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BUG: Add check for input array lengths in from_arrays method (GH13599)
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 58a92cfa5a784..f24bbe8e52ec8 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -728,3 +728,5 @@ Bug Fixes - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) - Bug in ``Index.union`` returns an incorrect result with a named empty index (:issue:`13432`) - Bugs in ``Index.difference`` and ``DataFrame.join`` raise in Python3 when using mixed-integer indexes (:issue:`13432`, :issue:`12814`) + +- Bug in ``MultiIndex.from_arrays`` didn't check for input array lengths (:issue:`13599`) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 365a971f82a3b..184744915bd8d 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -848,6 +848,12 @@ def from_arrays(cls, arrays, sortorder=None, names=None): name = None if names is None else names[0] return Index(arrays[0], name=name) + # Check if lengths of all arrays are equal or not, + # raise ValueError, if not + for i in range(1, len(arrays)): + if len(arrays[i]) != len(arrays[i - 1]): + raise ValueError('all arrays must be same length') + cats = [Categorical.from_array(arr, ordered=True) for arr in arrays] levels = [c.categories for c in cats] labels = [c.codes for c in cats] diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 2734e90a1971b..5597e1ffa8c87 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -632,6 +632,27 @@ def test_from_arrays_index_series_period(self): tm.assert_index_equal(result, result2) + def test_from_arrays_different_lengths(self): + # GH13599 + idx1 = [1, 2, 3] + idx2 = ['a', 'b'] + assertRaisesRegexp(ValueError, '^all arrays must be same length$', + MultiIndex.from_arrays, [idx1, idx2]) + + def test_from_arrays_different_lengths_first_array_zero_length(self): + # GH13599 + idx1 = [] + idx2 = ['a', 'b'] + assertRaisesRegexp(ValueError, '^all arrays must be same length$', + MultiIndex.from_arrays, [idx1, idx2]) + + def test_from_arrays_different_lengths_second_array_zero_length(self): + # GH13599 + idx1 = [1, 2, 3] + idx2 = [] + assertRaisesRegexp(ValueError, '^all arrays must be same length$', + MultiIndex.from_arrays, [idx1, idx2]) + def test_from_product(self): first = ['foo', 'bar', 'buz']
- [x] closes #13599 - [ ] tests added / passed - [x] whatsnew entry /cc @jreback
https://api.github.com/repos/pandas-dev/pandas/pulls/13728
2016-07-20T22:10:30Z
2016-07-20T22:25:22Z
null
2016-07-20T22:27:17Z
BUG: Fix pd.Timedelta(None) to return NaT.
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 646e8822ed46f..3839acaed15e6 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -755,3 +755,4 @@ Bug Fixes - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) - Bug in ``Index.union`` returns an incorrect result with a named empty index (:issue:`13432`) - Bugs in ``Index.difference`` and ``DataFrame.join`` raise in Python3 when using mixed-integer indexes (:issue:`13432`, :issue:`12814`) +- Bug in ``pd.Timedelta(None)`` raises ``ValueError``. This is different from ``pd.Timestamp(None)`` (:issue:`13687`) diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 659101cb4cad2..0bdf8590ec487 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -188,6 +188,8 @@ def test_construction(self): self.assertEqual(Timedelta('').value, iNaT) self.assertEqual(Timedelta('nat').value, iNaT) self.assertEqual(Timedelta('NAT').value, iNaT) + self.assertEqual(Timedelta(None).value, iNaT) + self.assertEqual(Timedelta(np.nan).value, iNaT) self.assertTrue(isnull(Timedelta('nat'))) # offset diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 5f487eedd1683..bc42adbab62b1 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -2592,10 +2592,10 @@ class Timedelta(_Timedelta): """ - def __new__(cls, object value=None, unit=None, **kwargs): + def __new__(cls, object value=_no_input, unit=None, **kwargs): cdef _Timedelta td_base - if value is None: + if value is _no_input: if not len(kwargs): raise ValueError("cannot construct a Timedelta without a value/unit or descriptive keywords (days,seconds....)")
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Before this commit pd.Timedelta raised ValueError when None is passed. This behavior is inconsistent because - pd.Timestamp(None) and pd.Period(None) return NaT - pd.Timedelta returns NaT if '', 'nat', 'NAT' or np.nan is passed This PR is related to https://github.com/pydata/pandas/pull/13687#issuecomment-233204955
https://api.github.com/repos/pandas-dev/pandas/pulls/13723
2016-07-20T17:00:53Z
2016-07-24T16:00:24Z
null
2016-07-24T16:03:34Z
CLN: Remove a test case about Timestamp to TestTimestamp
diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 36ae479c3dfcc..659101cb4cad2 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -188,7 +188,6 @@ def test_construction(self): self.assertEqual(Timedelta('').value, iNaT) self.assertEqual(Timedelta('nat').value, iNaT) self.assertEqual(Timedelta('NAT').value, iNaT) - self.assertTrue(isnull(Timestamp('nat'))) self.assertTrue(isnull(Timedelta('nat'))) # offset diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 59fc147ead4eb..9c97749c87103 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -4389,6 +4389,8 @@ def check(val, unit=None, h=1, s=1, us=0): result = Timestamp('NaT') self.assertIs(result, NaT) + self.assertTrue(isnull(Timestamp('nat'))) + def test_roundtrip(self): # test value to string and back conversions
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13722
2016-07-20T17:00:07Z
2016-07-20T17:25:33Z
2016-07-20T17:25:33Z
2016-07-20T17:27:38Z
BLD: Use tempita for cython templating
diff --git a/ci/lint.sh b/ci/lint.sh index 144febcfcece5..3adfa8d1e3d33 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -23,7 +23,7 @@ if [ "$LINT" ]; then for path in 'window.pyx' do echo "linting -> pandas/$path" - flake8 pandas/$path --filename '*.pyx' --select=E501,E302,E203,E226,E111,E114,E221,E303,E128,E231,E126,E128 + flake8 pandas/$path --filename '*.pyx' --select=E501,E302,E203,E226,E111,E114,E221,E303,E128,E231,E126 if [ $? -ne "0" ]; then RET=1 fi @@ -31,6 +31,18 @@ if [ "$LINT" ]; then done echo "Linting *.pyx DONE" + echo "Linting *.pxi.in" + for path in 'src' + do + echo "linting -> pandas/$path" + flake8 pandas/$path --filename '*.pxi.in' --select=E501,E302,E203,E111,E114,E221,E303,E231,E126 + if [ $? -ne "0" ]; then + RET=1 + fi + + done + echo "Linting *.pxi.in DONE" + echo "Check for invalid testing" grep -r -E --include '*.py' --exclude nosetester.py --exclude testing.py '(numpy|np)\.testing' pandas if [ $? = "0" ]; then diff --git a/pandas/algos.pyx b/pandas/algos.pyx index 8e659a8566adb..cccc5377d0dec 100644 --- a/pandas/algos.pyx +++ b/pandas/algos.pyx @@ -13,6 +13,7 @@ cdef float64_t FP_ERR = 1e-13 cimport util from libc.stdlib cimport malloc, free +from libc.string cimport memmove from numpy cimport NPY_INT8 as NPY_int8 from numpy cimport NPY_INT16 as NPY_int16 @@ -41,10 +42,14 @@ cdef extern from "src/headers/math.h": double fabs(double) nogil # this is our util.pxd -from util cimport numeric +from util cimport numeric, get_nat +cimport lib +from lib cimport is_null_datetimelike from pandas import lib +cdef int64_t iNaT = get_nat() + cdef: int TIEBREAK_AVERAGE = 0 int TIEBREAK_MIN = 1 @@ -1334,5 +1339,11 @@ cdef inline float64_t _median_linear(float64_t* a, int n): return result + include "join.pyx" -include "generated.pyx" + +# generated from template +include "algos_common_helper.pxi" +include "algos_groupby_helper.pxi" +include "algos_join_helper.pxi" +include "algos_take_helper.pxi" diff --git a/pandas/src/algos_common_helper.pxi b/pandas/src/algos_common_helper.pxi new file mode 100644 index 0000000000000..59b3ddff46dec --- /dev/null +++ b/pandas/src/algos_common_helper.pxi @@ -0,0 +1,2925 @@ +""" +Template for each `dtype` helper function using 1-d template + +# 1-d template +- map_indices +- pad +- pad_1d +- pad_2d +- backfill +- backfill_1d +- backfill_2d +- is_monotonic +- groupby +- arrmap + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +#---------------------------------------------------------------------- +# 1-d template +#---------------------------------------------------------------------- + + +@cython.wraparound(False) +@cython.boundscheck(False) +cpdef map_indices_float64(ndarray[float64_t] index): + """ + Produce a dict mapping the values of the input array to their respective + locations. + + Example: + array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} + + Better to do this with Cython because of the enormous speed boost. + """ + cdef Py_ssize_t i, length + cdef dict result = {} + + length = len(index) + + for i in range(length): + result[index[i]] = i + + return result + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_float64(ndarray[float64_t] old, ndarray[float64_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef float64_t cur, next + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: + return indexer + + i = j = 0 + + cur = old[0] + + while j <= nright - 1 and new[j] < cur: + j += 1 + + while True: + if j == nright: + break + + if i == nleft - 1: + while j < nright: + if new[j] == cur: + indexer[j] = i + elif new[j] > cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + break + + next = old[i + 1] + + while j < nright and cur <= new[j] < next: + if new[j] == cur: + indexer[j] = i + elif fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + + fill_count = 0 + i += 1 + cur = next + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_inplace_float64(ndarray[float64_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef float64_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[0] + for i in range(N): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_2d_inplace_float64(ndarray[float64_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef float64_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, 0] + for i in range(N): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + +""" +Backfilling logic for generating fill vector + +Diagram of what's going on + +Old New Fill vector Mask + . 0 1 + . 0 1 + . 0 1 +A A 0 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 +B B 1 1 + . 2 1 + . 2 1 + . 2 1 +C C 2 1 + . 0 + . 0 +D +""" + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_float64(ndarray[float64_t] old, ndarray[float64_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef float64_t cur, prev + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: + return indexer + + i = nleft - 1 + j = nright - 1 + + cur = old[nleft - 1] + + while j >= 0 and new[j] > cur: + j -= 1 + + while True: + if j < 0: + break + + if i == 0: + while j >= 0: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + break + + prev = old[i - 1] + + while j >= 0 and prev < new[j] <= cur: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + + fill_count = 0 + i -= 1 + cur = prev + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_inplace_float64(ndarray[float64_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef float64_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[N - 1] + for i in range(N - 1, -1, -1): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_2d_inplace_float64(ndarray[float64_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef float64_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, N - 1] + for i in range(N - 1, -1, -1): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def is_monotonic_float64(ndarray[float64_t] arr, bint timelike): + """ + Returns + ------- + is_monotonic_inc, is_monotonic_dec + """ + cdef: + Py_ssize_t i, n + float64_t prev, cur + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 + + n = len(arr) + + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False + else: + return True, True + elif n < 2: + return True, True + + if timelike and arr[0] == iNaT: + return False, False + + with nogil: + prev = arr[0] + for i in range(1, n): + cur = arr[i] + if timelike and cur == iNaT: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if cur < prev: + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 + elif cur == prev: + pass # is_unique = 0 + else: + # cur or prev is NaN + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if not is_monotonic_inc and not is_monotonic_dec: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + prev = cur + return is_monotonic_inc, is_monotonic_dec + + +@cython.wraparound(False) +@cython.boundscheck(False) +def groupby_float64(ndarray[float64_t] index, ndarray labels): + cdef dict result = {} + cdef Py_ssize_t i, length + cdef list members + cdef object idx, key + + length = len(index) + + if not length == len(labels): + raise AssertionError("len(index) != len(labels)") + + for i in range(length): + key = util.get_value_1d(labels, i) + + if is_null_datetimelike(key): + continue + + idx = index[i] + if key in result: + members = result[key] + members.append(idx) + else: + result[key] = [idx] + + return result + + +@cython.wraparound(False) +@cython.boundscheck(False) +def arrmap_float64(ndarray[float64_t] index, object func): + cdef Py_ssize_t length = index.shape[0] + cdef Py_ssize_t i = 0 + + cdef ndarray[object] result = np.empty(length, dtype=np.object_) + + from pandas.lib import maybe_convert_objects + + for i in range(length): + result[i] = func(index[i]) + + return maybe_convert_objects(result) + + +@cython.wraparound(False) +@cython.boundscheck(False) +cpdef map_indices_float32(ndarray[float32_t] index): + """ + Produce a dict mapping the values of the input array to their respective + locations. + + Example: + array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} + + Better to do this with Cython because of the enormous speed boost. + """ + cdef Py_ssize_t i, length + cdef dict result = {} + + length = len(index) + + for i in range(length): + result[index[i]] = i + + return result + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_float32(ndarray[float32_t] old, ndarray[float32_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef float32_t cur, next + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: + return indexer + + i = j = 0 + + cur = old[0] + + while j <= nright - 1 and new[j] < cur: + j += 1 + + while True: + if j == nright: + break + + if i == nleft - 1: + while j < nright: + if new[j] == cur: + indexer[j] = i + elif new[j] > cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + break + + next = old[i + 1] + + while j < nright and cur <= new[j] < next: + if new[j] == cur: + indexer[j] = i + elif fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + + fill_count = 0 + i += 1 + cur = next + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_inplace_float32(ndarray[float32_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef float32_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[0] + for i in range(N): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_2d_inplace_float32(ndarray[float32_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef float32_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, 0] + for i in range(N): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + +""" +Backfilling logic for generating fill vector + +Diagram of what's going on + +Old New Fill vector Mask + . 0 1 + . 0 1 + . 0 1 +A A 0 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 +B B 1 1 + . 2 1 + . 2 1 + . 2 1 +C C 2 1 + . 0 + . 0 +D +""" + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_float32(ndarray[float32_t] old, ndarray[float32_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef float32_t cur, prev + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: + return indexer + + i = nleft - 1 + j = nright - 1 + + cur = old[nleft - 1] + + while j >= 0 and new[j] > cur: + j -= 1 + + while True: + if j < 0: + break + + if i == 0: + while j >= 0: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + break + + prev = old[i - 1] + + while j >= 0 and prev < new[j] <= cur: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + + fill_count = 0 + i -= 1 + cur = prev + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_inplace_float32(ndarray[float32_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef float32_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[N - 1] + for i in range(N - 1, -1, -1): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_2d_inplace_float32(ndarray[float32_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef float32_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, N - 1] + for i in range(N - 1, -1, -1): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def is_monotonic_float32(ndarray[float32_t] arr, bint timelike): + """ + Returns + ------- + is_monotonic_inc, is_monotonic_dec + """ + cdef: + Py_ssize_t i, n + float32_t prev, cur + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 + + n = len(arr) + + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False + else: + return True, True + elif n < 2: + return True, True + + if timelike and arr[0] == iNaT: + return False, False + + with nogil: + prev = arr[0] + for i in range(1, n): + cur = arr[i] + if timelike and cur == iNaT: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if cur < prev: + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 + elif cur == prev: + pass # is_unique = 0 + else: + # cur or prev is NaN + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if not is_monotonic_inc and not is_monotonic_dec: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + prev = cur + return is_monotonic_inc, is_monotonic_dec + + +@cython.wraparound(False) +@cython.boundscheck(False) +def groupby_float32(ndarray[float32_t] index, ndarray labels): + cdef dict result = {} + cdef Py_ssize_t i, length + cdef list members + cdef object idx, key + + length = len(index) + + if not length == len(labels): + raise AssertionError("len(index) != len(labels)") + + for i in range(length): + key = util.get_value_1d(labels, i) + + if is_null_datetimelike(key): + continue + + idx = index[i] + if key in result: + members = result[key] + members.append(idx) + else: + result[key] = [idx] + + return result + + +@cython.wraparound(False) +@cython.boundscheck(False) +def arrmap_float32(ndarray[float32_t] index, object func): + cdef Py_ssize_t length = index.shape[0] + cdef Py_ssize_t i = 0 + + cdef ndarray[object] result = np.empty(length, dtype=np.object_) + + from pandas.lib import maybe_convert_objects + + for i in range(length): + result[i] = func(index[i]) + + return maybe_convert_objects(result) + + +@cython.wraparound(False) +@cython.boundscheck(False) +cpdef map_indices_object(ndarray[object] index): + """ + Produce a dict mapping the values of the input array to their respective + locations. + + Example: + array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} + + Better to do this with Cython because of the enormous speed boost. + """ + cdef Py_ssize_t i, length + cdef dict result = {} + + length = len(index) + + for i in range(length): + result[index[i]] = i + + return result + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_object(ndarray[object] old, ndarray[object] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef object cur, next + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: + return indexer + + i = j = 0 + + cur = old[0] + + while j <= nright - 1 and new[j] < cur: + j += 1 + + while True: + if j == nright: + break + + if i == nleft - 1: + while j < nright: + if new[j] == cur: + indexer[j] = i + elif new[j] > cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + break + + next = old[i + 1] + + while j < nright and cur <= new[j] < next: + if new[j] == cur: + indexer[j] = i + elif fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + + fill_count = 0 + i += 1 + cur = next + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_inplace_object(ndarray[object] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef object val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[0] + for i in range(N): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_2d_inplace_object(ndarray[object, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef object val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, 0] + for i in range(N): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + +""" +Backfilling logic for generating fill vector + +Diagram of what's going on + +Old New Fill vector Mask + . 0 1 + . 0 1 + . 0 1 +A A 0 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 +B B 1 1 + . 2 1 + . 2 1 + . 2 1 +C C 2 1 + . 0 + . 0 +D +""" + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_object(ndarray[object] old, ndarray[object] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef object cur, prev + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: + return indexer + + i = nleft - 1 + j = nright - 1 + + cur = old[nleft - 1] + + while j >= 0 and new[j] > cur: + j -= 1 + + while True: + if j < 0: + break + + if i == 0: + while j >= 0: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + break + + prev = old[i - 1] + + while j >= 0 and prev < new[j] <= cur: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + + fill_count = 0 + i -= 1 + cur = prev + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_inplace_object(ndarray[object] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef object val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[N - 1] + for i in range(N - 1, -1, -1): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_2d_inplace_object(ndarray[object, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef object val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, N - 1] + for i in range(N - 1, -1, -1): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def is_monotonic_object(ndarray[object] arr, bint timelike): + """ + Returns + ------- + is_monotonic_inc, is_monotonic_dec + """ + cdef: + Py_ssize_t i, n + object prev, cur + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 + + n = len(arr) + + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False + else: + return True, True + elif n < 2: + return True, True + + if timelike and arr[0] == iNaT: + return False, False + + + prev = arr[0] + for i in range(1, n): + cur = arr[i] + if timelike and cur == iNaT: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if cur < prev: + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 + elif cur == prev: + pass # is_unique = 0 + else: + # cur or prev is NaN + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if not is_monotonic_inc and not is_monotonic_dec: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + prev = cur + return is_monotonic_inc, is_monotonic_dec + + +@cython.wraparound(False) +@cython.boundscheck(False) +def groupby_object(ndarray[object] index, ndarray labels): + cdef dict result = {} + cdef Py_ssize_t i, length + cdef list members + cdef object idx, key + + length = len(index) + + if not length == len(labels): + raise AssertionError("len(index) != len(labels)") + + for i in range(length): + key = util.get_value_1d(labels, i) + + if is_null_datetimelike(key): + continue + + idx = index[i] + if key in result: + members = result[key] + members.append(idx) + else: + result[key] = [idx] + + return result + + +@cython.wraparound(False) +@cython.boundscheck(False) +def arrmap_object(ndarray[object] index, object func): + cdef Py_ssize_t length = index.shape[0] + cdef Py_ssize_t i = 0 + + cdef ndarray[object] result = np.empty(length, dtype=np.object_) + + from pandas.lib import maybe_convert_objects + + for i in range(length): + result[i] = func(index[i]) + + return maybe_convert_objects(result) + + +@cython.wraparound(False) +@cython.boundscheck(False) +cpdef map_indices_int32(ndarray[int32_t] index): + """ + Produce a dict mapping the values of the input array to their respective + locations. + + Example: + array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} + + Better to do this with Cython because of the enormous speed boost. + """ + cdef Py_ssize_t i, length + cdef dict result = {} + + length = len(index) + + for i in range(length): + result[index[i]] = i + + return result + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_int32(ndarray[int32_t] old, ndarray[int32_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef int32_t cur, next + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: + return indexer + + i = j = 0 + + cur = old[0] + + while j <= nright - 1 and new[j] < cur: + j += 1 + + while True: + if j == nright: + break + + if i == nleft - 1: + while j < nright: + if new[j] == cur: + indexer[j] = i + elif new[j] > cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + break + + next = old[i + 1] + + while j < nright and cur <= new[j] < next: + if new[j] == cur: + indexer[j] = i + elif fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + + fill_count = 0 + i += 1 + cur = next + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_inplace_int32(ndarray[int32_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef int32_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[0] + for i in range(N): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_2d_inplace_int32(ndarray[int32_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef int32_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, 0] + for i in range(N): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + +""" +Backfilling logic for generating fill vector + +Diagram of what's going on + +Old New Fill vector Mask + . 0 1 + . 0 1 + . 0 1 +A A 0 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 +B B 1 1 + . 2 1 + . 2 1 + . 2 1 +C C 2 1 + . 0 + . 0 +D +""" + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_int32(ndarray[int32_t] old, ndarray[int32_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef int32_t cur, prev + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: + return indexer + + i = nleft - 1 + j = nright - 1 + + cur = old[nleft - 1] + + while j >= 0 and new[j] > cur: + j -= 1 + + while True: + if j < 0: + break + + if i == 0: + while j >= 0: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + break + + prev = old[i - 1] + + while j >= 0 and prev < new[j] <= cur: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + + fill_count = 0 + i -= 1 + cur = prev + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_inplace_int32(ndarray[int32_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef int32_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[N - 1] + for i in range(N - 1, -1, -1): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_2d_inplace_int32(ndarray[int32_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef int32_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, N - 1] + for i in range(N - 1, -1, -1): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def is_monotonic_int32(ndarray[int32_t] arr, bint timelike): + """ + Returns + ------- + is_monotonic_inc, is_monotonic_dec + """ + cdef: + Py_ssize_t i, n + int32_t prev, cur + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 + + n = len(arr) + + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False + else: + return True, True + elif n < 2: + return True, True + + if timelike and arr[0] == iNaT: + return False, False + + with nogil: + prev = arr[0] + for i in range(1, n): + cur = arr[i] + if timelike and cur == iNaT: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if cur < prev: + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 + elif cur == prev: + pass # is_unique = 0 + else: + # cur or prev is NaN + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if not is_monotonic_inc and not is_monotonic_dec: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + prev = cur + return is_monotonic_inc, is_monotonic_dec + + +@cython.wraparound(False) +@cython.boundscheck(False) +def groupby_int32(ndarray[int32_t] index, ndarray labels): + cdef dict result = {} + cdef Py_ssize_t i, length + cdef list members + cdef object idx, key + + length = len(index) + + if not length == len(labels): + raise AssertionError("len(index) != len(labels)") + + for i in range(length): + key = util.get_value_1d(labels, i) + + if is_null_datetimelike(key): + continue + + idx = index[i] + if key in result: + members = result[key] + members.append(idx) + else: + result[key] = [idx] + + return result + + +@cython.wraparound(False) +@cython.boundscheck(False) +def arrmap_int32(ndarray[int32_t] index, object func): + cdef Py_ssize_t length = index.shape[0] + cdef Py_ssize_t i = 0 + + cdef ndarray[object] result = np.empty(length, dtype=np.object_) + + from pandas.lib import maybe_convert_objects + + for i in range(length): + result[i] = func(index[i]) + + return maybe_convert_objects(result) + + +@cython.wraparound(False) +@cython.boundscheck(False) +cpdef map_indices_int64(ndarray[int64_t] index): + """ + Produce a dict mapping the values of the input array to their respective + locations. + + Example: + array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} + + Better to do this with Cython because of the enormous speed boost. + """ + cdef Py_ssize_t i, length + cdef dict result = {} + + length = len(index) + + for i in range(length): + result[index[i]] = i + + return result + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_int64(ndarray[int64_t] old, ndarray[int64_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef int64_t cur, next + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: + return indexer + + i = j = 0 + + cur = old[0] + + while j <= nright - 1 and new[j] < cur: + j += 1 + + while True: + if j == nright: + break + + if i == nleft - 1: + while j < nright: + if new[j] == cur: + indexer[j] = i + elif new[j] > cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + break + + next = old[i + 1] + + while j < nright and cur <= new[j] < next: + if new[j] == cur: + indexer[j] = i + elif fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + + fill_count = 0 + i += 1 + cur = next + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_inplace_int64(ndarray[int64_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef int64_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[0] + for i in range(N): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_2d_inplace_int64(ndarray[int64_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef int64_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, 0] + for i in range(N): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + +""" +Backfilling logic for generating fill vector + +Diagram of what's going on + +Old New Fill vector Mask + . 0 1 + . 0 1 + . 0 1 +A A 0 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 +B B 1 1 + . 2 1 + . 2 1 + . 2 1 +C C 2 1 + . 0 + . 0 +D +""" + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_int64(ndarray[int64_t] old, ndarray[int64_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef int64_t cur, prev + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: + return indexer + + i = nleft - 1 + j = nright - 1 + + cur = old[nleft - 1] + + while j >= 0 and new[j] > cur: + j -= 1 + + while True: + if j < 0: + break + + if i == 0: + while j >= 0: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + break + + prev = old[i - 1] + + while j >= 0 and prev < new[j] <= cur: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + + fill_count = 0 + i -= 1 + cur = prev + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_inplace_int64(ndarray[int64_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef int64_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[N - 1] + for i in range(N - 1, -1, -1): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_2d_inplace_int64(ndarray[int64_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef int64_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, N - 1] + for i in range(N - 1, -1, -1): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def is_monotonic_int64(ndarray[int64_t] arr, bint timelike): + """ + Returns + ------- + is_monotonic_inc, is_monotonic_dec + """ + cdef: + Py_ssize_t i, n + int64_t prev, cur + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 + + n = len(arr) + + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False + else: + return True, True + elif n < 2: + return True, True + + if timelike and arr[0] == iNaT: + return False, False + + with nogil: + prev = arr[0] + for i in range(1, n): + cur = arr[i] + if timelike and cur == iNaT: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if cur < prev: + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 + elif cur == prev: + pass # is_unique = 0 + else: + # cur or prev is NaN + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if not is_monotonic_inc and not is_monotonic_dec: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + prev = cur + return is_monotonic_inc, is_monotonic_dec + + +@cython.wraparound(False) +@cython.boundscheck(False) +def groupby_int64(ndarray[int64_t] index, ndarray labels): + cdef dict result = {} + cdef Py_ssize_t i, length + cdef list members + cdef object idx, key + + length = len(index) + + if not length == len(labels): + raise AssertionError("len(index) != len(labels)") + + for i in range(length): + key = util.get_value_1d(labels, i) + + if is_null_datetimelike(key): + continue + + idx = index[i] + if key in result: + members = result[key] + members.append(idx) + else: + result[key] = [idx] + + return result + + +@cython.wraparound(False) +@cython.boundscheck(False) +def arrmap_int64(ndarray[int64_t] index, object func): + cdef Py_ssize_t length = index.shape[0] + cdef Py_ssize_t i = 0 + + cdef ndarray[object] result = np.empty(length, dtype=np.object_) + + from pandas.lib import maybe_convert_objects + + for i in range(length): + result[i] = func(index[i]) + + return maybe_convert_objects(result) + + +@cython.wraparound(False) +@cython.boundscheck(False) +cpdef map_indices_bool(ndarray[uint8_t] index): + """ + Produce a dict mapping the values of the input array to their respective + locations. + + Example: + array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} + + Better to do this with Cython because of the enormous speed boost. + """ + cdef Py_ssize_t i, length + cdef dict result = {} + + length = len(index) + + for i in range(length): + result[index[i]] = i + + return result + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_bool(ndarray[uint8_t] old, ndarray[uint8_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef uint8_t cur, next + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: + return indexer + + i = j = 0 + + cur = old[0] + + while j <= nright - 1 and new[j] < cur: + j += 1 + + while True: + if j == nright: + break + + if i == nleft - 1: + while j < nright: + if new[j] == cur: + indexer[j] = i + elif new[j] > cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + break + + next = old[i + 1] + + while j < nright and cur <= new[j] < next: + if new[j] == cur: + indexer[j] = i + elif fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + + fill_count = 0 + i += 1 + cur = next + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_inplace_bool(ndarray[uint8_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef uint8_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[0] + for i in range(N): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_2d_inplace_bool(ndarray[uint8_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef uint8_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, 0] + for i in range(N): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + +""" +Backfilling logic for generating fill vector + +Diagram of what's going on + +Old New Fill vector Mask + . 0 1 + . 0 1 + . 0 1 +A A 0 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 +B B 1 1 + . 2 1 + . 2 1 + . 2 1 +C C 2 1 + . 0 + . 0 +D +""" + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_bool(ndarray[uint8_t] old, ndarray[uint8_t] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef uint8_t cur, prev + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: + return indexer + + i = nleft - 1 + j = nright - 1 + + cur = old[nleft - 1] + + while j >= 0 and new[j] > cur: + j -= 1 + + while True: + if j < 0: + break + + if i == 0: + while j >= 0: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + break + + prev = old[i - 1] + + while j >= 0 and prev < new[j] <= cur: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + + fill_count = 0 + i -= 1 + cur = prev + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_inplace_bool(ndarray[uint8_t] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef uint8_t val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[N - 1] + for i in range(N - 1, -1, -1): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_2d_inplace_bool(ndarray[uint8_t, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef uint8_t val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, N - 1] + for i in range(N - 1, -1, -1): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def is_monotonic_bool(ndarray[uint8_t] arr, bint timelike): + """ + Returns + ------- + is_monotonic_inc, is_monotonic_dec + """ + cdef: + Py_ssize_t i, n + uint8_t prev, cur + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 + + n = len(arr) + + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False + else: + return True, True + elif n < 2: + return True, True + + if timelike and arr[0] == iNaT: + return False, False + + with nogil: + prev = arr[0] + for i in range(1, n): + cur = arr[i] + if timelike and cur == iNaT: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if cur < prev: + is_monotonic_inc = 0 + elif cur > prev: + is_monotonic_dec = 0 + elif cur == prev: + pass # is_unique = 0 + else: + # cur or prev is NaN + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + if not is_monotonic_inc and not is_monotonic_dec: + is_monotonic_inc = 0 + is_monotonic_dec = 0 + break + prev = cur + return is_monotonic_inc, is_monotonic_dec + + +@cython.wraparound(False) +@cython.boundscheck(False) +def groupby_bool(ndarray[uint8_t] index, ndarray labels): + cdef dict result = {} + cdef Py_ssize_t i, length + cdef list members + cdef object idx, key + + length = len(index) + + if not length == len(labels): + raise AssertionError("len(index) != len(labels)") + + for i in range(length): + key = util.get_value_1d(labels, i) + + if is_null_datetimelike(key): + continue + + idx = index[i] + if key in result: + members = result[key] + members.append(idx) + else: + result[key] = [idx] + + return result + + +@cython.wraparound(False) +@cython.boundscheck(False) +def arrmap_bool(ndarray[uint8_t] index, object func): + cdef Py_ssize_t length = index.shape[0] + cdef Py_ssize_t i = 0 + + cdef ndarray[object] result = np.empty(length, dtype=np.object_) + + from pandas.lib import maybe_convert_objects + + for i in range(length): + result[i] = func(index[i]) + + return maybe_convert_objects(result) + +#---------------------------------------------------------------------- +# put template +#---------------------------------------------------------------------- + + +@cython.boundscheck(False) +@cython.wraparound(False) +def diff_2d_float64(ndarray[float64_t, ndim=2] arr, + ndarray[float64_t, ndim=2] out, + Py_ssize_t periods, int axis): + cdef: + Py_ssize_t i, j, sx, sy + + sx, sy = (<object> arr).shape + if arr.flags.f_contiguous: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for j in range(sy): + for i in range(start, stop): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for j in range(start, stop): + for i in range(sx): + out[i, j] = arr[i, j] - arr[i, j - periods] + else: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for i in range(start, stop): + for j in range(sy): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for i in range(sx): + for j in range(start, stop): + out[i, j] = arr[i, j] - arr[i, j - periods] + + +def put2d_float64_float64(ndarray[float64_t, ndim=2, cast=True] values, + ndarray[int64_t] indexer, Py_ssize_t loc, + ndarray[float64_t] out): + cdef: + Py_ssize_t i, j, k + + k = len(values) + for j from 0 <= j < k: + i = indexer[j] + out[i] = values[j, loc] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def diff_2d_float32(ndarray[float32_t, ndim=2] arr, + ndarray[float32_t, ndim=2] out, + Py_ssize_t periods, int axis): + cdef: + Py_ssize_t i, j, sx, sy + + sx, sy = (<object> arr).shape + if arr.flags.f_contiguous: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for j in range(sy): + for i in range(start, stop): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for j in range(start, stop): + for i in range(sx): + out[i, j] = arr[i, j] - arr[i, j - periods] + else: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for i in range(start, stop): + for j in range(sy): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for i in range(sx): + for j in range(start, stop): + out[i, j] = arr[i, j] - arr[i, j - periods] + + +def put2d_float32_float32(ndarray[float32_t, ndim=2, cast=True] values, + ndarray[int64_t] indexer, Py_ssize_t loc, + ndarray[float32_t] out): + cdef: + Py_ssize_t i, j, k + + k = len(values) + for j from 0 <= j < k: + i = indexer[j] + out[i] = values[j, loc] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def diff_2d_int8(ndarray[int8_t, ndim=2] arr, + ndarray[float32_t, ndim=2] out, + Py_ssize_t periods, int axis): + cdef: + Py_ssize_t i, j, sx, sy + + sx, sy = (<object> arr).shape + if arr.flags.f_contiguous: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for j in range(sy): + for i in range(start, stop): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for j in range(start, stop): + for i in range(sx): + out[i, j] = arr[i, j] - arr[i, j - periods] + else: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for i in range(start, stop): + for j in range(sy): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for i in range(sx): + for j in range(start, stop): + out[i, j] = arr[i, j] - arr[i, j - periods] + + +def put2d_int8_float32(ndarray[int8_t, ndim=2, cast=True] values, + ndarray[int64_t] indexer, Py_ssize_t loc, + ndarray[float32_t] out): + cdef: + Py_ssize_t i, j, k + + k = len(values) + for j from 0 <= j < k: + i = indexer[j] + out[i] = values[j, loc] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def diff_2d_int16(ndarray[int16_t, ndim=2] arr, + ndarray[float32_t, ndim=2] out, + Py_ssize_t periods, int axis): + cdef: + Py_ssize_t i, j, sx, sy + + sx, sy = (<object> arr).shape + if arr.flags.f_contiguous: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for j in range(sy): + for i in range(start, stop): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for j in range(start, stop): + for i in range(sx): + out[i, j] = arr[i, j] - arr[i, j - periods] + else: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for i in range(start, stop): + for j in range(sy): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for i in range(sx): + for j in range(start, stop): + out[i, j] = arr[i, j] - arr[i, j - periods] + + +def put2d_int16_float32(ndarray[int16_t, ndim=2, cast=True] values, + ndarray[int64_t] indexer, Py_ssize_t loc, + ndarray[float32_t] out): + cdef: + Py_ssize_t i, j, k + + k = len(values) + for j from 0 <= j < k: + i = indexer[j] + out[i] = values[j, loc] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def diff_2d_int32(ndarray[int32_t, ndim=2] arr, + ndarray[float64_t, ndim=2] out, + Py_ssize_t periods, int axis): + cdef: + Py_ssize_t i, j, sx, sy + + sx, sy = (<object> arr).shape + if arr.flags.f_contiguous: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for j in range(sy): + for i in range(start, stop): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for j in range(start, stop): + for i in range(sx): + out[i, j] = arr[i, j] - arr[i, j - periods] + else: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for i in range(start, stop): + for j in range(sy): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for i in range(sx): + for j in range(start, stop): + out[i, j] = arr[i, j] - arr[i, j - periods] + + +def put2d_int32_float64(ndarray[int32_t, ndim=2, cast=True] values, + ndarray[int64_t] indexer, Py_ssize_t loc, + ndarray[float64_t] out): + cdef: + Py_ssize_t i, j, k + + k = len(values) + for j from 0 <= j < k: + i = indexer[j] + out[i] = values[j, loc] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def diff_2d_int64(ndarray[int64_t, ndim=2] arr, + ndarray[float64_t, ndim=2] out, + Py_ssize_t periods, int axis): + cdef: + Py_ssize_t i, j, sx, sy + + sx, sy = (<object> arr).shape + if arr.flags.f_contiguous: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for j in range(sy): + for i in range(start, stop): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for j in range(start, stop): + for i in range(sx): + out[i, j] = arr[i, j] - arr[i, j - periods] + else: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for i in range(start, stop): + for j in range(sy): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for i in range(sx): + for j in range(start, stop): + out[i, j] = arr[i, j] - arr[i, j - periods] + + +def put2d_int64_float64(ndarray[int64_t, ndim=2, cast=True] values, + ndarray[int64_t] indexer, Py_ssize_t loc, + ndarray[float64_t] out): + cdef: + Py_ssize_t i, j, k + + k = len(values) + for j from 0 <= j < k: + i = indexer[j] + out[i] = values[j, loc] + +#---------------------------------------------------------------------- +# ensure_dtype +#---------------------------------------------------------------------- + +cdef int PLATFORM_INT = (<ndarray> np.arange(0, dtype=np.int_)).descr.type_num + +cpdef ensure_platform_int(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == PLATFORM_INT: + return arr + else: + return arr.astype(np.int_) + else: + return np.array(arr, dtype=np.int_) + +cpdef ensure_object(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_OBJECT: + return arr + else: + return arr.astype(np.object_) + elif hasattr(arr, 'asobject'): + return arr.asobject + else: + return np.array(arr, dtype=np.object_) + +cpdef ensure_float64(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_FLOAT64: + return arr + else: + return arr.astype(np.float64) + else: + return np.array(arr, dtype=np.float64) + +cpdef ensure_float32(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_FLOAT32: + return arr + else: + return arr.astype(np.float32) + else: + return np.array(arr, dtype=np.float32) + +cpdef ensure_int8(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_INT8: + return arr + else: + return arr.astype(np.int8) + else: + return np.array(arr, dtype=np.int8) + +cpdef ensure_int16(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_INT16: + return arr + else: + return arr.astype(np.int16) + else: + return np.array(arr, dtype=np.int16) + +cpdef ensure_int32(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_INT32: + return arr + else: + return arr.astype(np.int32) + else: + return np.array(arr, dtype=np.int32) + +cpdef ensure_int64(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_INT64: + return arr + else: + return arr.astype(np.int64) + else: + return np.array(arr, dtype=np.int64) diff --git a/pandas/src/algos_common_helper.pxi.in b/pandas/src/algos_common_helper.pxi.in new file mode 100644 index 0000000000000..2327f10389cb5 --- /dev/null +++ b/pandas/src/algos_common_helper.pxi.in @@ -0,0 +1,603 @@ +""" +Template for each `dtype` helper function using 1-d template + +# 1-d template +- map_indices +- pad +- pad_1d +- pad_2d +- backfill +- backfill_1d +- backfill_2d +- is_monotonic +- groupby +- arrmap + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +#---------------------------------------------------------------------- +# 1-d template +#---------------------------------------------------------------------- + +{{py: + +# name, c_type, dtype, can_hold_na, nogil +dtypes = [('float64', 'float64_t', 'np.float64', True, True), + ('float32', 'float32_t', 'np.float32', True, True), + ('object', 'object', 'object', True, False), + ('int32', 'int32_t', 'np.int32', False, True), + ('int64', 'int64_t', 'np.int64', False, True), + ('bool', 'uint8_t', 'np.bool', False, True)] + +def get_dispatch(dtypes): + + for name, c_type, dtype, can_hold_na, nogil in dtypes: + + nogil_str = 'with nogil:' if nogil else '' + tab = ' ' if nogil else '' + yield name, c_type, dtype, can_hold_na, nogil_str, tab +}} + +{{for name, c_type, dtype, can_hold_na, nogil_str, tab + in get_dispatch(dtypes)}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +cpdef map_indices_{{name}}(ndarray[{{c_type}}] index): + """ + Produce a dict mapping the values of the input array to their respective + locations. + + Example: + array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} + + Better to do this with Cython because of the enormous speed boost. + """ + cdef Py_ssize_t i, length + cdef dict result = {} + + length = len(index) + + for i in range(length): + result[index[i]] = i + + return result + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_{{name}}(ndarray[{{c_type}}] old, ndarray[{{c_type}}] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef {{c_type}} cur, next + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: + return indexer + + i = j = 0 + + cur = old[0] + + while j <= nright - 1 and new[j] < cur: + j += 1 + + while True: + if j == nright: + break + + if i == nleft - 1: + while j < nright: + if new[j] == cur: + indexer[j] = i + elif new[j] > cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + break + + next = old[i + 1] + + while j < nright and cur <= new[j] < next: + if new[j] == cur: + indexer[j] = i + elif fill_count < lim: + indexer[j] = i + fill_count += 1 + j += 1 + + fill_count = 0 + i += 1 + cur = next + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_inplace_{{name}}(ndarray[{{c_type}}] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef {{c_type}} val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[0] + for i in range(N): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def pad_2d_inplace_{{name}}(ndarray[{{c_type}}, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef {{c_type}} val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, 0] + for i in range(N): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + +""" +Backfilling logic for generating fill vector + +Diagram of what's going on + +Old New Fill vector Mask + . 0 1 + . 0 1 + . 0 1 +A A 0 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 + . 1 1 +B B 1 1 + . 2 1 + . 2 1 + . 2 1 +C C 2 1 + . 0 + . 0 +D +""" + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_{{name}}(ndarray[{{c_type}}] old, ndarray[{{c_type}}] new, + limit=None): + cdef Py_ssize_t i, j, nleft, nright + cdef ndarray[int64_t, ndim=1] indexer + cdef {{c_type}} cur, prev + cdef int lim, fill_count = 0 + + nleft = len(old) + nright = len(new) + indexer = np.empty(nright, dtype=np.int64) + indexer.fill(-1) + + if limit is None: + lim = nright + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: + return indexer + + i = nleft - 1 + j = nright - 1 + + cur = old[nleft - 1] + + while j >= 0 and new[j] > cur: + j -= 1 + + while True: + if j < 0: + break + + if i == 0: + while j >= 0: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + break + + prev = old[i - 1] + + while j >= 0 and prev < new[j] <= cur: + if new[j] == cur: + indexer[j] = i + elif new[j] < cur and fill_count < lim: + indexer[j] = i + fill_count += 1 + j -= 1 + + fill_count = 0 + i -= 1 + cur = prev + + return indexer + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_inplace_{{name}}(ndarray[{{c_type}}] values, + ndarray[uint8_t, cast=True] mask, + limit=None): + cdef Py_ssize_t i, N + cdef {{c_type}} val + cdef int lim, fill_count = 0 + + N = len(values) + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + val = values[N - 1] + for i in range(N - 1, -1, -1): + if mask[i]: + if fill_count >= lim: + continue + fill_count += 1 + values[i] = val + else: + fill_count = 0 + val = values[i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def backfill_2d_inplace_{{name}}(ndarray[{{c_type}}, ndim=2] values, + ndarray[uint8_t, ndim=2] mask, + limit=None): + cdef Py_ssize_t i, j, N, K + cdef {{c_type}} val + cdef int lim, fill_count = 0 + + K, N = (<object> values).shape + + # GH 2778 + if N == 0: + return + + if limit is None: + lim = N + else: + if limit < 0: + raise ValueError('Limit must be non-negative') + lim = limit + + for j in range(K): + fill_count = 0 + val = values[j, N - 1] + for i in range(N - 1, -1, -1): + if mask[j, i]: + if fill_count >= lim: + continue + fill_count += 1 + values[j, i] = val + else: + fill_count = 0 + val = values[j, i] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def is_monotonic_{{name}}(ndarray[{{c_type}}] arr, bint timelike): + """ + Returns + ------- + is_monotonic_inc, is_monotonic_dec + """ + cdef: + Py_ssize_t i, n + {{c_type}} prev, cur + bint is_monotonic_inc = 1 + bint is_monotonic_dec = 1 + + n = len(arr) + + if n == 1: + if arr[0] != arr[0] or (timelike and arr[0] == iNaT): + # single value is NaN + return False, False + else: + return True, True + elif n < 2: + return True, True + + if timelike and arr[0] == iNaT: + return False, False + + {{nogil_str}} + {{tab}}prev = arr[0] + {{tab}}for i in range(1, n): + {{tab}} cur = arr[i] + {{tab}} if timelike and cur == iNaT: + {{tab}} is_monotonic_inc = 0 + {{tab}} is_monotonic_dec = 0 + {{tab}} break + {{tab}} if cur < prev: + {{tab}} is_monotonic_inc = 0 + {{tab}} elif cur > prev: + {{tab}} is_monotonic_dec = 0 + {{tab}} elif cur == prev: + {{tab}} pass # is_unique = 0 + {{tab}} else: + {{tab}} # cur or prev is NaN + {{tab}} is_monotonic_inc = 0 + {{tab}} is_monotonic_dec = 0 + {{tab}} break + {{tab}} if not is_monotonic_inc and not is_monotonic_dec: + {{tab}} is_monotonic_inc = 0 + {{tab}} is_monotonic_dec = 0 + {{tab}} break + {{tab}} prev = cur + return is_monotonic_inc, is_monotonic_dec + + +@cython.wraparound(False) +@cython.boundscheck(False) +def groupby_{{name}}(ndarray[{{c_type}}] index, ndarray labels): + cdef dict result = {} + cdef Py_ssize_t i, length + cdef list members + cdef object idx, key + + length = len(index) + + if not length == len(labels): + raise AssertionError("len(index) != len(labels)") + + for i in range(length): + key = util.get_value_1d(labels, i) + + if is_null_datetimelike(key): + continue + + idx = index[i] + if key in result: + members = result[key] + members.append(idx) + else: + result[key] = [idx] + + return result + + +@cython.wraparound(False) +@cython.boundscheck(False) +def arrmap_{{name}}(ndarray[{{c_type}}] index, object func): + cdef Py_ssize_t length = index.shape[0] + cdef Py_ssize_t i = 0 + + cdef ndarray[object] result = np.empty(length, dtype=np.object_) + + from pandas.lib import maybe_convert_objects + + for i in range(length): + result[i] = func(index[i]) + + return maybe_convert_objects(result) + +{{endfor}} + +#---------------------------------------------------------------------- +# put template +#---------------------------------------------------------------------- + +{{py: + +# name, c_type, dest_type, dest_dtype +dtypes = [('float64', 'float64_t', 'float64_t', 'np.float64'), + ('float32', 'float32_t', 'float32_t', 'np.float32'), + ('int8', 'int8_t', 'float32_t', 'np.float32'), + ('int16', 'int16_t', 'float32_t', 'np.float32'), + ('int32', 'int32_t', 'float64_t', 'np.float64'), + ('int64', 'int64_t', 'float64_t', 'np.float64')] + +def get_dispatch(dtypes): + + for name, c_type, dest_type, dest_dtype, in dtypes: + + dest_type2 = dest_type + dest_type = dest_type.replace('_t', '') + + yield name, c_type, dest_type, dest_type2, dest_dtype + +}} + +{{for name, c_type, dest_type, dest_type2, dest_dtype + in get_dispatch(dtypes)}} + + +@cython.boundscheck(False) +@cython.wraparound(False) +def diff_2d_{{name}}(ndarray[{{c_type}}, ndim=2] arr, + ndarray[{{dest_type2}}, ndim=2] out, + Py_ssize_t periods, int axis): + cdef: + Py_ssize_t i, j, sx, sy + + sx, sy = (<object> arr).shape + if arr.flags.f_contiguous: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for j in range(sy): + for i in range(start, stop): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for j in range(start, stop): + for i in range(sx): + out[i, j] = arr[i, j] - arr[i, j - periods] + else: + if axis == 0: + if periods >= 0: + start, stop = periods, sx + else: + start, stop = 0, sx + periods + for i in range(start, stop): + for j in range(sy): + out[i, j] = arr[i, j] - arr[i - periods, j] + else: + if periods >= 0: + start, stop = periods, sy + else: + start, stop = 0, sy + periods + for i in range(sx): + for j in range(start, stop): + out[i, j] = arr[i, j] - arr[i, j - periods] + + +def put2d_{{name}}_{{dest_type}}(ndarray[{{c_type}}, ndim=2, cast=True] values, + ndarray[int64_t] indexer, Py_ssize_t loc, + ndarray[{{dest_type2}}] out): + cdef: + Py_ssize_t i, j, k + + k = len(values) + for j from 0 <= j < k: + i = indexer[j] + out[i] = values[j, loc] + +{{endfor}} + +#---------------------------------------------------------------------- +# ensure_dtype +#---------------------------------------------------------------------- + +cdef int PLATFORM_INT = (<ndarray> np.arange(0, dtype=np.int_)).descr.type_num + +cpdef ensure_platform_int(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == PLATFORM_INT: + return arr + else: + return arr.astype(np.int_) + else: + return np.array(arr, dtype=np.int_) + +cpdef ensure_object(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_OBJECT: + return arr + else: + return arr.astype(np.object_) + elif hasattr(arr, 'asobject'): + return arr.asobject + else: + return np.array(arr, dtype=np.object_) + +{{py: + +# name, c_type, dtype +dtypes = [('float64', 'FLOAT64', 'float64'), + ('float32', 'FLOAT32', 'float32'), + ('int8', 'INT8', 'int8'), + ('int16', 'INT16', 'int16'), + ('int32', 'INT32', 'int32'), + ('int64', 'INT64', 'int64'), + # ('platform_int', 'INT', 'int_'), + # ('object', 'OBJECT', 'object_'), +] + +def get_dispatch(dtypes): + + for name, c_type, dtype in dtypes: + yield name, c_type, dtype +}} + +{{for name, c_type, dtype in get_dispatch(dtypes)}} + +cpdef ensure_{{name}}(object arr): + if util.is_array(arr): + if (<ndarray> arr).descr.type_num == NPY_{{c_type}}: + return arr + else: + return arr.astype(np.{{dtype}}) + else: + return np.array(arr, dtype=np.{{dtype}}) + +{{endfor}} \ No newline at end of file diff --git a/pandas/src/algos_groupby_helper.pxi b/pandas/src/algos_groupby_helper.pxi new file mode 100644 index 0000000000000..fb86c4efb7314 --- /dev/null +++ b/pandas/src/algos_groupby_helper.pxi @@ -0,0 +1,1369 @@ +""" +Template for each `dtype` helper function using groupby + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +cdef extern from "numpy/npy_math.h": + double NAN "NPY_NAN" +_int64_max = np.iinfo(np.int64).max + +#---------------------------------------------------------------------- +# group_add, group_prod, group_var, group_mean, group_ohlc +#---------------------------------------------------------------------- + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_add_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, count + ndarray[float64_t, ndim=2] sumx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + sumx = np.zeros_like(out) + + N, K = (<object> values).shape + + with nogil: + + if K > 1: + + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + sumx[lab, j] += val + + else: + + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val: + nobs[lab, 0] += 1 + sumx[lab, 0] += val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = sumx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_prod_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, count + ndarray[float64_t, ndim=2] prodx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + prodx = np.ones_like(out) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + prodx[lab, j] *= val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val: + nobs[lab, 0] += 1 + prodx[lab, 0] *= val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = prodx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +@cython.cdivision(True) +def group_var_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, ct, oldmean + ndarray[float64_t, ndim=2] nobs, mean + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + mean = np.zeros_like(out) + + N, K = (<object> values).shape + + out[:, :] = 0.0 + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + oldmean = mean[lab, j] + mean[lab, j] += (val - oldmean) / nobs[lab, j] + out[lab, j] += (val - mean[lab, j]) * (val - oldmean) + + for i in range(ncounts): + for j in range(K): + ct = nobs[i, j] + if ct < 2: + out[i, j] = NAN + else: + out[i, j] /= (ct - 1) +# add passing bin edges, instead of labels + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_mean_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, count + ndarray[float64_t, ndim=2] sumx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + sumx = np.zeros_like(out) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + # not nan + if val == val: + nobs[lab, j] += 1 + sumx[lab, j] += val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + # not nan + if val == val: + nobs[lab, 0] += 1 + sumx[lab, 0] += val + + for i in range(ncounts): + for j in range(K): + count = nobs[i, j] + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = sumx[i, j] / count + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_ohlc_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab + float64_t val, count + Py_ssize_t ngroups = len(counts) + + if len(labels) == 0: + return + + N, K = (<object> values).shape + + if out.shape[1] != 4: + raise ValueError('Output array must have 4 columns') + + if K > 1: + raise NotImplementedError("Argument 'values' must have only " + "one dimension") + out.fill(np.nan) + + with nogil: + for i in range(N): + lab = labels[i] + if lab == -1: + continue + + counts[lab] += 1 + val = values[i, 0] + if val != val: + continue + + if out[lab, 0] != out[lab, 0]: + out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val + else: + out[lab, 1] = max(out[lab, 1], val) + out[lab, 2] = min(out[lab, 2], val) + out[lab, 3] = val + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_add_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, count + ndarray[float32_t, ndim=2] sumx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + sumx = np.zeros_like(out) + + N, K = (<object> values).shape + + with nogil: + + if K > 1: + + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + sumx[lab, j] += val + + else: + + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val: + nobs[lab, 0] += 1 + sumx[lab, 0] += val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = sumx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_prod_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, count + ndarray[float32_t, ndim=2] prodx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + prodx = np.ones_like(out) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + prodx[lab, j] *= val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val: + nobs[lab, 0] += 1 + prodx[lab, 0] *= val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = prodx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +@cython.cdivision(True) +def group_var_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, ct, oldmean + ndarray[float32_t, ndim=2] nobs, mean + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + mean = np.zeros_like(out) + + N, K = (<object> values).shape + + out[:, :] = 0.0 + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + oldmean = mean[lab, j] + mean[lab, j] += (val - oldmean) / nobs[lab, j] + out[lab, j] += (val - mean[lab, j]) * (val - oldmean) + + for i in range(ncounts): + for j in range(K): + ct = nobs[i, j] + if ct < 2: + out[i, j] = NAN + else: + out[i, j] /= (ct - 1) +# add passing bin edges, instead of labels + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_mean_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, count + ndarray[float32_t, ndim=2] sumx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + sumx = np.zeros_like(out) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + # not nan + if val == val: + nobs[lab, j] += 1 + sumx[lab, j] += val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + # not nan + if val == val: + nobs[lab, 0] += 1 + sumx[lab, 0] += val + + for i in range(ncounts): + for j in range(K): + count = nobs[i, j] + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = sumx[i, j] / count + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_ohlc_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab + float32_t val, count + Py_ssize_t ngroups = len(counts) + + if len(labels) == 0: + return + + N, K = (<object> values).shape + + if out.shape[1] != 4: + raise ValueError('Output array must have 4 columns') + + if K > 1: + raise NotImplementedError("Argument 'values' must have only " + "one dimension") + out.fill(np.nan) + + with nogil: + for i in range(N): + lab = labels[i] + if lab == -1: + continue + + counts[lab] += 1 + val = values[i, 0] + if val != val: + continue + + if out[lab, 0] != out[lab, 0]: + out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val + else: + out[lab, 1] = max(out[lab, 1], val) + out[lab, 2] = min(out[lab, 2], val) + out[lab, 3] = val + +#---------------------------------------------------------------------- +# group_nth, group_last +#---------------------------------------------------------------------- + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_last_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, count + ndarray[float64_t, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + nobs[lab, j] += 1 + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = resx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_nth_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels, int64_t rank): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, count + ndarray[float64_t, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + nobs[lab, j] += 1 + if nobs[lab, j] == rank: + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = resx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_last_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, count + ndarray[float32_t, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + nobs[lab, j] += 1 + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = resx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_nth_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels, int64_t rank): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, count + ndarray[float32_t, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + nobs[lab, j] += 1 + if nobs[lab, j] == rank: + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = resx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_last_int64(ndarray[int64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[int64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + int64_t val, count + ndarray[int64_t, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != iNaT: + nobs[lab, j] += 1 + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = iNaT + else: + out[i, j] = resx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_nth_int64(ndarray[int64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[int64_t, ndim=2] values, + ndarray[int64_t] labels, int64_t rank): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + int64_t val, count + ndarray[int64_t, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != iNaT: + nobs[lab, j] += 1 + if nobs[lab, j] == rank: + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = iNaT + else: + out[i, j] = resx[i, j] + +#---------------------------------------------------------------------- +# group_min, group_max +#---------------------------------------------------------------------- + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_max_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, count + ndarray[float64_t, ndim=2] maxx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + maxx = np.empty_like(out) + maxx.fill(-np.inf) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + nobs[lab, j] += 1 + if val > maxx[lab, j]: + maxx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != NAN: + nobs[lab, 0] += 1 + if val > maxx[lab, 0]: + maxx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = maxx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_min_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float64_t val, count + ndarray[float64_t, ndim=2] minx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + minx = np.empty_like(out) + minx.fill(np.inf) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + + nobs[lab, j] += 1 + if val < minx[lab, j]: + minx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != NAN: + nobs[lab, 0] += 1 + if val < minx[lab, 0]: + minx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = minx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_max_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, count + ndarray[float32_t, ndim=2] maxx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + maxx = np.empty_like(out) + maxx.fill(-np.inf) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + nobs[lab, j] += 1 + if val > maxx[lab, j]: + maxx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != NAN: + nobs[lab, 0] += 1 + if val > maxx[lab, 0]: + maxx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = maxx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_min_float32(ndarray[float32_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float32_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + float32_t val, count + ndarray[float32_t, ndim=2] minx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + minx = np.empty_like(out) + minx.fill(np.inf) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != NAN: + + nobs[lab, j] += 1 + if val < minx[lab, j]: + minx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != NAN: + nobs[lab, 0] += 1 + if val < minx[lab, 0]: + minx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = minx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_max_int64(ndarray[int64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[int64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + int64_t val, count + ndarray[int64_t, ndim=2] maxx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + maxx = np.empty_like(out) + maxx.fill(-_int64_max) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != iNaT: + nobs[lab, j] += 1 + if val > maxx[lab, j]: + maxx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != iNaT: + nobs[lab, 0] += 1 + if val > maxx[lab, 0]: + maxx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = iNaT + else: + out[i, j] = maxx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_min_int64(ndarray[int64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[int64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + int64_t val, count + ndarray[int64_t, ndim=2] minx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + minx = np.empty_like(out) + minx.fill(_int64_max) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != iNaT: + + nobs[lab, j] += 1 + if val < minx[lab, j]: + minx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != iNaT: + nobs[lab, 0] += 1 + if val < minx[lab, 0]: + minx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = iNaT + else: + out[i, j] = minx[i, j] + +#---------------------------------------------------------------------- +# other grouping functions not needing a template +#---------------------------------------------------------------------- + + +def group_median_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, ngroups, size + ndarray[int64_t] _counts + ndarray data + float64_t* ptr + ngroups = len(counts) + N, K = (<object> values).shape + + indexer, _counts = groupsort_indexer(labels, ngroups) + counts[:] = _counts[1:] + + data = np.empty((K, N), dtype=np.float64) + ptr = <float64_t*> data.data + + take_2d_axis1_float64_float64(values.T, indexer, out=data) + + for i in range(K): + # exclude NA group + ptr += _counts[0] + for j in range(ngroups): + size = _counts[j + 1] + out[j, i] = _median_linear(ptr, size) + ptr += size + + +@cython.boundscheck(False) +@cython.wraparound(False) +def group_cumprod_float64(float64_t[:, :] out, + float64_t[:, :] values, + int64_t[:] labels, + float64_t[:, :] accum): + """ + Only transforms on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, size + float64_t val + int64_t lab + + N, K = (<object> values).shape + accum = np.ones_like(accum) + + with nogil: + for i in range(N): + lab = labels[i] + + if lab < 0: + continue + for j in range(K): + val = values[i, j] + if val == val: + accum[lab, j] *= val + out[i, j] = accum[lab, j] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def group_cumsum(numeric[:, :] out, + numeric[:, :] values, + int64_t[:] labels, + numeric[:, :] accum): + """ + Only transforms on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, size + numeric val + int64_t lab + + N, K = (<object> values).shape + accum = np.zeros_like(accum) + + with nogil: + for i in range(N): + lab = labels[i] + + if lab < 0: + continue + for j in range(K): + val = values[i, j] + if val == val: + accum[lab, j] += val + out[i, j] = accum[lab, j] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def group_shift_indexer(int64_t[:] out, int64_t[:] labels, + int ngroups, int periods): + cdef: + Py_ssize_t N, i, j, ii + int offset, sign + int64_t lab, idxer, idxer_slot + int64_t[:] label_seen = np.zeros(ngroups, dtype=np.int64) + int64_t[:, :] label_indexer + + N, = (<object> labels).shape + + if periods < 0: + periods = -periods + offset = N - 1 + sign = -1 + elif periods > 0: + offset = 0 + sign = 1 + + if periods == 0: + with nogil: + for i in range(N): + out[i] = i + else: + # array of each previous indexer seen + label_indexer = np.zeros((ngroups, periods), dtype=np.int64) + with nogil: + for i in range(N): + ## reverse iterator if shifting backwards + ii = offset + sign * i + lab = labels[ii] + label_seen[lab] += 1 + + idxer_slot = label_seen[lab] % periods + idxer = label_indexer[lab, idxer_slot] + + if label_seen[lab] > periods: + out[ii] = idxer + else: + out[ii] = -1 + + label_indexer[lab, idxer_slot] = ii diff --git a/pandas/src/algos_groupby_helper.pxi.in b/pandas/src/algos_groupby_helper.pxi.in new file mode 100644 index 0000000000000..6b9d8f07587bc --- /dev/null +++ b/pandas/src/algos_groupby_helper.pxi.in @@ -0,0 +1,713 @@ +""" +Template for each `dtype` helper function using groupby + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +cdef extern from "numpy/npy_math.h": + double NAN "NPY_NAN" +_int64_max = np.iinfo(np.int64).max + +#---------------------------------------------------------------------- +# group_add, group_prod, group_var, group_mean, group_ohlc +#---------------------------------------------------------------------- + +{{py: + +# name, c_type, dest_type, dest_dtype +dtypes = [('float64', 'float64_t', 'float64_t', 'np.float64'), + ('float32', 'float32_t', 'float32_t', 'np.float32')] + +def get_dispatch(dtypes): + + for name, c_type, dest_type, dest_dtype in dtypes: + + dest_type2 = dest_type + dest_type = dest_type.replace('_t', '') + + yield name, c_type, dest_type, dest_type2, dest_dtype +}} + +{{for name, c_type, dest_type, dest_type2, dest_dtype in get_dispatch(dtypes)}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_add_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{c_type}}, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, count + ndarray[{{dest_type2}}, ndim=2] sumx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + sumx = np.zeros_like(out) + + N, K = (<object> values).shape + + with nogil: + + if K > 1: + + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + sumx[lab, j] += val + + else: + + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val: + nobs[lab, 0] += 1 + sumx[lab, 0] += val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = sumx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_prod_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{c_type}}, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, count + ndarray[{{dest_type2}}, ndim=2] prodx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + prodx = np.ones_like(out) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + prodx[lab, j] *= val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val: + nobs[lab, 0] += 1 + prodx[lab, 0] *= val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = prodx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +@cython.cdivision(True) +def group_var_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{dest_type2}}, ndim=2] values, + ndarray[int64_t] labels): + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, ct, oldmean + ndarray[{{dest_type2}}, ndim=2] nobs, mean + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + mean = np.zeros_like(out) + + N, K = (<object> values).shape + + out[:, :] = 0.0 + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + + for j in range(K): + val = values[i, j] + + # not nan + if val == val: + nobs[lab, j] += 1 + oldmean = mean[lab, j] + mean[lab, j] += (val - oldmean) / nobs[lab, j] + out[lab, j] += (val - mean[lab, j]) * (val - oldmean) + + for i in range(ncounts): + for j in range(K): + ct = nobs[i, j] + if ct < 2: + out[i, j] = NAN + else: + out[i, j] /= (ct - 1) +# add passing bin edges, instead of labels + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_mean_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{dest_type2}}, ndim=2] values, + ndarray[int64_t] labels): + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, count + ndarray[{{dest_type2}}, ndim=2] sumx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + sumx = np.zeros_like(out) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + # not nan + if val == val: + nobs[lab, j] += 1 + sumx[lab, j] += val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + # not nan + if val == val: + nobs[lab, 0] += 1 + sumx[lab, 0] += val + + for i in range(ncounts): + for j in range(K): + count = nobs[i, j] + if nobs[i, j] == 0: + out[i, j] = NAN + else: + out[i, j] = sumx[i, j] / count + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_ohlc_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{dest_type2}}, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab + {{dest_type2}} val, count + Py_ssize_t ngroups = len(counts) + + if len(labels) == 0: + return + + N, K = (<object> values).shape + + if out.shape[1] != 4: + raise ValueError('Output array must have 4 columns') + + if K > 1: + raise NotImplementedError("Argument 'values' must have only " + "one dimension") + out.fill(np.nan) + + with nogil: + for i in range(N): + lab = labels[i] + if lab == -1: + continue + + counts[lab] += 1 + val = values[i, 0] + if val != val: + continue + + if out[lab, 0] != out[lab, 0]: + out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val + else: + out[lab, 1] = max(out[lab, 1], val) + out[lab, 2] = min(out[lab, 2], val) + out[lab, 3] = val + +{{endfor}} + +#---------------------------------------------------------------------- +# group_nth, group_last +#---------------------------------------------------------------------- + +{{py: + +# name, c_type, dest_type2, nan_val +dtypes = [('float64', 'float64_t', 'float64_t', 'NAN'), + ('float32', 'float32_t', 'float32_t', 'NAN'), + ('int64', 'int64_t', 'int64_t', 'iNaT')] + +def get_dispatch(dtypes): + + for name, c_type, dest_type2, nan_val in dtypes: + + yield name, c_type, dest_type2, nan_val +}} + + +{{for name, c_type, dest_type2, nan_val in get_dispatch(dtypes)}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_last_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{c_type}}, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, count + ndarray[{{dest_type2}}, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != {{nan_val}}: + nobs[lab, j] += 1 + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = {{nan_val}} + else: + out[i, j] = resx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_nth_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{c_type}}, ndim=2] values, + ndarray[int64_t] labels, int64_t rank): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, count + ndarray[{{dest_type2}}, ndim=2] resx + ndarray[int64_t, ndim=2] nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros((<object> out).shape, dtype=np.int64) + resx = np.empty_like(out) + + N, K = (<object> values).shape + + with nogil: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != {{nan_val}}: + nobs[lab, j] += 1 + if nobs[lab, j] == rank: + resx[lab, j] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = {{nan_val}} + else: + out[i, j] = resx[i, j] + +{{endfor}} + +#---------------------------------------------------------------------- +# group_min, group_max +#---------------------------------------------------------------------- + +{{py: + +# name, c_type, dest_type2, nan_val +dtypes = [('float64', 'float64_t', 'NAN', 'np.inf'), + ('float32', 'float32_t', 'NAN', 'np.inf'), + ('int64', 'int64_t', 'iNaT', '_int64_max')] + +def get_dispatch(dtypes): + + for name, dest_type2, nan_val, inf_val in dtypes: + yield name, dest_type2, nan_val, inf_val +}} + + +{{for name, dest_type2, nan_val, inf_val in get_dispatch(dtypes)}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_max_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{dest_type2}}, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, count + ndarray[{{dest_type2}}, ndim=2] maxx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + maxx = np.empty_like(out) + maxx.fill(-{{inf_val}}) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != {{nan_val}}: + nobs[lab, j] += 1 + if val > maxx[lab, j]: + maxx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != {{nan_val}}: + nobs[lab, 0] += 1 + if val > maxx[lab, 0]: + maxx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = {{nan_val}} + else: + out[i, j] = maxx[i, j] + + +@cython.wraparound(False) +@cython.boundscheck(False) +def group_min_{{name}}(ndarray[{{dest_type2}}, ndim=2] out, + ndarray[int64_t] counts, + ndarray[{{dest_type2}}, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, lab, ncounts = len(counts) + {{dest_type2}} val, count + ndarray[{{dest_type2}}, ndim=2] minx, nobs + + if not len(values) == len(labels): + raise AssertionError("len(index) != len(labels)") + + nobs = np.zeros_like(out) + + minx = np.empty_like(out) + minx.fill({{inf_val}}) + + N, K = (<object> values).shape + + with nogil: + if K > 1: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + for j in range(K): + val = values[i, j] + + # not nan + if val == val and val != {{nan_val}}: + + nobs[lab, j] += 1 + if val < minx[lab, j]: + minx[lab, j] = val + else: + for i in range(N): + lab = labels[i] + if lab < 0: + continue + + counts[lab] += 1 + val = values[i, 0] + + # not nan + if val == val and val != {{nan_val}}: + nobs[lab, 0] += 1 + if val < minx[lab, 0]: + minx[lab, 0] = val + + for i in range(ncounts): + for j in range(K): + if nobs[i, j] == 0: + out[i, j] = {{nan_val}} + else: + out[i, j] = minx[i, j] + +{{endfor}} + +#---------------------------------------------------------------------- +# other grouping functions not needing a template +#---------------------------------------------------------------------- + + +def group_median_float64(ndarray[float64_t, ndim=2] out, + ndarray[int64_t] counts, + ndarray[float64_t, ndim=2] values, + ndarray[int64_t] labels): + """ + Only aggregates on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, ngroups, size + ndarray[int64_t] _counts + ndarray data + float64_t* ptr + ngroups = len(counts) + N, K = (<object> values).shape + + indexer, _counts = groupsort_indexer(labels, ngroups) + counts[:] = _counts[1:] + + data = np.empty((K, N), dtype=np.float64) + ptr = <float64_t*> data.data + + take_2d_axis1_float64_float64(values.T, indexer, out=data) + + for i in range(K): + # exclude NA group + ptr += _counts[0] + for j in range(ngroups): + size = _counts[j + 1] + out[j, i] = _median_linear(ptr, size) + ptr += size + + +@cython.boundscheck(False) +@cython.wraparound(False) +def group_cumprod_float64(float64_t[:, :] out, + float64_t[:, :] values, + int64_t[:] labels, + float64_t[:, :] accum): + """ + Only transforms on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, size + float64_t val + int64_t lab + + N, K = (<object> values).shape + accum = np.ones_like(accum) + + with nogil: + for i in range(N): + lab = labels[i] + + if lab < 0: + continue + for j in range(K): + val = values[i, j] + if val == val: + accum[lab, j] *= val + out[i, j] = accum[lab, j] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def group_cumsum(numeric[:, :] out, + numeric[:, :] values, + int64_t[:] labels, + numeric[:, :] accum): + """ + Only transforms on axis=0 + """ + cdef: + Py_ssize_t i, j, N, K, size + numeric val + int64_t lab + + N, K = (<object> values).shape + accum = np.zeros_like(accum) + + with nogil: + for i in range(N): + lab = labels[i] + + if lab < 0: + continue + for j in range(K): + val = values[i, j] + if val == val: + accum[lab, j] += val + out[i, j] = accum[lab, j] + + +@cython.boundscheck(False) +@cython.wraparound(False) +def group_shift_indexer(int64_t[:] out, int64_t[:] labels, + int ngroups, int periods): + cdef: + Py_ssize_t N, i, j, ii + int offset, sign + int64_t lab, idxer, idxer_slot + int64_t[:] label_seen = np.zeros(ngroups, dtype=np.int64) + int64_t[:, :] label_indexer + + N, = (<object> labels).shape + + if periods < 0: + periods = -periods + offset = N - 1 + sign = -1 + elif periods > 0: + offset = 0 + sign = 1 + + if periods == 0: + with nogil: + for i in range(N): + out[i] = i + else: + # array of each previous indexer seen + label_indexer = np.zeros((ngroups, periods), dtype=np.int64) + with nogil: + for i in range(N): + ## reverse iterator if shifting backwards + ii = offset + sign * i + lab = labels[ii] + label_seen[lab] += 1 + + idxer_slot = label_seen[lab] % periods + idxer = label_indexer[lab, idxer_slot] + + if label_seen[lab] > periods: + out[ii] = idxer + else: + out[ii] = -1 + + label_indexer[lab, idxer_slot] = ii diff --git a/pandas/src/algos_join_helper.pxi b/pandas/src/algos_join_helper.pxi new file mode 100644 index 0000000000000..44b8159351492 --- /dev/null +++ b/pandas/src/algos_join_helper.pxi @@ -0,0 +1,1899 @@ +""" +Template for each `dtype` helper function for join + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +#---------------------------------------------------------------------- +# left_join_indexer, inner_join_indexer, outer_join_indexer +#---------------------------------------------------------------------- + +# Joins on ordered, unique indices + +# right might contain non-unique values + + +@cython.wraparound(False) +@cython.boundscheck(False) +def left_join_indexer_unique_float64(ndarray[float64_t] left, + ndarray[float64_t] right): + cdef: + Py_ssize_t i, j, nleft, nright + ndarray[int64_t] indexer + float64_t lval, rval + + i = 0 + j = 0 + nleft = len(left) + nright = len(right) + + indexer = np.empty(nleft, dtype=np.int64) + while True: + if i == nleft: + break + + if j == nright: + indexer[i] = -1 + i += 1 + continue + + rval = right[j] + + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + + if left[i] == right[j]: + indexer[i] = j + i += 1 + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + j += 1 + elif left[i] > rval: + indexer[i] = -1 + j += 1 + else: + indexer[i] = -1 + i += 1 + return indexer + + +# @cython.wraparound(False) +# @cython.boundscheck(False) +def left_join_indexer_float64(ndarray[float64_t] left, + ndarray[float64_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + float64_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[float64_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.float64) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + i += 1 + count += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def inner_join_indexer_float64(ndarray[float64_t] left, + ndarray[float64_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + float64_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[float64_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.float64) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = rval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def outer_join_indexer_float64(ndarray[float64_t] left, + ndarray[float64_t] right): + cdef: + Py_ssize_t i, j, nright, nleft, count + float64_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[float64_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft == 0: + count = nright + elif nright == 0: + count = nleft + else: + while True: + if i == nleft: + count += nright - j + break + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + count += 1 + j += 1 + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.float64) + + # do it again, but populate the indexers / result + + i = 0 + j = 0 + count = 0 + if nleft == 0: + for j in range(nright): + lindexer[j] = -1 + rindexer[j] = j + result[j] = right[j] + elif nright == 0: + for i in range(nleft): + lindexer[i] = i + rindexer[i] = -1 + result[i] = left[i] + else: + while True: + if i == nleft: + while j < nright: + lindexer[count] = -1 + rindexer[count] = j + result[count] = right[j] + count += 1 + j += 1 + break + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = lval + count += 1 + i += 1 + else: + lindexer[count] = -1 + rindexer[count] = j + result[count] = rval + count += 1 + j += 1 + + return result, lindexer, rindexer + +# Joins on ordered, unique indices + +# right might contain non-unique values + + +@cython.wraparound(False) +@cython.boundscheck(False) +def left_join_indexer_unique_float32(ndarray[float32_t] left, + ndarray[float32_t] right): + cdef: + Py_ssize_t i, j, nleft, nright + ndarray[int64_t] indexer + float32_t lval, rval + + i = 0 + j = 0 + nleft = len(left) + nright = len(right) + + indexer = np.empty(nleft, dtype=np.int64) + while True: + if i == nleft: + break + + if j == nright: + indexer[i] = -1 + i += 1 + continue + + rval = right[j] + + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + + if left[i] == right[j]: + indexer[i] = j + i += 1 + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + j += 1 + elif left[i] > rval: + indexer[i] = -1 + j += 1 + else: + indexer[i] = -1 + i += 1 + return indexer + + +# @cython.wraparound(False) +# @cython.boundscheck(False) +def left_join_indexer_float32(ndarray[float32_t] left, + ndarray[float32_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + float32_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[float32_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.float32) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + i += 1 + count += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def inner_join_indexer_float32(ndarray[float32_t] left, + ndarray[float32_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + float32_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[float32_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.float32) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = rval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def outer_join_indexer_float32(ndarray[float32_t] left, + ndarray[float32_t] right): + cdef: + Py_ssize_t i, j, nright, nleft, count + float32_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[float32_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft == 0: + count = nright + elif nright == 0: + count = nleft + else: + while True: + if i == nleft: + count += nright - j + break + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + count += 1 + j += 1 + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.float32) + + # do it again, but populate the indexers / result + + i = 0 + j = 0 + count = 0 + if nleft == 0: + for j in range(nright): + lindexer[j] = -1 + rindexer[j] = j + result[j] = right[j] + elif nright == 0: + for i in range(nleft): + lindexer[i] = i + rindexer[i] = -1 + result[i] = left[i] + else: + while True: + if i == nleft: + while j < nright: + lindexer[count] = -1 + rindexer[count] = j + result[count] = right[j] + count += 1 + j += 1 + break + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = lval + count += 1 + i += 1 + else: + lindexer[count] = -1 + rindexer[count] = j + result[count] = rval + count += 1 + j += 1 + + return result, lindexer, rindexer + +# Joins on ordered, unique indices + +# right might contain non-unique values + + +@cython.wraparound(False) +@cython.boundscheck(False) +def left_join_indexer_unique_object(ndarray[object] left, + ndarray[object] right): + cdef: + Py_ssize_t i, j, nleft, nright + ndarray[int64_t] indexer + object lval, rval + + i = 0 + j = 0 + nleft = len(left) + nright = len(right) + + indexer = np.empty(nleft, dtype=np.int64) + while True: + if i == nleft: + break + + if j == nright: + indexer[i] = -1 + i += 1 + continue + + rval = right[j] + + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + + if left[i] == right[j]: + indexer[i] = j + i += 1 + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + j += 1 + elif left[i] > rval: + indexer[i] = -1 + j += 1 + else: + indexer[i] = -1 + i += 1 + return indexer + + +# @cython.wraparound(False) +# @cython.boundscheck(False) +def left_join_indexer_object(ndarray[object] left, + ndarray[object] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + object lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[object] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=object) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + i += 1 + count += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def inner_join_indexer_object(ndarray[object] left, + ndarray[object] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + object lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[object] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=object) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = rval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def outer_join_indexer_object(ndarray[object] left, + ndarray[object] right): + cdef: + Py_ssize_t i, j, nright, nleft, count + object lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[object] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft == 0: + count = nright + elif nright == 0: + count = nleft + else: + while True: + if i == nleft: + count += nright - j + break + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + count += 1 + j += 1 + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=object) + + # do it again, but populate the indexers / result + + i = 0 + j = 0 + count = 0 + if nleft == 0: + for j in range(nright): + lindexer[j] = -1 + rindexer[j] = j + result[j] = right[j] + elif nright == 0: + for i in range(nleft): + lindexer[i] = i + rindexer[i] = -1 + result[i] = left[i] + else: + while True: + if i == nleft: + while j < nright: + lindexer[count] = -1 + rindexer[count] = j + result[count] = right[j] + count += 1 + j += 1 + break + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = lval + count += 1 + i += 1 + else: + lindexer[count] = -1 + rindexer[count] = j + result[count] = rval + count += 1 + j += 1 + + return result, lindexer, rindexer + +# Joins on ordered, unique indices + +# right might contain non-unique values + + +@cython.wraparound(False) +@cython.boundscheck(False) +def left_join_indexer_unique_int32(ndarray[int32_t] left, + ndarray[int32_t] right): + cdef: + Py_ssize_t i, j, nleft, nright + ndarray[int64_t] indexer + int32_t lval, rval + + i = 0 + j = 0 + nleft = len(left) + nright = len(right) + + indexer = np.empty(nleft, dtype=np.int64) + while True: + if i == nleft: + break + + if j == nright: + indexer[i] = -1 + i += 1 + continue + + rval = right[j] + + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + + if left[i] == right[j]: + indexer[i] = j + i += 1 + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + j += 1 + elif left[i] > rval: + indexer[i] = -1 + j += 1 + else: + indexer[i] = -1 + i += 1 + return indexer + + +# @cython.wraparound(False) +# @cython.boundscheck(False) +def left_join_indexer_int32(ndarray[int32_t] left, + ndarray[int32_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + int32_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[int32_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.int32) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + i += 1 + count += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def inner_join_indexer_int32(ndarray[int32_t] left, + ndarray[int32_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + int32_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[int32_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.int32) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = rval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def outer_join_indexer_int32(ndarray[int32_t] left, + ndarray[int32_t] right): + cdef: + Py_ssize_t i, j, nright, nleft, count + int32_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[int32_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft == 0: + count = nright + elif nright == 0: + count = nleft + else: + while True: + if i == nleft: + count += nright - j + break + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + count += 1 + j += 1 + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.int32) + + # do it again, but populate the indexers / result + + i = 0 + j = 0 + count = 0 + if nleft == 0: + for j in range(nright): + lindexer[j] = -1 + rindexer[j] = j + result[j] = right[j] + elif nright == 0: + for i in range(nleft): + lindexer[i] = i + rindexer[i] = -1 + result[i] = left[i] + else: + while True: + if i == nleft: + while j < nright: + lindexer[count] = -1 + rindexer[count] = j + result[count] = right[j] + count += 1 + j += 1 + break + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = lval + count += 1 + i += 1 + else: + lindexer[count] = -1 + rindexer[count] = j + result[count] = rval + count += 1 + j += 1 + + return result, lindexer, rindexer + +# Joins on ordered, unique indices + +# right might contain non-unique values + + +@cython.wraparound(False) +@cython.boundscheck(False) +def left_join_indexer_unique_int64(ndarray[int64_t] left, + ndarray[int64_t] right): + cdef: + Py_ssize_t i, j, nleft, nright + ndarray[int64_t] indexer + int64_t lval, rval + + i = 0 + j = 0 + nleft = len(left) + nright = len(right) + + indexer = np.empty(nleft, dtype=np.int64) + while True: + if i == nleft: + break + + if j == nright: + indexer[i] = -1 + i += 1 + continue + + rval = right[j] + + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + + if left[i] == right[j]: + indexer[i] = j + i += 1 + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + j += 1 + elif left[i] > rval: + indexer[i] = -1 + j += 1 + else: + indexer[i] = -1 + i += 1 + return indexer + + +# @cython.wraparound(False) +# @cython.boundscheck(False) +def left_join_indexer_int64(ndarray[int64_t] left, + ndarray[int64_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + int64_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[int64_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.int64) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + i += 1 + count += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def inner_join_indexer_int64(ndarray[int64_t] left, + ndarray[int64_t] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + int64_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[int64_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.int64) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = rval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def outer_join_indexer_int64(ndarray[int64_t] left, + ndarray[int64_t] right): + cdef: + Py_ssize_t i, j, nright, nleft, count + int64_t lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[int64_t] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft == 0: + count = nright + elif nright == 0: + count = nleft + else: + while True: + if i == nleft: + count += nright - j + break + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + count += 1 + j += 1 + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype=np.int64) + + # do it again, but populate the indexers / result + + i = 0 + j = 0 + count = 0 + if nleft == 0: + for j in range(nright): + lindexer[j] = -1 + rindexer[j] = j + result[j] = right[j] + elif nright == 0: + for i in range(nleft): + lindexer[i] = i + rindexer[i] = -1 + result[i] = left[i] + else: + while True: + if i == nleft: + while j < nright: + lindexer[count] = -1 + rindexer[count] = j + result[count] = right[j] + count += 1 + j += 1 + break + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = lval + count += 1 + i += 1 + else: + lindexer[count] = -1 + rindexer[count] = j + result[count] = rval + count += 1 + j += 1 + + return result, lindexer, rindexer diff --git a/pandas/src/algos_join_helper.pxi.in b/pandas/src/algos_join_helper.pxi.in new file mode 100644 index 0000000000000..5b55ec2b1bf6d --- /dev/null +++ b/pandas/src/algos_join_helper.pxi.in @@ -0,0 +1,407 @@ +""" +Template for each `dtype` helper function for join + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +#---------------------------------------------------------------------- +# left_join_indexer, inner_join_indexer, outer_join_indexer +#---------------------------------------------------------------------- + +{{py: + +# name, c_type, dtype +dtypes = [('float64', 'float64_t', 'np.float64'), + ('float32', 'float32_t', 'np.float32'), + ('object', 'object', 'object'), + ('int32', 'int32_t', 'np.int32'), + ('int64', 'int64_t', 'np.int64')] + +def get_dispatch(dtypes): + + for name, c_type, dtype in dtypes: + yield name, c_type, dtype + +}} + +{{for name, c_type, dtype in get_dispatch(dtypes)}} + +# Joins on ordered, unique indices + +# right might contain non-unique values + + +@cython.wraparound(False) +@cython.boundscheck(False) +def left_join_indexer_unique_{{name}}(ndarray[{{c_type}}] left, + ndarray[{{c_type}}] right): + cdef: + Py_ssize_t i, j, nleft, nright + ndarray[int64_t] indexer + {{c_type}} lval, rval + + i = 0 + j = 0 + nleft = len(left) + nright = len(right) + + indexer = np.empty(nleft, dtype=np.int64) + while True: + if i == nleft: + break + + if j == nright: + indexer[i] = -1 + i += 1 + continue + + rval = right[j] + + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + + if left[i] == right[j]: + indexer[i] = j + i += 1 + while i < nleft - 1 and left[i] == rval: + indexer[i] = j + i += 1 + j += 1 + elif left[i] > rval: + indexer[i] = -1 + j += 1 + else: + indexer[i] = -1 + i += 1 + return indexer + + +# @cython.wraparound(False) +# @cython.boundscheck(False) +def left_join_indexer_{{name}}(ndarray[{{c_type}}] left, + ndarray[{{c_type}}] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + {{c_type}} lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[{{c_type}}] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype={{dtype}}) + + i = 0 + j = 0 + count = 0 + if nleft > 0: + while i < nleft: + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + i += 1 + count += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def inner_join_indexer_{{name}}(ndarray[{{c_type}}] left, + ndarray[{{c_type}}] right): + """ + Two-pass algorithm for monotonic indexes. Handles many-to-one merges + """ + cdef: + Py_ssize_t i, j, k, nright, nleft, count + {{c_type}} lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[{{c_type}}] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + # do it again now that result size is known + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype={{dtype}}) + + i = 0 + j = 0 + count = 0 + if nleft > 0 and nright > 0: + while True: + if i == nleft: + break + if j == nright: + break + + lval = left[i] + rval = right[j] + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = rval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + i += 1 + else: + j += 1 + + return result, lindexer, rindexer + + +@cython.wraparound(False) +@cython.boundscheck(False) +def outer_join_indexer_{{name}}(ndarray[{{c_type}}] left, + ndarray[{{c_type}}] right): + cdef: + Py_ssize_t i, j, nright, nleft, count + {{c_type}} lval, rval + ndarray[int64_t] lindexer, rindexer + ndarray[{{c_type}}] result + + nleft = len(left) + nright = len(right) + + i = 0 + j = 0 + count = 0 + if nleft == 0: + count = nright + elif nright == 0: + count = nleft + else: + while True: + if i == nleft: + count += nright - j + break + if j == nright: + count += nleft - i + break + + lval = left[i] + rval = right[j] + if lval == rval: + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + count += 1 + i += 1 + else: + count += 1 + j += 1 + + lindexer = np.empty(count, dtype=np.int64) + rindexer = np.empty(count, dtype=np.int64) + result = np.empty(count, dtype={{dtype}}) + + # do it again, but populate the indexers / result + + i = 0 + j = 0 + count = 0 + if nleft == 0: + for j in range(nright): + lindexer[j] = -1 + rindexer[j] = j + result[j] = right[j] + elif nright == 0: + for i in range(nleft): + lindexer[i] = i + rindexer[i] = -1 + result[i] = left[i] + else: + while True: + if i == nleft: + while j < nright: + lindexer[count] = -1 + rindexer[count] = j + result[count] = right[j] + count += 1 + j += 1 + break + if j == nright: + while i < nleft: + lindexer[count] = i + rindexer[count] = -1 + result[count] = left[i] + count += 1 + i += 1 + break + + lval = left[i] + rval = right[j] + + if lval == rval: + lindexer[count] = i + rindexer[count] = j + result[count] = lval + count += 1 + if i < nleft - 1: + if j < nright - 1 and right[j + 1] == rval: + j += 1 + else: + i += 1 + if left[i] != rval: + j += 1 + elif j < nright - 1: + j += 1 + if lval != right[j]: + i += 1 + else: + # end of the road + break + elif lval < rval: + lindexer[count] = i + rindexer[count] = -1 + result[count] = lval + count += 1 + i += 1 + else: + lindexer[count] = -1 + rindexer[count] = j + result[count] = rval + count += 1 + j += 1 + + return result, lindexer, rindexer + +{{endfor}} \ No newline at end of file diff --git a/pandas/src/algos_take_helper.pxi b/pandas/src/algos_take_helper.pxi new file mode 100644 index 0000000000000..d8fb05804d4e5 --- /dev/null +++ b/pandas/src/algos_take_helper.pxi @@ -0,0 +1,4949 @@ +""" +Template for each `dtype` helper function for take + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +#---------------------------------------------------------------------- +# take_1d, take_2d +#---------------------------------------------------------------------- + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_bool_bool_memview(uint8_t[:] values, + int64_t[:] indexer, + uint8_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + uint8_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_bool_bool(ndarray[uint8_t, ndim=1] values, + int64_t[:] indexer, + uint8_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_bool_bool_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + uint8_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_bool_bool_memview(uint8_t[:, :] values, + int64_t[:] indexer, + uint8_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + uint8_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + uint8_t *v + uint8_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(uint8_t) and + sizeof(uint8_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(uint8_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_bool_bool(ndarray[uint8_t, ndim=2] values, + ndarray[int64_t] indexer, + uint8_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_bool_bool_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + uint8_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + uint8_t *v + uint8_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(uint8_t) and + sizeof(uint8_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(uint8_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_bool_bool_memview(uint8_t[:, :] values, + int64_t[:] indexer, + uint8_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + uint8_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_bool_bool(ndarray[uint8_t, ndim=2] values, + ndarray[int64_t] indexer, + uint8_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_bool_bool_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + uint8_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_bool_bool(ndarray[uint8_t, ndim=2] values, + indexer, + ndarray[uint8_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + uint8_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_bool_object_memview(uint8_t[:] values, + int64_t[:] indexer, + object[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + object fv + + n = indexer.shape[0] + + fv = fill_value + + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = True if values[idx] > 0 else False + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_bool_object(ndarray[uint8_t, ndim=1] values, + int64_t[:] indexer, + object[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_bool_object_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + object fv + + n = indexer.shape[0] + + fv = fill_value + + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = True if values[idx] > 0 else False + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_bool_object_memview(uint8_t[:, :] values, + int64_t[:] indexer, + object[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + object *v + object *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(object) and + sizeof(object) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(object) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = True if values[idx, j] > 0 else False + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_bool_object(ndarray[uint8_t, ndim=2] values, + ndarray[int64_t] indexer, + object[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_bool_object_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + object *v + object *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(object) and + sizeof(object) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(object) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = True if values[idx, j] > 0 else False + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_bool_object_memview(uint8_t[:, :] values, + int64_t[:] indexer, + object[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = True if values[i, idx] > 0 else False + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_bool_object(ndarray[uint8_t, ndim=2] values, + ndarray[int64_t] indexer, + object[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_bool_object_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = True if values[i, idx] > 0 else False + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_bool_object(ndarray[uint8_t, ndim=2] values, + indexer, + ndarray[object, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + object fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = True if values[idx, idx1[j]] > 0 else False + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int8_int8_memview(int8_t[:] values, + int64_t[:] indexer, + int8_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int8_t fv + + n = indexer.shape[0] + + fv = fill_value + + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int8_int8(ndarray[int8_t, ndim=1] values, + int64_t[:] indexer, + int8_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int8_int8_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int8_t fv + + n = indexer.shape[0] + + fv = fill_value + + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int8_int8_memview(int8_t[:, :] values, + int64_t[:] indexer, + int8_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int8_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int8_t *v + int8_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int8_t) and + sizeof(int8_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int8_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int8_int8(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + int8_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int8_int8_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int8_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int8_t *v + int8_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int8_t) and + sizeof(int8_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int8_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int8_int8_memview(int8_t[:, :] values, + int64_t[:] indexer, + int8_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int8_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int8_int8(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + int8_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int8_int8_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int8_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int8_int8(ndarray[int8_t, ndim=2] values, + indexer, + ndarray[int8_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int8_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int8_int32_memview(int8_t[:] values, + int64_t[:] indexer, + int32_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int8_int32(ndarray[int8_t, ndim=1] values, + int64_t[:] indexer, + int32_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int8_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int8_int32_memview(int8_t[:, :] values, + int64_t[:] indexer, + int32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int32_t *v + int32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int32_t) and + sizeof(int32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int8_int32(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + int32_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int8_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int32_t *v + int32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int32_t) and + sizeof(int32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int8_int32_memview(int8_t[:, :] values, + int64_t[:] indexer, + int32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int8_int32(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + int32_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int8_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int8_int32(ndarray[int8_t, ndim=2] values, + indexer, + ndarray[int32_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int32_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int8_int64_memview(int8_t[:] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int8_int64(ndarray[int8_t, ndim=1] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int8_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int8_int64_memview(int8_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int8_int64(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int8_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int8_int64_memview(int8_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int8_int64(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int8_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int8_int64(ndarray[int8_t, ndim=2] values, + indexer, + ndarray[int64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int8_float64_memview(int8_t[:] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int8_float64(ndarray[int8_t, ndim=1] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int8_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int8_float64_memview(int8_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int8_float64(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int8_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int8_float64_memview(int8_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int8_float64(ndarray[int8_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int8_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int8_float64(ndarray[int8_t, ndim=2] values, + indexer, + ndarray[float64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + float64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int16_int16_memview(int16_t[:] values, + int64_t[:] indexer, + int16_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int16_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int16_int16(ndarray[int16_t, ndim=1] values, + int64_t[:] indexer, + int16_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int16_int16_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int16_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int16_int16_memview(int16_t[:, :] values, + int64_t[:] indexer, + int16_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int16_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int16_t *v + int16_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int16_t) and + sizeof(int16_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int16_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int16_int16(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + int16_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int16_int16_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int16_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int16_t *v + int16_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int16_t) and + sizeof(int16_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int16_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int16_int16_memview(int16_t[:, :] values, + int64_t[:] indexer, + int16_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int16_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int16_int16(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + int16_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int16_int16_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int16_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int16_int16(ndarray[int16_t, ndim=2] values, + indexer, + ndarray[int16_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int16_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int16_int32_memview(int16_t[:] values, + int64_t[:] indexer, + int32_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int16_int32(ndarray[int16_t, ndim=1] values, + int64_t[:] indexer, + int32_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int16_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int16_int32_memview(int16_t[:, :] values, + int64_t[:] indexer, + int32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int32_t *v + int32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int32_t) and + sizeof(int32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int16_int32(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + int32_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int16_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int32_t *v + int32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int32_t) and + sizeof(int32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int16_int32_memview(int16_t[:, :] values, + int64_t[:] indexer, + int32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int16_int32(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + int32_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int16_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int16_int32(ndarray[int16_t, ndim=2] values, + indexer, + ndarray[int32_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int32_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int16_int64_memview(int16_t[:] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int16_int64(ndarray[int16_t, ndim=1] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int16_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int16_int64_memview(int16_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int16_int64(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int16_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int16_int64_memview(int16_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int16_int64(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int16_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int16_int64(ndarray[int16_t, ndim=2] values, + indexer, + ndarray[int64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int16_float64_memview(int16_t[:] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int16_float64(ndarray[int16_t, ndim=1] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int16_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int16_float64_memview(int16_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int16_float64(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int16_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int16_float64_memview(int16_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int16_float64(ndarray[int16_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int16_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int16_float64(ndarray[int16_t, ndim=2] values, + indexer, + ndarray[float64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + float64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int32_int32_memview(int32_t[:] values, + int64_t[:] indexer, + int32_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int32_int32(ndarray[int32_t, ndim=1] values, + int64_t[:] indexer, + int32_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int32_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int32_int32_memview(int32_t[:, :] values, + int64_t[:] indexer, + int32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int32_t *v + int32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int32_t) and + sizeof(int32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int32_int32(ndarray[int32_t, ndim=2] values, + ndarray[int64_t] indexer, + int32_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int32_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int32_t *v + int32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int32_t) and + sizeof(int32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int32_int32_memview(int32_t[:, :] values, + int64_t[:] indexer, + int32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int32_int32(ndarray[int32_t, ndim=2] values, + ndarray[int64_t] indexer, + int32_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int32_int32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int32_int32(ndarray[int32_t, ndim=2] values, + indexer, + ndarray[int32_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int32_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int32_int64_memview(int32_t[:] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int32_int64(ndarray[int32_t, ndim=1] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int32_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int32_int64_memview(int32_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int32_int64(ndarray[int32_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int32_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int32_int64_memview(int32_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int32_int64(ndarray[int32_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int32_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int32_int64(ndarray[int32_t, ndim=2] values, + indexer, + ndarray[int64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int32_float64_memview(int32_t[:] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int32_float64(ndarray[int32_t, ndim=1] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int32_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int32_float64_memview(int32_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int32_float64(ndarray[int32_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int32_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int32_float64_memview(int32_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int32_float64(ndarray[int32_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int32_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int32_float64(ndarray[int32_t, ndim=2] values, + indexer, + ndarray[float64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + float64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int64_int64_memview(int64_t[:] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int64_int64(ndarray[int64_t, ndim=1] values, + int64_t[:] indexer, + int64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int64_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + int64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int64_int64_memview(int64_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int64_int64(ndarray[int64_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int64_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + int64_t *v + int64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(int64_t) and + sizeof(int64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(int64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int64_int64_memview(int64_t[:, :] values, + int64_t[:] indexer, + int64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int64_int64(ndarray[int64_t, ndim=2] values, + ndarray[int64_t] indexer, + int64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int64_int64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + int64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int64_int64(ndarray[int64_t, ndim=2] values, + indexer, + ndarray[int64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + int64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_int64_float64_memview(int64_t[:] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_int64_float64(ndarray[int64_t, ndim=1] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_int64_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_int64_float64_memview(int64_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_int64_float64(ndarray[int64_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_int64_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_int64_float64_memview(int64_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_int64_float64(ndarray[int64_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_int64_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_int64_float64(ndarray[int64_t, ndim=2] values, + indexer, + ndarray[float64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + float64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_float32_float32_memview(float32_t[:] values, + int64_t[:] indexer, + float32_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + float32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_float32_float32(ndarray[float32_t, ndim=1] values, + int64_t[:] indexer, + float32_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_float32_float32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + float32_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_float32_float32_memview(float32_t[:, :] values, + int64_t[:] indexer, + float32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + float32_t *v + float32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float32_t) and + sizeof(float32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_float32_float32(ndarray[float32_t, ndim=2] values, + ndarray[int64_t] indexer, + float32_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_float32_float32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float32_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + float32_t *v + float32_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float32_t) and + sizeof(float32_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float32_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_float32_float32_memview(float32_t[:, :] values, + int64_t[:] indexer, + float32_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_float32_float32(ndarray[float32_t, ndim=2] values, + ndarray[int64_t] indexer, + float32_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_float32_float32_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float32_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_float32_float32(ndarray[float32_t, ndim=2] values, + indexer, + ndarray[float32_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + float32_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_float32_float64_memview(float32_t[:] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_float32_float64(ndarray[float32_t, ndim=1] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_float32_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_float32_float64_memview(float32_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_float32_float64(ndarray[float32_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_float32_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_float32_float64_memview(float32_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_float32_float64(ndarray[float32_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_float32_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_float32_float64(ndarray[float32_t, ndim=2] values, + indexer, + ndarray[float64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + float64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_float64_float64_memview(float64_t[:] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_float64_float64(ndarray[float64_t, ndim=1] values, + int64_t[:] indexer, + float64_t[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_float64_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + float64_t fv + + n = indexer.shape[0] + + fv = fill_value + + with nogil: + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_float64_float64_memview(float64_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_float64_float64(ndarray[float64_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_float64_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF True: + cdef: + float64_t *v + float64_t *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(float64_t) and + sizeof(float64_t) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(float64_t) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_float64_float64_memview(float64_t[:, :] values, + int64_t[:] indexer, + float64_t[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_float64_float64(ndarray[float64_t, ndim=2] values, + ndarray[int64_t] indexer, + float64_t[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_float64_float64_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + float64_t fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_float64_float64(ndarray[float64_t, ndim=2] values, + indexer, + ndarray[float64_t, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + float64_t fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_object_object_memview(object[:] values, + int64_t[:] indexer, + object[:] out, + fill_value=np.nan): + + + + cdef: + Py_ssize_t i, n, idx + object fv + + n = indexer.shape[0] + + fv = fill_value + + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_object_object(ndarray[object, ndim=1] values, + int64_t[:] indexer, + object[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_object_object_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + + cdef: + Py_ssize_t i, n, idx + object fv + + n = indexer.shape[0] + + fv = fill_value + + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + out[i] = fv + else: + out[i] = values[idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_object_object_memview(object[:, :] values, + int64_t[:] indexer, + object[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + object *v + object *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(object) and + sizeof(object) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(object) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_object_object(ndarray[object, ndim=2] values, + ndarray[int64_t] indexer, + object[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_object_object_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF False: + cdef: + object *v + object *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(object) and + sizeof(object) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(object) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = values[idx, j] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_object_object_memview(object[:, :] values, + int64_t[:] indexer, + object[:, :] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_object_object(ndarray[object, ndim=2] values, + ndarray[int64_t] indexer, + object[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_object_object_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. + cdef: + Py_ssize_t i, j, k, n, idx + object fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = values[i, idx] + + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_object_object(ndarray[object, ndim=2] values, + indexer, + ndarray[object, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + object fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = values[idx, idx1[j]] diff --git a/pandas/src/algos_take_helper.pxi.in b/pandas/src/algos_take_helper.pxi.in new file mode 100644 index 0000000000000..e9abbcd13f499 --- /dev/null +++ b/pandas/src/algos_take_helper.pxi.in @@ -0,0 +1,261 @@ +""" +Template for each `dtype` helper function for take + +WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in +""" + +#---------------------------------------------------------------------- +# take_1d, take_2d +#---------------------------------------------------------------------- + +{{py: + +# name, dest, c_type_in, c_type_out, preval, postval, can_copy, nogil +dtypes = [ + ('bool', 'bool', 'uint8_t', 'uint8_t', '', '', True, True), + ('bool', 'object', 'uint8_t', 'object', + 'True if ', ' > 0 else False', False, False), + ('int8', 'int8', 'int8_t', 'int8_t', '', '', True, False), + ('int8', 'int32', 'int8_t', 'int32_t', '', '', False, True), + ('int8', 'int64', 'int8_t', 'int64_t', '', '', False, True), + ('int8', 'float64', 'int8_t', 'float64_t', '', '', False, True), + ('int16', 'int16', 'int16_t', 'int16_t', '', '', True, True), + ('int16', 'int32', 'int16_t', 'int32_t', '', '', False, True), + ('int16', 'int64', 'int16_t', 'int64_t', '', '', False, True), + ('int16', 'float64', 'int16_t', 'float64_t', '', '', False, True), + ('int32', 'int32', 'int32_t', 'int32_t', '', '', True, True), + ('int32', 'int64', 'int32_t', 'int64_t', '', '', False, True), + ('int32', 'float64', 'int32_t', 'float64_t', '', '', False, True), + ('int64', 'int64', 'int64_t', 'int64_t', '', '', True, True), + ('int64', 'float64', 'int64_t', 'float64_t', '', '', False, True), + ('float32', 'float32', 'float32_t', 'float32_t', '', '', True, True), + ('float32', 'float64', 'float32_t', 'float64_t', '', '', False, True), + ('float64', 'float64', 'float64_t', 'float64_t', '', '', True, True), + ('object', 'object', 'object', 'object', '', '', False, False)] + + +def get_dispatch(dtypes): + + inner_take_1d_template = """ + cdef: + Py_ssize_t i, n, idx + %(c_type_out)s fv + + n = indexer.shape[0] + + fv = fill_value + + %(nogil_str)s + %(tab)sfor i from 0 <= i < n: + %(tab)s idx = indexer[i] + %(tab)s if idx == -1: + %(tab)s out[i] = fv + %(tab)s else: + %(tab)s out[i] = %(preval)svalues[idx]%(postval)s +""" + + inner_take_2d_axis0_template = """\ + cdef: + Py_ssize_t i, j, k, n, idx + %(c_type_out)s fv + + n = len(indexer) + k = values.shape[1] + + fv = fill_value + + IF %(can_copy)s: + cdef: + %(c_type_out)s *v + %(c_type_out)s *o + + #GH3130 + if (values.strides[1] == out.strides[1] and + values.strides[1] == sizeof(%(c_type_out)s) and + sizeof(%(c_type_out)s) * n >= 256): + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + v = &values[idx, 0] + o = &out[i, 0] + memmove(o, v, <size_t>(sizeof(%(c_type_out)s) * k)) + return + + for i from 0 <= i < n: + idx = indexer[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + out[i, j] = %(preval)svalues[idx, j]%(postval)s +""" + + inner_take_2d_axis1_template = """\ + cdef: + Py_ssize_t i, j, k, n, idx + %(c_type_out)s fv + + n = len(values) + k = len(indexer) + + if n == 0 or k == 0: + return + + fv = fill_value + + for i from 0 <= i < n: + for j from 0 <= j < k: + idx = indexer[j] + if idx == -1: + out[i, j] = fv + else: + out[i, j] = %(preval)svalues[i, idx]%(postval)s +""" + + for (name, dest, c_type_in, c_type_out, preval, postval, + can_copy, nogil) in dtypes: + if nogil: + nogil_str = "with nogil:" + tab = ' ' + else: + nogil_str = '' + tab = '' + + args = dict(name=name, dest=dest, c_type_in=c_type_in, + c_type_out=c_type_out, preval=preval, postval=postval, + can_copy=can_copy, nogil_str=nogil_str, tab=tab) + + inner_take_1d = inner_take_1d_template % args + inner_take_2d_axis0 = inner_take_2d_axis0_template % args + inner_take_2d_axis1 = inner_take_2d_axis1_template % args + + yield (name, dest, c_type_in, c_type_out, preval, postval, can_copy, + inner_take_1d, inner_take_2d_axis0, inner_take_2d_axis1) + +}} + + +{{for name, dest, c_type_in, c_type_out, preval, postval, can_copy, + inner_take_1d, inner_take_2d_axis0, inner_take_2d_axis1 + in get_dispatch(dtypes)}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_1d_{{name}}_{{dest}}_memview({{c_type_in}}[:] values, + int64_t[:] indexer, + {{c_type_out}}[:] out, + fill_value=np.nan): + + +{{inner_take_1d}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_1d_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=1] values, + int64_t[:] indexer, + {{c_type_out}}[:] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_1d_{{name}}_{{dest}}_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. +{{inner_take_1d}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis0_{{name}}_{{dest}}_memview({{c_type_in}}[:, :] values, + int64_t[:] indexer, + {{c_type_out}}[:, :] out, + fill_value=np.nan): +{{inner_take_2d_axis0}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis0_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values, + ndarray[int64_t] indexer, + {{c_type_out}}[:, :] out, + fill_value=np.nan): + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis0_{{name}}_{{dest}}_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. +{{inner_take_2d_axis0}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +cdef inline take_2d_axis1_{{name}}_{{dest}}_memview({{c_type_in}}[:, :] values, + int64_t[:] indexer, + {{c_type_out}}[:, :] out, + fill_value=np.nan): +{{inner_take_2d_axis1}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_axis1_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values, + ndarray[int64_t] indexer, + {{c_type_out}}[:, :] out, + fill_value=np.nan): + + if values.flags.writeable: + # We can call the memoryview version of the code + take_2d_axis1_{{name}}_{{dest}}_memview(values, indexer, out, + fill_value=fill_value) + return + + # We cannot use the memoryview version on readonly-buffers due to + # a limitation of Cython's typed memoryviews. Instead we can use + # the slightly slower Cython ndarray type directly. +{{inner_take_2d_axis1}} + + +@cython.wraparound(False) +@cython.boundscheck(False) +def take_2d_multi_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values, + indexer, + ndarray[{{c_type_out}}, ndim=2] out, + fill_value=np.nan): + cdef: + Py_ssize_t i, j, k, n, idx + ndarray[int64_t] idx0 = indexer[0] + ndarray[int64_t] idx1 = indexer[1] + {{c_type_out}} fv + + n = len(idx0) + k = len(idx1) + + fv = fill_value + for i from 0 <= i < n: + idx = idx0[i] + if idx == -1: + for j from 0 <= j < k: + out[i, j] = fv + else: + for j from 0 <= j < k: + if idx1[j] == -1: + out[i, j] = fv + else: + out[i, j] = {{preval}}values[idx, idx1[j]]{{postval}} + +{{endfor}} \ No newline at end of file diff --git a/pandas/src/generate_code.py b/pandas/src/generate_code.py deleted file mode 100644 index 309a81b38f4e1..0000000000000 --- a/pandas/src/generate_code.py +++ /dev/null @@ -1,2182 +0,0 @@ -""" -This file generates `generated.pyx` which is then included in `../algos.pyx` -during building. To regenerate `generated.pyx`, just run: - - `python generate_code.py`. - -""" - -# flake8: noqa - -from __future__ import print_function -import os -from pandas.compat import StringIO -import numpy as np - -_int64_max = np.iinfo(np.int64).max - -warning_to_new_contributors = """ -# DO NOT EDIT THIS FILE: This file was autogenerated from generate_code.py, so -# please edit that file and then run `python2 generate_code.py` to re-generate -# this file. -""" - -header = """ -cimport numpy as np -cimport cython - -from libc.string cimport memmove - -from numpy cimport * - -from cpython cimport (PyDict_New, PyDict_GetItem, PyDict_SetItem, - PyDict_Contains, PyDict_Keys, - Py_INCREF, PyTuple_SET_ITEM, - PyTuple_SetItem, - PyTuple_New) -from cpython cimport PyFloat_Check -cimport cpython - -cdef extern from "numpy/npy_math.h": - double NAN "NPY_NAN" - -import numpy as np -isnan = np.isnan - -from datetime import datetime as pydatetime - -# this is our datetime.pxd -from datetime cimport * - -from khash cimport * - -ctypedef unsigned char UChar - -cimport util -from util cimport is_array, _checknull, _checknan, get_nat -cimport lib -from lib cimport is_null_datetimelike - -cdef int64_t iNaT = get_nat() - -# import datetime C API -PyDateTime_IMPORT - -# initialize numpy -import_array() -import_ufunc() - -cdef int PLATFORM_INT = (<ndarray> np.arange(0, dtype=np.int_)).descr.type_num - -cpdef ensure_platform_int(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == PLATFORM_INT: - return arr - else: - return arr.astype(np.int_) - else: - return np.array(arr, dtype=np.int_) - -cpdef ensure_object(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_OBJECT: - return arr - else: - return arr.astype(np.object_) - elif hasattr(arr,'asobject'): - return arr.asobject - else: - return np.array(arr, dtype=np.object_) -""" - - -inner_take_1d_template = """\ - cdef: - Py_ssize_t i, n, idx - %(c_type_out)s fv - - n = indexer.shape[0] - - fv = fill_value - - %(nogil)s - %(tab)sfor i from 0 <= i < n: - %(tab)s idx = indexer[i] - %(tab)s if idx == -1: - %(tab)s out[i] = fv - %(tab)s else: - %(tab)s out[i] = %(preval)svalues[idx]%(postval)s -""" - -take_1d_template = """\ -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_%(name)s_%(dest)s_memview(%(c_type_in)s[:] values, - int64_t[:] indexer, - %(c_type_out)s[:] out, - fill_value=np.nan): -""" + inner_take_1d_template + """ - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_%(name)s_%(dest)s(ndarray[%(c_type_in)s, ndim=1] values, - int64_t[:] indexer, - %(c_type_out)s[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_%(name)s_%(dest)s_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. -""" + inner_take_1d_template - -inner_take_2d_axis0_template = """\ - cdef: - Py_ssize_t i, j, k, n, idx - %(c_type_out)s fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF %(can_copy)s: - cdef: - %(c_type_out)s *v - %(c_type_out)s *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(%(c_type_out)s) and - sizeof(%(c_type_out)s) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(%(c_type_out)s) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = %(preval)svalues[idx, j]%(postval)s -""" - -take_2d_axis0_template = """\ -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_%(name)s_%(dest)s_memview(%(c_type_in)s[:, :] values, - int64_t[:] indexer, - %(c_type_out)s[:, :] out, - fill_value=np.nan): -""" + inner_take_2d_axis0_template + """ - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_%(name)s_%(dest)s(ndarray[%(c_type_in)s, ndim=2] values, - ndarray[int64_t] indexer, - %(c_type_out)s[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_%(name)s_%(dest)s_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. -""" + inner_take_2d_axis0_template - - -inner_take_2d_axis1_template = """\ - cdef: - Py_ssize_t i, j, k, n, idx - %(c_type_out)s fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = %(preval)svalues[i, idx]%(postval)s -""" - -take_2d_axis1_template = """\ -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_%(name)s_%(dest)s_memview(%(c_type_in)s[:, :] values, - int64_t[:] indexer, - %(c_type_out)s[:, :] out, - fill_value=np.nan): -""" + inner_take_2d_axis1_template + """ - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_%(name)s_%(dest)s(ndarray[%(c_type_in)s, ndim=2] values, - ndarray[int64_t] indexer, - %(c_type_out)s[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_%(name)s_%(dest)s_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. -""" + inner_take_2d_axis1_template - - -take_2d_multi_template = """@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_%(name)s_%(dest)s(ndarray[%(c_type_in)s, ndim=2] values, - indexer, - ndarray[%(c_type_out)s, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - %(c_type_out)s fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = %(preval)svalues[idx, idx1[j]]%(postval)s -""" - - - -""" -Backfilling logic for generating fill vector - -Diagram of what's going on - -Old New Fill vector Mask - . 0 1 - . 0 1 - . 0 1 -A A 0 1 - . 1 1 - . 1 1 - . 1 1 - . 1 1 - . 1 1 -B B 1 1 - . 2 1 - . 2 1 - . 2 1 -C C 2 1 - . 0 - . 0 -D -""" - -backfill_template = """@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_%(name)s(ndarray[%(c_type)s] old, ndarray[%(c_type)s] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef %(c_type)s cur, prev - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: - return indexer - - i = nleft - 1 - j = nright - 1 - - cur = old[nleft - 1] - - while j >= 0 and new[j] > cur: - j -= 1 - - while True: - if j < 0: - break - - if i == 0: - while j >= 0: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - break - - prev = old[i - 1] - - while j >= 0 and prev < new[j] <= cur: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - - fill_count = 0 - i -= 1 - cur = prev - - return indexer -""" - - -pad_template = """@cython.boundscheck(False) -@cython.wraparound(False) -def pad_%(name)s(ndarray[%(c_type)s] old, ndarray[%(c_type)s] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef %(c_type)s cur, next - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: - return indexer - - i = j = 0 - - cur = old[0] - - while j <= nright - 1 and new[j] < cur: - j += 1 - - while True: - if j == nright: - break - - if i == nleft - 1: - while j < nright: - if new[j] == cur: - indexer[j] = i - elif new[j] > cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - break - - next = old[i + 1] - - while j < nright and cur <= new[j] < next: - if new[j] == cur: - indexer[j] = i - elif fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - - fill_count = 0 - i += 1 - cur = next - - return indexer -""" - -pad_1d_template = """@cython.boundscheck(False) -@cython.wraparound(False) -def pad_inplace_%(name)s(ndarray[%(c_type)s] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef %(c_type)s val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[0] - for i in range(N): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] -""" - -pad_2d_template = """@cython.boundscheck(False) -@cython.wraparound(False) -def pad_2d_inplace_%(name)s(ndarray[%(c_type)s, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef %(c_type)s val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, 0] - for i in range(N): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] -""" - -backfill_2d_template = """@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_2d_inplace_%(name)s(ndarray[%(c_type)s, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef %(c_type)s val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, N - 1] - for i in range(N - 1, -1 , -1): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] -""" - -backfill_1d_template = """@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_inplace_%(name)s(ndarray[%(c_type)s] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef %(c_type)s val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[N - 1] - for i in range(N - 1, -1 , -1): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] -""" - - -diff_2d_template = """@cython.boundscheck(False) -@cython.wraparound(False) -def diff_2d_%(name)s(ndarray[%(c_type)s, ndim=2] arr, - ndarray[%(dest_type2)s, ndim=2] out, - Py_ssize_t periods, int axis): - cdef: - Py_ssize_t i, j, sx, sy - - sx, sy = (<object> arr).shape - if arr.flags.f_contiguous: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for j in range(sy): - for i in range(start, stop): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for j in range(start, stop): - for i in range(sx): - out[i, j] = arr[i, j] - arr[i, j - periods] - else: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for i in range(start, stop): - for j in range(sy): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for i in range(sx): - for j in range(start, stop): - out[i, j] = arr[i, j] - arr[i, j - periods] -""" - -is_monotonic_template = '''@cython.boundscheck(False) -@cython.wraparound(False) -def is_monotonic_%(name)s(ndarray[%(c_type)s] arr, bint timelike): - """ - Returns - ------- - is_monotonic_inc, is_monotonic_dec - """ - cdef: - Py_ssize_t i, n - %(c_type)s prev, cur - bint is_monotonic_inc = 1 - bint is_monotonic_dec = 1 - - n = len(arr) - - if n == 1: - if arr[0] != arr[0] or (timelike and arr[0] == iNaT): - # single value is NaN - return False, False - else: - return True, True - elif n < 2: - return True, True - - if timelike and arr[0] == iNaT: - return False, False - - %(nogil)s - %(tab)sprev = arr[0] - %(tab)sfor i in range(1, n): - %(tab)s cur = arr[i] - %(tab)s if timelike and cur == iNaT: - %(tab)s is_monotonic_inc = 0 - %(tab)s is_monotonic_dec = 0 - %(tab)s break - %(tab)s if cur < prev: - %(tab)s is_monotonic_inc = 0 - %(tab)s elif cur > prev: - %(tab)s is_monotonic_dec = 0 - %(tab)s elif cur == prev: - %(tab)s pass # is_unique = 0 - %(tab)s else: - %(tab)s # cur or prev is NaN - %(tab)s is_monotonic_inc = 0 - %(tab)s is_monotonic_dec = 0 - %(tab)s break - %(tab)s if not is_monotonic_inc and not is_monotonic_dec: - %(tab)s is_monotonic_inc = 0 - %(tab)s is_monotonic_dec = 0 - %(tab)s break - %(tab)s prev = cur - return is_monotonic_inc, is_monotonic_dec -''' - -map_indices_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -cpdef map_indices_%(name)s(ndarray[%(c_type)s] index): - """ - Produce a dict mapping the values of the input array to their respective - locations. - - Example: - array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} - - Better to do this with Cython because of the enormous speed boost. - """ - cdef Py_ssize_t i, length - cdef dict result = {} - - length = len(index) - - for i in range(length): - result[index[i]] = i - - return result -''' - -groupby_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def groupby_%(name)s(ndarray[%(c_type)s] index, ndarray labels): - cdef dict result = {} - cdef Py_ssize_t i, length - cdef list members - cdef object idx, key - - length = len(index) - - if not length == len(labels): - raise AssertionError("len(index) != len(labels)") - - for i in range(length): - key = util.get_value_1d(labels, i) - - if is_null_datetimelike(key): - continue - - idx = index[i] - if key in result: - members = result[key] - members.append(idx) - else: - result[key] = [idx] - - return result -''' - -group_last_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_last_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(c_type)s, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, count - ndarray[%(dest_type2)s, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != %(nan_val)s: - nobs[lab, j] += 1 - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = %(nan_val)s - else: - out[i, j] = resx[i, j] -''' - -group_nth_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_nth_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(c_type)s, ndim=2] values, - ndarray[int64_t] labels, int64_t rank): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, count - ndarray[%(dest_type2)s, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != %(nan_val)s: - nobs[lab, j] += 1 - if nobs[lab, j] == rank: - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = %(nan_val)s - else: - out[i, j] = resx[i, j] -''' - -group_add_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_add_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(c_type)s, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, count - ndarray[%(dest_type2)s, ndim=2] sumx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - sumx = np.zeros_like(out) - - N, K = (<object> values).shape - - - with nogil: - - if K > 1: - - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - sumx[lab, j] += val - - else: - - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val: - nobs[lab, 0] += 1 - sumx[lab, 0] += val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = sumx[i, j] -''' - -group_prod_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_prod_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(c_type)s, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, count - ndarray[%(dest_type2)s, ndim=2] prodx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - prodx = np.ones_like(out) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - prodx[lab, j] *= val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val: - nobs[lab, 0] += 1 - prodx[lab, 0] *= val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = prodx[i, j] -''' - -group_var_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -@cython.cdivision(True) -def group_var_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(dest_type2)s, ndim=2] values, - ndarray[int64_t] labels): - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, ct, oldmean - ndarray[%(dest_type2)s, ndim=2] nobs, mean - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - mean = np.zeros_like(out) - - N, K = (<object> values).shape - - out[:, :] = 0.0 - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - oldmean = mean[lab, j] - mean[lab, j] += (val - oldmean) / nobs[lab, j] - out[lab, j] += (val - mean[lab, j]) * (val - oldmean) - - for i in range(ncounts): - for j in range(K): - ct = nobs[i, j] - if ct < 2: - out[i, j] = NAN - else: - out[i, j] /= (ct - 1) - -''' - -# add passing bin edges, instead of labels - - -#---------------------------------------------------------------------- -# group_min, group_max - -group_max_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_max_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(dest_type2)s, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, count - ndarray[%(dest_type2)s, ndim=2] maxx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - maxx = np.empty_like(out) - maxx.fill(-%(inf_val)s) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != %(nan_val)s: - nobs[lab, j] += 1 - if val > maxx[lab, j]: - maxx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != %(nan_val)s: - nobs[lab, 0] += 1 - if val > maxx[lab, 0]: - maxx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = %(nan_val)s - else: - out[i, j] = maxx[i, j] -''' - -group_min_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_min_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(dest_type2)s, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, count - ndarray[%(dest_type2)s, ndim=2] minx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - minx = np.empty_like(out) - minx.fill(%(inf_val)s) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != %(nan_val)s: - - nobs[lab, j] += 1 - if val < minx[lab, j]: - minx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != %(nan_val)s: - nobs[lab, 0] += 1 - if val < minx[lab, 0]: - minx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = %(nan_val)s - else: - out[i, j] = minx[i, j] -''' - - -group_mean_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_mean_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(dest_type2)s, ndim=2] values, - ndarray[int64_t] labels): - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - %(dest_type2)s val, count - ndarray[%(dest_type2)s, ndim=2] sumx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - sumx = np.zeros_like(out) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - # not nan - if val == val: - nobs[lab, j] += 1 - sumx[lab, j] += val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - # not nan - if val == val: - nobs[lab, 0] += 1 - sumx[lab, 0] += val - - for i in range(ncounts): - for j in range(K): - count = nobs[i, j] - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = sumx[i, j] / count -''' - -group_ohlc_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def group_ohlc_%(name)s(ndarray[%(dest_type2)s, ndim=2] out, - ndarray[int64_t] counts, - ndarray[%(dest_type2)s, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab - %(dest_type2)s val, count - Py_ssize_t ngroups = len(counts) - - if len(labels) == 0: - return - - N, K = (<object> values).shape - - if out.shape[1] != 4: - raise ValueError('Output array must have 4 columns') - - if K > 1: - raise NotImplementedError("Argument 'values' must have only " - "one dimension") - out.fill(np.nan) - - with nogil: - for i in range(N): - lab = labels[i] - if lab == -1: - continue - - counts[lab] += 1 - val = values[i, 0] - if val != val: - continue - - if out[lab, 0] != out[lab, 0]: - out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val - else: - out[lab, 1] = max(out[lab, 1], val) - out[lab, 2] = min(out[lab, 2], val) - out[lab, 3] = val -''' - -arrmap_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def arrmap_%(name)s(ndarray[%(c_type)s] index, object func): - cdef Py_ssize_t length = index.shape[0] - cdef Py_ssize_t i = 0 - - cdef ndarray[object] result = np.empty(length, dtype=np.object_) - - from pandas.lib import maybe_convert_objects - - for i in range(length): - result[i] = func(index[i]) - - return maybe_convert_objects(result) -''' - -#---------------------------------------------------------------------- -# Joins on ordered, unique indices - -# right might contain non-unique values - -left_join_unique_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def left_join_indexer_unique_%(name)s(ndarray[%(c_type)s] left, - ndarray[%(c_type)s] right): - cdef: - Py_ssize_t i, j, nleft, nright - ndarray[int64_t] indexer - %(c_type)s lval, rval - - i = 0 - j = 0 - nleft = len(left) - nright = len(right) - - indexer = np.empty(nleft, dtype=np.int64) - while True: - if i == nleft: - break - - if j == nright: - indexer[i] = -1 - i += 1 - continue - - rval = right[j] - - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - - if left[i] == right[j]: - indexer[i] = j - i += 1 - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - j += 1 - elif left[i] > rval: - indexer[i] = -1 - j += 1 - else: - indexer[i] = -1 - i += 1 - return indexer -''' - -# @cython.wraparound(False) -# @cython.boundscheck(False) - -left_join_template = '''def left_join_indexer_%(name)s(ndarray[%(c_type)s] left, - ndarray[%(c_type)s] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - %(c_type)s lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[%(c_type)s] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=%(dtype)s) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - i += 1 - count += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - else: - j += 1 - - return result, lindexer, rindexer -''' - - -inner_join_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def inner_join_indexer_%(name)s(ndarray[%(c_type)s] left, - ndarray[%(c_type)s] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - %(c_type)s lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[%(c_type)s] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=%(dtype)s) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = rval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - return result, lindexer, rindexer -''' - - -outer_join_template2 = '''@cython.wraparound(False) -@cython.boundscheck(False) -def outer_join_indexer_%(name)s(ndarray[%(c_type)s] left, - ndarray[%(c_type)s] right): - cdef: - Py_ssize_t i, j, nright, nleft, count - %(c_type)s lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[%(c_type)s] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft == 0: - count = nright - elif nright == 0: - count = nleft - else: - while True: - if i == nleft: - count += nright - j - break - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - count += 1 - j += 1 - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=%(dtype)s) - - # do it again, but populate the indexers / result - - i = 0 - j = 0 - count = 0 - if nleft == 0: - for j in range(nright): - lindexer[j] = -1 - rindexer[j] = j - result[j] = right[j] - elif nright == 0: - for i in range(nleft): - lindexer[i] = i - rindexer[i] = -1 - result[i] = left[i] - else: - while True: - if i == nleft: - while j < nright: - lindexer[count] = -1 - rindexer[count] = j - result[count] = right[j] - count += 1 - j += 1 - break - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = lval - count += 1 - i += 1 - else: - lindexer[count] = -1 - rindexer[count] = j - result[count] = rval - count += 1 - j += 1 - - return result, lindexer, rindexer -''' - -outer_join_template = '''@cython.wraparound(False) -@cython.boundscheck(False) -def outer_join_indexer_%(name)s(ndarray[%(c_type)s] left, - ndarray[%(c_type)s] right): - cdef: - Py_ssize_t i, j, nright, nleft, count - %(c_type)s lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[%(c_type)s] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - while True: - if i == nleft: - if j == nright: - # we are done - break - else: - while j < nright: - j += 1 - count += 1 - break - elif j == nright: - while i < nleft: - i += 1 - count += 1 - break - else: - if left[i] == right[j]: - i += 1 - j += 1 - elif left[i] < right[j]: - i += 1 - else: - j += 1 - - count += 1 - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=%(dtype)s) - - # do it again, but populate the indexers / result - - i = 0 - j = 0 - count = 0 - while True: - if i == nleft: - if j == nright: - # we are done - break - else: - while j < nright: - lindexer[count] = -1 - rindexer[count] = j - result[count] = right[j] - j += 1 - count += 1 - break - elif j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - i += 1 - count += 1 - break - else: - lval = left[i] - rval = right[j] - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - i += 1 - j += 1 - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = lval - i += 1 - else: - lindexer[count] = -1 - rindexer[count] = j - result[count] = rval - j += 1 - - count += 1 - - return result, lindexer, rindexer -''' - -# ensure_dtype functions - -ensure_dtype_template = """ -cpdef ensure_%(name)s(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_%(ctype)s: - return arr - else: - return arr.astype(np.%(dtype)s) - else: - return np.array(arr, dtype=np.%(dtype)s) -""" - -ensure_functions = [ - ('float64', 'FLOAT64', 'float64'), - ('float32', 'FLOAT32', 'float32'), - ('int8', 'INT8', 'int8'), - ('int16', 'INT16', 'int16'), - ('int32', 'INT32', 'int32'), - ('int64', 'INT64', 'int64'), - # ('platform_int', 'INT', 'int_'), - #('object', 'OBJECT', 'object_'), -] - -def generate_ensure_dtypes(): - output = StringIO() - for name, ctype, dtype in ensure_functions: - filled = ensure_dtype_template % locals() - output.write(filled) - return output.getvalue() - -#---------------------------------------------------------------------- -# Fast "put" logic for speeding up interleaving logic - -put2d_template = """ -def put2d_%(name)s_%(dest_type)s(ndarray[%(c_type)s, ndim=2, cast=True] values, - ndarray[int64_t] indexer, Py_ssize_t loc, - ndarray[%(dest_type2)s] out): - cdef: - Py_ssize_t i, j, k - - k = len(values) - for j from 0 <= j < k: - i = indexer[j] - out[i] = values[j, loc] -""" - -#---------------------------------------------------------------------- -# other grouping functions not needing a template -grouping_no_template = '''def group_median_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, ngroups, size - ndarray[int64_t] _counts - ndarray data - float64_t* ptr - ngroups = len(counts) - N, K = (<object> values).shape - - indexer, _counts = groupsort_indexer(labels, ngroups) - counts[:] = _counts[1:] - - data = np.empty((K, N), dtype=np.float64) - ptr = <float64_t*> data.data - - take_2d_axis1_float64_float64(values.T, indexer, out=data) - - for i in range(K): - # exclude NA group - ptr += _counts[0] - for j in range(ngroups): - size = _counts[j + 1] - out[j, i] = _median_linear(ptr, size) - ptr += size - - -@cython.boundscheck(False) -@cython.wraparound(False) -def group_cumprod_float64(float64_t[:,:] out, - float64_t[:,:] values, - int64_t[:] labels, - float64_t[:,:] accum): - """ - Only transforms on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, size - float64_t val - int64_t lab - - N, K = (<object> values).shape - accum = np.ones_like(accum) - - with nogil: - for i in range(N): - lab = labels[i] - - if lab < 0: - continue - for j in range(K): - val = values[i, j] - if val == val: - accum[lab, j] *= val - out[i, j] = accum[lab, j] - -@cython.boundscheck(False) -@cython.wraparound(False) -def group_cumsum(numeric[:,:] out, - numeric[:,:] values, - int64_t[:] labels, - numeric[:,:] accum): - """ - Only transforms on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, size - numeric val - int64_t lab - - N, K = (<object> values).shape - accum = np.zeros_like(accum) - - with nogil: - for i in range(N): - lab = labels[i] - - if lab < 0: - continue - for j in range(K): - val = values[i,j] - if val == val: - accum[lab,j] += val - out[i,j] = accum[lab,j] - -@cython.boundscheck(False) -@cython.wraparound(False) -def group_shift_indexer(int64_t[:] out, int64_t[:] labels, - int ngroups, int periods): - cdef: - Py_ssize_t N, i, j, ii - int offset, sign - int64_t lab, idxer, idxer_slot - int64_t[:] label_seen = np.zeros(ngroups, dtype=np.int64) - int64_t[:,:] label_indexer - - N, = (<object> labels).shape - - if periods < 0: - periods = -periods - offset = N - 1 - sign = -1 - elif periods > 0: - offset = 0 - sign = 1 - - if periods == 0: - with nogil: - for i in range(N): - out[i] = i - else: - # array of each previous indexer seen - label_indexer = np.zeros((ngroups, periods), dtype=np.int64) - with nogil: - for i in range(N): - ## reverse iterator if shifting backwards - ii = offset + sign * i - lab = labels[ii] - label_seen[lab] += 1 - - idxer_slot = label_seen[lab] % periods - idxer = label_indexer[lab, idxer_slot] - - if label_seen[lab] > periods: - out[ii] = idxer - else: - out[ii] = -1 - - label_indexer[lab, idxer_slot] = ii -''' - - -#------------------------------------------------------------------------- -# Generators - -def generate_put_template(template, use_ints=True, use_floats=True, - use_objects=False, use_datelikes=False): - floats_list = [ - ('float64', 'float64_t', 'float64_t', 'np.float64', True), - ('float32', 'float32_t', 'float32_t', 'np.float32', True), - ] - ints_list = [ - ('int8', 'int8_t', 'float32_t', 'np.float32', True), - ('int16', 'int16_t', 'float32_t', 'np.float32', True), - ('int32', 'int32_t', 'float64_t', 'np.float64', True), - ('int64', 'int64_t', 'float64_t', 'np.float64', True), - ] - date_like_list = [ - ('int64', 'int64_t', 'float64_t', 'np.float64', True), - ] - object_list = [('object', 'object', 'object', 'np.object_', False)] - function_list = [] - if use_floats: - function_list.extend(floats_list) - if use_ints: - function_list.extend(ints_list) - if use_objects: - function_list.extend(object_list) - if use_datelikes: - function_list.extend(date_like_list) - - output = StringIO() - for name, c_type, dest_type, dest_dtype, nogil in function_list: - func = template % {'name': name, - 'c_type': c_type, - 'dest_type': dest_type.replace('_t', ''), - 'dest_type2': dest_type, - 'dest_dtype': dest_dtype, - 'nogil' : 'with nogil:' if nogil else '', - 'tab' : ' ' if nogil else '' } - output.write(func) - output.write("\n") - return output.getvalue() - -def generate_put_min_max_template(template, use_ints=True, use_floats=True, - use_objects=False, use_datelikes=False): - floats_list = [ - ('float64', 'float64_t', 'NAN', 'np.inf', True), - ('float32', 'float32_t', 'NAN', 'np.inf', True), - ] - ints_list = [ - ('int64', 'int64_t', 'iNaT', _int64_max, True), - ] - date_like_list = [ - ('int64', 'int64_t', 'iNaT', _int64_max, True), - ] - object_list = [('object', 'object', 'np.nan', 'np.inf', False)] - function_list = [] - if use_floats: - function_list.extend(floats_list) - if use_ints: - function_list.extend(ints_list) - if use_objects: - function_list.extend(object_list) - if use_datelikes: - function_list.extend(date_like_list) - - output = StringIO() - for name, dest_type, nan_val, inf_val, nogil in function_list: - func = template % {'name': name, - 'dest_type2': dest_type, - 'nan_val': nan_val, - 'inf_val': inf_val, - 'nogil' : "with nogil:" if nogil else '', - 'tab' : ' ' if nogil else '' } - output.write(func) - output.write("\n") - return output.getvalue() - -def generate_put_selection_template(template, use_ints=True, use_floats=True, - use_objects=False, use_datelikes=False): - floats_list = [ - ('float64', 'float64_t', 'float64_t', 'NAN', True), - ('float32', 'float32_t', 'float32_t', 'NAN', True), - ] - ints_list = [ - ('int64', 'int64_t', 'int64_t', 'iNaT', True), - ] - date_like_list = [ - ('int64', 'int64_t', 'int64_t', 'iNaT', True), - ] - object_list = [('object', 'object', 'object', 'np.nan', False)] - function_list = [] - if use_floats: - function_list.extend(floats_list) - if use_ints: - function_list.extend(ints_list) - if use_objects: - function_list.extend(object_list) - if use_datelikes: - function_list.extend(date_like_list) - - output = StringIO() - for name, c_type, dest_type, nan_val, nogil in function_list: - - if nogil: - nogil = "with nogil:" - tab = ' ' - else: - nogil = '' - tab = '' - - func = template % {'name': name, - 'c_type': c_type, - 'dest_type2': dest_type, - 'nan_val': nan_val, - 'nogil' : nogil, - 'tab' : tab } - output.write(func) - output.write("\n") - return output.getvalue() - -def generate_take_template(template, exclude=None): - # name, dest, ctypein, ctypeout, preval, postval, cancopy, nogil - function_list = [ - ('bool', 'bool', 'uint8_t', 'uint8_t', '', '', True, True), - ('bool', 'object', 'uint8_t', 'object', - 'True if ', ' > 0 else False', False, False), - ('int8', 'int8', 'int8_t', 'int8_t', '', '', True, False), - ('int8', 'int32', 'int8_t', 'int32_t', '', '', False, True), - ('int8', 'int64', 'int8_t', 'int64_t', '', '', False, True), - ('int8', 'float64', 'int8_t', 'float64_t', '', '', False, True), - ('int16', 'int16', 'int16_t', 'int16_t', '', '', True, True), - ('int16', 'int32', 'int16_t', 'int32_t', '', '', False, True), - ('int16', 'int64', 'int16_t', 'int64_t', '', '', False, True), - ('int16', 'float64', 'int16_t', 'float64_t', '', '', False, True), - ('int32', 'int32', 'int32_t', 'int32_t', '', '', True, True), - ('int32', 'int64', 'int32_t', 'int64_t', '', '', False, True), - ('int32', 'float64', 'int32_t', 'float64_t', '', '', False, True), - ('int64', 'int64', 'int64_t', 'int64_t', '', '', True, True), - ('int64', 'float64', 'int64_t', 'float64_t', '', '', False, True), - ('float32', 'float32', 'float32_t', 'float32_t', '', '', True, True), - ('float32', 'float64', 'float32_t', 'float64_t', '', '', False, True), - ('float64', 'float64', 'float64_t', 'float64_t', '', '', True, True), - ('object', 'object', 'object', 'object', '', '', False, False), - ] - - output = StringIO() - for (name, dest, c_type_in, c_type_out, - preval, postval, can_copy, nogil) in function_list: - - if exclude is not None and name in exclude: - continue - - if nogil: - nogil = "with nogil:" - tab = ' ' - else: - nogil = '' - tab = '' - - func = template % {'name': name, 'dest': dest, - 'c_type_in': c_type_in, 'c_type_out': c_type_out, - 'preval': preval, 'postval': postval, - 'can_copy': 'True' if can_copy else 'False', - 'nogil' : nogil, - 'tab' : tab } - output.write(func) - output.write("\n") - return output.getvalue() - -def generate_from_template(template, exclude=None): - # name, ctype, capable of holding NA - function_list = [ - ('float64', 'float64_t', 'np.float64', True, True), - ('float32', 'float32_t', 'np.float32', True, True), - ('object', 'object', 'object', True, False), - ('int32', 'int32_t', 'np.int32', False, True), - ('int64', 'int64_t', 'np.int64', False, True), - ('bool', 'uint8_t', 'np.bool', False, True) - ] - - output = StringIO() - for name, c_type, dtype, can_hold_na, nogil in function_list: - if exclude is not None and name in exclude: - continue - - func = template % {'name': name, 'c_type': c_type, - 'dtype': dtype, - 'raise_on_na': 'False' if can_hold_na else 'True', - 'nogil' : 'with nogil:' if nogil else '', - 'tab' : ' ' if nogil else '' } - output.write(func) - output.write("\n") - return output.getvalue() - -put_2d = [diff_2d_template] - -groupbys = [group_add_template, - group_prod_template, - group_var_template, - group_mean_template, - group_ohlc_template] - -groupby_selection = [group_last_template, - group_nth_template] - -groupby_min_max = [group_min_template, - group_max_template] - -templates_1d = [map_indices_template, - pad_template, - backfill_template, - pad_1d_template, - backfill_1d_template, - pad_2d_template, - backfill_2d_template, - is_monotonic_template, - groupby_template, - arrmap_template] - -nobool_1d_templates = [left_join_unique_template, - left_join_template, - outer_join_template2, - inner_join_template] - -take_templates = [take_1d_template, - take_2d_axis0_template, - take_2d_axis1_template, - take_2d_multi_template] - - -def generate_take_cython_file(): - # Put `generated.pyx` in the same directory as this file - directory = os.path.dirname(os.path.realpath(__file__)) - filename = 'generated.pyx' - path = os.path.join(directory, filename) - - with open(path, 'w') as f: - print(warning_to_new_contributors, file=f) - print(header, file=f) - - print(generate_ensure_dtypes(), file=f) - - for template in templates_1d: - print(generate_from_template(template), file=f) - - for template in take_templates: - print(generate_take_template(template), file=f) - - for template in put_2d: - print(generate_put_template(template), file=f) - - for template in groupbys: - print(generate_put_template(template, use_ints=False), file=f) - - for template in groupby_selection: - print(generate_put_selection_template(template, use_ints=True), - file=f) - - for template in groupby_min_max: - print(generate_put_min_max_template(template, use_ints=True), - file=f) - - print(grouping_no_template, file=f) - - for template in nobool_1d_templates: - print(generate_from_template(template, exclude=['bool']), file=f) - - -if __name__ == '__main__': - generate_take_cython_file() diff --git a/pandas/src/generated.pyx b/pandas/src/generated.pyx deleted file mode 100644 index c6dcd609a2c6e..0000000000000 --- a/pandas/src/generated.pyx +++ /dev/null @@ -1,10522 +0,0 @@ - -# DO NOT EDIT THIS FILE: This file was autogenerated from generate_code.py, so -# please edit that file and then run `python2 generate_code.py` to re-generate -# this file. - - -cimport numpy as np -cimport cython - -from libc.string cimport memmove - -from numpy cimport * - -from cpython cimport (PyDict_New, PyDict_GetItem, PyDict_SetItem, - PyDict_Contains, PyDict_Keys, - Py_INCREF, PyTuple_SET_ITEM, - PyTuple_SetItem, - PyTuple_New) -from cpython cimport PyFloat_Check -cimport cpython - -cdef extern from "numpy/npy_math.h": - double NAN "NPY_NAN" - -import numpy as np -isnan = np.isnan - -from datetime import datetime as pydatetime - -# this is our datetime.pxd -from datetime cimport * - -from khash cimport * - -ctypedef unsigned char UChar - -cimport util -from util cimport is_array, _checknull, _checknan, get_nat -cimport lib -from lib cimport is_null_datetimelike - -cdef int64_t iNaT = get_nat() - -# import datetime C API -PyDateTime_IMPORT - -# initialize numpy -import_array() -import_ufunc() - -cdef int PLATFORM_INT = (<ndarray> np.arange(0, dtype=np.int_)).descr.type_num - -cpdef ensure_platform_int(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == PLATFORM_INT: - return arr - else: - return arr.astype(np.int_) - else: - return np.array(arr, dtype=np.int_) - -cpdef ensure_object(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_OBJECT: - return arr - else: - return arr.astype(np.object_) - elif hasattr(arr,'asobject'): - return arr.asobject - else: - return np.array(arr, dtype=np.object_) - - -cpdef ensure_float64(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_FLOAT64: - return arr - else: - return arr.astype(np.float64) - else: - return np.array(arr, dtype=np.float64) - -cpdef ensure_float32(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_FLOAT32: - return arr - else: - return arr.astype(np.float32) - else: - return np.array(arr, dtype=np.float32) - -cpdef ensure_int8(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_INT8: - return arr - else: - return arr.astype(np.int8) - else: - return np.array(arr, dtype=np.int8) - -cpdef ensure_int16(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_INT16: - return arr - else: - return arr.astype(np.int16) - else: - return np.array(arr, dtype=np.int16) - -cpdef ensure_int32(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_INT32: - return arr - else: - return arr.astype(np.int32) - else: - return np.array(arr, dtype=np.int32) - -cpdef ensure_int64(object arr): - if util.is_array(arr): - if (<ndarray> arr).descr.type_num == NPY_INT64: - return arr - else: - return arr.astype(np.int64) - else: - return np.array(arr, dtype=np.int64) - -@cython.wraparound(False) -@cython.boundscheck(False) -cpdef map_indices_float64(ndarray[float64_t] index): - """ - Produce a dict mapping the values of the input array to their respective - locations. - - Example: - array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} - - Better to do this with Cython because of the enormous speed boost. - """ - cdef Py_ssize_t i, length - cdef dict result = {} - - length = len(index) - - for i in range(length): - result[index[i]] = i - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -cpdef map_indices_float32(ndarray[float32_t] index): - """ - Produce a dict mapping the values of the input array to their respective - locations. - - Example: - array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} - - Better to do this with Cython because of the enormous speed boost. - """ - cdef Py_ssize_t i, length - cdef dict result = {} - - length = len(index) - - for i in range(length): - result[index[i]] = i - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -cpdef map_indices_object(ndarray[object] index): - """ - Produce a dict mapping the values of the input array to their respective - locations. - - Example: - array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} - - Better to do this with Cython because of the enormous speed boost. - """ - cdef Py_ssize_t i, length - cdef dict result = {} - - length = len(index) - - for i in range(length): - result[index[i]] = i - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -cpdef map_indices_int32(ndarray[int32_t] index): - """ - Produce a dict mapping the values of the input array to their respective - locations. - - Example: - array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} - - Better to do this with Cython because of the enormous speed boost. - """ - cdef Py_ssize_t i, length - cdef dict result = {} - - length = len(index) - - for i in range(length): - result[index[i]] = i - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -cpdef map_indices_int64(ndarray[int64_t] index): - """ - Produce a dict mapping the values of the input array to their respective - locations. - - Example: - array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} - - Better to do this with Cython because of the enormous speed boost. - """ - cdef Py_ssize_t i, length - cdef dict result = {} - - length = len(index) - - for i in range(length): - result[index[i]] = i - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -cpdef map_indices_bool(ndarray[uint8_t] index): - """ - Produce a dict mapping the values of the input array to their respective - locations. - - Example: - array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1} - - Better to do this with Cython because of the enormous speed boost. - """ - cdef Py_ssize_t i, length - cdef dict result = {} - - length = len(index) - - for i in range(length): - result[index[i]] = i - - return result - - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_float64(ndarray[float64_t] old, ndarray[float64_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef float64_t cur, next - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: - return indexer - - i = j = 0 - - cur = old[0] - - while j <= nright - 1 and new[j] < cur: - j += 1 - - while True: - if j == nright: - break - - if i == nleft - 1: - while j < nright: - if new[j] == cur: - indexer[j] = i - elif new[j] > cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - break - - next = old[i + 1] - - while j < nright and cur <= new[j] < next: - if new[j] == cur: - indexer[j] = i - elif fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - - fill_count = 0 - i += 1 - cur = next - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_float32(ndarray[float32_t] old, ndarray[float32_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef float32_t cur, next - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: - return indexer - - i = j = 0 - - cur = old[0] - - while j <= nright - 1 and new[j] < cur: - j += 1 - - while True: - if j == nright: - break - - if i == nleft - 1: - while j < nright: - if new[j] == cur: - indexer[j] = i - elif new[j] > cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - break - - next = old[i + 1] - - while j < nright and cur <= new[j] < next: - if new[j] == cur: - indexer[j] = i - elif fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - - fill_count = 0 - i += 1 - cur = next - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_object(ndarray[object] old, ndarray[object] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef object cur, next - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: - return indexer - - i = j = 0 - - cur = old[0] - - while j <= nright - 1 and new[j] < cur: - j += 1 - - while True: - if j == nright: - break - - if i == nleft - 1: - while j < nright: - if new[j] == cur: - indexer[j] = i - elif new[j] > cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - break - - next = old[i + 1] - - while j < nright and cur <= new[j] < next: - if new[j] == cur: - indexer[j] = i - elif fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - - fill_count = 0 - i += 1 - cur = next - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_int32(ndarray[int32_t] old, ndarray[int32_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef int32_t cur, next - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: - return indexer - - i = j = 0 - - cur = old[0] - - while j <= nright - 1 and new[j] < cur: - j += 1 - - while True: - if j == nright: - break - - if i == nleft - 1: - while j < nright: - if new[j] == cur: - indexer[j] = i - elif new[j] > cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - break - - next = old[i + 1] - - while j < nright and cur <= new[j] < next: - if new[j] == cur: - indexer[j] = i - elif fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - - fill_count = 0 - i += 1 - cur = next - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_int64(ndarray[int64_t] old, ndarray[int64_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef int64_t cur, next - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: - return indexer - - i = j = 0 - - cur = old[0] - - while j <= nright - 1 and new[j] < cur: - j += 1 - - while True: - if j == nright: - break - - if i == nleft - 1: - while j < nright: - if new[j] == cur: - indexer[j] = i - elif new[j] > cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - break - - next = old[i + 1] - - while j < nright and cur <= new[j] < next: - if new[j] == cur: - indexer[j] = i - elif fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - - fill_count = 0 - i += 1 - cur = next - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_bool(ndarray[uint8_t] old, ndarray[uint8_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef uint8_t cur, next - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[nright - 1] < old[0]: - return indexer - - i = j = 0 - - cur = old[0] - - while j <= nright - 1 and new[j] < cur: - j += 1 - - while True: - if j == nright: - break - - if i == nleft - 1: - while j < nright: - if new[j] == cur: - indexer[j] = i - elif new[j] > cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - break - - next = old[i + 1] - - while j < nright and cur <= new[j] < next: - if new[j] == cur: - indexer[j] = i - elif fill_count < lim: - indexer[j] = i - fill_count += 1 - j += 1 - - fill_count = 0 - i += 1 - cur = next - - return indexer - - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_float64(ndarray[float64_t] old, ndarray[float64_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef float64_t cur, prev - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: - return indexer - - i = nleft - 1 - j = nright - 1 - - cur = old[nleft - 1] - - while j >= 0 and new[j] > cur: - j -= 1 - - while True: - if j < 0: - break - - if i == 0: - while j >= 0: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - break - - prev = old[i - 1] - - while j >= 0 and prev < new[j] <= cur: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - - fill_count = 0 - i -= 1 - cur = prev - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_float32(ndarray[float32_t] old, ndarray[float32_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef float32_t cur, prev - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: - return indexer - - i = nleft - 1 - j = nright - 1 - - cur = old[nleft - 1] - - while j >= 0 and new[j] > cur: - j -= 1 - - while True: - if j < 0: - break - - if i == 0: - while j >= 0: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - break - - prev = old[i - 1] - - while j >= 0 and prev < new[j] <= cur: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - - fill_count = 0 - i -= 1 - cur = prev - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_object(ndarray[object] old, ndarray[object] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef object cur, prev - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: - return indexer - - i = nleft - 1 - j = nright - 1 - - cur = old[nleft - 1] - - while j >= 0 and new[j] > cur: - j -= 1 - - while True: - if j < 0: - break - - if i == 0: - while j >= 0: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - break - - prev = old[i - 1] - - while j >= 0 and prev < new[j] <= cur: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - - fill_count = 0 - i -= 1 - cur = prev - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_int32(ndarray[int32_t] old, ndarray[int32_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef int32_t cur, prev - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: - return indexer - - i = nleft - 1 - j = nright - 1 - - cur = old[nleft - 1] - - while j >= 0 and new[j] > cur: - j -= 1 - - while True: - if j < 0: - break - - if i == 0: - while j >= 0: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - break - - prev = old[i - 1] - - while j >= 0 and prev < new[j] <= cur: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - - fill_count = 0 - i -= 1 - cur = prev - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_int64(ndarray[int64_t] old, ndarray[int64_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef int64_t cur, prev - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: - return indexer - - i = nleft - 1 - j = nright - 1 - - cur = old[nleft - 1] - - while j >= 0 and new[j] > cur: - j -= 1 - - while True: - if j < 0: - break - - if i == 0: - while j >= 0: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - break - - prev = old[i - 1] - - while j >= 0 and prev < new[j] <= cur: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - - fill_count = 0 - i -= 1 - cur = prev - - return indexer - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_bool(ndarray[uint8_t] old, ndarray[uint8_t] new, - limit=None): - cdef Py_ssize_t i, j, nleft, nright - cdef ndarray[int64_t, ndim=1] indexer - cdef uint8_t cur, prev - cdef int lim, fill_count = 0 - - nleft = len(old) - nright = len(new) - indexer = np.empty(nright, dtype=np.int64) - indexer.fill(-1) - - if limit is None: - lim = nright - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]: - return indexer - - i = nleft - 1 - j = nright - 1 - - cur = old[nleft - 1] - - while j >= 0 and new[j] > cur: - j -= 1 - - while True: - if j < 0: - break - - if i == 0: - while j >= 0: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - break - - prev = old[i - 1] - - while j >= 0 and prev < new[j] <= cur: - if new[j] == cur: - indexer[j] = i - elif new[j] < cur and fill_count < lim: - indexer[j] = i - fill_count += 1 - j -= 1 - - fill_count = 0 - i -= 1 - cur = prev - - return indexer - - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_inplace_float64(ndarray[float64_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef float64_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[0] - for i in range(N): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_inplace_float32(ndarray[float32_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef float32_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[0] - for i in range(N): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_inplace_object(ndarray[object] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef object val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[0] - for i in range(N): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_inplace_int32(ndarray[int32_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef int32_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[0] - for i in range(N): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_inplace_int64(ndarray[int64_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef int64_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[0] - for i in range(N): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_inplace_bool(ndarray[uint8_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef uint8_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[0] - for i in range(N): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_inplace_float64(ndarray[float64_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef float64_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[N - 1] - for i in range(N - 1, -1 , -1): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_inplace_float32(ndarray[float32_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef float32_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[N - 1] - for i in range(N - 1, -1 , -1): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_inplace_object(ndarray[object] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef object val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[N - 1] - for i in range(N - 1, -1 , -1): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_inplace_int32(ndarray[int32_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef int32_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[N - 1] - for i in range(N - 1, -1 , -1): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_inplace_int64(ndarray[int64_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef int64_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[N - 1] - for i in range(N - 1, -1 , -1): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_inplace_bool(ndarray[uint8_t] values, - ndarray[uint8_t, cast=True] mask, - limit=None): - cdef Py_ssize_t i, N - cdef uint8_t val - cdef int lim, fill_count = 0 - - N = len(values) - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - val = values[N - 1] - for i in range(N - 1, -1 , -1): - if mask[i]: - if fill_count >= lim: - continue - fill_count += 1 - values[i] = val - else: - fill_count = 0 - val = values[i] - - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_2d_inplace_float64(ndarray[float64_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef float64_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, 0] - for i in range(N): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_2d_inplace_float32(ndarray[float32_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef float32_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, 0] - for i in range(N): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_2d_inplace_object(ndarray[object, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef object val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, 0] - for i in range(N): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_2d_inplace_int32(ndarray[int32_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef int32_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, 0] - for i in range(N): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_2d_inplace_int64(ndarray[int64_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef int64_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, 0] - for i in range(N): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def pad_2d_inplace_bool(ndarray[uint8_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef uint8_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, 0] - for i in range(N): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_2d_inplace_float64(ndarray[float64_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef float64_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, N - 1] - for i in range(N - 1, -1 , -1): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_2d_inplace_float32(ndarray[float32_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef float32_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, N - 1] - for i in range(N - 1, -1 , -1): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_2d_inplace_object(ndarray[object, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef object val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, N - 1] - for i in range(N - 1, -1 , -1): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_2d_inplace_int32(ndarray[int32_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef int32_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, N - 1] - for i in range(N - 1, -1 , -1): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_2d_inplace_int64(ndarray[int64_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef int64_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, N - 1] - for i in range(N - 1, -1 , -1): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - -@cython.boundscheck(False) -@cython.wraparound(False) -def backfill_2d_inplace_bool(ndarray[uint8_t, ndim=2] values, - ndarray[uint8_t, ndim=2] mask, - limit=None): - cdef Py_ssize_t i, j, N, K - cdef uint8_t val - cdef int lim, fill_count = 0 - - K, N = (<object> values).shape - - # GH 2778 - if N == 0: - return - - if limit is None: - lim = N - else: - if limit < 0: - raise ValueError('Limit must be non-negative') - lim = limit - - for j in range(K): - fill_count = 0 - val = values[j, N - 1] - for i in range(N - 1, -1 , -1): - if mask[j, i]: - if fill_count >= lim: - continue - fill_count += 1 - values[j, i] = val - else: - fill_count = 0 - val = values[j, i] - - -@cython.boundscheck(False) -@cython.wraparound(False) -def is_monotonic_float64(ndarray[float64_t] arr, bint timelike): - """ - Returns - ------- - is_monotonic_inc, is_monotonic_dec - """ - cdef: - Py_ssize_t i, n - float64_t prev, cur - bint is_monotonic_inc = 1 - bint is_monotonic_dec = 1 - - n = len(arr) - - if n == 1: - if arr[0] != arr[0] or (timelike and arr[0] == iNaT): - # single value is NaN - return False, False - else: - return True, True - elif n < 2: - return True, True - - if timelike and arr[0] == iNaT: - return False, False - - with nogil: - prev = arr[0] - for i in range(1, n): - cur = arr[i] - if timelike and cur == iNaT: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if cur < prev: - is_monotonic_inc = 0 - elif cur > prev: - is_monotonic_dec = 0 - elif cur == prev: - pass # is_unique = 0 - else: - # cur or prev is NaN - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if not is_monotonic_inc and not is_monotonic_dec: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - prev = cur - return is_monotonic_inc, is_monotonic_dec - -@cython.boundscheck(False) -@cython.wraparound(False) -def is_monotonic_float32(ndarray[float32_t] arr, bint timelike): - """ - Returns - ------- - is_monotonic_inc, is_monotonic_dec - """ - cdef: - Py_ssize_t i, n - float32_t prev, cur - bint is_monotonic_inc = 1 - bint is_monotonic_dec = 1 - - n = len(arr) - - if n == 1: - if arr[0] != arr[0] or (timelike and arr[0] == iNaT): - # single value is NaN - return False, False - else: - return True, True - elif n < 2: - return True, True - - if timelike and arr[0] == iNaT: - return False, False - - with nogil: - prev = arr[0] - for i in range(1, n): - cur = arr[i] - if timelike and cur == iNaT: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if cur < prev: - is_monotonic_inc = 0 - elif cur > prev: - is_monotonic_dec = 0 - elif cur == prev: - pass # is_unique = 0 - else: - # cur or prev is NaN - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if not is_monotonic_inc and not is_monotonic_dec: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - prev = cur - return is_monotonic_inc, is_monotonic_dec - -@cython.boundscheck(False) -@cython.wraparound(False) -def is_monotonic_object(ndarray[object] arr, bint timelike): - """ - Returns - ------- - is_monotonic_inc, is_monotonic_dec - """ - cdef: - Py_ssize_t i, n - object prev, cur - bint is_monotonic_inc = 1 - bint is_monotonic_dec = 1 - - n = len(arr) - - if n == 1: - if arr[0] != arr[0] or (timelike and arr[0] == iNaT): - # single value is NaN - return False, False - else: - return True, True - elif n < 2: - return True, True - - if timelike and arr[0] == iNaT: - return False, False - - - prev = arr[0] - for i in range(1, n): - cur = arr[i] - if timelike and cur == iNaT: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if cur < prev: - is_monotonic_inc = 0 - elif cur > prev: - is_monotonic_dec = 0 - elif cur == prev: - pass # is_unique = 0 - else: - # cur or prev is NaN - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if not is_monotonic_inc and not is_monotonic_dec: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - prev = cur - return is_monotonic_inc, is_monotonic_dec - -@cython.boundscheck(False) -@cython.wraparound(False) -def is_monotonic_int32(ndarray[int32_t] arr, bint timelike): - """ - Returns - ------- - is_monotonic_inc, is_monotonic_dec - """ - cdef: - Py_ssize_t i, n - int32_t prev, cur - bint is_monotonic_inc = 1 - bint is_monotonic_dec = 1 - - n = len(arr) - - if n == 1: - if arr[0] != arr[0] or (timelike and arr[0] == iNaT): - # single value is NaN - return False, False - else: - return True, True - elif n < 2: - return True, True - - if timelike and arr[0] == iNaT: - return False, False - - with nogil: - prev = arr[0] - for i in range(1, n): - cur = arr[i] - if timelike and cur == iNaT: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if cur < prev: - is_monotonic_inc = 0 - elif cur > prev: - is_monotonic_dec = 0 - elif cur == prev: - pass # is_unique = 0 - else: - # cur or prev is NaN - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if not is_monotonic_inc and not is_monotonic_dec: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - prev = cur - return is_monotonic_inc, is_monotonic_dec - -@cython.boundscheck(False) -@cython.wraparound(False) -def is_monotonic_int64(ndarray[int64_t] arr, bint timelike): - """ - Returns - ------- - is_monotonic_inc, is_monotonic_dec - """ - cdef: - Py_ssize_t i, n - int64_t prev, cur - bint is_monotonic_inc = 1 - bint is_monotonic_dec = 1 - - n = len(arr) - - if n == 1: - if arr[0] != arr[0] or (timelike and arr[0] == iNaT): - # single value is NaN - return False, False - else: - return True, True - elif n < 2: - return True, True - - if timelike and arr[0] == iNaT: - return False, False - - with nogil: - prev = arr[0] - for i in range(1, n): - cur = arr[i] - if timelike and cur == iNaT: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if cur < prev: - is_monotonic_inc = 0 - elif cur > prev: - is_monotonic_dec = 0 - elif cur == prev: - pass # is_unique = 0 - else: - # cur or prev is NaN - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if not is_monotonic_inc and not is_monotonic_dec: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - prev = cur - return is_monotonic_inc, is_monotonic_dec - -@cython.boundscheck(False) -@cython.wraparound(False) -def is_monotonic_bool(ndarray[uint8_t] arr, bint timelike): - """ - Returns - ------- - is_monotonic_inc, is_monotonic_dec - """ - cdef: - Py_ssize_t i, n - uint8_t prev, cur - bint is_monotonic_inc = 1 - bint is_monotonic_dec = 1 - - n = len(arr) - - if n == 1: - if arr[0] != arr[0] or (timelike and arr[0] == iNaT): - # single value is NaN - return False, False - else: - return True, True - elif n < 2: - return True, True - - if timelike and arr[0] == iNaT: - return False, False - - with nogil: - prev = arr[0] - for i in range(1, n): - cur = arr[i] - if timelike and cur == iNaT: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if cur < prev: - is_monotonic_inc = 0 - elif cur > prev: - is_monotonic_dec = 0 - elif cur == prev: - pass # is_unique = 0 - else: - # cur or prev is NaN - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - if not is_monotonic_inc and not is_monotonic_dec: - is_monotonic_inc = 0 - is_monotonic_dec = 0 - break - prev = cur - return is_monotonic_inc, is_monotonic_dec - - -@cython.wraparound(False) -@cython.boundscheck(False) -def groupby_float64(ndarray[float64_t] index, ndarray labels): - cdef dict result = {} - cdef Py_ssize_t i, length - cdef list members - cdef object idx, key - - length = len(index) - - if not length == len(labels): - raise AssertionError("len(index) != len(labels)") - - for i in range(length): - key = util.get_value_1d(labels, i) - - if is_null_datetimelike(key): - continue - - idx = index[i] - if key in result: - members = result[key] - members.append(idx) - else: - result[key] = [idx] - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -def groupby_float32(ndarray[float32_t] index, ndarray labels): - cdef dict result = {} - cdef Py_ssize_t i, length - cdef list members - cdef object idx, key - - length = len(index) - - if not length == len(labels): - raise AssertionError("len(index) != len(labels)") - - for i in range(length): - key = util.get_value_1d(labels, i) - - if is_null_datetimelike(key): - continue - - idx = index[i] - if key in result: - members = result[key] - members.append(idx) - else: - result[key] = [idx] - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -def groupby_object(ndarray[object] index, ndarray labels): - cdef dict result = {} - cdef Py_ssize_t i, length - cdef list members - cdef object idx, key - - length = len(index) - - if not length == len(labels): - raise AssertionError("len(index) != len(labels)") - - for i in range(length): - key = util.get_value_1d(labels, i) - - if is_null_datetimelike(key): - continue - - idx = index[i] - if key in result: - members = result[key] - members.append(idx) - else: - result[key] = [idx] - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -def groupby_int32(ndarray[int32_t] index, ndarray labels): - cdef dict result = {} - cdef Py_ssize_t i, length - cdef list members - cdef object idx, key - - length = len(index) - - if not length == len(labels): - raise AssertionError("len(index) != len(labels)") - - for i in range(length): - key = util.get_value_1d(labels, i) - - if is_null_datetimelike(key): - continue - - idx = index[i] - if key in result: - members = result[key] - members.append(idx) - else: - result[key] = [idx] - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -def groupby_int64(ndarray[int64_t] index, ndarray labels): - cdef dict result = {} - cdef Py_ssize_t i, length - cdef list members - cdef object idx, key - - length = len(index) - - if not length == len(labels): - raise AssertionError("len(index) != len(labels)") - - for i in range(length): - key = util.get_value_1d(labels, i) - - if is_null_datetimelike(key): - continue - - idx = index[i] - if key in result: - members = result[key] - members.append(idx) - else: - result[key] = [idx] - - return result - -@cython.wraparound(False) -@cython.boundscheck(False) -def groupby_bool(ndarray[uint8_t] index, ndarray labels): - cdef dict result = {} - cdef Py_ssize_t i, length - cdef list members - cdef object idx, key - - length = len(index) - - if not length == len(labels): - raise AssertionError("len(index) != len(labels)") - - for i in range(length): - key = util.get_value_1d(labels, i) - - if is_null_datetimelike(key): - continue - - idx = index[i] - if key in result: - members = result[key] - members.append(idx) - else: - result[key] = [idx] - - return result - - -@cython.wraparound(False) -@cython.boundscheck(False) -def arrmap_float64(ndarray[float64_t] index, object func): - cdef Py_ssize_t length = index.shape[0] - cdef Py_ssize_t i = 0 - - cdef ndarray[object] result = np.empty(length, dtype=np.object_) - - from pandas.lib import maybe_convert_objects - - for i in range(length): - result[i] = func(index[i]) - - return maybe_convert_objects(result) - -@cython.wraparound(False) -@cython.boundscheck(False) -def arrmap_float32(ndarray[float32_t] index, object func): - cdef Py_ssize_t length = index.shape[0] - cdef Py_ssize_t i = 0 - - cdef ndarray[object] result = np.empty(length, dtype=np.object_) - - from pandas.lib import maybe_convert_objects - - for i in range(length): - result[i] = func(index[i]) - - return maybe_convert_objects(result) - -@cython.wraparound(False) -@cython.boundscheck(False) -def arrmap_object(ndarray[object] index, object func): - cdef Py_ssize_t length = index.shape[0] - cdef Py_ssize_t i = 0 - - cdef ndarray[object] result = np.empty(length, dtype=np.object_) - - from pandas.lib import maybe_convert_objects - - for i in range(length): - result[i] = func(index[i]) - - return maybe_convert_objects(result) - -@cython.wraparound(False) -@cython.boundscheck(False) -def arrmap_int32(ndarray[int32_t] index, object func): - cdef Py_ssize_t length = index.shape[0] - cdef Py_ssize_t i = 0 - - cdef ndarray[object] result = np.empty(length, dtype=np.object_) - - from pandas.lib import maybe_convert_objects - - for i in range(length): - result[i] = func(index[i]) - - return maybe_convert_objects(result) - -@cython.wraparound(False) -@cython.boundscheck(False) -def arrmap_int64(ndarray[int64_t] index, object func): - cdef Py_ssize_t length = index.shape[0] - cdef Py_ssize_t i = 0 - - cdef ndarray[object] result = np.empty(length, dtype=np.object_) - - from pandas.lib import maybe_convert_objects - - for i in range(length): - result[i] = func(index[i]) - - return maybe_convert_objects(result) - -@cython.wraparound(False) -@cython.boundscheck(False) -def arrmap_bool(ndarray[uint8_t] index, object func): - cdef Py_ssize_t length = index.shape[0] - cdef Py_ssize_t i = 0 - - cdef ndarray[object] result = np.empty(length, dtype=np.object_) - - from pandas.lib import maybe_convert_objects - - for i in range(length): - result[i] = func(index[i]) - - return maybe_convert_objects(result) - - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_bool_bool_memview(uint8_t[:] values, - int64_t[:] indexer, - uint8_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - uint8_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_bool_bool(ndarray[uint8_t, ndim=1] values, - int64_t[:] indexer, - uint8_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_bool_bool_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - uint8_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_bool_object_memview(uint8_t[:] values, - int64_t[:] indexer, - object[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - object fv - - n = indexer.shape[0] - - fv = fill_value - - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = True if values[idx] > 0 else False - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_bool_object(ndarray[uint8_t, ndim=1] values, - int64_t[:] indexer, - object[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_bool_object_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - object fv - - n = indexer.shape[0] - - fv = fill_value - - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = True if values[idx] > 0 else False - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int8_int8_memview(int8_t[:] values, - int64_t[:] indexer, - int8_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int8_t fv - - n = indexer.shape[0] - - fv = fill_value - - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int8_int8(ndarray[int8_t, ndim=1] values, - int64_t[:] indexer, - int8_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int8_int8_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int8_t fv - - n = indexer.shape[0] - - fv = fill_value - - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int8_int32_memview(int8_t[:] values, - int64_t[:] indexer, - int32_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int8_int32(ndarray[int8_t, ndim=1] values, - int64_t[:] indexer, - int32_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int8_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int8_int64_memview(int8_t[:] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int8_int64(ndarray[int8_t, ndim=1] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int8_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int8_float64_memview(int8_t[:] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int8_float64(ndarray[int8_t, ndim=1] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int8_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int16_int16_memview(int16_t[:] values, - int64_t[:] indexer, - int16_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int16_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int16_int16(ndarray[int16_t, ndim=1] values, - int64_t[:] indexer, - int16_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int16_int16_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int16_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int16_int32_memview(int16_t[:] values, - int64_t[:] indexer, - int32_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int16_int32(ndarray[int16_t, ndim=1] values, - int64_t[:] indexer, - int32_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int16_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int16_int64_memview(int16_t[:] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int16_int64(ndarray[int16_t, ndim=1] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int16_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int16_float64_memview(int16_t[:] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int16_float64(ndarray[int16_t, ndim=1] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int16_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int32_int32_memview(int32_t[:] values, - int64_t[:] indexer, - int32_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int32_int32(ndarray[int32_t, ndim=1] values, - int64_t[:] indexer, - int32_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int32_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int32_int64_memview(int32_t[:] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int32_int64(ndarray[int32_t, ndim=1] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int32_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int32_float64_memview(int32_t[:] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int32_float64(ndarray[int32_t, ndim=1] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int32_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int64_int64_memview(int64_t[:] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int64_int64(ndarray[int64_t, ndim=1] values, - int64_t[:] indexer, - int64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int64_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - int64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_int64_float64_memview(int64_t[:] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_int64_float64(ndarray[int64_t, ndim=1] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_int64_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_float32_float32_memview(float32_t[:] values, - int64_t[:] indexer, - float32_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - float32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_float32_float32(ndarray[float32_t, ndim=1] values, - int64_t[:] indexer, - float32_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_float32_float32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - float32_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_float32_float64_memview(float32_t[:] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_float32_float64(ndarray[float32_t, ndim=1] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_float32_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_float64_float64_memview(float64_t[:] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_float64_float64(ndarray[float64_t, ndim=1] values, - int64_t[:] indexer, - float64_t[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_float64_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - float64_t fv - - n = indexer.shape[0] - - fv = fill_value - - with nogil: - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_1d_object_object_memview(object[:] values, - int64_t[:] indexer, - object[:] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, n, idx - object fv - - n = indexer.shape[0] - - fv = fill_value - - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_1d_object_object(ndarray[object, ndim=1] values, - int64_t[:] indexer, - object[:] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_1d_object_object_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, n, idx - object fv - - n = indexer.shape[0] - - fv = fill_value - - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - out[i] = fv - else: - out[i] = values[idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_bool_bool_memview(uint8_t[:, :] values, - int64_t[:] indexer, - uint8_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - uint8_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - uint8_t *v - uint8_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(uint8_t) and - sizeof(uint8_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(uint8_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_bool_bool(ndarray[uint8_t, ndim=2] values, - ndarray[int64_t] indexer, - uint8_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_bool_bool_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - uint8_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - uint8_t *v - uint8_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(uint8_t) and - sizeof(uint8_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(uint8_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_bool_object_memview(uint8_t[:, :] values, - int64_t[:] indexer, - object[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - object *v - object *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(object) and - sizeof(object) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(object) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = True if values[idx, j] > 0 else False - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_bool_object(ndarray[uint8_t, ndim=2] values, - ndarray[int64_t] indexer, - object[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_bool_object_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - object *v - object *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(object) and - sizeof(object) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(object) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = True if values[idx, j] > 0 else False - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int8_int8_memview(int8_t[:, :] values, - int64_t[:] indexer, - int8_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int8_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int8_t *v - int8_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int8_t) and - sizeof(int8_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int8_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int8_int8(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - int8_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int8_int8_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int8_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int8_t *v - int8_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int8_t) and - sizeof(int8_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int8_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int8_int32_memview(int8_t[:, :] values, - int64_t[:] indexer, - int32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int32_t *v - int32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int32_t) and - sizeof(int32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int8_int32(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - int32_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int8_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int32_t *v - int32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int32_t) and - sizeof(int32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int8_int64_memview(int8_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int8_int64(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int8_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int8_float64_memview(int8_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int8_float64(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int8_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int16_int16_memview(int16_t[:, :] values, - int64_t[:] indexer, - int16_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int16_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int16_t *v - int16_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int16_t) and - sizeof(int16_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int16_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int16_int16(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - int16_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int16_int16_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int16_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int16_t *v - int16_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int16_t) and - sizeof(int16_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int16_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int16_int32_memview(int16_t[:, :] values, - int64_t[:] indexer, - int32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int32_t *v - int32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int32_t) and - sizeof(int32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int16_int32(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - int32_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int16_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int32_t *v - int32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int32_t) and - sizeof(int32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int16_int64_memview(int16_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int16_int64(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int16_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int16_float64_memview(int16_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int16_float64(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int16_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int32_int32_memview(int32_t[:, :] values, - int64_t[:] indexer, - int32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int32_t *v - int32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int32_t) and - sizeof(int32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int32_int32(ndarray[int32_t, ndim=2] values, - ndarray[int64_t] indexer, - int32_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int32_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int32_t *v - int32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int32_t) and - sizeof(int32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int32_int64_memview(int32_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int32_int64(ndarray[int32_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int32_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int32_float64_memview(int32_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int32_float64(ndarray[int32_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int32_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int64_int64_memview(int64_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int64_int64(ndarray[int64_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int64_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - int64_t *v - int64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(int64_t) and - sizeof(int64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(int64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_int64_float64_memview(int64_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_int64_float64(ndarray[int64_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_int64_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_float32_float32_memview(float32_t[:, :] values, - int64_t[:] indexer, - float32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - float32_t *v - float32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float32_t) and - sizeof(float32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_float32_float32(ndarray[float32_t, ndim=2] values, - ndarray[int64_t] indexer, - float32_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_float32_float32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float32_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - float32_t *v - float32_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float32_t) and - sizeof(float32_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float32_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_float32_float64_memview(float32_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_float32_float64(ndarray[float32_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_float32_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_float64_float64_memview(float64_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_float64_float64(ndarray[float64_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_float64_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF True: - cdef: - float64_t *v - float64_t *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(float64_t) and - sizeof(float64_t) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(float64_t) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis0_object_object_memview(object[:, :] values, - int64_t[:] indexer, - object[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - object *v - object *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(object) and - sizeof(object) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(object) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis0_object_object(ndarray[object, ndim=2] values, - ndarray[int64_t] indexer, - object[:, :] out, - fill_value=np.nan): - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis0_object_object_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(indexer) - k = values.shape[1] - - fv = fill_value - - IF False: - cdef: - object *v - object *o - - #GH3130 - if (values.strides[1] == out.strides[1] and - values.strides[1] == sizeof(object) and - sizeof(object) * n >= 256): - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - v = &values[idx, 0] - o = &out[i, 0] - memmove(o, v, <size_t>(sizeof(object) * k)) - return - - for i from 0 <= i < n: - idx = indexer[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - out[i, j] = values[idx, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_bool_bool_memview(uint8_t[:, :] values, - int64_t[:] indexer, - uint8_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - uint8_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_bool_bool(ndarray[uint8_t, ndim=2] values, - ndarray[int64_t] indexer, - uint8_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_bool_bool_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - uint8_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_bool_object_memview(uint8_t[:, :] values, - int64_t[:] indexer, - object[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = True if values[i, idx] > 0 else False - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_bool_object(ndarray[uint8_t, ndim=2] values, - ndarray[int64_t] indexer, - object[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_bool_object_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = True if values[i, idx] > 0 else False - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int8_int8_memview(int8_t[:, :] values, - int64_t[:] indexer, - int8_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int8_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int8_int8(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - int8_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int8_int8_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int8_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int8_int32_memview(int8_t[:, :] values, - int64_t[:] indexer, - int32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int8_int32(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - int32_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int8_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int8_int64_memview(int8_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int8_int64(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int8_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int8_float64_memview(int8_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int8_float64(ndarray[int8_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int8_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int16_int16_memview(int16_t[:, :] values, - int64_t[:] indexer, - int16_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int16_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int16_int16(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - int16_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int16_int16_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int16_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int16_int32_memview(int16_t[:, :] values, - int64_t[:] indexer, - int32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int16_int32(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - int32_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int16_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int16_int64_memview(int16_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int16_int64(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int16_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int16_float64_memview(int16_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int16_float64(ndarray[int16_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int16_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int32_int32_memview(int32_t[:, :] values, - int64_t[:] indexer, - int32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int32_int32(ndarray[int32_t, ndim=2] values, - ndarray[int64_t] indexer, - int32_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int32_int32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int32_int64_memview(int32_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int32_int64(ndarray[int32_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int32_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int32_float64_memview(int32_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int32_float64(ndarray[int32_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int32_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int64_int64_memview(int64_t[:, :] values, - int64_t[:] indexer, - int64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int64_int64(ndarray[int64_t, ndim=2] values, - ndarray[int64_t] indexer, - int64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int64_int64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - int64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_int64_float64_memview(int64_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_int64_float64(ndarray[int64_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_int64_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_float32_float32_memview(float32_t[:, :] values, - int64_t[:] indexer, - float32_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_float32_float32(ndarray[float32_t, ndim=2] values, - ndarray[int64_t] indexer, - float32_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_float32_float32_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float32_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_float32_float64_memview(float32_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_float32_float64(ndarray[float32_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_float32_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_float64_float64_memview(float64_t[:, :] values, - int64_t[:] indexer, - float64_t[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_float64_float64(ndarray[float64_t, ndim=2] values, - ndarray[int64_t] indexer, - float64_t[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_float64_float64_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - float64_t fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - -@cython.wraparound(False) -@cython.boundscheck(False) -cdef inline take_2d_axis1_object_object_memview(object[:, :] values, - int64_t[:] indexer, - object[:, :] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_axis1_object_object(ndarray[object, ndim=2] values, - ndarray[int64_t] indexer, - object[:, :] out, - fill_value=np.nan): - - if values.flags.writeable: - # We can call the memoryview version of the code - take_2d_axis1_object_object_memview(values, indexer, out, - fill_value=fill_value) - return - - # We cannot use the memoryview version on readonly-buffers due to - # a limitation of Cython's typed memoryviews. Instead we can use - # the slightly slower Cython ndarray type directly. - cdef: - Py_ssize_t i, j, k, n, idx - object fv - - n = len(values) - k = len(indexer) - - if n == 0 or k == 0: - return - - fv = fill_value - - for i from 0 <= i < n: - for j from 0 <= j < k: - idx = indexer[j] - if idx == -1: - out[i, j] = fv - else: - out[i, j] = values[i, idx] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_bool_bool(ndarray[uint8_t, ndim=2] values, - indexer, - ndarray[uint8_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - uint8_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_bool_object(ndarray[uint8_t, ndim=2] values, - indexer, - ndarray[object, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - object fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = True if values[idx, idx1[j]] > 0 else False - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int8_int8(ndarray[int8_t, ndim=2] values, - indexer, - ndarray[int8_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int8_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int8_int32(ndarray[int8_t, ndim=2] values, - indexer, - ndarray[int32_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int32_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int8_int64(ndarray[int8_t, ndim=2] values, - indexer, - ndarray[int64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int8_float64(ndarray[int8_t, ndim=2] values, - indexer, - ndarray[float64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - float64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int16_int16(ndarray[int16_t, ndim=2] values, - indexer, - ndarray[int16_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int16_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int16_int32(ndarray[int16_t, ndim=2] values, - indexer, - ndarray[int32_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int32_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int16_int64(ndarray[int16_t, ndim=2] values, - indexer, - ndarray[int64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int16_float64(ndarray[int16_t, ndim=2] values, - indexer, - ndarray[float64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - float64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int32_int32(ndarray[int32_t, ndim=2] values, - indexer, - ndarray[int32_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int32_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int32_int64(ndarray[int32_t, ndim=2] values, - indexer, - ndarray[int64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int32_float64(ndarray[int32_t, ndim=2] values, - indexer, - ndarray[float64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - float64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int64_int64(ndarray[int64_t, ndim=2] values, - indexer, - ndarray[int64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - int64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_int64_float64(ndarray[int64_t, ndim=2] values, - indexer, - ndarray[float64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - float64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_float32_float32(ndarray[float32_t, ndim=2] values, - indexer, - ndarray[float32_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - float32_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_float32_float64(ndarray[float32_t, ndim=2] values, - indexer, - ndarray[float64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - float64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_float64_float64(ndarray[float64_t, ndim=2] values, - indexer, - ndarray[float64_t, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - float64_t fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - -@cython.wraparound(False) -@cython.boundscheck(False) -def take_2d_multi_object_object(ndarray[object, ndim=2] values, - indexer, - ndarray[object, ndim=2] out, - fill_value=np.nan): - cdef: - Py_ssize_t i, j, k, n, idx - ndarray[int64_t] idx0 = indexer[0] - ndarray[int64_t] idx1 = indexer[1] - object fv - - n = len(idx0) - k = len(idx1) - - fv = fill_value - for i from 0 <= i < n: - idx = idx0[i] - if idx == -1: - for j from 0 <= j < k: - out[i, j] = fv - else: - for j from 0 <= j < k: - if idx1[j] == -1: - out[i, j] = fv - else: - out[i, j] = values[idx, idx1[j]] - - -@cython.boundscheck(False) -@cython.wraparound(False) -def diff_2d_float64(ndarray[float64_t, ndim=2] arr, - ndarray[float64_t, ndim=2] out, - Py_ssize_t periods, int axis): - cdef: - Py_ssize_t i, j, sx, sy - - sx, sy = (<object> arr).shape - if arr.flags.f_contiguous: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for j in range(sy): - for i in range(start, stop): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for j in range(start, stop): - for i in range(sx): - out[i, j] = arr[i, j] - arr[i, j - periods] - else: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for i in range(start, stop): - for j in range(sy): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for i in range(sx): - for j in range(start, stop): - out[i, j] = arr[i, j] - arr[i, j - periods] - -@cython.boundscheck(False) -@cython.wraparound(False) -def diff_2d_float32(ndarray[float32_t, ndim=2] arr, - ndarray[float32_t, ndim=2] out, - Py_ssize_t periods, int axis): - cdef: - Py_ssize_t i, j, sx, sy - - sx, sy = (<object> arr).shape - if arr.flags.f_contiguous: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for j in range(sy): - for i in range(start, stop): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for j in range(start, stop): - for i in range(sx): - out[i, j] = arr[i, j] - arr[i, j - periods] - else: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for i in range(start, stop): - for j in range(sy): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for i in range(sx): - for j in range(start, stop): - out[i, j] = arr[i, j] - arr[i, j - periods] - -@cython.boundscheck(False) -@cython.wraparound(False) -def diff_2d_int8(ndarray[int8_t, ndim=2] arr, - ndarray[float32_t, ndim=2] out, - Py_ssize_t periods, int axis): - cdef: - Py_ssize_t i, j, sx, sy - - sx, sy = (<object> arr).shape - if arr.flags.f_contiguous: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for j in range(sy): - for i in range(start, stop): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for j in range(start, stop): - for i in range(sx): - out[i, j] = arr[i, j] - arr[i, j - periods] - else: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for i in range(start, stop): - for j in range(sy): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for i in range(sx): - for j in range(start, stop): - out[i, j] = arr[i, j] - arr[i, j - periods] - -@cython.boundscheck(False) -@cython.wraparound(False) -def diff_2d_int16(ndarray[int16_t, ndim=2] arr, - ndarray[float32_t, ndim=2] out, - Py_ssize_t periods, int axis): - cdef: - Py_ssize_t i, j, sx, sy - - sx, sy = (<object> arr).shape - if arr.flags.f_contiguous: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for j in range(sy): - for i in range(start, stop): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for j in range(start, stop): - for i in range(sx): - out[i, j] = arr[i, j] - arr[i, j - periods] - else: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for i in range(start, stop): - for j in range(sy): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for i in range(sx): - for j in range(start, stop): - out[i, j] = arr[i, j] - arr[i, j - periods] - -@cython.boundscheck(False) -@cython.wraparound(False) -def diff_2d_int32(ndarray[int32_t, ndim=2] arr, - ndarray[float64_t, ndim=2] out, - Py_ssize_t periods, int axis): - cdef: - Py_ssize_t i, j, sx, sy - - sx, sy = (<object> arr).shape - if arr.flags.f_contiguous: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for j in range(sy): - for i in range(start, stop): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for j in range(start, stop): - for i in range(sx): - out[i, j] = arr[i, j] - arr[i, j - periods] - else: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for i in range(start, stop): - for j in range(sy): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for i in range(sx): - for j in range(start, stop): - out[i, j] = arr[i, j] - arr[i, j - periods] - -@cython.boundscheck(False) -@cython.wraparound(False) -def diff_2d_int64(ndarray[int64_t, ndim=2] arr, - ndarray[float64_t, ndim=2] out, - Py_ssize_t periods, int axis): - cdef: - Py_ssize_t i, j, sx, sy - - sx, sy = (<object> arr).shape - if arr.flags.f_contiguous: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for j in range(sy): - for i in range(start, stop): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for j in range(start, stop): - for i in range(sx): - out[i, j] = arr[i, j] - arr[i, j - periods] - else: - if axis == 0: - if periods >= 0: - start, stop = periods, sx - else: - start, stop = 0, sx + periods - for i in range(start, stop): - for j in range(sy): - out[i, j] = arr[i, j] - arr[i - periods, j] - else: - if periods >= 0: - start, stop = periods, sy - else: - start, stop = 0, sy + periods - for i in range(sx): - for j in range(start, stop): - out[i, j] = arr[i, j] - arr[i, j - periods] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_add_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, count - ndarray[float64_t, ndim=2] sumx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - sumx = np.zeros_like(out) - - N, K = (<object> values).shape - - - with nogil: - - if K > 1: - - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - sumx[lab, j] += val - - else: - - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val: - nobs[lab, 0] += 1 - sumx[lab, 0] += val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = sumx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_add_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, count - ndarray[float32_t, ndim=2] sumx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - sumx = np.zeros_like(out) - - N, K = (<object> values).shape - - - with nogil: - - if K > 1: - - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - sumx[lab, j] += val - - else: - - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val: - nobs[lab, 0] += 1 - sumx[lab, 0] += val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = sumx[i, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_prod_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, count - ndarray[float64_t, ndim=2] prodx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - prodx = np.ones_like(out) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - prodx[lab, j] *= val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val: - nobs[lab, 0] += 1 - prodx[lab, 0] *= val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = prodx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_prod_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, count - ndarray[float32_t, ndim=2] prodx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - prodx = np.ones_like(out) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - prodx[lab, j] *= val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val: - nobs[lab, 0] += 1 - prodx[lab, 0] *= val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = prodx[i, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -@cython.cdivision(True) -def group_var_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, ct, oldmean - ndarray[float64_t, ndim=2] nobs, mean - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - mean = np.zeros_like(out) - - N, K = (<object> values).shape - - out[:, :] = 0.0 - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - oldmean = mean[lab, j] - mean[lab, j] += (val - oldmean) / nobs[lab, j] - out[lab, j] += (val - mean[lab, j]) * (val - oldmean) - - for i in range(ncounts): - for j in range(K): - ct = nobs[i, j] - if ct < 2: - out[i, j] = NAN - else: - out[i, j] /= (ct - 1) - - -@cython.wraparound(False) -@cython.boundscheck(False) -@cython.cdivision(True) -def group_var_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, ct, oldmean - ndarray[float32_t, ndim=2] nobs, mean - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - mean = np.zeros_like(out) - - N, K = (<object> values).shape - - out[:, :] = 0.0 - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - - for j in range(K): - val = values[i, j] - - # not nan - if val == val: - nobs[lab, j] += 1 - oldmean = mean[lab, j] - mean[lab, j] += (val - oldmean) / nobs[lab, j] - out[lab, j] += (val - mean[lab, j]) * (val - oldmean) - - for i in range(ncounts): - for j in range(K): - ct = nobs[i, j] - if ct < 2: - out[i, j] = NAN - else: - out[i, j] /= (ct - 1) - - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_mean_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, count - ndarray[float64_t, ndim=2] sumx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - sumx = np.zeros_like(out) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - # not nan - if val == val: - nobs[lab, j] += 1 - sumx[lab, j] += val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - # not nan - if val == val: - nobs[lab, 0] += 1 - sumx[lab, 0] += val - - for i in range(ncounts): - for j in range(K): - count = nobs[i, j] - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = sumx[i, j] / count - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_mean_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, count - ndarray[float32_t, ndim=2] sumx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - sumx = np.zeros_like(out) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - # not nan - if val == val: - nobs[lab, j] += 1 - sumx[lab, j] += val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - # not nan - if val == val: - nobs[lab, 0] += 1 - sumx[lab, 0] += val - - for i in range(ncounts): - for j in range(K): - count = nobs[i, j] - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = sumx[i, j] / count - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_ohlc_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab - float64_t val, count - Py_ssize_t ngroups = len(counts) - - if len(labels) == 0: - return - - N, K = (<object> values).shape - - if out.shape[1] != 4: - raise ValueError('Output array must have 4 columns') - - if K > 1: - raise NotImplementedError("Argument 'values' must have only " - "one dimension") - out.fill(np.nan) - - with nogil: - for i in range(N): - lab = labels[i] - if lab == -1: - continue - - counts[lab] += 1 - val = values[i, 0] - if val != val: - continue - - if out[lab, 0] != out[lab, 0]: - out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val - else: - out[lab, 1] = max(out[lab, 1], val) - out[lab, 2] = min(out[lab, 2], val) - out[lab, 3] = val - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_ohlc_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab - float32_t val, count - Py_ssize_t ngroups = len(counts) - - if len(labels) == 0: - return - - N, K = (<object> values).shape - - if out.shape[1] != 4: - raise ValueError('Output array must have 4 columns') - - if K > 1: - raise NotImplementedError("Argument 'values' must have only " - "one dimension") - out.fill(np.nan) - - with nogil: - for i in range(N): - lab = labels[i] - if lab == -1: - continue - - counts[lab] += 1 - val = values[i, 0] - if val != val: - continue - - if out[lab, 0] != out[lab, 0]: - out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val - else: - out[lab, 1] = max(out[lab, 1], val) - out[lab, 2] = min(out[lab, 2], val) - out[lab, 3] = val - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_last_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, count - ndarray[float64_t, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - nobs[lab, j] += 1 - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = resx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_last_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, count - ndarray[float32_t, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - nobs[lab, j] += 1 - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = resx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_last_int64(ndarray[int64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[int64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - int64_t val, count - ndarray[int64_t, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != iNaT: - nobs[lab, j] += 1 - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = iNaT - else: - out[i, j] = resx[i, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_nth_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels, int64_t rank): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, count - ndarray[float64_t, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - nobs[lab, j] += 1 - if nobs[lab, j] == rank: - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = resx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_nth_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels, int64_t rank): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, count - ndarray[float32_t, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - nobs[lab, j] += 1 - if nobs[lab, j] == rank: - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = resx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_nth_int64(ndarray[int64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[int64_t, ndim=2] values, - ndarray[int64_t] labels, int64_t rank): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - int64_t val, count - ndarray[int64_t, ndim=2] resx - ndarray[int64_t, ndim=2] nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros((<object> out).shape, dtype=np.int64) - resx = np.empty_like(out) - - N, K = (<object> values).shape - - with nogil: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != iNaT: - nobs[lab, j] += 1 - if nobs[lab, j] == rank: - resx[lab, j] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = iNaT - else: - out[i, j] = resx[i, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_min_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, count - ndarray[float64_t, ndim=2] minx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - minx = np.empty_like(out) - minx.fill(np.inf) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - - nobs[lab, j] += 1 - if val < minx[lab, j]: - minx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != NAN: - nobs[lab, 0] += 1 - if val < minx[lab, 0]: - minx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = minx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_min_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, count - ndarray[float32_t, ndim=2] minx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - minx = np.empty_like(out) - minx.fill(np.inf) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - - nobs[lab, j] += 1 - if val < minx[lab, j]: - minx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != NAN: - nobs[lab, 0] += 1 - if val < minx[lab, 0]: - minx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = minx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_min_int64(ndarray[int64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[int64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - int64_t val, count - ndarray[int64_t, ndim=2] minx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - minx = np.empty_like(out) - minx.fill(9223372036854775807) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != iNaT: - - nobs[lab, j] += 1 - if val < minx[lab, j]: - minx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != iNaT: - nobs[lab, 0] += 1 - if val < minx[lab, 0]: - minx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = iNaT - else: - out[i, j] = minx[i, j] - - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_max_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float64_t val, count - ndarray[float64_t, ndim=2] maxx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - maxx = np.empty_like(out) - maxx.fill(-np.inf) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - nobs[lab, j] += 1 - if val > maxx[lab, j]: - maxx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != NAN: - nobs[lab, 0] += 1 - if val > maxx[lab, 0]: - maxx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = maxx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_max_float32(ndarray[float32_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float32_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - float32_t val, count - ndarray[float32_t, ndim=2] maxx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - maxx = np.empty_like(out) - maxx.fill(-np.inf) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != NAN: - nobs[lab, j] += 1 - if val > maxx[lab, j]: - maxx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != NAN: - nobs[lab, 0] += 1 - if val > maxx[lab, 0]: - maxx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = NAN - else: - out[i, j] = maxx[i, j] - -@cython.wraparound(False) -@cython.boundscheck(False) -def group_max_int64(ndarray[int64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[int64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, lab, ncounts = len(counts) - int64_t val, count - ndarray[int64_t, ndim=2] maxx, nobs - - if not len(values) == len(labels): - raise AssertionError("len(index) != len(labels)") - - nobs = np.zeros_like(out) - - maxx = np.empty_like(out) - maxx.fill(-9223372036854775807) - - N, K = (<object> values).shape - - with nogil: - if K > 1: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - for j in range(K): - val = values[i, j] - - # not nan - if val == val and val != iNaT: - nobs[lab, j] += 1 - if val > maxx[lab, j]: - maxx[lab, j] = val - else: - for i in range(N): - lab = labels[i] - if lab < 0: - continue - - counts[lab] += 1 - val = values[i, 0] - - # not nan - if val == val and val != iNaT: - nobs[lab, 0] += 1 - if val > maxx[lab, 0]: - maxx[lab, 0] = val - - for i in range(ncounts): - for j in range(K): - if nobs[i, j] == 0: - out[i, j] = iNaT - else: - out[i, j] = maxx[i, j] - - -def group_median_float64(ndarray[float64_t, ndim=2] out, - ndarray[int64_t] counts, - ndarray[float64_t, ndim=2] values, - ndarray[int64_t] labels): - """ - Only aggregates on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, ngroups, size - ndarray[int64_t] _counts - ndarray data - float64_t* ptr - ngroups = len(counts) - N, K = (<object> values).shape - - indexer, _counts = groupsort_indexer(labels, ngroups) - counts[:] = _counts[1:] - - data = np.empty((K, N), dtype=np.float64) - ptr = <float64_t*> data.data - - take_2d_axis1_float64_float64(values.T, indexer, out=data) - - for i in range(K): - # exclude NA group - ptr += _counts[0] - for j in range(ngroups): - size = _counts[j + 1] - out[j, i] = _median_linear(ptr, size) - ptr += size - - -@cython.boundscheck(False) -@cython.wraparound(False) -def group_cumprod_float64(float64_t[:,:] out, - float64_t[:,:] values, - int64_t[:] labels, - float64_t[:,:] accum): - """ - Only transforms on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, size - float64_t val - int64_t lab - - N, K = (<object> values).shape - accum = np.ones_like(accum) - - with nogil: - for i in range(N): - lab = labels[i] - - if lab < 0: - continue - for j in range(K): - val = values[i, j] - if val == val: - accum[lab, j] *= val - out[i, j] = accum[lab, j] - -@cython.boundscheck(False) -@cython.wraparound(False) -def group_cumsum(numeric[:,:] out, - numeric[:,:] values, - int64_t[:] labels, - numeric[:,:] accum): - """ - Only transforms on axis=0 - """ - cdef: - Py_ssize_t i, j, N, K, size - numeric val - int64_t lab - - N, K = (<object> values).shape - accum = np.zeros_like(accum) - - with nogil: - for i in range(N): - lab = labels[i] - - if lab < 0: - continue - for j in range(K): - val = values[i,j] - if val == val: - accum[lab,j] += val - out[i,j] = accum[lab,j] - -@cython.boundscheck(False) -@cython.wraparound(False) -def group_shift_indexer(int64_t[:] out, int64_t[:] labels, - int ngroups, int periods): - cdef: - Py_ssize_t N, i, j, ii - int offset, sign - int64_t lab, idxer, idxer_slot - int64_t[:] label_seen = np.zeros(ngroups, dtype=np.int64) - int64_t[:,:] label_indexer - - N, = (<object> labels).shape - - if periods < 0: - periods = -periods - offset = N - 1 - sign = -1 - elif periods > 0: - offset = 0 - sign = 1 - - if periods == 0: - with nogil: - for i in range(N): - out[i] = i - else: - # array of each previous indexer seen - label_indexer = np.zeros((ngroups, periods), dtype=np.int64) - with nogil: - for i in range(N): - ## reverse iterator if shifting backwards - ii = offset + sign * i - lab = labels[ii] - label_seen[lab] += 1 - - idxer_slot = label_seen[lab] % periods - idxer = label_indexer[lab, idxer_slot] - - if label_seen[lab] > periods: - out[ii] = idxer - else: - out[ii] = -1 - - label_indexer[lab, idxer_slot] = ii - -@cython.wraparound(False) -@cython.boundscheck(False) -def left_join_indexer_unique_float64(ndarray[float64_t] left, - ndarray[float64_t] right): - cdef: - Py_ssize_t i, j, nleft, nright - ndarray[int64_t] indexer - float64_t lval, rval - - i = 0 - j = 0 - nleft = len(left) - nright = len(right) - - indexer = np.empty(nleft, dtype=np.int64) - while True: - if i == nleft: - break - - if j == nright: - indexer[i] = -1 - i += 1 - continue - - rval = right[j] - - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - - if left[i] == right[j]: - indexer[i] = j - i += 1 - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - j += 1 - elif left[i] > rval: - indexer[i] = -1 - j += 1 - else: - indexer[i] = -1 - i += 1 - return indexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def left_join_indexer_unique_float32(ndarray[float32_t] left, - ndarray[float32_t] right): - cdef: - Py_ssize_t i, j, nleft, nright - ndarray[int64_t] indexer - float32_t lval, rval - - i = 0 - j = 0 - nleft = len(left) - nright = len(right) - - indexer = np.empty(nleft, dtype=np.int64) - while True: - if i == nleft: - break - - if j == nright: - indexer[i] = -1 - i += 1 - continue - - rval = right[j] - - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - - if left[i] == right[j]: - indexer[i] = j - i += 1 - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - j += 1 - elif left[i] > rval: - indexer[i] = -1 - j += 1 - else: - indexer[i] = -1 - i += 1 - return indexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def left_join_indexer_unique_object(ndarray[object] left, - ndarray[object] right): - cdef: - Py_ssize_t i, j, nleft, nright - ndarray[int64_t] indexer - object lval, rval - - i = 0 - j = 0 - nleft = len(left) - nright = len(right) - - indexer = np.empty(nleft, dtype=np.int64) - while True: - if i == nleft: - break - - if j == nright: - indexer[i] = -1 - i += 1 - continue - - rval = right[j] - - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - - if left[i] == right[j]: - indexer[i] = j - i += 1 - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - j += 1 - elif left[i] > rval: - indexer[i] = -1 - j += 1 - else: - indexer[i] = -1 - i += 1 - return indexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def left_join_indexer_unique_int32(ndarray[int32_t] left, - ndarray[int32_t] right): - cdef: - Py_ssize_t i, j, nleft, nright - ndarray[int64_t] indexer - int32_t lval, rval - - i = 0 - j = 0 - nleft = len(left) - nright = len(right) - - indexer = np.empty(nleft, dtype=np.int64) - while True: - if i == nleft: - break - - if j == nright: - indexer[i] = -1 - i += 1 - continue - - rval = right[j] - - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - - if left[i] == right[j]: - indexer[i] = j - i += 1 - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - j += 1 - elif left[i] > rval: - indexer[i] = -1 - j += 1 - else: - indexer[i] = -1 - i += 1 - return indexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def left_join_indexer_unique_int64(ndarray[int64_t] left, - ndarray[int64_t] right): - cdef: - Py_ssize_t i, j, nleft, nright - ndarray[int64_t] indexer - int64_t lval, rval - - i = 0 - j = 0 - nleft = len(left) - nright = len(right) - - indexer = np.empty(nleft, dtype=np.int64) - while True: - if i == nleft: - break - - if j == nright: - indexer[i] = -1 - i += 1 - continue - - rval = right[j] - - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - - if left[i] == right[j]: - indexer[i] = j - i += 1 - while i < nleft - 1 and left[i] == rval: - indexer[i] = j - i += 1 - j += 1 - elif left[i] > rval: - indexer[i] = -1 - j += 1 - else: - indexer[i] = -1 - i += 1 - return indexer - - -def left_join_indexer_float64(ndarray[float64_t] left, - ndarray[float64_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - float64_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[float64_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.float64) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - i += 1 - count += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -def left_join_indexer_float32(ndarray[float32_t] left, - ndarray[float32_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - float32_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[float32_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.float32) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - i += 1 - count += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -def left_join_indexer_object(ndarray[object] left, - ndarray[object] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - object lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[object] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=object) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - i += 1 - count += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -def left_join_indexer_int32(ndarray[int32_t] left, - ndarray[int32_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - int32_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[int32_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.int32) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - i += 1 - count += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -def left_join_indexer_int64(ndarray[int64_t] left, - ndarray[int64_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - int64_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[int64_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.int64) - - i = 0 - j = 0 - count = 0 - if nleft > 0: - while i < nleft: - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - i += 1 - count += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - - -@cython.wraparound(False) -@cython.boundscheck(False) -def outer_join_indexer_float64(ndarray[float64_t] left, - ndarray[float64_t] right): - cdef: - Py_ssize_t i, j, nright, nleft, count - float64_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[float64_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft == 0: - count = nright - elif nright == 0: - count = nleft - else: - while True: - if i == nleft: - count += nright - j - break - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - count += 1 - j += 1 - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.float64) - - # do it again, but populate the indexers / result - - i = 0 - j = 0 - count = 0 - if nleft == 0: - for j in range(nright): - lindexer[j] = -1 - rindexer[j] = j - result[j] = right[j] - elif nright == 0: - for i in range(nleft): - lindexer[i] = i - rindexer[i] = -1 - result[i] = left[i] - else: - while True: - if i == nleft: - while j < nright: - lindexer[count] = -1 - rindexer[count] = j - result[count] = right[j] - count += 1 - j += 1 - break - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = lval - count += 1 - i += 1 - else: - lindexer[count] = -1 - rindexer[count] = j - result[count] = rval - count += 1 - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def outer_join_indexer_float32(ndarray[float32_t] left, - ndarray[float32_t] right): - cdef: - Py_ssize_t i, j, nright, nleft, count - float32_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[float32_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft == 0: - count = nright - elif nright == 0: - count = nleft - else: - while True: - if i == nleft: - count += nright - j - break - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - count += 1 - j += 1 - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.float32) - - # do it again, but populate the indexers / result - - i = 0 - j = 0 - count = 0 - if nleft == 0: - for j in range(nright): - lindexer[j] = -1 - rindexer[j] = j - result[j] = right[j] - elif nright == 0: - for i in range(nleft): - lindexer[i] = i - rindexer[i] = -1 - result[i] = left[i] - else: - while True: - if i == nleft: - while j < nright: - lindexer[count] = -1 - rindexer[count] = j - result[count] = right[j] - count += 1 - j += 1 - break - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = lval - count += 1 - i += 1 - else: - lindexer[count] = -1 - rindexer[count] = j - result[count] = rval - count += 1 - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def outer_join_indexer_object(ndarray[object] left, - ndarray[object] right): - cdef: - Py_ssize_t i, j, nright, nleft, count - object lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[object] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft == 0: - count = nright - elif nright == 0: - count = nleft - else: - while True: - if i == nleft: - count += nright - j - break - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - count += 1 - j += 1 - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=object) - - # do it again, but populate the indexers / result - - i = 0 - j = 0 - count = 0 - if nleft == 0: - for j in range(nright): - lindexer[j] = -1 - rindexer[j] = j - result[j] = right[j] - elif nright == 0: - for i in range(nleft): - lindexer[i] = i - rindexer[i] = -1 - result[i] = left[i] - else: - while True: - if i == nleft: - while j < nright: - lindexer[count] = -1 - rindexer[count] = j - result[count] = right[j] - count += 1 - j += 1 - break - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = lval - count += 1 - i += 1 - else: - lindexer[count] = -1 - rindexer[count] = j - result[count] = rval - count += 1 - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def outer_join_indexer_int32(ndarray[int32_t] left, - ndarray[int32_t] right): - cdef: - Py_ssize_t i, j, nright, nleft, count - int32_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[int32_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft == 0: - count = nright - elif nright == 0: - count = nleft - else: - while True: - if i == nleft: - count += nright - j - break - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - count += 1 - j += 1 - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.int32) - - # do it again, but populate the indexers / result - - i = 0 - j = 0 - count = 0 - if nleft == 0: - for j in range(nright): - lindexer[j] = -1 - rindexer[j] = j - result[j] = right[j] - elif nright == 0: - for i in range(nleft): - lindexer[i] = i - rindexer[i] = -1 - result[i] = left[i] - else: - while True: - if i == nleft: - while j < nright: - lindexer[count] = -1 - rindexer[count] = j - result[count] = right[j] - count += 1 - j += 1 - break - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = lval - count += 1 - i += 1 - else: - lindexer[count] = -1 - rindexer[count] = j - result[count] = rval - count += 1 - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def outer_join_indexer_int64(ndarray[int64_t] left, - ndarray[int64_t] right): - cdef: - Py_ssize_t i, j, nright, nleft, count - int64_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[int64_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft == 0: - count = nright - elif nright == 0: - count = nleft - else: - while True: - if i == nleft: - count += nright - j - break - if j == nright: - count += nleft - i - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - count += 1 - i += 1 - else: - count += 1 - j += 1 - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.int64) - - # do it again, but populate the indexers / result - - i = 0 - j = 0 - count = 0 - if nleft == 0: - for j in range(nright): - lindexer[j] = -1 - rindexer[j] = j - result[j] = right[j] - elif nright == 0: - for i in range(nleft): - lindexer[i] = i - rindexer[i] = -1 - result[i] = left[i] - else: - while True: - if i == nleft: - while j < nright: - lindexer[count] = -1 - rindexer[count] = j - result[count] = right[j] - count += 1 - j += 1 - break - if j == nright: - while i < nleft: - lindexer[count] = i - rindexer[count] = -1 - result[count] = left[i] - count += 1 - i += 1 - break - - lval = left[i] - rval = right[j] - - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = lval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - lindexer[count] = i - rindexer[count] = -1 - result[count] = lval - count += 1 - i += 1 - else: - lindexer[count] = -1 - rindexer[count] = j - result[count] = rval - count += 1 - j += 1 - - return result, lindexer, rindexer - - -@cython.wraparound(False) -@cython.boundscheck(False) -def inner_join_indexer_float64(ndarray[float64_t] left, - ndarray[float64_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - float64_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[float64_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.float64) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = rval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def inner_join_indexer_float32(ndarray[float32_t] left, - ndarray[float32_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - float32_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[float32_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.float32) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = rval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def inner_join_indexer_object(ndarray[object] left, - ndarray[object] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - object lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[object] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=object) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = rval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def inner_join_indexer_int32(ndarray[int32_t] left, - ndarray[int32_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - int32_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[int32_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.int32) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = rval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - -@cython.wraparound(False) -@cython.boundscheck(False) -def inner_join_indexer_int64(ndarray[int64_t] left, - ndarray[int64_t] right): - """ - Two-pass algorithm for monotonic indexes. Handles many-to-one merges - """ - cdef: - Py_ssize_t i, j, k, nright, nleft, count - int64_t lval, rval - ndarray[int64_t] lindexer, rindexer - ndarray[int64_t] result - - nleft = len(left) - nright = len(right) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - # do it again now that result size is known - - lindexer = np.empty(count, dtype=np.int64) - rindexer = np.empty(count, dtype=np.int64) - result = np.empty(count, dtype=np.int64) - - i = 0 - j = 0 - count = 0 - if nleft > 0 and nright > 0: - while True: - if i == nleft: - break - if j == nright: - break - - lval = left[i] - rval = right[j] - if lval == rval: - lindexer[count] = i - rindexer[count] = j - result[count] = rval - count += 1 - if i < nleft - 1: - if j < nright - 1 and right[j + 1] == rval: - j += 1 - else: - i += 1 - if left[i] != rval: - j += 1 - elif j < nright - 1: - j += 1 - if lval != right[j]: - i += 1 - else: - # end of the road - break - elif lval < rval: - i += 1 - else: - j += 1 - - return result, lindexer, rindexer - - diff --git a/setup.py b/setup.py index 937b3509cf493..86777f5579a09 100755 --- a/setup.py +++ b/setup.py @@ -90,11 +90,47 @@ def is_platform_mac(): except ImportError: cython = False + +if cython: + try: + try: + from Cython import Tempita as tempita + except ImportError: + import tempita + except ImportError: + raise ImportError('Building pandas requires Tempita: ' + 'pip install Tempita') + + from os.path import join as pjoin +_pxipath = pjoin('pandas', 'src') +_pxifiles = ['algos_common_helper.pxi.in', 'algos_groupby_helper.pxi.in', + 'algos_join_helper.pxi.in', 'algos_take_helper.pxi.in'] + + class build_ext(_build_ext): def build_extensions(self): + + for _pxifile in _pxifiles: + # build pxifiles first, template extention must be .pxi.in + assert _pxifile.endswith('.pxi.in') + pxifile = pjoin(_pxipath, _pxifile) + outfile = pxifile[:-3] + + if (os.path.exists(outfile) and + os.stat(pxifile).st_mtime < os.stat(outfile).st_mtime): + # if .pxi.in is not updated, no need to output .pxi + continue + + with open(pxifile, "r") as f: + tmpl = f.read() + pyxcontent = tempita.sub(tmpl) + + with open(outfile, "w") as f: + f.write(pyxcontent) + numpy_incl = pkg_resources.resource_filename('numpy', 'core/include') for ext in self.extensions:
- [x] closes #13399 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` not sure what the `setup.py` 's best practice be... CC: @gfyoung
https://api.github.com/repos/pandas-dev/pandas/pulls/13716
2016-07-20T11:59:15Z
2016-07-26T10:51:50Z
null
2016-07-26T11:14:07Z
ENH: Enable automatic writing of dates to Stata files
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index efa6e5575fa79..d1ee506ba294c 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -251,6 +251,7 @@ Other enhancements - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) - ``Series.append`` now supports the ``ignore_index`` option (:issue:`13677`) - ``.to_stata()`` and ```StataWriter`` can now write variable labels to Stata dta files using a dictionary to make column names to labels (:issue:`13535`, :issue:`13536`) +- ``.to_stata()`` and ```StataWriter`` will automatically convert ``datetime[ns]`` columns to Stata format ``%tc`` rather than raising a ``ValueError`` (:issue:`12259`) .. _whatsnew_0190.api: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 4fe7b318b3a18..a59668320de3d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1473,31 +1473,42 @@ def to_stata(self, fname, convert_dates=None, write_index=True, Parameters ---------- - fname : file path or buffer - Where to save the dta file. + fname : str or buffer + String path of file-like object convert_dates : dict - Dictionary mapping column of datetime types to the stata internal - format that you want to use for the dates. Options are - 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either a - number or a name. + Dictionary mapping columns containing datetime types to stata internal + format to use when wirting the dates. Options are 'tc', 'td', 'tm', + 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. + Datetime columns that do not have a conversion type specified will be + converted to 'tc'. Raises NotImplementedError if a datetime column has + timezone information write_index : bool Write the index to Stata dataset. encoding : str - Default is latin-1. Note that Stata does not support unicode. + Default is latin-1. Unicode is not supported byteorder : str - Can be ">", "<", "little", or "big". The default is None which uses - `sys.byteorder` + Can be ">", "<", "little", or "big". default is `sys.byteorder` time_stamp : datetime - A date time to use when writing the file. Can be None, in which - case the current time is used. + A datetime to use as file creation date. Default is the current time dataset_label : str - A label for the data set. Should be 80 characters or smaller. + A label for the data set. Must be 80 characters or smaller. .. versionadded:: 0.19.0 variable_labels : dict - Dictionary containing columns as keys and variable labels as - values. Each label must be 80 characters or smaller. + Dictionary containing columns as keys and variable labels as values. + Each label must be 80 characters or smaller. + + Raises + ------ + NotImplementedError + * If datetimes contain timezone information + * Column dtype is not representable in Stata + ValueError + * Columns listed in convert_dates are noth either datetime64[ns] + or datetime.datetime + * Column listed in convert_dates is not in DataFrame + * Categorical label contains more than 32,000 characters Examples -------- diff --git a/pandas/io/stata.py b/pandas/io/stata.py index d35466e8896ba..5528b2803eb21 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -432,7 +432,7 @@ def parse_dates_safe(dates, delta=False, year=False, days=False): d = parse_dates_safe(dates, year=True) conv_dates = d.year else: - raise ValueError("fmt %s not understood" % fmt) + raise ValueError("Format %s is not a known Stata date format" % fmt) conv_dates = Series(conv_dates, dtype=np.float64) missing_value = struct.unpack('<d', b'\x00\x00\x00\x00\x00\x00\xe0\x7f')[0] @@ -1709,7 +1709,7 @@ def _convert_datetime_to_stata_type(fmt): "%tq", "th", "%th", "ty", "%ty"]: return np.float64 # Stata expects doubles for SIFs else: - raise ValueError("fmt %s not understood" % fmt) + raise NotImplementedError("Format %s not implemented" % fmt) def _maybe_convert_to_int_keys(convert_dates, varlist): @@ -1721,9 +1721,8 @@ def _maybe_convert_to_int_keys(convert_dates, varlist): new_dict.update({varlist.index(key): convert_dates[key]}) else: if not isinstance(key, int): - raise ValueError( - "convert_dates key is not in varlist and is not an int" - ) + raise ValueError("convert_dates key must be a " + "column or an integer") new_dict.update({key: convert_dates[key]}) return new_dict @@ -1763,8 +1762,7 @@ def _dtype_to_stata_type(dtype, column): elif dtype == np.int8: return chr(251) else: # pragma : no cover - raise ValueError("Data type %s not currently understood. " - "Please report an error to the developers." % dtype) + raise NotImplementedError("Data type %s not supported." % dtype) def _dtype_to_default_stata_fmt(dtype, column): @@ -1801,35 +1799,36 @@ def _dtype_to_default_stata_fmt(dtype, column): elif dtype == np.int8 or dtype == np.int16: return "%8.0g" else: # pragma : no cover - raise ValueError("Data type %s not currently understood. " - "Please report an error to the developers." % dtype) + raise NotImplementedError("Data type %s not supported." % dtype) class StataWriter(StataParser): """ - A class for writing Stata binary dta files from array-like objects + A class for writing Stata binary dta files Parameters ---------- - fname : file path or buffer - Where to save the dta file. - data : array-like - Array-like input to save. Pandas objects are also accepted. + fname : str or buffer + String path of file-like object + data : DataFrame + Input to save convert_dates : dict - Dictionary mapping column of datetime types to the stata internal - format that you want to use for the dates. Options are - 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either a - number or a name. + Dictionary mapping columns containing datetime types to stata internal + format to use when wirting the dates. Options are 'tc', 'td', 'tm', + 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. + Datetime columns that do not have a conversion type specified will be + converted to 'tc'. Raises NotImplementedError if a datetime column has + timezone information + write_index : bool + Write the index to Stata dataset. encoding : str - Default is latin-1. Note that Stata does not support unicode. + Default is latin-1. Unicode is not supported byteorder : str - Can be ">", "<", "little", or "big". The default is None which uses - `sys.byteorder` + Can be ">", "<", "little", or "big". default is `sys.byteorder` time_stamp : datetime - A date time to use when writing the file. Can be None, in which - case the current time is used. + A datetime to use as file creation date. Default is the current time dataset_label : str - A label for the data set. Should be 80 characters or smaller. + A label for the data set. Must be 80 characters or smaller. .. versionadded:: 0.19.0 @@ -1843,6 +1842,17 @@ class StataWriter(StataParser): The StataWriter instance has a write_file method, which will write the file to the given `fname`. + Raises + ------ + NotImplementedError + * If datetimes contain timezone information + * Column dtype is not representable in Stata + ValueError + * Columns listed in convert_dates are noth either datetime64[ns] + or datetime.datetime + * Column listed in convert_dates is not in DataFrame + * Categorical label contains more than 32,000 characters + Examples -------- >>> import pandas as pd @@ -1861,7 +1871,7 @@ def __init__(self, fname, data, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, data_label=None, variable_labels=None): super(StataWriter, self).__init__(encoding) - self._convert_dates = convert_dates + self._convert_dates = {} if convert_dates is None else convert_dates self._write_index = write_index self._time_stamp = time_stamp self._data_label = data_label @@ -2041,15 +2051,22 @@ def _prepare_pandas(self, data): self.varlist = data.columns.tolist() dtypes = data.dtypes - if self._convert_dates is not None: - self._convert_dates = _maybe_convert_to_int_keys( - self._convert_dates, self.varlist + + # Ensure all date columns are converted + for col in data: + if col in self._convert_dates: + continue + if is_datetime64_dtype(data[col]): + self._convert_dates[col] = 'tc' + + self._convert_dates = _maybe_convert_to_int_keys(self._convert_dates, + self.varlist) + for key in self._convert_dates: + new_type = _convert_datetime_to_stata_type( + self._convert_dates[key] ) - for key in self._convert_dates: - new_type = _convert_datetime_to_stata_type( - self._convert_dates[key] - ) - dtypes[key] = np.dtype(new_type) + dtypes[key] = np.dtype(new_type) + self.typlist = [] self.fmtlist = [] for col, dtype in dtypes.iteritems(): diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 91850e6ffe9b9..009e40c84f94b 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -11,17 +11,17 @@ import nose import numpy as np +from pandas.tslib import NaT import pandas as pd import pandas.util.testing as tm from pandas import compat from pandas.compat import iterkeys from pandas.core.frame import DataFrame, Series -from pandas.types.common import is_categorical_dtype -from pandas.tslib import NaT from pandas.io.parsers import read_csv from pandas.io.stata import (read_stata, StataReader, InvalidColumnName, PossiblePrecisionLoss, StataMissingValue) +from pandas.types.common import is_categorical_dtype class TestStata(tm.TestCase): @@ -1165,6 +1165,52 @@ def test_write_variable_label_errors(self): with tm.ensure_clean() as path: original.to_stata(path, variable_labels=variable_labels_long) + def test_default_date_conversion(self): + # GH 12259 + dates = [dt.datetime(1999, 12, 31, 12, 12, 12, 12000), + dt.datetime(2012, 12, 21, 12, 21, 12, 21000), + dt.datetime(1776, 7, 4, 7, 4, 7, 4000)] + original = pd.DataFrame({'nums': [1.0, 2.0, 3.0], + 'strs': ['apple', 'banana', 'cherry'], + 'dates': dates}) + + with tm.ensure_clean() as path: + original.to_stata(path, write_index=False) + reread = read_stata(path, convert_dates=True) + tm.assert_frame_equal(original, reread) + + original.to_stata(path, + write_index=False, + convert_dates={'dates': 'tc'}) + direct = read_stata(path, convert_dates=True) + tm.assert_frame_equal(reread, direct) + + def test_unsupported_type(self): + original = pd.DataFrame({'a': [1 + 2j, 2 + 4j]}) + + with tm.assertRaises(NotImplementedError): + with tm.ensure_clean() as path: + original.to_stata(path) + + def test_unsupported_datetype(self): + dates = [dt.datetime(1999, 12, 31, 12, 12, 12, 12000), + dt.datetime(2012, 12, 21, 12, 21, 12, 21000), + dt.datetime(1776, 7, 4, 7, 4, 7, 4000)] + original = pd.DataFrame({'nums': [1.0, 2.0, 3.0], + 'strs': ['apple', 'banana', 'cherry'], + 'dates': dates}) + + with tm.assertRaises(NotImplementedError): + with tm.ensure_clean() as path: + original.to_stata(path, convert_dates={'dates': 'tC'}) + + dates = pd.date_range('1-1-1990',periods=3,tz='Asia/Hong_Kong') + original = pd.DataFrame({'nums': [1.0, 2.0, 3.0], + 'strs': ['apple', 'banana', 'cherry'], + 'dates': dates}) + with tm.assertRaises(NotImplementedError): + with tm.ensure_clean() as path: + original.to_stata(path) if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- [x] closes #12259 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Automatically select type %tc for datetime[ns] columns Change ValueErrors to NotImplementedError for unsupported types Add tests for select exceptions closes #12259
https://api.github.com/repos/pandas-dev/pandas/pulls/13710
2016-07-19T17:14:07Z
2016-07-21T10:59:35Z
null
2017-01-24T21:31:00Z
DOC/DEPR: pivot_annual
diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 0dbc79415af0b..38a816060e1bc 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -679,6 +679,19 @@ The :ref:`Pivot <reshaping.pivot>` docs. 'Employed' : lambda x : sum(x), 'Grade' : lambda x : sum(x) / len(x)}) +`Plot pandas DataFrame with year over year data +<http://stackoverflow.com/questions/30379789/plot-pandas-data-frame-with-year-over-year-data>`__ + +To create year and month crosstabulation: + +.. ipython:: python + + df = pd.DataFrame({'value': np.random.randn(36)}, + index=pd.date_range('2011-01-01', freq='M', periods=36)) + + pd.pivot_table(df, index=df.index.month, columns=df.index.year, + values='value', aggfunc='sum') + Apply ***** diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index f65f7d57d5d08..b4c6328c0f5fa 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -529,7 +529,7 @@ Deprecations - ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) - top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`) - ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`) - +- ``pivot_annual`` is deprecated. Use ``pivot_table`` as alternative, an example is :ref:`here <cookbook.pivot>` (:issue:`736`) .. _whatsnew_0190.prior_deprecations: diff --git a/pandas/tseries/tests/test_util.py b/pandas/tseries/tests/test_util.py index 9c5c9b7a03445..9d992995df3a7 100644 --- a/pandas/tseries/tests/test_util.py +++ b/pandas/tseries/tests/test_util.py @@ -21,7 +21,8 @@ def test_daily(self): rng = date_range('1/1/2000', '12/31/2004', freq='D') ts = Series(np.random.randn(len(rng)), index=rng) - annual = pivot_annual(ts, 'D') + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + annual = pivot_annual(ts, 'D') doy = ts.index.dayofyear doy[(~isleapyear(ts.index.year)) & (doy >= 60)] += 1 @@ -53,7 +54,8 @@ def test_hourly(self): hoy[~isleapyear(ts_hourly.index.year) & (hoy >= 1416)] += 24 hoy += 1 - annual = pivot_annual(ts_hourly) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + annual = pivot_annual(ts_hourly) ts_hourly = ts_hourly.astype(float) for i in [1, 1416, 1417, 1418, 1439, 1440, 1441, 8784]: @@ -78,7 +80,8 @@ def test_monthly(self): rng = date_range('1/1/2000', '12/31/2004', freq='M') ts = Series(np.random.randn(len(rng)), index=rng) - annual = pivot_annual(ts, 'M') + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + annual = pivot_annual(ts, 'M') month = ts.index.month for i in range(1, 13): diff --git a/pandas/tseries/util.py b/pandas/tseries/util.py index 98a93d22b09a6..7bac0567ea5c6 100644 --- a/pandas/tseries/util.py +++ b/pandas/tseries/util.py @@ -1,3 +1,5 @@ +import warnings + from pandas.compat import lrange import numpy as np from pandas.types.common import _ensure_platform_int @@ -7,6 +9,8 @@ def pivot_annual(series, freq=None): """ + Deprecated. Use ``pivot_table`` instead. + Group a series by years, taking leap years into account. The output has as many rows as distinct years in the original series, @@ -35,6 +39,10 @@ def pivot_annual(series, freq=None): ------- annual : DataFrame """ + + msg = "pivot_annual is deprecated. Use pivot_table instead" + warnings.warn(msg, FutureWarning) + index = series.index year = index.year years = nanops.unique1d(year)
- [x] closes #736 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Updated cookbook attaching SO link of original issue. BTW, `pivot_annual` has a similar functionality but it looks to be replaced by `pivot_table` and datetime property access. Because the function is not on top `pd` namespace, shouldn't deprecate it?
https://api.github.com/repos/pandas-dev/pandas/pulls/13706
2016-07-19T12:34:43Z
2016-07-20T21:11:50Z
null
2016-07-20T22:05:22Z
PERF: Improve Period hashing
diff --git a/asv_bench/benchmarks/period.py b/asv_bench/benchmarks/period.py index 012030a71ac82..c1b89ae1db75b 100644 --- a/asv_bench/benchmarks/period.py +++ b/asv_bench/benchmarks/period.py @@ -1,4 +1,4 @@ -from pandas import PeriodIndex, date_range +from pandas import Series, Period, PeriodIndex, date_range class create_period_index_from_date_range(object): @@ -7,3 +7,27 @@ class create_period_index_from_date_range(object): def time_period_index(self): # Simulate irregular PeriodIndex PeriodIndex(date_range('1985', periods=1000).to_pydatetime(), freq='D') + + +class period_algorithm(object): + goal_time = 0.2 + + def setup(self): + data = [Period('2011-01', freq='M'), Period('2011-02', freq='M'), + Period('2011-03', freq='M'), Period('2011-04', freq='M')] + self.s = Series(data * 1000) + self.i = PeriodIndex(data, freq='M') + + def time_period_series_drop_duplicates(self): + self.s.drop_duplicates() + + def time_period_index_drop_duplicates(self): + self.i.drop_duplicates() + + def time_period_series_value_counts(self): + self.s.value_counts() + + def time_period_index_value_counts(self): + self.i.value_counts() + + diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index efa6e5575fa79..5750b991aa950 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -567,6 +567,8 @@ Performance Improvements - Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) - Improved performance of ``Index.difference`` (:issue:`12044`) - Improved performance of datetime string parsing in ``DatetimeIndex`` (:issue:`13692`) +- Improved performance of hashing ``Period`` (:issue:`12817`) + .. _whatsnew_0190.bug_fixes: diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index 37f265ede07e7..45743d1cf70ff 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -727,7 +727,7 @@ cdef class _Period(object): (type(self).__name__, type(other).__name__)) def __hash__(self): - return hash((self.ordinal, self.freq)) + return hash((self.ordinal, self.freqstr)) def _add_delta(self, other): if isinstance(other, (timedelta, np.timedelta64, offsets.Tick, Timedelta)): diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 4aa1e2f5d33dd..05f7d9d9ce7b8 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -491,13 +491,15 @@ def test_value_counts_unique(self): for tz in [None, 'UTC', 'Asia/Tokyo', 'US/Eastern']: idx = pd.date_range('2011-01-01 09:00', freq='H', periods=10) # create repeated values, 'n'th element is repeated by n+1 times - idx = DatetimeIndex( - np.repeat(idx.values, range(1, len(idx) + 1)), tz=tz) + idx = DatetimeIndex(np.repeat(idx.values, range(1, len(idx) + 1)), + tz=tz) exp_idx = pd.date_range('2011-01-01 18:00', freq='-1H', periods=10, tz=tz) expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64') - tm.assert_series_equal(idx.value_counts(), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) expected = pd.date_range('2011-01-01 09:00', freq='H', periods=10, tz=tz) @@ -507,15 +509,20 @@ def test_value_counts_unique(self): '2013-01-01 09:00', '2013-01-01 08:00', '2013-01-01 08:00', pd.NaT], tz=tz) - exp_idx = DatetimeIndex( - ['2013-01-01 09:00', '2013-01-01 08:00'], tz=tz) + exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00'], + tz=tz) expected = Series([3, 2], index=exp_idx) - tm.assert_series_equal(idx.value_counts(), expected) - exp_idx = DatetimeIndex( - ['2013-01-01 09:00', '2013-01-01 08:00', pd.NaT], tz=tz) + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00', + pd.NaT], tz=tz) expected = Series([3, 2, 1], index=exp_idx) - tm.assert_series_equal(idx.value_counts(dropna=False), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(dropna=False), + expected) tm.assert_index_equal(idx.unique(), exp_idx) @@ -654,6 +661,27 @@ def test_drop_duplicates_metadata(self): self.assert_index_equal(idx, result) self.assertIsNone(result.freq) + def test_drop_duplicates(self): + # to check Index/Series compat + base = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx') + idx = base.append(base[:5]) + + res = idx.drop_duplicates() + tm.assert_index_equal(res, base) + res = Series(idx).drop_duplicates() + tm.assert_series_equal(res, Series(base)) + + res = idx.drop_duplicates(keep='last') + exp = base[5:].append(base[:5]) + tm.assert_index_equal(res, exp) + res = Series(idx).drop_duplicates(keep='last') + tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36))) + + res = idx.drop_duplicates(keep=False) + tm.assert_index_equal(res, base[5:]) + res = Series(idx).drop_duplicates(keep=False) + tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31))) + def test_take(self): # GH 10295 idx1 = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx') @@ -1303,23 +1331,29 @@ def test_value_counts_unique(self): exp_idx = timedelta_range('1 days 18:00:00', freq='-1H', periods=10) expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64') - tm.assert_series_equal(idx.value_counts(), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) expected = timedelta_range('1 days 09:00:00', freq='H', periods=10) tm.assert_index_equal(idx.unique(), expected) - idx = TimedeltaIndex( - ['1 days 09:00:00', '1 days 09:00:00', '1 days 09:00:00', - '1 days 08:00:00', '1 days 08:00:00', pd.NaT]) + idx = TimedeltaIndex(['1 days 09:00:00', '1 days 09:00:00', + '1 days 09:00:00', '1 days 08:00:00', + '1 days 08:00:00', pd.NaT]) exp_idx = TimedeltaIndex(['1 days 09:00:00', '1 days 08:00:00']) expected = Series([3, 2], index=exp_idx) - tm.assert_series_equal(idx.value_counts(), expected) - exp_idx = TimedeltaIndex(['1 days 09:00:00', '1 days 08:00:00', pd.NaT - ]) + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + exp_idx = TimedeltaIndex(['1 days 09:00:00', '1 days 08:00:00', + pd.NaT]) expected = Series([3, 2, 1], index=exp_idx) - tm.assert_series_equal(idx.value_counts(dropna=False), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(dropna=False), expected) tm.assert_index_equal(idx.unique(), exp_idx) @@ -1454,6 +1488,27 @@ def test_drop_duplicates_metadata(self): self.assert_index_equal(idx, result) self.assertIsNone(result.freq) + def test_drop_duplicates(self): + # to check Index/Series compat + base = pd.timedelta_range('1 day', '31 day', freq='D', name='idx') + idx = base.append(base[:5]) + + res = idx.drop_duplicates() + tm.assert_index_equal(res, base) + res = Series(idx).drop_duplicates() + tm.assert_series_equal(res, Series(base)) + + res = idx.drop_duplicates(keep='last') + exp = base[5:].append(base[:5]) + tm.assert_index_equal(res, exp) + res = Series(idx).drop_duplicates(keep='last') + tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36))) + + res = idx.drop_duplicates(keep=False) + tm.assert_index_equal(res, base[5:]) + res = Series(idx).drop_duplicates(keep=False) + tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31))) + def test_take(self): # GH 10295 idx1 = pd.timedelta_range('1 day', '31 day', freq='D', name='idx') @@ -2121,8 +2176,8 @@ def test_value_counts_unique(self): # GH 7735 idx = pd.period_range('2011-01-01 09:00', freq='H', periods=10) # create repeated values, 'n'th element is repeated by n+1 times - idx = PeriodIndex( - np.repeat(idx.values, range(1, len(idx) + 1)), freq='H') + idx = PeriodIndex(np.repeat(idx.values, range(1, len(idx) + 1)), + freq='H') exp_idx = PeriodIndex(['2011-01-01 18:00', '2011-01-01 17:00', '2011-01-01 16:00', '2011-01-01 15:00', @@ -2131,24 +2186,31 @@ def test_value_counts_unique(self): '2011-01-01 10:00', '2011-01-01 09:00'], freq='H') expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64') - tm.assert_series_equal(idx.value_counts(), expected) - expected = pd.period_range('2011-01-01 09:00', freq='H', periods=10) + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + expected = pd.period_range('2011-01-01 09:00', freq='H', + periods=10) tm.assert_index_equal(idx.unique(), expected) idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 09:00', '2013-01-01 09:00', '2013-01-01 08:00', '2013-01-01 08:00', pd.NaT], freq='H') - exp_idx = PeriodIndex( - ['2013-01-01 09:00', '2013-01-01 08:00'], freq='H') + exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00'], + freq='H') expected = Series([3, 2], index=exp_idx) - tm.assert_series_equal(idx.value_counts(), expected) - exp_idx = PeriodIndex( - ['2013-01-01 09:00', '2013-01-01 08:00', pd.NaT], freq='H') + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00', + pd.NaT], freq='H') expected = Series([3, 2, 1], index=exp_idx) - tm.assert_series_equal(idx.value_counts(dropna=False), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(dropna=False), expected) tm.assert_index_equal(idx.unique(), exp_idx) @@ -2164,6 +2226,28 @@ def test_drop_duplicates_metadata(self): self.assert_index_equal(idx, result) self.assertEqual(idx.freq, result.freq) + def test_drop_duplicates(self): + # to check Index/Series compat + base = pd.period_range('2011-01-01', '2011-01-31', freq='D', + name='idx') + idx = base.append(base[:5]) + + res = idx.drop_duplicates() + tm.assert_index_equal(res, base) + res = Series(idx).drop_duplicates() + tm.assert_series_equal(res, Series(base)) + + res = idx.drop_duplicates(keep='last') + exp = base[5:].append(base[:5]) + tm.assert_index_equal(res, exp) + res = Series(idx).drop_duplicates(keep='last') + tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36))) + + res = idx.drop_duplicates(keep=False) + tm.assert_index_equal(res, base[5:]) + res = Series(idx).drop_duplicates(keep=False) + tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31))) + def test_order_compat(self): def _check_freq(index, expected_index): if isinstance(index, PeriodIndex): diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index c90cbbf80086a..e3a67289a587b 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -462,6 +462,19 @@ def test_period_deprecated_freq(self): p = Period('2016-03-01 09:00', freq=exp) tm.assertIsInstance(p, Period) + def test_hash(self): + self.assertEqual(hash(Period('2011-01', freq='M')), + hash(Period('2011-01', freq='M'))) + + self.assertNotEqual(hash(Period('2011-01-01', freq='D')), + hash(Period('2011-01', freq='M'))) + + self.assertNotEqual(hash(Period('2011-01', freq='3M')), + hash(Period('2011-01', freq='2M'))) + + self.assertNotEqual(hash(Period('2011-01', freq='M')), + hash(Period('2011-02', freq='M'))) + def test_repr(self): p = Period('Jan-2000') self.assertIn('2000-01', repr(p))
- [x] closes #12817 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry ``` # asv - 33.75ms 3.22ms 0.10 period.period_algorithm.time_period_series_drop_duplicates ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13705
2016-07-19T12:23:27Z
2016-07-20T21:23:30Z
null
2016-07-20T21:34:54Z
MAINT: Removed some warnings in tests
diff --git a/pandas/core/internals.py b/pandas/core/internals.py index ff12cfddbe9cd..8e77486457546 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1490,7 +1490,7 @@ def putmask(self, mask, new, align=True, inplace=False, axis=0, if isinstance(new, np.ndarray) and len(new) == len(mask): new = new[mask] - mask = mask.reshape(new_values.shape) + mask = _safe_reshape(mask, new_values.shape) new_values[mask] = new new_values = self._try_coerce_result(new_values) return [self.make_block(values=new_values)] diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 35b1b8c1bf341..57b8bb1531551 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1,7 +1,6 @@ # -*- coding: utf-8 -*- # pylint: disable=E1101,E1103,W0232 -import os import sys from datetime import datetime from distutils.version import LooseVersion @@ -2906,54 +2905,41 @@ def test_value_counts(self): tm.assert_series_equal(res, exp) def test_value_counts_with_nan(self): - # https://github.com/pydata/pandas/issues/9443 + # see gh-9443 + # sanity check s = pd.Series(["a", "b", "a"], dtype="category") - tm.assert_series_equal( - s.value_counts(dropna=True), - pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"]))) - tm.assert_series_equal( - s.value_counts(dropna=False), - pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"]))) + exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) - s = pd.Series(["a", "b", None, "a", None, None], dtype="category") - tm.assert_series_equal( - s.value_counts(dropna=True), - pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"]))) - tm.assert_series_equal( - s.value_counts(dropna=False), - pd.Series([3, 2, 1], index=pd.CategoricalIndex([np.nan, "a", "b"]))) - # When we aren't sorting by counts, and np.nan isn't a - # category, it should be last. - tm.assert_series_equal( - s.value_counts(dropna=False, sort=False), - pd.Series([2, 1, 3], - index=pd.CategoricalIndex(["a", "b", np.nan]))) + res = s.value_counts(dropna=True) + tm.assert_series_equal(res, exp) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - s = pd.Series(pd.Categorical(["a", "b", "a"], - categories=["a", "b", np.nan])) + res = s.value_counts(dropna=True) + tm.assert_series_equal(res, exp) - # internal categories are different because of NaN - exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) - tm.assert_series_equal(s.value_counts(dropna=True), exp, - check_categorical=False) - exp = pd.Series([2, 1, 0], - index=pd.CategoricalIndex(["a", "b", np.nan])) - tm.assert_series_equal(s.value_counts(dropna=False), exp, - check_categorical=False) + # same Series via two different constructions --> same behaviour + series = [ + pd.Series(["a", "b", None, "a", None, None], dtype="category"), + pd.Series(pd.Categorical(["a", "b", None, "a", None, None], + categories=["a", "b"])) + ] - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - s = pd.Series(pd.Categorical(["a", "b", None, "a", None, None], - categories=["a", "b", np.nan])) + for s in series: + # None is a NaN value, so we exclude its count here + exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) + res = s.value_counts(dropna=True) + tm.assert_series_equal(res, exp) - exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) - tm.assert_series_equal(s.value_counts(dropna=True), exp, - check_categorical=False) - exp = pd.Series([3, 2, 1], - index=pd.CategoricalIndex([np.nan, "a", "b"])) - tm.assert_series_equal(s.value_counts(dropna=False), exp, - check_categorical=False) + # we don't exclude the count of None and sort by counts + exp = pd.Series([3, 2, 1], index=pd.CategoricalIndex([np.nan, "a", "b"])) + res = s.value_counts(dropna=False) + tm.assert_series_equal(res, exp) + + # When we aren't sorting by counts, and np.nan isn't a + # category, it should be last. + exp = pd.Series([2, 1, 3], index=pd.CategoricalIndex(["a", "b", np.nan])) + res = s.value_counts(dropna=False, sort=False) + tm.assert_series_equal(res, exp) def test_groupby(self): @@ -4113,16 +4099,11 @@ def f(): res = df.dropna() tm.assert_frame_equal(res, df_exp_drop_all) - # make sure that fillna takes both missing values and NA categories - # into account - c = Categorical(["a", "b", np.nan]) - with tm.assert_produces_warning(FutureWarning): - c.set_categories(["a", "b", np.nan], rename=True, inplace=True) - - c[0] = np.nan + # make sure that fillna takes missing values into account + c = Categorical([np.nan, "b", np.nan], categories=["a", "b"]) df = pd.DataFrame({"cats": c, "vals": [1, 2, 3]}) - cat_exp = Categorical(["a", "b", "a"], categories=["a", "b", np.nan]) + cat_exp = Categorical(["a", "b", "a"], categories=["a", "b"]) df_exp = pd.DataFrame({"cats": cat_exp, "vals": [1, 2, 3]}) res = df.fillna("a")
Per discussion with @jreback <a href="https://github.com/pydata/pandas/pull/13671#issuecomment-233508468">here</a>.
https://api.github.com/repos/pandas-dev/pandas/pulls/13702
2016-07-19T07:20:34Z
2016-07-20T21:27:06Z
null
2016-07-21T00:54:41Z
CLN: Removed the return_type param in StringMethods.split
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 57b0d8895f67b..05379a0fd3f55 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -544,6 +544,7 @@ Removal of prior version deprecations/changes - ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) - ``DataFrame.to_sql()`` has dropped the ``mysql`` option for the ``flavor`` parameter (:issue:`13611`) +- ``str.split`` has dropped the ``return_type`` parameter in favor of ``expand`` (:issue:`13701`) - Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (:issue:`13590`) Previous Behavior: diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 3150fc5d0143a..b49761367b9b5 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -15,7 +15,7 @@ from pandas.core.algorithms import take_1d import pandas.compat as compat from pandas.core.base import AccessorProperty, NoNewAttributesMixin -from pandas.util.decorators import Appender, deprecate_kwarg +from pandas.util.decorators import Appender import re import pandas.lib as lib import warnings @@ -1401,8 +1401,6 @@ def cat(self, others=None, sep=None, na_rep=None): result = str_cat(data, others=others, sep=sep, na_rep=na_rep) return self._wrap_result(result, use_codes=(not self._is_categorical)) - @deprecate_kwarg('return_type', 'expand', mapping={'series': False, - 'frame': True}) @copy(str_split) def split(self, pat=None, n=-1, expand=False): result = str_split(self._data, pat, n=n) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index fcdbec8fbc5c4..92fa7b976eb0e 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -1906,45 +1906,6 @@ def test_split_no_pat_with_nonzero_n(self): def test_split_to_dataframe(self): s = Series(['nosplit', 'alsonosplit']) - - with tm.assert_produces_warning(FutureWarning): - result = s.str.split('_', return_type='frame') - - exp = DataFrame({0: Series(['nosplit', 'alsonosplit'])}) - tm.assert_frame_equal(result, exp) - - s = Series(['some_equal_splits', 'with_no_nans']) - with tm.assert_produces_warning(FutureWarning): - result = s.str.split('_', return_type='frame') - exp = DataFrame({0: ['some', 'with'], - 1: ['equal', 'no'], - 2: ['splits', 'nans']}) - tm.assert_frame_equal(result, exp) - - s = Series(['some_unequal_splits', 'one_of_these_things_is_not']) - with tm.assert_produces_warning(FutureWarning): - result = s.str.split('_', return_type='frame') - exp = DataFrame({0: ['some', 'one'], - 1: ['unequal', 'of'], - 2: ['splits', 'these'], - 3: [NA, 'things'], - 4: [NA, 'is'], - 5: [NA, 'not']}) - tm.assert_frame_equal(result, exp) - - s = Series(['some_splits', 'with_index'], index=['preserve', 'me']) - with tm.assert_produces_warning(FutureWarning): - result = s.str.split('_', return_type='frame') - exp = DataFrame({0: ['some', 'with'], 1: ['splits', 'index']}, - index=['preserve', 'me']) - tm.assert_frame_equal(result, exp) - - with tm.assertRaisesRegexp(ValueError, "expand must be"): - with tm.assert_produces_warning(FutureWarning): - s.str.split('_', return_type="some_invalid_type") - - def test_split_to_dataframe_expand(self): - s = Series(['nosplit', 'alsonosplit']) result = s.str.split('_', expand=True) exp = DataFrame({0: Series(['nosplit', 'alsonosplit'])}) tm.assert_frame_equal(result, exp) @@ -1973,8 +1934,7 @@ def test_split_to_dataframe_expand(self): tm.assert_frame_equal(result, exp) with tm.assertRaisesRegexp(ValueError, "expand must be"): - with tm.assert_produces_warning(FutureWarning): - s.str.split('_', return_type="some_invalid_type") + s.str.split('_', expand="not_a_boolean") def test_split_to_multiindex_expand(self): idx = Index(['nosplit', 'alsonosplit']) @@ -1999,8 +1959,7 @@ def test_split_to_multiindex_expand(self): self.assertEqual(result.nlevels, 6) with tm.assertRaisesRegexp(ValueError, "expand must be"): - with tm.assert_produces_warning(FutureWarning): - idx.str.split('_', return_type="some_invalid_type") + idx.str.split('_', expand="not_a_boolean") def test_rsplit_to_dataframe_expand(self): s = Series(['nosplit', 'alsonosplit'])
Continues where #10085 left off by removing the `return_type` parameter for good.
https://api.github.com/repos/pandas-dev/pandas/pulls/13701
2016-07-19T05:43:05Z
2016-07-22T08:50:40Z
2016-07-22T08:50:40Z
2016-07-22T13:37:23Z
BUG: merge_asof not handling allow_exact_matches and tolerance on first entry
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..dd528669b47ef 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -46,7 +46,7 @@ The following are now part of this API: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A long-time requested feature has been added through the :func:`merge_asof` function, to -support asof style joining of time-series. (:issue:`1870`). Full documentation is +support asof style joining of time-series. (:issue:`1870`, :issue:`13695`). Full documentation is :ref:`here <merging.merge_asof>` The :func:`merge_asof` performs an asof merge, which is similar to a left-join diff --git a/pandas/src/join.pyx b/pandas/src/join.pyx index a81ac0aa35d4e..ad3b1d4e4a90e 100644 --- a/pandas/src/join.pyx +++ b/pandas/src/join.pyx @@ -193,11 +193,12 @@ def left_outer_asof_join(ndarray[int64_t] left, ndarray[int64_t] right, diff = left_val - right_val # do we allow exact matches - if allow_exact_matches and diff > tol: - right_indexer[indexer] = -1 - continue + if allow_exact_matches: + if diff > tol: + right_indexer[indexer] = -1 + continue elif not allow_exact_matches: - if diff >= tol: + if diff >= tol or lc == rc: right_indexer[indexer] = -1 continue @@ -220,13 +221,14 @@ def left_outer_asof_join(ndarray[int64_t] left, ndarray[int64_t] right, diff = left_val - right_val # do we allow exact matches - if allow_exact_matches and diff > tol: - right_indexer[indexer] = -1 - continue + if allow_exact_matches: + if diff > tol: + right_indexer[indexer] = -1 + continue # we don't allow exact matches elif not allow_exact_matches: - if diff >= tol or not right_pos: + if diff >= tol or lc == rc: right_indexer[indexer] = -1 else: right_indexer[indexer] = right_pos - 1 diff --git a/pandas/tools/tests/test_merge_asof.py b/pandas/tools/tests/test_merge_asof.py index 5d78ccf199ed3..bcbb0f0fadb49 100644 --- a/pandas/tools/tests/test_merge_asof.py +++ b/pandas/tools/tests/test_merge_asof.py @@ -347,6 +347,39 @@ def test_allow_exact_matches_and_tolerance(self): expected = self.allow_exact_matches_and_tolerance assert_frame_equal(result, expected) + def test_allow_exact_matches_and_tolerance2(self): + # GH 13695 + df1 = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob']}) + df2 = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.000', + '2016-07-15 13:30:00.030']), + 'version': [1, 2]}) + + result = pd.merge_asof(df1, df2, on='time') + expected = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob'], + 'version': [2]}) + assert_frame_equal(result, expected) + + result = pd.merge_asof(df1, df2, on='time', allow_exact_matches=False) + expected = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob'], + 'version': [1]}) + assert_frame_equal(result, expected) + + result = pd.merge_asof(df1, df2, on='time', allow_exact_matches=False, + tolerance=pd.Timedelta('10ms')) + expected = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob'], + 'version': [np.nan]}) + assert_frame_equal(result, expected) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False)
closes #13695
https://api.github.com/repos/pandas-dev/pandas/pulls/13698
2016-07-18T21:41:52Z
2016-07-19T01:16:41Z
null
2016-07-19T01:16:41Z
Test case for patch, plus fix to not swallow exceptions
https://github.com/pandas-dev/pandas/pull/13693.diff
- [x] closes #13652 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13693
2016-07-18T14:43:01Z
2016-07-20T21:54:09Z
null
2016-07-20T21:54:19Z
PERF: improve DTI string parse
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index f65f7d57d5d08..69200d7142b9f 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -566,6 +566,7 @@ Performance Improvements - Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`) - Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) - Improved performance of ``Index.difference`` (:issue:`12044`) +- Improved performance of datetime string parsing in ``DatetimeIndex`` (:issue:`13692`) .. _whatsnew_0190.bug_fixes: @@ -631,6 +632,7 @@ Bug Fixes - Bug in checking for any null objects in a ``TimedeltaIndex``, which always returned ``True`` (:issue:`13603`) + - Bug in ``Series`` arithmetic raises ``TypeError`` if it contains datetime-like as ``object`` dtype (:issue:`13043`) @@ -654,6 +656,8 @@ Bug Fixes - Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) - Bug in invalid ``Timedelta`` arithmetic and comparison may raise ``ValueError`` rather than ``TypeError`` (:issue:`13624`) +- Bug in invalid datetime parsing in ``to_datetime`` and ``DatetimeIndex`` may raise ``TypeError`` rather than ``ValueError`` (:issue:`11169`, :issue:`11287`) +- Bug in ``Index`` created with tz-aware ``Timestamp`` and mismatched ``tz`` option incorrectly coerces timezone (:issue:`13692`) - Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 84ea2a92b8026..f6a84ea9debaa 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -2440,7 +2440,7 @@ def converter(*date_cols): strs = _concat_date_cols(date_cols) try: - return tools._to_datetime( + return tools.to_datetime( _ensure_object(strs), utc=None, box=False, diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index af44767ae5be5..378e8c545ec83 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -170,16 +170,6 @@ def test_construction_index_with_mixed_timezones(self): self.assert_index_equal(result, exp, exact=True) self.assertFalse(isinstance(result, DatetimeIndex)) - # passing tz results in DatetimeIndex - result = Index([Timestamp('2011-01-01 10:00'), - Timestamp('2011-01-02 10:00', tz='US/Eastern')], - tz='Asia/Tokyo', name='idx') - exp = DatetimeIndex([Timestamp('2011-01-01 19:00'), - Timestamp('2011-01-03 00:00')], - tz='Asia/Tokyo', name='idx') - self.assert_index_equal(result, exp, exact=True) - self.assertTrue(isinstance(result, DatetimeIndex)) - # length = 1 result = Index([Timestamp('2011-01-01')], name='idx') exp = DatetimeIndex([Timestamp('2011-01-01')], name='idx') @@ -253,17 +243,6 @@ def test_construction_index_with_mixed_timezones_with_NaT(self): self.assert_index_equal(result, exp, exact=True) self.assertFalse(isinstance(result, DatetimeIndex)) - # passing tz results in DatetimeIndex - result = Index([pd.NaT, Timestamp('2011-01-01 10:00'), - pd.NaT, Timestamp('2011-01-02 10:00', - tz='US/Eastern')], - tz='Asia/Tokyo', name='idx') - exp = DatetimeIndex([pd.NaT, Timestamp('2011-01-01 19:00'), - pd.NaT, Timestamp('2011-01-03 00:00')], - tz='Asia/Tokyo', name='idx') - self.assert_index_equal(result, exp, exact=True) - self.assertTrue(isinstance(result, DatetimeIndex)) - # all NaT result = Index([pd.NaT, pd.NaT], name='idx') exp = DatetimeIndex([pd.NaT, pd.NaT], name='idx') @@ -323,12 +302,13 @@ def test_construction_dti_with_mixed_timezones(self): self.assertTrue(isinstance(result, DatetimeIndex)) # tz mismatch affecting to tz-aware raises TypeError/ValueError + with tm.assertRaises(ValueError): DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'), Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx') - with tm.assertRaises(TypeError): + with tm.assertRaisesRegexp(TypeError, 'data is already tz-aware'): DatetimeIndex([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00', tz='US/Eastern')], tz='Asia/Tokyo', name='idx') @@ -338,6 +318,13 @@ def test_construction_dti_with_mixed_timezones(self): Timestamp('2011-01-02 10:00', tz='US/Eastern')], tz='US/Eastern', name='idx') + with tm.assertRaisesRegexp(TypeError, 'data is already tz-aware'): + # passing tz should results in DatetimeIndex, then mismatch raises + # TypeError + Index([pd.NaT, Timestamp('2011-01-01 10:00'), + pd.NaT, Timestamp('2011-01-02 10:00', tz='US/Eastern')], + tz='Asia/Tokyo', name='idx') + def test_construction_base_constructor(self): arr = [pd.Timestamp('2011-01-01'), pd.NaT, pd.Timestamp('2011-01-03')] tm.assert_index_equal(pd.Index(arr), pd.DatetimeIndex(arr)) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 47bb69b8d7ad6..d448ca9878b99 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -292,55 +292,32 @@ def __new__(cls, data=None, raise ValueError('DatetimeIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) - # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) - data = np.asarray(data, dtype='O') + elif isinstance(data, ABCSeries): + data = data._values - # try a few ways to make it datetime64 - if lib.is_string_array(data): - data = tslib.parse_str_array_to_datetime(data, freq=freq, - dayfirst=dayfirst, - yearfirst=yearfirst) - else: - data = tools.to_datetime(data, errors='raise') - data.offset = freq - if isinstance(data, DatetimeIndex): - if name is not None: - data.name = name - - if tz is not None: - - # we might already be localized to this tz - # so passing the same tz is ok - # however any other tz is a no-no - if data.tz is None: - return data.tz_localize(tz, ambiguous=ambiguous) - elif str(tz) != str(data.tz): - raise TypeError("Already tz-aware, use tz_convert " - "to convert.") - - return data._deepcopy_if_needed(ref_to_data, copy) - - if issubclass(data.dtype.type, compat.string_types): - data = tslib.parse_str_array_to_datetime(data, freq=freq, - dayfirst=dayfirst, - yearfirst=yearfirst) + # data must be Index or np.ndarray here + if not (is_datetime64_dtype(data) or is_datetimetz(data) or + is_integer_dtype(data)): + data = tools.to_datetime(data, dayfirst=dayfirst, + yearfirst=yearfirst) if issubclass(data.dtype.type, np.datetime64) or is_datetimetz(data): - if isinstance(data, ABCSeries): - data = data._values + if isinstance(data, DatetimeIndex): if tz is None: tz = data.tz - + elif data.tz is None: + data = data.tz_localize(tz, ambiguous=ambiguous) else: # the tz's must match if str(tz) != str(data.tz): - raise TypeError("Already tz-aware, use tz_convert " - "to convert.") + msg = ('data is already tz-aware {0}, unable to ' + 'set specified tz: {1}') + raise TypeError(msg.format(data.tz, tz)) subarr = data.values @@ -356,35 +333,6 @@ def __new__(cls, data=None, if isinstance(data, Int64Index): raise TypeError('cannot convert Int64Index->DatetimeIndex') subarr = data.view(_NS_DTYPE) - else: - if isinstance(data, (ABCSeries, Index)): - values = data._values - else: - values = data - - if lib.is_string_array(values): - subarr = tslib.parse_str_array_to_datetime( - values, freq=freq, dayfirst=dayfirst, yearfirst=yearfirst) - else: - try: - subarr = tools.to_datetime(data, box=False) - - # make sure that we have a index/ndarray like (and not a - # Series) - if isinstance(subarr, ABCSeries): - subarr = subarr._values - if subarr.dtype == np.object_: - subarr = tools._to_datetime(subarr, box=False) - - except ValueError: - # tz aware - subarr = tools._to_datetime(data, box=False, utc=True) - - # we may not have been able to convert - if not (is_datetimetz(subarr) or - np.issubdtype(subarr.dtype, np.datetime64)): - raise ValueError('Unable to convert %s to datetime dtype' - % str(data)) if isinstance(subarr, DatetimeIndex): if tz is None: @@ -399,27 +347,21 @@ def __new__(cls, data=None, ints = subarr.view('i8') subarr = tslib.tz_localize_to_utc(ints, tz, ambiguous=ambiguous) - subarr = subarr.view(_NS_DTYPE) subarr = cls._simple_new(subarr, name=name, freq=freq, tz=tz) - - # if dtype is provided, coerce here if dtype is not None: - if not is_dtype_equal(subarr.dtype, dtype): - + # dtype must be coerced to DatetimeTZDtype above if subarr.tz is not None: raise ValueError("cannot localize from non-UTC data") - dtype = DatetimeTZDtype.construct_from_string(dtype) - subarr = subarr.tz_localize(dtype.tz) if verify_integrity and len(subarr) > 0: if freq is not None and not freq_infer: inferred = subarr.inferred_freq if inferred != freq.freqstr: - on_freq = cls._generate(subarr[0], None, len( - subarr), None, freq, tz=tz, ambiguous=ambiguous) + on_freq = cls._generate(subarr[0], None, len(subarr), None, + freq, tz=tz, ambiguous=ambiguous) if not np.array_equal(subarr.asi8, on_freq.asi8): raise ValueError('Inferred frequency {0} from passed ' 'dates does not conform to passed ' @@ -563,7 +505,6 @@ def _generate(cls, start, end, periods, name, offset, index = index[1:] if not right_closed and len(index) and index[-1] == end: index = index[:-1] - index = cls._simple_new(index, name=name, freq=offset, tz=tz) return index @@ -669,7 +610,7 @@ def _cached_range(cls, start=None, end=None, periods=None, offset=None, xdr = generate_range(offset=offset, start=_CACHE_START, end=_CACHE_END) - arr = tools._to_datetime(list(xdr), box=False) + arr = tools.to_datetime(list(xdr), box=False) cachedRange = DatetimeIndex._simple_new(arr) cachedRange.offset = offset diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 8d6955ab43711..e493e9d936b02 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -1046,7 +1046,12 @@ def _get_binner_for_grouping(self, obj): l = [] for key, group in grouper.get_iterator(self.ax): l.extend([key] * len(group)) - grouper = binner.__class__(l, freq=binner.freq, name=binner.name) + + if isinstance(self.ax, PeriodIndex): + grouper = binner.__class__(l, freq=binner.freq, name=binner.name) + else: + # resampling causes duplicated values, specifying freq is invalid + grouper = binner.__class__(l, name=binner.name) # since we may have had to sort # may need to reorder groups here diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 299ec374567e7..59fc147ead4eb 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -4087,8 +4087,9 @@ def test_dti_set_index_reindex(self): # 11314 # with tz - index = date_range(datetime(2015, 10, 1), datetime( - 2015, 10, 1, 23), freq='H', tz='US/Eastern') + index = date_range(datetime(2015, 10, 1), + datetime(2015, 10, 1, 23), + freq='H', tz='US/Eastern') df = DataFrame(np.random.randn(24, 1), columns=['a'], index=index) new_index = date_range(datetime(2015, 10, 2), datetime(2015, 10, 2, 23), diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index f30f01e66cb0b..22bb3bddbc742 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -7,7 +7,8 @@ import datetime import pandas as pd -from pandas.core.api import Timestamp, Series, Timedelta, Period, to_datetime +from pandas.core.api import (Timestamp, Index, Series, Timedelta, Period, + to_datetime) from pandas.tslib import get_timezone from pandas._period import period_asfreq, period_ordinal from pandas.tseries.index import date_range, DatetimeIndex @@ -698,14 +699,19 @@ def test_parsers(self): yearfirst=yearfirst) result2 = to_datetime(date_str, yearfirst=yearfirst) result3 = to_datetime([date_str], yearfirst=yearfirst) + # result5 is used below result4 = to_datetime(np.array([date_str], dtype=object), yearfirst=yearfirst) - result6 = DatetimeIndex([date_str], yearfirst=yearfirst)[0] - self.assertEqual(result1, expected) - self.assertEqual(result2, expected) - self.assertEqual(result3, expected) - self.assertEqual(result4, expected) - self.assertEqual(result6, expected) + result6 = DatetimeIndex([date_str], yearfirst=yearfirst) + # result7 is used below + result8 = DatetimeIndex(Index([date_str]), yearfirst=yearfirst) + result9 = DatetimeIndex(Series([date_str]), yearfirst=yearfirst) + + for res in [result1, result2]: + self.assertEqual(res, expected) + for res in [result3, result4, result6, result8, result9]: + exp = DatetimeIndex([pd.Timestamp(expected)]) + tm.assert_index_equal(res, exp) # these really need to have yearfist, but we don't support if not yearfirst: @@ -893,9 +899,7 @@ def test_parsers_monthfreq(self): for date_str, expected in compat.iteritems(cases): result1, _, _ = tools.parse_time_string(date_str, freq='M') - result2 = tools._to_datetime(date_str, freq='M') self.assertEqual(result1, expected) - self.assertEqual(result2, expected) def test_parsers_quarterly_with_freq(self): msg = ('Incorrect quarterly string is given, quarter ' diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index 067e8ec19f644..93d35ff964e69 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -295,22 +295,12 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, 1 loop, best of 3: 471 ms per loop """ - return _to_datetime(arg, errors=errors, dayfirst=dayfirst, - yearfirst=yearfirst, - utc=utc, box=box, format=format, exact=exact, - unit=unit, infer_datetime_format=infer_datetime_format) - -def _to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, - utc=None, box=True, format=None, exact=True, - unit=None, freq=None, infer_datetime_format=False): - """ - Same as to_datetime, but accept freq for - DatetimeIndex internal construction - """ from pandas.tseries.index import DatetimeIndex - def _convert_listlike(arg, box, format, name=None): + tz = 'utc' if utc else None + + def _convert_listlike(arg, box, format, name=None, tz=tz): if isinstance(arg, (list, tuple)): arg = np.array(arg, dtype='O') @@ -319,8 +309,7 @@ def _convert_listlike(arg, box, format, name=None): if is_datetime64_ns_dtype(arg): if box and not isinstance(arg, DatetimeIndex): try: - return DatetimeIndex(arg, tz='utc' if utc else None, - name=name) + return DatetimeIndex(arg, tz=tz, name=name) except ValueError: pass @@ -328,7 +317,7 @@ def _convert_listlike(arg, box, format, name=None): elif is_datetime64tz_dtype(arg): if not isinstance(arg, DatetimeIndex): - return DatetimeIndex(arg, tz='utc' if utc else None) + return DatetimeIndex(arg, tz=tz, name=name) if utc: arg = arg.tz_convert(None).tz_localize('UTC') return arg @@ -344,8 +333,7 @@ def _convert_listlike(arg, box, format, name=None): from pandas import Index return Index(result) - return DatetimeIndex(result, tz='utc' if utc else None, - name=name) + return DatetimeIndex(result, tz=tz, name=name) return result elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a string, datetime, list, tuple, ' @@ -382,8 +370,8 @@ def _convert_listlike(arg, box, format, name=None): # fallback if result is None: try: - result = tslib.array_strptime( - arg, format, exact=exact, errors=errors) + result = tslib.array_strptime(arg, format, exact=exact, + errors=errors) except tslib.OutOfBoundsDatetime: if errors == 'raise': raise @@ -404,14 +392,11 @@ def _convert_listlike(arg, box, format, name=None): utc=utc, dayfirst=dayfirst, yearfirst=yearfirst, - freq=freq, require_iso8601=require_iso8601 ) if is_datetime64_dtype(result) and box: - result = DatetimeIndex(result, - tz='utc' if utc else None, - name=name) + result = DatetimeIndex(result, tz=tz, name=name) return result except ValueError as e: diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 5624b84523705..016c49ea2b859 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -852,13 +852,6 @@ cdef inline bint _cmp_nat_dt(_NaT lhs, _Timestamp rhs, int op) except -1: return _nat_scalar_rules[op] -cdef _tz_format(object obj, object zone): - try: - return obj.strftime(' %%Z, tz=%s' % zone) - except: - return ', tz=%s' % zone - - cpdef object get_value_box(ndarray arr, object loc): cdef: Py_ssize_t i, sz @@ -1642,14 +1635,6 @@ cdef inline _check_dts_bounds(pandas_datetimestruct *dts): raise OutOfBoundsDatetime('Out of bounds nanosecond timestamp: %s' % fmt) -# elif isinstance(ts, _Timestamp): -# tmp = ts -# obj.value = (<_Timestamp> ts).value -# obj.dtval = -# elif isinstance(ts, object): -# # If all else fails -# obj.value = _dtlike_to_datetime64(ts, &obj.dts) -# obj.dtval = _dts_to_pydatetime(&obj.dts) def datetime_to_datetime64(ndarray[object] values): cdef: @@ -1689,7 +1674,7 @@ def datetime_to_datetime64(ndarray[object] values): cdef: set _not_datelike_strings = set(['a','A','m','M','p','P','t','T']) -cpdef object _does_string_look_like_datetime(object date_string): +cpdef bint _does_string_look_like_datetime(object date_string): if date_string.startswith('0'): # Strings starting with 0 are more consistent with a # date-like string than a number @@ -1827,8 +1812,14 @@ def parse_datetime_string(object date_string, object freq=None, except ValueError: pass - dt = parse_date(date_string, default=_DEFAULT_DATETIME, - dayfirst=dayfirst, yearfirst=yearfirst, **kwargs) + try: + dt = parse_date(date_string, default=_DEFAULT_DATETIME, + dayfirst=dayfirst, yearfirst=yearfirst, **kwargs) + except TypeError: + # following may be raised from dateutil + # TypeError: 'NoneType' object is not iterable + raise ValueError('Given date string not likely a datetime.') + return dt @@ -2214,7 +2205,7 @@ cpdef array_with_unit_to_datetime(ndarray values, unit, errors='coerce'): cpdef array_to_datetime(ndarray[object] values, errors='raise', - dayfirst=False, yearfirst=False, freq=None, + dayfirst=False, yearfirst=False, format=None, utc=None, require_iso8601=False): cdef: @@ -2343,7 +2334,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', try: py_dt = parse_datetime_string(val, dayfirst=dayfirst, - yearfirst=yearfirst, freq=freq) + yearfirst=yearfirst) except Exception: if is_coerce: iresult[i] = NPY_NAT @@ -2423,7 +2414,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', try: oresult[i] = parse_datetime_string(val, dayfirst=dayfirst, - yearfirst=yearfirst, freq=freq) + yearfirst=yearfirst) _pydatetime_to_dts(oresult[i], &dts) _check_dts_bounds(&dts) except Exception: @@ -2438,28 +2429,6 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', return oresult -def parse_str_array_to_datetime(ndarray values, dayfirst=False, - yearfirst=False, object freq=None): - """Shortcut to parse str array for quicker DatetimeIndex construction""" - cdef: - Py_ssize_t i, n = len(values) - object val, py_dt - ndarray[int64_t] iresult - _TSObject _ts - - iresult = np.empty(n, dtype='i8') - - for i in range(n): - val = values[i] - try: - py_dt = parse_datetime_string(val, dayfirst=dayfirst, - yearfirst=yearfirst, freq=freq) - except Exception: - raise ValueError - _ts = convert_to_tsobject(py_dt, None, None, 0, 0) - iresult[i] = _ts.value - - return iresult # Similar to Timestamp/datetime, this is a construction requirement for timedeltas # we need to do object instantiation in python
- [x] closes #11169, closes #11287 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry cleaned up `DatetimeIndex` constructor removing slower string-parsing path. ## Performance Improvement related to #7599, internally use `to_datetime` always as it tries some fastpath. ``` inp = np.array(['2011-01-01 09:00' for i in range(10000)]) # on current master %timeit pd.DatetimeIndex(inp) 1 loops, best of 3: 3.41 s per loop %timeit pd.to_datetime(inp) 100 loops, best of 3: 4.77 ms per loop # after the PR %timeit pd.DatetimeIndex(inp) #100 loops, best of 3: 4.23 ms per loop %timeit pd.to_datetime(inp) #100 loops, best of 3: 4.25 ms per loop ``` ## Bug Fixes The cleanup fixed these 2 kind of issues: #### 1. #11169 and #11287 Invalid string parsing may raise `TypeError` I met the same issue on travis and fixed with try-except clause (I can't reproduce it on my local Mac). #### 2. Index may incorrectly coerces mismatched tz on current master, `DatetimeIndex` and normal `Index` behaves differently. ``` # OK pd.DatetimeIndex([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific') # TypeError: Already tz-aware, use tz_convert to convert. # NG, it ignores mismatch and coerce to passed tz pd.Index([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific') DatetimeIndex(['2010-12-31 21:00:00-08:00'], dtype='datetime64[ns, US/Pacific]', freq=None) ``` after the PR both behave the same, showing understandable error. ``` pd.Index([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific') # TypeError: data is already tz-aware US/Eastern, unable to set specified tz: US/Pacific pd.DatetimeIndex([pd.Timestamp('2011-01-01', tz='US/Eastern')], tz='US/Pacific') # TypeError: data is already tz-aware US/Eastern, unable to set specified tz: US/Pacific ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13692
2016-07-18T12:57:41Z
2016-07-19T13:06:23Z
null
2016-07-19T13:07:55Z
BUG: Add type check for width parameter in str.pad method GH13598
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..5c4c1126cb078 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -614,3 +614,4 @@ Bug Fixes - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) +- Bug in ``pandas.Series.str.zfill``, when ``width`` was not of integer type no ``TypeError`` was raised, which resulted in a series with all NaN values (:issue:`13598`) \ No newline at end of file diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 6ec28f9735850..3150fc5d0143a 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -8,7 +8,8 @@ is_object_dtype, is_string_like, is_list_like, - is_scalar) + is_scalar, + is_integer) from pandas.core.common import _values_from_object from pandas.core.algorithms import take_1d @@ -914,6 +915,10 @@ def str_pad(arr, width, side='left', fillchar=' '): if len(fillchar) != 1: raise TypeError('fillchar must be a character, not str') + if not is_integer(width): + msg = 'width must be of integer type, not {0}' + raise TypeError(msg.format(type(width).__name__)) + if side == 'left': f = lambda x: x.rjust(width, fillchar) elif side == 'right': diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 4d23bed620265..2a4fc8b2db5c9 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -1603,6 +1603,15 @@ def test_pad_fillchar(self): "fillchar must be a character, not int"): result = values.str.pad(5, fillchar=5) + def test_pad_width(self): + values = Series(['1', '22', 'a', 'bb']) + s = Series(values) + + for f in ['center', 'ljust', 'rjust', 'zfill', 'pad']: + with tm.assertRaisesRegexp(TypeError, + "width must be of integer type, not*"): + getattr(s.str, f)('f') + def test_translate(self): def _check(result, expected):
- [x] closes #13598 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13690
2016-07-17T19:24:26Z
2016-07-19T01:11:18Z
null
2016-07-19T01:11:36Z
Closes #13688: added scroll to top button to div.footer via doc/_temp…
diff --git a/doc/source/themes/nature_with_gtoc/layout.html b/doc/source/themes/nature_with_gtoc/layout.html index fd0755e096023..ddf1e861f5f81 100644 --- a/doc/source/themes/nature_with_gtoc/layout.html +++ b/doc/source/themes/nature_with_gtoc/layout.html @@ -61,3 +61,37 @@ <h3 style="margin-top: 1.5em;">{{ _('Search') }}</h3> </div> </div> {%- endblock %} + +{%- block footer %} +<style type="text/css"> + .scrollToTop { + text-align: center; + font-weight: bold; + position: fixed; + bottom: 60px; + right: 40px; + display: none; + } +</style> +<a href="#" class="scrollToTop">Scroll To Top</a> +<script type="text/javascript"> +$(document).ready(function() { + //Check to see if the window is top if not then display button + $(window).scroll(function() { + if ($(this).scrollTop() > 200) { + $('.scrollToTop').fadeIn(); + } else { + $('.scrollToTop').fadeOut(); + } + }); + + //Click event to scroll to top + $('.scrollToTop').click(function() { + $('html, body').animate({ + scrollTop: 0 + }, 500); + return false; + }); +}); +</script> +{% endblock %} \ No newline at end of file
- [x] closes #13688 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Added scroll to top feature to documentation div.footer using Sphinx template
https://api.github.com/repos/pandas-dev/pandas/pulls/13689
2016-07-17T19:06:17Z
2016-07-23T16:25:20Z
2016-07-23T16:25:20Z
2016-07-25T21:47:08Z
BUG: Cast a key to NaT before get loc from Index
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..3e250c77f104d 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -614,3 +614,5 @@ Bug Fixes - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) + +- Bug in Checking for any NaT-like objects in a `TimedeltaIndex` always returns ``True`` (:issue:`13603`) diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index f9fb51ebf710c..78ab333be8ea5 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -697,6 +697,10 @@ def get_loc(self, key, method=None, tolerance=None): ------- loc : int """ + + if isnull(key): + key = tslib.NaT + if tolerance is not None: # try converting tolerance now, so errors don't get swallowed by # the try/except clauses below @@ -754,7 +758,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): def _get_string_slice(self, key, use_lhs=True, use_rhs=True): freq = getattr(self, 'freqstr', getattr(self, 'inferred_freq', None)) - if is_integer(key) or is_float(key): + if is_integer(key) or is_float(key) or key is tslib.NaT: self._invalid_indexer('slice', key) loc = self._partial_td_slice(key, freq, use_lhs=use_lhs, use_rhs=use_rhs) diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 4f985998d5e20..36ae479c3dfcc 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -30,6 +30,25 @@ class TestTimedeltas(tm.TestCase): def setUp(self): pass + def test_get_loc_nat(self): + tidx = TimedeltaIndex(['1 days 01:00:00', 'NaT', '2 days 01:00:00']) + + self.assertEqual(tidx.get_loc(pd.NaT), 1) + self.assertEqual(tidx.get_loc(None), 1) + self.assertEqual(tidx.get_loc(float('nan')), 1) + self.assertEqual(tidx.get_loc(np.nan), 1) + + def test_contains(self): + # Checking for any NaT-like objects + # GH 13603 + td = to_timedelta(range(5), unit='d') + pd.offsets.Hour(1) + for v in [pd.NaT, None, float('nan'), np.nan]: + self.assertFalse((v in td)) + + td = to_timedelta([pd.NaT]) + for v in [pd.NaT, None, float('nan'), np.nan]: + self.assertTrue((v in td)) + def test_construction(self): expected = np.timedelta64(10, 'D').astype('m8[ns]').view('i8')
- [x] closes #13603 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry pd.NaT, None, float('nan') and np.nan are converted NaT in TestTimedeltas. So we should convert keys if keys are these value. Fix #13603
https://api.github.com/repos/pandas-dev/pandas/pulls/13687
2016-07-17T16:16:56Z
2016-07-19T01:14:14Z
null
2016-07-19T01:16:33Z
DOC: Fix a name of option
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index c681cebd84836..5624b84523705 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -3028,7 +3028,7 @@ cdef inline bint is_timedelta(object o): def array_to_timedelta64(ndarray[object] values, unit='ns', errors='raise'): """ convert an ndarray to an array of ints that are timedeltas - force conversion if coerce = True, + force conversion if errors = 'coerce', else will raise if cannot convert """ cdef: Py_ssize_t i, n
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13686
2016-07-17T14:02:42Z
2016-07-17T18:32:25Z
2016-07-17T18:32:25Z
2016-07-17T18:32:35Z
DOC: slight change in .cum* function descriptions
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index d6e6f571be53a..6c1676fbdd7f4 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5504,7 +5504,7 @@ def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func, mask_a, mask_b): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) - @Appender("Return cumulative {0} over requested axis.".format(name) + + @Appender("Return {0} over requested axis.".format(desc) + _cnum_doc) def cum_func(self, axis=None, skipna=True, *args, **kwargs): skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
closes #13682 I changed the template function to use the _desc_ instead of the _name_ in generic.py. SIDENOTE: I accidentally committed to a branch with another PR, so I rebased without it and created a new branch with this commit.
https://api.github.com/repos/pandas-dev/pandas/pulls/13683
2016-07-17T08:21:38Z
2016-07-17T18:30:19Z
2016-07-17T18:30:19Z
2016-12-14T05:10:47Z
COMPAT: use mpl area legend if available
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index f93e8f4240787..4d6133a09ddae 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -849,6 +849,7 @@ Bug Fixes - Bug in ``pd.read_csv()``, which caused BOM files to be incorrectly parsed by not ignoring the BOM (:issue:`4793`) - Bug in ``io.json.json_normalize()``, where non-ascii keys raised an exception (:issue:`13213`) - Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`) +- Bug in area plot draws legend incorrectly if subplot is enabled or legend is moved after plot (matplotlib 1.5.0 is required to draw area plot legend properly) (issue:`9161`, :issue:`13544`) - Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`) - Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`) - Bug in ``Series`` construction from a tuple of integers on windows not returning default dtype (int64) (:issue:`13646`) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 4cf3364a03056..a61a21d259e57 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -1839,10 +1839,16 @@ def __init__(self, data, **kwargs): @classmethod def _plot(cls, ax, x, y, style=None, column_num=None, stacking_id=None, is_errorbar=False, **kwds): + if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(y)) y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label']) - lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds) + + # need to remove label, because subplots uses mpl legend as it is + line_kwds = kwds.copy() + if cls.mpl_ge_1_5_0(): + line_kwds.pop('label') + lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds) # get data from the line to get coordinates for fill_between xdata, y_values = lines[0].get_data(orig=False) @@ -1860,18 +1866,21 @@ def _plot(cls, ax, x, y, style=None, column_num=None, if 'color' not in kwds: kwds['color'] = lines[0].get_color() - if cls.mpl_ge_1_5_0(): # mpl 1.5 added real support for poly legends - kwds.pop('label') - ax.fill_between(xdata, start, y_values, **kwds) + rect = ax.fill_between(xdata, start, y_values, **kwds) cls._update_stacker(ax, stacking_id, y) - return lines + + # LinePlot expects list of artists + res = [rect] if cls.mpl_ge_1_5_0() else lines + return res def _add_legend_handle(self, handle, label, index=None): - from matplotlib.patches import Rectangle - # Because fill_between isn't supported in legend, - # specifically add Rectangle handle here - alpha = self.kwds.get('alpha', None) - handle = Rectangle((0, 0), 1, 1, fc=handle.get_color(), alpha=alpha) + if not self.mpl_ge_1_5_0(): + from matplotlib.patches import Rectangle + # Because fill_between isn't supported in legend, + # specifically add Rectangle handle here + alpha = self.kwds.get('alpha', None) + handle = Rectangle((0, 0), 1, 1, fc=handle.get_color(), + alpha=alpha) LinePlot._add_legend_handle(self, handle, label, index=index) def _post_plot_logic(self, ax, data):
- [x] closes #9161, closes #13544 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Do not use area legend workaround if mpl >= 1.5.0 is available. Legends are now displayed properly if mpl >= 1.5.0. #9161: ``` df = pd.DataFrame(np.random.rand(10, 3)) df.plot(kind='area', subplots=True, sharex=True, legend=True) ``` ![index](https://cloud.githubusercontent.com/assets/1696302/16897682/4ee78324-4bf3-11e6-8113-65d68ec28b09.png) #13544: ``` df = pd.DataFrame(np.random.rand(20, 5), columns=['A', 'B', 'C', 'D', 'E']) df.plot(kind='area', linewidth=0.1).legend(loc="center left", bbox_to_anchor=(1.02, 0.5)) ``` ![index2](https://cloud.githubusercontent.com/assets/1696302/16897684/61eb4370-4bf3-11e6-9472-cec27297b7d1.png)
https://api.github.com/repos/pandas-dev/pandas/pulls/13680
2016-07-16T22:54:06Z
2016-08-10T12:25:06Z
2016-08-10T12:25:06Z
2017-03-04T11:11:00Z
Merge pull request #1 from pydata/master
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Merge from pydata master
https://api.github.com/repos/pandas-dev/pandas/pulls/13679
2016-07-16T20:57:58Z
2016-07-16T20:58:28Z
null
2023-05-11T01:13:48Z
Add reference of DataFrame.rename_axis and Series.rename_axis to api.rst
diff --git a/doc/source/api.rst b/doc/source/api.rst index 0dde341d820e3..e8fe26e8a525d 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -380,6 +380,7 @@ Reindexing / Selection / Label manipulation Series.reindex Series.reindex_like Series.rename + Series.rename_axis Series.reset_index Series.sample Series.select @@ -889,6 +890,7 @@ Reindexing / Selection / Label manipulation DataFrame.reindex_axis DataFrame.reindex_like DataFrame.rename + DataFrame.rename_axis DataFrame.reset_index DataFrame.sample DataFrame.select
- [x] closes #13642 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13678
2016-07-16T19:58:39Z
2016-07-17T12:40:27Z
2016-07-17T12:40:27Z
2016-07-17T12:42:24Z
ENH: Series.append now has ignore_index kw
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..a69617bfbec55 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -249,6 +249,7 @@ Other enhancements - ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) - A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) +- ``Series.append`` now supports ``ignore_index`` option (:issue:`13677`) .. _whatsnew_0190.api: diff --git a/pandas/core/series.py b/pandas/core/series.py index 3c1f834c3d479..c3f5b1b8e641c 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1511,13 +1511,18 @@ def searchsorted(self, v, side='left', sorter=None): # ------------------------------------------------------------------- # Combination - def append(self, to_append, verify_integrity=False): + def append(self, to_append, ignore_index=False, verify_integrity=False): """ Concatenate two or more Series. Parameters ---------- to_append : Series or list/tuple of Series + ignore_index : boolean, default False + If True, do not use the index labels. + + .. versionadded: 0.19.0 + verify_integrity : boolean, default False If True, raise Exception on creating index with duplicates @@ -1548,6 +1553,17 @@ def append(self, to_append, verify_integrity=False): 5 6 dtype: int64 + With `ignore_index` set to True: + + >>> s1.append(s2, ignore_index=True) + 0 1 + 1 2 + 2 3 + 3 4 + 4 5 + 5 6 + dtype: int64 + With `verify_integrity` set to True: >>> s1.append(s2, verify_integrity=True) @@ -1561,7 +1577,7 @@ def append(self, to_append, verify_integrity=False): to_concat = [self] + to_append else: to_concat = [self, to_append] - return concat(to_concat, ignore_index=False, + return concat(to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity) def _binop(self, other, func, level=None, fill_value=None): diff --git a/pandas/tests/series/test_combine_concat.py b/pandas/tests/series/test_combine_concat.py index eb560d4a17055..fd6fd90cd631f 100644 --- a/pandas/tests/series/test_combine_concat.py +++ b/pandas/tests/series/test_combine_concat.py @@ -39,6 +39,27 @@ def test_append_many(self): result = pieces[0].append(pieces[1:]) assert_series_equal(result, self.ts) + def test_append_duplicates(self): + # GH 13677 + s1 = pd.Series([1, 2, 3]) + s2 = pd.Series([4, 5, 6]) + exp = pd.Series([1, 2, 3, 4, 5, 6], index=[0, 1, 2, 0, 1, 2]) + tm.assert_series_equal(s1.append(s2), exp) + tm.assert_series_equal(pd.concat([s1, s2]), exp) + + # the result must have RangeIndex + exp = pd.Series([1, 2, 3, 4, 5, 6]) + tm.assert_series_equal(s1.append(s2, ignore_index=True), + exp, check_index_type=True) + tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), + exp, check_index_type=True) + + msg = 'Indexes have overlapping values:' + with tm.assertRaisesRegexp(ValueError, msg): + s1.append(s2, verify_integrity=True) + with tm.assertRaisesRegexp(ValueError, msg): + pd.concat([s1, s2], verify_integrity=True) + def test_combine_first(self): values = tm.makeIntIndex(20).values.astype(float) series = Series(values, index=tm.makeIntIndex(20))
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry not sure if there is any reason we haven't supported...
https://api.github.com/repos/pandas-dev/pandas/pulls/13677
2016-07-16T19:14:46Z
2016-07-19T01:05:36Z
null
2016-07-19T02:46:11Z
TST: assert message shows unnecessary diff
diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 4442eed898b60..402613d3f1728 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1010,14 +1010,15 @@ def raise_assert_detail(obj, message, left, right, diff=None): if isinstance(right, np.ndarray): right = pprint_thing(right) - if diff is not None: - diff = "\n[diff]: {diff}".format(diff=diff) - msg = """{0} are different {1} [left]: {2} -[right]: {3}{4}""".format(obj, message, left, right, diff) +[right]: {3}""".format(obj, message, left, right) + + if diff is not None: + msg = msg + "\n[diff]: {diff}".format(diff=diff) + raise AssertionError(msg)
- [x] passes `git diff upstream/master | flake8 --diff` removed unnecessary trailing `None`. ``` pd.util.testing.assert_index_equal(pd.Index([1, 2, 3]), pd.Index([1 ,2, 4])) # AssertionError: Index are different # # Index values are different (33.33333 %) # [left]: Int64Index([1, 2, 3], dtype='int64') # [right]: Int64Index([1, 2, 4], dtype='int64')None ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13676
2016-07-16T19:09:46Z
2016-07-18T22:08:26Z
2016-07-18T22:08:26Z
2016-07-18T22:26:18Z
DOC: resample warnings
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index f65f7d57d5d08..126b8f023c967 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -637,7 +637,7 @@ Bug Fixes - Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`) - Bug in ``pd.to_datetime()`` which overflowed on ``int8``, and ``int16`` dtypes (:issue:`13451`) - Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`) - +- Bug in ``.resample(..)`` where incorrect warnings were triggered by IPython introspection (:issue:`13618`) - Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) - Bug in ``Series`` comparison may output incorrect result if rhs contains ``NaT`` (:issue:`9005`) - Bug in ``Series`` and ``Index`` comparison may output incorrect result if it contains ``NaT`` with ``object`` dtype (:issue:`13592`) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 8d6955ab43711..c3de30e5d6b9d 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -60,12 +60,14 @@ class Resampler(_GroupBy): 'loffset', 'base', 'kind'] # API compat of allowed attributes - _deprecated_valids = _attributes + ['_ipython_display_', '__doc__', - '_cache', '_attributes', 'binner', - 'grouper', 'groupby', 'keys', - 'sort', 'kind', 'squeeze', - 'group_keys', 'as_index', - 'exclusions', '_groupby'] + _deprecated_valids = _attributes + ['__doc__', '_cache', '_attributes', + 'binner', 'grouper', 'groupby', + 'sort', 'kind', 'squeeze', 'keys', + 'group_keys', 'as_index', 'exclusions', + '_groupby'] + # don't raise deprecation warning on attributes starting with these + # patterns - prevents warnings caused by IPython introspection + _deprecated_valid_patterns = ['_ipython', '_repr'] # API compat of disallowed attributes _deprecated_invalids = ['iloc', 'loc', 'ix', 'iat', 'at'] @@ -109,9 +111,12 @@ def _typ(self): return 'series' return 'dataframe' - def _deprecated(self): - warnings.warn(".resample() is now a deferred operation\n" - "use .resample(...).mean() instead of .resample(...)", + def _deprecated(self, op): + warnings.warn(("\n.resample() is now a deferred operation\n" + "You called {op}(...) on this deferred object " + "which materialized it into a {klass}\nby implicitly " + "taking the mean. Use .resample(...).mean() " + "instead").format(op=op, klass=self._typ), FutureWarning, stacklevel=3) return self.mean() @@ -119,20 +124,20 @@ def _make_deprecated_binop(op): # op is a string def _evaluate_numeric_binop(self, other): - result = self._deprecated() + result = self._deprecated(op) return getattr(result, op)(other) return _evaluate_numeric_binop - def _make_deprecated_unary(op): + def _make_deprecated_unary(op, name): # op is a callable def _evaluate_numeric_unary(self): - result = self._deprecated() + result = self._deprecated(name) return op(result) return _evaluate_numeric_unary def __array__(self): - return self._deprecated().__array__() + return self._deprecated('__array__').__array__() __gt__ = _make_deprecated_binop('__gt__') __ge__ = _make_deprecated_binop('__ge__') @@ -148,10 +153,10 @@ def __array__(self): __truediv__ = __rtruediv__ = _make_deprecated_binop('__truediv__') if not compat.PY3: __div__ = __rdiv__ = _make_deprecated_binop('__div__') - __neg__ = _make_deprecated_unary(lambda x: -x) - __pos__ = _make_deprecated_unary(lambda x: x) - __abs__ = _make_deprecated_unary(lambda x: np.abs(x)) - __inv__ = _make_deprecated_unary(lambda x: -x) + __neg__ = _make_deprecated_unary(lambda x: -x, '__neg__') + __pos__ = _make_deprecated_unary(lambda x: x, '__pos__') + __abs__ = _make_deprecated_unary(lambda x: np.abs(x), '__abs__') + __inv__ = _make_deprecated_unary(lambda x: -x, '__inv__') def __getattr__(self, attr): if attr in self._internal_names_set: @@ -165,8 +170,12 @@ def __getattr__(self, attr): raise ValueError(".resample() is now a deferred operation\n" "\tuse .resample(...).mean() instead of " ".resample(...)") - if attr not in self._deprecated_valids: - self = self._deprecated() + + matches_pattern = any(attr.startswith(x) for x + in self._deprecated_valid_patterns) + if not matches_pattern and attr not in self._deprecated_valids: + self = self._deprecated(attr) + return object.__getattribute__(self, attr) def __setattr__(self, attr, value): @@ -182,7 +191,7 @@ def __getitem__(self, key): # compat for deprecated if isinstance(self.obj, com.ABCSeries): - return self._deprecated()[key] + return self._deprecated('__getitem__')[key] raise @@ -230,7 +239,7 @@ def _assure_grouper(self): def plot(self, *args, **kwargs): # for compat with prior versions, we want to # have the warnings shown here and just have this work - return self._deprecated().plot(*args, **kwargs) + return self._deprecated('plot').plot(*args, **kwargs) def aggregate(self, arg, *args, **kwargs): """ diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 518f69485004c..85d8cd52e1866 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -168,6 +168,13 @@ def f(): check_stacklevel=False): self.assertIsInstance(getattr(r, op)(2), pd.Series) + # IPython introspection shouldn't trigger warning GH 13618 + for op in ['_repr_json', '_repr_latex', + '_ipython_canary_method_should_not_exist_']: + r = self.series.resample('H') + with tm.assert_produces_warning(None): + getattr(r, op, None) + # getitem compat df = self.series.to_frame('foo')
- [ ] closes #13618, closes #13520 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Prevents warnings triggered by IPython by matching against `'_repr'` or `'_ipython'`, and expands the `resample` warning message to indicate what caused the deferred object to be materialized.
https://api.github.com/repos/pandas-dev/pandas/pulls/13675
2016-07-16T17:57:53Z
2016-07-19T13:15:22Z
null
2016-07-19T13:15:26Z
CLN: Replace float64_t with int64_t in _ensure_components
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 650b4c7979d8d..8f3c4fb8c9a1e 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -2507,20 +2507,19 @@ cdef class _Timedelta(timedelta): """ compute the components """ - cdef int64_t sfrac, ifrac, ivalue = self.value - cdef float64_t frac + cdef int64_t sfrac, ifrac, frac, ivalue = self.value if self.is_populated: return # put frac in seconds - frac = float(ivalue)/1e9 + frac = ivalue/(1000*1000*1000) if frac < 0: self._sign = -1 # even fraction - if int(-frac/86400) != -frac/86400.0: - self._d = int(-frac/86400.0+1) + if (-frac % 86400) != 0: + self._d = -frac/86400 + 1 frac += 86400*self._d else: frac = -frac @@ -2529,39 +2528,37 @@ cdef class _Timedelta(timedelta): self._d = 0 if frac >= 86400: - self._d += int(frac / 86400) + self._d += frac / 86400 frac -= self._d * 86400 if frac >= 3600: - self._h = int(frac / 3600) + self._h = frac / 3600 frac -= self._h * 3600 else: self._h = 0 if frac >= 60: - self._m = int(frac / 60) + self._m = frac / 60 frac -= self._m * 60 else: self._m = 0 if frac >= 0: - self._s = int(frac) + self._s = frac frac -= self._s else: self._s = 0 - if frac != 0: - - # reset so we don't lose precision - sfrac = int((self._h*3600 + self._m*60 + self._s)*1e9) - if self._sign < 0: - ifrac = ivalue + self._d*DAY_NS - sfrac - else: - ifrac = ivalue - (self._d*DAY_NS + sfrac) + sfrac = (self._h*3600 + self._m*60 + self._s)*(1000*1000*1000) + if self._sign < 0: + ifrac = ivalue + self._d*DAY_NS - sfrac + else: + ifrac = ivalue - (self._d*DAY_NS + sfrac) - self._ms = int(ifrac/1e6) + if ifrac != 0: + self._ms = ifrac/(1000*1000) ifrac -= self._ms*1000*1000 - self._us = int(ifrac/1e3) + self._us = ifrac/1000 ifrac -= self._us*1000 self._ns = ifrac else:
- [x] passes `git diff upstream/master | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/13673
2016-07-16T16:18:31Z
2016-07-20T22:16:28Z
2016-07-20T22:16:28Z
2016-07-20T22:16:32Z
CLN: removed setter method of categorical's ordered attribute
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..92285676d36e3 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -506,6 +506,7 @@ Removal of prior version deprecations/changes - ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`) - ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) +- ``pd.Categorical`` has dropped setting of the ``ordered`` attribute directly in favor of the ``set_ordered`` method (:issue:`13671`) - ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index a26cc5125db78..39e140e962821 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -571,12 +571,6 @@ def _get_categories(self): _ordered = None - def _set_ordered(self, value): - """ Sets the ordered attribute to the boolean value """ - warn("Setting 'ordered' directly is deprecated, use 'set_ordered'", - FutureWarning, stacklevel=2) - self.set_ordered(value, inplace=True) - def set_ordered(self, value, inplace=False): """ Sets the ordered attribute to the boolean value @@ -624,7 +618,7 @@ def _get_ordered(self): """ Gets the ordered attribute """ return self._ordered - ordered = property(fget=_get_ordered, fset=_set_ordered) + ordered = property(fget=_get_ordered) def set_categories(self, new_categories, ordered=None, rename=False, inplace=False): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 1edd9443fe356..35b1b8c1bf341 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -808,13 +808,12 @@ def test_set_ordered(self): cat2.set_ordered(False, inplace=True) self.assertFalse(cat2.ordered) - # deperecated in v0.16.0 - with tm.assert_produces_warning(FutureWarning): - cat.ordered = False - self.assertFalse(cat.ordered) - with tm.assert_produces_warning(FutureWarning): + # removed in 0.19.0 + msg = "can\'t set attribute" + with tm.assertRaisesRegexp(AttributeError, msg): cat.ordered = True - self.assertTrue(cat.ordered) + with tm.assertRaisesRegexp(AttributeError, msg): + cat.ordered = False def test_set_categories(self): cat = Categorical(["a", "b", "c", "a"], ordered=True)
Deprecated back in `0.16.0` <a href="https://github.com/pydata/pandas/pull/9611">here</a>.
https://api.github.com/repos/pandas-dev/pandas/pulls/13671
2016-07-16T07:41:48Z
2016-07-19T01:44:45Z
null
2016-07-19T02:13:44Z
CLN: removed pandas.sandbox
diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index 8fafe8ec9eaa2..0d010b47f393a 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -93,6 +93,12 @@ targets the IPython Notebook environment. `Plotly’s <https://plot.ly/>`__ `Python API <https://plot.ly/python/>`__ enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and `D3.js <http://d3js.org/>`__. The library supports plotting directly from a pandas DataFrame and cloud-based collaboration. Users of `matplotlib, ggplot for Python, and Seaborn <https://plot.ly/python/matplotlib-to-plotly-tutorial/>`__ can convert figures into interactive web-based plots. Plots can be drawn in `IPython Notebooks <https://plot.ly/ipython-notebooks/>`__ , edited with R or MATLAB, modified in a GUI, or embedded in apps and dashboards. Plotly is free for unlimited sharing, and has `cloud <https://plot.ly/product/plans/>`__, `offline <https://plot.ly/python/offline/>`__, or `on-premise <https://plot.ly/product/enterprise/>`__ accounts for private use. +`Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Spun off from the main pandas library, the `Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__ +library enables DataFrame visualization and manipulation in PyQt4 and PySide applications. + .. _ecosystem.ide: IDE diff --git a/doc/source/faq.rst b/doc/source/faq.rst index e5d659cc31606..d23e0ca59254d 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -110,78 +110,6 @@ details. Visualizing Data in Qt applications ----------------------------------- -.. warning:: - - The ``qt`` support is **deprecated and will be removed in a future version**. - We refer users to the external package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_. - -There is experimental support for visualizing DataFrames in PyQt4 and PySide -applications. At the moment you can display and edit the values of the cells -in the DataFrame. Qt will take care of displaying just the portion of the -DataFrame that is currently visible and the edits will be immediately saved to -the underlying DataFrame - -To demonstrate this we will create a simple PySide application that will switch -between two editable DataFrames. For this will use the ``DataFrameModel`` class -that handles the access to the DataFrame, and the ``DataFrameWidget``, which is -just a thin layer around the ``QTableView``. - -.. code-block:: python - - import numpy as np - import pandas as pd - from pandas.sandbox.qtpandas import DataFrameModel, DataFrameWidget - from PySide import QtGui, QtCore - - # Or if you use PyQt4: - # from PyQt4 import QtGui, QtCore - - class MainWidget(QtGui.QWidget): - def __init__(self, parent=None): - super(MainWidget, self).__init__(parent) - - # Create two DataFrames - self.df1 = pd.DataFrame(np.arange(9).reshape(3, 3), - columns=['foo', 'bar', 'baz']) - self.df2 = pd.DataFrame({ - 'int': [1, 2, 3], - 'float': [1.5, 2.5, 3.5], - 'string': ['a', 'b', 'c'], - 'nan': [np.nan, np.nan, np.nan] - }, index=['AAA', 'BBB', 'CCC'], - columns=['int', 'float', 'string', 'nan']) - - # Create the widget and set the first DataFrame - self.widget = DataFrameWidget(self.df1) - - # Create the buttons for changing DataFrames - self.button_first = QtGui.QPushButton('First') - self.button_first.clicked.connect(self.on_first_click) - self.button_second = QtGui.QPushButton('Second') - self.button_second.clicked.connect(self.on_second_click) - - # Set the layout - vbox = QtGui.QVBoxLayout() - vbox.addWidget(self.widget) - hbox = QtGui.QHBoxLayout() - hbox.addWidget(self.button_first) - hbox.addWidget(self.button_second) - vbox.addLayout(hbox) - self.setLayout(vbox) - - def on_first_click(self): - '''Sets the first DataFrame''' - self.widget.setDataFrame(self.df1) - - def on_second_click(self): - '''Sets the second DataFrame''' - self.widget.setDataFrame(self.df2) - - if __name__ == '__main__': - import sys - - # Initialize the application - app = QtGui.QApplication(sys.argv) - mw = MainWidget() - mw.show() - app.exec_() +There is no support for such visualization in pandas. However, the external +package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_ does +provide this functionality. diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..34b70826f2bf3 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -504,6 +504,7 @@ Deprecations Removal of prior version deprecations/changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +- The ``pd.sandbox`` module has been removed in favor of the external library ``pandas-qt`` (:issue:`13670`) - ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`) - ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) - ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) diff --git a/pandas/api/tests/test_api.py b/pandas/api/tests/test_api.py index 3f6c97441d659..0aefdbeae0518 100644 --- a/pandas/api/tests/test_api.py +++ b/pandas/api/tests/test_api.py @@ -28,7 +28,7 @@ class TestPDApi(Base, tm.TestCase): # these are optionally imported based on testing # & need to be ignored - ignored = ['tests', 'rpy', 'sandbox', 'locale'] + ignored = ['tests', 'rpy', 'locale'] # top-level sub-packages lib = ['api', 'compat', 'computation', 'core', diff --git a/pandas/sandbox/__init__.py b/pandas/sandbox/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/pandas/sandbox/qtpandas.py b/pandas/sandbox/qtpandas.py deleted file mode 100644 index b6af40a0e2156..0000000000000 --- a/pandas/sandbox/qtpandas.py +++ /dev/null @@ -1,145 +0,0 @@ -""" -Easy integration of DataFrame into pyqt framework - -@author: Jev Kuznetsov -""" - -# flake8: noqa - -# GH9615 - -import warnings -warnings.warn("The pandas.sandbox.qtpandas module is deprecated and will be " - "removed in a future version. We refer users to the external package " - "here: https://github.com/datalyze-solutions/pandas-qt") - -try: - from PyQt4.QtCore import QAbstractTableModel, Qt, QVariant, QModelIndex - from PyQt4.QtGui import ( - QApplication, QDialog, QVBoxLayout, QTableView, QWidget) -except ImportError: - from PySide.QtCore import QAbstractTableModel, Qt, QModelIndex - from PySide.QtGui import ( - QApplication, QDialog, QVBoxLayout, QTableView, QWidget) - QVariant = lambda value=None: value - -from pandas import DataFrame, Index - - -class DataFrameModel(QAbstractTableModel): - """ data model for a DataFrame class """ - def __init__(self): - super(DataFrameModel, self).__init__() - self.df = DataFrame() - - def setDataFrame(self, dataFrame): - self.df = dataFrame - - def signalUpdate(self): - """ tell viewers to update their data (this is full update, not - efficient)""" - self.layoutChanged.emit() - - #------------- table display functions ----------------- - def headerData(self, section, orientation, role=Qt.DisplayRole): - if role != Qt.DisplayRole: - return QVariant() - - if orientation == Qt.Horizontal: - try: - return self.df.columns.tolist()[section] - except (IndexError, ): - return QVariant() - elif orientation == Qt.Vertical: - try: - # return self.df.index.tolist() - return self.df.index.tolist()[section] - except (IndexError, ): - return QVariant() - - def data(self, index, role=Qt.DisplayRole): - if role != Qt.DisplayRole: - return QVariant() - - if not index.isValid(): - return QVariant() - - return QVariant(str(self.df.ix[index.row(), index.column()])) - - def flags(self, index): - flags = super(DataFrameModel, self).flags(index) - flags |= Qt.ItemIsEditable - return flags - - def setData(self, index, value, role): - row = self.df.index[index.row()] - col = self.df.columns[index.column()] - if hasattr(value, 'toPyObject'): - # PyQt4 gets a QVariant - value = value.toPyObject() - else: - # PySide gets an unicode - dtype = self.df[col].dtype - if dtype != object: - value = None if value == '' else dtype.type(value) - self.df.set_value(row, col, value) - return True - - def rowCount(self, index=QModelIndex()): - return self.df.shape[0] - - def columnCount(self, index=QModelIndex()): - return self.df.shape[1] - - -class DataFrameWidget(QWidget): - """ a simple widget for using DataFrames in a gui """ - def __init__(self, dataFrame, parent=None): - super(DataFrameWidget, self).__init__(parent) - - self.dataModel = DataFrameModel() - self.dataTable = QTableView() - self.dataTable.setModel(self.dataModel) - - layout = QVBoxLayout() - layout.addWidget(self.dataTable) - self.setLayout(layout) - # Set DataFrame - self.setDataFrame(dataFrame) - - def setDataFrame(self, dataFrame): - self.dataModel.setDataFrame(dataFrame) - self.dataModel.signalUpdate() - self.dataTable.resizeColumnsToContents() - -#-----------------stand alone test code - - -def testDf(): - """ creates test dataframe """ - data = {'int': [1, 2, 3], 'float': [1.5, 2.5, 3.5], - 'string': ['a', 'b', 'c'], 'nan': [np.nan, np.nan, np.nan]} - return DataFrame(data, index=Index(['AAA', 'BBB', 'CCC']), - columns=['int', 'float', 'string', 'nan']) - - -class Form(QDialog): - def __init__(self, parent=None): - super(Form, self).__init__(parent) - - df = testDf() # make up some data - widget = DataFrameWidget(df) - widget.resizeColumnsToContents() - - layout = QVBoxLayout() - layout.addWidget(widget) - self.setLayout(layout) - -if __name__ == '__main__': - import sys - import numpy as np - - app = QApplication(sys.argv) - form = Form() - form.show() - app.exec_() diff --git a/setup.py b/setup.py index c77ca4d9e60fe..0bff49c4976b8 100755 --- a/setup.py +++ b/setup.py @@ -560,7 +560,6 @@ def pxd(name): 'pandas.io.sas', 'pandas.formats', 'pandas.rpy', - 'pandas.sandbox', 'pandas.sparse', 'pandas.sparse.tests', 'pandas.stats',
Deprecated back in `0.16.0` <a href="https://github.com/pydata/pandas/pull/9615">here</a>, and the module seems to be rarely, if at all, used according to @jorisvandenbossche .
https://api.github.com/repos/pandas-dev/pandas/pulls/13670
2016-07-16T06:56:51Z
2016-07-19T01:20:18Z
null
2016-07-19T01:21:33Z
CLN: removed the 'diff' method for Index
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 57b0d8895f67b..95d8011f0e10c 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -543,6 +543,7 @@ Removal of prior version deprecations/changes - ``pd.Categorical`` has dropped setting of the ``ordered`` attribute directly in favor of the ``set_ordered`` method (:issue:`13671`) - ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) - ``DataFrame.to_sql()`` has dropped the ``mysql`` option for the ``flavor`` parameter (:issue:`13611`) +- ``pd.Index`` has dropped the ``diff`` method in favour of ``difference`` (:issue:`13669`) - Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (:issue:`13590`) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 567d2a458dafa..850d049ef9f45 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1965,8 +1965,6 @@ def difference(self, other): return this._shallow_copy(the_diff, name=result_name) - diff = deprecate('diff', difference) - def symmetric_difference(self, other, result_name=None): """ Compute the symmetric difference of two Index objects.
Deprecated all the way back in `0.15.0` <a href="https://github.com/pydata/pandas/pull/8227">here</a>.
https://api.github.com/repos/pandas-dev/pandas/pulls/13669
2016-07-16T04:58:34Z
2016-07-20T21:28:11Z
null
2016-07-21T00:55:17Z
MAINT: Make pandasSQL_builder private
diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 8485a3f13f047..6f18962dba284 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -167,9 +167,9 @@ def execute(sql, con, cur=None, params=None): Results Iterable """ if cur is None: - pandas_sql = pandasSQL_builder(con) + pandas_sql = _pandasSQL_builder(con) else: - pandas_sql = pandasSQL_builder(cur, is_cursor=True) + pandas_sql = _pandasSQL_builder(cur, is_cursor=True) args = _convert_params(sql, params) return pandas_sql.execute(*args) @@ -427,7 +427,7 @@ def read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, read_sql """ - pandas_sql = pandasSQL_builder(con) + pandas_sql = _pandasSQL_builder(con) return pandas_sql.read_query( sql, index_col=index_col, params=params, coerce_float=coerce_float, parse_dates=parse_dates, chunksize=chunksize) @@ -492,7 +492,7 @@ def read_sql(sql, con, index_col=None, coerce_float=True, params=None, read_sql_query : Read SQL query into a DataFrame """ - pandas_sql = pandasSQL_builder(con) + pandas_sql = _pandasSQL_builder(con) if isinstance(pandas_sql, SQLiteDatabase): return pandas_sql.read_query( @@ -560,7 +560,7 @@ def to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', if if_exists not in ('fail', 'replace', 'append'): raise ValueError("'{0}' is not valid for if_exists".format(if_exists)) - pandas_sql = pandasSQL_builder(con, schema=schema, flavor=flavor) + pandas_sql = _pandasSQL_builder(con, schema=schema, flavor=flavor) if isinstance(frame, Series): frame = frame.to_frame() @@ -597,7 +597,7 @@ def has_table(table_name, con, flavor='sqlite', schema=None): ------- boolean """ - pandas_sql = pandasSQL_builder(con, flavor=flavor, schema=schema) + pandas_sql = _pandasSQL_builder(con, flavor=flavor, schema=schema) return pandas_sql.has_table(table_name) table_exists = has_table @@ -626,8 +626,8 @@ def _engine_builder(con): return con -def pandasSQL_builder(con, flavor=None, schema=None, meta=None, - is_cursor=False): +def _pandasSQL_builder(con, flavor=None, schema=None, meta=None, + is_cursor=False): """ Convenience function to return the correct PandasSQL subclass based on the provided parameters @@ -1716,5 +1716,5 @@ def get_schema(frame, name, flavor='sqlite', keys=None, con=None, dtype=None): """ - pandas_sql = pandasSQL_builder(con=con, flavor=flavor) + pandas_sql = _pandasSQL_builder(con=con, flavor=flavor) return pandas_sql._create_sql_schema(frame, name, keys=keys, dtype=dtype)
Per discussion with @jorisvandenbossche <a href="https://github.com/pydata/pandas/pull/13611#discussion_r71048508">here</a>.
https://api.github.com/repos/pandas-dev/pandas/pulls/13668
2016-07-16T03:01:13Z
2016-07-24T19:24:00Z
null
2023-05-11T01:13:48Z
API: Index doesn't results in PeriodIndex if Period contains NaT
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..6755b54b195ef 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -267,6 +267,8 @@ API changes - ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`) - ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`) - ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`) +- Passing ``Period`` with multiple frequencies to normal ``Index`` now returns ``Index`` with ``object`` dtype (:issue:`13664`) +- ``PeriodIndex.fillna`` with ``Period`` has different freq now coerces to ``object`` dtype (:issue:`13664`) .. _whatsnew_0190.api.tolist: @@ -601,7 +603,6 @@ Bug Fixes - Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`) - Clean some compile time warnings in datetime parsing (:issue:`13607`) - - Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`) - Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) - Bug in ``groupby(..).nth()`` where the group key is included inconsistently if called after ``.head()/.tail()`` (:issue:`12839`) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index d76f011df3dd8..44e3be32c23df 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -31,7 +31,7 @@ is_list_like, _ensure_object) from pandas.types.cast import _maybe_upcast_putmask -from pandas.types.generic import ABCSeries, ABCIndex +from pandas.types.generic import ABCSeries, ABCIndex, ABCPeriodIndex # ----------------------------------------------------------------------------- # Functions that add arithmetic methods to objects, given arithmetic factory @@ -773,6 +773,15 @@ def wrapper(self, other, axis=None): if (not lib.isscalar(lib.item_from_zerodim(other)) and len(self) != len(other)): raise ValueError('Lengths must match to compare') + + if isinstance(other, ABCPeriodIndex): + # temp workaround until fixing GH 13637 + # tested in test_nat_comparisons + # (pandas.tests.series.test_operators.TestSeriesOperators) + return self._constructor(na_op(self.values, + other.asobject.values), + index=self.index) + return self._constructor(na_op(self.values, np.asarray(other)), index=self.index).__finalize__(self) elif isinstance(other, pd.Categorical): diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index b013d6ccb0b8e..3b0e8327e5509 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -224,7 +224,8 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, pass # maybe coerce to a sub-class - from pandas.tseries.period import PeriodIndex + from pandas.tseries.period import (PeriodIndex, + IncompatibleFrequency) if isinstance(data, PeriodIndex): return PeriodIndex(data, copy=copy, name=name, **kwargs) if issubclass(data.dtype.type, np.integer): @@ -265,13 +266,15 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, return DatetimeIndex(subarr, copy=copy, name=name, **kwargs) - elif (inferred.startswith('timedelta') or - lib.is_timedelta_array(subarr)): + elif inferred.startswith('timedelta'): from pandas.tseries.tdi import TimedeltaIndex return TimedeltaIndex(subarr, copy=copy, name=name, **kwargs) elif inferred == 'period': - return PeriodIndex(subarr, name=name, **kwargs) + try: + return PeriodIndex(subarr, name=name, **kwargs) + except IncompatibleFrequency: + pass return cls._simple_new(subarr, name) elif hasattr(data, '__array__'): @@ -866,6 +869,16 @@ def _convert_can_do_setop(self, other): result_name = self.name if self.name == other.name else None return other, result_name + def _convert_for_op(self, value): + """ Convert value to be insertable to ndarray """ + return value + + def _assert_can_do_op(self, value): + """ Check value is valid for scalar op """ + if not lib.isscalar(value): + msg = "'value' must be a scalar, passed: {0}" + raise TypeError(msg.format(type(value).__name__)) + @property def nlevels(self): return 1 @@ -1508,16 +1521,6 @@ def hasnans(self): else: return False - def _convert_for_op(self, value): - """ Convert value to be insertable to ndarray """ - return value - - def _assert_can_do_op(self, value): - """ Check value is valid for scalar op """ - if not is_scalar(value): - msg = "'value' must be a scalar, passed: {0}" - raise TypeError(msg.format(type(value).__name__)) - def putmask(self, mask, value): """ return a new Index of the values set with the mask diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index 9f96037c97c62..fe4748eb0eba0 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -270,7 +270,7 @@ cdef inline bint is_null_datetimelike(v): cdef inline bint is_null_datetime64(v): - # determine if we have a null for a datetime (or integer versions)x, + # determine if we have a null for a datetime (or integer versions), # excluding np.timedelta64('nat') if util._checknull(v): return True @@ -282,7 +282,7 @@ cdef inline bint is_null_datetime64(v): cdef inline bint is_null_timedelta64(v): - # determine if we have a null for a timedelta (or integer versions)x, + # determine if we have a null for a timedelta (or integer versions), # excluding np.datetime64('nat') if util._checknull(v): return True @@ -293,6 +293,16 @@ cdef inline bint is_null_timedelta64(v): return False +cdef inline bint is_null_period(v): + # determine if we have a null for a Period (or integer versions), + # excluding np.datetime64('nat') and np.timedelta64('nat') + if util._checknull(v): + return True + elif v is NaT: + return True + return False + + cdef inline bint is_datetime(object o): return PyDateTime_Check(o) @@ -531,6 +541,7 @@ def is_timedelta_array(ndarray values): return False return null_count != n + def is_timedelta64_array(ndarray values): cdef Py_ssize_t i, null_count = 0, n = len(values) cdef object v @@ -546,6 +557,7 @@ def is_timedelta64_array(ndarray values): return False return null_count != n + def is_timedelta_or_timedelta64_array(ndarray values): """ infer with timedeltas and/or nat/none """ cdef Py_ssize_t i, null_count = 0, n = len(values) @@ -562,6 +574,7 @@ def is_timedelta_or_timedelta64_array(ndarray values): return False return null_count != n + def is_date_array(ndarray[object] values): cdef Py_ssize_t i, n = len(values) if n == 0: @@ -571,6 +584,7 @@ def is_date_array(ndarray[object] values): return False return True + def is_time_array(ndarray[object] values): cdef Py_ssize_t i, n = len(values) if n == 0: @@ -582,15 +596,21 @@ def is_time_array(ndarray[object] values): def is_period_array(ndarray[object] values): - cdef Py_ssize_t i, n = len(values) - from pandas.tseries.period import Period - + cdef Py_ssize_t i, null_count = 0, n = len(values) + cdef object v if n == 0: return False + + # return False for all nulls for i in range(n): - if not isinstance(values[i], Period): + v = values[i] + if is_null_period(v): + # we are a regular null + if util._checknull(v): + null_count += 1 + elif not is_period(v): return False - return True + return null_count != n cdef extern from "parse_helper.h": diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index 5c21f71d64660..af44767ae5be5 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -119,10 +119,10 @@ def test_pickle_compat_construction(self): def test_construction_index_with_mixed_timezones(self): # GH 11488 # no tz results in DatetimeIndex - result = Index( - [Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx') - exp = DatetimeIndex( - [Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx') + result = Index([Timestamp('2011-01-01'), + Timestamp('2011-01-02')], name='idx') + exp = DatetimeIndex([Timestamp('2011-01-01'), + Timestamp('2011-01-02')], name='idx') self.assert_index_equal(result, exp, exact=True) self.assertTrue(isinstance(result, DatetimeIndex)) self.assertIsNone(result.tz) @@ -295,9 +295,9 @@ def test_construction_dti_with_mixed_timezones(self): Timestamp('2011-01-02 10:00', tz='Asia/Tokyo')], name='idx') - exp = DatetimeIndex( - [Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00') - ], tz='Asia/Tokyo', name='idx') + exp = DatetimeIndex([Timestamp('2011-01-01 10:00'), + Timestamp('2011-01-02 10:00')], + tz='Asia/Tokyo', name='idx') self.assert_index_equal(result, exp, exact=True) self.assertTrue(isinstance(result, DatetimeIndex)) @@ -338,6 +338,17 @@ def test_construction_dti_with_mixed_timezones(self): Timestamp('2011-01-02 10:00', tz='US/Eastern')], tz='US/Eastern', name='idx') + def test_construction_base_constructor(self): + arr = [pd.Timestamp('2011-01-01'), pd.NaT, pd.Timestamp('2011-01-03')] + tm.assert_index_equal(pd.Index(arr), pd.DatetimeIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.DatetimeIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, pd.Timestamp('2011-01-03')] + tm.assert_index_equal(pd.Index(arr), pd.DatetimeIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.DatetimeIndex(np.array(arr))) + def test_astype(self): # GH 13149, GH 13209 idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN]) @@ -699,12 +710,11 @@ def test_fillna_datetime64(self): pd.Timestamp('2011-01-01 11:00')], dtype=object) self.assert_index_equal(idx.fillna('x'), exp) - idx = pd.DatetimeIndex( - ['2011-01-01 09:00', pd.NaT, '2011-01-01 11:00'], tz=tz) + idx = pd.DatetimeIndex(['2011-01-01 09:00', pd.NaT, + '2011-01-01 11:00'], tz=tz) - exp = pd.DatetimeIndex( - ['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00' - ], tz=tz) + exp = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 10:00', + '2011-01-01 11:00'], tz=tz) self.assert_index_equal( idx.fillna(pd.Timestamp('2011-01-01 10:00', tz=tz)), exp) @@ -734,6 +744,26 @@ def setUp(self): def create_index(self): return period_range('20130101', periods=5, freq='D') + def test_construction_base_constructor(self): + # GH 13664 + arr = [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='M')] + tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.PeriodIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, pd.Period('2011-03', freq='M')] + tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.PeriodIndex(np.array(arr))) + + arr = [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='D')] + tm.assert_index_equal(pd.Index(arr), pd.Index(arr, dtype=object)) + + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.Index(np.array(arr), dtype=object)) + def test_astype(self): # GH 13149, GH 13209 idx = PeriodIndex(['2016-05-16', 'NaT', NaT, np.NaN], freq='D') @@ -874,7 +904,6 @@ def test_repeat(self): self.assertEqual(res.freqstr, 'D') def test_period_index_indexer(self): - # GH4125 idx = pd.period_range('2002-01', '2003-12', freq='M') df = pd.DataFrame(pd.np.random.randn(24, 10), index=idx) @@ -886,12 +915,11 @@ def test_period_index_indexer(self): def test_fillna_period(self): # GH 11343 - idx = pd.PeriodIndex( - ['2011-01-01 09:00', pd.NaT, '2011-01-01 11:00'], freq='H') + idx = pd.PeriodIndex(['2011-01-01 09:00', pd.NaT, + '2011-01-01 11:00'], freq='H') - exp = pd.PeriodIndex( - ['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00' - ], freq='H') + exp = pd.PeriodIndex(['2011-01-01 09:00', '2011-01-01 10:00', + '2011-01-01 11:00'], freq='H') self.assert_index_equal( idx.fillna(pd.Period('2011-01-01 10:00', freq='H')), exp) @@ -899,10 +927,11 @@ def test_fillna_period(self): pd.Period('2011-01-01 11:00', freq='H')], dtype=object) self.assert_index_equal(idx.fillna('x'), exp) - with tm.assertRaisesRegexp( - ValueError, - 'Input has different freq=D from PeriodIndex\\(freq=H\\)'): - idx.fillna(pd.Period('2011-01-01', freq='D')) + exp = pd.Index([pd.Period('2011-01-01 09:00', freq='H'), + pd.Period('2011-01-01', freq='D'), + pd.Period('2011-01-01 11:00', freq='H')], dtype=object) + self.assert_index_equal(idx.fillna(pd.Period('2011-01-01', freq='D')), + exp) def test_no_millisecond_field(self): with self.assertRaises(AttributeError): @@ -923,6 +952,17 @@ def setUp(self): def create_index(self): return pd.to_timedelta(range(5), unit='d') + pd.offsets.Hour(1) + def test_construction_base_constructor(self): + arr = [pd.Timedelta('1 days'), pd.NaT, pd.Timedelta('3 days')] + tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.TimedeltaIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, pd.Timedelta('1 days')] + tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.TimedeltaIndex(np.array(arr))) + def test_shift(self): # test shift for TimedeltaIndex # err8083 diff --git a/pandas/tests/types/test_inference.py b/pandas/tests/types/test_inference.py index 34d10ee9dfa42..9a12220f5b41d 100644 --- a/pandas/tests/types/test_inference.py +++ b/pandas/tests/types/test_inference.py @@ -431,6 +431,33 @@ def test_infer_dtype_timedelta(self): dtype=object) self.assertEqual(lib.infer_dtype(arr), 'mixed') + def test_infer_dtype_period(self): + # GH 13664 + arr = np.array([pd.Period('2011-01', freq='D'), + pd.Period('2011-02', freq='D')]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + arr = np.array([pd.Period('2011-01', freq='D'), + pd.Period('2011-02', freq='M')]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + # starts with nan + for n in [pd.NaT, np.nan]: + arr = np.array([n, pd.Period('2011-01', freq='D')]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + arr = np.array([n, pd.Period('2011-01', freq='D'), n]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + # different type of nat + arr = np.array([np.datetime64('nat'), pd.Period('2011-01', freq='M')], + dtype=object) + self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') + + arr = np.array([pd.Period('2011-01', freq='M'), np.datetime64('nat')], + dtype=object) + self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') + def test_infer_dtype_all_nan_nat_like(self): arr = np.array([np.nan, np.nan]) self.assertEqual(lib.infer_dtype(arr), 'floating') diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index fe0440170383b..188f538372092 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -800,12 +800,15 @@ def _ensure_datetimelike_to_i8(other): if lib.isscalar(other) and isnull(other): other = tslib.iNaT elif isinstance(other, ABCIndexClass): - # convert tz if needed if getattr(other, 'tz', None) is not None: other = other.tz_localize(None).asi8 else: other = other.asi8 else: - other = np.array(other, copy=False).view('i8') + try: + other = np.array(other, copy=False).view('i8') + except TypeError: + # period array cannot be coerces to int + other = Index(other).asi8 return other diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 958a10c329a46..2243f12a6cd06 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -1731,9 +1731,9 @@ def test_representation_to_series(self): 2 2013 dtype: object""" - exp6 = """0 2011-01-01 09:00 -1 2012-02-01 10:00 -2 NaT + exp6 = """0 2011-01-01 09:00 +1 2012-02-01 10:00 +2 NaT dtype: object""" exp7 = """0 2013Q1
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry This improve `Period` handling a little. - normal `Index` constructor can accept: - `Period` and `NaT` mixed input (result is `PeriodIndex`) `Period` version of #13477) - `Period` with multiple freq (result is `object` dtype) - `PeriodIndex.fillna` can accept `Period` with different freq (result is `object` dtype) The below describes the behavior on current master ``` # this should be PeriodIndex pd.Index([pd.Period('2011-01', freq='M'), pd.NaT]) # Index([2011-01, NaT], dtype='object') # this should be object dtype pd.Index([pd.Period('2011-01', freq='M'), pd.Period('2011-01-01', freq='D')]) # pandas._period.IncompatibleFrequency: Input has different freq=D from PeriodIndex(freq=M) # this should be object dtype pd.PeriodIndex(['2011-01', 'NaT'], freq='M').fillna(pd.Period('2011-01-01', freq='D')) # pandas._period.IncompatibleFrequency: Input has different freq=D from PeriodIndex(freq=M) ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13664
2016-07-15T13:17:55Z
2016-07-19T02:07:54Z
null
2016-07-19T02:45:01Z
COMPAT/TST Matplotlib 2.0 compatability
diff --git a/ci/install_travis.sh b/ci/install_travis.sh index 3d9651d4f579b..98ce36acc096e 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -138,5 +138,9 @@ else fi +if [ "$JOB_NAME" == "34_slow" ]; then + conda install -c conda-forge/label/rc -c conda-forge matplotlib +fi + echo "done" exit 0 diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 222bd250034d8..0483cb184ee19 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -427,7 +427,7 @@ Other enhancements - Added documentation to :ref:`I/O<io.dtypes>` regarding the perils of reading in columns with mixed dtypes and how to handle it (:issue:`13746`) - Raise ``ImportError`` in the sql functions when ``sqlalchemy`` is not installed and a connection string is used (:issue:`11920`). - +- Compatibility with matplotlib 2.0. Older versions of pandas should also work with matplotlib 2.0 (:issue:`13333`) .. _whatsnew_0190.api: diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py index faf16430fc94f..7dcc3d6e5734f 100644 --- a/pandas/tests/plotting/common.py +++ b/pandas/tests/plotting/common.py @@ -52,6 +52,7 @@ def setUp(self): self.mpl_ge_1_3_1 = plotting._mpl_ge_1_3_1() self.mpl_ge_1_4_0 = plotting._mpl_ge_1_4_0() self.mpl_ge_1_5_0 = plotting._mpl_ge_1_5_0() + self.mpl_ge_2_0_0 = plotting._mpl_ge_2_0_0() if self.mpl_ge_1_4_0: self.bp_n_objects = 7 @@ -64,6 +65,11 @@ def setUp(self): else: self.polycollection_factor = 1 + if self.mpl_ge_2_0_0: + self.default_figsize = (6.4, 4.8) + else: + self.default_figsize = (8.0, 6.0) + self.default_tick_position = 'left' if self.mpl_ge_2_0_0 else 'default' # common test data from pandas import read_csv path = os.path.join(os.path.dirname(curpath()), 'data', 'iris.csv') @@ -189,7 +195,9 @@ def _check_colors(self, collections, linecolors=None, facecolors=None, """ from matplotlib.lines import Line2D - from matplotlib.collections import Collection, PolyCollection + from matplotlib.collections import ( + Collection, PolyCollection, LineCollection + ) conv = self.colorconverter if linecolors is not None: @@ -203,7 +211,7 @@ def _check_colors(self, collections, linecolors=None, facecolors=None, result = patch.get_color() # Line2D may contains string color expression result = conv.to_rgba(result) - elif isinstance(patch, PolyCollection): + elif isinstance(patch, (PolyCollection, LineCollection)): result = tuple(patch.get_edgecolor()[0]) else: result = patch.get_edgecolor() @@ -318,7 +326,7 @@ def _check_ax_scales(self, axes, xaxis='linear', yaxis='linear'): self.assertEqual(ax.yaxis.get_scale(), yaxis) def _check_axes_shape(self, axes, axes_num=None, layout=None, - figsize=(8.0, 6.0)): + figsize=None): """ Check expected number of axes is drawn in expected layout @@ -333,6 +341,8 @@ def _check_axes_shape(self, axes, axes_num=None, layout=None, figsize : tuple expected figsize. default is matplotlib default """ + if figsize is None: + figsize = self.default_figsize visible_axes = self._flatten_visible(axes) if axes_num is not None: @@ -346,7 +356,7 @@ def _check_axes_shape(self, axes, axes_num=None, layout=None, self.assertEqual(result, layout) self.assert_numpy_array_equal( - np.round(visible_axes[0].figure.get_size_inches()), + visible_axes[0].figure.get_size_inches(), np.array(figsize, dtype=np.float64)) def _get_axes_layout(self, axes): diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py index 492b9edff0122..0f7bc02e24915 100644 --- a/pandas/tests/plotting/test_datetimelike.py +++ b/pandas/tests/plotting/test_datetimelike.py @@ -26,6 +26,7 @@ class TestTSPlot(TestPlotBase): def setUp(self): TestPlotBase.setUp(self) + freq = ['S', 'T', 'H', 'D', 'W', 'M', 'Q', 'A'] idx = [period_range('12/31/1999', freq=x, periods=100) for x in freq] self.period_ser = [Series(np.random.randn(len(x)), x) for x in idx] @@ -122,7 +123,8 @@ def test_tsplot(self): _check_plot_works(s.plot, ax=ax) ax = ts.plot(style='k') - self.assertEqual((0., 0., 0.), ax.get_lines()[0].get_color()) + color = (0., 0., 0., 1) if self.mpl_ge_2_0_0 else (0., 0., 0.) + self.assertEqual(color, ax.get_lines()[0].get_color()) def test_both_style_and_color(self): import matplotlib.pyplot as plt # noqa @@ -575,7 +577,8 @@ def test_secondary_y(self): plt.close(fig) ax2 = ser2.plot() - self.assertEqual(ax2.get_yaxis().get_ticks_position(), 'default') + self.assertEqual(ax2.get_yaxis().get_ticks_position(), + self.default_tick_position) plt.close(ax2.get_figure()) ax = ser2.plot() @@ -605,7 +608,8 @@ def test_secondary_y_ts(self): plt.close(fig) ax2 = ser2.plot() - self.assertEqual(ax2.get_yaxis().get_ticks_position(), 'default') + self.assertEqual(ax2.get_yaxis().get_ticks_position(), + self.default_tick_position) plt.close(ax2.get_figure()) ax = ser2.plot() @@ -639,7 +643,8 @@ def test_secondary_frame(self): df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c']) axes = df.plot(secondary_y=['a', 'c'], subplots=True) self.assertEqual(axes[0].get_yaxis().get_ticks_position(), 'right') - self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'default') + self.assertEqual(axes[1].get_yaxis().get_ticks_position(), + self.default_tick_position) self.assertEqual(axes[2].get_yaxis().get_ticks_position(), 'right') @slow @@ -647,7 +652,8 @@ def test_secondary_bar_frame(self): df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c']) axes = df.plot(kind='bar', secondary_y=['a', 'c'], subplots=True) self.assertEqual(axes[0].get_yaxis().get_ticks_position(), 'right') - self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'default') + self.assertEqual(axes[1].get_yaxis().get_ticks_position(), + self.default_tick_position) self.assertEqual(axes[2].get_yaxis().get_ticks_position(), 'right') def test_mixed_freq_regular_first(self): diff --git a/pandas/tests/plotting/test_frame.py b/pandas/tests/plotting/test_frame.py index 11180c3e9b4f7..91be0a7a73e35 100644 --- a/pandas/tests/plotting/test_frame.py +++ b/pandas/tests/plotting/test_frame.py @@ -10,6 +10,7 @@ import pandas as pd from pandas import (Series, DataFrame, MultiIndex, PeriodIndex, date_range, bdate_range) +from pandas.types.api import is_list_like from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip, PY3) from pandas.formats.printing import pprint_thing import pandas.util.testing as tm @@ -952,9 +953,12 @@ def test_scatter_colors(self): with tm.assertRaises(TypeError): df.plot.scatter(x='a', y='b', c='c', color='green') + default_colors = self._maybe_unpack_cycler(self.plt.rcParams) + ax = df.plot.scatter(x='a', y='b', c='c') - tm.assert_numpy_array_equal(ax.collections[0].get_facecolor()[0], - np.array([0, 0, 1, 1], dtype=np.float64)) + tm.assert_numpy_array_equal( + ax.collections[0].get_facecolor()[0], + np.array(self.colorconverter.to_rgba(default_colors[0]))) ax = df.plot.scatter(x='a', y='b', color='white') tm.assert_numpy_array_equal(ax.collections[0].get_facecolor()[0], @@ -1623,6 +1627,8 @@ def test_line_colors_and_styles_subplots(self): axes = df.plot(subplots=True) for ax, c in zip(axes, list(default_colors)): + if self.mpl_ge_2_0_0: + c = [c] self._check_colors(ax.get_lines(), linecolors=c) tm.close() @@ -1703,9 +1709,14 @@ def test_area_colors(self): self._check_colors(poly, facecolors=custom_colors) handles, labels = ax.get_legend_handles_labels() - # legend is stored as Line2D, thus check linecolors - linehandles = [x for x in handles if not isinstance(x, PolyCollection)] - self._check_colors(linehandles, linecolors=custom_colors) + if self.mpl_ge_1_5_0: + self._check_colors(handles, facecolors=custom_colors) + else: + # legend is stored as Line2D, thus check linecolors + linehandles = [x for x in handles + if not isinstance(x, PolyCollection)] + self._check_colors(linehandles, linecolors=custom_colors) + for h in handles: self.assertTrue(h.get_alpha() is None) tm.close() @@ -1717,8 +1728,12 @@ def test_area_colors(self): self._check_colors(poly, facecolors=jet_colors) handles, labels = ax.get_legend_handles_labels() - linehandles = [x for x in handles if not isinstance(x, PolyCollection)] - self._check_colors(linehandles, linecolors=jet_colors) + if self.mpl_ge_1_5_0: + self._check_colors(handles, facecolors=jet_colors) + else: + linehandles = [x for x in handles + if not isinstance(x, PolyCollection)] + self._check_colors(linehandles, linecolors=jet_colors) for h in handles: self.assertTrue(h.get_alpha() is None) tm.close() @@ -1731,8 +1746,12 @@ def test_area_colors(self): self._check_colors(poly, facecolors=jet_with_alpha) handles, labels = ax.get_legend_handles_labels() - # Line2D can't have alpha in its linecolor - self._check_colors(handles[:len(jet_colors)], linecolors=jet_colors) + if self.mpl_ge_1_5_0: + linecolors = jet_with_alpha + else: + # Line2D can't have alpha in its linecolor + linecolors = jet_colors + self._check_colors(handles[:len(jet_colors)], linecolors=linecolors) for h in handles: self.assertEqual(h.get_alpha(), 0.5) @@ -1855,7 +1874,10 @@ def test_kde_colors_and_styles_subplots(self): @slow def test_boxplot_colors(self): def _check_colors(bp, box_c, whiskers_c, medians_c, caps_c='k', - fliers_c='b'): + fliers_c=None): + # TODO: outside this func? + if fliers_c is None: + fliers_c = 'k' if self.mpl_ge_2_0_0 else 'b' self._check_colors(bp['boxes'], linecolors=[box_c] * len(bp['boxes'])) self._check_colors(bp['whiskers'], @@ -2232,16 +2254,24 @@ def test_errorbar_asymmetrical(self): np.random.seed(0) err = np.random.rand(3, 2, 5) - data = np.random.randn(5, 3) - df = DataFrame(data) + # each column is [0, 1, 2, 3, 4], [3, 4, 5, 6, 7]... + df = DataFrame(np.arange(15).reshape(3, 5)).T + data = df.values ax = df.plot(yerr=err, xerr=err / 2) - self.assertEqual(ax.lines[7].get_ydata()[0], data[0, 1] - err[1, 0, 0]) - self.assertEqual(ax.lines[8].get_ydata()[0], data[0, 1] + err[1, 1, 0]) + if self.mpl_ge_2_0_0: + yerr_0_0 = ax.collections[1].get_paths()[0].vertices[:, 1] + expected_0_0 = err[0, :, 0] * np.array([-1, 1]) + tm.assert_almost_equal(yerr_0_0, expected_0_0) + else: + self.assertEqual(ax.lines[7].get_ydata()[0], + data[0, 1] - err[1, 0, 0]) + self.assertEqual(ax.lines[8].get_ydata()[0], + data[0, 1] + err[1, 1, 0]) - self.assertEqual(ax.lines[5].get_xdata()[0], -err[1, 0, 0] / 2) - self.assertEqual(ax.lines[6].get_xdata()[0], err[1, 1, 0] / 2) + self.assertEqual(ax.lines[5].get_xdata()[0], -err[1, 0, 0] / 2) + self.assertEqual(ax.lines[6].get_xdata()[0], err[1, 1, 0] / 2) with tm.assertRaises(ValueError): df.plot(yerr=err.T) @@ -2277,9 +2307,17 @@ def test_errorbar_scatter(self): self._check_has_errorbars(ax, xerr=1, yerr=1) def _check_errorbar_color(containers, expected, has_err='has_xerr'): - errs = [c.lines[1][0] - for c in ax.containers if getattr(c, has_err, False)] - self._check_colors(errs, linecolors=[expected] * len(errs)) + lines = [] + errs = [c.lines + for c in ax.containers if getattr(c, has_err, False)][0] + for el in errs: + if is_list_like(el): + lines.extend(el) + else: + lines.append(el) + err_lines = [x for x in lines if x in ax.collections] + self._check_colors( + err_lines, linecolors=np.array([expected] * len(err_lines))) # GH 8081 df = DataFrame( diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py index 8b9a4fe05bb2e..a484217da5969 100644 --- a/pandas/tests/plotting/test_misc.py +++ b/pandas/tests/plotting/test_misc.py @@ -103,7 +103,10 @@ def test_scatter_matrix_axis(self): axes0_labels = axes[0][0].yaxis.get_majorticklabels() # GH 5662 - expected = ['-2', '-1', '0', '1', '2'] + if self.mpl_ge_2_0_0: + expected = ['-2', '0', '2'] + else: + expected = ['-2', '-1', '0', '1', '2'] self._check_text_labels(axes0_labels, expected) self._check_ticks_props( axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) @@ -115,7 +118,10 @@ def test_scatter_matrix_axis(self): axes = _check_plot_works(scatter_matrix, filterwarnings='always', frame=df, range_padding=.1) axes0_labels = axes[0][0].yaxis.get_majorticklabels() - expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0'] + if self.mpl_ge_2_0_0: + expected = ['-1.0', '-0.5', '0.0'] + else: + expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0'] self._check_text_labels(axes0_labels, expected) self._check_ticks_props( axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) diff --git a/pandas/tests/plotting/test_series.py b/pandas/tests/plotting/test_series.py index 2bd2f8255569d..e752197c6ad77 100644 --- a/pandas/tests/plotting/test_series.py +++ b/pandas/tests/plotting/test_series.py @@ -218,12 +218,13 @@ def test_bar_log(self): expected = np.hstack((1.0e-04, expected, 1.0e+01)) ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind='bar') - self.assertEqual(ax.get_ylim(), (0.001, 0.10000000000000001)) + ymax = 0.12589254117941673 if self.mpl_ge_2_0_0 else .10000000000000001 + self.assertEqual(ax.get_ylim(), (0.001, ymax)) tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected) tm.close() ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind='barh') - self.assertEqual(ax.get_xlim(), (0.001, 0.10000000000000001)) + self.assertEqual(ax.get_xlim(), (0.001, ymax)) tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected) @slow diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index a61a21d259e57..1abd11017dbfe 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -141,6 +141,14 @@ def _mpl_ge_1_5_0(): except ImportError: return False + +def _mpl_ge_2_0_0(): + try: + import matplotlib + return matplotlib.__version__ >= LooseVersion('2.0') + except ImportError: + return False + if _mpl_ge_1_5_0(): # Compat with mp 1.5, which uses cycler. import cycler
- [x] closes #13333 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Just a WIP for now, I want to cleanup how I handle the errorbar changes. Will wait for a matplotlib RC before changing the Travis matrix I suppose?
https://api.github.com/repos/pandas-dev/pandas/pulls/13662
2016-07-15T01:39:21Z
2016-08-22T20:35:12Z
2016-08-22T20:35:12Z
2017-04-05T02:07:03Z
BUG: construction of Series with integers on windows not default to int64
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index c9f501c682a18..747fc70f858b4 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -534,7 +534,7 @@ Bug Fixes - Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`) - Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`) - Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`) - +- Bug in ``Series`` construction from a tuple of integers on windows not returning default dtype (int64) (:issue:`13646`) - Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`) - Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`) diff --git a/pandas/core/series.py b/pandas/core/series.py index b933f68cfad62..3c1f834c3d479 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2820,7 +2820,7 @@ def _try_cast(arr, take_fast_path): subarr = data.copy() return subarr - elif isinstance(data, list) and len(data) > 0: + elif isinstance(data, (list, tuple)) and len(data) > 0: if dtype is not None: try: subarr = _try_cast(data, False) diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index e2e0f568e4098..c91585a28d867 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -1196,7 +1196,7 @@ def test_alignment_non_pandas(self): align = pd.core.ops._align_method_FRAME - for val in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3])]: + for val in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3], dtype=np.intp)]: tm.assert_series_equal(align(df, val, 'index'), Series([1, 2, 3], index=df.index)) diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index b7ec4d570f18b..c8e04f1ffd75f 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -109,6 +109,17 @@ def test_constructor_iterator(self): result = Series(range(10), dtype='int64') assert_series_equal(result, expected) + def test_constructor_list_like(self): + + # make sure that we are coercing different + # list-likes to standard dtypes and not + # platform specific + expected = Series([1, 2, 3], dtype='int64') + for obj in [[1, 2, 3], (1, 2, 3), + np.array([1, 2, 3], dtype='int64')]: + result = Series(obj, index=[0, 1, 2]) + assert_series_equal(result, expected) + def test_constructor_generator(self): gen = (i for i in range(10)) diff --git a/pandas/types/cast.py b/pandas/types/cast.py index e55cb91d36430..ca23d8d26a426 100644 --- a/pandas/types/cast.py +++ b/pandas/types/cast.py @@ -33,7 +33,7 @@ def _possibly_convert_platform(values): """ try to do platform conversion, allow ndarray or list here """ if isinstance(values, (list, tuple)): - values = lib.list_to_object_array(values) + values = lib.list_to_object_array(list(values)) if getattr(values, 'dtype', None) == np.object_: if hasattr(values, '_values'): values = values._values
closes #13646
https://api.github.com/repos/pandas-dev/pandas/pulls/13661
2016-07-15T00:54:01Z
2016-07-15T10:21:32Z
null
2016-07-15T10:21:32Z
BUG: concat/append misc fixes
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 777bc01e71833..ca5f3dfc2a8f2 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -1356,6 +1356,13 @@ Bug Fixes - Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`) - Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`) +- Bug in ``pd.concat`` and ``.append`` may coerces ``datetime64`` and ``timedelta`` to ``object`` dtype containing python built-in ``datetime`` or ``timedelta`` rather than ``Timestamp`` or ``Timedelta`` (:issue:`13626`) +- Bug in ``PeriodIndex.append`` may raises ``AttributeError`` when the result is ``object`` dtype (:issue:`13221`) +- Bug in ``CategoricalIndex.append`` may accept normal ``list`` (:issue:`13626`) +- Bug in ``pd.concat`` and ``.append`` with the same timezone get reset to UTC (:issue:`7795`) +- Bug in ``Series`` and ``DataFrame`` ``.append`` raises ``AmbiguousTimeError`` if data contains datetime near DST boundary (:issue:`13626`) + + - Bug in ``DataFrame.to_csv()`` in which float values were being quoted even though quotations were specified for non-numeric values only (:issue:`12922`, :issue:`13259`) - Bug in ``DataFrame.describe()`` raising ``ValueError`` with only boolean columns (:issue:`13898`) - Bug in ``MultiIndex`` slicing where extra elements were returned when level is non-unique (:issue:`12896`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 46a1d22a4114b..ac3e5d2aabef7 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4384,14 +4384,20 @@ def append(self, other, ignore_index=False, verify_integrity=False): raise TypeError('Can only append a Series if ignore_index=True' ' or if the Series has a name') - index = None if other.name is None else [other.name] + if other.name is None: + index = None + else: + # other must have the same index name as self, otherwise + # index name will be reset + index = Index([other.name], name=self.index.name) + combined_columns = self.columns.tolist() + self.columns.union( other.index).difference(self.columns).tolist() other = other.reindex(combined_columns, copy=False) other = DataFrame(other.values.reshape((1, len(other))), - index=index, columns=combined_columns) + index=index, + columns=combined_columns) other = other._convert(datetime=True, timedelta=True) - if not self.columns.equals(combined_columns): self = self.reindex(columns=combined_columns) elif isinstance(other, list) and not isinstance(other[0], DataFrame): diff --git a/pandas/core/series.py b/pandas/core/series.py index 01d6f6f078d17..8379c8bcdcae8 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -289,7 +289,6 @@ def _set_axis(self, axis, labels, fastpath=False): is_all_dates = labels.is_all_dates if is_all_dates: - if not isinstance(labels, (DatetimeIndex, PeriodIndex, TimedeltaIndex)): try: @@ -297,8 +296,11 @@ def _set_axis(self, axis, labels, fastpath=False): # need to set here becuase we changed the index if fastpath: self._data.set_axis(axis, labels) - except tslib.OutOfBoundsDatetime: + except (tslib.OutOfBoundsDatetime, ValueError): + # labels may exceeds datetime bounds, + # or not be a DatetimeIndex pass + self._set_subtyp(is_all_dates) object.__setattr__(self, '_index', labels) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 15cd2064624d9..d6b6d01b1e444 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1392,15 +1392,19 @@ def __getitem__(self, key): else: return result - def _ensure_compat_append(self, other): + def append(self, other): """ - prepare the append + Append a collection of Index options together + + Parameters + ---------- + other : Index or list/tuple of indices Returns ------- - list of to_concat, name of result Index + appended : Index """ - name = self.name + to_concat = [self] if isinstance(other, (list, tuple)): @@ -1409,46 +1413,29 @@ def _ensure_compat_append(self, other): to_concat.append(other) for obj in to_concat: - if (isinstance(obj, Index) and obj.name != name and - obj.name is not None): - name = None - break + if not isinstance(obj, Index): + raise TypeError('all inputs must be Index') - to_concat = self._ensure_compat_concat(to_concat) - to_concat = [x._values if isinstance(x, Index) else x - for x in to_concat] - return to_concat, name + names = set([obj.name for obj in to_concat]) + name = None if len(names) > 1 else self.name - def append(self, other): - """ - Append a collection of Index options together + typs = _concat.get_dtype_kinds(to_concat) - Parameters - ---------- - other : Index or list/tuple of indices + if 'category' in typs: + # if any of the to_concat is category + from pandas.indexes.category import CategoricalIndex + return CategoricalIndex._append_same_dtype(self, to_concat, name) - Returns - ------- - appended : Index - """ - to_concat, name = self._ensure_compat_append(other) - attribs = self._get_attributes_dict() - attribs['name'] = name - return self._shallow_copy_with_infer( - np.concatenate(to_concat), **attribs) - - @staticmethod - def _ensure_compat_concat(indexes): - from pandas.tseries.api import (DatetimeIndex, PeriodIndex, - TimedeltaIndex) - klasses = DatetimeIndex, PeriodIndex, TimedeltaIndex - - is_ts = [isinstance(idx, klasses) for idx in indexes] + if len(typs) == 1: + return self._append_same_dtype(to_concat, name=name) + return _concat._concat_index_asobject(to_concat, name=name) - if any(is_ts) and not all(is_ts): - return [_maybe_box(idx) for idx in indexes] - - return indexes + def _append_same_dtype(self, to_concat, name): + """ + Concatenate to_concat which has the same class + """ + # must be overrided in specific classes + return _concat._concat_index_asobject(to_concat, name) _index_shared_docs['take'] = """ return a new %(klass)s of the values selected by the indices @@ -3634,16 +3621,6 @@ def _ensure_has_len(seq): return seq -def _maybe_box(idx): - from pandas.tseries.api import DatetimeIndex, PeriodIndex, TimedeltaIndex - klasses = DatetimeIndex, PeriodIndex, TimedeltaIndex - - if isinstance(idx, klasses): - return idx.asobject - - return idx - - def _trim_front(strings): """ Trims zeros and decimal points diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 251886ebdd974..1666d8f7bc078 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -569,26 +569,17 @@ def insert(self, loc, item): codes = np.concatenate((codes[:loc], code, codes[loc:])) return self._create_from_codes(codes) - def append(self, other): + def _append_same_dtype(self, to_concat, name): """ - Append a collection of CategoricalIndex options together - - Parameters - ---------- - other : Index or list/tuple of indices - - Returns - ------- - appended : Index - - Raises - ------ + Concatenate to_concat which has the same class ValueError if other is not in the categories """ - to_concat, name = self._ensure_compat_append(other) to_concat = [self._is_dtype_compat(c) for c in to_concat] codes = np.concatenate([c.codes for c in to_concat]) - return self._create_from_codes(codes, name=name) + result = self._create_from_codes(codes, name=name) + # if name is None, _create_from_codes sets self.name + result.name = name + return result @classmethod def _add_comparison_methods(cls): diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index b0e50491b8e9d..cb8452479f616 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -271,12 +271,12 @@ def test_append(self): lambda: ci.append(ci.values.reorder_categories(list('abc')))) # with objects - result = ci.append(['c', 'a']) + result = ci.append(Index(['c', 'a'])) expected = CategoricalIndex(list('aabbcaca'), categories=categories) tm.assert_index_equal(result, expected, exact=True) # invalid objects - self.assertRaises(TypeError, lambda: ci.append(['a', 'd'])) + self.assertRaises(TypeError, lambda: ci.append(Index(['a', 'd']))) def test_insert(self): diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 675193e1538b2..d49ac40631d37 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -6,8 +6,8 @@ import re import warnings -from pandas import (DataFrame, date_range, MultiIndex, Index, CategoricalIndex, - compat) +from pandas import (DataFrame, date_range, period_range, MultiIndex, Index, + CategoricalIndex, compat) from pandas.core.common import PerformanceWarning from pandas.indexes.base import InvalidIndexError from pandas.compat import range, lrange, u, PY3, long, lzip @@ -769,6 +769,40 @@ def test_append(self): result = self.index.append([]) self.assertTrue(result.equals(self.index)) + def test_append_mixed_dtypes(self): + # GH 13660 + dti = date_range('2011-01-01', freq='M', periods=3,) + dti_tz = date_range('2011-01-01', freq='M', periods=3, tz='US/Eastern') + pi = period_range('2011-01', freq='M', periods=3) + + mi = MultiIndex.from_arrays([[1, 2, 3], + [1.1, np.nan, 3.3], + ['a', 'b', 'c'], + dti, dti_tz, pi]) + self.assertEqual(mi.nlevels, 6) + + res = mi.append(mi) + exp = MultiIndex.from_arrays([[1, 2, 3, 1, 2, 3], + [1.1, np.nan, 3.3, 1.1, np.nan, 3.3], + ['a', 'b', 'c', 'a', 'b', 'c'], + dti.append(dti), + dti_tz.append(dti_tz), + pi.append(pi)]) + tm.assert_index_equal(res, exp) + + other = MultiIndex.from_arrays([['x', 'y', 'z'], ['x', 'y', 'z'], + ['x', 'y', 'z'], ['x', 'y', 'z'], + ['x', 'y', 'z'], ['x', 'y', 'z']]) + + res = mi.append(other) + exp = MultiIndex.from_arrays([[1, 2, 3, 'x', 'y', 'z'], + [1.1, np.nan, 3.3, 'x', 'y', 'z'], + ['a', 'b', 'c', 'x', 'y', 'z'], + dti.append(pd.Index(['x', 'y', 'z'])), + dti_tz.append(pd.Index(['x', 'y', 'z'])), + pi.append(pd.Index(['x', 'y', 'z']))]) + tm.assert_index_equal(res, exp) + def test_get_level_values(self): result = self.index.get_level_values(0) expected = Index(['foo', 'foo', 'bar', 'baz', 'qux', 'qux'], diff --git a/pandas/tests/types/test_concat.py b/pandas/tests/types/test_concat.py new file mode 100644 index 0000000000000..6403dcb5a5350 --- /dev/null +++ b/pandas/tests/types/test_concat.py @@ -0,0 +1,86 @@ +# -*- coding: utf-8 -*- + +import nose +import pandas as pd +import pandas.types.concat as _concat +import pandas.util.testing as tm + + +class TestConcatCompat(tm.TestCase): + + _multiprocess_can_split_ = True + + def check_concat(self, to_concat, exp): + for klass in [pd.Index, pd.Series]: + to_concat_klass = [klass(c) for c in to_concat] + res = _concat.get_dtype_kinds(to_concat_klass) + self.assertEqual(res, set(exp)) + + def test_get_dtype_kinds(self): + to_concat = [['a'], [1, 2]] + self.check_concat(to_concat, ['i', 'object']) + + to_concat = [[3, 4], [1, 2]] + self.check_concat(to_concat, ['i']) + + to_concat = [[3, 4], [1, 2.1]] + self.check_concat(to_concat, ['i', 'f']) + + def test_get_dtype_kinds_datetimelike(self): + to_concat = [pd.DatetimeIndex(['2011-01-01']), + pd.DatetimeIndex(['2011-01-02'])] + self.check_concat(to_concat, ['datetime']) + + to_concat = [pd.TimedeltaIndex(['1 days']), + pd.TimedeltaIndex(['2 days'])] + self.check_concat(to_concat, ['timedelta']) + + def test_get_dtype_kinds_datetimelike_object(self): + to_concat = [pd.DatetimeIndex(['2011-01-01']), + pd.DatetimeIndex(['2011-01-02'], tz='US/Eastern')] + self.check_concat(to_concat, + ['datetime', 'datetime64[ns, US/Eastern]']) + + to_concat = [pd.DatetimeIndex(['2011-01-01'], tz='Asia/Tokyo'), + pd.DatetimeIndex(['2011-01-02'], tz='US/Eastern')] + self.check_concat(to_concat, + ['datetime64[ns, Asia/Tokyo]', + 'datetime64[ns, US/Eastern]']) + + # timedelta has single type + to_concat = [pd.TimedeltaIndex(['1 days']), + pd.TimedeltaIndex(['2 hours'])] + self.check_concat(to_concat, ['timedelta']) + + to_concat = [pd.DatetimeIndex(['2011-01-01'], tz='Asia/Tokyo'), + pd.TimedeltaIndex(['1 days'])] + self.check_concat(to_concat, + ['datetime64[ns, Asia/Tokyo]', 'timedelta']) + + def test_get_dtype_kinds_period(self): + # because we don't have Period dtype (yet), + # Series results in object dtype + to_concat = [pd.PeriodIndex(['2011-01'], freq='M'), + pd.PeriodIndex(['2011-01'], freq='M')] + res = _concat.get_dtype_kinds(to_concat) + self.assertEqual(res, set(['period[M]'])) + + to_concat = [pd.Series([pd.Period('2011-01', freq='M')]), + pd.Series([pd.Period('2011-02', freq='M')])] + res = _concat.get_dtype_kinds(to_concat) + self.assertEqual(res, set(['object'])) + + to_concat = [pd.PeriodIndex(['2011-01'], freq='M'), + pd.PeriodIndex(['2011-01'], freq='D')] + res = _concat.get_dtype_kinds(to_concat) + self.assertEqual(res, set(['period[M]', 'period[D]'])) + + to_concat = [pd.Series([pd.Period('2011-01', freq='M')]), + pd.Series([pd.Period('2011-02', freq='D')])] + res = _concat.get_dtype_kinds(to_concat) + self.assertEqual(res, set(['object'])) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index 3e2b7c3af460e..94b464f6fca6c 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -523,6 +523,9 @@ def _normalize(table, normalize, margins): column_margin = table.loc[:, 'All'].drop('All') index_margin = table.loc['All', :].drop('All') table = table.drop('All', axis=1).drop('All') + # to keep index and columns names + table_index_names = table.index.names + table_columns_names = table.columns.names # Normalize core table = _normalize(table, normalize=normalize, margins=False) @@ -550,6 +553,9 @@ def _normalize(table, normalize, margins): else: raise ValueError("Not a valid normalize argument") + table.index.names = table_index_names + table.columns.names = table_columns_names + else: raise ValueError("Not a valid margins argument") diff --git a/pandas/tools/tests/test_concat.py b/pandas/tools/tests/test_concat.py index 17ccfb27d4b42..102f21bcdc535 100644 --- a/pandas/tools/tests/test_concat.py +++ b/pandas/tools/tests/test_concat.py @@ -4,7 +4,7 @@ from numpy.random import randn from datetime import datetime -from pandas.compat import StringIO +from pandas.compat import StringIO, iteritems import pandas as pd from pandas import (DataFrame, concat, read_csv, isnull, Series, date_range, @@ -27,6 +27,430 @@ def setUp(self): self.mixed_frame['foo'] = 'bar' +class TestConcatAppendCommon(ConcatenateBase): + + """ + Test common dtype coercion rules between concat and append. + """ + + def setUp(self): + + dt_data = [pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02'), + pd.Timestamp('2011-01-03')] + tz_data = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-01-02', tz='US/Eastern'), + pd.Timestamp('2011-01-03', tz='US/Eastern')] + + td_data = [pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Timedelta('3 days')] + + period_data = [pd.Period('2011-01', freq='M'), + pd.Period('2011-02', freq='M'), + pd.Period('2011-03', freq='M')] + + self.data = {'bool': [True, False, True], + 'int64': [1, 2, 3], + 'float64': [1.1, np.nan, 3.3], + 'category': pd.Categorical(['X', 'Y', 'Z']), + 'object': ['a', 'b', 'c'], + 'datetime64[ns]': dt_data, + 'datetime64[ns, US/Eastern]': tz_data, + 'timedelta64[ns]': td_data, + 'period[M]': period_data} + + def _check_expected_dtype(self, obj, label): + """ + Check whether obj has expected dtype depending on label + considering not-supported dtypes + """ + if isinstance(obj, pd.Index): + if label == 'bool': + self.assertEqual(obj.dtype, 'object') + else: + self.assertEqual(obj.dtype, label) + elif isinstance(obj, pd.Series): + if label.startswith('period'): + self.assertEqual(obj.dtype, 'object') + else: + self.assertEqual(obj.dtype, label) + else: + raise ValueError + + def test_dtypes(self): + # to confirm test case covers intended dtypes + for typ, vals in iteritems(self.data): + self._check_expected_dtype(pd.Index(vals), typ) + self._check_expected_dtype(pd.Series(vals), typ) + + def test_concatlike_same_dtypes(self): + # GH 13660 + for typ1, vals1 in iteritems(self.data): + + vals2 = vals1 + vals3 = vals1 + + if typ1 == 'category': + exp_data = pd.Categorical(list(vals1) + list(vals2)) + exp_data3 = pd.Categorical(list(vals1) + list(vals2) + + list(vals3)) + else: + exp_data = vals1 + vals2 + exp_data3 = vals1 + vals2 + vals3 + + # ----- Index ----- # + + # index.append + res = pd.Index(vals1).append(pd.Index(vals2)) + exp = pd.Index(exp_data) + tm.assert_index_equal(res, exp) + + # 3 elements + res = pd.Index(vals1).append([pd.Index(vals2), pd.Index(vals3)]) + exp = pd.Index(exp_data3) + tm.assert_index_equal(res, exp) + + # index.append name mismatch + i1 = pd.Index(vals1, name='x') + i2 = pd.Index(vals2, name='y') + res = i1.append(i2) + exp = pd.Index(exp_data) + tm.assert_index_equal(res, exp) + + # index.append name match + i1 = pd.Index(vals1, name='x') + i2 = pd.Index(vals2, name='x') + res = i1.append(i2) + exp = pd.Index(exp_data, name='x') + tm.assert_index_equal(res, exp) + + # cannot append non-index + with tm.assertRaisesRegexp(TypeError, 'all inputs must be Index'): + pd.Index(vals1).append(vals2) + + with tm.assertRaisesRegexp(TypeError, 'all inputs must be Index'): + pd.Index(vals1).append([pd.Index(vals2), vals3]) + + # ----- Series ----- # + + # series.append + res = pd.Series(vals1).append(pd.Series(vals2), + ignore_index=True) + exp = pd.Series(exp_data) + tm.assert_series_equal(res, exp, check_index_type=True) + + # concat + res = pd.concat([pd.Series(vals1), pd.Series(vals2)], + ignore_index=True) + tm.assert_series_equal(res, exp, check_index_type=True) + + # 3 elements + res = pd.Series(vals1).append([pd.Series(vals2), pd.Series(vals3)], + ignore_index=True) + exp = pd.Series(exp_data3) + tm.assert_series_equal(res, exp) + + res = pd.concat([pd.Series(vals1), pd.Series(vals2), + pd.Series(vals3)], ignore_index=True) + tm.assert_series_equal(res, exp) + + # name mismatch + s1 = pd.Series(vals1, name='x') + s2 = pd.Series(vals2, name='y') + res = s1.append(s2, ignore_index=True) + exp = pd.Series(exp_data) + tm.assert_series_equal(res, exp, check_index_type=True) + + res = pd.concat([s1, s2], ignore_index=True) + tm.assert_series_equal(res, exp, check_index_type=True) + + # name match + s1 = pd.Series(vals1, name='x') + s2 = pd.Series(vals2, name='x') + res = s1.append(s2, ignore_index=True) + exp = pd.Series(exp_data, name='x') + tm.assert_series_equal(res, exp, check_index_type=True) + + res = pd.concat([s1, s2], ignore_index=True) + tm.assert_series_equal(res, exp, check_index_type=True) + + # cannot append non-index + msg = "cannot concatenate a non-NDFrame object" + with tm.assertRaisesRegexp(TypeError, msg): + pd.Series(vals1).append(vals2) + + with tm.assertRaisesRegexp(TypeError, msg): + pd.Series(vals1).append([pd.Series(vals2), vals3]) + + with tm.assertRaisesRegexp(TypeError, msg): + pd.concat([pd.Series(vals1), vals2]) + + with tm.assertRaisesRegexp(TypeError, msg): + pd.concat([pd.Series(vals1), pd.Series(vals2), vals3]) + + def test_concatlike_dtypes_coercion(self): + # GH 13660 + for typ1, vals1 in iteritems(self.data): + for typ2, vals2 in iteritems(self.data): + + vals3 = vals2 + + # basically infer + exp_index_dtype = None + exp_series_dtype = None + + if typ1 == typ2: + # same dtype is tested in test_concatlike_same_dtypes + continue + elif typ1 == 'category' or typ2 == 'category': + # ToDo: suspicious + continue + + # specify expected dtype + if typ1 == 'bool' and typ2 in ('int64', 'float64'): + # series coerces to numeric based on numpy rule + # index doesn't because bool is object dtype + exp_series_dtype = typ2 + elif typ2 == 'bool' and typ1 in ('int64', 'float64'): + exp_series_dtype = typ1 + elif (typ1 == 'datetime64[ns, US/Eastern]' or + typ2 == 'datetime64[ns, US/Eastern]' or + typ1 == 'timedelta64[ns]' or + typ2 == 'timedelta64[ns]'): + exp_index_dtype = object + exp_series_dtype = object + + exp_data = vals1 + vals2 + exp_data3 = vals1 + vals2 + vals3 + + # ----- Index ----- # + + # index.append + res = pd.Index(vals1).append(pd.Index(vals2)) + exp = pd.Index(exp_data, dtype=exp_index_dtype) + tm.assert_index_equal(res, exp) + + # 3 elements + res = pd.Index(vals1).append([pd.Index(vals2), + pd.Index(vals3)]) + exp = pd.Index(exp_data3, dtype=exp_index_dtype) + tm.assert_index_equal(res, exp) + + # ----- Series ----- # + + # series.append + res = pd.Series(vals1).append(pd.Series(vals2), + ignore_index=True) + exp = pd.Series(exp_data, dtype=exp_series_dtype) + tm.assert_series_equal(res, exp, check_index_type=True) + + # concat + res = pd.concat([pd.Series(vals1), pd.Series(vals2)], + ignore_index=True) + tm.assert_series_equal(res, exp, check_index_type=True) + + # 3 elements + res = pd.Series(vals1).append([pd.Series(vals2), + pd.Series(vals3)], + ignore_index=True) + exp = pd.Series(exp_data3, dtype=exp_series_dtype) + tm.assert_series_equal(res, exp) + + res = pd.concat([pd.Series(vals1), pd.Series(vals2), + pd.Series(vals3)], ignore_index=True) + tm.assert_series_equal(res, exp) + + def test_concatlike_common_coerce_to_pandas_object(self): + # GH 13626 + # result must be Timestamp/Timedelta, not datetime.datetime/timedelta + dti = pd.DatetimeIndex(['2011-01-01', '2011-01-02']) + tdi = pd.TimedeltaIndex(['1 days', '2 days']) + + exp = pd.Index([pd.Timestamp('2011-01-01'), + pd.Timestamp('2011-01-02'), + pd.Timedelta('1 days'), + pd.Timedelta('2 days')]) + + res = dti.append(tdi) + tm.assert_index_equal(res, exp) + tm.assertIsInstance(res[0], pd.Timestamp) + tm.assertIsInstance(res[-1], pd.Timedelta) + + dts = pd.Series(dti) + tds = pd.Series(tdi) + res = dts.append(tds) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + tm.assertIsInstance(res.iloc[0], pd.Timestamp) + tm.assertIsInstance(res.iloc[-1], pd.Timedelta) + + res = pd.concat([dts, tds]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + tm.assertIsInstance(res.iloc[0], pd.Timestamp) + tm.assertIsInstance(res.iloc[-1], pd.Timedelta) + + def test_concatlike_datetimetz(self): + # GH 7795 + for tz in ['UTC', 'US/Eastern', 'Asia/Tokyo']: + dti1 = pd.DatetimeIndex(['2011-01-01', '2011-01-02'], tz=tz) + dti2 = pd.DatetimeIndex(['2012-01-01', '2012-01-02'], tz=tz) + + exp = pd.DatetimeIndex(['2011-01-01', '2011-01-02', + '2012-01-01', '2012-01-02'], tz=tz) + + res = dti1.append(dti2) + tm.assert_index_equal(res, exp) + + dts1 = pd.Series(dti1) + dts2 = pd.Series(dti2) + res = dts1.append(dts2) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + res = pd.concat([dts1, dts2]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + def test_concatlike_datetimetz_short(self): + # GH 7795 + for tz in ['UTC', 'US/Eastern', 'Asia/Tokyo', 'EST5EDT']: + + ix1 = pd.DatetimeIndex(start='2014-07-15', end='2014-07-17', + freq='D', tz=tz) + ix2 = pd.DatetimeIndex(['2014-07-11', '2014-07-21'], tz=tz) + df1 = pd.DataFrame(0, index=ix1, columns=['A', 'B']) + df2 = pd.DataFrame(0, index=ix2, columns=['A', 'B']) + + exp_idx = pd.DatetimeIndex(['2014-07-15', '2014-07-16', + '2014-07-17', '2014-07-11', + '2014-07-21'], tz=tz) + exp = pd.DataFrame(0, index=exp_idx, columns=['A', 'B']) + + tm.assert_frame_equal(df1.append(df2), exp) + tm.assert_frame_equal(pd.concat([df1, df2]), exp) + + def test_concatlike_datetimetz_to_object(self): + # GH 13660 + + # different tz coerces to object + for tz in ['UTC', 'US/Eastern', 'Asia/Tokyo']: + dti1 = pd.DatetimeIndex(['2011-01-01', '2011-01-02'], tz=tz) + dti2 = pd.DatetimeIndex(['2012-01-01', '2012-01-02']) + + exp = pd.Index([pd.Timestamp('2011-01-01', tz=tz), + pd.Timestamp('2011-01-02', tz=tz), + pd.Timestamp('2012-01-01'), + pd.Timestamp('2012-01-02')], dtype=object) + + res = dti1.append(dti2) + tm.assert_index_equal(res, exp) + + dts1 = pd.Series(dti1) + dts2 = pd.Series(dti2) + res = dts1.append(dts2) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + res = pd.concat([dts1, dts2]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + # different tz + dti3 = pd.DatetimeIndex(['2012-01-01', '2012-01-02'], + tz='US/Pacific') + + exp = pd.Index([pd.Timestamp('2011-01-01', tz=tz), + pd.Timestamp('2011-01-02', tz=tz), + pd.Timestamp('2012-01-01', tz='US/Pacific'), + pd.Timestamp('2012-01-02', tz='US/Pacific')], + dtype=object) + + res = dti1.append(dti3) + # tm.assert_index_equal(res, exp) + + dts1 = pd.Series(dti1) + dts3 = pd.Series(dti3) + res = dts1.append(dts3) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + res = pd.concat([dts1, dts3]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + def test_concatlike_common_period(self): + # GH 13660 + pi1 = pd.PeriodIndex(['2011-01', '2011-02'], freq='M') + pi2 = pd.PeriodIndex(['2012-01', '2012-02'], freq='M') + + exp = pd.PeriodIndex(['2011-01', '2011-02', '2012-01', + '2012-02'], freq='M') + + res = pi1.append(pi2) + tm.assert_index_equal(res, exp) + + ps1 = pd.Series(pi1) + ps2 = pd.Series(pi2) + res = ps1.append(ps2) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + res = pd.concat([ps1, ps2]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + def test_concatlike_common_period_diff_freq_to_object(self): + # GH 13221 + pi1 = pd.PeriodIndex(['2011-01', '2011-02'], freq='M') + pi2 = pd.PeriodIndex(['2012-01-01', '2012-02-01'], freq='D') + + exp = pd.Index([pd.Period('2011-01', freq='M'), + pd.Period('2011-02', freq='M'), + pd.Period('2012-01-01', freq='D'), + pd.Period('2012-02-01', freq='D')], dtype=object) + + res = pi1.append(pi2) + tm.assert_index_equal(res, exp) + + ps1 = pd.Series(pi1) + ps2 = pd.Series(pi2) + res = ps1.append(ps2) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + res = pd.concat([ps1, ps2]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + def test_concatlike_common_period_mixed_dt_to_object(self): + # GH 13221 + # different datetimelike + pi1 = pd.PeriodIndex(['2011-01', '2011-02'], freq='M') + tdi = pd.TimedeltaIndex(['1 days', '2 days']) + exp = pd.Index([pd.Period('2011-01', freq='M'), + pd.Period('2011-02', freq='M'), + pd.Timedelta('1 days'), + pd.Timedelta('2 days')], dtype=object) + + res = pi1.append(tdi) + tm.assert_index_equal(res, exp) + + ps1 = pd.Series(pi1) + tds = pd.Series(tdi) + res = ps1.append(tds) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + res = pd.concat([ps1, tds]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + # inverse + exp = pd.Index([pd.Timedelta('1 days'), + pd.Timedelta('2 days'), + pd.Period('2011-01', freq='M'), + pd.Period('2011-02', freq='M')], dtype=object) + + res = tdi.append(pi1) + tm.assert_index_equal(res, exp) + + ps1 = pd.Series(pi1) + tds = pd.Series(tdi) + res = tds.append(ps1) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + res = pd.concat([tds, ps1]) + tm.assert_series_equal(res, pd.Series(exp, index=[0, 1, 0, 1])) + + class TestAppend(ConcatenateBase): def test_append(self): diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index cda2343fbb842..75c6db23b4bc7 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -895,7 +895,9 @@ def test_crosstab_margins(self): all_cols = result['All', ''] exp_cols = df.groupby(['a']).size().astype('i8') - exp_cols = exp_cols.append(Series([len(df)], index=['All'])) + # to keep index.name + exp_margin = Series([len(df)], index=Index(['All'], name='a')) + exp_cols = exp_cols.append(exp_margin) exp_cols.name = ('All', '') tm.assert_series_equal(all_cols, exp_cols) @@ -1084,7 +1086,6 @@ def test_crosstab_normalize(self): dtype='object'), columns=pd.Index([3, 4, 'All'], name='b')) - tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='index', margins=True), row_normal_margins) tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='columns', diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index f0c6e334925c4..45e2a2d6c0720 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -26,6 +26,7 @@ from pandas.core.index import Index from pandas.indexes.base import _index_shared_docs from pandas.util.decorators import Appender, cache_readonly +import pandas.types.concat as _concat import pandas.tseries.frequencies as frequencies import pandas.algos as _algos @@ -795,6 +796,23 @@ def summary(self, name=None): result = result.replace("'", "") return result + def _append_same_dtype(self, to_concat, name): + """ + Concatenate to_concat which has the same class + """ + attribs = self._get_attributes_dict() + attribs['name'] = name + + if not isinstance(self, ABCPeriodIndex): + # reset freq + attribs['freq'] = None + + if getattr(self, 'tz', None) is not None: + return _concat._concat_datetimetz(to_concat, name) + else: + new_data = np.concatenate([c.asi8 for c in to_concat]) + return self._simple_new(new_data, **attribs) + def _ensure_datetimelike_to_i8(other): """ helper for coercing an input scalar or array to i8 """ diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index f78574521ffeb..ee0e88b993f55 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1008,36 +1008,6 @@ def union_many(self, others): this.offset = to_offset(this.inferred_freq) return this - def append(self, other): - """ - Append a collection of Index options together - - Parameters - ---------- - other : Index or list/tuple of indices - - Returns - ------- - appended : Index - """ - name = self.name - to_concat = [self] - - if isinstance(other, (list, tuple)): - to_concat = to_concat + list(other) - else: - to_concat.append(other) - - for obj in to_concat: - if isinstance(obj, Index) and obj.name != name: - name = None - break - - to_concat = self._ensure_compat_concat(to_concat) - to_concat, factory = _process_concat_data(to_concat, name) - - return factory(to_concat) - def join(self, other, how='left', level=None, return_indexers=False): """ See Index.join @@ -2180,56 +2150,3 @@ def _use_cached_range(offset, _normalized, start, end): def _time_to_micros(time): seconds = time.hour * 60 * 60 + 60 * time.minute + time.second return 1000000 * seconds + time.microsecond - - -def _process_concat_data(to_concat, name): - klass = Index - kwargs = {} - concat = np.concatenate - - all_dti = True - need_utc_convert = False - has_naive = False - tz = None - - for x in to_concat: - if not isinstance(x, DatetimeIndex): - all_dti = False - else: - if tz is None: - tz = x.tz - - if x.tz is None: - has_naive = True - - if x.tz != tz: - need_utc_convert = True - tz = 'UTC' - - if all_dti: - need_obj_convert = False - if has_naive and tz is not None: - need_obj_convert = True - - if need_obj_convert: - to_concat = [x.asobject.values for x in to_concat] - - else: - if need_utc_convert: - to_concat = [x.tz_convert('UTC').values for x in to_concat] - else: - to_concat = [x.values for x in to_concat] - - # well, technically not a "class" anymore...oh well - klass = DatetimeIndex._simple_new - kwargs = {'tz': tz} - concat = _concat._concat_compat - else: - for i, x in enumerate(to_concat): - if isinstance(x, DatetimeIndex): - to_concat[i] = x.asobject.values - elif isinstance(x, Index): - to_concat[i] = x.values - - factory_func = lambda x: klass(concat(x), name=name, **kwargs) - return to_concat, factory_func diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 7fb0f19b04486..363f2419889d1 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -974,45 +974,6 @@ def _format_native_types(self, na_rep=u('NaT'), date_format=None, values = np.array([formatter(dt) for dt in values]) return values - def append(self, other): - """ - Append a collection of Index options together - - Parameters - ---------- - other : Index or list/tuple of indices - - Returns - ------- - appended : Index - """ - name = self.name - to_concat = [self] - - if isinstance(other, (list, tuple)): - to_concat = to_concat + list(other) - else: - to_concat.append(other) - - for obj in to_concat: - if isinstance(obj, Index) and obj.name != name: - name = None - break - - to_concat = self._ensure_compat_concat(to_concat) - - if isinstance(to_concat[0], PeriodIndex): - if len(set([x.freq for x in to_concat])) > 1: - # box - to_concat = [x.asobject.values for x in to_concat] - else: - cat_values = np.concatenate([x._values for x in to_concat]) - return PeriodIndex(cat_values, freq=self.freq, name=name) - - to_concat = [x._values if isinstance(x, Index) else x - for x in to_concat] - return Index(com._concat_compat(to_concat), name=name) - def __setstate__(self, state): """Necessary for making this object picklable""" diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index a17eda3ac4288..7c7cac83aef53 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -505,34 +505,6 @@ def union(self, other): result.freq = to_offset(result.inferred_freq) return result - def append(self, other): - """ - Append a collection of Index options together - - Parameters - ---------- - other : Index or list/tuple of indices - - Returns - ------- - appended : Index - """ - name = self.name - to_concat = [self] - - if isinstance(other, (list, tuple)): - to_concat = to_concat + list(other) - else: - to_concat.append(other) - - for obj in to_concat: - if isinstance(obj, Index) and obj.name != name: - name = None - break - - to_concat = self._ensure_compat_concat(to_concat) - return Index(_concat._concat_compat(to_concat), name=name) - def join(self, other, how='left', level=None, return_indexers=False): """ See Index.join diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py index 7ec0d09c20841..a7a015f273320 100644 --- a/pandas/tseries/tests/test_timezones.py +++ b/pandas/tseries/tests/test_timezones.py @@ -1296,27 +1296,59 @@ def test_append_aware(self): tz='US/Eastern') rng2 = date_range('1/1/2011 02:00', periods=1, freq='H', tz='US/Eastern') - ts1 = Series(np.random.randn(len(rng1)), index=rng1) - ts2 = Series(np.random.randn(len(rng2)), index=rng2) + ts1 = Series([1], index=rng1) + ts2 = Series([2], index=rng2) ts_result = ts1.append(ts2) + + exp_index = DatetimeIndex(['2011-01-01 01:00', '2011-01-01 02:00'], + tz='US/Eastern') + exp = Series([1, 2], index=exp_index) + self.assert_series_equal(ts_result, exp) self.assertEqual(ts_result.index.tz, rng1.tz) rng1 = date_range('1/1/2011 01:00', periods=1, freq='H', tz='UTC') rng2 = date_range('1/1/2011 02:00', periods=1, freq='H', tz='UTC') - ts1 = Series(np.random.randn(len(rng1)), index=rng1) - ts2 = Series(np.random.randn(len(rng2)), index=rng2) + ts1 = Series([1], index=rng1) + ts2 = Series([2], index=rng2) ts_result = ts1.append(ts2) + + exp_index = DatetimeIndex(['2011-01-01 01:00', '2011-01-01 02:00'], + tz='UTC') + exp = Series([1, 2], index=exp_index) + self.assert_series_equal(ts_result, exp) utc = rng1.tz self.assertEqual(utc, ts_result.index.tz) + # GH 7795 + # different tz coerces to object dtype, not UTC rng1 = date_range('1/1/2011 01:00', periods=1, freq='H', tz='US/Eastern') rng2 = date_range('1/1/2011 02:00', periods=1, freq='H', tz='US/Central') - ts1 = Series(np.random.randn(len(rng1)), index=rng1) - ts2 = Series(np.random.randn(len(rng2)), index=rng2) + ts1 = Series([1], index=rng1) + ts2 = Series([2], index=rng2) ts_result = ts1.append(ts2) - self.assertEqual(utc, ts_result.index.tz) + exp_index = Index([Timestamp('1/1/2011 01:00', tz='US/Eastern'), + Timestamp('1/1/2011 02:00', tz='US/Central')]) + exp = Series([1, 2], index=exp_index) + self.assert_series_equal(ts_result, exp) + + def test_append_dst(self): + rng1 = date_range('1/1/2016 01:00', periods=3, freq='H', + tz='US/Eastern') + rng2 = date_range('8/1/2016 01:00', periods=3, freq='H', + tz='US/Eastern') + ts1 = Series([1, 2, 3], index=rng1) + ts2 = Series([10, 11, 12], index=rng2) + ts_result = ts1.append(ts2) + + exp_index = DatetimeIndex(['2016-01-01 01:00', '2016-01-01 02:00', + '2016-01-01 03:00', '2016-08-01 01:00', + '2016-08-01 02:00', '2016-08-01 03:00'], + tz='US/Eastern') + exp = Series([1, 2, 3, 10, 11, 12], index=exp_index) + tm.assert_series_equal(ts_result, exp) + self.assertEqual(ts_result.index.tz, rng1.tz) def test_append_aware_naive(self): rng1 = date_range('1/1/2011 01:00', periods=1, freq='H') diff --git a/pandas/types/concat.py b/pandas/types/concat.py index a7fd692cfb9cf..29a0fe7d9f8d0 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -12,11 +12,14 @@ is_datetimetz, is_datetime64_dtype, is_timedelta64_dtype, + is_period_dtype, is_object_dtype, is_bool_dtype, is_dtype_equal, _NS_DTYPE, _TD_DTYPE) +from pandas.types.generic import (ABCDatetimeIndex, ABCTimedeltaIndex, + ABCPeriodIndex) def get_dtype_kinds(l): @@ -39,7 +42,9 @@ def get_dtype_kinds(l): elif is_sparse(arr): typ = 'sparse' elif is_datetimetz(arr): - typ = 'datetimetz' + # if to_concat contains different tz, + # the result must be object dtype + typ = str(arr.dtype) elif is_datetime64_dtype(dtype): typ = 'datetime' elif is_timedelta64_dtype(dtype): @@ -48,6 +53,8 @@ def get_dtype_kinds(l): typ = 'object' elif is_bool_dtype(dtype): typ = 'bool' + elif is_period_dtype(dtype): + typ = str(arr.dtype) else: typ = dtype.kind typs.add(typ) @@ -127,7 +134,10 @@ def is_nonempty(x): typs = get_dtype_kinds(to_concat) # these are mandated to handle empties as well - if 'datetime' in typs or 'datetimetz' in typs or 'timedelta' in typs: + _contains_datetime = any(typ.startswith('datetime') for typ in typs) + _contains_period = any(typ.startswith('period') for typ in typs) + + if _contains_datetime or 'timedelta' in typs or _contains_period: return _concat_datetime(to_concat, axis=axis, typs=typs) elif 'sparse' in typs: @@ -319,12 +329,13 @@ def convert_to_pydatetime(x, axis): x = x.asobject.values else: shape = x.shape - x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) + x = tslib.ints_to_pydatetime(x.view(np.int64).ravel(), + box=True) x = x.reshape(shape) elif x.dtype == _TD_DTYPE: shape = x.shape - x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) + x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel(), box=True) x = x.reshape(shape) if axis == 1: @@ -336,34 +347,71 @@ def convert_to_pydatetime(x, axis): # must be single dtype if len(typs) == 1: + _contains_datetime = any(typ.startswith('datetime') for typ in typs) + _contains_period = any(typ.startswith('period') for typ in typs) - if 'datetimetz' in typs: - # datetime with no tz should be stored as "datetime" in typs, - # thus no need to care - - # we require ALL of the same tz for datetimetz - tzs = set([str(x.tz) for x in to_concat]) - if len(tzs) == 1: - from pandas.tseries.index import DatetimeIndex - new_values = np.concatenate([x.tz_localize(None).asi8 - for x in to_concat]) - return DatetimeIndex(new_values, tz=list(tzs)[0]) + if _contains_datetime: - elif 'datetime' in typs: - new_values = np.concatenate([x.view(np.int64) for x in to_concat], - axis=axis) - return new_values.view(_NS_DTYPE) + if 'datetime' in typs: + new_values = np.concatenate([x.view(np.int64) for x in + to_concat], axis=axis) + return new_values.view(_NS_DTYPE) + else: + # when to_concat has different tz, len(typs) > 1. + # thus no need to care + return _concat_datetimetz(to_concat) elif 'timedelta' in typs: new_values = np.concatenate([x.view(np.int64) for x in to_concat], axis=axis) return new_values.view(_TD_DTYPE) + elif _contains_period: + # PeriodIndex must be handled by PeriodIndex, + # Thus can't meet this condition ATM + # Must be changed when we adding PeriodDtype + raise NotImplementedError + # need to coerce to object to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] return np.concatenate(to_concat, axis=axis) +def _concat_datetimetz(to_concat, name=None): + """ + concat DatetimeIndex with the same tz + all inputs must be DatetimeIndex + it is used in DatetimeIndex.append also + """ + # do not pass tz to set because tzlocal cannot be hashed + if len(set([str(x.dtype) for x in to_concat])) != 1: + raise ValueError('to_concat must have the same tz') + tz = to_concat[0].tz + # no need to localize because internal repr will not be changed + new_values = np.concatenate([x.asi8 for x in to_concat]) + return to_concat[0]._simple_new(new_values, tz=tz, name=name) + + +def _concat_index_asobject(to_concat, name=None): + """ + concat all inputs as object. DatetimeIndex, TimedeltaIndex and + PeriodIndex are converted to object dtype before concatenation + """ + + klasses = ABCDatetimeIndex, ABCTimedeltaIndex, ABCPeriodIndex + to_concat = [x.asobject if isinstance(x, klasses) else x + for x in to_concat] + + from pandas import Index + self = to_concat[0] + attribs = self._get_attributes_dict() + attribs['name'] = name + + to_concat = [x._values if isinstance(x, Index) else x + for x in to_concat] + return self._shallow_copy_with_infer(np.concatenate(to_concat), **attribs) + + def _concat_sparse(to_concat, axis=0, typs=None): """ provide concatenation of an sparse/dense array of arrays each of which is a
closes #13626 closes #7795
https://api.github.com/repos/pandas-dev/pandas/pulls/13660
2016-07-14T23:51:28Z
2016-09-03T09:20:29Z
2016-09-03T09:20:29Z
2017-01-08T06:28:26Z
better error message for non-unique columns
diff --git a/pandas/formats/style.py b/pandas/formats/style.py index 472fd958d35eb..fa4ae82bac28d 100644 --- a/pandas/formats/style.py +++ b/pandas/formats/style.py @@ -137,8 +137,10 @@ def __init__(self, data, precision=None, table_styles=None, uuid=None, raise TypeError("``data`` must be a Series or DataFrame") if data.ndim == 1: data = data.to_frame() - if not data.index.is_unique or not data.columns.is_unique: + if not data.index.is_unique: raise ValueError("style is not supported for non-unique indicies.") + if not data.columns.is_unique: + raise ValueError("style is not supported for non-unique columns.") self.data = data self.index = data.index
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry I ran into this error with my code and the error message didn't help me figure out I was dealing with duplicate columns that couldn't be handled. I think this is more helpful
https://api.github.com/repos/pandas-dev/pandas/pulls/13656
2016-07-14T16:15:04Z
2016-11-16T22:21:25Z
null
2016-11-16T22:21:26Z
DOC/BLD: pin IPython version to 4.2.0 (#13639)
diff --git a/ci/requirements-2.7_DOC_BUILD.run b/ci/requirements-2.7_DOC_BUILD.run index a07721c75cf34..cde0719aa027e 100644 --- a/ci/requirements-2.7_DOC_BUILD.run +++ b/ci/requirements-2.7_DOC_BUILD.run @@ -1,4 +1,4 @@ -ipython=4 +ipython=4.2.0 ipykernel sphinx nbconvert
xref #13639
https://api.github.com/repos/pandas-dev/pandas/pulls/13647
2016-07-13T21:24:04Z
2016-07-14T07:12:52Z
2016-07-14T07:12:52Z
2016-07-14T07:13:01Z
CLN: Fix compile time warnings
diff --git a/pandas/src/datetime/np_datetime.c b/pandas/src/datetime/np_datetime.c index c30b404d2b8b2..80703c8b08de6 100644 --- a/pandas/src/datetime/np_datetime.c +++ b/pandas/src/datetime/np_datetime.c @@ -576,7 +576,7 @@ void pandas_datetime_to_datetimestruct(npy_datetime val, PANDAS_DATETIMEUNIT fr, } PANDAS_DATETIMEUNIT get_datetime64_unit(PyObject *obj) { - return ((PyDatetimeScalarObject *) obj)->obmeta.base; + return (PANDAS_DATETIMEUNIT)((PyDatetimeScalarObject *) obj)->obmeta.base; } diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 1080e9548ba56..75de63acbd7d6 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -493,7 +493,7 @@ static void *NpyDateTimeScalarToJSON(JSOBJ _obj, JSONTypeContext *tc, void *outV PyDatetimeScalarObject *obj = (PyDatetimeScalarObject *) _obj; PRINTMARK(); - pandas_datetime_to_datetimestruct(obj->obval, obj->obmeta.base, &dts); + pandas_datetime_to_datetimestruct(obj->obval, (PANDAS_DATETIMEUNIT)obj->obmeta.base, &dts); return PandasDateTimeStructToJSON(&dts, tc, outValue, _outLen); }
- [x] passes `git diff upstream/master | flake8 --diff` This commit suppresses these warnings warning: implicit conversion from enumeration type\ 'NPY_DATETIMEUNIT' to different enumeration type\ 'PANDAS_DATETIMEUNIT' [-Wenum-conversion]
https://api.github.com/repos/pandas-dev/pandas/pulls/13643
2016-07-13T16:20:52Z
2016-07-14T10:48:43Z
null
2016-07-14T11:22:50Z
BF(TST): allow AttributeError being raised (in addition to TypeError) from matplotlib
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 3a5b0117948b7..5493eb37c358b 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1330,7 +1330,8 @@ def test_plot(self): self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) df = DataFrame({'x': [1, 2], 'y': [3, 4]}) - with tm.assertRaises(TypeError): + # mpl >= 1.5.2 (or slightly below) throw AttributError + with tm.assertRaises((TypeError, AttributeError)): df.plot.line(blarg=True) df = DataFrame(np.random.rand(10, 3),
Closes #13570 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry: imho not worth it? origin: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=827938
https://api.github.com/repos/pandas-dev/pandas/pulls/13641
2016-07-13T15:01:57Z
2016-07-14T08:44:19Z
2016-07-14T08:44:19Z
2016-07-14T08:44:27Z
BLD: included pandas.api.* in setup.py
diff --git a/setup.py b/setup.py index 8f8865ecc3b7a..650357588570a 100755 --- a/setup.py +++ b/setup.py @@ -547,6 +547,9 @@ def pxd(name): maintainer=AUTHOR, version=versioneer.get_version(), packages=['pandas', + 'pandas.api', + 'pandas.api.tests', + 'pandas.api.types', 'pandas.compat', 'pandas.compat.numpy', 'pandas.computation',
Lots of good refactoring in #13147 , but can't use them if they aren't included!
https://api.github.com/repos/pandas-dev/pandas/pulls/13640
2016-07-13T14:25:13Z
2016-07-13T14:38:09Z
2016-07-13T14:38:09Z
2016-07-13T14:44:54Z
BUG: Add check for array lengths in from_arrays method (GH13599)
diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json index 7b9fe353df2e3..f5fa849464881 100644 --- a/asv_bench/asv.conf.json +++ b/asv_bench/asv.conf.json @@ -77,11 +77,11 @@ // On conda install pytables, otherwise tables {"environment_type": "conda", "tables": ""}, {"environment_type": "conda", "pytables": null}, - {"environment_type": "virtualenv", "tables": null}, - {"environment_type": "virtualenv", "pytables": ""}, + {"environment_type": "(?!conda).*", "tables": null}, + {"environment_type": "(?!conda).*", "pytables": ""}, // On conda&win32, install libpython {"sys_platform": "(?!win32).*", "libpython": ""}, - {"sys_platform": "win32", "libpython": null}, + {"environment_type": "conda", "sys_platform": "win32", "libpython": null}, {"environment_type": "(?!conda).*", "libpython": ""} ], "include": [], diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py index 8c65f09937df4..a0a1b560d36f3 100644 --- a/asv_bench/benchmarks/index_object.py +++ b/asv_bench/benchmarks/index_object.py @@ -63,6 +63,27 @@ def time_index_datetime_union(self): self.rng.union(self.rng2) +class index_datetime_set_difference(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.A = self.N - 20000 + self.B = self.N + 20000 + self.idx1 = DatetimeIndex(range(self.N)) + self.idx2 = DatetimeIndex(range(self.A, self.B)) + self.idx3 = DatetimeIndex(range(self.N, self.B)) + + def time_index_datetime_difference(self): + self.idx1.difference(self.idx2) + + def time_index_datetime_difference_disjoint(self): + self.idx1.difference(self.idx3) + + def time_index_datetime_symmetric_difference(self): + self.idx1.symmetric_difference(self.idx2) + + class index_float64_boolean_indexer(object): goal_time = 0.2 @@ -183,6 +204,40 @@ def time_index_int64_union(self): self.left.union(self.right) +class index_int64_set_difference(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.options = np.arange(self.N) + self.left = Index(self.options.take( + np.random.permutation(self.N)[:(self.N // 2)])) + self.right = Index(self.options.take( + np.random.permutation(self.N)[:(self.N // 2)])) + + def time_index_int64_difference(self): + self.left.difference(self.right) + + def time_index_int64_symmetric_difference(self): + self.left.symmetric_difference(self.right) + + +class index_str_set_difference(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.strs = tm.rands_array(10, self.N) + self.left = Index(self.strs[:self.N * 2 // 3]) + self.right = Index(self.strs[self.N // 3:]) + + def time_str_difference(self): + self.left.difference(self.right) + + def time_str_symmetric_difference(self): + self.left.symmetric_difference(self.right) + + class index_str_boolean_indexer(object): goal_time = 0.2 diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py index 53d37a8161f43..094ae23a92fad 100644 --- a/asv_bench/benchmarks/indexing.py +++ b/asv_bench/benchmarks/indexing.py @@ -19,24 +19,6 @@ def time_dataframe_getitem_scalar(self): self.df[self.col][self.idx] -class datamatrix_getitem_scalar(object): - goal_time = 0.2 - - def setup(self): - try: - self.klass = DataMatrix - except: - self.klass = DataFrame - self.index = tm.makeStringIndex(1000) - self.columns = tm.makeStringIndex(30) - self.df = self.klass(np.random.rand(1000, 30), index=self.index, columns=self.columns) - self.idx = self.index[100] - self.col = self.columns[10] - - def time_datamatrix_getitem_scalar(self): - self.df[self.col][self.idx] - - class series_get_value(object): goal_time = 0.2 @@ -498,5 +480,3 @@ def setup(self): def time_float_loc(self): self.ind.get_loc(0) - - diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py index 6809c351beade..ee9d3104be4b1 100644 --- a/asv_bench/benchmarks/inference.py +++ b/asv_bench/benchmarks/inference.py @@ -143,12 +143,12 @@ class to_numeric(object): param_names = ['data', 'downcast'] params = [ - [(['1'] * N / 2) + ([2] * N / 2), - (['-1'] * N / 2) + ([2] * N / 2), - np.repeat(np.array('1970-01-01', '1970-01-02', + [(['1'] * (N / 2)) + ([2] * (N / 2)), + (['-1'] * (N / 2)) + ([2] * (N / 2)), + np.repeat(np.array(['1970-01-01', '1970-01-02'], dtype='datetime64[D]'), N), - (['1.1'] * N / 2) + ([2] * N / 2), - ([1] * N / 2) + ([2] * N / 2), + (['1.1'] * (N / 2)) + ([2] * (N / 2)), + ([1] * (N / 2)) + ([2] * (N / 2)), np.repeat(np.int32(1), N)], [None, 'integer', 'signed', 'unsigned', 'float'], ] diff --git a/asv_bench/benchmarks/join_merge.py b/asv_bench/benchmarks/join_merge.py index 39ebd9cb1cb73..dcd07911f2ff0 100644 --- a/asv_bench/benchmarks/join_merge.py +++ b/asv_bench/benchmarks/join_merge.py @@ -179,10 +179,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -210,10 +206,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -241,10 +233,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -272,10 +260,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) diff --git a/asv_bench/benchmarks/period.py b/asv_bench/benchmarks/period.py index 012030a71ac82..c1b89ae1db75b 100644 --- a/asv_bench/benchmarks/period.py +++ b/asv_bench/benchmarks/period.py @@ -1,4 +1,4 @@ -from pandas import PeriodIndex, date_range +from pandas import Series, Period, PeriodIndex, date_range class create_period_index_from_date_range(object): @@ -7,3 +7,27 @@ class create_period_index_from_date_range(object): def time_period_index(self): # Simulate irregular PeriodIndex PeriodIndex(date_range('1985', periods=1000).to_pydatetime(), freq='D') + + +class period_algorithm(object): + goal_time = 0.2 + + def setup(self): + data = [Period('2011-01', freq='M'), Period('2011-02', freq='M'), + Period('2011-03', freq='M'), Period('2011-04', freq='M')] + self.s = Series(data * 1000) + self.i = PeriodIndex(data, freq='M') + + def time_period_series_drop_duplicates(self): + self.s.drop_duplicates() + + def time_period_index_drop_duplicates(self): + self.i.drop_duplicates() + + def time_period_series_value_counts(self): + self.s.value_counts() + + def time_period_index_value_counts(self): + self.i.value_counts() + + diff --git a/ci/lint.sh b/ci/lint.sh index a4c960084040f..144febcfcece5 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -8,7 +8,7 @@ RET=0 if [ "$LINT" ]; then echo "Linting" - for path in 'core' 'indexes' 'types' 'formats' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' + for path in 'api' 'core' 'indexes' 'types' 'formats' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' do echo "linting -> pandas/$path" flake8 pandas/$path --filename '*.py' @@ -17,7 +17,19 @@ if [ "$LINT" ]; then fi done - echo "Linting DONE" + echo "Linting *.py DONE" + + echo "Linting *.pyx" + for path in 'window.pyx' + do + echo "linting -> pandas/$path" + flake8 pandas/$path --filename '*.pyx' --select=E501,E302,E203,E226,E111,E114,E221,E303,E128,E231,E126,E128 + if [ $? -ne "0" ]; then + RET=1 + fi + + done + echo "Linting *.pyx DONE" echo "Check for invalid testing" grep -r -E --include '*.py' --exclude nosetester.py --exclude testing.py '(numpy|np)\.testing' pandas diff --git a/ci/requirements-2.7_DOC_BUILD.run b/ci/requirements-2.7_DOC_BUILD.run index b87a41df4191d..cde0719aa027e 100644 --- a/ci/requirements-2.7_DOC_BUILD.run +++ b/ci/requirements-2.7_DOC_BUILD.run @@ -1,4 +1,4 @@ -ipython +ipython=4.2.0 ipykernel sphinx nbconvert diff --git a/doc/source/api.rst b/doc/source/api.rst index 0dde341d820e3..e8fe26e8a525d 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -380,6 +380,7 @@ Reindexing / Selection / Label manipulation Series.reindex Series.reindex_like Series.rename + Series.rename_axis Series.reset_index Series.sample Series.select @@ -889,6 +890,7 @@ Reindexing / Selection / Label manipulation DataFrame.reindex_axis DataFrame.reindex_like DataFrame.rename + DataFrame.rename_axis DataFrame.reset_index DataFrame.sample DataFrame.select diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 59675e33e724b..12e0ecfba97da 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -391,6 +391,91 @@ For some windowing functions, additional parameters must be specified: such that the weights are normalized with respect to each other. Weights of ``[1, 1, 1]`` and ``[2, 2, 2]`` yield the same result. +.. _stats.moments.ts: + +Time-aware Rolling +~~~~~~~~~~~~~~~~~~ + +.. versionadded:: 0.19.0 + +New in version 0.19.0 are the ability to pass an offset (or convertible) to a ``.rolling()`` method and have it produce +variable sized windows based on the passed time window. For each time point, this includes all preceding values occurring +within the indicated time delta. + +This can be particularly useful for a non-regular time frequency index. + +.. ipython:: python + + dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index=pd.date_range('20130101 09:00:00', periods=5, freq='s')) + dft + +This is a regular frequency index. Using an integer window parameter works to roll along the window frequency. + +.. ipython:: python + + dft.rolling(2).sum() + dft.rolling(2, min_periods=1).sum() + +Specifying an offset allows a more intuitive specification of the rolling frequency. + +.. ipython:: python + + dft.rolling('2s').sum() + +Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation. + + +.. ipython:: python + + + dft = DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index = pd.Index([pd.Timestamp('20130101 09:00:00'), + pd.Timestamp('20130101 09:00:02'), + pd.Timestamp('20130101 09:00:03'), + pd.Timestamp('20130101 09:00:05'), + pd.Timestamp('20130101 09:00:06')], + name='foo')) + + dft + dft.rolling(2).sum() + + +Using the time-specification generates variable windows for this sparse data. + +.. ipython:: python + + dft.rolling('2s').sum() + +Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the +default of the index) in a DataFrame. + +.. ipython:: python + + dft = dft.reset_index() + dft + dft.rolling('2s', on='foo').sum() + +.. _stats.moments.ts-versus-resampling: + +Time-aware Rolling vs. Resampling +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Using ``.rolling()`` with a time-based index is quite similar to :ref:`resampling <timeseries.resampling>`. They +both operate and perform reductive operations on time-indexed pandas objects. + +When using ``.rolling()`` with an offset. The offset is a time-delta. Take a backwards-in-time looking window, and +aggregate all of the values in that window (including the end-point, but not the start-point). This is the new value +at that point in the result. These are variable sized windows in time-space for each point of the input. You will get +a same sized result as the input. + +When using ``.resample()`` with an offset. Construct a new index that is the frequency of the offset. For each frequency +bin, aggregate points from the input within a backwards-in-time looking window that fall in that bin. The result of this +aggregation is the output for that frequency point. The windows are fixed size size in the frequency space. Your result +will have the shape of a regular frequency between the min and the max of the original input object. + +To summarize, ``.rolling()`` is a time-based window operation, while ``.resample()`` is a frequency-based window operation. + Centering Windows ~~~~~~~~~~~~~~~~~ diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 0dbc79415af0b..38a816060e1bc 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -679,6 +679,19 @@ The :ref:`Pivot <reshaping.pivot>` docs. 'Employed' : lambda x : sum(x), 'Grade' : lambda x : sum(x) / len(x)}) +`Plot pandas DataFrame with year over year data +<http://stackoverflow.com/questions/30379789/plot-pandas-data-frame-with-year-over-year-data>`__ + +To create year and month crosstabulation: + +.. ipython:: python + + df = pd.DataFrame({'value': np.random.randn(36)}, + index=pd.date_range('2011-01-01', freq='M', periods=36)) + + pd.pivot_table(df, index=df.index.month, columns=df.index.year, + values='value', aggfunc='sum') + Apply ***** diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index 8fafe8ec9eaa2..0d010b47f393a 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -93,6 +93,12 @@ targets the IPython Notebook environment. `Plotly’s <https://plot.ly/>`__ `Python API <https://plot.ly/python/>`__ enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and `D3.js <http://d3js.org/>`__. The library supports plotting directly from a pandas DataFrame and cloud-based collaboration. Users of `matplotlib, ggplot for Python, and Seaborn <https://plot.ly/python/matplotlib-to-plotly-tutorial/>`__ can convert figures into interactive web-based plots. Plots can be drawn in `IPython Notebooks <https://plot.ly/ipython-notebooks/>`__ , edited with R or MATLAB, modified in a GUI, or embedded in apps and dashboards. Plotly is free for unlimited sharing, and has `cloud <https://plot.ly/product/plans/>`__, `offline <https://plot.ly/python/offline/>`__, or `on-premise <https://plot.ly/product/enterprise/>`__ accounts for private use. +`Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Spun off from the main pandas library, the `Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__ +library enables DataFrame visualization and manipulation in PyQt4 and PySide applications. + .. _ecosystem.ide: IDE diff --git a/doc/source/faq.rst b/doc/source/faq.rst index e5d659cc31606..d23e0ca59254d 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -110,78 +110,6 @@ details. Visualizing Data in Qt applications ----------------------------------- -.. warning:: - - The ``qt`` support is **deprecated and will be removed in a future version**. - We refer users to the external package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_. - -There is experimental support for visualizing DataFrames in PyQt4 and PySide -applications. At the moment you can display and edit the values of the cells -in the DataFrame. Qt will take care of displaying just the portion of the -DataFrame that is currently visible and the edits will be immediately saved to -the underlying DataFrame - -To demonstrate this we will create a simple PySide application that will switch -between two editable DataFrames. For this will use the ``DataFrameModel`` class -that handles the access to the DataFrame, and the ``DataFrameWidget``, which is -just a thin layer around the ``QTableView``. - -.. code-block:: python - - import numpy as np - import pandas as pd - from pandas.sandbox.qtpandas import DataFrameModel, DataFrameWidget - from PySide import QtGui, QtCore - - # Or if you use PyQt4: - # from PyQt4 import QtGui, QtCore - - class MainWidget(QtGui.QWidget): - def __init__(self, parent=None): - super(MainWidget, self).__init__(parent) - - # Create two DataFrames - self.df1 = pd.DataFrame(np.arange(9).reshape(3, 3), - columns=['foo', 'bar', 'baz']) - self.df2 = pd.DataFrame({ - 'int': [1, 2, 3], - 'float': [1.5, 2.5, 3.5], - 'string': ['a', 'b', 'c'], - 'nan': [np.nan, np.nan, np.nan] - }, index=['AAA', 'BBB', 'CCC'], - columns=['int', 'float', 'string', 'nan']) - - # Create the widget and set the first DataFrame - self.widget = DataFrameWidget(self.df1) - - # Create the buttons for changing DataFrames - self.button_first = QtGui.QPushButton('First') - self.button_first.clicked.connect(self.on_first_click) - self.button_second = QtGui.QPushButton('Second') - self.button_second.clicked.connect(self.on_second_click) - - # Set the layout - vbox = QtGui.QVBoxLayout() - vbox.addWidget(self.widget) - hbox = QtGui.QHBoxLayout() - hbox.addWidget(self.button_first) - hbox.addWidget(self.button_second) - vbox.addLayout(hbox) - self.setLayout(vbox) - - def on_first_click(self): - '''Sets the first DataFrame''' - self.widget.setDataFrame(self.df1) - - def on_second_click(self): - '''Sets the second DataFrame''' - self.widget.setDataFrame(self.df2) - - if __name__ == '__main__': - import sys - - # Initialize the application - app = QtGui.QApplication(sys.argv) - mw = MainWidget() - mw.show() - app.exec_() +There is no support for such visualization in pandas. However, the external +package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_ does +provide this functionality. diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 7e832af14c051..fd31eb1b584a8 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -752,7 +752,7 @@ calculate significantly slower and will raise a ``PerformanceWarning`` rng + BQuarterEnd() -.. _timeseries.alias: +.. _timeseries.custombusinessdays: Custom Business Days (Experimental) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -953,6 +953,8 @@ You can use keyword arguments suported by either ``BusinessHour`` and ``CustomBu # Monday is skipped because it's a holiday, business hour starts from 10:00 dt + bhour_mon * 2 +.. _timeseries.alias: + Offset Aliases ~~~~~~~~~~~~~~ @@ -1103,48 +1105,6 @@ it is rolled forward to the next anchor point. pd.Timestamp('2014-01-01') + MonthBegin(n=0) pd.Timestamp('2014-01-31') + MonthEnd(n=0) -.. _timeseries.legacyaliases: - -Legacy Aliases -~~~~~~~~~~~~~~ -Note that prior to v0.8.0, time rules had a slightly different look. These are -deprecated in v0.17.0, and removed in future version. - -.. csv-table:: - :header: "Legacy Time Rule", "Offset Alias" - :widths: 15, 65 - - "WEEKDAY", "B" - "EOM", "BM" - "W\@MON", "W\-MON" - "W\@TUE", "W\-TUE" - "W\@WED", "W\-WED" - "W\@THU", "W\-THU" - "W\@FRI", "W\-FRI" - "W\@SAT", "W\-SAT" - "W\@SUN", "W\-SUN" - "Q\@JAN", "BQ\-JAN" - "Q\@FEB", "BQ\-FEB" - "Q\@MAR", "BQ\-MAR" - "A\@JAN", "BA\-JAN" - "A\@FEB", "BA\-FEB" - "A\@MAR", "BA\-MAR" - "A\@APR", "BA\-APR" - "A\@MAY", "BA\-MAY" - "A\@JUN", "BA\-JUN" - "A\@JUL", "BA\-JUL" - "A\@AUG", "BA\-AUG" - "A\@SEP", "BA\-SEP" - "A\@OCT", "BA\-OCT" - "A\@NOV", "BA\-NOV" - "A\@DEC", "BA\-DEC" - - -As you can see, legacy quarterly and annual frequencies are business quarters -and business year ends. Please also note the legacy time rule for milliseconds -``ms`` versus the new offset alias for month start ``MS``. This means that -offset alias parsing is case sensitive. - .. _timeseries.holiday: Holidays / Holiday Calendars @@ -1324,7 +1284,11 @@ performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. -``resample`` is a time-based groupby, followed by a reduction method on each of its groups. +``.resample()`` is a time-based groupby, followed by a reduction method on each of its groups. + +.. note:: + + ``.resample()`` is similar to using a ``.rolling()`` operation with a time-based offset, see a discussion `here <stats.moments.ts-versus-resampling>` See some :ref:`cookbook examples <cookbook.resample>` for some advanced strategies diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index f457b8d4bd1f6..48612442f9e84 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -3,15 +3,17 @@ v0.19.0 (August ??, 2016) ------------------------- -This is a major release from 0.18.2 and includes a small number of API changes, several new features, +This is a major release from 0.18.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. Highlights include: - :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0190.enhancements.asof_merge>` +- ``.rolling()`` are now time-series aware, see :ref:`here <whatsnew_0190.enhancements.rolling_ts>` +- pandas development api, see :ref:`here <whatsnew_0190.dev_api>` -.. contents:: What's new in v0.18.2 +.. contents:: What's new in v0.19.0 :local: :backlinks: none @@ -20,13 +22,32 @@ Highlights include: New features ~~~~~~~~~~~~ +.. _whatsnew_0190.dev_api: + +pandas development API +^^^^^^^^^^^^^^^^^^^^^^ + +As part of making pandas APi more uniform and accessible in the future, we have created a standard +sub-package of pandas, ``pandas.api`` to hold public API's. We are starting by exposing type +introspection functions in ``pandas.api.types``. More sub-packages and officially sanctioned API's +will be published in future versions of pandas. + +The following are now part of this API: + +.. ipython:: python + + import pprint + from pandas.api import types + funcs = [ f for f in dir(types) if not f.startswith('_') ] + pprint.pprint(funcs) + .. _whatsnew_0190.enhancements.asof_merge: :func:`merge_asof` for asof-style time-series joining ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A long-time requested feature has been added through the :func:`merge_asof` function, to -support asof style joining of time-series. (:issue:`1870`). Full documentation is +support asof style joining of time-series. (:issue:`1870`, :issue:`13695`). Full documentation is :ref:`here <merging.merge_asof>` The :func:`merge_asof` performs an asof merge, which is similar to a left-join @@ -111,6 +132,64 @@ that forward filling happens automatically taking the most recent non-NaN value. This returns a merged DataFrame with the entries in the same order as the original left passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged. +.. _whatsnew_0190.enhancements.rolling_ts: + +``.rolling()`` are now time-series aware +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``.rolling()`` objects are now time-series aware and can accept a time-series offset (or convertible) for the ``window`` argument (:issue:`13327`, :issue:`12995`) +See the full documentation :ref:`here <stats.moments.ts>`. + +.. ipython:: python + + dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index=pd.date_range('20130101 09:00:00', periods=5, freq='s')) + dft + +This is a regular frequency index. Using an integer window parameter works to roll along the window frequency. + +.. ipython:: python + + dft.rolling(2).sum() + dft.rolling(2, min_periods=1).sum() + +Specifying an offset allows a more intuitive specification of the rolling frequency. + +.. ipython:: python + + dft.rolling('2s').sum() + +Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation. + +.. ipython:: python + + + dft = DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index = pd.Index([pd.Timestamp('20130101 09:00:00'), + pd.Timestamp('20130101 09:00:02'), + pd.Timestamp('20130101 09:00:03'), + pd.Timestamp('20130101 09:00:05'), + pd.Timestamp('20130101 09:00:06')], + name='foo')) + + dft + dft.rolling(2).sum() + +Using the time-specification generates variable windows for this sparse data. + +.. ipython:: python + + dft.rolling('2s').sum() + +Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the +default of the index) in a DataFrame. + +.. ipython:: python + + dft = dft.reset_index() + dft + dft.rolling('2s', on='foo').sum() + .. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support: :func:`read_csv` has improved support for duplicate column names @@ -227,8 +306,10 @@ Other enhancements - Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`) - The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`) - ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) -- A top-level function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) +- A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) +- ``Series.append`` now supports the ``ignore_index`` option (:issue:`13677`) +- ``.to_stata()`` and ```StataWriter`` can now write variable labels to Stata dta files using a dictionary to make column names to labels (:issue:`13535`, :issue:`13536`) .. _whatsnew_0190.api: @@ -236,6 +317,7 @@ API changes ~~~~~~~~~~~ +- ``Index.reshape`` will raise a ``NotImplementedError`` exception when called (:issue: `12882`) - Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception (:issue:`10001`) - ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`) - An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`) @@ -246,6 +328,8 @@ API changes - ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`) - ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`) - ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`) +- Passing ``Period`` with multiple frequencies to normal ``Index`` now returns ``Index`` with ``object`` dtype (:issue:`13664`) +- ``PeriodIndex.fillna`` with ``Period`` has different freq now coerces to ``object`` dtype (:issue:`13664`) .. _whatsnew_0190.api.tolist: @@ -374,7 +458,7 @@ resulting dtype will be upcast, which is unchanged from previous. pd.merge(df1, df2, how='outer', on='key') pd.merge(df1, df2, how='outer', on='key').dtypes -.. _whatsnew_0190.describe: +.. _whatsnew_0190.api.describe: ``.describe()`` changes ^^^^^^^^^^^^^^^^^^^^^^^ @@ -425,26 +509,112 @@ Furthermore: - Passing duplicated ``percentiles`` will now raise a ``ValueError``. - Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`) +.. _whatsnew_0190.api.periodnat: + +``Period('NaT')`` now returns ``pd.NaT`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Previously, ``Period`` has its own ``Period('NaT')`` representation different from ``pd.NaT``. Now ``Period('NaT')`` has been changed to return ``pd.NaT``. (:issue:`12759`, :issue:`13582`) + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.Period('NaT', freq='D') + Out[5]: Period('NaT', 'D') + +New Behavior: + +.. ipython:: python + + pd.Period('NaT') + + +To be compat with ``Period`` addition and subtraction, ``pd.NaT`` now supports addition and subtraction with ``int``. Previously it raises ``ValueError``. + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.NaT + 1 + ... + ValueError: Cannot add integral value to Timestamp without freq. + +New Behavior: + +.. ipython:: python + + pd.NaT + 1 + pd.NaT - 1 + +.. _whatsnew_0190.api.difference: + +``Index.difference`` and ``.symmetric_difference`` changes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``Index.difference`` and ``Index.symmetric_difference`` will now, more consistently, treat ``NaN`` values as any other values. (:issue:`13514`) + +.. ipython:: python + + idx1 = pd.Index([1, 2, 3, np.nan]) + idx2 = pd.Index([0, 1, np.nan]) + +Previous Behavior: + +.. code-block:: ipython + + In [3]: idx1.difference(idx2) + Out[3]: Float64Index([nan, 2.0, 3.0], dtype='float64') + + In [4]: idx1.symmetric_difference(idx2) + Out[4]: Float64Index([0.0, nan, 2.0, 3.0], dtype='float64') + +New Behavior: + +.. ipython:: python + + idx1.difference(idx2) + idx1.symmetric_difference(idx2) + .. _whatsnew_0190.deprecations: Deprecations ^^^^^^^^^^^^ +- ``Categorical.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`) +- ``Series.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`) +- ``DataFrame.to_sql()`` has deprecated the ``flavor`` parameter, as it is superfluous when SQLAlchemy is not installed (:issue:`13611`) - ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`) - ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`) - ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) - top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`) - ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`) - +- ``pivot_annual`` is deprecated. Use ``pivot_table`` as alternative, an example is :ref:`here <cookbook.pivot>` (:issue:`736`) .. _whatsnew_0190.prior_deprecations: Removal of prior version deprecations/changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +- The ``pd.sandbox`` module has been removed in favor of the external library ``pandas-qt`` (:issue:`13670`) - ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`) - ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) +- ``pd.Categorical`` has dropped setting of the ``ordered`` attribute directly in favor of the ``set_ordered`` method (:issue:`13671`) +- ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) +- ``DataFrame.to_sql()`` has dropped the ``mysql`` option for the ``flavor`` parameter (:issue:`13611`) +- ``pd.Index`` has dropped the ``diff`` method in favour of ``difference`` (:issue:`13669`) + +- Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (:issue:`13590`) + + Previous Behavior: + + .. code-block:: ipython + + In [2]: pd.date_range('2016-07-01', freq='W@MON', periods=3) + pandas/tseries/frequencies.py:465: FutureWarning: Freq "W@MON" is deprecated, use "W-MON" as alternative. + Out[2]: DatetimeIndex(['2016-07-04', '2016-07-11', '2016-07-18'], dtype='datetime64[ns]', freq='W-MON') + Now legacy time rules raises ``ValueError``. For the list of currently supported offsets, see :ref:`here <timeseries.alias>` .. _whatsnew_0190.performance: @@ -457,6 +627,9 @@ Performance Improvements - Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`) - Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) +- Improved performance of ``Index.difference`` (:issue:`12044`) +- Improved performance of datetime string parsing in ``DatetimeIndex`` (:issue:`13692`) +- Improved performance of hashing ``Period`` (:issue:`12817`) .. _whatsnew_0190.bug_fixes: @@ -472,7 +645,7 @@ Bug Fixes - Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`) - Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`) - Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`) - +- Bug in ``Series`` construction from a tuple of integers on windows not returning default dtype (int64) (:issue:`13646`) - Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`) - Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`) @@ -519,6 +692,8 @@ Bug Fixes - Bug in ``pd.read_csv()`` with ``engine=='c'`` in which null ``quotechar`` was not accepted even though ``quoting`` was specified as ``None`` (:issue:`13411`) - Bug in ``pd.read_csv()`` with ``engine=='c'`` in which fields were not properly cast to float when quoting was specified as non-numeric (:issue:`13411`) - Bug in ``pd.pivot_table()`` where ``margins_name`` is ignored when ``aggfunc`` is a list (:issue:`13354`) +- Bug in ``pd.Series.str.zfill``, ``center``, ``ljust``, ``rjust``, and ``pad`` when passing non-integers, did not raise ``TypeError`` (:issue:`13598`) +- Bug in checking for any null objects in a ``TimedeltaIndex``, which always returned ``True`` (:issue:`13603`) @@ -528,7 +703,7 @@ Bug Fixes - Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`) - Bug in ``pd.to_datetime()`` which overflowed on ``int8``, and ``int16`` dtypes (:issue:`13451`) - Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`) - +- Bug in ``.resample(..)`` where incorrect warnings were triggered by IPython introspection (:issue:`13618`) - Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) - Bug in ``Series`` comparison may output incorrect result if rhs contains ``NaT`` (:issue:`9005`) - Bug in ``Series`` and ``Index`` comparison may output incorrect result if it contains ``NaT`` with ``object`` dtype (:issue:`13592`) @@ -536,7 +711,7 @@ Bug Fixes - Bug in ``Peirod`` and ``Series`` or ``Index`` comparison raises ``TypeError`` (:issue:`13200`) - Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) - Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`) - +- Clean some compile time warnings in datetime parsing (:issue:`13607`) - Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`) - Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) @@ -545,8 +720,15 @@ Bug Fixes - Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) - Bug in invalid ``Timedelta`` arithmetic and comparison may raise ``ValueError`` rather than ``TypeError`` (:issue:`13624`) +- Bug in invalid datetime parsing in ``to_datetime`` and ``DatetimeIndex`` may raise ``TypeError`` rather than ``ValueError`` (:issue:`11169`, :issue:`11287`) +- Bug in ``Index`` created with tz-aware ``Timestamp`` and mismatched ``tz`` option incorrectly coerces timezone (:issue:`13692`) - Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) + +-Bug in ``MultiIndex.from_arrays`` didn't check for input array lengths (:issue:`13599`) + +- Bug in ``Index.union`` returns an incorrect result with a named empty index (:issue:`13432`) +- Bugs in ``Index.difference`` and ``DataFrame.join`` raise in Python3 when using mixed-integer indexes (:issue:`13432`, :issue:`12814`) diff --git a/pandas/__init__.py b/pandas/__init__.py index 350898c9925e7..2d91c97144e3c 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -16,7 +16,7 @@ if missing_dependencies: raise ImportError("Missing required dependencies {0}".format(missing_dependencies)) - +del hard_dependencies, dependency, missing_dependencies # numpy compat from pandas.compat.numpy import * diff --git a/pandas/api/__init__.py b/pandas/api/__init__.py new file mode 100644 index 0000000000000..fcbf42f6dabc4 --- /dev/null +++ b/pandas/api/__init__.py @@ -0,0 +1 @@ +""" public toolkit API """ diff --git a/pandas/sandbox/__init__.py b/pandas/api/tests/__init__.py similarity index 100% rename from pandas/sandbox/__init__.py rename to pandas/api/tests/__init__.py diff --git a/pandas/api/tests/test_api.py b/pandas/api/tests/test_api.py new file mode 100644 index 0000000000000..0aefdbeae0518 --- /dev/null +++ b/pandas/api/tests/test_api.py @@ -0,0 +1,213 @@ +# -*- coding: utf-8 -*- + +import pandas as pd +from pandas.core import common as com +from pandas import api +from pandas.api import types +from pandas.util import testing as tm + +_multiprocess_can_split_ = True + + +class Base(object): + + def check(self, namespace, expected, ignored=None): + # see which names are in the namespace, minus optional + # ignored ones + # compare vs the expected + + result = sorted([f for f in dir(namespace) if not f.startswith('_')]) + if ignored is not None: + result = sorted(list(set(result) - set(ignored))) + + expected = sorted(expected) + tm.assert_almost_equal(result, expected) + + +class TestPDApi(Base, tm.TestCase): + + # these are optionally imported based on testing + # & need to be ignored + ignored = ['tests', 'rpy', 'locale'] + + # top-level sub-packages + lib = ['api', 'compat', 'computation', 'core', + 'indexes', 'formats', 'pandas', + 'test', 'tools', 'tseries', + 'types', 'util', 'options', 'io'] + + # top-level packages that are c-imports, should rename to _* + # to avoid naming conflicts + lib_to_rename = ['algos', 'hashtable', 'tslib', 'msgpack', 'sparse', + 'json', 'lib', 'index', 'parser'] + + # these are already deprecated; awaiting removal + deprecated_modules = ['ols', 'stats'] + + # misc + misc = ['IndexSlice', 'NaT'] + + # top-level classes + classes = ['Categorical', 'CategoricalIndex', 'DataFrame', 'DateOffset', + 'DatetimeIndex', 'ExcelFile', 'ExcelWriter', 'Float64Index', + 'Grouper', 'HDFStore', 'Index', 'Int64Index', 'MultiIndex', + 'Period', 'PeriodIndex', 'RangeIndex', + 'Series', 'SparseArray', 'SparseDataFrame', + 'SparseSeries', 'TimeGrouper', 'Timedelta', + 'TimedeltaIndex', 'Timestamp'] + + # these are already deprecated; awaiting removal + deprecated_classes = ['SparsePanel', 'TimeSeries', 'WidePanel', + 'SparseTimeSeries'] + + # these should be deperecated in the future + deprecated_classes_in_future = ['Panel', 'Panel4D', + 'SparseList', 'Term'] + + # these should be removed from top-level namespace + remove_classes_from_top_level_namespace = ['Expr'] + + # external modules exposed in pandas namespace + modules = ['np', 'datetime', 'datetools'] + + # top-level functions + funcs = ['bdate_range', 'concat', 'crosstab', 'cut', + 'date_range', 'eval', + 'factorize', 'get_dummies', 'get_store', + 'infer_freq', 'isnull', 'lreshape', + 'match', 'melt', 'notnull', 'offsets', + 'merge', 'merge_ordered', 'merge_asof', + 'period_range', + 'pivot', 'pivot_table', 'plot_params', 'qcut', + 'scatter_matrix', + 'show_versions', 'timedelta_range', 'unique', + 'value_counts', 'wide_to_long'] + + # top-level option funcs + funcs_option = ['reset_option', 'describe_option', 'get_option', + 'option_context', 'set_option', + 'set_eng_float_format'] + + # top-level read_* funcs + funcs_read = ['read_clipboard', 'read_csv', 'read_excel', 'read_fwf', + 'read_gbq', 'read_hdf', 'read_html', 'read_json', + 'read_msgpack', 'read_pickle', 'read_sas', 'read_sql', + 'read_sql_query', 'read_sql_table', 'read_stata', + 'read_table'] + + # top-level to_* funcs + funcs_to = ['to_datetime', 'to_msgpack', + 'to_numeric', 'to_pickle', 'to_timedelta'] + + # these should be deperecated in the future + deprecated_funcs_in_future = ['pnow', 'groupby', 'info'] + + # these are already deprecated; awaiting removal + deprecated_funcs = ['ewma', 'ewmcorr', 'ewmcov', 'ewmstd', 'ewmvar', + 'ewmvol', 'expanding_apply', 'expanding_corr', + 'expanding_count', 'expanding_cov', 'expanding_kurt', + 'expanding_max', 'expanding_mean', 'expanding_median', + 'expanding_min', 'expanding_quantile', + 'expanding_skew', 'expanding_std', 'expanding_sum', + 'expanding_var', 'fama_macbeth', 'rolling_apply', + 'rolling_corr', 'rolling_count', 'rolling_cov', + 'rolling_kurt', 'rolling_max', 'rolling_mean', + 'rolling_median', 'rolling_min', 'rolling_quantile', + 'rolling_skew', 'rolling_std', 'rolling_sum', + 'rolling_var', 'rolling_window', 'ordered_merge'] + + def test_api(self): + + self.check(pd, + self.lib + self.lib_to_rename + self.misc + + self.modules + self.deprecated_modules + + self.classes + self.deprecated_classes + + self.deprecated_classes_in_future + + self.remove_classes_from_top_level_namespace + + self.funcs + self.funcs_option + + self.funcs_read + self.funcs_to + + self.deprecated_funcs + + self.deprecated_funcs_in_future, + self.ignored) + + +class TestApi(Base, tm.TestCase): + + allowed = ['tests', 'types'] + + def test_api(self): + + self.check(api, self.allowed) + + +class TestTypes(Base, tm.TestCase): + + allowed = ['is_any_int_dtype', 'is_bool', 'is_bool_dtype', + 'is_categorical', 'is_categorical_dtype', 'is_complex', + 'is_complex_dtype', 'is_datetime64_any_dtype', + 'is_datetime64_dtype', 'is_datetime64_ns_dtype', + 'is_datetime64tz_dtype', 'is_datetimetz', 'is_dtype_equal', + 'is_extension_type', 'is_float', 'is_float_dtype', + 'is_floating_dtype', 'is_int64_dtype', 'is_integer', + 'is_integer_dtype', 'is_number', 'is_numeric_dtype', + 'is_object_dtype', 'is_scalar', 'is_sparse', + 'is_string_dtype', 'is_timedelta64_dtype', + 'is_timedelta64_ns_dtype', + 'is_re', 'is_re_compilable', + 'is_dict_like', 'is_iterator', + 'is_list_like', 'is_hashable', + 'is_named_tuple', 'is_sequence', + 'pandas_dtype'] + + def test_types(self): + + self.check(types, self.allowed) + + def check_deprecation(self, fold, fnew): + with tm.assert_produces_warning(FutureWarning): + try: + result = fold('foo') + expected = fnew('foo') + self.assertEqual(result, expected) + except TypeError: + self.assertRaises(TypeError, + lambda: fnew('foo')) + except AttributeError: + self.assertRaises(AttributeError, + lambda: fnew('foo')) + + def test_deprecation_core_common(self): + + # test that we are in fact deprecating + # the pandas.core.common introspectors + for t in self.allowed: + self.check_deprecation(getattr(com, t), getattr(types, t)) + + def test_deprecation_core_common_moved(self): + + # these are in pandas.types.common + l = ['is_datetime_arraylike', + 'is_datetime_or_timedelta_dtype', + 'is_datetimelike', + 'is_datetimelike_v_numeric', + 'is_datetimelike_v_object', + 'is_datetimetz', + 'is_int_or_datetime_dtype', + 'is_period_arraylike', + 'is_string_like', + 'is_string_like_dtype'] + + from pandas.types import common as c + for t in l: + self.check_deprecation(getattr(com, t), getattr(c, t)) + + def test_removed_from_core_common(self): + + for t in ['is_null_datelike_scalar', + 'ensure_float']: + self.assertRaises(AttributeError, lambda: getattr(com, t)) + +if __name__ == '__main__': + import nose + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/api/types/__init__.py b/pandas/api/types/__init__.py new file mode 100644 index 0000000000000..ee217543f0420 --- /dev/null +++ b/pandas/api/types/__init__.py @@ -0,0 +1,4 @@ +""" public toolkit API """ + +from pandas.types.api import * # noqa +del np # noqa diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index 15bf6d31b7109..adc17c7514832 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -21,7 +21,8 @@ from numpy import ndarray from pandas.util.validators import (validate_args, validate_kwargs, validate_args_and_kwargs) -from pandas.core.common import is_bool, is_integer, UnsupportedFunctionCall +from pandas.core.common import UnsupportedFunctionCall +from pandas.types.common import is_integer, is_bool from pandas.compat import OrderedDict diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index 7a0743f6b2778..96a04cff9372e 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -7,11 +7,11 @@ import numpy as np +from pandas.types.common import is_list_like, is_scalar import pandas as pd from pandas.compat import PY3, string_types, text_type import pandas.core.common as com from pandas.formats.printing import pprint_thing, pprint_thing_encoded -import pandas.lib as lib from pandas.core.base import StringMixin from pandas.computation.common import _ensure_decoded, _result_type_many from pandas.computation.scope import _DEFAULT_GLOBALS @@ -100,7 +100,7 @@ def update(self, value): @property def isscalar(self): - return lib.isscalar(self._value) + return is_scalar(self._value) @property def type(self): @@ -229,7 +229,7 @@ def _in(x, y): try: return x.isin(y) except AttributeError: - if com.is_list_like(x): + if is_list_like(x): try: return y.isin(x) except AttributeError: @@ -244,7 +244,7 @@ def _not_in(x, y): try: return ~x.isin(y) except AttributeError: - if com.is_list_like(x): + if is_list_like(x): try: return ~y.isin(x) except AttributeError: diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py index d6d55d15fec30..e375716b0d606 100644 --- a/pandas/computation/pytables.py +++ b/pandas/computation/pytables.py @@ -7,6 +7,8 @@ from datetime import datetime, timedelta import numpy as np import pandas as pd + +from pandas.types.common import is_list_like import pandas.core.common as com from pandas.compat import u, string_types, DeepChainMap from pandas.core.base import StringMixin @@ -127,7 +129,7 @@ def pr(left, right): def conform(self, rhs): """ inplace conform rhs """ - if not com.is_list_like(rhs): + if not is_list_like(rhs): rhs = [rhs] if isinstance(rhs, np.ndarray): rhs = rhs.ravel() diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index 5019dd392a567..066df0521fef6 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -13,6 +13,7 @@ from numpy.random import randn, rand, randint import numpy as np +from pandas.types.common import is_list_like, is_scalar import pandas as pd from pandas.core import common as com from pandas import DataFrame, Series, Panel, date_range @@ -200,7 +201,7 @@ def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2): ex = '(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)'.format(cmp1=cmp1, binop=binop, cmp2=cmp2) - scalar_with_in_notin = (lib.isscalar(rhs) and (cmp1 in skip_these or + scalar_with_in_notin = (is_scalar(rhs) and (cmp1 in skip_these or cmp2 in skip_these)) if scalar_with_in_notin: with tm.assertRaises(TypeError): @@ -253,7 +254,7 @@ def check_operands(left, right, cmp_op): def check_simple_cmp_op(self, lhs, cmp1, rhs): ex = 'lhs {0} rhs'.format(cmp1) - if cmp1 in ('in', 'not in') and not com.is_list_like(rhs): + if cmp1 in ('in', 'not in') and not is_list_like(rhs): self.assertRaises(TypeError, pd.eval, ex, engine=self.engine, parser=self.parser, local_dict={'lhs': lhs, 'rhs': rhs}) @@ -331,7 +332,7 @@ def check_pow(self, lhs, arith1, rhs): expected = self.get_expected_pow_result(lhs, rhs) result = pd.eval(ex, engine=self.engine, parser=self.parser) - if (lib.isscalar(lhs) and lib.isscalar(rhs) and + if (is_scalar(lhs) and is_scalar(rhs) and _is_py3_complex_incompat(result, expected)): self.assertRaises(AssertionError, tm.assert_numpy_array_equal, result, expected) @@ -364,16 +365,16 @@ def check_compound_invert_op(self, lhs, cmp1, rhs): skip_these = 'in', 'not in' ex = '~(lhs {0} rhs)'.format(cmp1) - if lib.isscalar(rhs) and cmp1 in skip_these: + if is_scalar(rhs) and cmp1 in skip_these: self.assertRaises(TypeError, pd.eval, ex, engine=self.engine, parser=self.parser, local_dict={'lhs': lhs, 'rhs': rhs}) else: # compound - if lib.isscalar(lhs) and lib.isscalar(rhs): + if is_scalar(lhs) and is_scalar(rhs): lhs, rhs = map(lambda x: np.array([x]), (lhs, rhs)) expected = _eval_single_bin(lhs, cmp1, rhs, self.engine) - if lib.isscalar(expected): + if is_scalar(expected): expected = not expected else: expected = ~expected @@ -643,17 +644,17 @@ def test_identical(self): x = 1 result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, 1) - self.assertTrue(lib.isscalar(result)) + self.assertTrue(is_scalar(result)) x = 1.5 result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, 1.5) - self.assertTrue(lib.isscalar(result)) + self.assertTrue(is_scalar(result)) x = False result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, False) - self.assertTrue(lib.isscalar(result)) + self.assertTrue(is_scalar(result)) x = np.array([1]) result = pd.eval('x', engine=self.engine, parser=self.parser) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 4b40bce79cbb5..5cc54e61f6b2a 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -7,10 +7,31 @@ import numpy as np from pandas import compat, lib, tslib, _np_version_under1p8 +from pandas.types.cast import _maybe_promote +from pandas.types.generic import ABCPeriodIndex, ABCDatetimeIndex +from pandas.types.common import (is_integer_dtype, + is_int64_dtype, + is_categorical_dtype, + is_extension_type, + is_datetimetz, + is_period_arraylike, + is_datetime_or_timedelta_dtype, + is_float_dtype, + needs_i8_conversion, + is_categorical, + is_datetime64_dtype, + is_timedelta64_dtype, + is_scalar, + _ensure_platform_int, + _ensure_object, + _ensure_float64, + _ensure_int64, + is_list_like) +from pandas.types.missing import isnull + import pandas.core.common as com import pandas.algos as algos import pandas.hashtable as htable -from pandas.types import api as gt from pandas.compat import string_types from pandas.tslib import iNaT @@ -105,12 +126,12 @@ def isin(comps, values): boolean array same length as comps """ - if not com.is_list_like(comps): + if not is_list_like(comps): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(comps).__name__)) comps = np.asarray(comps) - if not com.is_list_like(values): + if not is_list_like(values): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(values).__name__)) @@ -126,15 +147,15 @@ def isin(comps, values): f = lambda x, y: lib.ismember_int64(x, set(y)) # may need i8 conversion for proper membership testing - if com.is_datetime64_dtype(comps): + if is_datetime64_dtype(comps): from pandas.tseries.tools import to_datetime values = to_datetime(values)._values.view('i8') comps = comps.view('i8') - elif com.is_timedelta64_dtype(comps): + elif is_timedelta64_dtype(comps): from pandas.tseries.timedeltas import to_timedelta values = to_timedelta(values)._values.view('i8') comps = comps.view('i8') - elif com.is_int64_dtype(comps): + elif is_int64_dtype(comps): pass else: f = lambda x, y: lib.ismember(x, set(values)) @@ -142,6 +163,104 @@ def isin(comps, values): return f(comps, values) +def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False): + """ + Sort ``values`` and reorder corresponding ``labels``. + ``values`` should be unique if ``labels`` is not None. + Safe for use with mixed types (int, str), orders ints before strs. + + .. versionadded:: 0.19.0 + + Parameters + ---------- + values : list-like + Sequence; must be unique if ``labels`` is not None. + labels : list_like + Indices to ``values``. All out of bound indices are treated as + "not found" and will be masked with ``na_sentinel``. + na_sentinel : int, default -1 + Value in ``labels`` to mark "not found". + Ignored when ``labels`` is None. + assume_unique : bool, default False + When True, ``values`` are assumed to be unique, which can speed up + the calculation. Ignored when ``labels`` is None. + + Returns + ------- + ordered : ndarray + Sorted ``values`` + new_labels : ndarray + Reordered ``labels``; returned when ``labels`` is not None. + + Raises + ------ + TypeError + * If ``values`` is not list-like or if ``labels`` is neither None + nor list-like + * If ``values`` cannot be sorted + ValueError + * If ``labels`` is not None and ``values`` contain duplicates. + """ + if not is_list_like(values): + raise TypeError("Only list-like objects are allowed to be passed to" + "safe_sort as values") + values = np.array(values, copy=False) + + def sort_mixed(values): + # order ints before strings, safe in py3 + str_pos = np.array([isinstance(x, string_types) for x in values], + dtype=bool) + nums = np.sort(values[~str_pos]) + strs = np.sort(values[str_pos]) + return _ensure_object(np.concatenate([nums, strs])) + + sorter = None + if compat.PY3 and lib.infer_dtype(values) == 'mixed-integer': + # unorderable in py3 if mixed str/int + ordered = sort_mixed(values) + else: + try: + sorter = values.argsort() + ordered = values.take(sorter) + except TypeError: + # try this anyway + ordered = sort_mixed(values) + + # labels: + + if labels is None: + return ordered + + if not is_list_like(labels): + raise TypeError("Only list-like objects or None are allowed to be" + "passed to safe_sort as labels") + labels = _ensure_platform_int(np.asarray(labels)) + + from pandas import Index + if not assume_unique and not Index(values).is_unique: + raise ValueError("values should be unique if labels is not None") + + if sorter is None: + # mixed types + (hash_klass, _), values = _get_data_algo(values, _hashtables) + t = hash_klass(len(values)) + t.map_locations(values) + sorter = _ensure_platform_int(t.lookup(ordered)) + + reverse_indexer = np.empty(len(sorter), dtype=np.int_) + reverse_indexer.put(sorter, np.arange(len(sorter))) + + mask = (labels < -len(values)) | (labels >= len(values)) | \ + (labels == na_sentinel) + + # (Out of bound indices will be masked with `na_sentinel` next, so we may + # deal with them here without performance loss using `mode='wrap'`.) + new_labels = reverse_indexer.take(labels, mode='wrap') + np.putmask(new_labels, mask, na_sentinel) + + return ordered, new_labels + + def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): """ Encode input values as an enumerated type or categorical variable @@ -171,51 +290,28 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): vals = np.asarray(values) # localize to UTC - is_datetimetz = com.is_datetimetz(values) - if is_datetimetz: + is_datetimetz_type = is_datetimetz(values) + if is_datetimetz_type: values = DatetimeIndex(values) vals = values.tz_localize(None) - is_datetime = com.is_datetime64_dtype(vals) - is_timedelta = com.is_timedelta64_dtype(vals) + is_datetime = is_datetime64_dtype(vals) + is_timedelta = is_timedelta64_dtype(vals) (hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables) table = hash_klass(size_hint or len(vals)) uniques = vec_klass() labels = table.get_labels(vals, uniques, 0, na_sentinel, True) - labels = com._ensure_platform_int(labels) + labels = _ensure_platform_int(labels) uniques = uniques.to_array() if sort and len(uniques) > 0: - try: - sorter = uniques.argsort() - except: - # unorderable in py3 if mixed str/int - t = hash_klass(len(uniques)) - t.map_locations(com._ensure_object(uniques)) - - # order ints before strings - ordered = np.concatenate([ - np.sort(np.array([e for i, e in enumerate(uniques) if f(e)], - dtype=object)) for f in - [lambda x: not isinstance(x, string_types), - lambda x: isinstance(x, string_types)]]) - sorter = com._ensure_platform_int(t.lookup( - com._ensure_object(ordered))) - - reverse_indexer = np.empty(len(sorter), dtype=np.int_) - reverse_indexer.put(sorter, np.arange(len(sorter))) - - mask = labels < 0 - labels = reverse_indexer.take(labels) - np.putmask(labels, mask, -1) - - uniques = uniques.take(sorter) - - if is_datetimetz: + uniques, labels = safe_sort(uniques, labels, na_sentinel=na_sentinel, + assume_unique=True) + if is_datetimetz_type: # reset tz uniques = DatetimeIndex(uniques.astype('M8[ns]')).tz_localize( values.tz) @@ -267,7 +363,7 @@ def value_counts(values, sort=True, ascending=False, normalize=False, raise TypeError("bins argument only works with numeric data.") values = cat.codes - if com.is_extension_type(values) and not com.is_datetimetz(values): + if is_extension_type(values) and not is_datetimetz(values): # handle Categorical and sparse, # datetime tz can be handeled in ndarray path result = Series(values).values.value_counts(dropna=dropna) @@ -298,9 +394,9 @@ def value_counts(values, sort=True, ascending=False, normalize=False, def _value_counts_arraylike(values, dropna=True): - is_datetimetz = com.is_datetimetz(values) - is_period = (isinstance(values, gt.ABCPeriodIndex) or - com.is_period_arraylike(values)) + is_datetimetz_type = is_datetimetz(values) + is_period = (isinstance(values, ABCPeriodIndex) or + is_period_arraylike(values)) orig = values @@ -308,7 +404,7 @@ def _value_counts_arraylike(values, dropna=True): values = Series(values).values dtype = values.dtype - if com.is_datetime_or_timedelta_dtype(dtype) or is_period: + if is_datetime_or_timedelta_dtype(dtype) or is_period: from pandas.tseries.index import DatetimeIndex from pandas.tseries.period import PeriodIndex @@ -327,8 +423,8 @@ def _value_counts_arraylike(values, dropna=True): keys = keys.astype(dtype) # dtype handling - if is_datetimetz: - if isinstance(orig, gt.ABCDatetimeIndex): + if is_datetimetz_type: + if isinstance(orig, ABCDatetimeIndex): tz = orig.tz else: tz = orig.dt.tz @@ -336,15 +432,15 @@ def _value_counts_arraylike(values, dropna=True): if is_period: keys = PeriodIndex._simple_new(keys, freq=freq) - elif com.is_integer_dtype(dtype): - values = com._ensure_int64(values) + elif is_integer_dtype(dtype): + values = _ensure_int64(values) keys, counts = htable.value_count_scalar64(values, dropna) - elif com.is_float_dtype(dtype): - values = com._ensure_float64(values) + elif is_float_dtype(dtype): + values = _ensure_float64(values) keys, counts = htable.value_count_scalar64(values, dropna) else: - values = com._ensure_object(values) - mask = com.isnull(values) + values = _ensure_object(values) + mask = isnull(values) keys, counts = htable.value_count_object(values, mask) if not dropna and mask.any(): keys = np.insert(keys, 0, np.NaN) @@ -366,8 +462,8 @@ def mode(values): constructor = Series dtype = values.dtype - if com.is_integer_dtype(values): - values = com._ensure_int64(values) + if is_integer_dtype(values): + values = _ensure_int64(values) result = constructor(sorted(htable.mode_int64(values)), dtype=dtype) elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)): @@ -375,11 +471,11 @@ def mode(values): values = values.view(np.int64) result = constructor(sorted(htable.mode_int64(values)), dtype=dtype) - elif com.is_categorical_dtype(values): + elif is_categorical_dtype(values): result = constructor(values.mode()) else: - mask = com.isnull(values) - values = com._ensure_object(values) + mask = isnull(values) + values = _ensure_object(values) res = htable.mode_object(values, mask) try: res = sorted(res) @@ -459,7 +555,7 @@ def quantile(x, q, interpolation_method='fraction'): """ x = np.asarray(x) - mask = com.isnull(x) + mask = isnull(x) x = x[~mask] @@ -486,7 +582,7 @@ def _get_score(at): return score - if lib.isscalar(q): + if is_scalar(q): return _get_score(q) else: q = np.asarray(q, np.float64) @@ -593,18 +689,18 @@ def _hashtable_algo(f, dtype, return_dtype=None): """ f(HashTable, type_caster) -> result """ - if com.is_float_dtype(dtype): - return f(htable.Float64HashTable, com._ensure_float64) - elif com.is_integer_dtype(dtype): - return f(htable.Int64HashTable, com._ensure_int64) - elif com.is_datetime64_dtype(dtype): + if is_float_dtype(dtype): + return f(htable.Float64HashTable, _ensure_float64) + elif is_integer_dtype(dtype): + return f(htable.Int64HashTable, _ensure_int64) + elif is_datetime64_dtype(dtype): return_dtype = return_dtype or 'M8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) - elif com.is_timedelta64_dtype(dtype): + return f(htable.Int64HashTable, _ensure_int64).view(return_dtype) + elif is_timedelta64_dtype(dtype): return_dtype = return_dtype or 'm8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) + return f(htable.Int64HashTable, _ensure_int64).view(return_dtype) else: - return f(htable.PyObjectHashTable, com._ensure_object) + return f(htable.PyObjectHashTable, _ensure_object) _hashtables = { 'float64': (htable.Float64HashTable, htable.Float64Vector), @@ -614,20 +710,20 @@ def _hashtable_algo(f, dtype, return_dtype=None): def _get_data_algo(values, func_map): - if com.is_float_dtype(values): + if is_float_dtype(values): f = func_map['float64'] - values = com._ensure_float64(values) + values = _ensure_float64(values) - elif com.needs_i8_conversion(values): + elif needs_i8_conversion(values): f = func_map['int64'] values = values.view('i8') - elif com.is_integer_dtype(values): + elif is_integer_dtype(values): f = func_map['int64'] - values = com._ensure_int64(values) + values = _ensure_int64(values) else: f = func_map['generic'] - values = com._ensure_object(values) + values = _ensure_object(values) return f, values @@ -689,7 +785,7 @@ def _take_nd_generic(arr, indexer, out, axis, fill_value, mask_info): if arr.dtype != out.dtype: arr = arr.astype(out.dtype) if arr.shape[axis] > 0: - arr.take(com._ensure_platform_int(indexer), axis=axis, out=out) + arr.take(_ensure_platform_int(indexer), axis=axis, out=out) if needs_masking: outindexer = [slice(None)] * arr.ndim outindexer[axis] = mask @@ -830,7 +926,7 @@ def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None): return func def func(arr, indexer, out, fill_value=np.nan): - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) _take_nd_generic(arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info) @@ -854,7 +950,7 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, out : ndarray or None, default None Optional output array, must be appropriate type to hold input and fill_value together, if indexer has any -1 value entries; call - common._maybe_promote to determine this type for any fill_value + _maybe_promote to determine this type for any fill_value fill_value : any, default np.nan Fill value to replace -1 values with mask_info : tuple of (ndarray, boolean) @@ -868,24 +964,24 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, """ # dispatch to internal type takes - if com.is_categorical(arr): + if is_categorical(arr): return arr.take_nd(indexer, fill_value=fill_value, allow_fill=allow_fill) - elif com.is_datetimetz(arr): + elif is_datetimetz(arr): return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) if indexer is None: indexer = np.arange(arr.shape[axis], dtype=np.int64) dtype, fill_value = arr.dtype, arr.dtype.type() else: - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) if not allow_fill: dtype, fill_value = arr.dtype, arr.dtype.type() mask_info = None, False else: # check for promotion based on types only (do this first because # it's faster than computing a mask) - dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + dtype, fill_value = _maybe_promote(arr.dtype, fill_value) if dtype != arr.dtype and (out is None or out.dtype != dtype): # check if promotion is actually required based on indexer if mask_info is not None: @@ -931,7 +1027,7 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info) - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) func(arr, indexer, out, fill_value) if flip_order: @@ -957,11 +1053,11 @@ def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, if row_idx is None: row_idx = np.arange(arr.shape[0], dtype=np.int64) else: - row_idx = com._ensure_int64(row_idx) + row_idx = _ensure_int64(row_idx) if col_idx is None: col_idx = np.arange(arr.shape[1], dtype=np.int64) else: - col_idx = com._ensure_int64(col_idx) + col_idx = _ensure_int64(col_idx) indexer = row_idx, col_idx if not allow_fill: dtype, fill_value = arr.dtype, arr.dtype.type() @@ -969,7 +1065,7 @@ def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, else: # check for promotion based on types only (do this first because # it's faster than computing a mask) - dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + dtype, fill_value = _maybe_promote(arr.dtype, fill_value) if dtype != arr.dtype and (out is None or out.dtype != dtype): # check if promotion is actually required based on indexer if mask_info is not None: @@ -1032,7 +1128,7 @@ def diff(arr, n, axis=0): na = np.nan dtype = arr.dtype is_timedelta = False - if com.needs_i8_conversion(arr): + if needs_i8_conversion(arr): dtype = np.float64 arr = arr.view('i8') na = tslib.iNaT diff --git a/pandas/core/api.py b/pandas/core/api.py index 0a6992bfebd70..579f21eb4ada8 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -5,7 +5,7 @@ import numpy as np from pandas.core.algorithms import factorize, match, unique, value_counts -from pandas.core.common import isnull, notnull +from pandas.types.missing import isnull, notnull from pandas.core.categorical import Categorical from pandas.core.groupby import Grouper from pandas.formats.format import set_eng_float_format diff --git a/pandas/core/base.py b/pandas/core/base.py index 13a6b4b7b4ce0..a0dfebdfde356 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -4,6 +4,12 @@ from pandas import compat from pandas.compat import builtins import numpy as np + +from pandas.types.missing import isnull +from pandas.types.generic import ABCDataFrame, ABCSeries, ABCIndex +from pandas.types.common import (_ensure_object, is_object_dtype, + is_list_like, is_scalar) + from pandas.core import common as com import pandas.core.nanops as nanops import pandas.lib as lib @@ -11,7 +17,6 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError -from pandas.types import api as gt from pandas.formats.printing import pprint_thing _shared_docs = dict() @@ -121,7 +126,7 @@ def __sizeof__(self): """ if hasattr(self, 'memory_usage'): mem = self.memory_usage(deep=True) - if not lib.isscalar(mem): + if not is_scalar(mem): mem = mem.sum() return int(mem) @@ -293,15 +298,15 @@ def name(self): @property def _selection_list(self): - if not isinstance(self._selection, (list, tuple, gt.ABCSeries, - gt.ABCIndex, np.ndarray)): + if not isinstance(self._selection, (list, tuple, ABCSeries, + ABCIndex, np.ndarray)): return [self._selection] return self._selection @cache_readonly def _selected_obj(self): - if self._selection is None or isinstance(self.obj, gt.ABCSeries): + if self._selection is None or isinstance(self.obj, ABCSeries): return self.obj else: return self.obj[self._selection] @@ -313,7 +318,7 @@ def ndim(self): @cache_readonly def _obj_with_exclusions(self): if self._selection is not None and isinstance(self.obj, - gt.ABCDataFrame): + ABCDataFrame): return self.obj.reindex(columns=self._selection_list) if len(self.exclusions) > 0: @@ -325,7 +330,7 @@ def __getitem__(self, key): if self._selection is not None: raise Exception('Column(s) %s already selected' % self._selection) - if isinstance(key, (list, tuple, gt.ABCSeries, gt.ABCIndex, + if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)): if len(self.obj.columns.intersection(key)) != len(key): bad_keys = list(set(key).difference(self.obj.columns)) @@ -553,7 +558,7 @@ def _agg(arg, func): if isinstance(result, list): result = concat(result, keys=keys, axis=1) elif isinstance(list(compat.itervalues(result))[0], - gt.ABCDataFrame): + ABCDataFrame): result = concat([result[k] for k in keys], keys=keys, axis=1) else: from pandas import DataFrame @@ -682,7 +687,7 @@ def _gotitem(self, key, ndim, subset=None): **kwargs) self._reset_cache() if subset.ndim == 2: - if lib.isscalar(key) and key in subset or com.is_list_like(key): + if is_scalar(key) and key in subset or is_list_like(key): self._selection = key return self @@ -903,7 +908,7 @@ def argmin(self, axis=None): @cache_readonly def hasnans(self): """ return if I have any nans; enables various perf speedups """ - return com.isnull(self).any() + return isnull(self).any() def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): @@ -980,7 +985,7 @@ def nunique(self, dropna=True): """ uniqs = self.unique() n = len(uniqs) - if dropna and com.isnull(uniqs).any(): + if dropna and isnull(uniqs).any(): n -= 1 return n @@ -1053,7 +1058,7 @@ def memory_usage(self, deep=False): return self.values.memory_usage(deep=deep) v = self.values.nbytes - if deep and com.is_object_dtype(self): + if deep and is_object_dtype(self): v += lib.memory_usage_of_objects(self.values) return v @@ -1195,7 +1200,7 @@ def drop_duplicates(self, keep='first', inplace=False): False: 'first'}) @Appender(_shared_docs['duplicated'] % _indexops_doc_kwargs) def duplicated(self, keep='first'): - keys = com._values_from_object(com._ensure_object(self.values)) + keys = com._values_from_object(_ensure_object(self.values)) duplicated = lib.duplicated(keys, keep=keep) try: return self._constructor(duplicated, diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index f4aeaf9184d09..39e140e962821 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -7,6 +7,22 @@ from pandas import compat, lib from pandas.compat import u +from pandas.types.generic import ABCSeries, ABCIndexClass, ABCCategoricalIndex +from pandas.types.missing import isnull, notnull +from pandas.types.cast import (_possibly_infer_to_datetimelike, + _coerce_indexer_dtype) +from pandas.types.dtypes import CategoricalDtype +from pandas.types.common import (_ensure_int64, + _ensure_object, + _ensure_platform_int, + is_dtype_equal, + is_datetimelike, + is_categorical_dtype, + is_integer_dtype, is_bool, + is_list_like, is_sequence, + is_scalar) +from pandas.core.common import is_null_slice + from pandas.core.algorithms import factorize, take_1d from pandas.core.base import (PandasObject, PandasDelegate, NoNewAttributesMixin, _shared_docs) @@ -16,13 +32,6 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) -from pandas.core.common import ( - ABCSeries, ABCIndexClass, ABCCategoricalIndex, isnull, notnull, - is_dtype_equal, is_categorical_dtype, is_integer_dtype, - _possibly_infer_to_datetimelike, is_list_like, - is_sequence, is_null_slice, is_bool, _ensure_object, _ensure_int64, - _coerce_indexer_dtype) -from pandas.types.api import CategoricalDtype from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option @@ -64,7 +73,7 @@ def f(self, other): # With cat[0], for example, being ``np.int64(1)`` by the time it gets # into this function would become ``np.array(1)``. other = lib.item_from_zerodim(other) - if lib.isscalar(other): + if is_scalar(other): if other in self.categories: i = self.categories.get_loc(other) return getattr(self._codes, op)(i) @@ -219,8 +228,8 @@ class Categorical(PandasObject): __array_priority__ = 1000 _typ = 'categorical' - def __init__(self, values, categories=None, ordered=False, name=None, - fastpath=False, levels=None): + def __init__(self, values, categories=None, ordered=False, + name=None, fastpath=False): if fastpath: # fast path @@ -236,17 +245,6 @@ def __init__(self, values, categories=None, ordered=False, name=None, "name=\"something\")'") warn(msg, UserWarning, stacklevel=2) - # TODO: Remove after deprecation period in 2017/ after 0.18 - if levels is not None: - warn("Creating a 'Categorical' with 'levels' is deprecated, use " - "'categories' instead", FutureWarning, stacklevel=2) - if categories is None: - categories = levels - else: - raise ValueError("Cannot pass in both 'categories' and " - "(deprecated) 'levels', use only " - "'categories'", stacklevel=2) - # sanitize input if is_categorical_dtype(values): @@ -374,11 +372,28 @@ def itemsize(self): def reshape(self, new_shape, *args, **kwargs): """ - An ndarray-compatible method that returns - `self` because categorical instances cannot - actually be reshaped. + DEPRECATED: calling this method will raise an error in a + future release. + + An ndarray-compatible method that returns `self` because + `Categorical` instances cannot actually be reshaped. + + Parameters + ---------- + new_shape : int or tuple of ints + A 1-D array of integers that correspond to the new + shape of the `Categorical`. For more information on + the parameter, please refer to `np.reshape`. """ + warn("reshape is deprecated and will raise " + "in a subsequent release", FutureWarning, stacklevel=2) + nv.validate_reshape(args, kwargs) + + # while the 'new_shape' parameter has no effect, + # we should still enforce valid shape parameters + np.reshape(self.codes, new_shape) + return self @property @@ -554,29 +569,8 @@ def _get_categories(self): categories = property(fget=_get_categories, fset=_set_categories, doc=_categories_doc) - def _set_levels(self, levels): - """ set new levels (deprecated, use "categories") """ - warn("Assigning to 'levels' is deprecated, use 'categories'", - FutureWarning, stacklevel=2) - self.categories = levels - - def _get_levels(self): - """ Gets the levels (deprecated, use "categories") """ - warn("Accessing 'levels' is deprecated, use 'categories'", - FutureWarning, stacklevel=2) - return self.categories - - # TODO: Remove after deprecation period in 2017/ after 0.18 - levels = property(fget=_get_levels, fset=_set_levels) - _ordered = None - def _set_ordered(self, value): - """ Sets the ordered attribute to the boolean value """ - warn("Setting 'ordered' directly is deprecated, use 'set_ordered'", - FutureWarning, stacklevel=2) - self.set_ordered(value, inplace=True) - def set_ordered(self, value, inplace=False): """ Sets the ordered attribute to the boolean value @@ -624,7 +618,7 @@ def _get_ordered(self): """ Gets the ordered attribute """ return self._ordered - ordered = property(fget=_get_ordered, fset=_set_ordered) + ordered = property(fget=_get_ordered) def set_categories(self, new_categories, ordered=None, rename=False, inplace=False): @@ -968,7 +962,7 @@ def shift(self, periods): if codes.ndim > 1: raise NotImplementedError("Categorical with ndim > 1.") if np.prod(codes.shape) and (periods != 0): - codes = np.roll(codes, com._ensure_platform_int(periods), axis=0) + codes = np.roll(codes, _ensure_platform_int(periods), axis=0) if periods > 0: codes[:periods] = -1 else: @@ -1148,7 +1142,7 @@ def value_counts(self, dropna=True): counts : Series """ from numpy import bincount - from pandas.core.common import isnull + from pandas.types.missing import isnull from pandas.core.series import Series from pandas.core.index import CategoricalIndex @@ -1182,7 +1176,7 @@ def get_values(self): Index if datetime / periods """ # if we are a datetime and period index, return Index to keep metadata - if com.is_datetimelike(self.categories): + if is_datetimelike(self.categories): return self.categories.take(self._codes, fill_value=np.nan) return np.array(self) @@ -1933,7 +1927,7 @@ def _convert_to_list_like(list_like): if (is_sequence(list_like) or isinstance(list_like, tuple) or isinstance(list_like, types.GeneratorType)): return list(list_like) - elif lib.isscalar(list_like): + elif is_scalar(list_like): return [list_like] else: # is this reached? diff --git a/pandas/core/common.py b/pandas/core/common.py index 28bae362a3411..99dd2e9f5b8a9 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2,23 +2,66 @@ Misc tools for implementing data structures """ -import re -import collections -import numbers +import sys +import warnings from datetime import datetime, timedelta from functools import partial import numpy as np -import pandas as pd -import pandas.algos as algos import pandas.lib as lib import pandas.tslib as tslib from pandas import compat -from pandas.compat import (long, zip, map, string_types, - iteritems) -from pandas.types import api as gt -from pandas.types.api import * # noqa +from pandas.compat import long, zip, iteritems from pandas.core.config import get_option +from pandas.types.generic import ABCSeries +from pandas.types.common import _NS_DTYPE, is_integer +from pandas.types.inference import _iterable_not_string +from pandas.types.missing import isnull +from pandas.api import types +from pandas.types import common + +# back-compat of public API +# deprecate these functions +m = sys.modules['pandas.core.common'] +for t in [t for t in dir(types) if not t.startswith('_')]: + + def outer(t=t): + + def wrapper(*args, **kwargs): + warnings.warn("pandas.core.common.{t} is deprecated. " + "import from the public API: " + "pandas.api.types.{t} instead".format(t=t), + FutureWarning, stacklevel=2) + return getattr(types, t)(*args, **kwargs) + return wrapper + + setattr(m, t, outer(t)) + +# back-compat for non-public functions +# deprecate these functions +for t in ['is_datetime_arraylike', + 'is_datetime_or_timedelta_dtype', + 'is_datetimelike', + 'is_datetimelike_v_numeric', + 'is_datetimelike_v_object', + 'is_datetimetz', + 'is_int_or_datetime_dtype', + 'is_period_arraylike', + 'is_string_like', + 'is_string_like_dtype']: + + def outer(t=t): + + def wrapper(*args, **kwargs): + warnings.warn("pandas.core.common.{t} is deprecated. " + "These are not longer public API functions, " + "but can be imported from " + "pandas.types.common.{t} instead".format(t=t), + FutureWarning, stacklevel=2) + return getattr(common, t)(*args, **kwargs) + return wrapper + + setattr(m, t, outer(t)) class PandasError(Exception): @@ -58,322 +101,6 @@ def __str__(self): self.class_instance.__class__.__name__) -_POSSIBLY_CAST_DTYPES = set([np.dtype(t).name - for t in ['O', 'int8', 'uint8', 'int16', 'uint16', - 'int32', 'uint32', 'int64', 'uint64']]) - -_NS_DTYPE = np.dtype('M8[ns]') -_TD_DTYPE = np.dtype('m8[ns]') -_INT64_DTYPE = np.dtype(np.int64) -_DATELIKE_DTYPES = set([np.dtype(t) - for t in ['M8[ns]', '<M8[ns]', '>M8[ns]', - 'm8[ns]', '<m8[ns]', '>m8[ns]']]) -_int8_max = np.iinfo(np.int8).max -_int16_max = np.iinfo(np.int16).max -_int32_max = np.iinfo(np.int32).max -_int64_max = np.iinfo(np.int64).max - - -def isnull(obj): - """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) - - Parameters - ---------- - arr : ndarray or object value - Object to check for null-ness - - Returns - ------- - isnulled : array-like of bool or bool - Array or bool indicating whether an object is null or if an array is - given which of the element is null. - - See also - -------- - pandas.notnull: boolean inverse of pandas.isnull - """ - return _isnull(obj) - - -def _isnull_new(obj): - if lib.isscalar(obj): - return lib.checknull(obj) - # hack (for now) because MI registers as ndarray - elif isinstance(obj, pd.MultiIndex): - raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): - return _isnull_ndarraylike(obj) - elif isinstance(obj, gt.ABCGeneric): - return obj._constructor(obj._data.isnull(func=isnull)) - elif isinstance(obj, list) or hasattr(obj, '__array__'): - return _isnull_ndarraylike(np.asarray(obj)) - else: - return obj is None - - -def _isnull_old(obj): - """Detect missing values. Treat None, NaN, INF, -INF as null. - - Parameters - ---------- - arr: ndarray or object value - - Returns - ------- - boolean ndarray or boolean - """ - if lib.isscalar(obj): - return lib.checknull_old(obj) - # hack (for now) because MI registers as ndarray - elif isinstance(obj, pd.MultiIndex): - raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): - return _isnull_ndarraylike_old(obj) - elif isinstance(obj, gt.ABCGeneric): - return obj._constructor(obj._data.isnull(func=_isnull_old)) - elif isinstance(obj, list) or hasattr(obj, '__array__'): - return _isnull_ndarraylike_old(np.asarray(obj)) - else: - return obj is None - - -_isnull = _isnull_new - - -def _use_inf_as_null(key): - """Option change callback for null/inf behaviour - Choose which replacement for numpy.isnan / ~numpy.isfinite is used. - - Parameters - ---------- - flag: bool - True means treat None, NaN, INF, -INF as null (old way), - False means None and NaN are null, but INF, -INF are not null - (new way). - - Notes - ----- - This approach to setting global module values is discussed and - approved here: - - * http://stackoverflow.com/questions/4859217/ - programmatically-creating-variables-in-python/4859312#4859312 - """ - flag = get_option(key) - if flag: - globals()['_isnull'] = _isnull_old - else: - globals()['_isnull'] = _isnull_new - - -def _isnull_ndarraylike(obj): - - values = getattr(obj, 'values', obj) - dtype = values.dtype - - if is_string_dtype(dtype): - if is_categorical_dtype(values): - from pandas import Categorical - if not isinstance(values, Categorical): - values = values.values - result = values.isnull() - else: - - # Working around NumPy ticket 1542 - shape = values.shape - - if is_string_like_dtype(dtype): - result = np.zeros(values.shape, dtype=bool) - else: - result = np.empty(shape, dtype=bool) - vec = lib.isnullobj(values.ravel()) - result[...] = vec.reshape(shape) - - elif is_datetimelike(obj): - # this is the NaT pattern - result = values.view('i8') == tslib.iNaT - else: - result = np.isnan(values) - - # box - if isinstance(obj, gt.ABCSeries): - from pandas import Series - result = Series(result, index=obj.index, name=obj.name, copy=False) - - return result - - -def _isnull_ndarraylike_old(obj): - values = getattr(obj, 'values', obj) - dtype = values.dtype - - if is_string_dtype(dtype): - # Working around NumPy ticket 1542 - shape = values.shape - - if is_string_like_dtype(dtype): - result = np.zeros(values.shape, dtype=bool) - else: - result = np.empty(shape, dtype=bool) - vec = lib.isnullobj_old(values.ravel()) - result[:] = vec.reshape(shape) - - elif dtype in _DATELIKE_DTYPES: - # this is the NaT pattern - result = values.view('i8') == tslib.iNaT - else: - result = ~np.isfinite(values) - - # box - if isinstance(obj, gt.ABCSeries): - from pandas import Series - result = Series(result, index=obj.index, name=obj.name, copy=False) - - return result - - -def notnull(obj): - """Replacement for numpy.isfinite / ~numpy.isnan which is suitable for use - on object arrays. - - Parameters - ---------- - arr : ndarray or object value - Object to check for *not*-null-ness - - Returns - ------- - isnulled : array-like of bool or bool - Array or bool indicating whether an object is *not* null or if an array - is given which of the element is *not* null. - - See also - -------- - pandas.isnull : boolean inverse of pandas.notnull - """ - res = isnull(obj) - if lib.isscalar(res): - return not res - return ~res - - -def is_null_datelike_scalar(other): - """ test whether the object is a null datelike, e.g. Nat - but guard against passing a non-scalar """ - if other is pd.NaT or other is None: - return True - elif lib.isscalar(other): - - # a timedelta - if hasattr(other, 'dtype'): - return other.view('i8') == tslib.iNaT - elif is_integer(other) and other == tslib.iNaT: - return True - return isnull(other) - return False - - -def array_equivalent(left, right, strict_nan=False): - """ - True if two arrays, left and right, have equal non-NaN elements, and NaNs - in corresponding locations. False otherwise. It is assumed that left and - right are NumPy arrays of the same dtype. The behavior of this function - (particularly with respect to NaNs) is not defined if the dtypes are - different. - - Parameters - ---------- - left, right : ndarrays - strict_nan : bool, default False - If True, consider NaN and None to be different. - - Returns - ------- - b : bool - Returns True if the arrays are equivalent. - - Examples - -------- - >>> array_equivalent( - ... np.array([1, 2, np.nan]), - ... np.array([1, 2, np.nan])) - True - >>> array_equivalent( - ... np.array([1, np.nan, 2]), - ... np.array([1, 2, np.nan])) - False - """ - - left, right = np.asarray(left), np.asarray(right) - - # shape compat - if left.shape != right.shape: - return False - - # Object arrays can contain None, NaN and NaT. - # string dtypes must be come to this path for NumPy 1.7.1 compat - if is_string_dtype(left) or is_string_dtype(right): - - if not strict_nan: - # pd.isnull considers NaN and None to be equivalent. - return lib.array_equivalent_object(_ensure_object(left.ravel()), - _ensure_object(right.ravel())) - - for left_value, right_value in zip(left, right): - if left_value is tslib.NaT and right_value is not tslib.NaT: - return False - - elif isinstance(left_value, float) and np.isnan(left_value): - if (not isinstance(right_value, float) or - not np.isnan(right_value)): - return False - else: - if left_value != right_value: - return False - return True - - # NaNs can occur in float and complex arrays. - if is_float_dtype(left) or is_complex_dtype(left): - return ((left == right) | (np.isnan(left) & np.isnan(right))).all() - - # numpy will will not allow this type of datetimelike vs integer comparison - elif is_datetimelike_v_numeric(left, right): - return False - - # M8/m8 - elif needs_i8_conversion(left) and needs_i8_conversion(right): - if not is_dtype_equal(left.dtype, right.dtype): - return False - - left = left.view('i8') - right = right.view('i8') - - # NaNs cannot occur otherwise. - try: - return np.array_equal(left, right) - except AttributeError: - # see gh-13388 - # - # NumPy v1.7.1 has a bug in its array_equal - # function that prevents it from correctly - # comparing two arrays with complex dtypes. - # This bug is corrected in v1.8.0, so remove - # this try-except block as soon as we stop - # supporting NumPy versions < 1.8.0 - if not is_dtype_equal(left.dtype, right.dtype): - return False - - left = left.tolist() - right = right.tolist() - - return left == right - - -def _iterable_not_string(x): - return (isinstance(x, collections.Iterable) and - not isinstance(x, compat.string_types)) - - def flatten(l): """Flatten an arbitrarily nested sequence. @@ -398,510 +125,6 @@ def flatten(l): yield el -def _coerce_indexer_dtype(indexer, categories): - """ coerce the indexer input array to the smallest dtype possible """ - l = len(categories) - if l < _int8_max: - return _ensure_int8(indexer) - elif l < _int16_max: - return _ensure_int16(indexer) - elif l < _int32_max: - return _ensure_int32(indexer) - return _ensure_int64(indexer) - - -def _coerce_to_dtypes(result, dtypes): - """ given a dtypes and a result set, coerce the result elements to the - dtypes - """ - if len(result) != len(dtypes): - raise AssertionError("_coerce_to_dtypes requires equal len arrays") - - from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type - - def conv(r, dtype): - try: - if isnull(r): - pass - elif dtype == _NS_DTYPE: - r = lib.Timestamp(r) - elif dtype == _TD_DTYPE: - r = _coerce_scalar_to_timedelta_type(r) - elif dtype == np.bool_: - # messy. non 0/1 integers do not get converted. - if is_integer(r) and r not in [0, 1]: - return int(r) - r = bool(r) - elif dtype.kind == 'f': - r = float(r) - elif dtype.kind == 'i': - r = int(r) - except: - pass - - return r - - return [conv(r, dtype) for r, dtype in zip(result, dtypes)] - - -def _infer_fill_value(val): - """ - infer the fill value for the nan/NaT from the provided - scalar/ndarray/list-like if we are a NaT, return the correct dtyped - element to provide proper block construction - """ - - if not is_list_like(val): - val = [val] - val = np.array(val, copy=False) - if is_datetimelike(val): - return np.array('NaT', dtype=val.dtype) - elif is_object_dtype(val.dtype): - dtype = lib.infer_dtype(_ensure_object(val)) - if dtype in ['datetime', 'datetime64']: - return np.array('NaT', dtype=_NS_DTYPE) - elif dtype in ['timedelta', 'timedelta64']: - return np.array('NaT', dtype=_TD_DTYPE) - return np.nan - - -def _infer_dtype_from_scalar(val): - """ interpret the dtype from a scalar """ - - dtype = np.object_ - - # a 1-element ndarray - if isinstance(val, np.ndarray): - if val.ndim != 0: - raise ValueError( - "invalid ndarray passed to _infer_dtype_from_scalar") - - dtype = val.dtype - val = val.item() - - elif isinstance(val, compat.string_types): - - # If we create an empty array using a string to infer - # the dtype, NumPy will only allocate one character per entry - # so this is kind of bad. Alternately we could use np.repeat - # instead of np.empty (but then you still don't want things - # coming out as np.str_! - - dtype = np.object_ - - elif isinstance(val, (np.datetime64, - datetime)) and getattr(val, 'tzinfo', None) is None: - val = lib.Timestamp(val).value - dtype = np.dtype('M8[ns]') - - elif isinstance(val, (np.timedelta64, timedelta)): - val = lib.Timedelta(val).value - dtype = np.dtype('m8[ns]') - - elif is_bool(val): - dtype = np.bool_ - - elif is_integer(val): - if isinstance(val, np.integer): - dtype = type(val) - else: - dtype = np.int64 - - elif is_float(val): - if isinstance(val, np.floating): - dtype = type(val) - else: - dtype = np.float64 - - elif is_complex(val): - dtype = np.complex_ - - return dtype, val - - -def _is_na_compat(arr, fill_value=np.nan): - """ - Parameters - ---------- - arr: a numpy array - fill_value: fill value, default to np.nan - - Returns - ------- - True if we can fill using this fill_value - """ - dtype = arr.dtype - if isnull(fill_value): - return not (is_bool_dtype(dtype) or - is_integer_dtype(dtype)) - return True - - -def _maybe_fill(arr, fill_value=np.nan): - """ - if we have a compatiable fill_value and arr dtype, then fill - """ - if _is_na_compat(arr, fill_value): - arr.fill(fill_value) - return arr - - -def _maybe_promote(dtype, fill_value=np.nan): - - # if we passed an array here, determine the fill value by dtype - if isinstance(fill_value, np.ndarray): - if issubclass(fill_value.dtype.type, (np.datetime64, np.timedelta64)): - fill_value = tslib.iNaT - else: - - # we need to change to object type as our - # fill_value is of object type - if fill_value.dtype == np.object_: - dtype = np.dtype(np.object_) - fill_value = np.nan - - # returns tuple of (dtype, fill_value) - if issubclass(dtype.type, (np.datetime64, np.timedelta64)): - # for now: refuse to upcast datetime64 - # (this is because datetime64 will not implicitly upconvert - # to object correctly as of numpy 1.6.1) - if isnull(fill_value): - fill_value = tslib.iNaT - else: - if issubclass(dtype.type, np.datetime64): - try: - fill_value = lib.Timestamp(fill_value).value - except: - # the proper thing to do here would probably be to upcast - # to object (but numpy 1.6.1 doesn't do this properly) - fill_value = tslib.iNaT - elif issubclass(dtype.type, np.timedelta64): - try: - fill_value = lib.Timedelta(fill_value).value - except: - # as for datetimes, cannot upcast to object - fill_value = tslib.iNaT - else: - fill_value = tslib.iNaT - elif is_datetimetz(dtype): - if isnull(fill_value): - fill_value = tslib.iNaT - elif is_float(fill_value): - if issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif issubclass(dtype.type, np.integer): - dtype = np.float64 - elif is_bool(fill_value): - if not issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif is_integer(fill_value): - if issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif issubclass(dtype.type, np.integer): - # upcast to prevent overflow - arr = np.asarray(fill_value) - if arr != arr.astype(dtype): - dtype = arr.dtype - elif is_complex(fill_value): - if issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif issubclass(dtype.type, (np.integer, np.floating)): - dtype = np.complex128 - elif fill_value is None: - if is_float_dtype(dtype) or is_complex_dtype(dtype): - fill_value = np.nan - elif is_integer_dtype(dtype): - dtype = np.float64 - fill_value = np.nan - elif is_datetime_or_timedelta_dtype(dtype): - fill_value = tslib.iNaT - else: - dtype = np.object_ - else: - dtype = np.object_ - - # in case we have a string that looked like a number - if is_categorical_dtype(dtype): - pass - elif is_datetimetz(dtype): - pass - elif issubclass(np.dtype(dtype).type, compat.string_types): - dtype = np.object_ - - return dtype, fill_value - - -def _maybe_upcast_putmask(result, mask, other): - """ - A safe version of putmask that potentially upcasts the result - - Parameters - ---------- - result : ndarray - The destination array. This will be mutated in-place if no upcasting is - necessary. - mask : boolean ndarray - other : ndarray or scalar - The source array or value - - Returns - ------- - result : ndarray - changed : boolean - Set to true if the result array was upcasted - """ - - if mask.any(): - # Two conversions for date-like dtypes that can't be done automatically - # in np.place: - # NaN -> NaT - # integer or integer array -> date-like array - if result.dtype in _DATELIKE_DTYPES: - if lib.isscalar(other): - if isnull(other): - other = result.dtype.type('nat') - elif is_integer(other): - other = np.array(other, dtype=result.dtype) - elif is_integer_dtype(other): - other = np.array(other, dtype=result.dtype) - - def changeit(): - - # try to directly set by expanding our array to full - # length of the boolean - try: - om = other[mask] - om_at = om.astype(result.dtype) - if (om == om_at).all(): - new_result = result.values.copy() - new_result[mask] = om_at - result[:] = new_result - return result, False - except: - pass - - # we are forced to change the dtype of the result as the input - # isn't compatible - r, _ = _maybe_upcast(result, fill_value=other, copy=True) - np.place(r, mask, other) - - return r, True - - # we want to decide whether place will work - # if we have nans in the False portion of our mask then we need to - # upcast (possibly), otherwise we DON't want to upcast (e.g. if we - # have values, say integers, in the success portion then it's ok to not - # upcast) - new_dtype, _ = _maybe_promote(result.dtype, other) - if new_dtype != result.dtype: - - # we have a scalar or len 0 ndarray - # and its nan and we are changing some values - if (lib.isscalar(other) or - (isinstance(other, np.ndarray) and other.ndim < 1)): - if isnull(other): - return changeit() - - # we have an ndarray and the masking has nans in it - else: - - if isnull(other[mask]).any(): - return changeit() - - try: - np.place(result, mask, other) - except: - return changeit() - - return result, False - - -def _maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): - """ provide explict type promotion and coercion - - Parameters - ---------- - values : the ndarray that we want to maybe upcast - fill_value : what we want to fill with - dtype : if None, then use the dtype of the values, else coerce to this type - copy : if True always make a copy even if no upcast is required - """ - - if is_extension_type(values): - if copy: - values = values.copy() - else: - if dtype is None: - dtype = values.dtype - new_dtype, fill_value = _maybe_promote(dtype, fill_value) - if new_dtype != values.dtype: - values = values.astype(new_dtype) - elif copy: - values = values.copy() - - return values, fill_value - - -def _possibly_cast_item(obj, item, dtype): - chunk = obj[item] - - if chunk.values.dtype != dtype: - if dtype in (np.object_, np.bool_): - obj[item] = chunk.astype(np.object_) - elif not issubclass(dtype, (np.integer, np.bool_)): # pragma: no cover - raise ValueError("Unexpected dtype encountered: %s" % dtype) - - -def _possibly_downcast_to_dtype(result, dtype): - """ try to cast to the specified dtype (e.g. convert back to bool/int - or could be an astype of float64->float32 - """ - - if lib.isscalar(result): - return result - - def trans(x): - return x - - if isinstance(dtype, compat.string_types): - if dtype == 'infer': - inferred_type = lib.infer_dtype(_ensure_object(result.ravel())) - if inferred_type == 'boolean': - dtype = 'bool' - elif inferred_type == 'integer': - dtype = 'int64' - elif inferred_type == 'datetime64': - dtype = 'datetime64[ns]' - elif inferred_type == 'timedelta64': - dtype = 'timedelta64[ns]' - - # try to upcast here - elif inferred_type == 'floating': - dtype = 'int64' - if issubclass(result.dtype.type, np.number): - - def trans(x): # noqa - return x.round() - else: - dtype = 'object' - - if isinstance(dtype, compat.string_types): - dtype = np.dtype(dtype) - - try: - - # don't allow upcasts here (except if empty) - if dtype.kind == result.dtype.kind: - if (result.dtype.itemsize <= dtype.itemsize and - np.prod(result.shape)): - return result - - if issubclass(dtype.type, np.floating): - return result.astype(dtype) - elif dtype == np.bool_ or issubclass(dtype.type, np.integer): - - # if we don't have any elements, just astype it - if not np.prod(result.shape): - return trans(result).astype(dtype) - - # do a test on the first element, if it fails then we are done - r = result.ravel() - arr = np.array([r[0]]) - - # if we have any nulls, then we are done - if isnull(arr).any() or not np.allclose(arr, - trans(arr).astype(dtype)): - return result - - # a comparable, e.g. a Decimal may slip in here - elif not isinstance(r[0], (np.integer, np.floating, np.bool, int, - float, bool)): - return result - - if (issubclass(result.dtype.type, (np.object_, np.number)) and - notnull(result).all()): - new_result = trans(result).astype(dtype) - try: - if np.allclose(new_result, result): - return new_result - except: - - # comparison of an object dtype with a number type could - # hit here - if (new_result == result).all(): - return new_result - - # a datetimelike - elif dtype.kind in ['M', 'm'] and result.dtype.kind in ['i']: - try: - result = result.astype(dtype) - except: - if dtype.tz: - # convert to datetime and change timezone - result = pd.to_datetime(result).tz_localize(dtype.tz) - - except: - pass - - return result - - -def _maybe_convert_string_to_object(values): - """ - - Convert string-like and string-like array to convert object dtype. - This is to avoid numpy to handle the array as str dtype. - """ - if isinstance(values, string_types): - values = np.array([values], dtype=object) - elif (isinstance(values, np.ndarray) and - issubclass(values.dtype.type, (np.string_, np.unicode_))): - values = values.astype(object) - return values - - -def _maybe_convert_scalar(values): - """ - Convert a python scalar to the appropriate numpy dtype if possible - This avoids numpy directly converting according to platform preferences - """ - if lib.isscalar(values): - dtype, values = _infer_dtype_from_scalar(values) - try: - values = dtype(values) - except TypeError: - pass - return values - - -def _lcd_dtypes(a_dtype, b_dtype): - """ return the lcd dtype to hold these types """ - - if is_datetime64_dtype(a_dtype) or is_datetime64_dtype(b_dtype): - return _NS_DTYPE - elif is_timedelta64_dtype(a_dtype) or is_timedelta64_dtype(b_dtype): - return _TD_DTYPE - elif is_complex_dtype(a_dtype): - if is_complex_dtype(b_dtype): - return a_dtype - return np.float64 - elif is_integer_dtype(a_dtype): - if is_integer_dtype(b_dtype): - if a_dtype.itemsize == b_dtype.itemsize: - return a_dtype - return np.int64 - return np.float64 - elif is_float_dtype(a_dtype): - if is_float_dtype(b_dtype): - if a_dtype.itemsize == b_dtype.itemsize: - return a_dtype - else: - return np.float64 - elif is_integer(b_dtype): - return np.float64 - return np.object - - def _consensus_name_attr(objs): name = objs[0].name for obj in objs[1:]: @@ -909,66 +132,20 @@ def _consensus_name_attr(objs): return None return name -# ---------------------------------------------------------------------- -# Lots of little utilities - - -def _validate_date_like_dtype(dtype): - try: - typ = np.datetime_data(dtype)[0] - except ValueError as e: - raise TypeError('%s' % e) - if typ != 'generic' and typ != 'ns': - raise ValueError('%r is too specific of a frequency, try passing %r' % - (dtype.name, dtype.type.__name__)) - - -def _invalidate_string_dtypes(dtype_set): - """Change string like dtypes to object for - ``DataFrame.select_dtypes()``. - """ - non_string_dtypes = dtype_set - _string_dtypes - if non_string_dtypes != dtype_set: - raise TypeError("string dtypes are not allowed, use 'object' instead") - - -def _get_dtype_from_object(dtype): - """Get a numpy dtype.type-style object. This handles the datetime64[ns] - and datetime64[ns, TZ] compat - - Notes - ----- - If nothing can be found, returns ``object``. - """ - # type object from a dtype - if isinstance(dtype, type) and issubclass(dtype, np.generic): - return dtype - elif is_categorical(dtype): - return gt.CategoricalDtype().type - elif is_datetimetz(dtype): - return gt.DatetimeTZDtype(dtype).type - elif isinstance(dtype, np.dtype): # dtype object - try: - _validate_date_like_dtype(dtype) - except TypeError: - # should still pass if we don't have a datelike - pass - return dtype.type - elif isinstance(dtype, compat.string_types): - if dtype == 'datetime' or dtype == 'timedelta': - dtype += '64' - - try: - return _get_dtype_from_object(getattr(np, dtype)) - except (AttributeError, TypeError): - # handles cases like _get_dtype(int) - # i.e., python objects that are valid dtypes (unlike user-defined - # types, in general) - # TypeError handles the float16 typecode of 'e' - # further handle internal types - pass - return _get_dtype_from_object(np.dtype(dtype)) +def _maybe_match_name(a, b): + a_has = hasattr(a, 'name') + b_has = hasattr(b, 'name') + if a_has and b_has: + if a.name == b.name: + return a.name + else: + return None + elif a_has: + return a.name + elif b_has: + return b.name + return None def _get_info_slice(obj, indexer): @@ -1005,225 +182,8 @@ def _maybe_box_datetimelike(value): _values_from_object = lib.values_from_object -def _possibly_castable(arr): - # return False to force a non-fastpath - - # check datetime64[ns]/timedelta64[ns] are valid - # otherwise try to coerce - kind = arr.dtype.kind - if kind == 'M' or kind == 'm': - return arr.dtype in _DATELIKE_DTYPES - - return arr.dtype.name not in _POSSIBLY_CAST_DTYPES - - -def _possibly_convert_platform(values): - """ try to do platform conversion, allow ndarray or list here """ - - if isinstance(values, (list, tuple)): - values = lib.list_to_object_array(values) - if getattr(values, 'dtype', None) == np.object_: - if hasattr(values, '_values'): - values = values._values - values = lib.maybe_convert_objects(values) - - return values - - -def _possibly_cast_to_datetime(value, dtype, errors='raise'): - """ try to cast the array/value to a datetimelike dtype, converting float - nan to iNaT - """ - from pandas.tseries.timedeltas import to_timedelta - from pandas.tseries.tools import to_datetime - - if dtype is not None: - if isinstance(dtype, compat.string_types): - dtype = np.dtype(dtype) - - is_datetime64 = is_datetime64_dtype(dtype) - is_datetime64tz = is_datetime64tz_dtype(dtype) - is_timedelta64 = is_timedelta64_dtype(dtype) - - if is_datetime64 or is_datetime64tz or is_timedelta64: - - # force the dtype if needed - if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE): - if dtype.name == 'datetime64[ns]': - dtype = _NS_DTYPE - else: - raise TypeError("cannot convert datetimelike to " - "dtype [%s]" % dtype) - elif is_datetime64tz: - - # our NaT doesn't support tz's - # this will coerce to DatetimeIndex with - # a matching dtype below - if lib.isscalar(value) and isnull(value): - value = [value] - - elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE): - if dtype.name == 'timedelta64[ns]': - dtype = _TD_DTYPE - else: - raise TypeError("cannot convert timedeltalike to " - "dtype [%s]" % dtype) - - if lib.isscalar(value): - if value == tslib.iNaT or isnull(value): - value = tslib.iNaT - else: - value = np.array(value, copy=False) - - # have a scalar array-like (e.g. NaT) - if value.ndim == 0: - value = tslib.iNaT - - # we have an array of datetime or timedeltas & nulls - elif np.prod(value.shape) or not is_dtype_equal(value.dtype, - dtype): - try: - if is_datetime64: - value = to_datetime(value, errors=errors)._values - elif is_datetime64tz: - # input has to be UTC at this point, so just - # localize - value = to_datetime( - value, - errors=errors).tz_localize(dtype.tz) - elif is_timedelta64: - value = to_timedelta(value, errors=errors)._values - except (AttributeError, ValueError, TypeError): - pass - - # coerce datetimelike to object - elif is_datetime64_dtype(value) and not is_datetime64_dtype(dtype): - if is_object_dtype(dtype): - ints = np.asarray(value).view('i8') - return tslib.ints_to_pydatetime(ints) - - # we have a non-castable dtype that was passed - raise TypeError('Cannot cast datetime64 to %s' % dtype) - - else: - - is_array = isinstance(value, np.ndarray) - - # catch a datetime/timedelta that is not of ns variety - # and no coercion specified - if is_array and value.dtype.kind in ['M', 'm']: - dtype = value.dtype - - if dtype.kind == 'M' and dtype != _NS_DTYPE: - value = value.astype(_NS_DTYPE) - - elif dtype.kind == 'm' and dtype != _TD_DTYPE: - value = to_timedelta(value) - - # only do this if we have an array and the dtype of the array is not - # setup already we are not an integer/object, so don't bother with this - # conversion - elif not (is_array and not (issubclass(value.dtype.type, np.integer) or - value.dtype == np.object_)): - value = _possibly_infer_to_datetimelike(value) - - return value - - -def _possibly_infer_to_datetimelike(value, convert_dates=False): - """ - we might have an array (or single object) that is datetime like, - and no dtype is passed don't change the value unless we find a - datetime/timedelta set - - this is pretty strict in that a datetime/timedelta is REQUIRED - in addition to possible nulls/string likes - - ONLY strings are NOT datetimelike - - Parameters - ---------- - value : np.array / Series / Index / list-like - convert_dates : boolean, default False - if True try really hard to convert dates (such as datetime.date), other - leave inferred dtype 'date' alone - - """ - - if isinstance(value, (gt.ABCDatetimeIndex, gt.ABCPeriodIndex)): - return value - elif isinstance(value, gt.ABCSeries): - if isinstance(value._values, gt.ABCDatetimeIndex): - return value._values - - v = value - if not is_list_like(v): - v = [v] - v = np.array(v, copy=False) - shape = v.shape - if not v.ndim == 1: - v = v.ravel() - - if len(v): - - def _try_datetime(v): - # safe coerce to datetime64 - try: - v = tslib.array_to_datetime(v, errors='raise') - except ValueError: - - # we might have a sequence of the same-datetimes with tz's - # if so coerce to a DatetimeIndex; if they are not the same, - # then these stay as object dtype - try: - from pandas import to_datetime - return to_datetime(v) - except: - pass - - except: - pass - - return v.reshape(shape) - - def _try_timedelta(v): - # safe coerce to timedelta64 - - # will try first with a string & object conversion - from pandas.tseries.timedeltas import to_timedelta - try: - return to_timedelta(v)._values.reshape(shape) - except: - return v - - # do a quick inference for perf - sample = v[:min(3, len(v))] - inferred_type = lib.infer_dtype(sample) - - if (inferred_type in ['datetime', 'datetime64'] or - (convert_dates and inferred_type in ['date'])): - value = _try_datetime(v) - elif inferred_type in ['timedelta', 'timedelta64']: - value = _try_timedelta(v) - - # It's possible to have nulls intermixed within the datetime or - # timedelta. These will in general have an inferred_type of 'mixed', - # so have to try both datetime and timedelta. - - # try timedelta first to avoid spurious datetime conversions - # e.g. '00:00:01' is a timedelta but technically is also a datetime - elif inferred_type in ['mixed']: - - if lib.is_possible_datetimelike_array(_ensure_object(v)): - value = _try_timedelta(v) - if lib.infer_dtype(value) in ['mixed']: - value = _try_datetime(v) - - return value - - def is_bool_indexer(key): - if isinstance(key, (gt.ABCSeries, np.ndarray)): + if isinstance(key, (ABCSeries, np.ndarray)): if key.dtype == np.object_: key = np.asarray(_values_from_object(key)) @@ -1250,12 +210,6 @@ def _default_index(n): return RangeIndex(0, n, name=None) -def ensure_float(arr): - if issubclass(arr.dtype.type, (np.integer, np.bool_)): - arr = arr.astype(float) - return arr - - def _mut_exclusive(**kwargs): item1, item2 = kwargs.items() label1, val1 = item1 @@ -1287,6 +241,10 @@ def _all_not_none(*args): return True +def _count_not_none(*args): + return sum(x is not None for x in args) + + def _try_sort(iterable): listed = list(iterable) try: @@ -1295,10 +253,6 @@ def _try_sort(iterable): return listed -def _count_not_none(*args): - return sum(x is not None for x in args) - - def iterpairs(seq): """ Parameters @@ -1451,349 +405,6 @@ def _maybe_make_list(obj): return [obj] return obj -# TYPE TESTING - -is_bool = lib.is_bool - -is_integer = lib.is_integer - -is_float = lib.is_float - -is_complex = lib.is_complex - - -def is_string_like(obj): - return isinstance(obj, (compat.text_type, compat.string_types)) - - -def is_iterator(obj): - # python 3 generators have __next__ instead of next - return hasattr(obj, 'next') or hasattr(obj, '__next__') - - -def is_number(obj): - return isinstance(obj, (numbers.Number, np.number)) - - -def is_period_arraylike(arr): - """ return if we are period arraylike / PeriodIndex """ - if isinstance(arr, pd.PeriodIndex): - return True - elif isinstance(arr, (np.ndarray, gt.ABCSeries)): - return arr.dtype == object and lib.infer_dtype(arr) == 'period' - return getattr(arr, 'inferred_type', None) == 'period' - - -def is_datetime_arraylike(arr): - """ return if we are datetime arraylike / DatetimeIndex """ - if isinstance(arr, gt.ABCDatetimeIndex): - return True - elif isinstance(arr, (np.ndarray, gt.ABCSeries)): - return arr.dtype == object and lib.infer_dtype(arr) == 'datetime' - return getattr(arr, 'inferred_type', None) == 'datetime' - - -def is_datetimelike(arr): - return (arr.dtype in _DATELIKE_DTYPES or - isinstance(arr, gt.ABCPeriodIndex) or - is_datetimetz(arr)) - - -def _coerce_to_dtype(dtype): - """ coerce a string / np.dtype to a dtype """ - if is_categorical_dtype(dtype): - dtype = gt.CategoricalDtype() - elif is_datetime64tz_dtype(dtype): - dtype = gt.DatetimeTZDtype(dtype) - else: - dtype = np.dtype(dtype) - return dtype - - -def _get_dtype(arr_or_dtype): - if isinstance(arr_or_dtype, np.dtype): - return arr_or_dtype - elif isinstance(arr_or_dtype, type): - return np.dtype(arr_or_dtype) - elif isinstance(arr_or_dtype, gt.CategoricalDtype): - return arr_or_dtype - elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): - return arr_or_dtype - elif isinstance(arr_or_dtype, compat.string_types): - if is_categorical_dtype(arr_or_dtype): - return gt.CategoricalDtype.construct_from_string(arr_or_dtype) - elif is_datetime64tz_dtype(arr_or_dtype): - return gt.DatetimeTZDtype.construct_from_string(arr_or_dtype) - - if hasattr(arr_or_dtype, 'dtype'): - arr_or_dtype = arr_or_dtype.dtype - return np.dtype(arr_or_dtype) - - -def _get_dtype_type(arr_or_dtype): - if isinstance(arr_or_dtype, np.dtype): - return arr_or_dtype.type - elif isinstance(arr_or_dtype, type): - return np.dtype(arr_or_dtype).type - elif isinstance(arr_or_dtype, gt.CategoricalDtype): - return gt.CategoricalDtypeType - elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): - return gt.DatetimeTZDtypeType - elif isinstance(arr_or_dtype, compat.string_types): - if is_categorical_dtype(arr_or_dtype): - return gt.CategoricalDtypeType - elif is_datetime64tz_dtype(arr_or_dtype): - return gt.DatetimeTZDtypeType - return _get_dtype_type(np.dtype(arr_or_dtype)) - try: - return arr_or_dtype.dtype.type - except AttributeError: - return type(None) - - -def is_dtype_equal(source, target): - """ return a boolean if the dtypes are equal """ - try: - source = _get_dtype(source) - target = _get_dtype(target) - return source == target - except (TypeError, AttributeError): - - # invalid comparison - # object == category will hit this - return False - - -def is_any_int_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.integer) - - -def is_integer_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return (issubclass(tipo, np.integer) and - not issubclass(tipo, (np.datetime64, np.timedelta64))) - - -def is_int64_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.int64) - - -def is_int_or_datetime_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return (issubclass(tipo, np.integer) or - issubclass(tipo, (np.datetime64, np.timedelta64))) - - -def is_datetime64_dtype(arr_or_dtype): - try: - tipo = _get_dtype_type(arr_or_dtype) - except TypeError: - return False - return issubclass(tipo, np.datetime64) - - -def is_datetime64tz_dtype(arr_or_dtype): - return gt.DatetimeTZDtype.is_dtype(arr_or_dtype) - - -def is_datetime64_any_dtype(arr_or_dtype): - return (is_datetime64_dtype(arr_or_dtype) or - is_datetime64tz_dtype(arr_or_dtype)) - - -def is_datetime64_ns_dtype(arr_or_dtype): - try: - tipo = _get_dtype(arr_or_dtype) - except TypeError: - return False - return tipo == _NS_DTYPE - - -def is_timedelta64_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.timedelta64) - - -def is_timedelta64_ns_dtype(arr_or_dtype): - tipo = _get_dtype(arr_or_dtype) - return tipo == _TD_DTYPE - - -def is_datetime_or_timedelta_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, (np.datetime64, np.timedelta64)) - - -def is_numeric_v_string_like(a, b): - """ - numpy doesn't like to compare numeric arrays vs scalar string-likes - - return a boolean result if this is the case for a,b or b,a - - """ - is_a_array = isinstance(a, np.ndarray) - is_b_array = isinstance(b, np.ndarray) - - is_a_numeric_array = is_a_array and is_numeric_dtype(a) - is_b_numeric_array = is_b_array and is_numeric_dtype(b) - is_a_string_array = is_a_array and is_string_like_dtype(a) - is_b_string_array = is_b_array and is_string_like_dtype(b) - - is_a_scalar_string_like = not is_a_array and is_string_like(a) - is_b_scalar_string_like = not is_b_array and is_string_like(b) - - return ((is_a_numeric_array and is_b_scalar_string_like) or - (is_b_numeric_array and is_a_scalar_string_like) or - (is_a_numeric_array and is_b_string_array) or - (is_b_numeric_array and is_a_string_array)) - - -def is_datetimelike_v_numeric(a, b): - # return if we have an i8 convertible and numeric comparison - if not hasattr(a, 'dtype'): - a = np.asarray(a) - if not hasattr(b, 'dtype'): - b = np.asarray(b) - - def is_numeric(x): - return is_integer_dtype(x) or is_float_dtype(x) - - is_datetimelike = needs_i8_conversion - return ((is_datetimelike(a) and is_numeric(b)) or - (is_datetimelike(b) and is_numeric(a))) - - -def is_datetimelike_v_object(a, b): - # return if we have an i8 convertible and object comparsion - if not hasattr(a, 'dtype'): - a = np.asarray(a) - if not hasattr(b, 'dtype'): - b = np.asarray(b) - - def f(x): - return is_object_dtype(x) - - def is_object(x): - return is_integer_dtype(x) or is_float_dtype(x) - - is_datetimelike = needs_i8_conversion - return ((is_datetimelike(a) and is_object(b)) or - (is_datetimelike(b) and is_object(a))) - - -def needs_i8_conversion(arr_or_dtype): - return (is_datetime_or_timedelta_dtype(arr_or_dtype) or - is_datetime64tz_dtype(arr_or_dtype)) - - -def is_numeric_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return (issubclass(tipo, (np.number, np.bool_)) and - not issubclass(tipo, (np.datetime64, np.timedelta64))) - - -def is_string_dtype(arr_or_dtype): - dtype = _get_dtype(arr_or_dtype) - return dtype.kind in ('O', 'S', 'U') - - -def is_string_like_dtype(arr_or_dtype): - # exclude object as its a mixed dtype - dtype = _get_dtype(arr_or_dtype) - return dtype.kind in ('S', 'U') - - -def is_float_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.floating) - - -def is_floating_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return isinstance(tipo, np.floating) - - -def is_bool_dtype(arr_or_dtype): - try: - tipo = _get_dtype_type(arr_or_dtype) - except ValueError: - # this isn't even a dtype - return False - return issubclass(tipo, np.bool_) - - -def is_sparse(array): - """ return if we are a sparse array """ - return isinstance(array, (gt.ABCSparseArray, gt.ABCSparseSeries)) - - -def is_datetimetz(array): - """ return if we are a datetime with tz array """ - return ((isinstance(array, gt.ABCDatetimeIndex) and - getattr(array, 'tz', None) is not None) or - is_datetime64tz_dtype(array)) - - -def is_extension_type(value): - """ - if we are a klass that is preserved by the internals - these are internal klasses that we represent (and don't use a np.array) - """ - if is_categorical(value): - return True - elif is_sparse(value): - return True - elif is_datetimetz(value): - return True - return False - - -def is_categorical(array): - """ return if we are a categorical possibility """ - return isinstance(array, gt.ABCCategorical) or is_categorical_dtype(array) - - -def is_categorical_dtype(arr_or_dtype): - return gt.CategoricalDtype.is_dtype(arr_or_dtype) - - -def is_complex_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.complexfloating) - - -def is_object_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.object_) - - -def is_re(obj): - return isinstance(obj, re._pattern_type) - - -def is_re_compilable(obj): - try: - re.compile(obj) - except TypeError: - return False - else: - return True - - -def is_list_like(arg): - return (hasattr(arg, '__iter__') and - not isinstance(arg, compat.string_and_binary_types)) - - -def is_dict_like(arg): - return hasattr(arg, '__getitem__') and hasattr(arg, 'keys') - - -def is_named_tuple(arg): - return isinstance(arg, tuple) and hasattr(arg, '_fields') - def is_null_slice(obj): """ we have a null slice """ @@ -1807,47 +418,6 @@ def is_full_slice(obj, l): obj.step is None) -def is_hashable(arg): - """Return True if hash(arg) will succeed, False otherwise. - - Some types will pass a test against collections.Hashable but fail when they - are actually hashed with hash(). - - Distinguish between these and other types by trying the call to hash() and - seeing if they raise TypeError. - - Examples - -------- - >>> a = ([],) - >>> isinstance(a, collections.Hashable) - True - >>> is_hashable(a) - False - """ - # unfortunately, we can't use isinstance(arg, collections.Hashable), which - # can be faster than calling hash, because numpy scalars on Python 3 fail - # this test - - # reconsider this decision once this numpy bug is fixed: - # https://github.com/numpy/numpy/issues/5562 - - try: - hash(arg) - except TypeError: - return False - else: - return True - - -def is_sequence(x): - try: - iter(x) - len(x) # it has a length - return not isinstance(x, compat.string_and_binary_types) - except (TypeError, AttributeError): - return False - - def _get_callable_name(obj): # typical case has name if hasattr(obj, '__name__'): @@ -1875,74 +445,6 @@ def _apply_if_callable(maybe_callable, obj, **kwargs): return maybe_callable -_string_dtypes = frozenset(map(_get_dtype_from_object, (compat.binary_type, - compat.text_type))) - -_ensure_float64 = algos.ensure_float64 -_ensure_float32 = algos.ensure_float32 -_ensure_int64 = algos.ensure_int64 -_ensure_int32 = algos.ensure_int32 -_ensure_int16 = algos.ensure_int16 -_ensure_int8 = algos.ensure_int8 -_ensure_platform_int = algos.ensure_platform_int -_ensure_object = algos.ensure_object - - -def _astype_nansafe(arr, dtype, copy=True): - """ return a view if copy is False, but - need to be very careful as the result shape could change! """ - if not isinstance(dtype, np.dtype): - dtype = _coerce_to_dtype(dtype) - - if issubclass(dtype.type, compat.text_type): - # in Py3 that's str, in Py2 that's unicode - return lib.astype_unicode(arr.ravel()).reshape(arr.shape) - elif issubclass(dtype.type, compat.string_types): - return lib.astype_str(arr.ravel()).reshape(arr.shape) - elif is_datetime64_dtype(arr): - if dtype == object: - return tslib.ints_to_pydatetime(arr.view(np.int64)) - elif dtype == np.int64: - return arr.view(dtype) - elif dtype != _NS_DTYPE: - raise TypeError("cannot astype a datetimelike from [%s] to [%s]" % - (arr.dtype, dtype)) - return arr.astype(_NS_DTYPE) - elif is_timedelta64_dtype(arr): - if dtype == np.int64: - return arr.view(dtype) - elif dtype == object: - return tslib.ints_to_pytimedelta(arr.view(np.int64)) - - # in py3, timedelta64[ns] are int64 - elif ((compat.PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE]) or - (not compat.PY3 and dtype != _TD_DTYPE)): - - # allow frequency conversions - if dtype.kind == 'm': - mask = isnull(arr) - result = arr.astype(dtype).astype(np.float64) - result[mask] = np.nan - return result - - raise TypeError("cannot astype a timedelta from [%s] to [%s]" % - (arr.dtype, dtype)) - - return arr.astype(_TD_DTYPE) - elif (np.issubdtype(arr.dtype, np.floating) and - np.issubdtype(dtype, np.integer)): - - if np.isnan(arr).any(): - raise ValueError('Cannot convert NA to integer') - elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer): - # work around NumPy brokenness, #1987 - return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape) - - if copy: - return arr.astype(dtype) - return arr.view(dtype) - - def _all_none(*args): for arg in args: if arg is not None: @@ -1988,6 +490,9 @@ class Sentinel(object): return Sentinel() +# ---------------------------------------------------------------------- +# Detect our environment + def in_interactive_session(): """ check if we're running in an interactive shell @@ -2055,21 +560,6 @@ def in_ipython_frontend(): return False -def _maybe_match_name(a, b): - a_has = hasattr(a, 'name') - b_has = hasattr(b, 'name') - if a_has and b_has: - if a.name == b.name: - return a.name - else: - return None - elif a_has: - return a.name - elif b_has: - return b.name - return None - - def _random_state(state=None): """ Helper function for processing random_state arguments. diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 3ca2c6cd014bc..5cbc968f06fa7 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -366,7 +366,7 @@ def mpl_style_cb(key): def use_inf_as_null_cb(key): - from pandas.core.common import _use_inf_as_null + from pandas.types.missing import _use_inf_as_null _use_inf_as_null(key) with cf.config_prefix('mode'): diff --git a/pandas/core/convert.py b/pandas/core/convert.py deleted file mode 100644 index 7f4fe73c688f8..0000000000000 --- a/pandas/core/convert.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Functions for converting object to other types -""" - -import numpy as np - -import pandas as pd -from pandas.core.common import (_possibly_cast_to_datetime, is_object_dtype, - isnull) -import pandas.lib as lib - - -# TODO: Remove in 0.18 or 2017, which ever is sooner -def _possibly_convert_objects(values, convert_dates=True, convert_numeric=True, - convert_timedeltas=True, copy=True): - """ if we have an object dtype, try to coerce dates and/or numbers """ - - # if we have passed in a list or scalar - if isinstance(values, (list, tuple)): - values = np.array(values, dtype=np.object_) - if not hasattr(values, 'dtype'): - values = np.array([values], dtype=np.object_) - - # convert dates - if convert_dates and values.dtype == np.object_: - - # we take an aggressive stance and convert to datetime64[ns] - if convert_dates == 'coerce': - new_values = _possibly_cast_to_datetime(values, 'M8[ns]', - errors='coerce') - - # if we are all nans then leave me alone - if not isnull(new_values).all(): - values = new_values - - else: - values = lib.maybe_convert_objects(values, - convert_datetime=convert_dates) - - # convert timedeltas - if convert_timedeltas and values.dtype == np.object_: - - if convert_timedeltas == 'coerce': - from pandas.tseries.timedeltas import to_timedelta - new_values = to_timedelta(values, coerce=True) - - # if we are all nans then leave me alone - if not isnull(new_values).all(): - values = new_values - - else: - values = lib.maybe_convert_objects( - values, convert_timedelta=convert_timedeltas) - - # convert to numeric - if values.dtype == np.object_: - if convert_numeric: - try: - new_values = lib.maybe_convert_numeric(values, set(), - coerce_numeric=True) - - # if we are all nans then leave me alone - if not isnull(new_values).all(): - values = new_values - - except: - pass - else: - # soft-conversion - values = lib.maybe_convert_objects(values) - - values = values.copy() if copy else values - - return values - - -def _soft_convert_objects(values, datetime=True, numeric=True, timedelta=True, - coerce=False, copy=True): - """ if we have an object dtype, try to coerce dates and/or numbers """ - - conversion_count = sum((datetime, numeric, timedelta)) - if conversion_count == 0: - raise ValueError('At least one of datetime, numeric or timedelta must ' - 'be True.') - elif conversion_count > 1 and coerce: - raise ValueError("Only one of 'datetime', 'numeric' or " - "'timedelta' can be True when when coerce=True.") - - if isinstance(values, (list, tuple)): - # List or scalar - values = np.array(values, dtype=np.object_) - elif not hasattr(values, 'dtype'): - values = np.array([values], dtype=np.object_) - elif not is_object_dtype(values.dtype): - # If not object, do not attempt conversion - values = values.copy() if copy else values - return values - - # If 1 flag is coerce, ensure 2 others are False - if coerce: - # Immediate return if coerce - if datetime: - return pd.to_datetime(values, errors='coerce', box=False) - elif timedelta: - return pd.to_timedelta(values, errors='coerce', box=False) - elif numeric: - return pd.to_numeric(values, errors='coerce') - - # Soft conversions - if datetime: - values = lib.maybe_convert_objects(values, convert_datetime=datetime) - - if timedelta and is_object_dtype(values.dtype): - # Object check to ensure only run if previous did not convert - values = lib.maybe_convert_objects(values, convert_timedelta=timedelta) - - if numeric and is_object_dtype(values.dtype): - try: - converted = lib.maybe_convert_numeric(values, set(), - coerce_numeric=True) - # If all NaNs, then do not-alter - values = converted if not isnull(converted).all() else values - values = values.copy() if copy else values - except: - pass - - return values diff --git a/pandas/core/frame.py b/pandas/core/frame.py index e01fc6dca6be3..4fe7b318b3a18 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -23,12 +23,43 @@ import numpy as np import numpy.ma as ma -from pandas.core.common import ( - isnull, notnull, PandasError, _try_sort, _default_index, _maybe_upcast, - is_sequence, _infer_dtype_from_scalar, _values_from_object, is_list_like, - _maybe_box_datetimelike, is_categorical_dtype, is_object_dtype, - is_extension_type, is_datetimetz, _possibly_infer_to_datetimelike, - _dict_compat) +from pandas.types.cast import (_maybe_upcast, + _infer_dtype_from_scalar, + _possibly_cast_to_datetime, + _possibly_infer_to_datetimelike, + _possibly_convert_platform, + _possibly_downcast_to_dtype, + _invalidate_string_dtypes, + _coerce_to_dtypes, + _maybe_upcast_putmask) +from pandas.types.common import (is_categorical_dtype, + is_object_dtype, + is_extension_type, + is_datetimetz, + is_datetime64_dtype, + is_bool_dtype, + is_integer_dtype, + is_float_dtype, + is_integer, + is_scalar, + needs_i8_conversion, + _get_dtype_from_object, + _lcd_dtypes, + _ensure_float, + _ensure_float64, + _ensure_int64, + _ensure_platform_int, + is_list_like, + is_iterator, + is_sequence, + is_named_tuple) +from pandas.types.missing import isnull, notnull + +from pandas.core.common import (PandasError, _try_sort, + _default_index, + _values_from_object, + _maybe_box_datetimelike, + _dict_compat) from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import (maybe_droplevels, convert_to_index_sliceable, @@ -268,7 +299,7 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, data = list(data) if len(data) > 0: if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1: - if com.is_named_tuple(data[0]) and columns is None: + if is_named_tuple(data[0]) and columns is None: columns = data[0]._fields arrays, columns = _to_arrays(data, columns, dtype=dtype) columns = _ensure_index(columns) @@ -940,7 +971,7 @@ def from_records(cls, data, index=None, exclude=None, columns=None, if columns is not None: columns = _ensure_index(columns) - if com.is_iterator(data): + if is_iterator(data): if nrows == 0: return cls() @@ -1051,7 +1082,7 @@ def to_records(self, index=True, convert_datetime64=True): y : recarray """ if index: - if com.is_datetime64_dtype(self.index) and convert_datetime64: + if is_datetime64_dtype(self.index) and convert_datetime64: ix_vals = [self.index.to_pydatetime()] else: if isinstance(self.index, MultiIndex): @@ -1436,7 +1467,7 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', def to_stata(self, fname, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, - data_label=None): + data_label=None, variable_labels=None): """ A class for writing Stata binary dta files from array-like objects @@ -1449,11 +1480,24 @@ def to_stata(self, fname, convert_dates=None, write_index=True, format that you want to use for the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either a number or a name. + write_index : bool + Write the index to Stata dataset. encoding : str Default is latin-1. Note that Stata does not support unicode. byteorder : str Can be ">", "<", "little", or "big". The default is None which uses `sys.byteorder` + time_stamp : datetime + A date time to use when writing the file. Can be None, in which + case the current time is used. + dataset_label : str + A label for the data set. Should be 80 characters or smaller. + + .. versionadded:: 0.19.0 + + variable_labels : dict + Dictionary containing columns as keys and variable labels as + values. Each label must be 80 characters or smaller. Examples -------- @@ -1469,7 +1513,8 @@ def to_stata(self, fname, convert_dates=None, write_index=True, writer = StataWriter(fname, self, convert_dates=convert_dates, encoding=encoding, byteorder=byteorder, time_stamp=time_stamp, data_label=data_label, - write_index=write_index) + write_index=write_index, + variable_labels=variable_labels) writer.write_file() @Appender(fmt.docstring_to_string, indents=1) @@ -1920,7 +1965,7 @@ def _ixs(self, i, axis=0): copy = True else: new_values = self._data.fast_xs(i) - if lib.isscalar(new_values): + if is_scalar(new_values): return new_values # if we are a copy, mark as such @@ -2072,7 +2117,7 @@ def _getitem_multilevel(self, key): return self._get_item_cache(key) def _getitem_frame(self, key): - if key.values.size and not com.is_bool_dtype(key.values): + if key.values.size and not is_bool_dtype(key.values): raise ValueError('Must pass DataFrame with boolean values only') return self.where(key) @@ -2289,7 +2334,7 @@ def select_dtypes(self, include=None, exclude=None): 5 False """ include, exclude = include or (), exclude or () - if not (com.is_list_like(include) and com.is_list_like(exclude)): + if not (is_list_like(include) and is_list_like(exclude)): raise TypeError('include and exclude must both be non-string' ' sequences') selection = tuple(map(frozenset, (include, exclude))) @@ -2300,9 +2345,9 @@ def select_dtypes(self, include=None, exclude=None): # convert the myriad valid dtypes object to a single representation include, exclude = map( - lambda x: frozenset(map(com._get_dtype_from_object, x)), selection) + lambda x: frozenset(map(_get_dtype_from_object, x)), selection) for dtypes in (include, exclude): - com._invalidate_string_dtypes(dtypes) + _invalidate_string_dtypes(dtypes) # can't both include AND exclude! if not include.isdisjoint(exclude): @@ -2392,7 +2437,7 @@ def _setitem_array(self, key, value): def _setitem_frame(self, key, value): # support boolean setting with DataFrame input, e.g. # df[df > df2] = 0 - if key.values.size and not com.is_bool_dtype(key.values): + if key.values.size and not is_bool_dtype(key.values): raise TypeError('Must pass DataFrame with boolean values only') self._check_inplace_setting(value) @@ -2586,7 +2631,7 @@ def reindexer(value): value = _sanitize_index(value, self.index, copy=False) if not isinstance(value, (np.ndarray, Index)): if isinstance(value, list) and len(value) > 0: - value = com._possibly_convert_platform(value) + value = _possibly_convert_platform(value) else: value = com._asarray_tuplesafe(value) elif value.ndim == 2: @@ -2602,7 +2647,7 @@ def reindexer(value): # upcast the scalar dtype, value = _infer_dtype_from_scalar(value) value = np.repeat(value, len(self.index)).astype(dtype) - value = com._possibly_cast_to_datetime(value, dtype) + value = _possibly_cast_to_datetime(value, dtype) # return internal types directly if is_extension_type(value): @@ -2916,8 +2961,8 @@ def _maybe_casted_values(index, labels=None): mask = labels == -1 values = values.take(labels) if mask.any(): - values, changed = com._maybe_upcast_putmask(values, mask, - np.nan) + values, changed = _maybe_upcast_putmask(values, mask, + np.nan) return values new_index = _default_index(len(new_obj)) @@ -3131,14 +3176,14 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False, raise ValueError('When sorting by column, axis must be 0 (rows)') if not isinstance(by, list): by = [by] - if com.is_sequence(ascending) and len(by) != len(ascending): + if is_sequence(ascending) and len(by) != len(ascending): raise ValueError('Length of ascending (%d) != length of by (%d)' % (len(ascending), len(by))) if len(by) > 1: from pandas.core.groupby import _lexsort_indexer def trans(v): - if com.needs_i8_conversion(v): + if needs_i8_conversion(v): return v.view('i8') return v @@ -3151,7 +3196,7 @@ def trans(v): keys.append(trans(k)) indexer = _lexsort_indexer(keys, orders=ascending, na_position=na_position) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) else: from pandas.core.groupby import _nargsort @@ -3320,7 +3365,7 @@ def sortlevel(self, level=0, axis=0, ascending=True, inplace=False, inplace=inplace, sort_remaining=sort_remaining) def _nsorted(self, columns, n, method, keep): - if not com.is_list_like(columns): + if not is_list_like(columns): columns = [columns] columns = list(columns) ser = getattr(self[columns[0]], method)(n, keep=keep) @@ -3658,28 +3703,28 @@ def combine(self, other, func, fill_value=None, overwrite=True): # if we have different dtypes, possibily promote new_dtype = this_dtype if this_dtype != other_dtype: - new_dtype = com._lcd_dtypes(this_dtype, other_dtype) + new_dtype = _lcd_dtypes(this_dtype, other_dtype) series = series.astype(new_dtype) otherSeries = otherSeries.astype(new_dtype) # see if we need to be represented as i8 (datetimelike) # try to keep us at this dtype - needs_i8_conversion = com.needs_i8_conversion(new_dtype) - if needs_i8_conversion: + needs_i8_conversion_i = needs_i8_conversion(new_dtype) + if needs_i8_conversion_i: this_dtype = new_dtype arr = func(series, otherSeries, True) else: arr = func(series, otherSeries) if do_fill: - arr = com.ensure_float(arr) + arr = _ensure_float(arr) arr[this_mask & other_mask] = NA # try to downcast back to the original dtype - if needs_i8_conversion: - arr = com._possibly_cast_to_datetime(arr, this_dtype) + if needs_i8_conversion_i: + arr = _possibly_cast_to_datetime(arr, this_dtype) else: - arr = com._possibly_downcast_to_dtype(arr, this_dtype) + arr = _possibly_downcast_to_dtype(arr, this_dtype) result[col] = arr @@ -4581,7 +4626,7 @@ def _dict_round(df, decimals): yield vals def _series_round(s, decimals): - if com.is_integer_dtype(s) or com.is_float_dtype(s): + if is_integer_dtype(s) or is_float_dtype(s): return s.round(decimals) return s @@ -4592,7 +4637,7 @@ def _series_round(s, decimals): if not decimals.index.is_unique: raise ValueError("Index of decimals must be unique") new_cols = [col for col in _dict_round(self, decimals)] - elif com.is_integer(decimals): + elif is_integer(decimals): # Dispatch to Series.round new_cols = [_series_round(v, decimals) for _, v in self.iteritems()] @@ -4634,14 +4679,14 @@ def corr(self, method='pearson', min_periods=1): mat = numeric_df.values if method == 'pearson': - correl = _algos.nancorr(com._ensure_float64(mat), minp=min_periods) + correl = _algos.nancorr(_ensure_float64(mat), minp=min_periods) elif method == 'spearman': - correl = _algos.nancorr_spearman(com._ensure_float64(mat), + correl = _algos.nancorr_spearman(_ensure_float64(mat), minp=min_periods) else: if min_periods is None: min_periods = 1 - mat = com._ensure_float64(mat).T + mat = _ensure_float64(mat).T corrf = nanops.get_corr_func(method) K = len(cols) correl = np.empty((K, K), dtype=float) @@ -4696,7 +4741,7 @@ def cov(self, min_periods=None): baseCov = np.cov(mat.T) baseCov = baseCov.reshape((len(cols), len(cols))) else: - baseCov = _algos.nancorr(com._ensure_float64(mat), cov=True, + baseCov = _algos.nancorr(_ensure_float64(mat), cov=True, minp=min_periods) return self._constructor(baseCov, index=cols, columns=cols) @@ -4825,7 +4870,7 @@ def _count_level(self, level, axis=0, numeric_only=False): level = count_axis._get_level_number(level) level_index = count_axis.levels[level] - labels = com._ensure_int64(count_axis.labels[level]) + labels = _ensure_int64(count_axis.labels[level]) counts = lib.count_level_2d(mask, labels, len(level_index), axis=0) result = DataFrame(counts, index=level_index, columns=agg_axis) @@ -4906,7 +4951,7 @@ def f(x): # try to coerce to the original dtypes item by item if we can if axis == 0: - result = com._coerce_to_dtypes(result, self.dtypes) + result = _coerce_to_dtypes(result, self.dtypes) return Series(result, index=labels) @@ -5376,13 +5421,13 @@ def _prep_ndarray(values, copy=True): return np.empty((0, 0), dtype=object) def convert(v): - return com._possibly_convert_platform(v) + return _possibly_convert_platform(v) # we could have a 1-dim or 2-dim list here # this is equiv of np.asarray, but does object conversion # and platform dtype preservation try: - if com.is_list_like(values[0]) or hasattr(values[0], 'len'): + if is_list_like(values[0]) or hasattr(values[0], 'len'): values = np.array([convert(v) for v in values]) else: values = convert(values) @@ -5570,7 +5615,7 @@ def _convert_object_array(content, columns, coerce_float=False, dtype=None): def convert(arr): if dtype != object and dtype != np.object: arr = lib.maybe_convert_objects(arr, try_float=coerce_float) - arr = com._possibly_cast_to_datetime(arr, dtype) + arr = _possibly_cast_to_datetime(arr, dtype) return arr arrays = [convert(arr) for arr in content] diff --git a/pandas/core/generic.py b/pandas/core/generic.py index b4bcae47cbbdf..dd4be571ef2b4 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -8,6 +8,29 @@ import pandas.lib as lib import pandas as pd + + +from pandas.types.common import (_coerce_to_dtype, + _ensure_int64, + needs_i8_conversion, + is_scalar, + is_integer, is_bool, + is_bool_dtype, + is_numeric_dtype, + is_datetime64_dtype, + is_timedelta64_dtype, + is_list_like, + is_dict_like, + is_re_compilable) +from pandas.types.cast import _maybe_promote, _maybe_upcast_putmask +from pandas.types.missing import isnull, notnull +from pandas.types.generic import ABCSeries, ABCPanel + +from pandas.core.common import (_values_from_object, + _maybe_box_datetimelike, + SettingWithCopyError, SettingWithCopyWarning, + AbstractMethodError) + from pandas.core.base import PandasObject from pandas.core.index import (Index, MultiIndex, _ensure_index, InvalidIndexError) @@ -25,11 +48,6 @@ from pandas.compat.numpy import function as nv from pandas.compat import (map, zip, lrange, string_types, isidentifier, set_function_name) -from pandas.core.common import (isnull, notnull, is_list_like, - _values_from_object, _maybe_promote, - _maybe_box_datetimelike, ABCSeries, - SettingWithCopyError, SettingWithCopyWarning, - AbstractMethodError) import pandas.core.nanops as nanops from pandas.util.decorators import Appender, Substitution, deprecate_kwarg from pandas.core import config @@ -46,10 +64,6 @@ Name or list of names which refer to the axis items.""") -def is_dictlike(x): - return isinstance(x, (dict, com.ABCSeries)) - - def _single_replace(self, to_replace, method, inplace, limit): if self.ndim != 1: raise TypeError('cannot replace {0} with method {1} on a {2}' @@ -116,7 +130,7 @@ def _validate_dtype(self, dtype): """ validate the passed dtype """ if dtype is not None: - dtype = com._coerce_to_dtype(dtype) + dtype = _coerce_to_dtype(dtype) # a compound dtype if dtype.kind == 'V': @@ -310,7 +324,7 @@ def _from_axes(cls, data, axes, **kwargs): def _get_axis_number(self, axis): axis = self._AXIS_ALIASES.get(axis, axis) - if com.is_integer(axis): + if is_integer(axis): if axis in self._AXIS_NAMES: return axis else: @@ -717,8 +731,8 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): 1 2 5 2 3 6 """ - non_mapper = lib.isscalar(mapper) or (com.is_list_like(mapper) and not - com.is_dict_like(mapper)) + non_mapper = is_scalar(mapper) or (is_list_like(mapper) and not + is_dict_like(mapper)) if non_mapper: return self._set_axis_name(mapper, axis=axis) else: @@ -912,7 +926,7 @@ def bool(self): v = self.squeeze() if isinstance(v, (bool, np.bool_)): return bool(v) - elif lib.isscalar(v): + elif is_scalar(v): raise ValueError("bool cannot act on a non-boolean single element " "{0}".format(self.__class__.__name__)) @@ -1130,7 +1144,7 @@ def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs): return packers.to_msgpack(path_or_buf, self, encoding=encoding, **kwargs) - def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', + def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None): """ Write records stored in a DataFrame to a SQL database. @@ -1141,12 +1155,11 @@ def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', Name of SQL table con : SQLAlchemy engine or DBAPI2 connection (legacy mode) Using SQLAlchemy makes it possible to use any DB supported by that - library. - If a DBAPI2 object, only sqlite3 is supported. - flavor : {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy engine. - 'mysql' is deprecated and will be removed in future versions, but - it will be further supported through SQLAlchemy engines. + library. If a DBAPI2 object, only sqlite3 is supported. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version, + as 'sqlite' is the only supported option if SQLAlchemy is not + installed. schema : string, default None Specify the schema (if database flavor supports this). If None, use default schema. @@ -1764,10 +1777,10 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): else: return self.take(loc, axis=axis, convert=True) - if not lib.isscalar(loc): + if not is_scalar(loc): new_index = self.index[loc] - if lib.isscalar(loc): + if is_scalar(loc): new_values = self._data.fast_xs(loc) # may need to box a datelike-scalar @@ -2340,7 +2353,7 @@ def _reindex_with_indexers(self, reindexers, fill_value=np.nan, copy=False, index = _ensure_index(index) if indexer is not None: - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) # TODO: speed up on homogeneous DataFrame objects new_data = new_data.reindex_indexer(index, indexer, axis=baxis, @@ -3202,10 +3215,10 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, return self if self.ndim == 1: - if isinstance(value, (dict, com.ABCSeries)): + if isinstance(value, (dict, ABCSeries)): from pandas import Series value = Series(value) - elif not com.is_list_like(value): + elif not is_list_like(value): pass else: raise ValueError("invalid fill value with a %s" % @@ -3215,7 +3228,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, inplace=inplace, downcast=downcast) - elif isinstance(value, (dict, com.ABCSeries)): + elif isinstance(value, (dict, ABCSeries)): if axis == 1: raise NotImplementedError('Currently only can fill ' 'with dict/Series column ' @@ -3228,7 +3241,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, obj = result[k] obj.fillna(v, limit=limit, inplace=True) return result - elif not com.is_list_like(value): + elif not is_list_like(value): new_data = self._data.fillna(value=value, limit=limit, inplace=inplace, downcast=downcast) @@ -3354,7 +3367,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, and play with this method to gain intuition about how it works. """ - if not com.is_bool(regex) and to_replace is not None: + if not is_bool(regex) and to_replace is not None: raise AssertionError("'to_replace' must be 'None' if 'regex' is " "not a bool") if axis is not None: @@ -3367,15 +3380,15 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, if value is None: # passing a single value that is scalar like # when value is None (GH5319), for compat - if not is_dictlike(to_replace) and not is_dictlike(regex): + if not is_dict_like(to_replace) and not is_dict_like(regex): to_replace = [to_replace] if isinstance(to_replace, (tuple, list)): return _single_replace(self, to_replace, method, inplace, limit) - if not is_dictlike(to_replace): - if not is_dictlike(regex): + if not is_dict_like(to_replace): + if not is_dict_like(regex): raise TypeError('If "to_replace" and "value" are both None' ' and "to_replace" is not a list, then ' 'regex must be a mapping') @@ -3385,7 +3398,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, items = list(compat.iteritems(to_replace)) keys, values = zip(*items) - are_mappings = [is_dictlike(v) for v in values] + are_mappings = [is_dict_like(v) for v in values] if any(are_mappings): if not all(are_mappings): @@ -3418,8 +3431,8 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, return self new_data = self._data - if is_dictlike(to_replace): - if is_dictlike(value): # {'A' : NA} -> {'A' : 0} + if is_dict_like(to_replace): + if is_dict_like(value): # {'A' : NA} -> {'A' : 0} res = self if inplace else self.copy() for c, src in compat.iteritems(to_replace): if c in value and c in self: @@ -3429,7 +3442,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, return None if inplace else res # {'A': NA} -> 0 - elif not com.is_list_like(value): + elif not is_list_like(value): for k, src in compat.iteritems(to_replace): if k in self: new_data = new_data.replace(to_replace=src, @@ -3441,8 +3454,8 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, raise TypeError('value argument must be scalar, dict, or ' 'Series') - elif com.is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] - if com.is_list_like(value): + elif is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] + if is_list_like(value): if len(to_replace) != len(value): raise ValueError('Replacement lists must match ' 'in length. Expecting %d got %d ' % @@ -3458,8 +3471,8 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, value=value, inplace=inplace, regex=regex) elif to_replace is None: - if not (com.is_re_compilable(regex) or - com.is_list_like(regex) or is_dictlike(regex)): + if not (is_re_compilable(regex) or + is_list_like(regex) or is_dict_like(regex)): raise TypeError("'regex' must be a string or a compiled " "regular expression or a list or dict of " "strings or regular expressions, you " @@ -3470,7 +3483,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, else: # dest iterable dict-like - if is_dictlike(value): # NA -> {'A' : 0, 'B' : -1} + if is_dict_like(value): # NA -> {'A' : 0, 'B' : -1} new_data = self._data for k, v in compat.iteritems(value): @@ -3480,7 +3493,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, inplace=inplace, regex=regex) - elif not com.is_list_like(value): # NA -> 0 + elif not is_list_like(value): # NA -> 0 new_data = self._data.replace(to_replace=to_replace, value=value, inplace=inplace, regex=regex) @@ -3792,14 +3805,14 @@ def clip(self, lower=None, upper=None, axis=None, *args, **kwargs): 3 0.230930 0.000000 4 1.100000 0.570967 """ - if isinstance(self, com.ABCPanel): + if isinstance(self, ABCPanel): raise NotImplementedError("clip is not supported yet for panels") axis = nv.validate_clip_with_axis(axis, args, kwargs) # GH 2747 (arguments were reversed) if lower is not None and upper is not None: - if lib.isscalar(lower) and lib.isscalar(upper): + if is_scalar(lower) and is_scalar(upper): lower, upper = min(lower, upper), max(lower, upper) result = self @@ -4485,10 +4498,12 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, new_other = np.array(other, dtype=self.dtype) except ValueError: new_other = np.array(other) + except TypeError: + new_other = other # we can end up comparing integers and m8[ns] # which is a numpy no no - is_i8 = com.needs_i8_conversion(self.dtype) + is_i8 = needs_i8_conversion(self.dtype) if is_i8: matches = False else: @@ -4497,7 +4512,7 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, if matches is False or not matches.all(): # coerce other to a common dtype if we can - if com.needs_i8_conversion(self.dtype): + if needs_i8_conversion(self.dtype): try: other = np.array(other, dtype=self.dtype) except: @@ -4550,7 +4565,7 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, dtype, fill_value = _maybe_promote(other.dtype) new_other = np.empty(len(icond), dtype=dtype) new_other.fill(fill_value) - com._maybe_upcast_putmask(new_other, icond, other) + _maybe_upcast_putmask(new_other, icond, other) other = new_other else: @@ -5058,7 +5073,7 @@ def describe_categorical_1d(data): if result[1] > 0: top, freq = objcounts.index[0], objcounts.iloc[0] - if com.is_datetime64_dtype(data): + if is_datetime64_dtype(data): asint = data.dropna().values.view('i8') names += ['top', 'freq', 'first', 'last'] result += [lib.Timestamp(top), freq, @@ -5071,11 +5086,11 @@ def describe_categorical_1d(data): return pd.Series(result, index=names, name=data.name) def describe_1d(data): - if com.is_bool_dtype(data): + if is_bool_dtype(data): return describe_categorical_1d(data) - elif com.is_numeric_dtype(data): + elif is_numeric_dtype(data): return describe_numeric_1d(data) - elif com.is_timedelta64_dtype(data): + elif is_timedelta64_dtype(data): return describe_numeric_1d(data) else: return describe_categorical_1d(data) @@ -5162,7 +5177,7 @@ def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, rs = (data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1) if freq is None: - mask = com.isnull(_values_from_object(self)) + mask = isnull(_values_from_object(self)) np.putmask(rs.values, mask, np.nan) return rs @@ -5327,11 +5342,12 @@ def _add_series_or_dataframe_operations(cls): @Appender(rwindow.rolling.__doc__) def rolling(self, window, min_periods=None, freq=None, center=False, - win_type=None, axis=0): + win_type=None, on=None, axis=0): axis = self._get_axis_number(axis) return rwindow.rolling(self, window=window, min_periods=min_periods, freq=freq, - center=center, win_type=win_type, axis=axis) + center=center, win_type=win_type, + on=on, axis=axis) cls.rolling = rolling @@ -5488,7 +5504,7 @@ def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func, mask_a, mask_b): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) - @Appender("Return cumulative {0} over requested axis.".format(name) + + @Appender("Return {0} over requested axis.".format(desc) + _cnum_doc) def cum_func(self, axis=None, skipna=True, *args, **kwargs): skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 077acc1e81444..6179857978b7b 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -13,6 +13,25 @@ from pandas import compat from pandas.compat.numpy import function as nv from pandas.compat.numpy import _np_version_under1p8 + +from pandas.types.common import (_DATELIKE_DTYPES, + is_numeric_dtype, + is_timedelta64_dtype, is_datetime64_dtype, + is_categorical_dtype, + is_datetime_or_timedelta_dtype, + is_bool, is_integer_dtype, + is_complex_dtype, + is_bool_dtype, + is_scalar, + _ensure_float64, + _ensure_platform_int, + _ensure_int64, + _ensure_object, + _ensure_float) +from pandas.types.cast import _possibly_downcast_to_dtype +from pandas.types.missing import isnull, notnull, _maybe_fill + +from pandas.core.common import _values_from_object, AbstractMethodError from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.categorical import Categorical @@ -30,14 +49,7 @@ import pandas.core.algorithms as algos import pandas.core.common as com -from pandas.core.common import(_possibly_downcast_to_dtype, isnull, - notnull, _DATELIKE_DTYPES, is_numeric_dtype, - is_timedelta64_dtype, is_datetime64_dtype, - is_categorical_dtype, _values_from_object, - is_datetime_or_timedelta_dtype, is_bool, - is_bool_dtype, AbstractMethodError, - _maybe_fill) -from pandas.core.config import option_context, is_callable +from pandas.core.config import option_context import pandas.lib as lib from pandas.lib import Timestamp import pandas.tslib as tslib @@ -662,7 +674,7 @@ def apply(self, func, *args, **kwargs): # resolve functions to their callable functions prior, this # wouldn't be needed if args or kwargs: - if is_callable(func): + if callable(func): @wraps(func) def f(g): @@ -752,7 +764,7 @@ def _try_cast(self, result, obj): else: dtype = obj.dtype - if not lib.isscalar(result): + if not is_scalar(result): result = _possibly_downcast_to_dtype(result, dtype) return result @@ -817,7 +829,7 @@ def _python_agg_general(self, func, *args, **kwargs): # since we are masking, make sure that we have a float object values = result if is_numeric_dtype(values.dtype): - values = com.ensure_float(values) + values = _ensure_float(values) output[name] = self._try_cast(values[mask], result) @@ -1595,7 +1607,7 @@ def size(self): """ ids, _, ngroup = self.group_info - ids = com._ensure_platform_int(ids) + ids = _ensure_platform_int(ids) out = np.bincount(ids[ids != -1], minlength=ngroup or None) return Series(out, index=self.result_index, dtype='int64') @@ -1631,7 +1643,7 @@ def group_info(self): comp_ids, obs_group_ids = self._get_compressed_labels() ngroups = len(obs_group_ids) - comp_ids = com._ensure_int64(comp_ids) + comp_ids = _ensure_int64(comp_ids) return comp_ids, obs_group_ids, ngroups def _get_compressed_labels(self): @@ -1671,7 +1683,7 @@ def get_group_levels(self): name_list = [] for ping, labels in zip(self.groupings, self.recons_labels): - labels = com._ensure_platform_int(labels) + labels = _ensure_platform_int(labels) levels = ping.group_index.take(labels) name_list.append(levels) @@ -1780,11 +1792,11 @@ def _cython_operation(self, kind, values, how, axis): values = values.view('int64') is_numeric = True elif is_bool_dtype(values.dtype): - values = _algos.ensure_float64(values) - elif com.is_integer_dtype(values): + values = _ensure_float64(values) + elif is_integer_dtype(values): values = values.astype('int64', copy=False) - elif is_numeric and not com.is_complex_dtype(values): - values = _algos.ensure_float64(values) + elif is_numeric and not is_complex_dtype(values): + values = _ensure_float64(values) else: values = values.astype(object) @@ -1793,7 +1805,7 @@ def _cython_operation(self, kind, values, how, axis): kind, how, values, is_numeric) except NotImplementedError: if is_numeric: - values = _algos.ensure_float64(values) + values = _ensure_float64(values) func, dtype_str = self._get_cython_function( kind, how, values, is_numeric) else: @@ -1821,7 +1833,7 @@ def _cython_operation(self, kind, values, how, axis): result = self._transform( result, accum, values, labels, func, is_numeric) - if com.is_integer_dtype(result): + if is_integer_dtype(result): if len(result[result == tslib.iNaT]) > 0: result = result.astype('float64') result[result == tslib.iNaT] = np.nan @@ -1834,7 +1846,7 @@ def _cython_operation(self, kind, values, how, axis): result, (counts > 0).view(np.uint8)) except ValueError: result = lib.row_bool_subset_object( - com._ensure_object(result), + _ensure_object(result), (counts > 0).view(np.uint8)) else: result = result[counts > 0] @@ -1996,7 +2008,7 @@ def generate_bins_generic(values, binner, closed): class BinGrouper(BaseGrouper): def __init__(self, bins, binlabels, filter_empty=False, mutated=False): - self.bins = com._ensure_int64(bins) + self.bins = _ensure_int64(bins) self.binlabels = _ensure_index(binlabels) self._filter_empty_groups = filter_empty self.mutated = mutated @@ -2061,7 +2073,7 @@ def group_info(self): obs_group_ids = np.arange(ngroups) rep = np.diff(np.r_[0, self.bins]) - rep = com._ensure_platform_int(rep) + rep = _ensure_platform_int(rep) if ngroups == len(self.bins): comp_ids = np.repeat(np.arange(ngroups), rep) else: @@ -2449,7 +2461,7 @@ def is_in_obj(gpr): def _is_label_like(val): return (isinstance(val, compat.string_types) or - (val is not None and lib.isscalar(val))) + (val is not None and is_scalar(val))) def _convert_grouper(axis, grouper): @@ -2671,7 +2683,7 @@ def _aggregate_multiple_funcs(self, arg, _level): results[name] = obj.aggregate(func) if isinstance(list(compat.itervalues(results))[0], - com.ABCDataFrame): + DataFrame): # let higher level handle if _level: @@ -2870,9 +2882,9 @@ def nunique(self, dropna=True): 'val.dtype must be object, got %s' % val.dtype val, _ = algos.factorize(val, sort=False) sorter = np.lexsort((val, ids)) - isnull = lambda a: a == -1 + _isnull = lambda a: a == -1 else: - isnull = com.isnull + _isnull = isnull ids, val = ids[sorter], val[sorter] @@ -2882,7 +2894,7 @@ def nunique(self, dropna=True): inc = np.r_[1, val[1:] != val[:-1]] # 1st item of each group is a new unique observation - mask = isnull(val) + mask = _isnull(val) if dropna: inc[idx] = 1 inc[mask] = 0 @@ -2998,8 +3010,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False, mi = MultiIndex(levels=levels, labels=labels, names=names, verify_integrity=False) - if com.is_integer_dtype(out): - out = com._ensure_int64(out) + if is_integer_dtype(out): + out = _ensure_int64(out) return Series(out, index=mi, name=self.name) # for compat. with algos.value_counts need to ensure every @@ -3029,8 +3041,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False, mi = MultiIndex(levels=levels, labels=labels, names=names, verify_integrity=False) - if com.is_integer_dtype(out): - out = com._ensure_int64(out) + if is_integer_dtype(out): + out = _ensure_int64(out) return Series(out, index=mi, name=self.name) def count(self): @@ -3039,7 +3051,7 @@ def count(self): val = self.obj.get_values() mask = (ids != -1) & ~isnull(val) - ids = com._ensure_platform_int(ids) + ids = _ensure_platform_int(ids) out = np.bincount(ids[mask], minlength=ngroups or None) return Series(out, @@ -3616,7 +3628,7 @@ def filter(self, func, dropna=True, *args, **kwargs): # noqa pass # interpret the result of the filter - if is_bool(res) or (lib.isscalar(res) and isnull(res)): + if is_bool(res) or (is_scalar(res) and isnull(res)): if res and notnull(res): indices.append(self._get_index(name)) else: @@ -3813,7 +3825,7 @@ def count(self): """ Compute count of group, excluding missing values """ from functools import partial from pandas.lib import count_level_2d - from pandas.core.common import _isnull_ndarraylike as isnull + from pandas.types.missing import _isnull_ndarraylike as isnull data, _ = self._get_data_to_aggregate() ids, _, ngroups = self.grouper.group_info @@ -3934,7 +3946,7 @@ class DataSplitter(object): def __init__(self, data, labels, ngroups, axis=0): self.data = data - self.labels = com._ensure_int64(labels) + self.labels = _ensure_int64(labels) self.ngroups = ngroups self.axis = axis @@ -4115,7 +4127,7 @@ def loop(labels, shape): def maybe_lift(lab, size): # pormote nan values return (lab + 1, size + 1) if (lab == -1).any() else (lab, size) - labels = map(com._ensure_int64, labels) + labels = map(_ensure_int64, labels) if not xnull: labels, shape = map(list, zip(*map(maybe_lift, labels, shape))) @@ -4331,9 +4343,9 @@ def _get_group_index_sorter(group_index, ngroups): alpha = 0.0 # taking complexities literally; there may be beta = 1.0 # some room for fine-tuning these parameters if alpha + beta * ngroups < count * np.log(count): - sorter, _ = _algos.groupsort_indexer(com._ensure_int64(group_index), + sorter, _ = _algos.groupsort_indexer(_ensure_int64(group_index), ngroups) - return com._ensure_platform_int(sorter) + return _ensure_platform_int(sorter) else: return group_index.argsort(kind='mergesort') @@ -4348,7 +4360,7 @@ def _compress_group_index(group_index, sort=True): size_hint = min(len(group_index), _hash._SIZE_HINT_LIMIT) table = _hash.Int64HashTable(size_hint) - group_index = com._ensure_int64(group_index) + group_index = _ensure_int64(group_index) # note, group labels come out ascending (ie, 1,2,3 etc) comp_ids, obs_group_ids = table.get_labels_groupby(group_index) @@ -4390,7 +4402,7 @@ def _groupby_indices(values): _, counts = _hash.value_count_scalar64(codes, False) else: reverse, codes, counts = _algos.group_labels( - _values_from_object(com._ensure_object(values))) + _values_from_object(_ensure_object(values))) return _algos.groupby_indices(reverse, codes, counts) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 9485f50ed07f1..0cba8308c1c53 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1,17 +1,24 @@ # pylint: disable=W0223 -from pandas.core.index import Index, MultiIndex +import numpy as np from pandas.compat import range, zip import pandas.compat as compat +from pandas.types.generic import ABCDataFrame, ABCPanel, ABCSeries +from pandas.types.common import (is_integer_dtype, + is_integer, is_float, + is_categorical_dtype, + is_list_like, + is_sequence, + is_scalar, + _ensure_platform_int) +from pandas.types.missing import isnull, _infer_fill_value + +from pandas.core.index import Index, MultiIndex + import pandas.core.common as com -import pandas.lib as lib -from pandas.core.common import (is_bool_indexer, is_integer_dtype, - _asarray_tuplesafe, is_list_like, isnull, - is_null_slice, is_full_slice, ABCSeries, - ABCDataFrame, ABCPanel, is_float, - _values_from_object, _infer_fill_value, - is_integer) -import numpy as np +from pandas.core.common import (is_bool_indexer, _asarray_tuplesafe, + is_null_slice, is_full_slice, + _values_from_object) # the supported indexers @@ -67,7 +74,7 @@ def __getitem__(self, key): key = tuple(com._apply_if_callable(x, self.obj) for x in key) try: values = self.obj.get_value(*key) - if lib.isscalar(values): + if is_scalar(values): return values except Exception: pass @@ -625,7 +632,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False): # we have a frame, with multiple indexers on both axes; and a # series, so need to broadcast (see GH5206) if (sum_aligners == self.ndim and - all([com.is_sequence(_) for _ in indexer])): + all([is_sequence(_) for _ in indexer])): ser = ser.reindex(obj.axes[0][indexer[0]], copy=True)._values # single indexer @@ -639,7 +646,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False): ax = obj.axes[i] # multiple aligners (or null slices) - if com.is_sequence(idx) or isinstance(idx, slice): + if is_sequence(idx) or isinstance(idx, slice): if single_aligner and is_null_slice(idx): continue new_ix = ax[idx] @@ -685,7 +692,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False): return ser - elif lib.isscalar(indexer): + elif is_scalar(indexer): ax = self.obj._get_axis(1) if ser.index.equals(ax): @@ -710,7 +717,7 @@ def _align_frame(self, indexer, df): sindexers = [] for i, ix in enumerate(indexer): ax = self.obj.axes[i] - if com.is_sequence(ix) or isinstance(ix, slice): + if is_sequence(ix) or isinstance(ix, slice): if idx is None: idx = ax[ix].ravel() elif cols is None: @@ -761,7 +768,7 @@ def _align_frame(self, indexer, df): val = df.reindex(index=ax)._values return val - elif lib.isscalar(indexer) and is_panel: + elif is_scalar(indexer) and is_panel: idx = self.obj.axes[1] cols = self.obj.axes[2] @@ -857,7 +864,7 @@ def _convert_for_reindex(self, key, axis=0): keyarr = _asarray_tuplesafe(key) if is_integer_dtype(keyarr) and not labels.is_integer(): - keyarr = com._ensure_platform_int(keyarr) + keyarr = _ensure_platform_int(keyarr) return labels.take(keyarr) return keyarr @@ -968,7 +975,7 @@ def _getitem_nested_tuple(self, tup): axis += 1 # if we have a scalar, we are done - if lib.isscalar(obj) or not hasattr(obj, 'ndim'): + if is_scalar(obj) or not hasattr(obj, 'ndim'): break # has the dim of the obj changed? @@ -1038,7 +1045,7 @@ def _getitem_iterable(self, key, axis=0): # asarray can be unsafe, NumPy strings are weird keyarr = _asarray_tuplesafe(key) - if com.is_categorical_dtype(labels): + if is_categorical_dtype(labels): keyarr = labels._shallow_copy(keyarr) # have the index handle the indexer and possibly return @@ -1799,7 +1806,7 @@ def check_bool_indexer(ax, key): result = key if isinstance(key, ABCSeries) and not key.index.equals(ax): result = result.reindex(ax) - mask = com.isnull(result._values) + mask = isnull(result._values) if mask.any(): raise IndexingError('Unalignable boolean Series key provided') @@ -1941,9 +1948,9 @@ def _non_reducing_slice(slice_): def pred(part): # true when slice does *not* reduce - return isinstance(part, slice) or com.is_list_like(part) + return isinstance(part, slice) or is_list_like(part) - if not com.is_list_like(slice_): + if not is_list_like(slice_): if not isinstance(slice_, slice): # a 1-d slice, like df.loc[1] slice_ = [[slice_]] diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 1ea567f15cb7f..8e77486457546 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -10,29 +10,48 @@ from pandas.core.base import PandasObject -from pandas.core.common import (_possibly_downcast_to_dtype, isnull, _NS_DTYPE, - _TD_DTYPE, ABCSeries, is_list_like, - _infer_dtype_from_scalar, is_null_slice, - is_dtype_equal, is_null_datelike_scalar, - _maybe_promote, is_timedelta64_dtype, - is_datetime64_dtype, is_datetimetz, is_sparse, - array_equivalent, _is_na_compat, - _maybe_convert_string_to_object, - _maybe_convert_scalar, - is_categorical, is_datetimelike_v_numeric, - is_numeric_v_string_like, is_extension_type) +from pandas.types.dtypes import DatetimeTZDtype, CategoricalDtype +from pandas.types.common import (_TD_DTYPE, _NS_DTYPE, + _ensure_int64, _ensure_platform_int, + is_integer, + is_dtype_equal, + is_timedelta64_dtype, + is_datetime64_dtype, is_datetimetz, is_sparse, + is_categorical, is_categorical_dtype, + is_integer_dtype, + is_datetime64tz_dtype, + is_object_dtype, + is_datetimelike_v_numeric, + is_numeric_v_string_like, is_extension_type, + is_list_like, + is_re, + is_re_compilable, + is_scalar, + _get_dtype) +from pandas.types.cast import (_possibly_downcast_to_dtype, + _maybe_convert_string_to_object, + _maybe_upcast, + _maybe_convert_scalar, _maybe_promote, + _infer_dtype_from_scalar, + _soft_convert_objects, + _possibly_convert_objects, + _astype_nansafe) +from pandas.types.missing import (isnull, array_equivalent, + _is_na_compat, + is_null_datelike_scalar) +import pandas.types.concat as _concat + +from pandas.types.generic import ABCSeries +from pandas.core.common import is_null_slice import pandas.core.algorithms as algos -from pandas.types.api import DatetimeTZDtype from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import maybe_convert_indices, length_of_indexer from pandas.core.categorical import Categorical, maybe_to_categorical from pandas.tseries.index import DatetimeIndex from pandas.formats.printing import pprint_thing -import pandas.core.common as com -import pandas.types.concat as _concat + import pandas.core.missing as missing -import pandas.core.convert as convert from pandas.sparse.array import _maybe_to_sparse, SparseArray import pandas.lib as lib import pandas.tslib as tslib @@ -112,8 +131,8 @@ def is_categorical_astype(self, dtype): validate that we have a astypeable to categorical, returns a boolean if we are a categorical """ - if com.is_categorical_dtype(dtype): - if dtype == com.CategoricalDtype(): + if is_categorical_dtype(dtype): + if dtype == CategoricalDtype(): return True # this is a pd.Categorical, but is not @@ -137,7 +156,7 @@ def get_values(self, dtype=None): return an internal format, currently just the ndarray this is often overriden to handle to_dense like operations """ - if com.is_object_dtype(dtype): + if is_object_dtype(dtype): return self.values.astype(object) return self.values @@ -481,7 +500,7 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, values = self.get_values(dtype=dtype) # _astype_nansafe works fine with 1-d only - values = com._astype_nansafe(values.ravel(), dtype, copy=True) + values = _astype_nansafe(values.ravel(), dtype, copy=True) values = values.reshape(self.shape) newb = make_block(values, placement=self.mgr_locs, dtype=dtype, @@ -651,7 +670,7 @@ def setitem(self, indexer, value, mgr=None): # cast the values to a type that can hold nan (if necessary) if not self._can_hold_element(value): - dtype, _ = com._maybe_promote(arr_value.dtype) + dtype, _ = _maybe_promote(arr_value.dtype) values = values.astype(dtype) transf = (lambda x: x.T) if self.ndim == 2 else (lambda x: x) @@ -684,7 +703,7 @@ def _is_scalar_indexer(indexer): if arr_value.ndim == 1: if not isinstance(indexer, tuple): indexer = tuple([indexer]) - return all([lib.isscalar(idx) for idx in indexer]) + return all([is_scalar(idx) for idx in indexer]) return False def _is_empty_indexer(indexer): @@ -724,7 +743,7 @@ def _is_empty_indexer(indexer): if hasattr(value, 'dtype') and is_dtype_equal(values.dtype, value.dtype): dtype = value.dtype - elif lib.isscalar(value): + elif is_scalar(value): dtype, _ = _infer_dtype_from_scalar(value) else: dtype = 'infer' @@ -838,7 +857,7 @@ def putmask(self, mask, new, align=True, inplace=False, axis=0, n = np.array(new) # type of the new block - dtype, _ = com._maybe_promote(n.dtype) + dtype, _ = _maybe_promote(n.dtype) # we need to explicitly astype here to make a copy n = n.astype(dtype) @@ -1027,7 +1046,7 @@ def shift(self, periods, axis=0, mgr=None): # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also - new_values, fill_value = com._maybe_upcast(self.values) + new_values, fill_value = _maybe_upcast(self.values) # make sure array sent to np.roll is c_contiguous f_ordered = new_values.flags.f_contiguous @@ -1036,7 +1055,7 @@ def shift(self, periods, axis=0, mgr=None): axis = new_values.ndim - axis - 1 if np.prod(new_values.shape): - new_values = np.roll(new_values, com._ensure_platform_int(periods), + new_values = np.roll(new_values, _ensure_platform_int(periods), axis=axis) axis_indexer = [slice(None)] * self.ndim @@ -1306,7 +1325,7 @@ def quantile(self, qs, interpolation='linear', axis=0, mgr=None): from pandas import Float64Index is_empty = values.shape[axis] == 0 - if com.is_list_like(qs): + if is_list_like(qs): ax = Float64Index(qs) if is_empty: @@ -1350,7 +1369,7 @@ def quantile(self, qs, interpolation='linear', axis=0, mgr=None): ndim = getattr(result, 'ndim', None) or 0 result = self._try_coerce_result(result) - if lib.isscalar(result): + if is_scalar(result): return ax, self.make_block_scalar(result) return ax, make_block(result, placement=np.arange(len(result)), @@ -1471,7 +1490,7 @@ def putmask(self, mask, new, align=True, inplace=False, axis=0, if isinstance(new, np.ndarray) and len(new) == len(mask): new = new[mask] - mask = mask.reshape(new_values.shape) + mask = _safe_reshape(mask, new_values.shape) new_values[mask] = new new_values = self._try_coerce_result(new_values) return [self.make_block(values=new_values)] @@ -1591,7 +1610,7 @@ def _can_hold_element(self, element): tipo = element.dtype.type return (issubclass(tipo, np.integer) and not issubclass(tipo, (np.datetime64, np.timedelta64))) - return com.is_integer(element) + return is_integer(element) def _try_cast(self, element): try: @@ -1600,7 +1619,7 @@ def _try_cast(self, element): return element def should_store(self, value): - return com.is_integer_dtype(value) and value.dtype == self.dtype + return is_integer_dtype(value) and value.dtype == self.dtype class DatetimeLikeBlockMixin(object): @@ -1621,7 +1640,7 @@ def get_values(self, dtype=None): """ return object dtype as boxed values, such as Timestamps/Timedelta """ - if com.is_object_dtype(dtype): + if is_object_dtype(dtype): return lib.map_infer(self.values.ravel(), self._box_func).reshape(self.values.shape) return self.values @@ -1641,7 +1660,7 @@ def fillna(self, value, **kwargs): # allow filling with integers to be # interpreted as seconds - if not isinstance(value, np.timedelta64) and com.is_integer(value): + if not isinstance(value, np.timedelta64) and is_integer(value): value = Timedelta(value, unit='s') return super(TimeDeltaBlock, self).fillna(value, **kwargs) @@ -1795,10 +1814,10 @@ def convert(self, *args, **kwargs): new_style |= kw in kwargs if new_style: - fn = convert._soft_convert_objects + fn = _soft_convert_objects fn_inputs = new_inputs else: - fn = convert._possibly_convert_objects + fn = _possibly_convert_objects fn_inputs = ['convert_dates', 'convert_numeric', 'convert_timedeltas'] fn_inputs += ['copy'] @@ -1820,7 +1839,7 @@ def convert(self, *args, **kwargs): try: values = values.reshape(shape) values = _block_shape(values, ndim=self.ndim) - except AttributeError: + except (AttributeError, NotImplementedError): pass newb = make_block(values, ndim=self.ndim, placement=[rl]) blocks.append(newb) @@ -1884,15 +1903,15 @@ def should_store(self, value): def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mgr=None): - to_rep_is_list = com.is_list_like(to_replace) - value_is_list = com.is_list_like(value) + to_rep_is_list = is_list_like(to_replace) + value_is_list = is_list_like(value) both_lists = to_rep_is_list and value_is_list either_list = to_rep_is_list or value_is_list result_blocks = [] blocks = [self] - if not either_list and com.is_re(to_replace): + if not either_list and is_re(to_replace): return self._replace_single(to_replace, value, inplace=inplace, filter=filter, regex=True, convert=convert, mgr=mgr) @@ -1930,10 +1949,10 @@ def replace(self, to_replace, value, inplace=False, filter=None, def _replace_single(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mgr=None): # to_replace is regex compilable - to_rep_re = regex and com.is_re_compilable(to_replace) + to_rep_re = regex and is_re_compilable(to_replace) # regex is regex compilable - regex_re = com.is_re_compilable(regex) + regex_re = is_re_compilable(regex) # only one will survive if to_rep_re and regex_re: @@ -2046,7 +2065,7 @@ def _try_coerce_result(self, result): # GH12564: CategoricalBlock is 1-dim only # while returned results could be any dim - if ((not com.is_categorical_dtype(result)) and + if ((not is_categorical_dtype(result)) and isinstance(result, np.ndarray)): result = _block_shape(result, ndim=self.ndim) @@ -2151,7 +2170,7 @@ def _astype(self, dtype, mgr=None, **kwargs): """ # if we are passed a datetime64[ns, tz] - if com.is_datetime64tz_dtype(dtype): + if is_datetime64tz_dtype(dtype): dtype = DatetimeTZDtype(dtype) values = self.values @@ -2167,7 +2186,7 @@ def _can_hold_element(self, element): if is_list_like(element): element = np.array(element) return element.dtype == _NS_DTYPE or element.dtype == np.int64 - return (com.is_integer(element) or isinstance(element, datetime) or + return (is_integer(element) or isinstance(element, datetime) or isnull(element)) def _try_cast(self, element): @@ -2209,7 +2228,7 @@ def _try_coerce_args(self, values, other): "naive Block") other_mask = isnull(other) other = other.asm8.view('i8') - elif hasattr(other, 'dtype') and com.is_integer_dtype(other): + elif hasattr(other, 'dtype') and is_integer_dtype(other): other = other.view('i8') else: try: @@ -2315,7 +2334,7 @@ def external_values(self): def get_values(self, dtype=None): # return object dtype as Timestamps with the zones - if com.is_object_dtype(dtype): + if is_object_dtype(dtype): f = lambda x: lib.Timestamp(x, tz=self.values.tz) return lib.map_infer( self.values.ravel(), f).reshape(self.values.shape) @@ -2561,7 +2580,7 @@ def shift(self, periods, axis=0, mgr=None): new_values = self.values.to_dense().take(indexer) # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also - new_values, fill_value = com._maybe_upcast(new_values) + new_values, fill_value = _maybe_upcast(new_values) if periods > 0: new_values[:periods] = fill_value else: @@ -3491,7 +3510,7 @@ def get(self, item, fastpath=True): indexer = np.arange(len(self.items))[isnull(self.items)] # allow a single nan location indexer - if not lib.isscalar(indexer): + if not is_scalar(indexer): if len(indexer) == 1: loc = indexer.item() else: @@ -3597,7 +3616,7 @@ def value_getitem(placement): return value else: if value.ndim == self.ndim - 1: - value = value.reshape((1,) + value.shape) + value = _safe_reshape(value, (1,) + value.shape) def value_getitem(placement): return value @@ -3823,7 +3842,7 @@ def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None): return [blk.getitem_block(slobj, new_mgr_locs=slice(0, sllen))] elif not allow_fill or self.ndim == 1: if allow_fill and fill_tuple[0] is None: - _, fill_value = com._maybe_promote(blk.dtype) + _, fill_value = _maybe_promote(blk.dtype) fill_tuple = (fill_value, ) return [blk.take_nd(slobj, axis=0, @@ -3881,7 +3900,7 @@ def _make_na_block(self, placement, fill_value=None): block_shape = list(self.shape) block_shape[0] = len(placement) - dtype, fill_value = com._infer_dtype_from_scalar(fill_value) + dtype, fill_value = _infer_dtype_from_scalar(fill_value) block_values = np.empty(block_shape, dtype=dtype) block_values.fill(fill_value) return make_block(block_values, placement=placement) @@ -4560,7 +4579,7 @@ def _possibly_compare(a, b, op): else: result = op(a, b) - if lib.isscalar(result) and (is_a_array or is_b_array): + if is_scalar(result) and (is_a_array or is_b_array): type_names = [type(a).__name__, type(b).__name__] if is_a_array: @@ -4611,7 +4630,7 @@ def _factor_indexer(shape, labels): expanded label indexer """ mult = np.array(shape)[::-1].cumprod()[::-1] - return com._ensure_platform_int( + return _ensure_platform_int( np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T) @@ -4631,7 +4650,7 @@ def _get_blkno_placements(blknos, blk_count, group=True): """ - blknos = com._ensure_int64(blknos) + blknos = _ensure_int64(blknos) # FIXME: blk_count is unused, but it may avoid the use of dicts in cython for blkno, indexer in lib.get_blkno_indexers(blknos, group): @@ -4667,6 +4686,28 @@ def rrenamer(x): _transform_index(right, rrenamer)) +def _safe_reshape(arr, new_shape): + """ + If possible, reshape `arr` to have shape `new_shape`, + with a couple of exceptions (see gh-13012): + + 1) If `arr` is a Categorical or Index, `arr` will be + returned as is. + 2) If `arr` is a Series, the `_values` attribute will + be reshaped and returned. + + Parameters + ---------- + arr : array-like, object to be reshaped + new_shape : int or tuple of ints, the new shape + """ + if isinstance(arr, ABCSeries): + arr = arr._values + if not isinstance(arr, Categorical): + arr = arr.reshape(new_shape) + return arr + + def _transform_index(index, func): """ Apply function to all values found in index. @@ -4721,7 +4762,7 @@ def _putmask_smart(v, m, n): pass # change the dtype - dtype, _ = com._maybe_promote(n.dtype) + dtype, _ = _maybe_promote(n.dtype) nv = v.astype(dtype) try: nv[m] = n[m] @@ -4787,9 +4828,9 @@ def get_empty_dtype_and_na(join_units): if dtype is None: continue - if com.is_categorical_dtype(dtype): + if is_categorical_dtype(dtype): upcast_cls = 'category' - elif com.is_datetimetz(dtype): + elif is_datetimetz(dtype): upcast_cls = 'datetimetz' elif issubclass(dtype.type, np.bool_): upcast_cls = 'bool' @@ -5062,8 +5103,8 @@ def dtype(self): if not self.needs_filling: return self.block.dtype else: - return com._get_dtype(com._maybe_promote(self.block.dtype, - self.block.fill_value)[0]) + return _get_dtype(_maybe_promote(self.block.dtype, + self.block.fill_value)[0]) return self._dtype diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 911fcaf529f98..b847415f274db 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -5,10 +5,15 @@ import numpy as np from distutils.version import LooseVersion -import pandas.core.common as com import pandas.algos as algos import pandas.lib as lib from pandas.compat import range, string_types +from pandas.types.common import (is_numeric_v_string_like, + is_float_dtype, is_datetime64_dtype, + is_integer_dtype, _ensure_float64, + is_scalar, + _DATELIKE_DTYPES) +from pandas.types.missing import isnull def mask_missing(arr, values_to_mask): @@ -24,7 +29,7 @@ def mask_missing(arr, values_to_mask): except Exception: values_to_mask = np.array(values_to_mask, dtype=object) - na_mask = com.isnull(values_to_mask) + na_mask = isnull(values_to_mask) nonna = values_to_mask[~na_mask] mask = None @@ -32,28 +37,28 @@ def mask_missing(arr, values_to_mask): if mask is None: # numpy elementwise comparison warning - if com.is_numeric_v_string_like(arr, x): + if is_numeric_v_string_like(arr, x): mask = False else: mask = arr == x # if x is a string and arr is not, then we get False and we must # expand the mask to size arr.shape - if lib.isscalar(mask): + if is_scalar(mask): mask = np.zeros(arr.shape, dtype=bool) else: # numpy elementwise comparison warning - if com.is_numeric_v_string_like(arr, x): + if is_numeric_v_string_like(arr, x): mask |= False else: mask |= arr == x if na_mask.any(): if mask is None: - mask = com.isnull(arr) + mask = isnull(arr) else: - mask |= com.isnull(arr) + mask |= isnull(arr) return mask @@ -110,7 +115,7 @@ def interpolate_1d(xvalues, yvalues, method='linear', limit=None, """ # Treat the original, non-scipy methods first. - invalid = com.isnull(yvalues) + invalid = isnull(yvalues) valid = ~invalid if not valid.any(): @@ -442,12 +447,12 @@ def pad_1d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'pad_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _pad_1d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.pad_inplace_float64 elif values.dtype == np.object_: _method = algos.pad_inplace_object @@ -456,7 +461,7 @@ def pad_1d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for pad_1d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) _method(values, mask, limit=limit) return values @@ -467,12 +472,12 @@ def backfill_1d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'backfill_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _backfill_1d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.backfill_inplace_float64 elif values.dtype == np.object_: _method = algos.backfill_inplace_object @@ -481,7 +486,7 @@ def backfill_1d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for backfill_1d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) _method(values, mask, limit=limit) @@ -493,12 +498,12 @@ def pad_2d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'pad_2d_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _pad_2d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.pad_2d_inplace_float64 elif values.dtype == np.object_: _method = algos.pad_2d_inplace_object @@ -507,7 +512,7 @@ def pad_2d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for pad_2d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) if np.all(values.shape): @@ -523,12 +528,12 @@ def backfill_2d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'backfill_2d_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _backfill_2d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.backfill_2d_inplace_float64 elif values.dtype == np.object_: _method = algos.backfill_2d_inplace_object @@ -537,7 +542,7 @@ def backfill_2d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for backfill_2d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) if np.all(values.shape): @@ -570,22 +575,22 @@ def fill_zeros(result, x, y, name, fill): mask the nan's from x """ - if fill is None or com.is_float_dtype(result): + if fill is None or is_float_dtype(result): return result if name.startswith(('r', '__r')): x, y = y, x - is_typed_variable = (hasattr(y, 'dtype') or hasattr(y, 'type')) - is_scalar = lib.isscalar(y) + is_variable_type = (hasattr(y, 'dtype') or hasattr(y, 'type')) + is_scalar_type = is_scalar(y) - if not is_typed_variable and not is_scalar: + if not is_variable_type and not is_scalar_type: return result - if is_scalar: + if is_scalar_type: y = np.array(y) - if com.is_integer_dtype(y): + if is_integer_dtype(y): if (y == 0).any(): diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index f390e3f04a6c3..7b89373dda7ba 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -11,16 +11,19 @@ import pandas.hashtable as _hash from pandas import compat, lib, algos, tslib -from pandas.core.common import (isnull, notnull, _values_from_object, - _maybe_upcast_putmask, _ensure_float64, - _ensure_int64, _ensure_object, is_float, - is_integer, is_complex, is_float_dtype, - is_complex_dtype, is_integer_dtype, - is_bool_dtype, is_object_dtype, - is_datetime64_dtype, is_timedelta64_dtype, - is_datetime_or_timedelta_dtype, _get_dtype, - is_int_or_datetime_dtype, is_any_int_dtype, - _int64_max) +from pandas.types.common import (_ensure_int64, _ensure_object, + _ensure_float64, _get_dtype, + is_float, is_scalar, + is_integer, is_complex, is_float_dtype, + is_complex_dtype, is_integer_dtype, + is_bool_dtype, is_object_dtype, + is_datetime64_dtype, is_timedelta64_dtype, + is_datetime_or_timedelta_dtype, + is_int_or_datetime_dtype, is_any_int_dtype) +from pandas.types.cast import _int64_max, _maybe_upcast_putmask +from pandas.types.missing import isnull, notnull + +from pandas.core.common import _values_from_object class disallow(object): @@ -351,7 +354,7 @@ def _get_counts_nanvar(mask, axis, ddof, dtype=float): d = count - dtype.type(ddof) # always return NaN, never inf - if lib.isscalar(count): + if is_scalar(count): if count <= ddof: count = np.nan d = np.nan @@ -623,7 +626,7 @@ def _get_counts(mask, axis, dtype=float): return dtype.type(mask.size - mask.sum()) count = mask.shape[axis] - mask.sum(axis) - if lib.isscalar(count): + if is_scalar(count): return dtype.type(count) try: return count.astype(dtype) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 3aaca1eea486e..44e3be32c23df 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -13,21 +13,25 @@ from pandas import compat, lib, tslib import pandas.index as _index from pandas.util.decorators import Appender -import pandas.core.common as com import pandas.computation.expressions as expressions from pandas.lib import isscalar from pandas.tslib import iNaT from pandas.compat import bind_method import pandas.core.missing as missing import pandas.algos as _algos -from pandas.core.common import (is_list_like, notnull, isnull, - _values_from_object, _maybe_match_name, - needs_i8_conversion, is_datetimelike_v_numeric, - is_integer_dtype, is_categorical_dtype, - is_object_dtype, is_timedelta64_dtype, - is_datetime64_dtype, is_datetime64tz_dtype, - is_bool_dtype, PerformanceWarning, - ABCSeries, ABCIndex) +from pandas.core.common import (_values_from_object, _maybe_match_name, + PerformanceWarning) +from pandas.types.missing import notnull, isnull +from pandas.types.common import (needs_i8_conversion, + is_datetimelike_v_numeric, + is_integer_dtype, is_categorical_dtype, + is_object_dtype, is_timedelta64_dtype, + is_datetime64_dtype, is_datetime64tz_dtype, + is_bool_dtype, is_datetimetz, + is_list_like, + _ensure_object) +from pandas.types.cast import _maybe_upcast_putmask +from pandas.types.generic import ABCSeries, ABCIndex, ABCPeriodIndex # ----------------------------------------------------------------------------- # Functions that add arithmetic methods to objects, given arithmetic factory @@ -446,7 +450,7 @@ def _convert_to_array(self, values, name=None, other=None): supplied_dtype = values.dtype inferred_type = supplied_dtype or lib.infer_dtype(values) if (inferred_type in ('datetime64', 'datetime', 'date', 'time') or - com.is_datetimetz(inferred_type)): + is_datetimetz(inferred_type)): # if we have a other of timedelta, but use pd.NaT here we # we are in the wrong path if (supplied_dtype is None and other is not None and @@ -463,7 +467,7 @@ def _convert_to_array(self, values, name=None, other=None): hasattr(ovalues, 'tz')): values = pd.DatetimeIndex(values) # datetime array with tz - elif com.is_datetimetz(values): + elif is_datetimetz(values): if isinstance(values, ABCSeries): values = values._values elif not (isinstance(values, (np.ndarray, ABCSeries)) and @@ -625,7 +629,7 @@ def na_op(x, y): "{op}".format(typ=type(x).__name__, op=str_rep)) - result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) + result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result @@ -769,6 +773,15 @@ def wrapper(self, other, axis=None): if (not lib.isscalar(lib.item_from_zerodim(other)) and len(self) != len(other)): raise ValueError('Lengths must match to compare') + + if isinstance(other, ABCPeriodIndex): + # temp workaround until fixing GH 13637 + # tested in test_nat_comparisons + # (pandas.tests.series.test_operators.TestSeriesOperators) + return self._constructor(na_op(self.values, + other.asobject.values), + index=self.index) + return self._constructor(na_op(self.values, np.asarray(other)), index=self.index).__finalize__(self) elif isinstance(other, pd.Categorical): @@ -820,8 +833,8 @@ def na_op(x, y): if (is_bool_dtype(x.dtype) and is_bool_dtype(y.dtype)): result = op(x, y) # when would this be hit? else: - x = com._ensure_object(x) - y = com._ensure_object(y) + x = _ensure_object(x) + y = _ensure_object(y) result = lib.vec_binop(x, y, op) else: try: @@ -1095,7 +1108,7 @@ def na_op(x, y): "objects of type {x} and {y}".format( op=name, x=type(x), y=type(y))) - result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) + result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = result.reshape(x.shape) result = missing.fill_zeros(result, x, y, name, fill_zeros) @@ -1220,7 +1233,7 @@ def na_op(x, y): result = np.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) - result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) + result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 7d0bedcc2b381..4d61563cccce5 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -8,17 +8,21 @@ import numpy as np +from pandas.types.cast import (_infer_dtype_from_scalar, + _possibly_cast_item) +from pandas.types.common import (is_integer, is_list_like, + is_string_like, is_scalar) +from pandas.types.missing import notnull + import pandas.computation.expressions as expressions import pandas.core.common as com import pandas.core.ops as ops import pandas.core.missing as missing from pandas import compat -from pandas import lib from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict) from pandas.compat.numpy import function as nv from pandas.core.categorical import Categorical -from pandas.core.common import (PandasError, _try_sort, _default_index, - _infer_dtype_from_scalar, is_list_like) +from pandas.core.common import PandasError, _try_sort, _default_index from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import (Index, MultiIndex, _ensure_index, @@ -168,7 +172,7 @@ def _init_data(self, data, copy, dtype, **kwargs): mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy) copy = False dtype = None - elif lib.isscalar(data) and all(x is not None for x in passed_axes): + elif is_scalar(data) and all(x is not None for x in passed_axes): if dtype is None: dtype, data = _infer_dtype_from_scalar(data) values = np.empty([len(x) for x in passed_axes], dtype=dtype) @@ -552,7 +556,7 @@ def set_value(self, *args, **kwargs): made_bigger = not np.array_equal(axes[0], self._info_axis) # how to make this logic simpler? if made_bigger: - com._possibly_cast_item(result, args[0], likely_dtype) + _possibly_cast_item(result, args[0], likely_dtype) return result.set_value(*args) @@ -582,7 +586,7 @@ def __setitem__(self, key, value): 'object was {1}'.format( shape[1:], tuple(map(int, value.shape)))) mat = np.asarray(value) - elif lib.isscalar(value): + elif is_scalar(value): dtype, value = _infer_dtype_from_scalar(value) mat = np.empty(shape[1:], dtype=dtype) mat.fill(value) @@ -653,7 +657,7 @@ def round(self, decimals=0, *args, **kwargs): """ nv.validate_round(args, kwargs) - if com.is_integer(decimals): + if is_integer(decimals): result = np.apply_along_axis(np.round, 0, self.values) return self._wrap_result(result, axis=0) raise TypeError("decimals must be an integer") @@ -687,7 +691,7 @@ def dropna(self, axis=0, how='any', inplace=False): axis = self._get_axis_number(axis) values = self.values - mask = com.notnull(values) + mask = notnull(values) for ax in reversed(sorted(set(range(self._AXIS_LEN)) - set([axis]))): mask = mask.sum(ax) @@ -711,7 +715,7 @@ def _combine(self, other, func, axis=0): return self._combine_panel(other, func) elif isinstance(other, DataFrame): return self._combine_frame(other, func, axis=axis) - elif lib.isscalar(other): + elif is_scalar(other): return self._combine_const(other, func) else: raise NotImplementedError("%s is not supported in combine " @@ -924,7 +928,7 @@ def to_frame(self, filter_observations=True): if filter_observations: # shaped like the return DataFrame - mask = com.notnull(self.values).all(axis=0) + mask = notnull(self.values).all(axis=0) # size = mask.sum() selector = mask.ravel() else: @@ -1218,7 +1222,7 @@ def transpose(self, *args, **kwargs): # check if a list of axes was passed in instead as a # single *args element if (len(args) == 1 and hasattr(args[0], '__iter__') and - not com.is_string_like(args[0])): + not is_string_like(args[0])): axes = args[0] else: axes = args diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 8d237016d1b33..4f601a2d377a6 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -6,6 +6,11 @@ import numpy as np +from pandas.types.common import _ensure_platform_int, is_list_like +from pandas.types.cast import _maybe_promote +from pandas.types.missing import notnull +import pandas.types.concat as _concat + from pandas.core.series import Series from pandas.core.frame import DataFrame @@ -14,11 +19,8 @@ from pandas._sparse import IntIndex from pandas.core.categorical import Categorical -from pandas.core.common import notnull, _ensure_platform_int, _maybe_promote from pandas.core.groupby import get_group_index, _compress_group_index -import pandas.core.common as com -import pandas.types.concat as _concat import pandas.core.algorithms as algos import pandas.algos as _algos @@ -1063,7 +1065,7 @@ def check_len(item, name): length_msg = ("Length of '{0}' ({1}) did not match the length of " "the columns being encoded ({2}).") - if com.is_list_like(item): + if is_list_like(item): if not len(item) == len(columns_to_encode): raise ValueError(length_msg.format(name, len(item), len(columns_to_encode))) diff --git a/pandas/core/series.py b/pandas/core/series.py index 8015670212181..c3f5b1b8e641c 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -13,18 +13,33 @@ import numpy as np import numpy.ma as ma -from pandas.core.common import (isnull, notnull, is_bool_indexer, - _default_index, _maybe_upcast, - _asarray_tuplesafe, _infer_dtype_from_scalar, - is_list_like, _values_from_object, - is_categorical_dtype, - _possibly_cast_to_datetime, - _possibly_castable, _possibly_convert_platform, - _try_sort, is_extension_type, is_datetimetz, - _maybe_match_name, ABCSparseArray, - _coerce_to_dtype, SettingWithCopyError, - _maybe_box_datetimelike, ABCDataFrame, - _dict_compat, is_integer) +from pandas.types.common import (_coerce_to_dtype, is_categorical_dtype, + is_integer, is_integer_dtype, + is_float_dtype, + is_extension_type, is_datetimetz, + is_datetimelike, + is_timedelta64_dtype, + is_list_like, + is_hashable, + is_iterator, + is_dict_like, + is_scalar, + _ensure_platform_int) +from pandas.types.generic import ABCSparseArray, ABCDataFrame +from pandas.types.cast import (_maybe_upcast, _infer_dtype_from_scalar, + _possibly_convert_platform, + _possibly_cast_to_datetime, _possibly_castable) +from pandas.types.missing import isnull, notnull + +from pandas.core.common import (is_bool_indexer, + _default_index, + _asarray_tuplesafe, + _values_from_object, + _try_sort, + _maybe_match_name, + SettingWithCopyError, + _maybe_box_datetimelike, + _dict_compat) from pandas.core.index import (Index, MultiIndex, InvalidIndexError, Float64Index, _ensure_index) from pandas.core.indexing import check_bool_indexer, maybe_convert_indices @@ -303,7 +318,7 @@ def name(self): @name.setter def name(self, value): - if value is not None and not com.is_hashable(value): + if value is not None and not is_hashable(value): raise TypeError('Series.name must be a hashable type') object.__setattr__(self, '_name', value) @@ -580,7 +595,7 @@ def __getitem__(self, key): try: result = self.index.get_value(self, key) - if not lib.isscalar(result): + if not is_scalar(result): if is_list_like(result) and not isinstance(result, Series): # we need to box if we have a non-unique index here @@ -613,10 +628,10 @@ def __getitem__(self, key): except Exception: raise - if com.is_iterator(key): + if is_iterator(key): key = list(key) - if is_bool_indexer(key): + if com.is_bool_indexer(key): key = check_bool_indexer(self.index, key) return self._get_with(key) @@ -710,9 +725,9 @@ def setitem(key, value): elif key is Ellipsis: self[:] = value return - elif is_bool_indexer(key): + elif com.is_bool_indexer(key): pass - elif com.is_timedelta64_dtype(self.dtype): + elif is_timedelta64_dtype(self.dtype): # reassign a null value to iNaT if isnull(value): value = tslib.iNaT @@ -736,7 +751,7 @@ def setitem(key, value): if 'unorderable' in str(e): # pragma: no cover raise IndexError(key) - if is_bool_indexer(key): + if com.is_bool_indexer(key): key = check_bool_indexer(self.index, key) try: self._where(~key, value, inplace=True) @@ -828,14 +843,22 @@ def repeat(self, reps, *args, **kwargs): def reshape(self, *args, **kwargs): """ - Return the values attribute of `self` with shape `args`. - However, if the specified shape matches exactly the current - shape, `self` is returned for compatibility reasons. + DEPRECATED: calling this method will raise an error in a + future release. Please call ``.values.reshape(...)`` instead. + + return an ndarray with the values shape + if the specified shape matches exactly the current shape, then + return self (for compat) See also -------- numpy.ndarray.reshape """ + warnings.warn("reshape is deprecated and will raise " + "in a subsequent release. Please use " + ".values.reshape(...) instead", FutureWarning, + stacklevel=2) + if len(args) == 1 and hasattr(args[0], '__iter__'): shape = args[0] else: @@ -1060,7 +1083,7 @@ def _get_repr(self, name=False, header=True, index=True, length=True, def __iter__(self): """ provide iteration over the values of the Series box values if necessary """ - if com.is_datetimelike(self): + if is_datetimelike(self): return (_maybe_box_datetimelike(x) for x in self._values) else: return iter(self._values) @@ -1349,7 +1372,7 @@ def quantile(self, q=0.5, interpolation='linear'): result = self._data.quantile(qs=q, interpolation=interpolation) - if com.is_list_like(q): + if is_list_like(q): return self._constructor(result, index=Float64Index(q), name=self.name) @@ -1481,20 +1504,25 @@ def dot(self, other): @Appender(base._shared_docs['searchsorted']) def searchsorted(self, v, side='left', sorter=None): if sorter is not None: - sorter = com._ensure_platform_int(sorter) + sorter = _ensure_platform_int(sorter) return self._values.searchsorted(Series(v)._values, side=side, sorter=sorter) # ------------------------------------------------------------------- # Combination - def append(self, to_append, verify_integrity=False): + def append(self, to_append, ignore_index=False, verify_integrity=False): """ Concatenate two or more Series. Parameters ---------- to_append : Series or list/tuple of Series + ignore_index : boolean, default False + If True, do not use the index labels. + + .. versionadded: 0.19.0 + verify_integrity : boolean, default False If True, raise Exception on creating index with duplicates @@ -1525,6 +1553,17 @@ def append(self, to_append, verify_integrity=False): 5 6 dtype: int64 + With `ignore_index` set to True: + + >>> s1.append(s2, ignore_index=True) + 0 1 + 1 2 + 2 3 + 3 4 + 4 5 + 5 6 + dtype: int64 + With `verify_integrity` set to True: >>> s1.append(s2, verify_integrity=True) @@ -1538,7 +1577,7 @@ def append(self, to_append, verify_integrity=False): to_concat = [self] + to_append else: to_concat = [self, to_append] - return concat(to_concat, ignore_index=False, + return concat(to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity) def _binop(self, other, func, level=None, fill_value=None): @@ -1727,7 +1766,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, elif isinstance(index, MultiIndex): from pandas.core.groupby import _lexsort_indexer indexer = _lexsort_indexer(index.labels, orders=ascending) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) new_index = index.take(indexer) else: new_index, indexer = index.sort_values(return_indexer=True, @@ -2265,8 +2304,8 @@ def align(self, other, join='outer', axis=None, level=None, copy=True, @Appender(generic._shared_docs['rename'] % _shared_doc_kwargs) def rename(self, index=None, **kwargs): - non_mapping = lib.isscalar(index) or (com.is_list_like(index) and - not com.is_dict_like(index)) + non_mapping = is_scalar(index) or (is_list_like(index) and + not is_dict_like(index)) if non_mapping: return self._set_name(index, inplace=kwargs.get('inplace')) return super(Series, self).rename(index=index, **kwargs) @@ -2345,7 +2384,7 @@ def take(self, indices, axis=0, convert=True, is_copy=False, **kwargs): if convert: indices = maybe_convert_indices(indices, len(self._get_axis(axis))) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) new_index = self.index.take(indices) new_values = self._values.take(indices) return self._constructor(new_values, @@ -2771,7 +2810,7 @@ def _try_cast(arr, take_fast_path): subarr = np.array(data, copy=False) # possibility of nan -> garbage - if com.is_float_dtype(data.dtype) and com.is_integer_dtype(dtype): + if is_float_dtype(data.dtype) and is_integer_dtype(dtype): if not isnull(data).any(): subarr = _try_cast(data, True) elif copy: @@ -2797,7 +2836,7 @@ def _try_cast(arr, take_fast_path): subarr = data.copy() return subarr - elif isinstance(data, list) and len(data) > 0: + elif isinstance(data, (list, tuple)) and len(data) > 0: if dtype is not None: try: subarr = _try_cast(data, False) diff --git a/pandas/core/strings.py b/pandas/core/strings.py index a3f687b7fd73c..3150fc5d0143a 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -1,14 +1,20 @@ import numpy as np from pandas.compat import zip -from pandas.core.common import (isnull, notnull, _values_from_object, - is_bool_dtype, - is_list_like, is_categorical_dtype, - is_object_dtype, is_string_like) +from pandas.types.generic import ABCSeries, ABCIndex +from pandas.types.missing import isnull, notnull +from pandas.types.common import (is_bool_dtype, + is_categorical_dtype, + is_object_dtype, + is_string_like, + is_list_like, + is_scalar, + is_integer) +from pandas.core.common import _values_from_object + from pandas.core.algorithms import take_1d import pandas.compat as compat from pandas.core.base import AccessorProperty, NoNewAttributesMixin -from pandas.types import api as gt from pandas.util.decorators import Appender, deprecate_kwarg import re import pandas.lib as lib @@ -152,7 +158,7 @@ def _map(f, arr, na_mask=False, na_value=np.nan, dtype=object): if not len(arr): return np.ndarray(0, dtype=dtype) - if isinstance(arr, gt.ABCSeries): + if isinstance(arr, ABCSeries): arr = arr.values if not isinstance(arr, np.ndarray): arr = np.asarray(arr, dtype=object) @@ -343,7 +349,7 @@ def str_repeat(arr, repeats): ------- repeated : Series/Index of objects """ - if lib.isscalar(repeats): + if is_scalar(repeats): def rep(x): try: @@ -696,7 +702,7 @@ def str_extractall(arr, pat, flags=0): if regex.groups == 0: raise ValueError("pattern contains no capture groups") - if isinstance(arr, gt.ABCIndex): + if isinstance(arr, ABCIndex): arr = arr.to_series().reset_index(drop=True) names = dict(zip(regex.groupindex.values(), regex.groupindex.keys())) @@ -909,6 +915,10 @@ def str_pad(arr, width, side='left', fillchar=' '): if len(fillchar) != 1: raise TypeError('fillchar must be a character, not str') + if not is_integer(width): + msg = 'width must be of integer type, not {0}' + raise TypeError(msg.format(type(width).__name__)) + if side == 'left': f = lambda x: x.rjust(width, fillchar) elif side == 'right': @@ -1538,7 +1548,7 @@ def rjust(self, width, fillchar=' '): return self.pad(width, side='left', fillchar=fillchar) def zfill(self, width): - """" + """ Filling left side of strings in the Series/Index with 0. Equivalent to :meth:`str.zfill`. @@ -1820,7 +1830,7 @@ class StringAccessorMixin(object): def _make_str_accessor(self): from pandas.core.index import Index - if (isinstance(self, gt.ABCSeries) and + if (isinstance(self, ABCSeries) and not ((is_categorical_dtype(self.dtype) and is_object_dtype(self.values.categories)) or (is_object_dtype(self.dtype)))): diff --git a/pandas/core/window.py b/pandas/core/window.py index 1e34d18fe3e54..9e2a27adc25a7 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -11,17 +11,33 @@ import numpy as np from collections import defaultdict +from pandas.types.generic import (ABCSeries, + ABCDataFrame, + ABCDatetimeIndex, + ABCTimedeltaIndex, + ABCPeriodIndex) +from pandas.types.common import (is_integer, + is_bool, + is_float_dtype, + is_integer_dtype, + needs_i8_conversion, + is_timedelta64_dtype, + is_list_like, + _ensure_float64) import pandas as pd from pandas.lib import isscalar from pandas.core.base import (PandasObject, SelectionMixin, GroupByMixin) import pandas.core.common as com import pandas._window as _window +from pandas.tseries.offsets import DateOffset from pandas import compat from pandas.compat.numpy import function as nv -from pandas.util.decorators import Substitution, Appender +from pandas.util.decorators import (Substitution, Appender, + cache_readonly) from textwrap import dedent + _shared_docs = dict() _doc_template = """ @@ -38,19 +54,21 @@ class _Window(PandasObject, SelectionMixin): _attributes = ['window', 'min_periods', 'freq', 'center', 'win_type', - 'axis'] + 'axis', 'on'] exclusions = set() def __init__(self, obj, window=None, min_periods=None, freq=None, - center=False, win_type=None, axis=0, **kwargs): + center=False, win_type=None, axis=0, on=None, **kwargs): if freq is not None: warnings.warn("The freq kw is deprecated and will be removed in a " "future version. You can resample prior to passing " "to a window function", FutureWarning, stacklevel=3) + self.__dict__.update(kwargs) self.blocks = [] self.obj = obj + self.on = on self.window = window self.min_periods = min_periods self.freq = freq @@ -63,19 +81,32 @@ def __init__(self, obj, window=None, min_periods=None, freq=None, def _constructor(self): return Window + @property + def is_datetimelike(self): + return None + + @property + def _on(self): + return None + + @property + def is_freq_type(self): + return self.win_type == 'freq' + def validate(self): - if self.center is not None and not com.is_bool(self.center): + if self.center is not None and not is_bool(self.center): raise ValueError("center must be a boolean") if self.min_periods is not None and not \ - com.is_integer(self.min_periods): + is_integer(self.min_periods): raise ValueError("min_periods must be an integer") def _convert_freq(self, how=None): """ resample according to the how, return a new object """ obj = self._selected_obj + index = None if (self.freq is not None and - isinstance(obj, (com.ABCSeries, com.ABCDataFrame))): + isinstance(obj, (ABCSeries, ABCDataFrame))): if how is not None: warnings.warn("The how kw argument is deprecated and removed " "in a future version. You can resample prior " @@ -83,13 +114,24 @@ def _convert_freq(self, how=None): stacklevel=6) obj = obj.resample(self.freq).aggregate(how or 'asfreq') - return obj + + return obj, index def _create_blocks(self, how): """ split data into blocks & return conformed data """ - obj = self._convert_freq(how) - return obj.as_blocks(copy=False).values(), obj + obj, index = self._convert_freq(how) + if index is not None: + index = self._on + + # filter out the on from the object + if self.on is not None: + if obj.ndim == 2: + obj = obj.reindex(columns=obj.columns.difference([self.on]), + copy=False) + blocks = obj.as_blocks(copy=False).values() + + return blocks, obj, index def _gotitem(self, key, ndim, subset=None): """ @@ -111,7 +153,7 @@ def _gotitem(self, key, ndim, subset=None): self = self._shallow_copy(subset) self._reset_cache() if subset.ndim == 2: - if isscalar(key) and key in subset or com.is_list_like(key): + if isscalar(key) and key in subset or is_list_like(key): self._selection = key return self @@ -143,6 +185,21 @@ def __unicode__(self): return "{klass} [{attrs}]".format(klass=self._window_type, attrs=','.join(attrs)) + def _get_index(self, index=None): + """ + Return index as ndarrays + + Returns + ------- + tuple of (index, index_as_ndarray) + """ + + if self.is_freq_type: + if index is None: + index = self._on + return index, index.asi8 + return index, index + def _prep_values(self, values=None, kill_inf=True, how=None): if values is None: @@ -150,11 +207,11 @@ def _prep_values(self, values=None, kill_inf=True, how=None): # GH #12373 : rolling functions error on float32 data # make sure the data is coerced to float64 - if com.is_float_dtype(values.dtype): - values = com._ensure_float64(values) - elif com.is_integer_dtype(values.dtype): - values = com._ensure_float64(values) - elif com.needs_i8_conversion(values.dtype): + if is_float_dtype(values.dtype): + values = _ensure_float64(values) + elif is_integer_dtype(values.dtype): + values = _ensure_float64(values) + elif needs_i8_conversion(values.dtype): raise NotImplementedError("ops for {action} for this " "dtype {dtype} are not " "implemented".format( @@ -162,7 +219,7 @@ def _prep_values(self, values=None, kill_inf=True, how=None): dtype=values.dtype)) else: try: - values = com._ensure_float64(values) + values = _ensure_float64(values) except (ValueError, TypeError): raise TypeError("cannot handle this type -> {0}" "".format(values.dtype)) @@ -178,13 +235,13 @@ def _wrap_result(self, result, block=None, obj=None): if obj is None: obj = self._selected_obj - index = obj.index + if isinstance(result, np.ndarray): # coerce if necessary if block is not None: - if com.is_timedelta64_dtype(block.values.dtype): + if is_timedelta64_dtype(block.values.dtype): result = pd.to_timedelta( result.ravel(), unit='ns').values.reshape(result.shape) @@ -206,6 +263,9 @@ def _wrap_results(self, results, blocks, obj): obj : conformed data (may be resampled) """ + from pandas import Series + from pandas.core.index import _ensure_index + final = [] for result, block in zip(results, blocks): @@ -214,9 +274,31 @@ def _wrap_results(self, results, blocks, obj): return result final.append(result) + # if we have an 'on' column + # we want to put it back into the results + # in the same location + columns = self._selected_obj.columns + if self.on is not None \ + and not self._on.equals(obj.index): + + name = self._on.name + final.append(Series(self._on, index=obj.index, name=name)) + + if self._selection is not None: + + selection = _ensure_index(self._selection) + + # need to reorder to include original location of + # the on column (if its not already there) + if name not in selection: + columns = self.obj.columns + indexer = columns.get_indexer(selection.tolist() + [name]) + columns = columns.take(sorted(indexer)) + if not len(final): return obj.astype('float64') - return pd.concat(final, axis=1).reindex(columns=obj.columns) + return pd.concat(final, axis=1).reindex(columns=columns, + copy=False) def _center_window(self, result, window): """ center the result in the window """ @@ -262,18 +344,24 @@ def aggregate(self, arg, *args, **kwargs): class Window(_Window): """ - Provides rolling transformations. + Provides rolling window calculcations. .. versionadded:: 0.18.0 Parameters ---------- - window : int - Size of the moving window. This is the number of observations used for - calculating the statistic. + window : int, or offset + Size of the moving window. This is the number of observations used for + calculating the statistic. Each window will be a fixed size. + + If its an offset then this will be the time period of each window. Each + window will be a variable sized based on the observations included in + the time-period. This is only valid for datetimelike indexes. This is + new in 0.19.0 min_periods : int, default None Minimum number of observations in window required to have a value - (otherwise result is NA). + (otherwise result is NA). For a window that is specified by an offset, + this will default to 1. freq : string or DateOffset object, optional (default None) (DEPRECATED) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. @@ -281,11 +369,91 @@ class Window(_Window): Set the labels at the center of the window. win_type : string, default None Provide a window type. See the notes below. - axis : int, default 0 + on : string, optional + For a DataFrame, column on which to calculate + the rolling window, rather than the index + + .. versionadded:: 0.19.0 + + axis : int or string, default 0 Returns ------- - a Window sub-classed for the particular operation + a Window or Rolling sub-classed for the particular operation + + Examples + -------- + + >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) + >>> df + B + 0 0.0 + 1 1.0 + 2 2.0 + 3 NaN + 4 4.0 + + Rolling sum with a window length of 2, using the 'triang' + window type. + + >>> df.rolling(2, win_type='triang').sum() + B + 0 NaN + 1 1.0 + 2 2.5 + 3 NaN + 4 NaN + + Rolling sum with a window length of 2, min_periods defaults + to the window length. + + >>> df.rolling(2).sum() + B + 0 NaN + 1 1.0 + 2 3.0 + 3 NaN + 4 NaN + + Same as above, but explicity set the min_periods + + >>> df.rolling(2, min_periods=1).sum() + B + 0 0.0 + 1 1.0 + 2 3.0 + 3 2.0 + 4 4.0 + + A ragged (meaning not-a-regular frequency), time-indexed DataFrame + + >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, + ....: index = [pd.Timestamp('20130101 09:00:00'), + ....: pd.Timestamp('20130101 09:00:02'), + ....: pd.Timestamp('20130101 09:00:03'), + ....: pd.Timestamp('20130101 09:00:05'), + ....: pd.Timestamp('20130101 09:00:06')]) + + >>> df + B + 2013-01-01 09:00:00 0.0 + 2013-01-01 09:00:02 1.0 + 2013-01-01 09:00:03 2.0 + 2013-01-01 09:00:05 NaN + 2013-01-01 09:00:06 4.0 + + + Contrasting to an integer rolling window, this will roll a variable + length window corresponding to the time period. + The default for min_periods is 1. + + >>> df.rolling('2s').sum() + B + 2013-01-01 09:00:00 0.0 + 2013-01-01 09:00:02 1.0 + 2013-01-01 09:00:03 3.0 + 2013-01-01 09:00:05 NaN + 2013-01-01 09:00:06 4.0 Notes ----- @@ -296,7 +464,10 @@ class Window(_Window): frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). - The recognized window types are: + To learn more about the offsets & frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. + + The recognized win_types are: * ``boxcar`` * ``triang`` @@ -312,7 +483,8 @@ class Window(_Window): * ``gaussian`` (needs std) * ``general_gaussian`` (needs power, width) * ``slepian`` (needs width). -""" + + """ def validate(self): super(Window, self).validate() @@ -320,7 +492,7 @@ def validate(self): window = self.window if isinstance(window, (list, tuple, np.ndarray)): pass - elif com.is_integer(window): + elif is_integer(window): if window < 0: raise ValueError("window must be non-negative") try: @@ -345,7 +517,7 @@ def _prep_window(self, **kwargs): window = self._get_window() if isinstance(window, (list, tuple, np.ndarray)): return com._asarray_tuplesafe(window).astype(float) - elif com.is_integer(window): + elif is_integer(window): import scipy.signal as sig # the below may pop from kwargs @@ -391,7 +563,7 @@ def _apply_window(self, mean=True, how=None, **kwargs): window = self._prep_window(**kwargs) center = self.center - blocks, obj = self._create_blocks(how=how) + blocks, obj, index = self._create_blocks(how=how) results = [] for b in blocks: try: @@ -520,7 +692,8 @@ def _apply(self, func, name=None, window=None, center=None, if check_minp is None: check_minp = _use_window - blocks, obj = self._create_blocks(how=how) + blocks, obj, index = self._create_blocks(how=how) + index, indexi = self._get_index(index=index) results = [] for b in blocks: try: @@ -542,9 +715,10 @@ def _apply(self, func, name=None, window=None, center=None, def func(arg, window, min_periods=None): minp = check_minp(min_periods, window) - # GH #12373: rolling functions error on float32 data - return cfunc(com._ensure_float64(arg), - window, minp, **kwargs) + # ensure we are only rolling on floats + arg = _ensure_float64(arg) + return cfunc(arg, + window, minp, indexi, **kwargs) # calculation function if center: @@ -578,15 +752,17 @@ class _Rolling_and_Expanding(_Rolling): observations inside provided window.""" def count(self): - obj = self._convert_freq() + + blocks, obj, index = self._create_blocks(how=None) + index, indexi = self._get_index(index=index) + window = self._get_window() window = min(window, len(obj)) if not self.center else window - blocks, obj = self._create_blocks(how=None) results = [] for b in blocks: - if com.needs_i8_conversion(b.values): + if needs_i8_conversion(b.values): result = b.notnull().astype(int) else: try: @@ -616,10 +792,12 @@ def apply(self, func, args=(), kwargs={}): _level = kwargs.pop('_level', None) # noqa window = self._get_window() offset = _offset(window, self.center) + index, indexi = self._get_index() def f(arg, window, min_periods): minp = _use_window(min_periods, window) - return _window.roll_generic(arg, window, minp, offset, func, args, + return _window.roll_generic(arg, window, minp, indexi, + offset, func, args, kwargs) return self._apply(f, func, args=args, kwargs=kwargs, @@ -686,10 +864,12 @@ def median(self, how=None, **kwargs): def std(self, ddof=1, *args, **kwargs): nv.validate_window_func('std', args, kwargs) window = self._get_window() + index, indexi = self._get_index() def f(arg, *args, **kwargs): minp = _require_min_periods(1)(self.min_periods, window) - return _zsqrt(_window.roll_var(arg, window, minp, ddof)) + return _zsqrt(_window.roll_var(arg, window, minp, indexi, + ddof)) return self._apply(f, 'std', check_minp=_require_min_periods(1), ddof=ddof, **kwargs) @@ -731,10 +911,12 @@ def kurt(self, **kwargs): def quantile(self, quantile, **kwargs): window = self._get_window() + index, indexi = self._get_index() def f(arg, *args, **kwargs): minp = _use_window(self.min_periods, window) - return _window.roll_quantile(arg, window, minp, quantile) + return _window.roll_quantile(arg, window, minp, indexi, + quantile) return self._apply(f, 'quantile', quantile=quantile, **kwargs) @@ -814,43 +996,63 @@ def _get_corr(a, b): class Rolling(_Rolling_and_Expanding): - """ - Provides rolling window calculcations. - - .. versionadded:: 0.18.0 - Parameters - ---------- - window : int - Size of the moving window. This is the number of observations used for - calculating the statistic. - min_periods : int, default None - Minimum number of observations in window required to have a value - (otherwise result is NA). - freq : string or DateOffset object, optional (default None) (DEPRECATED) - Frequency to conform the data to before computing the statistic. - Specified as a frequency string or DateOffset object. - center : boolean, default False - Set the labels at the center of the window. - axis : int, default 0 + @cache_readonly + def is_datetimelike(self): + return isinstance(self._on, + (ABCDatetimeIndex, + ABCTimedeltaIndex, + ABCPeriodIndex)) + + @cache_readonly + def _on(self): + + if self.on is None: + return self.obj.index + elif (isinstance(self.obj, ABCDataFrame) and + self.on in self.obj.columns): + return pd.Index(self.obj[self.on]) + else: + raise ValueError("invalid on specified as {0}, " + "must be a column (if DataFrame) " + "or None".format(self.on)) - Returns - ------- - a Window sub-classed for the particular operation + def validate(self): + super(Rolling, self).validate() - Notes - ----- - By default, the result is set to the right edge of the window. This can be - changed to the center of the window by setting ``center=True``. + # we allow rolling on a datetimelike index + if (self.is_datetimelike and + isinstance(self.window, (compat.string_types, DateOffset))): - The `freq` keyword is used to conform time series data to a specified - frequency by resampling the data. This is done with the default parameters - of :meth:`~pandas.Series.resample` (i.e. using the `mean`). - """ + # must be monotonic for on + if not self._on.is_monotonic: + formatted = self.on or 'index' + raise ValueError("{0} must be " + "monotonic".format(formatted)) - def validate(self): - super(Rolling, self).validate() - if not com.is_integer(self.window): + from pandas.tseries.frequencies import to_offset + try: + freq = to_offset(self.window) + except (TypeError, ValueError): + raise ValueError("passed window {0} in not " + "compat with a datetimelike " + "index".format(self.window)) + + # we don't allow center + if self.center: + raise NotImplementedError("center is not implemented " + "for datetimelike and offset " + "based windows") + + # this will raise ValueError on non-fixed freqs + self.window = freq.nanos + self.win_type = 'freq' + + # min_periods must be an integer + if self.min_periods is None: + self.min_periods = 1 + + elif not is_integer(self.window): raise ValueError("window must be an integer") elif self.window < 0: raise ValueError("window must be non-negative") @@ -867,6 +1069,11 @@ def aggregate(self, arg, *args, **kwargs): @Appender(_doc_template) @Appender(_shared_docs['count']) def count(self): + + # different impl for freq counting + if self.is_freq_type: + return self._apply('roll_count', 'count') + return super(Rolling, self).count() @Substitution(name='rolling') @@ -984,12 +1191,31 @@ class Expanding(_Rolling_and_Expanding): Specified as a frequency string or DateOffset object. center : boolean, default False Set the labels at the center of the window. - axis : int, default 0 + axis : int or string, default 0 Returns ------- a Window sub-classed for the particular operation + Examples + -------- + + >>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + B + 0 0.0 + 1 1.0 + 2 2.0 + 3 NaN + 4 4.0 + + >>> df.expanding(2).sum() + B + 0 NaN + 1 1.0 + 2 3.0 + 3 3.0 + 4 7.0 + Notes ----- By default, the result is set to the right edge of the window. This can be @@ -1196,6 +1422,25 @@ class EWM(_Rolling): ------- a Window sub-classed for the particular operation + Examples + -------- + + >>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + B + 0 0.0 + 1 1.0 + 2 2.0 + 3 NaN + 4 4.0 + + >>> df.ewm(com=0.5).mean() + B + 0 0.000000 + 1 0.750000 + 2 1.615385 + 3 1.615385 + 4 3.670213 + Notes ----- Exactly one of center of mass, span, half-life, and alpha must be provided. @@ -1239,6 +1484,7 @@ def __init__(self, obj, com=None, span=None, halflife=None, alpha=None, self.adjust = adjust self.ignore_na = ignore_na self.axis = axis + self.on = None @property def _constructor(self): @@ -1267,7 +1513,7 @@ def _apply(self, func, how=None, **kwargs): y : type of input argument """ - blocks, obj = self._create_blocks(how=how) + blocks, obj, index = self._create_blocks(how=how) results = [] for b in blocks: try: @@ -1484,7 +1730,7 @@ def _get_center_of_mass(com, span, halflife, alpha): def _offset(window, center): - if not com.is_integer(window): + if not is_integer(window): window = len(window) offset = (window - 1) / 2. if center else 0 try: diff --git a/pandas/formats/format.py b/pandas/formats/format.py index cc46ed57aeff0..436a9d5d5d4c8 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -10,8 +10,19 @@ import sys +from pandas.types.missing import isnull, notnull +from pandas.types.common import (is_categorical_dtype, + is_float_dtype, + is_period_arraylike, + is_integer_dtype, + is_datetimetz, + is_integer, + is_float, + is_numeric_dtype, + is_datetime64_dtype, + is_timedelta64_dtype) + from pandas.core.base import PandasObject -from pandas.core.common import isnull, notnull, is_numeric_dtype from pandas.core.index import Index, MultiIndex, _ensure_index from pandas import compat from pandas.compat import (StringIO, lzip, range, map, zip, reduce, u, @@ -194,7 +205,7 @@ def _get_footer(self): # level infos are added to the end and in a new line, like it is done # for Categoricals - if com.is_categorical_dtype(self.tr_series.dtype): + if is_categorical_dtype(self.tr_series.dtype): level_info = self.tr_series._values._repr_categories_info() if footer: footer += "\n" @@ -316,12 +327,12 @@ def should_show_dimensions(self): def _get_formatter(self, i): if isinstance(self.formatters, (list, tuple)): - if com.is_integer(i): + if is_integer(i): return self.formatters[i] else: return None else: - if com.is_integer(i) and i not in self.columns: + if is_integer(i) and i not in self.columns: i = self.columns[i] return self.formatters.get(i, None) @@ -1646,7 +1657,7 @@ def __init__(self, df, na_rep='', float_format=None, cols=None, def _format_value(self, val): if lib.checknull(val): val = self.na_rep - elif com.is_float(val): + elif is_float(val): if lib.isposinf_scalar(val): val = self.inf_rep elif lib.isneginf_scalar(val): @@ -1867,19 +1878,19 @@ def get_formatted_cells(self): def format_array(values, formatter, float_format=None, na_rep='NaN', digits=None, space=None, justify='right', decimal='.'): - if com.is_categorical_dtype(values): + if is_categorical_dtype(values): fmt_klass = CategoricalArrayFormatter - elif com.is_float_dtype(values.dtype): + elif is_float_dtype(values.dtype): fmt_klass = FloatArrayFormatter - elif com.is_period_arraylike(values): + elif is_period_arraylike(values): fmt_klass = PeriodArrayFormatter - elif com.is_integer_dtype(values.dtype): + elif is_integer_dtype(values.dtype): fmt_klass = IntArrayFormatter - elif com.is_datetimetz(values): + elif is_datetimetz(values): fmt_klass = Datetime64TZFormatter - elif com.is_datetime64_dtype(values.dtype): + elif is_datetime64_dtype(values.dtype): fmt_klass = Datetime64Formatter - elif com.is_timedelta64_dtype(values.dtype): + elif is_timedelta64_dtype(values.dtype): fmt_klass = Timedelta64Formatter else: fmt_klass = GenericArrayFormatter @@ -1949,14 +1960,14 @@ def _format(x): if isinstance(vals, Index): vals = vals._values - is_float = lib.map_infer(vals, com.is_float) & notnull(vals) - leading_space = is_float.any() + is_float_type = lib.map_infer(vals, is_float) & notnull(vals) + leading_space = is_float_type.any() fmt_values = [] for i, v in enumerate(vals): - if not is_float[i] and leading_space: + if not is_float_type[i] and leading_space: fmt_values.append(' %s' % _format(v)) - elif is_float[i]: + elif is_float_type[i]: fmt_values.append(float_format(v)) else: fmt_values.append(' %s' % _format(v)) diff --git a/pandas/formats/printing.py b/pandas/formats/printing.py index a4eaec8d5334b..37bd4b63d6f7a 100644 --- a/pandas/formats/printing.py +++ b/pandas/formats/printing.py @@ -2,9 +2,9 @@ printing tools """ +from pandas.types.inference import is_sequence from pandas import compat from pandas.compat import u -import pandas.core.common as com from pandas.core.config import get_option @@ -213,7 +213,7 @@ def as_escaped_unicode(thing, escape_chars=escape_chars): _nest_lvl < get_option("display.pprint_nest_depth")): result = _pprint_dict(thing, _nest_lvl, quote_strings=True, max_seq_items=max_seq_items) - elif (com.is_sequence(thing) and + elif (is_sequence(thing) and _nest_lvl < get_option("display.pprint_nest_depth")): result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars, quote_strings=quote_strings, diff --git a/pandas/formats/style.py b/pandas/formats/style.py index 477ecccc03f4f..472fd958d35eb 100644 --- a/pandas/formats/style.py +++ b/pandas/formats/style.py @@ -17,10 +17,11 @@ "or `pip install Jinja2`" raise ImportError(msg) +from pandas.types.common import is_float, is_string_like + import numpy as np import pandas as pd from pandas.compat import lzip, range -import pandas.core.common as com from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice try: import matplotlib.pyplot as plt @@ -153,7 +154,7 @@ def __init__(self, data, precision=None, table_styles=None, uuid=None, # display_funcs maps (row, col) -> formatting function def default_display_func(x): - if com.is_float(x): + if is_float(x): return '{:>.{precision}g}'.format(x, precision=self.precision) else: return x @@ -893,7 +894,7 @@ def _highlight_extrema(data, color='yellow', max_=True): def _maybe_wrap_formatter(formatter): - if com.is_string_like(formatter): + if is_string_like(formatter): return lambda x: formatter.format(x) elif callable(formatter): return formatter diff --git a/pandas/index.pyx b/pandas/index.pyx index 71717dd2d771b..bc985100692fc 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -80,7 +80,7 @@ cdef class IndexEngine: cdef: bint unique, monotonic_inc, monotonic_dec - bint initialized, monotonic_check, unique_check + bint initialized, monotonic_check def __init__(self, vgetter, n): self.vgetter = vgetter @@ -91,7 +91,6 @@ cdef class IndexEngine: self.monotonic_check = 0 self.unique = 0 - self.unique_check = 0 self.monotonic_inc = 0 self.monotonic_dec = 0 @@ -211,8 +210,8 @@ cdef class IndexEngine: property is_unique: def __get__(self): - if not self.unique_check: - self._do_unique_check() + if not self.initialized: + self.initialize() return self.unique == 1 @@ -246,9 +245,6 @@ cdef class IndexEngine: cdef _get_index_values(self): return self.vgetter() - cdef inline _do_unique_check(self): - self._ensure_mapping_populated() - def _call_monotonic(self, values): raise NotImplementedError @@ -270,7 +266,6 @@ cdef class IndexEngine: if len(self.mapping) == len(values): self.unique = 1 - self.unique_check = 1 self.initialized = 1 diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 0bb80be013275..850d049ef9f45 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -12,6 +12,28 @@ from pandas.compat import range, u from pandas.compat.numpy import function as nv from pandas import compat + + +from pandas.types.generic import ABCSeries, ABCMultiIndex, ABCPeriodIndex +from pandas.types.missing import isnull, array_equivalent +from pandas.types.common import (_ensure_int64, _ensure_object, + _ensure_platform_int, + is_datetimetz, + is_integer, + is_float, + is_dtype_equal, + is_object_dtype, + is_categorical_dtype, + is_bool_dtype, + is_integer_dtype, is_float_dtype, + needs_i8_conversion, + is_iterator, is_list_like, + is_scalar) +from pandas.types.cast import _coerce_indexer_dtype +from pandas.core.common import (is_bool_indexer, + _values_from_object, + _asarray_tuplesafe) + from pandas.core.base import (PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin) import pandas.core.base as base @@ -22,15 +44,6 @@ import pandas.core.missing as missing import pandas.core.algorithms as algos from pandas.formats.printing import pprint_thing -from pandas.core.common import (isnull, array_equivalent, - is_object_dtype, is_datetimetz, ABCSeries, - ABCPeriodIndex, ABCMultiIndex, - _values_from_object, is_float, is_integer, - is_iterator, is_categorical_dtype, - _ensure_object, _ensure_int64, is_bool_indexer, - is_list_like, is_bool_dtype, - is_integer_dtype, is_float_dtype, - needs_i8_conversion) from pandas.core.ops import _comp_method_OBJECT_ARRAY from pandas.core.strings import StringAccessorMixin @@ -211,7 +224,8 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, pass # maybe coerce to a sub-class - from pandas.tseries.period import PeriodIndex + from pandas.tseries.period import (PeriodIndex, + IncompatibleFrequency) if isinstance(data, PeriodIndex): return PeriodIndex(data, copy=copy, name=name, **kwargs) if issubclass(data.dtype.type, np.integer): @@ -223,7 +237,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data): subarr = data.astype('object') else: - subarr = com._asarray_tuplesafe(data, dtype=object) + subarr = _asarray_tuplesafe(data, dtype=object) # _asarray_tuplesafe does not always copy underlying data, # so need to make sure that this happens @@ -252,19 +266,21 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, return DatetimeIndex(subarr, copy=copy, name=name, **kwargs) - elif (inferred.startswith('timedelta') or - lib.is_timedelta_array(subarr)): + elif inferred.startswith('timedelta'): from pandas.tseries.tdi import TimedeltaIndex return TimedeltaIndex(subarr, copy=copy, name=name, **kwargs) elif inferred == 'period': - return PeriodIndex(subarr, name=name, **kwargs) + try: + return PeriodIndex(subarr, name=name, **kwargs) + except IncompatibleFrequency: + pass return cls._simple_new(subarr, name) elif hasattr(data, '__array__'): return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs) - elif data is None or lib.isscalar(data): + elif data is None or is_scalar(data): cls._scalar_data_error(data) else: if (tupleize_cols and isinstance(data, list) and data and @@ -284,7 +300,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, # python2 - MultiIndex fails on mixed types pass # other iterable of some kind - subarr = com._asarray_tuplesafe(data, dtype=object) + subarr = _asarray_tuplesafe(data, dtype=object) return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs) """ @@ -539,7 +555,7 @@ def _coerce_to_ndarray(cls, data): """ if not isinstance(data, (np.ndarray, Index)): - if data is None or lib.isscalar(data): + if data is None or is_scalar(data): cls._scalar_data_error(data) # other iterable of some kind @@ -841,7 +857,7 @@ def to_datetime(self, dayfirst=False): return DatetimeIndex(self.values) def _assert_can_do_setop(self, other): - if not com.is_list_like(other): + if not is_list_like(other): raise TypeError('Input must be Index or array-like') return True @@ -853,6 +869,16 @@ def _convert_can_do_setop(self, other): result_name = self.name if self.name == other.name else None return other, result_name + def _convert_for_op(self, value): + """ Convert value to be insertable to ndarray """ + return value + + def _assert_can_do_op(self, value): + """ Check value is valid for scalar op """ + if not lib.isscalar(value): + msg = "'value' must be a scalar, passed: {0}" + raise TypeError(msg.format(type(value).__name__)) + @property def nlevels(self): return 1 @@ -944,6 +970,16 @@ def rename(self, name, inplace=False): """ return self.set_names([name], inplace=inplace) + def reshape(self, *args, **kwargs): + """ + NOT IMPLEMENTED: do not call this method, as reshaping is not + supported for Index objects and will raise an error. + + Reshape an Index. + """ + raise NotImplementedError("reshaping is not supported " + "for Index objects") + @property def _has_complex_internals(self): # to disable groupby tricks in MultiIndex @@ -1325,7 +1361,7 @@ def __getitem__(self, key): getitem = self._data.__getitem__ promote = self._shallow_copy - if lib.isscalar(key): + if is_scalar(key): return getitem(key) if isinstance(key, slice): @@ -1338,7 +1374,7 @@ def __getitem__(self, key): key = _values_from_object(key) result = getitem(key) - if not lib.isscalar(result): + if not is_scalar(result): return promote(result) else: return result @@ -1426,7 +1462,7 @@ def _ensure_compat_concat(indexes): def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): nv.validate_take(tuple(), kwargs) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) if self._can_hold_na: taken = self._assert_take_fillable(self.values, indices, allow_fill=allow_fill, @@ -1442,7 +1478,7 @@ def take(self, indices, axis=0, allow_fill=True, def _assert_take_fillable(self, values, indices, allow_fill=True, fill_value=None, na_value=np.nan): """ Internal method to handle NA filling of take """ - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) # only fill if we are passing a non-None fill_value if allow_fill and fill_value is not None: @@ -1485,16 +1521,6 @@ def hasnans(self): else: return False - def _convert_for_op(self, value): - """ Convert value to be insertable to ndarray """ - return value - - def _assert_can_do_op(self, value): - """ Check value is valid for scalar op """ - if not lib.isscalar(value): - msg = "'value' must be a scalar, passed: {0}" - raise TypeError(msg.format(type(value).__name__)) - def putmask(self, mask, value): """ return a new Index of the values set with the mask @@ -1706,7 +1732,7 @@ def argsort(self, *args, **kwargs): return result.argsort(*args, **kwargs) def __add__(self, other): - if com.is_list_like(other): + if is_list_like(other): warnings.warn("using '+' to provide set union with Indexes is " "deprecated, use '|' or .union()", FutureWarning, stacklevel=2) @@ -1750,7 +1776,7 @@ def _get_consensus_name(self, other): else: name = None if self.name != name: - return other._shallow_copy(name=name) + return self._shallow_copy(name=name) return self def union(self, other): @@ -1783,7 +1809,7 @@ def union(self, other): if len(self) == 0: return other._get_consensus_name(self) - if not com.is_dtype_equal(self.dtype, other.dtype): + if not is_dtype_equal(self.dtype, other.dtype): this = self.astype('O') other = other.astype('O') return this.union(other) @@ -1866,7 +1892,7 @@ def intersection(self, other): if self.equals(other): return self._get_consensus_name(other) - if not com.is_dtype_equal(self.dtype, other.dtype): + if not is_dtype_equal(self.dtype, other.dtype): this = self.astype('O') other = other.astype('O') return this.intersection(other) @@ -1897,7 +1923,8 @@ def difference(self, other): Return a new Index with elements from the index that are not in `other`. - This is the sorted set difference of two Index objects. + This is the set difference of two Index objects. + It's sorted if sorting is possible. Parameters ---------- @@ -1923,14 +1950,25 @@ def difference(self, other): other, result_name = self._convert_can_do_setop(other) - theDiff = sorted(set(self) - set(other)) - return Index(theDiff, name=result_name) + this = self._get_unique_index() - diff = deprecate('diff', difference) + indexer = this.get_indexer(other) + indexer = indexer.take((indexer != -1).nonzero()[0]) + + label_diff = np.setdiff1d(np.arange(this.size), indexer, + assume_unique=True) + the_diff = this.values.take(label_diff) + try: + the_diff = algos.safe_sort(the_diff) + except TypeError: + pass + + return this._shallow_copy(the_diff, name=result_name) def symmetric_difference(self, other, result_name=None): """ - Compute the sorted symmetric difference of two Index objects. + Compute the symmetric difference of two Index objects. + It's sorted if sorting is possible. Parameters ---------- @@ -1947,9 +1985,6 @@ def symmetric_difference(self, other, result_name=None): ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by ``(idx1 - idx2) + (idx2 - idx1)`` with duplicates dropped. - The sorting of a result containing ``NaN`` values is not guaranteed - across Python versions. See GitHub issue #6444. - Examples -------- >>> idx1 = Index([1, 2, 3, 4]) @@ -1967,8 +2002,26 @@ def symmetric_difference(self, other, result_name=None): if result_name is None: result_name = result_name_update - the_diff = sorted(set((self.difference(other)). - union(other.difference(self)))) + this = self._get_unique_index() + other = other._get_unique_index() + indexer = this.get_indexer(other) + + # {this} minus {other} + common_indexer = indexer.take((indexer != -1).nonzero()[0]) + left_indexer = np.setdiff1d(np.arange(this.size), common_indexer, + assume_unique=True) + left_diff = this.values.take(left_indexer) + + # {other} minus {this} + right_indexer = (indexer == -1).nonzero()[0] + right_diff = other.values.take(right_indexer) + + the_diff = _concat._concat_compat([left_diff, right_diff]) + try: + the_diff = algos.safe_sort(the_diff) + except TypeError: + pass + attribs = self._get_attributes_dict() attribs['name'] = result_name if 'freq' in attribs: @@ -1977,6 +2030,36 @@ def symmetric_difference(self, other, result_name=None): sym_diff = deprecate('sym_diff', symmetric_difference) + def _get_unique_index(self, dropna=False): + """ + Returns an index containing unique values. + + Parameters + ---------- + dropna : bool + If True, NaN values are dropped. + + Returns + ------- + uniques : index + """ + if self.is_unique and not dropna: + return self + + values = self.values + + if not self.is_unique: + values = self.unique() + + if dropna: + try: + if self.hasnans: + values = values[~isnull(values)] + except NotImplementedError: + pass + + return self._shallow_copy(values) + def get_loc(self, key, method=None, tolerance=None): """ Get integer location for requested label @@ -2028,7 +2111,7 @@ def get_value(self, series, key): # if we have something that is Index-like, then # use this, e.g. DatetimeIndex s = getattr(series, '_values', None) - if isinstance(s, Index) and lib.isscalar(key): + if isinstance(s, Index) and is_scalar(key): try: return s[key] except (IndexError, ValueError): @@ -2061,7 +2144,7 @@ def get_value(self, series, key): raise e1 except TypeError: # python 3 - if lib.isscalar(key): # pragma: no cover + if is_scalar(key): # pragma: no cover raise IndexError(key) raise InvalidIndexError(key) @@ -2137,7 +2220,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): return pself.get_indexer(ptarget, method=method, limit=limit, tolerance=tolerance) - if not com.is_dtype_equal(self.dtype, target.dtype): + if not is_dtype_equal(self.dtype, target.dtype): this = self.astype(object) target = target.astype(object) return this.get_indexer(target, method=method, limit=limit, @@ -2161,7 +2244,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): indexer = self._engine.get_indexer(target._values) - return com._ensure_platform_int(indexer) + return _ensure_platform_int(indexer) def _convert_tolerance(self, tolerance): # override this method on subclasses @@ -2443,7 +2526,7 @@ def _reindex_non_unique(self, target): if len(missing): l = np.arange(len(indexer)) - missing = com._ensure_platform_int(missing) + missing = _ensure_platform_int(missing) missing_labels = target.take(missing) missing_indexer = _ensure_int64(l[~check]) cur_labels = self.take(indexer[check])._values @@ -2541,7 +2624,7 @@ def join(self, other, how='left', level=None, return_indexers=False): result = x, z, y return result - if not com.is_dtype_equal(self.dtype, other.dtype): + if not is_dtype_equal(self.dtype, other.dtype): this = self.astype('O') other = other.astype('O') return this.join(other, how=how, return_indexers=return_indexers) @@ -2637,8 +2720,8 @@ def _join_non_unique(self, other, how='left', return_indexers=False): [other._values], how=how, sort=True) - left_idx = com._ensure_platform_int(left_idx) - right_idx = com._ensure_platform_int(right_idx) + left_idx = _ensure_platform_int(left_idx) + right_idx = _ensure_platform_int(right_idx) join_index = self.values.take(left_idx) mask = left_idx == -1 @@ -2850,9 +2933,9 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): kind=kind) # return a slice - if not lib.isscalar(start_slice): + if not is_scalar(start_slice): raise AssertionError("Start slice bound is non-scalar") - if not lib.isscalar(end_slice): + if not is_scalar(end_slice): raise AssertionError("End slice bound is non-scalar") return slice(start_slice, end_slice, step) @@ -3483,7 +3566,7 @@ def _get_na_value(dtype): def _ensure_frozen(array_like, categories, copy=False): - array_like = com._coerce_indexer_dtype(array_like, categories) + array_like = _coerce_indexer_dtype(array_like, categories) array_like = array_like.view(FrozenNDArray) if copy: array_like = array_like.copy() diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 84b8926f4177f..f1d4fe2f26bdd 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -1,15 +1,21 @@ import numpy as np -import pandas.lib as lib import pandas.index as _index from pandas import compat from pandas.compat.numpy import function as nv +from pandas.types.generic import ABCCategorical, ABCSeries +from pandas.types.common import (is_categorical_dtype, + _ensure_platform_int, + is_list_like, + is_scalar) +from pandas.types.missing import array_equivalent + + from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg) from pandas.core.config import get_option from pandas.indexes.base import Index, _index_shared_docs import pandas.core.base as base -import pandas.core.common as com import pandas.core.missing as missing import pandas.indexes.base as ibase @@ -49,7 +55,7 @@ def __new__(cls, data=None, categories=None, ordered=None, dtype=None, if name is None and hasattr(data, 'name'): name = data.name - if isinstance(data, com.ABCCategorical): + if isinstance(data, ABCCategorical): data = cls._create_categorical(cls, data, categories, ordered) elif isinstance(data, CategoricalIndex): data = data._data @@ -58,7 +64,7 @@ def __new__(cls, data=None, categories=None, ordered=None, dtype=None, # don't allow scalars # if data is None, then categories must be provided - if lib.isscalar(data): + if is_scalar(data): if data is not None or categories is None: cls._scalar_data_error(data) data = [] @@ -116,7 +122,7 @@ def _create_categorical(self, data, categories=None, ordered=None): ------- Categorical """ - if not isinstance(data, com.ABCCategorical): + if not isinstance(data, ABCCategorical): from pandas.core.categorical import Categorical data = Categorical(data, categories=categories, ordered=ordered) else: @@ -164,7 +170,7 @@ def _is_dtype_compat(self, other): ------ TypeError if the dtypes are not compatible """ - if com.is_categorical_dtype(other): + if is_categorical_dtype(other): if isinstance(other, CategoricalIndex): other = other._values if not other.is_dtype_equal(self): @@ -172,7 +178,7 @@ def _is_dtype_compat(self, other): "when appending") else: values = other - if not com.is_list_like(values): + if not is_list_like(values): values = [values] other = CategoricalIndex(self._create_categorical( self, other, categories=self.categories, ordered=self.ordered)) @@ -191,7 +197,7 @@ def equals(self, other): try: other = self._is_dtype_compat(other) - return com.array_equivalent(self._data, other) + return array_equivalent(self._data, other) except (TypeError, ValueError): pass @@ -360,7 +366,7 @@ def reindex(self, target, method=None, level=None, limit=None, target = ibase._ensure_index(target) - if not com.is_categorical_dtype(target) and not target.is_unique: + if not is_categorical_dtype(target) and not target.is_unique: raise ValueError("cannot reindex with a non-unique indexer") indexer, missing = self.get_indexer_non_unique(np.array(target)) @@ -388,7 +394,7 @@ def reindex(self, target, method=None, level=None, limit=None, # unless we had an inital Categorical to begin with # in which case we are going to conform to the passed Categorical new_target = np.asarray(new_target) - if com.is_categorical_dtype(target): + if is_categorical_dtype(target): new_target = target._shallow_copy(new_target, name=self.name) else: new_target = Index(new_target, name=self.name) @@ -460,7 +466,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): codes = self.categories.get_indexer(target) indexer, _ = self._engine.get_indexer_non_unique(codes) - return com._ensure_platform_int(indexer) + return _ensure_platform_int(indexer) def get_indexer_non_unique(self, target): """ this is the same for a CategoricalIndex for get_indexer; the API @@ -491,7 +497,7 @@ def _convert_list_indexer(self, keyarr, kind=None): def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): nv.validate_take(tuple(), kwargs) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) taken = self._assert_take_fillable(self.codes, indices, allow_fill=allow_fill, fill_value=fill_value, @@ -591,12 +597,12 @@ def _evaluate_compare(self, other): self, other._values, categories=self.categories, ordered=self.ordered) - if isinstance(other, (com.ABCCategorical, np.ndarray, - com.ABCSeries)): + if isinstance(other, (ABCCategorical, np.ndarray, + ABCSeries)): if len(self.values) != len(other): raise ValueError("Lengths must match to compare") - if isinstance(other, com.ABCCategorical): + if isinstance(other, ABCCategorical): if not self.values.is_dtype_equal(other): raise TypeError("categorical index comparisions must " "have the same categories and ordered " @@ -619,7 +625,7 @@ def _delegate_method(self, name, *args, **kwargs): if 'inplace' in kwargs: raise ValueError("cannot use inplace with CategoricalIndex") res = method(*args, **kwargs) - if lib.isscalar(res): + if is_scalar(res): return res return CategoricalIndex(res, name=self.name) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 05b2045a4850f..184744915bd8d 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -13,6 +13,21 @@ from pandas.compat import range, zip, lrange, lzip, map from pandas.compat.numpy import function as nv from pandas import compat + + +from pandas.types.common import (_ensure_int64, + _ensure_platform_int, + is_object_dtype, + is_iterator, + is_list_like, + is_scalar) +from pandas.types.missing import isnull, array_equivalent +from pandas.core.common import (_values_from_object, + is_bool_indexer, + is_null_slice, + PerformanceWarning) + + from pandas.core.base import FrozenList import pandas.core.base as base from pandas.util.decorators import (Appender, cache_readonly, @@ -21,13 +36,6 @@ import pandas.core.missing as missing import pandas.core.algorithms as algos from pandas.formats.printing import pprint_thing -from pandas.core.common import (isnull, array_equivalent, - is_object_dtype, - _values_from_object, - is_iterator, - _ensure_int64, is_bool_indexer, - is_list_like, is_null_slice, - PerformanceWarning) from pandas.core.config import get_option @@ -798,7 +806,7 @@ def lexsort_depth(self): else: return 0 - int64_labels = [com._ensure_int64(lab) for lab in self.labels] + int64_labels = [_ensure_int64(lab) for lab in self.labels] for k in range(self.nlevels, 0, -1): if lib.is_lexsorted(int64_labels[:k]): return k @@ -840,6 +848,12 @@ def from_arrays(cls, arrays, sortorder=None, names=None): name = None if names is None else names[0] return Index(arrays[0], name=name) + # Check if lengths of all arrays are equal or not, + # raise ValueError, if not + for i in range(1, len(arrays)): + if len(arrays[i]) != len(arrays[i - 1]): + raise ValueError('all arrays must be same length') + cats = [Categorical.from_array(arr, ordered=True) for arr in arrays] levels = [c.categories for c in cats] labels = [c.codes for c in cats] @@ -984,7 +998,7 @@ def __setstate__(self, state): self._reset_identity() def __getitem__(self, key): - if lib.isscalar(key): + if is_scalar(key): retval = [] for lev, lab in zip(self.levels, self.labels): if lab[key] == -1: @@ -1011,7 +1025,7 @@ def __getitem__(self, key): def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): nv.validate_take(tuple(), kwargs) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) taken = self._assert_take_fillable(self.labels, indices, allow_fill=allow_fill, fill_value=fill_value, @@ -1313,7 +1327,7 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): if not ascending: indexer = indexer[::-1] - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) new_labels = [lab.take(indexer) for lab in self.labels] new_index = MultiIndex(labels=new_labels, levels=self.levels, @@ -1377,7 +1391,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): else: indexer = self_index._engine.get_indexer(target._values) - return com._ensure_platform_int(indexer) + return _ensure_platform_int(indexer) def reindex(self, target, method=None, level=None, limit=None, tolerance=None): @@ -1759,7 +1773,7 @@ def convert_indexer(start, stop, step, indexer=indexer, labels=labels): # selected from pandas import Series mapper = Series(indexer) - indexer = labels.take(com._ensure_platform_int(indexer)) + indexer = labels.take(_ensure_platform_int(indexer)) result = Series(Index(indexer).isin(r).nonzero()[0]) m = result.map(mapper)._values diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 89fc05fdcc5f5..86d22e141f781 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -3,13 +3,15 @@ import pandas.algos as _algos import pandas.index as _index +from pandas.types.common import (is_dtype_equal, pandas_dtype, + is_float_dtype, is_object_dtype, + is_integer_dtype, is_scalar) +from pandas.types.missing import array_equivalent, isnull +from pandas.core.common import _values_from_object + from pandas import compat from pandas.indexes.base import Index, InvalidIndexError, _index_shared_docs from pandas.util.decorators import Appender, cache_readonly -import pandas.core.common as com -from pandas.core.common import (is_dtype_equal, isnull, pandas_dtype, - is_float_dtype, is_object_dtype, - is_integer_dtype) import pandas.indexes.base as ibase @@ -164,8 +166,8 @@ def equals(self, other): if self.is_(other): return True - return com.array_equivalent(com._values_from_object(self), - com._values_from_object(other)) + return array_equivalent(_values_from_object(self), + _values_from_object(other)) def _wrap_joined_index(self, joined, other): name = self.name if self.name == other.name else None @@ -287,17 +289,17 @@ def _format_native_types(self, na_rep='', float_format=None, decimal='.', def get_value(self, series, key): """ we always want to get an index value, never a value """ - if not lib.isscalar(key): + if not is_scalar(key): raise InvalidIndexError from pandas.core.indexing import maybe_droplevels from pandas.core.series import Series - k = com._values_from_object(key) + k = _values_from_object(key) loc = self.get_loc(k) - new_values = com._values_from_object(series)[loc] + new_values = _values_from_object(series)[loc] - if lib.isscalar(new_values) or new_values is None: + if is_scalar(new_values) or new_values is None: return new_values new_index = self[loc] diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 168143fdea047..f680d2da0161e 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -4,14 +4,16 @@ import numpy as np import pandas.index as _index +from pandas.types.common import (is_integer, + is_scalar, + is_int64_dtype) + from pandas import compat from pandas.compat import lrange, range from pandas.compat.numpy import function as nv from pandas.indexes.base import Index, _index_shared_docs from pandas.util.decorators import Appender, cache_readonly -import pandas.core.common as com import pandas.indexes.base as ibase -import pandas.lib as lib from pandas.indexes.numeric import Int64Index @@ -120,7 +122,7 @@ def _simple_new(cls, start, stop=None, step=None, name=None, result = object.__new__(cls) # handle passed None, non-integers - if start is None or not com.is_integer(start): + if start is None or not is_integer(start): try: return RangeIndex(start, stop, step, name=name, **kwargs) except TypeError: @@ -139,7 +141,7 @@ def _simple_new(cls, start, stop=None, step=None, name=None, @staticmethod def _validate_dtype(dtype): """ require dtype to be None or int64 """ - if not (dtype is None or com.is_int64_dtype(dtype)): + if not (dtype is None or is_int64_dtype(dtype)): raise TypeError('Invalid to pass a non-int64 dtype to RangeIndex') @cache_readonly @@ -448,7 +450,7 @@ def __getitem__(self, key): """ super_getitem = super(RangeIndex, self).__getitem__ - if lib.isscalar(key): + if is_scalar(key): n = int(key) if n != key: return super_getitem(key) @@ -510,7 +512,7 @@ def __getitem__(self, key): return super_getitem(key) def __floordiv__(self, other): - if com.is_integer(other): + if is_integer(other): if (len(self) == 0 or self._start % other == 0 and self._step % other == 0): @@ -560,7 +562,7 @@ def _evaluate_numeric_binop(self, other): # we don't have a representable op # so return a base index - if not com.is_integer(rstep) or not rstep: + if not is_integer(rstep) or not rstep: raise ValueError else: @@ -577,7 +579,7 @@ def _evaluate_numeric_binop(self, other): # for compat with numpy / Int64Index # even if we can represent as a RangeIndex, return # as a Float64Index if we have float-like descriptors - if not all([com.is_integer(x) for x in + if not all([is_integer(x) for x in [rstart, rstop, rstep]]): result = result.astype('float64') diff --git a/pandas/io/common.py b/pandas/io/common.py index 76395928eb011..6f9bddd0fdf9b 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -11,8 +11,8 @@ from pandas.compat import StringIO, BytesIO, string_types, text_type from pandas import compat from pandas.formats.printing import pprint_thing -from pandas.core.common import is_number, AbstractMethodError - +from pandas.core.common import AbstractMethodError +from pandas.types.common import is_number try: import pathlib diff --git a/pandas/io/data.py b/pandas/io/data.py index 5fa440e7bb1ff..68151fbb091fa 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -19,7 +19,9 @@ ) import pandas.compat as compat from pandas import Panel, DataFrame, Series, read_csv, concat, to_datetime, DatetimeIndex, DateOffset -from pandas.core.common import is_list_like, PandasError + +from pandas.types.common import is_list_like +from pandas.core.common import PandasError from pandas.io.common import urlopen, ZipFile, urlencode from pandas.tseries.offsets import MonthEnd from pandas.util.testing import _network_error_classes diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 775465ea9372d..703cdbeaa7a8f 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -10,6 +10,9 @@ import abc import numpy as np +from pandas.types.common import (is_integer, is_float, + is_bool, is_list_like) + from pandas.core.frame import DataFrame from pandas.io.parsers import TextParser from pandas.io.common import (_is_url, _urlopen, _validate_header_arg, @@ -22,7 +25,6 @@ from pandas.formats.printing import pprint_thing import pandas.compat as compat import pandas.compat.openpyxl_compat as openpyxl_compat -import pandas.core.common as com from warnings import warn from distutils.version import LooseVersion @@ -423,17 +425,17 @@ def _parse_cell(cell_contents, cell_typ): output[asheetname] = DataFrame() continue - if com.is_list_like(header) and len(header) == 1: + if is_list_like(header) and len(header) == 1: header = header[0] # forward fill and pull out names for MultiIndex column header_names = None if header is not None: - if com.is_list_like(header): + if is_list_like(header): header_names = [] control_row = [True for x in data[0]] for row in header: - if com.is_integer(skiprows): + if is_integer(skiprows): row += skiprows data[row], control_row = _fill_mi_header( @@ -444,9 +446,9 @@ def _parse_cell(cell_contents, cell_typ): else: data[header] = _trim_excel_header(data[header]) - if com.is_list_like(index_col): + if is_list_like(index_col): # forward fill values for MultiIndex index - if not com.is_list_like(header): + if not is_list_like(header): offset = 1 + header else: offset = 1 + max(header) @@ -459,7 +461,7 @@ def _parse_cell(cell_contents, cell_typ): else: last = data[row][col] - if com.is_list_like(header) and len(header) > 1: + if is_list_like(header) and len(header) > 1: has_index_names = True # GH 12292 : error when read one empty column from excel file @@ -556,21 +558,21 @@ def _pop_header_name(row, index_col): return none_fill(row[0]), row[1:] else: # pop out header name and fill w/ blank - i = index_col if not com.is_list_like(index_col) else max(index_col) + i = index_col if not is_list_like(index_col) else max(index_col) return none_fill(row[i]), row[:i] + [''] + row[i + 1:] def _conv_value(val): # Convert numpy types to Python types for the Excel writers. - if com.is_integer(val): + if is_integer(val): val = int(val) - elif com.is_float(val): + elif is_float(val): val = float(val) - elif com.is_bool(val): + elif is_bool(val): val = bool(val) elif isinstance(val, Period): val = "%s" % val - elif com.is_list_like(val): + elif is_list_like(val): val = str(val) return val diff --git a/pandas/io/html.py b/pandas/io/html.py index 609642e248eda..e0d84a9617ae4 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -12,12 +12,12 @@ import numpy as np +from pandas.types.common import is_list_like from pandas.io.common import (EmptyDataError, _is_url, urlopen, parse_url, _validate_header_arg) from pandas.io.parsers import TextParser from pandas.compat import (lrange, lmap, u, string_types, iteritems, raise_with_traceback, binary_type) -from pandas.core import common as com from pandas import Series from pandas.core.common import AbstractMethodError from pandas.formats.printing import pprint_thing @@ -107,7 +107,7 @@ def _get_skiprows(skiprows): """ if isinstance(skiprows, slice): return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1) - elif isinstance(skiprows, numbers.Integral) or com.is_list_like(skiprows): + elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows): return skiprows elif skiprows is None: return 0 diff --git a/pandas/io/packers.py b/pandas/io/packers.py index ff06a5f212f8b..94f390955dddd 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -47,6 +47,10 @@ import numpy as np from pandas import compat from pandas.compat import u, u_safe + +from pandas.types.common import (is_categorical_dtype, is_object_dtype, + needs_i8_conversion, pandas_dtype) + from pandas import (Timestamp, Period, Series, DataFrame, # noqa Index, MultiIndex, Float64Index, Int64Index, Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT, @@ -55,11 +59,9 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame -from pandas.core.common import (PerformanceWarning, - is_categorical_dtype, is_object_dtype, - needs_i8_conversion, pandas_dtype) +from pandas.core.common import PerformanceWarning from pandas.io.common import get_filepath_or_buffer -from pandas.core.internals import BlockManager, make_block +from pandas.core.internals import BlockManager, make_block, _safe_reshape import pandas.core.internals as internals from pandas.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType @@ -620,8 +622,9 @@ def decode(obj): axes = obj[u'axes'] def create_block(b): - values = unconvert(b[u'values'], dtype_for(b[u'dtype']), - b[u'compress']).reshape(b[u'shape']) + values = _safe_reshape(unconvert( + b[u'values'], dtype_for(b[u'dtype']), + b[u'compress']), b[u'shape']) # locs handles duplicate column names, and should be used instead # of items; see GH 9618 diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index dc9455289b757..f6a84ea9debaa 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -2,20 +2,22 @@ Module contains tools for processing files into DataFrames or other objects """ from __future__ import print_function -from pandas.compat import (range, lrange, StringIO, lzip, zip, - string_types, map, OrderedDict) -from pandas import compat from collections import defaultdict import re import csv import warnings +import datetime import numpy as np +from pandas import compat +from pandas.compat import range, lrange, StringIO, lzip, zip, string_types, map +from pandas.types.common import (is_integer, _ensure_object, + is_list_like, is_integer_dtype, + is_float, + is_scalar) from pandas.core.index import Index, MultiIndex from pandas.core.frame import DataFrame -import datetime -import pandas.core.common as com from pandas.core.common import AbstractMethodError from pandas.core.config import get_option from pandas.io.date_converters import generic_parser @@ -326,11 +328,11 @@ def _validate_nrows(nrows): msg = "'nrows' must be an integer" if nrows is not None: - if com.is_float(nrows): + if is_float(nrows): if int(nrows) != nrows: raise ValueError(msg) nrows = int(nrows) - elif not com.is_integer(nrows): + elif not is_integer(nrows): raise ValueError(msg) return nrows @@ -869,7 +871,7 @@ def _clean_options(self, options, engine): # handle skiprows; this is internally handled by the # c-engine, so only need for python parsers if engine != 'c': - if com.is_integer(skiprows): + if is_integer(skiprows): skiprows = lrange(skiprows) skiprows = set() if skiprows is None else set(skiprows) @@ -961,7 +963,7 @@ def _validate_parse_dates_arg(parse_dates): "for the 'parse_dates' parameter") if parse_dates is not None: - if lib.isscalar(parse_dates): + if is_scalar(parse_dates): if not lib.is_bool(parse_dates): raise TypeError(msg) @@ -1021,8 +1023,8 @@ def __init__(self, kwds): is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray)) if not (is_sequence and - all(map(com.is_integer, self.index_col)) or - com.is_integer(self.index_col)): + all(map(is_integer, self.index_col)) or + is_integer(self.index_col)): raise ValueError("index_col must only contain row numbers " "when specifying a multi-index header") @@ -1047,7 +1049,7 @@ def _should_parse_dates(self, i): name = self.index_names[i] j = self.index_col[i] - if lib.isscalar(self.parse_dates): + if is_scalar(self.parse_dates): return (j == self.parse_dates) or (name == self.parse_dates) else: return (j in self.parse_dates) or (name in self.parse_dates) @@ -1281,7 +1283,7 @@ def _convert_types(self, values, na_values, try_num_bool=True): mask = lib.ismember(values, na_values) na_count = mask.sum() if na_count > 0: - if com.is_integer_dtype(values): + if is_integer_dtype(values): values = values.astype(np.float64) np.putmask(values, mask, np.nan) return values, na_count @@ -1407,10 +1409,10 @@ def _set_noconvert_columns(self): usecols = self.usecols def _set(x): - if usecols and com.is_integer(x): + if usecols and is_integer(x): x = list(usecols)[x] - if not com.is_integer(x): + if not is_integer(x): x = names.index(x) self._reader.set_noconvert(x) @@ -1790,7 +1792,7 @@ def _set_no_thousands_columns(self): noconvert_columns = set() def _set(x): - if com.is_integer(x): + if is_integer(x): noconvert_columns.add(x) else: noconvert_columns.add(self.columns.index(x)) @@ -1954,7 +1956,7 @@ def _convert_data(self, data): def _to_recarray(self, data, columns): dtypes = [] - o = OrderedDict() + o = compat.OrderedDict() # use the columns to "order" the keys # in the unordered 'data' dictionary @@ -2438,8 +2440,8 @@ def converter(*date_cols): strs = _concat_date_cols(date_cols) try: - return tools._to_datetime( - com._ensure_object(strs), + return tools.to_datetime( + _ensure_object(strs), utc=None, box=False, dayfirst=dayfirst, @@ -2492,7 +2494,7 @@ def _isindex(colspec): if isinstance(parse_spec, list): # list of column lists for colspec in parse_spec: - if lib.isscalar(colspec): + if is_scalar(colspec): if isinstance(colspec, int) and colspec not in data_dict: colspec = orig_names[colspec] if _isindex(colspec): @@ -2569,7 +2571,7 @@ def _clean_na_values(na_values, keep_default_na=True): (k, _floatify_na_values(v)) for k, v in na_values.items() # noqa ]) else: - if not com.is_list_like(na_values): + if not is_list_like(na_values): na_values = [na_values] na_values = _stringify_na_values(na_values) if keep_default_na: @@ -2622,7 +2624,7 @@ def _get_empty_meta(columns, index_col, index_names, dtype=None): if not isinstance(dtype, dict): dtype = defaultdict(lambda: dtype) # Convert column indexes to column names. - dtype = dict((columns[k] if com.is_integer(k) else k, v) + dtype = dict((columns[k] if is_integer(k) else k, v) for k, v in compat.iteritems(dtype)) if index_col is None or index_col is False: diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py index c19dae7f3545e..2358c296f782e 100644 --- a/pandas/io/pickle.py +++ b/pandas/io/pickle.py @@ -3,7 +3,7 @@ import numpy as np from numpy.lib.format import read_array, write_array from pandas.compat import BytesIO, cPickle as pkl, pickle_compat as pc, PY3 -import pandas.core.common as com +from pandas.types.common import is_datetime64_dtype, _NS_DTYPE def to_pickle(obj, path): @@ -86,7 +86,7 @@ def _unpickle_array(bytes): # All datetimes should be stored as M8[ns]. When unpickling with # numpy1.6, it will read these as M8[us]. So this ensures all # datetime64 types are read as MS[ns] - if com.is_datetime64_dtype(arr): - arr = arr.view(com._NS_DTYPE) + if is_datetime64_dtype(arr): + arr = arr.view(_NS_DTYPE) return arr diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index d4ca717ddbc4e..038ca7ac7775b 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -12,11 +12,21 @@ import warnings import os +from pandas.types.common import (is_list_like, + is_categorical_dtype, + is_timedelta64_dtype, + is_datetime64tz_dtype, + is_datetime64_dtype, + _ensure_object, + _ensure_int64, + _ensure_platform_int) +from pandas.types.missing import array_equivalent + import numpy as np import pandas as pd from pandas import (Series, DataFrame, Panel, Panel4D, Index, - MultiIndex, Int64Index) + MultiIndex, Int64Index, isnull) from pandas.core import config from pandas.io.common import _stringify_path from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel @@ -32,7 +42,6 @@ _block2d_to_blocknd, _factor_indexer, _block_shape) from pandas.core.index import _ensure_index -import pandas.core.common as com from pandas.tools.merge import concat from pandas import compat from pandas.compat import u_safe as u, PY3, range, lrange, string_types, filter @@ -1677,7 +1686,7 @@ def validate_metadata(self, handler): new_metadata = self.metadata cur_metadata = handler.read_metadata(self.cname) if new_metadata is not None and cur_metadata is not None \ - and not com.array_equivalent(new_metadata, cur_metadata): + and not array_equivalent(new_metadata, cur_metadata): raise ValueError("cannot append a categorical with " "different categories to the existing") @@ -2566,7 +2575,7 @@ def write_array(self, key, value, items=None): empty_array = self._is_empty_array(value.shape) transposed = False - if com.is_categorical_dtype(value): + if is_categorical_dtype(value): raise NotImplementedError('Cannot store a category dtype in ' 'a HDF5 dataset that uses format=' '"fixed". Use format="table".') @@ -2621,12 +2630,12 @@ def write_array(self, key, value, items=None): if empty_array: self.write_array_empty(key, value) else: - if com.is_datetime64_dtype(value.dtype): + if is_datetime64_dtype(value.dtype): self._handle.create_array( self.group, key, value.view('i8')) getattr( self.group, key)._v_attrs.value_type = 'datetime64' - elif com.is_datetime64tz_dtype(value.dtype): + elif is_datetime64tz_dtype(value.dtype): # store as UTC # with a zone self._handle.create_array(self.group, key, @@ -2635,7 +2644,7 @@ def write_array(self, key, value, items=None): node = getattr(self.group, key) node._v_attrs.tz = _get_tz(value.tz) node._v_attrs.value_type = 'datetime64' - elif com.is_timedelta64_dtype(value.dtype): + elif is_timedelta64_dtype(value.dtype): self._handle.create_array( self.group, key, value.view('i8')) getattr( @@ -3756,8 +3765,8 @@ def read(self, where=None, columns=None, **kwargs): if len(unique(key)) == len(key): sorter, _ = algos.groupsort_indexer( - com._ensure_int64(key), np.prod(N)) - sorter = com._ensure_platform_int(sorter) + _ensure_int64(key), np.prod(N)) + sorter = _ensure_platform_int(sorter) # create the objs for c in self.values_axes: @@ -3802,7 +3811,7 @@ def read(self, where=None, columns=None, **kwargs): unique_tuples = _asarray_tuplesafe(unique_tuples) indexer = match(unique_tuples, tuple_index) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) new_index = long_index.take(indexer) new_values = lp.values.take(indexer, axis=0) @@ -3903,7 +3912,7 @@ def write_data(self, chunksize, dropna=False): # figure the mask: only do if we can successfully process this # column, otherwise ignore the mask - mask = com.isnull(a.data).all(axis=0) + mask = isnull(a.data).all(axis=0) if isinstance(mask, np.ndarray): masks.append(mask.astype('u1', copy=False)) @@ -4522,7 +4531,7 @@ def _convert_string_array(data, encoding, itemsize=None): # create the sized dtype if itemsize is None: - itemsize = lib.max_len_string_array(com._ensure_object(data.ravel())) + itemsize = lib.max_len_string_array(_ensure_object(data.ravel())) data = np.asarray(data, dtype="S%d" % itemsize) return data @@ -4551,7 +4560,7 @@ def _unconvert_string_array(data, nan_rep=None, encoding=None): encoding = _ensure_encoding(encoding) if encoding is not None and len(data): - itemsize = lib.max_len_string_array(com._ensure_object(data)) + itemsize = lib.max_len_string_array(_ensure_object(data)) if compat.PY3: dtype = "U{0}".format(itemsize) else: @@ -4619,7 +4628,7 @@ def __init__(self, table, where=None, start=None, stop=None, **kwargs): self.terms = None self.coordinates = None - if com.is_list_like(where): + if is_list_like(where): # see if we have a passed coordinate like try: diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 324988360c9fe..b9eaa0e4d657b 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -13,13 +13,15 @@ import numpy as np import pandas.lib as lib -import pandas.core.common as com +from pandas.types.missing import isnull +from pandas.types.dtypes import DatetimeTZDtype +from pandas.types.common import (is_list_like, + is_datetime64tz_dtype) + from pandas.compat import (lzip, map, zip, raise_with_traceback, string_types, text_type) from pandas.core.api import DataFrame, Series -from pandas.core.common import isnull from pandas.core.base import PandasObject -from pandas.types.api import DatetimeTZDtype from pandas.tseries.tools import to_datetime from contextlib import contextmanager @@ -39,6 +41,24 @@ class DatabaseError(IOError): _SQLALCHEMY_INSTALLED = None +def _validate_flavor_parameter(flavor): + """ + Checks whether a database 'flavor' was specified. + If not None, produces FutureWarning if 'sqlite' and + raises a ValueError if anything else. + """ + if flavor is not None: + if flavor == 'sqlite': + warnings.warn("the 'flavor' parameter is deprecated " + "and will be removed in a future version, " + "as 'sqlite' is the only supported option " + "when SQLAlchemy is not installed.", + FutureWarning, stacklevel=2) + else: + raise ValueError("database flavor {flavor} is not " + "supported".format(flavor=flavor)) + + def _is_sqlalchemy_connectable(con): global _SQLALCHEMY_INSTALLED if _SQLALCHEMY_INSTALLED is None: @@ -90,7 +110,7 @@ def _handle_date_column(col, format=None): # parse dates as timestamp format = 's' if format is None else format return to_datetime(col, errors='coerce', unit=format, utc=True) - elif com.is_datetime64tz_dtype(col): + elif is_datetime64tz_dtype(col): # coerce to UTC timezone # GH11216 return (to_datetime(col, errors='coerce') @@ -123,7 +143,7 @@ def _parse_date_columns(data_frame, parse_dates): # we could in theory do a 'nice' conversion from a FixedOffset tz # GH11216 for col_name, df_col in data_frame.iteritems(): - if com.is_datetime64tz_dtype(df_col): + if is_datetime64tz_dtype(df_col): data_frame[col_name] = _handle_date_column(df_col) return data_frame @@ -515,7 +535,7 @@ def read_sql(sql, con, index_col=None, coerce_float=True, params=None, chunksize=chunksize) -def to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', +def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None): """ Write records stored in a DataFrame to a SQL database. @@ -530,10 +550,8 @@ def to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. - flavor : {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy connectable. - 'mysql' is deprecated and will be removed in future versions, but it - will be further supported through SQLAlchemy connectables. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version schema : string, default None Name of SQL schema in database to write to (if database flavor supports this). If None, use default schema (default). @@ -571,7 +589,7 @@ def to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', chunksize=chunksize, dtype=dtype) -def has_table(table_name, con, flavor='sqlite', schema=None): +def has_table(table_name, con, flavor=None, schema=None): """ Check if DataBase has named table. @@ -583,10 +601,8 @@ def has_table(table_name, con, flavor='sqlite', schema=None): Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. - flavor: {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy connectable. - 'mysql' is deprecated and will be removed in future versions, but it - will be further supported through SQLAlchemy connectables. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version schema : string, default None Name of SQL schema in database to write to (if database flavor supports this). If None, use default schema (default). @@ -601,12 +617,6 @@ def has_table(table_name, con, flavor='sqlite', schema=None): table_exists = has_table -_MYSQL_WARNING = ("The 'mysql' flavor with DBAPI connection is deprecated " - "and will be removed in future versions. " - "MySQL will be further supported with SQLAlchemy " - "connectables.") - - def _engine_builder(con): """ Returns a SQLAlchemy engine from a URI (if con is a string) @@ -630,15 +640,15 @@ def pandasSQL_builder(con, flavor=None, schema=None, meta=None, Convenience function to return the correct PandasSQL subclass based on the provided parameters """ + _validate_flavor_parameter(flavor) + # When support for DBAPI connections is removed, # is_cursor should not be necessary. con = _engine_builder(con) if _is_sqlalchemy_connectable(con): return SQLDatabase(con, schema=schema, meta=meta) else: - if flavor == 'mysql': - warnings.warn(_MYSQL_WARNING, FutureWarning, stacklevel=3) - return SQLiteDatabase(con, flavor, is_cursor=is_cursor) + return SQLiteDatabase(con, is_cursor=is_cursor) class SQLTable(PandasObject): @@ -876,7 +886,7 @@ def _create_table_setup(self): for name, typ, is_index in column_names_and_types] if self.keys is not None: - if not com.is_list_like(self.keys): + if not is_list_like(self.keys): keys = [self.keys] else: keys = self.keys @@ -1033,11 +1043,11 @@ class PandasSQL(PandasObject): def read_sql(self, *args, **kwargs): raise ValueError("PandasSQL must be created with an SQLAlchemy " - "connectable or connection+sql flavor") + "connectable or sqlite connection") def to_sql(self, *args, **kwargs): raise ValueError("PandasSQL must be created with an SQLAlchemy " - "connectable or connection+sql flavor") + "connectable or sqlite connection") class SQLDatabase(PandasSQL): @@ -1306,38 +1316,16 @@ def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): # ---- SQL without SQLAlchemy --- -# Flavour specific sql strings and handler class for access to DBs without -# SQLAlchemy installed -# SQL type convertions for each DB +# sqlite-specific sql strings and handler class +# dictionary used for readability purposes _SQL_TYPES = { - 'string': { - 'mysql': 'VARCHAR (63)', - 'sqlite': 'TEXT', - }, - 'floating': { - 'mysql': 'DOUBLE', - 'sqlite': 'REAL', - }, - 'integer': { - 'mysql': 'BIGINT', - 'sqlite': 'INTEGER', - }, - 'datetime': { - 'mysql': 'DATETIME', - 'sqlite': 'TIMESTAMP', - }, - 'date': { - 'mysql': 'DATE', - 'sqlite': 'DATE', - }, - 'time': { - 'mysql': 'TIME', - 'sqlite': 'TIME', - }, - 'boolean': { - 'mysql': 'BOOLEAN', - 'sqlite': 'INTEGER', - } + 'string': 'TEXT', + 'floating': 'REAL', + 'integer': 'INTEGER', + 'datetime': 'TIMESTAMP', + 'date': 'DATE', + 'time': 'TIME', + 'boolean': 'INTEGER', } @@ -1349,22 +1337,6 @@ def _get_unicode_name(name): return uname -def _get_valid_mysql_name(name): - # Filter for unquoted identifiers - # See http://dev.mysql.com/doc/refman/5.0/en/identifiers.html - uname = _get_unicode_name(name) - if not len(uname): - raise ValueError("Empty table or column name specified") - - basere = r'[0-9,a-z,A-Z$_]' - for c in uname: - if not re.match(basere, c): - if not (0x80 < ord(c) < 0xFFFF): - raise ValueError("Invalid MySQL identifier '%s'" % uname) - - return '`' + uname + '`' - - def _get_valid_sqlite_name(name): # See http://stackoverflow.com/questions/6514274/how-do-you-escape-strings\ # -for-sqlite-table-column-names-in-python @@ -1383,19 +1355,6 @@ def _get_valid_sqlite_name(name): return '"' + uname.replace('"', '""') + '"' -# SQL enquote and wildcard symbols -_SQL_WILDCARD = { - 'mysql': '%s', - 'sqlite': '?' -} - -# Validate and return escaped identifier -_SQL_GET_IDENTIFIER = { - 'mysql': _get_valid_mysql_name, - 'sqlite': _get_valid_sqlite_name, -} - - _SAFE_NAMES_WARNING = ("The spaces in these column names will not be changed. " "In pandas versions < 0.14, spaces were converted to " "underscores.") @@ -1426,9 +1385,8 @@ def _execute_create(self): def insert_statement(self): names = list(map(text_type, self.frame.columns)) - flv = self.pd_sql.flavor - wld = _SQL_WILDCARD[flv] # wildcard char - escape = _SQL_GET_IDENTIFIER[flv] + wld = '?' # wildcard char + escape = _get_valid_sqlite_name if self.index is not None: [names.insert(0, idx) for idx in self.index[::-1]] @@ -1458,14 +1416,13 @@ def _create_table_setup(self): if any(map(pat.search, column_names)): warnings.warn(_SAFE_NAMES_WARNING, stacklevel=6) - flv = self.pd_sql.flavor - escape = _SQL_GET_IDENTIFIER[flv] + escape = _get_valid_sqlite_name create_tbl_stmts = [escape(cname) + ' ' + ctype for cname, ctype, _ in column_names_and_types] if self.keys is not None and len(self.keys): - if not com.is_list_like(self.keys): + if not is_list_like(self.keys): keys = [self.keys] else: keys = self.keys @@ -1512,7 +1469,7 @@ def _sql_type_name(self, col): if col_type not in _SQL_TYPES: col_type = "string" - return _SQL_TYPES[col_type][self.pd_sql.flavor] + return _SQL_TYPES[col_type] class SQLiteDatabase(PandasSQL): @@ -1520,25 +1477,17 @@ class SQLiteDatabase(PandasSQL): Version of SQLDatabase to support sqlite connections (fallback without sqlalchemy). This should only be used internally. - For now still supports `flavor` argument to deal with 'mysql' database - for backwards compatibility, but this will be removed in future versions. - Parameters ---------- con : sqlite connection object """ - def __init__(self, con, flavor, is_cursor=False): + def __init__(self, con, flavor=None, is_cursor=False): + _validate_flavor_parameter(flavor) + self.is_cursor = is_cursor self.con = con - if flavor is None: - flavor = 'sqlite' - if flavor not in ['sqlite', 'mysql']: - raise NotImplementedError("flavors other than SQLite and MySQL " - "are not supported") - else: - self.flavor = flavor @contextmanager def run_transaction(self): @@ -1663,15 +1612,12 @@ def to_sql(self, frame, name, if_exists='fail', index=True, def has_table(self, name, schema=None): # TODO(wesm): unused? - # escape = _SQL_GET_IDENTIFIER[self.flavor] + # escape = _get_valid_sqlite_name # esc_name = escape(name) - wld = _SQL_WILDCARD[self.flavor] - flavor_map = { - 'sqlite': ("SELECT name FROM sqlite_master " - "WHERE type='table' AND name=%s;") % wld, - 'mysql': "SHOW TABLES LIKE %s" % wld} - query = flavor_map.get(self.flavor) + wld = '?' + query = ("SELECT name FROM sqlite_master " + "WHERE type='table' AND name=%s;") % wld return len(self.execute(query, [name, ]).fetchall()) > 0 @@ -1679,8 +1625,7 @@ def get_table(self, table_name, schema=None): return None # not supported in fallback mode def drop_table(self, name, schema=None): - escape = _SQL_GET_IDENTIFIER[self.flavor] - drop_sql = "DROP TABLE %s" % escape(name) + drop_sql = "DROP TABLE %s" % _get_valid_sqlite_name(name) self.execute(drop_sql) def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): @@ -1689,7 +1634,7 @@ def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): return str(table.sql_schema()) -def get_schema(frame, name, flavor='sqlite', keys=None, con=None, dtype=None): +def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None): """ Get the SQL db table schema for the given frame. @@ -1698,16 +1643,14 @@ def get_schema(frame, name, flavor='sqlite', keys=None, con=None, dtype=None): frame : DataFrame name : string name of SQL table - flavor : {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy connectable. - 'mysql' is deprecated and will be removed in future versions, but it - will be further supported through SQLAlchemy engines. keys : string or sequence, default: None columns to use a primary key con: an open SQL database connection object or a SQLAlchemy connectable Using SQLAlchemy makes it possible to use any DB supported by that library, default: None If a DBAPI2 object, only sqlite3 is supported. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version dtype : dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection. diff --git a/pandas/io/stata.py b/pandas/io/stata.py index c7390cf240f8a..d35466e8896ba 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -14,6 +14,10 @@ import sys import struct from dateutil.relativedelta import relativedelta + +from pandas.types.common import (is_categorical_dtype, is_datetime64_dtype, + _ensure_object) + from pandas.core.base import StringMixin from pandas.core.categorical import Categorical from pandas.core.frame import DataFrame @@ -24,7 +28,7 @@ zip, BytesIO from pandas.util.decorators import Appender import pandas as pd -import pandas.core.common as com + from pandas.io.common import get_filepath_or_buffer, BaseIterator from pandas.lib import max_len_string_array, infer_dtype from pandas.tslib import NaT, Timestamp @@ -358,7 +362,7 @@ def _datetime_to_stata_elapsed_vec(dates, fmt): def parse_dates_safe(dates, delta=False, year=False, days=False): d = {} - if com.is_datetime64_dtype(dates.values): + if is_datetime64_dtype(dates.values): if delta: delta = dates - stata_epoch d['delta'] = delta.values.astype( @@ -396,7 +400,7 @@ def parse_dates_safe(dates, delta=False, year=False, days=False): index = dates.index if bad_loc.any(): dates = Series(dates) - if com.is_datetime64_dtype(dates): + if is_datetime64_dtype(dates): dates[bad_loc] = to_datetime(stata_epoch) else: dates[bad_loc] = stata_epoch @@ -1055,7 +1059,7 @@ def _read_new_header(self, first_char): self.lbllist = self._get_lbllist() self.path_or_buf.seek(self._seek_variable_labels) - self.vlblist = self._get_vlblist() + self._variable_labels = self._get_variable_labels() # Get data type information, works for versions 117-118. def _get_dtypes(self, seek_vartypes): @@ -1123,7 +1127,7 @@ def _get_lbllist(self): return [self._null_terminate(self.path_or_buf.read(b)) for i in range(self.nvar)] - def _get_vlblist(self): + def _get_variable_labels(self): if self.format_version == 118: vlblist = [self._decode(self.path_or_buf.read(321)) for i in range(self.nvar)] @@ -1238,7 +1242,7 @@ def _read_old_header(self, first_char): self.lbllist = self._get_lbllist() - self.vlblist = self._get_vlblist() + self._variable_labels = self._get_variable_labels() # ignore expansion fields (Format 105 and later) # When reading, read five bytes; the last four bytes now tell you @@ -1302,11 +1306,11 @@ def _read_value_labels(self): while True: if self.format_version >= 117: if self.path_or_buf.read(5) == b'</val': # <lbl> - break # end of variable label table + break # end of value label table slength = self.path_or_buf.read(4) if not slength: - break # end of variable label table (format < 117) + break # end of value label table (format < 117) if self.format_version <= 117: labname = self._null_terminate(self.path_or_buf.read(33)) else: @@ -1662,7 +1666,7 @@ def variable_labels(self): """Returns variable labels as a dict, associating each variable name with corresponding label """ - return dict(zip(self.varlist, self.vlblist)) + return dict(zip(self.varlist, self._variable_labels)) def value_labels(self): """Returns a dict, associating each variable name a dict, associating @@ -1692,7 +1696,7 @@ def _set_endianness(endianness): def _pad_bytes(name, length): """ - Takes a char string and pads it wih null bytes until it's length chars + Takes a char string and pads it with null bytes until it's length chars """ return name + "\x00" * (length - len(name)) @@ -1746,7 +1750,7 @@ def _dtype_to_stata_type(dtype, column): elif dtype.type == np.object_: # try to coerce it to the biggest string # not memory efficient, what else could we # do? - itemsize = max_len_string_array(com._ensure_object(column.values)) + itemsize = max_len_string_array(_ensure_object(column.values)) return chr(max(itemsize, 1)) elif dtype == np.float64: return chr(255) @@ -1784,7 +1788,7 @@ def _dtype_to_default_stata_fmt(dtype, column): if not (inferred_dtype in ('string', 'unicode') or len(column) == 0): raise ValueError('Writing general object arrays is not supported') - itemsize = max_len_string_array(com._ensure_object(column.values)) + itemsize = max_len_string_array(_ensure_object(column.values)) if itemsize > 244: raise ValueError(excessive_string_length_error % column.name) return "%" + str(max(itemsize, 1)) + "s" @@ -1827,6 +1831,12 @@ class StataWriter(StataParser): dataset_label : str A label for the data set. Should be 80 characters or smaller. + .. versionadded:: 0.19.0 + + variable_labels : dict + Dictionary containing columns as keys and variable labels as values. + Each label must be 80 characters or smaller. + Returns ------- writer : StataWriter instance @@ -1849,12 +1859,13 @@ class StataWriter(StataParser): def __init__(self, fname, data, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, - data_label=None): + data_label=None, variable_labels=None): super(StataWriter, self).__init__(encoding) self._convert_dates = convert_dates self._write_index = write_index self._time_stamp = time_stamp self._data_label = data_label + self._variable_labels = variable_labels # attach nobs, nvars, data, varlist, typlist self._prepare_pandas(data) @@ -1880,7 +1891,7 @@ def _prepare_categoricals(self, data): """Check for categorical columns, retain categorical information for Stata file and convert categorical data to int""" - is_cat = [com.is_categorical_dtype(data[col]) for col in data] + is_cat = [is_categorical_dtype(data[col]) for col in data] self._is_col_cat = is_cat self._value_labels = [] if not any(is_cat): @@ -2131,11 +2142,29 @@ def _write_descriptors(self, typlist=None, varlist=None, srtlist=None, else: # Default is empty label self._write(_pad_bytes("", 33)) - def _write_variable_labels(self, labels=None): - nvar = self.nvar - if labels is None: - for i in range(nvar): - self._write(_pad_bytes("", 81)) + def _write_variable_labels(self): + # Missing labels are 80 blank characters plus null termination + blank = _pad_bytes('', 81) + + if self._variable_labels is None: + for i in range(self.nvar): + self._write(blank) + return + + for col in self.data: + if col in self._variable_labels: + label = self._variable_labels[col] + if len(label) > 80: + raise ValueError('Variable labels must be 80 characters ' + 'or fewer') + is_latin1 = all(ord(c) < 256 for c in label) + if not is_latin1: + raise ValueError('Variable labels must contain only ' + 'characters that can be encoded in ' + 'Latin-1') + self._write(_pad_bytes(label, 81)) + else: + self._write(blank) def _prepare_data(self): data = self.data diff --git a/pandas/tests/data/categorical_0_14_1.pickle b/pandas/io/tests/data/categorical_0_14_1.pickle similarity index 100% rename from pandas/tests/data/categorical_0_14_1.pickle rename to pandas/io/tests/data/categorical_0_14_1.pickle diff --git a/pandas/tests/data/categorical_0_15_2.pickle b/pandas/io/tests/data/categorical_0_15_2.pickle similarity index 100% rename from pandas/tests/data/categorical_0_15_2.pickle rename to pandas/io/tests/data/categorical_0_15_2.pickle diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index 55c14fee9e3ed..6019144d59698 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -231,6 +231,44 @@ def python_unpickler(path): result = python_unpickler(path) self.compare_element(result, expected, typ) + def test_pickle_v0_14_1(self): + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_14_1.pickle') + # This code was executed once on v0.14.1 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + + def test_pickle_v0_15_2(self): + # ordered -> _ordered + # GH 9347 + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_15_2.pickle') + # This code was executed once on v0.15.2 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 9a995c17f0445..41be39f9abaa6 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -13,7 +13,7 @@ common methods, `_TestSQLAlchemyConn` tests the API with a SQLAlchemy Connection object. The different tested flavors (sqlite3, MySQL, PostgreSQL) derive from the base class - - Tests for the fallback mode (`TestSQLiteFallback` and `TestMySQLLegacy`) + - Tests for the fallback mode (`TestSQLiteFallback`) """ @@ -31,11 +31,12 @@ from datetime import datetime, date, time +from pandas.types.common import (is_object_dtype, is_datetime64_dtype, + is_datetime64tz_dtype) from pandas import DataFrame, Series, Index, MultiIndex, isnull, concat from pandas import date_range, to_datetime, to_timedelta, Timestamp import pandas.compat as compat from pandas.compat import StringIO, range, lrange, string_types -from pandas.core import common as com from pandas.core.datetools import format as date_format import pandas.io.sql as sql @@ -525,30 +526,29 @@ def test_read_sql_view(self): self._check_iris_loaded_frame(iris_frame) def test_to_sql(self): - sql.to_sql(self.test_frame1, 'test_frame1', self.conn, flavor='sqlite') + sql.to_sql(self.test_frame1, 'test_frame1', self.conn) self.assertTrue( - sql.has_table('test_frame1', self.conn, flavor='sqlite'), + sql.has_table('test_frame1', self.conn), 'Table not written to DB') def test_to_sql_fail(self): sql.to_sql(self.test_frame1, 'test_frame2', - self.conn, flavor='sqlite', if_exists='fail') + self.conn, if_exists='fail') self.assertTrue( - sql.has_table('test_frame2', self.conn, flavor='sqlite'), + sql.has_table('test_frame2', self.conn), 'Table not written to DB') self.assertRaises(ValueError, sql.to_sql, self.test_frame1, - 'test_frame2', self.conn, flavor='sqlite', - if_exists='fail') + 'test_frame2', self.conn, if_exists='fail') def test_to_sql_replace(self): sql.to_sql(self.test_frame1, 'test_frame3', - self.conn, flavor='sqlite', if_exists='fail') + self.conn, if_exists='fail') # Add to table again sql.to_sql(self.test_frame1, 'test_frame3', - self.conn, flavor='sqlite', if_exists='replace') + self.conn, if_exists='replace') self.assertTrue( - sql.has_table('test_frame3', self.conn, flavor='sqlite'), + sql.has_table('test_frame3', self.conn), 'Table not written to DB') num_entries = len(self.test_frame1) @@ -559,13 +559,13 @@ def test_to_sql_replace(self): def test_to_sql_append(self): sql.to_sql(self.test_frame1, 'test_frame4', - self.conn, flavor='sqlite', if_exists='fail') + self.conn, if_exists='fail') # Add to table again sql.to_sql(self.test_frame1, 'test_frame4', - self.conn, flavor='sqlite', if_exists='append') + self.conn, if_exists='append') self.assertTrue( - sql.has_table('test_frame4', self.conn, flavor='sqlite'), + sql.has_table('test_frame4', self.conn), 'Table not written to DB') num_entries = 2 * len(self.test_frame1) @@ -575,26 +575,25 @@ def test_to_sql_append(self): num_rows, num_entries, "not the same number of rows as entries") def test_to_sql_type_mapping(self): - sql.to_sql(self.test_frame3, 'test_frame5', - self.conn, flavor='sqlite', index=False) + sql.to_sql(self.test_frame3, 'test_frame5', self.conn, index=False) result = sql.read_sql("SELECT * FROM test_frame5", self.conn) tm.assert_frame_equal(self.test_frame3, result) def test_to_sql_series(self): s = Series(np.arange(5, dtype='int64'), name='series') - sql.to_sql(s, "test_series", self.conn, flavor='sqlite', index=False) + sql.to_sql(s, "test_series", self.conn, index=False) s2 = sql.read_sql_query("SELECT * FROM test_series", self.conn) tm.assert_frame_equal(s.to_frame(), s2) def test_to_sql_panel(self): panel = tm.makePanel() self.assertRaises(NotImplementedError, sql.to_sql, panel, - 'test_panel', self.conn, flavor='sqlite') + 'test_panel', self.conn) def test_roundtrip(self): sql.to_sql(self.test_frame1, 'test_frame_roundtrip', - con=self.conn, flavor='sqlite') + con=self.conn) result = sql.read_sql_query( 'SELECT * FROM test_frame_roundtrip', con=self.conn) @@ -608,7 +607,7 @@ def test_roundtrip(self): def test_roundtrip_chunksize(self): sql.to_sql(self.test_frame1, 'test_frame_roundtrip', con=self.conn, - index=False, flavor='sqlite', chunksize=2) + index=False, chunksize=2) result = sql.read_sql_query( 'SELECT * FROM test_frame_roundtrip', con=self.conn) @@ -763,27 +762,25 @@ def test_integer_col_names(self): if_exists='replace') def test_get_schema(self): - create_sql = sql.get_schema(self.test_frame1, 'test', 'sqlite', - con=self.conn) + create_sql = sql.get_schema(self.test_frame1, 'test', con=self.conn) self.assertTrue('CREATE' in create_sql) def test_get_schema_dtypes(self): float_frame = DataFrame({'a': [1.1, 1.2], 'b': [2.1, 2.2]}) dtype = sqlalchemy.Integer if self.mode == 'sqlalchemy' else 'INTEGER' - create_sql = sql.get_schema(float_frame, 'test', 'sqlite', + create_sql = sql.get_schema(float_frame, 'test', con=self.conn, dtype={'b': dtype}) self.assertTrue('CREATE' in create_sql) self.assertTrue('INTEGER' in create_sql) def test_get_schema_keys(self): frame = DataFrame({'Col1': [1.1, 1.2], 'Col2': [2.1, 2.2]}) - create_sql = sql.get_schema(frame, 'test', 'sqlite', - con=self.conn, keys='Col1') + create_sql = sql.get_schema(frame, 'test', con=self.conn, keys='Col1') constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("Col1")' self.assertTrue(constraint_sentence in create_sql) # multiple columns as key (GH10385) - create_sql = sql.get_schema(self.test_frame1, 'test', 'sqlite', + create_sql = sql.get_schema(self.test_frame1, 'test', con=self.conn, keys=['A', 'B']) constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("A", "B")' self.assertTrue(constraint_sentence in create_sql) @@ -1043,8 +1040,8 @@ def test_sql_open_close(self): with tm.ensure_clean() as name: conn = self.connect(name) - sql.to_sql(self.test_frame3, "test_frame3_legacy", conn, - flavor="sqlite", index=False) + sql.to_sql(self.test_frame3, "test_frame3_legacy", + conn, index=False) conn.close() conn = self.connect(name) @@ -1066,12 +1063,11 @@ def test_safe_names_warning(self): df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b ']) # has a space # warns on create table with spaces in names with tm.assert_produces_warning(): - sql.to_sql(df, "test_frame3_legacy", self.conn, - flavor="sqlite", index=False) + sql.to_sql(df, "test_frame3_legacy", self.conn, index=False) def test_get_schema2(self): # without providing a connection object (available for backwards comp) - create_sql = sql.get_schema(self.test_frame1, 'test', 'sqlite') + create_sql = sql.get_schema(self.test_frame1, 'test') self.assertTrue('CREATE' in create_sql) def test_tquery(self): @@ -1097,7 +1093,7 @@ def test_sqlite_type_mapping(self): # Test Timestamp objects (no datetime64 because of timezone) (GH9085) df = DataFrame({'time': to_datetime(['201412120154', '201412110254'], utc=True)}) - db = sql.SQLiteDatabase(self.conn, self.flavor) + db = sql.SQLiteDatabase(self.conn) table = sql.SQLiteTable("test_type", db, frame=df) schema = table.sql_schema() self.assertEqual(self._get_sqlite_column_type(schema, 'time'), @@ -1275,7 +1271,7 @@ def test_datetime_with_timezone(self): def check(col): # check that a column is either datetime64[ns] # or datetime64[ns, UTC] - if com.is_datetime64_dtype(col.dtype): + if is_datetime64_dtype(col.dtype): # "2000-01-01 00:00:00-08:00" should convert to # "2000-01-01 08:00:00" @@ -1285,7 +1281,7 @@ def check(col): # "2000-06-01 07:00:00" self.assertEqual(col[1], Timestamp('2000-06-01 07:00:00')) - elif com.is_datetime64tz_dtype(col.dtype): + elif is_datetime64tz_dtype(col.dtype): self.assertTrue(str(col.dt.tz) == 'UTC') # "2000-01-01 00:00:00-08:00" should convert to @@ -1311,9 +1307,9 @@ def check(col): # even with the same versions of psycopg2 & sqlalchemy, possibly a # Postgrsql server version difference col = df.DateColWithTz - self.assertTrue(com.is_object_dtype(col.dtype) or - com.is_datetime64_dtype(col.dtype) or - com.is_datetime64tz_dtype(col.dtype), + self.assertTrue(is_object_dtype(col.dtype) or + is_datetime64_dtype(col.dtype) or + is_datetime64tz_dtype(col.dtype), "DateCol loaded with incorrect type -> {0}" .format(col.dtype)) @@ -1327,7 +1323,7 @@ def check(col): self.conn, chunksize=1)), ignore_index=True) col = df.DateColWithTz - self.assertTrue(com.is_datetime64tz_dtype(col.dtype), + self.assertTrue(is_datetime64tz_dtype(col.dtype), "DateCol loaded with incorrect type -> {0}" .format(col.dtype)) self.assertTrue(str(col.dt.tz) == 'UTC') @@ -1907,16 +1903,12 @@ def connect(cls): def setUp(self): self.conn = self.connect() - self.pandasSQL = sql.SQLiteDatabase(self.conn, 'sqlite') + self.pandasSQL = sql.SQLiteDatabase(self.conn) self._load_iris_data() self._load_test1_data() - def test_invalid_flavor(self): - self.assertRaises( - NotImplementedError, sql.SQLiteDatabase, self.conn, 'oracle') - def test_read_sql(self): self._read_sql_iris() @@ -1964,7 +1956,7 @@ def test_execute_sql(self): def test_datetime_date(self): # test support for datetime.date df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"]) - df.to_sql('test_date', self.conn, index=False, flavor=self.flavor) + df.to_sql('test_date', self.conn, index=False) res = read_sql_query('SELECT * FROM test_date', self.conn) if self.flavor == 'sqlite': # comes back as strings @@ -1975,7 +1967,7 @@ def test_datetime_date(self): def test_datetime_time(self): # test support for datetime.time, GH #8341 df = DataFrame([time(9, 0, 0), time(9, 1, 30)], columns=["a"]) - df.to_sql('test_time', self.conn, index=False, flavor=self.flavor) + df.to_sql('test_time', self.conn, index=False) res = read_sql_query('SELECT * FROM test_time', self.conn) if self.flavor == 'sqlite': # comes back as strings @@ -2050,130 +2042,22 @@ def test_illegal_names(self): df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) # Raise error on blank - self.assertRaises(ValueError, df.to_sql, "", self.conn, - flavor=self.flavor) + self.assertRaises(ValueError, df.to_sql, "", self.conn) for ndx, weird_name in enumerate( ['test_weird_name]', 'test_weird_name[', 'test_weird_name`', 'test_weird_name"', 'test_weird_name\'', '_b.test_weird_name_01-30', '"_b.test_weird_name_01-30"', '99beginswithnumber', '12345', u'\xe9']): - df.to_sql(weird_name, self.conn, flavor=self.flavor) + df.to_sql(weird_name, self.conn) sql.table_exists(weird_name, self.conn) df2 = DataFrame([[1, 2], [3, 4]], columns=['a', weird_name]) c_tbl = 'test_weird_col_name%d' % ndx - df2.to_sql(c_tbl, self.conn, flavor=self.flavor) + df2.to_sql(c_tbl, self.conn) sql.table_exists(c_tbl, self.conn) -class TestMySQLLegacy(MySQLMixIn, TestSQLiteFallback): - """ - Test the legacy mode against a MySQL database. - - """ - flavor = 'mysql' - - @classmethod - def setUpClass(cls): - cls.setup_driver() - - # test connection - try: - cls.connect() - except cls.driver.err.OperationalError: - raise nose.SkipTest( - "{0} - can't connect to MySQL server".format(cls)) - - @classmethod - def setup_driver(cls): - try: - import pymysql - cls.driver = pymysql - except ImportError: - raise nose.SkipTest('pymysql not installed') - - @classmethod - def connect(cls): - return cls.driver.connect(host='127.0.0.1', user='root', passwd='', - db='pandas_nosetest') - - def _count_rows(self, table_name): - cur = self._get_exec() - cur.execute( - "SELECT count(*) AS count_1 FROM %s" % table_name) - rows = cur.fetchall() - return rows[0][0] - - def setUp(self): - try: - self.conn = self.connect() - except self.driver.err.OperationalError: - raise nose.SkipTest("Can't connect to MySQL server") - - self.pandasSQL = sql.SQLiteDatabase(self.conn, 'mysql') - - self._load_iris_data() - self._load_test1_data() - - def test_a_deprecation(self): - with tm.assert_produces_warning(FutureWarning): - sql.to_sql(self.test_frame1, 'test_frame1', self.conn, - flavor='mysql') - self.assertTrue( - sql.has_table('test_frame1', self.conn, flavor='mysql'), - 'Table not written to DB') - - def _get_index_columns(self, tbl_name): - ixs = sql.read_sql_query( - "SHOW INDEX IN %s" % tbl_name, self.conn) - ix_cols = {} - for ix_name, ix_col in zip(ixs.Key_name, ixs.Column_name): - if ix_name not in ix_cols: - ix_cols[ix_name] = [] - ix_cols[ix_name].append(ix_col) - return list(ix_cols.values()) - - # TODO: cruft? - # def test_to_sql_save_index(self): - # self._to_sql_save_index() - - # for ix_name, ix_col in zip(ixs.Key_name, ixs.Column_name): - # if ix_name not in ix_cols: - # ix_cols[ix_name] = [] - # ix_cols[ix_name].append(ix_col) - # return ix_cols.values() - - def test_to_sql_save_index(self): - self._to_sql_save_index() - - def test_illegal_names(self): - df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) - - # These tables and columns should be ok - for ndx, ok_name in enumerate(['99beginswithnumber', '12345']): - df.to_sql(ok_name, self.conn, flavor=self.flavor, index=False, - if_exists='replace') - df2 = DataFrame([[1, 2], [3, 4]], columns=['a', ok_name]) - - df2.to_sql('test_ok_col_name', self.conn, - flavor=self.flavor, index=False, - if_exists='replace') - - # For MySQL, these should raise ValueError - for ndx, illegal_name in enumerate( - ['test_illegal_name]', 'test_illegal_name[', - 'test_illegal_name`', 'test_illegal_name"', - 'test_illegal_name\'', '']): - self.assertRaises(ValueError, df.to_sql, illegal_name, self.conn, - flavor=self.flavor, index=False) - - df2 = DataFrame([[1, 2], [3, 4]], columns=['a', illegal_name]) - self.assertRaises(ValueError, df2.to_sql, - 'test_illegal_col_name%d' % ndx, - self.conn, flavor=self.flavor, index=False) - - # ----------------------------------------------------------------------------- # -- Old tests from 0.13.1 (before refactor using sqlalchemy) @@ -2227,7 +2111,7 @@ def test_write_row_by_row(self): frame = tm.makeTimeDataFrame() frame.ix[0, 0] = np.nan - create_sql = sql.get_schema(frame, 'test', 'sqlite') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() cur.execute(create_sql) @@ -2246,7 +2130,7 @@ def test_write_row_by_row(self): def test_execute(self): frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'sqlite') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() cur.execute(create_sql) ins = "INSERT INTO test VALUES (?, ?, ?, ?)" @@ -2261,7 +2145,7 @@ def test_execute(self): def test_schema(self): frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'sqlite') + create_sql = sql.get_schema(frame, 'test') lines = create_sql.splitlines() for l in lines: tokens = l.split(' ') @@ -2269,7 +2153,7 @@ def test_schema(self): self.assertTrue(tokens[1] == 'DATETIME') frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'sqlite', keys=['A', 'B'],) + create_sql = sql.get_schema(frame, 'test', keys=['A', 'B']) lines = create_sql.splitlines() self.assertTrue('PRIMARY KEY ("A", "B")' in create_sql) cur = self.conn.cursor() @@ -2424,44 +2308,68 @@ def clean_up(test_table_to_drop): frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='notvalidvalue') clean_up(table_name) # test if_exists='fail' - sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='fail') + sql.to_sql(frame=df_if_exists_1, con=self.conn, + name=table_name, if_exists='fail') self.assertRaises(ValueError, sql.to_sql, frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='fail') # test if_exists='replace' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='sqlite', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) # test if_exists='append' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='fail', index=False) + if_exists='fail', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='sqlite', if_exists='append', index=False) + if_exists='append', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) +class TestSQLFlavorDeprecation(tm.TestCase): + """ + gh-13611: test that the 'flavor' parameter + is appropriately deprecated by checking the + functions that directly raise the warning + """ + + con = 1234 # don't need real connection for this + funcs = ['SQLiteDatabase', 'pandasSQL_builder'] + + def test_unsupported_flavor(self): + msg = 'is not supported' + + for func in self.funcs: + tm.assertRaisesRegexp(ValueError, msg, getattr(sql, func), + self.con, flavor='mysql') + + def test_deprecated_flavor(self): + for func in self.funcs: + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + getattr(sql, func)(self.con, flavor='sqlite') + + +@unittest.skip("gh-13611: there is no support for MySQL " + "if SQLAlchemy is not installed") class TestXMySQL(MySQLMixIn, tm.TestCase): @classmethod @@ -2530,7 +2438,7 @@ def test_write_row_by_row(self): frame = tm.makeTimeDataFrame() frame.ix[0, 0] = np.nan drop_sql = "DROP TABLE IF EXISTS test" - create_sql = sql.get_schema(frame, 'test', 'mysql') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() cur.execute(drop_sql) cur.execute(create_sql) @@ -2552,7 +2460,7 @@ def test_chunksize_read_type(self): drop_sql = "DROP TABLE IF EXISTS test" cur = self.conn.cursor() cur.execute(drop_sql) - sql.to_sql(frame, name='test', con=self.conn, flavor='mysql') + sql.to_sql(frame, name='test', con=self.conn) query = "select * from test" chunksize = 5 chunk_gen = pd.read_sql_query(sql=query, con=self.conn, @@ -2564,7 +2472,7 @@ def test_execute(self): _skip_if_no_pymysql() frame = tm.makeTimeDataFrame() drop_sql = "DROP TABLE IF EXISTS test" - create_sql = sql.get_schema(frame, 'test', 'mysql') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() with warnings.catch_warnings(): warnings.filterwarnings("ignore", "Unknown table.*") @@ -2583,7 +2491,7 @@ def test_execute(self): def test_schema(self): _skip_if_no_pymysql() frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'mysql') + create_sql = sql.get_schema(frame, 'test') lines = create_sql.splitlines() for l in lines: tokens = l.split(' ') @@ -2592,7 +2500,7 @@ def test_schema(self): frame = tm.makeTimeDataFrame() drop_sql = "DROP TABLE IF EXISTS test" - create_sql = sql.get_schema(frame, 'test', 'mysql', keys=['A', 'B'],) + create_sql = sql.get_schema(frame, 'test', keys=['A', 'B']) lines = create_sql.splitlines() self.assertTrue('PRIMARY KEY (`A`, `B`)' in create_sql) cur = self.conn.cursor() @@ -2665,8 +2573,7 @@ def _check_roundtrip(self, frame): with warnings.catch_warnings(): warnings.filterwarnings("ignore", "Unknown table.*") cur.execute(drop_sql) - sql.to_sql(frame, name='test_table', - con=self.conn, flavor='mysql', index=False) + sql.to_sql(frame, name='test_table', con=self.conn, index=False) result = sql.read_sql("select * from test_table", self.conn) # HACK! Change this once indexes are handled properly. @@ -2686,7 +2593,7 @@ def _check_roundtrip(self, frame): warnings.filterwarnings("ignore", "Unknown table.*") cur.execute(drop_sql) sql.to_sql(frame2, name='test_table2', - con=self.conn, flavor='mysql', index=False) + con=self.conn, index=False) result = sql.read_sql("select * from test_table2", self.conn, index_col='Idx') expected = frame.copy() @@ -2706,7 +2613,7 @@ def test_tquery(self): cur = self.conn.cursor() cur.execute(drop_sql) sql.to_sql(frame, name='test_table', - con=self.conn, flavor='mysql', index=False) + con=self.conn, index=False) result = sql.tquery("select A from test_table", self.conn) expected = Series(frame.A.values, frame.index) # not to have name result = Series(result, frame.index) @@ -2732,7 +2639,7 @@ def test_uquery(self): cur = self.conn.cursor() cur.execute(drop_sql) sql.to_sql(frame, name='test_table', - con=self.conn, flavor='mysql', index=False) + con=self.conn, index=False) stmt = 'INSERT INTO test_table VALUES(2.314, -123.1, 1.234, 2.3)' self.assertEqual(sql.uquery(stmt, con=self.conn), 1) @@ -2752,7 +2659,7 @@ def test_keyword_as_column_names(self): _skip_if_no_pymysql() df = DataFrame({'From': np.ones(5)}) sql.to_sql(df, con=self.conn, name='testkeywords', - if_exists='replace', flavor='mysql', index=False) + if_exists='replace', index=False) def test_if_exists(self): _skip_if_no_pymysql() @@ -2775,39 +2682,37 @@ def clean_up(test_table_to_drop): frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='notvalidvalue') clean_up(table_name) # test if_exists='fail' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='fail', index=False) + if_exists='fail', index=False) self.assertRaises(ValueError, sql.to_sql, frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='fail') # test if_exists='replace' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='mysql', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) # test if_exists='append' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='fail', index=False) + if_exists='fail', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='mysql', if_exists='append', index=False) + if_exists='append', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 830c68d62efad..91850e6ffe9b9 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -1,27 +1,27 @@ # -*- coding: utf-8 -*- # pylint: disable=E1101 -from datetime import datetime import datetime as dt import os -import warnings -import nose import struct import sys +import warnings +from datetime import datetime from distutils.version import LooseVersion +import nose import numpy as np import pandas as pd +import pandas.util.testing as tm +from pandas import compat from pandas.compat import iterkeys from pandas.core.frame import DataFrame, Series -from pandas.core.common import is_categorical_dtype +from pandas.types.common import is_categorical_dtype +from pandas.tslib import NaT from pandas.io.parsers import read_csv from pandas.io.stata import (read_stata, StataReader, InvalidColumnName, PossiblePrecisionLoss, StataMissingValue) -import pandas.util.testing as tm -from pandas.tslib import NaT -from pandas import compat class TestStata(tm.TestCase): @@ -1113,6 +1113,58 @@ def test_read_chunks_columns(self): tm.assert_frame_equal(from_frame, chunk, check_dtype=False) pos += chunksize + def test_write_variable_labels(self): + # GH 13631, add support for writing variable labels + original = pd.DataFrame({'a': [1, 2, 3, 4], + 'b': [1.0, 3.0, 27.0, 81.0], + 'c': ['Atlanta', 'Birmingham', + 'Cincinnati', 'Detroit']}) + original.index.name = 'index' + variable_labels = {'a': 'City Rank', 'b': 'City Exponent', 'c': 'City'} + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels) + with StataReader(path) as sr: + read_labels = sr.variable_labels() + expected_labels = {'index': '', + 'a': 'City Rank', + 'b': 'City Exponent', + 'c': 'City'} + tm.assert_equal(read_labels, expected_labels) + + variable_labels['index'] = 'The Index' + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels) + with StataReader(path) as sr: + read_labels = sr.variable_labels() + tm.assert_equal(read_labels, variable_labels) + + def test_write_variable_label_errors(self): + original = pd.DataFrame({'a': [1, 2, 3, 4], + 'b': [1.0, 3.0, 27.0, 81.0], + 'c': ['Atlanta', 'Birmingham', + 'Cincinnati', 'Detroit']}) + values = [u'\u03A1', u'\u0391', + u'\u039D', u'\u0394', + u'\u0391', u'\u03A3'] + + variable_labels_utf8 = {'a': 'City Rank', + 'b': 'City Exponent', + 'c': u''.join(values)} + + with tm.assertRaises(ValueError): + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels_utf8) + + variable_labels_long = {'a': 'City Rank', + 'b': 'City Exponent', + 'c': 'A very, very, very long variable label ' + 'that is too long for Stata which means ' + 'that it has more than 80 characters'} + + with tm.assertRaises(ValueError): + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels_long) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/sandbox/qtpandas.py b/pandas/sandbox/qtpandas.py deleted file mode 100644 index b6af40a0e2156..0000000000000 --- a/pandas/sandbox/qtpandas.py +++ /dev/null @@ -1,145 +0,0 @@ -""" -Easy integration of DataFrame into pyqt framework - -@author: Jev Kuznetsov -""" - -# flake8: noqa - -# GH9615 - -import warnings -warnings.warn("The pandas.sandbox.qtpandas module is deprecated and will be " - "removed in a future version. We refer users to the external package " - "here: https://github.com/datalyze-solutions/pandas-qt") - -try: - from PyQt4.QtCore import QAbstractTableModel, Qt, QVariant, QModelIndex - from PyQt4.QtGui import ( - QApplication, QDialog, QVBoxLayout, QTableView, QWidget) -except ImportError: - from PySide.QtCore import QAbstractTableModel, Qt, QModelIndex - from PySide.QtGui import ( - QApplication, QDialog, QVBoxLayout, QTableView, QWidget) - QVariant = lambda value=None: value - -from pandas import DataFrame, Index - - -class DataFrameModel(QAbstractTableModel): - """ data model for a DataFrame class """ - def __init__(self): - super(DataFrameModel, self).__init__() - self.df = DataFrame() - - def setDataFrame(self, dataFrame): - self.df = dataFrame - - def signalUpdate(self): - """ tell viewers to update their data (this is full update, not - efficient)""" - self.layoutChanged.emit() - - #------------- table display functions ----------------- - def headerData(self, section, orientation, role=Qt.DisplayRole): - if role != Qt.DisplayRole: - return QVariant() - - if orientation == Qt.Horizontal: - try: - return self.df.columns.tolist()[section] - except (IndexError, ): - return QVariant() - elif orientation == Qt.Vertical: - try: - # return self.df.index.tolist() - return self.df.index.tolist()[section] - except (IndexError, ): - return QVariant() - - def data(self, index, role=Qt.DisplayRole): - if role != Qt.DisplayRole: - return QVariant() - - if not index.isValid(): - return QVariant() - - return QVariant(str(self.df.ix[index.row(), index.column()])) - - def flags(self, index): - flags = super(DataFrameModel, self).flags(index) - flags |= Qt.ItemIsEditable - return flags - - def setData(self, index, value, role): - row = self.df.index[index.row()] - col = self.df.columns[index.column()] - if hasattr(value, 'toPyObject'): - # PyQt4 gets a QVariant - value = value.toPyObject() - else: - # PySide gets an unicode - dtype = self.df[col].dtype - if dtype != object: - value = None if value == '' else dtype.type(value) - self.df.set_value(row, col, value) - return True - - def rowCount(self, index=QModelIndex()): - return self.df.shape[0] - - def columnCount(self, index=QModelIndex()): - return self.df.shape[1] - - -class DataFrameWidget(QWidget): - """ a simple widget for using DataFrames in a gui """ - def __init__(self, dataFrame, parent=None): - super(DataFrameWidget, self).__init__(parent) - - self.dataModel = DataFrameModel() - self.dataTable = QTableView() - self.dataTable.setModel(self.dataModel) - - layout = QVBoxLayout() - layout.addWidget(self.dataTable) - self.setLayout(layout) - # Set DataFrame - self.setDataFrame(dataFrame) - - def setDataFrame(self, dataFrame): - self.dataModel.setDataFrame(dataFrame) - self.dataModel.signalUpdate() - self.dataTable.resizeColumnsToContents() - -#-----------------stand alone test code - - -def testDf(): - """ creates test dataframe """ - data = {'int': [1, 2, 3], 'float': [1.5, 2.5, 3.5], - 'string': ['a', 'b', 'c'], 'nan': [np.nan, np.nan, np.nan]} - return DataFrame(data, index=Index(['AAA', 'BBB', 'CCC']), - columns=['int', 'float', 'string', 'nan']) - - -class Form(QDialog): - def __init__(self, parent=None): - super(Form, self).__init__(parent) - - df = testDf() # make up some data - widget = DataFrameWidget(df) - widget.resizeColumnsToContents() - - layout = QVBoxLayout() - layout.addWidget(widget) - self.setLayout(layout) - -if __name__ == '__main__': - import sys - import numpy as np - - app = QApplication(sys.argv) - form = Form() - form.show() - app.exec_() diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 0312fb023f7fd..35233d1b6ba94 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -15,6 +15,14 @@ from pandas.compat import range from pandas.compat.numpy import function as nv +from pandas.types.generic import ABCSparseArray, ABCSparseSeries +from pandas.types.common import (is_float, is_integer, + is_integer_dtype, _ensure_platform_int, + is_list_like, + is_scalar) +from pandas.types.cast import _possibly_convert_platform +from pandas.types.missing import isnull, notnull + from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib import pandas.index as _index @@ -40,13 +48,13 @@ def wrapper(self, other): if len(self) != len(other): raise AssertionError("length mismatch: %d vs. %d" % (len(self), len(other))) - if not isinstance(other, com.ABCSparseArray): + if not isinstance(other, ABCSparseArray): other = SparseArray(other, fill_value=self.fill_value) if name[0] == 'r': return _sparse_array_op(other, self, op, name[1:]) else: return _sparse_array_op(self, other, op, name) - elif lib.isscalar(other): + elif is_scalar(other): new_fill_value = op(np.float64(self.fill_value), np.float64(other)) return _wrap_result(name, op(self.sp_values, other), @@ -120,7 +128,7 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', if index is not None: if data is None: data = np.nan - if not lib.isscalar(data): + if not is_scalar(data): raise Exception("must only pass scalars with an index ") values = np.empty(len(index), dtype='float64') values.fill(data) @@ -177,7 +185,7 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', @classmethod def _simple_new(cls, data, sp_index, fill_value): - if (com.is_integer_dtype(data) and com.is_float(fill_value) and + if (is_integer_dtype(data) and is_float(fill_value) and sp_index.ngaps > 0): # if float fill_value is being included in dense repr, # convert values to float @@ -288,7 +296,7 @@ def __getitem__(self, key): """ """ - if com.is_integer(key): + if is_integer(key): return self._get_val_at(key) elif isinstance(key, tuple): data_slice = self.values[key] @@ -340,11 +348,11 @@ def take(self, indices, axis=0, allow_fill=True, if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) - if com.is_integer(indices): + if is_integer(indices): # return scalar return self[indices] - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) n = len(self) if allow_fill and fill_value is not None: # allow -1 to indicate self.fill_value, @@ -380,7 +388,7 @@ def take(self, indices, axis=0, allow_fill=True, return self._simple_new(new_values, sp_index, self.fill_value) def __setitem__(self, key, value): - # if com.is_integer(key): + # if is_integer(key): # self.values[key] = value # else: # raise Exception("SparseArray does not support seting non-scalars @@ -395,7 +403,7 @@ def __setslice__(self, i, j, value): j = 0 slobj = slice(i, j) # noqa - # if not lib.isscalar(value): + # if not is_scalar(value): # raise Exception("SparseArray does not support seting non-scalars # via slices") @@ -445,12 +453,12 @@ def count(self): @property def _null_fill_value(self): - return com.isnull(self.fill_value) + return isnull(self.fill_value) @property def _valid_sp_values(self): sp_vals = self.sp_values - mask = com.notnull(sp_vals) + mask = notnull(sp_vals) return sp_vals[mask] @Appender(_index_shared_docs['fillna'] % _sparray_doc_kwargs) @@ -466,7 +474,7 @@ def fillna(self, value, downcast=None): fill_value=value) else: new_values = self.sp_values.copy() - new_values[com.isnull(new_values)] = value + new_values[isnull(new_values)] = value return self._simple_new(new_values, self.sp_index, fill_value=self.fill_value) @@ -498,7 +506,7 @@ def cumsum(self, axis=0, *args, **kwargs): nv.validate_cumsum(args, kwargs) # TODO: gh-12855 - return a SparseArray here - if com.notnull(self.fill_value): + if notnull(self.fill_value): return self.to_dense().cumsum() # TODO: what if sp_values contains NaN?? @@ -569,7 +577,7 @@ def _maybe_to_dense(obj): def _maybe_to_sparse(array): - if isinstance(array, com.ABCSparseSeries): + if isinstance(array, ABCSparseSeries): array = SparseArray(array.values, sparse_index=array.sp_index, fill_value=array.fill_value, copy=True) if not isinstance(array, SparseArray): @@ -588,15 +596,15 @@ def _sanitize_values(arr): else: # scalar - if lib.isscalar(arr): + if is_scalar(arr): arr = [arr] # ndarray if isinstance(arr, np.ndarray): pass - elif com.is_list_like(arr) and len(arr) > 0: - arr = com._possibly_convert_platform(arr) + elif is_list_like(arr) and len(arr) > 0: + arr = _possibly_convert_platform(arr) else: arr = np.asarray(arr) @@ -624,8 +632,8 @@ def make_sparse(arr, kind='block', fill_value=nan): if arr.ndim > 1: raise TypeError("expected dimension <= 1 data") - if com.isnull(fill_value): - mask = com.notnull(arr) + if isnull(fill_value): + mask = notnull(arr) else: mask = arr != fill_value diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 52a6e6edf0896..811d8019c7fee 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -10,13 +10,15 @@ from pandas import compat import numpy as np +from pandas.types.missing import isnull, notnull +from pandas.types.common import _ensure_platform_int + +from pandas.core.common import _try_sort from pandas.compat.numpy import function as nv -from pandas.core.common import isnull, _try_sort from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.series import Series from pandas.core.frame import (DataFrame, extract_index, _prep_ndarray, _default_index) -import pandas.core.common as com import pandas.core.algorithms as algos from pandas.core.internals import (BlockManager, create_block_manager_from_arrays) @@ -520,7 +522,7 @@ def _reindex_index(self, index, method, copy, level, fill_value=np.nan, return SparseDataFrame(index=index, columns=self.columns) indexer = self.index.get_indexer(index, method, limit=limit) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) mask = indexer == -1 need_mask = mask.any() @@ -546,7 +548,7 @@ def _reindex_columns(self, columns, copy, level, fill_value, limit=None, if level is not None: raise TypeError('Reindex by level not supported for sparse') - if com.notnull(fill_value): + if notnull(fill_value): raise NotImplementedError("'fill_value' argument is not supported") if limit: diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py index bc10b73a47723..666dae8071053 100644 --- a/pandas/sparse/list.py +++ b/pandas/sparse/list.py @@ -2,9 +2,9 @@ from pandas.core.base import PandasObject from pandas.formats.printing import pprint_thing +from pandas.types.common import is_scalar from pandas.sparse.array import SparseArray import pandas._sparse as splib -import pandas.lib as lib class SparseList(PandasObject): @@ -121,7 +121,7 @@ def append(self, value): ---------- value: scalar or array-like """ - if lib.isscalar(value): + if is_scalar(value): value = [value] sparr = SparseArray(value, fill_value=self.fill_value) diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index 88f396d20a91e..0996cd3bd826a 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -10,6 +10,7 @@ from pandas import compat import numpy as np +from pandas.types.common import is_list_like, is_scalar from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.frame import DataFrame from pandas.core.panel import Panel @@ -18,7 +19,6 @@ import pandas.core.common as com import pandas.core.ops as ops -import pandas.lib as lib class SparsePanelAxis(object): @@ -186,7 +186,7 @@ def _ixs(self, i, axis=0): key = self._get_axis(axis)[i] # xs cannot handle a non-scalar key, so just reindex here - if com.is_list_like(key): + if is_list_like(key): return self.reindex(**{self._get_axis_name(axis): key}) return self.xs(key, axis=axis) @@ -393,7 +393,7 @@ def _combine(self, other, func, axis=0): return self._combineFrame(other, func, axis=axis) elif isinstance(other, Panel): return self._combinePanel(other, func) - elif lib.isscalar(other): + elif is_scalar(other): new_frames = dict((k, func(v, other)) for k, v in self.iteritems()) return self._new_like(new_frames) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 5c7762c56ec6d..951c2ae0c0d5a 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -8,8 +8,11 @@ import numpy as np import warnings +from pandas.types.missing import isnull +from pandas.types.common import is_scalar +from pandas.core.common import _values_from_object, _maybe_match_name + from pandas.compat.numpy import function as nv -from pandas.core.common import isnull, _values_from_object, _maybe_match_name from pandas.core.index import Index, _ensure_index, InvalidIndexError from pandas.core.series import Series from pandas.core.frame import DataFrame @@ -18,7 +21,6 @@ import pandas.core.common as com import pandas.core.ops as ops import pandas.index as _index -import pandas.lib as lib from pandas.util.decorators import Appender from pandas.sparse.array import (make_sparse, _sparse_array_op, SparseArray, @@ -54,7 +56,7 @@ def wrapper(self, other): return _sparse_series_op(self, other, op, name) elif isinstance(other, DataFrame): return NotImplemented - elif lib.isscalar(other): + elif is_scalar(other): if isnull(other) or isnull(self.fill_value): new_fill_value = np.nan else: diff --git a/pandas/src/datetime/np_datetime.c b/pandas/src/datetime/np_datetime.c index c30b404d2b8b2..80703c8b08de6 100644 --- a/pandas/src/datetime/np_datetime.c +++ b/pandas/src/datetime/np_datetime.c @@ -576,7 +576,7 @@ void pandas_datetime_to_datetimestruct(npy_datetime val, PANDAS_DATETIMEUNIT fr, } PANDAS_DATETIMEUNIT get_datetime64_unit(PyObject *obj) { - return ((PyDatetimeScalarObject *) obj)->obmeta.base; + return (PANDAS_DATETIMEUNIT)((PyDatetimeScalarObject *) obj)->obmeta.base; } diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c index 3a1d37f86cc28..b633d6cde0820 100644 --- a/pandas/src/datetime/np_datetime_strings.c +++ b/pandas/src/datetime/np_datetime_strings.c @@ -460,7 +460,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -503,7 +503,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -975,7 +975,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -1005,11 +1005,6 @@ get_datetime_iso_8601_strlen(int local, PANDAS_DATETIMEUNIT base) { int len = 0; - /* If no unit is provided, return the maximum length */ - if (base == -1) { - return PANDAS_DATETIME_MAX_ISO8601_STRLEN; - } - switch (base) { /* Generic units can only be used to represent NaT */ /*case PANDAS_FR_GENERIC:*/ @@ -1146,28 +1141,13 @@ make_iso_8601_datetime(pandas_datetimestruct *dts, char *outstr, int outlen, local = 0; } - /* Automatically detect a good unit */ - if (base == -1) { - base = lossless_unit_from_datetimestruct(dts); - /* - * If there's a timezone, use at least minutes precision, - * and never split up hours and minutes by default - */ - if ((base < PANDAS_FR_m && local) || base == PANDAS_FR_h) { - base = PANDAS_FR_m; - } - /* Don't split up dates by default */ - else if (base < PANDAS_FR_D) { - base = PANDAS_FR_D; - } - } /* * Print weeks with the same precision as days. * * TODO: Could print weeks with YYYY-Www format if the week * epoch is a Monday. */ - else if (base == PANDAS_FR_W) { + if (base == PANDAS_FR_W) { base = PANDAS_FR_D; } diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index 9f96037c97c62..fe4748eb0eba0 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -270,7 +270,7 @@ cdef inline bint is_null_datetimelike(v): cdef inline bint is_null_datetime64(v): - # determine if we have a null for a datetime (or integer versions)x, + # determine if we have a null for a datetime (or integer versions), # excluding np.timedelta64('nat') if util._checknull(v): return True @@ -282,7 +282,7 @@ cdef inline bint is_null_datetime64(v): cdef inline bint is_null_timedelta64(v): - # determine if we have a null for a timedelta (or integer versions)x, + # determine if we have a null for a timedelta (or integer versions), # excluding np.datetime64('nat') if util._checknull(v): return True @@ -293,6 +293,16 @@ cdef inline bint is_null_timedelta64(v): return False +cdef inline bint is_null_period(v): + # determine if we have a null for a Period (or integer versions), + # excluding np.datetime64('nat') and np.timedelta64('nat') + if util._checknull(v): + return True + elif v is NaT: + return True + return False + + cdef inline bint is_datetime(object o): return PyDateTime_Check(o) @@ -531,6 +541,7 @@ def is_timedelta_array(ndarray values): return False return null_count != n + def is_timedelta64_array(ndarray values): cdef Py_ssize_t i, null_count = 0, n = len(values) cdef object v @@ -546,6 +557,7 @@ def is_timedelta64_array(ndarray values): return False return null_count != n + def is_timedelta_or_timedelta64_array(ndarray values): """ infer with timedeltas and/or nat/none """ cdef Py_ssize_t i, null_count = 0, n = len(values) @@ -562,6 +574,7 @@ def is_timedelta_or_timedelta64_array(ndarray values): return False return null_count != n + def is_date_array(ndarray[object] values): cdef Py_ssize_t i, n = len(values) if n == 0: @@ -571,6 +584,7 @@ def is_date_array(ndarray[object] values): return False return True + def is_time_array(ndarray[object] values): cdef Py_ssize_t i, n = len(values) if n == 0: @@ -582,15 +596,21 @@ def is_time_array(ndarray[object] values): def is_period_array(ndarray[object] values): - cdef Py_ssize_t i, n = len(values) - from pandas.tseries.period import Period - + cdef Py_ssize_t i, null_count = 0, n = len(values) + cdef object v if n == 0: return False + + # return False for all nulls for i in range(n): - if not isinstance(values[i], Period): + v = values[i] + if is_null_period(v): + # we are a regular null + if util._checknull(v): + null_count += 1 + elif not is_period(v): return False - return True + return null_count != n cdef extern from "parse_helper.h": diff --git a/pandas/src/join.pyx b/pandas/src/join.pyx index a81ac0aa35d4e..ad3b1d4e4a90e 100644 --- a/pandas/src/join.pyx +++ b/pandas/src/join.pyx @@ -193,11 +193,12 @@ def left_outer_asof_join(ndarray[int64_t] left, ndarray[int64_t] right, diff = left_val - right_val # do we allow exact matches - if allow_exact_matches and diff > tol: - right_indexer[indexer] = -1 - continue + if allow_exact_matches: + if diff > tol: + right_indexer[indexer] = -1 + continue elif not allow_exact_matches: - if diff >= tol: + if diff >= tol or lc == rc: right_indexer[indexer] = -1 continue @@ -220,13 +221,14 @@ def left_outer_asof_join(ndarray[int64_t] left, ndarray[int64_t] right, diff = left_val - right_val # do we allow exact matches - if allow_exact_matches and diff > tol: - right_indexer[indexer] = -1 - continue + if allow_exact_matches: + if diff > tol: + right_indexer[indexer] = -1 + continue # we don't allow exact matches elif not allow_exact_matches: - if diff >= tol or not right_pos: + if diff >= tol or lc == rc: right_indexer[indexer] = -1 else: right_indexer[indexer] = right_pos - 1 diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index af2e295ae0cfc..45743d1cf70ff 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -472,7 +472,11 @@ def extract_ordinals(ndarray[object] values, freq): except AttributeError: p = Period(p, freq=freq) - ordinals[i] = p.ordinal + if p is tslib.NaT: + # input may contain NaT-like string + ordinals[i] = tslib.iNaT + else: + ordinals[i] = p.ordinal return ordinals @@ -665,24 +669,8 @@ class IncompatibleFrequency(ValueError): pass -cdef class Period(object): - """ - Represents an period of time +cdef class _Period(object): - Parameters - ---------- - value : Period or compat.string_types, default None - The time period represented (e.g., '4Q2005') - freq : str, default None - One of pandas period strings or corresponding objects - year : int, default None - month : int, default 1 - quarter : int, default None - day : int, default 1 - hour : int, default 0 - minute : int, default 0 - second : int, default 0 - """ cdef public: int64_t ordinal object freq @@ -711,97 +699,22 @@ cdef class Period(object): @classmethod def _from_ordinal(cls, ordinal, freq): """ fast creation from an ordinal and freq that are already validated! """ - self = Period.__new__(cls) - self.ordinal = ordinal - self.freq = cls._maybe_convert_freq(freq) - return self - - def __init__(self, value=None, freq=None, ordinal=None, - year=None, month=1, quarter=None, day=1, - hour=0, minute=0, second=0): - # freq points to a tuple (base, mult); base is one of the defined - # periods such as A, Q, etc. Every five minutes would be, e.g., - # ('T', 5) but may be passed in as a string like '5T' - - # ordinal is the period offset from the gregorian proleptic epoch - - if ordinal is not None and value is not None: - raise ValueError(("Only value or ordinal but not both should be " - "given but not both")) - elif ordinal is not None: - if not lib.is_integer(ordinal): - raise ValueError("Ordinal must be an integer") - if freq is None: - raise ValueError('Must supply freq for ordinal value') - - elif value is None: - if freq is None: - raise ValueError("If value is None, freq cannot be None") - ordinal = _ordinal_from_fields(year, month, quarter, day, - hour, minute, second, freq) - - elif isinstance(value, Period): - other = value - if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): - ordinal = other.ordinal - freq = other.freq - else: - converted = other.asfreq(freq) - ordinal = converted.ordinal - - elif is_null_datetimelike(value) or value in tslib._nat_strings: - ordinal = tslib.iNaT - if freq is None: - raise ValueError("If value is NaT, freq cannot be None " - "because it cannot be inferred") - - elif isinstance(value, compat.string_types) or lib.is_integer(value): - if lib.is_integer(value): - value = str(value) - value = value.upper() - dt, _, reso = parse_time_string(value, freq) - - if freq is None: - try: - freq = frequencies.Resolution.get_freq(reso) - except KeyError: - raise ValueError("Invalid frequency or could not infer: %s" % reso) - - elif isinstance(value, datetime): - dt = value - if freq is None: - raise ValueError('Must supply freq for datetime value') - elif isinstance(value, np.datetime64): - dt = Timestamp(value) - if freq is None: - raise ValueError('Must supply freq for datetime value') - elif isinstance(value, date): - dt = datetime(year=value.year, month=value.month, day=value.day) - if freq is None: - raise ValueError('Must supply freq for datetime value') - else: - msg = "Value must be Period, string, integer, or datetime" - raise ValueError(msg) - - base, mult = frequencies.get_freq_code(freq) - - if ordinal is None: - self.ordinal = get_period_ordinal(dt.year, dt.month, dt.day, - dt.hour, dt.minute, dt.second, - dt.microsecond, 0, base) + if ordinal == tslib.iNaT: + return tslib.NaT else: + self = _Period.__new__(cls) self.ordinal = ordinal - - self.freq = self._maybe_convert_freq(freq) + self.freq = cls._maybe_convert_freq(freq) + return self def __richcmp__(self, other, op): if isinstance(other, Period): if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT: - return _nat_scalar_rules[op] return PyObject_RichCompareBool(self.ordinal, other.ordinal, op) + elif other is tslib.NaT: + return _nat_scalar_rules[op] # index/series like elif hasattr(other, '_typ'): return NotImplemented @@ -814,7 +727,7 @@ cdef class Period(object): (type(self).__name__, type(other).__name__)) def __hash__(self): - return hash((self.ordinal, self.freq)) + return hash((self.ordinal, self.freqstr)) def _add_delta(self, other): if isinstance(other, (timedelta, np.timedelta64, offsets.Tick, Timedelta)): @@ -824,10 +737,7 @@ cdef class Period(object): offset_nanos = tslib._delta_to_nanoseconds(offset) if nanos % offset_nanos == 0: - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + (nanos // offset_nanos) + ordinal = self.ordinal + (nanos // offset_nanos) return Period(ordinal=ordinal, freq=self.freq) msg = 'Input cannot be converted to Period(freq={0})' raise IncompatibleFrequency(msg.format(self.freqstr)) @@ -835,10 +745,7 @@ cdef class Period(object): freqstr = frequencies.get_standard_freq(other) base = frequencies.get_base_alias(freqstr) if base == self.freq.rule_code: - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + other.n + ordinal = self.ordinal + other.n return Period(ordinal=ordinal, freq=self.freq) msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) @@ -853,10 +760,7 @@ cdef class Period(object): elif other is tslib.NaT: return tslib.NaT elif lib.is_integer(other): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + other * self.freq.n + ordinal = self.ordinal + other * self.freq.n return Period(ordinal=ordinal, freq=self.freq) else: # pragma: no cover return NotImplemented @@ -872,17 +776,12 @@ cdef class Period(object): neg_other = -other return self + neg_other elif lib.is_integer(other): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal - other * self.freq.n + ordinal = self.ordinal - other * self.freq.n return Period(ordinal=ordinal, freq=self.freq) elif isinstance(other, Period): if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT: - return Period(ordinal=tslib.iNaT, freq=self.freq) return self.ordinal - other.ordinal elif getattr(other, '_typ', None) == 'periodindex': return -other.__sub__(self) @@ -914,16 +813,13 @@ cdef class Period(object): base1, mult1 = frequencies.get_freq_code(self.freq) base2, mult2 = frequencies.get_freq_code(freq) - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal + # mult1 can't be negative or 0 + end = how == 'E' + if end: + ordinal = self.ordinal + mult1 - 1 else: - # mult1 can't be negative or 0 - end = how == 'E' - if end: - ordinal = self.ordinal + mult1 - 1 - else: - ordinal = self.ordinal - ordinal = period_asfreq(ordinal, base1, base2, end) + ordinal = self.ordinal + ordinal = period_asfreq(ordinal, base1, base2, end) return Period(ordinal=ordinal, freq=freq) @@ -933,12 +829,9 @@ cdef class Period(object): @property def end_time(self): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - # freq.n can't be negative or 0 - # ordinal = (self + self.freq.n).start_time.value - 1 - ordinal = (self + 1).start_time.value - 1 + # freq.n can't be negative or 0 + # ordinal = (self + self.freq.n).start_time.value - 1 + ordinal = (self + 1).start_time.value - 1 return Timestamp(ordinal) def to_timestamp(self, freq=None, how='start', tz=None): @@ -1199,8 +1092,114 @@ cdef class Period(object): return period_format(self.ordinal, base, fmt) -def _ordinal_from_fields(year, month, quarter, day, hour, minute, - second, freq): +class Period(_Period): + """ + Represents an period of time + + Parameters + ---------- + value : Period or compat.string_types, default None + The time period represented (e.g., '4Q2005') + freq : str, default None + One of pandas period strings or corresponding objects + year : int, default None + month : int, default 1 + quarter : int, default None + day : int, default 1 + hour : int, default 0 + minute : int, default 0 + second : int, default 0 + """ + + def __new__(cls, value=None, freq=None, ordinal=None, + year=None, month=None, quarter=None, day=None, + hour=None, minute=None, second=None): + # freq points to a tuple (base, mult); base is one of the defined + # periods such as A, Q, etc. Every five minutes would be, e.g., + # ('T', 5) but may be passed in as a string like '5T' + + # ordinal is the period offset from the gregorian proleptic epoch + + cdef _Period self + + if ordinal is not None and value is not None: + raise ValueError(("Only value or ordinal but not both should be " + "given but not both")) + elif ordinal is not None: + if not lib.is_integer(ordinal): + raise ValueError("Ordinal must be an integer") + if freq is None: + raise ValueError('Must supply freq for ordinal value') + + elif value is None: + if (year is None and month is None and quarter is None and + day is None and hour is None and minute is None and second is None): + ordinal = tslib.iNaT + else: + if freq is None: + raise ValueError("If value is None, freq cannot be None") + + # set defaults + month = 1 if month is None else month + day = 1 if day is None else day + hour = 0 if hour is None else hour + minute = 0 if minute is None else minute + second = 0 if second is None else second + + ordinal = _ordinal_from_fields(year, month, quarter, day, + hour, minute, second, freq) + + elif isinstance(value, Period): + other = value + if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): + ordinal = other.ordinal + freq = other.freq + else: + converted = other.asfreq(freq) + ordinal = converted.ordinal + + elif is_null_datetimelike(value) or value in tslib._nat_strings: + ordinal = tslib.iNaT + + elif isinstance(value, compat.string_types) or lib.is_integer(value): + if lib.is_integer(value): + value = str(value) + value = value.upper() + dt, _, reso = parse_time_string(value, freq) + + if freq is None: + try: + freq = frequencies.Resolution.get_freq(reso) + except KeyError: + raise ValueError("Invalid frequency or could not infer: %s" % reso) + + elif isinstance(value, datetime): + dt = value + if freq is None: + raise ValueError('Must supply freq for datetime value') + elif isinstance(value, np.datetime64): + dt = Timestamp(value) + if freq is None: + raise ValueError('Must supply freq for datetime value') + elif isinstance(value, date): + dt = datetime(year=value.year, month=value.month, day=value.day) + if freq is None: + raise ValueError('Must supply freq for datetime value') + else: + msg = "Value must be Period, string, integer, or datetime" + raise ValueError(msg) + + if ordinal is None: + base, mult = frequencies.get_freq_code(freq) + ordinal = get_period_ordinal(dt.year, dt.month, dt.day, + dt.hour, dt.minute, dt.second, + dt.microsecond, 0, base) + + return cls._from_ordinal(ordinal, freq) + + +def _ordinal_from_fields(year, month, quarter, day, + hour, minute, second, freq): base, mult = frequencies.get_freq_code(freq) if quarter is not None: year, month = _quarter_to_myear(year, quarter, freq) diff --git a/pandas/src/testing.pyx b/pandas/src/testing.pyx index 6780cf311c244..e9563d9168206 100644 --- a/pandas/src/testing.pyx +++ b/pandas/src/testing.pyx @@ -1,7 +1,8 @@ import numpy as np from pandas import compat -from pandas.core.common import isnull, array_equivalent, is_dtype_equal +from pandas.types.missing import isnull, array_equivalent +from pandas.types.common import is_dtype_equal cdef NUMERIC_TYPES = ( bool, @@ -145,8 +146,15 @@ cpdef assert_almost_equal(a, b, if na != nb: from pandas.util.testing import raise_assert_detail + + # if we have a small diff set, print it + if abs(na-nb) < 10: + r = list(set(a) ^ set(b)) + else: + r = None + raise_assert_detail(obj, '{0} length are different'.format(obj), - na, nb) + na, nb, r) for i in xrange(len(a)): try: diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 925c18cd23d8f..75de63acbd7d6 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -450,7 +450,7 @@ static void *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue, si static void *PandasDateTimeStructToJSON(pandas_datetimestruct *dts, JSONTypeContext *tc, void *outValue, size_t *_outLen) { - int base = ((PyObjectEncoder*) tc->encoder)->datetimeUnit; + PANDAS_DATETIMEUNIT base = ((PyObjectEncoder*) tc->encoder)->datetimeUnit; if (((PyObjectEncoder*) tc->encoder)->datetimeIso) { @@ -493,7 +493,7 @@ static void *NpyDateTimeScalarToJSON(JSOBJ _obj, JSONTypeContext *tc, void *outV PyDatetimeScalarObject *obj = (PyDatetimeScalarObject *) _obj; PRINTMARK(); - pandas_datetime_to_datetimestruct(obj->obval, obj->obmeta.base, &dts); + pandas_datetime_to_datetimestruct(obj->obval, (PANDAS_DATETIMEUNIT)obj->obmeta.base, &dts); return PandasDateTimeStructToJSON(&dts, tc, outValue, _outLen); } diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index 46d30ab7fe313..bb475e47206c2 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -6,7 +6,7 @@ import warnings import numpy as np -from pandas import lib +from pandas.types.common import is_scalar from pandas.core.api import DataFrame, Series from pandas.util.decorators import Substitution, Appender @@ -226,7 +226,7 @@ def ensure_compat(dispatch, name, arg, func_kw=None, *args, **kwargs): aargs += ',' def f(a, b): - if lib.isscalar(b): + if is_scalar(b): return "{a}={b}".format(a=a, b=b) return "{a}=<{b}>".format(a=a, b=type(b).__name__) aargs = ','.join([f(a, b) for a, b in kwds.items() if b is not None]) diff --git a/pandas/stats/ols.py b/pandas/stats/ols.py index 678689f2d2b30..b533d255bd196 100644 --- a/pandas/stats/ols.py +++ b/pandas/stats/ols.py @@ -13,7 +13,7 @@ from pandas.core.api import DataFrame, Series, isnull from pandas.core.base import StringMixin -from pandas.core.common import _ensure_float64 +from pandas.types.common import _ensure_float64 from pandas.core.index import MultiIndex from pandas.core.panel import Panel from pandas.util.decorators import cache_readonly diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py index 2b619b84a5994..020b7f1f1ab9d 100644 --- a/pandas/tests/frame/test_apply.py +++ b/pandas/tests/frame/test_apply.py @@ -10,7 +10,7 @@ from pandas import (notnull, DataFrame, Series, MultiIndex, date_range, Timestamp, compat) import pandas as pd -import pandas.core.common as com +from pandas.types.dtypes import CategoricalDtype from pandas.util.testing import (assert_series_equal, assert_frame_equal) import pandas.util.testing as tm @@ -45,8 +45,8 @@ def test_apply(self): 'c1': ['C', 'C', 'D', 'D']}) df = df.apply(lambda ts: ts.astype('category')) self.assertEqual(df.shape, (4, 2)) - self.assertTrue(isinstance(df['c0'].dtype, com.CategoricalDtype)) - self.assertTrue(isinstance(df['c1'].dtype, com.CategoricalDtype)) + self.assertTrue(isinstance(df['c0'].dtype, CategoricalDtype)) + self.assertTrue(isinstance(df['c1'].dtype, CategoricalDtype)) def test_apply_mixed_datetimelike(self): # mixed datetimelike diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index b42aef9447373..d21db5ba52a45 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -14,6 +14,7 @@ import numpy.ma as ma import numpy.ma.mrecords as mrecords +from pandas.types.common import is_integer_dtype from pandas.compat import (lmap, long, zip, range, lrange, lzip, OrderedDict, is_platform_little_endian) from pandas import compat @@ -809,7 +810,7 @@ def test_constructor_list_of_lists(self): # GH #484 l = [[1, 'a'], [2, 'b']] df = DataFrame(data=l, columns=["num", "str"]) - self.assertTrue(com.is_integer_dtype(df['num'])) + self.assertTrue(is_integer_dtype(df['num'])) self.assertEqual(df['str'].dtype, np.object_) # GH 4851 diff --git a/pandas/tests/frame/test_dtypes.py b/pandas/tests/frame/test_dtypes.py index 5f95ff6b6b601..c650436eefaf3 100644 --- a/pandas/tests/frame/test_dtypes.py +++ b/pandas/tests/frame/test_dtypes.py @@ -1,15 +1,13 @@ # -*- coding: utf-8 -*- from __future__ import print_function - from datetime import timedelta import numpy as np - from pandas import (DataFrame, Series, date_range, Timedelta, Timestamp, compat, option_context) from pandas.compat import u -from pandas.core import common as com +from pandas.types.dtypes import DatetimeTZDtype from pandas.tests.frame.common import TestData from pandas.util.testing import (assert_series_equal, assert_frame_equal, @@ -84,8 +82,8 @@ def test_datetime_with_tz_dtypes(self): tzframe.iloc[1, 2] = pd.NaT result = tzframe.dtypes.sort_index() expected = Series([np.dtype('datetime64[ns]'), - com.DatetimeTZDtype('datetime64[ns, US/Eastern]'), - com.DatetimeTZDtype('datetime64[ns, CET]')], + DatetimeTZDtype('datetime64[ns, US/Eastern]'), + DatetimeTZDtype('datetime64[ns, CET]')], ['A', 'B', 'C']) assert_series_equal(result, expected) diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index d7fed8131a4f4..578df5ba9101e 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -17,6 +17,9 @@ date_range) import pandas as pd +from pandas.types.common import (is_float_dtype, + is_integer, + is_scalar) from pandas.util.testing import (assert_almost_equal, assert_numpy_array_equal, assert_series_equal, @@ -26,7 +29,6 @@ from pandas.core.indexing import IndexingError import pandas.util.testing as tm -import pandas.lib as lib from pandas.tests.frame.common import TestData @@ -1419,15 +1421,15 @@ def test_setitem_single_column_mixed_datetime(self): # set an allowable datetime64 type from pandas import tslib df.ix['b', 'timestamp'] = tslib.iNaT - self.assertTrue(com.isnull(df.ix['b', 'timestamp'])) + self.assertTrue(isnull(df.ix['b', 'timestamp'])) # allow this syntax df.ix['c', 'timestamp'] = nan - self.assertTrue(com.isnull(df.ix['c', 'timestamp'])) + self.assertTrue(isnull(df.ix['c', 'timestamp'])) # allow this syntax df.ix['d', :] = nan - self.assertTrue(com.isnull(df.ix['c', :]).all() == False) # noqa + self.assertTrue(isnull(df.ix['c', :]).all() == False) # noqa # as of GH 3216 this will now work! # try to set with a list like item @@ -1619,7 +1621,7 @@ def test_set_value_resize(self): res = self.frame.copy() res3 = res.set_value('foobar', 'baz', 5) - self.assertTrue(com.is_float_dtype(res3['baz'])) + self.assertTrue(is_float_dtype(res3['baz'])) self.assertTrue(isnull(res3['baz'].drop(['foobar'])).all()) self.assertRaises(ValueError, res3.set_value, 'foobar', 'baz', 'sam') @@ -1662,7 +1664,7 @@ def test_single_element_ix_dont_upcast(self): (int, np.integer))) result = self.frame.ix[self.frame.index[5], 'E'] - self.assertTrue(com.is_integer(result)) + self.assertTrue(is_integer(result)) def test_irow(self): df = DataFrame(np.random.randn(10, 4), index=lrange(0, 20, 2)) @@ -2268,7 +2270,7 @@ def _check_align(df, cond, other, check_dtypes=True): d = df[k].values c = cond[k].reindex(df[k].index).fillna(False).values - if lib.isscalar(other): + if is_scalar(other): o = other else: if isinstance(other, np.ndarray): diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index e2e0f568e4098..c91585a28d867 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -1196,7 +1196,7 @@ def test_alignment_non_pandas(self): align = pd.core.ops._align_method_FRAME - for val in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3])]: + for val in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3], dtype=np.intp)]: tm.assert_series_equal(align(df, val, 'index'), Series([1, 2, 3], index=df.index)) diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index d6f7493bb25f9..92560363be8fe 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -287,6 +287,45 @@ def test_duplicates(self): self.assertEqual(result.name, 'foo') self.assert_index_equal(result, Index([ind[0]], name='foo')) + def test_get_unique_index(self): + for ind in self.indices.values(): + + # MultiIndex tested separately + if not len(ind) or isinstance(ind, MultiIndex): + continue + + idx = ind[[0] * 5] + idx_unique = ind[[0]] + # We test against `idx_unique`, so first we make sure it's unique + # and doesn't contain nans. + self.assertTrue(idx_unique.is_unique) + try: + self.assertFalse(idx_unique.hasnans) + except NotImplementedError: + pass + + for dropna in [False, True]: + result = idx._get_unique_index(dropna=dropna) + self.assert_index_equal(result, idx_unique) + + # nans: + + if not ind._can_hold_na: + continue + + vals = ind.values[[0] * 5] + vals[0] = np.nan + vals_unique = vals[:2] + idx_nan = ind._shallow_copy(vals) + idx_unique_nan = ind._shallow_copy(vals_unique) + self.assertTrue(idx_unique_nan.is_unique) + + for dropna, expected in zip([False, True], + [idx_unique_nan, idx_unique]): + for i in [idx_nan, idx_unique_nan]: + result = i._get_unique_index(dropna=dropna) + self.assert_index_equal(result, expected) + def test_sort(self): for ind in self.indices.values(): self.assertRaises(TypeError, ind.sort) diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index 67869901b068e..cc5dd24292bb8 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -640,47 +640,56 @@ def test_union(self): first = Index(list('ab'), name='A') second = Index(list('ab'), name='B') union = first.union(second) - self.assertIsNone(union.name) + expected = Index(list('ab'), name=None) + tm.assert_index_equal(union, expected) first = Index(list('ab'), name='A') second = Index([], name='B') union = first.union(second) - self.assertIsNone(union.name) + expected = Index(list('ab'), name=None) + tm.assert_index_equal(union, expected) first = Index([], name='A') second = Index(list('ab'), name='B') union = first.union(second) - self.assertIsNone(union.name) + expected = Index(list('ab'), name=None) + tm.assert_index_equal(union, expected) first = Index(list('ab')) second = Index(list('ab'), name='B') union = first.union(second) - self.assertEqual(union.name, 'B') + expected = Index(list('ab'), name='B') + tm.assert_index_equal(union, expected) first = Index([]) second = Index(list('ab'), name='B') union = first.union(second) - self.assertEqual(union.name, 'B') + expected = Index(list('ab'), name='B') + tm.assert_index_equal(union, expected) first = Index(list('ab')) second = Index([], name='B') union = first.union(second) - self.assertEqual(union.name, 'B') + expected = Index(list('ab'), name='B') + tm.assert_index_equal(union, expected) first = Index(list('ab'), name='A') second = Index(list('ab')) union = first.union(second) - self.assertEqual(union.name, 'A') + expected = Index(list('ab'), name='A') + tm.assert_index_equal(union, expected) first = Index(list('ab'), name='A') second = Index([]) union = first.union(second) - self.assertEqual(union.name, 'A') + expected = Index(list('ab'), name='A') + tm.assert_index_equal(union, expected) first = Index([], name='A') second = Index(list('ab')) union = first.union(second) - self.assertEqual(union.name, 'A') + expected = Index(list('ab'), name='A') + tm.assert_index_equal(union, expected) def test_add(self): @@ -803,17 +812,19 @@ def test_symmetric_difference(self): self.assertTrue(tm.equalContents(result, expected)) # nans: - # GH #6444, sorting of nans. Make sure the number of nans is right - # and the correct non-nan values are there. punt on sorting. - idx1 = Index([1, 2, 3, np.nan]) + # GH 13514 change: {nan} - {nan} == {} + # (GH 6444, sorting of nans, is no longer an issue) + idx1 = Index([1, np.nan, 2, 3]) idx2 = Index([0, 1, np.nan]) + idx3 = Index([0, 1]) + result = idx1.symmetric_difference(idx2) - # expected = Index([0.0, np.nan, 2.0, 3.0, np.nan]) + expected = Index([0.0, 2.0, 3.0]) + tm.assert_index_equal(result, expected) - nans = pd.isnull(result) - self.assertEqual(nans.sum(), 1) - self.assertEqual((~nans).sum(), 3) - [self.assertIn(x, result) for x in [0.0, 2.0, 3.0]] + result = idx1.symmetric_difference(idx3) + expected = Index([0.0, 2.0, 3.0, np.nan]) + tm.assert_index_equal(result, expected) # other not an Index: idx1 = Index([1, 2, 3, 4], name='idx1') @@ -1413,6 +1424,12 @@ def test_take_fill_value(self): with tm.assertRaises(IndexError): idx.take(np.array([1, -5])) + def test_reshape_raise(self): + msg = "reshaping is not supported" + idx = pd.Index([0, 1, 2]) + tm.assertRaisesRegexp(NotImplementedError, msg, + idx.reshape, idx.shape) + def test_reindex_preserves_name_if_target_is_list_or_ndarray(self): # GH6552 idx = pd.Index([0, 1, 2]) @@ -1659,6 +1676,149 @@ def test_string_index_repr(self): self.assertEqual(coerce(idx), expected) +class TestMixedIntIndex(Base, tm.TestCase): + # Mostly the tests from common.py for which the results differ + # in py2 and py3 because ints and strings are uncomparable in py3 + # (GH 13514) + + _holder = Index + _multiprocess_can_split_ = True + + def setUp(self): + self.indices = dict(mixedIndex=Index([0, 'a', 1, 'b', 2, 'c'])) + self.setup_indices() + + def create_index(self): + return self.mixedIndex + + def test_order(self): + idx = self.create_index() + # 9816 deprecated + if PY3: + with tm.assertRaisesRegexp(TypeError, "unorderable types"): + with tm.assert_produces_warning(FutureWarning): + idx.order() + else: + with tm.assert_produces_warning(FutureWarning): + idx.order() + + def test_argsort(self): + idx = self.create_index() + if PY3: + with tm.assertRaisesRegexp(TypeError, "unorderable types"): + result = idx.argsort() + else: + result = idx.argsort() + expected = np.array(idx).argsort() + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + def test_numpy_argsort(self): + idx = self.create_index() + if PY3: + with tm.assertRaisesRegexp(TypeError, "unorderable types"): + result = np.argsort(idx) + else: + result = np.argsort(idx) + expected = idx.argsort() + tm.assert_numpy_array_equal(result, expected) + + def test_copy_name(self): + # Check that "name" argument passed at initialization is honoured + # GH12309 + idx = self.create_index() + + first = idx.__class__(idx, copy=True, name='mario') + second = first.__class__(first, copy=False) + + # Even though "copy=False", we want a new object. + self.assertIsNot(first, second) + # Not using tm.assert_index_equal() since names differ: + self.assertTrue(idx.equals(first)) + + self.assertEqual(first.name, 'mario') + self.assertEqual(second.name, 'mario') + + s1 = Series(2, index=first) + s2 = Series(3, index=second[:-1]) + if PY3: + with tm.assert_produces_warning(RuntimeWarning): + # unorderable types + s3 = s1 * s2 + else: + s3 = s1 * s2 + self.assertEqual(s3.index.name, 'mario') + + def test_union_base(self): + idx = self.create_index() + first = idx[3:] + second = idx[:5] + + if PY3: + with tm.assert_produces_warning(RuntimeWarning): + # unorderable types + result = first.union(second) + expected = Index(['b', 2, 'c', 0, 'a', 1]) + self.assert_index_equal(result, expected) + else: + result = first.union(second) + expected = Index(['b', 2, 'c', 0, 'a', 1]) + self.assert_index_equal(result, expected) + + # GH 10149 + cases = [klass(second.values) + for klass in [np.array, Series, list]] + for case in cases: + if PY3: + with tm.assert_produces_warning(RuntimeWarning): + # unorderable types + result = first.union(case) + self.assertTrue(tm.equalContents(result, idx)) + else: + result = first.union(case) + self.assertTrue(tm.equalContents(result, idx)) + + def test_intersection_base(self): + # (same results for py2 and py3 but sortedness not tested elsewhere) + idx = self.create_index() + first = idx[:5] + second = idx[:3] + result = first.intersection(second) + expected = Index([0, 'a', 1]) + self.assert_index_equal(result, expected) + + # GH 10149 + cases = [klass(second.values) + for klass in [np.array, Series, list]] + for case in cases: + result = first.intersection(case) + self.assertTrue(tm.equalContents(result, second)) + + def test_difference_base(self): + # (same results for py2 and py3 but sortedness not tested elsewhere) + idx = self.create_index() + first = idx[:4] + second = idx[3:] + + result = first.difference(second) + expected = Index([0, 1, 'a']) + self.assert_index_equal(result, expected) + + def test_symmetric_difference(self): + # (same results for py2 and py3 but sortedness not tested elsewhere) + idx = self.create_index() + first = idx[:4] + second = idx[3:] + + result = first.symmetric_difference(second) + expected = Index([0, 1, 2, 'a', 'c']) + self.assert_index_equal(result, expected) + + def test_logical_compat(self): + idx = self.create_index() + self.assertEqual(idx.all(), idx.values.all()) + self.assertEqual(idx.any(), idx.values.any()) + + def test_get_combined_index(): from pandas.core.index import _get_combined_index result = _get_combined_index([]) diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index 9eba481a66685..378e8c545ec83 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -119,10 +119,10 @@ def test_pickle_compat_construction(self): def test_construction_index_with_mixed_timezones(self): # GH 11488 # no tz results in DatetimeIndex - result = Index( - [Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx') - exp = DatetimeIndex( - [Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx') + result = Index([Timestamp('2011-01-01'), + Timestamp('2011-01-02')], name='idx') + exp = DatetimeIndex([Timestamp('2011-01-01'), + Timestamp('2011-01-02')], name='idx') self.assert_index_equal(result, exp, exact=True) self.assertTrue(isinstance(result, DatetimeIndex)) self.assertIsNone(result.tz) @@ -170,16 +170,6 @@ def test_construction_index_with_mixed_timezones(self): self.assert_index_equal(result, exp, exact=True) self.assertFalse(isinstance(result, DatetimeIndex)) - # passing tz results in DatetimeIndex - result = Index([Timestamp('2011-01-01 10:00'), - Timestamp('2011-01-02 10:00', tz='US/Eastern')], - tz='Asia/Tokyo', name='idx') - exp = DatetimeIndex([Timestamp('2011-01-01 19:00'), - Timestamp('2011-01-03 00:00')], - tz='Asia/Tokyo', name='idx') - self.assert_index_equal(result, exp, exact=True) - self.assertTrue(isinstance(result, DatetimeIndex)) - # length = 1 result = Index([Timestamp('2011-01-01')], name='idx') exp = DatetimeIndex([Timestamp('2011-01-01')], name='idx') @@ -253,17 +243,6 @@ def test_construction_index_with_mixed_timezones_with_NaT(self): self.assert_index_equal(result, exp, exact=True) self.assertFalse(isinstance(result, DatetimeIndex)) - # passing tz results in DatetimeIndex - result = Index([pd.NaT, Timestamp('2011-01-01 10:00'), - pd.NaT, Timestamp('2011-01-02 10:00', - tz='US/Eastern')], - tz='Asia/Tokyo', name='idx') - exp = DatetimeIndex([pd.NaT, Timestamp('2011-01-01 19:00'), - pd.NaT, Timestamp('2011-01-03 00:00')], - tz='Asia/Tokyo', name='idx') - self.assert_index_equal(result, exp, exact=True) - self.assertTrue(isinstance(result, DatetimeIndex)) - # all NaT result = Index([pd.NaT, pd.NaT], name='idx') exp = DatetimeIndex([pd.NaT, pd.NaT], name='idx') @@ -295,9 +274,9 @@ def test_construction_dti_with_mixed_timezones(self): Timestamp('2011-01-02 10:00', tz='Asia/Tokyo')], name='idx') - exp = DatetimeIndex( - [Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00') - ], tz='Asia/Tokyo', name='idx') + exp = DatetimeIndex([Timestamp('2011-01-01 10:00'), + Timestamp('2011-01-02 10:00')], + tz='Asia/Tokyo', name='idx') self.assert_index_equal(result, exp, exact=True) self.assertTrue(isinstance(result, DatetimeIndex)) @@ -323,12 +302,13 @@ def test_construction_dti_with_mixed_timezones(self): self.assertTrue(isinstance(result, DatetimeIndex)) # tz mismatch affecting to tz-aware raises TypeError/ValueError + with tm.assertRaises(ValueError): DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'), Timestamp('2011-01-02 10:00', tz='US/Eastern')], name='idx') - with tm.assertRaises(TypeError): + with tm.assertRaisesRegexp(TypeError, 'data is already tz-aware'): DatetimeIndex([Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00', tz='US/Eastern')], tz='Asia/Tokyo', name='idx') @@ -338,6 +318,24 @@ def test_construction_dti_with_mixed_timezones(self): Timestamp('2011-01-02 10:00', tz='US/Eastern')], tz='US/Eastern', name='idx') + with tm.assertRaisesRegexp(TypeError, 'data is already tz-aware'): + # passing tz should results in DatetimeIndex, then mismatch raises + # TypeError + Index([pd.NaT, Timestamp('2011-01-01 10:00'), + pd.NaT, Timestamp('2011-01-02 10:00', tz='US/Eastern')], + tz='Asia/Tokyo', name='idx') + + def test_construction_base_constructor(self): + arr = [pd.Timestamp('2011-01-01'), pd.NaT, pd.Timestamp('2011-01-03')] + tm.assert_index_equal(pd.Index(arr), pd.DatetimeIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.DatetimeIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, pd.Timestamp('2011-01-03')] + tm.assert_index_equal(pd.Index(arr), pd.DatetimeIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.DatetimeIndex(np.array(arr))) + def test_astype(self): # GH 13149, GH 13209 idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN]) @@ -699,12 +697,11 @@ def test_fillna_datetime64(self): pd.Timestamp('2011-01-01 11:00')], dtype=object) self.assert_index_equal(idx.fillna('x'), exp) - idx = pd.DatetimeIndex( - ['2011-01-01 09:00', pd.NaT, '2011-01-01 11:00'], tz=tz) + idx = pd.DatetimeIndex(['2011-01-01 09:00', pd.NaT, + '2011-01-01 11:00'], tz=tz) - exp = pd.DatetimeIndex( - ['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00' - ], tz=tz) + exp = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 10:00', + '2011-01-01 11:00'], tz=tz) self.assert_index_equal( idx.fillna(pd.Timestamp('2011-01-01 10:00', tz=tz)), exp) @@ -734,6 +731,26 @@ def setUp(self): def create_index(self): return period_range('20130101', periods=5, freq='D') + def test_construction_base_constructor(self): + # GH 13664 + arr = [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='M')] + tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.PeriodIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, pd.Period('2011-03', freq='M')] + tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.PeriodIndex(np.array(arr))) + + arr = [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='D')] + tm.assert_index_equal(pd.Index(arr), pd.Index(arr, dtype=object)) + + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.Index(np.array(arr), dtype=object)) + def test_astype(self): # GH 13149, GH 13209 idx = PeriodIndex(['2016-05-16', 'NaT', NaT, np.NaN], freq='D') @@ -741,14 +758,7 @@ def test_astype(self): result = idx.astype(object) expected = Index([Period('2016-05-16', freq='D')] + [Period(NaT, freq='D')] * 3, dtype='object') - # Hack because of lack of support for Period null checking (GH12759) - tm.assert_index_equal(result[:1], expected[:1]) - result_arr = np.asarray([p.ordinal for p in result], dtype=np.int64) - expected_arr = np.asarray([p.ordinal for p in expected], - dtype=np.int64) - tm.assert_numpy_array_equal(result_arr, expected_arr) - # TODO: When GH12759 is resolved, change the above hack to: - # tm.assert_index_equal(result, expected) # now, it raises. + tm.assert_index_equal(result, expected) result = idx.astype(int) expected = Int64Index([16937] + [-9223372036854775808] * 3, @@ -881,7 +891,6 @@ def test_repeat(self): self.assertEqual(res.freqstr, 'D') def test_period_index_indexer(self): - # GH4125 idx = pd.period_range('2002-01', '2003-12', freq='M') df = pd.DataFrame(pd.np.random.randn(24, 10), index=idx) @@ -893,12 +902,11 @@ def test_period_index_indexer(self): def test_fillna_period(self): # GH 11343 - idx = pd.PeriodIndex( - ['2011-01-01 09:00', pd.NaT, '2011-01-01 11:00'], freq='H') + idx = pd.PeriodIndex(['2011-01-01 09:00', pd.NaT, + '2011-01-01 11:00'], freq='H') - exp = pd.PeriodIndex( - ['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00' - ], freq='H') + exp = pd.PeriodIndex(['2011-01-01 09:00', '2011-01-01 10:00', + '2011-01-01 11:00'], freq='H') self.assert_index_equal( idx.fillna(pd.Period('2011-01-01 10:00', freq='H')), exp) @@ -906,10 +914,11 @@ def test_fillna_period(self): pd.Period('2011-01-01 11:00', freq='H')], dtype=object) self.assert_index_equal(idx.fillna('x'), exp) - with tm.assertRaisesRegexp( - ValueError, - 'Input has different freq=D from PeriodIndex\\(freq=H\\)'): - idx.fillna(pd.Period('2011-01-01', freq='D')) + exp = pd.Index([pd.Period('2011-01-01 09:00', freq='H'), + pd.Period('2011-01-01', freq='D'), + pd.Period('2011-01-01 11:00', freq='H')], dtype=object) + self.assert_index_equal(idx.fillna(pd.Period('2011-01-01', freq='D')), + exp) def test_no_millisecond_field(self): with self.assertRaises(AttributeError): @@ -930,6 +939,17 @@ def setUp(self): def create_index(self): return pd.to_timedelta(range(5), unit='d') + pd.offsets.Hour(1) + def test_construction_base_constructor(self): + arr = [pd.Timedelta('1 days'), pd.NaT, pd.Timedelta('3 days')] + tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.TimedeltaIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, pd.Timedelta('1 days')] + tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), + pd.TimedeltaIndex(np.array(arr))) + def test_shift(self): # test shift for TimedeltaIndex # err8083 diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index e6a8aafc32be4..173a33aaffd6d 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -632,6 +632,13 @@ def test_from_arrays_index_series_period(self): tm.assert_index_equal(result, result2) + def test_from_arrays_different_lengths(self): + # GH13599 + idx1 = [1, 2, 3] + idx2 = ['a', 'b'] + assertRaisesRegexp(ValueError, '^all arrays must be same length$', + MultiIndex.from_arrays, [idx1, idx2]) + def test_from_product(self): first = ['foo', 'bar', 'buz'] @@ -1877,6 +1884,15 @@ def test_duplicate_meta_data(self): self.assertTrue(idx.has_duplicates) self.assertEqual(idx.drop_duplicates().names, idx.names) + def test_get_unique_index(self): + idx = self.index[[0, 1, 0, 1, 1, 0, 0]] + expected = self.index._shallow_copy(idx[[0, 1]]) + + for dropna in [False, True]: + result = idx._get_unique_index(dropna=dropna) + self.assertTrue(result.unique) + self.assert_index_equal(result, expected) + def test_tolist(self): result = self.index.tolist() exp = list(self.index.values) diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index a6246790f83cb..44c7f2277293d 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -6,6 +6,9 @@ import warnings from datetime import datetime +from pandas.types.common import (is_integer_dtype, + is_float_dtype, + is_scalar) from pandas.compat import range, lrange, lzip, StringIO, lmap, map from pandas.tslib import NaT from numpy import nan @@ -22,7 +25,7 @@ assert_frame_equal, assert_panel_equal, assert_attr_equal, slow) from pandas.formats.printing import pprint_thing -from pandas import concat, lib +from pandas import concat from pandas.core.common import PerformanceWarning import pandas.util.testing as tm @@ -200,7 +203,7 @@ def _print(result, error=None): return try: - if lib.isscalar(rs) and lib.isscalar(xp): + if is_scalar(rs) and is_scalar(xp): self.assertEqual(rs, xp) elif xp.ndim == 1: assert_series_equal(rs, xp) @@ -775,7 +778,7 @@ def test_ix_loc_consistency(self): # this is not an exhaustive case def compare(result, expected): - if lib.isscalar(expected): + if is_scalar(expected): self.assertEqual(result, expected) else: self.assertTrue(expected.equals(result)) @@ -2888,8 +2891,8 @@ def test_setitem_dtype_upcast(self): columns=['foo', 'bar', 'baz']) assert_frame_equal(left, right) - self.assertTrue(com.is_integer_dtype(left['foo'])) - self.assertTrue(com.is_integer_dtype(left['baz'])) + self.assertTrue(is_integer_dtype(left['foo'])) + self.assertTrue(is_integer_dtype(left['baz'])) left = DataFrame(np.arange(6, dtype='int64').reshape(2, 3) / 10.0, index=list('ab'), @@ -2900,8 +2903,8 @@ def test_setitem_dtype_upcast(self): columns=['foo', 'bar', 'baz']) assert_frame_equal(left, right) - self.assertTrue(com.is_float_dtype(left['foo'])) - self.assertTrue(com.is_float_dtype(left['baz'])) + self.assertTrue(is_float_dtype(left['foo'])) + self.assertTrue(is_float_dtype(left['baz'])) def test_setitem_iloc(self): diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index d9e2d8096c8d7..34cfb2f0c1529 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1554,49 +1554,63 @@ def test_shift_categorical(self): assert_index_equal(s.values.categories, sp1.values.categories) assert_index_equal(s.values.categories, sn2.values.categories) - def test_reshape_non_2d(self): - # GH 4554 - x = Series(np.random.random(201), name='x') - self.assertTrue(x.reshape(x.shape, ) is x) + def test_reshape_deprecate(self): + x = Series(np.random.random(10), name='x') + tm.assert_produces_warning(FutureWarning, x.reshape, x.shape) - # GH 2719 - a = Series([1, 2, 3, 4]) - result = a.reshape(2, 2) - expected = a.values.reshape(2, 2) - tm.assert_numpy_array_equal(result, expected) - self.assertIsInstance(result, type(expected)) + def test_reshape_non_2d(self): + # see gh-4554 + with tm.assert_produces_warning(FutureWarning): + x = Series(np.random.random(201), name='x') + self.assertTrue(x.reshape(x.shape, ) is x) + + # see gh-2719 + with tm.assert_produces_warning(FutureWarning): + a = Series([1, 2, 3, 4]) + result = a.reshape(2, 2) + expected = a.values.reshape(2, 2) + tm.assert_numpy_array_equal(result, expected) + self.assertIsInstance(result, type(expected)) def test_reshape_2d_return_array(self): x = Series(np.random.random(201), name='x') - result = x.reshape((-1, 1)) - self.assertNotIsInstance(result, Series) - result2 = np.reshape(x, (-1, 1)) - self.assertNotIsInstance(result2, Series) + with tm.assert_produces_warning(FutureWarning): + result = x.reshape((-1, 1)) + self.assertNotIsInstance(result, Series) - result = x[:, None] - expected = x.reshape((-1, 1)) - assert_almost_equal(result, expected) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result2 = np.reshape(x, (-1, 1)) + self.assertNotIsInstance(result2, Series) + + with tm.assert_produces_warning(FutureWarning): + result = x[:, None] + expected = x.reshape((-1, 1)) + assert_almost_equal(result, expected) def test_reshape_bad_kwarg(self): a = Series([1, 2, 3, 4]) - msg = "'foo' is an invalid keyword argument for this function" - tm.assertRaisesRegexp(TypeError, msg, a.reshape, (2, 2), foo=2) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "'foo' is an invalid keyword argument for this function" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, (2, 2), foo=2) - msg = "reshape\(\) got an unexpected keyword argument 'foo'" - tm.assertRaisesRegexp(TypeError, msg, a.reshape, a.shape, foo=2) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "reshape\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, a.shape, foo=2) def test_numpy_reshape(self): a = Series([1, 2, 3, 4]) - result = np.reshape(a, (2, 2)) - expected = a.values.reshape(2, 2) - tm.assert_numpy_array_equal(result, expected) - self.assertIsInstance(result, type(expected)) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result = np.reshape(a, (2, 2)) + expected = a.values.reshape(2, 2) + tm.assert_numpy_array_equal(result, expected) + self.assertIsInstance(result, type(expected)) - result = np.reshape(a, a.shape) - tm.assert_series_equal(result, a) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result = np.reshape(a, a.shape) + tm.assert_series_equal(result, a) def test_unstack(self): from numpy import nan diff --git a/pandas/tests/series/test_combine_concat.py b/pandas/tests/series/test_combine_concat.py index eb560d4a17055..fd6fd90cd631f 100644 --- a/pandas/tests/series/test_combine_concat.py +++ b/pandas/tests/series/test_combine_concat.py @@ -39,6 +39,27 @@ def test_append_many(self): result = pieces[0].append(pieces[1:]) assert_series_equal(result, self.ts) + def test_append_duplicates(self): + # GH 13677 + s1 = pd.Series([1, 2, 3]) + s2 = pd.Series([4, 5, 6]) + exp = pd.Series([1, 2, 3, 4, 5, 6], index=[0, 1, 2, 0, 1, 2]) + tm.assert_series_equal(s1.append(s2), exp) + tm.assert_series_equal(pd.concat([s1, s2]), exp) + + # the result must have RangeIndex + exp = pd.Series([1, 2, 3, 4, 5, 6]) + tm.assert_series_equal(s1.append(s2, ignore_index=True), + exp, check_index_type=True) + tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), + exp, check_index_type=True) + + msg = 'Indexes have overlapping values:' + with tm.assertRaisesRegexp(ValueError, msg): + s1.append(s2, verify_integrity=True) + with tm.assertRaisesRegexp(ValueError, msg): + pd.concat([s1, s2], verify_integrity=True) + def test_combine_first(self): values = tm.makeIntIndex(20).values.astype(float) series = Series(values, index=tm.makeIntIndex(20)) diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 2a7e8a957977f..c8e04f1ffd75f 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -8,10 +8,11 @@ import numpy.ma as ma import pandas as pd +from pandas.types.common import is_categorical_dtype, is_datetime64tz_dtype from pandas import Index, Series, isnull, date_range, period_range from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp, DatetimeIndex -import pandas.core.common as com + import pandas.lib as lib from pandas.compat import lrange, range, zip, OrderedDict, long @@ -108,6 +109,17 @@ def test_constructor_iterator(self): result = Series(range(10), dtype='int64') assert_series_equal(result, expected) + def test_constructor_list_like(self): + + # make sure that we are coercing different + # list-likes to standard dtypes and not + # platform specific + expected = Series([1, 2, 3], dtype='int64') + for obj in [[1, 2, 3], (1, 2, 3), + np.array([1, 2, 3], dtype='int64')]: + result = Series(obj, index=[0, 1, 2]) + assert_series_equal(result, expected) + def test_constructor_generator(self): gen = (i for i in range(10)) @@ -144,11 +156,11 @@ def test_constructor_categorical(self): ValueError, lambda: Series(pd.Categorical([1, 2, 3]), dtype='int64')) cat = Series(pd.Categorical([1, 2, 3]), dtype='category') - self.assertTrue(com.is_categorical_dtype(cat)) - self.assertTrue(com.is_categorical_dtype(cat.dtype)) + self.assertTrue(is_categorical_dtype(cat)) + self.assertTrue(is_categorical_dtype(cat.dtype)) s = Series([1, 2, 3], dtype='category') - self.assertTrue(com.is_categorical_dtype(s)) - self.assertTrue(com.is_categorical_dtype(s.dtype)) + self.assertTrue(is_categorical_dtype(s)) + self.assertTrue(is_categorical_dtype(s.dtype)) def test_constructor_maskedarray(self): data = ma.masked_all((3, ), dtype=float) @@ -429,7 +441,7 @@ def test_constructor_with_datetime_tz(self): s = Series(dr) self.assertTrue(s.dtype.name == 'datetime64[ns, US/Eastern]') self.assertTrue(s.dtype == 'datetime64[ns, US/Eastern]') - self.assertTrue(com.is_datetime64tz_dtype(s.dtype)) + self.assertTrue(is_datetime64tz_dtype(s.dtype)) self.assertTrue('datetime64[ns, US/Eastern]' in str(s)) # export diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index 6e82f81f901a9..c25895548dcb9 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -6,6 +6,7 @@ import numpy as np import pandas as pd +from pandas.types.common import is_integer_dtype, is_list_like from pandas import (Index, Series, DataFrame, bdate_range, date_range, period_range, timedelta_range) from pandas.tseries.period import PeriodIndex @@ -49,16 +50,16 @@ def test_dt_namespace_accessor(self): def get_expected(s, name): result = getattr(Index(s._values), prop) if isinstance(result, np.ndarray): - if com.is_integer_dtype(result): + if is_integer_dtype(result): result = result.astype('int64') - elif not com.is_list_like(result): + elif not is_list_like(result): return result return Series(result, index=s.index, name=s.name) def compare(s, name): a = getattr(s.dt, prop) b = get_expected(s, prop) - if not (com.is_list_like(a) and com.is_list_like(b)): + if not (is_list_like(a) and is_list_like(b)): self.assertEqual(a, b) else: tm.assert_series_equal(a, b) diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index 15ca238ee32a0..64ebaa63cc10f 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -7,16 +7,14 @@ import numpy as np import pandas as pd +from pandas.types.common import is_integer, is_scalar from pandas import Index, Series, DataFrame, isnull, date_range from pandas.core.index import MultiIndex from pandas.core.indexing import IndexingError from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta -import pandas.core.common as com import pandas.core.datetools as datetools -import pandas.lib as lib - from pandas.compat import lrange, range from pandas import compat from pandas.util.testing import assert_series_equal, assert_almost_equal @@ -375,7 +373,7 @@ def test_getitem_ambiguous_keyerror(self): def test_getitem_unordered_dup(self): obj = Series(lrange(5), index=['c', 'a', 'a', 'b', 'b']) - self.assertTrue(lib.isscalar(obj['c'])) + self.assertTrue(is_scalar(obj['c'])) self.assertEqual(obj['c'], 0) def test_getitem_dups_with_missing(self): @@ -1174,23 +1172,23 @@ def test_where_numeric_with_string(self): s = pd.Series([1, 2, 3]) w = s.where(s > 1, 'X') - self.assertFalse(com.is_integer(w[0])) - self.assertTrue(com.is_integer(w[1])) - self.assertTrue(com.is_integer(w[2])) + self.assertFalse(is_integer(w[0])) + self.assertTrue(is_integer(w[1])) + self.assertTrue(is_integer(w[2])) self.assertTrue(isinstance(w[0], str)) self.assertTrue(w.dtype == 'object') w = s.where(s > 1, ['X', 'Y', 'Z']) - self.assertFalse(com.is_integer(w[0])) - self.assertTrue(com.is_integer(w[1])) - self.assertTrue(com.is_integer(w[2])) + self.assertFalse(is_integer(w[0])) + self.assertTrue(is_integer(w[1])) + self.assertTrue(is_integer(w[2])) self.assertTrue(isinstance(w[0], str)) self.assertTrue(w.dtype == 'object') w = s.where(s > 1, np.array(['X', 'Y', 'Z'])) - self.assertFalse(com.is_integer(w[0])) - self.assertTrue(com.is_integer(w[1])) - self.assertTrue(com.is_integer(w[2])) + self.assertFalse(is_integer(w[0])) + self.assertTrue(is_integer(w[1])) + self.assertTrue(is_integer(w[2])) self.assertTrue(isinstance(w[0], str)) self.assertTrue(w.dtype == 'object') diff --git a/pandas/tests/series/test_quantile.py b/pandas/tests/series/test_quantile.py index e0bff7fbd39e4..7d2517987e526 100644 --- a/pandas/tests/series/test_quantile.py +++ b/pandas/tests/series/test_quantile.py @@ -7,7 +7,7 @@ from pandas import (Index, Series, _np_version_under1p9) from pandas.tseries.index import Timestamp -import pandas.core.common as com +from pandas.types.common import is_integer import pandas.util.testing as tm from .common import TestData @@ -96,11 +96,11 @@ def test_quantile_interpolation_dtype(self): # interpolation = linear (default case) q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='lower') self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) - self.assertTrue(com.is_integer(q)) + self.assertTrue(is_integer(q)) q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='higher') self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) - self.assertTrue(com.is_integer(q)) + self.assertTrue(is_integer(q)) def test_quantile_interpolation_np_lt_1p9(self): # GH #10174 diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index cb90110c953c1..f18d869b3843d 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -56,6 +56,80 @@ def test_strings(self): tm.assert_series_equal(result, expected) +class TestSafeSort(tm.TestCase): + _multiprocess_can_split_ = True + + def test_basic_sort(self): + values = [3, 1, 2, 0, 4] + result = algos.safe_sort(values) + expected = np.array([0, 1, 2, 3, 4]) + tm.assert_numpy_array_equal(result, expected) + + values = list("baaacb") + result = algos.safe_sort(values) + expected = np.array(list("aaabbc")) + tm.assert_numpy_array_equal(result, expected) + + values = [] + result = algos.safe_sort(values) + expected = np.array([]) + tm.assert_numpy_array_equal(result, expected) + + def test_labels(self): + values = [3, 1, 2, 0, 4] + expected = np.array([0, 1, 2, 3, 4]) + + labels = [0, 1, 1, 2, 3, 0, -1, 4] + result, result_labels = algos.safe_sort(values, labels) + expected_labels = np.array([3, 1, 1, 2, 0, 3, -1, 4]) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + # na_sentinel + labels = [0, 1, 1, 2, 3, 0, 99, 4] + result, result_labels = algos.safe_sort(values, labels, + na_sentinel=99) + expected_labels = np.array([3, 1, 1, 2, 0, 3, 99, 4]) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + # out of bound indices + labels = [0, 101, 102, 2, 3, 0, 99, 4] + result, result_labels = algos.safe_sort(values, labels) + expected_labels = np.array([3, -1, -1, 2, 0, 3, -1, 4]) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + labels = [] + result, result_labels = algos.safe_sort(values, labels) + expected_labels = np.array([], dtype=np.int_) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + def test_mixed_integer(self): + values = np.array(['b', 1, 0, 'a', 0, 'b'], dtype=object) + result = algos.safe_sort(values) + expected = np.array([0, 0, 1, 'a', 'b', 'b'], dtype=object) + tm.assert_numpy_array_equal(result, expected) + + values = np.array(['b', 1, 0, 'a'], dtype=object) + labels = [0, 1, 2, 3, 0, -1, 1] + result, result_labels = algos.safe_sort(values, labels) + expected = np.array([0, 1, 'a', 'b'], dtype=object) + + def test_exceptions(self): + with tm.assertRaisesRegexp(TypeError, + "Only list-like objects are allowed"): + algos.safe_sort(values=1) + + with tm.assertRaisesRegexp(TypeError, + "Only list-like objects or None"): + algos.safe_sort(values=[0, 1, 2], labels=1) + + with tm.assertRaisesRegexp(ValueError, "values should be unique"): + algos.safe_sort(values=[0, 1, 2, 1], labels=[0, 1]) + + class TestFactorize(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 77ae3ca20d123..2721d8d0e5e69 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -9,7 +9,7 @@ import pandas as pd import pandas.compat as compat -import pandas.core.common as com +from pandas.types.common import is_object_dtype, is_datetimetz import pandas.util.testing as tm from pandas import (Series, Index, DatetimeIndex, TimedeltaIndex, PeriodIndex, Timedelta) @@ -517,7 +517,7 @@ def test_value_counts_unique_nunique(self): continue # special assign to the numpy array - if com.is_datetimetz(o): + if is_datetimetz(o): if isinstance(o, DatetimeIndex): v = o.asi8 v[0:2] = pd.tslib.iNaT @@ -982,8 +982,8 @@ def test_memory_usage(self): res = o.memory_usage() res_deep = o.memory_usage(deep=True) - if (com.is_object_dtype(o) or (isinstance(o, Series) and - com.is_object_dtype(o.index))): + if (is_object_dtype(o) or (isinstance(o, Series) and + is_object_dtype(o.index))): # if there are objects, only deep will pick them up self.assertTrue(res_deep > res) else: diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 90876a4541da6..57b8bb1531551 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1,19 +1,23 @@ # -*- coding: utf-8 -*- # pylint: disable=E1101,E1103,W0232 -import os import sys from datetime import datetime from distutils.version import LooseVersion import numpy as np +from pandas.types.dtypes import CategoricalDtype +from pandas.types.common import (is_categorical_dtype, + is_object_dtype, + is_float_dtype, + is_integer_dtype) + import pandas as pd import pandas.compat as compat -import pandas.core.common as com import pandas.util.testing as tm from pandas import (Categorical, Index, Series, DataFrame, PeriodIndex, - Timestamp, CategoricalIndex) + Timestamp, CategoricalIndex, isnull) from pandas.compat import range, lrange, u, PY3 from pandas.core.config import option_context @@ -195,18 +199,18 @@ def f(): # This should result in integer categories, not float! cat = pd.Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) - self.assertTrue(com.is_integer_dtype(cat.categories)) + self.assertTrue(is_integer_dtype(cat.categories)) # https://github.com/pydata/pandas/issues/3678 cat = pd.Categorical([np.nan, 1, 2, 3]) - self.assertTrue(com.is_integer_dtype(cat.categories)) + self.assertTrue(is_integer_dtype(cat.categories)) # this should result in floats cat = pd.Categorical([np.nan, 1, 2., 3]) - self.assertTrue(com.is_float_dtype(cat.categories)) + self.assertTrue(is_float_dtype(cat.categories)) cat = pd.Categorical([np.nan, 1., 2., 3.]) - self.assertTrue(com.is_float_dtype(cat.categories)) + self.assertTrue(is_float_dtype(cat.categories)) # Deprecating NaNs in categoires (GH #10748) # preserve int as far as possible by converting to object if NaN is in @@ -214,23 +218,23 @@ def f(): with tm.assert_produces_warning(FutureWarning): cat = pd.Categorical([np.nan, 1, 2, 3], categories=[np.nan, 1, 2, 3]) - self.assertTrue(com.is_object_dtype(cat.categories)) + self.assertTrue(is_object_dtype(cat.categories)) # This doesn't work -> this would probably need some kind of "remember # the original type" feature to try to cast the array interface result # to... # vals = np.asarray(cat[cat.notnull()]) - # self.assertTrue(com.is_integer_dtype(vals)) + # self.assertTrue(is_integer_dtype(vals)) with tm.assert_produces_warning(FutureWarning): cat = pd.Categorical([np.nan, "a", "b", "c"], categories=[np.nan, "a", "b", "c"]) - self.assertTrue(com.is_object_dtype(cat.categories)) + self.assertTrue(is_object_dtype(cat.categories)) # but don't do it for floats with tm.assert_produces_warning(FutureWarning): cat = pd.Categorical([np.nan, 1., 2., 3.], categories=[np.nan, 1., 2., 3.]) - self.assertTrue(com.is_float_dtype(cat.categories)) + self.assertTrue(is_float_dtype(cat.categories)) # corner cases cat = pd.Categorical([1]) @@ -552,7 +556,7 @@ def test_na_flags_int_categories(self): cat = Categorical(labels, categories, fastpath=True) repr(cat) - self.assert_numpy_array_equal(com.isnull(cat), labels == -1) + self.assert_numpy_array_equal(isnull(cat), labels == -1) def test_categories_none(self): factor = Categorical(['a', 'b', 'b', 'a', @@ -803,13 +807,12 @@ def test_set_ordered(self): cat2.set_ordered(False, inplace=True) self.assertFalse(cat2.ordered) - # deperecated in v0.16.0 - with tm.assert_produces_warning(FutureWarning): - cat.ordered = False - self.assertFalse(cat.ordered) - with tm.assert_produces_warning(FutureWarning): + # removed in 0.19.0 + msg = "can\'t set attribute" + with tm.assertRaisesRegexp(AttributeError, msg): cat.ordered = True - self.assertTrue(cat.ordered) + with tm.assertRaisesRegexp(AttributeError, msg): + cat.ordered = False def test_set_categories(self): cat = Categorical(["a", "b", "c", "a"], ordered=True) @@ -1554,18 +1557,6 @@ def test_deprecated_labels(self): res = cat.labels self.assert_numpy_array_equal(res, exp) - def test_deprecated_levels(self): - # TODO: levels is deprecated and should be removed in 0.18 or 2017, - # whatever is earlier - cat = pd.Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) - exp = cat.categories - with tm.assert_produces_warning(FutureWarning): - res = cat.levels - self.assert_index_equal(res, exp) - with tm.assert_produces_warning(FutureWarning): - res = pd.Categorical([1, 2, 3, np.nan], levels=[1, 2, 3]) - self.assert_index_equal(res.categories, exp) - def test_removed_names_produces_warning(self): # 10482 @@ -2076,15 +2067,15 @@ def test_assignment_to_dataframe(self): result = df.dtypes expected = Series( - [np.dtype('int32'), com.CategoricalDtype()], index=['value', 'D']) + [np.dtype('int32'), CategoricalDtype()], index=['value', 'D']) tm.assert_series_equal(result, expected) df['E'] = s str(df) result = df.dtypes - expected = Series([np.dtype('int32'), com.CategoricalDtype(), - com.CategoricalDtype()], + expected = Series([np.dtype('int32'), CategoricalDtype(), + CategoricalDtype()], index=['value', 'D', 'E']) tm.assert_series_equal(result, expected) @@ -2914,54 +2905,41 @@ def test_value_counts(self): tm.assert_series_equal(res, exp) def test_value_counts_with_nan(self): - # https://github.com/pydata/pandas/issues/9443 + # see gh-9443 + # sanity check s = pd.Series(["a", "b", "a"], dtype="category") - tm.assert_series_equal( - s.value_counts(dropna=True), - pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"]))) - tm.assert_series_equal( - s.value_counts(dropna=False), - pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"]))) + exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) - s = pd.Series(["a", "b", None, "a", None, None], dtype="category") - tm.assert_series_equal( - s.value_counts(dropna=True), - pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"]))) - tm.assert_series_equal( - s.value_counts(dropna=False), - pd.Series([3, 2, 1], index=pd.CategoricalIndex([np.nan, "a", "b"]))) - # When we aren't sorting by counts, and np.nan isn't a - # category, it should be last. - tm.assert_series_equal( - s.value_counts(dropna=False, sort=False), - pd.Series([2, 1, 3], - index=pd.CategoricalIndex(["a", "b", np.nan]))) + res = s.value_counts(dropna=True) + tm.assert_series_equal(res, exp) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - s = pd.Series(pd.Categorical(["a", "b", "a"], - categories=["a", "b", np.nan])) + res = s.value_counts(dropna=True) + tm.assert_series_equal(res, exp) - # internal categories are different because of NaN - exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) - tm.assert_series_equal(s.value_counts(dropna=True), exp, - check_categorical=False) - exp = pd.Series([2, 1, 0], - index=pd.CategoricalIndex(["a", "b", np.nan])) - tm.assert_series_equal(s.value_counts(dropna=False), exp, - check_categorical=False) + # same Series via two different constructions --> same behaviour + series = [ + pd.Series(["a", "b", None, "a", None, None], dtype="category"), + pd.Series(pd.Categorical(["a", "b", None, "a", None, None], + categories=["a", "b"])) + ] - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - s = pd.Series(pd.Categorical(["a", "b", None, "a", None, None], - categories=["a", "b", np.nan])) + for s in series: + # None is a NaN value, so we exclude its count here + exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) + res = s.value_counts(dropna=True) + tm.assert_series_equal(res, exp) - exp = pd.Series([2, 1], index=pd.CategoricalIndex(["a", "b"])) - tm.assert_series_equal(s.value_counts(dropna=True), exp, - check_categorical=False) - exp = pd.Series([3, 2, 1], - index=pd.CategoricalIndex([np.nan, "a", "b"])) - tm.assert_series_equal(s.value_counts(dropna=False), exp, - check_categorical=False) + # we don't exclude the count of None and sort by counts + exp = pd.Series([3, 2, 1], index=pd.CategoricalIndex([np.nan, "a", "b"])) + res = s.value_counts(dropna=False) + tm.assert_series_equal(res, exp) + + # When we aren't sorting by counts, and np.nan isn't a + # category, it should be last. + exp = pd.Series([2, 1, 3], index=pd.CategoricalIndex(["a", "b", np.nan])) + res = s.value_counts(dropna=False, sort=False) + tm.assert_series_equal(res, exp) def test_groupby(self): @@ -3234,7 +3212,7 @@ def test_slicing_and_getting_ops(self): # frame res_df = df.iloc[2:4, :] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) # row res_row = df.iloc[2, :] @@ -3244,7 +3222,7 @@ def test_slicing_and_getting_ops(self): # col res_col = df.iloc[:, 0] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) # single value res_val = df.iloc[2, 0] @@ -3254,7 +3232,7 @@ def test_slicing_and_getting_ops(self): # frame res_df = df.loc["j":"k", :] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) # row res_row = df.loc["j", :] @@ -3264,7 +3242,7 @@ def test_slicing_and_getting_ops(self): # col res_col = df.loc[:, "cats"] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) # single value res_val = df.loc["j", "cats"] @@ -3275,7 +3253,7 @@ def test_slicing_and_getting_ops(self): # res_df = df.ix["j":"k",[0,1]] # doesn't work? res_df = df.ix["j":"k", :] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) # row res_row = df.ix["j", :] @@ -3285,7 +3263,7 @@ def test_slicing_and_getting_ops(self): # col res_col = df.ix[:, "cats"] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) # single value res_val = df.ix["j", 0] @@ -3318,23 +3296,23 @@ def test_slicing_and_getting_ops(self): res_df = df.iloc[slice(2, 4)] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) res_df = df.iloc[[2, 3]] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) res_col = df.iloc[:, 0] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) res_df = df.iloc[:, slice(0, 2)] tm.assert_frame_equal(res_df, df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) res_df = df.iloc[:, [0, 1]] tm.assert_frame_equal(res_df, df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) def test_slicing_doc_examples(self): @@ -4053,13 +4031,40 @@ def test_numpy_repeat(self): msg = "the 'axis' parameter is not supported" tm.assertRaisesRegexp(ValueError, msg, np.repeat, cat, 2, axis=1) + def test_reshape(self): + cat = pd.Categorical([], categories=["a", "b"]) + tm.assert_produces_warning(FutureWarning, cat.reshape, 0) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical([], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape(0), cat) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical([], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape((5, -1)), cat) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape(cat.shape), cat) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape(cat.size), cat) + + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "can only specify one unknown dimension" + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + tm.assertRaisesRegexp(ValueError, msg, cat.reshape, (-2, -1)) + def test_numpy_reshape(self): - cat = pd.Categorical(["a", "b"], categories=["a", "b"]) - self.assert_categorical_equal(np.reshape(cat, cat.shape), cat) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(np.reshape(cat, cat.shape), cat) - msg = "the 'order' parameter is not supported" - tm.assertRaisesRegexp(ValueError, msg, np.reshape, - cat, cat.shape, order='F') + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.reshape, + cat, cat.shape, order='F') def test_na_actions(self): @@ -4094,16 +4099,11 @@ def f(): res = df.dropna() tm.assert_frame_equal(res, df_exp_drop_all) - # make sure that fillna takes both missing values and NA categories - # into account - c = Categorical(["a", "b", np.nan]) - with tm.assert_produces_warning(FutureWarning): - c.set_categories(["a", "b", np.nan], rename=True, inplace=True) - - c[0] = np.nan + # make sure that fillna takes missing values into account + c = Categorical([np.nan, "b", np.nan], categories=["a", "b"]) df = pd.DataFrame({"cats": c, "vals": [1, 2, 3]}) - cat_exp = Categorical(["a", "b", "a"], categories=["a", "b", np.nan]) + cat_exp = Categorical(["a", "b", "a"], categories=["a", "b"]) df_exp = pd.DataFrame({"cats": cat_exp, "vals": [1, 2, 3]}) res = df.fillna("a") @@ -4114,7 +4114,7 @@ def test_astype_to_other(self): s = self.cat['value_group'] expected = s tm.assert_series_equal(s.astype('category'), expected) - tm.assert_series_equal(s.astype(com.CategoricalDtype()), expected) + tm.assert_series_equal(s.astype(CategoricalDtype()), expected) self.assertRaises(ValueError, lambda: s.astype('float64')) cat = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c'])) @@ -4139,10 +4139,10 @@ def cmp(a, b): # valid conversion for valid in [lambda x: x.astype('category'), - lambda x: x.astype(com.CategoricalDtype()), + lambda x: x.astype(CategoricalDtype()), lambda x: x.astype('object').astype('category'), lambda x: x.astype('object').astype( - com.CategoricalDtype()) + CategoricalDtype()) ]: result = valid(s) @@ -4399,44 +4399,6 @@ def test_dt_accessor_api_for_categorical(self): invalid.dt self.assertFalse(hasattr(invalid, 'str')) - def test_pickle_v0_14_1(self): - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_14_1.pickle') - # This code was executed once on v0.14.1 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - self.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - - def test_pickle_v0_15_2(self): - # ordered -> _ordered - # GH 9347 - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_15_2.pickle') - # This code was executed once on v0.15.2 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - self.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - def test_concat_categorical(self): # See GH 10177 df1 = pd.DataFrame( diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 56b1b542d547e..09dd3f7ab517c 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -1,21 +1,12 @@ # -*- coding: utf-8 -*- -import collections -from datetime import datetime, timedelta -import re import nose import numpy as np -import pandas as pd -from pandas.tslib import iNaT, NaT -from pandas import (Series, DataFrame, date_range, DatetimeIndex, - TimedeltaIndex, Timestamp, Float64Index) -from pandas import compat -from pandas.compat import range, lrange, lmap, u -from pandas.core.common import notnull, isnull, array_equivalent + +from pandas import Series, Timestamp +from pandas.compat import range, lmap import pandas.core.common as com -import pandas.core.convert as convert import pandas.util.testing as tm -import pandas.core.config as cf _multiprocess_can_split_ = True @@ -28,22 +19,6 @@ def test_mut_exclusive(): assert com._mut_exclusive(major=None, major_axis=None) is None -def test_is_sequence(): - is_seq = com.is_sequence - assert (is_seq((1, 2))) - assert (is_seq([1, 2])) - assert (not is_seq("abcd")) - assert (not is_seq(u("abcd"))) - assert (not is_seq(np.int64)) - - class A(object): - - def __getitem__(self): - return 1 - - assert (not is_seq(A())) - - def test_get_callable_name(): from functools import partial getname = com._get_callable_name @@ -68,407 +43,6 @@ def __call__(self): assert getname(1) is None -class TestInferDtype(tm.TestCase): - - def test_infer_dtype_from_scalar(self): - # Test that _infer_dtype_from_scalar is returning correct dtype for int - # and float. - - for dtypec in [np.uint8, np.int8, np.uint16, np.int16, np.uint32, - np.int32, np.uint64, np.int64]: - data = dtypec(12) - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, type(data)) - - data = 12 - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.int64) - - for dtypec in [np.float16, np.float32, np.float64]: - data = dtypec(12) - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, dtypec) - - data = np.float(12) - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.float64) - - for data in [True, False]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.bool_) - - for data in [np.complex64(1), np.complex128(1)]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.complex_) - - import datetime - for data in [np.datetime64(1, 'ns'), pd.Timestamp(1), - datetime.datetime(2000, 1, 1, 0, 0)]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, 'M8[ns]') - - for data in [np.timedelta64(1, 'ns'), pd.Timedelta(1), - datetime.timedelta(1)]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, 'm8[ns]') - - for data in [datetime.date(2000, 1, 1), - pd.Timestamp(1, tz='US/Eastern'), 'foo']: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.object_) - - -def test_notnull(): - assert notnull(1.) - assert not notnull(None) - assert not notnull(np.NaN) - - with cf.option_context("mode.use_inf_as_null", False): - assert notnull(np.inf) - assert notnull(-np.inf) - - arr = np.array([1.5, np.inf, 3.5, -np.inf]) - result = notnull(arr) - assert result.all() - - with cf.option_context("mode.use_inf_as_null", True): - assert not notnull(np.inf) - assert not notnull(-np.inf) - - arr = np.array([1.5, np.inf, 3.5, -np.inf]) - result = notnull(arr) - assert result.sum() == 2 - - with cf.option_context("mode.use_inf_as_null", False): - for s in [tm.makeFloatSeries(), tm.makeStringSeries(), - tm.makeObjectSeries(), tm.makeTimeSeries(), - tm.makePeriodSeries()]: - assert (isinstance(isnull(s), Series)) - - -def test_isnull(): - assert not isnull(1.) - assert isnull(None) - assert isnull(np.NaN) - assert not isnull(np.inf) - assert not isnull(-np.inf) - - # series - for s in [tm.makeFloatSeries(), tm.makeStringSeries(), - tm.makeObjectSeries(), tm.makeTimeSeries(), - tm.makePeriodSeries()]: - assert (isinstance(isnull(s), Series)) - - # frame - for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(), - tm.makeMixedDataFrame()]: - result = isnull(df) - expected = df.apply(isnull) - tm.assert_frame_equal(result, expected) - - # panel - for p in [tm.makePanel(), tm.makePeriodPanel(), tm.add_nans(tm.makePanel()) - ]: - result = isnull(p) - expected = p.apply(isnull) - tm.assert_panel_equal(result, expected) - - # panel 4d - for p in [tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D())]: - result = isnull(p) - expected = p.apply(isnull) - tm.assert_panel4d_equal(result, expected) - - -def test_isnull_lists(): - result = isnull([[False]]) - exp = np.array([[False]]) - assert (np.array_equal(result, exp)) - - result = isnull([[1], [2]]) - exp = np.array([[False], [False]]) - assert (np.array_equal(result, exp)) - - # list of strings / unicode - result = isnull(['foo', 'bar']) - assert (not result.any()) - - result = isnull([u('foo'), u('bar')]) - assert (not result.any()) - - -def test_isnull_nat(): - result = isnull([NaT]) - exp = np.array([True]) - assert (np.array_equal(result, exp)) - - result = isnull(np.array([NaT], dtype=object)) - exp = np.array([True]) - assert (np.array_equal(result, exp)) - - -def test_isnull_numpy_nat(): - arr = np.array([NaT, np.datetime64('NaT'), np.timedelta64('NaT'), - np.datetime64('NaT', 's')]) - result = isnull(arr) - expected = np.array([True] * 4) - tm.assert_numpy_array_equal(result, expected) - - -def test_isnull_datetime(): - assert (not isnull(datetime.now())) - assert notnull(datetime.now()) - - idx = date_range('1/1/1990', periods=20) - assert (notnull(idx).all()) - - idx = np.asarray(idx) - idx[0] = iNaT - idx = DatetimeIndex(idx) - mask = isnull(idx) - assert (mask[0]) - assert (not mask[1:].any()) - - # GH 9129 - pidx = idx.to_period(freq='M') - mask = isnull(pidx) - assert (mask[0]) - assert (not mask[1:].any()) - - mask = isnull(pidx[1:]) - assert (not mask.any()) - - -class TestIsNull(tm.TestCase): - - def test_0d_array(self): - self.assertTrue(isnull(np.array(np.nan))) - self.assertFalse(isnull(np.array(0.0))) - self.assertFalse(isnull(np.array(0))) - # test object dtype - self.assertTrue(isnull(np.array(np.nan, dtype=object))) - self.assertFalse(isnull(np.array(0.0, dtype=object))) - self.assertFalse(isnull(np.array(0, dtype=object))) - - -class TestNumberScalar(tm.TestCase): - - def test_is_number(self): - - self.assertTrue(com.is_number(True)) - self.assertTrue(com.is_number(1)) - self.assertTrue(com.is_number(1.1)) - self.assertTrue(com.is_number(1 + 3j)) - self.assertTrue(com.is_number(np.bool(False))) - self.assertTrue(com.is_number(np.int64(1))) - self.assertTrue(com.is_number(np.float64(1.1))) - self.assertTrue(com.is_number(np.complex128(1 + 3j))) - self.assertTrue(com.is_number(np.nan)) - - self.assertFalse(com.is_number(None)) - self.assertFalse(com.is_number('x')) - self.assertFalse(com.is_number(datetime(2011, 1, 1))) - self.assertFalse(com.is_number(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_number(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_number(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_number(timedelta(1000))) - self.assertFalse(com.is_number(pd.Timedelta('1 days'))) - - # questionable - self.assertFalse(com.is_number(np.bool_(False))) - self.assertTrue(com.is_number(np.timedelta64(1, 'D'))) - - def test_is_bool(self): - self.assertTrue(com.is_bool(True)) - self.assertTrue(com.is_bool(np.bool(False))) - self.assertTrue(com.is_bool(np.bool_(False))) - - self.assertFalse(com.is_bool(1)) - self.assertFalse(com.is_bool(1.1)) - self.assertFalse(com.is_bool(1 + 3j)) - self.assertFalse(com.is_bool(np.int64(1))) - self.assertFalse(com.is_bool(np.float64(1.1))) - self.assertFalse(com.is_bool(np.complex128(1 + 3j))) - self.assertFalse(com.is_bool(np.nan)) - self.assertFalse(com.is_bool(None)) - self.assertFalse(com.is_bool('x')) - self.assertFalse(com.is_bool(datetime(2011, 1, 1))) - self.assertFalse(com.is_bool(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_bool(timedelta(1000))) - self.assertFalse(com.is_bool(np.timedelta64(1, 'D'))) - self.assertFalse(com.is_bool(pd.Timedelta('1 days'))) - - def test_is_integer(self): - self.assertTrue(com.is_integer(1)) - self.assertTrue(com.is_integer(np.int64(1))) - - self.assertFalse(com.is_integer(True)) - self.assertFalse(com.is_integer(1.1)) - self.assertFalse(com.is_integer(1 + 3j)) - self.assertFalse(com.is_integer(np.bool(False))) - self.assertFalse(com.is_integer(np.bool_(False))) - self.assertFalse(com.is_integer(np.float64(1.1))) - self.assertFalse(com.is_integer(np.complex128(1 + 3j))) - self.assertFalse(com.is_integer(np.nan)) - self.assertFalse(com.is_integer(None)) - self.assertFalse(com.is_integer('x')) - self.assertFalse(com.is_integer(datetime(2011, 1, 1))) - self.assertFalse(com.is_integer(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_integer(timedelta(1000))) - self.assertFalse(com.is_integer(pd.Timedelta('1 days'))) - - # questionable - self.assertTrue(com.is_integer(np.timedelta64(1, 'D'))) - - def test_is_float(self): - self.assertTrue(com.is_float(1.1)) - self.assertTrue(com.is_float(np.float64(1.1))) - self.assertTrue(com.is_float(np.nan)) - - self.assertFalse(com.is_float(True)) - self.assertFalse(com.is_float(1)) - self.assertFalse(com.is_float(1 + 3j)) - self.assertFalse(com.is_float(np.bool(False))) - self.assertFalse(com.is_float(np.bool_(False))) - self.assertFalse(com.is_float(np.int64(1))) - self.assertFalse(com.is_float(np.complex128(1 + 3j))) - self.assertFalse(com.is_float(None)) - self.assertFalse(com.is_float('x')) - self.assertFalse(com.is_float(datetime(2011, 1, 1))) - self.assertFalse(com.is_float(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_float(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_float(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_float(timedelta(1000))) - self.assertFalse(com.is_float(np.timedelta64(1, 'D'))) - self.assertFalse(com.is_float(pd.Timedelta('1 days'))) - - -def test_downcast_conv(): - # test downcasting - - arr = np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]) - result = com._possibly_downcast_to_dtype(arr, 'infer') - assert (np.array_equal(result, arr)) - - arr = np.array([8., 8., 8., 8., 8.9999999999995]) - result = com._possibly_downcast_to_dtype(arr, 'infer') - expected = np.array([8, 8, 8, 8, 9]) - assert (np.array_equal(result, expected)) - - arr = np.array([8., 8., 8., 8., 9.0000000000005]) - result = com._possibly_downcast_to_dtype(arr, 'infer') - expected = np.array([8, 8, 8, 8, 9]) - assert (np.array_equal(result, expected)) - - # conversions - - expected = np.array([1, 2]) - for dtype in [np.float64, object, np.int64]: - arr = np.array([1.0, 2.0], dtype=dtype) - result = com._possibly_downcast_to_dtype(arr, 'infer') - tm.assert_almost_equal(result, expected, check_dtype=False) - - for dtype in [np.float64, object]: - expected = np.array([1.0, 2.0, np.nan], dtype=dtype) - arr = np.array([1.0, 2.0, np.nan], dtype=dtype) - result = com._possibly_downcast_to_dtype(arr, 'infer') - tm.assert_almost_equal(result, expected) - - # empties - for dtype in [np.int32, np.float64, np.float32, np.bool_, - np.int64, object]: - arr = np.array([], dtype=dtype) - result = com._possibly_downcast_to_dtype(arr, 'int64') - tm.assert_almost_equal(result, np.array([], dtype=np.int64)) - assert result.dtype == np.int64 - - -def test_array_equivalent(): - assert array_equivalent(np.array([np.nan, np.nan]), - np.array([np.nan, np.nan])) - assert array_equivalent(np.array([np.nan, 1, np.nan]), - np.array([np.nan, 1, np.nan])) - assert array_equivalent(np.array([np.nan, None], dtype='object'), - np.array([np.nan, None], dtype='object')) - assert array_equivalent(np.array([np.nan, 1 + 1j], dtype='complex'), - np.array([np.nan, 1 + 1j], dtype='complex')) - assert not array_equivalent( - np.array([np.nan, 1 + 1j], dtype='complex'), np.array( - [np.nan, 1 + 2j], dtype='complex')) - assert not array_equivalent( - np.array([np.nan, 1, np.nan]), np.array([np.nan, 2, np.nan])) - assert not array_equivalent( - np.array(['a', 'b', 'c', 'd']), np.array(['e', 'e'])) - assert array_equivalent(Float64Index([0, np.nan]), - Float64Index([0, np.nan])) - assert not array_equivalent( - Float64Index([0, np.nan]), Float64Index([1, np.nan])) - assert array_equivalent(DatetimeIndex([0, np.nan]), - DatetimeIndex([0, np.nan])) - assert not array_equivalent( - DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan])) - assert array_equivalent(TimedeltaIndex([0, np.nan]), - TimedeltaIndex([0, np.nan])) - assert not array_equivalent( - TimedeltaIndex([0, np.nan]), TimedeltaIndex([1, np.nan])) - assert array_equivalent(DatetimeIndex([0, np.nan], tz='US/Eastern'), - DatetimeIndex([0, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan], tz='US/Eastern'), DatetimeIndex( - [1, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan]), DatetimeIndex( - [0, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan], tz='CET'), DatetimeIndex( - [0, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan]), TimedeltaIndex([0, np.nan])) - - -def test_array_equivalent_str(): - for dtype in ['O', 'S', 'U']: - assert array_equivalent(np.array(['A', 'B'], dtype=dtype), - np.array(['A', 'B'], dtype=dtype)) - assert not array_equivalent(np.array(['A', 'B'], dtype=dtype), - np.array(['A', 'X'], dtype=dtype)) - - -def test_datetimeindex_from_empty_datetime64_array(): - for unit in ['ms', 'us', 'ns']: - idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit)) - assert (len(idx) == 0) - - -def test_nan_to_nat_conversions(): - - df = DataFrame(dict({ - 'A': np.asarray( - lrange(10), dtype='float64'), - 'B': Timestamp('20010101') - })) - df.iloc[3:6, :] = np.nan - result = df.loc[4, 'B'].value - assert (result == iNaT) - - s = df['B'].copy() - s._data = s._data.setitem(indexer=tuple([slice(8, 9)]), value=np.nan) - assert (isnull(s[8])) - - # numpy < 1.7.0 is wrong - from distutils.version import LooseVersion - if LooseVersion(np.__version__) >= '1.7.0': - assert (s[8].value == np.datetime64('NaT').astype(np.int64)) - - def test_any_none(): assert (com._any_none(1, 2, 3, None)) assert (not com._any_none(1, 2, 3, 4)) @@ -567,122 +141,6 @@ def test_groupby(): assert v == expected[k] -def test_is_list_like(): - passes = ([], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]), - Series([]), Series(['a']).str) - fails = (1, '2', object()) - - for p in passes: - assert com.is_list_like(p) - - for f in fails: - assert not com.is_list_like(f) - - -def test_is_dict_like(): - passes = [{}, {'A': 1}, pd.Series([1])] - fails = ['1', 1, [1, 2], (1, 2), range(2), pd.Index([1])] - - for p in passes: - assert com.is_dict_like(p) - - for f in fails: - assert not com.is_dict_like(f) - - -def test_is_named_tuple(): - passes = (collections.namedtuple('Test', list('abc'))(1, 2, 3), ) - fails = ((1, 2, 3), 'a', Series({'pi': 3.14})) - - for p in passes: - assert com.is_named_tuple(p) - - for f in fails: - assert not com.is_named_tuple(f) - - -def test_is_hashable(): - - # all new-style classes are hashable by default - class HashableClass(object): - pass - - class UnhashableClass1(object): - __hash__ = None - - class UnhashableClass2(object): - - def __hash__(self): - raise TypeError("Not hashable") - - hashable = (1, - 3.14, - np.float64(3.14), - 'a', - tuple(), - (1, ), - HashableClass(), ) - not_hashable = ([], UnhashableClass1(), ) - abc_hashable_not_really_hashable = (([], ), UnhashableClass2(), ) - - for i in hashable: - assert com.is_hashable(i) - for i in not_hashable: - assert not com.is_hashable(i) - for i in abc_hashable_not_really_hashable: - assert not com.is_hashable(i) - - # numpy.array is no longer collections.Hashable as of - # https://github.com/numpy/numpy/pull/5326, just test - # pandas.common.is_hashable() - assert not com.is_hashable(np.array([])) - - # old-style classes in Python 2 don't appear hashable to - # collections.Hashable but also seem to support hash() by default - if compat.PY2: - - class OldStyleClass(): - pass - - c = OldStyleClass() - assert not isinstance(c, collections.Hashable) - assert com.is_hashable(c) - hash(c) # this will not raise - - -def test_ensure_int32(): - values = np.arange(10, dtype=np.int32) - result = com._ensure_int32(values) - assert (result.dtype == np.int32) - - values = np.arange(10, dtype=np.int64) - result = com._ensure_int32(values) - assert (result.dtype == np.int32) - - -def test_is_re(): - passes = re.compile('ad'), - fails = 'x', 2, 3, object() - - for p in passes: - assert com.is_re(p) - - for f in fails: - assert not com.is_re(f) - - -def test_is_recompilable(): - passes = (r'a', u('x'), r'asdf', re.compile('adsf'), u(r'\u2233\s*'), - re.compile(r'')) - fails = 1, [], object() - - for p in passes: - assert com.is_re_compilable(p) - - for f in fails: - assert not com.is_re_compilable(f) - - def test_random_state(): import numpy.random as npr # Check with seed @@ -730,83 +188,6 @@ def test_maybe_match_name(): assert (matched == 'y') -class TestMaybe(tm.TestCase): - - def test_maybe_convert_string_to_array(self): - result = com._maybe_convert_string_to_object('x') - tm.assert_numpy_array_equal(result, np.array(['x'], dtype=object)) - self.assertTrue(result.dtype == object) - - result = com._maybe_convert_string_to_object(1) - self.assertEqual(result, 1) - - arr = np.array(['x', 'y'], dtype=str) - result = com._maybe_convert_string_to_object(arr) - tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) - self.assertTrue(result.dtype == object) - - # unicode - arr = np.array(['x', 'y']).astype('U') - result = com._maybe_convert_string_to_object(arr) - tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) - self.assertTrue(result.dtype == object) - - # object - arr = np.array(['x', 2], dtype=object) - result = com._maybe_convert_string_to_object(arr) - tm.assert_numpy_array_equal(result, np.array(['x', 2], dtype=object)) - self.assertTrue(result.dtype == object) - - def test_maybe_convert_scalar(self): - - # pass thru - result = com._maybe_convert_scalar('x') - self.assertEqual(result, 'x') - result = com._maybe_convert_scalar(np.array([1])) - self.assertEqual(result, np.array([1])) - - # leave scalar dtype - result = com._maybe_convert_scalar(np.int64(1)) - self.assertEqual(result, np.int64(1)) - result = com._maybe_convert_scalar(np.int32(1)) - self.assertEqual(result, np.int32(1)) - result = com._maybe_convert_scalar(np.float32(1)) - self.assertEqual(result, np.float32(1)) - result = com._maybe_convert_scalar(np.int64(1)) - self.assertEqual(result, np.float64(1)) - - # coerce - result = com._maybe_convert_scalar(1) - self.assertEqual(result, np.int64(1)) - result = com._maybe_convert_scalar(1.0) - self.assertEqual(result, np.float64(1)) - result = com._maybe_convert_scalar(pd.Timestamp('20130101')) - self.assertEqual(result, pd.Timestamp('20130101').value) - result = com._maybe_convert_scalar(datetime(2013, 1, 1)) - self.assertEqual(result, pd.Timestamp('20130101').value) - result = com._maybe_convert_scalar(pd.Timedelta('1 day 1 min')) - self.assertEqual(result, pd.Timedelta('1 day 1 min').value) - - -class TestConvert(tm.TestCase): - - def test_possibly_convert_objects_copy(self): - values = np.array([1, 2]) - - out = convert._possibly_convert_objects(values, copy=False) - self.assertTrue(values is out) - - out = convert._possibly_convert_objects(values, copy=True) - self.assertTrue(values is not out) - - values = np.array(['apply', 'banana']) - out = convert._possibly_convert_objects(values, copy=False) - self.assertTrue(values is out) - - out = convert._possibly_convert_objects(values, copy=True) - self.assertTrue(values is not out) - - def test_dict_compat(): data_datetime64 = {np.datetime64('1990-03-15'): 1, np.datetime64('2015-03-15'): 2} @@ -817,39 +198,6 @@ def test_dict_compat(): assert (com._dict_compat(data_unchanged) == data_unchanged) -def test_is_timedelta(): - assert (com.is_timedelta64_dtype('timedelta64')) - assert (com.is_timedelta64_dtype('timedelta64[ns]')) - assert (not com.is_timedelta64_ns_dtype('timedelta64')) - assert (com.is_timedelta64_ns_dtype('timedelta64[ns]')) - - tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64') - assert (com.is_timedelta64_dtype(tdi)) - assert (com.is_timedelta64_ns_dtype(tdi)) - assert (com.is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]'))) - # Conversion to Int64Index: - assert (not com.is_timedelta64_ns_dtype(tdi.astype('timedelta64'))) - assert (not com.is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]'))) - - -def test_array_equivalent_compat(): - # see gh-13388 - m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - n = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - assert (com.array_equivalent(m, n, strict_nan=True)) - assert (com.array_equivalent(m, n, strict_nan=False)) - - m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - n = np.array([(1, 2), (4, 3)], dtype=[('a', int), ('b', float)]) - assert (not com.array_equivalent(m, n, strict_nan=True)) - assert (not com.array_equivalent(m, n, strict_nan=False)) - - m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - n = np.array([(1, 2), (3, 4)], dtype=[('b', int), ('a', float)]) - assert (not com.array_equivalent(m, n, strict_nan=True)) - assert (not com.array_equivalent(m, n, strict_nan=False)) - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 2f4c2b414cc30..a53e79439b017 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -7,12 +7,12 @@ from numpy import nan import pandas as pd +from pandas.types.common import is_scalar from pandas import (Index, Series, DataFrame, Panel, isnull, date_range, period_range, Panel4D) from pandas.core.index import MultiIndex import pandas.formats.printing as printing -import pandas.lib as lib from pandas.compat import range, zip, PY3 from pandas import compat @@ -53,7 +53,7 @@ def _construct(self, shape, value=None, dtype=None, **kwargs): if isinstance(shape, int): shape = tuple([shape] * self._ndim) if value is not None: - if lib.isscalar(value): + if is_scalar(value): if value == 'empty': arr = None diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index bd19a83ce2b64..5493eb37c358b 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -9,6 +9,7 @@ from datetime import datetime, date +from pandas.types.common import is_list_like import pandas as pd from pandas import (Series, DataFrame, MultiIndex, PeriodIndex, date_range, bdate_range) @@ -16,7 +17,6 @@ iteritems, OrderedDict, PY3) from pandas.util.decorators import cache_readonly from pandas.formats.printing import pprint_thing -import pandas.core.common as com import pandas.util.testing as tm from pandas.util.testing import (ensure_clean, assert_is_valid_plot_return_object, slow) @@ -157,7 +157,7 @@ def _check_visible(self, collections, visible=True): """ from matplotlib.collections import Collection if not isinstance(collections, - Collection) and not com.is_list_like(collections): + Collection) and not is_list_like(collections): collections = [collections] for patch in collections: @@ -242,7 +242,7 @@ def _check_text_labels(self, texts, expected): expected : str or list-like which has the same length as texts expected text label, or its list """ - if not com.is_list_like(texts): + if not is_list_like(texts): self.assertEqual(texts.get_text(), expected) else: labels = [t.get_text() for t in texts] @@ -1330,7 +1330,8 @@ def test_plot(self): self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) df = DataFrame({'x': [1, 2], 'y': [3, 4]}) - with tm.assertRaises(TypeError): + # mpl >= 1.5.2 (or slightly below) throw AttributError + with tm.assertRaises((TypeError, AttributeError)): df.plot.line(blarg=True) df = DataFrame(np.random.rand(10, 3), diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index a52f22fe2032a..258f36cb1b68f 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -5,7 +5,8 @@ from datetime import datetime from numpy import nan -from pandas import date_range, bdate_range, Timestamp +from pandas.types.common import _ensure_platform_int +from pandas import date_range, bdate_range, Timestamp, isnull from pandas.core.index import Index, MultiIndex, CategoricalIndex from pandas.core.api import Categorical, DataFrame from pandas.core.common import UnsupportedFunctionCall @@ -163,9 +164,9 @@ def test_first_last_nth(self): grouped['B'].nth(0) self.df.loc[self.df['A'] == 'foo', 'B'] = np.nan - self.assertTrue(com.isnull(grouped['B'].first()['foo'])) - self.assertTrue(com.isnull(grouped['B'].last()['foo'])) - self.assertTrue(com.isnull(grouped['B'].nth(0)['foo'])) + self.assertTrue(isnull(grouped['B'].first()['foo'])) + self.assertTrue(isnull(grouped['B'].last()['foo'])) + self.assertTrue(isnull(grouped['B'].nth(0)['foo'])) # v0.14.0 whatsnew df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B']) @@ -1079,8 +1080,9 @@ def test_transform_fast(self): grp = df.groupby('id')['val'] values = np.repeat(grp.mean().values, - com._ensure_platform_int(grp.count().values)) + _ensure_platform_int(grp.count().values)) expected = pd.Series(values, index=df.index, name='val') + result = grp.transform(np.mean) assert_series_equal(result, expected) @@ -3208,6 +3210,18 @@ def test_groupby_nonstring_columns(self): expected = df.groupby(df[0]).mean() assert_frame_equal(result, expected) + def test_groupby_mixed_type_columns(self): + # GH 13432, unorderable types in py3 + df = DataFrame([[0, 1, 2]], columns=['A', 'B', 0]) + expected = DataFrame([[1, 2]], columns=['B', 0], + index=Index([0], name='A')) + + result = df.groupby('A').first() + tm.assert_frame_equal(result, expected) + + result = df.groupby('A').sum() + tm.assert_frame_equal(result, expected) + def test_cython_grouper_series_bug_noncontig(self): arr = np.empty((100, 100)) arr.fill(np.nan) diff --git a/pandas/tests/test_infer_and_convert.py b/pandas/tests/test_infer_and_convert.py deleted file mode 100644 index 5f016322f101f..0000000000000 --- a/pandas/tests/test_infer_and_convert.py +++ /dev/null @@ -1,653 +0,0 @@ -# -*- coding: utf-8 -*- - -from datetime import datetime, timedelta, date, time - -import numpy as np -import pandas as pd -import pandas.lib as lib -import pandas.util.testing as tm -from pandas import Index - -from pandas.compat import long, u, PY2 - - -class TestInference(tm.TestCase): - - def test_infer_dtype_bytes(self): - compare = 'string' if PY2 else 'bytes' - - # string array of bytes - arr = np.array(list('abc'), dtype='S1') - self.assertEqual(pd.lib.infer_dtype(arr), compare) - - # object array of bytes - arr = arr.astype(object) - self.assertEqual(pd.lib.infer_dtype(arr), compare) - - def test_isinf_scalar(self): - # GH 11352 - self.assertTrue(lib.isposinf_scalar(float('inf'))) - self.assertTrue(lib.isposinf_scalar(np.inf)) - self.assertFalse(lib.isposinf_scalar(-np.inf)) - self.assertFalse(lib.isposinf_scalar(1)) - self.assertFalse(lib.isposinf_scalar('a')) - - self.assertTrue(lib.isneginf_scalar(float('-inf'))) - self.assertTrue(lib.isneginf_scalar(-np.inf)) - self.assertFalse(lib.isneginf_scalar(np.inf)) - self.assertFalse(lib.isneginf_scalar(1)) - self.assertFalse(lib.isneginf_scalar('a')) - - def test_maybe_convert_numeric_infinities(self): - # see gh-13274 - infinities = ['inf', 'inF', 'iNf', 'Inf', - 'iNF', 'InF', 'INf', 'INF'] - na_values = set(['', 'NULL', 'nan']) - - pos = np.array(['inf'], dtype=np.float64) - neg = np.array(['-inf'], dtype=np.float64) - - msg = "Unable to parse string" - - for infinity in infinities: - for maybe_int in (True, False): - out = lib.maybe_convert_numeric( - np.array([infinity], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, pos) - - out = lib.maybe_convert_numeric( - np.array(['-' + infinity], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, neg) - - out = lib.maybe_convert_numeric( - np.array([u(infinity)], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, pos) - - out = lib.maybe_convert_numeric( - np.array(['+' + infinity], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, pos) - - # too many characters - with tm.assertRaisesRegexp(ValueError, msg): - lib.maybe_convert_numeric( - np.array(['foo_' + infinity], dtype=object), - na_values, maybe_int) - - def test_maybe_convert_numeric_post_floatify_nan(self): - # see gh-13314 - data = np.array(['1.200', '-999.000', '4.500'], dtype=object) - expected = np.array([1.2, np.nan, 4.5], dtype=np.float64) - nan_values = set([-999, -999.0]) - - for coerce_type in (True, False): - out = lib.maybe_convert_numeric(data, nan_values, coerce_type) - tm.assert_numpy_array_equal(out, expected) - - def test_convert_infs(self): - arr = np.array(['inf', 'inf', 'inf'], dtype='O') - result = lib.maybe_convert_numeric(arr, set(), False) - self.assertTrue(result.dtype == np.float64) - - arr = np.array(['-inf', '-inf', '-inf'], dtype='O') - result = lib.maybe_convert_numeric(arr, set(), False) - self.assertTrue(result.dtype == np.float64) - - def test_scientific_no_exponent(self): - # See PR 12215 - arr = np.array(['42E', '2E', '99e', '6e'], dtype='O') - result = lib.maybe_convert_numeric(arr, set(), False, True) - self.assertTrue(np.all(np.isnan(result))) - - def test_convert_non_hashable(self): - # GH13324 - # make sure that we are handing non-hashables - arr = np.array([[10.0, 2], 1.0, 'apple']) - result = lib.maybe_convert_numeric(arr, set(), False, True) - tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan])) - - -class TestTypeInference(tm.TestCase): - _multiprocess_can_split_ = True - - def test_length_zero(self): - result = lib.infer_dtype(np.array([], dtype='i4')) - self.assertEqual(result, 'integer') - - result = lib.infer_dtype([]) - self.assertEqual(result, 'empty') - - def test_integers(self): - arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'integer') - - arr = np.array([1, 2, 3, np.int64(4), np.int32(5), 'foo'], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed-integer') - - arr = np.array([1, 2, 3, 4, 5], dtype='i4') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'integer') - - def test_bools(self): - arr = np.array([True, False, True, True, True], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'boolean') - - arr = np.array([np.bool_(True), np.bool_(False)], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'boolean') - - arr = np.array([True, False, True, 'foo'], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed') - - arr = np.array([True, False, True], dtype=bool) - result = lib.infer_dtype(arr) - self.assertEqual(result, 'boolean') - - def test_floats(self): - arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'floating') - - arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'], - dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed-integer') - - arr = np.array([1, 2, 3, 4, 5], dtype='f4') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'floating') - - arr = np.array([1, 2, 3, 4, 5], dtype='f8') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'floating') - - def test_string(self): - pass - - def test_unicode(self): - pass - - def test_datetime(self): - - dates = [datetime(2012, 1, x) for x in range(1, 20)] - index = Index(dates) - self.assertEqual(index.inferred_type, 'datetime64') - - def test_infer_dtype_datetime(self): - - arr = np.array([pd.Timestamp('2011-01-01'), - pd.Timestamp('2011-01-02')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([np.datetime64('2011-01-01'), - np.datetime64('2011-01-01')], dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime64') - - arr = np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - # starts with nan - for n in [pd.NaT, np.nan]: - arr = np.array([n, pd.Timestamp('2011-01-02')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([n, np.datetime64('2011-01-02')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime64') - - arr = np.array([n, datetime(2011, 1, 1)]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([n, pd.Timestamp('2011-01-02'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([n, np.datetime64('2011-01-02'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime64') - - arr = np.array([n, datetime(2011, 1, 1), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - # different type of nat - arr = np.array([np.timedelta64('nat'), - np.datetime64('2011-01-02')], dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - arr = np.array([np.datetime64('2011-01-02'), - np.timedelta64('nat')], dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - # mixed datetime - arr = np.array([datetime(2011, 1, 1), - pd.Timestamp('2011-01-02')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - # should be datetime? - arr = np.array([np.datetime64('2011-01-01'), - pd.Timestamp('2011-01-02')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - arr = np.array([pd.Timestamp('2011-01-02'), - np.datetime64('2011-01-01')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed-integer') - - arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1.1]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - arr = np.array([np.nan, '2011-01-01', pd.Timestamp('2011-01-02')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - def test_infer_dtype_timedelta(self): - - arr = np.array([pd.Timedelta('1 days'), - pd.Timedelta('2 days')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([np.timedelta64(1, 'D'), - np.timedelta64(2, 'D')], dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([timedelta(1), timedelta(2)]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - # starts with nan - for n in [pd.NaT, np.nan]: - arr = np.array([n, pd.Timedelta('1 days')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([n, np.timedelta64(1, 'D')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([n, timedelta(1)]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([n, pd.Timedelta('1 days'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([n, np.timedelta64(1, 'D'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([n, timedelta(1), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - # different type of nat - arr = np.array([np.datetime64('nat'), np.timedelta64(1, 'D')], - dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - arr = np.array([np.timedelta64(1, 'D'), np.datetime64('nat')], - dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - def test_infer_dtype_all_nan_nat_like(self): - arr = np.array([np.nan, np.nan]) - self.assertEqual(pd.lib.infer_dtype(arr), 'floating') - - # nan and None mix are result in mixed - arr = np.array([np.nan, np.nan, None]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - arr = np.array([None, np.nan, np.nan]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - # pd.NaT - arr = np.array([pd.NaT]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([pd.NaT, np.nan]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([np.nan, pd.NaT]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([np.nan, pd.NaT, np.nan]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - arr = np.array([None, pd.NaT, None]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime') - - # np.datetime64(nat) - arr = np.array([np.datetime64('nat')]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime64') - - for n in [np.nan, pd.NaT, None]: - arr = np.array([n, np.datetime64('nat'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime64') - - arr = np.array([pd.NaT, n, np.datetime64('nat'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'datetime64') - - arr = np.array([np.timedelta64('nat')], dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - for n in [np.nan, pd.NaT, None]: - arr = np.array([n, np.timedelta64('nat'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - arr = np.array([pd.NaT, n, np.timedelta64('nat'), n]) - self.assertEqual(pd.lib.infer_dtype(arr), 'timedelta') - - # datetime / timedelta mixed - arr = np.array([pd.NaT, np.datetime64('nat'), - np.timedelta64('nat'), np.nan]) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - arr = np.array([np.timedelta64('nat'), np.datetime64('nat')], - dtype=object) - self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') - - def test_is_datetimelike_array_all_nan_nat_like(self): - arr = np.array([np.nan, pd.NaT, np.datetime64('nat')]) - self.assertTrue(pd.lib.is_datetime_array(arr)) - self.assertTrue(pd.lib.is_datetime64_array(arr)) - self.assertFalse(pd.lib.is_timedelta_array(arr)) - self.assertFalse(pd.lib.is_timedelta64_array(arr)) - self.assertFalse(pd.lib.is_timedelta_or_timedelta64_array(arr)) - - arr = np.array([np.nan, pd.NaT, np.timedelta64('nat')]) - self.assertFalse(pd.lib.is_datetime_array(arr)) - self.assertFalse(pd.lib.is_datetime64_array(arr)) - self.assertTrue(pd.lib.is_timedelta_array(arr)) - self.assertTrue(pd.lib.is_timedelta64_array(arr)) - self.assertTrue(pd.lib.is_timedelta_or_timedelta64_array(arr)) - - arr = np.array([np.nan, pd.NaT, np.datetime64('nat'), - np.timedelta64('nat')]) - self.assertFalse(pd.lib.is_datetime_array(arr)) - self.assertFalse(pd.lib.is_datetime64_array(arr)) - self.assertFalse(pd.lib.is_timedelta_array(arr)) - self.assertFalse(pd.lib.is_timedelta64_array(arr)) - self.assertFalse(pd.lib.is_timedelta_or_timedelta64_array(arr)) - - arr = np.array([np.nan, pd.NaT]) - self.assertTrue(pd.lib.is_datetime_array(arr)) - self.assertTrue(pd.lib.is_datetime64_array(arr)) - self.assertTrue(pd.lib.is_timedelta_array(arr)) - self.assertTrue(pd.lib.is_timedelta64_array(arr)) - self.assertTrue(pd.lib.is_timedelta_or_timedelta64_array(arr)) - - arr = np.array([np.nan, np.nan], dtype=object) - self.assertFalse(pd.lib.is_datetime_array(arr)) - self.assertFalse(pd.lib.is_datetime64_array(arr)) - self.assertFalse(pd.lib.is_timedelta_array(arr)) - self.assertFalse(pd.lib.is_timedelta64_array(arr)) - self.assertFalse(pd.lib.is_timedelta_or_timedelta64_array(arr)) - - def test_date(self): - - dates = [date(2012, 1, x) for x in range(1, 20)] - index = Index(dates) - self.assertEqual(index.inferred_type, 'date') - - def test_to_object_array_tuples(self): - r = (5, 6) - values = [r] - result = lib.to_object_array_tuples(values) - - try: - # make sure record array works - from collections import namedtuple - record = namedtuple('record', 'x y') - r = record(5, 6) - values = [r] - result = lib.to_object_array_tuples(values) # noqa - except ImportError: - pass - - def test_to_object_array_width(self): - # see gh-13320 - rows = [[1, 2, 3], [4, 5, 6]] - - expected = np.array(rows, dtype=object) - out = lib.to_object_array(rows) - tm.assert_numpy_array_equal(out, expected) - - expected = np.array(rows, dtype=object) - out = lib.to_object_array(rows, min_width=1) - tm.assert_numpy_array_equal(out, expected) - - expected = np.array([[1, 2, 3, None, None], - [4, 5, 6, None, None]], dtype=object) - out = lib.to_object_array(rows, min_width=5) - tm.assert_numpy_array_equal(out, expected) - - def test_object(self): - - # GH 7431 - # cannot infer more than this as only a single element - arr = np.array([None], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed') - - def test_categorical(self): - - # GH 8974 - from pandas import Categorical, Series - arr = Categorical(list('abc')) - result = lib.infer_dtype(arr) - self.assertEqual(result, 'categorical') - - result = lib.infer_dtype(Series(arr)) - self.assertEqual(result, 'categorical') - - arr = Categorical(list('abc'), categories=['cegfab'], ordered=True) - result = lib.infer_dtype(arr) - self.assertEqual(result, 'categorical') - - result = lib.infer_dtype(Series(arr)) - self.assertEqual(result, 'categorical') - - def test_is_period(self): - self.assertTrue(lib.is_period(pd.Period('2011-01', freq='M'))) - self.assertFalse(lib.is_period(pd.PeriodIndex(['2011-01'], freq='M'))) - self.assertFalse(lib.is_period(pd.Timestamp('2011-01'))) - self.assertFalse(lib.is_period(1)) - self.assertFalse(lib.is_period(np.nan)) - - -class TestConvert(tm.TestCase): - - def test_convert_objects(self): - arr = np.array(['a', 'b', np.nan, np.nan, 'd', 'e', 'f'], dtype='O') - result = lib.maybe_convert_objects(arr) - self.assertTrue(result.dtype == np.object_) - - def test_convert_objects_ints(self): - # test that we can detect many kinds of integers - dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'] - - for dtype_str in dtypes: - arr = np.array(list(np.arange(20, dtype=dtype_str)), dtype='O') - self.assertTrue(arr[0].dtype == np.dtype(dtype_str)) - result = lib.maybe_convert_objects(arr) - self.assertTrue(issubclass(result.dtype.type, np.integer)) - - def test_convert_objects_complex_number(self): - for dtype in np.sctypes['complex']: - arr = np.array(list(1j * np.arange(20, dtype=dtype)), dtype='O') - self.assertTrue(arr[0].dtype == np.dtype(dtype)) - result = lib.maybe_convert_objects(arr) - self.assertTrue(issubclass(result.dtype.type, np.complexfloating)) - - -class Testisscalar(tm.TestCase): - - def test_isscalar_builtin_scalars(self): - self.assertTrue(lib.isscalar(None)) - self.assertTrue(lib.isscalar(True)) - self.assertTrue(lib.isscalar(False)) - self.assertTrue(lib.isscalar(0.)) - self.assertTrue(lib.isscalar(np.nan)) - self.assertTrue(lib.isscalar('foobar')) - self.assertTrue(lib.isscalar(b'foobar')) - self.assertTrue(lib.isscalar(u('efoobar'))) - self.assertTrue(lib.isscalar(datetime(2014, 1, 1))) - self.assertTrue(lib.isscalar(date(2014, 1, 1))) - self.assertTrue(lib.isscalar(time(12, 0))) - self.assertTrue(lib.isscalar(timedelta(hours=1))) - self.assertTrue(lib.isscalar(pd.NaT)) - - def test_isscalar_builtin_nonscalars(self): - self.assertFalse(lib.isscalar({})) - self.assertFalse(lib.isscalar([])) - self.assertFalse(lib.isscalar([1])) - self.assertFalse(lib.isscalar(())) - self.assertFalse(lib.isscalar((1, ))) - self.assertFalse(lib.isscalar(slice(None))) - self.assertFalse(lib.isscalar(Ellipsis)) - - def test_isscalar_numpy_array_scalars(self): - self.assertTrue(lib.isscalar(np.int64(1))) - self.assertTrue(lib.isscalar(np.float64(1.))) - self.assertTrue(lib.isscalar(np.int32(1))) - self.assertTrue(lib.isscalar(np.object_('foobar'))) - self.assertTrue(lib.isscalar(np.str_('foobar'))) - self.assertTrue(lib.isscalar(np.unicode_(u('foobar')))) - self.assertTrue(lib.isscalar(np.bytes_(b'foobar'))) - self.assertTrue(lib.isscalar(np.datetime64('2014-01-01'))) - self.assertTrue(lib.isscalar(np.timedelta64(1, 'h'))) - - def test_isscalar_numpy_zerodim_arrays(self): - for zerodim in [np.array(1), np.array('foobar'), - np.array(np.datetime64('2014-01-01')), - np.array(np.timedelta64(1, 'h')), - np.array(np.datetime64('NaT'))]: - self.assertFalse(lib.isscalar(zerodim)) - self.assertTrue(lib.isscalar(lib.item_from_zerodim(zerodim))) - - def test_isscalar_numpy_arrays(self): - self.assertFalse(lib.isscalar(np.array([]))) - self.assertFalse(lib.isscalar(np.array([[]]))) - self.assertFalse(lib.isscalar(np.matrix('1; 2'))) - - def test_isscalar_pandas_scalars(self): - self.assertTrue(lib.isscalar(pd.Timestamp('2014-01-01'))) - self.assertTrue(lib.isscalar(pd.Timedelta(hours=1))) - self.assertTrue(lib.isscalar(pd.Period('2014-01-01'))) - - def test_lisscalar_pandas_containers(self): - self.assertFalse(lib.isscalar(pd.Series())) - self.assertFalse(lib.isscalar(pd.Series([1]))) - self.assertFalse(lib.isscalar(pd.DataFrame())) - self.assertFalse(lib.isscalar(pd.DataFrame([[1]]))) - self.assertFalse(lib.isscalar(pd.Panel())) - self.assertFalse(lib.isscalar(pd.Panel([[[1]]]))) - self.assertFalse(lib.isscalar(pd.Index([]))) - self.assertFalse(lib.isscalar(pd.Index([1]))) - - -class TestParseSQL(tm.TestCase): - - def test_convert_sql_column_floats(self): - arr = np.array([1.5, None, 3, 4.2], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_strings(self): - arr = np.array(['1.5', None, '3', '4.2'], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_unicode(self): - arr = np.array([u('1.5'), None, u('3'), u('4.2')], - dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], - dtype=object) - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_ints(self): - arr = np.array([1, 2, 3, 4], dtype='O') - arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') - result = lib.convert_sql_column(arr) - result2 = lib.convert_sql_column(arr2) - expected = np.array([1, 2, 3, 4], dtype='i8') - self.assert_numpy_array_equal(result, expected) - self.assert_numpy_array_equal(result2, expected) - - arr = np.array([1, 2, 3, None, 4], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_longs(self): - arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, 4], dtype='i8') - self.assert_numpy_array_equal(result, expected) - - arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_bools(self): - arr = np.array([True, False, True, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, True, False], dtype=bool) - self.assert_numpy_array_equal(result, expected) - - arr = np.array([True, False, None, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, np.nan, False], dtype=object) - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_decimals(self): - from decimal import Decimal - arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_downcast_int64(self): - from pandas.parser import na_values - - arr = np.array([1, 2, 7, 8, 10], dtype=np.int64) - expected = np.array([1, 2, 7, 8, 10], dtype=np.int8) - - # default argument - result = lib.downcast_int64(arr, na_values) - self.assert_numpy_array_equal(result, expected) - - result = lib.downcast_int64(arr, na_values, use_unsigned=False) - self.assert_numpy_array_equal(result, expected) - - expected = np.array([1, 2, 7, 8, 10], dtype=np.uint8) - result = lib.downcast_int64(arr, na_values, use_unsigned=True) - self.assert_numpy_array_equal(result, expected) - - # still cast to int8 despite use_unsigned=True - # because of the negative number as an element - arr = np.array([1, 2, -7, 8, 10], dtype=np.int64) - expected = np.array([1, 2, -7, 8, 10], dtype=np.int8) - result = lib.downcast_int64(arr, na_values, use_unsigned=True) - self.assert_numpy_array_equal(result, expected) - - arr = np.array([1, 2, 7, 8, 300], dtype=np.int64) - expected = np.array([1, 2, 7, 8, 300], dtype=np.int16) - result = lib.downcast_int64(arr, na_values) - self.assert_numpy_array_equal(result, expected) - - int8_na = na_values[np.int8] - int64_na = na_values[np.int64] - arr = np.array([int64_na, 2, 3, 10, 15], dtype=np.int64) - expected = np.array([int8_na, 2, 3, 10, 15], dtype=np.int8) - result = lib.downcast_int64(arr, na_values) - self.assert_numpy_array_equal(result, expected) - - -if __name__ == '__main__': - import nose - - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index 10a6bb5c75b01..84d7226f1b2f5 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -222,6 +222,7 @@ def test_duplicated_with_nas(): expected = trues + trues assert (np.array_equal(result, expected)) + if __name__ == '__main__': import nose diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index 1b1db90ea713d..f3b0becccf596 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -10,6 +10,7 @@ from pandas.core.index import Index, MultiIndex from pandas import Panel, DataFrame, Series, notnull, isnull, Timestamp +from pandas.types.common import is_float_dtype, is_integer_dtype from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assertRaisesRegexp) import pandas.core.common as com @@ -787,8 +788,8 @@ def test_delevel_infer_dtype(self): df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'], index=index) deleveled = df.reset_index() - self.assertTrue(com.is_integer_dtype(deleveled['prm1'])) - self.assertTrue(com.is_float_dtype(deleveled['prm2'])) + self.assertTrue(is_integer_dtype(deleveled['prm1'])) + self.assertTrue(is_float_dtype(deleveled['prm2'])) def test_reset_index_with_drop(self): deleveled = self.ymd.reset_index(drop=True) diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py index 904bedde03312..eeeddc278c714 100644 --- a/pandas/tests/test_nanops.py +++ b/pandas/tests/test_nanops.py @@ -5,8 +5,8 @@ import warnings import numpy as np -from pandas import Series -from pandas.core.common import isnull, is_integer_dtype +from pandas import Series, isnull +from pandas.types.common import is_integer_dtype import pandas.core.nanops as nanops import pandas.util.testing as tm diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index b1f09ad2685e3..f2e13867d3bf0 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -10,12 +10,13 @@ import numpy as np import pandas as pd +from pandas.types.common import is_float_dtype from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex from pandas.core.datetools import bday from pandas.core.nanops import nanall, nanany from pandas.core.panel import Panel from pandas.core.series import remove_na -import pandas.core.common as com + from pandas.formats.printing import pprint_thing from pandas import compat from pandas.compat import range, lrange, StringIO, OrderedDict, signature @@ -903,7 +904,7 @@ def test_set_value(self): self.assertEqual(res.get_value('ItemE', 'foo', 'bar'), 1.5) res3 = self.panel.set_value('ItemE', 'foobar', 'baz', 5) - self.assertTrue(com.is_float_dtype(res3['ItemE'].values)) + self.assertTrue(is_float_dtype(res3['ItemE'].values)) with tm.assertRaisesRegexp(TypeError, "There must be an argument for each axis" " plus the value provided"): diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 607048df29faa..16a55c7ec4aeb 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -6,12 +6,12 @@ import numpy as np +from pandas.types.common import is_float_dtype from pandas import Series, Index, isnull, notnull from pandas.core.datetools import bday from pandas.core.panel import Panel from pandas.core.panel4d import Panel4D from pandas.core.series import remove_na -import pandas.core.common as com from pandas.util.testing import (assert_panel_equal, assert_panel4d_equal, @@ -595,7 +595,7 @@ def test_set_value(self): self.assertEqual(res.get_value('l4', 'ItemE', 'foo', 'bar'), 1.5) res3 = self.panel4d.set_value('l4', 'ItemE', 'foobar', 'baz', 5) - self.assertTrue(com.is_float_dtype(res3['l4'].values)) + self.assertTrue(is_float_dtype(res3['l4'].values)) class TestPanel4d(tm.TestCase, CheckIndexing, SafeForSparse, diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 67d171bb8efda..fcdbec8fbc5c4 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -12,8 +12,7 @@ from pandas.compat import range, u import pandas.compat as compat -from pandas import (Index, Series, DataFrame, isnull, MultiIndex) -import pandas.core.common as com +from pandas import (Index, Series, DataFrame, isnull, MultiIndex, notnull) from pandas.util.testing import assert_series_equal import pandas.util.testing as tm @@ -1350,7 +1349,7 @@ def test_len(self): values = Series(['foo', 'fooo', 'fooooo', np.nan, 'fooooooo']) result = values.str.len() - exp = values.map(lambda x: len(x) if com.notnull(x) else NA) + exp = values.map(lambda x: len(x) if notnull(x) else NA) tm.assert_series_equal(result, exp) # mixed @@ -1368,7 +1367,7 @@ def test_len(self): 'fooooooo')]) result = values.str.len() - exp = values.map(lambda x: len(x) if com.notnull(x) else NA) + exp = values.map(lambda x: len(x) if notnull(x) else NA) tm.assert_series_equal(result, exp) def test_findall(self): @@ -1604,6 +1603,15 @@ def test_pad_fillchar(self): "fillchar must be a character, not int"): result = values.str.pad(5, fillchar=5) + def test_pad_width(self): + # GH 13598 + s = Series(['1', '22', 'a', 'bb']) + + for f in ['center', 'ljust', 'rjust', 'zfill', 'pad']: + with tm.assertRaisesRegexp(TypeError, + "width must be of integer type, not*"): + getattr(s.str, f)('f') + def test_translate(self): def _check(result, expected): diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 3693ebdb12e2f..7a35682eee3b0 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -11,7 +11,7 @@ import pandas as pd from pandas import (Series, DataFrame, Panel, bdate_range, isnull, - notnull, concat) + notnull, concat, Timestamp) import pandas.core.datetools as datetools import pandas.stats.moments as mom import pandas.core.window as rwindow @@ -101,7 +101,7 @@ def tests_skip_nuisance(self): expected = pd.concat([r[['A', 'B']].sum(), df[['C']]], axis=1) result = r.sum() - tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result, expected, check_like=True) def test_agg(self): df = DataFrame({'A': range(5), 'B': range(0, 10, 2)}) @@ -319,6 +319,13 @@ class TestRolling(Base): def setUp(self): self._create_data() + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + df + df.rolling(2).sum() + df.rolling(2, min_periods=1).sum() + def test_constructor(self): # GH 12669 @@ -372,6 +379,12 @@ class TestExpanding(Base): def setUp(self): self._create_data() + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + df + df.expanding(2).sum() + def test_constructor(self): # GH 12669 @@ -408,6 +421,12 @@ class TestEWM(Base): def setUp(self): self._create_data() + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + df + df.ewm(com=0.5).mean() + def test_constructor(self): for o in [self.series, self.frame]: c = o.ewm @@ -565,6 +584,7 @@ def _create_data(self): def test_dtypes(self): self._create_data() for f_name, d_name in product(self.funcs.keys(), self.data.keys()): + f = self.funcs[f_name] d = self.data[d_name] exp = self.expects[d_name][f_name] @@ -958,6 +978,7 @@ def test_rolling_median(self): name='median') def test_rolling_min(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): self._check_moment_func(mom.rolling_min, np.min, name='min') @@ -970,6 +991,7 @@ def test_rolling_min(self): window=3, min_periods=5) def test_rolling_max(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): self._check_moment_func(mom.rolling_max, np.max, name='max') @@ -2890,6 +2912,7 @@ def test_rolling_median_memory_error(self): Series(np.random.randn(n)).rolling(window=2, center=False).median() def test_rolling_min_max_numeric_types(self): + # GH12373 types_test = [np.dtype("f{}".format(width)) for width in [4, 8]] types_test.extend([np.dtype("{}{}".format(sign, width)) @@ -2961,6 +2984,7 @@ def test_rolling(self): r = g.rolling(window=4) for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() expected = g.apply(lambda x: getattr(x.rolling(4), f)()) tm.assert_frame_equal(result, expected) @@ -3007,6 +3031,7 @@ def test_expanding(self): r = g.expanding() for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() expected = g.apply(lambda x: getattr(x.expanding(), f)()) tm.assert_frame_equal(result, expected) @@ -3047,3 +3072,547 @@ def test_expanding_apply(self): result = r.apply(lambda x: x.sum()) expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum())) tm.assert_frame_equal(result, expected) + + +class TestRollingTS(tm.TestCase): + + # rolling time-series friendly + # xref GH13327 + + def setUp(self): + + self.regular = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': range(5)}).set_index('A') + + self.ragged = DataFrame({'B': range(5)}) + self.ragged.index = [Timestamp('20130101 09:00:00'), + Timestamp('20130101 09:00:02'), + Timestamp('20130101 09:00:03'), + Timestamp('20130101 09:00:05'), + Timestamp('20130101 09:00:06')] + + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index=[Timestamp('20130101 09:00:00'), + Timestamp('20130101 09:00:02'), + Timestamp('20130101 09:00:03'), + Timestamp('20130101 09:00:05'), + Timestamp('20130101 09:00:06')]) + df + df.rolling('2s').sum() + + def test_valid(self): + + df = self.regular + + # not a valid freq + with self.assertRaises(ValueError): + df.rolling(window='foobar') + + # not a datetimelike index + with self.assertRaises(ValueError): + df.reset_index().rolling(window='foobar') + + # non-fixed freqs + for freq in ['2MS', pd.offsets.MonthBegin(2)]: + with self.assertRaises(ValueError): + df.rolling(window=freq) + + for freq in ['1D', pd.offsets.Day(2), '2ms']: + df.rolling(window=freq) + + # non-integer min_periods + for minp in [1.0, 'foo', np.array([1, 2, 3])]: + with self.assertRaises(ValueError): + df.rolling(window='1D', min_periods=minp) + + # center is not implemented + with self.assertRaises(NotImplementedError): + df.rolling(window='1D', center=True) + + def test_on(self): + + df = self.regular + + # not a valid column + with self.assertRaises(ValueError): + df.rolling(window='2s', on='foobar') + + # column is valid + df = df.copy() + df['C'] = pd.date_range('20130101', periods=len(df)) + df.rolling(window='2d', on='C').sum() + + # invalid columns + with self.assertRaises(ValueError): + df.rolling(window='2d', on='B') + + # ok even though on non-selected + df.rolling(window='2d', on='C').B.sum() + + def test_monotonic_on(self): + + # on/index must be monotonic + df = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': range(5)}) + + self.assertTrue(df.A.is_monotonic) + df.rolling('2s', on='A').sum() + + df = df.set_index('A') + self.assertTrue(df.index.is_monotonic) + df.rolling('2s').sum() + + # non-monotonic + df.index = reversed(df.index.tolist()) + self.assertFalse(df.index.is_monotonic) + + with self.assertRaises(ValueError): + df.rolling('2s').sum() + + df = df.reset_index() + with self.assertRaises(ValueError): + df.rolling('2s', on='A').sum() + + def test_frame_on(self): + + df = DataFrame({'B': range(5), + 'C': pd.date_range('20130101 09:00:00', + periods=5, + freq='3s')}) + + df['A'] = [Timestamp('20130101 09:00:00'), + Timestamp('20130101 09:00:02'), + Timestamp('20130101 09:00:03'), + Timestamp('20130101 09:00:05'), + Timestamp('20130101 09:00:06')] + + # we are doing simulating using 'on' + expected = (df.set_index('A') + .rolling('2s') + .B + .sum() + .reset_index(drop=True) + ) + + result = (df.rolling('2s', on='A') + .B + .sum() + ) + tm.assert_series_equal(result, expected) + + # test as a frame + # we should be ignoring the 'on' as an aggregation column + # note that the expected is setting, computing, and reseting + # so the columns need to be switched compared + # to the actual result where they are ordered as in the + # original + expected = (df.set_index('A') + .rolling('2s')[['B']] + .sum() + .reset_index()[['B', 'A']] + ) + + result = (df.rolling('2s', on='A')[['B']] + .sum() + ) + tm.assert_frame_equal(result, expected) + + def test_frame_on2(self): + + # using multiple aggregation columns + df = DataFrame({'A': [0, 1, 2, 3, 4], + 'B': [0, 1, 2, np.nan, 4], + 'C': pd.Index([pd.Timestamp('20130101 09:00:00'), + pd.Timestamp('20130101 09:00:02'), + pd.Timestamp('20130101 09:00:03'), + pd.Timestamp('20130101 09:00:05'), + pd.Timestamp('20130101 09:00:06')])}, + columns=['A', 'C', 'B']) + + expected1 = DataFrame({'A': [0., 1, 3, 3, 7], + 'B': [0, 1, 3, np.nan, 4], + 'C': df['C']}, + columns=['A', 'C', 'B']) + + result = df.rolling('2s', on='C').sum() + expected = expected1 + tm.assert_frame_equal(result, expected) + + expected = Series([0, 1, 3, np.nan, 4], name='B') + result = df.rolling('2s', on='C').B.sum() + tm.assert_series_equal(result, expected) + + expected = expected1[['A', 'B', 'C']] + result = df.rolling('2s', on='C')[['A', 'B', 'C']].sum() + tm.assert_frame_equal(result, expected) + + def test_basic_regular(self): + + df = self.regular.copy() + + df.index = pd.date_range('20130101', periods=5, freq='D') + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window='1D').sum() + tm.assert_frame_equal(result, expected) + + df.index = pd.date_range('20130101', periods=5, freq='2D') + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window='2D', min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window='2D', min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(window=1).sum() + result = df.rolling(window='2D').sum() + tm.assert_frame_equal(result, expected) + + def test_min_periods(self): + + # compare for min_periods + df = self.regular + + # these slightly different + expected = df.rolling(2, min_periods=1).sum() + result = df.rolling('2s').sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(2, min_periods=1).sum() + result = df.rolling('2s', min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + def test_ragged_sum(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 3, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=2).sum() + expected = df.copy() + expected['B'] = [np.nan, np.nan, 3, np.nan, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 5, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s').sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 5, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='4s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 6, 9] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='4s', min_periods=3).sum() + expected = df.copy() + expected['B'] = [np.nan, np.nan, 3, 6, 9] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 6, 10] + tm.assert_frame_equal(result, expected) + + def test_ragged_mean(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).mean() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).mean() + expected = df.copy() + expected['B'] = [0.0, 1, 1.5, 3.0, 3.5] + tm.assert_frame_equal(result, expected) + + def test_ragged_median(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).median() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).median() + expected = df.copy() + expected['B'] = [0.0, 1, 1.5, 3.0, 3.5] + tm.assert_frame_equal(result, expected) + + def test_ragged_quantile(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).quantile(0.5) + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).quantile(0.5) + expected = df.copy() + expected['B'] = [0.0, 1, 1.0, 3.0, 3.0] + tm.assert_frame_equal(result, expected) + + def test_ragged_std(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).std(ddof=0) + expected = df.copy() + expected['B'] = [0.0] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='1s', min_periods=1).std(ddof=1) + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s', min_periods=1).std(ddof=0) + expected = df.copy() + expected['B'] = [0.0] + [0.5] * 4 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).std(ddof=1) + expected = df.copy() + expected['B'] = [np.nan, 0.707107, 1.0, 1.0, 1.290994] + tm.assert_frame_equal(result, expected) + + def test_ragged_var(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).var(ddof=0) + expected = df.copy() + expected['B'] = [0.0] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='1s', min_periods=1).var(ddof=1) + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s', min_periods=1).var(ddof=0) + expected = df.copy() + expected['B'] = [0.0] + [0.25] * 4 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).var(ddof=1) + expected = df.copy() + expected['B'] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.] + tm.assert_frame_equal(result, expected) + + def test_ragged_skew(self): + + df = self.ragged + result = df.rolling(window='3s', min_periods=1).skew() + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).skew() + expected = df.copy() + expected['B'] = [np.nan] * 2 + [0.0, 0.0, 0.0] + tm.assert_frame_equal(result, expected) + + def test_ragged_kurt(self): + + df = self.ragged + result = df.rolling(window='3s', min_periods=1).kurt() + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).kurt() + expected = df.copy() + expected['B'] = [np.nan] * 4 + [-1.2] + tm.assert_frame_equal(result, expected) + + def test_ragged_count(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).count() + expected = df.copy() + expected['B'] = [1.0, 1, 1, 1, 1] + tm.assert_frame_equal(result, expected) + + df = self.ragged + result = df.rolling(window='1s').count() + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).count() + expected = df.copy() + expected['B'] = [1.0, 1, 2, 1, 2] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=2).count() + expected = df.copy() + expected['B'] = [np.nan, np.nan, 2, np.nan, 2] + tm.assert_frame_equal(result, expected) + + def test_regular_min(self): + + df = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': [0.0, 1, 2, 3, 4]}).set_index('A') + result = df.rolling('1s').min() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + df = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': [5, 4, 3, 4, 5]}).set_index('A') + + tm.assert_frame_equal(result, expected) + result = df.rolling('2s').min() + expected = df.copy() + expected['B'] = [5.0, 4, 3, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling('5s').min() + expected = df.copy() + expected['B'] = [5.0, 4, 3, 3, 3] + tm.assert_frame_equal(result, expected) + + def test_ragged_min(self): + + df = self.ragged + + result = df.rolling(window='1s', min_periods=1).min() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).min() + expected = df.copy() + expected['B'] = [0.0, 1, 1, 3, 3] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).min() + expected = df.copy() + expected['B'] = [0.0, 0, 0, 1, 1] + tm.assert_frame_equal(result, expected) + + def test_perf_min(self): + + N = 10000 + + dfp = DataFrame({'B': np.random.randn(N)}, + index=pd.date_range('20130101', + periods=N, + freq='s')) + expected = dfp.rolling(2, min_periods=1).min() + result = dfp.rolling('2s').min() + self.assertTrue(((result - expected) < 0.01).all().bool()) + + expected = dfp.rolling(200, min_periods=1).min() + result = dfp.rolling('200s').min() + self.assertTrue(((result - expected) < 0.01).all().bool()) + + def test_ragged_max(self): + + df = self.ragged + + result = df.rolling(window='1s', min_periods=1).max() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).max() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).max() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + def test_ragged_apply(self): + + df = self.ragged + + f = lambda x: 1 + result = df.rolling(window='1s', min_periods=1).apply(f) + expected = df.copy() + expected['B'] = 1. + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).apply(f) + expected = df.copy() + expected['B'] = 1. + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).apply(f) + expected = df.copy() + expected['B'] = 1. + tm.assert_frame_equal(result, expected) + + def test_all(self): + + # simple comparision of integer vs time-based windowing + df = self.regular * 2 + er = df.rolling(window=1) + r = df.rolling(window='1s') + + for f in ['sum', 'mean', 'count', 'median', 'std', + 'var', 'kurt', 'skew', 'min', 'max']: + + result = getattr(r, f)() + expected = getattr(er, f)() + tm.assert_frame_equal(result, expected) + + result = r.quantile(0.5) + expected = er.quantile(0.5) + tm.assert_frame_equal(result, expected) + + result = r.apply(lambda x: 1) + expected = er.apply(lambda x: 1) + tm.assert_frame_equal(result, expected) + + def test_all2(self): + + # more sophisticated comparision of integer vs. + # time-based windowing + df = DataFrame({'B': np.arange(50)}, + index=pd.date_range('20130101', + periods=50, freq='H') + ) + # in-range data + dft = df.between_time("09:00", "16:00") + + r = dft.rolling(window='5H') + + for f in ['sum', 'mean', 'count', 'median', 'std', + 'var', 'kurt', 'skew', 'min', 'max']: + + result = getattr(r, f)() + + # we need to roll the days separately + # to compare with a time-based roll + # finally groupby-apply will return a multi-index + # so we need to drop the day + def agg_by_day(x): + x = x.between_time("09:00", "16:00") + return getattr(x.rolling(5, min_periods=1), f)() + expected = df.groupby(df.index.day).apply( + agg_by_day).reset_index(level=0, drop=True) + + tm.assert_frame_equal(result, expected) diff --git a/pandas/tests/types/test_cast.py b/pandas/tests/types/test_cast.py new file mode 100644 index 0000000000000..dd3f07ea8157f --- /dev/null +++ b/pandas/tests/types/test_cast.py @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- + +""" +These test the private routines in types/cast.py + +""" + + +import nose +from datetime import datetime +import numpy as np + +from pandas import Timedelta, Timestamp +from pandas.types.cast import (_possibly_downcast_to_dtype, + _possibly_convert_objects, + _infer_dtype_from_scalar, + _maybe_convert_string_to_object, + _maybe_convert_scalar) +from pandas.util import testing as tm + +_multiprocess_can_split_ = True + + +def test_downcast_conv(): + # test downcasting + + arr = np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]) + result = _possibly_downcast_to_dtype(arr, 'infer') + assert (np.array_equal(result, arr)) + + arr = np.array([8., 8., 8., 8., 8.9999999999995]) + result = _possibly_downcast_to_dtype(arr, 'infer') + expected = np.array([8, 8, 8, 8, 9]) + assert (np.array_equal(result, expected)) + + arr = np.array([8., 8., 8., 8., 9.0000000000005]) + result = _possibly_downcast_to_dtype(arr, 'infer') + expected = np.array([8, 8, 8, 8, 9]) + assert (np.array_equal(result, expected)) + + # conversions + + expected = np.array([1, 2]) + for dtype in [np.float64, object, np.int64]: + arr = np.array([1.0, 2.0], dtype=dtype) + result = _possibly_downcast_to_dtype(arr, 'infer') + tm.assert_almost_equal(result, expected, check_dtype=False) + + for dtype in [np.float64, object]: + expected = np.array([1.0, 2.0, np.nan], dtype=dtype) + arr = np.array([1.0, 2.0, np.nan], dtype=dtype) + result = _possibly_downcast_to_dtype(arr, 'infer') + tm.assert_almost_equal(result, expected) + + # empties + for dtype in [np.int32, np.float64, np.float32, np.bool_, + np.int64, object]: + arr = np.array([], dtype=dtype) + result = _possibly_downcast_to_dtype(arr, 'int64') + tm.assert_almost_equal(result, np.array([], dtype=np.int64)) + assert result.dtype == np.int64 + + +class TestInferDtype(tm.TestCase): + + def test_infer_dtype_from_scalar(self): + # Test that _infer_dtype_from_scalar is returning correct dtype for int + # and float. + + for dtypec in [np.uint8, np.int8, np.uint16, np.int16, np.uint32, + np.int32, np.uint64, np.int64]: + data = dtypec(12) + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, type(data)) + + data = 12 + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.int64) + + for dtypec in [np.float16, np.float32, np.float64]: + data = dtypec(12) + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, dtypec) + + data = np.float(12) + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.float64) + + for data in [True, False]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.bool_) + + for data in [np.complex64(1), np.complex128(1)]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.complex_) + + import datetime + for data in [np.datetime64(1, 'ns'), Timestamp(1), + datetime.datetime(2000, 1, 1, 0, 0)]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, 'M8[ns]') + + for data in [np.timedelta64(1, 'ns'), Timedelta(1), + datetime.timedelta(1)]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, 'm8[ns]') + + for data in [datetime.date(2000, 1, 1), + Timestamp(1, tz='US/Eastern'), 'foo']: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.object_) + + +class TestMaybe(tm.TestCase): + + def test_maybe_convert_string_to_array(self): + result = _maybe_convert_string_to_object('x') + tm.assert_numpy_array_equal(result, np.array(['x'], dtype=object)) + self.assertTrue(result.dtype == object) + + result = _maybe_convert_string_to_object(1) + self.assertEqual(result, 1) + + arr = np.array(['x', 'y'], dtype=str) + result = _maybe_convert_string_to_object(arr) + tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) + self.assertTrue(result.dtype == object) + + # unicode + arr = np.array(['x', 'y']).astype('U') + result = _maybe_convert_string_to_object(arr) + tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) + self.assertTrue(result.dtype == object) + + # object + arr = np.array(['x', 2], dtype=object) + result = _maybe_convert_string_to_object(arr) + tm.assert_numpy_array_equal(result, np.array(['x', 2], dtype=object)) + self.assertTrue(result.dtype == object) + + def test_maybe_convert_scalar(self): + + # pass thru + result = _maybe_convert_scalar('x') + self.assertEqual(result, 'x') + result = _maybe_convert_scalar(np.array([1])) + self.assertEqual(result, np.array([1])) + + # leave scalar dtype + result = _maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.int64(1)) + result = _maybe_convert_scalar(np.int32(1)) + self.assertEqual(result, np.int32(1)) + result = _maybe_convert_scalar(np.float32(1)) + self.assertEqual(result, np.float32(1)) + result = _maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.float64(1)) + + # coerce + result = _maybe_convert_scalar(1) + self.assertEqual(result, np.int64(1)) + result = _maybe_convert_scalar(1.0) + self.assertEqual(result, np.float64(1)) + result = _maybe_convert_scalar(Timestamp('20130101')) + self.assertEqual(result, Timestamp('20130101').value) + result = _maybe_convert_scalar(datetime(2013, 1, 1)) + self.assertEqual(result, Timestamp('20130101').value) + result = _maybe_convert_scalar(Timedelta('1 day 1 min')) + self.assertEqual(result, Timedelta('1 day 1 min').value) + + +class TestConvert(tm.TestCase): + + def test_possibly_convert_objects_copy(self): + values = np.array([1, 2]) + + out = _possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) + + out = _possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) + + values = np.array(['apply', 'banana']) + out = _possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) + + out = _possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_common.py b/pandas/tests/types/test_common.py new file mode 100644 index 0000000000000..0a586410ad5a0 --- /dev/null +++ b/pandas/tests/types/test_common.py @@ -0,0 +1,22 @@ +# -*- coding: utf-8 -*- + +import nose +import numpy as np + +from pandas.types.dtypes import DatetimeTZDtype, CategoricalDtype +from pandas.types.common import pandas_dtype + +_multiprocess_can_split_ = True + + +def test_pandas_dtype(): + + assert pandas_dtype('datetime64[ns, US/Eastern]') == DatetimeTZDtype( + 'datetime64[ns, US/Eastern]') + assert pandas_dtype('category') == CategoricalDtype() + for dtype in ['M8[ns]', 'm8[ns]', 'object', 'float64', 'int64']: + assert pandas_dtype(dtype) == np.dtype(dtype) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_dtypes.py b/pandas/tests/types/test_dtypes.py index d48b9baf64777..1743e80ae01a9 100644 --- a/pandas/tests/types/test_dtypes.py +++ b/pandas/tests/types/test_dtypes.py @@ -4,13 +4,14 @@ import nose import numpy as np from pandas import Series, Categorical, date_range -import pandas.core.common as com -from pandas.types.api import CategoricalDtype -from pandas.core.common import (is_categorical_dtype, - is_categorical, DatetimeTZDtype, - is_datetime64tz_dtype, is_datetimetz, - is_dtype_equal, is_datetime64_ns_dtype, - is_datetime64_dtype) + +from pandas.types.dtypes import CategoricalDtype +from pandas.types.common import (is_categorical_dtype, + is_categorical, DatetimeTZDtype, + is_datetime64tz_dtype, is_datetimetz, + is_dtype_equal, is_datetime64_ns_dtype, + is_datetime64_dtype, + _coerce_to_dtype) import pandas.util.testing as tm _multiprocess_can_split_ = True @@ -124,9 +125,9 @@ def test_subclass(self): self.assertTrue(issubclass(type(a), type(b))) def test_coerce_to_dtype(self): - self.assertEqual(com._coerce_to_dtype('datetime64[ns, US/Eastern]'), + self.assertEqual(_coerce_to_dtype('datetime64[ns, US/Eastern]'), DatetimeTZDtype('ns', 'US/Eastern')) - self.assertEqual(com._coerce_to_dtype('datetime64[ns, Asia/Tokyo]'), + self.assertEqual(_coerce_to_dtype('datetime64[ns, Asia/Tokyo]'), DatetimeTZDtype('ns', 'Asia/Tokyo')) def test_compat(self): diff --git a/pandas/tests/types/test_generic.py b/pandas/tests/types/test_generic.py index 5549a3a376992..89913de6f6069 100644 --- a/pandas/tests/types/test_generic.py +++ b/pandas/tests/types/test_generic.py @@ -3,8 +3,8 @@ import nose import numpy as np import pandas as pd -import pandas.core.common as com import pandas.util.testing as tm +from pandas.types import generic as gt _multiprocess_can_split_ = True @@ -22,24 +22,24 @@ class TestABCClasses(tm.TestCase): sparse_array = pd.SparseArray(np.random.randn(10)) def test_abc_types(self): - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index) - self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index) - self.assertIsInstance(self.multi_index, com.ABCMultiIndex) - self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex) - self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex) - self.assertIsInstance(self.period_index, com.ABCPeriodIndex) + self.assertIsInstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index) + self.assertIsInstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index) + self.assertIsInstance(self.multi_index, gt.ABCMultiIndex) + self.assertIsInstance(self.datetime_index, gt.ABCDatetimeIndex) + self.assertIsInstance(self.timedelta_index, gt.ABCTimedeltaIndex) + self.assertIsInstance(self.period_index, gt.ABCPeriodIndex) self.assertIsInstance(self.categorical_df.index, - com.ABCCategoricalIndex) - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass) - self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries) - self.assertIsInstance(self.df, com.ABCDataFrame) - self.assertIsInstance(self.df.to_panel(), com.ABCPanel) - self.assertIsInstance(self.sparse_series, com.ABCSparseSeries) - self.assertIsInstance(self.sparse_array, com.ABCSparseArray) - self.assertIsInstance(self.categorical, com.ABCCategorical) - self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod) + gt.ABCCategoricalIndex) + self.assertIsInstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass) + self.assertIsInstance(pd.Series([1, 2, 3]), gt.ABCSeries) + self.assertIsInstance(self.df, gt.ABCDataFrame) + self.assertIsInstance(self.df.to_panel(), gt.ABCPanel) + self.assertIsInstance(self.sparse_series, gt.ABCSparseSeries) + self.assertIsInstance(self.sparse_array, gt.ABCSparseArray) + self.assertIsInstance(self.categorical, gt.ABCCategorical) + self.assertIsInstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod) if __name__ == '__main__': diff --git a/pandas/tests/types/test_inference.py b/pandas/tests/types/test_inference.py new file mode 100644 index 0000000000000..9a12220f5b41d --- /dev/null +++ b/pandas/tests/types/test_inference.py @@ -0,0 +1,847 @@ +# -*- coding: utf-8 -*- + +""" +These the test the public routines exposed in types/common.py +related to inference and not otherwise tested in types/test_common.py + +""" + +import nose +import collections +import re +from datetime import datetime, date, timedelta, time +import numpy as np + +import pandas as pd +from pandas import lib, tslib +from pandas import (Series, Index, DataFrame, Timedelta, + DatetimeIndex, TimedeltaIndex, Timestamp, + Panel, Period) +from pandas.compat import u, PY2, lrange +from pandas.types import inference +from pandas.types.common import (is_timedelta64_dtype, + is_timedelta64_ns_dtype, + is_number, + is_integer, + is_float, + is_bool, + is_scalar, + _ensure_int32) +from pandas.types.missing import isnull +from pandas.util import testing as tm + +_multiprocess_can_split_ = True + + +def test_is_sequence(): + is_seq = inference.is_sequence + assert (is_seq((1, 2))) + assert (is_seq([1, 2])) + assert (not is_seq("abcd")) + assert (not is_seq(u("abcd"))) + assert (not is_seq(np.int64)) + + class A(object): + + def __getitem__(self): + return 1 + + assert (not is_seq(A())) + + +def test_is_list_like(): + passes = ([], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]), + Series([]), Series(['a']).str) + fails = (1, '2', object()) + + for p in passes: + assert inference.is_list_like(p) + + for f in fails: + assert not inference.is_list_like(f) + + +def test_is_dict_like(): + passes = [{}, {'A': 1}, Series([1])] + fails = ['1', 1, [1, 2], (1, 2), range(2), Index([1])] + + for p in passes: + assert inference.is_dict_like(p) + + for f in fails: + assert not inference.is_dict_like(f) + + +def test_is_named_tuple(): + passes = (collections.namedtuple('Test', list('abc'))(1, 2, 3), ) + fails = ((1, 2, 3), 'a', Series({'pi': 3.14})) + + for p in passes: + assert inference.is_named_tuple(p) + + for f in fails: + assert not inference.is_named_tuple(f) + + +def test_is_hashable(): + + # all new-style classes are hashable by default + class HashableClass(object): + pass + + class UnhashableClass1(object): + __hash__ = None + + class UnhashableClass2(object): + + def __hash__(self): + raise TypeError("Not hashable") + + hashable = (1, + 3.14, + np.float64(3.14), + 'a', + tuple(), + (1, ), + HashableClass(), ) + not_hashable = ([], UnhashableClass1(), ) + abc_hashable_not_really_hashable = (([], ), UnhashableClass2(), ) + + for i in hashable: + assert inference.is_hashable(i) + for i in not_hashable: + assert not inference.is_hashable(i) + for i in abc_hashable_not_really_hashable: + assert not inference.is_hashable(i) + + # numpy.array is no longer collections.Hashable as of + # https://github.com/numpy/numpy/pull/5326, just test + # is_hashable() + assert not inference.is_hashable(np.array([])) + + # old-style classes in Python 2 don't appear hashable to + # collections.Hashable but also seem to support hash() by default + if PY2: + + class OldStyleClass(): + pass + + c = OldStyleClass() + assert not isinstance(c, collections.Hashable) + assert inference.is_hashable(c) + hash(c) # this will not raise + + +def test_is_re(): + passes = re.compile('ad'), + fails = 'x', 2, 3, object() + + for p in passes: + assert inference.is_re(p) + + for f in fails: + assert not inference.is_re(f) + + +def test_is_recompilable(): + passes = (r'a', u('x'), r'asdf', re.compile('adsf'), u(r'\u2233\s*'), + re.compile(r'')) + fails = 1, [], object() + + for p in passes: + assert inference.is_re_compilable(p) + + for f in fails: + assert not inference.is_re_compilable(f) + + +class TestInference(tm.TestCase): + + def test_infer_dtype_bytes(self): + compare = 'string' if PY2 else 'bytes' + + # string array of bytes + arr = np.array(list('abc'), dtype='S1') + self.assertEqual(lib.infer_dtype(arr), compare) + + # object array of bytes + arr = arr.astype(object) + self.assertEqual(lib.infer_dtype(arr), compare) + + def test_isinf_scalar(self): + # GH 11352 + self.assertTrue(lib.isposinf_scalar(float('inf'))) + self.assertTrue(lib.isposinf_scalar(np.inf)) + self.assertFalse(lib.isposinf_scalar(-np.inf)) + self.assertFalse(lib.isposinf_scalar(1)) + self.assertFalse(lib.isposinf_scalar('a')) + + self.assertTrue(lib.isneginf_scalar(float('-inf'))) + self.assertTrue(lib.isneginf_scalar(-np.inf)) + self.assertFalse(lib.isneginf_scalar(np.inf)) + self.assertFalse(lib.isneginf_scalar(1)) + self.assertFalse(lib.isneginf_scalar('a')) + + def test_maybe_convert_numeric_infinities(self): + # see gh-13274 + infinities = ['inf', 'inF', 'iNf', 'Inf', + 'iNF', 'InF', 'INf', 'INF'] + na_values = set(['', 'NULL', 'nan']) + + pos = np.array(['inf'], dtype=np.float64) + neg = np.array(['-inf'], dtype=np.float64) + + msg = "Unable to parse string" + + for infinity in infinities: + for maybe_int in (True, False): + out = lib.maybe_convert_numeric( + np.array([infinity], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, pos) + + out = lib.maybe_convert_numeric( + np.array(['-' + infinity], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, neg) + + out = lib.maybe_convert_numeric( + np.array([u(infinity)], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, pos) + + out = lib.maybe_convert_numeric( + np.array(['+' + infinity], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, pos) + + # too many characters + with tm.assertRaisesRegexp(ValueError, msg): + lib.maybe_convert_numeric( + np.array(['foo_' + infinity], dtype=object), + na_values, maybe_int) + + def test_maybe_convert_numeric_post_floatify_nan(self): + # see gh-13314 + data = np.array(['1.200', '-999.000', '4.500'], dtype=object) + expected = np.array([1.2, np.nan, 4.5], dtype=np.float64) + nan_values = set([-999, -999.0]) + + for coerce_type in (True, False): + out = lib.maybe_convert_numeric(data, nan_values, coerce_type) + tm.assert_numpy_array_equal(out, expected) + + def test_convert_infs(self): + arr = np.array(['inf', 'inf', 'inf'], dtype='O') + result = lib.maybe_convert_numeric(arr, set(), False) + self.assertTrue(result.dtype == np.float64) + + arr = np.array(['-inf', '-inf', '-inf'], dtype='O') + result = lib.maybe_convert_numeric(arr, set(), False) + self.assertTrue(result.dtype == np.float64) + + def test_scientific_no_exponent(self): + # See PR 12215 + arr = np.array(['42E', '2E', '99e', '6e'], dtype='O') + result = lib.maybe_convert_numeric(arr, set(), False, True) + self.assertTrue(np.all(np.isnan(result))) + + def test_convert_non_hashable(self): + # GH13324 + # make sure that we are handing non-hashables + arr = np.array([[10.0, 2], 1.0, 'apple']) + result = lib.maybe_convert_numeric(arr, set(), False, True) + tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan])) + + +class TestTypeInference(tm.TestCase): + _multiprocess_can_split_ = True + + def test_length_zero(self): + result = lib.infer_dtype(np.array([], dtype='i4')) + self.assertEqual(result, 'integer') + + result = lib.infer_dtype([]) + self.assertEqual(result, 'empty') + + def test_integers(self): + arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'integer') + + arr = np.array([1, 2, 3, np.int64(4), np.int32(5), 'foo'], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed-integer') + + arr = np.array([1, 2, 3, 4, 5], dtype='i4') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'integer') + + def test_bools(self): + arr = np.array([True, False, True, True, True], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'boolean') + + arr = np.array([np.bool_(True), np.bool_(False)], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'boolean') + + arr = np.array([True, False, True, 'foo'], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed') + + arr = np.array([True, False, True], dtype=bool) + result = lib.infer_dtype(arr) + self.assertEqual(result, 'boolean') + + def test_floats(self): + arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'floating') + + arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'], + dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed-integer') + + arr = np.array([1, 2, 3, 4, 5], dtype='f4') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'floating') + + arr = np.array([1, 2, 3, 4, 5], dtype='f8') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'floating') + + def test_string(self): + pass + + def test_unicode(self): + pass + + def test_datetime(self): + + dates = [datetime(2012, 1, x) for x in range(1, 20)] + index = Index(dates) + self.assertEqual(index.inferred_type, 'datetime64') + + def test_infer_dtype_datetime(self): + + arr = np.array([Timestamp('2011-01-01'), + Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([np.datetime64('2011-01-01'), + np.datetime64('2011-01-01')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # starts with nan + for n in [pd.NaT, np.nan]: + arr = np.array([n, pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([n, np.datetime64('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([n, datetime(2011, 1, 1)]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([n, pd.Timestamp('2011-01-02'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([n, np.datetime64('2011-01-02'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([n, datetime(2011, 1, 1), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # different type of nat + arr = np.array([np.timedelta64('nat'), + np.datetime64('2011-01-02')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.datetime64('2011-01-02'), + np.timedelta64('nat')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + # mixed datetime + arr = np.array([datetime(2011, 1, 1), + pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # should be datetime? + arr = np.array([np.datetime64('2011-01-01'), + pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([pd.Timestamp('2011-01-02'), + np.datetime64('2011-01-01')]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1]) + self.assertEqual(lib.infer_dtype(arr), 'mixed-integer') + + arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1.1]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.nan, '2011-01-01', pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + def test_infer_dtype_timedelta(self): + + arr = np.array([pd.Timedelta('1 days'), + pd.Timedelta('2 days')]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([np.timedelta64(1, 'D'), + np.timedelta64(2, 'D')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([timedelta(1), timedelta(2)]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + # starts with nan + for n in [pd.NaT, np.nan]: + arr = np.array([n, Timedelta('1 days')]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, np.timedelta64(1, 'D')]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, timedelta(1)]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, pd.Timedelta('1 days'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, np.timedelta64(1, 'D'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, timedelta(1), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + # different type of nat + arr = np.array([np.datetime64('nat'), np.timedelta64(1, 'D')], + dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.timedelta64(1, 'D'), np.datetime64('nat')], + dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + def test_infer_dtype_period(self): + # GH 13664 + arr = np.array([pd.Period('2011-01', freq='D'), + pd.Period('2011-02', freq='D')]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + arr = np.array([pd.Period('2011-01', freq='D'), + pd.Period('2011-02', freq='M')]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + # starts with nan + for n in [pd.NaT, np.nan]: + arr = np.array([n, pd.Period('2011-01', freq='D')]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + arr = np.array([n, pd.Period('2011-01', freq='D'), n]) + self.assertEqual(pd.lib.infer_dtype(arr), 'period') + + # different type of nat + arr = np.array([np.datetime64('nat'), pd.Period('2011-01', freq='M')], + dtype=object) + self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') + + arr = np.array([pd.Period('2011-01', freq='M'), np.datetime64('nat')], + dtype=object) + self.assertEqual(pd.lib.infer_dtype(arr), 'mixed') + + def test_infer_dtype_all_nan_nat_like(self): + arr = np.array([np.nan, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'floating') + + # nan and None mix are result in mixed + arr = np.array([np.nan, np.nan, None]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([None, np.nan, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + # pd.NaT + arr = np.array([pd.NaT]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([pd.NaT, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([np.nan, pd.NaT]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([np.nan, pd.NaT, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([None, pd.NaT, None]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # np.datetime64(nat) + arr = np.array([np.datetime64('nat')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + for n in [np.nan, pd.NaT, None]: + arr = np.array([n, np.datetime64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([pd.NaT, n, np.datetime64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([np.timedelta64('nat')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + for n in [np.nan, pd.NaT, None]: + arr = np.array([n, np.timedelta64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([pd.NaT, n, np.timedelta64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + # datetime / timedelta mixed + arr = np.array([pd.NaT, np.datetime64('nat'), + np.timedelta64('nat'), np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.timedelta64('nat'), np.datetime64('nat')], + dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + def test_is_datetimelike_array_all_nan_nat_like(self): + arr = np.array([np.nan, pd.NaT, np.datetime64('nat')]) + self.assertTrue(lib.is_datetime_array(arr)) + self.assertTrue(lib.is_datetime64_array(arr)) + self.assertFalse(lib.is_timedelta_array(arr)) + self.assertFalse(lib.is_timedelta64_array(arr)) + self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, pd.NaT, np.timedelta64('nat')]) + self.assertFalse(lib.is_datetime_array(arr)) + self.assertFalse(lib.is_datetime64_array(arr)) + self.assertTrue(lib.is_timedelta_array(arr)) + self.assertTrue(lib.is_timedelta64_array(arr)) + self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, pd.NaT, np.datetime64('nat'), + np.timedelta64('nat')]) + self.assertFalse(lib.is_datetime_array(arr)) + self.assertFalse(lib.is_datetime64_array(arr)) + self.assertFalse(lib.is_timedelta_array(arr)) + self.assertFalse(lib.is_timedelta64_array(arr)) + self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, pd.NaT]) + self.assertTrue(lib.is_datetime_array(arr)) + self.assertTrue(lib.is_datetime64_array(arr)) + self.assertTrue(lib.is_timedelta_array(arr)) + self.assertTrue(lib.is_timedelta64_array(arr)) + self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, np.nan], dtype=object) + self.assertFalse(lib.is_datetime_array(arr)) + self.assertFalse(lib.is_datetime64_array(arr)) + self.assertFalse(lib.is_timedelta_array(arr)) + self.assertFalse(lib.is_timedelta64_array(arr)) + self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr)) + + def test_date(self): + + dates = [date(2012, 1, x) for x in range(1, 20)] + index = Index(dates) + self.assertEqual(index.inferred_type, 'date') + + def test_to_object_array_tuples(self): + r = (5, 6) + values = [r] + result = lib.to_object_array_tuples(values) + + try: + # make sure record array works + from collections import namedtuple + record = namedtuple('record', 'x y') + r = record(5, 6) + values = [r] + result = lib.to_object_array_tuples(values) # noqa + except ImportError: + pass + + def test_object(self): + + # GH 7431 + # cannot infer more than this as only a single element + arr = np.array([None], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed') + + def test_to_object_array_width(self): + # see gh-13320 + rows = [[1, 2, 3], [4, 5, 6]] + + expected = np.array(rows, dtype=object) + out = lib.to_object_array(rows) + tm.assert_numpy_array_equal(out, expected) + + expected = np.array(rows, dtype=object) + out = lib.to_object_array(rows, min_width=1) + tm.assert_numpy_array_equal(out, expected) + + expected = np.array([[1, 2, 3, None, None], + [4, 5, 6, None, None]], dtype=object) + out = lib.to_object_array(rows, min_width=5) + tm.assert_numpy_array_equal(out, expected) + + def test_is_period(self): + self.assertTrue(lib.is_period(pd.Period('2011-01', freq='M'))) + self.assertFalse(lib.is_period(pd.PeriodIndex(['2011-01'], freq='M'))) + self.assertFalse(lib.is_period(pd.Timestamp('2011-01'))) + self.assertFalse(lib.is_period(1)) + self.assertFalse(lib.is_period(np.nan)) + + def test_categorical(self): + + # GH 8974 + from pandas import Categorical, Series + arr = Categorical(list('abc')) + result = lib.infer_dtype(arr) + self.assertEqual(result, 'categorical') + + result = lib.infer_dtype(Series(arr)) + self.assertEqual(result, 'categorical') + + arr = Categorical(list('abc'), categories=['cegfab'], ordered=True) + result = lib.infer_dtype(arr) + self.assertEqual(result, 'categorical') + + result = lib.infer_dtype(Series(arr)) + self.assertEqual(result, 'categorical') + + +class TestNumberScalar(tm.TestCase): + + def test_is_number(self): + + self.assertTrue(is_number(True)) + self.assertTrue(is_number(1)) + self.assertTrue(is_number(1.1)) + self.assertTrue(is_number(1 + 3j)) + self.assertTrue(is_number(np.bool(False))) + self.assertTrue(is_number(np.int64(1))) + self.assertTrue(is_number(np.float64(1.1))) + self.assertTrue(is_number(np.complex128(1 + 3j))) + self.assertTrue(is_number(np.nan)) + + self.assertFalse(is_number(None)) + self.assertFalse(is_number('x')) + self.assertFalse(is_number(datetime(2011, 1, 1))) + self.assertFalse(is_number(np.datetime64('2011-01-01'))) + self.assertFalse(is_number(Timestamp('2011-01-01'))) + self.assertFalse(is_number(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_number(timedelta(1000))) + self.assertFalse(is_number(Timedelta('1 days'))) + + # questionable + self.assertFalse(is_number(np.bool_(False))) + self.assertTrue(is_number(np.timedelta64(1, 'D'))) + + def test_is_bool(self): + self.assertTrue(is_bool(True)) + self.assertTrue(is_bool(np.bool(False))) + self.assertTrue(is_bool(np.bool_(False))) + + self.assertFalse(is_bool(1)) + self.assertFalse(is_bool(1.1)) + self.assertFalse(is_bool(1 + 3j)) + self.assertFalse(is_bool(np.int64(1))) + self.assertFalse(is_bool(np.float64(1.1))) + self.assertFalse(is_bool(np.complex128(1 + 3j))) + self.assertFalse(is_bool(np.nan)) + self.assertFalse(is_bool(None)) + self.assertFalse(is_bool('x')) + self.assertFalse(is_bool(datetime(2011, 1, 1))) + self.assertFalse(is_bool(np.datetime64('2011-01-01'))) + self.assertFalse(is_bool(Timestamp('2011-01-01'))) + self.assertFalse(is_bool(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_bool(timedelta(1000))) + self.assertFalse(is_bool(np.timedelta64(1, 'D'))) + self.assertFalse(is_bool(Timedelta('1 days'))) + + def test_is_integer(self): + self.assertTrue(is_integer(1)) + self.assertTrue(is_integer(np.int64(1))) + + self.assertFalse(is_integer(True)) + self.assertFalse(is_integer(1.1)) + self.assertFalse(is_integer(1 + 3j)) + self.assertFalse(is_integer(np.bool(False))) + self.assertFalse(is_integer(np.bool_(False))) + self.assertFalse(is_integer(np.float64(1.1))) + self.assertFalse(is_integer(np.complex128(1 + 3j))) + self.assertFalse(is_integer(np.nan)) + self.assertFalse(is_integer(None)) + self.assertFalse(is_integer('x')) + self.assertFalse(is_integer(datetime(2011, 1, 1))) + self.assertFalse(is_integer(np.datetime64('2011-01-01'))) + self.assertFalse(is_integer(Timestamp('2011-01-01'))) + self.assertFalse(is_integer(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_integer(timedelta(1000))) + self.assertFalse(is_integer(Timedelta('1 days'))) + + # questionable + self.assertTrue(is_integer(np.timedelta64(1, 'D'))) + + def test_is_float(self): + self.assertTrue(is_float(1.1)) + self.assertTrue(is_float(np.float64(1.1))) + self.assertTrue(is_float(np.nan)) + + self.assertFalse(is_float(True)) + self.assertFalse(is_float(1)) + self.assertFalse(is_float(1 + 3j)) + self.assertFalse(is_float(np.bool(False))) + self.assertFalse(is_float(np.bool_(False))) + self.assertFalse(is_float(np.int64(1))) + self.assertFalse(is_float(np.complex128(1 + 3j))) + self.assertFalse(is_float(None)) + self.assertFalse(is_float('x')) + self.assertFalse(is_float(datetime(2011, 1, 1))) + self.assertFalse(is_float(np.datetime64('2011-01-01'))) + self.assertFalse(is_float(Timestamp('2011-01-01'))) + self.assertFalse(is_float(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_float(timedelta(1000))) + self.assertFalse(is_float(np.timedelta64(1, 'D'))) + self.assertFalse(is_float(Timedelta('1 days'))) + + def test_is_timedelta(self): + self.assertTrue(is_timedelta64_dtype('timedelta64')) + self.assertTrue(is_timedelta64_dtype('timedelta64[ns]')) + self.assertFalse(is_timedelta64_ns_dtype('timedelta64')) + self.assertTrue(is_timedelta64_ns_dtype('timedelta64[ns]')) + + tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64') + self.assertTrue(is_timedelta64_dtype(tdi)) + self.assertTrue(is_timedelta64_ns_dtype(tdi)) + self.assertTrue(is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]'))) + + # Conversion to Int64Index: + self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64'))) + self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]'))) + + +class Testisscalar(tm.TestCase): + + def test_isscalar_builtin_scalars(self): + self.assertTrue(is_scalar(None)) + self.assertTrue(is_scalar(True)) + self.assertTrue(is_scalar(False)) + self.assertTrue(is_scalar(0.)) + self.assertTrue(is_scalar(np.nan)) + self.assertTrue(is_scalar('foobar')) + self.assertTrue(is_scalar(b'foobar')) + self.assertTrue(is_scalar(u('efoobar'))) + self.assertTrue(is_scalar(datetime(2014, 1, 1))) + self.assertTrue(is_scalar(date(2014, 1, 1))) + self.assertTrue(is_scalar(time(12, 0))) + self.assertTrue(is_scalar(timedelta(hours=1))) + self.assertTrue(is_scalar(pd.NaT)) + + def test_isscalar_builtin_nonscalars(self): + self.assertFalse(is_scalar({})) + self.assertFalse(is_scalar([])) + self.assertFalse(is_scalar([1])) + self.assertFalse(is_scalar(())) + self.assertFalse(is_scalar((1, ))) + self.assertFalse(is_scalar(slice(None))) + self.assertFalse(is_scalar(Ellipsis)) + + def test_isscalar_numpy_array_scalars(self): + self.assertTrue(is_scalar(np.int64(1))) + self.assertTrue(is_scalar(np.float64(1.))) + self.assertTrue(is_scalar(np.int32(1))) + self.assertTrue(is_scalar(np.object_('foobar'))) + self.assertTrue(is_scalar(np.str_('foobar'))) + self.assertTrue(is_scalar(np.unicode_(u('foobar')))) + self.assertTrue(is_scalar(np.bytes_(b'foobar'))) + self.assertTrue(is_scalar(np.datetime64('2014-01-01'))) + self.assertTrue(is_scalar(np.timedelta64(1, 'h'))) + + def test_isscalar_numpy_zerodim_arrays(self): + for zerodim in [np.array(1), np.array('foobar'), + np.array(np.datetime64('2014-01-01')), + np.array(np.timedelta64(1, 'h')), + np.array(np.datetime64('NaT'))]: + self.assertFalse(is_scalar(zerodim)) + self.assertTrue(is_scalar(lib.item_from_zerodim(zerodim))) + + def test_isscalar_numpy_arrays(self): + self.assertFalse(is_scalar(np.array([]))) + self.assertFalse(is_scalar(np.array([[]]))) + self.assertFalse(is_scalar(np.matrix('1; 2'))) + + def test_isscalar_pandas_scalars(self): + self.assertTrue(is_scalar(Timestamp('2014-01-01'))) + self.assertTrue(is_scalar(Timedelta(hours=1))) + self.assertTrue(is_scalar(Period('2014-01-01'))) + + def test_lisscalar_pandas_containers(self): + self.assertFalse(is_scalar(Series())) + self.assertFalse(is_scalar(Series([1]))) + self.assertFalse(is_scalar(DataFrame())) + self.assertFalse(is_scalar(DataFrame([[1]]))) + self.assertFalse(is_scalar(Panel())) + self.assertFalse(is_scalar(Panel([[[1]]]))) + self.assertFalse(is_scalar(Index([]))) + self.assertFalse(is_scalar(Index([1]))) + + +def test_datetimeindex_from_empty_datetime64_array(): + for unit in ['ms', 'us', 'ns']: + idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit)) + assert (len(idx) == 0) + + +def test_nan_to_nat_conversions(): + + df = DataFrame(dict({ + 'A': np.asarray( + lrange(10), dtype='float64'), + 'B': Timestamp('20010101') + })) + df.iloc[3:6, :] = np.nan + result = df.loc[4, 'B'].value + assert (result == tslib.iNaT) + + s = df['B'].copy() + s._data = s._data.setitem(indexer=tuple([slice(8, 9)]), value=np.nan) + assert (isnull(s[8])) + + # numpy < 1.7.0 is wrong + from distutils.version import LooseVersion + if LooseVersion(np.__version__) >= '1.7.0': + assert (s[8].value == np.datetime64('NaT').astype(np.int64)) + + +def test_ensure_int32(): + values = np.arange(10, dtype=np.int32) + result = _ensure_int32(values) + assert (result.dtype == np.int32) + + values = np.arange(10, dtype=np.int64) + result = _ensure_int32(values) + assert (result.dtype == np.int32) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_io.py b/pandas/tests/types/test_io.py new file mode 100644 index 0000000000000..545edf8f1386c --- /dev/null +++ b/pandas/tests/types/test_io.py @@ -0,0 +1,116 @@ +# -*- coding: utf-8 -*- + +import numpy as np +import pandas.lib as lib +import pandas.util.testing as tm + +from pandas.compat import long, u + + +class TestParseSQL(tm.TestCase): + + def test_convert_sql_column_floats(self): + arr = np.array([1.5, None, 3, 4.2], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_strings(self): + arr = np.array(['1.5', None, '3', '4.2'], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_unicode(self): + arr = np.array([u('1.5'), None, u('3'), u('4.2')], + dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], + dtype=object) + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_ints(self): + arr = np.array([1, 2, 3, 4], dtype='O') + arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') + result = lib.convert_sql_column(arr) + result2 = lib.convert_sql_column(arr2) + expected = np.array([1, 2, 3, 4], dtype='i8') + self.assert_numpy_array_equal(result, expected) + self.assert_numpy_array_equal(result2, expected) + + arr = np.array([1, 2, 3, None, 4], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_longs(self): + arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, 4], dtype='i8') + self.assert_numpy_array_equal(result, expected) + + arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_bools(self): + arr = np.array([True, False, True, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, True, False], dtype=bool) + self.assert_numpy_array_equal(result, expected) + + arr = np.array([True, False, None, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, np.nan, False], dtype=object) + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_decimals(self): + from decimal import Decimal + arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_downcast_int64(self): + from pandas.parser import na_values + + arr = np.array([1, 2, 7, 8, 10], dtype=np.int64) + expected = np.array([1, 2, 7, 8, 10], dtype=np.int8) + + # default argument + result = lib.downcast_int64(arr, na_values) + self.assert_numpy_array_equal(result, expected) + + result = lib.downcast_int64(arr, na_values, use_unsigned=False) + self.assert_numpy_array_equal(result, expected) + + expected = np.array([1, 2, 7, 8, 10], dtype=np.uint8) + result = lib.downcast_int64(arr, na_values, use_unsigned=True) + self.assert_numpy_array_equal(result, expected) + + # still cast to int8 despite use_unsigned=True + # because of the negative number as an element + arr = np.array([1, 2, -7, 8, 10], dtype=np.int64) + expected = np.array([1, 2, -7, 8, 10], dtype=np.int8) + result = lib.downcast_int64(arr, na_values, use_unsigned=True) + self.assert_numpy_array_equal(result, expected) + + arr = np.array([1, 2, 7, 8, 300], dtype=np.int64) + expected = np.array([1, 2, 7, 8, 300], dtype=np.int16) + result = lib.downcast_int64(arr, na_values) + self.assert_numpy_array_equal(result, expected) + + int8_na = na_values[np.int8] + int64_na = na_values[np.int64] + arr = np.array([int64_na, 2, 3, 10, 15], dtype=np.int64) + expected = np.array([int8_na, 2, 3, 10, 15], dtype=np.int8) + result = lib.downcast_int64(arr, na_values) + self.assert_numpy_array_equal(result, expected) + + +if __name__ == '__main__': + import nose + + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_missing.py b/pandas/tests/types/test_missing.py new file mode 100644 index 0000000000000..edcb69de7bfad --- /dev/null +++ b/pandas/tests/types/test_missing.py @@ -0,0 +1,243 @@ +# -*- coding: utf-8 -*- + +import nose +import numpy as np +from datetime import datetime +from pandas.util import testing as tm + +from pandas.core import config as cf +from pandas.compat import u +from pandas.tslib import iNaT +from pandas import (NaT, Float64Index, Series, + DatetimeIndex, TimedeltaIndex, date_range) +from pandas.types.dtypes import DatetimeTZDtype +from pandas.types.missing import (array_equivalent, isnull, notnull, + na_value_for_dtype) + +_multiprocess_can_split_ = True + + +def test_notnull(): + assert notnull(1.) + assert not notnull(None) + assert not notnull(np.NaN) + + with cf.option_context("mode.use_inf_as_null", False): + assert notnull(np.inf) + assert notnull(-np.inf) + + arr = np.array([1.5, np.inf, 3.5, -np.inf]) + result = notnull(arr) + assert result.all() + + with cf.option_context("mode.use_inf_as_null", True): + assert not notnull(np.inf) + assert not notnull(-np.inf) + + arr = np.array([1.5, np.inf, 3.5, -np.inf]) + result = notnull(arr) + assert result.sum() == 2 + + with cf.option_context("mode.use_inf_as_null", False): + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries(), tm.makeTimeSeries(), + tm.makePeriodSeries()]: + assert (isinstance(isnull(s), Series)) + + +def test_isnull(): + assert not isnull(1.) + assert isnull(None) + assert isnull(np.NaN) + assert not isnull(np.inf) + assert not isnull(-np.inf) + + # series + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries(), tm.makeTimeSeries(), + tm.makePeriodSeries()]: + assert (isinstance(isnull(s), Series)) + + # frame + for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(), + tm.makeMixedDataFrame()]: + result = isnull(df) + expected = df.apply(isnull) + tm.assert_frame_equal(result, expected) + + # panel + for p in [tm.makePanel(), tm.makePeriodPanel(), tm.add_nans(tm.makePanel()) + ]: + result = isnull(p) + expected = p.apply(isnull) + tm.assert_panel_equal(result, expected) + + # panel 4d + for p in [tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D())]: + result = isnull(p) + expected = p.apply(isnull) + tm.assert_panel4d_equal(result, expected) + + +def test_isnull_lists(): + result = isnull([[False]]) + exp = np.array([[False]]) + assert (np.array_equal(result, exp)) + + result = isnull([[1], [2]]) + exp = np.array([[False], [False]]) + assert (np.array_equal(result, exp)) + + # list of strings / unicode + result = isnull(['foo', 'bar']) + assert (not result.any()) + + result = isnull([u('foo'), u('bar')]) + assert (not result.any()) + + +def test_isnull_nat(): + result = isnull([NaT]) + exp = np.array([True]) + assert (np.array_equal(result, exp)) + + result = isnull(np.array([NaT], dtype=object)) + exp = np.array([True]) + assert (np.array_equal(result, exp)) + + +def test_isnull_numpy_nat(): + arr = np.array([NaT, np.datetime64('NaT'), np.timedelta64('NaT'), + np.datetime64('NaT', 's')]) + result = isnull(arr) + expected = np.array([True] * 4) + tm.assert_numpy_array_equal(result, expected) + + +def test_isnull_datetime(): + assert (not isnull(datetime.now())) + assert notnull(datetime.now()) + + idx = date_range('1/1/1990', periods=20) + assert (notnull(idx).all()) + + idx = np.asarray(idx) + idx[0] = iNaT + idx = DatetimeIndex(idx) + mask = isnull(idx) + assert (mask[0]) + assert (not mask[1:].any()) + + # GH 9129 + pidx = idx.to_period(freq='M') + mask = isnull(pidx) + assert (mask[0]) + assert (not mask[1:].any()) + + mask = isnull(pidx[1:]) + assert (not mask.any()) + + +class TestIsNull(tm.TestCase): + + def test_0d_array(self): + self.assertTrue(isnull(np.array(np.nan))) + self.assertFalse(isnull(np.array(0.0))) + self.assertFalse(isnull(np.array(0))) + # test object dtype + self.assertTrue(isnull(np.array(np.nan, dtype=object))) + self.assertFalse(isnull(np.array(0.0, dtype=object))) + self.assertFalse(isnull(np.array(0, dtype=object))) + + +def test_array_equivalent(): + assert array_equivalent(np.array([np.nan, np.nan]), + np.array([np.nan, np.nan])) + assert array_equivalent(np.array([np.nan, 1, np.nan]), + np.array([np.nan, 1, np.nan])) + assert array_equivalent(np.array([np.nan, None], dtype='object'), + np.array([np.nan, None], dtype='object')) + assert array_equivalent(np.array([np.nan, 1 + 1j], dtype='complex'), + np.array([np.nan, 1 + 1j], dtype='complex')) + assert not array_equivalent( + np.array([np.nan, 1 + 1j], dtype='complex'), np.array( + [np.nan, 1 + 2j], dtype='complex')) + assert not array_equivalent( + np.array([np.nan, 1, np.nan]), np.array([np.nan, 2, np.nan])) + assert not array_equivalent( + np.array(['a', 'b', 'c', 'd']), np.array(['e', 'e'])) + assert array_equivalent(Float64Index([0, np.nan]), + Float64Index([0, np.nan])) + assert not array_equivalent( + Float64Index([0, np.nan]), Float64Index([1, np.nan])) + assert array_equivalent(DatetimeIndex([0, np.nan]), + DatetimeIndex([0, np.nan])) + assert not array_equivalent( + DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan])) + assert array_equivalent(TimedeltaIndex([0, np.nan]), + TimedeltaIndex([0, np.nan])) + assert not array_equivalent( + TimedeltaIndex([0, np.nan]), TimedeltaIndex([1, np.nan])) + assert array_equivalent(DatetimeIndex([0, np.nan], tz='US/Eastern'), + DatetimeIndex([0, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan], tz='US/Eastern'), DatetimeIndex( + [1, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan]), DatetimeIndex( + [0, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan], tz='CET'), DatetimeIndex( + [0, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan]), TimedeltaIndex([0, np.nan])) + + +def test_array_equivalent_compat(): + # see gh-13388 + m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + n = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + assert (array_equivalent(m, n, strict_nan=True)) + assert (array_equivalent(m, n, strict_nan=False)) + + m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + n = np.array([(1, 2), (4, 3)], dtype=[('a', int), ('b', float)]) + assert (not array_equivalent(m, n, strict_nan=True)) + assert (not array_equivalent(m, n, strict_nan=False)) + + m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + n = np.array([(1, 2), (3, 4)], dtype=[('b', int), ('a', float)]) + assert (not array_equivalent(m, n, strict_nan=True)) + assert (not array_equivalent(m, n, strict_nan=False)) + + +def test_array_equivalent_str(): + for dtype in ['O', 'S', 'U']: + assert array_equivalent(np.array(['A', 'B'], dtype=dtype), + np.array(['A', 'B'], dtype=dtype)) + assert not array_equivalent(np.array(['A', 'B'], dtype=dtype), + np.array(['A', 'X'], dtype=dtype)) + + +def test_na_value_for_dtype(): + for dtype in [np.dtype('M8[ns]'), np.dtype('m8[ns]'), + DatetimeTZDtype('datetime64[ns, US/Eastern]')]: + assert na_value_for_dtype(dtype) is NaT + + for dtype in ['u1', 'u2', 'u4', 'u8', + 'i1', 'i2', 'i4', 'i8']: + assert na_value_for_dtype(np.dtype(dtype)) == 0 + + for dtype in ['bool']: + assert na_value_for_dtype(np.dtype(dtype)) is False + + for dtype in ['f2', 'f4', 'f8']: + assert np.isnan(na_value_for_dtype(np.dtype(dtype))) + + for dtype in ['O']: + assert np.isnan(na_value_for_dtype(np.dtype(dtype))) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_types.py b/pandas/tests/types/test_types.py deleted file mode 100644 index b9f6006cab731..0000000000000 --- a/pandas/tests/types/test_types.py +++ /dev/null @@ -1,40 +0,0 @@ -# -*- coding: utf-8 -*- -import nose -import numpy as np - -from pandas import NaT -from pandas.types.api import (DatetimeTZDtype, CategoricalDtype, - na_value_for_dtype, pandas_dtype) - - -def test_pandas_dtype(): - - assert pandas_dtype('datetime64[ns, US/Eastern]') == DatetimeTZDtype( - 'datetime64[ns, US/Eastern]') - assert pandas_dtype('category') == CategoricalDtype() - for dtype in ['M8[ns]', 'm8[ns]', 'object', 'float64', 'int64']: - assert pandas_dtype(dtype) == np.dtype(dtype) - - -def test_na_value_for_dtype(): - for dtype in [np.dtype('M8[ns]'), np.dtype('m8[ns]'), - DatetimeTZDtype('datetime64[ns, US/Eastern]')]: - assert na_value_for_dtype(dtype) is NaT - - for dtype in ['u1', 'u2', 'u4', 'u8', - 'i1', 'i2', 'i4', 'i8']: - assert na_value_for_dtype(np.dtype(dtype)) == 0 - - for dtype in ['bool']: - assert na_value_for_dtype(np.dtype(dtype)) is False - - for dtype in ['f2', 'f4', 'f8']: - assert np.isnan(na_value_for_dtype(np.dtype(dtype))) - - for dtype in ['O']: - assert np.isnan(na_value_for_dtype(np.dtype(dtype))) - - -if __name__ == '__main__': - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 075dff9cf6c38..e7d165354ec6c 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -12,6 +12,21 @@ from pandas import (Categorical, DataFrame, Series, Index, MultiIndex, Timedelta) from pandas.core.frame import _merge_doc +from pandas.types.generic import ABCSeries +from pandas.types.common import (is_datetime64tz_dtype, + is_datetime64_dtype, + needs_i8_conversion, + is_int64_dtype, + is_integer, + is_int_or_datetime_dtype, + is_dtype_equal, + is_bool, + is_list_like, + _ensure_int64, + _ensure_platform_int, + _ensure_object) +from pandas.types.missing import na_value_for_dtype + from pandas.core.generic import NDFrame from pandas.core.index import (_get_combined_index, _ensure_index, _get_consensus_names, @@ -19,18 +34,10 @@ from pandas.core.internals import (items_overlap_with_suffix, concatenate_block_managers) from pandas.util.decorators import Appender, Substitution -from pandas.core.common import (ABCSeries, is_dtype_equal, - is_datetime64_dtype, - is_int64_dtype, - is_integer, - is_bool, - is_list_like, - needs_i8_conversion) import pandas.core.algorithms as algos import pandas.core.common as com import pandas.types.concat as _concat -from pandas.types.api import na_value_for_dtype import pandas.algos as _algos import pandas.hashtable as _hash @@ -436,7 +443,7 @@ def _merger(x, y): # if we DO have duplicates, then # we cannot guarantee order - sorter = com._ensure_platform_int( + sorter = _ensure_platform_int( np.concatenate([groupby.indices[g] for g, _ in groupby])) if len(result) != len(sorter): if check_duplicates: @@ -1111,8 +1118,8 @@ def _get_single_indexer(join_key, index, sort=False): left_key, right_key, count = _factorize_keys(join_key, index, sort=sort) left_indexer, right_indexer = _algos.left_outer_join( - com._ensure_int64(left_key), - com._ensure_int64(right_key), + _ensure_int64(left_key), + _ensure_int64(right_key), count, sort=sort) return left_indexer, right_indexer @@ -1158,18 +1165,17 @@ def _right_outer_join(x, y, max_groups): def _factorize_keys(lk, rk, sort=True): - if com.is_datetime64tz_dtype(lk) and com.is_datetime64tz_dtype(rk): + if is_datetime64tz_dtype(lk) and is_datetime64tz_dtype(rk): lk = lk.values rk = rk.values - - if com.is_int_or_datetime_dtype(lk) and com.is_int_or_datetime_dtype(rk): + if is_int_or_datetime_dtype(lk) and is_int_or_datetime_dtype(rk): klass = _hash.Int64Factorizer - lk = com._ensure_int64(com._values_from_object(lk)) - rk = com._ensure_int64(com._values_from_object(rk)) + lk = _ensure_int64(com._values_from_object(lk)) + rk = _ensure_int64(com._values_from_object(rk)) else: klass = _hash.Factorizer - lk = com._ensure_object(lk) - rk = com._ensure_object(rk) + lk = _ensure_object(lk) + rk = _ensure_object(rk) rizer = klass(max(len(lk), len(rk))) @@ -1203,16 +1209,12 @@ def _sort_labels(uniques, left, right): # tuplesafe uniques = Index(uniques).values - sorter = uniques.argsort() - - reverse_indexer = np.empty(len(sorter), dtype=np.int64) - reverse_indexer.put(sorter, np.arange(len(sorter))) - - new_left = reverse_indexer.take(com._ensure_platform_int(left)) - np.putmask(new_left, left == -1, -1) + l = len(left) + labels = np.concatenate([left, right]) - new_right = reverse_indexer.take(com._ensure_platform_int(right)) - np.putmask(new_right, right == -1, -1) + _, new_labels = algos.safe_sort(uniques, labels, na_sentinel=-1) + new_labels = _ensure_int64(new_labels) + new_left, new_right = new_labels[:l], new_labels[l:] return new_left, new_right diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index e1405bc9e6add..3e2b7c3af460e 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -1,6 +1,7 @@ # pylint: disable=E1103 +from pandas.types.common import is_list_like, is_scalar from pandas import Series, DataFrame from pandas.core.index import MultiIndex, Index from pandas.core.groupby import Grouper @@ -9,7 +10,6 @@ from pandas.compat import range, lrange, zip from pandas import compat import pandas.core.common as com -import pandas.lib as lib import numpy as np @@ -95,7 +95,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', values_passed = values is not None if values_passed: - if com.is_list_like(values): + if is_list_like(values): values_multi = True values = list(values) else: @@ -361,7 +361,7 @@ def _all_key(): def _convert_by(by): if by is None: by = [] - elif (lib.isscalar(by) or + elif (is_scalar(by) or isinstance(by, (np.ndarray, Index, Series, Grouper)) or hasattr(by, '__call__')): by = [by] diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index b6c1926c1e7fc..4cf3364a03056 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -11,10 +11,17 @@ import numpy as np +from pandas.types.common import (is_list_like, + is_integer, + is_number, + is_hashable, + is_iterator) +from pandas.types.missing import isnull, notnull + from pandas.util.decorators import cache_readonly, deprecate_kwarg from pandas.core.base import PandasObject -import pandas.core.common as com -from pandas.core.common import AbstractMethodError + +from pandas.core.common import AbstractMethodError, _try_sort from pandas.core.generic import _shared_docs, _shared_doc_kwargs from pandas.core.index import Index, MultiIndex from pandas.core.series import Series, remove_na @@ -161,7 +168,7 @@ def _get_standard_colors(num_colors=None, colormap=None, color_type='default', if colormap is not None: warnings.warn("'color' and 'colormap' cannot be used " "simultaneously. Using 'color'") - colors = list(color) if com.is_list_like(color) else color + colors = list(color) if is_list_like(color) else color else: if color_type == 'default': # need to call list() on the result to copy so we don't @@ -336,7 +343,7 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, # no gaps between subplots fig.subplots_adjust(wspace=0, hspace=0) - mask = com.notnull(df) + mask = notnull(df) marker = _get_marker_compat(marker) @@ -980,7 +987,7 @@ def _validate_color_args(self): "simultaneously. Using 'color'") if 'color' in self.kwds and self.style is not None: - if com.is_list_like(self.style): + if is_list_like(self.style): styles = self.style else: styles = [self.style] @@ -1001,7 +1008,7 @@ def _iter_data(self, data=None, keep_index=False, fillna=None): # TODO: unused? # if self.sort_columns: - # columns = com._try_sort(data.columns) + # columns = _try_sort(data.columns) # else: # columns = data.columns @@ -1099,13 +1106,13 @@ def result(self): Return result axes """ if self.subplots: - if self.layout is not None and not com.is_list_like(self.ax): + if self.layout is not None and not is_list_like(self.ax): return self.axes.reshape(*self.layout) else: return self.axes else: sec_true = isinstance(self.secondary_y, bool) and self.secondary_y - all_sec = (com.is_list_like(self.secondary_y) and + all_sec = (is_list_like(self.secondary_y) and len(self.secondary_y) == self.nseries) if (sec_true or all_sec): # if all data is plotted on secondary, return right axes @@ -1322,7 +1329,7 @@ def _get_xticks(self, convert_period=False): @classmethod def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds): - mask = com.isnull(y) + mask = isnull(y) if mask.any(): y = np.ma.array(y) y = np.ma.masked_where(mask, y) @@ -1463,8 +1470,8 @@ def match_labels(data, e): err = np.atleast_2d(evalues) err = np.tile(err, (self.nseries, 1)) - elif com.is_list_like(err): - if com.is_iterator(err): + elif is_list_like(err): + if is_iterator(err): err = np.atleast_2d(list(err)) else: # raw error values @@ -1486,7 +1493,7 @@ def match_labels(data, e): if len(err) == 1: err = np.tile(err, (self.nseries, 1)) - elif com.is_number(err): + elif is_number(err): err = np.tile([err], (self.nseries, len(self.data))) else: @@ -1543,9 +1550,9 @@ def __init__(self, data, x, y, **kwargs): MPLPlot.__init__(self, data, **kwargs) if x is None or y is None: raise ValueError(self._kind + ' requires and x and y column') - if com.is_integer(x) and not self.data.columns.holds_integer(): + if is_integer(x) and not self.data.columns.holds_integer(): x = self.data.columns[x] - if com.is_integer(y) and not self.data.columns.holds_integer(): + if is_integer(y) and not self.data.columns.holds_integer(): y = self.data.columns[y] self.x = x self.y = y @@ -1569,7 +1576,7 @@ def __init__(self, data, x, y, s=None, c=None, **kwargs): # the handling of this argument later s = 20 super(ScatterPlot, self).__init__(data, x, y, s=s, **kwargs) - if com.is_integer(c) and not self.data.columns.holds_integer(): + if is_integer(c) and not self.data.columns.holds_integer(): c = self.data.columns[c] self.c = c @@ -1577,7 +1584,7 @@ def _make_plot(self): x, y, c, data = self.x, self.y, self.c, self.data ax = self.axes[0] - c_is_column = com.is_hashable(c) and c in self.data.columns + c_is_column = is_hashable(c) and c in self.data.columns # plot a colorbar only if a colormap is provided or necessary cb = self.kwds.pop('colorbar', self.colormap or c_is_column) @@ -1629,7 +1636,7 @@ class HexBinPlot(PlanePlot): def __init__(self, data, x, y, C=None, **kwargs): super(HexBinPlot, self).__init__(data, x, y, **kwargs) - if com.is_integer(C) and not self.data.columns.holds_integer(): + if is_integer(C) and not self.data.columns.holds_integer(): C = self.data.columns[C] self.C = C @@ -1912,9 +1919,9 @@ def __init__(self, data, **kwargs): self.ax_pos = self.tick_pos - self.tickoffset def _args_adjust(self): - if com.is_list_like(self.bottom): + if is_list_like(self.bottom): self.bottom = np.array(self.bottom) - if com.is_list_like(self.left): + if is_list_like(self.left): self.left = np.array(self.left) @classmethod @@ -2027,18 +2034,18 @@ def __init__(self, data, bins=10, bottom=0, **kwargs): MPLPlot.__init__(self, data, **kwargs) def _args_adjust(self): - if com.is_integer(self.bins): + if is_integer(self.bins): # create common bin edge values = (self.data._convert(datetime=True)._get_numeric_data()) values = np.ravel(values) - values = values[~com.isnull(values)] + values = values[~isnull(values)] hist, self.bins = np.histogram( values, bins=self.bins, range=self.kwds.get('range', None), weights=self.kwds.get('weights', None)) - if com.is_list_like(self.bottom): + if is_list_like(self.bottom): self.bottom = np.array(self.bottom) @classmethod @@ -2046,7 +2053,7 @@ def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, stacking_id=None, **kwds): if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(bins) - 1) - y = y[~com.isnull(y)] + y = y[~isnull(y)] base = np.zeros(len(bins) - 1) bottom = bottom + \ @@ -2411,7 +2418,7 @@ def _plot(data, x=None, y=None, subplots=False, msg = "{0} requires either y column or 'subplots=True'" raise ValueError(msg.format(kind)) elif y is not None: - if com.is_integer(y) and not data.columns.holds_integer(): + if is_integer(y) and not data.columns.holds_integer(): y = data.columns[y] # converted to series actually. copy to not modify data = data[y].copy() @@ -2420,12 +2427,12 @@ def _plot(data, x=None, y=None, subplots=False, else: if isinstance(data, DataFrame): if x is not None: - if com.is_integer(x) and not data.columns.holds_integer(): + if is_integer(x) and not data.columns.holds_integer(): x = data.columns[x] data = data.set_index(x) if y is not None: - if com.is_integer(y) and not data.columns.holds_integer(): + if is_integer(y) and not data.columns.holds_integer(): y = data.columns[y] label = kwds['label'] if 'label' in kwds else y series = data[y].copy() # Don't modify @@ -2434,7 +2441,7 @@ def _plot(data, x=None, y=None, subplots=False, for kw in ['xerr', 'yerr']: if (kw in kwds) and \ (isinstance(kwds[kw], string_types) or - com.is_integer(kwds[kw])): + is_integer(kwds[kw])): try: kwds[kw] = data[kwds[kw]] except (IndexError, KeyError, TypeError): @@ -2897,7 +2904,7 @@ def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, layout=layout) _axes = _flatten(axes) - for i, col in enumerate(com._try_sort(data.columns)): + for i, col in enumerate(_try_sort(data.columns)): ax = _axes[i] ax.hist(data[col].dropna().values, bins=bins, **kwds) ax.set_title(col) @@ -3345,7 +3352,7 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True, if ax is None: fig = plt.figure(**fig_kw) else: - if com.is_list_like(ax): + if is_list_like(ax): ax = _flatten(ax) if layout is not None: warnings.warn("When passing multiple axes, layout keyword is " @@ -3487,7 +3494,7 @@ def _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey): def _flatten(axes): - if not com.is_list_like(axes): + if not is_list_like(axes): return np.array([axes]) elif isinstance(axes, (np.ndarray, Index)): return axes.ravel() diff --git a/pandas/tools/tests/test_concat.py b/pandas/tools/tests/test_concat.py index a8c86657a48cc..568cf63c02e30 100644 --- a/pandas/tools/tests/test_concat.py +++ b/pandas/tools/tests/test_concat.py @@ -17,7 +17,7 @@ assert_almost_equal) -class TestConcatenate(tm.TestCase): +class ConcatenateBase(tm.TestCase): _multiprocess_can_split_ = True @@ -26,6 +26,9 @@ def setUp(self): self.mixed_frame = self.frame.copy() self.mixed_frame['foo'] = 'bar' + +class TestAppend(ConcatenateBase): + def test_append(self): begin_index = self.frame.index[:5] end_index = self.frame.index[5:] @@ -142,42 +145,32 @@ def test_append_preserve_index_name(self): result = df1.append(df2) self.assertEqual(result.index.name, 'A') - def test_join_many(self): - df = DataFrame(np.random.randn(10, 6), columns=list('abcdef')) - df_list = [df[['a', 'b']], df[['c', 'd']], df[['e', 'f']]] - - joined = df_list[0].join(df_list[1:]) - tm.assert_frame_equal(joined, df) - - df_list = [df[['a', 'b']][:-2], - df[['c', 'd']][2:], df[['e', 'f']][1:9]] - - def _check_diff_index(df_list, result, exp_index): - reindexed = [x.reindex(exp_index) for x in df_list] - expected = reindexed[0].join(reindexed[1:]) - tm.assert_frame_equal(result, expected) - - # different join types - joined = df_list[0].join(df_list[1:], how='outer') - _check_diff_index(df_list, joined, df.index) - - joined = df_list[0].join(df_list[1:]) - _check_diff_index(df_list, joined, df_list[0].index) - - joined = df_list[0].join(df_list[1:], how='inner') - _check_diff_index(df_list, joined, df.index[2:8]) - - self.assertRaises(ValueError, df_list[0].join, df_list[1:], on='a') - - def test_join_many_mixed(self): - df = DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) - df['key'] = ['foo', 'bar'] * 4 - df1 = df.ix[:, ['A', 'B']] - df2 = df.ix[:, ['C', 'D']] - df3 = df.ix[:, ['key']] - - result = df1.join([df2, df3]) - assert_frame_equal(result, df) + def test_append_dtype_coerce(self): + + # GH 4993 + # appending with datetime will incorrectly convert datetime64 + import datetime as dt + from pandas import NaT + + df1 = DataFrame(index=[1, 2], data=[dt.datetime(2013, 1, 1, 0, 0), + dt.datetime(2013, 1, 2, 0, 0)], + columns=['start_time']) + df2 = DataFrame(index=[4, 5], data=[[dt.datetime(2013, 1, 3, 0, 0), + dt.datetime(2013, 1, 3, 6, 10)], + [dt.datetime(2013, 1, 4, 0, 0), + dt.datetime(2013, 1, 4, 7, 10)]], + columns=['start_time', 'end_time']) + + expected = concat([Series([NaT, NaT, dt.datetime(2013, 1, 3, 6, 10), + dt.datetime(2013, 1, 4, 7, 10)], + name='end_time'), + Series([dt.datetime(2013, 1, 1, 0, 0), + dt.datetime(2013, 1, 2, 0, 0), + dt.datetime(2013, 1, 3, 0, 0), + dt.datetime(2013, 1, 4, 0, 0)], + name='start_time')], axis=1) + result = df1.append(df2, ignore_index=True) + assert_frame_equal(result, expected) def test_append_missing_column_proper_upcast(self): df1 = DataFrame({'A': np.array([1, 2, 3, 4], dtype='i8')}) @@ -188,6 +181,9 @@ def test_append_missing_column_proper_upcast(self): self.assertEqual(appended['A'].dtype, 'f8') self.assertEqual(appended['B'].dtype, 'O') + +class TestConcatenate(ConcatenateBase): + def test_concat_copy(self): df = DataFrame(np.random.randn(4, 3)) @@ -524,35 +520,6 @@ def test_with_mixed_tuples(self): # it works concat([df1, df2]) - def test_join_dups(self): - - # joining dups - df = concat([DataFrame(np.random.randn(10, 4), - columns=['A', 'A', 'B', 'B']), - DataFrame(np.random.randint(0, 10, size=20) - .reshape(10, 2), - columns=['A', 'C'])], - axis=1) - - expected = concat([df, df], axis=1) - result = df.join(df, rsuffix='_2') - result.columns = expected.columns - assert_frame_equal(result, expected) - - # GH 4975, invalid join on dups - w = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - x = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - y = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - z = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - - dta = x.merge(y, left_index=True, right_index=True).merge( - z, left_index=True, right_index=True, how="outer") - dta = dta.merge(w, left_index=True, right_index=True) - expected = concat([x, y, z, w], axis=1) - expected.columns = ['x_x', 'y_x', 'x_y', - 'y_y', 'x_x', 'y_x', 'x_y', 'y_y'] - assert_frame_equal(dta, expected) - def test_handle_empty_objects(self): df = DataFrame(np.random.randn(10, 4), columns=list('abcd')) @@ -649,86 +616,40 @@ def test_concat_mixed_objs(self): panel = tm.makePanel() self.assertRaises(ValueError, lambda: concat([panel, s1], axis=1)) - def test_panel_join(self): - panel = tm.makePanel() - tm.add_nans(panel) - - p1 = panel.ix[:2, :10, :3] - p2 = panel.ix[2:, 5:, 2:] - - # left join - result = p1.join(p2) - expected = p1.copy() - expected['ItemC'] = p2['ItemC'] - tm.assert_panel_equal(result, expected) - - # right join - result = p1.join(p2, how='right') - expected = p2.copy() - expected['ItemA'] = p1['ItemA'] - expected['ItemB'] = p1['ItemB'] - expected = expected.reindex(items=['ItemA', 'ItemB', 'ItemC']) - tm.assert_panel_equal(result, expected) - - # inner join - result = p1.join(p2, how='inner') - expected = panel.ix[:, 5:10, 2:3] - tm.assert_panel_equal(result, expected) - - # outer join - result = p1.join(p2, how='outer') - expected = p1.reindex(major=panel.major_axis, - minor=panel.minor_axis) - expected = expected.join(p2.reindex(major=panel.major_axis, - minor=panel.minor_axis)) - tm.assert_panel_equal(result, expected) - - def test_panel_join_overlap(self): - panel = tm.makePanel() - tm.add_nans(panel) - - p1 = panel.ix[['ItemA', 'ItemB', 'ItemC']] - p2 = panel.ix[['ItemB', 'ItemC']] - - # Expected index is - # - # ItemA, ItemB_p1, ItemC_p1, ItemB_p2, ItemC_p2 - joined = p1.join(p2, lsuffix='_p1', rsuffix='_p2') - p1_suf = p1.ix[['ItemB', 'ItemC']].add_suffix('_p1') - p2_suf = p2.ix[['ItemB', 'ItemC']].add_suffix('_p2') - no_overlap = panel.ix[['ItemA']] - expected = no_overlap.join(p1_suf.join(p2_suf)) - tm.assert_panel_equal(joined, expected) - - def test_panel_join_many(self): - tm.K = 10 - panel = tm.makePanel() - tm.K = 4 + def test_empty_dtype_coerce(self): - panels = [panel.ix[:2], panel.ix[2:6], panel.ix[6:]] + # xref to #12411 + # xref to #12045 + # xref to #11594 + # see below - joined = panels[0].join(panels[1:]) - tm.assert_panel_equal(joined, panel) + # 10571 + df1 = DataFrame(data=[[1, None], [2, None]], columns=['a', 'b']) + df2 = DataFrame(data=[[3, None], [4, None]], columns=['a', 'b']) + result = concat([df1, df2]) + expected = df1.dtypes + tm.assert_series_equal(result.dtypes, expected) - panels = [panel.ix[:2, :-5], panel.ix[2:6, 2:], panel.ix[6:, 5:-7]] + def test_dtype_coerceion(self): - data_dict = {} - for p in panels: - data_dict.update(p.iteritems()) + # 12411 + df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), + pd.NaT]}) - joined = panels[0].join(panels[1:], how='inner') - expected = Panel.from_dict(data_dict, intersect=True) - tm.assert_panel_equal(joined, expected) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) - joined = panels[0].join(panels[1:], how='outer') - expected = Panel.from_dict(data_dict, intersect=False) - tm.assert_panel_equal(joined, expected) + # 12045 + import datetime + df = DataFrame({'date': [datetime.datetime(2012, 1, 1), + datetime.datetime(1012, 1, 2)]}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) - # edge cases - self.assertRaises(ValueError, panels[0].join, panels[1:], - how='outer', lsuffix='foo', rsuffix='bar') - self.assertRaises(ValueError, panels[0].join, panels[1:], - how='right') + # 11594 + df = DataFrame({'text': ['some words'] + [None] * 9}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) def test_panel_concat_other_axes(self): panel = tm.makePanel() @@ -1080,6 +1001,239 @@ def test_concat_invalid_first_argument(self): expected = read_csv(StringIO(data)) assert_frame_equal(result, expected) + def test_concat_NaT_series(self): + # GH 11693 + # test for merging NaT series with datetime series. + x = Series(date_range('20151124 08:00', '20151124 09:00', + freq='1h', tz='US/Eastern')) + y = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]') + expected = Series([x[0], x[1], pd.NaT, pd.NaT]) + + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # all NaT with tz + expected = Series(pd.NaT, index=range(4), + dtype='datetime64[ns, US/Eastern]') + result = pd.concat([y, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # without tz + x = pd.Series(pd.date_range('20151124 08:00', + '20151124 09:00', freq='1h')) + y = pd.Series(pd.date_range('20151124 10:00', + '20151124 11:00', freq='1h')) + y[:] = pd.NaT + expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT]) + result = pd.concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # all NaT without tz + x[:] = pd.NaT + expected = pd.Series(pd.NaT, index=range(4), + dtype='datetime64[ns]') + result = pd.concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + def test_concat_tz_frame(self): + df2 = DataFrame(dict(A=pd.Timestamp('20130102', tz='US/Eastern'), + B=pd.Timestamp('20130603', tz='CET')), + index=range(5)) + + # concat + df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1) + assert_frame_equal(df2, df3) + + def test_concat_tz_series(self): + # GH 11755 + # tz and no tz + x = Series(date_range('20151124 08:00', + '20151124 09:00', + freq='1h', tz='UTC')) + y = Series(date_range('2012-01-01', '2012-01-02')) + expected = Series([x[0], x[1], y[0], y[1]], + dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # GH 11887 + # concat tz and object + x = Series(date_range('20151124 08:00', + '20151124 09:00', + freq='1h', tz='UTC')) + y = Series(['a', 'b']) + expected = Series([x[0], x[1], y[0], y[1]], + dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # 12217 + # 12306 fixed I think + + # Concat'ing two UTC times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('UTC') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('UTC') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, UTC]') + + # Concat'ing two London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 2+1 London times + first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 1+2 London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + def test_concat_tz_series_with_datetimelike(self): + # GH 12620 + # tz and timedelta + x = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-02-01', tz='US/Eastern')] + y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + + # tz and period + y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + + def test_concat_tz_series_tzlocal(self): + # GH 13583 + tm._skip_if_no_dateutil() + import dateutil + x = [pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2011-02-01', tz=dateutil.tz.tzlocal())] + y = [pd.Timestamp('2012-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2012-02-01', tz=dateutil.tz.tzlocal())] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y)) + self.assertEqual(result.dtype, 'datetime64[ns, tzlocal()]') + + def test_concat_period_series(self): + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + # different freq + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + # non-period + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(['A', 'B']) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + def test_concat_empty_series(self): + # GH 11082 + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) + tm.assert_frame_equal(res, exp) + + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=0) + # name will be reset + exp = pd.Series([1, 2, 3]) + tm.assert_series_equal(res, exp) + + # empty Series with no name + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name=None) + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, + columns=['x', 0]) + tm.assert_frame_equal(res, exp) + + def test_default_index(self): + # is_series and ignore_index + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series([4, 5, 6], name='y') + res = pd.concat([s1, s2], axis=1, ignore_index=True) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + # use check_index_type=True to check the result have + # RangeIndex (default index) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_series and all inputs have no names + s1 = pd.Series([1, 2, 3]) + s2 = pd.Series([4, 5, 6]) + res = pd.concat([s1, s2], axis=1, ignore_index=False) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + exp.columns = pd.RangeIndex(2) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_dataframe and ignore_index + df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) + df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) + + res = pd.concat([df1, df2], axis=0, ignore_index=True) + exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], + columns=['A', 'B']) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + res = pd.concat([df1, df2], axis=1, ignore_index=True) + exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tools/tests/test_join.py b/pandas/tools/tests/test_join.py new file mode 100644 index 0000000000000..cb84c1f06653b --- /dev/null +++ b/pandas/tools/tests/test_join.py @@ -0,0 +1,804 @@ +# pylint: disable=E1103 + +import nose + +from numpy.random import randn +import numpy as np + +import pandas as pd +from pandas.compat import lrange +import pandas.compat as compat +from pandas.tools.merge import merge, concat +from pandas.util.testing import assert_frame_equal +from pandas import DataFrame, MultiIndex, Series + +import pandas.algos as algos +import pandas.util.testing as tm +from pandas.tools.tests.test_merge import get_test_data, N, NGROUPS + + +a_ = np.array + + +class TestJoin(tm.TestCase): + + _multiprocess_can_split_ = True + + def setUp(self): + # aggregate multiple columns + self.df = DataFrame({'key1': get_test_data(), + 'key2': get_test_data(), + 'data1': np.random.randn(N), + 'data2': np.random.randn(N)}) + + # exclude a couple keys for fun + self.df = self.df[self.df['key2'] > 1] + + self.df2 = DataFrame({'key1': get_test_data(n=N // 5), + 'key2': get_test_data(ngroups=NGROUPS // 2, + n=N // 5), + 'value': np.random.randn(N // 5)}) + + index, data = tm.getMixedTypeDict() + self.target = DataFrame(data, index=index) + + # Join on string value + self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']}, + index=data['C']) + + def test_cython_left_outer_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) + max_group = 5 + + ls, rs = algos.left_outer_join(left, right, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, + 6, 6, 7, 7, 8, 8, 9, 10]) + exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, + 4, 5, 4, 5, 4, 5, -1, -1]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_cython_right_outer_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) + max_group = 5 + + rs, ls = algos.left_outer_join(right, left, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + # 0 1 1 1 + exp_li = a_([0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5, + # 2 2 4 + 6, 7, 8, 6, 7, 8, -1]) + exp_ri = a_([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, + 4, 4, 4, 5, 5, 5, 6]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_cython_inner_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.int64) + max_group = 5 + + ls, rs = algos.inner_join(left, right, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, + 6, 6, 7, 7, 8, 8]) + exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, + 4, 5, 4, 5, 4, 5]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_left_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='left') + + joined_both = merge(self.df, self.df2) + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='left') + + def test_right_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='right') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='right') + + joined_both = merge(self.df, self.df2, how='right') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='right') + + def test_full_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='outer') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='outer') + + joined_both = merge(self.df, self.df2, how='outer') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='outer') + + def test_inner_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='inner') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='inner') + + joined_both = merge(self.df, self.df2, how='inner') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='inner') + + def test_handle_overlap(self): + joined = merge(self.df, self.df2, on='key2', + suffixes=['.foo', '.bar']) + + self.assertIn('key1.foo', joined) + self.assertIn('key1.bar', joined) + + def test_handle_overlap_arbitrary_key(self): + joined = merge(self.df, self.df2, + left_on='key2', right_on='key1', + suffixes=['.foo', '.bar']) + self.assertIn('key1.foo', joined) + self.assertIn('key2.bar', joined) + + def test_join_on(self): + target = self.target + source = self.source + + merged = target.join(source, on='C') + self.assert_series_equal(merged['MergedA'], target['A'], + check_names=False) + self.assert_series_equal(merged['MergedD'], target['D'], + check_names=False) + + # join with duplicates (fix regression from DataFrame/Matrix merge) + df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) + joined = df.join(df2, on='key') + expected = DataFrame({'key': ['a', 'a', 'b', 'b', 'c'], + 'value': [0, 0, 1, 1, 2]}) + assert_frame_equal(joined, expected) + + # Test when some are missing + df_a = DataFrame([[1], [2], [3]], index=['a', 'b', 'c'], + columns=['one']) + df_b = DataFrame([['foo'], ['bar']], index=[1, 2], + columns=['two']) + df_c = DataFrame([[1], [2]], index=[1, 2], + columns=['three']) + joined = df_a.join(df_b, on='one') + joined = joined.join(df_c, on='one') + self.assertTrue(np.isnan(joined['two']['c'])) + self.assertTrue(np.isnan(joined['three']['c'])) + + # merge column not p resent + self.assertRaises(KeyError, target.join, source, on='E') + + # overlap + source_copy = source.copy() + source_copy['A'] = 0 + self.assertRaises(ValueError, target.join, source_copy, on='A') + + def test_join_on_fails_with_different_right_index(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}, + index=tm.makeCustomIndex(10, 2)) + merge(df, df2, left_on='a', right_index=True) + + def test_join_on_fails_with_different_left_index(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}, + index=tm.makeCustomIndex(10, 2)) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}) + merge(df, df2, right_on='b', left_index=True) + + def test_join_on_fails_with_different_column_counts(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}, + index=tm.makeCustomIndex(10, 2)) + merge(df, df2, right_on='a', left_on=['a', 'b']) + + def test_join_on_fails_with_wrong_object_type(self): + # GH12081 + wrongly_typed = [Series([0, 1]), 2, 'str', None, np.array([0, 1])] + df = DataFrame({'a': [1, 1]}) + + for obj in wrongly_typed: + with tm.assertRaisesRegexp(ValueError, str(type(obj))): + merge(obj, df, left_on='a', right_on='a') + with tm.assertRaisesRegexp(ValueError, str(type(obj))): + merge(df, obj, left_on='a', right_on='a') + + def test_join_on_pass_vector(self): + expected = self.target.join(self.source, on='C') + del expected['C'] + + join_col = self.target.pop('C') + result = self.target.join(self.source, on=join_col) + assert_frame_equal(result, expected) + + def test_join_with_len0(self): + # nothing to merge + merged = self.target.join(self.source.reindex([]), on='C') + for col in self.source: + self.assertIn(col, merged) + self.assertTrue(merged[col].isnull().all()) + + merged2 = self.target.join(self.source.reindex([]), on='C', + how='inner') + self.assert_index_equal(merged2.columns, merged.columns) + self.assertEqual(len(merged2), 0) + + def test_join_on_inner(self): + df = DataFrame({'key': ['a', 'a', 'd', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1]}, index=['a', 'b']) + + joined = df.join(df2, on='key', how='inner') + + expected = df.join(df2, on='key') + expected = expected[expected['value'].notnull()] + self.assert_series_equal(joined['key'], expected['key'], + check_dtype=False) + self.assert_series_equal(joined['value'], expected['value'], + check_dtype=False) + self.assert_index_equal(joined.index, expected.index) + + def test_join_on_singlekey_list(self): + df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) + + # corner cases + joined = df.join(df2, on=['key']) + expected = df.join(df2, on='key') + + assert_frame_equal(joined, expected) + + def test_join_on_series(self): + result = self.target.join(self.source['MergedA'], on='C') + expected = self.target.join(self.source[['MergedA']], on='C') + assert_frame_equal(result, expected) + + def test_join_on_series_buglet(self): + # GH #638 + df = DataFrame({'a': [1, 1]}) + ds = Series([2], index=[1], name='b') + result = df.join(ds, on='a') + expected = DataFrame({'a': [1, 1], + 'b': [2, 2]}, index=df.index) + tm.assert_frame_equal(result, expected) + + def test_join_index_mixed(self): + df1 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, + index=np.arange(10), + columns=['A', 'B', 'C', 'D']) + self.assertEqual(df1['B'].dtype, np.int64) + self.assertEqual(df1['D'].dtype, np.bool_) + + df2 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, + index=np.arange(0, 10, 2), + columns=['A', 'B', 'C', 'D']) + + # overlap + joined = df1.join(df2, lsuffix='_one', rsuffix='_two') + expected_columns = ['A_one', 'B_one', 'C_one', 'D_one', + 'A_two', 'B_two', 'C_two', 'D_two'] + df1.columns = expected_columns[:4] + df2.columns = expected_columns[4:] + expected = _join_by_hand(df1, df2) + assert_frame_equal(joined, expected) + + # no overlapping blocks + df1 = DataFrame(index=np.arange(10)) + df1['bool'] = True + df1['string'] = 'foo' + + df2 = DataFrame(index=np.arange(5, 15)) + df2['int'] = 1 + df2['float'] = 1. + + for kind in ['inner', 'outer', 'left', 'right']: + + joined = df1.join(df2, how=kind) + expected = _join_by_hand(df1, df2, how=kind) + assert_frame_equal(joined, expected) + + joined = df2.join(df1, how=kind) + expected = _join_by_hand(df2, df1, how=kind) + assert_frame_equal(joined, expected) + + def test_join_empty_bug(self): + # generated an exception in 0.4.3 + x = DataFrame() + x.join(DataFrame([3], index=[0], columns=['A']), how='outer') + + def test_join_unconsolidated(self): + # GH #331 + a = DataFrame(randn(30, 2), columns=['a', 'b']) + c = Series(randn(30)) + a['c'] = c + d = DataFrame(randn(30, 1), columns=['q']) + + # it works! + a.join(d) + d.join(a) + + def test_join_multiindex(self): + index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'], + [1, 2, 3, 1, 2, 3]], + names=['first', 'second']) + + index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'], + [1, 2, 3, 1, 2, 3]], + names=['first', 'second']) + + df1 = DataFrame(data=np.random.randn(6), index=index1, + columns=['var X']) + df2 = DataFrame(data=np.random.randn(6), index=index2, + columns=['var Y']) + + df1 = df1.sortlevel(0) + df2 = df2.sortlevel(0) + + joined = df1.join(df2, how='outer') + ex_index = index1._tuple_index.union(index2._tuple_index) + expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) + expected.index.names = index1.names + assert_frame_equal(joined, expected) + self.assertEqual(joined.index.names, index1.names) + + df1 = df1.sortlevel(1) + df2 = df2.sortlevel(1) + + joined = df1.join(df2, how='outer').sortlevel(0) + ex_index = index1._tuple_index.union(index2._tuple_index) + expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) + expected.index.names = index1.names + + assert_frame_equal(joined, expected) + self.assertEqual(joined.index.names, index1.names) + + def test_join_inner_multiindex(self): + key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux', + 'qux', 'snap'] + key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two', + 'three', 'one'] + + data = np.random.randn(len(key1)) + data = DataFrame({'key1': key1, 'key2': key2, + 'data': data}) + + index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], + ['one', 'two', 'three']], + labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], + [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=['first', 'second']) + to_join = DataFrame(np.random.randn(10, 3), index=index, + columns=['j_one', 'j_two', 'j_three']) + + joined = data.join(to_join, on=['key1', 'key2'], how='inner') + expected = merge(data, to_join.reset_index(), + left_on=['key1', 'key2'], + right_on=['first', 'second'], how='inner', + sort=False) + + expected2 = merge(to_join, data, + right_on=['key1', 'key2'], left_index=True, + how='inner', sort=False) + assert_frame_equal(joined, expected2.reindex_like(joined)) + + expected2 = merge(to_join, data, right_on=['key1', 'key2'], + left_index=True, how='inner', sort=False) + + expected = expected.drop(['first', 'second'], axis=1) + expected.index = joined.index + + self.assertTrue(joined.index.is_monotonic) + assert_frame_equal(joined, expected) + + # _assert_same_contents(expected, expected2.ix[:, expected.columns]) + + def test_join_hierarchical_mixed(self): + # GH 2024 + df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'c']) + new_df = df.groupby(['a']).agg({'b': [np.mean, np.sum]}) + other_df = DataFrame( + [(1, 2, 3), (7, 10, 6)], columns=['a', 'b', 'd']) + other_df.set_index('a', inplace=True) + # GH 9455, 12219 + with tm.assert_produces_warning(UserWarning): + result = merge(new_df, other_df, left_index=True, right_index=True) + self.assertTrue(('b', 'mean') in result) + self.assertTrue('b' in result) + + def test_join_float64_float32(self): + + a = DataFrame(randn(10, 2), columns=['a', 'b'], dtype=np.float64) + b = DataFrame(randn(10, 1), columns=['c'], dtype=np.float32) + joined = a.join(b) + self.assertEqual(joined.dtypes['a'], 'float64') + self.assertEqual(joined.dtypes['b'], 'float64') + self.assertEqual(joined.dtypes['c'], 'float32') + + a = np.random.randint(0, 5, 100).astype('int64') + b = np.random.random(100).astype('float64') + c = np.random.random(100).astype('float32') + df = DataFrame({'a': a, 'b': b, 'c': c}) + xpdf = DataFrame({'a': a, 'b': b, 'c': c}) + s = DataFrame(np.random.random(5).astype('float32'), columns=['md']) + rs = df.merge(s, left_on='a', right_index=True) + self.assertEqual(rs.dtypes['a'], 'int64') + self.assertEqual(rs.dtypes['b'], 'float64') + self.assertEqual(rs.dtypes['c'], 'float32') + self.assertEqual(rs.dtypes['md'], 'float32') + + xp = xpdf.merge(s, left_on='a', right_index=True) + assert_frame_equal(rs, xp) + + def test_join_many_non_unique_index(self): + df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]}) + df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]}) + df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]}) + idf1 = df1.set_index(["a", "b"]) + idf2 = df2.set_index(["a", "b"]) + idf3 = df3.set_index(["a", "b"]) + + result = idf1.join([idf2, idf3], how='outer') + + df_partially_merged = merge(df1, df2, on=['a', 'b'], how='outer') + expected = merge(df_partially_merged, df3, on=['a', 'b'], how='outer') + + result = result.reset_index() + expected = expected[result.columns] + expected['a'] = expected.a.astype('int64') + expected['b'] = expected.b.astype('int64') + assert_frame_equal(result, expected) + + df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]}) + df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]}) + df3 = DataFrame( + {"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]}) + idf1 = df1.set_index(["a", "b"]) + idf2 = df2.set_index(["a", "b"]) + idf3 = df3.set_index(["a", "b"]) + result = idf1.join([idf2, idf3], how='inner') + + df_partially_merged = merge(df1, df2, on=['a', 'b'], how='inner') + expected = merge(df_partially_merged, df3, on=['a', 'b'], how='inner') + + result = result.reset_index() + + assert_frame_equal(result, expected.ix[:, result.columns]) + + # GH 11519 + df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8), + 'D': np.random.randn(8)}) + s = Series(np.repeat(np.arange(8), 2), + index=np.repeat(np.arange(8), 2), name='TEST') + inner = df.join(s, how='inner') + outer = df.join(s, how='outer') + left = df.join(s, how='left') + right = df.join(s, how='right') + assert_frame_equal(inner, outer) + assert_frame_equal(inner, left) + assert_frame_equal(inner, right) + + def test_join_sort(self): + left = DataFrame({'key': ['foo', 'bar', 'baz', 'foo'], + 'value': [1, 2, 3, 4]}) + right = DataFrame({'value2': ['a', 'b', 'c']}, + index=['bar', 'baz', 'foo']) + + joined = left.join(right, on='key', sort=True) + expected = DataFrame({'key': ['bar', 'baz', 'foo', 'foo'], + 'value': [2, 3, 1, 4], + 'value2': ['a', 'b', 'c', 'c']}, + index=[1, 2, 0, 3]) + assert_frame_equal(joined, expected) + + # smoke test + joined = left.join(right, on='key', sort=False) + self.assert_index_equal(joined.index, pd.Index(lrange(4))) + + def test_join_mixed_non_unique_index(self): + # GH 12814, unorderable types in py3 with a non-unique index + df1 = DataFrame({'a': [1, 2, 3, 4]}, index=[1, 2, 3, 'a']) + df2 = DataFrame({'b': [5, 6, 7, 8]}, index=[1, 3, 3, 4]) + result = df1.join(df2) + expected = DataFrame({'a': [1, 2, 3, 3, 4], + 'b': [5, np.nan, 6, 7, np.nan]}, + index=[1, 2, 3, 3, 'a']) + tm.assert_frame_equal(result, expected) + + df3 = DataFrame({'a': [1, 2, 3, 4]}, index=[1, 2, 2, 'a']) + df4 = DataFrame({'b': [5, 6, 7, 8]}, index=[1, 2, 3, 4]) + result = df3.join(df4) + expected = DataFrame({'a': [1, 2, 3, 4], 'b': [5, 6, 6, np.nan]}, + index=[1, 2, 2, 'a']) + tm.assert_frame_equal(result, expected) + + def test_mixed_type_join_with_suffix(self): + # GH #916 + df = DataFrame(np.random.randn(20, 6), + columns=['a', 'b', 'c', 'd', 'e', 'f']) + df.insert(0, 'id', 0) + df.insert(5, 'dt', 'foo') + + grouped = df.groupby('id') + mn = grouped.mean() + cn = grouped.count() + + # it works! + mn.join(cn, rsuffix='_right') + + def test_join_many(self): + df = DataFrame(np.random.randn(10, 6), columns=list('abcdef')) + df_list = [df[['a', 'b']], df[['c', 'd']], df[['e', 'f']]] + + joined = df_list[0].join(df_list[1:]) + tm.assert_frame_equal(joined, df) + + df_list = [df[['a', 'b']][:-2], + df[['c', 'd']][2:], df[['e', 'f']][1:9]] + + def _check_diff_index(df_list, result, exp_index): + reindexed = [x.reindex(exp_index) for x in df_list] + expected = reindexed[0].join(reindexed[1:]) + tm.assert_frame_equal(result, expected) + + # different join types + joined = df_list[0].join(df_list[1:], how='outer') + _check_diff_index(df_list, joined, df.index) + + joined = df_list[0].join(df_list[1:]) + _check_diff_index(df_list, joined, df_list[0].index) + + joined = df_list[0].join(df_list[1:], how='inner') + _check_diff_index(df_list, joined, df.index[2:8]) + + self.assertRaises(ValueError, df_list[0].join, df_list[1:], on='a') + + def test_join_many_mixed(self): + df = DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) + df['key'] = ['foo', 'bar'] * 4 + df1 = df.ix[:, ['A', 'B']] + df2 = df.ix[:, ['C', 'D']] + df3 = df.ix[:, ['key']] + + result = df1.join([df2, df3]) + assert_frame_equal(result, df) + + def test_join_dups(self): + + # joining dups + df = concat([DataFrame(np.random.randn(10, 4), + columns=['A', 'A', 'B', 'B']), + DataFrame(np.random.randint(0, 10, size=20) + .reshape(10, 2), + columns=['A', 'C'])], + axis=1) + + expected = concat([df, df], axis=1) + result = df.join(df, rsuffix='_2') + result.columns = expected.columns + assert_frame_equal(result, expected) + + # GH 4975, invalid join on dups + w = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + x = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + y = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + z = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + + dta = x.merge(y, left_index=True, right_index=True).merge( + z, left_index=True, right_index=True, how="outer") + dta = dta.merge(w, left_index=True, right_index=True) + expected = concat([x, y, z, w], axis=1) + expected.columns = ['x_x', 'y_x', 'x_y', + 'y_y', 'x_x', 'y_x', 'x_y', 'y_y'] + assert_frame_equal(dta, expected) + + def test_panel_join(self): + panel = tm.makePanel() + tm.add_nans(panel) + + p1 = panel.ix[:2, :10, :3] + p2 = panel.ix[2:, 5:, 2:] + + # left join + result = p1.join(p2) + expected = p1.copy() + expected['ItemC'] = p2['ItemC'] + tm.assert_panel_equal(result, expected) + + # right join + result = p1.join(p2, how='right') + expected = p2.copy() + expected['ItemA'] = p1['ItemA'] + expected['ItemB'] = p1['ItemB'] + expected = expected.reindex(items=['ItemA', 'ItemB', 'ItemC']) + tm.assert_panel_equal(result, expected) + + # inner join + result = p1.join(p2, how='inner') + expected = panel.ix[:, 5:10, 2:3] + tm.assert_panel_equal(result, expected) + + # outer join + result = p1.join(p2, how='outer') + expected = p1.reindex(major=panel.major_axis, + minor=panel.minor_axis) + expected = expected.join(p2.reindex(major=panel.major_axis, + minor=panel.minor_axis)) + tm.assert_panel_equal(result, expected) + + def test_panel_join_overlap(self): + panel = tm.makePanel() + tm.add_nans(panel) + + p1 = panel.ix[['ItemA', 'ItemB', 'ItemC']] + p2 = panel.ix[['ItemB', 'ItemC']] + + # Expected index is + # + # ItemA, ItemB_p1, ItemC_p1, ItemB_p2, ItemC_p2 + joined = p1.join(p2, lsuffix='_p1', rsuffix='_p2') + p1_suf = p1.ix[['ItemB', 'ItemC']].add_suffix('_p1') + p2_suf = p2.ix[['ItemB', 'ItemC']].add_suffix('_p2') + no_overlap = panel.ix[['ItemA']] + expected = no_overlap.join(p1_suf.join(p2_suf)) + tm.assert_panel_equal(joined, expected) + + def test_panel_join_many(self): + tm.K = 10 + panel = tm.makePanel() + tm.K = 4 + + panels = [panel.ix[:2], panel.ix[2:6], panel.ix[6:]] + + joined = panels[0].join(panels[1:]) + tm.assert_panel_equal(joined, panel) + + panels = [panel.ix[:2, :-5], panel.ix[2:6, 2:], panel.ix[6:, 5:-7]] + + data_dict = {} + for p in panels: + data_dict.update(p.iteritems()) + + joined = panels[0].join(panels[1:], how='inner') + expected = pd.Panel.from_dict(data_dict, intersect=True) + tm.assert_panel_equal(joined, expected) + + joined = panels[0].join(panels[1:], how='outer') + expected = pd.Panel.from_dict(data_dict, intersect=False) + tm.assert_panel_equal(joined, expected) + + # edge cases + self.assertRaises(ValueError, panels[0].join, panels[1:], + how='outer', lsuffix='foo', rsuffix='bar') + self.assertRaises(ValueError, panels[0].join, panels[1:], + how='right') + + +def _check_join(left, right, result, join_col, how='left', + lsuffix='_x', rsuffix='_y'): + + # some smoke tests + for c in join_col: + assert(result[c].notnull().all()) + + left_grouped = left.groupby(join_col) + right_grouped = right.groupby(join_col) + + for group_key, group in result.groupby(join_col): + l_joined = _restrict_to_columns(group, left.columns, lsuffix) + r_joined = _restrict_to_columns(group, right.columns, rsuffix) + + try: + lgroup = left_grouped.get_group(group_key) + except KeyError: + if how in ('left', 'inner'): + raise AssertionError('key %s should not have been in the join' + % str(group_key)) + + _assert_all_na(l_joined, left.columns, join_col) + else: + _assert_same_contents(l_joined, lgroup) + + try: + rgroup = right_grouped.get_group(group_key) + except KeyError: + if how in ('right', 'inner'): + raise AssertionError('key %s should not have been in the join' + % str(group_key)) + + _assert_all_na(r_joined, right.columns, join_col) + else: + _assert_same_contents(r_joined, rgroup) + + +def _restrict_to_columns(group, columns, suffix): + found = [c for c in group.columns + if c in columns or c.replace(suffix, '') in columns] + + # filter + group = group.ix[:, found] + + # get rid of suffixes, if any + group = group.rename(columns=lambda x: x.replace(suffix, '')) + + # put in the right order... + group = group.ix[:, columns] + + return group + + +def _assert_same_contents(join_chunk, source): + NA_SENTINEL = -1234567 # drop_duplicates not so NA-friendly... + + jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values + svalues = source.fillna(NA_SENTINEL).drop_duplicates().values + + rows = set(tuple(row) for row in jvalues) + assert(len(rows) == len(source)) + assert(all(tuple(row) in rows for row in svalues)) + + +def _assert_all_na(join_chunk, source_columns, join_col): + for c in source_columns: + if c in join_col: + continue + assert(join_chunk[c].isnull().all()) + + +def _join_by_hand(a, b, how='left'): + join_index = a.index.join(b.index, how=how) + + a_re = a.reindex(join_index) + b_re = b.reindex(join_index) + + result_columns = a.columns.append(b.columns) + + for col, s in compat.iteritems(b_re): + a_re[col] = s + return a_re.reindex(columns=result_columns) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 6c448de741e0c..396b095fabbd6 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -9,23 +9,17 @@ import random import pandas as pd -from pandas.compat import range, lrange, lzip +from pandas.compat import lrange, lzip from pandas.tools.merge import merge, concat, MergeError from pandas.util.testing import (assert_frame_equal, assert_series_equal, slow) -from pandas import (DataFrame, Index, MultiIndex, - Series, date_range, Categorical, - compat) -import pandas.algos as algos +from pandas import DataFrame, Index, MultiIndex, Series, Categorical import pandas.util.testing as tm -a_ = np.array - N = 50 NGROUPS = 8 -JOIN_TYPES = ['inner', 'outer', 'left', 'right'] def get_test_data(ngroups=NGROUPS, n=N): @@ -58,496 +52,16 @@ def setUp(self): n=N // 5), 'value': np.random.randn(N // 5)}) - index, data = tm.getMixedTypeDict() - self.target = DataFrame(data, index=index) - - # Join on string value - self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']}, - index=data['C']) - self.left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'], 'v1': np.random.randn(7)}) self.right = DataFrame({'v2': np.random.randn(4)}, index=['d', 'b', 'c', 'a']) - def test_cython_left_outer_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) - max_group = 5 - - ls, rs = algos.left_outer_join(left, right, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, - 6, 6, 7, 7, 8, 8, 9, 10]) - exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, - 4, 5, 4, 5, 4, 5, -1, -1]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) - self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) - - def test_cython_right_outer_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) - max_group = 5 - - rs, ls = algos.left_outer_join(right, left, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - # 0 1 1 1 - exp_li = a_([0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5, - # 2 2 4 - 6, 7, 8, 6, 7, 8, -1]) - exp_ri = a_([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, - 4, 4, 4, 5, 5, 5, 6]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) - self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) - - def test_cython_inner_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.int64) - max_group = 5 - - ls, rs = algos.inner_join(left, right, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, - 6, 6, 7, 7, 8, 8]) - exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, - 4, 5, 4, 5, 4, 5]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) - self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) - - def test_left_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='left') - - joined_both = merge(self.df, self.df2) - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='left') - - def test_right_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='right') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='right') - - joined_both = merge(self.df, self.df2, how='right') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='right') - - def test_full_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='outer') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='outer') - - joined_both = merge(self.df, self.df2, how='outer') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='outer') - - def test_inner_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='inner') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='inner') - - joined_both = merge(self.df, self.df2, how='inner') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='inner') - - def test_handle_overlap(self): - joined = merge(self.df, self.df2, on='key2', - suffixes=['.foo', '.bar']) - - self.assertIn('key1.foo', joined) - self.assertIn('key1.bar', joined) - - def test_handle_overlap_arbitrary_key(self): - joined = merge(self.df, self.df2, - left_on='key2', right_on='key1', - suffixes=['.foo', '.bar']) - self.assertIn('key1.foo', joined) - self.assertIn('key2.bar', joined) - def test_merge_common(self): joined = merge(self.df, self.df2) exp = merge(self.df, self.df2, on=['key1', 'key2']) tm.assert_frame_equal(joined, exp) - def test_join_on(self): - target = self.target - source = self.source - - merged = target.join(source, on='C') - self.assert_series_equal(merged['MergedA'], target['A'], - check_names=False) - self.assert_series_equal(merged['MergedD'], target['D'], - check_names=False) - - # join with duplicates (fix regression from DataFrame/Matrix merge) - df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) - joined = df.join(df2, on='key') - expected = DataFrame({'key': ['a', 'a', 'b', 'b', 'c'], - 'value': [0, 0, 1, 1, 2]}) - assert_frame_equal(joined, expected) - - # Test when some are missing - df_a = DataFrame([[1], [2], [3]], index=['a', 'b', 'c'], - columns=['one']) - df_b = DataFrame([['foo'], ['bar']], index=[1, 2], - columns=['two']) - df_c = DataFrame([[1], [2]], index=[1, 2], - columns=['three']) - joined = df_a.join(df_b, on='one') - joined = joined.join(df_c, on='one') - self.assertTrue(np.isnan(joined['two']['c'])) - self.assertTrue(np.isnan(joined['three']['c'])) - - # merge column not p resent - self.assertRaises(KeyError, target.join, source, on='E') - - # overlap - source_copy = source.copy() - source_copy['A'] = 0 - self.assertRaises(ValueError, target.join, source_copy, on='A') - - def test_join_on_fails_with_different_right_index(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}, - index=tm.makeCustomIndex(10, 2)) - merge(df, df2, left_on='a', right_index=True) - - def test_join_on_fails_with_different_left_index(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}, - index=tm.makeCustomIndex(10, 2)) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}) - merge(df, df2, right_on='b', left_index=True) - - def test_join_on_fails_with_different_column_counts(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}, - index=tm.makeCustomIndex(10, 2)) - merge(df, df2, right_on='a', left_on=['a', 'b']) - - def test_join_on_fails_with_wrong_object_type(self): - # GH12081 - wrongly_typed = [Series([0, 1]), 2, 'str', None, np.array([0, 1])] - df = DataFrame({'a': [1, 1]}) - - for obj in wrongly_typed: - with tm.assertRaisesRegexp(ValueError, str(type(obj))): - merge(obj, df, left_on='a', right_on='a') - with tm.assertRaisesRegexp(ValueError, str(type(obj))): - merge(df, obj, left_on='a', right_on='a') - - def test_join_on_pass_vector(self): - expected = self.target.join(self.source, on='C') - del expected['C'] - - join_col = self.target.pop('C') - result = self.target.join(self.source, on=join_col) - assert_frame_equal(result, expected) - - def test_join_with_len0(self): - # nothing to merge - merged = self.target.join(self.source.reindex([]), on='C') - for col in self.source: - self.assertIn(col, merged) - self.assertTrue(merged[col].isnull().all()) - - merged2 = self.target.join(self.source.reindex([]), on='C', - how='inner') - self.assert_index_equal(merged2.columns, merged.columns) - self.assertEqual(len(merged2), 0) - - def test_join_on_inner(self): - df = DataFrame({'key': ['a', 'a', 'd', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1]}, index=['a', 'b']) - - joined = df.join(df2, on='key', how='inner') - - expected = df.join(df2, on='key') - expected = expected[expected['value'].notnull()] - self.assert_series_equal(joined['key'], expected['key'], - check_dtype=False) - self.assert_series_equal(joined['value'], expected['value'], - check_dtype=False) - self.assert_index_equal(joined.index, expected.index) - - def test_join_on_singlekey_list(self): - df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) - - # corner cases - joined = df.join(df2, on=['key']) - expected = df.join(df2, on='key') - - assert_frame_equal(joined, expected) - - def test_join_on_series(self): - result = self.target.join(self.source['MergedA'], on='C') - expected = self.target.join(self.source[['MergedA']], on='C') - assert_frame_equal(result, expected) - - def test_join_on_series_buglet(self): - # GH #638 - df = DataFrame({'a': [1, 1]}) - ds = Series([2], index=[1], name='b') - result = df.join(ds, on='a') - expected = DataFrame({'a': [1, 1], - 'b': [2, 2]}, index=df.index) - tm.assert_frame_equal(result, expected) - - def test_join_index_mixed(self): - df1 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, - index=np.arange(10), - columns=['A', 'B', 'C', 'D']) - self.assertEqual(df1['B'].dtype, np.int64) - self.assertEqual(df1['D'].dtype, np.bool_) - - df2 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, - index=np.arange(0, 10, 2), - columns=['A', 'B', 'C', 'D']) - - # overlap - joined = df1.join(df2, lsuffix='_one', rsuffix='_two') - expected_columns = ['A_one', 'B_one', 'C_one', 'D_one', - 'A_two', 'B_two', 'C_two', 'D_two'] - df1.columns = expected_columns[:4] - df2.columns = expected_columns[4:] - expected = _join_by_hand(df1, df2) - assert_frame_equal(joined, expected) - - # no overlapping blocks - df1 = DataFrame(index=np.arange(10)) - df1['bool'] = True - df1['string'] = 'foo' - - df2 = DataFrame(index=np.arange(5, 15)) - df2['int'] = 1 - df2['float'] = 1. - - for kind in JOIN_TYPES: - - joined = df1.join(df2, how=kind) - expected = _join_by_hand(df1, df2, how=kind) - assert_frame_equal(joined, expected) - - joined = df2.join(df1, how=kind) - expected = _join_by_hand(df2, df1, how=kind) - assert_frame_equal(joined, expected) - - def test_join_empty_bug(self): - # generated an exception in 0.4.3 - x = DataFrame() - x.join(DataFrame([3], index=[0], columns=['A']), how='outer') - - def test_join_unconsolidated(self): - # GH #331 - a = DataFrame(randn(30, 2), columns=['a', 'b']) - c = Series(randn(30)) - a['c'] = c - d = DataFrame(randn(30, 1), columns=['q']) - - # it works! - a.join(d) - d.join(a) - - def test_join_multiindex(self): - index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'], - [1, 2, 3, 1, 2, 3]], - names=['first', 'second']) - - index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'], - [1, 2, 3, 1, 2, 3]], - names=['first', 'second']) - - df1 = DataFrame(data=np.random.randn(6), index=index1, - columns=['var X']) - df2 = DataFrame(data=np.random.randn(6), index=index2, - columns=['var Y']) - - df1 = df1.sortlevel(0) - df2 = df2.sortlevel(0) - - joined = df1.join(df2, how='outer') - ex_index = index1._tuple_index.union(index2._tuple_index) - expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) - expected.index.names = index1.names - assert_frame_equal(joined, expected) - self.assertEqual(joined.index.names, index1.names) - - df1 = df1.sortlevel(1) - df2 = df2.sortlevel(1) - - joined = df1.join(df2, how='outer').sortlevel(0) - ex_index = index1._tuple_index.union(index2._tuple_index) - expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) - expected.index.names = index1.names - - assert_frame_equal(joined, expected) - self.assertEqual(joined.index.names, index1.names) - - def test_join_inner_multiindex(self): - key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux', - 'qux', 'snap'] - key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two', - 'three', 'one'] - - data = np.random.randn(len(key1)) - data = DataFrame({'key1': key1, 'key2': key2, - 'data': data}) - - index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], - ['one', 'two', 'three']], - labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], - [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], - names=['first', 'second']) - to_join = DataFrame(np.random.randn(10, 3), index=index, - columns=['j_one', 'j_two', 'j_three']) - - joined = data.join(to_join, on=['key1', 'key2'], how='inner') - expected = merge(data, to_join.reset_index(), - left_on=['key1', 'key2'], - right_on=['first', 'second'], how='inner', - sort=False) - - expected2 = merge(to_join, data, - right_on=['key1', 'key2'], left_index=True, - how='inner', sort=False) - assert_frame_equal(joined, expected2.reindex_like(joined)) - - expected2 = merge(to_join, data, right_on=['key1', 'key2'], - left_index=True, how='inner', sort=False) - - expected = expected.drop(['first', 'second'], axis=1) - expected.index = joined.index - - self.assertTrue(joined.index.is_monotonic) - assert_frame_equal(joined, expected) - - # _assert_same_contents(expected, expected2.ix[:, expected.columns]) - - def test_join_hierarchical_mixed(self): - # GH 2024 - df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'c']) - new_df = df.groupby(['a']).agg({'b': [np.mean, np.sum]}) - other_df = DataFrame( - [(1, 2, 3), (7, 10, 6)], columns=['a', 'b', 'd']) - other_df.set_index('a', inplace=True) - # GH 9455, 12219 - with tm.assert_produces_warning(UserWarning): - result = merge(new_df, other_df, left_index=True, right_index=True) - self.assertTrue(('b', 'mean') in result) - self.assertTrue('b' in result) - - def test_join_float64_float32(self): - - a = DataFrame(randn(10, 2), columns=['a', 'b'], dtype=np.float64) - b = DataFrame(randn(10, 1), columns=['c'], dtype=np.float32) - joined = a.join(b) - self.assertEqual(joined.dtypes['a'], 'float64') - self.assertEqual(joined.dtypes['b'], 'float64') - self.assertEqual(joined.dtypes['c'], 'float32') - - a = np.random.randint(0, 5, 100).astype('int64') - b = np.random.random(100).astype('float64') - c = np.random.random(100).astype('float32') - df = DataFrame({'a': a, 'b': b, 'c': c}) - xpdf = DataFrame({'a': a, 'b': b, 'c': c}) - s = DataFrame(np.random.random(5).astype('float32'), columns=['md']) - rs = df.merge(s, left_on='a', right_index=True) - self.assertEqual(rs.dtypes['a'], 'int64') - self.assertEqual(rs.dtypes['b'], 'float64') - self.assertEqual(rs.dtypes['c'], 'float32') - self.assertEqual(rs.dtypes['md'], 'float32') - - xp = xpdf.merge(s, left_on='a', right_index=True) - assert_frame_equal(rs, xp) - - def test_join_many_non_unique_index(self): - df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]}) - df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]}) - df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]}) - idf1 = df1.set_index(["a", "b"]) - idf2 = df2.set_index(["a", "b"]) - idf3 = df3.set_index(["a", "b"]) - - result = idf1.join([idf2, idf3], how='outer') - - df_partially_merged = merge(df1, df2, on=['a', 'b'], how='outer') - expected = merge(df_partially_merged, df3, on=['a', 'b'], how='outer') - - result = result.reset_index() - expected = expected[result.columns] - expected['a'] = expected.a.astype('int64') - expected['b'] = expected.b.astype('int64') - assert_frame_equal(result, expected) - - df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]}) - df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]}) - df3 = DataFrame( - {"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]}) - idf1 = df1.set_index(["a", "b"]) - idf2 = df2.set_index(["a", "b"]) - idf3 = df3.set_index(["a", "b"]) - result = idf1.join([idf2, idf3], how='inner') - - df_partially_merged = merge(df1, df2, on=['a', 'b'], how='inner') - expected = merge(df_partially_merged, df3, on=['a', 'b'], how='inner') - - result = result.reset_index() - - assert_frame_equal(result, expected.ix[:, result.columns]) - - # GH 11519 - df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo'], - 'B': ['one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three'], - 'C': np.random.randn(8), - 'D': np.random.randn(8)}) - s = Series(np.repeat(np.arange(8), 2), - index=np.repeat(np.arange(8), 2), name='TEST') - inner = df.join(s, how='inner') - outer = df.join(s, how='outer') - left = df.join(s, how='left') - right = df.join(s, how='right') - assert_frame_equal(inner, outer) - assert_frame_equal(inner, left) - assert_frame_equal(inner, right) - def test_merge_index_singlekey_right_vs_left(self): left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'], 'v1': np.random.randn(7)}) @@ -651,23 +165,6 @@ def test_merge_nocopy(self): merged['d'] = 'peekaboo' self.assertTrue((right['d'] == 'peekaboo').all()) - def test_join_sort(self): - left = DataFrame({'key': ['foo', 'bar', 'baz', 'foo'], - 'value': [1, 2, 3, 4]}) - right = DataFrame({'value2': ['a', 'b', 'c']}, - index=['bar', 'baz', 'foo']) - - joined = left.join(right, on='key', sort=True) - expected = DataFrame({'key': ['bar', 'baz', 'foo', 'foo'], - 'value': [2, 3, 1, 4], - 'value2': ['a', 'b', 'c', 'c']}, - index=[1, 2, 0, 3]) - assert_frame_equal(joined, expected) - - # smoke test - joined = left.join(right, on='key', sort=False) - self.assert_index_equal(joined.index, pd.Index(lrange(4))) - def test_intelligently_handle_join_key(self): # #733, be a bit more 1337 about not returning unconsolidated DataFrame @@ -737,20 +234,6 @@ def test_handle_join_key_pass_array(self): merged = merge(left, right, left_index=True, right_on=key, how='outer') self.assert_series_equal(merged['key_0'], Series(key, name='key_0')) - def test_mixed_type_join_with_suffix(self): - # GH #916 - df = DataFrame(np.random.randn(20, 6), - columns=['a', 'b', 'c', 'd', 'e', 'f']) - df.insert(0, 'id', 0) - df.insert(5, 'dt', 'foo') - - grouped = df.groupby('id') - mn = grouped.mean() - cn = grouped.count() - - # it works! - mn.join(cn, rsuffix='_right') - def test_no_overlap_more_informative_error(self): dt = datetime.now() df1 = DataFrame({'x': ['a']}, index=[dt]) @@ -963,68 +446,6 @@ def _constructor(self): tm.assertIsInstance(result, NotADataFrame) - def test_empty_dtype_coerce(self): - - # xref to #12411 - # xref to #12045 - # xref to #11594 - # see below - - # 10571 - df1 = DataFrame(data=[[1, None], [2, None]], columns=['a', 'b']) - df2 = DataFrame(data=[[3, None], [4, None]], columns=['a', 'b']) - result = concat([df1, df2]) - expected = df1.dtypes - assert_series_equal(result.dtypes, expected) - - def test_dtype_coerceion(self): - - # 12411 - df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), - pd.NaT]}) - - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - # 12045 - import datetime - df = DataFrame({'date': [datetime.datetime(2012, 1, 1), - datetime.datetime(1012, 1, 2)]}) - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - # 11594 - df = DataFrame({'text': ['some words'] + [None] * 9}) - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - def test_append_dtype_coerce(self): - - # GH 4993 - # appending with datetime will incorrectly convert datetime64 - import datetime as dt - from pandas import NaT - - df1 = DataFrame(index=[1, 2], data=[dt.datetime(2013, 1, 1, 0, 0), - dt.datetime(2013, 1, 2, 0, 0)], - columns=['start_time']) - df2 = DataFrame(index=[4, 5], data=[[dt.datetime(2013, 1, 3, 0, 0), - dt.datetime(2013, 1, 3, 6, 10)], - [dt.datetime(2013, 1, 4, 0, 0), - dt.datetime(2013, 1, 4, 7, 10)]], - columns=['start_time', 'end_time']) - - expected = concat([Series([NaT, NaT, dt.datetime(2013, 1, 3, 6, 10), - dt.datetime(2013, 1, 4, 7, 10)], - name='end_time'), - Series([dt.datetime(2013, 1, 1, 0, 0), - dt.datetime(2013, 1, 2, 0, 0), - dt.datetime(2013, 1, 3, 0, 0), - dt.datetime(2013, 1, 4, 0, 0)], - name='start_time')], axis=1) - result = df1.append(df2, ignore_index=True) - assert_frame_equal(result, expected) - def test_join_append_timedeltas(self): import datetime as dt @@ -1140,239 +561,6 @@ def test_merge_on_periods(self): self.assertEqual(result['value_x'].dtype, 'object') self.assertEqual(result['value_y'].dtype, 'object') - def test_concat_NaT_series(self): - # GH 11693 - # test for merging NaT series with datetime series. - x = Series(date_range('20151124 08:00', '20151124 09:00', - freq='1h', tz='US/Eastern')) - y = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]') - expected = Series([x[0], x[1], pd.NaT, pd.NaT]) - - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # all NaT with tz - expected = Series(pd.NaT, index=range(4), - dtype='datetime64[ns, US/Eastern]') - result = pd.concat([y, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # without tz - x = pd.Series(pd.date_range('20151124 08:00', - '20151124 09:00', freq='1h')) - y = pd.Series(pd.date_range('20151124 10:00', - '20151124 11:00', freq='1h')) - y[:] = pd.NaT - expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT]) - result = pd.concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # all NaT without tz - x[:] = pd.NaT - expected = pd.Series(pd.NaT, index=range(4), - dtype='datetime64[ns]') - result = pd.concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - def test_concat_tz_frame(self): - df2 = DataFrame(dict(A=pd.Timestamp('20130102', tz='US/Eastern'), - B=pd.Timestamp('20130603', tz='CET')), - index=range(5)) - - # concat - df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1) - assert_frame_equal(df2, df3) - - def test_concat_tz_series(self): - # GH 11755 - # tz and no tz - x = Series(date_range('20151124 08:00', - '20151124 09:00', - freq='1h', tz='UTC')) - y = Series(date_range('2012-01-01', '2012-01-02')) - expected = Series([x[0], x[1], y[0], y[1]], - dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # GH 11887 - # concat tz and object - x = Series(date_range('20151124 08:00', - '20151124 09:00', - freq='1h', tz='UTC')) - y = Series(['a', 'b']) - expected = Series([x[0], x[1], y[0], y[1]], - dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # 12217 - # 12306 fixed I think - - # Concat'ing two UTC times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('UTC') - - second = pd.DataFrame([[datetime(2016, 1, 2)]]) - second[0] = second[0].dt.tz_localize('UTC') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, UTC]') - - # Concat'ing two London times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 2)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - # Concat'ing 2+1 London times - first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 3)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - # Concat'ing 1+2 London times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - def test_concat_tz_series_with_datetimelike(self): - # GH 12620 - # tz and timedelta - x = [pd.Timestamp('2011-01-01', tz='US/Eastern'), - pd.Timestamp('2011-02-01', tz='US/Eastern')] - y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) - - # tz and period - y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) - - def test_concat_tz_series_tzlocal(self): - # GH 13583 - tm._skip_if_no_dateutil() - import dateutil - x = [pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()), - pd.Timestamp('2011-02-01', tz=dateutil.tz.tzlocal())] - y = [pd.Timestamp('2012-01-01', tz=dateutil.tz.tzlocal()), - pd.Timestamp('2012-02-01', tz=dateutil.tz.tzlocal())] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y)) - self.assertEqual(result.dtype, 'datetime64[ns, tzlocal()]') - - def test_concat_period_series(self): - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - # different freq - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - # non-period - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(['A', 'B']) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - def test_concat_empty_series(self): - # GH 11082 - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name='y') - res = pd.concat([s1, s2], axis=1) - exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) - tm.assert_frame_equal(res, exp) - - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name='y') - res = pd.concat([s1, s2], axis=0) - # name will be reset - exp = pd.Series([1, 2, 3]) - tm.assert_series_equal(res, exp) - - # empty Series with no name - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name=None) - res = pd.concat([s1, s2], axis=1) - exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, - columns=['x', 0]) - tm.assert_frame_equal(res, exp) - - def test_default_index(self): - # is_series and ignore_index - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series([4, 5, 6], name='y') - res = pd.concat([s1, s2], axis=1, ignore_index=True) - self.assertIsInstance(res.columns, pd.RangeIndex) - exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) - # use check_index_type=True to check the result have - # RangeIndex (default index) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - # is_series and all inputs have no names - s1 = pd.Series([1, 2, 3]) - s2 = pd.Series([4, 5, 6]) - res = pd.concat([s1, s2], axis=1, ignore_index=False) - self.assertIsInstance(res.columns, pd.RangeIndex) - exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) - exp.columns = pd.RangeIndex(2) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - # is_dataframe and ignore_index - df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) - df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) - - res = pd.concat([df1, df2], axis=0, ignore_index=True) - exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], - columns=['A', 'B']) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - res = pd.concat([df1, df2], axis=1, ignore_index=True) - exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [ @@ -2134,90 +1322,6 @@ def f(): self.assertRaises(NotImplementedError, f) -def _check_join(left, right, result, join_col, how='left', - lsuffix='_x', rsuffix='_y'): - - # some smoke tests - for c in join_col: - assert(result[c].notnull().all()) - - left_grouped = left.groupby(join_col) - right_grouped = right.groupby(join_col) - - for group_key, group in result.groupby(join_col): - l_joined = _restrict_to_columns(group, left.columns, lsuffix) - r_joined = _restrict_to_columns(group, right.columns, rsuffix) - - try: - lgroup = left_grouped.get_group(group_key) - except KeyError: - if how in ('left', 'inner'): - raise AssertionError('key %s should not have been in the join' - % str(group_key)) - - _assert_all_na(l_joined, left.columns, join_col) - else: - _assert_same_contents(l_joined, lgroup) - - try: - rgroup = right_grouped.get_group(group_key) - except KeyError: - if how in ('right', 'inner'): - raise AssertionError('key %s should not have been in the join' - % str(group_key)) - - _assert_all_na(r_joined, right.columns, join_col) - else: - _assert_same_contents(r_joined, rgroup) - - -def _restrict_to_columns(group, columns, suffix): - found = [c for c in group.columns - if c in columns or c.replace(suffix, '') in columns] - - # filter - group = group.ix[:, found] - - # get rid of suffixes, if any - group = group.rename(columns=lambda x: x.replace(suffix, '')) - - # put in the right order... - group = group.ix[:, columns] - - return group - - -def _assert_same_contents(join_chunk, source): - NA_SENTINEL = -1234567 # drop_duplicates not so NA-friendly... - - jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values - svalues = source.fillna(NA_SENTINEL).drop_duplicates().values - - rows = set(tuple(row) for row in jvalues) - assert(len(rows) == len(source)) - assert(all(tuple(row) in rows for row in svalues)) - - -def _assert_all_na(join_chunk, source_columns, join_col): - for c in source_columns: - if c in join_col: - continue - assert(join_chunk[c].isnull().all()) - - -def _join_by_hand(a, b, how='left'): - join_index = a.index.join(b.index, how=how) - - a_re = a.reindex(join_index) - b_re = b.reindex(join_index) - - result_columns = a.columns.append(b.columns) - - for col, s in compat.iteritems(b_re): - a_re[col] = s - return a_re.reindex(columns=result_columns) - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tools/tests/test_merge_asof.py b/pandas/tools/tests/test_merge_asof.py index 5d78ccf199ed3..bcbb0f0fadb49 100644 --- a/pandas/tools/tests/test_merge_asof.py +++ b/pandas/tools/tests/test_merge_asof.py @@ -347,6 +347,39 @@ def test_allow_exact_matches_and_tolerance(self): expected = self.allow_exact_matches_and_tolerance assert_frame_equal(result, expected) + def test_allow_exact_matches_and_tolerance2(self): + # GH 13695 + df1 = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob']}) + df2 = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.000', + '2016-07-15 13:30:00.030']), + 'version': [1, 2]}) + + result = pd.merge_asof(df1, df2, on='time') + expected = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob'], + 'version': [2]}) + assert_frame_equal(result, expected) + + result = pd.merge_asof(df1, df2, on='time', allow_exact_matches=False) + expected = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob'], + 'version': [1]}) + assert_frame_equal(result, expected) + + result = pd.merge_asof(df1, df2, on='time', allow_exact_matches=False, + tolerance=pd.Timedelta('10ms')) + expected = pd.DataFrame({ + 'time': pd.to_datetime(['2016-07-15 13:30:00.030']), + 'username': ['bob'], + 'version': [np.nan]}) + assert_frame_equal(result, expected) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index b0bbf8ba70354..62bbfc2f630a5 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -2,12 +2,14 @@ Quantilization functions and related stuff """ +from pandas.types.missing import isnull +from pandas.types.common import (is_float, is_integer, + is_scalar) + from pandas.core.api import Series from pandas.core.categorical import Categorical import pandas.core.algorithms as algos -import pandas.core.common as com import pandas.core.nanops as nanops -import pandas.lib as lib from pandas.compat import zip import numpy as np @@ -80,7 +82,7 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3, """ # NOTE: this binning code is changed a bit from histogram for var(x) == 0 if not np.iterable(bins): - if lib.isscalar(bins) and bins < 1: + if is_scalar(bins) and bins < 1: raise ValueError("`bins` should be a positive integer.") try: # for array-like sz = x.size @@ -164,7 +166,7 @@ def qcut(x, q, labels=None, retbins=False, precision=3): >>> pd.qcut(range(5), 4, labels=False) array([0, 0, 1, 2, 3], dtype=int64) """ - if com.is_integer(q): + if is_integer(q): quantiles = np.linspace(0, 1, q + 1) else: quantiles = q @@ -194,7 +196,7 @@ def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False, if include_lowest: ids[x == bins[0]] = 1 - na_mask = com.isnull(x) | (ids == len(bins)) | (ids == 0) + na_mask = isnull(x) | (ids == len(bins)) | (ids == 0) has_nas = na_mask.any() if labels is not False: @@ -264,7 +266,7 @@ def _format_label(x, precision=3): fmt_str = '%%.%dg' % precision if np.isinf(x): return str(x) - elif com.is_float(x): + elif is_float(x): frac, whole = np.modf(x) sgn = '-' if x < 0 else '' whole = abs(whole) diff --git a/pandas/tools/util.py b/pandas/tools/util.py index d70904e1bf286..b8b28663387cc 100644 --- a/pandas/tools/util.py +++ b/pandas/tools/util.py @@ -1,6 +1,12 @@ import numpy as np import pandas.lib as lib +from pandas.types.common import (is_number, + is_numeric_dtype, + is_datetime_or_timedelta_dtype, + _ensure_object) +from pandas.types.cast import _possibly_downcast_to_dtype + import pandas as pd from pandas.compat import reduce from pandas.core.index import Index @@ -141,7 +147,7 @@ def to_numeric(arg, errors='raise', downcast=None): elif isinstance(arg, (list, tuple)): values = np.array(arg, dtype='O') elif np.isscalar(arg): - if com.is_number(arg): + if is_number(arg): return arg is_scalar = True values = np.array([arg], dtype='O') @@ -151,14 +157,13 @@ def to_numeric(arg, errors='raise', downcast=None): values = arg try: - if com.is_numeric_dtype(values): + if is_numeric_dtype(values): pass - elif com.is_datetime_or_timedelta_dtype(values): + elif is_datetime_or_timedelta_dtype(values): values = values.astype(np.int64) else: - values = com._ensure_object(values) + values = _ensure_object(values) coerce_numeric = False if errors in ('ignore', 'raise') else True - values = lib.maybe_convert_numeric(values, set(), coerce_numeric=coerce_numeric) @@ -168,7 +173,7 @@ def to_numeric(arg, errors='raise', downcast=None): # attempt downcast only if the data has been successfully converted # to a numerical dtype and if a downcast method has been specified - if downcast is not None and com.is_numeric_dtype(values): + if downcast is not None and is_numeric_dtype(values): typecodes = None if downcast in ('integer', 'signed'): @@ -189,7 +194,7 @@ def to_numeric(arg, errors='raise', downcast=None): # from smallest to largest for dtype in typecodes: if np.dtype(dtype).itemsize < values.dtype.itemsize: - values = com._possibly_downcast_to_dtype( + values = _possibly_downcast_to_dtype( values, dtype) # successful conversion diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 4bafac873ea09..188f538372092 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -9,10 +9,16 @@ from pandas.compat.numpy import function as nv import numpy as np - +from pandas.types.common import (is_integer, is_float, + is_bool_dtype, _ensure_int64, + is_scalar, + is_list_like) +from pandas.types.generic import (ABCIndex, ABCSeries, + ABCPeriodIndex, ABCIndexClass) +from pandas.types.missing import isnull from pandas.core import common as com, algorithms -from pandas.core.common import (is_integer, is_float, is_bool_dtype, - AbstractMethodError) +from pandas.core.common import AbstractMethodError + import pandas.formats.printing as printing import pandas.tslib as tslib import pandas._period as prlib @@ -111,9 +117,9 @@ def _join_i8_wrapper(joinf, dtype, with_indexers=True): @staticmethod def wrapper(left, right): - if isinstance(left, (np.ndarray, com.ABCIndex, com.ABCSeries)): + if isinstance(left, (np.ndarray, ABCIndex, ABCSeries)): left = left.view('i8') - if isinstance(right, (np.ndarray, com.ABCIndex, com.ABCSeries)): + if isinstance(right, (np.ndarray, ABCIndex, ABCSeries)): right = right.view('i8') results = joinf(left, right) if with_indexers: @@ -133,10 +139,10 @@ def _evaluate_compare(self, other, op): # coerce to a similar object if not isinstance(other, type(self)): - if not com.is_list_like(other): + if not is_list_like(other): # scalar other = [other] - elif lib.isscalar(lib.item_from_zerodim(other)): + elif is_scalar(lib.item_from_zerodim(other)): # ndarray scalar other = [other.item()] other = type(self)(other) @@ -174,7 +180,7 @@ def _ensure_localized(self, result): # reconvert to local tz if getattr(self, 'tz', None) is not None: - if not isinstance(result, com.ABCIndexClass): + if not isinstance(result, ABCIndexClass): result = self._simple_new(result) result = result.tz_localize(self.tz) return result @@ -202,7 +208,7 @@ def _format_with_header(self, header, **kwargs): def __contains__(self, key): try: res = self.get_loc(key) - return lib.isscalar(res) or type(res) == slice or np.any(res) + return is_scalar(res) or type(res) == slice or np.any(res) except (KeyError, TypeError, ValueError): return False @@ -213,7 +219,7 @@ def __getitem__(self, key): """ is_int = is_integer(key) - if lib.isscalar(key) and not is_int: + if is_scalar(key) and not is_int: raise ValueError getitem = self._data.__getitem__ @@ -282,7 +288,7 @@ def _nat_new(self, box=True): return result attribs = self._get_attributes_dict() - if not isinstance(self, com.ABCPeriodIndex): + if not isinstance(self, ABCPeriodIndex): attribs['freq'] = None return self._simple_new(result, **attribs) @@ -312,7 +318,7 @@ def sort_values(self, return_indexer=False, ascending=True): attribs = self._get_attributes_dict() freq = attribs['freq'] - if freq is not None and not isinstance(self, com.ABCPeriodIndex): + if freq is not None and not isinstance(self, ABCPeriodIndex): if freq.n > 0 and not ascending: freq = freq * -1 elif freq.n < 0 and ascending: @@ -328,7 +334,7 @@ def sort_values(self, return_indexer=False, ascending=True): def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): nv.validate_take(tuple(), kwargs) - indices = com._ensure_int64(indices) + indices = _ensure_int64(indices) maybe_slice = lib.maybe_indices_to_slice(indices, len(self)) if isinstance(maybe_slice, slice): @@ -340,7 +346,7 @@ def take(self, indices, axis=0, allow_fill=True, na_value=tslib.iNaT) # keep freq in PeriodIndex, reset otherwise - freq = self.freq if isinstance(self, com.ABCPeriodIndex) else None + freq = self.freq if isinstance(self, ABCPeriodIndex) else None return self._shallow_copy(taken, freq=freq) def get_duplicates(self): @@ -545,7 +551,7 @@ def _convert_scalar_indexer(self, key, kind=None): # we don't allow integer/float indexing for loc # we don't allow float indexing for ix/getitem - if lib.isscalar(key): + if is_scalar(key): is_int = is_integer(key) is_flt = is_float(key) if kind in ['loc'] and (is_int or is_flt): @@ -591,7 +597,7 @@ def __add__(self, other): elif isinstance(other, (DateOffset, timedelta, np.timedelta64, tslib.Timedelta)): return self._add_delta(other) - elif com.is_integer(other): + elif is_integer(other): return self.shift(other) elif isinstance(other, (tslib.Timestamp, datetime)): return self._add_datelike(other) @@ -619,7 +625,7 @@ def __sub__(self, other): elif isinstance(other, (DateOffset, timedelta, np.timedelta64, tslib.Timedelta)): return self._add_delta(-other) - elif com.is_integer(other): + elif is_integer(other): return self.shift(-other) elif isinstance(other, (tslib.Timestamp, datetime)): return self._sub_datelike(other) @@ -791,15 +797,18 @@ def summary(self, name=None): def _ensure_datetimelike_to_i8(other): """ helper for coercing an input scalar or array to i8 """ - if lib.isscalar(other) and com.isnull(other): + if lib.isscalar(other) and isnull(other): other = tslib.iNaT - elif isinstance(other, com.ABCIndexClass): - + elif isinstance(other, ABCIndexClass): # convert tz if needed if getattr(other, 'tz', None) is not None: other = other.tz_localize(None).asi8 else: other = other.asi8 else: - other = np.array(other, copy=False).view('i8') + try: + other = np.array(other, copy=False).view('i8') + except TypeError: + # period array cannot be coerces to int + other = Index(other).asi8 return other diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 8937e83c7009a..46e8bd43e8ff8 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -3,19 +3,21 @@ """ import numpy as np + +from pandas.types.common import (_NS_DTYPE, _TD_DTYPE, + is_period_arraylike, + is_datetime_arraylike, is_integer_dtype, + is_datetime64_dtype, is_datetime64tz_dtype, + is_timedelta64_dtype, is_categorical_dtype, + is_list_like) + from pandas.core.base import PandasDelegate, NoNewAttributesMixin -from pandas.core import common as com from pandas.tseries.index import DatetimeIndex from pandas._period import IncompatibleFrequency # flake8: noqa from pandas.tseries.period import PeriodIndex from pandas.tseries.tdi import TimedeltaIndex from pandas import tslib from pandas.core.algorithms import take_1d -from pandas.core.common import (_NS_DTYPE, _TD_DTYPE, is_period_arraylike, - is_datetime_arraylike, is_integer_dtype, - is_list_like, - is_datetime64_dtype, is_datetime64tz_dtype, - is_timedelta64_dtype, is_categorical_dtype) def is_datetimelike(data): @@ -129,7 +131,7 @@ def _delegate_method(self, name, *args, **kwargs): method = getattr(self.values, name) result = method(*args, **kwargs) - if not com.is_list_like(result): + if not is_list_like(result): return result result = Series(result, index=self.index, name=self.name) diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py index 78b185ae8cf31..fc23f4f99449b 100644 --- a/pandas/tseries/converter.py +++ b/pandas/tseries/converter.py @@ -10,6 +10,14 @@ from matplotlib.ticker import Formatter, AutoLocator, Locator from matplotlib.transforms import nonsingular + +from pandas.types.common import (is_float, is_integer, + is_integer_dtype, + is_float_dtype, + is_datetime64_ns_dtype, + is_period_arraylike, + ) + from pandas.compat import lrange import pandas.compat as compat import pandas.lib as lib @@ -73,8 +81,8 @@ class TimeConverter(units.ConversionInterface): @staticmethod def convert(value, unit, axis): valid_types = (str, pydt.time) - if (isinstance(value, valid_types) or com.is_integer(value) or - com.is_float(value)): + if (isinstance(value, valid_types) or is_integer(value) or + is_float(value)): return time2num(value) if isinstance(value, Index): return value.map(time2num) @@ -129,14 +137,14 @@ def convert(values, units, axis): raise TypeError('Axis must have `freq` set to convert to Periods') valid_types = (compat.string_types, datetime, Period, pydt.date, pydt.time) - if (isinstance(values, valid_types) or com.is_integer(values) or - com.is_float(values)): + if (isinstance(values, valid_types) or is_integer(values) or + is_float(values)): return get_datevalue(values, axis.freq) if isinstance(values, PeriodIndex): return values.asfreq(axis.freq).values if isinstance(values, Index): return values.map(lambda x: get_datevalue(x, axis.freq)) - if com.is_period_arraylike(values): + if is_period_arraylike(values): return PeriodIndex(values, freq=axis.freq).values if isinstance(values, (list, tuple, np.ndarray, Index)): return [get_datevalue(x, axis.freq) for x in values] @@ -149,7 +157,7 @@ def get_datevalue(date, freq): elif isinstance(date, (compat.string_types, datetime, pydt.date, pydt.time)): return Period(date, freq).ordinal - elif (com.is_integer(date) or com.is_float(date) or + elif (is_integer(date) or is_float(date) or (isinstance(date, (np.ndarray, Index)) and (date.size == 1))): return date elif date is None: @@ -163,8 +171,8 @@ def _dt_to_float_ordinal(dt): preserving hours, minutes, seconds and microseconds. Return value is a :func:`float`. """ - if (isinstance(dt, (np.ndarray, Index, Series)) and - com.is_datetime64_ns_dtype(dt)): + if (isinstance(dt, (np.ndarray, Index, Series) + ) and is_datetime64_ns_dtype(dt)): base = dates.epoch2num(dt.asi8 / 1.0E9) else: base = dates.date2num(dt) @@ -188,7 +196,7 @@ def try_parse(values): return _dt_to_float_ordinal(lib.Timestamp(values)) elif isinstance(values, pydt.time): return dates.date2num(values) - elif (com.is_integer(values) or com.is_float(values)): + elif (is_integer(values) or is_float(values)): return values elif isinstance(values, compat.string_types): return try_parse(values) @@ -198,7 +206,7 @@ def try_parse(values): if not isinstance(values, np.ndarray): values = com._asarray_tuplesafe(values) - if com.is_integer_dtype(values) or com.is_float_dtype(values): + if is_integer_dtype(values) or is_float_dtype(values): return values try: diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 3f1d0c6d969a6..8b3785d78d260 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -1,17 +1,22 @@ from datetime import timedelta -from pandas.compat import range, long, zip +from pandas.compat import long, zip from pandas import compat import re import warnings import numpy as np +from pandas.types.generic import ABCSeries +from pandas.types.common import (is_integer, + is_period_arraylike, + is_timedelta64_dtype, + is_datetime64_dtype) + import pandas.core.algorithms as algos from pandas.core.algorithms import unique from pandas.tseries.offsets import DateOffset from pandas.util.decorators import cache_readonly import pandas.tseries.offsets as offsets -import pandas.core.common as com import pandas.lib as lib import pandas.tslib as tslib from pandas.tslib import Timedelta @@ -255,8 +260,8 @@ def get_freq_code(freqstr): freqstr = (freqstr.rule_code, freqstr.n) if isinstance(freqstr, tuple): - if (com.is_integer(freqstr[0]) and - com.is_integer(freqstr[1])): + if (is_integer(freqstr[0]) and + is_integer(freqstr[1])): # e.g., freqstr = (2000, 1) return freqstr else: @@ -265,13 +270,13 @@ def get_freq_code(freqstr): code = _period_str_to_code(freqstr[0]) stride = freqstr[1] except: - if com.is_integer(freqstr[1]): + if is_integer(freqstr[1]): raise code = _period_str_to_code(freqstr[1]) stride = freqstr[0] return code, stride - if com.is_integer(freqstr): + if is_integer(freqstr): return (freqstr, 1) base, stride = _base_and_stride(freqstr) @@ -351,34 +356,6 @@ def get_period_alias(offset_str): """ alias to closest period strings BQ->Q etc""" return _offset_to_period_map.get(offset_str, None) -_rule_aliases = { - # Legacy rules that will continue to map to their original values - # essentially for the rest of time - 'WEEKDAY': 'B', - 'EOM': 'BM', - 'W@MON': 'W-MON', - 'W@TUE': 'W-TUE', - 'W@WED': 'W-WED', - 'W@THU': 'W-THU', - 'W@FRI': 'W-FRI', - 'W@SAT': 'W-SAT', - 'W@SUN': 'W-SUN', - 'Q@JAN': 'BQ-JAN', - 'Q@FEB': 'BQ-FEB', - 'Q@MAR': 'BQ-MAR', - 'A@JAN': 'BA-JAN', - 'A@FEB': 'BA-FEB', - 'A@MAR': 'BA-MAR', - 'A@APR': 'BA-APR', - 'A@MAY': 'BA-MAY', - 'A@JUN': 'BA-JUN', - 'A@JUL': 'BA-JUL', - 'A@AUG': 'BA-AUG', - 'A@SEP': 'BA-SEP', - 'A@OCT': 'BA-OCT', - 'A@NOV': 'BA-NOV', - 'A@DEC': 'BA-DEC', -} _lite_rule_alias = { 'W': 'W-SUN', @@ -396,17 +373,6 @@ def get_period_alias(offset_str): 'ns': 'N' } -# TODO: Can this be killed? -for _i, _weekday in enumerate(['MON', 'TUE', 'WED', 'THU', 'FRI']): - for _iweek in range(4): - _name = 'WOM-%d%s' % (_iweek + 1, _weekday) - _rule_aliases[_name.replace('-', '@')] = _name - -# Note that _rule_aliases is not 1:1 (d[BA]==d[A@DEC]), and so traversal -# order matters when constructing an inverse. we pick one. #2331 -# Used in get_legacy_offset_name -_legacy_reverse_map = dict((v, k) for k, v in - reversed(sorted(compat.iteritems(_rule_aliases)))) _name_to_offset_map = {'days': Day(1), 'hours': Hour(1), @@ -417,6 +383,9 @@ def get_period_alias(offset_str): 'nanoseconds': Nano(1)} +_INVALID_FREQ_ERROR = "Invalid frequency: {0}" + + def to_offset(freqstr): """ Return DateOffset object from string representation or @@ -455,7 +424,7 @@ def to_offset(freqstr): else: delta = delta + offset except Exception: - raise ValueError("Could not evaluate %s" % freqstr) + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) else: delta = None @@ -474,10 +443,10 @@ def to_offset(freqstr): else: delta = delta + offset except Exception: - raise ValueError("Could not evaluate %s" % freqstr) + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) if delta is None: - raise ValueError('Unable to understand %s as a frequency' % freqstr) + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) return delta @@ -521,9 +490,6 @@ def get_base_alias(freqstr): _dont_uppercase = set(('MS', 'ms')) -_LEGACY_FREQ_WARNING = 'Freq "{0}" is deprecated, use "{1}" as alternative.' - - def get_offset(name): """ Return DateOffset object associated with rule name @@ -534,27 +500,9 @@ def get_offset(name): """ if name not in _dont_uppercase: name = name.upper() - - if name in _rule_aliases: - new = _rule_aliases[name] - warnings.warn(_LEGACY_FREQ_WARNING.format(name, new), - FutureWarning, stacklevel=2) - name = new - elif name.lower() in _rule_aliases: - new = _rule_aliases[name.lower()] - warnings.warn(_LEGACY_FREQ_WARNING.format(name, new), - FutureWarning, stacklevel=2) - name = new - name = _lite_rule_alias.get(name, name) name = _lite_rule_alias.get(name.lower(), name) - else: - if name in _rule_aliases: - new = _rule_aliases[name] - warnings.warn(_LEGACY_FREQ_WARNING.format(name, new), - FutureWarning, stacklevel=2) - name = new name = _lite_rule_alias.get(name, name) if name not in _offset_map: @@ -566,7 +514,7 @@ def get_offset(name): offset = klass._from_name(*split[1:]) except (ValueError, TypeError, KeyError): # bad prefix or suffix - raise ValueError('Bad rule name requested: %s.' % name) + raise ValueError(_INVALID_FREQ_ERROR.format(name)) # cache _offset_map[name] = offset # do not return cache because it's mutable @@ -590,17 +538,6 @@ def get_offset_name(offset): return offset.freqstr -def get_legacy_offset_name(offset): - """ - Return the pre pandas 0.8.0 name for the date offset - """ - - # This only used in test_timeseries_legacy.py - - name = offset.name - return _legacy_reverse_map.get(name, name) - - def get_standard_freq(freq): """ Return the standardized frequency string @@ -791,36 +728,18 @@ def _period_alias_dictionary(): def _period_str_to_code(freqstr): - # hack - if freqstr in _rule_aliases: - new = _rule_aliases[freqstr] - warnings.warn(_LEGACY_FREQ_WARNING.format(freqstr, new), - FutureWarning, stacklevel=3) - freqstr = new freqstr = _lite_rule_alias.get(freqstr, freqstr) if freqstr not in _dont_uppercase: lower = freqstr.lower() - if lower in _rule_aliases: - new = _rule_aliases[lower] - warnings.warn(_LEGACY_FREQ_WARNING.format(lower, new), - FutureWarning, stacklevel=3) - freqstr = new freqstr = _lite_rule_alias.get(lower, freqstr) + if freqstr not in _dont_uppercase: + freqstr = freqstr.upper() try: - if freqstr not in _dont_uppercase: - freqstr = freqstr.upper() return _period_code_map[freqstr] except KeyError: - try: - alias = _period_alias_dict[freqstr] - warnings.warn(_LEGACY_FREQ_WARNING.format(freqstr, alias), - FutureWarning, stacklevel=3) - except KeyError: - raise ValueError("Unknown freqstr: %s" % freqstr) - - return _period_code_map[alias] + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) def infer_freq(index, warn=True): @@ -843,16 +762,16 @@ def infer_freq(index, warn=True): """ import pandas as pd - if isinstance(index, com.ABCSeries): + if isinstance(index, ABCSeries): values = index._values - if not (com.is_datetime64_dtype(values) or - com.is_timedelta64_dtype(values) or + if not (is_datetime64_dtype(values) or + is_timedelta64_dtype(values) or values.dtype == object): raise TypeError("cannot infer freq from a non-convertible " "dtype on a Series of {0}".format(index.dtype)) index = values - if com.is_period_arraylike(index): + if is_period_arraylike(index): raise TypeError("PeriodIndex given. Check the `freq` attribute " "instead of using infer_freq.") elif isinstance(index, pd.TimedeltaIndex): diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 9b36bc5907066..d448ca9878b99 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -6,13 +6,25 @@ from datetime import timedelta import numpy as np from pandas.core.base import _shared_docs -from pandas.core.common import (_INT64_DTYPE, _NS_DTYPE, _maybe_box, - _values_from_object, ABCSeries, - DatetimeTZDtype, PerformanceWarning, - is_datetimetz, is_datetime64_dtype, - is_datetime64_ns_dtype, is_dtype_equal, - is_float, is_integer, is_integer_dtype, - is_object_dtype, is_string_dtype) + +from pandas.types.common import (_NS_DTYPE, _INT64_DTYPE, + is_object_dtype, is_datetime64_dtype, + is_datetimetz, is_dtype_equal, + is_integer, is_float, + is_integer_dtype, + is_datetime64_ns_dtype, + is_bool_dtype, + is_string_dtype, + is_list_like, + is_scalar, + _ensure_int64) +from pandas.types.generic import ABCSeries +from pandas.types.dtypes import DatetimeTZDtype +from pandas.types.missing import isnull + +import pandas.types.concat as _concat +from pandas.core.common import (_values_from_object, _maybe_box, + PerformanceWarning) from pandas.core.index import Index, Int64Index, Float64Index from pandas.indexes.base import _index_shared_docs @@ -27,7 +39,6 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) import pandas.core.common as com -import pandas.types.concat as _concat import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools @@ -87,7 +98,7 @@ def wrapper(self, other): isinstance(other, compat.string_types)): other = _to_m8(other, tz=self.tz) result = func(other) - if com.isnull(other): + if isnull(other): result.fill(nat_result) else: if isinstance(other, list): @@ -109,7 +120,7 @@ def wrapper(self, other): result[self._isnan] = nat_result # support of bool dtype indexers - if com.is_bool_dtype(result): + if is_bool_dtype(result): return result return Index(result) @@ -277,59 +288,36 @@ def __new__(cls, data=None, ambiguous=ambiguous) if not isinstance(data, (np.ndarray, Index, ABCSeries)): - if lib.isscalar(data): + if is_scalar(data): raise ValueError('DatetimeIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) - # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) - data = np.asarray(data, dtype='O') + elif isinstance(data, ABCSeries): + data = data._values - # try a few ways to make it datetime64 - if lib.is_string_array(data): - data = tslib.parse_str_array_to_datetime(data, freq=freq, - dayfirst=dayfirst, - yearfirst=yearfirst) - else: - data = tools.to_datetime(data, errors='raise') - data.offset = freq - if isinstance(data, DatetimeIndex): - if name is not None: - data.name = name - - if tz is not None: - - # we might already be localized to this tz - # so passing the same tz is ok - # however any other tz is a no-no - if data.tz is None: - return data.tz_localize(tz, ambiguous=ambiguous) - elif str(tz) != str(data.tz): - raise TypeError("Already tz-aware, use tz_convert " - "to convert.") - - return data._deepcopy_if_needed(ref_to_data, copy) - - if issubclass(data.dtype.type, compat.string_types): - data = tslib.parse_str_array_to_datetime(data, freq=freq, - dayfirst=dayfirst, - yearfirst=yearfirst) + # data must be Index or np.ndarray here + if not (is_datetime64_dtype(data) or is_datetimetz(data) or + is_integer_dtype(data)): + data = tools.to_datetime(data, dayfirst=dayfirst, + yearfirst=yearfirst) if issubclass(data.dtype.type, np.datetime64) or is_datetimetz(data): - if isinstance(data, ABCSeries): - data = data._values + if isinstance(data, DatetimeIndex): if tz is None: tz = data.tz - + elif data.tz is None: + data = data.tz_localize(tz, ambiguous=ambiguous) else: # the tz's must match if str(tz) != str(data.tz): - raise TypeError("Already tz-aware, use tz_convert " - "to convert.") + msg = ('data is already tz-aware {0}, unable to ' + 'set specified tz: {1}') + raise TypeError(msg.format(data.tz, tz)) subarr = data.values @@ -345,35 +333,6 @@ def __new__(cls, data=None, if isinstance(data, Int64Index): raise TypeError('cannot convert Int64Index->DatetimeIndex') subarr = data.view(_NS_DTYPE) - else: - if isinstance(data, (ABCSeries, Index)): - values = data._values - else: - values = data - - if lib.is_string_array(values): - subarr = tslib.parse_str_array_to_datetime( - values, freq=freq, dayfirst=dayfirst, yearfirst=yearfirst) - else: - try: - subarr = tools.to_datetime(data, box=False) - - # make sure that we have a index/ndarray like (and not a - # Series) - if isinstance(subarr, ABCSeries): - subarr = subarr._values - if subarr.dtype == np.object_: - subarr = tools._to_datetime(subarr, box=False) - - except ValueError: - # tz aware - subarr = tools._to_datetime(data, box=False, utc=True) - - # we may not have been able to convert - if not (is_datetimetz(subarr) or - np.issubdtype(subarr.dtype, np.datetime64)): - raise ValueError('Unable to convert %s to datetime dtype' - % str(data)) if isinstance(subarr, DatetimeIndex): if tz is None: @@ -388,27 +347,21 @@ def __new__(cls, data=None, ints = subarr.view('i8') subarr = tslib.tz_localize_to_utc(ints, tz, ambiguous=ambiguous) - subarr = subarr.view(_NS_DTYPE) subarr = cls._simple_new(subarr, name=name, freq=freq, tz=tz) - - # if dtype is provided, coerce here if dtype is not None: - if not is_dtype_equal(subarr.dtype, dtype): - + # dtype must be coerced to DatetimeTZDtype above if subarr.tz is not None: raise ValueError("cannot localize from non-UTC data") - dtype = DatetimeTZDtype.construct_from_string(dtype) - subarr = subarr.tz_localize(dtype.tz) if verify_integrity and len(subarr) > 0: if freq is not None and not freq_infer: inferred = subarr.inferred_freq if inferred != freq.freqstr: - on_freq = cls._generate(subarr[0], None, len( - subarr), None, freq, tz=tz, ambiguous=ambiguous) + on_freq = cls._generate(subarr[0], None, len(subarr), None, + freq, tz=tz, ambiguous=ambiguous) if not np.array_equal(subarr.asi8, on_freq.asi8): raise ValueError('Inferred frequency {0} from passed ' 'dates does not conform to passed ' @@ -537,7 +490,7 @@ def _generate(cls, start, end, periods, name, offset, index = _generate_regular_range(start, end, periods, offset) if tz is not None and getattr(index, 'tz', None) is None: - index = tslib.tz_localize_to_utc(com._ensure_int64(index), tz, + index = tslib.tz_localize_to_utc(_ensure_int64(index), tz, ambiguous=ambiguous) index = index.view(_NS_DTYPE) @@ -552,7 +505,6 @@ def _generate(cls, start, end, periods, name, offset, index = index[1:] if not right_closed and len(index) and index[-1] == end: index = index[:-1] - index = cls._simple_new(index, name=name, freq=offset, tz=tz) return index @@ -601,7 +553,7 @@ def _simple_new(cls, values, name=None, freq=None, tz=None, return cls(values, name=name, freq=freq, tz=tz, dtype=dtype, **kwargs).values elif not is_datetime64_dtype(values): - values = com._ensure_int64(values).view(_NS_DTYPE) + values = _ensure_int64(values).view(_NS_DTYPE) result = object.__new__(cls) result._data = values @@ -658,7 +610,7 @@ def _cached_range(cls, start=None, end=None, periods=None, offset=None, xdr = generate_range(offset=offset, start=_CACHE_START, end=_CACHE_END) - arr = tools._to_datetime(list(xdr), box=False) + arr = tools.to_datetime(list(xdr), box=False) cachedRange = DatetimeIndex._simple_new(arr) cachedRange.offset = offset @@ -1683,7 +1635,7 @@ def inferred_type(self): def dtype(self): if self.tz is None: return _NS_DTYPE - return com.DatetimeTZDtype('ns', self.tz) + return DatetimeTZDtype('ns', self.tz) @property def is_all_dates(self): @@ -1787,9 +1739,9 @@ def delete(self, loc): if loc in (0, -len(self), -1, len(self) - 1): freq = self.freq else: - if com.is_list_like(loc): + if is_list_like(loc): loc = lib.maybe_indices_to_slice( - com._ensure_int64(np.array(loc)), len(self)) + _ensure_int64(np.array(loc)), len(self)) if isinstance(loc, slice) and loc.step in (1, None): if (loc.start in (0, None) or loc.stop in (len(self), None)): freq = self.freq diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index d0b1fd746d0d5..f12ba8083f545 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -3,9 +3,9 @@ from pandas import compat import numpy as np +from pandas.types.generic import ABCSeries, ABCDatetimeIndex, ABCPeriod from pandas.tseries.tools import to_datetime, normalize_date -from pandas.core.common import (ABCSeries, ABCDatetimeIndex, ABCPeriod, - AbstractMethodError) +from pandas.core.common import AbstractMethodError # import after tools, dateutil check from dateutil.relativedelta import relativedelta, weekday diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 750e7a5553ef6..dffb71cff526a 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -1,6 +1,24 @@ # pylint: disable=E1101,E1103,W0232 from datetime import datetime, timedelta import numpy as np + + +from pandas.core import common as com +from pandas.types.common import (is_integer, + is_float, + is_object_dtype, + is_integer_dtype, + is_float_dtype, + is_scalar, + is_timedelta64_dtype, + is_bool_dtype, + _ensure_int64, + _ensure_object) + +from pandas.types.generic import ABCSeries +from pandas.types.missing import isnull + + import pandas.tseries.frequencies as frequencies from pandas.tseries.frequencies import get_freq_code as _gfc from pandas.tseries.index import DatetimeIndex, Int64Index, Index @@ -17,15 +35,10 @@ from pandas.core.base import _shared_docs from pandas.indexes.base import _index_shared_docs -import pandas.core.common as com -from pandas.core.common import ( - _maybe_box, _values_from_object, ABCSeries, is_float, is_integer, - is_integer_dtype, is_object_dtype, isnull) from pandas import compat from pandas.compat.numpy import function as nv from pandas.util.decorators import Appender, cache_readonly, Substitution from pandas.lib import Timedelta -import pandas.lib as lib import pandas.tslib as tslib import pandas.core.missing as missing from pandas.compat import zip, u @@ -79,13 +92,14 @@ def wrapper(self, other): result[mask] = nat_result return result + elif other is tslib.NaT: + result = np.empty(len(self.values), dtype=bool) + result.fill(nat_result) else: other = Period(other, freq=self.freq) func = getattr(self.values, opname) result = func(other.ordinal) - if other.ordinal == tslib.iNaT: - result.fill(nat_result) mask = self.values == tslib.iNaT if mask.any(): result[mask] = nat_result @@ -209,7 +223,7 @@ def _generate_range(cls, start, end, periods, freq, fields): def _from_arraylike(cls, data, freq, tz): if not isinstance(data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): - if lib.isscalar(data) or isinstance(data, Period): + if is_scalar(data) or isinstance(data, Period): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) @@ -219,13 +233,13 @@ def _from_arraylike(cls, data, freq, tz): data = list(data) try: - data = com._ensure_int64(data) + data = _ensure_int64(data) if freq is None: raise ValueError('freq not specified') - data = np.array([Period(x, freq=freq).ordinal for x in data], + data = np.array([Period(x, freq=freq) for x in data], dtype=np.int64) except (TypeError, ValueError): - data = com._ensure_object(data) + data = _ensure_object(data) if freq is None: freq = period.extract_freq(data) @@ -242,7 +256,7 @@ def _from_arraylike(cls, data, freq, tz): base1, base2, 1) else: - if freq is None and com.is_object_dtype(data): + if freq is None and is_object_dtype(data): # must contain Period instance and thus extract ordinals freq = period.extract_freq(data) data = period.extract_ordinals(data, freq) @@ -256,9 +270,9 @@ def _from_arraylike(cls, data, freq, tz): data = dt64arr_to_periodarr(data, freq, tz) else: try: - data = com._ensure_int64(data) + data = _ensure_int64(data) except (TypeError, ValueError): - data = com._ensure_object(data) + data = _ensure_object(data) data = period.extract_ordinals(data, freq) return data, freq @@ -266,9 +280,9 @@ def _from_arraylike(cls, data, freq, tz): @classmethod def _simple_new(cls, values, name=None, freq=None, **kwargs): - if not com.is_integer_dtype(values): + if not is_integer_dtype(values): values = np.array(values, copy=False) - if (len(values) > 0 and com.is_float_dtype(values)): + if (len(values) > 0 and is_float_dtype(values)): raise TypeError("PeriodIndex can't take floats") else: return PeriodIndex(values, name=name, freq=freq, **kwargs) @@ -309,15 +323,18 @@ def _na_value(self): return self._box_func(tslib.iNaT) def __contains__(self, key): - if not isinstance(key, Period) or key.freq != self.freq: - if isinstance(key, compat.string_types): - try: - self.get_loc(key) - return True - except Exception: - return False + if isinstance(key, Period): + if key.freq != self.freq: + return False + else: + return key.ordinal in self._engine + else: + try: + self.get_loc(key) + return True + except Exception: + return False return False - return key.ordinal in self._engine def __array_wrap__(self, result, context=None): """ @@ -339,7 +356,7 @@ def __array_wrap__(self, result, context=None): # from here because numpy catches. raise ValueError(msg.format(func.__name__)) - if com.is_bool_dtype(result): + if is_bool_dtype(result): return result return PeriodIndex(result, freq=self.freq, name=self.name) @@ -580,9 +597,9 @@ def _maybe_convert_timedelta(self, other): msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) elif isinstance(other, np.ndarray): - if com.is_integer_dtype(other): + if is_integer_dtype(other): return other - elif com.is_timedelta64_dtype(other): + elif is_timedelta64_dtype(other): offset = frequencies.to_offset(self.freq) if isinstance(offset, offsets.Tick): nanos = tslib._delta_to_nanoseconds(other) @@ -609,17 +626,13 @@ def _sub_period(self, other): msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if other.ordinal == tslib.iNaT: - new_data = np.empty(len(self)) - new_data.fill(np.nan) - else: - asi8 = self.asi8 - new_data = asi8 - other.ordinal + asi8 = self.asi8 + new_data = asi8 - other.ordinal - if self.hasnans: - mask = asi8 == tslib.iNaT - new_data = new_data.astype(np.float64) - new_data[mask] = np.nan + if self.hasnans: + mask = asi8 == tslib.iNaT + new_data = new_data.astype(np.float64) + new_data[mask] = np.nan # result must be Int64Index or Float64Index return Index(new_data, name=self.name) @@ -657,10 +670,11 @@ def get_value(self, series, key): Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing """ - s = _values_from_object(series) + s = com._values_from_object(series) try: - return _maybe_box(self, super(PeriodIndex, self).get_value(s, key), - series, key) + return com._maybe_box(self, + super(PeriodIndex, self).get_value(s, key), + series, key) except (KeyError, IndexError): try: asdt, parsed, reso = parse_time_string(key, self.freq) @@ -683,16 +697,16 @@ def get_value(self, series, key): return series[key] elif grp == freqn: key = Period(asdt, freq=self.freq).ordinal - return _maybe_box(self, self._engine.get_value(s, key), - series, key) + return com._maybe_box(self, self._engine.get_value(s, key), + series, key) else: raise KeyError(key) except TypeError: pass key = Period(key, self.freq).ordinal - return _maybe_box(self, self._engine.get_value(s, key), - series, key) + return com._maybe_box(self, self._engine.get_value(s, key), + series, key) def get_indexer(self, target, method=None, limit=None, tolerance=None): if hasattr(target, 'freq') and target.freq != self.freq: @@ -726,8 +740,10 @@ def get_loc(self, key, method=None, tolerance=None): # we cannot construct the Period # as we have an invalid type raise KeyError(key) + try: - return Index.get_loc(self, key.ordinal, method, tolerance) + ordinal = tslib.iNaT if key is tslib.NaT else key.ordinal + return Index.get_loc(self, ordinal, method, tolerance) except KeyError: raise KeyError(key) @@ -849,7 +865,7 @@ def _apply_meta(self, rawarr): def __getitem__(self, key): getitem = self._data.__getitem__ - if lib.isscalar(key): + if is_scalar(key): val = getitem(key) return Period(ordinal=val, freq=self.freq) else: @@ -1030,8 +1046,7 @@ def _get_ordinal_range(start, end, periods, freq, mult=1): if is_start_per and is_end_per and start.freq != end.freq: raise ValueError('Start and end must have same freq') - if ((is_start_per and start.ordinal == tslib.iNaT) or - (is_end_per and end.ordinal == tslib.iNaT)): + if (start is tslib.NaT or end is tslib.NaT): raise ValueError('Start and end must not be NaT') if freq is None: diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 8d6955ab43711..38c2e009a01f3 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -60,12 +60,15 @@ class Resampler(_GroupBy): 'loffset', 'base', 'kind'] # API compat of allowed attributes - _deprecated_valids = _attributes + ['_ipython_display_', '__doc__', - '_cache', '_attributes', 'binner', - 'grouper', 'groupby', 'keys', - 'sort', 'kind', 'squeeze', - 'group_keys', 'as_index', - 'exclusions', '_groupby'] + _deprecated_valids = _attributes + ['__doc__', '_cache', '_attributes', + 'binner', 'grouper', 'groupby', + 'sort', 'kind', 'squeeze', 'keys', + 'group_keys', 'as_index', 'exclusions', + '_groupby'] + + # don't raise deprecation warning on attributes starting with these + # patterns - prevents warnings caused by IPython introspection + _deprecated_valid_patterns = ['_ipython', '_repr'] # API compat of disallowed attributes _deprecated_invalids = ['iloc', 'loc', 'ix', 'iat', 'at'] @@ -109,9 +112,12 @@ def _typ(self): return 'series' return 'dataframe' - def _deprecated(self): - warnings.warn(".resample() is now a deferred operation\n" - "use .resample(...).mean() instead of .resample(...)", + def _deprecated(self, op): + warnings.warn(("\n.resample() is now a deferred operation\n" + "You called {op}(...) on this deferred object " + "which materialized it into a {klass}\nby implicitly " + "taking the mean. Use .resample(...).mean() " + "instead").format(op=op, klass=self._typ), FutureWarning, stacklevel=3) return self.mean() @@ -119,20 +125,20 @@ def _make_deprecated_binop(op): # op is a string def _evaluate_numeric_binop(self, other): - result = self._deprecated() + result = self._deprecated(op) return getattr(result, op)(other) return _evaluate_numeric_binop - def _make_deprecated_unary(op): + def _make_deprecated_unary(op, name): # op is a callable def _evaluate_numeric_unary(self): - result = self._deprecated() + result = self._deprecated(name) return op(result) return _evaluate_numeric_unary def __array__(self): - return self._deprecated().__array__() + return self._deprecated('__array__').__array__() __gt__ = _make_deprecated_binop('__gt__') __ge__ = _make_deprecated_binop('__ge__') @@ -148,10 +154,10 @@ def __array__(self): __truediv__ = __rtruediv__ = _make_deprecated_binop('__truediv__') if not compat.PY3: __div__ = __rdiv__ = _make_deprecated_binop('__div__') - __neg__ = _make_deprecated_unary(lambda x: -x) - __pos__ = _make_deprecated_unary(lambda x: x) - __abs__ = _make_deprecated_unary(lambda x: np.abs(x)) - __inv__ = _make_deprecated_unary(lambda x: -x) + __neg__ = _make_deprecated_unary(lambda x: -x, '__neg__') + __pos__ = _make_deprecated_unary(lambda x: x, '__pos__') + __abs__ = _make_deprecated_unary(lambda x: np.abs(x), '__abs__') + __inv__ = _make_deprecated_unary(lambda x: -x, '__inv__') def __getattr__(self, attr): if attr in self._internal_names_set: @@ -165,8 +171,12 @@ def __getattr__(self, attr): raise ValueError(".resample() is now a deferred operation\n" "\tuse .resample(...).mean() instead of " ".resample(...)") - if attr not in self._deprecated_valids: - self = self._deprecated() + + matches_pattern = any(attr.startswith(x) for x + in self._deprecated_valid_patterns) + if not matches_pattern and attr not in self._deprecated_valids: + self = self._deprecated(attr) + return object.__getattribute__(self, attr) def __setattr__(self, attr, value): @@ -182,7 +192,7 @@ def __getitem__(self, key): # compat for deprecated if isinstance(self.obj, com.ABCSeries): - return self._deprecated()[key] + return self._deprecated('__getitem__')[key] raise @@ -230,7 +240,7 @@ def _assure_grouper(self): def plot(self, *args, **kwargs): # for compat with prior versions, we want to # have the warnings shown here and just have this work - return self._deprecated().plot(*args, **kwargs) + return self._deprecated('plot').plot(*args, **kwargs) def aggregate(self, arg, *args, **kwargs): """ @@ -1046,7 +1056,12 @@ def _get_binner_for_grouping(self, obj): l = [] for key, group in grouper.get_iterator(self.ax): l.extend([key] * len(group)) - grouper = binner.__class__(l, freq=binner.freq, name=binner.name) + + if isinstance(self.ax, PeriodIndex): + grouper = binner.__class__(l, freq=binner.freq, name=binner.name) + else: + # resampling causes duplicated values, specifying freq is invalid + grouper = binner.__class__(l, name=binner.name) # since we may have had to sort # may need to reorder groups here diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index dbc0078b67ae7..78ab333be8ea5 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -2,11 +2,20 @@ from datetime import timedelta import numpy as np -from pandas.core.common import (ABCSeries, _TD_DTYPE, _maybe_box, - _values_from_object, isnull, - is_integer, is_float, is_integer_dtype, - is_object_dtype, is_timedelta64_dtype, - is_timedelta64_ns_dtype) +from pandas.types.common import (_TD_DTYPE, + is_integer, is_float, + is_bool_dtype, + is_list_like, + is_scalar, + is_integer_dtype, + is_object_dtype, + is_timedelta64_dtype, + is_timedelta64_ns_dtype, + _ensure_int64) +from pandas.types.missing import isnull +from pandas.types.generic import ABCSeries +from pandas.core.common import _maybe_box, _values_from_object + from pandas.core.index import Index, Int64Index import pandas.compat as compat from pandas.compat import u @@ -44,10 +53,10 @@ def wrapper(self, other): # failed to parse as timedelta raise TypeError(msg.format(type(other))) result = func(other) - if com.isnull(other): + if isnull(other): result.fill(nat_result) else: - if not com.is_list_like(other): + if not is_list_like(other): raise TypeError(msg.format(type(other))) other = TimedeltaIndex(other).values @@ -66,7 +75,7 @@ def wrapper(self, other): result[self._isnan] = nat_result # support of bool dtype indexers - if com.is_bool_dtype(result): + if is_bool_dtype(result): return result return Index(result) @@ -175,7 +184,7 @@ def __new__(cls, data=None, unit=None, data = to_timedelta(data, unit=unit, box=False) if not isinstance(data, (np.ndarray, Index, ABCSeries)): - if lib.isscalar(data): + if is_scalar(data): raise ValueError('TimedeltaIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) @@ -261,7 +270,7 @@ def _simple_new(cls, values, name=None, freq=None, **kwargs): if values.dtype == np.object_: values = tslib.array_to_timedelta64(values) if values.dtype != _TD_DTYPE: - values = com._ensure_int64(values).view(_TD_DTYPE) + values = _ensure_int64(values).view(_TD_DTYPE) result = object.__new__(cls) result._data = values @@ -688,6 +697,10 @@ def get_loc(self, key, method=None, tolerance=None): ------- loc : int """ + + if isnull(key): + key = tslib.NaT + if tolerance is not None: # try converting tolerance now, so errors don't get swallowed by # the try/except clauses below @@ -745,7 +758,7 @@ def _maybe_cast_slice_bound(self, label, side, kind): def _get_string_slice(self, key, use_lhs=True, use_rhs=True): freq = getattr(self, 'freqstr', getattr(self, 'inferred_freq', None)) - if is_integer(key) or is_float(key): + if is_integer(key) or is_float(key) or key is tslib.NaT: self._invalid_indexer('slice', key) loc = self._partial_td_slice(key, freq, use_lhs=use_lhs, use_rhs=use_rhs) @@ -905,9 +918,9 @@ def delete(self, loc): if loc in (0, -len(self), -1, len(self) - 1): freq = self.freq else: - if com.is_list_like(loc): + if is_list_like(loc): loc = lib.maybe_indices_to_slice( - com._ensure_int64(np.array(loc)), len(self)) + _ensure_int64(np.array(loc)), len(self)) if isinstance(loc, slice) and loc.step in (1, None): if (loc.start in (0, None) or loc.stop in (len(self), None)): freq = self.freq diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 68cea17ba3fc9..05f7d9d9ce7b8 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -160,9 +160,11 @@ def test_round(self): tm.assert_index_equal(rng.round(freq='H'), expected_rng) self.assertEqual(elt.round(freq='H'), expected_elt) - msg = "Could not evaluate foo" - tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='foo') - tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with tm.assertRaisesRegexp(ValueError, msg): + rng.round(freq='foo') + with tm.assertRaisesRegexp(ValueError, msg): + elt.round(freq='foo') msg = "<MonthEnd> is a non-fixed frequency" tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='M') @@ -489,13 +491,15 @@ def test_value_counts_unique(self): for tz in [None, 'UTC', 'Asia/Tokyo', 'US/Eastern']: idx = pd.date_range('2011-01-01 09:00', freq='H', periods=10) # create repeated values, 'n'th element is repeated by n+1 times - idx = DatetimeIndex( - np.repeat(idx.values, range(1, len(idx) + 1)), tz=tz) + idx = DatetimeIndex(np.repeat(idx.values, range(1, len(idx) + 1)), + tz=tz) exp_idx = pd.date_range('2011-01-01 18:00', freq='-1H', periods=10, tz=tz) expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64') - tm.assert_series_equal(idx.value_counts(), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) expected = pd.date_range('2011-01-01 09:00', freq='H', periods=10, tz=tz) @@ -505,15 +509,20 @@ def test_value_counts_unique(self): '2013-01-01 09:00', '2013-01-01 08:00', '2013-01-01 08:00', pd.NaT], tz=tz) - exp_idx = DatetimeIndex( - ['2013-01-01 09:00', '2013-01-01 08:00'], tz=tz) + exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00'], + tz=tz) expected = Series([3, 2], index=exp_idx) - tm.assert_series_equal(idx.value_counts(), expected) - exp_idx = DatetimeIndex( - ['2013-01-01 09:00', '2013-01-01 08:00', pd.NaT], tz=tz) + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00', + pd.NaT], tz=tz) expected = Series([3, 2, 1], index=exp_idx) - tm.assert_series_equal(idx.value_counts(dropna=False), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(dropna=False), + expected) tm.assert_index_equal(idx.unique(), exp_idx) @@ -652,6 +661,27 @@ def test_drop_duplicates_metadata(self): self.assert_index_equal(idx, result) self.assertIsNone(result.freq) + def test_drop_duplicates(self): + # to check Index/Series compat + base = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx') + idx = base.append(base[:5]) + + res = idx.drop_duplicates() + tm.assert_index_equal(res, base) + res = Series(idx).drop_duplicates() + tm.assert_series_equal(res, Series(base)) + + res = idx.drop_duplicates(keep='last') + exp = base[5:].append(base[:5]) + tm.assert_index_equal(res, exp) + res = Series(idx).drop_duplicates(keep='last') + tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36))) + + res = idx.drop_duplicates(keep=False) + tm.assert_index_equal(res, base[5:]) + res = Series(idx).drop_duplicates(keep=False) + tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31))) + def test_take(self): # GH 10295 idx1 = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx') @@ -847,9 +877,11 @@ def test_round(self): tm.assert_index_equal(td.round(freq='H'), expected_rng) self.assertEqual(elt.round(freq='H'), expected_elt) - msg = "Could not evaluate foo" - tm.assertRaisesRegexp(ValueError, msg, td.round, freq='foo') - tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + td.round(freq='foo') + with tm.assertRaisesRegexp(ValueError, msg): + elt.round(freq='foo') msg = "<MonthEnd> is a non-fixed frequency" tm.assertRaisesRegexp(ValueError, msg, td.round, freq='M') @@ -1299,23 +1331,29 @@ def test_value_counts_unique(self): exp_idx = timedelta_range('1 days 18:00:00', freq='-1H', periods=10) expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64') - tm.assert_series_equal(idx.value_counts(), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) expected = timedelta_range('1 days 09:00:00', freq='H', periods=10) tm.assert_index_equal(idx.unique(), expected) - idx = TimedeltaIndex( - ['1 days 09:00:00', '1 days 09:00:00', '1 days 09:00:00', - '1 days 08:00:00', '1 days 08:00:00', pd.NaT]) + idx = TimedeltaIndex(['1 days 09:00:00', '1 days 09:00:00', + '1 days 09:00:00', '1 days 08:00:00', + '1 days 08:00:00', pd.NaT]) exp_idx = TimedeltaIndex(['1 days 09:00:00', '1 days 08:00:00']) expected = Series([3, 2], index=exp_idx) - tm.assert_series_equal(idx.value_counts(), expected) - exp_idx = TimedeltaIndex(['1 days 09:00:00', '1 days 08:00:00', pd.NaT - ]) + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + exp_idx = TimedeltaIndex(['1 days 09:00:00', '1 days 08:00:00', + pd.NaT]) expected = Series([3, 2, 1], index=exp_idx) - tm.assert_series_equal(idx.value_counts(dropna=False), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(dropna=False), expected) tm.assert_index_equal(idx.unique(), exp_idx) @@ -1450,6 +1488,27 @@ def test_drop_duplicates_metadata(self): self.assert_index_equal(idx, result) self.assertIsNone(result.freq) + def test_drop_duplicates(self): + # to check Index/Series compat + base = pd.timedelta_range('1 day', '31 day', freq='D', name='idx') + idx = base.append(base[:5]) + + res = idx.drop_duplicates() + tm.assert_index_equal(res, base) + res = Series(idx).drop_duplicates() + tm.assert_series_equal(res, Series(base)) + + res = idx.drop_duplicates(keep='last') + exp = base[5:].append(base[:5]) + tm.assert_index_equal(res, exp) + res = Series(idx).drop_duplicates(keep='last') + tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36))) + + res = idx.drop_duplicates(keep=False) + tm.assert_index_equal(res, base[5:]) + res = Series(idx).drop_duplicates(keep=False) + tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31))) + def test_take(self): # GH 10295 idx1 = pd.timedelta_range('1 day', '31 day', freq='D', name='idx') @@ -1587,17 +1646,16 @@ def test_asobject_tolist(self): result = idx.asobject self.assertTrue(isinstance(result, Index)) self.assertEqual(result.dtype, object) + tm.assert_index_equal(result, expected) for i in [0, 1, 3]: - self.assertTrue(result[i], expected[i]) - self.assertTrue(result[2].ordinal, pd.tslib.iNaT) - self.assertTrue(result[2].freq, 'D') + self.assertEqual(result[i], expected[i]) + self.assertIs(result[2], pd.NaT) self.assertEqual(result.name, expected.name) result_list = idx.tolist() for i in [0, 1, 3]: - self.assertTrue(result_list[i], expected_list[i]) - self.assertTrue(result_list[2].ordinal, pd.tslib.iNaT) - self.assertTrue(result_list[2].freq, 'D') + self.assertEqual(result_list[i], expected_list[i]) + self.assertIs(result_list[2], pd.NaT) def test_minmax(self): @@ -1623,18 +1681,15 @@ def test_minmax(self): # Return NaT obj = PeriodIndex([], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) obj = PeriodIndex([pd.NaT], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) obj = PeriodIndex([pd.NaT, pd.NaT, pd.NaT], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) def test_numpy_minmax(self): pr = pd.period_range(start='2016-01-15', end='2016-01-20') @@ -2121,8 +2176,8 @@ def test_value_counts_unique(self): # GH 7735 idx = pd.period_range('2011-01-01 09:00', freq='H', periods=10) # create repeated values, 'n'th element is repeated by n+1 times - idx = PeriodIndex( - np.repeat(idx.values, range(1, len(idx) + 1)), freq='H') + idx = PeriodIndex(np.repeat(idx.values, range(1, len(idx) + 1)), + freq='H') exp_idx = PeriodIndex(['2011-01-01 18:00', '2011-01-01 17:00', '2011-01-01 16:00', '2011-01-01 15:00', @@ -2131,24 +2186,31 @@ def test_value_counts_unique(self): '2011-01-01 10:00', '2011-01-01 09:00'], freq='H') expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64') - tm.assert_series_equal(idx.value_counts(), expected) - expected = pd.period_range('2011-01-01 09:00', freq='H', periods=10) + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + expected = pd.period_range('2011-01-01 09:00', freq='H', + periods=10) tm.assert_index_equal(idx.unique(), expected) idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 09:00', '2013-01-01 09:00', '2013-01-01 08:00', '2013-01-01 08:00', pd.NaT], freq='H') - exp_idx = PeriodIndex( - ['2013-01-01 09:00', '2013-01-01 08:00'], freq='H') + exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00'], + freq='H') expected = Series([3, 2], index=exp_idx) - tm.assert_series_equal(idx.value_counts(), expected) - exp_idx = PeriodIndex( - ['2013-01-01 09:00', '2013-01-01 08:00', pd.NaT], freq='H') + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(), expected) + + exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00', + pd.NaT], freq='H') expected = Series([3, 2, 1], index=exp_idx) - tm.assert_series_equal(idx.value_counts(dropna=False), expected) + + for obj in [idx, Series(idx)]: + tm.assert_series_equal(obj.value_counts(dropna=False), expected) tm.assert_index_equal(idx.unique(), exp_idx) @@ -2164,6 +2226,28 @@ def test_drop_duplicates_metadata(self): self.assert_index_equal(idx, result) self.assertEqual(idx.freq, result.freq) + def test_drop_duplicates(self): + # to check Index/Series compat + base = pd.period_range('2011-01-01', '2011-01-31', freq='D', + name='idx') + idx = base.append(base[:5]) + + res = idx.drop_duplicates() + tm.assert_index_equal(res, base) + res = Series(idx).drop_duplicates() + tm.assert_series_equal(res, Series(base)) + + res = idx.drop_duplicates(keep='last') + exp = base[5:].append(base[:5]) + tm.assert_index_equal(res, exp) + res = Series(idx).drop_duplicates(keep='last') + tm.assert_series_equal(res, Series(exp, index=np.arange(5, 36))) + + res = idx.drop_duplicates(keep=False) + tm.assert_index_equal(res, base[5:]) + res = Series(idx).drop_duplicates(keep=False) + tm.assert_series_equal(res, Series(base[5:], index=np.arange(5, 31))) + def test_order_compat(self): def _check_freq(index, expected_index): if isinstance(index, PeriodIndex): diff --git a/pandas/tseries/tests/test_bin_groupby.py b/pandas/tseries/tests/test_bin_groupby.py index 6b6c468b7c391..08c0833be0cd6 100644 --- a/pandas/tseries/tests/test_bin_groupby.py +++ b/pandas/tseries/tests/test_bin_groupby.py @@ -3,12 +3,12 @@ from numpy import nan import numpy as np +from pandas.types.common import _ensure_int64 from pandas import Index, isnull from pandas.util.testing import assert_almost_equal import pandas.util.testing as tm import pandas.lib as lib import pandas.algos as algos -from pandas.core import common as com def test_series_grouper(): @@ -90,8 +90,8 @@ def _check(dtype): bins = np.array([6, 12, 20]) out = np.zeros((3, 4), dtype) counts = np.zeros(len(out), dtype=np.int64) - labels = com._ensure_int64(np.repeat(np.arange(3), - np.diff(np.r_[0, bins]))) + labels = _ensure_int64(np.repeat(np.arange(3), + np.diff(np.r_[0, bins]))) func = getattr(algos, 'group_ohlc_%s' % dtype) func(out, counts, obj[:, None], labels) diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py index 1f06b7ad4361b..268933fada7a2 100644 --- a/pandas/tseries/tests/test_frequencies.py +++ b/pandas/tseries/tests/test_frequencies.py @@ -245,10 +245,10 @@ def _assert_depr(freq, expected, aliases): assert isinstance(aliases, list) assert (frequencies._period_str_to_code(freq) == expected) + msg = frequencies._INVALID_FREQ_ERROR for alias in aliases: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - assert (frequencies._period_str_to_code(alias) == expected) + with tm.assertRaisesRegexp(ValueError, msg): + frequencies._period_str_to_code(alias) _assert_depr("M", 3000, ["MTH", "MONTH", "MONTHLY"]) @@ -699,8 +699,9 @@ def test_series(self): s = Series(period_range('2013', periods=10, freq=freq)) self.assertRaises(TypeError, lambda: frequencies.infer_freq(s)) for freq in ['Y']: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + + msg = frequencies._INVALID_FREQ_ERROR + with tm.assertRaisesRegexp(ValueError, msg): s = Series(period_range('2013', periods=10, freq=freq)) self.assertRaises(TypeError, lambda: frequencies.infer_freq(s)) @@ -715,17 +716,23 @@ def test_series(self): self.assertEqual(inferred, 'D') def test_legacy_offset_warnings(self): - for k, v in compat.iteritems(frequencies._rule_aliases): - with tm.assert_produces_warning(FutureWarning): - result = frequencies.get_offset(k) - exp = frequencies.get_offset(v) - self.assertEqual(result, exp) - - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - idx = date_range('2011-01-01', periods=5, freq=k) - exp = date_range('2011-01-01', periods=5, freq=v) - self.assert_index_equal(idx, exp) + freqs = ['WEEKDAY', 'EOM', 'W@MON', 'W@TUE', 'W@WED', 'W@THU', + 'W@FRI', 'W@SAT', 'W@SUN', 'Q@JAN', 'Q@FEB', 'Q@MAR', + 'A@JAN', 'A@FEB', 'A@MAR', 'A@APR', 'A@MAY', 'A@JUN', + 'A@JUL', 'A@AUG', 'A@SEP', 'A@OCT', 'A@NOV', 'A@DEC', + 'WOM@1MON', 'WOM@2MON', 'WOM@3MON', 'WOM@4MON', + 'WOM@1TUE', 'WOM@2TUE', 'WOM@3TUE', 'WOM@4TUE', + 'WOM@1WED', 'WOM@2WED', 'WOM@3WED', 'WOM@4WED', + 'WOM@1THU', 'WOM@2THU', 'WOM@3THU', 'WOM@4THU' + 'WOM@1FRI', 'WOM@2FRI', 'WOM@3FRI', 'WOM@4FRI'] + + msg = frequencies._INVALID_FREQ_ERROR + for freq in freqs: + with tm.assertRaisesRegexp(ValueError, msg): + frequencies.get_offset(freq) + + with tm.assertRaisesRegexp(ValueError, msg): + date_range('2011-01-01', periods=5, freq=freq) MONTHS = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 5965a661699a6..b31e4d54c551f 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -23,7 +23,7 @@ from pandas.core.series import Series from pandas.tseries.frequencies import (_offset_map, get_freq_code, - _get_freq_str) + _get_freq_str, _INVALID_FREQ_ERROR) from pandas.tseries.index import _to_m8, DatetimeIndex, _daterange_cache from pandas.tseries.tools import parse_time_string, DateParseError import pandas.tseries.offsets as offsets @@ -4531,8 +4531,11 @@ def test_get_offset_name(self): def test_get_offset(): - assertRaisesRegexp(ValueError, "rule.*GIBBERISH", get_offset, 'gibberish') - assertRaisesRegexp(ValueError, "rule.*QS-JAN-B", get_offset, 'QS-JAN-B') + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_offset('gibberish') + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_offset('QS-JAN-B') + pairs = [ ('B', BDay()), ('b', BDay()), ('bm', BMonthEnd()), ('Bm', BMonthEnd()), ('W-MON', Week(weekday=0)), @@ -4558,10 +4561,8 @@ def test_get_offset(): def test_get_offset_legacy(): pairs = [('w@Sat', Week(weekday=5))] for name, expected in pairs: - with tm.assert_produces_warning(FutureWarning): - offset = get_offset(name) - assert offset == expected, ("Expected %r to yield %r (actual: %r)" % - (name, expected, offset)) + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_offset(name) class TestParseTimeString(tm.TestCase): @@ -4595,16 +4596,14 @@ def test_get_standard_freq(): assert fstr == get_standard_freq('1w') assert fstr == get_standard_freq(('W', 1)) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = get_standard_freq('WeEk') - assert fstr == result + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_standard_freq('WeEk') fstr = get_standard_freq('5Q') assert fstr == get_standard_freq('5q') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = get_standard_freq('5QuarTer') - assert fstr == result + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_standard_freq('5QuarTer') assert fstr == get_standard_freq(('q', 5)) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 807fb86b1b4da..e3a67289a587b 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -36,14 +36,17 @@ def test_quarterly_negative_ordinals(self): p = Period(ordinal=-1, freq='Q-DEC') self.assertEqual(p.year, 1969) self.assertEqual(p.quarter, 4) + self.assertIsInstance(p, Period) p = Period(ordinal=-2, freq='Q-DEC') self.assertEqual(p.year, 1969) self.assertEqual(p.quarter, 3) + self.assertIsInstance(p, Period) p = Period(ordinal=-2, freq='M') self.assertEqual(p.year, 1969) self.assertEqual(p.month, 11) + self.assertIsInstance(p, Period) def test_period_cons_quarterly(self): # bugs in scikits.timeseries @@ -67,6 +70,7 @@ def test_period_cons_annual(self): stamp = exp.to_timestamp('D', how='end') + timedelta(days=30) p = Period(stamp, freq=freq) self.assertEqual(p, exp + 1) + self.assertIsInstance(p, Period) def test_period_cons_weekly(self): for num in range(10, 17): @@ -77,34 +81,46 @@ def test_period_cons_weekly(self): result = Period(daystr, freq=freq) expected = Period(daystr, freq='D').asfreq(freq) self.assertEqual(result, expected) + self.assertIsInstance(result, Period) + + def test_period_from_ordinal(self): + p = pd.Period('2011-01', freq='M') + res = pd.Period._from_ordinal(p.ordinal, freq='M') + self.assertEqual(p, res) + self.assertIsInstance(res, Period) def test_period_cons_nat(self): p = Period('NaT', freq='M') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'M') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period('nat', freq='W-SUN') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'W-SUN') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period(tslib.iNaT, freq='D') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'D') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period(tslib.iNaT, freq='3D') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, offsets.Day(3)) - self.assertEqual(p.freqstr, '3D') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) + + p = Period('NaT') + self.assertIs(p, pd.NaT) + + p = Period(tslib.iNaT) + self.assertIs(p, pd.NaT) + + def test_cons_null_like(self): + # check Timestamp compat + self.assertIs(Timestamp('NaT'), pd.NaT) + self.assertIs(Period('NaT'), pd.NaT) + + self.assertIs(Timestamp(None), pd.NaT) + self.assertIs(Period(None), pd.NaT) - self.assertRaises(ValueError, Period, 'NaT') + self.assertIs(Timestamp(float('nan')), pd.NaT) + self.assertIs(Period(float('nan')), pd.NaT) + + self.assertIs(Timestamp(np.nan), pd.NaT) + self.assertIs(Period(np.nan), pd.NaT) def test_period_cons_mult(self): p1 = Period('2011-01', freq='3M') @@ -197,13 +213,6 @@ def test_timestamp_tz_arg_dateutil_from_string(self): freq='M').to_timestamp(tz='dateutil/Europe/Brussels') self.assertEqual(p.tz, gettz('Europe/Brussels')) - def test_timestamp_nat_tz(self): - t = Period('NaT', freq='M').to_timestamp() - self.assertTrue(t is tslib.NaT) - - t = Period('NaT', freq='M').to_timestamp(tz='Asia/Tokyo') - self.assertTrue(t is tslib.NaT) - def test_timestamp_mult(self): p = pd.Period('2011-01', freq='M') self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) @@ -213,12 +222,6 @@ def test_timestamp_mult(self): self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) self.assertEqual(p.to_timestamp(how='E'), pd.Timestamp('2011-03-31')) - def test_timestamp_nat_mult(self): - for freq in ['M', '3M']: - p = pd.Period('NaT', freq=freq) - self.assertTrue(p.to_timestamp(how='S') is pd.NaT) - self.assertTrue(p.to_timestamp(how='E') is pd.NaT) - def test_period_constructor(self): i1 = Period('1/1/2005', freq='M') i2 = Period('Jan 2005') @@ -448,13 +451,29 @@ def test_period_deprecated_freq(self): "L": ["MILLISECOND", "MILLISECONDLY", "millisecond"], "U": ["MICROSECOND", "MICROSECONDLY", "microsecond"], "N": ["NANOSECOND", "NANOSECONDLY", "nanosecond"]} + + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR for exp, freqs in iteritems(cases): for freq in freqs: + with self.assertRaisesRegexp(ValueError, msg): + Period('2016-03-01 09:00', freq=freq) + + # check supported freq-aliases still works + p = Period('2016-03-01 09:00', freq=exp) + tm.assertIsInstance(p, Period) - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - res = pd.Period('2016-03-01 09:00', freq=freq) - self.assertEqual(res, Period('2016-03-01 09:00', freq=exp)) + def test_hash(self): + self.assertEqual(hash(Period('2011-01', freq='M')), + hash(Period('2011-01', freq='M'))) + + self.assertNotEqual(hash(Period('2011-01-01', freq='D')), + hash(Period('2011-01', freq='M'))) + + self.assertNotEqual(hash(Period('2011-01', freq='3M')), + hash(Period('2011-01', freq='2M'))) + + self.assertNotEqual(hash(Period('2011-01', freq='M')), + hash(Period('2011-02', freq='M'))) def test_repr(self): p = Period('Jan-2000') @@ -552,9 +571,6 @@ def _ex(p): result = p.to_timestamp('5S', how='start') self.assertEqual(result, expected) - p = Period('NaT', freq='W') - self.assertTrue(p.to_timestamp() is tslib.NaT) - def test_start_time(self): freq_lst = ['A', 'Q', 'M', 'D', 'H', 'T', 'S'] xp = datetime(2012, 1, 1) @@ -566,9 +582,6 @@ def test_start_time(self): self.assertEqual(Period('2012', freq='W').start_time, datetime(2011, 12, 26)) - p = Period('NaT', freq='W') - self.assertTrue(p.start_time is tslib.NaT) - def test_end_time(self): p = Period('2012', freq='A') @@ -607,9 +620,6 @@ def _ex(*args): xp = _ex(2012, 1, 16) self.assertEqual(xp, p.end_time) - p = Period('NaT', freq='W') - self.assertTrue(p.end_time is tslib.NaT) - def test_anchor_week_end_time(self): def _ex(*args): return Timestamp(Timestamp(datetime(*args)).value - 1) @@ -665,19 +675,21 @@ def test_properties_weekly(self): def test_properties_weekly_legacy(self): # Test properties on Periods with daily frequency. - with tm.assert_produces_warning(FutureWarning): - w_date = Period(freq='WK', year=2007, month=1, day=7) - # + w_date = Period(freq='W', year=2007, month=1, day=7) self.assertEqual(w_date.year, 2007) self.assertEqual(w_date.quarter, 1) self.assertEqual(w_date.month, 1) self.assertEqual(w_date.week, 1) self.assertEqual((w_date - 1).week, 52) self.assertEqual(w_date.days_in_month, 31) - with tm.assert_produces_warning(FutureWarning): - exp = Period(freq='WK', year=2012, month=2, day=1) + + exp = Period(freq='W', year=2012, month=2, day=1) self.assertEqual(exp.days_in_month, 29) + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK', year=2007, month=1, day=7) + def test_properties_daily(self): # Test properties on Periods with daily frequency. b_date = Period(freq='B', year=2007, month=1, day=1) @@ -758,15 +770,14 @@ def test_properties_secondly(self): def test_properties_nat(self): p_nat = Period('NaT', freq='M') t_nat = pd.Timestamp('NaT') + self.assertIs(p_nat, t_nat) + # confirm Period('NaT') work identical with Timestamp('NaT') for f in ['year', 'month', 'day', 'hour', 'minute', 'second', 'week', 'dayofyear', 'quarter', 'days_in_month']: self.assertTrue(np.isnan(getattr(p_nat, f))) self.assertTrue(np.isnan(getattr(t_nat, f))) - for f in ['weekofyear', 'dayofweek', 'weekday', 'qyear']: - self.assertTrue(np.isnan(getattr(p_nat, f))) - def test_pnow(self): dt = datetime.now() @@ -789,7 +800,7 @@ def test_constructor_corner(self): self.assertRaises(ValueError, Period, 1.6, freq='D') self.assertRaises(ValueError, Period, ordinal=1.6, freq='D') self.assertRaises(ValueError, Period, ordinal=2, value=1, freq='D') - self.assertRaises(ValueError, Period) + self.assertIs(Period(None), pd.NaT) self.assertRaises(ValueError, Period, month=1) p = Period('2007-01-01', freq='D') @@ -826,10 +837,11 @@ def test_asfreq_MS(self): self.assertEqual(initial.asfreq(freq="M", how="S"), Period('2013-01', 'M')) - with self.assertRaisesRegexp(ValueError, "Unknown freqstr"): + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): initial.asfreq(freq="MS", how="S") - with tm.assertRaisesRegexp(ValueError, "Unknown freqstr: MS"): + with tm.assertRaisesRegexp(ValueError, msg): pd.Period('2013-01', 'MS') self.assertTrue(_period_code_map.get("MS") is None) @@ -1129,123 +1141,28 @@ def test_conv_weekly(self): self.assertEqual(ival_W.asfreq('W'), ival_W) + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + ival_W.asfreq('WK') + def test_conv_weekly_legacy(self): # frequency conversion tests: from Weekly Frequency - - with tm.assert_produces_warning(FutureWarning): - ival_W = Period(freq='WK', year=2007, month=1, day=1) - - with tm.assert_produces_warning(FutureWarning): - ival_WSUN = Period(freq='WK', year=2007, month=1, day=7) - with tm.assert_produces_warning(FutureWarning): - ival_WSAT = Period(freq='WK-SAT', year=2007, month=1, day=6) - with tm.assert_produces_warning(FutureWarning): - ival_WFRI = Period(freq='WK-FRI', year=2007, month=1, day=5) - with tm.assert_produces_warning(FutureWarning): - ival_WTHU = Period(freq='WK-THU', year=2007, month=1, day=4) - with tm.assert_produces_warning(FutureWarning): - ival_WWED = Period(freq='WK-WED', year=2007, month=1, day=3) - with tm.assert_produces_warning(FutureWarning): - ival_WTUE = Period(freq='WK-TUE', year=2007, month=1, day=2) - with tm.assert_produces_warning(FutureWarning): - ival_WMON = Period(freq='WK-MON', year=2007, month=1, day=1) - - ival_WSUN_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_WSUN_to_D_end = Period(freq='D', year=2007, month=1, day=7) - ival_WSAT_to_D_start = Period(freq='D', year=2006, month=12, day=31) - ival_WSAT_to_D_end = Period(freq='D', year=2007, month=1, day=6) - ival_WFRI_to_D_start = Period(freq='D', year=2006, month=12, day=30) - ival_WFRI_to_D_end = Period(freq='D', year=2007, month=1, day=5) - ival_WTHU_to_D_start = Period(freq='D', year=2006, month=12, day=29) - ival_WTHU_to_D_end = Period(freq='D', year=2007, month=1, day=4) - ival_WWED_to_D_start = Period(freq='D', year=2006, month=12, day=28) - ival_WWED_to_D_end = Period(freq='D', year=2007, month=1, day=3) - ival_WTUE_to_D_start = Period(freq='D', year=2006, month=12, day=27) - ival_WTUE_to_D_end = Period(freq='D', year=2007, month=1, day=2) - ival_WMON_to_D_start = Period(freq='D', year=2006, month=12, day=26) - ival_WMON_to_D_end = Period(freq='D', year=2007, month=1, day=1) - - with tm.assert_produces_warning(FutureWarning): - ival_W_end_of_year = Period(freq='WK', year=2007, month=12, day=31) - with tm.assert_produces_warning(FutureWarning): - ival_W_end_of_quarter = Period(freq='WK', year=2007, month=3, - day=31) - with tm.assert_produces_warning(FutureWarning): - ival_W_end_of_month = Period(freq='WK', year=2007, month=1, day=31) - ival_W_to_A = Period(freq='A', year=2007) - ival_W_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_W_to_M = Period(freq='M', year=2007, month=1) - - if Period(freq='D', year=2007, month=12, day=31).weekday == 6: - ival_W_to_A_end_of_year = Period(freq='A', year=2007) - else: - ival_W_to_A_end_of_year = Period(freq='A', year=2008) - - if Period(freq='D', year=2007, month=3, day=31).weekday == 6: - ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=1) - else: - ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=2) - - if Period(freq='D', year=2007, month=1, day=31).weekday == 6: - ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=1) - else: - ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=2) - - ival_W_to_B_start = Period(freq='B', year=2007, month=1, day=1) - ival_W_to_B_end = Period(freq='B', year=2007, month=1, day=5) - ival_W_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_W_to_D_end = Period(freq='D', year=2007, month=1, day=7) - ival_W_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_W_to_H_end = Period(freq='H', year=2007, month=1, day=7, hour=23) - ival_W_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_W_to_T_end = Period(freq='Min', year=2007, month=1, day=7, - hour=23, minute=59) - ival_W_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_W_to_S_end = Period(freq='S', year=2007, month=1, day=7, hour=23, - minute=59, second=59) - - self.assertEqual(ival_W.asfreq('A'), ival_W_to_A) - self.assertEqual(ival_W_end_of_year.asfreq('A'), - ival_W_to_A_end_of_year) - self.assertEqual(ival_W.asfreq('Q'), ival_W_to_Q) - self.assertEqual(ival_W_end_of_quarter.asfreq('Q'), - ival_W_to_Q_end_of_quarter) - self.assertEqual(ival_W.asfreq('M'), ival_W_to_M) - self.assertEqual(ival_W_end_of_month.asfreq('M'), - ival_W_to_M_end_of_month) - - self.assertEqual(ival_W.asfreq('B', 'S'), ival_W_to_B_start) - self.assertEqual(ival_W.asfreq('B', 'E'), ival_W_to_B_end) - - self.assertEqual(ival_W.asfreq('D', 'S'), ival_W_to_D_start) - self.assertEqual(ival_W.asfreq('D', 'E'), ival_W_to_D_end) - - self.assertEqual(ival_WSUN.asfreq('D', 'S'), ival_WSUN_to_D_start) - self.assertEqual(ival_WSUN.asfreq('D', 'E'), ival_WSUN_to_D_end) - self.assertEqual(ival_WSAT.asfreq('D', 'S'), ival_WSAT_to_D_start) - self.assertEqual(ival_WSAT.asfreq('D', 'E'), ival_WSAT_to_D_end) - self.assertEqual(ival_WFRI.asfreq('D', 'S'), ival_WFRI_to_D_start) - self.assertEqual(ival_WFRI.asfreq('D', 'E'), ival_WFRI_to_D_end) - self.assertEqual(ival_WTHU.asfreq('D', 'S'), ival_WTHU_to_D_start) - self.assertEqual(ival_WTHU.asfreq('D', 'E'), ival_WTHU_to_D_end) - self.assertEqual(ival_WWED.asfreq('D', 'S'), ival_WWED_to_D_start) - self.assertEqual(ival_WWED.asfreq('D', 'E'), ival_WWED_to_D_end) - self.assertEqual(ival_WTUE.asfreq('D', 'S'), ival_WTUE_to_D_start) - self.assertEqual(ival_WTUE.asfreq('D', 'E'), ival_WTUE_to_D_end) - self.assertEqual(ival_WMON.asfreq('D', 'S'), ival_WMON_to_D_start) - self.assertEqual(ival_WMON.asfreq('D', 'E'), ival_WMON_to_D_end) - - self.assertEqual(ival_W.asfreq('H', 'S'), ival_W_to_H_start) - self.assertEqual(ival_W.asfreq('H', 'E'), ival_W_to_H_end) - self.assertEqual(ival_W.asfreq('Min', 'S'), ival_W_to_T_start) - self.assertEqual(ival_W.asfreq('Min', 'E'), ival_W_to_T_end) - self.assertEqual(ival_W.asfreq('S', 'S'), ival_W_to_S_start) - self.assertEqual(ival_W.asfreq('S', 'E'), ival_W_to_S_end) - - with tm.assert_produces_warning(FutureWarning): - self.assertEqual(ival_W.asfreq('WK'), ival_W) + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK', year=2007, month=1, day=1) + + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-SAT', year=2007, month=1, day=6) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-FRI', year=2007, month=1, day=5) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-THU', year=2007, month=1, day=4) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-WED', year=2007, month=1, day=3) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-TUE', year=2007, month=1, day=2) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-MON', year=2007, month=1, day=1) def test_conv_business(self): # frequency conversion tests: from Business Frequency" @@ -1526,12 +1443,6 @@ def test_conv_secondly(self): self.assertEqual(ival_S.asfreq('S'), ival_S) - def test_asfreq_nat(self): - p = Period('NaT', freq='A') - result = p.asfreq('M') - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') - def test_asfreq_mult(self): # normal freq to mult freq p = Period(freq='A', year=2007) @@ -1603,21 +1514,6 @@ def test_asfreq_mult(self): self.assertEqual(result.ordinal, expected.ordinal) self.assertEqual(result.freq, expected.freq) - def test_asfreq_mult_nat(self): - # normal freq to mult freq - for p in [Period('NaT', freq='A'), Period('NaT', freq='3A'), - Period('NaT', freq='2M'), Period('NaT', freq='3D')]: - for freq in ['3A', offsets.YearEnd(3)]: - result = p.asfreq(freq) - expected = Period('NaT', freq='3A') - self.assertEqual(result.ordinal, pd.tslib.iNaT) - self.assertEqual(result.freq, expected.freq) - - result = p.asfreq(freq, how='S') - expected = Period('NaT', freq='3A') - self.assertEqual(result.ordinal, pd.tslib.iNaT) - self.assertEqual(result.freq, expected.freq) - class TestPeriodIndex(tm.TestCase): def setUp(self): @@ -1995,6 +1891,19 @@ def test_getitem_datetime(self): rs = ts[dt1:dt4] tm.assert_series_equal(rs, ts) + def test_getitem_nat(self): + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertEqual(idx[0], pd.Period('2011-01', freq='M')) + self.assertIs(idx[1], tslib.NaT) + + s = pd.Series([0, 1, 2], index=idx) + self.assertEqual(s[pd.NaT], 1) + + s = pd.Series(idx, index=idx) + self.assertEqual(s[pd.Period('2011-01', freq='M')], + pd.Period('2011-01', freq='M')) + self.assertIs(s[pd.NaT], tslib.NaT) + def test_slice_with_negative_step(self): ts = Series(np.arange(20), period_range('2014-01', periods=20, freq='M')) @@ -2038,6 +1947,20 @@ def test_contains(self): self.assertFalse(Period('2007-01', freq='D') in rng) self.assertFalse(Period('2007-01', freq='2M') in rng) + def test_contains_nat(self): + # GH13582 + idx = period_range('2007-01', freq='M', periods=10) + self.assertFalse(pd.NaT in idx) + self.assertFalse(None in idx) + self.assertFalse(float('nan') in idx) + self.assertFalse(np.nan in idx) + + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertTrue(pd.NaT in idx) + self.assertTrue(None in idx) + self.assertTrue(float('nan') in idx) + self.assertTrue(np.nan in idx) + def test_sub(self): rng = period_range('2007-01', periods=50) @@ -2895,11 +2818,14 @@ def test_to_period_monthish(self): prng = rng.to_period() self.assertEqual(prng.freq, 'M') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - rng = date_range('01-Jan-2012', periods=8, freq='EOM') + rng = date_range('01-Jan-2012', periods=8, freq='M') prng = rng.to_period() self.assertEqual(prng.freq, 'M') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + date_range('01-Jan-2012', periods=8, freq='EOM') + def test_multiples(self): result1 = Period('1989', freq='2A') result2 = Period('1989', freq='A') @@ -3292,6 +3218,17 @@ def test_get_loc_msg(self): except KeyError as inst: self.assertEqual(inst.args[0], bad_period) + def test_get_loc_nat(self): + didx = DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03']) + pidx = PeriodIndex(['2011-01-01', 'NaT', '2011-01-03'], freq='M') + + # check DatetimeIndex compat + for idx in [didx, pidx]: + self.assertEqual(idx.get_loc(pd.NaT), 1) + self.assertEqual(idx.get_loc(None), 1) + self.assertEqual(idx.get_loc(float('nan')), 1) + self.assertEqual(idx.get_loc(np.nan), 1) + def test_append_concat(self): # #1815 d1 = date_range('12/31/1990', '12/31/1999', freq='A-DEC') @@ -3576,95 +3513,87 @@ def test_add_offset_nat(self): for freq in ['A', '2A', '3A']: p = Period('NaT', freq=freq) for o in [offsets.YearEnd(2)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) + # freq is Tick for freq in ['D', '2D', '3D']: p = Period('NaT', freq=freq) for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), np.timedelta64(3600 * 24, 's'), timedelta(-2), timedelta(hours=48)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23)]: - - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) for freq in ['H', '2H', '3H']: p = Period('NaT', freq=freq) for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), np.timedelta64(3600, 's'), timedelta(minutes=120), timedelta(days=4, minutes=180)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if not isinstance(o, np.timedelta64): - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(3200, 's'), timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) def test_sub_pdnat(self): # GH 13071 @@ -3749,24 +3678,22 @@ def test_sub_offset_nat(self): for freq in ['A', '2A', '3A']: p = Period('NaT', freq=freq) for o in [offsets.YearEnd(2)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) # freq is Tick for freq in ['D', '2D', '3D']: @@ -3774,37 +3701,33 @@ def test_sub_offset_nat(self): for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), np.timedelta64(3600 * 24, 's'), timedelta(-2), timedelta(hours=48)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) for freq in ['H', '2H', '3H']: p = Period('NaT', freq=freq) for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), np.timedelta64(3600, 's'), timedelta(minutes=120), timedelta(days=4, minutes=180)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(3200, 's'), timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) def test_nat_ops(self): for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) - self.assertEqual((p - 1).ordinal, tslib.iNaT) - self.assertEqual((p - Period('2011-01', freq=freq)).ordinal, - tslib.iNaT) - self.assertEqual((Period('2011-01', freq=freq) - p).ordinal, - tslib.iNaT) + self.assertIs(p + 1, tslib.NaT) + self.assertIs(1 + p, tslib.NaT) + self.assertIs(p - 1, tslib.NaT) + self.assertIs(p - Period('2011-01', freq=freq), tslib.NaT) + self.assertIs(Period('2011-01', freq=freq) - p, tslib.NaT) def test_period_ops_offset(self): p = Period('2011-04-01', freq='D') @@ -3830,18 +3753,17 @@ class TestPeriodIndexSeriesMethods(tm.TestCase): def _check(self, values, func, expected): idx = pd.PeriodIndex(values) result = func(idx) - tm.assert_index_equal(result, pd.PeriodIndex(expected)) + if isinstance(expected, pd.Index): + tm.assert_index_equal(result, expected) + else: + # comp op results in bool + tm.assert_numpy_array_equal(result, expected) s = pd.Series(values) result = func(s) - exp = pd.Series(expected) - # Period(NaT) != Period(NaT) - - lmask = result.map(lambda x: x.ordinal != tslib.iNaT) - rmask = exp.map(lambda x: x.ordinal != tslib.iNaT) - tm.assert_series_equal(lmask, rmask) - tm.assert_series_equal(result[lmask], exp[rmask]) + exp = pd.Series(expected, name=values.name) + tm.assert_series_equal(result, exp) def test_pi_ops(self): idx = PeriodIndex(['2011-01', '2011-02', '2011-03', @@ -3962,7 +3884,7 @@ def test_pi_sub_period(self): exp = pd.Index([12, 11, 10, 9], name='idx') tm.assert_index_equal(result, exp) - exp = pd.Index([np.nan, np.nan, np.nan, np.nan], name='idx') + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) @@ -3987,10 +3909,82 @@ def test_pi_sub_period_nat(self): exp = pd.Index([12, np.nan, 10, 9], name='idx') tm.assert_index_equal(result, exp) - exp = pd.Index([np.nan, np.nan, np.nan, np.nan], name='idx') + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) + def test_pi_comp_period(self): + idx = PeriodIndex(['2011-01', '2011-02', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > pd.Period('2011-03', freq='M') + exp = np.array([False, False, False, True], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + def test_pi_comp_period_nat(self): + idx = PeriodIndex(['2011-01', 'NaT', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x == tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: x != tslib.NaT + exp = np.array([True, True, True, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x < pd.Period('2011-03', freq='M') + exp = np.array([True, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: tslib.NaT >= x + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + class TestPeriodRepresentation(tm.TestCase): """ @@ -4326,10 +4320,10 @@ def test_NaT_scalar(self): series = Series([0, 1000, 2000, iNaT], dtype='period[D]') val = series[3] - self.assertTrue(com.isnull(val)) + self.assertTrue(isnull(val)) series[2] = val - self.assertTrue(com.isnull(series[2])) + self.assertTrue(isnull(series[2])) def test_NaT_cast(self): result = Series([np.nan]).astype('period[D]') diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 2236d20975eee..85d8cd52e1866 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -11,10 +11,11 @@ import pandas.util.testing as tm from pandas import (Series, DataFrame, Panel, Index, isnull, notnull, Timestamp) + +from pandas.types.generic import ABCSeries, ABCDataFrame from pandas.compat import range, lrange, zip, product, OrderedDict from pandas.core.base import SpecificationError -from pandas.core.common import (ABCSeries, ABCDataFrame, - UnsupportedFunctionCall) +from pandas.core.common import UnsupportedFunctionCall from pandas.core.groupby import DataError from pandas.tseries.frequencies import MONTHS, DAYS from pandas.tseries.frequencies import to_offset @@ -167,6 +168,13 @@ def f(): check_stacklevel=False): self.assertIsInstance(getattr(r, op)(2), pd.Series) + # IPython introspection shouldn't trigger warning GH 13618 + for op in ['_repr_json', '_repr_latex', + '_ipython_canary_method_should_not_exist_']: + r = self.series.resample('H') + with tm.assert_produces_warning(None): + getattr(r, op, None) + # getitem compat df = self.series.to_frame('foo') diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 4f985998d5e20..659101cb4cad2 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -30,6 +30,25 @@ class TestTimedeltas(tm.TestCase): def setUp(self): pass + def test_get_loc_nat(self): + tidx = TimedeltaIndex(['1 days 01:00:00', 'NaT', '2 days 01:00:00']) + + self.assertEqual(tidx.get_loc(pd.NaT), 1) + self.assertEqual(tidx.get_loc(None), 1) + self.assertEqual(tidx.get_loc(float('nan')), 1) + self.assertEqual(tidx.get_loc(np.nan), 1) + + def test_contains(self): + # Checking for any NaT-like objects + # GH 13603 + td = to_timedelta(range(5), unit='d') + pd.offsets.Hour(1) + for v in [pd.NaT, None, float('nan'), np.nan]: + self.assertFalse((v in td)) + + td = to_timedelta([pd.NaT]) + for v in [pd.NaT, None, float('nan'), np.nan]: + self.assertTrue((v in td)) + def test_construction(self): expected = np.timedelta64(10, 'D').astype('m8[ns]').view('i8') @@ -169,7 +188,6 @@ def test_construction(self): self.assertEqual(Timedelta('').value, iNaT) self.assertEqual(Timedelta('nat').value, iNaT) self.assertEqual(Timedelta('NAT').value, iNaT) - self.assertTrue(isnull(Timestamp('nat'))) self.assertTrue(isnull(Timedelta('nat'))) # offset diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index e594d31e57296..9c97749c87103 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -12,6 +12,7 @@ import pandas.lib as lib import pandas.tslib as tslib +from pandas.types.common import is_datetime64_ns_dtype import pandas as pd import pandas.compat as compat import pandas.core.common as com @@ -2282,7 +2283,7 @@ def test_to_datetime_tz_psycopg2(self): i = pd.DatetimeIndex([ '2000-01-01 08:00:00+00:00' ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)) - self.assertFalse(com.is_datetime64_ns_dtype(i)) + self.assertFalse(is_datetime64_ns_dtype(i)) # tz coerceion result = pd.to_datetime(i, errors='coerce') @@ -4086,8 +4087,9 @@ def test_dti_set_index_reindex(self): # 11314 # with tz - index = date_range(datetime(2015, 10, 1), datetime( - 2015, 10, 1, 23), freq='H', tz='US/Eastern') + index = date_range(datetime(2015, 10, 1), + datetime(2015, 10, 1, 23), + freq='H', tz='US/Eastern') df = DataFrame(np.random.randn(24, 1), columns=['a'], index=index) new_index = date_range(datetime(2015, 10, 2), datetime(2015, 10, 2, 23), @@ -4387,6 +4389,8 @@ def check(val, unit=None, h=1, s=1, us=0): result = Timestamp('NaT') self.assertIs(result, NaT) + self.assertTrue(isnull(Timestamp('nat'))) + def test_roundtrip(self): # test value to string and back conversions diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py index 71a041d5139a2..470aafafec547 100644 --- a/pandas/tseries/tests/test_timezones.py +++ b/pandas/tseries/tests/test_timezones.py @@ -5,6 +5,7 @@ import numpy as np import pytz +from pandas.types.dtypes import DatetimeTZDtype from pandas import (Index, Series, DataFrame, isnull, Timestamp) from pandas import DatetimeIndex, to_datetime, NaT @@ -17,7 +18,6 @@ from pytz import NonExistentTimeError import pandas.util.testing as tm -from pandas.types.api import DatetimeTZDtype from pandas.util.testing import assert_frame_equal, set_timezone from pandas.compat import lrange, zip diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index ce88edcf4249b..22bb3bddbc742 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -7,7 +7,8 @@ import datetime import pandas as pd -from pandas.core.api import Timestamp, Series, Timedelta, Period, to_datetime +from pandas.core.api import (Timestamp, Index, Series, Timedelta, Period, + to_datetime) from pandas.tslib import get_timezone from pandas._period import period_asfreq, period_ordinal from pandas.tseries.index import date_range, DatetimeIndex @@ -255,6 +256,18 @@ def test_constructor_keyword(self): hour=1, minute=2, second=3, microsecond=999999)), repr(Timestamp('2015-11-12 01:02:03.999999'))) + def test_constructor_fromordinal(self): + base = datetime.datetime(2000, 1, 1) + + ts = Timestamp.fromordinal(base.toordinal(), freq='D') + self.assertEqual(base, ts) + self.assertEqual(ts.freq, 'D') + self.assertEqual(base.toordinal(), ts.toordinal()) + + ts = Timestamp.fromordinal(base.toordinal(), tz='US/Eastern') + self.assertEqual(pd.Timestamp('2000-01-01', tz='US/Eastern'), ts) + self.assertEqual(base.toordinal(), ts.toordinal()) + def test_constructor_offset_depr(self): # GH 12160 with tm.assert_produces_warning(FutureWarning, @@ -270,6 +283,21 @@ def test_constructor_offset_depr(self): with tm.assertRaisesRegexp(TypeError, msg): Timestamp('2011-01-01', offset='D', freq='D') + def test_constructor_offset_depr_fromordinal(self): + # GH 12160 + base = datetime.datetime(2000, 1, 1) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + ts = Timestamp.fromordinal(base.toordinal(), offset='D') + self.assertEqual(pd.Timestamp('2000-01-01'), ts) + self.assertEqual(ts.freq, 'D') + self.assertEqual(base.toordinal(), ts.toordinal()) + + msg = "Can only specify freq or offset, not both" + with tm.assertRaisesRegexp(TypeError, msg): + Timestamp.fromordinal(base.toordinal(), offset='D', freq='D') + def test_conversion(self): # GH 9255 ts = Timestamp('2000-01-01') @@ -671,14 +699,19 @@ def test_parsers(self): yearfirst=yearfirst) result2 = to_datetime(date_str, yearfirst=yearfirst) result3 = to_datetime([date_str], yearfirst=yearfirst) + # result5 is used below result4 = to_datetime(np.array([date_str], dtype=object), yearfirst=yearfirst) - result6 = DatetimeIndex([date_str], yearfirst=yearfirst)[0] - self.assertEqual(result1, expected) - self.assertEqual(result2, expected) - self.assertEqual(result3, expected) - self.assertEqual(result4, expected) - self.assertEqual(result6, expected) + result6 = DatetimeIndex([date_str], yearfirst=yearfirst) + # result7 is used below + result8 = DatetimeIndex(Index([date_str]), yearfirst=yearfirst) + result9 = DatetimeIndex(Series([date_str]), yearfirst=yearfirst) + + for res in [result1, result2]: + self.assertEqual(res, expected) + for res in [result3, result4, result6, result8, result9]: + exp = DatetimeIndex([pd.Timestamp(expected)]) + tm.assert_index_equal(res, exp) # these really need to have yearfist, but we don't support if not yearfirst: @@ -866,9 +899,7 @@ def test_parsers_monthfreq(self): for date_str, expected in compat.iteritems(cases): result1, _, _ = tools.parse_time_string(date_str, freq='M') - result2 = tools._to_datetime(date_str, freq='M') self.assertEqual(result1, expected) - self.assertEqual(result2, expected) def test_parsers_quarterly_with_freq(self): msg = ('Incorrect quarterly string is given, quarter ' @@ -1197,6 +1228,13 @@ def test_nat_arithmetic(self): self.assertIs(left - right, pd.NaT) self.assertIs(right - left, pd.NaT) + # int addition / subtraction + for (left, right) in [(pd.NaT, 2), (pd.NaT, 0), (pd.NaT, -3)]: + self.assertIs(right + left, pd.NaT) + self.assertIs(left + right, pd.NaT) + self.assertIs(left - right, pd.NaT) + self.assertIs(right - left, pd.NaT) + def test_nat_arithmetic_index(self): # GH 11718 @@ -1388,16 +1426,14 @@ def _check_round(freq, expected): result = stamp.round(freq=freq) self.assertEqual(result, expected) - for freq, expected in [ - ('D', Timestamp('2000-01-05 00:00:00')), - ('H', Timestamp('2000-01-05 05:00:00')), - ('S', Timestamp('2000-01-05 05:09:15')) - ]: + for freq, expected in [('D', Timestamp('2000-01-05 00:00:00')), + ('H', Timestamp('2000-01-05 05:00:00')), + ('S', Timestamp('2000-01-05 05:09:15'))]: _check_round(freq, expected) - msg = "Could not evaluate" - tm.assertRaisesRegexp(ValueError, msg, - stamp.round, 'foo') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + stamp.round('foo') class TestTimestampOps(tm.TestCase): diff --git a/pandas/tseries/tests/test_util.py b/pandas/tseries/tests/test_util.py index 9c5c9b7a03445..9d992995df3a7 100644 --- a/pandas/tseries/tests/test_util.py +++ b/pandas/tseries/tests/test_util.py @@ -21,7 +21,8 @@ def test_daily(self): rng = date_range('1/1/2000', '12/31/2004', freq='D') ts = Series(np.random.randn(len(rng)), index=rng) - annual = pivot_annual(ts, 'D') + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + annual = pivot_annual(ts, 'D') doy = ts.index.dayofyear doy[(~isleapyear(ts.index.year)) & (doy >= 60)] += 1 @@ -53,7 +54,8 @@ def test_hourly(self): hoy[~isleapyear(ts_hourly.index.year) & (hoy >= 1416)] += 24 hoy += 1 - annual = pivot_annual(ts_hourly) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + annual = pivot_annual(ts_hourly) ts_hourly = ts_hourly.astype(float) for i in [1, 1416, 1417, 1418, 1439, 1440, 1441, 8784]: @@ -78,7 +80,8 @@ def test_monthly(self): rng = date_range('1/1/2000', '12/31/2004', freq='M') ts = Series(np.random.randn(len(rng)), index=rng) - annual = pivot_annual(ts, 'M') + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + annual = pivot_annual(ts, 'M') month = ts.index.month for i in range(1, 13): diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 5a28218500858..7f28ec86ec40d 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -4,9 +4,11 @@ import numpy as np import pandas.tslib as tslib -from pandas.core.common import (ABCSeries, is_integer_dtype, - is_timedelta64_dtype, is_list_like, - _ensure_object, ABCIndexClass) +from pandas.types.common import (_ensure_object, + is_integer_dtype, + is_timedelta64_dtype, + is_list_like) +from pandas.types.generic import ABCSeries, ABCIndexClass from pandas.util.decorators import deprecate_kwarg diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index efb8590dfccf4..93d35ff964e69 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -4,8 +4,17 @@ import pandas.lib as lib import pandas.tslib as tslib -import pandas.core.common as com -from pandas.core.common import ABCIndexClass, ABCSeries, ABCDataFrame + +from pandas.types.common import (_ensure_object, + is_datetime64_ns_dtype, + is_datetime64_dtype, + is_datetime64tz_dtype, + is_integer_dtype, + is_list_like) +from pandas.types.generic import (ABCIndexClass, ABCSeries, + ABCDataFrame) +from pandas.types.missing import notnull + import pandas.compat as compat from pandas.util.decorators import deprecate_kwarg @@ -161,7 +170,7 @@ def _guess_datetime_format(dt_str, dayfirst=False, def _guess_datetime_format_for_array(arr, **kwargs): # Try to guess the format based on the first non-NaN element - non_nan_elements = com.notnull(arr).nonzero()[0] + non_nan_elements = notnull(arr).nonzero()[0] if len(non_nan_elements): return _guess_datetime_format(arr[non_nan_elements[0]], **kwargs) @@ -286,40 +295,29 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, 1 loop, best of 3: 471 ms per loop """ - return _to_datetime(arg, errors=errors, dayfirst=dayfirst, - yearfirst=yearfirst, - utc=utc, box=box, format=format, exact=exact, - unit=unit, infer_datetime_format=infer_datetime_format) - -def _to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, - utc=None, box=True, format=None, exact=True, - unit=None, freq=None, infer_datetime_format=False): - """ - Same as to_datetime, but accept freq for - DatetimeIndex internal construction - """ from pandas.tseries.index import DatetimeIndex - def _convert_listlike(arg, box, format, name=None): + tz = 'utc' if utc else None + + def _convert_listlike(arg, box, format, name=None, tz=tz): if isinstance(arg, (list, tuple)): arg = np.array(arg, dtype='O') # these are shortcutable - if com.is_datetime64_ns_dtype(arg): + if is_datetime64_ns_dtype(arg): if box and not isinstance(arg, DatetimeIndex): try: - return DatetimeIndex(arg, tz='utc' if utc else None, - name=name) + return DatetimeIndex(arg, tz=tz, name=name) except ValueError: pass return arg - elif com.is_datetime64tz_dtype(arg): + elif is_datetime64tz_dtype(arg): if not isinstance(arg, DatetimeIndex): - return DatetimeIndex(arg, tz='utc' if utc else None) + return DatetimeIndex(arg, tz=tz, name=name) if utc: arg = arg.tz_convert(None).tz_localize('UTC') return arg @@ -335,14 +333,13 @@ def _convert_listlike(arg, box, format, name=None): from pandas import Index return Index(result) - return DatetimeIndex(result, tz='utc' if utc else None, - name=name) + return DatetimeIndex(result, tz=tz, name=name) return result elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a string, datetime, list, tuple, ' '1-d array, or Series') - arg = com._ensure_object(arg) + arg = _ensure_object(arg) require_iso8601 = False if infer_datetime_format and format is None: @@ -373,8 +370,8 @@ def _convert_listlike(arg, box, format, name=None): # fallback if result is None: try: - result = tslib.array_strptime( - arg, format, exact=exact, errors=errors) + result = tslib.array_strptime(arg, format, exact=exact, + errors=errors) except tslib.OutOfBoundsDatetime: if errors == 'raise': raise @@ -395,14 +392,11 @@ def _convert_listlike(arg, box, format, name=None): utc=utc, dayfirst=dayfirst, yearfirst=yearfirst, - freq=freq, require_iso8601=require_iso8601 ) - if com.is_datetime64_dtype(result) and box: - result = DatetimeIndex(result, - tz='utc' if utc else None, - name=name) + if is_datetime64_dtype(result) and box: + result = DatetimeIndex(result, tz=tz, name=name) return result except ValueError as e: @@ -424,7 +418,7 @@ def _convert_listlike(arg, box, format, name=None): return _assemble_from_unit_mappings(arg, errors=errors) elif isinstance(arg, ABCIndexClass): return _convert_listlike(arg, box, format, name=arg.name) - elif com.is_list_like(arg): + elif is_list_like(arg): return _convert_listlike(arg, box, format) return _convert_listlike(np.array([arg]), box, format)[0] @@ -511,7 +505,7 @@ def coerce(values): values = to_numeric(values, errors=errors) # prevent overflow in case of int8 or int16 - if com.is_integer_dtype(values): + if is_integer_dtype(values): values = values.astype('int64', copy=False) return values @@ -574,7 +568,7 @@ def calc_with_mask(carg, mask): # a float with actual np.nan try: carg = arg.astype(np.float64) - return calc_with_mask(carg, com.notnull(carg)) + return calc_with_mask(carg, notnull(carg)) except: pass @@ -654,7 +648,7 @@ def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None): def _guess_time_format_for_array(arr): # Try to guess the format based on the first non-NaN element - non_nan_elements = com.notnull(arr).nonzero()[0] + non_nan_elements = notnull(arr).nonzero()[0] if len(non_nan_elements): element = arr[non_nan_elements[0]] for time_format in _time_formats: @@ -705,7 +699,7 @@ def _convert_listlike(arg, format): raise TypeError('arg must be a string, datetime, list, tuple, ' '1-d array, or Series') - arg = com._ensure_object(arg) + arg = _ensure_object(arg) if infer_time_format and format is None: format = _guess_time_format_for_array(arg) @@ -762,7 +756,7 @@ def _convert_listlike(arg, format): return Series(values, index=arg.index, name=arg.name) elif isinstance(arg, ABCIndexClass): return _convert_listlike(arg, format) - elif com.is_list_like(arg): + elif is_list_like(arg): return _convert_listlike(arg, format) return _convert_listlike(np.array([arg]), format)[0] diff --git a/pandas/tseries/util.py b/pandas/tseries/util.py index 7e314657cb25c..7bac0567ea5c6 100644 --- a/pandas/tseries/util.py +++ b/pandas/tseries/util.py @@ -1,12 +1,16 @@ +import warnings + from pandas.compat import lrange import numpy as np -import pandas.core.common as com +from pandas.types.common import _ensure_platform_int from pandas.core.frame import DataFrame import pandas.core.nanops as nanops def pivot_annual(series, freq=None): """ + Deprecated. Use ``pivot_table`` instead. + Group a series by years, taking leap years into account. The output has as many rows as distinct years in the original series, @@ -35,6 +39,10 @@ def pivot_annual(series, freq=None): ------- annual : DataFrame """ + + msg = "pivot_annual is deprecated. Use pivot_table instead" + warnings.warn(msg, FutureWarning) + index = series.index year = index.year years = nanops.unique1d(year) @@ -69,7 +77,7 @@ def pivot_annual(series, freq=None): raise NotImplementedError(freq) flat_index = (year - years.min()) * width + offset - flat_index = com._ensure_platform_int(flat_index) + flat_index = _ensure_platform_int(flat_index) values = np.empty((len(years), width)) values.fill(np.nan) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 650b4c7979d8d..016c49ea2b859 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -235,12 +235,14 @@ class Timestamp(_Timestamp): ---------- ts_input : datetime-like, str, int, float Value to be converted to Timestamp - offset : str, DateOffset + freq : str, DateOffset Offset which Timestamp will have tz : string, pytz.timezone, dateutil.tz.tzfile or None Time zone for time which Timestamp will have. unit : string numpy unit used for conversion, if ts_input is int or float + offset : str, DateOffset + Deprecated, use freq The other two forms mimic the parameters from ``datetime.datetime``. They can be passed by either position or keyword, but not both mixed together. @@ -262,8 +264,21 @@ class Timestamp(_Timestamp): @classmethod def fromordinal(cls, ordinal, freq=None, tz=None, offset=None): - """ passed an ordinal, translate and convert to a ts - note: by definition there cannot be any tz info on the ordinal itself """ + """ + passed an ordinal, translate and convert to a ts + note: by definition there cannot be any tz info on the ordinal itself + + Parameters + ---------- + ordinal : int + date corresponding to a proleptic Gregorian ordinal + freq : str, DateOffset + Offset which Timestamp will have + tz : string, pytz.timezone, dateutil.tz.tzfile or None + Time zone for time which Timestamp will have. + offset : str, DateOffset + Deprecated, use freq + """ return cls(datetime.fromordinal(ordinal), freq=freq, tz=tz, offset=offset) @classmethod @@ -837,13 +852,6 @@ cdef inline bint _cmp_nat_dt(_NaT lhs, _Timestamp rhs, int op) except -1: return _nat_scalar_rules[op] -cdef _tz_format(object obj, object zone): - try: - return obj.strftime(' %%Z, tz=%s' % zone) - except: - return ', tz=%s' % zone - - cpdef object get_value_box(ndarray arr, object loc): cdef: Py_ssize_t i, sz @@ -1082,7 +1090,10 @@ cdef class _Timestamp(datetime): return Timestamp(self.value + other_int, tz=self.tzinfo, freq=self.freq) elif is_integer_object(other): - if self.freq is None: + if self is NaT: + # to be compat with Period + return NaT + elif self.freq is None: raise ValueError("Cannot add integral value to Timestamp " "without freq.") return Timestamp((self.freq * other).apply(self), freq=self.freq) @@ -1624,14 +1635,6 @@ cdef inline _check_dts_bounds(pandas_datetimestruct *dts): raise OutOfBoundsDatetime('Out of bounds nanosecond timestamp: %s' % fmt) -# elif isinstance(ts, _Timestamp): -# tmp = ts -# obj.value = (<_Timestamp> ts).value -# obj.dtval = -# elif isinstance(ts, object): -# # If all else fails -# obj.value = _dtlike_to_datetime64(ts, &obj.dts) -# obj.dtval = _dts_to_pydatetime(&obj.dts) def datetime_to_datetime64(ndarray[object] values): cdef: @@ -1671,7 +1674,7 @@ def datetime_to_datetime64(ndarray[object] values): cdef: set _not_datelike_strings = set(['a','A','m','M','p','P','t','T']) -cpdef object _does_string_look_like_datetime(object date_string): +cpdef bint _does_string_look_like_datetime(object date_string): if date_string.startswith('0'): # Strings starting with 0 are more consistent with a # date-like string than a number @@ -1809,8 +1812,14 @@ def parse_datetime_string(object date_string, object freq=None, except ValueError: pass - dt = parse_date(date_string, default=_DEFAULT_DATETIME, - dayfirst=dayfirst, yearfirst=yearfirst, **kwargs) + try: + dt = parse_date(date_string, default=_DEFAULT_DATETIME, + dayfirst=dayfirst, yearfirst=yearfirst, **kwargs) + except TypeError: + # following may be raised from dateutil + # TypeError: 'NoneType' object is not iterable + raise ValueError('Given date string not likely a datetime.') + return dt @@ -2196,7 +2205,7 @@ cpdef array_with_unit_to_datetime(ndarray values, unit, errors='coerce'): cpdef array_to_datetime(ndarray[object] values, errors='raise', - dayfirst=False, yearfirst=False, freq=None, + dayfirst=False, yearfirst=False, format=None, utc=None, require_iso8601=False): cdef: @@ -2325,7 +2334,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', try: py_dt = parse_datetime_string(val, dayfirst=dayfirst, - yearfirst=yearfirst, freq=freq) + yearfirst=yearfirst) except Exception: if is_coerce: iresult[i] = NPY_NAT @@ -2405,7 +2414,7 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', try: oresult[i] = parse_datetime_string(val, dayfirst=dayfirst, - yearfirst=yearfirst, freq=freq) + yearfirst=yearfirst) _pydatetime_to_dts(oresult[i], &dts) _check_dts_bounds(&dts) except Exception: @@ -2420,28 +2429,6 @@ cpdef array_to_datetime(ndarray[object] values, errors='raise', return oresult -def parse_str_array_to_datetime(ndarray values, dayfirst=False, - yearfirst=False, object freq=None): - """Shortcut to parse str array for quicker DatetimeIndex construction""" - cdef: - Py_ssize_t i, n = len(values) - object val, py_dt - ndarray[int64_t] iresult - _TSObject _ts - - iresult = np.empty(n, dtype='i8') - - for i in range(n): - val = values[i] - try: - py_dt = parse_datetime_string(val, dayfirst=dayfirst, - yearfirst=yearfirst, freq=freq) - except Exception: - raise ValueError - _ts = convert_to_tsobject(py_dt, None, None, 0, 0) - iresult[i] = _ts.value - - return iresult # Similar to Timestamp/datetime, this is a construction requirement for timedeltas # we need to do object instantiation in python @@ -3010,7 +2997,7 @@ cdef inline bint is_timedelta(object o): def array_to_timedelta64(ndarray[object] values, unit='ns', errors='raise'): """ convert an ndarray to an array of ints that are timedeltas - force conversion if coerce = True, + force conversion if errors = 'coerce', else will raise if cannot convert """ cdef: Py_ssize_t i, n diff --git a/pandas/types/api.py b/pandas/types/api.py index 721d8d29bba8b..2d68e041f632e 100644 --- a/pandas/types/api.py +++ b/pandas/types/api.py @@ -1,75 +1,54 @@ # flake8: noqa import numpy as np -from pandas.compat import string_types -from .dtypes import (CategoricalDtype, CategoricalDtypeType, - DatetimeTZDtype, DatetimeTZDtypeType) -from .generic import (ABCIndex, ABCInt64Index, ABCRangeIndex, - ABCFloat64Index, ABCMultiIndex, - ABCDatetimeIndex, - ABCTimedeltaIndex, ABCPeriodIndex, - ABCCategoricalIndex, - ABCIndexClass, - ABCSeries, ABCDataFrame, ABCPanel, - ABCSparseSeries, ABCSparseArray, - ABCCategorical, ABCPeriod, - ABCGeneric) - -def pandas_dtype(dtype): - """ - Converts input into a pandas only dtype object or a numpy dtype object. - - Parameters - ---------- - dtype : object to be converted - - Returns - ------- - np.dtype or a pandas dtype - """ - if isinstance(dtype, DatetimeTZDtype): - return dtype - elif isinstance(dtype, CategoricalDtype): - return dtype - elif isinstance(dtype, string_types): - try: - return DatetimeTZDtype.construct_from_string(dtype) - except TypeError: - pass - - try: - return CategoricalDtype.construct_from_string(dtype) - except TypeError: - pass - - return np.dtype(dtype) - -def na_value_for_dtype(dtype): - """ - Return a dtype compat na value - - Parameters - ---------- - dtype : string / dtype - - Returns - ------- - dtype compat na value - """ - - from pandas.core import common as com - from pandas import NaT - dtype = pandas_dtype(dtype) - - if (com.is_datetime64_dtype(dtype) or - com.is_datetime64tz_dtype(dtype) or - com.is_timedelta64_dtype(dtype)): - return NaT - elif com.is_float_dtype(dtype): - return np.nan - elif com.is_integer_dtype(dtype): - return 0 - elif com.is_bool_dtype(dtype): - return False - return np.nan +from .common import (pandas_dtype, + is_dtype_equal, + is_extension_type, + + # categorical + is_categorical, + is_categorical_dtype, + + # datetimelike + is_datetimetz, + is_datetime64_dtype, + is_datetime64tz_dtype, + is_datetime64_any_dtype, + is_datetime64_ns_dtype, + is_timedelta64_dtype, + is_timedelta64_ns_dtype, + + # string-like + is_string_dtype, + is_object_dtype, + + # sparse + is_sparse, + + # numeric types + is_scalar, + is_sparse, + is_bool, + is_integer, + is_float, + is_complex, + is_number, + is_any_int_dtype, + is_integer_dtype, + is_int64_dtype, + is_numeric_dtype, + is_float_dtype, + is_floating_dtype, + is_bool_dtype, + is_complex_dtype, + + # like + is_re, + is_re_compilable, + is_dict_like, + is_iterator, + is_list_like, + is_hashable, + is_named_tuple, + is_sequence) diff --git a/pandas/types/cast.py b/pandas/types/cast.py new file mode 100644 index 0000000000000..ca23d8d26a426 --- /dev/null +++ b/pandas/types/cast.py @@ -0,0 +1,860 @@ +""" routings for casting """ + +from datetime import datetime, timedelta +import numpy as np +from pandas import lib, tslib +from pandas.tslib import iNaT +from pandas.compat import string_types, text_type, PY3 +from .common import (_ensure_object, is_bool, is_integer, is_float, + is_complex, is_datetimetz, is_categorical_dtype, + is_extension_type, is_object_dtype, + is_datetime64tz_dtype, is_datetime64_dtype, + is_timedelta64_dtype, is_dtype_equal, + is_float_dtype, is_complex_dtype, + is_integer_dtype, is_datetime_or_timedelta_dtype, + is_scalar, + _string_dtypes, + _coerce_to_dtype, + _ensure_int8, _ensure_int16, + _ensure_int32, _ensure_int64, + _NS_DTYPE, _TD_DTYPE, _INT64_DTYPE, + _DATELIKE_DTYPES, _POSSIBLY_CAST_DTYPES) +from .generic import ABCDatetimeIndex, ABCPeriodIndex, ABCSeries +from .missing import isnull, notnull +from .inference import is_list_like + +_int8_max = np.iinfo(np.int8).max +_int16_max = np.iinfo(np.int16).max +_int32_max = np.iinfo(np.int32).max +_int64_max = np.iinfo(np.int64).max + + +def _possibly_convert_platform(values): + """ try to do platform conversion, allow ndarray or list here """ + + if isinstance(values, (list, tuple)): + values = lib.list_to_object_array(list(values)) + if getattr(values, 'dtype', None) == np.object_: + if hasattr(values, '_values'): + values = values._values + values = lib.maybe_convert_objects(values) + + return values + + +def _possibly_downcast_to_dtype(result, dtype): + """ try to cast to the specified dtype (e.g. convert back to bool/int + or could be an astype of float64->float32 + """ + + if is_scalar(result): + return result + + def trans(x): + return x + + if isinstance(dtype, string_types): + if dtype == 'infer': + inferred_type = lib.infer_dtype(_ensure_object(result.ravel())) + if inferred_type == 'boolean': + dtype = 'bool' + elif inferred_type == 'integer': + dtype = 'int64' + elif inferred_type == 'datetime64': + dtype = 'datetime64[ns]' + elif inferred_type == 'timedelta64': + dtype = 'timedelta64[ns]' + + # try to upcast here + elif inferred_type == 'floating': + dtype = 'int64' + if issubclass(result.dtype.type, np.number): + + def trans(x): # noqa + return x.round() + else: + dtype = 'object' + + if isinstance(dtype, string_types): + dtype = np.dtype(dtype) + + try: + + # don't allow upcasts here (except if empty) + if dtype.kind == result.dtype.kind: + if (result.dtype.itemsize <= dtype.itemsize and + np.prod(result.shape)): + return result + + if issubclass(dtype.type, np.floating): + return result.astype(dtype) + elif dtype == np.bool_ or issubclass(dtype.type, np.integer): + + # if we don't have any elements, just astype it + if not np.prod(result.shape): + return trans(result).astype(dtype) + + # do a test on the first element, if it fails then we are done + r = result.ravel() + arr = np.array([r[0]]) + + # if we have any nulls, then we are done + if isnull(arr).any() or not np.allclose(arr, + trans(arr).astype(dtype)): + return result + + # a comparable, e.g. a Decimal may slip in here + elif not isinstance(r[0], (np.integer, np.floating, np.bool, int, + float, bool)): + return result + + if (issubclass(result.dtype.type, (np.object_, np.number)) and + notnull(result).all()): + new_result = trans(result).astype(dtype) + try: + if np.allclose(new_result, result): + return new_result + except: + + # comparison of an object dtype with a number type could + # hit here + if (new_result == result).all(): + return new_result + + # a datetimelike + elif dtype.kind in ['M', 'm'] and result.dtype.kind in ['i']: + try: + result = result.astype(dtype) + except: + if dtype.tz: + # convert to datetime and change timezone + from pandas import to_datetime + result = to_datetime(result).tz_localize(dtype.tz) + + except: + pass + + return result + + +def _maybe_upcast_putmask(result, mask, other): + """ + A safe version of putmask that potentially upcasts the result + + Parameters + ---------- + result : ndarray + The destination array. This will be mutated in-place if no upcasting is + necessary. + mask : boolean ndarray + other : ndarray or scalar + The source array or value + + Returns + ------- + result : ndarray + changed : boolean + Set to true if the result array was upcasted + """ + + if mask.any(): + # Two conversions for date-like dtypes that can't be done automatically + # in np.place: + # NaN -> NaT + # integer or integer array -> date-like array + if result.dtype in _DATELIKE_DTYPES: + if is_scalar(other): + if isnull(other): + other = result.dtype.type('nat') + elif is_integer(other): + other = np.array(other, dtype=result.dtype) + elif is_integer_dtype(other): + other = np.array(other, dtype=result.dtype) + + def changeit(): + + # try to directly set by expanding our array to full + # length of the boolean + try: + om = other[mask] + om_at = om.astype(result.dtype) + if (om == om_at).all(): + new_result = result.values.copy() + new_result[mask] = om_at + result[:] = new_result + return result, False + except: + pass + + # we are forced to change the dtype of the result as the input + # isn't compatible + r, _ = _maybe_upcast(result, fill_value=other, copy=True) + np.place(r, mask, other) + + return r, True + + # we want to decide whether place will work + # if we have nans in the False portion of our mask then we need to + # upcast (possibly), otherwise we DON't want to upcast (e.g. if we + # have values, say integers, in the success portion then it's ok to not + # upcast) + new_dtype, _ = _maybe_promote(result.dtype, other) + if new_dtype != result.dtype: + + # we have a scalar or len 0 ndarray + # and its nan and we are changing some values + if (is_scalar(other) or + (isinstance(other, np.ndarray) and other.ndim < 1)): + if isnull(other): + return changeit() + + # we have an ndarray and the masking has nans in it + else: + + if isnull(other[mask]).any(): + return changeit() + + try: + np.place(result, mask, other) + except: + return changeit() + + return result, False + + +def _maybe_promote(dtype, fill_value=np.nan): + + # if we passed an array here, determine the fill value by dtype + if isinstance(fill_value, np.ndarray): + if issubclass(fill_value.dtype.type, (np.datetime64, np.timedelta64)): + fill_value = iNaT + else: + + # we need to change to object type as our + # fill_value is of object type + if fill_value.dtype == np.object_: + dtype = np.dtype(np.object_) + fill_value = np.nan + + # returns tuple of (dtype, fill_value) + if issubclass(dtype.type, (np.datetime64, np.timedelta64)): + # for now: refuse to upcast datetime64 + # (this is because datetime64 will not implicitly upconvert + # to object correctly as of numpy 1.6.1) + if isnull(fill_value): + fill_value = iNaT + else: + if issubclass(dtype.type, np.datetime64): + try: + fill_value = lib.Timestamp(fill_value).value + except: + # the proper thing to do here would probably be to upcast + # to object (but numpy 1.6.1 doesn't do this properly) + fill_value = iNaT + elif issubclass(dtype.type, np.timedelta64): + try: + fill_value = lib.Timedelta(fill_value).value + except: + # as for datetimes, cannot upcast to object + fill_value = iNaT + else: + fill_value = iNaT + elif is_datetimetz(dtype): + if isnull(fill_value): + fill_value = iNaT + elif is_float(fill_value): + if issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif issubclass(dtype.type, np.integer): + dtype = np.float64 + elif is_bool(fill_value): + if not issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif is_integer(fill_value): + if issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif issubclass(dtype.type, np.integer): + # upcast to prevent overflow + arr = np.asarray(fill_value) + if arr != arr.astype(dtype): + dtype = arr.dtype + elif is_complex(fill_value): + if issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif issubclass(dtype.type, (np.integer, np.floating)): + dtype = np.complex128 + elif fill_value is None: + if is_float_dtype(dtype) or is_complex_dtype(dtype): + fill_value = np.nan + elif is_integer_dtype(dtype): + dtype = np.float64 + fill_value = np.nan + elif is_datetime_or_timedelta_dtype(dtype): + fill_value = iNaT + else: + dtype = np.object_ + else: + dtype = np.object_ + + # in case we have a string that looked like a number + if is_categorical_dtype(dtype): + pass + elif is_datetimetz(dtype): + pass + elif issubclass(np.dtype(dtype).type, string_types): + dtype = np.object_ + + return dtype, fill_value + + +def _infer_dtype_from_scalar(val): + """ interpret the dtype from a scalar """ + + dtype = np.object_ + + # a 1-element ndarray + if isinstance(val, np.ndarray): + if val.ndim != 0: + raise ValueError( + "invalid ndarray passed to _infer_dtype_from_scalar") + + dtype = val.dtype + val = val.item() + + elif isinstance(val, string_types): + + # If we create an empty array using a string to infer + # the dtype, NumPy will only allocate one character per entry + # so this is kind of bad. Alternately we could use np.repeat + # instead of np.empty (but then you still don't want things + # coming out as np.str_! + + dtype = np.object_ + + elif isinstance(val, (np.datetime64, + datetime)) and getattr(val, 'tzinfo', None) is None: + val = lib.Timestamp(val).value + dtype = np.dtype('M8[ns]') + + elif isinstance(val, (np.timedelta64, timedelta)): + val = lib.Timedelta(val).value + dtype = np.dtype('m8[ns]') + + elif is_bool(val): + dtype = np.bool_ + + elif is_integer(val): + if isinstance(val, np.integer): + dtype = type(val) + else: + dtype = np.int64 + + elif is_float(val): + if isinstance(val, np.floating): + dtype = type(val) + else: + dtype = np.float64 + + elif is_complex(val): + dtype = np.complex_ + + return dtype, val + + +def _maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): + """ provide explict type promotion and coercion + + Parameters + ---------- + values : the ndarray that we want to maybe upcast + fill_value : what we want to fill with + dtype : if None, then use the dtype of the values, else coerce to this type + copy : if True always make a copy even if no upcast is required + """ + + if is_extension_type(values): + if copy: + values = values.copy() + else: + if dtype is None: + dtype = values.dtype + new_dtype, fill_value = _maybe_promote(dtype, fill_value) + if new_dtype != values.dtype: + values = values.astype(new_dtype) + elif copy: + values = values.copy() + + return values, fill_value + + +def _possibly_cast_item(obj, item, dtype): + chunk = obj[item] + + if chunk.values.dtype != dtype: + if dtype in (np.object_, np.bool_): + obj[item] = chunk.astype(np.object_) + elif not issubclass(dtype, (np.integer, np.bool_)): # pragma: no cover + raise ValueError("Unexpected dtype encountered: %s" % dtype) + + +def _invalidate_string_dtypes(dtype_set): + """Change string like dtypes to object for + ``DataFrame.select_dtypes()``. + """ + non_string_dtypes = dtype_set - _string_dtypes + if non_string_dtypes != dtype_set: + raise TypeError("string dtypes are not allowed, use 'object' instead") + + +def _maybe_convert_string_to_object(values): + """ + + Convert string-like and string-like array to convert object dtype. + This is to avoid numpy to handle the array as str dtype. + """ + if isinstance(values, string_types): + values = np.array([values], dtype=object) + elif (isinstance(values, np.ndarray) and + issubclass(values.dtype.type, (np.string_, np.unicode_))): + values = values.astype(object) + return values + + +def _maybe_convert_scalar(values): + """ + Convert a python scalar to the appropriate numpy dtype if possible + This avoids numpy directly converting according to platform preferences + """ + if is_scalar(values): + dtype, values = _infer_dtype_from_scalar(values) + try: + values = dtype(values) + except TypeError: + pass + return values + + +def _coerce_indexer_dtype(indexer, categories): + """ coerce the indexer input array to the smallest dtype possible """ + l = len(categories) + if l < _int8_max: + return _ensure_int8(indexer) + elif l < _int16_max: + return _ensure_int16(indexer) + elif l < _int32_max: + return _ensure_int32(indexer) + return _ensure_int64(indexer) + + +def _coerce_to_dtypes(result, dtypes): + """ + given a dtypes and a result set, coerce the result elements to the + dtypes + """ + if len(result) != len(dtypes): + raise AssertionError("_coerce_to_dtypes requires equal len arrays") + + from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type + + def conv(r, dtype): + try: + if isnull(r): + pass + elif dtype == _NS_DTYPE: + r = lib.Timestamp(r) + elif dtype == _TD_DTYPE: + r = _coerce_scalar_to_timedelta_type(r) + elif dtype == np.bool_: + # messy. non 0/1 integers do not get converted. + if is_integer(r) and r not in [0, 1]: + return int(r) + r = bool(r) + elif dtype.kind == 'f': + r = float(r) + elif dtype.kind == 'i': + r = int(r) + except: + pass + + return r + + return [conv(r, dtype) for r, dtype in zip(result, dtypes)] + + +def _astype_nansafe(arr, dtype, copy=True): + """ return a view if copy is False, but + need to be very careful as the result shape could change! """ + if not isinstance(dtype, np.dtype): + dtype = _coerce_to_dtype(dtype) + + if issubclass(dtype.type, text_type): + # in Py3 that's str, in Py2 that's unicode + return lib.astype_unicode(arr.ravel()).reshape(arr.shape) + elif issubclass(dtype.type, string_types): + return lib.astype_str(arr.ravel()).reshape(arr.shape) + elif is_datetime64_dtype(arr): + if dtype == object: + return tslib.ints_to_pydatetime(arr.view(np.int64)) + elif dtype == np.int64: + return arr.view(dtype) + elif dtype != _NS_DTYPE: + raise TypeError("cannot astype a datetimelike from [%s] to [%s]" % + (arr.dtype, dtype)) + return arr.astype(_NS_DTYPE) + elif is_timedelta64_dtype(arr): + if dtype == np.int64: + return arr.view(dtype) + elif dtype == object: + return tslib.ints_to_pytimedelta(arr.view(np.int64)) + + # in py3, timedelta64[ns] are int64 + elif ((PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE]) or + (not PY3 and dtype != _TD_DTYPE)): + + # allow frequency conversions + if dtype.kind == 'm': + mask = isnull(arr) + result = arr.astype(dtype).astype(np.float64) + result[mask] = np.nan + return result + + raise TypeError("cannot astype a timedelta from [%s] to [%s]" % + (arr.dtype, dtype)) + + return arr.astype(_TD_DTYPE) + elif (np.issubdtype(arr.dtype, np.floating) and + np.issubdtype(dtype, np.integer)): + + if np.isnan(arr).any(): + raise ValueError('Cannot convert NA to integer') + elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer): + # work around NumPy brokenness, #1987 + return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape) + + if copy: + return arr.astype(dtype) + return arr.view(dtype) + + +def _possibly_convert_objects(values, convert_dates=True, convert_numeric=True, + convert_timedeltas=True, copy=True): + """ if we have an object dtype, try to coerce dates and/or numbers """ + + # if we have passed in a list or scalar + if isinstance(values, (list, tuple)): + values = np.array(values, dtype=np.object_) + if not hasattr(values, 'dtype'): + values = np.array([values], dtype=np.object_) + + # convert dates + if convert_dates and values.dtype == np.object_: + + # we take an aggressive stance and convert to datetime64[ns] + if convert_dates == 'coerce': + new_values = _possibly_cast_to_datetime(values, 'M8[ns]', + errors='coerce') + + # if we are all nans then leave me alone + if not isnull(new_values).all(): + values = new_values + + else: + values = lib.maybe_convert_objects(values, + convert_datetime=convert_dates) + + # convert timedeltas + if convert_timedeltas and values.dtype == np.object_: + + if convert_timedeltas == 'coerce': + from pandas.tseries.timedeltas import to_timedelta + new_values = to_timedelta(values, coerce=True) + + # if we are all nans then leave me alone + if not isnull(new_values).all(): + values = new_values + + else: + values = lib.maybe_convert_objects( + values, convert_timedelta=convert_timedeltas) + + # convert to numeric + if values.dtype == np.object_: + if convert_numeric: + try: + new_values = lib.maybe_convert_numeric(values, set(), + coerce_numeric=True) + + # if we are all nans then leave me alone + if not isnull(new_values).all(): + values = new_values + + except: + pass + else: + # soft-conversion + values = lib.maybe_convert_objects(values) + + values = values.copy() if copy else values + + return values + + +def _soft_convert_objects(values, datetime=True, numeric=True, timedelta=True, + coerce=False, copy=True): + """ if we have an object dtype, try to coerce dates and/or numbers """ + + conversion_count = sum((datetime, numeric, timedelta)) + if conversion_count == 0: + raise ValueError('At least one of datetime, numeric or timedelta must ' + 'be True.') + elif conversion_count > 1 and coerce: + raise ValueError("Only one of 'datetime', 'numeric' or " + "'timedelta' can be True when when coerce=True.") + + if isinstance(values, (list, tuple)): + # List or scalar + values = np.array(values, dtype=np.object_) + elif not hasattr(values, 'dtype'): + values = np.array([values], dtype=np.object_) + elif not is_object_dtype(values.dtype): + # If not object, do not attempt conversion + values = values.copy() if copy else values + return values + + # If 1 flag is coerce, ensure 2 others are False + if coerce: + # Immediate return if coerce + if datetime: + from pandas import to_datetime + return to_datetime(values, errors='coerce', box=False) + elif timedelta: + from pandas import to_timedelta + return to_timedelta(values, errors='coerce', box=False) + elif numeric: + from pandas import to_numeric + return to_numeric(values, errors='coerce') + + # Soft conversions + if datetime: + values = lib.maybe_convert_objects(values, convert_datetime=datetime) + + if timedelta and is_object_dtype(values.dtype): + # Object check to ensure only run if previous did not convert + values = lib.maybe_convert_objects(values, convert_timedelta=timedelta) + + if numeric and is_object_dtype(values.dtype): + try: + converted = lib.maybe_convert_numeric(values, set(), + coerce_numeric=True) + # If all NaNs, then do not-alter + values = converted if not isnull(converted).all() else values + values = values.copy() if copy else values + except: + pass + + return values + + +def _possibly_castable(arr): + # return False to force a non-fastpath + + # check datetime64[ns]/timedelta64[ns] are valid + # otherwise try to coerce + kind = arr.dtype.kind + if kind == 'M' or kind == 'm': + return arr.dtype in _DATELIKE_DTYPES + + return arr.dtype.name not in _POSSIBLY_CAST_DTYPES + + +def _possibly_infer_to_datetimelike(value, convert_dates=False): + """ + we might have a array (or single object) that is datetime like, + and no dtype is passed don't change the value unless we find a + datetime/timedelta set + + this is pretty strict in that a datetime/timedelta is REQUIRED + in addition to possible nulls/string likes + + ONLY strings are NOT datetimelike + + Parameters + ---------- + value : np.array / Series / Index / list-like + convert_dates : boolean, default False + if True try really hard to convert dates (such as datetime.date), other + leave inferred dtype 'date' alone + + """ + + if isinstance(value, (ABCDatetimeIndex, ABCPeriodIndex)): + return value + elif isinstance(value, ABCSeries): + if isinstance(value._values, ABCDatetimeIndex): + return value._values + + v = value + + if not is_list_like(v): + v = [v] + v = np.array(v, copy=False) + shape = v.shape + if not v.ndim == 1: + v = v.ravel() + + if len(v): + + def _try_datetime(v): + # safe coerce to datetime64 + try: + v = tslib.array_to_datetime(v, errors='raise') + except ValueError: + + # we might have a sequence of the same-datetimes with tz's + # if so coerce to a DatetimeIndex; if they are not the same, + # then these stay as object dtype + try: + from pandas import to_datetime + return to_datetime(v) + except: + pass + + except: + pass + + return v.reshape(shape) + + def _try_timedelta(v): + # safe coerce to timedelta64 + + # will try first with a string & object conversion + from pandas import to_timedelta + try: + return to_timedelta(v)._values.reshape(shape) + except: + return v + + # do a quick inference for perf + sample = v[:min(3, len(v))] + inferred_type = lib.infer_dtype(sample) + + if (inferred_type in ['datetime', 'datetime64'] or + (convert_dates and inferred_type in ['date'])): + value = _try_datetime(v) + elif inferred_type in ['timedelta', 'timedelta64']: + value = _try_timedelta(v) + + # It's possible to have nulls intermixed within the datetime or + # timedelta. These will in general have an inferred_type of 'mixed', + # so have to try both datetime and timedelta. + + # try timedelta first to avoid spurious datetime conversions + # e.g. '00:00:01' is a timedelta but technically is also a datetime + elif inferred_type in ['mixed']: + + if lib.is_possible_datetimelike_array(_ensure_object(v)): + value = _try_timedelta(v) + if lib.infer_dtype(value) in ['mixed']: + value = _try_datetime(v) + + return value + + +def _possibly_cast_to_datetime(value, dtype, errors='raise'): + """ try to cast the array/value to a datetimelike dtype, converting float + nan to iNaT + """ + from pandas.tseries.timedeltas import to_timedelta + from pandas.tseries.tools import to_datetime + + if dtype is not None: + if isinstance(dtype, string_types): + dtype = np.dtype(dtype) + + is_datetime64 = is_datetime64_dtype(dtype) + is_datetime64tz = is_datetime64tz_dtype(dtype) + is_timedelta64 = is_timedelta64_dtype(dtype) + + if is_datetime64 or is_datetime64tz or is_timedelta64: + + # force the dtype if needed + if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE): + if dtype.name == 'datetime64[ns]': + dtype = _NS_DTYPE + else: + raise TypeError("cannot convert datetimelike to " + "dtype [%s]" % dtype) + elif is_datetime64tz: + + # our NaT doesn't support tz's + # this will coerce to DatetimeIndex with + # a matching dtype below + if is_scalar(value) and isnull(value): + value = [value] + + elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE): + if dtype.name == 'timedelta64[ns]': + dtype = _TD_DTYPE + else: + raise TypeError("cannot convert timedeltalike to " + "dtype [%s]" % dtype) + + if is_scalar(value): + if value == tslib.iNaT or isnull(value): + value = tslib.iNaT + else: + value = np.array(value, copy=False) + + # have a scalar array-like (e.g. NaT) + if value.ndim == 0: + value = tslib.iNaT + + # we have an array of datetime or timedeltas & nulls + elif np.prod(value.shape) or not is_dtype_equal(value.dtype, + dtype): + try: + if is_datetime64: + value = to_datetime(value, errors=errors)._values + elif is_datetime64tz: + # input has to be UTC at this point, so just + # localize + value = to_datetime( + value, + errors=errors).tz_localize(dtype.tz) + elif is_timedelta64: + value = to_timedelta(value, errors=errors)._values + except (AttributeError, ValueError, TypeError): + pass + + # coerce datetimelike to object + elif is_datetime64_dtype(value) and not is_datetime64_dtype(dtype): + if is_object_dtype(dtype): + ints = np.asarray(value).view('i8') + return tslib.ints_to_pydatetime(ints) + + # we have a non-castable dtype that was passed + raise TypeError('Cannot cast datetime64 to %s' % dtype) + + else: + + is_array = isinstance(value, np.ndarray) + + # catch a datetime/timedelta that is not of ns variety + # and no coercion specified + if is_array and value.dtype.kind in ['M', 'm']: + dtype = value.dtype + + if dtype.kind == 'M' and dtype != _NS_DTYPE: + value = value.astype(_NS_DTYPE) + + elif dtype.kind == 'm' and dtype != _TD_DTYPE: + value = to_timedelta(value) + + # only do this if we have an array and the dtype of the array is not + # setup already we are not an integer/object, so don't bother with this + # conversion + elif not (is_array and not (issubclass(value.dtype.type, np.integer) or + value.dtype == np.object_)): + value = _possibly_infer_to_datetimelike(value) + + return value diff --git a/pandas/types/common.py b/pandas/types/common.py new file mode 100644 index 0000000000000..9d0ccaac843ef --- /dev/null +++ b/pandas/types/common.py @@ -0,0 +1,448 @@ +""" common type operations """ + +import numpy as np +from pandas.compat import string_types, text_type, binary_type +from pandas import lib, algos +from .dtypes import (CategoricalDtype, CategoricalDtypeType, + DatetimeTZDtype, DatetimeTZDtypeType, + ExtensionDtype) +from .generic import (ABCCategorical, ABCPeriodIndex, + ABCDatetimeIndex, ABCSeries, + ABCSparseArray, ABCSparseSeries) +from .inference import is_integer, is_string_like +from .inference import * # noqa + + +_POSSIBLY_CAST_DTYPES = set([np.dtype(t).name + for t in ['O', 'int8', 'uint8', 'int16', 'uint16', + 'int32', 'uint32', 'int64', 'uint64']]) + +_NS_DTYPE = np.dtype('M8[ns]') +_TD_DTYPE = np.dtype('m8[ns]') +_INT64_DTYPE = np.dtype(np.int64) +_DATELIKE_DTYPES = set([np.dtype(t) + for t in ['M8[ns]', '<M8[ns]', '>M8[ns]', + 'm8[ns]', '<m8[ns]', '>m8[ns]']]) + +_ensure_float64 = algos.ensure_float64 +_ensure_float32 = algos.ensure_float32 + + +def _ensure_float(arr): + if issubclass(arr.dtype.type, (np.integer, np.bool_)): + arr = arr.astype(float) + return arr + +_ensure_int64 = algos.ensure_int64 +_ensure_int32 = algos.ensure_int32 +_ensure_int16 = algos.ensure_int16 +_ensure_int8 = algos.ensure_int8 +_ensure_platform_int = algos.ensure_platform_int +_ensure_object = algos.ensure_object + + +def is_object_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.object_) + + +def is_sparse(array): + """ return if we are a sparse array """ + return isinstance(array, (ABCSparseArray, ABCSparseSeries)) + + +def is_categorical(array): + """ return if we are a categorical possibility """ + return isinstance(array, ABCCategorical) or is_categorical_dtype(array) + + +def is_datetimetz(array): + """ return if we are a datetime with tz array """ + return ((isinstance(array, ABCDatetimeIndex) and + getattr(array, 'tz', None) is not None) or + is_datetime64tz_dtype(array)) + + +def is_datetime64_dtype(arr_or_dtype): + try: + tipo = _get_dtype_type(arr_or_dtype) + except TypeError: + return False + return issubclass(tipo, np.datetime64) + + +def is_datetime64tz_dtype(arr_or_dtype): + return DatetimeTZDtype.is_dtype(arr_or_dtype) + + +def is_timedelta64_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.timedelta64) + + +def is_categorical_dtype(arr_or_dtype): + return CategoricalDtype.is_dtype(arr_or_dtype) + + +def is_string_dtype(arr_or_dtype): + dtype = _get_dtype(arr_or_dtype) + return dtype.kind in ('O', 'S', 'U') + + +def is_period_arraylike(arr): + """ return if we are period arraylike / PeriodIndex """ + if isinstance(arr, ABCPeriodIndex): + return True + elif isinstance(arr, (np.ndarray, ABCSeries)): + return arr.dtype == object and lib.infer_dtype(arr) == 'period' + return getattr(arr, 'inferred_type', None) == 'period' + + +def is_datetime_arraylike(arr): + """ return if we are datetime arraylike / DatetimeIndex """ + if isinstance(arr, ABCDatetimeIndex): + return True + elif isinstance(arr, (np.ndarray, ABCSeries)): + return arr.dtype == object and lib.infer_dtype(arr) == 'datetime' + return getattr(arr, 'inferred_type', None) == 'datetime' + + +def is_datetimelike(arr): + return (arr.dtype in _DATELIKE_DTYPES or + isinstance(arr, ABCPeriodIndex) or + is_datetimetz(arr)) + + +def is_dtype_equal(source, target): + """ return a boolean if the dtypes are equal """ + try: + source = _get_dtype(source) + target = _get_dtype(target) + return source == target + except (TypeError, AttributeError): + + # invalid comparison + # object == category will hit this + return False + + +def is_any_int_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.integer) + + +def is_integer_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return (issubclass(tipo, np.integer) and + not issubclass(tipo, (np.datetime64, np.timedelta64))) + + +def is_int64_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.int64) + + +def is_int_or_datetime_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return (issubclass(tipo, np.integer) or + issubclass(tipo, (np.datetime64, np.timedelta64))) + + +def is_datetime64_any_dtype(arr_or_dtype): + return (is_datetime64_dtype(arr_or_dtype) or + is_datetime64tz_dtype(arr_or_dtype)) + + +def is_datetime64_ns_dtype(arr_or_dtype): + try: + tipo = _get_dtype(arr_or_dtype) + except TypeError: + return False + return tipo == _NS_DTYPE + + +def is_timedelta64_ns_dtype(arr_or_dtype): + tipo = _get_dtype(arr_or_dtype) + return tipo == _TD_DTYPE + + +def is_datetime_or_timedelta_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, (np.datetime64, np.timedelta64)) + + +def is_numeric_v_string_like(a, b): + """ + numpy doesn't like to compare numeric arrays vs scalar string-likes + + return a boolean result if this is the case for a,b or b,a + + """ + is_a_array = isinstance(a, np.ndarray) + is_b_array = isinstance(b, np.ndarray) + + is_a_numeric_array = is_a_array and is_numeric_dtype(a) + is_b_numeric_array = is_b_array and is_numeric_dtype(b) + is_a_string_array = is_a_array and is_string_like_dtype(a) + is_b_string_array = is_b_array and is_string_like_dtype(b) + + is_a_scalar_string_like = not is_a_array and is_string_like(a) + is_b_scalar_string_like = not is_b_array and is_string_like(b) + + return ((is_a_numeric_array and is_b_scalar_string_like) or + (is_b_numeric_array and is_a_scalar_string_like) or + (is_a_numeric_array and is_b_string_array) or + (is_b_numeric_array and is_a_string_array)) + + +def is_datetimelike_v_numeric(a, b): + # return if we have an i8 convertible and numeric comparison + if not hasattr(a, 'dtype'): + a = np.asarray(a) + if not hasattr(b, 'dtype'): + b = np.asarray(b) + + def is_numeric(x): + return is_integer_dtype(x) or is_float_dtype(x) + + is_datetimelike = needs_i8_conversion + return ((is_datetimelike(a) and is_numeric(b)) or + (is_datetimelike(b) and is_numeric(a))) + + +def is_datetimelike_v_object(a, b): + # return if we have an i8 convertible and object comparsion + if not hasattr(a, 'dtype'): + a = np.asarray(a) + if not hasattr(b, 'dtype'): + b = np.asarray(b) + + def f(x): + return is_object_dtype(x) + + def is_object(x): + return is_integer_dtype(x) or is_float_dtype(x) + + is_datetimelike = needs_i8_conversion + return ((is_datetimelike(a) and is_object(b)) or + (is_datetimelike(b) and is_object(a))) + + +def needs_i8_conversion(arr_or_dtype): + return (is_datetime_or_timedelta_dtype(arr_or_dtype) or + is_datetime64tz_dtype(arr_or_dtype)) + + +def is_numeric_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return (issubclass(tipo, (np.number, np.bool_)) and + not issubclass(tipo, (np.datetime64, np.timedelta64))) + + +def is_string_like_dtype(arr_or_dtype): + # exclude object as its a mixed dtype + dtype = _get_dtype(arr_or_dtype) + return dtype.kind in ('S', 'U') + + +def is_float_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.floating) + + +def is_floating_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return isinstance(tipo, np.floating) + + +def is_bool_dtype(arr_or_dtype): + try: + tipo = _get_dtype_type(arr_or_dtype) + except ValueError: + # this isn't even a dtype + return False + return issubclass(tipo, np.bool_) + + +def is_extension_type(value): + """ + if we are a klass that is preserved by the internals + these are internal klasses that we represent (and don't use a np.array) + """ + if is_categorical(value): + return True + elif is_sparse(value): + return True + elif is_datetimetz(value): + return True + return False + + +def is_complex_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.complexfloating) + + +def _coerce_to_dtype(dtype): + """ coerce a string / np.dtype to a dtype """ + if is_categorical_dtype(dtype): + dtype = CategoricalDtype() + elif is_datetime64tz_dtype(dtype): + dtype = DatetimeTZDtype(dtype) + else: + dtype = np.dtype(dtype) + return dtype + + +def _get_dtype(arr_or_dtype): + if isinstance(arr_or_dtype, np.dtype): + return arr_or_dtype + elif isinstance(arr_or_dtype, type): + return np.dtype(arr_or_dtype) + elif isinstance(arr_or_dtype, CategoricalDtype): + return arr_or_dtype + elif isinstance(arr_or_dtype, DatetimeTZDtype): + return arr_or_dtype + elif isinstance(arr_or_dtype, string_types): + if is_categorical_dtype(arr_or_dtype): + return CategoricalDtype.construct_from_string(arr_or_dtype) + elif is_datetime64tz_dtype(arr_or_dtype): + return DatetimeTZDtype.construct_from_string(arr_or_dtype) + + if hasattr(arr_or_dtype, 'dtype'): + arr_or_dtype = arr_or_dtype.dtype + return np.dtype(arr_or_dtype) + + +def _get_dtype_type(arr_or_dtype): + if isinstance(arr_or_dtype, np.dtype): + return arr_or_dtype.type + elif isinstance(arr_or_dtype, type): + return np.dtype(arr_or_dtype).type + elif isinstance(arr_or_dtype, CategoricalDtype): + return CategoricalDtypeType + elif isinstance(arr_or_dtype, DatetimeTZDtype): + return DatetimeTZDtypeType + elif isinstance(arr_or_dtype, string_types): + if is_categorical_dtype(arr_or_dtype): + return CategoricalDtypeType + elif is_datetime64tz_dtype(arr_or_dtype): + return DatetimeTZDtypeType + return _get_dtype_type(np.dtype(arr_or_dtype)) + try: + return arr_or_dtype.dtype.type + except AttributeError: + return type(None) + + +def _get_dtype_from_object(dtype): + """Get a numpy dtype.type-style object. This handles the datetime64[ns] + and datetime64[ns, TZ] compat + + Notes + ----- + If nothing can be found, returns ``object``. + """ + + # type object from a dtype + if isinstance(dtype, type) and issubclass(dtype, np.generic): + return dtype + elif is_categorical(dtype): + return CategoricalDtype().type + elif is_datetimetz(dtype): + return DatetimeTZDtype(dtype).type + elif isinstance(dtype, np.dtype): # dtype object + try: + _validate_date_like_dtype(dtype) + except TypeError: + # should still pass if we don't have a datelike + pass + return dtype.type + elif isinstance(dtype, string_types): + if dtype == 'datetime' or dtype == 'timedelta': + dtype += '64' + + try: + return _get_dtype_from_object(getattr(np, dtype)) + except (AttributeError, TypeError): + # handles cases like _get_dtype(int) + # i.e., python objects that are valid dtypes (unlike user-defined + # types, in general) + # TypeError handles the float16 typecode of 'e' + # further handle internal types + pass + + return _get_dtype_from_object(np.dtype(dtype)) + + +def _validate_date_like_dtype(dtype): + try: + typ = np.datetime_data(dtype)[0] + except ValueError as e: + raise TypeError('%s' % e) + if typ != 'generic' and typ != 'ns': + raise ValueError('%r is too specific of a frequency, try passing %r' % + (dtype.name, dtype.type.__name__)) + + +def _lcd_dtypes(a_dtype, b_dtype): + """ return the lcd dtype to hold these types """ + + if is_datetime64_dtype(a_dtype) or is_datetime64_dtype(b_dtype): + return _NS_DTYPE + elif is_timedelta64_dtype(a_dtype) or is_timedelta64_dtype(b_dtype): + return _TD_DTYPE + elif is_complex_dtype(a_dtype): + if is_complex_dtype(b_dtype): + return a_dtype + return np.float64 + elif is_integer_dtype(a_dtype): + if is_integer_dtype(b_dtype): + if a_dtype.itemsize == b_dtype.itemsize: + return a_dtype + return np.int64 + return np.float64 + elif is_float_dtype(a_dtype): + if is_float_dtype(b_dtype): + if a_dtype.itemsize == b_dtype.itemsize: + return a_dtype + else: + return np.float64 + elif is_integer(b_dtype): + return np.float64 + return np.object + +_string_dtypes = frozenset(map(_get_dtype_from_object, (binary_type, + text_type))) + + +def pandas_dtype(dtype): + """ + Converts input into a pandas only dtype object or a numpy dtype object. + + Parameters + ---------- + dtype : object to be converted + + Returns + ------- + np.dtype or a pandas dtype + """ + if isinstance(dtype, DatetimeTZDtype): + return dtype + elif isinstance(dtype, CategoricalDtype): + return dtype + elif isinstance(dtype, string_types): + try: + return DatetimeTZDtype.construct_from_string(dtype) + except TypeError: + pass + + try: + return CategoricalDtype.construct_from_string(dtype) + except TypeError: + pass + elif isinstance(dtype, ExtensionDtype): + return dtype + + return np.dtype(dtype) diff --git a/pandas/types/concat.py b/pandas/types/concat.py index 44338f26eb2e8..3b30531fb30ac 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -3,10 +3,19 @@ """ import numpy as np -import pandas.core.common as com import pandas.tslib as tslib from pandas import compat from pandas.compat import map +from .common import (is_categorical_dtype, + is_sparse, + is_datetimetz, + is_datetime64_dtype, + is_timedelta64_dtype, + is_object_dtype, + is_bool_dtype, + is_dtype_equal, + _NS_DTYPE, + _TD_DTYPE) def get_dtype_kinds(l): @@ -24,19 +33,19 @@ def get_dtype_kinds(l): for arr in l: dtype = arr.dtype - if com.is_categorical_dtype(dtype): + if is_categorical_dtype(dtype): typ = 'category' - elif com.is_sparse(arr): + elif is_sparse(arr): typ = 'sparse' - elif com.is_datetimetz(arr): + elif is_datetimetz(arr): typ = 'datetimetz' - elif com.is_datetime64_dtype(dtype): + elif is_datetime64_dtype(dtype): typ = 'datetime' - elif com.is_timedelta64_dtype(dtype): + elif is_timedelta64_dtype(dtype): typ = 'timedelta' - elif com.is_object_dtype(dtype): + elif is_object_dtype(dtype): typ = 'object' - elif com.is_bool_dtype(dtype): + elif is_bool_dtype(dtype): typ = 'bool' else: typ = dtype.kind @@ -51,14 +60,14 @@ def _get_series_result_type(result): """ if isinstance(result, dict): # concat Series with axis 1 - if all(com.is_sparse(c) for c in compat.itervalues(result)): + if all(is_sparse(c) for c in compat.itervalues(result)): from pandas.sparse.api import SparseDataFrame return SparseDataFrame else: from pandas.core.frame import DataFrame return DataFrame - elif com.is_sparse(result): + elif is_sparse(result): # concat Series with axis 1 from pandas.sparse.api import SparseSeries return SparseSeries @@ -165,7 +174,7 @@ def _concat_categorical(to_concat, axis=0): def convert_categorical(x): # coerce to object dtype - if com.is_categorical_dtype(x.dtype): + if is_categorical_dtype(x.dtype): return x.get_values() return x.ravel() @@ -177,7 +186,7 @@ def convert_categorical(x): # we could have object blocks and categoricals here # if we only have a single categoricals then combine everything # else its a non-compat categorical - categoricals = [x for x in to_concat if com.is_categorical_dtype(x.dtype)] + categoricals = [x for x in to_concat if is_categorical_dtype(x.dtype)] # validate the categories categories = categoricals[0] @@ -235,7 +244,7 @@ def union_categoricals(to_union): if any(c.ordered for c in to_union): raise TypeError("Can only combine unordered Categoricals") - if not all(com.is_dtype_equal(c.categories.dtype, first.categories.dtype) + if not all(is_dtype_equal(c.categories.dtype, first.categories.dtype) for c in to_union): raise TypeError("dtype of categories must be the same") @@ -272,7 +281,7 @@ def convert_to_pydatetime(x, axis): # coerce to an object dtype # if dtype is of datetimetz or timezone - if x.dtype.kind == com._NS_DTYPE.kind: + if x.dtype.kind == _NS_DTYPE.kind: if getattr(x, 'tz', None) is not None: x = x.asobject.values else: @@ -280,7 +289,7 @@ def convert_to_pydatetime(x, axis): x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) x = x.reshape(shape) - elif x.dtype == com._TD_DTYPE: + elif x.dtype == _TD_DTYPE: shape = x.shape x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) x = x.reshape(shape) @@ -310,12 +319,12 @@ def convert_to_pydatetime(x, axis): elif 'datetime' in typs: new_values = np.concatenate([x.view(np.int64) for x in to_concat], axis=axis) - return new_values.view(com._NS_DTYPE) + return new_values.view(_NS_DTYPE) elif 'timedelta' in typs: new_values = np.concatenate([x.view(np.int64) for x in to_concat], axis=axis) - return new_values.view(com._TD_DTYPE) + return new_values.view(_TD_DTYPE) # need to coerce to object to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] @@ -350,7 +359,7 @@ def convert_sparse(x, axis): return x if typs is None: - typs = com.get_dtype_kinds(to_concat) + typs = get_dtype_kinds(to_concat) if len(typs) == 1: # concat input as it is if all inputs are sparse @@ -374,7 +383,7 @@ def convert_sparse(x, axis): # input may be sparse / dense mixed and may have different fill_value # input must contain sparse at least 1 - sparses = [c for c in to_concat if com.is_sparse(c)] + sparses = [c for c in to_concat if is_sparse(c)] fill_values = [c.fill_value for c in sparses] sp_indexes = [c.sp_index for c in sparses] diff --git a/pandas/types/inference.py b/pandas/types/inference.py new file mode 100644 index 0000000000000..35a2dc2fb831b --- /dev/null +++ b/pandas/types/inference.py @@ -0,0 +1,104 @@ +""" basic inference routines """ + +import collections +import re +import numpy as np +from numbers import Number +from pandas.compat import (string_types, text_type, + string_and_binary_types) +from pandas import lib + +is_bool = lib.is_bool + +is_integer = lib.is_integer + +is_float = lib.is_float + +is_complex = lib.is_complex + +is_scalar = lib.isscalar + + +def is_number(obj): + return isinstance(obj, (Number, np.number)) + + +def is_string_like(obj): + return isinstance(obj, (text_type, string_types)) + + +def _iterable_not_string(x): + return (isinstance(x, collections.Iterable) and + not isinstance(x, string_types)) + + +def is_iterator(obj): + # python 3 generators have __next__ instead of next + return hasattr(obj, 'next') or hasattr(obj, '__next__') + + +def is_re(obj): + return isinstance(obj, re._pattern_type) + + +def is_re_compilable(obj): + try: + re.compile(obj) + except TypeError: + return False + else: + return True + + +def is_list_like(arg): + return (hasattr(arg, '__iter__') and + not isinstance(arg, string_and_binary_types)) + + +def is_dict_like(arg): + return hasattr(arg, '__getitem__') and hasattr(arg, 'keys') + + +def is_named_tuple(arg): + return isinstance(arg, tuple) and hasattr(arg, '_fields') + + +def is_hashable(arg): + """Return True if hash(arg) will succeed, False otherwise. + + Some types will pass a test against collections.Hashable but fail when they + are actually hashed with hash(). + + Distinguish between these and other types by trying the call to hash() and + seeing if they raise TypeError. + + Examples + -------- + >>> a = ([],) + >>> isinstance(a, collections.Hashable) + True + >>> is_hashable(a) + False + """ + # unfortunately, we can't use isinstance(arg, collections.Hashable), which + # can be faster than calling hash, because numpy scalars on Python 3 fail + # this test + + # reconsider this decision once this numpy bug is fixed: + # https://github.com/numpy/numpy/issues/5562 + + try: + hash(arg) + except TypeError: + return False + else: + return True + + +def is_sequence(x): + try: + iter(x) + len(x) # it has a length + return not isinstance(x, string_and_binary_types) + except (TypeError, AttributeError): + return False diff --git a/pandas/types/missing.py b/pandas/types/missing.py new file mode 100644 index 0000000000000..8b4193d02beb7 --- /dev/null +++ b/pandas/types/missing.py @@ -0,0 +1,394 @@ +""" +missing types & inference +""" +import numpy as np +from pandas import lib +from pandas.tslib import NaT, iNaT +from .generic import (ABCMultiIndex, ABCSeries, + ABCIndexClass, ABCGeneric) +from .common import (is_string_dtype, is_datetimelike, + is_datetimelike_v_numeric, is_float_dtype, + is_datetime64_dtype, is_datetime64tz_dtype, + is_timedelta64_dtype, + is_complex_dtype, is_categorical_dtype, + is_string_like_dtype, is_bool_dtype, + is_integer_dtype, is_dtype_equal, + needs_i8_conversion, _ensure_object, + pandas_dtype, + is_scalar, + is_object_dtype, + is_integer, + _TD_DTYPE, + _NS_DTYPE, + _DATELIKE_DTYPES) +from .inference import is_list_like + + +def isnull(obj): + """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) + + Parameters + ---------- + arr : ndarray or object value + Object to check for null-ness + + Returns + ------- + isnulled : array-like of bool or bool + Array or bool indicating whether an object is null or if an array is + given which of the element is null. + + See also + -------- + pandas.notnull: boolean inverse of pandas.isnull + """ + return _isnull(obj) + + +def _isnull_new(obj): + if is_scalar(obj): + return lib.checknull(obj) + # hack (for now) because MI registers as ndarray + elif isinstance(obj, ABCMultiIndex): + raise NotImplementedError("isnull is not defined for MultiIndex") + elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)): + return _isnull_ndarraylike(obj) + elif isinstance(obj, ABCGeneric): + return obj._constructor(obj._data.isnull(func=isnull)) + elif isinstance(obj, list) or hasattr(obj, '__array__'): + return _isnull_ndarraylike(np.asarray(obj)) + else: + return obj is None + + +def _isnull_old(obj): + """Detect missing values. Treat None, NaN, INF, -INF as null. + + Parameters + ---------- + arr: ndarray or object value + + Returns + ------- + boolean ndarray or boolean + """ + if is_scalar(obj): + return lib.checknull_old(obj) + # hack (for now) because MI registers as ndarray + elif isinstance(obj, ABCMultiIndex): + raise NotImplementedError("isnull is not defined for MultiIndex") + elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)): + return _isnull_ndarraylike_old(obj) + elif isinstance(obj, ABCGeneric): + return obj._constructor(obj._data.isnull(func=_isnull_old)) + elif isinstance(obj, list) or hasattr(obj, '__array__'): + return _isnull_ndarraylike_old(np.asarray(obj)) + else: + return obj is None + + +_isnull = _isnull_new + + +def _use_inf_as_null(key): + """Option change callback for null/inf behaviour + Choose which replacement for numpy.isnan / -numpy.isfinite is used. + + Parameters + ---------- + flag: bool + True means treat None, NaN, INF, -INF as null (old way), + False means None and NaN are null, but INF, -INF are not null + (new way). + + Notes + ----- + This approach to setting global module values is discussed and + approved here: + + * http://stackoverflow.com/questions/4859217/ + programmatically-creating-variables-in-python/4859312#4859312 + """ + from pandas.core.config import get_option + flag = get_option(key) + if flag: + globals()['_isnull'] = _isnull_old + else: + globals()['_isnull'] = _isnull_new + + +def _isnull_ndarraylike(obj): + + values = getattr(obj, 'values', obj) + dtype = values.dtype + + if is_string_dtype(dtype): + if is_categorical_dtype(values): + from pandas import Categorical + if not isinstance(values, Categorical): + values = values.values + result = values.isnull() + else: + + # Working around NumPy ticket 1542 + shape = values.shape + + if is_string_like_dtype(dtype): + result = np.zeros(values.shape, dtype=bool) + else: + result = np.empty(shape, dtype=bool) + vec = lib.isnullobj(values.ravel()) + result[...] = vec.reshape(shape) + + elif is_datetimelike(obj): + # this is the NaT pattern + result = values.view('i8') == iNaT + else: + result = np.isnan(values) + + # box + if isinstance(obj, ABCSeries): + from pandas import Series + result = Series(result, index=obj.index, name=obj.name, copy=False) + + return result + + +def _isnull_ndarraylike_old(obj): + values = getattr(obj, 'values', obj) + dtype = values.dtype + + if is_string_dtype(dtype): + # Working around NumPy ticket 1542 + shape = values.shape + + if is_string_like_dtype(dtype): + result = np.zeros(values.shape, dtype=bool) + else: + result = np.empty(shape, dtype=bool) + vec = lib.isnullobj_old(values.ravel()) + result[:] = vec.reshape(shape) + + elif dtype in _DATELIKE_DTYPES: + # this is the NaT pattern + result = values.view('i8') == iNaT + else: + result = ~np.isfinite(values) + + # box + if isinstance(obj, ABCSeries): + from pandas import Series + result = Series(result, index=obj.index, name=obj.name, copy=False) + + return result + + +def notnull(obj): + """Replacement for numpy.isfinite / -numpy.isnan which is suitable for use + on object arrays. + + Parameters + ---------- + arr : ndarray or object value + Object to check for *not*-null-ness + + Returns + ------- + isnulled : array-like of bool or bool + Array or bool indicating whether an object is *not* null or if an array + is given which of the element is *not* null. + + See also + -------- + pandas.isnull : boolean inverse of pandas.notnull + """ + res = isnull(obj) + if is_scalar(res): + return not res + return ~res + + +def is_null_datelike_scalar(other): + """ test whether the object is a null datelike, e.g. Nat + but guard against passing a non-scalar """ + if other is NaT or other is None: + return True + elif is_scalar(other): + + # a timedelta + if hasattr(other, 'dtype'): + return other.view('i8') == iNaT + elif is_integer(other) and other == iNaT: + return True + return isnull(other) + return False + + +def _is_na_compat(arr, fill_value=np.nan): + """ + Parameters + ---------- + arr: a numpy array + fill_value: fill value, default to np.nan + + Returns + ------- + True if we can fill using this fill_value + """ + dtype = arr.dtype + if isnull(fill_value): + return not (is_bool_dtype(dtype) or + is_integer_dtype(dtype)) + return True + + +def array_equivalent(left, right, strict_nan=False): + """ + True if two arrays, left and right, have equal non-NaN elements, and NaNs + in corresponding locations. False otherwise. It is assumed that left and + right are NumPy arrays of the same dtype. The behavior of this function + (particularly with respect to NaNs) is not defined if the dtypes are + different. + + Parameters + ---------- + left, right : ndarrays + strict_nan : bool, default False + If True, consider NaN and None to be different. + + Returns + ------- + b : bool + Returns True if the arrays are equivalent. + + Examples + -------- + >>> array_equivalent( + ... np.array([1, 2, np.nan]), + ... np.array([1, 2, np.nan])) + True + >>> array_equivalent( + ... np.array([1, np.nan, 2]), + ... np.array([1, 2, np.nan])) + False + """ + + left, right = np.asarray(left), np.asarray(right) + + # shape compat + if left.shape != right.shape: + return False + + # Object arrays can contain None, NaN and NaT. + # string dtypes must be come to this path for NumPy 1.7.1 compat + if is_string_dtype(left) or is_string_dtype(right): + + if not strict_nan: + # isnull considers NaN and None to be equivalent. + return lib.array_equivalent_object( + _ensure_object(left.ravel()), _ensure_object(right.ravel())) + + for left_value, right_value in zip(left, right): + if left_value is NaT and right_value is not NaT: + return False + + elif isinstance(left_value, float) and np.isnan(left_value): + if (not isinstance(right_value, float) or + not np.isnan(right_value)): + return False + else: + if left_value != right_value: + return False + return True + + # NaNs can occur in float and complex arrays. + if is_float_dtype(left) or is_complex_dtype(left): + return ((left == right) | (np.isnan(left) & np.isnan(right))).all() + + # numpy will will not allow this type of datetimelike vs integer comparison + elif is_datetimelike_v_numeric(left, right): + return False + + # M8/m8 + elif needs_i8_conversion(left) and needs_i8_conversion(right): + if not is_dtype_equal(left.dtype, right.dtype): + return False + + left = left.view('i8') + right = right.view('i8') + + # NaNs cannot occur otherwise. + try: + return np.array_equal(left, right) + except AttributeError: + # see gh-13388 + # + # NumPy v1.7.1 has a bug in its array_equal + # function that prevents it from correctly + # comparing two arrays with complex dtypes. + # This bug is corrected in v1.8.0, so remove + # this try-except block as soon as we stop + # supporting NumPy versions < 1.8.0 + if not is_dtype_equal(left.dtype, right.dtype): + return False + + left = left.tolist() + right = right.tolist() + + return left == right + + +def _infer_fill_value(val): + """ + infer the fill value for the nan/NaT from the provided + scalar/ndarray/list-like if we are a NaT, return the correct dtyped + element to provide proper block construction + """ + + if not is_list_like(val): + val = [val] + val = np.array(val, copy=False) + if is_datetimelike(val): + return np.array('NaT', dtype=val.dtype) + elif is_object_dtype(val.dtype): + dtype = lib.infer_dtype(_ensure_object(val)) + if dtype in ['datetime', 'datetime64']: + return np.array('NaT', dtype=_NS_DTYPE) + elif dtype in ['timedelta', 'timedelta64']: + return np.array('NaT', dtype=_TD_DTYPE) + return np.nan + + +def _maybe_fill(arr, fill_value=np.nan): + """ + if we have a compatiable fill_value and arr dtype, then fill + """ + if _is_na_compat(arr, fill_value): + arr.fill(fill_value) + return arr + + +def na_value_for_dtype(dtype): + """ + Return a dtype compat na value + + Parameters + ---------- + dtype : string / dtype + + Returns + ------- + np.dtype or a pandas dtype + """ + dtype = pandas_dtype(dtype) + + if (is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype) or + is_timedelta64_dtype(dtype)): + return NaT + elif is_float_dtype(dtype): + return np.nan + elif is_integer_dtype(dtype): + return 0 + elif is_bool_dtype(dtype): + return False + return np.nan diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 2961b2fb2241f..402613d3f1728 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -23,11 +23,14 @@ import numpy as np import pandas as pd -from pandas.core.common import (is_sequence, array_equivalent, - is_list_like, is_datetimelike_v_numeric, - is_datetimelike_v_object, - is_number, is_bool, - needs_i8_conversion, is_categorical_dtype) +from pandas.types.missing import array_equivalent +from pandas.types.common import (is_datetimelike_v_numeric, + is_datetimelike_v_object, + is_number, is_bool, + needs_i8_conversion, + is_categorical_dtype, + is_sequence, + is_list_like) from pandas.formats.printing import pprint_thing from pandas.core.algorithms import take_1d @@ -1001,7 +1004,7 @@ def assert_categorical_equal(left, right, check_dtype=True, assert_attr_equal('ordered', left, right, obj=obj) -def raise_assert_detail(obj, message, left, right): +def raise_assert_detail(obj, message, left, right, diff=None): if isinstance(left, np.ndarray): left = pprint_thing(left) if isinstance(right, np.ndarray): @@ -1012,6 +1015,10 @@ def raise_assert_detail(obj, message, left, right): {1} [left]: {2} [right]: {3}""".format(obj, message, left, right) + + if diff is not None: + msg = msg + "\n[diff]: {diff}".format(diff=diff) + raise AssertionError(msg) diff --git a/pandas/util/validators.py b/pandas/util/validators.py index bbfd24df9c13e..964fa9d9b38d5 100644 --- a/pandas/util/validators.py +++ b/pandas/util/validators.py @@ -3,6 +3,8 @@ for validating data or function arguments """ +from pandas.types.common import is_bool + def _check_arg_length(fname, args, max_fname_arg_count, compat_args): """ @@ -35,8 +37,6 @@ def _check_for_default_values(fname, arg_val_dict, compat_args): checked that arg_val_dict.keys() is a subset of compat_args """ - from pandas.core.common import is_bool - for key in arg_val_dict: # try checking equality directly with '=' operator, # as comparison may have been overriden for the left diff --git a/pandas/window.pyx b/pandas/window.pyx index bfe9152477a40..8235d68e2a88b 100644 --- a/pandas/window.pyx +++ b/pandas/window.pyx @@ -1,3 +1,6 @@ +# cython: profile=False +# cython: boundscheck=False, wraparound=False, cdivision=True + from numpy cimport * cimport numpy as np import numpy as np @@ -51,9 +54,10 @@ cdef double nan = NaN cdef inline int int_max(int a, int b): return a if a >= b else b cdef inline int int_min(int a, int b): return a if a <= b else b -# this is our util.pxd from util cimport numeric +from skiplist cimport * + cdef extern from "src/headers/math.h": double sqrt(double x) nogil int signbit(double) nogil @@ -69,16 +73,37 @@ include "skiplist.pyx" # - In Cython x * x is faster than x ** 2 for C types, this should be # periodically revisited to see if it's still true. # -# - -def _check_minp(win, minp, N, floor=1): + +def _check_minp(win, minp, N, floor=None): + """ + Parameters + ---------- + win: int + minp: int or None + N: len of window + floor: int, optional + default 1 + + Returns + ------- + minimum period + """ + + if minp is None: + minp = 1 + if not util.is_integer_object(minp): + raise ValueError("min_periods must be an integer") if minp > win: - raise ValueError('min_periods (%d) must be <= window (%d)' - % (minp, win)) + raise ValueError("min_periods (%d) must be <= " + "window (%d)" % (minp, win)) elif minp > N: minp = N + 1 elif minp < 0: raise ValueError('min_periods must be >= 0') + if floor is None: + floor = 1 + return max(minp, floor) # original C implementation by N. Devillard. @@ -96,757 +121,1227 @@ def _check_minp(win, minp, N, floor=1): # Physical description: 366 p. # Series: Prentice-Hall Series in Automatic Computation -#------------------------------------------------------------------------------- -# Rolling sum -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_sum(ndarray[double_t] input, int win, int minp): - cdef double val, prev, sum_x = 0 - cdef int nobs = 0, i - cdef int N = len(input) - - cdef ndarray[double_t] output = np.empty(N, dtype=float) +# ---------------------------------------------------------------------- +# The indexer objects for rolling +# These define start/end indexers to compute offsets - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] - # Not NaN - if val == val: - nobs += 1 - sum_x += val +cdef class WindowIndexer: - output[i] = NaN + cdef: + ndarray start, end + int64_t N, minp, win + bint is_variable - for i from minp - 1 <= i < N: - val = input[i] + def get_data(self): + return (self.start, self.end, <int64_t>self.N, + <int64_t>self.win, <int64_t>self.minp, + self.is_variable) - if val == val: - nobs += 1 - sum_x += val - if i > win - 1: - prev = input[i - win] - if prev == prev: - sum_x -= prev - nobs -= 1 +cdef class MockFixedWindowIndexer(WindowIndexer): + """ - if nobs >= minp: - output[i] = sum_x - else: - output[i] = NaN + We are just checking parameters of the indexer, + and returning a consistent API with fixed/variable + indexers. - return output + Parameters + ---------- + input: ndarray + input data array + win: int64_t + window size + minp: int64_t + min number of obs in a window to consider non-NaN + index: object + index of the input + floor: optional + unit for flooring -#------------------------------------------------------------------------------- -# Rolling mean -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_mean(ndarray[double_t] input, - int win, int minp): - cdef: - double val, prev, result, sum_x = 0 - Py_ssize_t nobs = 0, i, neg_ct = 0 - Py_ssize_t N = len(input) + """ + def __init__(self, ndarray input, int64_t win, int64_t minp, + object index=None, object floor=None): - cdef ndarray[double_t] output = np.empty(N, dtype=float) - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] + assert index is None + self.is_variable = 0 + self.N = len(input) + self.minp = _check_minp(win, minp, self.N, floor=floor) + self.start = np.empty(0, dtype='int64') + self.end = np.empty(0, dtype='int64') + self.win = win - # Not NaN - if val == val: - nobs += 1 - sum_x += val - if signbit(val): - neg_ct += 1 - output[i] = NaN +cdef class FixedWindowIndexer(WindowIndexer): + """ + create a fixed length window indexer object + that has start & end, that point to offsets in + the index object; these are defined based on the win + arguments - for i from minp - 1 <= i < N: - val = input[i] + Parameters + ---------- + input: ndarray + input data array + win: int64_t + window size + minp: int64_t + min number of obs in a window to consider non-NaN + index: object + index of the input + floor: optional + unit for flooring the unit - if val == val: - nobs += 1 - sum_x += val - if signbit(val): - neg_ct += 1 + """ + def __init__(self, ndarray input, int64_t win, int64_t minp, + object index=None, object floor=None): + cdef ndarray start_s, start_e, end_s, end_e - if i > win - 1: - prev = input[i - win] - if prev == prev: - sum_x -= prev - nobs -= 1 - if signbit(prev): - neg_ct -= 1 + assert index is None + self.is_variable = 0 + self.N = len(input) + self.minp = _check_minp(win, minp, self.N, floor=floor) - if nobs >= minp: - result = sum_x / nobs - if neg_ct == 0 and result < 0: - # all positive - output[i] = 0 - elif neg_ct == nobs and result > 0: - # all negative - output[i] = 0 - else: - output[i] = result - else: - output[i] = NaN + start_s = np.zeros(win, dtype='int64') + start_e = np.arange(win, self.N, dtype='int64') - win + 1 + self.start = np.concatenate([start_s, start_e]) - return output + end_s = np.arange(win, dtype='int64') + 1 + end_e = start_e + win + self.end = np.concatenate([end_s, end_e]) + self.win = win -#------------------------------------------------------------------------------- -# Exponentially weighted moving average -def ewma(ndarray[double_t] input, double_t com, int adjust, int ignore_na, int minp): +cdef class VariableWindowIndexer(WindowIndexer): """ - Compute exponentially-weighted moving average using center-of-mass. + create a variable length window indexer object + that has start & end, that point to offsets in + the index object; these are defined based on the win + arguments Parameters ---------- - input : ndarray (float64 type) - com : float64 - adjust: int - ignore_na: int - minp: int + input: ndarray + input data array + win: int64_t + window size + minp: int64_t + min number of obs in a window to consider non-NaN + index: ndarray + index of the input - Returns - ------- - y : ndarray """ + def __init__(self, ndarray input, int64_t win, int64_t minp, + ndarray index): - cdef Py_ssize_t N = len(input) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - if N == 0: - return output + self.is_variable = 1 + self.N = len(index) + self.minp = _check_minp(win, minp, self.N) - minp = max(minp, 1) + self.start = np.empty(self.N, dtype='int64') + self.start.fill(-1) - cdef double alpha, old_wt_factor, new_wt, weighted_avg, old_wt, cur - cdef Py_ssize_t i, nobs + self.end = np.empty(self.N, dtype='int64') + self.end.fill(-1) - alpha = 1. / (1. + com) - old_wt_factor = 1. - alpha - new_wt = 1. if adjust else alpha + self.build(index, win) - weighted_avg = input[0] - is_observation = (weighted_avg == weighted_avg) - nobs = int(is_observation) - output[0] = weighted_avg if (nobs >= minp) else NaN - old_wt = 1. + # max window size + self.win = (self.end - self.start).max() - for i from 1 <= i < N: - cur = input[i] - is_observation = (cur == cur) - nobs += int(is_observation) - if weighted_avg == weighted_avg: - if is_observation or (not ignore_na): - old_wt *= old_wt_factor - if is_observation: - if weighted_avg != cur: # avoid numerical errors on constant series - weighted_avg = ((old_wt * weighted_avg) + (new_wt * cur)) / (old_wt + new_wt) - if adjust: - old_wt += new_wt - else: - old_wt = 1. - elif is_observation: - weighted_avg = cur + def build(self, ndarray[int64_t] index, int64_t win): - output[i] = weighted_avg if (nobs >= minp) else NaN + cdef: + ndarray[int64_t] start, end + int64_t start_bound, end_bound, N + Py_ssize_t i, j - return output + start = self.start + end = self.end + N = self.N -#------------------------------------------------------------------------------- -# Exponentially weighted moving covariance + start[0] = 0 + end[0] = 1 -def ewmcov(ndarray[double_t] input_x, ndarray[double_t] input_y, - double_t com, int adjust, int ignore_na, int minp, int bias): + with nogil: + + # start is start of slice interval (including) + # end is end of slice interval (not including) + for i in range(1, N): + end_bound = index[i] + start_bound = index[i] - win + + # advance the start bound until we are + # within the constraint + start[i] = i + for j in range(start[i - 1], i): + if index[j] > start_bound: + start[i] = j + break + + # end bound is previous end + # or current index + if index[end[i - 1]] <= end_bound: + end[i] = i + 1 + else: + end[i] = end[i - 1] + + +def get_window_indexer(input, win, minp, index, floor=None, + use_mock=True): """ - Compute exponentially-weighted moving variance using center-of-mass. + return the correct window indexer for the computation Parameters ---------- - input_x : ndarray (float64 type) - input_y : ndarray (float64 type) - com : float64 - adjust: int - ignore_na: int - minp: int - bias: int + input: 1d ndarray + win: integer, window size + minp: integer, minimum periods + index: 1d ndarray, optional + index to the input array + floor: optional + unit for flooring the unit + use_mock: boolean, default True + if we are a fixed indexer, return a mock indexer + instead of the FixedWindow Indexer. This is a type + compat Indexer that allows us to use a standard + code path with all of the indexers. Returns ------- - y : ndarray - """ + tuple of 1d int64 ndarrays of the offsets & data about the window - cdef Py_ssize_t N = len(input_x) - if len(input_y) != N: - raise ValueError('arrays are of different lengths (%d and %d)' % (N, len(input_y))) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - if N == 0: - return output - - minp = max(minp, 1) + """ - cdef double alpha, old_wt_factor, new_wt, mean_x, mean_y, cov - cdef double sum_wt, sum_wt2, old_wt, cur_x, cur_y, old_mean_x, old_mean_y - cdef Py_ssize_t i, nobs + if index is not None: + indexer = VariableWindowIndexer(input, win, minp, index) + elif use_mock: + indexer = MockFixedWindowIndexer(input, win, minp, index, floor) + else: + indexer = FixedWindowIndexer(input, win, minp, index, floor) + return indexer.get_data() - alpha = 1. / (1. + com) - old_wt_factor = 1. - alpha - new_wt = 1. if adjust else alpha +# ---------------------------------------------------------------------- +# Rolling count +# this is only an impl for index not None, IOW, freq aware - mean_x = input_x[0] - mean_y = input_y[0] - is_observation = ((mean_x == mean_x) and (mean_y == mean_y)) - nobs = int(is_observation) - if not is_observation: - mean_x = NaN - mean_y = NaN - output[0] = (0. if bias else NaN) if (nobs >= minp) else NaN - cov = 0. - sum_wt = 1. - sum_wt2 = 1. - old_wt = 1. - for i from 1 <= i < N: - cur_x = input_x[i] - cur_y = input_y[i] - is_observation = ((cur_x == cur_x) and (cur_y == cur_y)) - nobs += int(is_observation) - if mean_x == mean_x: - if is_observation or (not ignore_na): - sum_wt *= old_wt_factor - sum_wt2 *= (old_wt_factor * old_wt_factor) - old_wt *= old_wt_factor - if is_observation: - old_mean_x = mean_x - old_mean_y = mean_y - if mean_x != cur_x: # avoid numerical errors on constant series - mean_x = ((old_wt * old_mean_x) + (new_wt * cur_x)) / (old_wt + new_wt) - if mean_y != cur_y: # avoid numerical errors on constant series - mean_y = ((old_wt * old_mean_y) + (new_wt * cur_y)) / (old_wt + new_wt) - cov = ((old_wt * (cov + ((old_mean_x - mean_x) * (old_mean_y - mean_y)))) + - (new_wt * ((cur_x - mean_x) * (cur_y - mean_y)))) / (old_wt + new_wt) - sum_wt += new_wt - sum_wt2 += (new_wt * new_wt) - old_wt += new_wt - if not adjust: - sum_wt /= old_wt - sum_wt2 /= (old_wt * old_wt) - old_wt = 1. - elif is_observation: - mean_x = cur_x - mean_y = cur_y +def roll_count(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, count_x = 0.0 + int64_t s, e, nobs, N + Py_ssize_t i, j + ndarray[int64_t] start, end + ndarray[double_t] output - if nobs >= minp: - if not bias: - numerator = sum_wt * sum_wt - denominator = numerator - sum_wt2 - output[i] = ((numerator / denominator) * cov) if (denominator > 0.) else NaN - else: - output[i] = cov - else: - output[i] = NaN + start, end, N, win, minp, _ = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) - return output + with nogil: -#---------------------------------------------------------------------- -# Rolling variance + for i in range(0, N): + s = start[i] + e = end[i] -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_var(ndarray[double_t] input, int win, int minp, int ddof=1): - """ - Numerically stable implementation using Welford's method. - """ - cdef double val, prev, mean_x = 0, ssqdm_x = 0, nobs = 0, delta - cdef Py_ssize_t i - cdef Py_ssize_t N = len(input) + if i == 0: - cdef ndarray[double_t] output = np.empty(N, dtype=float) + # setup + count_x = 0.0 + for j in range(s, e): + val = input[j] + if val == val: + count_x += 1.0 - minp = _check_minp(win, minp, N) + else: - # Check for windows larger than array, addresses #7297 - win = min(win, N) + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + if val == val: + count_x -= 1.0 - with nogil: - # Over the first window, observations can only be added, never removed - for i from 0 <= i < win: - val = input[i] + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + if val == val: + count_x += 1.0 - # Not NaN - if val == val: - nobs += 1 - delta = (val - mean_x) - mean_x += delta / nobs - ssqdm_x += delta * (val - mean_x) - - if (nobs >= minp) and (nobs > ddof): - #pathological case - if nobs == 1: - val = 0 - else: - val = ssqdm_x / (nobs - ddof) - if val < 0: - val = 0 + if count_x >= minp: + output[i] = count_x else: - val = NaN + output[i] = NaN - output[i] = val + return output - # After the first window, observations can both be added and removed - for i from win <= i < N: - val = input[i] - prev = input[i - win] +# ---------------------------------------------------------------------- +# Rolling sum - if val == val: - if prev == prev: - # Adding one observation and removing another one - delta = val - prev - prev -= mean_x - mean_x += delta / nobs - val -= mean_x - ssqdm_x += (val + prev) * delta - else: - # Adding one observation and not removing any - nobs += 1 - delta = (val - mean_x) - mean_x += delta / nobs - ssqdm_x += delta * (val - mean_x) - elif prev == prev: - # Adding no new observation, but removing one - nobs -= 1 - if nobs: - delta = (prev - mean_x) - mean_x -= delta / nobs - ssqdm_x -= delta * (prev - mean_x) - else: - mean_x = 0 - ssqdm_x = 0 - # Variance is unchanged if no observation is added or removed - - if (nobs >= minp) and (nobs > ddof): - #pathological case - if nobs == 1: - val = 0 - else: - val = ssqdm_x / (nobs - ddof) - if val < 0: - val = 0 - else: - val = NaN - output[i] = val +cdef inline double calc_sum(int64_t minp, int64_t nobs, double sum_x) nogil: + cdef double result - return output + if nobs >= minp: + result = sum_x + else: + result = NaN + return result -#------------------------------------------------------------------------------- -# Rolling skewness -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_skew(ndarray[double_t] input, int win, int minp): - cdef double val, prev - cdef double x = 0, xx = 0, xxx = 0 - cdef Py_ssize_t nobs = 0, i - cdef Py_ssize_t N = len(input) - cdef ndarray[double_t] output = np.empty(N, dtype=float) +cdef inline void add_sum(double val, int64_t *nobs, double *sum_x) nogil: + """ add a value from the sum calc """ - # 3 components of the skewness equation - cdef double A, B, C, R + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + sum_x[0] = sum_x[0] + val - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] - # Not NaN - if val == val: - nobs += 1 - x += val - xx += val * val - xxx += val * val * val +cdef inline void remove_sum(double val, int64_t *nobs, double *sum_x) nogil: + """ remove a value from the sum calc """ - output[i] = NaN + if val == val: + nobs[0] = nobs[0] - 1 + sum_x[0] = sum_x[0] - val - for i from minp - 1 <= i < N: - val = input[i] - if val == val: - nobs += 1 - x += val - xx += val * val - xxx += val * val * val +def roll_sum(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev_x, sum_x = 0 + int64_t s, e + int64_t nobs = 0, i, j, N + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output - if i > win - 1: - prev = input[i - win] - if prev == prev: - x -= prev - xx -= prev * prev - xxx -= prev * prev * prev + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) - nobs -= 1 - if nobs >= minp: - A = x / nobs - B = xx / nobs - A * A - C = xxx / nobs - A * A * A - 3 * A * B - if B <= 0 or nobs < 3: - output[i] = NaN - else: - R = sqrt(B) - output[i] = ((sqrt(nobs * (nobs - 1.)) * C) / - ((nobs-2) * R * R * R)) - else: - output[i] = NaN + # for performance we are going to iterate + # fixed windows separately, makes the code more complex as we have 2 paths + # but is faster - return output + if is_variable: -#------------------------------------------------------------------------------- -# Rolling kurtosis -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_kurt(ndarray[double_t] input, - int win, int minp): - cdef double val, prev - cdef double x = 0, xx = 0, xxx = 0, xxxx = 0 - cdef Py_ssize_t nobs = 0, i - cdef Py_ssize_t N = len(input) + # variable window + with nogil: - cdef ndarray[double_t] output = np.empty(N, dtype=float) + for i in range(0, N): + s = start[i] + e = end[i] - # 5 components of the kurtosis equation - cdef double A, B, C, D, R, K + if i == 0: - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] + # setup + sum_x = 0.0 + nobs = 0 + for j in range(s, e): + add_sum(input[j], &nobs, &sum_x) - # Not NaN - if val == val: - nobs += 1 + else: - # seriously don't ask me why this is faster - x += val - xx += val * val - xxx += val * val * val - xxxx += val * val * val * val + # calculate deletes + for j in range(start[i - 1], s): + remove_sum(input[j], &nobs, &sum_x) - output[i] = NaN + # calculate adds + for j in range(end[i - 1], e): + add_sum(input[j], &nobs, &sum_x) - for i from minp - 1 <= i < N: - val = input[i] + output[i] = calc_sum(minp, nobs, sum_x) - if val == val: - nobs += 1 - x += val - xx += val * val - xxx += val * val * val - xxxx += val * val * val * val + else: - if i > win - 1: - prev = input[i - win] - if prev == prev: - x -= prev - xx -= prev * prev - xxx -= prev * prev * prev - xxxx -= prev * prev * prev * prev + # fixed window - nobs -= 1 + with nogil: - if nobs >= minp: - A = x / nobs - R = A * A - B = xx / nobs - R - R = R * A - C = xxx / nobs - R - 3 * A * B - R = R * A - D = xxxx / nobs - R - 6*B*A*A - 4*C*A - - if B == 0 or nobs < 4: - output[i] = NaN + for i in range(0, minp - 1): + add_sum(input[i], &nobs, &sum_x) + output[i] = NaN - else: - K = (nobs * nobs - 1.)*D/(B*B) - 3*((nobs-1.)**2) - K = K / ((nobs - 2.)*(nobs-3.)) + for i in range(minp - 1, N): + val = input[i] + add_sum(val, &nobs, &sum_x) - output[i] = K + if i > win - 1: + prev_x = input[i - win] + remove_sum(prev_x, &nobs, &sum_x) - else: - output[i] = NaN + output[i] = calc_sum(minp, nobs, sum_x) return output -#------------------------------------------------------------------------------- -# Rolling median, min, max +# ---------------------------------------------------------------------- +# Rolling mean -from skiplist cimport * -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_median_c(ndarray[float64_t] arg, int win, int minp): - cdef: - double val, res, prev - bint err=0 - int ret=0 - skiplist_t *sl - Py_ssize_t midpoint, nobs = 0, i +cdef inline double calc_mean(int64_t minp, Py_ssize_t nobs, + Py_ssize_t neg_ct, double sum_x) nogil: + cdef double result + if nobs >= minp: + result = sum_x / <double>nobs + if neg_ct == 0 and result < 0: + # all positive + result = 0 + elif neg_ct == nobs and result > 0: + # all negative + result = 0 + else: + pass + else: + result = NaN + return result - cdef Py_ssize_t N = len(arg) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - sl = skiplist_init(win) - if sl == NULL: - raise MemoryError("skiplist_init failed") +cdef inline void add_mean(double val, Py_ssize_t *nobs, double *sum_x, + Py_ssize_t *neg_ct) nogil: + """ add a value from the mean calc """ - minp = _check_minp(win, minp, N) + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + sum_x[0] = sum_x[0] + val + if signbit(val): + neg_ct[0] = neg_ct[0] + 1 - with nogil: - for i from 0 <= i < minp - 1: - val = arg[i] - # Not NaN - if val == val: - nobs += 1 - err = skiplist_insert(sl, val) != 1 - if err: - break - output[i] = NaN +cdef inline void remove_mean(double val, Py_ssize_t *nobs, double *sum_x, + Py_ssize_t *neg_ct) nogil: + """ remove a value from the mean calc """ + + if val == val: + nobs[0] = nobs[0] - 1 + sum_x[0] = sum_x[0] - val + if signbit(val): + neg_ct[0] = neg_ct[0] - 1 - with nogil: - if not err: - for i from minp - 1 <= i < N: - val = arg[i] +def roll_mean(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev_x, result, sum_x = 0 + int64_t s, e + bint is_variable + Py_ssize_t nobs = 0, i, j, neg_ct = 0, N + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + # for performance we are going to iterate + # fixed windows separately, makes the code more complex as we have 2 paths + # but is faster + + if is_variable: + + with nogil: + + for i in range(0, N): + s = start[i] + e = end[i] + + if i == 0: + + # setup + sum_x = 0.0 + nobs = 0 + for j in range(s, e): + val = input[j] + add_mean(val, &nobs, &sum_x, &neg_ct) + + else: + + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + remove_mean(val, &nobs, &sum_x, &neg_ct) + + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + add_mean(val, &nobs, &sum_x, &neg_ct) + + output[i] = calc_mean(minp, nobs, neg_ct, sum_x) + + else: + + with nogil: + for i from 0 <= i < minp - 1: + val = input[i] + add_mean(val, &nobs, &sum_x, &neg_ct) + output[i] = NaN + + for i from minp - 1 <= i < N: + val = input[i] + add_mean(val, &nobs, &sum_x, &neg_ct) if i > win - 1: - prev = arg[i - win] + prev_x = input[i - win] + remove_mean(prev_x, &nobs, &sum_x, &neg_ct) + + output[i] = calc_mean(minp, nobs, neg_ct, sum_x) + + return output + +# ---------------------------------------------------------------------- +# Rolling variance + + +cdef inline double calc_var(int64_t minp, int ddof, double nobs, + double ssqdm_x) nogil: + cdef double result + + # Variance is unchanged if no observation is added or removed + if (nobs >= minp) and (nobs > ddof): + + # pathological case + if nobs == 1: + result = 0 + else: + result = ssqdm_x / (nobs - <double>ddof) + if result < 0: + result = 0 + else: + result = NaN + + return result + + +cdef inline void add_var(double val, double *nobs, double *mean_x, + double *ssqdm_x) nogil: + """ add a value from the var calc """ + cdef double delta + + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + + delta = (val - mean_x[0]) + mean_x[0] = mean_x[0] + delta / nobs[0] + ssqdm_x[0] = ssqdm_x[0] + delta * (val - mean_x[0]) + +cdef inline void remove_var(double val, double *nobs, double *mean_x, + double *ssqdm_x) nogil: + """ remove a value from the var calc """ + cdef double delta + + # Not NaN + if val == val: + nobs[0] = nobs[0] - 1 + if nobs[0]: + delta = (val - mean_x[0]) + mean_x[0] = mean_x[0] - delta / nobs[0] + ssqdm_x[0] = ssqdm_x[0] - delta * (val - mean_x[0]) + else: + mean_x[0] = 0 + ssqdm_x[0] = 0 + + +def roll_var(ndarray[double_t] input, int64_t win, int64_t minp, + object index, int ddof=1): + """ + Numerically stable implementation using Welford's method. + """ + cdef: + double val, prev, mean_x = 0, ssqdm_x = 0, nobs = 0, delta + int64_t s, e + bint is_variable + Py_ssize_t i, j, N + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + # Check for windows larger than array, addresses #7297 + win = min(win, N) + + # for performance we are going to iterate + # fixed windows separately, makes the code more complex as we + # have 2 paths but is faster + + if is_variable: + + with nogil: + + for i in range(0, N): + + s = start[i] + e = end[i] + + # Over the first window, observations can only be added + # never removed + if i == 0: + + for j in range(s, e): + add_var(input[j], &nobs, &mean_x, &ssqdm_x) + + else: + + # After the first window, observations can both be added + # and removed + + # calculate adds + for j in range(end[i - 1], e): + add_var(input[j], &nobs, &mean_x, &ssqdm_x) + + # calculate deletes + for j in range(start[i - 1], s): + remove_var(input[j], &nobs, &mean_x, &ssqdm_x) + + output[i] = calc_var(minp, ddof, nobs, ssqdm_x) + + else: + + with nogil: + + # Over the first window, observations can only be added, never + # removed + for i from 0 <= i < win: + add_var(input[i], &nobs, &mean_x, &ssqdm_x) + output[i] = calc_var(minp, ddof, nobs, ssqdm_x) + + # After the first window, observations can both be added and + # removed + for i from win <= i < N: + val = input[i] + prev = input[i - win] + + if val == val: if prev == prev: - skiplist_remove(sl, prev) - nobs -= 1 + # Adding one observation and removing another one + delta = val - prev + prev -= mean_x + mean_x += delta / nobs + val -= mean_x + ssqdm_x += (val + prev) * delta + + else: + add_var(val, &nobs, &mean_x, &ssqdm_x) + elif prev == prev: + remove_var(prev, &nobs, &mean_x, &ssqdm_x) + + output[i] = calc_var(minp, ddof, nobs, ssqdm_x) + + return output + + +# ---------------------------------------------------------------------- +# Rolling skewness + +cdef inline double calc_skew(int64_t minp, int64_t nobs, double x, double xx, + double xxx) nogil: + cdef double result, dnobs + cdef double A, B, C, R + + if nobs >= minp: + dnobs = <double>nobs + A = x / dnobs + B = xx / dnobs - A * A + C = xxx / dnobs - A * A * A - 3 * A * B + if B <= 0 or nobs < 3: + result = NaN + else: + R = sqrt(B) + result = ((sqrt(dnobs * (dnobs - 1.)) * C) / + ((dnobs - 2) * R * R * R)) + else: + result = NaN + + return result + +cdef inline void add_skew(double val, int64_t *nobs, double *x, double *xx, + double *xxx) nogil: + """ add a value from the skew calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + + # seriously don't ask me why this is faster + x[0] = x[0] + val + xx[0] = xx[0] + val * val + xxx[0] = xxx[0] + val * val * val + +cdef inline void remove_skew(double val, int64_t *nobs, double *x, double *xx, + double *xxx) nogil: + """ remove a value from the skew calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] - 1 + + # seriously don't ask me why this is faster + x[0] = x[0] - val + xx[0] = xx[0] - val * val + xxx[0] = xxx[0] - val * val * val + + +def roll_skew(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev + double x = 0, xx = 0, xxx = 0 + int64_t nobs = 0, i, j, N + int64_t s, e + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + if is_variable: + + with nogil: + + for i in range(0, N): + + s = start[i] + e = end[i] + + # Over the first window, observations can only be added + # never removed + if i == 0: + + for j in range(s, e): + val = input[j] + add_skew(val, &nobs, &x, &xx, &xxx) + + else: + + # After the first window, observations can both be added + # and removed + + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + add_skew(val, &nobs, &x, &xx, &xxx) + + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + remove_skew(val, &nobs, &x, &xx, &xxx) + + output[i] = calc_skew(minp, nobs, x, xx, xxx) + + else: + + with nogil: + for i from 0 <= i < minp - 1: + val = input[i] + add_skew(val, &nobs, &x, &xx, &xxx) + output[i] = NaN + + for i from minp - 1 <= i < N: + val = input[i] + add_skew(val, &nobs, &x, &xx, &xxx) + + if i > win - 1: + prev = input[i - win] + remove_skew(prev, &nobs, &x, &xx, &xxx) + + output[i] = calc_skew(minp, nobs, x, xx, xxx) + + return output + +# ---------------------------------------------------------------------- +# Rolling kurtosis + + +cdef inline double calc_kurt(int64_t minp, int64_t nobs, double x, double xx, + double xxx, double xxxx) nogil: + cdef double result, dnobs + cdef double A, B, C, D, R, K + + if nobs >= minp: + dnobs = <double>nobs + A = x / dnobs + R = A * A + B = xx / dnobs - R + R = R * A + C = xxx / dnobs - R - 3 * A * B + R = R * A + D = xxxx / dnobs - R - 6 * B * A * A - 4 * C * A + + if B == 0 or nobs < 4: + result = NaN + else: + K = (dnobs * dnobs - 1.) * D / (B * B) - 3 * ((dnobs - 1.) ** 2) + result = K / ((dnobs - 2.) * (dnobs - 3.)) + else: + result = NaN + + return result + +cdef inline void add_kurt(double val, int64_t *nobs, double *x, double *xx, + double *xxx, double *xxxx) nogil: + """ add a value from the kurotic calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + + # seriously don't ask me why this is faster + x[0] = x[0] + val + xx[0] = xx[0] + val * val + xxx[0] = xxx[0] + val * val * val + xxxx[0] = xxxx[0] + val * val * val * val + +cdef inline void remove_kurt(double val, int64_t *nobs, double *x, double *xx, + double *xxx, double *xxxx) nogil: + """ remove a value from the kurotic calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] - 1 + + # seriously don't ask me why this is faster + x[0] = x[0] - val + xx[0] = xx[0] - val * val + xxx[0] = xxx[0] - val * val * val + xxxx[0] = xxxx[0] - val * val * val * val + + +def roll_kurt(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev + double x = 0, xx = 0, xxx = 0, xxxx = 0 + int64_t nobs = 0, i, j, N + int64_t s, e + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + if is_variable: + + with nogil: + + for i in range(0, N): + + s = start[i] + e = end[i] + + # Over the first window, observations can only be added + # never removed + if i == 0: + + for j in range(s, e): + add_kurt(input[j], &nobs, &x, &xx, &xxx, &xxxx) + + else: + + # After the first window, observations can both be added + # and removed + + # calculate adds + for j in range(end[i - 1], e): + add_kurt(input[j], &nobs, &x, &xx, &xxx, &xxxx) + + # calculate deletes + for j in range(start[i - 1], s): + remove_kurt(input[j], &nobs, &x, &xx, &xxx, &xxxx) + + output[i] = calc_kurt(minp, nobs, x, xx, xxx, xxxx) + + else: + + with nogil: + + for i from 0 <= i < minp - 1: + add_kurt(input[i], &nobs, &x, &xx, &xxx, &xxxx) + output[i] = NaN + + for i from minp - 1 <= i < N: + add_kurt(input[i], &nobs, &x, &xx, &xxx, &xxxx) + + if i > win - 1: + prev = input[i - win] + remove_kurt(prev, &nobs, &x, &xx, &xxx, &xxxx) + + output[i] = calc_kurt(minp, nobs, x, xx, xxx, xxxx) + + return output + +# ---------------------------------------------------------------------- +# Rolling median, min, max + + +def roll_median_c(ndarray[float64_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, res, prev + bint err=0, is_variable + int ret=0 + skiplist_t *sl + Py_ssize_t i, j + int64_t nobs = 0, N, s, e + int midpoint + ndarray[int64_t] start, end + ndarray[double_t] output + + # we use the Fixed/Variable Indexer here as the + # actual skiplist ops outweigh any window computation costs + start, end, N, win, minp, is_variable = get_window_indexer( + input, win, + minp, index, + use_mock=False) + output = np.empty(N, dtype=float) + + sl = skiplist_init(<int>win) + if sl == NULL: + raise MemoryError("skiplist_init failed") + + with nogil: + + for i in range(0, N): + s = start[i] + e = end[i] + + if i == 0: + + # setup + val = input[i] if val == val: nobs += 1 err = skiplist_insert(sl, val) != 1 if err: break - if nobs >= minp: - midpoint = nobs / 2 - if nobs % 2: - res = skiplist_get(sl, midpoint, &ret) - else: - res = (skiplist_get(sl, midpoint, &ret) + - skiplist_get(sl, (midpoint - 1), &ret)) / 2 + else: + + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + if val == val: + skiplist_remove(sl, val) + nobs -= 1 + + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + if val == val: + nobs += 1 + err = skiplist_insert(sl, val) != 1 + if err: + break + + if nobs >= minp: + midpoint = <int>(nobs / 2) + if nobs % 2: + res = skiplist_get(sl, midpoint, &ret) else: - res = NaN + res = (skiplist_get(sl, midpoint, &ret) + + skiplist_get(sl, (midpoint - 1), &ret)) / 2 + else: + res = NaN - output[i] = res + output[i] = res - skiplist_destroy(sl) + skiplist_destroy(sl) if err: raise MemoryError("skiplist_insert failed") return output -#---------------------------------------------------------------------- +# ---------------------------------------------------------------------- # Moving maximum / minimum code taken from Bottleneck under the terms # of its Simplified BSD license # https://github.com/kwgoodman/bottleneck -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_max(ndarray[numeric] a, int window, int minp): + +cdef inline numeric init_mm(numeric ai, Py_ssize_t *nobs, bint is_max) nogil: + + if numeric in cython.floating: + if ai == ai: + nobs[0] = nobs[0] + 1 + elif is_max: + if numeric == cython.float: + ai = MINfloat32 + else: + ai = MINfloat64 + else: + if numeric == cython.float: + ai = MAXfloat32 + else: + ai = MAXfloat64 + + else: + nobs[0] = nobs[0] + 1 + + return ai + + +cdef inline void remove_mm(numeric aold, Py_ssize_t *nobs) nogil: + """ remove a value from the mm calc """ + if numeric in cython.floating and aold == aold: + nobs[0] = nobs[0] - 1 + + +cdef inline numeric calc_mm(int64_t minp, Py_ssize_t nobs, + numeric value) nogil: + cdef numeric result + + if numeric in cython.floating: + if nobs >= minp: + result = value + else: + result = NaN + else: + result = value + + return result + + +def roll_max(ndarray[numeric] input, int64_t win, int64_t minp, + object index): """ Moving max of 1d array of any numeric type along axis=0 ignoring NaNs. Parameters ---------- - a: numpy array + input: numpy array window: int, size of rolling window minp: if number of observations in window is below this, output a NaN + index: ndarray, optional + index for window computation """ - return _roll_min_max(a, window, minp, 1) + return _roll_min_max(input, win, minp, index, is_max=1) + -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_min(ndarray[numeric] a, int window, int minp): +def roll_min(ndarray[numeric] input, int64_t win, int64_t minp, + object index): """ Moving max of 1d array of any numeric type along axis=0 ignoring NaNs. Parameters ---------- - a: numpy array + input: numpy array window: int, size of rolling window minp: if number of observations in window is below this, output a NaN + index: ndarray, optional + index for window computation """ - return _roll_min_max(a, window, minp, 0) - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef _roll_min_max(ndarray[numeric] a, int window, int minp, bint is_max): - "Moving min/max of 1d array of any numeric type along axis=0 ignoring NaNs." - cdef numeric ai, aold - cdef Py_ssize_t count - cdef Py_ssize_t* death - cdef numeric* ring - cdef numeric* minvalue - cdef numeric* end - cdef numeric* last - cdef Py_ssize_t i0 - cdef np.npy_intp *dim - dim = PyArray_DIMS(a) - cdef Py_ssize_t n0 = dim[0] - cdef np.npy_intp *dims = [n0] - cdef bint should_replace - cdef np.ndarray[numeric, ndim=1] y = PyArray_EMPTY(1, dims, PyArray_TYPE(a), 0) - - if window < 1: - raise ValueError('Invalid window size %d' - % (window)) - - if minp > window: - raise ValueError('Invalid min_periods size %d greater than window %d' - % (minp, window)) - - minp = _check_minp(window, minp, n0) - with nogil: - ring = <numeric*>malloc(window * sizeof(numeric)) - death = <Py_ssize_t*>malloc(window * sizeof(Py_ssize_t)) - end = ring + window - last = ring + return _roll_min_max(input, win, minp, index, is_max=0) + +cdef _roll_min_max(ndarray[numeric] input, int64_t win, int64_t minp, + object index, bint is_max): + """ + Moving min/max of 1d array of any numeric type along axis=0 + ignoring NaNs. + """ + + cdef: + numeric ai + bint is_variable, should_replace + int64_t s, e, N, i, j, removed + Py_ssize_t nobs = 0 + ndarray[int64_t] starti, endi + ndarray[numeric, ndim=1] output + cdef: + int64_t* death + numeric* ring + numeric* minvalue + numeric* end + numeric* last + + cdef: + cdef numeric r + + starti, endi, N, win, minp, is_variable = get_window_indexer( + input, win, + minp, index) + + output = np.empty(N, dtype=input.dtype) + + if is_variable: + + with nogil: + + for i in range(N): + s = starti[i] + e = endi[i] + + r = input[s] + nobs = 0 + for j in range(s, e): + + # adds, death at the i offset + ai = init_mm(input[j], &nobs, is_max) + + if is_max: + if ai > r: + r = ai + else: + if ai < r: + r = ai + + output[i] = calc_mm(minp, nobs, r) + + else: + + # setup the rings of death! + ring = <numeric *>malloc(win * sizeof(numeric)) + death = <int64_t *>malloc(win * sizeof(int64_t)) + + end = ring + win + last = ring minvalue = ring - ai = a[0] - if numeric in cython.floating: - if ai == ai: - minvalue[0] = ai - elif is_max: - minvalue[0] = MINfloat64 - else: - minvalue[0] = MAXfloat64 - else: - minvalue[0] = ai - death[0] = window - - count = 0 - for i0 in range(n0): - ai = a[i0] - if numeric in cython.floating: - if ai == ai: - count += 1 - elif is_max: - ai = MINfloat64 + ai = input[0] + minvalue[0] = init_mm(input[0], &nobs, is_max) + death[0] = win + nobs = 0 + + with nogil: + + for i in range(N): + ai = init_mm(input[i], &nobs, is_max) + + if i >= win: + remove_mm(input[i - win], &nobs) + + if death[minvalue - ring] == i: + minvalue = minvalue + 1 + if minvalue >= end: + minvalue = ring + + if is_max: + should_replace = ai >= minvalue[0] else: - ai = MAXfloat64 - else: - count += 1 - if i0 >= window: - aold = a[i0 - window] - if aold == aold: - count -= 1 - if death[minvalue-ring] == i0: - minvalue += 1 - if minvalue >= end: - minvalue = ring - should_replace = ai >= minvalue[0] if is_max else ai <= minvalue[0] - if should_replace: - minvalue[0] = ai - death[minvalue-ring] = i0 + window - last = minvalue - else: - should_replace = last[0] <= ai if is_max else last[0] >= ai - while should_replace: - if last == ring: - last = end - last -= 1 - should_replace = last[0] <= ai if is_max else last[0] >= ai - last += 1 - if last == end: - last = ring - last[0] = ai - death[last - ring] = i0 + window - if numeric in cython.floating: - if count >= minp: - y[i0] = minvalue[0] + should_replace = ai <= minvalue[0] + if should_replace: + + minvalue[0] = ai + death[minvalue - ring] = i + win + last = minvalue + else: - y[i0] = NaN - else: - y[i0] = minvalue[0] - for i0 in range(minp - 1): - if numeric in cython.floating: - y[i0] = NaN - else: - y[i0] = 0 + if is_max: + should_replace = last[0] <= ai + else: + should_replace = last[0] >= ai + while should_replace: + if last == ring: + last = end + last -= 1 + if is_max: + should_replace = last[0] <= ai + else: + should_replace = last[0] >= ai + + last += 1 + if last == end: + last = ring + last[0] = ai + death[last - ring] = i + win + + output[i] = calc_mm(minp, nobs, minvalue[0]) + + for i in range(minp - 1): + if numeric in cython.floating: + output[i] = NaN + else: + output[i] = 0 + + free(ring) + free(death) + + # print("output: {0}".format(output)) + return output - free(ring) - free(death) - return y -def roll_quantile(ndarray[float64_t, cast=True] input, int win, - int minp, double quantile): +def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win, + int64_t minp, object index, double quantile): """ O(N log(window)) implementation using skip list """ - cdef double val, prev, midpoint - cdef IndexableSkiplist skiplist - cdef Py_ssize_t nobs = 0, i - cdef Py_ssize_t N = len(input) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - + cdef: + double val, prev, midpoint + IndexableSkiplist skiplist + int64_t nobs = 0, i, j, s, e, N + Py_ssize_t idx + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output + + # we use the Fixed/Variable Indexer here as the + # actual skiplist ops outweigh any window computation costs + start, end, N, win, minp, is_variable = get_window_indexer( + input, win, + minp, index, + use_mock=False) + output = np.empty(N, dtype=float) skiplist = IndexableSkiplist(win) - minp = _check_minp(win, minp, N) - - for i from 0 <= i < minp - 1: - val = input[i] + for i in range(0, N): + s = start[i] + e = end[i] - # Not NaN - if val == val: - nobs += 1 - skiplist.insert(val) + if i == 0: - output[i] = NaN - - for i from minp - 1 <= i < N: - val = input[i] + # setup + val = input[i] + if val == val: + nobs += 1 + skiplist.insert(val) - if i > win - 1: - prev = input[i - win] + else: - if prev == prev: - skiplist.remove(prev) - nobs -= 1 + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + if val == val: + skiplist.remove(val) + nobs -= 1 - if val == val: - nobs += 1 - skiplist.insert(val) + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + if val == val: + nobs += 1 + skiplist.insert(val) if nobs >= minp: - idx = int((quantile / 1.) * (nobs - 1)) + idx = int(quantile * <double>(nobs - 1)) output[i] = skiplist.get(idx) else: output[i] = NaN return output + def roll_generic(ndarray[float64_t, cast=True] input, - int win, int minp, int offset, - object func, object args, object kwargs): - cdef ndarray[double_t] output, counts, bufarr - cdef Py_ssize_t i, n - cdef float64_t *buf - cdef float64_t *oldbuf + int64_t win, int64_t minp, object index, + int offset, object func, + object args, object kwargs): + cdef: + ndarray[double_t] output, counts, bufarr + float64_t *buf + float64_t *oldbuf + int64_t nobs = 0, i, j, s, e, N + bint is_variable + ndarray[int64_t] start, end if not input.flags.c_contiguous: input = input.copy('C') @@ -855,36 +1350,60 @@ def roll_generic(ndarray[float64_t, cast=True] input, if n == 0: return input - minp = _check_minp(win, minp, n, floor=0) - output = np.empty(n, dtype=float) - counts = roll_sum(np.concatenate((np.isfinite(input).astype(float), np.array([0.] * offset))), win, minp)[offset:] + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index, + floor=0) + output = np.empty(N, dtype=float) - # truncated windows at the beginning, through first full-length window - for i from 0 <= i < (int_min(win, n) - offset): - if counts[i] >= minp: - output[i] = func(input[0 : (i + offset + 1)], *args, **kwargs) - else: - output[i] = NaN + counts = roll_sum(np.concatenate([np.isfinite(input).astype(float), + np.array([0.] * offset)]), + win, minp, index)[offset:] - # remaining full-length windows - buf = <float64_t*> input.data - bufarr = np.empty(win, dtype=float) - oldbuf = <float64_t*> bufarr.data - for i from (win - offset) <= i < (n - offset): - buf = buf + 1 - bufarr.data = <char*> buf - if counts[i] >= minp: - output[i] = func(bufarr, *args, **kwargs) - else: - output[i] = NaN - bufarr.data = <char*> oldbuf + if is_variable: - # truncated windows at the end - for i from int_max(n - offset, 0) <= i < n: - if counts[i] >= minp: - output[i] = func(input[int_max(i + offset - win + 1, 0) : n], *args, **kwargs) - else: - output[i] = NaN + # variable window + if offset != 0: + raise ValueError("unable to roll_generic with a non-zero offset") + + for i in range(0, N): + s = start[i] + e = end[i] + + if counts[i] >= minp: + output[i] = func(input[s:e], *args, **kwargs) + else: + output[i] = NaN + + else: + + # truncated windows at the beginning, through first full-length window + for i from 0 <= i < (int_min(win, N) - offset): + if counts[i] >= minp: + output[i] = func(input[0: (i + offset + 1)], *args, **kwargs) + else: + output[i] = NaN + + # remaining full-length windows + buf = <float64_t *> input.data + bufarr = np.empty(win, dtype=float) + oldbuf = <float64_t *> bufarr.data + for i from (win - offset) <= i < (N - offset): + buf = buf + 1 + bufarr.data = <char *> buf + if counts[i] >= minp: + output[i] = func(bufarr, *args, **kwargs) + else: + output[i] = NaN + bufarr.data = <char *> oldbuf + + # truncated windows at the end + for i from int_max(N - offset, 0) <= i < N: + if counts[i] >= minp: + output[i] = func(input[int_max(i + offset - win + 1, 0): N], + *args, + **kwargs) + else: + output[i] = NaN return output @@ -952,3 +1471,179 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] input, output[in_i] = NaN return output + +# ---------------------------------------------------------------------- +# Exponentially weighted moving average + + +def ewma(ndarray[double_t] input, double_t com, int adjust, int ignore_na, + int minp): + """ + Compute exponentially-weighted moving average using center-of-mass. + + Parameters + ---------- + input : ndarray (float64 type) + com : float64 + adjust: int + ignore_na: int + minp: int + + Returns + ------- + y : ndarray + """ + + cdef Py_ssize_t N = len(input) + cdef ndarray[double_t] output = np.empty(N, dtype=float) + if N == 0: + return output + + minp = max(minp, 1) + + cdef double alpha, old_wt_factor, new_wt, weighted_avg, old_wt, cur + cdef Py_ssize_t i, nobs + + alpha = 1. / (1. + com) + old_wt_factor = 1. - alpha + new_wt = 1. if adjust else alpha + + weighted_avg = input[0] + is_observation = (weighted_avg == weighted_avg) + nobs = int(is_observation) + output[0] = weighted_avg if (nobs >= minp) else NaN + old_wt = 1. + + for i from 1 <= i < N: + cur = input[i] + is_observation = (cur == cur) + nobs += int(is_observation) + if weighted_avg == weighted_avg: + + if is_observation or (not ignore_na): + + old_wt *= old_wt_factor + if is_observation: + + # avoid numerical errors on constant series + if weighted_avg != cur: + weighted_avg = ((old_wt * weighted_avg) + + (new_wt * cur)) / (old_wt + new_wt) + if adjust: + old_wt += new_wt + else: + old_wt = 1. + elif is_observation: + weighted_avg = cur + + output[i] = weighted_avg if (nobs >= minp) else NaN + + return output + +# ---------------------------------------------------------------------- +# Exponentially weighted moving covariance + + +def ewmcov(ndarray[double_t] input_x, ndarray[double_t] input_y, + double_t com, int adjust, int ignore_na, int minp, int bias): + """ + Compute exponentially-weighted moving variance using center-of-mass. + + Parameters + ---------- + input_x : ndarray (float64 type) + input_y : ndarray (float64 type) + com : float64 + adjust: int + ignore_na: int + minp: int + bias: int + + Returns + ------- + y : ndarray + """ + + cdef Py_ssize_t N = len(input_x) + if len(input_y) != N: + raise ValueError("arrays are of different lengths " + "(%d and %d)" % (N, len(input_y))) + cdef ndarray[double_t] output = np.empty(N, dtype=float) + if N == 0: + return output + + minp = max(minp, 1) + + cdef double alpha, old_wt_factor, new_wt, mean_x, mean_y, cov + cdef double sum_wt, sum_wt2, old_wt, cur_x, cur_y, old_mean_x, old_mean_y + cdef Py_ssize_t i, nobs + + alpha = 1. / (1. + com) + old_wt_factor = 1. - alpha + new_wt = 1. if adjust else alpha + + mean_x = input_x[0] + mean_y = input_y[0] + is_observation = ((mean_x == mean_x) and (mean_y == mean_y)) + nobs = int(is_observation) + if not is_observation: + mean_x = NaN + mean_y = NaN + output[0] = (0. if bias else NaN) if (nobs >= minp) else NaN + cov = 0. + sum_wt = 1. + sum_wt2 = 1. + old_wt = 1. + + for i from 1 <= i < N: + cur_x = input_x[i] + cur_y = input_y[i] + is_observation = ((cur_x == cur_x) and (cur_y == cur_y)) + nobs += int(is_observation) + if mean_x == mean_x: + if is_observation or (not ignore_na): + sum_wt *= old_wt_factor + sum_wt2 *= (old_wt_factor * old_wt_factor) + old_wt *= old_wt_factor + if is_observation: + old_mean_x = mean_x + old_mean_y = mean_y + + # avoid numerical errors on constant series + if mean_x != cur_x: + mean_x = ((old_wt * old_mean_x) + + (new_wt * cur_x)) / (old_wt + new_wt) + + # avoid numerical errors on constant series + if mean_y != cur_y: + mean_y = ((old_wt * old_mean_y) + + (new_wt * cur_y)) / (old_wt + new_wt) + cov = ((old_wt * (cov + ((old_mean_x - mean_x) * + (old_mean_y - mean_y)))) + + (new_wt * ((cur_x - mean_x) * + (cur_y - mean_y)))) / (old_wt + new_wt) + sum_wt += new_wt + sum_wt2 += (new_wt * new_wt) + old_wt += new_wt + if not adjust: + sum_wt /= old_wt + sum_wt2 /= (old_wt * old_wt) + old_wt = 1. + elif is_observation: + mean_x = cur_x + mean_y = cur_y + + if nobs >= minp: + if not bias: + numerator = sum_wt * sum_wt + denominator = numerator - sum_wt2 + if (denominator > 0.): + output[i] = ((numerator / denominator) * cov) + else: + output[i] = NaN + else: + output[i] = cov + else: + output[i] = NaN + + return output diff --git a/setup.py b/setup.py index 8f8865ecc3b7a..58965fe9ae6d6 100755 --- a/setup.py +++ b/setup.py @@ -430,9 +430,9 @@ def pxd(name): 'depends': [srcpath('generated', suffix='.pyx'), srcpath('join', suffix='.pyx')]}, _window={'pyxfile': 'window', - 'pxdfiles': ['src/skiplist','src/util'], - 'depends': ['pandas/src/skiplist.pyx', - 'pandas/src/skiplist.h']}, + 'pxdfiles': ['src/skiplist', 'src/util'], + 'depends': ['pandas/src/skiplist.pyx', + 'pandas/src/skiplist.h']}, parser={'pyxfile': 'parser', 'depends': ['pandas/src/parser/tokenizer.h', 'pandas/src/parser/io.h', @@ -547,6 +547,9 @@ def pxd(name): maintainer=AUTHOR, version=versioneer.get_version(), packages=['pandas', + 'pandas.api', + 'pandas.api.tests', + 'pandas.api.types', 'pandas.compat', 'pandas.compat.numpy', 'pandas.computation', @@ -557,7 +560,6 @@ def pxd(name): 'pandas.io.sas', 'pandas.formats', 'pandas.rpy', - 'pandas.sandbox', 'pandas.sparse', 'pandas.sparse.tests', 'pandas.stats', @@ -586,6 +588,7 @@ def pxd(name): 'tests/data/legacy_msgpack/*/*.msgpack', 'tests/data/*.csv*', 'tests/data/*.dta', + 'tests/data/*.pickle', 'tests/data/*.txt', 'tests/data/*.xls', 'tests/data/*.xlsx', @@ -602,8 +605,7 @@ def pxd(name): 'tests/data/html_encoding/*.html', 'tests/json/data/*.json'], 'pandas.tools': ['tests/data/*.csv'], - 'pandas.tests': ['data/*.pickle', - 'data/*.csv'], + 'pandas.tests': ['data/*.csv'], 'pandas.tests.formats': ['data/*.csv'], 'pandas.tests.indexes': ['data/*.pickle'], 'pandas.tseries.tests': ['data/*.pickle',
- [x] closes #13599 - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13633
2016-07-12T23:39:05Z
2016-07-20T21:56:46Z
null
2023-05-11T01:13:47Z
DOC: Add reference to frequency strings
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 7e832af14c051..12480bbac7d64 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -567,11 +567,11 @@ DateOffset objects ------------------ In the preceding examples, we created DatetimeIndex objects at various -frequencies by passing in frequency strings like 'M', 'W', and 'BM to the -``freq`` keyword. Under the hood, these frequency strings are being translated -into an instance of pandas ``DateOffset``, which represents a regular -frequency increment. Specific offset logic like "month", "business day", or -"one hour" is represented in its various subclasses. +frequencies by passing in :ref:`frequency strings <timeseries.offset_aliases>` +like 'M', 'W', and 'BM to the ``freq`` keyword. Under the hood, these frequency +strings are being translated into an instance of pandas ``DateOffset``, +which represents a regular frequency increment. Specific offset logic like +"month", "business day", or "one hour" is represented in its various subclasses. .. csv-table:: :header: "Class name", "Description" @@ -953,6 +953,9 @@ You can use keyword arguments suported by either ``BusinessHour`` and ``CustomBu # Monday is skipped because it's a holiday, business hour starts from 10:00 dt + bhour_mon * 2 + +.. _timeseries.offset_aliases: + Offset Aliases ~~~~~~~~~~~~~~ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index b4bcae47cbbdf..fb1808d46f08d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3938,6 +3938,9 @@ def asfreq(self, freq, method=None, how=None, normalize=False): Returns ------- converted : type of caller + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ from pandas.tseries.resample import asfreq return asfreq(self, freq, method=method, how=how, normalize=normalize) @@ -4009,6 +4012,9 @@ def resample(self, rule, how=None, axis=0, fill_method=None, closed=None, range from 0 through 4. Defaults to 0 + To learn more about the offset strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. + Examples -------- diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index 46d30ab7fe313..2db28779b4263 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -271,6 +271,9 @@ def rolling_count(arg, window, **kwargs): The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ return ensure_compat('rolling', 'count', arg, window=window, **kwargs) @@ -521,6 +524,9 @@ def rolling_quantile(arg, window, quantile, min_periods=None, freq=None, The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ return ensure_compat('rolling', 'quantile', @@ -570,6 +576,9 @@ def rolling_apply(arg, window, func, min_periods=None, freq=None, The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ return ensure_compat('rolling', 'apply', @@ -642,6 +651,9 @@ def rolling_window(arg, window=None, win_type=None, min_periods=None, The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ func = 'mean' if mean else 'sum' return ensure_compat('rolling', @@ -707,6 +719,9 @@ def expanding_count(arg, freq=None): The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ return ensure_compat('expanding', 'count', arg, freq=freq) @@ -735,6 +750,9 @@ def expanding_quantile(arg, quantile, min_periods=1, freq=None): The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ return ensure_compat('expanding', 'quantile', @@ -818,6 +836,9 @@ def expanding_apply(arg, func, min_periods=1, freq=None, The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ return ensure_compat('expanding', 'apply', diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 9b36bc5907066..79f3c139f5c03 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -178,6 +178,9 @@ class DatetimeIndex(DatelikeOps, TimelikeOps, DatetimeIndexOpsMixin, Attempt to infer fall dst-transition hours based on order name : object Name to be stored in the index + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ _typ = 'datetimeindex' @@ -2071,6 +2074,9 @@ def date_range(start=None, end=None, periods=None, freq='D', tz=None, ----- 2 of start, end, or periods must be specified + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. + Returns ------- rng : DatetimeIndex @@ -2111,6 +2117,9 @@ def bdate_range(start=None, end=None, periods=None, freq='B', tz=None, ----- 2 of start, end, or periods must be specified + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. + Returns ------- rng : DatetimeIndex @@ -2162,6 +2171,9 @@ def cdate_range(start=None, end=None, periods=None, freq='C', tz=None, ----- 2 of start, end, or periods must be specified + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. + Returns ------- rng : DatetimeIndex diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index dbc0078b67ae7..b7bf2f2370d2a 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -101,6 +101,9 @@ class TimedeltaIndex(DatetimeIndexOpsMixin, TimelikeOps, Int64Index): the 'left', 'right', or both sides (None) name : object Name to be stored in the index + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ _typ = 'timedeltaindex' @@ -1001,6 +1004,9 @@ def timedelta_range(start=None, end=None, periods=None, freq='D', Returns ------- rng : TimedeltaIndex + + To learn more about the frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. """ return TimedeltaIndex(start=start, end=end, periods=periods, freq=freq, name=name,
- [x] closes #13160
https://api.github.com/repos/pandas-dev/pandas/pulls/13632
2016-07-12T23:00:45Z
2016-07-21T14:31:18Z
null
2016-07-21T14:38:48Z
ENH: Add support for writing variable labels to Stata files
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 688f3b7ff6ada..a5478e3bed459 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -250,6 +250,8 @@ Other enhancements - A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) +- ``to_stata`` and ```StataWriter`` can now write variable labels to Stata dta files using a dictionary to make column names to labels (:issue:`13535`, :issue:`13535`) + .. _whatsnew_0190.api: API changes diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 334526b424be5..4fe7b318b3a18 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1467,7 +1467,7 @@ def to_excel(self, excel_writer, sheet_name='Sheet1', na_rep='', def to_stata(self, fname, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, - data_label=None): + data_label=None, variable_labels=None): """ A class for writing Stata binary dta files from array-like objects @@ -1480,11 +1480,24 @@ def to_stata(self, fname, convert_dates=None, write_index=True, format that you want to use for the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either a number or a name. + write_index : bool + Write the index to Stata dataset. encoding : str Default is latin-1. Note that Stata does not support unicode. byteorder : str Can be ">", "<", "little", or "big". The default is None which uses `sys.byteorder` + time_stamp : datetime + A date time to use when writing the file. Can be None, in which + case the current time is used. + dataset_label : str + A label for the data set. Should be 80 characters or smaller. + + .. versionadded:: 0.19.0 + + variable_labels : dict + Dictionary containing columns as keys and variable labels as + values. Each label must be 80 characters or smaller. Examples -------- @@ -1500,7 +1513,8 @@ def to_stata(self, fname, convert_dates=None, write_index=True, writer = StataWriter(fname, self, convert_dates=convert_dates, encoding=encoding, byteorder=byteorder, time_stamp=time_stamp, data_label=data_label, - write_index=write_index) + write_index=write_index, + variable_labels=variable_labels) writer.write_file() @Appender(fmt.docstring_to_string, indents=1) diff --git a/pandas/io/stata.py b/pandas/io/stata.py index bd19102c7f18c..d35466e8896ba 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -1059,7 +1059,7 @@ def _read_new_header(self, first_char): self.lbllist = self._get_lbllist() self.path_or_buf.seek(self._seek_variable_labels) - self.vlblist = self._get_vlblist() + self._variable_labels = self._get_variable_labels() # Get data type information, works for versions 117-118. def _get_dtypes(self, seek_vartypes): @@ -1127,7 +1127,7 @@ def _get_lbllist(self): return [self._null_terminate(self.path_or_buf.read(b)) for i in range(self.nvar)] - def _get_vlblist(self): + def _get_variable_labels(self): if self.format_version == 118: vlblist = [self._decode(self.path_or_buf.read(321)) for i in range(self.nvar)] @@ -1242,7 +1242,7 @@ def _read_old_header(self, first_char): self.lbllist = self._get_lbllist() - self.vlblist = self._get_vlblist() + self._variable_labels = self._get_variable_labels() # ignore expansion fields (Format 105 and later) # When reading, read five bytes; the last four bytes now tell you @@ -1306,11 +1306,11 @@ def _read_value_labels(self): while True: if self.format_version >= 117: if self.path_or_buf.read(5) == b'</val': # <lbl> - break # end of variable label table + break # end of value label table slength = self.path_or_buf.read(4) if not slength: - break # end of variable label table (format < 117) + break # end of value label table (format < 117) if self.format_version <= 117: labname = self._null_terminate(self.path_or_buf.read(33)) else: @@ -1666,7 +1666,7 @@ def variable_labels(self): """Returns variable labels as a dict, associating each variable name with corresponding label """ - return dict(zip(self.varlist, self.vlblist)) + return dict(zip(self.varlist, self._variable_labels)) def value_labels(self): """Returns a dict, associating each variable name a dict, associating @@ -1696,7 +1696,7 @@ def _set_endianness(endianness): def _pad_bytes(name, length): """ - Takes a char string and pads it wih null bytes until it's length chars + Takes a char string and pads it with null bytes until it's length chars """ return name + "\x00" * (length - len(name)) @@ -1831,6 +1831,12 @@ class StataWriter(StataParser): dataset_label : str A label for the data set. Should be 80 characters or smaller. + .. versionadded:: 0.19.0 + + variable_labels : dict + Dictionary containing columns as keys and variable labels as values. + Each label must be 80 characters or smaller. + Returns ------- writer : StataWriter instance @@ -1853,12 +1859,13 @@ class StataWriter(StataParser): def __init__(self, fname, data, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, - data_label=None): + data_label=None, variable_labels=None): super(StataWriter, self).__init__(encoding) self._convert_dates = convert_dates self._write_index = write_index self._time_stamp = time_stamp self._data_label = data_label + self._variable_labels = variable_labels # attach nobs, nvars, data, varlist, typlist self._prepare_pandas(data) @@ -2135,11 +2142,29 @@ def _write_descriptors(self, typlist=None, varlist=None, srtlist=None, else: # Default is empty label self._write(_pad_bytes("", 33)) - def _write_variable_labels(self, labels=None): - nvar = self.nvar - if labels is None: - for i in range(nvar): - self._write(_pad_bytes("", 81)) + def _write_variable_labels(self): + # Missing labels are 80 blank characters plus null termination + blank = _pad_bytes('', 81) + + if self._variable_labels is None: + for i in range(self.nvar): + self._write(blank) + return + + for col in self.data: + if col in self._variable_labels: + label = self._variable_labels[col] + if len(label) > 80: + raise ValueError('Variable labels must be 80 characters ' + 'or fewer') + is_latin1 = all(ord(c) < 256 for c in label) + if not is_latin1: + raise ValueError('Variable labels must contain only ' + 'characters that can be encoded in ' + 'Latin-1') + self._write(_pad_bytes(label, 81)) + else: + self._write(blank) def _prepare_data(self): data = self.data diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 5f45d1b547e62..91850e6ffe9b9 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -1,27 +1,27 @@ # -*- coding: utf-8 -*- # pylint: disable=E1101 -from datetime import datetime import datetime as dt import os -import warnings -import nose import struct import sys +import warnings +from datetime import datetime from distutils.version import LooseVersion +import nose import numpy as np import pandas as pd +import pandas.util.testing as tm +from pandas import compat from pandas.compat import iterkeys from pandas.core.frame import DataFrame, Series from pandas.types.common import is_categorical_dtype +from pandas.tslib import NaT from pandas.io.parsers import read_csv from pandas.io.stata import (read_stata, StataReader, InvalidColumnName, PossiblePrecisionLoss, StataMissingValue) -import pandas.util.testing as tm -from pandas.tslib import NaT -from pandas import compat class TestStata(tm.TestCase): @@ -1113,6 +1113,58 @@ def test_read_chunks_columns(self): tm.assert_frame_equal(from_frame, chunk, check_dtype=False) pos += chunksize + def test_write_variable_labels(self): + # GH 13631, add support for writing variable labels + original = pd.DataFrame({'a': [1, 2, 3, 4], + 'b': [1.0, 3.0, 27.0, 81.0], + 'c': ['Atlanta', 'Birmingham', + 'Cincinnati', 'Detroit']}) + original.index.name = 'index' + variable_labels = {'a': 'City Rank', 'b': 'City Exponent', 'c': 'City'} + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels) + with StataReader(path) as sr: + read_labels = sr.variable_labels() + expected_labels = {'index': '', + 'a': 'City Rank', + 'b': 'City Exponent', + 'c': 'City'} + tm.assert_equal(read_labels, expected_labels) + + variable_labels['index'] = 'The Index' + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels) + with StataReader(path) as sr: + read_labels = sr.variable_labels() + tm.assert_equal(read_labels, variable_labels) + + def test_write_variable_label_errors(self): + original = pd.DataFrame({'a': [1, 2, 3, 4], + 'b': [1.0, 3.0, 27.0, 81.0], + 'c': ['Atlanta', 'Birmingham', + 'Cincinnati', 'Detroit']}) + values = [u'\u03A1', u'\u0391', + u'\u039D', u'\u0394', + u'\u0391', u'\u03A3'] + + variable_labels_utf8 = {'a': 'City Rank', + 'b': 'City Exponent', + 'c': u''.join(values)} + + with tm.assertRaises(ValueError): + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels_utf8) + + variable_labels_long = {'a': 'City Rank', + 'b': 'City Exponent', + 'c': 'A very, very, very long variable label ' + 'that is too long for Stata which means ' + 'that it has more than 80 characters'} + + with tm.assertRaises(ValueError): + with tm.ensure_clean() as path: + original.to_stata(path, variable_labels=variable_labels_long) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- [x] closes #13536 - [x] closes #13535 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Add support for writing variable labels Fix documentation for to_stata Clean up function name to improve readability closes #13536 closes #13535
https://api.github.com/repos/pandas-dev/pandas/pulls/13631
2016-07-12T17:41:50Z
2016-07-19T01:59:54Z
null
2017-01-24T21:30:56Z
CLN: fix some issues in asv benchmark suite
diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json index 7b9fe353df2e3..f5fa849464881 100644 --- a/asv_bench/asv.conf.json +++ b/asv_bench/asv.conf.json @@ -77,11 +77,11 @@ // On conda install pytables, otherwise tables {"environment_type": "conda", "tables": ""}, {"environment_type": "conda", "pytables": null}, - {"environment_type": "virtualenv", "tables": null}, - {"environment_type": "virtualenv", "pytables": ""}, + {"environment_type": "(?!conda).*", "tables": null}, + {"environment_type": "(?!conda).*", "pytables": ""}, // On conda&win32, install libpython {"sys_platform": "(?!win32).*", "libpython": ""}, - {"sys_platform": "win32", "libpython": null}, + {"environment_type": "conda", "sys_platform": "win32", "libpython": null}, {"environment_type": "(?!conda).*", "libpython": ""} ], "include": [], diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py index 53d37a8161f43..094ae23a92fad 100644 --- a/asv_bench/benchmarks/indexing.py +++ b/asv_bench/benchmarks/indexing.py @@ -19,24 +19,6 @@ def time_dataframe_getitem_scalar(self): self.df[self.col][self.idx] -class datamatrix_getitem_scalar(object): - goal_time = 0.2 - - def setup(self): - try: - self.klass = DataMatrix - except: - self.klass = DataFrame - self.index = tm.makeStringIndex(1000) - self.columns = tm.makeStringIndex(30) - self.df = self.klass(np.random.rand(1000, 30), index=self.index, columns=self.columns) - self.idx = self.index[100] - self.col = self.columns[10] - - def time_datamatrix_getitem_scalar(self): - self.df[self.col][self.idx] - - class series_get_value(object): goal_time = 0.2 @@ -498,5 +480,3 @@ def setup(self): def time_float_loc(self): self.ind.get_loc(0) - - diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py index 6809c351beade..ee9d3104be4b1 100644 --- a/asv_bench/benchmarks/inference.py +++ b/asv_bench/benchmarks/inference.py @@ -143,12 +143,12 @@ class to_numeric(object): param_names = ['data', 'downcast'] params = [ - [(['1'] * N / 2) + ([2] * N / 2), - (['-1'] * N / 2) + ([2] * N / 2), - np.repeat(np.array('1970-01-01', '1970-01-02', + [(['1'] * (N / 2)) + ([2] * (N / 2)), + (['-1'] * (N / 2)) + ([2] * (N / 2)), + np.repeat(np.array(['1970-01-01', '1970-01-02'], dtype='datetime64[D]'), N), - (['1.1'] * N / 2) + ([2] * N / 2), - ([1] * N / 2) + ([2] * N / 2), + (['1.1'] * (N / 2)) + ([2] * (N / 2)), + ([1] * (N / 2)) + ([2] * (N / 2)), np.repeat(np.int32(1), N)], [None, 'integer', 'signed', 'unsigned', 'float'], ] diff --git a/asv_bench/benchmarks/join_merge.py b/asv_bench/benchmarks/join_merge.py index 39ebd9cb1cb73..dcd07911f2ff0 100644 --- a/asv_bench/benchmarks/join_merge.py +++ b/asv_bench/benchmarks/join_merge.py @@ -179,10 +179,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -210,10 +206,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -241,10 +233,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -272,10 +260,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D'])
Was trying to run benchmarks, and bumped into some issue (+ the DataMatrix, I just removed that as this is now removed from pandas, so not much sense having benchmarks for that)
https://api.github.com/repos/pandas-dev/pandas/pulls/13630
2016-07-12T15:26:07Z
2016-07-14T14:26:07Z
2016-07-14T14:26:07Z
2016-07-14T14:26:07Z
CLN: Removed outtype in DataFrame.to_dict
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 4cc16aac15f8b..393a961d26909 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -443,6 +443,7 @@ Removal of prior version deprecations/changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`) +- ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) .. _whatsnew_0190.performance: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b4509c999a5da..e01fc6dca6be3 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -818,7 +818,6 @@ def from_dict(cls, data, orient='columns', dtype=None): return cls(data, index=index, columns=columns, dtype=dtype) - @deprecate_kwarg(old_arg_name='outtype', new_arg_name='orient') def to_dict(self, orient='dict'): """Convert DataFrame to dictionary.
Follows up from #8486 in `0.15.0` by removing `outtype` in `DataFrame.to_dict()` Fortunately or unfortunately, no tests were written then to test the deprecation, so there was nothing to remove from the test suite this time around.
https://api.github.com/repos/pandas-dev/pandas/pulls/13627
2016-07-12T05:48:29Z
2016-07-12T17:19:49Z
2016-07-12T17:19:49Z
2016-07-13T01:46:25Z
BUG: Handle infinite values correctly in Series.argmax
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 4cc16aac15f8b..022d8545ca658 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -549,3 +549,5 @@ Bug Fixes - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) + +- Bug when calling ``Series.argmax`` with infinite values (:issue:`13595`) diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index f390e3f04a6c3..7c13c23f731da 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -454,8 +454,7 @@ def nanargmax(values, axis=None, skipna=True): """ Returns -1 in the NA case """ - values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf', - isfinite=True) + values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf') result = values.argmax(axis) result = _maybe_arg_null_out(result, axis, mask, skipna) return result @@ -465,8 +464,7 @@ def nanargmin(values, axis=None, skipna=True): """ Returns -1 in the NA case """ - values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf', - isfinite=True) + values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf') result = values.argmin(axis) result = _maybe_arg_null_out(result, axis, mask, skipna) return result diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index d9e2d8096c8d7..03c34b90c7a6c 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1282,6 +1282,14 @@ def test_idxmax(self): result = s.idxmin() self.assertEqual(result, 1.1) + # Infinite values + # GH 13595 + s = pd.Series([1, 2, np.inf]) + result = s.idxmax() + self.assertEqual(result, 2) + result = s.idxmax(skipna=False) + self.assertEqual(result, 2) + def test_numpy_argmax(self): # argmax is aliased to idxmax diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py index 904bedde03312..0c85ec9638ca9 100644 --- a/pandas/tests/test_nanops.py +++ b/pandas/tests/test_nanops.py @@ -41,6 +41,7 @@ def setUp(self): self.arr_inf = self.arr_float * np.inf self.arr_float_inf = np.vstack([self.arr_float, self.arr_inf]) + self.arr_float_neg_inf = -1 * self.arr_float_inf self.arr_float1_inf = np.vstack([self.arr_float1, self.arr_inf]) self.arr_inf_float1 = np.vstack([self.arr_inf, self.arr_float1]) self.arr_inf_inf = np.vstack([self.arr_inf, self.arr_inf]) @@ -234,7 +235,7 @@ def check_fun(self, testfunc, targfunc, testar, targar=None, def check_funs(self, testfunc, targfunc, allow_complex=True, allow_all_nan=True, allow_str=True, allow_date=True, - allow_tdelta=True, allow_obj=True, **kwargs): + allow_tdelta=True, allow_obj=True, allow_inf=True, **kwargs): self.check_fun(testfunc, targfunc, 'arr_float', **kwargs) self.check_fun(testfunc, targfunc, 'arr_float_nan', 'arr_float', **kwargs) @@ -287,6 +288,10 @@ def check_funs(self, testfunc, targfunc, allow_complex=True, allow_complex=allow_complex) self.check_fun(testfunc, targfunc, 'arr_obj', **kwargs) + if allow_inf: + self.check_fun(testfunc, targfunc, 'arr_float_inf', **kwargs) + self.check_fun(testfunc, targfunc, 'arr_float_neg_inf', **kwargs) + def check_funs_ddof(self, testfunc, targfunc, @@ -295,7 +300,7 @@ def check_funs_ddof(self, allow_str=True, allow_date=False, allow_tdelta=False, - allow_obj=True, ): + allow_obj=True): for ddof in range(3): try: self.check_funs(testfunc, targfunc, allow_complex,
- [ X] closes #13595 - [X ] tests added / passed - [X ] passes `git diff upstream/master | flake8 --diff` - [X ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13625
2016-07-11T23:46:55Z
2016-12-30T21:45:27Z
null
2016-12-30T23:37:49Z
BUG: Invalid Timedelta op may raise ValueError
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 4cc16aac15f8b..8661d87a617ba 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -543,7 +543,7 @@ Bug Fixes - Bug in ``.to_html``, ``.to_latex`` and ``.to_string`` silently ignore custom datetime formatter passed through the ``formatters`` key word (:issue:`10690`) - Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) - +- Bug in invalid ``Timedelta`` arithmetic and comparison may raise ``ValueError`` rather than ``TypeError`` (:issue:`13624`) - Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index af4c46e2d16fa..dbc0078b67ae7 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -35,16 +35,20 @@ def _td_index_cmp(opname, nat_result=False): """ def wrapper(self, other): + msg = "cannot compare a TimedeltaIndex with type {0}" func = getattr(super(TimedeltaIndex, self), opname) if _is_convertible_to_td(other) or other is tslib.NaT: - other = _to_m8(other) + try: + other = _to_m8(other) + except ValueError: + # failed to parse as timedelta + raise TypeError(msg.format(type(other))) result = func(other) if com.isnull(other): result.fill(nat_result) else: if not com.is_list_like(other): - raise TypeError("cannot compare a TimedeltaIndex with type " - "{0}".format(type(other))) + raise TypeError(msg.format(type(other))) other = TimedeltaIndex(other).values result = func(other) diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index c3bd62849bf82..4f985998d5e20 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -472,6 +472,21 @@ class Other: self.assertTrue(td.__mul__(other) is NotImplemented) self.assertTrue(td.__floordiv__(td) is NotImplemented) + def test_ops_error_str(self): + # GH 13624 + td = Timedelta('1 day') + + for l, r in [(td, 'a'), ('a', td)]: + + with tm.assertRaises(TypeError): + l + r + + with tm.assertRaises(TypeError): + l > r + + self.assertFalse(l == r) + self.assertTrue(l != r) + def test_fields(self): def check(value): # that we are int/long like @@ -1432,6 +1447,23 @@ def test_comparisons_nat(self): expected = np.array([True, True, True, True, True, False]) self.assert_numpy_array_equal(result, expected) + def test_ops_error_str(self): + # GH 13624 + tdi = TimedeltaIndex(['1 day', '2 days']) + + for l, r in [(tdi, 'a'), ('a', tdi)]: + with tm.assertRaises(TypeError): + l + r + + with tm.assertRaises(TypeError): + l > r + + with tm.assertRaises(TypeError): + l == r + + with tm.assertRaises(TypeError): + l != r + def test_map(self): rng = timedelta_range('1 day', periods=10) diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 7ff5d7adcaa35..5a28218500858 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -74,8 +74,8 @@ def _convert_listlike(arg, box, unit, name=None): value = arg.astype('timedelta64[{0}]'.format( unit)).astype('timedelta64[ns]', copy=False) else: - value = tslib.array_to_timedelta64( - _ensure_object(arg), unit=unit, errors=errors) + value = tslib.array_to_timedelta64(_ensure_object(arg), + unit=unit, errors=errors) value = value.astype('timedelta64[ns]', copy=False) if box: diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index fe4de11864522..650b4c7979d8d 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -2912,10 +2912,17 @@ class Timedelta(_Timedelta): if not self._validate_ops_compat(other): return NotImplemented - other = Timedelta(other) if other is NaT: return NaT + + try: + other = Timedelta(other) + except ValueError: + # failed to parse as timedelta + return NotImplemented + return Timedelta(op(self.value, other.value), unit='ns') + f.__name__ = name return f
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry `Timedelta` op may raise `ValueError` for invalid inputs because of internal conversion. ``` pd.Timedelta('1 days') + 'a' # ValueError: unit abbreviation w/o a number ``` Now it raises `TypeError`.
https://api.github.com/repos/pandas-dev/pandas/pulls/13624
2016-07-11T23:06:11Z
2016-07-12T10:52:19Z
null
2016-07-12T10:58:11Z
ENH: DataFrame sort columns by rows: sort_values(axis=1)
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 4cc16aac15f8b..7d3889505e098 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -229,6 +229,13 @@ Other enhancements - ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) - A top-level function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) +- ``DataFrame`` has gained support to re-order the columns based on the values in a row using ``df.sort_values(by='index_label', axis=1)`` (:issue:`10806`) + + .. ipython:: python + + df = pd.DataFrame({'A': [2, 7], 'B': [3, 5], 'C': [4, 8]}, + index=['row1', 'row2']) + df.sort_values(by='row2', axis=1) .. _whatsnew_0190.api: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b4509c999a5da..92440731c77e2 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3127,9 +3127,8 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last'): axis = self._get_axis_number(axis) + other_axis = 0 if axis == 1 else 1 - if axis != 0: - raise ValueError('When sorting by column, axis must be 0 (rows)') if not isinstance(by, list): by = [by] if com.is_sequence(ascending) and len(by) != len(ascending): @@ -3145,7 +3144,7 @@ def trans(v): keys = [] for x in by: - k = self[x].values + k = self.xs(x, axis=other_axis).values if k.ndim == 2: raise ValueError('Cannot sort by duplicate column %s' % str(x)) @@ -3157,7 +3156,7 @@ def trans(v): from pandas.core.groupby import _nargsort by = by[0] - k = self[by].values + k = self.xs(by, axis=other_axis).values if k.ndim == 2: # try to be helpful diff --git a/pandas/tests/frame/test_sorting.py b/pandas/tests/frame/test_sorting.py index 4d57216c8f870..b7a38e9e13ebd 100644 --- a/pandas/tests/frame/test_sorting.py +++ b/pandas/tests/frame/test_sorting.py @@ -84,7 +84,7 @@ def test_sort_values(self): frame = DataFrame([[1, 1, 2], [3, 1, 0], [4, 5, 6]], index=[1, 2, 3], columns=list('ABC')) - # by column + # by column (axis=0) sorted_df = frame.sort_values(by='A') indexer = frame['A'].argsort().values expected = frame.ix[frame.index[indexer]] @@ -116,9 +116,26 @@ def test_sort_values(self): self.assertRaises(ValueError, lambda: frame.sort_values( by=['A', 'B'], axis=2, inplace=True)) - msg = 'When sorting by column, axis must be 0' - with assertRaisesRegexp(ValueError, msg): - frame.sort_values(by='A', axis=1) + # by row (axis=1): GH 10806 + sorted_df = frame.sort_values(by=3, axis=1) + expected = frame + assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=3, axis=1, ascending=False) + expected = frame.reindex(columns=['C', 'B', 'A']) + assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=[1, 2], axis='columns') + expected = frame.reindex(columns=['B', 'A', 'C']) + assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=[1, 3], axis=1, + ascending=[True, False]) + assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=[1, 3], axis=1, ascending=False) + expected = frame.reindex(columns=['C', 'B', 'A']) + assert_frame_equal(sorted_df, expected) msg = r'Length of ascending \(5\) != length of by \(2\)' with assertRaisesRegexp(ValueError, msg): @@ -133,6 +150,11 @@ def test_sort_values_inplace(self): expected = frame.sort_values(by='A') assert_frame_equal(sorted_df, expected) + sorted_df = frame.copy() + sorted_df.sort_values(by=1, axis=1, inplace=True) + expected = frame.sort_values(by=1, axis=1) + assert_frame_equal(sorted_df, expected) + sorted_df = frame.copy() sorted_df.sort_values(by='A', ascending=False, inplace=True) expected = frame.sort_values(by='A', ascending=False) @@ -179,6 +201,10 @@ def test_sort_nan(self): sorted_df = df.sort_values(['A'], na_position='first', ascending=False) assert_frame_equal(sorted_df, expected) + expected = df.reindex(columns=['B', 'A']) + sorted_df = df.sort_values(by=1, axis=1, na_position='first') + assert_frame_equal(sorted_df, expected) + # na_position='last', order expected = DataFrame( {'A': [1, 1, 2, 4, 6, 8, nan],
- [X] closes #10806 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13622
2016-07-11T19:34:03Z
2016-07-21T15:05:26Z
null
2016-07-21T16:18:20Z
TST: Move plotting related tests to tests/plotting
diff --git a/pandas/tests/plotting/__init__.py b/pandas/tests/plotting/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py new file mode 100644 index 0000000000000..d80eb891c5bd6 --- /dev/null +++ b/pandas/tests/plotting/common.py @@ -0,0 +1,552 @@ +#!/usr/bin/env python +# coding: utf-8 + +import nose +import os +import warnings + +from pandas import DataFrame +from pandas.compat import zip, iteritems, OrderedDict +from pandas.util.decorators import cache_readonly +import pandas.core.common as com +import pandas.util.testing as tm +from pandas.util.testing import (ensure_clean, + assert_is_valid_plot_return_object) + +import numpy as np +from numpy import random + +import pandas.tools.plotting as plotting + + +""" +This is a common base class used for various plotting tests +""" + + +def _skip_if_no_scipy_gaussian_kde(): + try: + from scipy.stats import gaussian_kde # noqa + except ImportError: + raise nose.SkipTest("scipy version doesn't support gaussian_kde") + + +def _ok_for_gaussian_kde(kind): + if kind in ['kde', 'density']: + try: + from scipy.stats import gaussian_kde # noqa + except ImportError: + return False + return True + + +@tm.mplskip +class TestPlotBase(tm.TestCase): + + def setUp(self): + + import matplotlib as mpl + mpl.rcdefaults() + + self.mpl_le_1_2_1 = plotting._mpl_le_1_2_1() + self.mpl_ge_1_3_1 = plotting._mpl_ge_1_3_1() + self.mpl_ge_1_4_0 = plotting._mpl_ge_1_4_0() + self.mpl_ge_1_5_0 = plotting._mpl_ge_1_5_0() + + if self.mpl_ge_1_4_0: + self.bp_n_objects = 7 + else: + self.bp_n_objects = 8 + if self.mpl_ge_1_5_0: + # 1.5 added PolyCollections to legend handler + # so we have twice as many items. + self.polycollection_factor = 2 + else: + self.polycollection_factor = 1 + + # common test data + from pandas import read_csv + path = os.path.join(os.path.dirname(curpath()), 'data', 'iris.csv') + self.iris = read_csv(path) + + n = 100 + with tm.RNGContext(42): + gender = np.random.choice(['Male', 'Female'], size=n) + classroom = np.random.choice(['A', 'B', 'C'], size=n) + + self.hist_df = DataFrame({'gender': gender, + 'classroom': classroom, + 'height': random.normal(66, 4, size=n), + 'weight': random.normal(161, 32, size=n), + 'category': random.randint(4, size=n)}) + + self.tdf = tm.makeTimeDataFrame() + self.hexbin_df = DataFrame({"A": np.random.uniform(size=20), + "B": np.random.uniform(size=20), + "C": np.arange(20) + np.random.uniform( + size=20)}) + + def tearDown(self): + tm.close() + + @cache_readonly + def plt(self): + import matplotlib.pyplot as plt + return plt + + @cache_readonly + def colorconverter(self): + import matplotlib.colors as colors + return colors.colorConverter + + def _check_legend_labels(self, axes, labels=None, visible=True): + """ + Check each axes has expected legend labels + + Parameters + ---------- + axes : matplotlib Axes object, or its list-like + labels : list-like + expected legend labels + visible : bool + expected legend visibility. labels are checked only when visible is + True + """ + + if visible and (labels is None): + raise ValueError('labels must be specified when visible is True') + axes = self._flatten_visible(axes) + for ax in axes: + if visible: + self.assertTrue(ax.get_legend() is not None) + self._check_text_labels(ax.get_legend().get_texts(), labels) + else: + self.assertTrue(ax.get_legend() is None) + + def _check_data(self, xp, rs): + """ + Check each axes has identical lines + + Parameters + ---------- + xp : matplotlib Axes object + rs : matplotlib Axes object + """ + xp_lines = xp.get_lines() + rs_lines = rs.get_lines() + + def check_line(xpl, rsl): + xpdata = xpl.get_xydata() + rsdata = rsl.get_xydata() + tm.assert_almost_equal(xpdata, rsdata) + + self.assertEqual(len(xp_lines), len(rs_lines)) + [check_line(xpl, rsl) for xpl, rsl in zip(xp_lines, rs_lines)] + tm.close() + + def _check_visible(self, collections, visible=True): + """ + Check each artist is visible or not + + Parameters + ---------- + collections : matplotlib Artist or its list-like + target Artist or its list or collection + visible : bool + expected visibility + """ + from matplotlib.collections import Collection + if not isinstance(collections, + Collection) and not com.is_list_like(collections): + collections = [collections] + + for patch in collections: + self.assertEqual(patch.get_visible(), visible) + + def _get_colors_mapped(self, series, colors): + unique = series.unique() + # unique and colors length can be differed + # depending on slice value + mapped = dict(zip(unique, colors)) + return [mapped[v] for v in series.values] + + def _check_colors(self, collections, linecolors=None, facecolors=None, + mapping=None): + """ + Check each artist has expected line colors and face colors + + Parameters + ---------- + collections : list-like + list or collection of target artist + linecolors : list-like which has the same length as collections + list of expected line colors + facecolors : list-like which has the same length as collections + list of expected face colors + mapping : Series + Series used for color grouping key + used for andrew_curves, parallel_coordinates, radviz test + """ + + from matplotlib.lines import Line2D + from matplotlib.collections import Collection, PolyCollection + conv = self.colorconverter + if linecolors is not None: + + if mapping is not None: + linecolors = self._get_colors_mapped(mapping, linecolors) + linecolors = linecolors[:len(collections)] + + self.assertEqual(len(collections), len(linecolors)) + for patch, color in zip(collections, linecolors): + if isinstance(patch, Line2D): + result = patch.get_color() + # Line2D may contains string color expression + result = conv.to_rgba(result) + elif isinstance(patch, PolyCollection): + result = tuple(patch.get_edgecolor()[0]) + else: + result = patch.get_edgecolor() + + expected = conv.to_rgba(color) + self.assertEqual(result, expected) + + if facecolors is not None: + + if mapping is not None: + facecolors = self._get_colors_mapped(mapping, facecolors) + facecolors = facecolors[:len(collections)] + + self.assertEqual(len(collections), len(facecolors)) + for patch, color in zip(collections, facecolors): + if isinstance(patch, Collection): + # returned as list of np.array + result = patch.get_facecolor()[0] + else: + result = patch.get_facecolor() + + if isinstance(result, np.ndarray): + result = tuple(result) + + expected = conv.to_rgba(color) + self.assertEqual(result, expected) + + def _check_text_labels(self, texts, expected): + """ + Check each text has expected labels + + Parameters + ---------- + texts : matplotlib Text object, or its list-like + target text, or its list + expected : str or list-like which has the same length as texts + expected text label, or its list + """ + if not com.is_list_like(texts): + self.assertEqual(texts.get_text(), expected) + else: + labels = [t.get_text() for t in texts] + self.assertEqual(len(labels), len(expected)) + for l, e in zip(labels, expected): + self.assertEqual(l, e) + + def _check_ticks_props(self, axes, xlabelsize=None, xrot=None, + ylabelsize=None, yrot=None): + """ + Check each axes has expected tick properties + + Parameters + ---------- + axes : matplotlib Axes object, or its list-like + xlabelsize : number + expected xticks font size + xrot : number + expected xticks rotation + ylabelsize : number + expected yticks font size + yrot : number + expected yticks rotation + """ + from matplotlib.ticker import NullFormatter + axes = self._flatten_visible(axes) + for ax in axes: + if xlabelsize or xrot: + if isinstance(ax.xaxis.get_minor_formatter(), NullFormatter): + # If minor ticks has NullFormatter, rot / fontsize are not + # retained + labels = ax.get_xticklabels() + else: + labels = ax.get_xticklabels() + ax.get_xticklabels( + minor=True) + + for label in labels: + if xlabelsize is not None: + self.assertAlmostEqual(label.get_fontsize(), + xlabelsize) + if xrot is not None: + self.assertAlmostEqual(label.get_rotation(), xrot) + + if ylabelsize or yrot: + if isinstance(ax.yaxis.get_minor_formatter(), NullFormatter): + labels = ax.get_yticklabels() + else: + labels = ax.get_yticklabels() + ax.get_yticklabels( + minor=True) + + for label in labels: + if ylabelsize is not None: + self.assertAlmostEqual(label.get_fontsize(), + ylabelsize) + if yrot is not None: + self.assertAlmostEqual(label.get_rotation(), yrot) + + def _check_ax_scales(self, axes, xaxis='linear', yaxis='linear'): + """ + Check each axes has expected scales + + Parameters + ---------- + axes : matplotlib Axes object, or its list-like + xaxis : {'linear', 'log'} + expected xaxis scale + yaxis : {'linear', 'log'} + expected yaxis scale + """ + axes = self._flatten_visible(axes) + for ax in axes: + self.assertEqual(ax.xaxis.get_scale(), xaxis) + self.assertEqual(ax.yaxis.get_scale(), yaxis) + + def _check_axes_shape(self, axes, axes_num=None, layout=None, + figsize=(8.0, 6.0)): + """ + Check expected number of axes is drawn in expected layout + + Parameters + ---------- + axes : matplotlib Axes object, or its list-like + axes_num : number + expected number of axes. Unnecessary axes should be set to + invisible. + layout : tuple + expected layout, (expected number of rows , columns) + figsize : tuple + expected figsize. default is matplotlib default + """ + visible_axes = self._flatten_visible(axes) + + if axes_num is not None: + self.assertEqual(len(visible_axes), axes_num) + for ax in visible_axes: + # check something drawn on visible axes + self.assertTrue(len(ax.get_children()) > 0) + + if layout is not None: + result = self._get_axes_layout(plotting._flatten(axes)) + self.assertEqual(result, layout) + + self.assert_numpy_array_equal( + np.round(visible_axes[0].figure.get_size_inches()), + np.array(figsize, dtype=np.float64)) + + def _get_axes_layout(self, axes): + x_set = set() + y_set = set() + for ax in axes: + # check axes coordinates to estimate layout + points = ax.get_position().get_points() + x_set.add(points[0][0]) + y_set.add(points[0][1]) + return (len(y_set), len(x_set)) + + def _flatten_visible(self, axes): + """ + Flatten axes, and filter only visible + + Parameters + ---------- + axes : matplotlib Axes object, or its list-like + + """ + axes = plotting._flatten(axes) + axes = [ax for ax in axes if ax.get_visible()] + return axes + + def _check_has_errorbars(self, axes, xerr=0, yerr=0): + """ + Check axes has expected number of errorbars + + Parameters + ---------- + axes : matplotlib Axes object, or its list-like + xerr : number + expected number of x errorbar + yerr : number + expected number of y errorbar + """ + axes = self._flatten_visible(axes) + for ax in axes: + containers = ax.containers + xerr_count = 0 + yerr_count = 0 + for c in containers: + has_xerr = getattr(c, 'has_xerr', False) + has_yerr = getattr(c, 'has_yerr', False) + if has_xerr: + xerr_count += 1 + if has_yerr: + yerr_count += 1 + self.assertEqual(xerr, xerr_count) + self.assertEqual(yerr, yerr_count) + + def _check_box_return_type(self, returned, return_type, expected_keys=None, + check_ax_title=True): + """ + Check box returned type is correct + + Parameters + ---------- + returned : object to be tested, returned from boxplot + return_type : str + return_type passed to boxplot + expected_keys : list-like, optional + group labels in subplot case. If not passed, + the function checks assuming boxplot uses single ax + check_ax_title : bool + Whether to check the ax.title is the same as expected_key + Intended to be checked by calling from ``boxplot``. + Normal ``plot`` doesn't attach ``ax.title``, it must be disabled. + """ + from matplotlib.axes import Axes + types = {'dict': dict, 'axes': Axes, 'both': tuple} + if expected_keys is None: + # should be fixed when the returning default is changed + if return_type is None: + return_type = 'dict' + + self.assertTrue(isinstance(returned, types[return_type])) + if return_type == 'both': + self.assertIsInstance(returned.ax, Axes) + self.assertIsInstance(returned.lines, dict) + else: + # should be fixed when the returning default is changed + if return_type is None: + for r in self._flatten_visible(returned): + self.assertIsInstance(r, Axes) + return + + self.assertTrue(isinstance(returned, OrderedDict)) + self.assertEqual(sorted(returned.keys()), sorted(expected_keys)) + for key, value in iteritems(returned): + self.assertTrue(isinstance(value, types[return_type])) + # check returned dict has correct mapping + if return_type == 'axes': + if check_ax_title: + self.assertEqual(value.get_title(), key) + elif return_type == 'both': + if check_ax_title: + self.assertEqual(value.ax.get_title(), key) + self.assertIsInstance(value.ax, Axes) + self.assertIsInstance(value.lines, dict) + elif return_type == 'dict': + line = value['medians'][0] + axes = line.axes if self.mpl_ge_1_5_0 else line.get_axes() + if check_ax_title: + self.assertEqual(axes.get_title(), key) + else: + raise AssertionError + + def _check_grid_settings(self, obj, kinds, kws={}): + # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 + + import matplotlib as mpl + + def is_grid_on(): + xoff = all(not g.gridOn + for g in self.plt.gca().xaxis.get_major_ticks()) + yoff = all(not g.gridOn + for g in self.plt.gca().yaxis.get_major_ticks()) + return not (xoff and yoff) + + spndx = 1 + for kind in kinds: + if not _ok_for_gaussian_kde(kind): + continue + + self.plt.subplot(1, 4 * len(kinds), spndx) + spndx += 1 + mpl.rc('axes', grid=False) + obj.plot(kind=kind, **kws) + self.assertFalse(is_grid_on()) + + self.plt.subplot(1, 4 * len(kinds), spndx) + spndx += 1 + mpl.rc('axes', grid=True) + obj.plot(kind=kind, grid=False, **kws) + self.assertFalse(is_grid_on()) + + if kind != 'pie': + self.plt.subplot(1, 4 * len(kinds), spndx) + spndx += 1 + mpl.rc('axes', grid=True) + obj.plot(kind=kind, **kws) + self.assertTrue(is_grid_on()) + + self.plt.subplot(1, 4 * len(kinds), spndx) + spndx += 1 + mpl.rc('axes', grid=False) + obj.plot(kind=kind, grid=True, **kws) + self.assertTrue(is_grid_on()) + + def _maybe_unpack_cycler(self, rcParams, field='color'): + """ + Compat layer for MPL 1.5 change to color cycle + + Before: plt.rcParams['axes.color_cycle'] -> ['b', 'g', 'r'...] + After : plt.rcParams['axes.prop_cycle'] -> cycler(...) + """ + if self.mpl_ge_1_5_0: + cyl = rcParams['axes.prop_cycle'] + colors = [v[field] for v in cyl] + else: + colors = rcParams['axes.color_cycle'] + return colors + + +def _check_plot_works(f, filterwarnings='always', **kwargs): + import matplotlib.pyplot as plt + ret = None + with warnings.catch_warnings(): + warnings.simplefilter(filterwarnings) + try: + try: + fig = kwargs['figure'] + except KeyError: + fig = plt.gcf() + + plt.clf() + + ax = kwargs.get('ax', fig.add_subplot(211)) # noqa + ret = f(**kwargs) + + assert_is_valid_plot_return_object(ret) + + try: + kwargs['ax'] = fig.add_subplot(212) + ret = f(**kwargs) + except Exception: + pass + else: + assert_is_valid_plot_return_object(ret) + + with ensure_clean(return_filelike=True) as path: + plt.savefig(path) + finally: + tm.close(fig) + + return ret + + +def curpath(): + pth, _ = os.path.split(os.path.abspath(__file__)) + return pth diff --git a/pandas/tests/plotting/test_boxplot_method.py b/pandas/tests/plotting/test_boxplot_method.py new file mode 100644 index 0000000000000..d499540827ab0 --- /dev/null +++ b/pandas/tests/plotting/test_boxplot_method.py @@ -0,0 +1,374 @@ +#!/usr/bin/env python +# coding: utf-8 + +import nose +import itertools +import string +from distutils.version import LooseVersion + +from pandas import Series, DataFrame, MultiIndex +from pandas.compat import range, lzip +import pandas.util.testing as tm +from pandas.util.testing import slow + +import numpy as np +from numpy import random +from numpy.random import randn + +import pandas.tools.plotting as plotting + +from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works) + + +""" Test cases for .boxplot method """ + + +def _skip_if_mpl_14_or_dev_boxplot(): + # GH 8382 + # Boxplot failures on 1.4 and 1.4.1 + # Don't need try / except since that's done at class level + import matplotlib + if str(matplotlib.__version__) >= LooseVersion('1.4'): + raise nose.SkipTest("Matplotlib Regression in 1.4 and current dev.") + + +@tm.mplskip +class TestDataFramePlots(TestPlotBase): + + @slow + def test_boxplot_legacy(self): + df = DataFrame(randn(6, 4), + index=list(string.ascii_letters[:6]), + columns=['one', 'two', 'three', 'four']) + df['indic'] = ['foo', 'bar'] * 3 + df['indic2'] = ['foo', 'bar', 'foo'] * 2 + + _check_plot_works(df.boxplot, return_type='dict') + _check_plot_works(df.boxplot, column=[ + 'one', 'two'], return_type='dict') + # _check_plot_works adds an ax so catch warning. see GH #13188 + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.boxplot, column=['one', 'two'], + by='indic') + _check_plot_works(df.boxplot, column='one', by=['indic', 'indic2']) + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.boxplot, by='indic') + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.boxplot, by=['indic', 'indic2']) + _check_plot_works(plotting.boxplot, data=df['one'], return_type='dict') + _check_plot_works(df.boxplot, notch=1, return_type='dict') + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.boxplot, by='indic', notch=1) + + df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2']) + df['X'] = Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B']) + df['Y'] = Series(['A'] * 10) + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.boxplot, by='X') + + # When ax is supplied and required number of axes is 1, + # passed ax should be used: + fig, ax = self.plt.subplots() + axes = df.boxplot('Col1', by='X', ax=ax) + ax_axes = ax.axes if self.mpl_ge_1_5_0 else ax.get_axes() + self.assertIs(ax_axes, axes) + + fig, ax = self.plt.subplots() + axes = df.groupby('Y').boxplot(ax=ax, return_type='axes') + ax_axes = ax.axes if self.mpl_ge_1_5_0 else ax.get_axes() + self.assertIs(ax_axes, axes['A']) + + # Multiple columns with an ax argument should use same figure + fig, ax = self.plt.subplots() + with tm.assert_produces_warning(UserWarning): + axes = df.boxplot(column=['Col1', 'Col2'], + by='X', ax=ax, return_type='axes') + self.assertIs(axes['Col1'].get_figure(), fig) + + # When by is None, check that all relevant lines are present in the + # dict + fig, ax = self.plt.subplots() + d = df.boxplot(ax=ax, return_type='dict') + lines = list(itertools.chain.from_iterable(d.values())) + self.assertEqual(len(ax.get_lines()), len(lines)) + + @slow + def test_boxplot_return_type_legacy(self): + # API change in https://github.com/pydata/pandas/pull/7096 + import matplotlib as mpl # noqa + + df = DataFrame(randn(6, 4), + index=list(string.ascii_letters[:6]), + columns=['one', 'two', 'three', 'four']) + with tm.assertRaises(ValueError): + df.boxplot(return_type='NOTATYPE') + + with tm.assert_produces_warning(FutureWarning): + result = df.boxplot() + # change to Axes in future + self._check_box_return_type(result, 'dict') + + with tm.assert_produces_warning(False): + result = df.boxplot(return_type='dict') + self._check_box_return_type(result, 'dict') + + with tm.assert_produces_warning(False): + result = df.boxplot(return_type='axes') + self._check_box_return_type(result, 'axes') + + with tm.assert_produces_warning(False): + result = df.boxplot(return_type='both') + self._check_box_return_type(result, 'both') + + @slow + def test_boxplot_axis_limits(self): + + def _check_ax_limits(col, ax): + y_min, y_max = ax.get_ylim() + self.assertTrue(y_min <= col.min()) + self.assertTrue(y_max >= col.max()) + + df = self.hist_df.copy() + df['age'] = np.random.randint(1, 20, df.shape[0]) + # One full row + height_ax, weight_ax = df.boxplot(['height', 'weight'], by='category') + _check_ax_limits(df['height'], height_ax) + _check_ax_limits(df['weight'], weight_ax) + self.assertEqual(weight_ax._sharey, height_ax) + + # Two rows, one partial + p = df.boxplot(['height', 'weight', 'age'], by='category') + height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0] + dummy_ax = p[1, 1] + _check_ax_limits(df['height'], height_ax) + _check_ax_limits(df['weight'], weight_ax) + _check_ax_limits(df['age'], age_ax) + self.assertEqual(weight_ax._sharey, height_ax) + self.assertEqual(age_ax._sharey, height_ax) + self.assertIsNone(dummy_ax._sharey) + + @slow + def test_boxplot_empty_column(self): + _skip_if_mpl_14_or_dev_boxplot() + df = DataFrame(np.random.randn(20, 4)) + df.loc[:, 0] = np.nan + _check_plot_works(df.boxplot, return_type='axes') + + +@tm.mplskip +class TestDataFrameGroupByPlots(TestPlotBase): + + @slow + def test_boxplot_legacy(self): + grouped = self.hist_df.groupby(by='gender') + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(grouped.boxplot, return_type='axes') + self._check_axes_shape(list(axes.values()), axes_num=2, layout=(1, 2)) + + axes = _check_plot_works(grouped.boxplot, subplots=False, + return_type='axes') + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + tuples = lzip(string.ascii_letters[:10], range(10)) + df = DataFrame(np.random.rand(10, 3), + index=MultiIndex.from_tuples(tuples)) + + grouped = df.groupby(level=1) + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(grouped.boxplot, return_type='axes') + self._check_axes_shape(list(axes.values()), axes_num=10, layout=(4, 3)) + + axes = _check_plot_works(grouped.boxplot, subplots=False, + return_type='axes') + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + + grouped = df.unstack(level=1).groupby(level=0, axis=1) + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(grouped.boxplot, return_type='axes') + self._check_axes_shape(list(axes.values()), axes_num=3, layout=(2, 2)) + + axes = _check_plot_works(grouped.boxplot, subplots=False, + return_type='axes') + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + + @slow + def test_grouped_plot_fignums(self): + n = 10 + weight = Series(np.random.normal(166, 20, size=n)) + height = Series(np.random.normal(60, 10, size=n)) + with tm.RNGContext(42): + gender = np.random.choice(['male', 'female'], size=n) + df = DataFrame({'height': height, 'weight': weight, 'gender': gender}) + gb = df.groupby('gender') + + res = gb.plot() + self.assertEqual(len(self.plt.get_fignums()), 2) + self.assertEqual(len(res), 2) + tm.close() + + res = gb.boxplot(return_type='axes') + self.assertEqual(len(self.plt.get_fignums()), 1) + self.assertEqual(len(res), 2) + tm.close() + + # now works with GH 5610 as gender is excluded + res = df.groupby('gender').hist() + tm.close() + + @slow + def test_grouped_box_return_type(self): + df = self.hist_df + + # old style: return_type=None + result = df.boxplot(by='gender') + self.assertIsInstance(result, np.ndarray) + self._check_box_return_type( + result, None, + expected_keys=['height', 'weight', 'category']) + + # now for groupby + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result = df.groupby('gender').boxplot() + self._check_box_return_type( + result, 'dict', expected_keys=['Male', 'Female']) + + columns2 = 'X B C D A G Y N Q O'.split() + df2 = DataFrame(random.randn(50, 10), columns=columns2) + categories2 = 'A B C D E F G H I J'.split() + df2['category'] = categories2 * 5 + + for t in ['dict', 'axes', 'both']: + returned = df.groupby('classroom').boxplot(return_type=t) + self._check_box_return_type( + returned, t, expected_keys=['A', 'B', 'C']) + + returned = df.boxplot(by='classroom', return_type=t) + self._check_box_return_type( + returned, t, + expected_keys=['height', 'weight', 'category']) + + returned = df2.groupby('category').boxplot(return_type=t) + self._check_box_return_type(returned, t, expected_keys=categories2) + + returned = df2.boxplot(by='category', return_type=t) + self._check_box_return_type(returned, t, expected_keys=columns2) + + @slow + def test_grouped_box_layout(self): + df = self.hist_df + + self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], + by=df.gender, layout=(1, 1)) + self.assertRaises(ValueError, df.boxplot, + column=['height', 'weight', 'category'], + layout=(2, 1), return_type='dict') + self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], + by=df.gender, layout=(-1, -1)) + + # _check_plot_works adds an ax so catch warning. see GH #13188 + with tm.assert_produces_warning(UserWarning): + box = _check_plot_works(df.groupby('gender').boxplot, + column='height', return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2)) + + with tm.assert_produces_warning(UserWarning): + box = _check_plot_works(df.groupby('category').boxplot, + column='height', + return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2)) + + # GH 6769 + with tm.assert_produces_warning(UserWarning): + box = _check_plot_works(df.groupby('classroom').boxplot, + column='height', return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) + + # GH 5897 + axes = df.boxplot(column=['height', 'weight', 'category'], by='gender', + return_type='axes') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) + for ax in [axes['height']]: + self._check_visible(ax.get_xticklabels(), visible=False) + self._check_visible([ax.xaxis.get_label()], visible=False) + for ax in [axes['weight'], axes['category']]: + self._check_visible(ax.get_xticklabels()) + self._check_visible([ax.xaxis.get_label()]) + + box = df.groupby('classroom').boxplot( + column=['height', 'weight', 'category'], return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) + + with tm.assert_produces_warning(UserWarning): + box = _check_plot_works(df.groupby('category').boxplot, + column='height', + layout=(3, 2), return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) + with tm.assert_produces_warning(UserWarning): + box = _check_plot_works(df.groupby('category').boxplot, + column='height', + layout=(3, -1), return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) + + box = df.boxplot(column=['height', 'weight', 'category'], by='gender', + layout=(4, 1)) + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1)) + + box = df.boxplot(column=['height', 'weight', 'category'], by='gender', + layout=(-1, 1)) + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1)) + + box = df.groupby('classroom').boxplot( + column=['height', 'weight', 'category'], layout=(1, 4), + return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4)) + + box = df.groupby('classroom').boxplot( # noqa + column=['height', 'weight', 'category'], layout=(1, -1), + return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3)) + + @slow + def test_grouped_box_multiple_axes(self): + # GH 6970, GH 7069 + df = self.hist_df + + # check warning to ignore sharex / sharey + # this check should be done in the first function which + # passes multiple axes to plot, hist or boxplot + # location should be changed if other test is added + # which has earlier alphabetical order + with tm.assert_produces_warning(UserWarning): + fig, axes = self.plt.subplots(2, 2) + df.groupby('category').boxplot( + column='height', return_type='axes', ax=axes) + self._check_axes_shape(self.plt.gcf().axes, + axes_num=4, layout=(2, 2)) + + fig, axes = self.plt.subplots(2, 3) + with tm.assert_produces_warning(UserWarning): + returned = df.boxplot(column=['height', 'weight', 'category'], + by='gender', return_type='axes', ax=axes[0]) + returned = np.array(list(returned.values())) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[0]) + self.assertIs(returned[0].figure, fig) + + # draw on second row + with tm.assert_produces_warning(UserWarning): + returned = df.groupby('classroom').boxplot( + column=['height', 'weight', 'category'], + return_type='axes', ax=axes[1]) + returned = np.array(list(returned.values())) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[1]) + self.assertIs(returned[0].figure, fig) + + with tm.assertRaises(ValueError): + fig, axes = self.plt.subplots(2, 3) + # pass different number of axes from required + with tm.assert_produces_warning(UserWarning): + axes = df.groupby('classroom').boxplot(ax=axes) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tests/plotting/test_datetimelike.py similarity index 99% rename from pandas/tseries/tests/test_plotting.py rename to pandas/tests/plotting/test_datetimelike.py index 2255f9fae73de..3f09317915254 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tests/plotting/test_datetimelike.py @@ -14,12 +14,18 @@ from pandas.util.testing import assert_series_equal, ensure_clean, slow import pandas.util.testing as tm -from pandas.tests.test_graphics import _skip_if_no_scipy_gaussian_kde +from pandas.tests.plotting.common import (TestPlotBase, + _skip_if_no_scipy_gaussian_kde) + + +""" Test cases for time series specific (freq conversion, etc) """ @tm.mplskip -class TestTSPlot(tm.TestCase): +class TestTSPlot(TestPlotBase): + def setUp(self): + TestPlotBase.setUp(self) freq = ['S', 'T', 'H', 'D', 'W', 'M', 'Q', 'A'] idx = [period_range('12/31/1999', freq=x, periods=100) for x in freq] self.period_ser = [Series(np.random.randn(len(x)), x) for x in idx] diff --git a/pandas/tests/test_graphics.py b/pandas/tests/plotting/test_frame.py similarity index 68% rename from pandas/tests/test_graphics.py rename to pandas/tests/plotting/test_frame.py index 5493eb37c358b..311da4a92e45a 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/plotting/test_frame.py @@ -2,1293 +2,31 @@ # coding: utf-8 import nose -import itertools -import os import string import warnings from datetime import datetime, date -from pandas.types.common import is_list_like import pandas as pd from pandas import (Series, DataFrame, MultiIndex, PeriodIndex, date_range, bdate_range) -from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip, - iteritems, OrderedDict, PY3) -from pandas.util.decorators import cache_readonly +from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip, PY3) from pandas.formats.printing import pprint_thing import pandas.util.testing as tm -from pandas.util.testing import (ensure_clean, - assert_is_valid_plot_return_object, slow) +from pandas.util.testing import slow from pandas.core.config import set_option import numpy as np -from numpy import random from numpy.random import rand, randn import pandas.tools.plotting as plotting -""" -These tests are for ``Dataframe.plot`` and ``Series.plot``. -Other plot methods such as ``.hist``, ``.boxplot`` and other miscellaneous -are tested in test_graphics_others.py -""" +from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works, + _skip_if_no_scipy_gaussian_kde, + _ok_for_gaussian_kde) -def _skip_if_no_scipy_gaussian_kde(): - try: - from scipy.stats import gaussian_kde # noqa - except ImportError: - raise nose.SkipTest("scipy version doesn't support gaussian_kde") - - -def _ok_for_gaussian_kde(kind): - if kind in ['kde', 'density']: - try: - from scipy.stats import gaussian_kde # noqa - except ImportError: - return False - return True - - -@tm.mplskip -class TestPlotBase(tm.TestCase): - - def setUp(self): - - import matplotlib as mpl - mpl.rcdefaults() - - n = 100 - with tm.RNGContext(42): - gender = np.random.choice(['Male', 'Female'], size=n) - classroom = np.random.choice(['A', 'B', 'C'], size=n) - - self.hist_df = DataFrame({'gender': gender, - 'classroom': classroom, - 'height': random.normal(66, 4, size=n), - 'weight': random.normal(161, 32, size=n), - 'category': random.randint(4, size=n)}) - - self.mpl_le_1_2_1 = plotting._mpl_le_1_2_1() - self.mpl_ge_1_3_1 = plotting._mpl_ge_1_3_1() - self.mpl_ge_1_4_0 = plotting._mpl_ge_1_4_0() - self.mpl_ge_1_5_0 = plotting._mpl_ge_1_5_0() - - if self.mpl_ge_1_4_0: - self.bp_n_objects = 7 - else: - self.bp_n_objects = 8 - if self.mpl_ge_1_5_0: - # 1.5 added PolyCollections to legend handler - # so we have twice as many items. - self.polycollection_factor = 2 - else: - self.polycollection_factor = 1 - - def tearDown(self): - tm.close() - - @cache_readonly - def plt(self): - import matplotlib.pyplot as plt - return plt - - @cache_readonly - def colorconverter(self): - import matplotlib.colors as colors - return colors.colorConverter - - def _check_legend_labels(self, axes, labels=None, visible=True): - """ - Check each axes has expected legend labels - - Parameters - ---------- - axes : matplotlib Axes object, or its list-like - labels : list-like - expected legend labels - visible : bool - expected legend visibility. labels are checked only when visible is - True - """ - - if visible and (labels is None): - raise ValueError('labels must be specified when visible is True') - axes = self._flatten_visible(axes) - for ax in axes: - if visible: - self.assertTrue(ax.get_legend() is not None) - self._check_text_labels(ax.get_legend().get_texts(), labels) - else: - self.assertTrue(ax.get_legend() is None) - - def _check_data(self, xp, rs): - """ - Check each axes has identical lines - - Parameters - ---------- - xp : matplotlib Axes object - rs : matplotlib Axes object - """ - xp_lines = xp.get_lines() - rs_lines = rs.get_lines() - - def check_line(xpl, rsl): - xpdata = xpl.get_xydata() - rsdata = rsl.get_xydata() - tm.assert_almost_equal(xpdata, rsdata) - - self.assertEqual(len(xp_lines), len(rs_lines)) - [check_line(xpl, rsl) for xpl, rsl in zip(xp_lines, rs_lines)] - tm.close() - - def _check_visible(self, collections, visible=True): - """ - Check each artist is visible or not - - Parameters - ---------- - collections : matplotlib Artist or its list-like - target Artist or its list or collection - visible : bool - expected visibility - """ - from matplotlib.collections import Collection - if not isinstance(collections, - Collection) and not is_list_like(collections): - collections = [collections] - - for patch in collections: - self.assertEqual(patch.get_visible(), visible) - - def _get_colors_mapped(self, series, colors): - unique = series.unique() - # unique and colors length can be differed - # depending on slice value - mapped = dict(zip(unique, colors)) - return [mapped[v] for v in series.values] - - def _check_colors(self, collections, linecolors=None, facecolors=None, - mapping=None): - """ - Check each artist has expected line colors and face colors - - Parameters - ---------- - collections : list-like - list or collection of target artist - linecolors : list-like which has the same length as collections - list of expected line colors - facecolors : list-like which has the same length as collections - list of expected face colors - mapping : Series - Series used for color grouping key - used for andrew_curves, parallel_coordinates, radviz test - """ - - from matplotlib.lines import Line2D - from matplotlib.collections import Collection, PolyCollection - conv = self.colorconverter - if linecolors is not None: - - if mapping is not None: - linecolors = self._get_colors_mapped(mapping, linecolors) - linecolors = linecolors[:len(collections)] - - self.assertEqual(len(collections), len(linecolors)) - for patch, color in zip(collections, linecolors): - if isinstance(patch, Line2D): - result = patch.get_color() - # Line2D may contains string color expression - result = conv.to_rgba(result) - elif isinstance(patch, PolyCollection): - result = tuple(patch.get_edgecolor()[0]) - else: - result = patch.get_edgecolor() - - expected = conv.to_rgba(color) - self.assertEqual(result, expected) - - if facecolors is not None: - - if mapping is not None: - facecolors = self._get_colors_mapped(mapping, facecolors) - facecolors = facecolors[:len(collections)] - - self.assertEqual(len(collections), len(facecolors)) - for patch, color in zip(collections, facecolors): - if isinstance(patch, Collection): - # returned as list of np.array - result = patch.get_facecolor()[0] - else: - result = patch.get_facecolor() - - if isinstance(result, np.ndarray): - result = tuple(result) - - expected = conv.to_rgba(color) - self.assertEqual(result, expected) - - def _check_text_labels(self, texts, expected): - """ - Check each text has expected labels - - Parameters - ---------- - texts : matplotlib Text object, or its list-like - target text, or its list - expected : str or list-like which has the same length as texts - expected text label, or its list - """ - if not is_list_like(texts): - self.assertEqual(texts.get_text(), expected) - else: - labels = [t.get_text() for t in texts] - self.assertEqual(len(labels), len(expected)) - for l, e in zip(labels, expected): - self.assertEqual(l, e) - - def _check_ticks_props(self, axes, xlabelsize=None, xrot=None, - ylabelsize=None, yrot=None): - """ - Check each axes has expected tick properties - - Parameters - ---------- - axes : matplotlib Axes object, or its list-like - xlabelsize : number - expected xticks font size - xrot : number - expected xticks rotation - ylabelsize : number - expected yticks font size - yrot : number - expected yticks rotation - """ - from matplotlib.ticker import NullFormatter - axes = self._flatten_visible(axes) - for ax in axes: - if xlabelsize or xrot: - if isinstance(ax.xaxis.get_minor_formatter(), NullFormatter): - # If minor ticks has NullFormatter, rot / fontsize are not - # retained - labels = ax.get_xticklabels() - else: - labels = ax.get_xticklabels() + ax.get_xticklabels( - minor=True) - - for label in labels: - if xlabelsize is not None: - self.assertAlmostEqual(label.get_fontsize(), - xlabelsize) - if xrot is not None: - self.assertAlmostEqual(label.get_rotation(), xrot) - - if ylabelsize or yrot: - if isinstance(ax.yaxis.get_minor_formatter(), NullFormatter): - labels = ax.get_yticklabels() - else: - labels = ax.get_yticklabels() + ax.get_yticklabels( - minor=True) - - for label in labels: - if ylabelsize is not None: - self.assertAlmostEqual(label.get_fontsize(), - ylabelsize) - if yrot is not None: - self.assertAlmostEqual(label.get_rotation(), yrot) - - def _check_ax_scales(self, axes, xaxis='linear', yaxis='linear'): - """ - Check each axes has expected scales - - Parameters - ---------- - axes : matplotlib Axes object, or its list-like - xaxis : {'linear', 'log'} - expected xaxis scale - yaxis : {'linear', 'log'} - expected yaxis scale - """ - axes = self._flatten_visible(axes) - for ax in axes: - self.assertEqual(ax.xaxis.get_scale(), xaxis) - self.assertEqual(ax.yaxis.get_scale(), yaxis) - - def _check_axes_shape(self, axes, axes_num=None, layout=None, - figsize=(8.0, 6.0)): - """ - Check expected number of axes is drawn in expected layout - - Parameters - ---------- - axes : matplotlib Axes object, or its list-like - axes_num : number - expected number of axes. Unnecessary axes should be set to - invisible. - layout : tuple - expected layout, (expected number of rows , columns) - figsize : tuple - expected figsize. default is matplotlib default - """ - visible_axes = self._flatten_visible(axes) - - if axes_num is not None: - self.assertEqual(len(visible_axes), axes_num) - for ax in visible_axes: - # check something drawn on visible axes - self.assertTrue(len(ax.get_children()) > 0) - - if layout is not None: - result = self._get_axes_layout(plotting._flatten(axes)) - self.assertEqual(result, layout) - - self.assert_numpy_array_equal( - np.round(visible_axes[0].figure.get_size_inches()), - np.array(figsize, dtype=np.float64)) - - def _get_axes_layout(self, axes): - x_set = set() - y_set = set() - for ax in axes: - # check axes coordinates to estimate layout - points = ax.get_position().get_points() - x_set.add(points[0][0]) - y_set.add(points[0][1]) - return (len(y_set), len(x_set)) - - def _flatten_visible(self, axes): - """ - Flatten axes, and filter only visible - - Parameters - ---------- - axes : matplotlib Axes object, or its list-like - - """ - axes = plotting._flatten(axes) - axes = [ax for ax in axes if ax.get_visible()] - return axes - - def _check_has_errorbars(self, axes, xerr=0, yerr=0): - """ - Check axes has expected number of errorbars - - Parameters - ---------- - axes : matplotlib Axes object, or its list-like - xerr : number - expected number of x errorbar - yerr : number - expected number of y errorbar - """ - axes = self._flatten_visible(axes) - for ax in axes: - containers = ax.containers - xerr_count = 0 - yerr_count = 0 - for c in containers: - has_xerr = getattr(c, 'has_xerr', False) - has_yerr = getattr(c, 'has_yerr', False) - if has_xerr: - xerr_count += 1 - if has_yerr: - yerr_count += 1 - self.assertEqual(xerr, xerr_count) - self.assertEqual(yerr, yerr_count) - - def _check_box_return_type(self, returned, return_type, expected_keys=None, - check_ax_title=True): - """ - Check box returned type is correct - - Parameters - ---------- - returned : object to be tested, returned from boxplot - return_type : str - return_type passed to boxplot - expected_keys : list-like, optional - group labels in subplot case. If not passed, - the function checks assuming boxplot uses single ax - check_ax_title : bool - Whether to check the ax.title is the same as expected_key - Intended to be checked by calling from ``boxplot``. - Normal ``plot`` doesn't attach ``ax.title``, it must be disabled. - """ - from matplotlib.axes import Axes - types = {'dict': dict, 'axes': Axes, 'both': tuple} - if expected_keys is None: - # should be fixed when the returning default is changed - if return_type is None: - return_type = 'dict' - - self.assertTrue(isinstance(returned, types[return_type])) - if return_type == 'both': - self.assertIsInstance(returned.ax, Axes) - self.assertIsInstance(returned.lines, dict) - else: - # should be fixed when the returning default is changed - if return_type is None: - for r in self._flatten_visible(returned): - self.assertIsInstance(r, Axes) - return - - self.assertTrue(isinstance(returned, OrderedDict)) - self.assertEqual(sorted(returned.keys()), sorted(expected_keys)) - for key, value in iteritems(returned): - self.assertTrue(isinstance(value, types[return_type])) - # check returned dict has correct mapping - if return_type == 'axes': - if check_ax_title: - self.assertEqual(value.get_title(), key) - elif return_type == 'both': - if check_ax_title: - self.assertEqual(value.ax.get_title(), key) - self.assertIsInstance(value.ax, Axes) - self.assertIsInstance(value.lines, dict) - elif return_type == 'dict': - line = value['medians'][0] - axes = line.axes if self.mpl_ge_1_5_0 else line.get_axes() - if check_ax_title: - self.assertEqual(axes.get_title(), key) - else: - raise AssertionError - - def _check_grid_settings(self, obj, kinds, kws={}): - # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 - - import matplotlib as mpl - - def is_grid_on(): - xoff = all(not g.gridOn - for g in self.plt.gca().xaxis.get_major_ticks()) - yoff = all(not g.gridOn - for g in self.plt.gca().yaxis.get_major_ticks()) - return not (xoff and yoff) - - spndx = 1 - for kind in kinds: - if not _ok_for_gaussian_kde(kind): - continue - - self.plt.subplot(1, 4 * len(kinds), spndx) - spndx += 1 - mpl.rc('axes', grid=False) - obj.plot(kind=kind, **kws) - self.assertFalse(is_grid_on()) - - self.plt.subplot(1, 4 * len(kinds), spndx) - spndx += 1 - mpl.rc('axes', grid=True) - obj.plot(kind=kind, grid=False, **kws) - self.assertFalse(is_grid_on()) - - if kind != 'pie': - self.plt.subplot(1, 4 * len(kinds), spndx) - spndx += 1 - mpl.rc('axes', grid=True) - obj.plot(kind=kind, **kws) - self.assertTrue(is_grid_on()) - - self.plt.subplot(1, 4 * len(kinds), spndx) - spndx += 1 - mpl.rc('axes', grid=False) - obj.plot(kind=kind, grid=True, **kws) - self.assertTrue(is_grid_on()) - - def _maybe_unpack_cycler(self, rcParams, field='color'): - """ - Compat layer for MPL 1.5 change to color cycle - - Before: plt.rcParams['axes.color_cycle'] -> ['b', 'g', 'r'...] - After : plt.rcParams['axes.prop_cycle'] -> cycler(...) - """ - if self.mpl_ge_1_5_0: - cyl = rcParams['axes.prop_cycle'] - colors = [v[field] for v in cyl] - else: - colors = rcParams['axes.color_cycle'] - return colors - - -@tm.mplskip -class TestSeriesPlots(TestPlotBase): - - def setUp(self): - TestPlotBase.setUp(self) - import matplotlib as mpl - mpl.rcdefaults() - - self.ts = tm.makeTimeSeries() - self.ts.name = 'ts' - - self.series = tm.makeStringSeries() - self.series.name = 'series' - - self.iseries = tm.makePeriodSeries() - self.iseries.name = 'iseries' - - @slow - def test_plot(self): - _check_plot_works(self.ts.plot, label='foo') - _check_plot_works(self.ts.plot, use_index=False) - axes = _check_plot_works(self.ts.plot, rot=0) - self._check_ticks_props(axes, xrot=0) - - ax = _check_plot_works(self.ts.plot, style='.', logy=True) - self._check_ax_scales(ax, yaxis='log') - - ax = _check_plot_works(self.ts.plot, style='.', logx=True) - self._check_ax_scales(ax, xaxis='log') - - ax = _check_plot_works(self.ts.plot, style='.', loglog=True) - self._check_ax_scales(ax, xaxis='log', yaxis='log') - - _check_plot_works(self.ts[:10].plot.bar) - _check_plot_works(self.ts.plot.area, stacked=False) - _check_plot_works(self.iseries.plot) - - for kind in ['line', 'bar', 'barh', 'kde', 'hist', 'box']: - if not _ok_for_gaussian_kde(kind): - continue - _check_plot_works(self.series[:5].plot, kind=kind) - - _check_plot_works(self.series[:10].plot.barh) - ax = _check_plot_works(Series(randn(10)).plot.bar, color='black') - self._check_colors([ax.patches[0]], facecolors=['black']) - - # GH 6951 - ax = _check_plot_works(self.ts.plot, subplots=True) - self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) - - ax = _check_plot_works(self.ts.plot, subplots=True, layout=(-1, 1)) - self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) - ax = _check_plot_works(self.ts.plot, subplots=True, layout=(1, -1)) - self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) - - @slow - def test_plot_figsize_and_title(self): - # figsize and title - ax = self.series.plot(title='Test', figsize=(16, 8)) - self._check_text_labels(ax.title, 'Test') - self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16, 8)) - - def test_dont_modify_rcParams(self): - # GH 8242 - if self.mpl_ge_1_5_0: - key = 'axes.prop_cycle' - else: - key = 'axes.color_cycle' - colors = self.plt.rcParams[key] - Series([1, 2, 3]).plot() - self.assertEqual(colors, self.plt.rcParams[key]) - - def test_ts_line_lim(self): - ax = self.ts.plot() - xmin, xmax = ax.get_xlim() - lines = ax.get_lines() - self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0]) - self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1]) - tm.close() - - ax = self.ts.plot(secondary_y=True) - xmin, xmax = ax.get_xlim() - lines = ax.get_lines() - self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0]) - self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1]) - - def test_ts_area_lim(self): - ax = self.ts.plot.area(stacked=False) - xmin, xmax = ax.get_xlim() - line = ax.get_lines()[0].get_data(orig=False)[0] - self.assertEqual(xmin, line[0]) - self.assertEqual(xmax, line[-1]) - tm.close() - - # GH 7471 - ax = self.ts.plot.area(stacked=False, x_compat=True) - xmin, xmax = ax.get_xlim() - line = ax.get_lines()[0].get_data(orig=False)[0] - self.assertEqual(xmin, line[0]) - self.assertEqual(xmax, line[-1]) - tm.close() - - tz_ts = self.ts.copy() - tz_ts.index = tz_ts.tz_localize('GMT').tz_convert('CET') - ax = tz_ts.plot.area(stacked=False, x_compat=True) - xmin, xmax = ax.get_xlim() - line = ax.get_lines()[0].get_data(orig=False)[0] - self.assertEqual(xmin, line[0]) - self.assertEqual(xmax, line[-1]) - tm.close() - - ax = tz_ts.plot.area(stacked=False, secondary_y=True) - xmin, xmax = ax.get_xlim() - line = ax.get_lines()[0].get_data(orig=False)[0] - self.assertEqual(xmin, line[0]) - self.assertEqual(xmax, line[-1]) - - def test_label(self): - s = Series([1, 2]) - ax = s.plot(label='LABEL', legend=True) - self._check_legend_labels(ax, labels=['LABEL']) - self.plt.close() - ax = s.plot(legend=True) - self._check_legend_labels(ax, labels=['None']) - self.plt.close() - # get name from index - s.name = 'NAME' - ax = s.plot(legend=True) - self._check_legend_labels(ax, labels=['NAME']) - self.plt.close() - # override the default - ax = s.plot(legend=True, label='LABEL') - self._check_legend_labels(ax, labels=['LABEL']) - self.plt.close() - # Add lebel info, but don't draw - ax = s.plot(legend=False, label='LABEL') - self.assertEqual(ax.get_legend(), None) # Hasn't been drawn - ax.legend() # draw it - self._check_legend_labels(ax, labels=['LABEL']) - - def test_line_area_nan_series(self): - values = [1, 2, np.nan, 3] - s = Series(values) - ts = Series(values, index=tm.makeDateIndex(k=4)) - - for d in [s, ts]: - ax = _check_plot_works(d.plot) - masked = ax.lines[0].get_ydata() - # remove nan for comparison purpose - exp = np.array([1, 2, 3], dtype=np.float64) - self.assert_numpy_array_equal(np.delete(masked.data, 2), exp) - self.assert_numpy_array_equal( - masked.mask, np.array([False, False, True, False])) - - expected = np.array([1, 2, 0, 3], dtype=np.float64) - ax = _check_plot_works(d.plot, stacked=True) - self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected) - ax = _check_plot_works(d.plot.area) - self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected) - ax = _check_plot_works(d.plot.area, stacked=False) - self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected) - - def test_line_use_index_false(self): - s = Series([1, 2, 3], index=['a', 'b', 'c']) - s.index.name = 'The Index' - ax = s.plot(use_index=False) - label = ax.get_xlabel() - self.assertEqual(label, '') - ax2 = s.plot.bar(use_index=False) - label2 = ax2.get_xlabel() - self.assertEqual(label2, '') - - @slow - def test_bar_log(self): - expected = np.array([1., 10., 100., 1000.]) - - if not self.mpl_le_1_2_1: - expected = np.hstack((.1, expected, 1e4)) - - ax = Series([200, 500]).plot.bar(log=True) - tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected) - tm.close() - - ax = Series([200, 500]).plot.barh(log=True) - tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected) - tm.close() - - # GH 9905 - expected = np.array([1.0e-03, 1.0e-02, 1.0e-01, 1.0e+00]) - - if not self.mpl_le_1_2_1: - expected = np.hstack((1.0e-04, expected, 1.0e+01)) - - ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind='bar') - self.assertEqual(ax.get_ylim(), (0.001, 0.10000000000000001)) - tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected) - tm.close() - - ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind='barh') - self.assertEqual(ax.get_xlim(), (0.001, 0.10000000000000001)) - tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected) - - @slow - def test_bar_ignore_index(self): - df = Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']) - ax = df.plot.bar(use_index=False) - self._check_text_labels(ax.get_xticklabels(), ['0', '1', '2', '3']) - - def test_rotation(self): - df = DataFrame(randn(5, 5)) - # Default rot 0 - axes = df.plot() - self._check_ticks_props(axes, xrot=0) - - axes = df.plot(rot=30) - self._check_ticks_props(axes, xrot=30) - - def test_irregular_datetime(self): - rng = date_range('1/1/2000', '3/1/2000') - rng = rng[[0, 1, 2, 3, 5, 9, 10, 11, 12]] - ser = Series(randn(len(rng)), rng) - ax = ser.plot() - xp = datetime(1999, 1, 1).toordinal() - ax.set_xlim('1/1/1999', '1/1/2001') - self.assertEqual(xp, ax.get_xlim()[0]) - - @slow - def test_pie_series(self): - # if sum of values is less than 1.0, pie handle them as rate and draw - # semicircle. - series = Series(np.random.randint(1, 5), - index=['a', 'b', 'c', 'd', 'e'], name='YLABEL') - ax = _check_plot_works(series.plot.pie) - self._check_text_labels(ax.texts, series.index) - self.assertEqual(ax.get_ylabel(), 'YLABEL') - - # without wedge labels - ax = _check_plot_works(series.plot.pie, labels=None) - self._check_text_labels(ax.texts, [''] * 5) - - # with less colors than elements - color_args = ['r', 'g', 'b'] - ax = _check_plot_works(series.plot.pie, colors=color_args) - - color_expected = ['r', 'g', 'b', 'r', 'g'] - self._check_colors(ax.patches, facecolors=color_expected) - - # with labels and colors - labels = ['A', 'B', 'C', 'D', 'E'] - color_args = ['r', 'g', 'b', 'c', 'm'] - ax = _check_plot_works(series.plot.pie, labels=labels, - colors=color_args) - self._check_text_labels(ax.texts, labels) - self._check_colors(ax.patches, facecolors=color_args) - - # with autopct and fontsize - ax = _check_plot_works(series.plot.pie, colors=color_args, - autopct='%.2f', fontsize=7) - pcts = ['{0:.2f}'.format(s * 100) - for s in series.values / float(series.sum())] - iters = [iter(series.index), iter(pcts)] - expected_texts = list(next(it) for it in itertools.cycle(iters)) - self._check_text_labels(ax.texts, expected_texts) - for t in ax.texts: - self.assertEqual(t.get_fontsize(), 7) - - # includes negative value - with tm.assertRaises(ValueError): - series = Series([1, 2, 0, 4, -1], index=['a', 'b', 'c', 'd', 'e']) - series.plot.pie() - - # includes nan - series = Series([1, 2, np.nan, 4], index=['a', 'b', 'c', 'd'], - name='YLABEL') - ax = _check_plot_works(series.plot.pie) - self._check_text_labels(ax.texts, ['a', 'b', '', 'd']) - - def test_pie_nan(self): - s = Series([1, np.nan, 1, 1]) - ax = s.plot.pie(legend=True) - expected = ['0', '', '2', '3'] - result = [x.get_text() for x in ax.texts] - self.assertEqual(result, expected) - - @slow - def test_hist_df_kwargs(self): - df = DataFrame(np.random.randn(10, 2)) - ax = df.plot.hist(bins=5) - self.assertEqual(len(ax.patches), 10) - - @slow - def test_hist_df_with_nonnumerics(self): - # GH 9853 - with tm.RNGContext(1): - df = DataFrame( - np.random.randn(10, 4), columns=['A', 'B', 'C', 'D']) - df['E'] = ['x', 'y'] * 5 - ax = df.plot.hist(bins=5) - self.assertEqual(len(ax.patches), 20) - - ax = df.plot.hist() # bins=10 - self.assertEqual(len(ax.patches), 40) - - @slow - def test_hist_legacy(self): - _check_plot_works(self.ts.hist) - _check_plot_works(self.ts.hist, grid=False) - _check_plot_works(self.ts.hist, figsize=(8, 10)) - # _check_plot_works adds an ax so catch warning. see GH #13188 - with tm.assert_produces_warning(UserWarning): - _check_plot_works(self.ts.hist, - by=self.ts.index.month) - with tm.assert_produces_warning(UserWarning): - _check_plot_works(self.ts.hist, - by=self.ts.index.month, bins=5) - - fig, ax = self.plt.subplots(1, 1) - _check_plot_works(self.ts.hist, ax=ax) - _check_plot_works(self.ts.hist, ax=ax, figure=fig) - _check_plot_works(self.ts.hist, figure=fig) - tm.close() - - fig, (ax1, ax2) = self.plt.subplots(1, 2) - _check_plot_works(self.ts.hist, figure=fig, ax=ax1) - _check_plot_works(self.ts.hist, figure=fig, ax=ax2) - - with tm.assertRaises(ValueError): - self.ts.hist(by=self.ts.index, figure=fig) - - @slow - def test_hist_bins_legacy(self): - df = DataFrame(np.random.randn(10, 2)) - ax = df.hist(bins=2)[0][0] - self.assertEqual(len(ax.patches), 2) - - @slow - def test_hist_layout(self): - df = self.hist_df - with tm.assertRaises(ValueError): - df.height.hist(layout=(1, 1)) - - with tm.assertRaises(ValueError): - df.height.hist(layout=[1, 1]) - - @slow - def test_hist_layout_with_by(self): - df = self.hist_df - - # _check_plot_works adds an ax so catch warning. see GH #13188 - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, - by=df.gender, layout=(2, 1)) - self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, - by=df.gender, layout=(3, -1)) - self._check_axes_shape(axes, axes_num=2, layout=(3, 1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, - by=df.category, layout=(4, 1)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, - by=df.category, layout=(2, -1)) - self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, - by=df.category, layout=(3, -1)) - self._check_axes_shape(axes, axes_num=4, layout=(3, 2)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, - by=df.category, layout=(-1, 4)) - self._check_axes_shape(axes, axes_num=4, layout=(1, 4)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, - by=df.classroom, layout=(2, 2)) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 2), - figsize=(12, 7)) - - @slow - def test_hist_no_overlap(self): - from matplotlib.pyplot import subplot, gcf - x = Series(randn(2)) - y = Series(randn(2)) - subplot(121) - x.hist() - subplot(122) - y.hist() - fig = gcf() - axes = fig.axes if self.mpl_ge_1_5_0 else fig.get_axes() - self.assertEqual(len(axes), 2) - - @slow - def test_hist_secondary_legend(self): - # GH 9610 - df = DataFrame(np.random.randn(30, 4), columns=list('abcd')) - - # primary -> secondary - ax = df['a'].plot.hist(legend=True) - df['b'].plot.hist(ax=ax, legend=True, secondary_y=True) - # both legends are dran on left ax - # left and right axis must be visible - self._check_legend_labels(ax, labels=['a', 'b (right)']) - self.assertTrue(ax.get_yaxis().get_visible()) - self.assertTrue(ax.right_ax.get_yaxis().get_visible()) - tm.close() - - # secondary -> secondary - ax = df['a'].plot.hist(legend=True, secondary_y=True) - df['b'].plot.hist(ax=ax, legend=True, secondary_y=True) - # both legends are draw on left ax - # left axis must be invisible, right axis must be visible - self._check_legend_labels(ax.left_ax, - labels=['a (right)', 'b (right)']) - self.assertFalse(ax.left_ax.get_yaxis().get_visible()) - self.assertTrue(ax.get_yaxis().get_visible()) - tm.close() - - # secondary -> primary - ax = df['a'].plot.hist(legend=True, secondary_y=True) - # right axes is returned - df['b'].plot.hist(ax=ax, legend=True) - # both legends are draw on left ax - # left and right axis must be visible - self._check_legend_labels(ax.left_ax, labels=['a (right)', 'b']) - self.assertTrue(ax.left_ax.get_yaxis().get_visible()) - self.assertTrue(ax.get_yaxis().get_visible()) - tm.close() - - @slow - def test_df_series_secondary_legend(self): - # GH 9779 - df = DataFrame(np.random.randn(30, 3), columns=list('abc')) - s = Series(np.random.randn(30), name='x') - - # primary -> secondary (without passing ax) - ax = df.plot() - s.plot(legend=True, secondary_y=True) - # both legends are dran on left ax - # left and right axis must be visible - self._check_legend_labels(ax, labels=['a', 'b', 'c', 'x (right)']) - self.assertTrue(ax.get_yaxis().get_visible()) - self.assertTrue(ax.right_ax.get_yaxis().get_visible()) - tm.close() - - # primary -> secondary (with passing ax) - ax = df.plot() - s.plot(ax=ax, legend=True, secondary_y=True) - # both legends are dran on left ax - # left and right axis must be visible - self._check_legend_labels(ax, labels=['a', 'b', 'c', 'x (right)']) - self.assertTrue(ax.get_yaxis().get_visible()) - self.assertTrue(ax.right_ax.get_yaxis().get_visible()) - tm.close() - - # seconcary -> secondary (without passing ax) - ax = df.plot(secondary_y=True) - s.plot(legend=True, secondary_y=True) - # both legends are dran on left ax - # left axis must be invisible and right axis must be visible - expected = ['a (right)', 'b (right)', 'c (right)', 'x (right)'] - self._check_legend_labels(ax.left_ax, labels=expected) - self.assertFalse(ax.left_ax.get_yaxis().get_visible()) - self.assertTrue(ax.get_yaxis().get_visible()) - tm.close() - - # secondary -> secondary (with passing ax) - ax = df.plot(secondary_y=True) - s.plot(ax=ax, legend=True, secondary_y=True) - # both legends are dran on left ax - # left axis must be invisible and right axis must be visible - expected = ['a (right)', 'b (right)', 'c (right)', 'x (right)'] - self._check_legend_labels(ax.left_ax, expected) - self.assertFalse(ax.left_ax.get_yaxis().get_visible()) - self.assertTrue(ax.get_yaxis().get_visible()) - tm.close() - - # secondary -> secondary (with passing ax) - ax = df.plot(secondary_y=True, mark_right=False) - s.plot(ax=ax, legend=True, secondary_y=True) - # both legends are dran on left ax - # left axis must be invisible and right axis must be visible - expected = ['a', 'b', 'c', 'x (right)'] - self._check_legend_labels(ax.left_ax, expected) - self.assertFalse(ax.left_ax.get_yaxis().get_visible()) - self.assertTrue(ax.get_yaxis().get_visible()) - tm.close() - - @slow - def test_plot_fails_with_dupe_color_and_style(self): - x = Series(randn(2)) - with tm.assertRaises(ValueError): - x.plot(style='k--', color='k') - - @slow - def test_hist_kde(self): - ax = self.ts.plot.hist(logy=True) - self._check_ax_scales(ax, yaxis='log') - xlabels = ax.get_xticklabels() - # ticks are values, thus ticklabels are blank - self._check_text_labels(xlabels, [''] * len(xlabels)) - ylabels = ax.get_yticklabels() - self._check_text_labels(ylabels, [''] * len(ylabels)) - - tm._skip_if_no_scipy() - _skip_if_no_scipy_gaussian_kde() - _check_plot_works(self.ts.plot.kde) - _check_plot_works(self.ts.plot.density) - ax = self.ts.plot.kde(logy=True) - self._check_ax_scales(ax, yaxis='log') - xlabels = ax.get_xticklabels() - self._check_text_labels(xlabels, [''] * len(xlabels)) - ylabels = ax.get_yticklabels() - self._check_text_labels(ylabels, [''] * len(ylabels)) - - @slow - def test_kde_kwargs(self): - tm._skip_if_no_scipy() - _skip_if_no_scipy_gaussian_kde() - from numpy import linspace - _check_plot_works(self.ts.plot.kde, bw_method=.5, - ind=linspace(-100, 100, 20)) - _check_plot_works(self.ts.plot.density, bw_method=.5, - ind=linspace(-100, 100, 20)) - ax = self.ts.plot.kde(logy=True, bw_method=.5, - ind=linspace(-100, 100, 20)) - self._check_ax_scales(ax, yaxis='log') - self._check_text_labels(ax.yaxis.get_label(), 'Density') - - @slow - def test_kde_missing_vals(self): - tm._skip_if_no_scipy() - _skip_if_no_scipy_gaussian_kde() - s = Series(np.random.uniform(size=50)) - s[0] = np.nan - _check_plot_works(s.plot.kde) - - @slow - def test_hist_kwargs(self): - ax = self.ts.plot.hist(bins=5) - self.assertEqual(len(ax.patches), 5) - self._check_text_labels(ax.yaxis.get_label(), 'Frequency') - tm.close() - - if self.mpl_ge_1_3_1: - ax = self.ts.plot.hist(orientation='horizontal') - self._check_text_labels(ax.xaxis.get_label(), 'Frequency') - tm.close() - - ax = self.ts.plot.hist(align='left', stacked=True) - tm.close() - - @slow - def test_hist_kde_color(self): - ax = self.ts.plot.hist(logy=True, bins=10, color='b') - self._check_ax_scales(ax, yaxis='log') - self.assertEqual(len(ax.patches), 10) - self._check_colors(ax.patches, facecolors=['b'] * 10) - - tm._skip_if_no_scipy() - _skip_if_no_scipy_gaussian_kde() - ax = self.ts.plot.kde(logy=True, color='r') - self._check_ax_scales(ax, yaxis='log') - lines = ax.get_lines() - self.assertEqual(len(lines), 1) - self._check_colors(lines, ['r']) - - @slow - def test_boxplot_series(self): - ax = self.ts.plot.box(logy=True) - self._check_ax_scales(ax, yaxis='log') - xlabels = ax.get_xticklabels() - self._check_text_labels(xlabels, [self.ts.name]) - ylabels = ax.get_yticklabels() - self._check_text_labels(ylabels, [''] * len(ylabels)) - - @slow - def test_kind_both_ways(self): - s = Series(range(3)) - for kind in plotting._common_kinds + plotting._series_kinds: - if not _ok_for_gaussian_kde(kind): - continue - s.plot(kind=kind) - getattr(s.plot, kind)() - - @slow - def test_invalid_plot_data(self): - s = Series(list('abcd')) - for kind in plotting._common_kinds: - if not _ok_for_gaussian_kde(kind): - continue - with tm.assertRaises(TypeError): - s.plot(kind=kind) - - @slow - def test_valid_object_plot(self): - s = Series(lrange(10), dtype=object) - for kind in plotting._common_kinds: - if not _ok_for_gaussian_kde(kind): - continue - _check_plot_works(s.plot, kind=kind) - - def test_partially_invalid_plot_data(self): - s = Series(['a', 'b', 1.0, 2]) - for kind in plotting._common_kinds: - if not _ok_for_gaussian_kde(kind): - continue - with tm.assertRaises(TypeError): - s.plot(kind=kind) - - def test_invalid_kind(self): - s = Series([1, 2]) - with tm.assertRaises(ValueError): - s.plot(kind='aasdf') - - @slow - def test_dup_datetime_index_plot(self): - dr1 = date_range('1/1/2009', periods=4) - dr2 = date_range('1/2/2009', periods=4) - index = dr1.append(dr2) - values = randn(index.size) - s = Series(values, index=index) - _check_plot_works(s.plot) - - @slow - def test_errorbar_plot(self): - - s = Series(np.arange(10), name='x') - s_err = np.random.randn(10) - d_err = DataFrame(randn(10, 2), index=s.index, columns=['x', 'y']) - # test line and bar plots - kinds = ['line', 'bar'] - for kind in kinds: - ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind) - self._check_has_errorbars(ax, xerr=0, yerr=1) - ax = _check_plot_works(s.plot, yerr=s_err, kind=kind) - self._check_has_errorbars(ax, xerr=0, yerr=1) - ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind) - self._check_has_errorbars(ax, xerr=0, yerr=1) - ax = _check_plot_works(s.plot, yerr=d_err, kind=kind) - self._check_has_errorbars(ax, xerr=0, yerr=1) - ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind) - self._check_has_errorbars(ax, xerr=1, yerr=1) - - ax = _check_plot_works(s.plot, xerr=s_err) - self._check_has_errorbars(ax, xerr=1, yerr=0) - - # test time series plotting - ix = date_range('1/1/2000', '1/1/2001', freq='M') - ts = Series(np.arange(12), index=ix, name='x') - ts_err = Series(np.random.randn(12), index=ix) - td_err = DataFrame(randn(12, 2), index=ix, columns=['x', 'y']) - - ax = _check_plot_works(ts.plot, yerr=ts_err) - self._check_has_errorbars(ax, xerr=0, yerr=1) - ax = _check_plot_works(ts.plot, yerr=td_err) - self._check_has_errorbars(ax, xerr=0, yerr=1) - - # check incorrect lengths and types - with tm.assertRaises(ValueError): - s.plot(yerr=np.arange(11)) - - s_err = ['zzz'] * 10 - # in mpl 1.5+ this is a TypeError - with tm.assertRaises((ValueError, TypeError)): - s.plot(yerr=s_err) - - def test_table(self): - _check_plot_works(self.series.plot, table=True) - _check_plot_works(self.series.plot, table=self.series) - - @slow - def test_series_grid_settings(self): - # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 - self._check_grid_settings(Series([1, 2, 3]), - plotting._series_kinds + - plotting._common_kinds) - - @slow - def test_standard_colors(self): - for c in ['r', 'red', 'green', '#FF0000']: - result = plotting._get_standard_colors(1, color=c) - self.assertEqual(result, [c]) - - result = plotting._get_standard_colors(1, color=[c]) - self.assertEqual(result, [c]) - - result = plotting._get_standard_colors(3, color=c) - self.assertEqual(result, [c] * 3) - - result = plotting._get_standard_colors(3, color=[c]) - self.assertEqual(result, [c] * 3) - - @slow - def test_standard_colors_all(self): - import matplotlib.colors as colors - - # multiple colors like mediumaquamarine - for c in colors.cnames: - result = plotting._get_standard_colors(num_colors=1, color=c) - self.assertEqual(result, [c]) - - result = plotting._get_standard_colors(num_colors=1, color=[c]) - self.assertEqual(result, [c]) - - result = plotting._get_standard_colors(num_colors=3, color=c) - self.assertEqual(result, [c] * 3) - - result = plotting._get_standard_colors(num_colors=3, color=[c]) - self.assertEqual(result, [c] * 3) - - # single letter colors like k - for c in colors.ColorConverter.colors: - result = plotting._get_standard_colors(num_colors=1, color=c) - self.assertEqual(result, [c]) - - result = plotting._get_standard_colors(num_colors=1, color=[c]) - self.assertEqual(result, [c]) - - result = plotting._get_standard_colors(num_colors=3, color=c) - self.assertEqual(result, [c] * 3) - - result = plotting._get_standard_colors(num_colors=3, color=[c]) - self.assertEqual(result, [c] * 3) - - def test_series_plot_color_kwargs(self): - # GH1890 - ax = Series(np.arange(12) + 1).plot(color='green') - self._check_colors(ax.get_lines(), linecolors=['green']) - - def test_time_series_plot_color_kwargs(self): - # #1890 - ax = Series(np.arange(12) + 1, index=date_range( - '1/1/2000', periods=12)).plot(color='green') - self._check_colors(ax.get_lines(), linecolors=['green']) - - def test_time_series_plot_color_with_empty_kwargs(self): - import matplotlib as mpl - - if self.mpl_ge_1_5_0: - def_colors = self._maybe_unpack_cycler(mpl.rcParams) - else: - def_colors = mpl.rcParams['axes.color_cycle'] - index = date_range('1/1/2000', periods=12) - s = Series(np.arange(1, 13), index=index) - - ncolors = 3 - - for i in range(ncolors): - ax = s.plot() - self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors]) - - def test_xticklabels(self): - # GH11529 - s = Series(np.arange(10), index=['P%02d' % i for i in range(10)]) - ax = s.plot(xticks=[0, 3, 5, 9]) - exp = ['P%02d' % i for i in [0, 3, 5, 9]] - self._check_text_labels(ax.get_xticklabels(), exp) - - def test_custom_business_day_freq(self): - # GH7222 - from pandas.tseries.offsets import CustomBusinessDay - s = Series(range(100, 121), index=pd.bdate_range( - start='2014-05-01', end='2014-06-01', - freq=CustomBusinessDay(holidays=['2014-05-26']))) - - _check_plot_works(s.plot) +""" Test cases for DataFrame.plot """ @tm.mplskip @@ -1305,10 +43,6 @@ def setUp(self): "C": np.arange(20) + np.random.uniform( size=20)}) - from pandas import read_csv - path = os.path.join(curpath(), 'data', 'iris.csv') - self.iris = read_csv(path) - @slow def test_plot(self): df = self.tdf @@ -3939,103 +2673,6 @@ def test_rcParams_bar_colors(self): for c in barplot.patches]) -@tm.mplskip -class TestDataFrameGroupByPlots(TestPlotBase): - - def test_series_groupby_plotting_nominally_works(self): - n = 10 - weight = Series(np.random.normal(166, 20, size=n)) - height = Series(np.random.normal(60, 10, size=n)) - with tm.RNGContext(42): - gender = np.random.choice(['male', 'female'], size=n) - - weight.groupby(gender).plot() - tm.close() - height.groupby(gender).hist() - tm.close() - # Regression test for GH8733 - height.groupby(gender).plot(alpha=0.5) - tm.close() - - def test_plotting_with_float_index_works(self): - # GH 7025 - df = DataFrame({'def': [1, 1, 1, 2, 2, 2, 3, 3, 3], - 'val': np.random.randn(9)}, - index=[1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0]) - - df.groupby('def')['val'].plot() - tm.close() - df.groupby('def')['val'].apply(lambda x: x.plot()) - tm.close() - - def test_hist_single_row(self): - # GH10214 - bins = np.arange(80, 100 + 2, 1) - df = DataFrame({"Name": ["AAA", "BBB"], - "ByCol": [1, 2], - "Mark": [85, 89]}) - df["Mark"].hist(by=df["ByCol"], bins=bins) - df = DataFrame({"Name": ["AAA"], "ByCol": [1], "Mark": [85]}) - df["Mark"].hist(by=df["ByCol"], bins=bins) - - def test_plot_submethod_works(self): - df = DataFrame({'x': [1, 2, 3, 4, 5], - 'y': [1, 2, 3, 2, 1], - 'z': list('ababa')}) - df.groupby('z').plot.scatter('x', 'y') - tm.close() - df.groupby('z')['x'].plot.line() - tm.close() - - def test_plot_kwargs(self): - - df = DataFrame({'x': [1, 2, 3, 4, 5], - 'y': [1, 2, 3, 2, 1], - 'z': list('ababa')}) - - res = df.groupby('z').plot(kind='scatter', x='x', y='y') - # check that a scatter plot is effectively plotted: the axes should - # contain a PathCollection from the scatter plot (GH11805) - self.assertEqual(len(res['a'].collections), 1) - - res = df.groupby('z').plot.scatter(x='x', y='y') - self.assertEqual(len(res['a'].collections), 1) - - -def _check_plot_works(f, filterwarnings='always', **kwargs): - import matplotlib.pyplot as plt - ret = None - with warnings.catch_warnings(): - warnings.simplefilter(filterwarnings) - try: - try: - fig = kwargs['figure'] - except KeyError: - fig = plt.gcf() - - plt.clf() - - ax = kwargs.get('ax', fig.add_subplot(211)) # noqa - ret = f(**kwargs) - - assert_is_valid_plot_return_object(ret) - - try: - kwargs['ax'] = fig.add_subplot(212) - ret = f(**kwargs) - except Exception: - pass - else: - assert_is_valid_plot_return_object(ret) - - with ensure_clean(return_filelike=True) as path: - plt.savefig(path) - finally: - tm.close(fig) - - return ret - - def _generate_4_axes_via_gridspec(): import matplotlib.pyplot as plt import matplotlib as mpl @@ -4050,11 +2687,6 @@ def _generate_4_axes_via_gridspec(): return gs, [ax_tl, ax_ll, ax_tr, ax_lr] -def curpath(): - pth, _ = os.path.split(os.path.abspath(__file__)) - return pth - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/plotting/test_groupby.py b/pandas/tests/plotting/test_groupby.py new file mode 100644 index 0000000000000..101a6556c61bf --- /dev/null +++ b/pandas/tests/plotting/test_groupby.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# coding: utf-8 + +import nose + +from pandas import Series, DataFrame +import pandas.util.testing as tm + +import numpy as np + +from pandas.tests.plotting.common import TestPlotBase + + +""" Test cases for GroupBy.plot """ + + +@tm.mplskip +class TestDataFrameGroupByPlots(TestPlotBase): + + def test_series_groupby_plotting_nominally_works(self): + n = 10 + weight = Series(np.random.normal(166, 20, size=n)) + height = Series(np.random.normal(60, 10, size=n)) + with tm.RNGContext(42): + gender = np.random.choice(['male', 'female'], size=n) + + weight.groupby(gender).plot() + tm.close() + height.groupby(gender).hist() + tm.close() + # Regression test for GH8733 + height.groupby(gender).plot(alpha=0.5) + tm.close() + + def test_plotting_with_float_index_works(self): + # GH 7025 + df = DataFrame({'def': [1, 1, 1, 2, 2, 2, 3, 3, 3], + 'val': np.random.randn(9)}, + index=[1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0]) + + df.groupby('def')['val'].plot() + tm.close() + df.groupby('def')['val'].apply(lambda x: x.plot()) + tm.close() + + def test_hist_single_row(self): + # GH10214 + bins = np.arange(80, 100 + 2, 1) + df = DataFrame({"Name": ["AAA", "BBB"], + "ByCol": [1, 2], + "Mark": [85, 89]}) + df["Mark"].hist(by=df["ByCol"], bins=bins) + df = DataFrame({"Name": ["AAA"], "ByCol": [1], "Mark": [85]}) + df["Mark"].hist(by=df["ByCol"], bins=bins) + + def test_plot_submethod_works(self): + df = DataFrame({'x': [1, 2, 3, 4, 5], + 'y': [1, 2, 3, 2, 1], + 'z': list('ababa')}) + df.groupby('z').plot.scatter('x', 'y') + tm.close() + df.groupby('z')['x'].plot.line() + tm.close() + + def test_plot_kwargs(self): + + df = DataFrame({'x': [1, 2, 3, 4, 5], + 'y': [1, 2, 3, 2, 1], + 'z': list('ababa')}) + + res = df.groupby('z').plot(kind='scatter', x='x', y='y') + # check that a scatter plot is effectively plotted: the axes should + # contain a PathCollection from the scatter plot (GH11805) + self.assertEqual(len(res['a'].collections), 1) + + res = df.groupby('z').plot.scatter(x='x', y='y') + self.assertEqual(len(res['a'].collections), 1) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/plotting/test_hist_method.py b/pandas/tests/plotting/test_hist_method.py new file mode 100644 index 0000000000000..c7bff5a31fc02 --- /dev/null +++ b/pandas/tests/plotting/test_hist_method.py @@ -0,0 +1,426 @@ +#!/usr/bin/env python +# coding: utf-8 + +import nose + +from pandas import Series, DataFrame +import pandas.util.testing as tm +from pandas.util.testing import slow + +import numpy as np +from numpy.random import randn + +import pandas.tools.plotting as plotting +from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works) + + +""" Test cases for .hist method """ + + +@tm.mplskip +class TestSeriesPlots(TestPlotBase): + + def setUp(self): + TestPlotBase.setUp(self) + import matplotlib as mpl + mpl.rcdefaults() + + self.ts = tm.makeTimeSeries() + self.ts.name = 'ts' + + @slow + def test_hist_legacy(self): + _check_plot_works(self.ts.hist) + _check_plot_works(self.ts.hist, grid=False) + _check_plot_works(self.ts.hist, figsize=(8, 10)) + # _check_plot_works adds an ax so catch warning. see GH #13188 + with tm.assert_produces_warning(UserWarning): + _check_plot_works(self.ts.hist, by=self.ts.index.month) + with tm.assert_produces_warning(UserWarning): + _check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5) + + fig, ax = self.plt.subplots(1, 1) + _check_plot_works(self.ts.hist, ax=ax) + _check_plot_works(self.ts.hist, ax=ax, figure=fig) + _check_plot_works(self.ts.hist, figure=fig) + tm.close() + + fig, (ax1, ax2) = self.plt.subplots(1, 2) + _check_plot_works(self.ts.hist, figure=fig, ax=ax1) + _check_plot_works(self.ts.hist, figure=fig, ax=ax2) + + with tm.assertRaises(ValueError): + self.ts.hist(by=self.ts.index, figure=fig) + + @slow + def test_hist_bins_legacy(self): + df = DataFrame(np.random.randn(10, 2)) + ax = df.hist(bins=2)[0][0] + self.assertEqual(len(ax.patches), 2) + + @slow + def test_hist_layout(self): + df = self.hist_df + with tm.assertRaises(ValueError): + df.height.hist(layout=(1, 1)) + + with tm.assertRaises(ValueError): + df.height.hist(layout=[1, 1]) + + @slow + def test_hist_layout_with_by(self): + df = self.hist_df + + # _check_plot_works adds an `ax` kwarg to the method call + # so we get a warning about an axis being cleared, even + # though we don't explicing pass one, see GH #13188 + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, by=df.gender, + layout=(2, 1)) + self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, by=df.gender, + layout=(3, -1)) + self._check_axes_shape(axes, axes_num=2, layout=(3, 1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, by=df.category, + layout=(4, 1)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works( + df.height.hist, by=df.category, layout=(2, -1)) + self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works( + df.height.hist, by=df.category, layout=(3, -1)) + self._check_axes_shape(axes, axes_num=4, layout=(3, 2)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works( + df.height.hist, by=df.category, layout=(-1, 4)) + self._check_axes_shape(axes, axes_num=4, layout=(1, 4)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works( + df.height.hist, by=df.classroom, layout=(2, 2)) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7)) + self._check_axes_shape( + axes, axes_num=4, layout=(4, 2), figsize=(12, 7)) + + @slow + def test_hist_no_overlap(self): + from matplotlib.pyplot import subplot, gcf + x = Series(randn(2)) + y = Series(randn(2)) + subplot(121) + x.hist() + subplot(122) + y.hist() + fig = gcf() + axes = fig.axes if self.mpl_ge_1_5_0 else fig.get_axes() + self.assertEqual(len(axes), 2) + + @slow + def test_hist_by_no_extra_plots(self): + df = self.hist_df + axes = df.height.hist(by=df.gender) # noqa + self.assertEqual(len(self.plt.get_fignums()), 1) + + @slow + def test_plot_fails_when_ax_differs_from_figure(self): + from pylab import figure + fig1 = figure() + fig2 = figure() + ax1 = fig1.add_subplot(111) + with tm.assertRaises(AssertionError): + self.ts.hist(ax=ax1, figure=fig2) + + +@tm.mplskip +class TestDataFramePlots(TestPlotBase): + + @slow + def test_hist_df_legacy(self): + from matplotlib.patches import Rectangle + with tm.assert_produces_warning(UserWarning): + _check_plot_works(self.hist_df.hist) + + # make sure layout is handled + df = DataFrame(randn(100, 3)) + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.hist, grid=False) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + self.assertFalse(axes[1, 1].get_visible()) + + df = DataFrame(randn(100, 1)) + _check_plot_works(df.hist) + + # make sure layout is handled + df = DataFrame(randn(100, 6)) + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.hist, layout=(4, 2)) + self._check_axes_shape(axes, axes_num=6, layout=(4, 2)) + + # make sure sharex, sharey is handled + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.hist, sharex=True, sharey=True) + + # handle figsize arg + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.hist, figsize=(8, 10)) + + # check bins argument + with tm.assert_produces_warning(UserWarning): + _check_plot_works(df.hist, bins=5) + + # make sure xlabelsize and xrot are handled + ser = df[0] + xf, yf = 20, 18 + xrot, yrot = 30, 40 + axes = ser.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) + self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, + ylabelsize=yf, yrot=yrot) + + xf, yf = 20, 18 + xrot, yrot = 30, 40 + axes = df.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) + self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, + ylabelsize=yf, yrot=yrot) + + tm.close() + # make sure kwargs to hist are handled + ax = ser.hist(normed=True, cumulative=True, bins=4) + # height of last bin (index 5) must be 1.0 + rects = [x for x in ax.get_children() if isinstance(x, Rectangle)] + self.assertAlmostEqual(rects[-1].get_height(), 1.0) + + tm.close() + ax = ser.hist(log=True) + # scale of y must be 'log' + self._check_ax_scales(ax, yaxis='log') + + tm.close() + + # propagate attr exception from matplotlib.Axes.hist + with tm.assertRaises(AttributeError): + ser.hist(foo='bar') + + @slow + def test_hist_layout(self): + df = DataFrame(randn(100, 3)) + + layout_to_expected_size = ( + {'layout': None, 'expected_size': (2, 2)}, # default is 2x2 + {'layout': (2, 2), 'expected_size': (2, 2)}, + {'layout': (4, 1), 'expected_size': (4, 1)}, + {'layout': (1, 4), 'expected_size': (1, 4)}, + {'layout': (3, 3), 'expected_size': (3, 3)}, + {'layout': (-1, 4), 'expected_size': (1, 4)}, + {'layout': (4, -1), 'expected_size': (4, 1)}, + {'layout': (-1, 2), 'expected_size': (2, 2)}, + {'layout': (2, -1), 'expected_size': (2, 2)} + ) + + for layout_test in layout_to_expected_size: + axes = df.hist(layout=layout_test['layout']) + expected = layout_test['expected_size'] + self._check_axes_shape(axes, axes_num=3, layout=expected) + + # layout too small for all 4 plots + with tm.assertRaises(ValueError): + df.hist(layout=(1, 1)) + + # invalid format for layout + with tm.assertRaises(ValueError): + df.hist(layout=(1,)) + with tm.assertRaises(ValueError): + df.hist(layout=(-1, -1)) + + +@tm.mplskip +class TestDataFrameGroupByPlots(TestPlotBase): + + @slow + def test_grouped_hist_legacy(self): + from matplotlib.patches import Rectangle + + df = DataFrame(randn(500, 2), columns=['A', 'B']) + df['C'] = np.random.randint(0, 4, 500) + df['D'] = ['X'] * 500 + + axes = plotting.grouped_hist(df.A, by=df.C) + self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) + + tm.close() + axes = df.hist(by=df.C) + self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) + + tm.close() + # group by a key with single value + axes = df.hist(by='D', rot=30) + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + self._check_ticks_props(axes, xrot=30) + + tm.close() + # make sure kwargs to hist are handled + xf, yf = 20, 18 + xrot, yrot = 30, 40 + axes = plotting.grouped_hist(df.A, by=df.C, normed=True, + cumulative=True, bins=4, + xlabelsize=xf, xrot=xrot, + ylabelsize=yf, yrot=yrot) + # height of last bin (index 5) must be 1.0 + for ax in axes.ravel(): + rects = [x for x in ax.get_children() if isinstance(x, Rectangle)] + height = rects[-1].get_height() + self.assertAlmostEqual(height, 1.0) + self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, + ylabelsize=yf, yrot=yrot) + + tm.close() + axes = plotting.grouped_hist(df.A, by=df.C, log=True) + # scale of y must be 'log' + self._check_ax_scales(axes, yaxis='log') + + tm.close() + # propagate attr exception from matplotlib.Axes.hist + with tm.assertRaises(AttributeError): + plotting.grouped_hist(df.A, by=df.C, foo='bar') + + with tm.assert_produces_warning(FutureWarning): + df.hist(by='C', figsize='default') + + @slow + def test_grouped_hist_legacy2(self): + n = 10 + weight = Series(np.random.normal(166, 20, size=n)) + height = Series(np.random.normal(60, 10, size=n)) + with tm.RNGContext(42): + gender_int = np.random.choice([0, 1], size=n) + df_int = DataFrame({'height': height, 'weight': weight, + 'gender': gender_int}) + gb = df_int.groupby('gender') + axes = gb.hist() + self.assertEqual(len(axes), 2) + self.assertEqual(len(self.plt.get_fignums()), 2) + tm.close() + + @slow + def test_grouped_hist_layout(self): + df = self.hist_df + self.assertRaises(ValueError, df.hist, column='weight', by=df.gender, + layout=(1, 1)) + self.assertRaises(ValueError, df.hist, column='height', by=df.category, + layout=(1, 3)) + self.assertRaises(ValueError, df.hist, column='height', by=df.category, + layout=(-1, -1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.hist, column='height', by=df.gender, + layout=(2, 1)) + self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.hist, column='height', by=df.gender, + layout=(2, -1)) + self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) + + axes = df.hist(column='height', by=df.category, layout=(4, 1)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) + + axes = df.hist(column='height', by=df.category, layout=(-1, 1)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) + + axes = df.hist(column='height', by=df.category, + layout=(4, 2), figsize=(12, 8)) + self._check_axes_shape( + axes, axes_num=4, layout=(4, 2), figsize=(12, 8)) + tm.close() + + # GH 6769 + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works( + df.hist, column='height', by='classroom', layout=(2, 2)) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + # without column + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.hist, by='classroom') + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + axes = df.hist(by='gender', layout=(3, 5)) + self._check_axes_shape(axes, axes_num=2, layout=(3, 5)) + + axes = df.hist(column=['height', 'weight', 'category']) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + @slow + def test_grouped_hist_multiple_axes(self): + # GH 6970, GH 7069 + df = self.hist_df + + fig, axes = self.plt.subplots(2, 3) + returned = df.hist(column=['height', 'weight', 'category'], ax=axes[0]) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[0]) + self.assertIs(returned[0].figure, fig) + returned = df.hist(by='classroom', ax=axes[1]) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[1]) + self.assertIs(returned[0].figure, fig) + + with tm.assertRaises(ValueError): + fig, axes = self.plt.subplots(2, 3) + # pass different number of axes from required + axes = df.hist(column='height', ax=axes) + + @slow + def test_axis_share_x(self): + df = self.hist_df + # GH4089 + ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True) + + # share x + self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) + + # don't share y + self.assertFalse(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertFalse(ax2._shared_y_axes.joined(ax1, ax2)) + + @slow + def test_axis_share_y(self): + df = self.hist_df + ax1, ax2 = df.hist(column='height', by=df.gender, sharey=True) + + # share y + self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) + + # don't share x + self.assertFalse(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertFalse(ax2._shared_x_axes.joined(ax1, ax2)) + + @slow + def test_axis_share_xy(self): + df = self.hist_df + ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True, + sharey=True) + + # share both x and y + self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) + + self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py new file mode 100644 index 0000000000000..8b9a4fe05bb2e --- /dev/null +++ b/pandas/tests/plotting/test_misc.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python +# coding: utf-8 + +import nose + +from pandas import Series, DataFrame +from pandas.compat import lmap +import pandas.util.testing as tm +from pandas.util.testing import slow + +import numpy as np +from numpy import random +from numpy.random import randn + +import pandas.tools.plotting as plotting +from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works, + _ok_for_gaussian_kde) + + +""" Test cases for misc plot functions """ + + +@tm.mplskip +class TestSeriesPlots(TestPlotBase): + + def setUp(self): + TestPlotBase.setUp(self) + import matplotlib as mpl + mpl.rcdefaults() + + self.ts = tm.makeTimeSeries() + self.ts.name = 'ts' + + @slow + def test_autocorrelation_plot(self): + from pandas.tools.plotting import autocorrelation_plot + _check_plot_works(autocorrelation_plot, series=self.ts) + _check_plot_works(autocorrelation_plot, series=self.ts.values) + + ax = autocorrelation_plot(self.ts, label='Test') + self._check_legend_labels(ax, labels=['Test']) + + @slow + def test_lag_plot(self): + from pandas.tools.plotting import lag_plot + _check_plot_works(lag_plot, series=self.ts) + _check_plot_works(lag_plot, series=self.ts, lag=5) + + @slow + def test_bootstrap_plot(self): + from pandas.tools.plotting import bootstrap_plot + _check_plot_works(bootstrap_plot, series=self.ts, size=10) + + +@tm.mplskip +class TestDataFramePlots(TestPlotBase): + + @slow + def test_scatter_plot_legacy(self): + tm._skip_if_no_scipy() + + df = DataFrame(randn(100, 2)) + + def scat(**kwds): + return plotting.scatter_matrix(df, **kwds) + + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat) + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat, marker='+') + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat, vmin=0) + if _ok_for_gaussian_kde('kde'): + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat, diagonal='kde') + if _ok_for_gaussian_kde('density'): + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat, diagonal='density') + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat, diagonal='hist') + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat, range_padding=.1) + + def scat2(x, y, by=None, ax=None, figsize=None): + return plotting.scatter_plot(df, x, y, by, ax, figsize=None) + + _check_plot_works(scat2, x=0, y=1) + grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index) + with tm.assert_produces_warning(UserWarning): + _check_plot_works(scat2, x=0, y=1, by=grouper) + + def test_scatter_matrix_axis(self): + tm._skip_if_no_scipy() + scatter_matrix = plotting.scatter_matrix + + with tm.RNGContext(42): + df = DataFrame(randn(100, 3)) + + # we are plotting multiples on a sub-plot + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(scatter_matrix, filterwarnings='always', + frame=df, range_padding=.1) + axes0_labels = axes[0][0].yaxis.get_majorticklabels() + + # GH 5662 + expected = ['-2', '-1', '0', '1', '2'] + self._check_text_labels(axes0_labels, expected) + self._check_ticks_props( + axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) + + df[0] = ((df[0] - 2) / 3) + + # we are plotting multiples on a sub-plot + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(scatter_matrix, filterwarnings='always', + frame=df, range_padding=.1) + axes0_labels = axes[0][0].yaxis.get_majorticklabels() + expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0'] + self._check_text_labels(axes0_labels, expected) + self._check_ticks_props( + axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) + + @slow + def test_andrews_curves(self): + from pandas.tools.plotting import andrews_curves + from matplotlib import cm + + df = self.iris + + _check_plot_works(andrews_curves, frame=df, class_column='Name') + + rgba = ('#556270', '#4ECDC4', '#C7F464') + ax = _check_plot_works(andrews_curves, frame=df, + class_column='Name', color=rgba) + self._check_colors( + ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) + + cnames = ['dodgerblue', 'aquamarine', 'seagreen'] + ax = _check_plot_works(andrews_curves, frame=df, + class_column='Name', color=cnames) + self._check_colors( + ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) + + ax = _check_plot_works(andrews_curves, frame=df, + class_column='Name', colormap=cm.jet) + cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) + self._check_colors( + ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) + + length = 10 + df = DataFrame({"A": random.rand(length), + "B": random.rand(length), + "C": random.rand(length), + "Name": ["A"] * length}) + + _check_plot_works(andrews_curves, frame=df, class_column='Name') + + rgba = ('#556270', '#4ECDC4', '#C7F464') + ax = _check_plot_works(andrews_curves, frame=df, + class_column='Name', color=rgba) + self._check_colors( + ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) + + cnames = ['dodgerblue', 'aquamarine', 'seagreen'] + ax = _check_plot_works(andrews_curves, frame=df, + class_column='Name', color=cnames) + self._check_colors( + ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) + + ax = _check_plot_works(andrews_curves, frame=df, + class_column='Name', colormap=cm.jet) + cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) + self._check_colors( + ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) + + colors = ['b', 'g', 'r'] + df = DataFrame({"A": [1, 2, 3], + "B": [1, 2, 3], + "C": [1, 2, 3], + "Name": colors}) + ax = andrews_curves(df, 'Name', color=colors) + handles, labels = ax.get_legend_handles_labels() + self._check_colors(handles, linecolors=colors) + + with tm.assert_produces_warning(FutureWarning): + andrews_curves(data=df, class_column='Name') + + @slow + def test_parallel_coordinates(self): + from pandas.tools.plotting import parallel_coordinates + from matplotlib import cm + + df = self.iris + + ax = _check_plot_works(parallel_coordinates, + frame=df, class_column='Name') + nlines = len(ax.get_lines()) + nxticks = len(ax.xaxis.get_ticklabels()) + + rgba = ('#556270', '#4ECDC4', '#C7F464') + ax = _check_plot_works(parallel_coordinates, + frame=df, class_column='Name', color=rgba) + self._check_colors( + ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) + + cnames = ['dodgerblue', 'aquamarine', 'seagreen'] + ax = _check_plot_works(parallel_coordinates, + frame=df, class_column='Name', color=cnames) + self._check_colors( + ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) + + ax = _check_plot_works(parallel_coordinates, + frame=df, class_column='Name', colormap=cm.jet) + cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) + self._check_colors( + ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) + + ax = _check_plot_works(parallel_coordinates, + frame=df, class_column='Name', axvlines=False) + assert len(ax.get_lines()) == (nlines - nxticks) + + colors = ['b', 'g', 'r'] + df = DataFrame({"A": [1, 2, 3], + "B": [1, 2, 3], + "C": [1, 2, 3], + "Name": colors}) + ax = parallel_coordinates(df, 'Name', color=colors) + handles, labels = ax.get_legend_handles_labels() + self._check_colors(handles, linecolors=colors) + + with tm.assert_produces_warning(FutureWarning): + parallel_coordinates(data=df, class_column='Name') + with tm.assert_produces_warning(FutureWarning): + parallel_coordinates(df, 'Name', colors=colors) + + @slow + def test_radviz(self): + from pandas.tools.plotting import radviz + from matplotlib import cm + + df = self.iris + _check_plot_works(radviz, frame=df, class_column='Name') + + rgba = ('#556270', '#4ECDC4', '#C7F464') + ax = _check_plot_works( + radviz, frame=df, class_column='Name', color=rgba) + # skip Circle drawn as ticks + patches = [p for p in ax.patches[:20] if p.get_label() != ''] + self._check_colors( + patches[:10], facecolors=rgba, mapping=df['Name'][:10]) + + cnames = ['dodgerblue', 'aquamarine', 'seagreen'] + _check_plot_works(radviz, frame=df, class_column='Name', color=cnames) + patches = [p for p in ax.patches[:20] if p.get_label() != ''] + self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10]) + + _check_plot_works(radviz, frame=df, + class_column='Name', colormap=cm.jet) + cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) + patches = [p for p in ax.patches[:20] if p.get_label() != ''] + self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10]) + + colors = [[0., 0., 1., 1.], + [0., 0.5, 1., 1.], + [1., 0., 0., 1.]] + df = DataFrame({"A": [1, 2, 3], + "B": [2, 1, 3], + "C": [3, 2, 1], + "Name": ['b', 'g', 'r']}) + ax = radviz(df, 'Name', color=colors) + handles, labels = ax.get_legend_handles_labels() + self._check_colors(handles, facecolors=colors) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/plotting/test_series.py b/pandas/tests/plotting/test_series.py new file mode 100644 index 0000000000000..2bd2f8255569d --- /dev/null +++ b/pandas/tests/plotting/test_series.py @@ -0,0 +1,807 @@ +#!/usr/bin/env python +# coding: utf-8 + +import nose +import itertools + +from datetime import datetime + +import pandas as pd +from pandas import Series, DataFrame, date_range +from pandas.compat import range, lrange +import pandas.util.testing as tm +from pandas.util.testing import slow + +import numpy as np +from numpy.random import randn + +import pandas.tools.plotting as plotting +from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works, + _skip_if_no_scipy_gaussian_kde, + _ok_for_gaussian_kde) + + +""" Test cases for Series.plot """ + + +@tm.mplskip +class TestSeriesPlots(TestPlotBase): + + def setUp(self): + TestPlotBase.setUp(self) + import matplotlib as mpl + mpl.rcdefaults() + + self.ts = tm.makeTimeSeries() + self.ts.name = 'ts' + + self.series = tm.makeStringSeries() + self.series.name = 'series' + + self.iseries = tm.makePeriodSeries() + self.iseries.name = 'iseries' + + @slow + def test_plot(self): + _check_plot_works(self.ts.plot, label='foo') + _check_plot_works(self.ts.plot, use_index=False) + axes = _check_plot_works(self.ts.plot, rot=0) + self._check_ticks_props(axes, xrot=0) + + ax = _check_plot_works(self.ts.plot, style='.', logy=True) + self._check_ax_scales(ax, yaxis='log') + + ax = _check_plot_works(self.ts.plot, style='.', logx=True) + self._check_ax_scales(ax, xaxis='log') + + ax = _check_plot_works(self.ts.plot, style='.', loglog=True) + self._check_ax_scales(ax, xaxis='log', yaxis='log') + + _check_plot_works(self.ts[:10].plot.bar) + _check_plot_works(self.ts.plot.area, stacked=False) + _check_plot_works(self.iseries.plot) + + for kind in ['line', 'bar', 'barh', 'kde', 'hist', 'box']: + if not _ok_for_gaussian_kde(kind): + continue + _check_plot_works(self.series[:5].plot, kind=kind) + + _check_plot_works(self.series[:10].plot.barh) + ax = _check_plot_works(Series(randn(10)).plot.bar, color='black') + self._check_colors([ax.patches[0]], facecolors=['black']) + + # GH 6951 + ax = _check_plot_works(self.ts.plot, subplots=True) + self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) + + ax = _check_plot_works(self.ts.plot, subplots=True, layout=(-1, 1)) + self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) + ax = _check_plot_works(self.ts.plot, subplots=True, layout=(1, -1)) + self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) + + @slow + def test_plot_figsize_and_title(self): + # figsize and title + ax = self.series.plot(title='Test', figsize=(16, 8)) + self._check_text_labels(ax.title, 'Test') + self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16, 8)) + + def test_dont_modify_rcParams(self): + # GH 8242 + if self.mpl_ge_1_5_0: + key = 'axes.prop_cycle' + else: + key = 'axes.color_cycle' + colors = self.plt.rcParams[key] + Series([1, 2, 3]).plot() + self.assertEqual(colors, self.plt.rcParams[key]) + + def test_ts_line_lim(self): + ax = self.ts.plot() + xmin, xmax = ax.get_xlim() + lines = ax.get_lines() + self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0]) + self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1]) + tm.close() + + ax = self.ts.plot(secondary_y=True) + xmin, xmax = ax.get_xlim() + lines = ax.get_lines() + self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0]) + self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1]) + + def test_ts_area_lim(self): + ax = self.ts.plot.area(stacked=False) + xmin, xmax = ax.get_xlim() + line = ax.get_lines()[0].get_data(orig=False)[0] + self.assertEqual(xmin, line[0]) + self.assertEqual(xmax, line[-1]) + tm.close() + + # GH 7471 + ax = self.ts.plot.area(stacked=False, x_compat=True) + xmin, xmax = ax.get_xlim() + line = ax.get_lines()[0].get_data(orig=False)[0] + self.assertEqual(xmin, line[0]) + self.assertEqual(xmax, line[-1]) + tm.close() + + tz_ts = self.ts.copy() + tz_ts.index = tz_ts.tz_localize('GMT').tz_convert('CET') + ax = tz_ts.plot.area(stacked=False, x_compat=True) + xmin, xmax = ax.get_xlim() + line = ax.get_lines()[0].get_data(orig=False)[0] + self.assertEqual(xmin, line[0]) + self.assertEqual(xmax, line[-1]) + tm.close() + + ax = tz_ts.plot.area(stacked=False, secondary_y=True) + xmin, xmax = ax.get_xlim() + line = ax.get_lines()[0].get_data(orig=False)[0] + self.assertEqual(xmin, line[0]) + self.assertEqual(xmax, line[-1]) + + def test_label(self): + s = Series([1, 2]) + ax = s.plot(label='LABEL', legend=True) + self._check_legend_labels(ax, labels=['LABEL']) + self.plt.close() + ax = s.plot(legend=True) + self._check_legend_labels(ax, labels=['None']) + self.plt.close() + # get name from index + s.name = 'NAME' + ax = s.plot(legend=True) + self._check_legend_labels(ax, labels=['NAME']) + self.plt.close() + # override the default + ax = s.plot(legend=True, label='LABEL') + self._check_legend_labels(ax, labels=['LABEL']) + self.plt.close() + # Add lebel info, but don't draw + ax = s.plot(legend=False, label='LABEL') + self.assertEqual(ax.get_legend(), None) # Hasn't been drawn + ax.legend() # draw it + self._check_legend_labels(ax, labels=['LABEL']) + + def test_line_area_nan_series(self): + values = [1, 2, np.nan, 3] + s = Series(values) + ts = Series(values, index=tm.makeDateIndex(k=4)) + + for d in [s, ts]: + ax = _check_plot_works(d.plot) + masked = ax.lines[0].get_ydata() + # remove nan for comparison purpose + exp = np.array([1, 2, 3], dtype=np.float64) + self.assert_numpy_array_equal(np.delete(masked.data, 2), exp) + self.assert_numpy_array_equal( + masked.mask, np.array([False, False, True, False])) + + expected = np.array([1, 2, 0, 3], dtype=np.float64) + ax = _check_plot_works(d.plot, stacked=True) + self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected) + ax = _check_plot_works(d.plot.area) + self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected) + ax = _check_plot_works(d.plot.area, stacked=False) + self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected) + + def test_line_use_index_false(self): + s = Series([1, 2, 3], index=['a', 'b', 'c']) + s.index.name = 'The Index' + ax = s.plot(use_index=False) + label = ax.get_xlabel() + self.assertEqual(label, '') + ax2 = s.plot.bar(use_index=False) + label2 = ax2.get_xlabel() + self.assertEqual(label2, '') + + @slow + def test_bar_log(self): + expected = np.array([1., 10., 100., 1000.]) + + if not self.mpl_le_1_2_1: + expected = np.hstack((.1, expected, 1e4)) + + ax = Series([200, 500]).plot.bar(log=True) + tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected) + tm.close() + + ax = Series([200, 500]).plot.barh(log=True) + tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected) + tm.close() + + # GH 9905 + expected = np.array([1.0e-03, 1.0e-02, 1.0e-01, 1.0e+00]) + + if not self.mpl_le_1_2_1: + expected = np.hstack((1.0e-04, expected, 1.0e+01)) + + ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind='bar') + self.assertEqual(ax.get_ylim(), (0.001, 0.10000000000000001)) + tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected) + tm.close() + + ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind='barh') + self.assertEqual(ax.get_xlim(), (0.001, 0.10000000000000001)) + tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected) + + @slow + def test_bar_ignore_index(self): + df = Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']) + ax = df.plot.bar(use_index=False) + self._check_text_labels(ax.get_xticklabels(), ['0', '1', '2', '3']) + + def test_rotation(self): + df = DataFrame(randn(5, 5)) + # Default rot 0 + axes = df.plot() + self._check_ticks_props(axes, xrot=0) + + axes = df.plot(rot=30) + self._check_ticks_props(axes, xrot=30) + + def test_irregular_datetime(self): + rng = date_range('1/1/2000', '3/1/2000') + rng = rng[[0, 1, 2, 3, 5, 9, 10, 11, 12]] + ser = Series(randn(len(rng)), rng) + ax = ser.plot() + xp = datetime(1999, 1, 1).toordinal() + ax.set_xlim('1/1/1999', '1/1/2001') + self.assertEqual(xp, ax.get_xlim()[0]) + + @slow + def test_pie_series(self): + # if sum of values is less than 1.0, pie handle them as rate and draw + # semicircle. + series = Series(np.random.randint(1, 5), + index=['a', 'b', 'c', 'd', 'e'], name='YLABEL') + ax = _check_plot_works(series.plot.pie) + self._check_text_labels(ax.texts, series.index) + self.assertEqual(ax.get_ylabel(), 'YLABEL') + + # without wedge labels + ax = _check_plot_works(series.plot.pie, labels=None) + self._check_text_labels(ax.texts, [''] * 5) + + # with less colors than elements + color_args = ['r', 'g', 'b'] + ax = _check_plot_works(series.plot.pie, colors=color_args) + + color_expected = ['r', 'g', 'b', 'r', 'g'] + self._check_colors(ax.patches, facecolors=color_expected) + + # with labels and colors + labels = ['A', 'B', 'C', 'D', 'E'] + color_args = ['r', 'g', 'b', 'c', 'm'] + ax = _check_plot_works(series.plot.pie, labels=labels, + colors=color_args) + self._check_text_labels(ax.texts, labels) + self._check_colors(ax.patches, facecolors=color_args) + + # with autopct and fontsize + ax = _check_plot_works(series.plot.pie, colors=color_args, + autopct='%.2f', fontsize=7) + pcts = ['{0:.2f}'.format(s * 100) + for s in series.values / float(series.sum())] + iters = [iter(series.index), iter(pcts)] + expected_texts = list(next(it) for it in itertools.cycle(iters)) + self._check_text_labels(ax.texts, expected_texts) + for t in ax.texts: + self.assertEqual(t.get_fontsize(), 7) + + # includes negative value + with tm.assertRaises(ValueError): + series = Series([1, 2, 0, 4, -1], index=['a', 'b', 'c', 'd', 'e']) + series.plot.pie() + + # includes nan + series = Series([1, 2, np.nan, 4], index=['a', 'b', 'c', 'd'], + name='YLABEL') + ax = _check_plot_works(series.plot.pie) + self._check_text_labels(ax.texts, ['a', 'b', '', 'd']) + + def test_pie_nan(self): + s = Series([1, np.nan, 1, 1]) + ax = s.plot.pie(legend=True) + expected = ['0', '', '2', '3'] + result = [x.get_text() for x in ax.texts] + self.assertEqual(result, expected) + + @slow + def test_hist_df_kwargs(self): + df = DataFrame(np.random.randn(10, 2)) + ax = df.plot.hist(bins=5) + self.assertEqual(len(ax.patches), 10) + + @slow + def test_hist_df_with_nonnumerics(self): + # GH 9853 + with tm.RNGContext(1): + df = DataFrame( + np.random.randn(10, 4), columns=['A', 'B', 'C', 'D']) + df['E'] = ['x', 'y'] * 5 + ax = df.plot.hist(bins=5) + self.assertEqual(len(ax.patches), 20) + + ax = df.plot.hist() # bins=10 + self.assertEqual(len(ax.patches), 40) + + @slow + def test_hist_legacy(self): + _check_plot_works(self.ts.hist) + _check_plot_works(self.ts.hist, grid=False) + _check_plot_works(self.ts.hist, figsize=(8, 10)) + # _check_plot_works adds an ax so catch warning. see GH #13188 + with tm.assert_produces_warning(UserWarning): + _check_plot_works(self.ts.hist, + by=self.ts.index.month) + with tm.assert_produces_warning(UserWarning): + _check_plot_works(self.ts.hist, + by=self.ts.index.month, bins=5) + + fig, ax = self.plt.subplots(1, 1) + _check_plot_works(self.ts.hist, ax=ax) + _check_plot_works(self.ts.hist, ax=ax, figure=fig) + _check_plot_works(self.ts.hist, figure=fig) + tm.close() + + fig, (ax1, ax2) = self.plt.subplots(1, 2) + _check_plot_works(self.ts.hist, figure=fig, ax=ax1) + _check_plot_works(self.ts.hist, figure=fig, ax=ax2) + + with tm.assertRaises(ValueError): + self.ts.hist(by=self.ts.index, figure=fig) + + @slow + def test_hist_bins_legacy(self): + df = DataFrame(np.random.randn(10, 2)) + ax = df.hist(bins=2)[0][0] + self.assertEqual(len(ax.patches), 2) + + @slow + def test_hist_layout(self): + df = self.hist_df + with tm.assertRaises(ValueError): + df.height.hist(layout=(1, 1)) + + with tm.assertRaises(ValueError): + df.height.hist(layout=[1, 1]) + + @slow + def test_hist_layout_with_by(self): + df = self.hist_df + + # _check_plot_works adds an ax so catch warning. see GH #13188 + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, + by=df.gender, layout=(2, 1)) + self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, + by=df.gender, layout=(3, -1)) + self._check_axes_shape(axes, axes_num=2, layout=(3, 1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, + by=df.category, layout=(4, 1)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, + by=df.category, layout=(2, -1)) + self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, + by=df.category, layout=(3, -1)) + self._check_axes_shape(axes, axes_num=4, layout=(3, 2)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, + by=df.category, layout=(-1, 4)) + self._check_axes_shape(axes, axes_num=4, layout=(1, 4)) + + with tm.assert_produces_warning(UserWarning): + axes = _check_plot_works(df.height.hist, + by=df.classroom, layout=(2, 2)) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 2), + figsize=(12, 7)) + + @slow + def test_hist_no_overlap(self): + from matplotlib.pyplot import subplot, gcf + x = Series(randn(2)) + y = Series(randn(2)) + subplot(121) + x.hist() + subplot(122) + y.hist() + fig = gcf() + axes = fig.axes if self.mpl_ge_1_5_0 else fig.get_axes() + self.assertEqual(len(axes), 2) + + @slow + def test_hist_secondary_legend(self): + # GH 9610 + df = DataFrame(np.random.randn(30, 4), columns=list('abcd')) + + # primary -> secondary + ax = df['a'].plot.hist(legend=True) + df['b'].plot.hist(ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left and right axis must be visible + self._check_legend_labels(ax, labels=['a', 'b (right)']) + self.assertTrue(ax.get_yaxis().get_visible()) + self.assertTrue(ax.right_ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> secondary + ax = df['a'].plot.hist(legend=True, secondary_y=True) + df['b'].plot.hist(ax=ax, legend=True, secondary_y=True) + # both legends are draw on left ax + # left axis must be invisible, right axis must be visible + self._check_legend_labels(ax.left_ax, + labels=['a (right)', 'b (right)']) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> primary + ax = df['a'].plot.hist(legend=True, secondary_y=True) + # right axes is returned + df['b'].plot.hist(ax=ax, legend=True) + # both legends are draw on left ax + # left and right axis must be visible + self._check_legend_labels(ax.left_ax, labels=['a (right)', 'b']) + self.assertTrue(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + @slow + def test_df_series_secondary_legend(self): + # GH 9779 + df = DataFrame(np.random.randn(30, 3), columns=list('abc')) + s = Series(np.random.randn(30), name='x') + + # primary -> secondary (without passing ax) + ax = df.plot() + s.plot(legend=True, secondary_y=True) + # both legends are dran on left ax + # left and right axis must be visible + self._check_legend_labels(ax, labels=['a', 'b', 'c', 'x (right)']) + self.assertTrue(ax.get_yaxis().get_visible()) + self.assertTrue(ax.right_ax.get_yaxis().get_visible()) + tm.close() + + # primary -> secondary (with passing ax) + ax = df.plot() + s.plot(ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left and right axis must be visible + self._check_legend_labels(ax, labels=['a', 'b', 'c', 'x (right)']) + self.assertTrue(ax.get_yaxis().get_visible()) + self.assertTrue(ax.right_ax.get_yaxis().get_visible()) + tm.close() + + # seconcary -> secondary (without passing ax) + ax = df.plot(secondary_y=True) + s.plot(legend=True, secondary_y=True) + # both legends are dran on left ax + # left axis must be invisible and right axis must be visible + expected = ['a (right)', 'b (right)', 'c (right)', 'x (right)'] + self._check_legend_labels(ax.left_ax, labels=expected) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> secondary (with passing ax) + ax = df.plot(secondary_y=True) + s.plot(ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left axis must be invisible and right axis must be visible + expected = ['a (right)', 'b (right)', 'c (right)', 'x (right)'] + self._check_legend_labels(ax.left_ax, expected) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> secondary (with passing ax) + ax = df.plot(secondary_y=True, mark_right=False) + s.plot(ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left axis must be invisible and right axis must be visible + expected = ['a', 'b', 'c', 'x (right)'] + self._check_legend_labels(ax.left_ax, expected) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + @slow + def test_plot_fails_with_dupe_color_and_style(self): + x = Series(randn(2)) + with tm.assertRaises(ValueError): + x.plot(style='k--', color='k') + + @slow + def test_hist_kde(self): + ax = self.ts.plot.hist(logy=True) + self._check_ax_scales(ax, yaxis='log') + xlabels = ax.get_xticklabels() + # ticks are values, thus ticklabels are blank + self._check_text_labels(xlabels, [''] * len(xlabels)) + ylabels = ax.get_yticklabels() + self._check_text_labels(ylabels, [''] * len(ylabels)) + + tm._skip_if_no_scipy() + _skip_if_no_scipy_gaussian_kde() + _check_plot_works(self.ts.plot.kde) + _check_plot_works(self.ts.plot.density) + ax = self.ts.plot.kde(logy=True) + self._check_ax_scales(ax, yaxis='log') + xlabels = ax.get_xticklabels() + self._check_text_labels(xlabels, [''] * len(xlabels)) + ylabels = ax.get_yticklabels() + self._check_text_labels(ylabels, [''] * len(ylabels)) + + @slow + def test_kde_kwargs(self): + tm._skip_if_no_scipy() + _skip_if_no_scipy_gaussian_kde() + from numpy import linspace + _check_plot_works(self.ts.plot.kde, bw_method=.5, + ind=linspace(-100, 100, 20)) + _check_plot_works(self.ts.plot.density, bw_method=.5, + ind=linspace(-100, 100, 20)) + ax = self.ts.plot.kde(logy=True, bw_method=.5, + ind=linspace(-100, 100, 20)) + self._check_ax_scales(ax, yaxis='log') + self._check_text_labels(ax.yaxis.get_label(), 'Density') + + @slow + def test_kde_missing_vals(self): + tm._skip_if_no_scipy() + _skip_if_no_scipy_gaussian_kde() + s = Series(np.random.uniform(size=50)) + s[0] = np.nan + _check_plot_works(s.plot.kde) + + @slow + def test_hist_kwargs(self): + ax = self.ts.plot.hist(bins=5) + self.assertEqual(len(ax.patches), 5) + self._check_text_labels(ax.yaxis.get_label(), 'Frequency') + tm.close() + + if self.mpl_ge_1_3_1: + ax = self.ts.plot.hist(orientation='horizontal') + self._check_text_labels(ax.xaxis.get_label(), 'Frequency') + tm.close() + + ax = self.ts.plot.hist(align='left', stacked=True) + tm.close() + + @slow + def test_hist_kde_color(self): + ax = self.ts.plot.hist(logy=True, bins=10, color='b') + self._check_ax_scales(ax, yaxis='log') + self.assertEqual(len(ax.patches), 10) + self._check_colors(ax.patches, facecolors=['b'] * 10) + + tm._skip_if_no_scipy() + _skip_if_no_scipy_gaussian_kde() + ax = self.ts.plot.kde(logy=True, color='r') + self._check_ax_scales(ax, yaxis='log') + lines = ax.get_lines() + self.assertEqual(len(lines), 1) + self._check_colors(lines, ['r']) + + @slow + def test_boxplot_series(self): + ax = self.ts.plot.box(logy=True) + self._check_ax_scales(ax, yaxis='log') + xlabels = ax.get_xticklabels() + self._check_text_labels(xlabels, [self.ts.name]) + ylabels = ax.get_yticklabels() + self._check_text_labels(ylabels, [''] * len(ylabels)) + + @slow + def test_kind_both_ways(self): + s = Series(range(3)) + for kind in plotting._common_kinds + plotting._series_kinds: + if not _ok_for_gaussian_kde(kind): + continue + s.plot(kind=kind) + getattr(s.plot, kind)() + + @slow + def test_invalid_plot_data(self): + s = Series(list('abcd')) + for kind in plotting._common_kinds: + if not _ok_for_gaussian_kde(kind): + continue + with tm.assertRaises(TypeError): + s.plot(kind=kind) + + @slow + def test_valid_object_plot(self): + s = Series(lrange(10), dtype=object) + for kind in plotting._common_kinds: + if not _ok_for_gaussian_kde(kind): + continue + _check_plot_works(s.plot, kind=kind) + + def test_partially_invalid_plot_data(self): + s = Series(['a', 'b', 1.0, 2]) + for kind in plotting._common_kinds: + if not _ok_for_gaussian_kde(kind): + continue + with tm.assertRaises(TypeError): + s.plot(kind=kind) + + def test_invalid_kind(self): + s = Series([1, 2]) + with tm.assertRaises(ValueError): + s.plot(kind='aasdf') + + @slow + def test_dup_datetime_index_plot(self): + dr1 = date_range('1/1/2009', periods=4) + dr2 = date_range('1/2/2009', periods=4) + index = dr1.append(dr2) + values = randn(index.size) + s = Series(values, index=index) + _check_plot_works(s.plot) + + @slow + def test_errorbar_plot(self): + + s = Series(np.arange(10), name='x') + s_err = np.random.randn(10) + d_err = DataFrame(randn(10, 2), index=s.index, columns=['x', 'y']) + # test line and bar plots + kinds = ['line', 'bar'] + for kind in kinds: + ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind) + self._check_has_errorbars(ax, xerr=0, yerr=1) + ax = _check_plot_works(s.plot, yerr=s_err, kind=kind) + self._check_has_errorbars(ax, xerr=0, yerr=1) + ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind) + self._check_has_errorbars(ax, xerr=0, yerr=1) + ax = _check_plot_works(s.plot, yerr=d_err, kind=kind) + self._check_has_errorbars(ax, xerr=0, yerr=1) + ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind) + self._check_has_errorbars(ax, xerr=1, yerr=1) + + ax = _check_plot_works(s.plot, xerr=s_err) + self._check_has_errorbars(ax, xerr=1, yerr=0) + + # test time series plotting + ix = date_range('1/1/2000', '1/1/2001', freq='M') + ts = Series(np.arange(12), index=ix, name='x') + ts_err = Series(np.random.randn(12), index=ix) + td_err = DataFrame(randn(12, 2), index=ix, columns=['x', 'y']) + + ax = _check_plot_works(ts.plot, yerr=ts_err) + self._check_has_errorbars(ax, xerr=0, yerr=1) + ax = _check_plot_works(ts.plot, yerr=td_err) + self._check_has_errorbars(ax, xerr=0, yerr=1) + + # check incorrect lengths and types + with tm.assertRaises(ValueError): + s.plot(yerr=np.arange(11)) + + s_err = ['zzz'] * 10 + # in mpl 1.5+ this is a TypeError + with tm.assertRaises((ValueError, TypeError)): + s.plot(yerr=s_err) + + def test_table(self): + _check_plot_works(self.series.plot, table=True) + _check_plot_works(self.series.plot, table=self.series) + + @slow + def test_series_grid_settings(self): + # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 + self._check_grid_settings(Series([1, 2, 3]), + plotting._series_kinds + + plotting._common_kinds) + + @slow + def test_standard_colors(self): + for c in ['r', 'red', 'green', '#FF0000']: + result = plotting._get_standard_colors(1, color=c) + self.assertEqual(result, [c]) + + result = plotting._get_standard_colors(1, color=[c]) + self.assertEqual(result, [c]) + + result = plotting._get_standard_colors(3, color=c) + self.assertEqual(result, [c] * 3) + + result = plotting._get_standard_colors(3, color=[c]) + self.assertEqual(result, [c] * 3) + + @slow + def test_standard_colors_all(self): + import matplotlib.colors as colors + + # multiple colors like mediumaquamarine + for c in colors.cnames: + result = plotting._get_standard_colors(num_colors=1, color=c) + self.assertEqual(result, [c]) + + result = plotting._get_standard_colors(num_colors=1, color=[c]) + self.assertEqual(result, [c]) + + result = plotting._get_standard_colors(num_colors=3, color=c) + self.assertEqual(result, [c] * 3) + + result = plotting._get_standard_colors(num_colors=3, color=[c]) + self.assertEqual(result, [c] * 3) + + # single letter colors like k + for c in colors.ColorConverter.colors: + result = plotting._get_standard_colors(num_colors=1, color=c) + self.assertEqual(result, [c]) + + result = plotting._get_standard_colors(num_colors=1, color=[c]) + self.assertEqual(result, [c]) + + result = plotting._get_standard_colors(num_colors=3, color=c) + self.assertEqual(result, [c] * 3) + + result = plotting._get_standard_colors(num_colors=3, color=[c]) + self.assertEqual(result, [c] * 3) + + def test_series_plot_color_kwargs(self): + # GH1890 + ax = Series(np.arange(12) + 1).plot(color='green') + self._check_colors(ax.get_lines(), linecolors=['green']) + + def test_time_series_plot_color_kwargs(self): + # #1890 + ax = Series(np.arange(12) + 1, index=date_range( + '1/1/2000', periods=12)).plot(color='green') + self._check_colors(ax.get_lines(), linecolors=['green']) + + def test_time_series_plot_color_with_empty_kwargs(self): + import matplotlib as mpl + + if self.mpl_ge_1_5_0: + def_colors = self._maybe_unpack_cycler(mpl.rcParams) + else: + def_colors = mpl.rcParams['axes.color_cycle'] + index = date_range('1/1/2000', periods=12) + s = Series(np.arange(1, 13), index=index) + + ncolors = 3 + + for i in range(ncolors): + ax = s.plot() + self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors]) + + def test_xticklabels(self): + # GH11529 + s = Series(np.arange(10), index=['P%02d' % i for i in range(10)]) + ax = s.plot(xticks=[0, 3, 5, 9]) + exp = ['P%02d' % i for i in [0, 3, 5, 9]] + self._check_text_labels(ax.get_xticklabels(), exp) + + def test_custom_business_day_freq(self): + # GH7222 + from pandas.tseries.offsets import CustomBusinessDay + s = Series(range(100, 121), index=pd.bdate_range( + start='2014-05-01', end='2014-06-01', + freq=CustomBusinessDay(holidays=['2014-05-26']))) + + _check_plot_works(s.plot) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/test_graphics_others.py b/pandas/tests/test_graphics_others.py deleted file mode 100644 index f9a210a492594..0000000000000 --- a/pandas/tests/test_graphics_others.py +++ /dev/null @@ -1,1033 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -import nose -import itertools -import os -import string -from distutils.version import LooseVersion - -from pandas import Series, DataFrame, MultiIndex -from pandas.compat import range, lmap, lzip -import pandas.util.testing as tm -from pandas.util.testing import slow - -import numpy as np -from numpy import random -from numpy.random import randn - -import pandas.tools.plotting as plotting - -from pandas.tests.test_graphics import (TestPlotBase, _check_plot_works, - curpath, _ok_for_gaussian_kde) - - -""" -These tests are for ``DataFrame.hist``, ``DataFrame.boxplot`` and -other miscellaneous plots. -`Dataframe.plot`` and ``Series.plot`` are tested in test_graphics.py -""" - - -def _skip_if_mpl_14_or_dev_boxplot(): - # GH 8382 - # Boxplot failures on 1.4 and 1.4.1 - # Don't need try / except since that's done at class level - import matplotlib - if str(matplotlib.__version__) >= LooseVersion('1.4'): - raise nose.SkipTest("Matplotlib Regression in 1.4 and current dev.") - - -@tm.mplskip -class TestSeriesPlots(TestPlotBase): - - def setUp(self): - TestPlotBase.setUp(self) - import matplotlib as mpl - mpl.rcdefaults() - - self.ts = tm.makeTimeSeries() - self.ts.name = 'ts' - - self.series = tm.makeStringSeries() - self.series.name = 'series' - - self.iseries = tm.makePeriodSeries() - self.iseries.name = 'iseries' - - @slow - def test_hist_legacy(self): - _check_plot_works(self.ts.hist) - _check_plot_works(self.ts.hist, grid=False) - _check_plot_works(self.ts.hist, figsize=(8, 10)) - # _check_plot_works adds an ax so catch warning. see GH #13188 - with tm.assert_produces_warning(UserWarning): - _check_plot_works(self.ts.hist, by=self.ts.index.month) - with tm.assert_produces_warning(UserWarning): - _check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5) - - fig, ax = self.plt.subplots(1, 1) - _check_plot_works(self.ts.hist, ax=ax) - _check_plot_works(self.ts.hist, ax=ax, figure=fig) - _check_plot_works(self.ts.hist, figure=fig) - tm.close() - - fig, (ax1, ax2) = self.plt.subplots(1, 2) - _check_plot_works(self.ts.hist, figure=fig, ax=ax1) - _check_plot_works(self.ts.hist, figure=fig, ax=ax2) - - with tm.assertRaises(ValueError): - self.ts.hist(by=self.ts.index, figure=fig) - - @slow - def test_hist_bins_legacy(self): - df = DataFrame(np.random.randn(10, 2)) - ax = df.hist(bins=2)[0][0] - self.assertEqual(len(ax.patches), 2) - - @slow - def test_hist_layout(self): - df = self.hist_df - with tm.assertRaises(ValueError): - df.height.hist(layout=(1, 1)) - - with tm.assertRaises(ValueError): - df.height.hist(layout=[1, 1]) - - @slow - def test_hist_layout_with_by(self): - df = self.hist_df - - # _check_plot_works adds an `ax` kwarg to the method call - # so we get a warning about an axis being cleared, even - # though we don't explicing pass one, see GH #13188 - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, by=df.gender, - layout=(2, 1)) - self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, by=df.gender, - layout=(3, -1)) - self._check_axes_shape(axes, axes_num=2, layout=(3, 1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.height.hist, by=df.category, - layout=(4, 1)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works( - df.height.hist, by=df.category, layout=(2, -1)) - self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works( - df.height.hist, by=df.category, layout=(3, -1)) - self._check_axes_shape(axes, axes_num=4, layout=(3, 2)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works( - df.height.hist, by=df.category, layout=(-1, 4)) - self._check_axes_shape(axes, axes_num=4, layout=(1, 4)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works( - df.height.hist, by=df.classroom, layout=(2, 2)) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7)) - self._check_axes_shape( - axes, axes_num=4, layout=(4, 2), figsize=(12, 7)) - - @slow - def test_hist_no_overlap(self): - from matplotlib.pyplot import subplot, gcf - x = Series(randn(2)) - y = Series(randn(2)) - subplot(121) - x.hist() - subplot(122) - y.hist() - fig = gcf() - axes = fig.axes if self.mpl_ge_1_5_0 else fig.get_axes() - self.assertEqual(len(axes), 2) - - @slow - def test_hist_by_no_extra_plots(self): - df = self.hist_df - axes = df.height.hist(by=df.gender) # noqa - self.assertEqual(len(self.plt.get_fignums()), 1) - - @slow - def test_plot_fails_when_ax_differs_from_figure(self): - from pylab import figure - fig1 = figure() - fig2 = figure() - ax1 = fig1.add_subplot(111) - with tm.assertRaises(AssertionError): - self.ts.hist(ax=ax1, figure=fig2) - - @slow - def test_autocorrelation_plot(self): - from pandas.tools.plotting import autocorrelation_plot - _check_plot_works(autocorrelation_plot, series=self.ts) - _check_plot_works(autocorrelation_plot, series=self.ts.values) - - ax = autocorrelation_plot(self.ts, label='Test') - self._check_legend_labels(ax, labels=['Test']) - - @slow - def test_lag_plot(self): - from pandas.tools.plotting import lag_plot - _check_plot_works(lag_plot, series=self.ts) - _check_plot_works(lag_plot, series=self.ts, lag=5) - - @slow - def test_bootstrap_plot(self): - from pandas.tools.plotting import bootstrap_plot - _check_plot_works(bootstrap_plot, series=self.ts, size=10) - - -@tm.mplskip -class TestDataFramePlots(TestPlotBase): - - def setUp(self): - TestPlotBase.setUp(self) - import matplotlib as mpl - mpl.rcdefaults() - - self.tdf = tm.makeTimeDataFrame() - self.hexbin_df = DataFrame({ - "A": np.random.uniform(size=20), - "B": np.random.uniform(size=20), - "C": np.arange(20) + np.random.uniform(size=20)}) - - from pandas import read_csv - path = os.path.join(curpath(), 'data', 'iris.csv') - self.iris = read_csv(path) - - @slow - def test_boxplot_legacy(self): - df = DataFrame(randn(6, 4), - index=list(string.ascii_letters[:6]), - columns=['one', 'two', 'three', 'four']) - df['indic'] = ['foo', 'bar'] * 3 - df['indic2'] = ['foo', 'bar', 'foo'] * 2 - - _check_plot_works(df.boxplot, return_type='dict') - _check_plot_works(df.boxplot, column=[ - 'one', 'two'], return_type='dict') - # _check_plot_works adds an ax so catch warning. see GH #13188 - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.boxplot, column=['one', 'two'], - by='indic') - _check_plot_works(df.boxplot, column='one', by=['indic', 'indic2']) - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.boxplot, by='indic') - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.boxplot, by=['indic', 'indic2']) - _check_plot_works(plotting.boxplot, data=df['one'], return_type='dict') - _check_plot_works(df.boxplot, notch=1, return_type='dict') - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.boxplot, by='indic', notch=1) - - df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2']) - df['X'] = Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B']) - df['Y'] = Series(['A'] * 10) - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.boxplot, by='X') - - # When ax is supplied and required number of axes is 1, - # passed ax should be used: - fig, ax = self.plt.subplots() - axes = df.boxplot('Col1', by='X', ax=ax) - ax_axes = ax.axes if self.mpl_ge_1_5_0 else ax.get_axes() - self.assertIs(ax_axes, axes) - - fig, ax = self.plt.subplots() - axes = df.groupby('Y').boxplot(ax=ax, return_type='axes') - ax_axes = ax.axes if self.mpl_ge_1_5_0 else ax.get_axes() - self.assertIs(ax_axes, axes['A']) - - # Multiple columns with an ax argument should use same figure - fig, ax = self.plt.subplots() - with tm.assert_produces_warning(UserWarning): - axes = df.boxplot(column=['Col1', 'Col2'], - by='X', ax=ax, return_type='axes') - self.assertIs(axes['Col1'].get_figure(), fig) - - # When by is None, check that all relevant lines are present in the - # dict - fig, ax = self.plt.subplots() - d = df.boxplot(ax=ax, return_type='dict') - lines = list(itertools.chain.from_iterable(d.values())) - self.assertEqual(len(ax.get_lines()), len(lines)) - - @slow - def test_boxplot_return_type_legacy(self): - # API change in https://github.com/pydata/pandas/pull/7096 - import matplotlib as mpl # noqa - - df = DataFrame(randn(6, 4), - index=list(string.ascii_letters[:6]), - columns=['one', 'two', 'three', 'four']) - with tm.assertRaises(ValueError): - df.boxplot(return_type='NOTATYPE') - - with tm.assert_produces_warning(FutureWarning): - result = df.boxplot() - # change to Axes in future - self._check_box_return_type(result, 'dict') - - with tm.assert_produces_warning(False): - result = df.boxplot(return_type='dict') - self._check_box_return_type(result, 'dict') - - with tm.assert_produces_warning(False): - result = df.boxplot(return_type='axes') - self._check_box_return_type(result, 'axes') - - with tm.assert_produces_warning(False): - result = df.boxplot(return_type='both') - self._check_box_return_type(result, 'both') - - @slow - def test_boxplot_axis_limits(self): - - def _check_ax_limits(col, ax): - y_min, y_max = ax.get_ylim() - self.assertTrue(y_min <= col.min()) - self.assertTrue(y_max >= col.max()) - - df = self.hist_df.copy() - df['age'] = np.random.randint(1, 20, df.shape[0]) - # One full row - height_ax, weight_ax = df.boxplot(['height', 'weight'], by='category') - _check_ax_limits(df['height'], height_ax) - _check_ax_limits(df['weight'], weight_ax) - self.assertEqual(weight_ax._sharey, height_ax) - - # Two rows, one partial - p = df.boxplot(['height', 'weight', 'age'], by='category') - height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0] - dummy_ax = p[1, 1] - _check_ax_limits(df['height'], height_ax) - _check_ax_limits(df['weight'], weight_ax) - _check_ax_limits(df['age'], age_ax) - self.assertEqual(weight_ax._sharey, height_ax) - self.assertEqual(age_ax._sharey, height_ax) - self.assertIsNone(dummy_ax._sharey) - - @slow - def test_boxplot_empty_column(self): - _skip_if_mpl_14_or_dev_boxplot() - df = DataFrame(np.random.randn(20, 4)) - df.loc[:, 0] = np.nan - _check_plot_works(df.boxplot, return_type='axes') - - @slow - def test_hist_df_legacy(self): - from matplotlib.patches import Rectangle - with tm.assert_produces_warning(UserWarning): - _check_plot_works(self.hist_df.hist) - - # make sure layout is handled - df = DataFrame(randn(100, 3)) - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.hist, grid=False) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - self.assertFalse(axes[1, 1].get_visible()) - - df = DataFrame(randn(100, 1)) - _check_plot_works(df.hist) - - # make sure layout is handled - df = DataFrame(randn(100, 6)) - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.hist, layout=(4, 2)) - self._check_axes_shape(axes, axes_num=6, layout=(4, 2)) - - # make sure sharex, sharey is handled - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.hist, sharex=True, sharey=True) - - # handle figsize arg - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.hist, figsize=(8, 10)) - - # check bins argument - with tm.assert_produces_warning(UserWarning): - _check_plot_works(df.hist, bins=5) - - # make sure xlabelsize and xrot are handled - ser = df[0] - xf, yf = 20, 18 - xrot, yrot = 30, 40 - axes = ser.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) - self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, - ylabelsize=yf, yrot=yrot) - - xf, yf = 20, 18 - xrot, yrot = 30, 40 - axes = df.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) - self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, - ylabelsize=yf, yrot=yrot) - - tm.close() - # make sure kwargs to hist are handled - ax = ser.hist(normed=True, cumulative=True, bins=4) - # height of last bin (index 5) must be 1.0 - rects = [x for x in ax.get_children() if isinstance(x, Rectangle)] - self.assertAlmostEqual(rects[-1].get_height(), 1.0) - - tm.close() - ax = ser.hist(log=True) - # scale of y must be 'log' - self._check_ax_scales(ax, yaxis='log') - - tm.close() - - # propagate attr exception from matplotlib.Axes.hist - with tm.assertRaises(AttributeError): - ser.hist(foo='bar') - - @slow - def test_hist_layout(self): - df = DataFrame(randn(100, 3)) - - layout_to_expected_size = ( - {'layout': None, 'expected_size': (2, 2)}, # default is 2x2 - {'layout': (2, 2), 'expected_size': (2, 2)}, - {'layout': (4, 1), 'expected_size': (4, 1)}, - {'layout': (1, 4), 'expected_size': (1, 4)}, - {'layout': (3, 3), 'expected_size': (3, 3)}, - {'layout': (-1, 4), 'expected_size': (1, 4)}, - {'layout': (4, -1), 'expected_size': (4, 1)}, - {'layout': (-1, 2), 'expected_size': (2, 2)}, - {'layout': (2, -1), 'expected_size': (2, 2)} - ) - - for layout_test in layout_to_expected_size: - axes = df.hist(layout=layout_test['layout']) - expected = layout_test['expected_size'] - self._check_axes_shape(axes, axes_num=3, layout=expected) - - # layout too small for all 4 plots - with tm.assertRaises(ValueError): - df.hist(layout=(1, 1)) - - # invalid format for layout - with tm.assertRaises(ValueError): - df.hist(layout=(1,)) - with tm.assertRaises(ValueError): - df.hist(layout=(-1, -1)) - - @slow - def test_scatter_plot_legacy(self): - tm._skip_if_no_scipy() - - df = DataFrame(randn(100, 2)) - - def scat(**kwds): - return plotting.scatter_matrix(df, **kwds) - - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat) - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat, marker='+') - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat, vmin=0) - if _ok_for_gaussian_kde('kde'): - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat, diagonal='kde') - if _ok_for_gaussian_kde('density'): - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat, diagonal='density') - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat, diagonal='hist') - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat, range_padding=.1) - - def scat2(x, y, by=None, ax=None, figsize=None): - return plotting.scatter_plot(df, x, y, by, ax, figsize=None) - - _check_plot_works(scat2, x=0, y=1) - grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index) - with tm.assert_produces_warning(UserWarning): - _check_plot_works(scat2, x=0, y=1, by=grouper) - - def test_scatter_matrix_axis(self): - tm._skip_if_no_scipy() - scatter_matrix = plotting.scatter_matrix - - with tm.RNGContext(42): - df = DataFrame(randn(100, 3)) - - # we are plotting multiples on a sub-plot - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(scatter_matrix, filterwarnings='always', - frame=df, range_padding=.1) - axes0_labels = axes[0][0].yaxis.get_majorticklabels() - - # GH 5662 - expected = ['-2', '-1', '0', '1', '2'] - self._check_text_labels(axes0_labels, expected) - self._check_ticks_props( - axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) - - df[0] = ((df[0] - 2) / 3) - - # we are plotting multiples on a sub-plot - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(scatter_matrix, filterwarnings='always', - frame=df, range_padding=.1) - axes0_labels = axes[0][0].yaxis.get_majorticklabels() - expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0'] - self._check_text_labels(axes0_labels, expected) - self._check_ticks_props( - axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) - - @slow - def test_andrews_curves(self): - from pandas.tools.plotting import andrews_curves - from matplotlib import cm - - df = self.iris - - _check_plot_works(andrews_curves, frame=df, class_column='Name') - - rgba = ('#556270', '#4ECDC4', '#C7F464') - ax = _check_plot_works(andrews_curves, frame=df, - class_column='Name', color=rgba) - self._check_colors( - ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) - - cnames = ['dodgerblue', 'aquamarine', 'seagreen'] - ax = _check_plot_works(andrews_curves, frame=df, - class_column='Name', color=cnames) - self._check_colors( - ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) - - ax = _check_plot_works(andrews_curves, frame=df, - class_column='Name', colormap=cm.jet) - cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) - self._check_colors( - ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) - - length = 10 - df = DataFrame({"A": random.rand(length), - "B": random.rand(length), - "C": random.rand(length), - "Name": ["A"] * length}) - - _check_plot_works(andrews_curves, frame=df, class_column='Name') - - rgba = ('#556270', '#4ECDC4', '#C7F464') - ax = _check_plot_works(andrews_curves, frame=df, - class_column='Name', color=rgba) - self._check_colors( - ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) - - cnames = ['dodgerblue', 'aquamarine', 'seagreen'] - ax = _check_plot_works(andrews_curves, frame=df, - class_column='Name', color=cnames) - self._check_colors( - ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) - - ax = _check_plot_works(andrews_curves, frame=df, - class_column='Name', colormap=cm.jet) - cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) - self._check_colors( - ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) - - colors = ['b', 'g', 'r'] - df = DataFrame({"A": [1, 2, 3], - "B": [1, 2, 3], - "C": [1, 2, 3], - "Name": colors}) - ax = andrews_curves(df, 'Name', color=colors) - handles, labels = ax.get_legend_handles_labels() - self._check_colors(handles, linecolors=colors) - - with tm.assert_produces_warning(FutureWarning): - andrews_curves(data=df, class_column='Name') - - @slow - def test_parallel_coordinates(self): - from pandas.tools.plotting import parallel_coordinates - from matplotlib import cm - - df = self.iris - - ax = _check_plot_works(parallel_coordinates, - frame=df, class_column='Name') - nlines = len(ax.get_lines()) - nxticks = len(ax.xaxis.get_ticklabels()) - - rgba = ('#556270', '#4ECDC4', '#C7F464') - ax = _check_plot_works(parallel_coordinates, - frame=df, class_column='Name', color=rgba) - self._check_colors( - ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) - - cnames = ['dodgerblue', 'aquamarine', 'seagreen'] - ax = _check_plot_works(parallel_coordinates, - frame=df, class_column='Name', color=cnames) - self._check_colors( - ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) - - ax = _check_plot_works(parallel_coordinates, - frame=df, class_column='Name', colormap=cm.jet) - cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) - self._check_colors( - ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) - - ax = _check_plot_works(parallel_coordinates, - frame=df, class_column='Name', axvlines=False) - assert len(ax.get_lines()) == (nlines - nxticks) - - colors = ['b', 'g', 'r'] - df = DataFrame({"A": [1, 2, 3], - "B": [1, 2, 3], - "C": [1, 2, 3], - "Name": colors}) - ax = parallel_coordinates(df, 'Name', color=colors) - handles, labels = ax.get_legend_handles_labels() - self._check_colors(handles, linecolors=colors) - - with tm.assert_produces_warning(FutureWarning): - parallel_coordinates(data=df, class_column='Name') - with tm.assert_produces_warning(FutureWarning): - parallel_coordinates(df, 'Name', colors=colors) - - @slow - def test_radviz(self): - from pandas.tools.plotting import radviz - from matplotlib import cm - - df = self.iris - _check_plot_works(radviz, frame=df, class_column='Name') - - rgba = ('#556270', '#4ECDC4', '#C7F464') - ax = _check_plot_works( - radviz, frame=df, class_column='Name', color=rgba) - # skip Circle drawn as ticks - patches = [p for p in ax.patches[:20] if p.get_label() != ''] - self._check_colors( - patches[:10], facecolors=rgba, mapping=df['Name'][:10]) - - cnames = ['dodgerblue', 'aquamarine', 'seagreen'] - _check_plot_works(radviz, frame=df, class_column='Name', color=cnames) - patches = [p for p in ax.patches[:20] if p.get_label() != ''] - self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10]) - - _check_plot_works(radviz, frame=df, - class_column='Name', colormap=cm.jet) - cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) - patches = [p for p in ax.patches[:20] if p.get_label() != ''] - self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10]) - - colors = [[0., 0., 1., 1.], - [0., 0.5, 1., 1.], - [1., 0., 0., 1.]] - df = DataFrame({"A": [1, 2, 3], - "B": [2, 1, 3], - "C": [3, 2, 1], - "Name": ['b', 'g', 'r']}) - ax = radviz(df, 'Name', color=colors) - handles, labels = ax.get_legend_handles_labels() - self._check_colors(handles, facecolors=colors) - - -@tm.mplskip -class TestDataFrameGroupByPlots(TestPlotBase): - - @slow - def test_boxplot_legacy(self): - grouped = self.hist_df.groupby(by='gender') - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(grouped.boxplot, return_type='axes') - self._check_axes_shape(list(axes.values()), axes_num=2, layout=(1, 2)) - - axes = _check_plot_works(grouped.boxplot, subplots=False, - return_type='axes') - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - tuples = lzip(string.ascii_letters[:10], range(10)) - df = DataFrame(np.random.rand(10, 3), - index=MultiIndex.from_tuples(tuples)) - - grouped = df.groupby(level=1) - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(grouped.boxplot, return_type='axes') - self._check_axes_shape(list(axes.values()), axes_num=10, layout=(4, 3)) - - axes = _check_plot_works(grouped.boxplot, subplots=False, - return_type='axes') - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - - grouped = df.unstack(level=1).groupby(level=0, axis=1) - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(grouped.boxplot, return_type='axes') - self._check_axes_shape(list(axes.values()), axes_num=3, layout=(2, 2)) - - axes = _check_plot_works(grouped.boxplot, subplots=False, - return_type='axes') - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - - @slow - def test_grouped_plot_fignums(self): - n = 10 - weight = Series(np.random.normal(166, 20, size=n)) - height = Series(np.random.normal(60, 10, size=n)) - with tm.RNGContext(42): - gender = np.random.choice(['male', 'female'], size=n) - df = DataFrame({'height': height, 'weight': weight, 'gender': gender}) - gb = df.groupby('gender') - - res = gb.plot() - self.assertEqual(len(self.plt.get_fignums()), 2) - self.assertEqual(len(res), 2) - tm.close() - - res = gb.boxplot(return_type='axes') - self.assertEqual(len(self.plt.get_fignums()), 1) - self.assertEqual(len(res), 2) - tm.close() - - # now works with GH 5610 as gender is excluded - res = df.groupby('gender').hist() - tm.close() - - @slow - def test_grouped_hist_legacy(self): - from matplotlib.patches import Rectangle - - df = DataFrame(randn(500, 2), columns=['A', 'B']) - df['C'] = np.random.randint(0, 4, 500) - df['D'] = ['X'] * 500 - - axes = plotting.grouped_hist(df.A, by=df.C) - self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) - - tm.close() - axes = df.hist(by=df.C) - self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) - - tm.close() - # group by a key with single value - axes = df.hist(by='D', rot=30) - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - self._check_ticks_props(axes, xrot=30) - - tm.close() - # make sure kwargs to hist are handled - xf, yf = 20, 18 - xrot, yrot = 30, 40 - axes = plotting.grouped_hist(df.A, by=df.C, normed=True, - cumulative=True, bins=4, - xlabelsize=xf, xrot=xrot, - ylabelsize=yf, yrot=yrot) - # height of last bin (index 5) must be 1.0 - for ax in axes.ravel(): - rects = [x for x in ax.get_children() if isinstance(x, Rectangle)] - height = rects[-1].get_height() - self.assertAlmostEqual(height, 1.0) - self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, - ylabelsize=yf, yrot=yrot) - - tm.close() - axes = plotting.grouped_hist(df.A, by=df.C, log=True) - # scale of y must be 'log' - self._check_ax_scales(axes, yaxis='log') - - tm.close() - # propagate attr exception from matplotlib.Axes.hist - with tm.assertRaises(AttributeError): - plotting.grouped_hist(df.A, by=df.C, foo='bar') - - with tm.assert_produces_warning(FutureWarning): - df.hist(by='C', figsize='default') - - @slow - def test_grouped_hist_legacy2(self): - n = 10 - weight = Series(np.random.normal(166, 20, size=n)) - height = Series(np.random.normal(60, 10, size=n)) - with tm.RNGContext(42): - gender_int = np.random.choice([0, 1], size=n) - df_int = DataFrame({'height': height, 'weight': weight, - 'gender': gender_int}) - gb = df_int.groupby('gender') - axes = gb.hist() - self.assertEqual(len(axes), 2) - self.assertEqual(len(self.plt.get_fignums()), 2) - tm.close() - - @slow - def test_grouped_box_return_type(self): - df = self.hist_df - - # old style: return_type=None - result = df.boxplot(by='gender') - self.assertIsInstance(result, np.ndarray) - self._check_box_return_type( - result, None, - expected_keys=['height', 'weight', 'category']) - - # now for groupby - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = df.groupby('gender').boxplot() - self._check_box_return_type( - result, 'dict', expected_keys=['Male', 'Female']) - - columns2 = 'X B C D A G Y N Q O'.split() - df2 = DataFrame(random.randn(50, 10), columns=columns2) - categories2 = 'A B C D E F G H I J'.split() - df2['category'] = categories2 * 5 - - for t in ['dict', 'axes', 'both']: - returned = df.groupby('classroom').boxplot(return_type=t) - self._check_box_return_type( - returned, t, expected_keys=['A', 'B', 'C']) - - returned = df.boxplot(by='classroom', return_type=t) - self._check_box_return_type( - returned, t, - expected_keys=['height', 'weight', 'category']) - - returned = df2.groupby('category').boxplot(return_type=t) - self._check_box_return_type(returned, t, expected_keys=categories2) - - returned = df2.boxplot(by='category', return_type=t) - self._check_box_return_type(returned, t, expected_keys=columns2) - - @slow - def test_grouped_box_layout(self): - df = self.hist_df - - self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], - by=df.gender, layout=(1, 1)) - self.assertRaises(ValueError, df.boxplot, - column=['height', 'weight', 'category'], - layout=(2, 1), return_type='dict') - self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], - by=df.gender, layout=(-1, -1)) - - # _check_plot_works adds an ax so catch warning. see GH #13188 - with tm.assert_produces_warning(UserWarning): - box = _check_plot_works(df.groupby('gender').boxplot, - column='height', return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2)) - - with tm.assert_produces_warning(UserWarning): - box = _check_plot_works(df.groupby('category').boxplot, - column='height', - return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2)) - - # GH 6769 - with tm.assert_produces_warning(UserWarning): - box = _check_plot_works(df.groupby('classroom').boxplot, - column='height', return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) - - # GH 5897 - axes = df.boxplot(column=['height', 'weight', 'category'], by='gender', - return_type='axes') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) - for ax in [axes['height']]: - self._check_visible(ax.get_xticklabels(), visible=False) - self._check_visible([ax.xaxis.get_label()], visible=False) - for ax in [axes['weight'], axes['category']]: - self._check_visible(ax.get_xticklabels()) - self._check_visible([ax.xaxis.get_label()]) - - box = df.groupby('classroom').boxplot( - column=['height', 'weight', 'category'], return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) - - with tm.assert_produces_warning(UserWarning): - box = _check_plot_works(df.groupby('category').boxplot, - column='height', - layout=(3, 2), return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) - with tm.assert_produces_warning(UserWarning): - box = _check_plot_works(df.groupby('category').boxplot, - column='height', - layout=(3, -1), return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) - - box = df.boxplot(column=['height', 'weight', 'category'], by='gender', - layout=(4, 1)) - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1)) - - box = df.boxplot(column=['height', 'weight', 'category'], by='gender', - layout=(-1, 1)) - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1)) - - box = df.groupby('classroom').boxplot( - column=['height', 'weight', 'category'], layout=(1, 4), - return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4)) - - box = df.groupby('classroom').boxplot( # noqa - column=['height', 'weight', 'category'], layout=(1, -1), - return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3)) - - @slow - def test_grouped_box_multiple_axes(self): - # GH 6970, GH 7069 - df = self.hist_df - - # check warning to ignore sharex / sharey - # this check should be done in the first function which - # passes multiple axes to plot, hist or boxplot - # location should be changed if other test is added - # which has earlier alphabetical order - with tm.assert_produces_warning(UserWarning): - fig, axes = self.plt.subplots(2, 2) - df.groupby('category').boxplot( - column='height', return_type='axes', ax=axes) - self._check_axes_shape(self.plt.gcf().axes, - axes_num=4, layout=(2, 2)) - - fig, axes = self.plt.subplots(2, 3) - with tm.assert_produces_warning(UserWarning): - returned = df.boxplot(column=['height', 'weight', 'category'], - by='gender', return_type='axes', ax=axes[0]) - returned = np.array(list(returned.values())) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[0]) - self.assertIs(returned[0].figure, fig) - - # draw on second row - with tm.assert_produces_warning(UserWarning): - returned = df.groupby('classroom').boxplot( - column=['height', 'weight', 'category'], - return_type='axes', ax=axes[1]) - returned = np.array(list(returned.values())) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[1]) - self.assertIs(returned[0].figure, fig) - - with tm.assertRaises(ValueError): - fig, axes = self.plt.subplots(2, 3) - # pass different number of axes from required - with tm.assert_produces_warning(UserWarning): - axes = df.groupby('classroom').boxplot(ax=axes) - - @slow - def test_grouped_hist_layout(self): - df = self.hist_df - self.assertRaises(ValueError, df.hist, column='weight', by=df.gender, - layout=(1, 1)) - self.assertRaises(ValueError, df.hist, column='height', by=df.category, - layout=(1, 3)) - self.assertRaises(ValueError, df.hist, column='height', by=df.category, - layout=(-1, -1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.hist, column='height', by=df.gender, - layout=(2, 1)) - self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) - - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.hist, column='height', by=df.gender, - layout=(2, -1)) - self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) - - axes = df.hist(column='height', by=df.category, layout=(4, 1)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) - - axes = df.hist(column='height', by=df.category, layout=(-1, 1)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) - - axes = df.hist(column='height', by=df.category, - layout=(4, 2), figsize=(12, 8)) - self._check_axes_shape( - axes, axes_num=4, layout=(4, 2), figsize=(12, 8)) - tm.close() - - # GH 6769 - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works( - df.hist, column='height', by='classroom', layout=(2, 2)) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - # without column - with tm.assert_produces_warning(UserWarning): - axes = _check_plot_works(df.hist, by='classroom') - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - axes = df.hist(by='gender', layout=(3, 5)) - self._check_axes_shape(axes, axes_num=2, layout=(3, 5)) - - axes = df.hist(column=['height', 'weight', 'category']) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - @slow - def test_grouped_hist_multiple_axes(self): - # GH 6970, GH 7069 - df = self.hist_df - - fig, axes = self.plt.subplots(2, 3) - returned = df.hist(column=['height', 'weight', 'category'], ax=axes[0]) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[0]) - self.assertIs(returned[0].figure, fig) - returned = df.hist(by='classroom', ax=axes[1]) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[1]) - self.assertIs(returned[0].figure, fig) - - with tm.assertRaises(ValueError): - fig, axes = self.plt.subplots(2, 3) - # pass different number of axes from required - axes = df.hist(column='height', ax=axes) - - @slow - def test_axis_share_x(self): - df = self.hist_df - # GH4089 - ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True) - - # share x - self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) - - # don't share y - self.assertFalse(ax1._shared_y_axes.joined(ax1, ax2)) - self.assertFalse(ax2._shared_y_axes.joined(ax1, ax2)) - - @slow - def test_axis_share_y(self): - df = self.hist_df - ax1, ax2 = df.hist(column='height', by=df.gender, sharey=True) - - # share y - self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) - - # don't share x - self.assertFalse(ax1._shared_x_axes.joined(ax1, ax2)) - self.assertFalse(ax2._shared_x_axes.joined(ax1, ax2)) - - @slow - def test_axis_share_xy(self): - df = self.hist_df - ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True, - sharey=True) - - # share both x and y - self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) - - self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) - - -if __name__ == '__main__': - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/setup.py b/setup.py index 650357588570a..81698f42a03f5 100755 --- a/setup.py +++ b/setup.py @@ -572,6 +572,7 @@ def pxd(name): 'pandas.tests.formats', 'pandas.tests.types', 'pandas.tests.test_msgpack', + 'pandas.tests.plotting', 'pandas.tools', 'pandas.tools.tests', 'pandas.tseries',
- [x] related to #13579 - [x] tests passed - [x] passes `git diff upstream/master | flake8 --diff` Because `test_graphics` is getting huge, split them based on data types under `tests/plotting` to make visualization related tests easier.
https://api.github.com/repos/pandas-dev/pandas/pulls/13621
2016-07-11T17:03:57Z
2016-07-22T12:09:11Z
2016-07-22T12:09:11Z
2016-07-22T12:14:54Z
TST: reorganize tools.tests
diff --git a/pandas/tools/tests/test_concat.py b/pandas/tools/tests/test_concat.py index a8c86657a48cc..568cf63c02e30 100644 --- a/pandas/tools/tests/test_concat.py +++ b/pandas/tools/tests/test_concat.py @@ -17,7 +17,7 @@ assert_almost_equal) -class TestConcatenate(tm.TestCase): +class ConcatenateBase(tm.TestCase): _multiprocess_can_split_ = True @@ -26,6 +26,9 @@ def setUp(self): self.mixed_frame = self.frame.copy() self.mixed_frame['foo'] = 'bar' + +class TestAppend(ConcatenateBase): + def test_append(self): begin_index = self.frame.index[:5] end_index = self.frame.index[5:] @@ -142,42 +145,32 @@ def test_append_preserve_index_name(self): result = df1.append(df2) self.assertEqual(result.index.name, 'A') - def test_join_many(self): - df = DataFrame(np.random.randn(10, 6), columns=list('abcdef')) - df_list = [df[['a', 'b']], df[['c', 'd']], df[['e', 'f']]] - - joined = df_list[0].join(df_list[1:]) - tm.assert_frame_equal(joined, df) - - df_list = [df[['a', 'b']][:-2], - df[['c', 'd']][2:], df[['e', 'f']][1:9]] - - def _check_diff_index(df_list, result, exp_index): - reindexed = [x.reindex(exp_index) for x in df_list] - expected = reindexed[0].join(reindexed[1:]) - tm.assert_frame_equal(result, expected) - - # different join types - joined = df_list[0].join(df_list[1:], how='outer') - _check_diff_index(df_list, joined, df.index) - - joined = df_list[0].join(df_list[1:]) - _check_diff_index(df_list, joined, df_list[0].index) - - joined = df_list[0].join(df_list[1:], how='inner') - _check_diff_index(df_list, joined, df.index[2:8]) - - self.assertRaises(ValueError, df_list[0].join, df_list[1:], on='a') - - def test_join_many_mixed(self): - df = DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) - df['key'] = ['foo', 'bar'] * 4 - df1 = df.ix[:, ['A', 'B']] - df2 = df.ix[:, ['C', 'D']] - df3 = df.ix[:, ['key']] - - result = df1.join([df2, df3]) - assert_frame_equal(result, df) + def test_append_dtype_coerce(self): + + # GH 4993 + # appending with datetime will incorrectly convert datetime64 + import datetime as dt + from pandas import NaT + + df1 = DataFrame(index=[1, 2], data=[dt.datetime(2013, 1, 1, 0, 0), + dt.datetime(2013, 1, 2, 0, 0)], + columns=['start_time']) + df2 = DataFrame(index=[4, 5], data=[[dt.datetime(2013, 1, 3, 0, 0), + dt.datetime(2013, 1, 3, 6, 10)], + [dt.datetime(2013, 1, 4, 0, 0), + dt.datetime(2013, 1, 4, 7, 10)]], + columns=['start_time', 'end_time']) + + expected = concat([Series([NaT, NaT, dt.datetime(2013, 1, 3, 6, 10), + dt.datetime(2013, 1, 4, 7, 10)], + name='end_time'), + Series([dt.datetime(2013, 1, 1, 0, 0), + dt.datetime(2013, 1, 2, 0, 0), + dt.datetime(2013, 1, 3, 0, 0), + dt.datetime(2013, 1, 4, 0, 0)], + name='start_time')], axis=1) + result = df1.append(df2, ignore_index=True) + assert_frame_equal(result, expected) def test_append_missing_column_proper_upcast(self): df1 = DataFrame({'A': np.array([1, 2, 3, 4], dtype='i8')}) @@ -188,6 +181,9 @@ def test_append_missing_column_proper_upcast(self): self.assertEqual(appended['A'].dtype, 'f8') self.assertEqual(appended['B'].dtype, 'O') + +class TestConcatenate(ConcatenateBase): + def test_concat_copy(self): df = DataFrame(np.random.randn(4, 3)) @@ -524,35 +520,6 @@ def test_with_mixed_tuples(self): # it works concat([df1, df2]) - def test_join_dups(self): - - # joining dups - df = concat([DataFrame(np.random.randn(10, 4), - columns=['A', 'A', 'B', 'B']), - DataFrame(np.random.randint(0, 10, size=20) - .reshape(10, 2), - columns=['A', 'C'])], - axis=1) - - expected = concat([df, df], axis=1) - result = df.join(df, rsuffix='_2') - result.columns = expected.columns - assert_frame_equal(result, expected) - - # GH 4975, invalid join on dups - w = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - x = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - y = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - z = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - - dta = x.merge(y, left_index=True, right_index=True).merge( - z, left_index=True, right_index=True, how="outer") - dta = dta.merge(w, left_index=True, right_index=True) - expected = concat([x, y, z, w], axis=1) - expected.columns = ['x_x', 'y_x', 'x_y', - 'y_y', 'x_x', 'y_x', 'x_y', 'y_y'] - assert_frame_equal(dta, expected) - def test_handle_empty_objects(self): df = DataFrame(np.random.randn(10, 4), columns=list('abcd')) @@ -649,86 +616,40 @@ def test_concat_mixed_objs(self): panel = tm.makePanel() self.assertRaises(ValueError, lambda: concat([panel, s1], axis=1)) - def test_panel_join(self): - panel = tm.makePanel() - tm.add_nans(panel) - - p1 = panel.ix[:2, :10, :3] - p2 = panel.ix[2:, 5:, 2:] - - # left join - result = p1.join(p2) - expected = p1.copy() - expected['ItemC'] = p2['ItemC'] - tm.assert_panel_equal(result, expected) - - # right join - result = p1.join(p2, how='right') - expected = p2.copy() - expected['ItemA'] = p1['ItemA'] - expected['ItemB'] = p1['ItemB'] - expected = expected.reindex(items=['ItemA', 'ItemB', 'ItemC']) - tm.assert_panel_equal(result, expected) - - # inner join - result = p1.join(p2, how='inner') - expected = panel.ix[:, 5:10, 2:3] - tm.assert_panel_equal(result, expected) - - # outer join - result = p1.join(p2, how='outer') - expected = p1.reindex(major=panel.major_axis, - minor=panel.minor_axis) - expected = expected.join(p2.reindex(major=panel.major_axis, - minor=panel.minor_axis)) - tm.assert_panel_equal(result, expected) - - def test_panel_join_overlap(self): - panel = tm.makePanel() - tm.add_nans(panel) - - p1 = panel.ix[['ItemA', 'ItemB', 'ItemC']] - p2 = panel.ix[['ItemB', 'ItemC']] - - # Expected index is - # - # ItemA, ItemB_p1, ItemC_p1, ItemB_p2, ItemC_p2 - joined = p1.join(p2, lsuffix='_p1', rsuffix='_p2') - p1_suf = p1.ix[['ItemB', 'ItemC']].add_suffix('_p1') - p2_suf = p2.ix[['ItemB', 'ItemC']].add_suffix('_p2') - no_overlap = panel.ix[['ItemA']] - expected = no_overlap.join(p1_suf.join(p2_suf)) - tm.assert_panel_equal(joined, expected) - - def test_panel_join_many(self): - tm.K = 10 - panel = tm.makePanel() - tm.K = 4 + def test_empty_dtype_coerce(self): - panels = [panel.ix[:2], panel.ix[2:6], panel.ix[6:]] + # xref to #12411 + # xref to #12045 + # xref to #11594 + # see below - joined = panels[0].join(panels[1:]) - tm.assert_panel_equal(joined, panel) + # 10571 + df1 = DataFrame(data=[[1, None], [2, None]], columns=['a', 'b']) + df2 = DataFrame(data=[[3, None], [4, None]], columns=['a', 'b']) + result = concat([df1, df2]) + expected = df1.dtypes + tm.assert_series_equal(result.dtypes, expected) - panels = [panel.ix[:2, :-5], panel.ix[2:6, 2:], panel.ix[6:, 5:-7]] + def test_dtype_coerceion(self): - data_dict = {} - for p in panels: - data_dict.update(p.iteritems()) + # 12411 + df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), + pd.NaT]}) - joined = panels[0].join(panels[1:], how='inner') - expected = Panel.from_dict(data_dict, intersect=True) - tm.assert_panel_equal(joined, expected) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) - joined = panels[0].join(panels[1:], how='outer') - expected = Panel.from_dict(data_dict, intersect=False) - tm.assert_panel_equal(joined, expected) + # 12045 + import datetime + df = DataFrame({'date': [datetime.datetime(2012, 1, 1), + datetime.datetime(1012, 1, 2)]}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) - # edge cases - self.assertRaises(ValueError, panels[0].join, panels[1:], - how='outer', lsuffix='foo', rsuffix='bar') - self.assertRaises(ValueError, panels[0].join, panels[1:], - how='right') + # 11594 + df = DataFrame({'text': ['some words'] + [None] * 9}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) def test_panel_concat_other_axes(self): panel = tm.makePanel() @@ -1080,6 +1001,239 @@ def test_concat_invalid_first_argument(self): expected = read_csv(StringIO(data)) assert_frame_equal(result, expected) + def test_concat_NaT_series(self): + # GH 11693 + # test for merging NaT series with datetime series. + x = Series(date_range('20151124 08:00', '20151124 09:00', + freq='1h', tz='US/Eastern')) + y = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]') + expected = Series([x[0], x[1], pd.NaT, pd.NaT]) + + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # all NaT with tz + expected = Series(pd.NaT, index=range(4), + dtype='datetime64[ns, US/Eastern]') + result = pd.concat([y, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # without tz + x = pd.Series(pd.date_range('20151124 08:00', + '20151124 09:00', freq='1h')) + y = pd.Series(pd.date_range('20151124 10:00', + '20151124 11:00', freq='1h')) + y[:] = pd.NaT + expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT]) + result = pd.concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # all NaT without tz + x[:] = pd.NaT + expected = pd.Series(pd.NaT, index=range(4), + dtype='datetime64[ns]') + result = pd.concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + def test_concat_tz_frame(self): + df2 = DataFrame(dict(A=pd.Timestamp('20130102', tz='US/Eastern'), + B=pd.Timestamp('20130603', tz='CET')), + index=range(5)) + + # concat + df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1) + assert_frame_equal(df2, df3) + + def test_concat_tz_series(self): + # GH 11755 + # tz and no tz + x = Series(date_range('20151124 08:00', + '20151124 09:00', + freq='1h', tz='UTC')) + y = Series(date_range('2012-01-01', '2012-01-02')) + expected = Series([x[0], x[1], y[0], y[1]], + dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # GH 11887 + # concat tz and object + x = Series(date_range('20151124 08:00', + '20151124 09:00', + freq='1h', tz='UTC')) + y = Series(['a', 'b']) + expected = Series([x[0], x[1], y[0], y[1]], + dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # 12217 + # 12306 fixed I think + + # Concat'ing two UTC times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('UTC') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('UTC') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, UTC]') + + # Concat'ing two London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 2+1 London times + first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 1+2 London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + def test_concat_tz_series_with_datetimelike(self): + # GH 12620 + # tz and timedelta + x = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-02-01', tz='US/Eastern')] + y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + + # tz and period + y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + + def test_concat_tz_series_tzlocal(self): + # GH 13583 + tm._skip_if_no_dateutil() + import dateutil + x = [pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2011-02-01', tz=dateutil.tz.tzlocal())] + y = [pd.Timestamp('2012-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2012-02-01', tz=dateutil.tz.tzlocal())] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y)) + self.assertEqual(result.dtype, 'datetime64[ns, tzlocal()]') + + def test_concat_period_series(self): + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + # different freq + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + # non-period + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(['A', 'B']) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + def test_concat_empty_series(self): + # GH 11082 + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) + tm.assert_frame_equal(res, exp) + + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=0) + # name will be reset + exp = pd.Series([1, 2, 3]) + tm.assert_series_equal(res, exp) + + # empty Series with no name + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name=None) + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, + columns=['x', 0]) + tm.assert_frame_equal(res, exp) + + def test_default_index(self): + # is_series and ignore_index + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series([4, 5, 6], name='y') + res = pd.concat([s1, s2], axis=1, ignore_index=True) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + # use check_index_type=True to check the result have + # RangeIndex (default index) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_series and all inputs have no names + s1 = pd.Series([1, 2, 3]) + s2 = pd.Series([4, 5, 6]) + res = pd.concat([s1, s2], axis=1, ignore_index=False) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + exp.columns = pd.RangeIndex(2) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_dataframe and ignore_index + df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) + df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) + + res = pd.concat([df1, df2], axis=0, ignore_index=True) + exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], + columns=['A', 'B']) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + res = pd.concat([df1, df2], axis=1, ignore_index=True) + exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tools/tests/test_join.py b/pandas/tools/tests/test_join.py new file mode 100644 index 0000000000000..86aee0b4a01c9 --- /dev/null +++ b/pandas/tools/tests/test_join.py @@ -0,0 +1,787 @@ +# pylint: disable=E1103 + +import nose + +from numpy.random import randn +import numpy as np + +import pandas as pd +from pandas.compat import lrange +import pandas.compat as compat +from pandas.tools.merge import merge, concat +from pandas.util.testing import assert_frame_equal +from pandas import DataFrame, MultiIndex, Series + +import pandas.algos as algos +import pandas.util.testing as tm +from pandas.tools.tests.test_merge import get_test_data, N, NGROUPS + + +a_ = np.array + + +class TestJoin(tm.TestCase): + + _multiprocess_can_split_ = True + + def setUp(self): + # aggregate multiple columns + self.df = DataFrame({'key1': get_test_data(), + 'key2': get_test_data(), + 'data1': np.random.randn(N), + 'data2': np.random.randn(N)}) + + # exclude a couple keys for fun + self.df = self.df[self.df['key2'] > 1] + + self.df2 = DataFrame({'key1': get_test_data(n=N // 5), + 'key2': get_test_data(ngroups=NGROUPS // 2, + n=N // 5), + 'value': np.random.randn(N // 5)}) + + index, data = tm.getMixedTypeDict() + self.target = DataFrame(data, index=index) + + # Join on string value + self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']}, + index=data['C']) + + def test_cython_left_outer_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) + max_group = 5 + + ls, rs = algos.left_outer_join(left, right, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, + 6, 6, 7, 7, 8, 8, 9, 10]) + exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, + 4, 5, 4, 5, 4, 5, -1, -1]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_cython_right_outer_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) + max_group = 5 + + rs, ls = algos.left_outer_join(right, left, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + # 0 1 1 1 + exp_li = a_([0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5, + # 2 2 4 + 6, 7, 8, 6, 7, 8, -1]) + exp_ri = a_([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, + 4, 4, 4, 5, 5, 5, 6]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_cython_inner_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.int64) + max_group = 5 + + ls, rs = algos.inner_join(left, right, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, + 6, 6, 7, 7, 8, 8]) + exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, + 4, 5, 4, 5, 4, 5]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_left_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='left') + + joined_both = merge(self.df, self.df2) + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='left') + + def test_right_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='right') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='right') + + joined_both = merge(self.df, self.df2, how='right') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='right') + + def test_full_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='outer') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='outer') + + joined_both = merge(self.df, self.df2, how='outer') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='outer') + + def test_inner_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='inner') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='inner') + + joined_both = merge(self.df, self.df2, how='inner') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='inner') + + def test_handle_overlap(self): + joined = merge(self.df, self.df2, on='key2', + suffixes=['.foo', '.bar']) + + self.assertIn('key1.foo', joined) + self.assertIn('key1.bar', joined) + + def test_handle_overlap_arbitrary_key(self): + joined = merge(self.df, self.df2, + left_on='key2', right_on='key1', + suffixes=['.foo', '.bar']) + self.assertIn('key1.foo', joined) + self.assertIn('key2.bar', joined) + + def test_join_on(self): + target = self.target + source = self.source + + merged = target.join(source, on='C') + self.assert_series_equal(merged['MergedA'], target['A'], + check_names=False) + self.assert_series_equal(merged['MergedD'], target['D'], + check_names=False) + + # join with duplicates (fix regression from DataFrame/Matrix merge) + df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) + joined = df.join(df2, on='key') + expected = DataFrame({'key': ['a', 'a', 'b', 'b', 'c'], + 'value': [0, 0, 1, 1, 2]}) + assert_frame_equal(joined, expected) + + # Test when some are missing + df_a = DataFrame([[1], [2], [3]], index=['a', 'b', 'c'], + columns=['one']) + df_b = DataFrame([['foo'], ['bar']], index=[1, 2], + columns=['two']) + df_c = DataFrame([[1], [2]], index=[1, 2], + columns=['three']) + joined = df_a.join(df_b, on='one') + joined = joined.join(df_c, on='one') + self.assertTrue(np.isnan(joined['two']['c'])) + self.assertTrue(np.isnan(joined['three']['c'])) + + # merge column not p resent + self.assertRaises(KeyError, target.join, source, on='E') + + # overlap + source_copy = source.copy() + source_copy['A'] = 0 + self.assertRaises(ValueError, target.join, source_copy, on='A') + + def test_join_on_fails_with_different_right_index(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}, + index=tm.makeCustomIndex(10, 2)) + merge(df, df2, left_on='a', right_index=True) + + def test_join_on_fails_with_different_left_index(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}, + index=tm.makeCustomIndex(10, 2)) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}) + merge(df, df2, right_on='b', left_index=True) + + def test_join_on_fails_with_different_column_counts(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}, + index=tm.makeCustomIndex(10, 2)) + merge(df, df2, right_on='a', left_on=['a', 'b']) + + def test_join_on_fails_with_wrong_object_type(self): + # GH12081 + wrongly_typed = [Series([0, 1]), 2, 'str', None, np.array([0, 1])] + df = DataFrame({'a': [1, 1]}) + + for obj in wrongly_typed: + with tm.assertRaisesRegexp(ValueError, str(type(obj))): + merge(obj, df, left_on='a', right_on='a') + with tm.assertRaisesRegexp(ValueError, str(type(obj))): + merge(df, obj, left_on='a', right_on='a') + + def test_join_on_pass_vector(self): + expected = self.target.join(self.source, on='C') + del expected['C'] + + join_col = self.target.pop('C') + result = self.target.join(self.source, on=join_col) + assert_frame_equal(result, expected) + + def test_join_with_len0(self): + # nothing to merge + merged = self.target.join(self.source.reindex([]), on='C') + for col in self.source: + self.assertIn(col, merged) + self.assertTrue(merged[col].isnull().all()) + + merged2 = self.target.join(self.source.reindex([]), on='C', + how='inner') + self.assert_index_equal(merged2.columns, merged.columns) + self.assertEqual(len(merged2), 0) + + def test_join_on_inner(self): + df = DataFrame({'key': ['a', 'a', 'd', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1]}, index=['a', 'b']) + + joined = df.join(df2, on='key', how='inner') + + expected = df.join(df2, on='key') + expected = expected[expected['value'].notnull()] + self.assert_series_equal(joined['key'], expected['key'], + check_dtype=False) + self.assert_series_equal(joined['value'], expected['value'], + check_dtype=False) + self.assert_index_equal(joined.index, expected.index) + + def test_join_on_singlekey_list(self): + df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) + + # corner cases + joined = df.join(df2, on=['key']) + expected = df.join(df2, on='key') + + assert_frame_equal(joined, expected) + + def test_join_on_series(self): + result = self.target.join(self.source['MergedA'], on='C') + expected = self.target.join(self.source[['MergedA']], on='C') + assert_frame_equal(result, expected) + + def test_join_on_series_buglet(self): + # GH #638 + df = DataFrame({'a': [1, 1]}) + ds = Series([2], index=[1], name='b') + result = df.join(ds, on='a') + expected = DataFrame({'a': [1, 1], + 'b': [2, 2]}, index=df.index) + tm.assert_frame_equal(result, expected) + + def test_join_index_mixed(self): + df1 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, + index=np.arange(10), + columns=['A', 'B', 'C', 'D']) + self.assertEqual(df1['B'].dtype, np.int64) + self.assertEqual(df1['D'].dtype, np.bool_) + + df2 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, + index=np.arange(0, 10, 2), + columns=['A', 'B', 'C', 'D']) + + # overlap + joined = df1.join(df2, lsuffix='_one', rsuffix='_two') + expected_columns = ['A_one', 'B_one', 'C_one', 'D_one', + 'A_two', 'B_two', 'C_two', 'D_two'] + df1.columns = expected_columns[:4] + df2.columns = expected_columns[4:] + expected = _join_by_hand(df1, df2) + assert_frame_equal(joined, expected) + + # no overlapping blocks + df1 = DataFrame(index=np.arange(10)) + df1['bool'] = True + df1['string'] = 'foo' + + df2 = DataFrame(index=np.arange(5, 15)) + df2['int'] = 1 + df2['float'] = 1. + + for kind in ['inner', 'outer', 'left', 'right']: + + joined = df1.join(df2, how=kind) + expected = _join_by_hand(df1, df2, how=kind) + assert_frame_equal(joined, expected) + + joined = df2.join(df1, how=kind) + expected = _join_by_hand(df2, df1, how=kind) + assert_frame_equal(joined, expected) + + def test_join_empty_bug(self): + # generated an exception in 0.4.3 + x = DataFrame() + x.join(DataFrame([3], index=[0], columns=['A']), how='outer') + + def test_join_unconsolidated(self): + # GH #331 + a = DataFrame(randn(30, 2), columns=['a', 'b']) + c = Series(randn(30)) + a['c'] = c + d = DataFrame(randn(30, 1), columns=['q']) + + # it works! + a.join(d) + d.join(a) + + def test_join_multiindex(self): + index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'], + [1, 2, 3, 1, 2, 3]], + names=['first', 'second']) + + index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'], + [1, 2, 3, 1, 2, 3]], + names=['first', 'second']) + + df1 = DataFrame(data=np.random.randn(6), index=index1, + columns=['var X']) + df2 = DataFrame(data=np.random.randn(6), index=index2, + columns=['var Y']) + + df1 = df1.sortlevel(0) + df2 = df2.sortlevel(0) + + joined = df1.join(df2, how='outer') + ex_index = index1._tuple_index.union(index2._tuple_index) + expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) + expected.index.names = index1.names + assert_frame_equal(joined, expected) + self.assertEqual(joined.index.names, index1.names) + + df1 = df1.sortlevel(1) + df2 = df2.sortlevel(1) + + joined = df1.join(df2, how='outer').sortlevel(0) + ex_index = index1._tuple_index.union(index2._tuple_index) + expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) + expected.index.names = index1.names + + assert_frame_equal(joined, expected) + self.assertEqual(joined.index.names, index1.names) + + def test_join_inner_multiindex(self): + key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux', + 'qux', 'snap'] + key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two', + 'three', 'one'] + + data = np.random.randn(len(key1)) + data = DataFrame({'key1': key1, 'key2': key2, + 'data': data}) + + index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], + ['one', 'two', 'three']], + labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], + [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=['first', 'second']) + to_join = DataFrame(np.random.randn(10, 3), index=index, + columns=['j_one', 'j_two', 'j_three']) + + joined = data.join(to_join, on=['key1', 'key2'], how='inner') + expected = merge(data, to_join.reset_index(), + left_on=['key1', 'key2'], + right_on=['first', 'second'], how='inner', + sort=False) + + expected2 = merge(to_join, data, + right_on=['key1', 'key2'], left_index=True, + how='inner', sort=False) + assert_frame_equal(joined, expected2.reindex_like(joined)) + + expected2 = merge(to_join, data, right_on=['key1', 'key2'], + left_index=True, how='inner', sort=False) + + expected = expected.drop(['first', 'second'], axis=1) + expected.index = joined.index + + self.assertTrue(joined.index.is_monotonic) + assert_frame_equal(joined, expected) + + # _assert_same_contents(expected, expected2.ix[:, expected.columns]) + + def test_join_hierarchical_mixed(self): + # GH 2024 + df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'c']) + new_df = df.groupby(['a']).agg({'b': [np.mean, np.sum]}) + other_df = DataFrame( + [(1, 2, 3), (7, 10, 6)], columns=['a', 'b', 'd']) + other_df.set_index('a', inplace=True) + # GH 9455, 12219 + with tm.assert_produces_warning(UserWarning): + result = merge(new_df, other_df, left_index=True, right_index=True) + self.assertTrue(('b', 'mean') in result) + self.assertTrue('b' in result) + + def test_join_float64_float32(self): + + a = DataFrame(randn(10, 2), columns=['a', 'b'], dtype=np.float64) + b = DataFrame(randn(10, 1), columns=['c'], dtype=np.float32) + joined = a.join(b) + self.assertEqual(joined.dtypes['a'], 'float64') + self.assertEqual(joined.dtypes['b'], 'float64') + self.assertEqual(joined.dtypes['c'], 'float32') + + a = np.random.randint(0, 5, 100).astype('int64') + b = np.random.random(100).astype('float64') + c = np.random.random(100).astype('float32') + df = DataFrame({'a': a, 'b': b, 'c': c}) + xpdf = DataFrame({'a': a, 'b': b, 'c': c}) + s = DataFrame(np.random.random(5).astype('float32'), columns=['md']) + rs = df.merge(s, left_on='a', right_index=True) + self.assertEqual(rs.dtypes['a'], 'int64') + self.assertEqual(rs.dtypes['b'], 'float64') + self.assertEqual(rs.dtypes['c'], 'float32') + self.assertEqual(rs.dtypes['md'], 'float32') + + xp = xpdf.merge(s, left_on='a', right_index=True) + assert_frame_equal(rs, xp) + + def test_join_many_non_unique_index(self): + df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]}) + df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]}) + df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]}) + idf1 = df1.set_index(["a", "b"]) + idf2 = df2.set_index(["a", "b"]) + idf3 = df3.set_index(["a", "b"]) + + result = idf1.join([idf2, idf3], how='outer') + + df_partially_merged = merge(df1, df2, on=['a', 'b'], how='outer') + expected = merge(df_partially_merged, df3, on=['a', 'b'], how='outer') + + result = result.reset_index() + expected = expected[result.columns] + expected['a'] = expected.a.astype('int64') + expected['b'] = expected.b.astype('int64') + assert_frame_equal(result, expected) + + df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]}) + df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]}) + df3 = DataFrame( + {"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]}) + idf1 = df1.set_index(["a", "b"]) + idf2 = df2.set_index(["a", "b"]) + idf3 = df3.set_index(["a", "b"]) + result = idf1.join([idf2, idf3], how='inner') + + df_partially_merged = merge(df1, df2, on=['a', 'b'], how='inner') + expected = merge(df_partially_merged, df3, on=['a', 'b'], how='inner') + + result = result.reset_index() + + assert_frame_equal(result, expected.ix[:, result.columns]) + + # GH 11519 + df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8), + 'D': np.random.randn(8)}) + s = Series(np.repeat(np.arange(8), 2), + index=np.repeat(np.arange(8), 2), name='TEST') + inner = df.join(s, how='inner') + outer = df.join(s, how='outer') + left = df.join(s, how='left') + right = df.join(s, how='right') + assert_frame_equal(inner, outer) + assert_frame_equal(inner, left) + assert_frame_equal(inner, right) + + def test_join_sort(self): + left = DataFrame({'key': ['foo', 'bar', 'baz', 'foo'], + 'value': [1, 2, 3, 4]}) + right = DataFrame({'value2': ['a', 'b', 'c']}, + index=['bar', 'baz', 'foo']) + + joined = left.join(right, on='key', sort=True) + expected = DataFrame({'key': ['bar', 'baz', 'foo', 'foo'], + 'value': [2, 3, 1, 4], + 'value2': ['a', 'b', 'c', 'c']}, + index=[1, 2, 0, 3]) + assert_frame_equal(joined, expected) + + # smoke test + joined = left.join(right, on='key', sort=False) + self.assert_index_equal(joined.index, pd.Index(lrange(4))) + + def test_mixed_type_join_with_suffix(self): + # GH #916 + df = DataFrame(np.random.randn(20, 6), + columns=['a', 'b', 'c', 'd', 'e', 'f']) + df.insert(0, 'id', 0) + df.insert(5, 'dt', 'foo') + + grouped = df.groupby('id') + mn = grouped.mean() + cn = grouped.count() + + # it works! + mn.join(cn, rsuffix='_right') + + def test_join_many(self): + df = DataFrame(np.random.randn(10, 6), columns=list('abcdef')) + df_list = [df[['a', 'b']], df[['c', 'd']], df[['e', 'f']]] + + joined = df_list[0].join(df_list[1:]) + tm.assert_frame_equal(joined, df) + + df_list = [df[['a', 'b']][:-2], + df[['c', 'd']][2:], df[['e', 'f']][1:9]] + + def _check_diff_index(df_list, result, exp_index): + reindexed = [x.reindex(exp_index) for x in df_list] + expected = reindexed[0].join(reindexed[1:]) + tm.assert_frame_equal(result, expected) + + # different join types + joined = df_list[0].join(df_list[1:], how='outer') + _check_diff_index(df_list, joined, df.index) + + joined = df_list[0].join(df_list[1:]) + _check_diff_index(df_list, joined, df_list[0].index) + + joined = df_list[0].join(df_list[1:], how='inner') + _check_diff_index(df_list, joined, df.index[2:8]) + + self.assertRaises(ValueError, df_list[0].join, df_list[1:], on='a') + + def test_join_many_mixed(self): + df = DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) + df['key'] = ['foo', 'bar'] * 4 + df1 = df.ix[:, ['A', 'B']] + df2 = df.ix[:, ['C', 'D']] + df3 = df.ix[:, ['key']] + + result = df1.join([df2, df3]) + assert_frame_equal(result, df) + + def test_join_dups(self): + + # joining dups + df = concat([DataFrame(np.random.randn(10, 4), + columns=['A', 'A', 'B', 'B']), + DataFrame(np.random.randint(0, 10, size=20) + .reshape(10, 2), + columns=['A', 'C'])], + axis=1) + + expected = concat([df, df], axis=1) + result = df.join(df, rsuffix='_2') + result.columns = expected.columns + assert_frame_equal(result, expected) + + # GH 4975, invalid join on dups + w = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + x = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + y = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + z = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + + dta = x.merge(y, left_index=True, right_index=True).merge( + z, left_index=True, right_index=True, how="outer") + dta = dta.merge(w, left_index=True, right_index=True) + expected = concat([x, y, z, w], axis=1) + expected.columns = ['x_x', 'y_x', 'x_y', + 'y_y', 'x_x', 'y_x', 'x_y', 'y_y'] + assert_frame_equal(dta, expected) + + def test_panel_join(self): + panel = tm.makePanel() + tm.add_nans(panel) + + p1 = panel.ix[:2, :10, :3] + p2 = panel.ix[2:, 5:, 2:] + + # left join + result = p1.join(p2) + expected = p1.copy() + expected['ItemC'] = p2['ItemC'] + tm.assert_panel_equal(result, expected) + + # right join + result = p1.join(p2, how='right') + expected = p2.copy() + expected['ItemA'] = p1['ItemA'] + expected['ItemB'] = p1['ItemB'] + expected = expected.reindex(items=['ItemA', 'ItemB', 'ItemC']) + tm.assert_panel_equal(result, expected) + + # inner join + result = p1.join(p2, how='inner') + expected = panel.ix[:, 5:10, 2:3] + tm.assert_panel_equal(result, expected) + + # outer join + result = p1.join(p2, how='outer') + expected = p1.reindex(major=panel.major_axis, + minor=panel.minor_axis) + expected = expected.join(p2.reindex(major=panel.major_axis, + minor=panel.minor_axis)) + tm.assert_panel_equal(result, expected) + + def test_panel_join_overlap(self): + panel = tm.makePanel() + tm.add_nans(panel) + + p1 = panel.ix[['ItemA', 'ItemB', 'ItemC']] + p2 = panel.ix[['ItemB', 'ItemC']] + + # Expected index is + # + # ItemA, ItemB_p1, ItemC_p1, ItemB_p2, ItemC_p2 + joined = p1.join(p2, lsuffix='_p1', rsuffix='_p2') + p1_suf = p1.ix[['ItemB', 'ItemC']].add_suffix('_p1') + p2_suf = p2.ix[['ItemB', 'ItemC']].add_suffix('_p2') + no_overlap = panel.ix[['ItemA']] + expected = no_overlap.join(p1_suf.join(p2_suf)) + tm.assert_panel_equal(joined, expected) + + def test_panel_join_many(self): + tm.K = 10 + panel = tm.makePanel() + tm.K = 4 + + panels = [panel.ix[:2], panel.ix[2:6], panel.ix[6:]] + + joined = panels[0].join(panels[1:]) + tm.assert_panel_equal(joined, panel) + + panels = [panel.ix[:2, :-5], panel.ix[2:6, 2:], panel.ix[6:, 5:-7]] + + data_dict = {} + for p in panels: + data_dict.update(p.iteritems()) + + joined = panels[0].join(panels[1:], how='inner') + expected = pd.Panel.from_dict(data_dict, intersect=True) + tm.assert_panel_equal(joined, expected) + + joined = panels[0].join(panels[1:], how='outer') + expected = pd.Panel.from_dict(data_dict, intersect=False) + tm.assert_panel_equal(joined, expected) + + # edge cases + self.assertRaises(ValueError, panels[0].join, panels[1:], + how='outer', lsuffix='foo', rsuffix='bar') + self.assertRaises(ValueError, panels[0].join, panels[1:], + how='right') + + +def _check_join(left, right, result, join_col, how='left', + lsuffix='_x', rsuffix='_y'): + + # some smoke tests + for c in join_col: + assert(result[c].notnull().all()) + + left_grouped = left.groupby(join_col) + right_grouped = right.groupby(join_col) + + for group_key, group in result.groupby(join_col): + l_joined = _restrict_to_columns(group, left.columns, lsuffix) + r_joined = _restrict_to_columns(group, right.columns, rsuffix) + + try: + lgroup = left_grouped.get_group(group_key) + except KeyError: + if how in ('left', 'inner'): + raise AssertionError('key %s should not have been in the join' + % str(group_key)) + + _assert_all_na(l_joined, left.columns, join_col) + else: + _assert_same_contents(l_joined, lgroup) + + try: + rgroup = right_grouped.get_group(group_key) + except KeyError: + if how in ('right', 'inner'): + raise AssertionError('key %s should not have been in the join' + % str(group_key)) + + _assert_all_na(r_joined, right.columns, join_col) + else: + _assert_same_contents(r_joined, rgroup) + + +def _restrict_to_columns(group, columns, suffix): + found = [c for c in group.columns + if c in columns or c.replace(suffix, '') in columns] + + # filter + group = group.ix[:, found] + + # get rid of suffixes, if any + group = group.rename(columns=lambda x: x.replace(suffix, '')) + + # put in the right order... + group = group.ix[:, columns] + + return group + + +def _assert_same_contents(join_chunk, source): + NA_SENTINEL = -1234567 # drop_duplicates not so NA-friendly... + + jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values + svalues = source.fillna(NA_SENTINEL).drop_duplicates().values + + rows = set(tuple(row) for row in jvalues) + assert(len(rows) == len(source)) + assert(all(tuple(row) in rows for row in svalues)) + + +def _assert_all_na(join_chunk, source_columns, join_col): + for c in source_columns: + if c in join_col: + continue + assert(join_chunk[c].isnull().all()) + + +def _join_by_hand(a, b, how='left'): + join_index = a.index.join(b.index, how=how) + + a_re = a.reindex(join_index) + b_re = b.reindex(join_index) + + result_columns = a.columns.append(b.columns) + + for col, s in compat.iteritems(b_re): + a_re[col] = s + return a_re.reindex(columns=result_columns) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 6c448de741e0c..396b095fabbd6 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -9,23 +9,17 @@ import random import pandas as pd -from pandas.compat import range, lrange, lzip +from pandas.compat import lrange, lzip from pandas.tools.merge import merge, concat, MergeError from pandas.util.testing import (assert_frame_equal, assert_series_equal, slow) -from pandas import (DataFrame, Index, MultiIndex, - Series, date_range, Categorical, - compat) -import pandas.algos as algos +from pandas import DataFrame, Index, MultiIndex, Series, Categorical import pandas.util.testing as tm -a_ = np.array - N = 50 NGROUPS = 8 -JOIN_TYPES = ['inner', 'outer', 'left', 'right'] def get_test_data(ngroups=NGROUPS, n=N): @@ -58,496 +52,16 @@ def setUp(self): n=N // 5), 'value': np.random.randn(N // 5)}) - index, data = tm.getMixedTypeDict() - self.target = DataFrame(data, index=index) - - # Join on string value - self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']}, - index=data['C']) - self.left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'], 'v1': np.random.randn(7)}) self.right = DataFrame({'v2': np.random.randn(4)}, index=['d', 'b', 'c', 'a']) - def test_cython_left_outer_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) - max_group = 5 - - ls, rs = algos.left_outer_join(left, right, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, - 6, 6, 7, 7, 8, 8, 9, 10]) - exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, - 4, 5, 4, 5, 4, 5, -1, -1]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) - self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) - - def test_cython_right_outer_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) - max_group = 5 - - rs, ls = algos.left_outer_join(right, left, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - # 0 1 1 1 - exp_li = a_([0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5, - # 2 2 4 - 6, 7, 8, 6, 7, 8, -1]) - exp_ri = a_([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, - 4, 4, 4, 5, 5, 5, 6]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) - self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) - - def test_cython_inner_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.int64) - max_group = 5 - - ls, rs = algos.inner_join(left, right, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, - 6, 6, 7, 7, 8, 8]) - exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, - 4, 5, 4, 5, 4, 5]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) - self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) - - def test_left_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='left') - - joined_both = merge(self.df, self.df2) - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='left') - - def test_right_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='right') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='right') - - joined_both = merge(self.df, self.df2, how='right') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='right') - - def test_full_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='outer') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='outer') - - joined_both = merge(self.df, self.df2, how='outer') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='outer') - - def test_inner_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='inner') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='inner') - - joined_both = merge(self.df, self.df2, how='inner') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='inner') - - def test_handle_overlap(self): - joined = merge(self.df, self.df2, on='key2', - suffixes=['.foo', '.bar']) - - self.assertIn('key1.foo', joined) - self.assertIn('key1.bar', joined) - - def test_handle_overlap_arbitrary_key(self): - joined = merge(self.df, self.df2, - left_on='key2', right_on='key1', - suffixes=['.foo', '.bar']) - self.assertIn('key1.foo', joined) - self.assertIn('key2.bar', joined) - def test_merge_common(self): joined = merge(self.df, self.df2) exp = merge(self.df, self.df2, on=['key1', 'key2']) tm.assert_frame_equal(joined, exp) - def test_join_on(self): - target = self.target - source = self.source - - merged = target.join(source, on='C') - self.assert_series_equal(merged['MergedA'], target['A'], - check_names=False) - self.assert_series_equal(merged['MergedD'], target['D'], - check_names=False) - - # join with duplicates (fix regression from DataFrame/Matrix merge) - df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) - joined = df.join(df2, on='key') - expected = DataFrame({'key': ['a', 'a', 'b', 'b', 'c'], - 'value': [0, 0, 1, 1, 2]}) - assert_frame_equal(joined, expected) - - # Test when some are missing - df_a = DataFrame([[1], [2], [3]], index=['a', 'b', 'c'], - columns=['one']) - df_b = DataFrame([['foo'], ['bar']], index=[1, 2], - columns=['two']) - df_c = DataFrame([[1], [2]], index=[1, 2], - columns=['three']) - joined = df_a.join(df_b, on='one') - joined = joined.join(df_c, on='one') - self.assertTrue(np.isnan(joined['two']['c'])) - self.assertTrue(np.isnan(joined['three']['c'])) - - # merge column not p resent - self.assertRaises(KeyError, target.join, source, on='E') - - # overlap - source_copy = source.copy() - source_copy['A'] = 0 - self.assertRaises(ValueError, target.join, source_copy, on='A') - - def test_join_on_fails_with_different_right_index(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}, - index=tm.makeCustomIndex(10, 2)) - merge(df, df2, left_on='a', right_index=True) - - def test_join_on_fails_with_different_left_index(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}, - index=tm.makeCustomIndex(10, 2)) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}) - merge(df, df2, right_on='b', left_index=True) - - def test_join_on_fails_with_different_column_counts(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}, - index=tm.makeCustomIndex(10, 2)) - merge(df, df2, right_on='a', left_on=['a', 'b']) - - def test_join_on_fails_with_wrong_object_type(self): - # GH12081 - wrongly_typed = [Series([0, 1]), 2, 'str', None, np.array([0, 1])] - df = DataFrame({'a': [1, 1]}) - - for obj in wrongly_typed: - with tm.assertRaisesRegexp(ValueError, str(type(obj))): - merge(obj, df, left_on='a', right_on='a') - with tm.assertRaisesRegexp(ValueError, str(type(obj))): - merge(df, obj, left_on='a', right_on='a') - - def test_join_on_pass_vector(self): - expected = self.target.join(self.source, on='C') - del expected['C'] - - join_col = self.target.pop('C') - result = self.target.join(self.source, on=join_col) - assert_frame_equal(result, expected) - - def test_join_with_len0(self): - # nothing to merge - merged = self.target.join(self.source.reindex([]), on='C') - for col in self.source: - self.assertIn(col, merged) - self.assertTrue(merged[col].isnull().all()) - - merged2 = self.target.join(self.source.reindex([]), on='C', - how='inner') - self.assert_index_equal(merged2.columns, merged.columns) - self.assertEqual(len(merged2), 0) - - def test_join_on_inner(self): - df = DataFrame({'key': ['a', 'a', 'd', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1]}, index=['a', 'b']) - - joined = df.join(df2, on='key', how='inner') - - expected = df.join(df2, on='key') - expected = expected[expected['value'].notnull()] - self.assert_series_equal(joined['key'], expected['key'], - check_dtype=False) - self.assert_series_equal(joined['value'], expected['value'], - check_dtype=False) - self.assert_index_equal(joined.index, expected.index) - - def test_join_on_singlekey_list(self): - df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) - - # corner cases - joined = df.join(df2, on=['key']) - expected = df.join(df2, on='key') - - assert_frame_equal(joined, expected) - - def test_join_on_series(self): - result = self.target.join(self.source['MergedA'], on='C') - expected = self.target.join(self.source[['MergedA']], on='C') - assert_frame_equal(result, expected) - - def test_join_on_series_buglet(self): - # GH #638 - df = DataFrame({'a': [1, 1]}) - ds = Series([2], index=[1], name='b') - result = df.join(ds, on='a') - expected = DataFrame({'a': [1, 1], - 'b': [2, 2]}, index=df.index) - tm.assert_frame_equal(result, expected) - - def test_join_index_mixed(self): - df1 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, - index=np.arange(10), - columns=['A', 'B', 'C', 'D']) - self.assertEqual(df1['B'].dtype, np.int64) - self.assertEqual(df1['D'].dtype, np.bool_) - - df2 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, - index=np.arange(0, 10, 2), - columns=['A', 'B', 'C', 'D']) - - # overlap - joined = df1.join(df2, lsuffix='_one', rsuffix='_two') - expected_columns = ['A_one', 'B_one', 'C_one', 'D_one', - 'A_two', 'B_two', 'C_two', 'D_two'] - df1.columns = expected_columns[:4] - df2.columns = expected_columns[4:] - expected = _join_by_hand(df1, df2) - assert_frame_equal(joined, expected) - - # no overlapping blocks - df1 = DataFrame(index=np.arange(10)) - df1['bool'] = True - df1['string'] = 'foo' - - df2 = DataFrame(index=np.arange(5, 15)) - df2['int'] = 1 - df2['float'] = 1. - - for kind in JOIN_TYPES: - - joined = df1.join(df2, how=kind) - expected = _join_by_hand(df1, df2, how=kind) - assert_frame_equal(joined, expected) - - joined = df2.join(df1, how=kind) - expected = _join_by_hand(df2, df1, how=kind) - assert_frame_equal(joined, expected) - - def test_join_empty_bug(self): - # generated an exception in 0.4.3 - x = DataFrame() - x.join(DataFrame([3], index=[0], columns=['A']), how='outer') - - def test_join_unconsolidated(self): - # GH #331 - a = DataFrame(randn(30, 2), columns=['a', 'b']) - c = Series(randn(30)) - a['c'] = c - d = DataFrame(randn(30, 1), columns=['q']) - - # it works! - a.join(d) - d.join(a) - - def test_join_multiindex(self): - index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'], - [1, 2, 3, 1, 2, 3]], - names=['first', 'second']) - - index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'], - [1, 2, 3, 1, 2, 3]], - names=['first', 'second']) - - df1 = DataFrame(data=np.random.randn(6), index=index1, - columns=['var X']) - df2 = DataFrame(data=np.random.randn(6), index=index2, - columns=['var Y']) - - df1 = df1.sortlevel(0) - df2 = df2.sortlevel(0) - - joined = df1.join(df2, how='outer') - ex_index = index1._tuple_index.union(index2._tuple_index) - expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) - expected.index.names = index1.names - assert_frame_equal(joined, expected) - self.assertEqual(joined.index.names, index1.names) - - df1 = df1.sortlevel(1) - df2 = df2.sortlevel(1) - - joined = df1.join(df2, how='outer').sortlevel(0) - ex_index = index1._tuple_index.union(index2._tuple_index) - expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) - expected.index.names = index1.names - - assert_frame_equal(joined, expected) - self.assertEqual(joined.index.names, index1.names) - - def test_join_inner_multiindex(self): - key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux', - 'qux', 'snap'] - key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two', - 'three', 'one'] - - data = np.random.randn(len(key1)) - data = DataFrame({'key1': key1, 'key2': key2, - 'data': data}) - - index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], - ['one', 'two', 'three']], - labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], - [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], - names=['first', 'second']) - to_join = DataFrame(np.random.randn(10, 3), index=index, - columns=['j_one', 'j_two', 'j_three']) - - joined = data.join(to_join, on=['key1', 'key2'], how='inner') - expected = merge(data, to_join.reset_index(), - left_on=['key1', 'key2'], - right_on=['first', 'second'], how='inner', - sort=False) - - expected2 = merge(to_join, data, - right_on=['key1', 'key2'], left_index=True, - how='inner', sort=False) - assert_frame_equal(joined, expected2.reindex_like(joined)) - - expected2 = merge(to_join, data, right_on=['key1', 'key2'], - left_index=True, how='inner', sort=False) - - expected = expected.drop(['first', 'second'], axis=1) - expected.index = joined.index - - self.assertTrue(joined.index.is_monotonic) - assert_frame_equal(joined, expected) - - # _assert_same_contents(expected, expected2.ix[:, expected.columns]) - - def test_join_hierarchical_mixed(self): - # GH 2024 - df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'c']) - new_df = df.groupby(['a']).agg({'b': [np.mean, np.sum]}) - other_df = DataFrame( - [(1, 2, 3), (7, 10, 6)], columns=['a', 'b', 'd']) - other_df.set_index('a', inplace=True) - # GH 9455, 12219 - with tm.assert_produces_warning(UserWarning): - result = merge(new_df, other_df, left_index=True, right_index=True) - self.assertTrue(('b', 'mean') in result) - self.assertTrue('b' in result) - - def test_join_float64_float32(self): - - a = DataFrame(randn(10, 2), columns=['a', 'b'], dtype=np.float64) - b = DataFrame(randn(10, 1), columns=['c'], dtype=np.float32) - joined = a.join(b) - self.assertEqual(joined.dtypes['a'], 'float64') - self.assertEqual(joined.dtypes['b'], 'float64') - self.assertEqual(joined.dtypes['c'], 'float32') - - a = np.random.randint(0, 5, 100).astype('int64') - b = np.random.random(100).astype('float64') - c = np.random.random(100).astype('float32') - df = DataFrame({'a': a, 'b': b, 'c': c}) - xpdf = DataFrame({'a': a, 'b': b, 'c': c}) - s = DataFrame(np.random.random(5).astype('float32'), columns=['md']) - rs = df.merge(s, left_on='a', right_index=True) - self.assertEqual(rs.dtypes['a'], 'int64') - self.assertEqual(rs.dtypes['b'], 'float64') - self.assertEqual(rs.dtypes['c'], 'float32') - self.assertEqual(rs.dtypes['md'], 'float32') - - xp = xpdf.merge(s, left_on='a', right_index=True) - assert_frame_equal(rs, xp) - - def test_join_many_non_unique_index(self): - df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]}) - df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]}) - df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]}) - idf1 = df1.set_index(["a", "b"]) - idf2 = df2.set_index(["a", "b"]) - idf3 = df3.set_index(["a", "b"]) - - result = idf1.join([idf2, idf3], how='outer') - - df_partially_merged = merge(df1, df2, on=['a', 'b'], how='outer') - expected = merge(df_partially_merged, df3, on=['a', 'b'], how='outer') - - result = result.reset_index() - expected = expected[result.columns] - expected['a'] = expected.a.astype('int64') - expected['b'] = expected.b.astype('int64') - assert_frame_equal(result, expected) - - df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]}) - df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]}) - df3 = DataFrame( - {"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]}) - idf1 = df1.set_index(["a", "b"]) - idf2 = df2.set_index(["a", "b"]) - idf3 = df3.set_index(["a", "b"]) - result = idf1.join([idf2, idf3], how='inner') - - df_partially_merged = merge(df1, df2, on=['a', 'b'], how='inner') - expected = merge(df_partially_merged, df3, on=['a', 'b'], how='inner') - - result = result.reset_index() - - assert_frame_equal(result, expected.ix[:, result.columns]) - - # GH 11519 - df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo'], - 'B': ['one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three'], - 'C': np.random.randn(8), - 'D': np.random.randn(8)}) - s = Series(np.repeat(np.arange(8), 2), - index=np.repeat(np.arange(8), 2), name='TEST') - inner = df.join(s, how='inner') - outer = df.join(s, how='outer') - left = df.join(s, how='left') - right = df.join(s, how='right') - assert_frame_equal(inner, outer) - assert_frame_equal(inner, left) - assert_frame_equal(inner, right) - def test_merge_index_singlekey_right_vs_left(self): left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'], 'v1': np.random.randn(7)}) @@ -651,23 +165,6 @@ def test_merge_nocopy(self): merged['d'] = 'peekaboo' self.assertTrue((right['d'] == 'peekaboo').all()) - def test_join_sort(self): - left = DataFrame({'key': ['foo', 'bar', 'baz', 'foo'], - 'value': [1, 2, 3, 4]}) - right = DataFrame({'value2': ['a', 'b', 'c']}, - index=['bar', 'baz', 'foo']) - - joined = left.join(right, on='key', sort=True) - expected = DataFrame({'key': ['bar', 'baz', 'foo', 'foo'], - 'value': [2, 3, 1, 4], - 'value2': ['a', 'b', 'c', 'c']}, - index=[1, 2, 0, 3]) - assert_frame_equal(joined, expected) - - # smoke test - joined = left.join(right, on='key', sort=False) - self.assert_index_equal(joined.index, pd.Index(lrange(4))) - def test_intelligently_handle_join_key(self): # #733, be a bit more 1337 about not returning unconsolidated DataFrame @@ -737,20 +234,6 @@ def test_handle_join_key_pass_array(self): merged = merge(left, right, left_index=True, right_on=key, how='outer') self.assert_series_equal(merged['key_0'], Series(key, name='key_0')) - def test_mixed_type_join_with_suffix(self): - # GH #916 - df = DataFrame(np.random.randn(20, 6), - columns=['a', 'b', 'c', 'd', 'e', 'f']) - df.insert(0, 'id', 0) - df.insert(5, 'dt', 'foo') - - grouped = df.groupby('id') - mn = grouped.mean() - cn = grouped.count() - - # it works! - mn.join(cn, rsuffix='_right') - def test_no_overlap_more_informative_error(self): dt = datetime.now() df1 = DataFrame({'x': ['a']}, index=[dt]) @@ -963,68 +446,6 @@ def _constructor(self): tm.assertIsInstance(result, NotADataFrame) - def test_empty_dtype_coerce(self): - - # xref to #12411 - # xref to #12045 - # xref to #11594 - # see below - - # 10571 - df1 = DataFrame(data=[[1, None], [2, None]], columns=['a', 'b']) - df2 = DataFrame(data=[[3, None], [4, None]], columns=['a', 'b']) - result = concat([df1, df2]) - expected = df1.dtypes - assert_series_equal(result.dtypes, expected) - - def test_dtype_coerceion(self): - - # 12411 - df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), - pd.NaT]}) - - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - # 12045 - import datetime - df = DataFrame({'date': [datetime.datetime(2012, 1, 1), - datetime.datetime(1012, 1, 2)]}) - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - # 11594 - df = DataFrame({'text': ['some words'] + [None] * 9}) - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - def test_append_dtype_coerce(self): - - # GH 4993 - # appending with datetime will incorrectly convert datetime64 - import datetime as dt - from pandas import NaT - - df1 = DataFrame(index=[1, 2], data=[dt.datetime(2013, 1, 1, 0, 0), - dt.datetime(2013, 1, 2, 0, 0)], - columns=['start_time']) - df2 = DataFrame(index=[4, 5], data=[[dt.datetime(2013, 1, 3, 0, 0), - dt.datetime(2013, 1, 3, 6, 10)], - [dt.datetime(2013, 1, 4, 0, 0), - dt.datetime(2013, 1, 4, 7, 10)]], - columns=['start_time', 'end_time']) - - expected = concat([Series([NaT, NaT, dt.datetime(2013, 1, 3, 6, 10), - dt.datetime(2013, 1, 4, 7, 10)], - name='end_time'), - Series([dt.datetime(2013, 1, 1, 0, 0), - dt.datetime(2013, 1, 2, 0, 0), - dt.datetime(2013, 1, 3, 0, 0), - dt.datetime(2013, 1, 4, 0, 0)], - name='start_time')], axis=1) - result = df1.append(df2, ignore_index=True) - assert_frame_equal(result, expected) - def test_join_append_timedeltas(self): import datetime as dt @@ -1140,239 +561,6 @@ def test_merge_on_periods(self): self.assertEqual(result['value_x'].dtype, 'object') self.assertEqual(result['value_y'].dtype, 'object') - def test_concat_NaT_series(self): - # GH 11693 - # test for merging NaT series with datetime series. - x = Series(date_range('20151124 08:00', '20151124 09:00', - freq='1h', tz='US/Eastern')) - y = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]') - expected = Series([x[0], x[1], pd.NaT, pd.NaT]) - - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # all NaT with tz - expected = Series(pd.NaT, index=range(4), - dtype='datetime64[ns, US/Eastern]') - result = pd.concat([y, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # without tz - x = pd.Series(pd.date_range('20151124 08:00', - '20151124 09:00', freq='1h')) - y = pd.Series(pd.date_range('20151124 10:00', - '20151124 11:00', freq='1h')) - y[:] = pd.NaT - expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT]) - result = pd.concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # all NaT without tz - x[:] = pd.NaT - expected = pd.Series(pd.NaT, index=range(4), - dtype='datetime64[ns]') - result = pd.concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - def test_concat_tz_frame(self): - df2 = DataFrame(dict(A=pd.Timestamp('20130102', tz='US/Eastern'), - B=pd.Timestamp('20130603', tz='CET')), - index=range(5)) - - # concat - df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1) - assert_frame_equal(df2, df3) - - def test_concat_tz_series(self): - # GH 11755 - # tz and no tz - x = Series(date_range('20151124 08:00', - '20151124 09:00', - freq='1h', tz='UTC')) - y = Series(date_range('2012-01-01', '2012-01-02')) - expected = Series([x[0], x[1], y[0], y[1]], - dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # GH 11887 - # concat tz and object - x = Series(date_range('20151124 08:00', - '20151124 09:00', - freq='1h', tz='UTC')) - y = Series(['a', 'b']) - expected = Series([x[0], x[1], y[0], y[1]], - dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # 12217 - # 12306 fixed I think - - # Concat'ing two UTC times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('UTC') - - second = pd.DataFrame([[datetime(2016, 1, 2)]]) - second[0] = second[0].dt.tz_localize('UTC') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, UTC]') - - # Concat'ing two London times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 2)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - # Concat'ing 2+1 London times - first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 3)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - # Concat'ing 1+2 London times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - def test_concat_tz_series_with_datetimelike(self): - # GH 12620 - # tz and timedelta - x = [pd.Timestamp('2011-01-01', tz='US/Eastern'), - pd.Timestamp('2011-02-01', tz='US/Eastern')] - y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) - - # tz and period - y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) - - def test_concat_tz_series_tzlocal(self): - # GH 13583 - tm._skip_if_no_dateutil() - import dateutil - x = [pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()), - pd.Timestamp('2011-02-01', tz=dateutil.tz.tzlocal())] - y = [pd.Timestamp('2012-01-01', tz=dateutil.tz.tzlocal()), - pd.Timestamp('2012-02-01', tz=dateutil.tz.tzlocal())] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y)) - self.assertEqual(result.dtype, 'datetime64[ns, tzlocal()]') - - def test_concat_period_series(self): - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - # different freq - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - # non-period - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(['A', 'B']) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - def test_concat_empty_series(self): - # GH 11082 - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name='y') - res = pd.concat([s1, s2], axis=1) - exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) - tm.assert_frame_equal(res, exp) - - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name='y') - res = pd.concat([s1, s2], axis=0) - # name will be reset - exp = pd.Series([1, 2, 3]) - tm.assert_series_equal(res, exp) - - # empty Series with no name - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name=None) - res = pd.concat([s1, s2], axis=1) - exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, - columns=['x', 0]) - tm.assert_frame_equal(res, exp) - - def test_default_index(self): - # is_series and ignore_index - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series([4, 5, 6], name='y') - res = pd.concat([s1, s2], axis=1, ignore_index=True) - self.assertIsInstance(res.columns, pd.RangeIndex) - exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) - # use check_index_type=True to check the result have - # RangeIndex (default index) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - # is_series and all inputs have no names - s1 = pd.Series([1, 2, 3]) - s2 = pd.Series([4, 5, 6]) - res = pd.concat([s1, s2], axis=1, ignore_index=False) - self.assertIsInstance(res.columns, pd.RangeIndex) - exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) - exp.columns = pd.RangeIndex(2) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - # is_dataframe and ignore_index - df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) - df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) - - res = pd.concat([df1, df2], axis=0, ignore_index=True) - exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], - columns=['A', 'B']) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - res = pd.concat([df1, df2], axis=1, ignore_index=True) - exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [ @@ -2134,90 +1322,6 @@ def f(): self.assertRaises(NotImplementedError, f) -def _check_join(left, right, result, join_col, how='left', - lsuffix='_x', rsuffix='_y'): - - # some smoke tests - for c in join_col: - assert(result[c].notnull().all()) - - left_grouped = left.groupby(join_col) - right_grouped = right.groupby(join_col) - - for group_key, group in result.groupby(join_col): - l_joined = _restrict_to_columns(group, left.columns, lsuffix) - r_joined = _restrict_to_columns(group, right.columns, rsuffix) - - try: - lgroup = left_grouped.get_group(group_key) - except KeyError: - if how in ('left', 'inner'): - raise AssertionError('key %s should not have been in the join' - % str(group_key)) - - _assert_all_na(l_joined, left.columns, join_col) - else: - _assert_same_contents(l_joined, lgroup) - - try: - rgroup = right_grouped.get_group(group_key) - except KeyError: - if how in ('right', 'inner'): - raise AssertionError('key %s should not have been in the join' - % str(group_key)) - - _assert_all_na(r_joined, right.columns, join_col) - else: - _assert_same_contents(r_joined, rgroup) - - -def _restrict_to_columns(group, columns, suffix): - found = [c for c in group.columns - if c in columns or c.replace(suffix, '') in columns] - - # filter - group = group.ix[:, found] - - # get rid of suffixes, if any - group = group.rename(columns=lambda x: x.replace(suffix, '')) - - # put in the right order... - group = group.ix[:, columns] - - return group - - -def _assert_same_contents(join_chunk, source): - NA_SENTINEL = -1234567 # drop_duplicates not so NA-friendly... - - jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values - svalues = source.fillna(NA_SENTINEL).drop_duplicates().values - - rows = set(tuple(row) for row in jvalues) - assert(len(rows) == len(source)) - assert(all(tuple(row) in rows for row in svalues)) - - -def _assert_all_na(join_chunk, source_columns, join_col): - for c in source_columns: - if c in join_col: - continue - assert(join_chunk[c].isnull().all()) - - -def _join_by_hand(a, b, how='left'): - join_index = a.index.join(b.index, how=how) - - a_re = a.reindex(join_index) - b_re = b.reindex(join_index) - - result_columns = a.columns.append(b.columns) - - for col, s in compat.iteritems(b_re): - a_re[col] = s - return a_re.reindex(columns=result_columns) - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False)
- [x] tests passed - [x] passes `git diff upstream/master | flake8 --diff` `merge`, `join` and `concat` related tests are mixed each other. Cleaned up these tests.
https://api.github.com/repos/pandas-dev/pandas/pulls/13619
2016-07-11T17:00:02Z
2016-07-14T08:15:23Z
2016-07-14T08:15:23Z
2016-07-14T09:17:09Z
DOC: asfreq clarify original NaNs are not filled (GH9963)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 7b271df4085cc..a42d2b5789aa7 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3914,16 +3914,20 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, def asfreq(self, freq, method=None, how=None, normalize=False): """ - Convert all TimeSeries inside to specified frequency using DateOffset - objects. Optionally provide fill method to pad/backfill missing values. + Convert TimeSeries to specified frequency. + + Optionally provide filling method to pad/backfill missing values. Parameters ---------- freq : DateOffset object, or string - method : {'backfill', 'bfill', 'pad', 'ffill', None} - Method to use for filling holes in reindexed Series - pad / ffill: propagate last valid observation forward to next valid - backfill / bfill: use NEXT valid observation to fill method + method : {'backfill'/'bfill', 'pad'/'ffill'}, default None + Method to use for filling holes in reindexed Series (note this + does not fill NaNs that already were present): + + * 'pad' / 'ffill': propagate last valid observation forward to next + valid + * 'backfill' / 'bfill': use NEXT valid observation to fill how : {'start', 'end'}, default end For PeriodIndex only, see PeriodIndex.asfreq normalize : bool, default False
Closes #9963
https://api.github.com/repos/pandas-dev/pandas/pulls/13617
2016-07-11T13:42:11Z
2016-07-12T07:07:41Z
2016-07-12T07:07:41Z
2016-07-12T07:07:41Z
CLN: remove deprecated io.sql uquery and tquery functions
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index cdae0d5c27c7d..61ba29955bb16 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -615,6 +615,9 @@ Removal of prior version deprecations/changes Now legacy time rules raises ``ValueError``. For the list of currently supported offsets, see :ref:`here <timeseries.alias>` +- The ``tquery`` and ``uquery`` functions in the ``pandas.io.sql`` module are removed (:issue:`5950`). + + .. _whatsnew_0190.performance: Performance Improvements diff --git a/pandas/io/sql.py b/pandas/io/sql.py index b9eaa0e4d657b..dfc9e80aa27d1 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -8,7 +8,6 @@ from datetime import datetime, date, time import warnings -import traceback import re import numpy as np @@ -18,7 +17,7 @@ from pandas.types.common import (is_list_like, is_datetime64tz_dtype) -from pandas.compat import (lzip, map, zip, raise_with_traceback, +from pandas.compat import (map, zip, raise_with_traceback, string_types, text_type) from pandas.core.api import DataFrame, Series from pandas.core.base import PandasObject @@ -192,125 +191,6 @@ def execute(sql, con, cur=None, params=None): return pandas_sql.execute(*args) -# ----------------------------------------------------------------------------- -# -- Deprecated tquery and uquery - -def _safe_fetch(cur): - try: - result = cur.fetchall() - if not isinstance(result, list): - result = list(result) - return result - except Exception as e: # pragma: no cover - excName = e.__class__.__name__ - if excName == 'OperationalError': - return [] - - -def tquery(sql, con=None, cur=None, retry=True): - """ - DEPRECATED. Returns list of tuples corresponding to each row in given sql - query. - - If only one column selected, then plain list is returned. - - To obtain the same result in the future, you can use the following: - - >>> execute(sql, con, params).fetchall() - - Parameters - ---------- - sql: string - SQL query to be executed - con: DBAPI2 connection, default: None - cur: deprecated, cursor is obtained from connection, default: None - retry: boolean value to specify whether to retry after failure - default: True - - Returns - ------- - Results Iterable - - """ - warnings.warn( - "tquery is deprecated, and will be removed in future versions. " - "You can use ``execute(...).fetchall()`` instead.", - FutureWarning, stacklevel=2) - - cur = execute(sql, con, cur=cur) - result = _safe_fetch(cur) - - if con is not None: - try: - cur.close() - con.commit() - except Exception as e: - excName = e.__class__.__name__ - if excName == 'OperationalError': # pragma: no cover - print('Failed to commit, may need to restart interpreter') - else: - raise - - traceback.print_exc() - if retry: - return tquery(sql, con=con, retry=False) - - if result and len(result[0]) == 1: - # python 3 compat - result = list(lzip(*result)[0]) - elif result is None: # pragma: no cover - result = [] - - return result - - -def uquery(sql, con=None, cur=None, retry=True, params=None): - """ - DEPRECATED. Does the same thing as tquery, but instead of returning - results, it returns the number of rows affected. Good for update queries. - - To obtain the same result in the future, you can use the following: - - >>> execute(sql, con).rowcount - - Parameters - ---------- - sql: string - SQL query to be executed - con: DBAPI2 connection, default: None - cur: deprecated, cursor is obtained from connection, default: None - retry: boolean value to specify whether to retry after failure - default: True - params: list or tuple, optional, default: None - List of parameters to pass to execute method. - - Returns - ------- - Number of affected rows - - """ - warnings.warn( - "uquery is deprecated, and will be removed in future versions. " - "You can use ``execute(...).rowcount`` instead.", - FutureWarning, stacklevel=2) - - cur = execute(sql, con, cur=cur, params=params) - - result = cur.rowcount - try: - con.commit() - except Exception as e: - excName = e.__class__.__name__ - if excName != 'OperationalError': - raise - - traceback.print_exc() - if retry: - print('Looks like your connection failed, reconnecting...') - return uquery(sql, con, retry=False) - return result - - # ----------------------------------------------------------------------------- # -- Read and write to DataFrames diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 41be39f9abaa6..f4001420a77b6 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -1070,17 +1070,6 @@ def test_get_schema2(self): create_sql = sql.get_schema(self.test_frame1, 'test') self.assertTrue('CREATE' in create_sql) - def test_tquery(self): - with tm.assert_produces_warning(FutureWarning): - iris_results = sql.tquery("SELECT * FROM iris", con=self.conn) - row = iris_results[0] - tm.equalContents(row, [5.1, 3.5, 1.4, 0.2, 'Iris-setosa']) - - def test_uquery(self): - with tm.assert_produces_warning(FutureWarning): - rows = sql.uquery("SELECT * FROM iris LIMIT 1", con=self.conn) - self.assertEqual(rows, -1) - def _get_sqlite_column_type(self, schema, column): for col in schema.split('\n'): @@ -2091,6 +2080,15 @@ def format_query(sql, *args): return sql % tuple(processed_args) +def tquery(query, con=None, cur=None): + """Replace removed sql.tquery function""" + res = sql.execute(query, con=con, cur=cur).fetchall() + if res is None: + return None + else: + return list(res) + + def _skip_if_no_pymysql(): try: import pymysql # noqa @@ -2120,7 +2118,7 @@ def test_write_row_by_row(self): ins = "INSERT INTO test VALUES (%s, %s, %s, %s)" for idx, row in frame.iterrows(): fmt_sql = format_query(ins, *row) - sql.tquery(fmt_sql, cur=cur) + tquery(fmt_sql, cur=cur) self.conn.commit() @@ -2200,7 +2198,7 @@ def test_execute_closed_connection(self): self.conn.close() try: sys.stdout = StringIO() - self.assertRaises(Exception, sql.tquery, "select * from test", + self.assertRaises(Exception, tquery, "select * from test", con=self.conn) finally: sys.stdout = sys.__stdout__ @@ -2232,42 +2230,6 @@ def _check_roundtrip(self, frame): expected.index.name = 'Idx' tm.assert_frame_equal(expected, result) - def test_tquery(self): - frame = tm.makeTimeDataFrame() - sql.to_sql(frame, name='test_table', con=self.conn, index=False) - result = sql.tquery("select A from test_table", self.conn) - expected = Series(frame.A.values, frame.index) # not to have name - result = Series(result, frame.index) - tm.assert_series_equal(result, expected) - - try: - sys.stdout = StringIO() - self.assertRaises(sql.DatabaseError, sql.tquery, - 'select * from blah', con=self.conn) - - self.assertRaises(sql.DatabaseError, sql.tquery, - 'select * from blah', con=self.conn, retry=True) - finally: - sys.stdout = sys.__stdout__ - - def test_uquery(self): - frame = tm.makeTimeDataFrame() - sql.to_sql(frame, name='test_table', con=self.conn, index=False) - stmt = 'INSERT INTO test_table VALUES(2.314, -123.1, 1.234, 2.3)' - self.assertEqual(sql.uquery(stmt, con=self.conn), 1) - - try: - sys.stdout = StringIO() - - self.assertRaises(sql.DatabaseError, sql.tquery, - 'insert into blah values (1)', con=self.conn) - - self.assertRaises(sql.DatabaseError, sql.tquery, - 'insert into blah values (1)', con=self.conn, - retry=True) - finally: - sys.stdout = sys.__stdout__ - def test_keyword_as_column_names(self): df = DataFrame({'From': np.ones(5)}) sql.to_sql(df, con=self.conn, name='testkeywords', index=False) @@ -2324,22 +2286,22 @@ def clean_up(test_table_to_drop): # test if_exists='replace' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, if_exists='replace', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, if_exists='replace', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) # test if_exists='append' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, if_exists='fail', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, if_exists='append', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) @@ -2445,7 +2407,7 @@ def test_write_row_by_row(self): ins = "INSERT INTO test VALUES (%s, %s, %s, %s)" for idx, row in frame.iterrows(): fmt_sql = format_query(ins, *row) - sql.tquery(fmt_sql, cur=cur) + tquery(fmt_sql, cur=cur) self.conn.commit() @@ -2554,7 +2516,7 @@ def test_execute_closed_connection(self): self.conn.close() try: sys.stdout = StringIO() - self.assertRaises(Exception, sql.tquery, "select * from test", + self.assertRaises(Exception, tquery, "select * from test", con=self.conn) finally: sys.stdout = sys.__stdout__ @@ -2603,58 +2565,6 @@ def _check_roundtrip(self, frame): expected.index.names = result.index.names tm.assert_frame_equal(expected, result) - def test_tquery(self): - try: - import pymysql # noqa - except ImportError: - raise nose.SkipTest("no pymysql") - frame = tm.makeTimeDataFrame() - drop_sql = "DROP TABLE IF EXISTS test_table" - cur = self.conn.cursor() - cur.execute(drop_sql) - sql.to_sql(frame, name='test_table', - con=self.conn, index=False) - result = sql.tquery("select A from test_table", self.conn) - expected = Series(frame.A.values, frame.index) # not to have name - result = Series(result, frame.index) - tm.assert_series_equal(result, expected) - - try: - sys.stdout = StringIO() - self.assertRaises(sql.DatabaseError, sql.tquery, - 'select * from blah', con=self.conn) - - self.assertRaises(sql.DatabaseError, sql.tquery, - 'select * from blah', con=self.conn, retry=True) - finally: - sys.stdout = sys.__stdout__ - - def test_uquery(self): - try: - import pymysql # noqa - except ImportError: - raise nose.SkipTest("no pymysql") - frame = tm.makeTimeDataFrame() - drop_sql = "DROP TABLE IF EXISTS test_table" - cur = self.conn.cursor() - cur.execute(drop_sql) - sql.to_sql(frame, name='test_table', - con=self.conn, index=False) - stmt = 'INSERT INTO test_table VALUES(2.314, -123.1, 1.234, 2.3)' - self.assertEqual(sql.uquery(stmt, con=self.conn), 1) - - try: - sys.stdout = StringIO() - - self.assertRaises(sql.DatabaseError, sql.tquery, - 'insert into blah values (1)', con=self.conn) - - self.assertRaises(sql.DatabaseError, sql.tquery, - 'insert into blah values (1)', con=self.conn, - retry=True) - finally: - sys.stdout = sys.__stdout__ - def test_keyword_as_column_names(self): _skip_if_no_pymysql() df = DataFrame({'From': np.ones(5)}) @@ -2698,22 +2608,22 @@ def clean_up(test_table_to_drop): # test if_exists='replace' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, if_exists='replace', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, if_exists='replace', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) # test if_exists='append' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, if_exists='fail', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, if_exists='append', index=False) - self.assertEqual(sql.tquery(sql_select, con=self.conn), + self.assertEqual(tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name)
Were already deprecated since 0.14
https://api.github.com/repos/pandas-dev/pandas/pulls/13616
2016-07-11T12:55:20Z
2016-07-21T11:06:59Z
2016-07-21T11:06:59Z
2016-07-21T11:07:00Z
ENH: Allow to_sql to recognize single sql type (GH11886)
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index bafb351b2f678..646e8822ed46f 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -310,6 +310,7 @@ Other enhancements - ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) - A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) +- ``DataFrame.to_sql `` now allows a single value as the SQL type for all columns (:issue:`11886`). - ``Series.append`` now supports the ``ignore_index`` option (:issue:`13677`) - ``.to_stata()`` and ``StataWriter`` can now write variable labels to Stata dta files using a dictionary to make column names to labels (:issue:`13535`, :issue:`13536`) - ``.to_stata()`` and ``StataWriter`` will automatically convert ``datetime64[ns]`` columns to Stata format ``%tc``, rather than raising a ``ValueError`` (:issue:`12259`) @@ -322,7 +323,6 @@ Other enhancements index=['row1', 'row2']) df.sort_values(by='row2', axis=1) - .. _whatsnew_0190.api: API changes diff --git a/pandas/io/sql.py b/pandas/io/sql.py index dfc9e80aa27d1..49f277f6ba7bc 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -14,7 +14,7 @@ import pandas.lib as lib from pandas.types.missing import isnull from pandas.types.dtypes import DatetimeTZDtype -from pandas.types.common import (is_list_like, +from pandas.types.common import (is_list_like, is_dict_like, is_datetime64tz_dtype) from pandas.compat import (map, zip, raise_with_traceback, @@ -448,9 +448,10 @@ def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', chunksize : int, default None If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once. - dtype : dict of column name to SQL type, default None + dtype : single SQLtype or dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection. + If all columns are of the same type, one single value can be used. """ if if_exists not in ('fail', 'replace', 'append'): @@ -1121,11 +1122,15 @@ def to_sql(self, frame, name, if_exists='fail', index=True, chunksize : int, default None If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once. - dtype : dict of column name to SQL type, default None + dtype : single type or dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should - be a SQLAlchemy type. + be a SQLAlchemy type. If all columns are of the same type, one + single value can be used. """ + if dtype and not is_dict_like(dtype): + dtype = {col_name: dtype for col_name in frame} + if dtype is not None: from sqlalchemy.types import to_instance, TypeEngine for col, my_type in dtype.items(): @@ -1473,11 +1478,15 @@ def to_sql(self, frame, name, if_exists='fail', index=True, chunksize : int, default None If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once. - dtype : dict of column name to SQL type, default None + dtype : single type or dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should - be a string. + be a string. If all columns are of the same type, one single value + can be used. """ + if dtype and not is_dict_like(dtype): + dtype = {col_name: dtype for col_name in frame} + if dtype is not None: for col, my_type in dtype.items(): if not isinstance(my_type, str): diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index f4001420a77b6..21c3ea416e091 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -1537,6 +1537,15 @@ def test_dtype(self): self.assertTrue(isinstance(sqltype, sqlalchemy.String)) self.assertEqual(sqltype.length, 10) + # single dtype + df.to_sql('single_dtype_test', self.conn, dtype=sqlalchemy.TEXT) + meta = sqlalchemy.schema.MetaData(bind=self.conn) + meta.reflect() + sqltypea = meta.tables['single_dtype_test'].columns['A'].type + sqltypeb = meta.tables['single_dtype_test'].columns['B'].type + self.assertTrue(isinstance(sqltypea, sqlalchemy.TEXT)) + self.assertTrue(isinstance(sqltypeb, sqlalchemy.TEXT)) + def test_notnull_dtype(self): cols = {'Bool': Series([True, None]), 'Date': Series([datetime(2012, 5, 1), None]), @@ -2006,6 +2015,13 @@ def test_dtype(self): self.assertRaises(ValueError, df.to_sql, 'error', self.conn, dtype={'B': bool}) + # single dtype + df.to_sql('single_dtype_test', self.conn, dtype='STRING') + self.assertEqual( + self._get_sqlite_column_type('single_dtype_test', 'A'), 'STRING') + self.assertEqual( + self._get_sqlite_column_type('single_dtype_test', 'B'), 'STRING') + def test_notnull_dtype(self): if self.flavor == 'mysql': raise nose.SkipTest('Not applicable to MySQL legacy')
Rebase of PR https://github.com/pydata/pandas/pull/13252 Closes https://github.com/pydata/pandas/issues/11886
https://api.github.com/repos/pandas-dev/pandas/pulls/13614
2016-07-11T10:18:00Z
2016-07-23T19:28:48Z
2016-07-23T19:28:48Z
2016-07-23T19:28:48Z
CLN: Removed levels attribute from Categorical
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 688f3b7ff6ada..7858ab038a1b4 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -467,6 +467,7 @@ Removal of prior version deprecations/changes - ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`) - ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) +- ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) .. _whatsnew_0190.performance: diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 1d1a9f990e61a..a26cc5125db78 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -228,8 +228,8 @@ class Categorical(PandasObject): __array_priority__ = 1000 _typ = 'categorical' - def __init__(self, values, categories=None, ordered=False, name=None, - fastpath=False, levels=None): + def __init__(self, values, categories=None, ordered=False, + name=None, fastpath=False): if fastpath: # fast path @@ -245,17 +245,6 @@ def __init__(self, values, categories=None, ordered=False, name=None, "name=\"something\")'") warn(msg, UserWarning, stacklevel=2) - # TODO: Remove after deprecation period in 2017/ after 0.18 - if levels is not None: - warn("Creating a 'Categorical' with 'levels' is deprecated, use " - "'categories' instead", FutureWarning, stacklevel=2) - if categories is None: - categories = levels - else: - raise ValueError("Cannot pass in both 'categories' and " - "(deprecated) 'levels', use only " - "'categories'", stacklevel=2) - # sanitize input if is_categorical_dtype(values): @@ -580,21 +569,6 @@ def _get_categories(self): categories = property(fget=_get_categories, fset=_set_categories, doc=_categories_doc) - def _set_levels(self, levels): - """ set new levels (deprecated, use "categories") """ - warn("Assigning to 'levels' is deprecated, use 'categories'", - FutureWarning, stacklevel=2) - self.categories = levels - - def _get_levels(self): - """ Gets the levels (deprecated, use "categories") """ - warn("Accessing 'levels' is deprecated, use 'categories'", - FutureWarning, stacklevel=2) - return self.categories - - # TODO: Remove after deprecation period in 2017/ after 0.18 - levels = property(fget=_get_levels, fset=_set_levels) - _ordered = None def _set_ordered(self, value): diff --git a/pandas/tests/data/categorical_0_14_1.pickle b/pandas/io/tests/data/categorical_0_14_1.pickle similarity index 100% rename from pandas/tests/data/categorical_0_14_1.pickle rename to pandas/io/tests/data/categorical_0_14_1.pickle diff --git a/pandas/tests/data/categorical_0_15_2.pickle b/pandas/io/tests/data/categorical_0_15_2.pickle similarity index 100% rename from pandas/tests/data/categorical_0_15_2.pickle rename to pandas/io/tests/data/categorical_0_15_2.pickle diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index 55c14fee9e3ed..6019144d59698 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -231,6 +231,44 @@ def python_unpickler(path): result = python_unpickler(path) self.compare_element(result, expected, typ) + def test_pickle_v0_14_1(self): + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_14_1.pickle') + # This code was executed once on v0.14.1 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + + def test_pickle_v0_15_2(self): + # ordered -> _ordered + # GH 9347 + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_15_2.pickle') + # This code was executed once on v0.15.2 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index dd39861ac3114..1edd9443fe356 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1559,18 +1559,6 @@ def test_deprecated_labels(self): res = cat.labels self.assert_numpy_array_equal(res, exp) - def test_deprecated_levels(self): - # TODO: levels is deprecated and should be removed in 0.18 or 2017, - # whatever is earlier - cat = pd.Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) - exp = cat.categories - with tm.assert_produces_warning(FutureWarning): - res = cat.levels - self.assert_index_equal(res, exp) - with tm.assert_produces_warning(FutureWarning): - res = pd.Categorical([1, 2, 3, np.nan], levels=[1, 2, 3]) - self.assert_index_equal(res.categories, exp) - def test_removed_names_produces_warning(self): # 10482 @@ -4431,44 +4419,6 @@ def test_dt_accessor_api_for_categorical(self): invalid.dt self.assertFalse(hasattr(invalid, 'str')) - def test_pickle_v0_14_1(self): - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_14_1.pickle') - # This code was executed once on v0.14.1 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - self.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - - def test_pickle_v0_15_2(self): - # ordered -> _ordered - # GH 9347 - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_15_2.pickle') - # This code was executed once on v0.15.2 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - self.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - def test_concat_categorical(self): # See GH 10177 df1 = pd.DataFrame( diff --git a/setup.py b/setup.py index 650357588570a..c77ca4d9e60fe 100755 --- a/setup.py +++ b/setup.py @@ -589,6 +589,7 @@ def pxd(name): 'tests/data/legacy_msgpack/*/*.msgpack', 'tests/data/*.csv*', 'tests/data/*.dta', + 'tests/data/*.pickle', 'tests/data/*.txt', 'tests/data/*.xls', 'tests/data/*.xlsx', @@ -605,8 +606,7 @@ def pxd(name): 'tests/data/html_encoding/*.html', 'tests/json/data/*.json'], 'pandas.tools': ['tests/data/*.csv'], - 'pandas.tests': ['data/*.pickle', - 'data/*.csv'], + 'pandas.tests': ['data/*.csv'], 'pandas.tests.formats': ['data/*.csv'], 'pandas.tests.indexes': ['data/*.pickle'], 'pandas.tseries.tests': ['data/*.pickle',
Deprecated back in `0.15.0` and therefore long overdue. Closes #8376.
https://api.github.com/repos/pandas-dev/pandas/pulls/13612
2016-07-11T07:32:53Z
2016-07-15T10:28:35Z
null
2016-07-15T15:51:02Z
CLN: Removed the flavor parameter in DataFrame.to_sql
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index efa6e5575fa79..57b0d8895f67b 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -524,6 +524,7 @@ Deprecations - ``Categorical.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`) - ``Series.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`) +- ``DataFrame.to_sql()`` has deprecated the ``flavor`` parameter, as it is superfluous when SQLAlchemy is not installed (:issue:`13611`) - ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`) - ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`) - ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) @@ -541,6 +542,7 @@ Removal of prior version deprecations/changes - ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) - ``pd.Categorical`` has dropped setting of the ``ordered`` attribute directly in favor of the ``set_ordered`` method (:issue:`13671`) - ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) +- ``DataFrame.to_sql()`` has dropped the ``mysql`` option for the ``flavor`` parameter (:issue:`13611`) - Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (:issue:`13590`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 6c1676fbdd7f4..e59bec2dbd7e0 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1144,7 +1144,7 @@ def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs): return packers.to_msgpack(path_or_buf, self, encoding=encoding, **kwargs) - def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', + def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None): """ Write records stored in a DataFrame to a SQL database. @@ -1155,12 +1155,11 @@ def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail', Name of SQL table con : SQLAlchemy engine or DBAPI2 connection (legacy mode) Using SQLAlchemy makes it possible to use any DB supported by that - library. - If a DBAPI2 object, only sqlite3 is supported. - flavor : {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy engine. - 'mysql' is deprecated and will be removed in future versions, but - it will be further supported through SQLAlchemy engines. + library. If a DBAPI2 object, only sqlite3 is supported. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version, + as 'sqlite' is the only supported option if SQLAlchemy is not + installed. schema : string, default None Specify the schema (if database flavor supports this). If None, use default schema. diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 8485a3f13f047..b9eaa0e4d657b 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -41,6 +41,24 @@ class DatabaseError(IOError): _SQLALCHEMY_INSTALLED = None +def _validate_flavor_parameter(flavor): + """ + Checks whether a database 'flavor' was specified. + If not None, produces FutureWarning if 'sqlite' and + raises a ValueError if anything else. + """ + if flavor is not None: + if flavor == 'sqlite': + warnings.warn("the 'flavor' parameter is deprecated " + "and will be removed in a future version, " + "as 'sqlite' is the only supported option " + "when SQLAlchemy is not installed.", + FutureWarning, stacklevel=2) + else: + raise ValueError("database flavor {flavor} is not " + "supported".format(flavor=flavor)) + + def _is_sqlalchemy_connectable(con): global _SQLALCHEMY_INSTALLED if _SQLALCHEMY_INSTALLED is None: @@ -517,7 +535,7 @@ def read_sql(sql, con, index_col=None, coerce_float=True, params=None, chunksize=chunksize) -def to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', +def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None): """ Write records stored in a DataFrame to a SQL database. @@ -532,10 +550,8 @@ def to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. - flavor : {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy connectable. - 'mysql' is deprecated and will be removed in future versions, but it - will be further supported through SQLAlchemy connectables. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version schema : string, default None Name of SQL schema in database to write to (if database flavor supports this). If None, use default schema (default). @@ -573,7 +589,7 @@ def to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', chunksize=chunksize, dtype=dtype) -def has_table(table_name, con, flavor='sqlite', schema=None): +def has_table(table_name, con, flavor=None, schema=None): """ Check if DataBase has named table. @@ -585,10 +601,8 @@ def has_table(table_name, con, flavor='sqlite', schema=None): Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. - flavor: {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy connectable. - 'mysql' is deprecated and will be removed in future versions, but it - will be further supported through SQLAlchemy connectables. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version schema : string, default None Name of SQL schema in database to write to (if database flavor supports this). If None, use default schema (default). @@ -603,12 +617,6 @@ def has_table(table_name, con, flavor='sqlite', schema=None): table_exists = has_table -_MYSQL_WARNING = ("The 'mysql' flavor with DBAPI connection is deprecated " - "and will be removed in future versions. " - "MySQL will be further supported with SQLAlchemy " - "connectables.") - - def _engine_builder(con): """ Returns a SQLAlchemy engine from a URI (if con is a string) @@ -632,15 +640,15 @@ def pandasSQL_builder(con, flavor=None, schema=None, meta=None, Convenience function to return the correct PandasSQL subclass based on the provided parameters """ + _validate_flavor_parameter(flavor) + # When support for DBAPI connections is removed, # is_cursor should not be necessary. con = _engine_builder(con) if _is_sqlalchemy_connectable(con): return SQLDatabase(con, schema=schema, meta=meta) else: - if flavor == 'mysql': - warnings.warn(_MYSQL_WARNING, FutureWarning, stacklevel=3) - return SQLiteDatabase(con, flavor, is_cursor=is_cursor) + return SQLiteDatabase(con, is_cursor=is_cursor) class SQLTable(PandasObject): @@ -1035,11 +1043,11 @@ class PandasSQL(PandasObject): def read_sql(self, *args, **kwargs): raise ValueError("PandasSQL must be created with an SQLAlchemy " - "connectable or connection+sql flavor") + "connectable or sqlite connection") def to_sql(self, *args, **kwargs): raise ValueError("PandasSQL must be created with an SQLAlchemy " - "connectable or connection+sql flavor") + "connectable or sqlite connection") class SQLDatabase(PandasSQL): @@ -1308,38 +1316,16 @@ def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): # ---- SQL without SQLAlchemy --- -# Flavour specific sql strings and handler class for access to DBs without -# SQLAlchemy installed -# SQL type convertions for each DB +# sqlite-specific sql strings and handler class +# dictionary used for readability purposes _SQL_TYPES = { - 'string': { - 'mysql': 'VARCHAR (63)', - 'sqlite': 'TEXT', - }, - 'floating': { - 'mysql': 'DOUBLE', - 'sqlite': 'REAL', - }, - 'integer': { - 'mysql': 'BIGINT', - 'sqlite': 'INTEGER', - }, - 'datetime': { - 'mysql': 'DATETIME', - 'sqlite': 'TIMESTAMP', - }, - 'date': { - 'mysql': 'DATE', - 'sqlite': 'DATE', - }, - 'time': { - 'mysql': 'TIME', - 'sqlite': 'TIME', - }, - 'boolean': { - 'mysql': 'BOOLEAN', - 'sqlite': 'INTEGER', - } + 'string': 'TEXT', + 'floating': 'REAL', + 'integer': 'INTEGER', + 'datetime': 'TIMESTAMP', + 'date': 'DATE', + 'time': 'TIME', + 'boolean': 'INTEGER', } @@ -1351,22 +1337,6 @@ def _get_unicode_name(name): return uname -def _get_valid_mysql_name(name): - # Filter for unquoted identifiers - # See http://dev.mysql.com/doc/refman/5.0/en/identifiers.html - uname = _get_unicode_name(name) - if not len(uname): - raise ValueError("Empty table or column name specified") - - basere = r'[0-9,a-z,A-Z$_]' - for c in uname: - if not re.match(basere, c): - if not (0x80 < ord(c) < 0xFFFF): - raise ValueError("Invalid MySQL identifier '%s'" % uname) - - return '`' + uname + '`' - - def _get_valid_sqlite_name(name): # See http://stackoverflow.com/questions/6514274/how-do-you-escape-strings\ # -for-sqlite-table-column-names-in-python @@ -1385,19 +1355,6 @@ def _get_valid_sqlite_name(name): return '"' + uname.replace('"', '""') + '"' -# SQL enquote and wildcard symbols -_SQL_WILDCARD = { - 'mysql': '%s', - 'sqlite': '?' -} - -# Validate and return escaped identifier -_SQL_GET_IDENTIFIER = { - 'mysql': _get_valid_mysql_name, - 'sqlite': _get_valid_sqlite_name, -} - - _SAFE_NAMES_WARNING = ("The spaces in these column names will not be changed. " "In pandas versions < 0.14, spaces were converted to " "underscores.") @@ -1428,9 +1385,8 @@ def _execute_create(self): def insert_statement(self): names = list(map(text_type, self.frame.columns)) - flv = self.pd_sql.flavor - wld = _SQL_WILDCARD[flv] # wildcard char - escape = _SQL_GET_IDENTIFIER[flv] + wld = '?' # wildcard char + escape = _get_valid_sqlite_name if self.index is not None: [names.insert(0, idx) for idx in self.index[::-1]] @@ -1460,8 +1416,7 @@ def _create_table_setup(self): if any(map(pat.search, column_names)): warnings.warn(_SAFE_NAMES_WARNING, stacklevel=6) - flv = self.pd_sql.flavor - escape = _SQL_GET_IDENTIFIER[flv] + escape = _get_valid_sqlite_name create_tbl_stmts = [escape(cname) + ' ' + ctype for cname, ctype, _ in column_names_and_types] @@ -1514,7 +1469,7 @@ def _sql_type_name(self, col): if col_type not in _SQL_TYPES: col_type = "string" - return _SQL_TYPES[col_type][self.pd_sql.flavor] + return _SQL_TYPES[col_type] class SQLiteDatabase(PandasSQL): @@ -1522,25 +1477,17 @@ class SQLiteDatabase(PandasSQL): Version of SQLDatabase to support sqlite connections (fallback without sqlalchemy). This should only be used internally. - For now still supports `flavor` argument to deal with 'mysql' database - for backwards compatibility, but this will be removed in future versions. - Parameters ---------- con : sqlite connection object """ - def __init__(self, con, flavor, is_cursor=False): + def __init__(self, con, flavor=None, is_cursor=False): + _validate_flavor_parameter(flavor) + self.is_cursor = is_cursor self.con = con - if flavor is None: - flavor = 'sqlite' - if flavor not in ['sqlite', 'mysql']: - raise NotImplementedError("flavors other than SQLite and MySQL " - "are not supported") - else: - self.flavor = flavor @contextmanager def run_transaction(self): @@ -1665,15 +1612,12 @@ def to_sql(self, frame, name, if_exists='fail', index=True, def has_table(self, name, schema=None): # TODO(wesm): unused? - # escape = _SQL_GET_IDENTIFIER[self.flavor] + # escape = _get_valid_sqlite_name # esc_name = escape(name) - wld = _SQL_WILDCARD[self.flavor] - flavor_map = { - 'sqlite': ("SELECT name FROM sqlite_master " - "WHERE type='table' AND name=%s;") % wld, - 'mysql': "SHOW TABLES LIKE %s" % wld} - query = flavor_map.get(self.flavor) + wld = '?' + query = ("SELECT name FROM sqlite_master " + "WHERE type='table' AND name=%s;") % wld return len(self.execute(query, [name, ]).fetchall()) > 0 @@ -1681,8 +1625,7 @@ def get_table(self, table_name, schema=None): return None # not supported in fallback mode def drop_table(self, name, schema=None): - escape = _SQL_GET_IDENTIFIER[self.flavor] - drop_sql = "DROP TABLE %s" % escape(name) + drop_sql = "DROP TABLE %s" % _get_valid_sqlite_name(name) self.execute(drop_sql) def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): @@ -1691,7 +1634,7 @@ def _create_sql_schema(self, frame, table_name, keys=None, dtype=None): return str(table.sql_schema()) -def get_schema(frame, name, flavor='sqlite', keys=None, con=None, dtype=None): +def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None): """ Get the SQL db table schema for the given frame. @@ -1700,16 +1643,14 @@ def get_schema(frame, name, flavor='sqlite', keys=None, con=None, dtype=None): frame : DataFrame name : string name of SQL table - flavor : {'sqlite', 'mysql'}, default 'sqlite' - The flavor of SQL to use. Ignored when using SQLAlchemy connectable. - 'mysql' is deprecated and will be removed in future versions, but it - will be further supported through SQLAlchemy engines. keys : string or sequence, default: None columns to use a primary key con: an open SQL database connection object or a SQLAlchemy connectable Using SQLAlchemy makes it possible to use any DB supported by that library, default: None If a DBAPI2 object, only sqlite3 is supported. + flavor : 'sqlite', default None + DEPRECATED: this parameter will be removed in a future version dtype : dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection. diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index e5a49c5213a48..41be39f9abaa6 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -13,7 +13,7 @@ common methods, `_TestSQLAlchemyConn` tests the API with a SQLAlchemy Connection object. The different tested flavors (sqlite3, MySQL, PostgreSQL) derive from the base class - - Tests for the fallback mode (`TestSQLiteFallback` and `TestMySQLLegacy`) + - Tests for the fallback mode (`TestSQLiteFallback`) """ @@ -526,30 +526,29 @@ def test_read_sql_view(self): self._check_iris_loaded_frame(iris_frame) def test_to_sql(self): - sql.to_sql(self.test_frame1, 'test_frame1', self.conn, flavor='sqlite') + sql.to_sql(self.test_frame1, 'test_frame1', self.conn) self.assertTrue( - sql.has_table('test_frame1', self.conn, flavor='sqlite'), + sql.has_table('test_frame1', self.conn), 'Table not written to DB') def test_to_sql_fail(self): sql.to_sql(self.test_frame1, 'test_frame2', - self.conn, flavor='sqlite', if_exists='fail') + self.conn, if_exists='fail') self.assertTrue( - sql.has_table('test_frame2', self.conn, flavor='sqlite'), + sql.has_table('test_frame2', self.conn), 'Table not written to DB') self.assertRaises(ValueError, sql.to_sql, self.test_frame1, - 'test_frame2', self.conn, flavor='sqlite', - if_exists='fail') + 'test_frame2', self.conn, if_exists='fail') def test_to_sql_replace(self): sql.to_sql(self.test_frame1, 'test_frame3', - self.conn, flavor='sqlite', if_exists='fail') + self.conn, if_exists='fail') # Add to table again sql.to_sql(self.test_frame1, 'test_frame3', - self.conn, flavor='sqlite', if_exists='replace') + self.conn, if_exists='replace') self.assertTrue( - sql.has_table('test_frame3', self.conn, flavor='sqlite'), + sql.has_table('test_frame3', self.conn), 'Table not written to DB') num_entries = len(self.test_frame1) @@ -560,13 +559,13 @@ def test_to_sql_replace(self): def test_to_sql_append(self): sql.to_sql(self.test_frame1, 'test_frame4', - self.conn, flavor='sqlite', if_exists='fail') + self.conn, if_exists='fail') # Add to table again sql.to_sql(self.test_frame1, 'test_frame4', - self.conn, flavor='sqlite', if_exists='append') + self.conn, if_exists='append') self.assertTrue( - sql.has_table('test_frame4', self.conn, flavor='sqlite'), + sql.has_table('test_frame4', self.conn), 'Table not written to DB') num_entries = 2 * len(self.test_frame1) @@ -576,26 +575,25 @@ def test_to_sql_append(self): num_rows, num_entries, "not the same number of rows as entries") def test_to_sql_type_mapping(self): - sql.to_sql(self.test_frame3, 'test_frame5', - self.conn, flavor='sqlite', index=False) + sql.to_sql(self.test_frame3, 'test_frame5', self.conn, index=False) result = sql.read_sql("SELECT * FROM test_frame5", self.conn) tm.assert_frame_equal(self.test_frame3, result) def test_to_sql_series(self): s = Series(np.arange(5, dtype='int64'), name='series') - sql.to_sql(s, "test_series", self.conn, flavor='sqlite', index=False) + sql.to_sql(s, "test_series", self.conn, index=False) s2 = sql.read_sql_query("SELECT * FROM test_series", self.conn) tm.assert_frame_equal(s.to_frame(), s2) def test_to_sql_panel(self): panel = tm.makePanel() self.assertRaises(NotImplementedError, sql.to_sql, panel, - 'test_panel', self.conn, flavor='sqlite') + 'test_panel', self.conn) def test_roundtrip(self): sql.to_sql(self.test_frame1, 'test_frame_roundtrip', - con=self.conn, flavor='sqlite') + con=self.conn) result = sql.read_sql_query( 'SELECT * FROM test_frame_roundtrip', con=self.conn) @@ -609,7 +607,7 @@ def test_roundtrip(self): def test_roundtrip_chunksize(self): sql.to_sql(self.test_frame1, 'test_frame_roundtrip', con=self.conn, - index=False, flavor='sqlite', chunksize=2) + index=False, chunksize=2) result = sql.read_sql_query( 'SELECT * FROM test_frame_roundtrip', con=self.conn) @@ -764,27 +762,25 @@ def test_integer_col_names(self): if_exists='replace') def test_get_schema(self): - create_sql = sql.get_schema(self.test_frame1, 'test', 'sqlite', - con=self.conn) + create_sql = sql.get_schema(self.test_frame1, 'test', con=self.conn) self.assertTrue('CREATE' in create_sql) def test_get_schema_dtypes(self): float_frame = DataFrame({'a': [1.1, 1.2], 'b': [2.1, 2.2]}) dtype = sqlalchemy.Integer if self.mode == 'sqlalchemy' else 'INTEGER' - create_sql = sql.get_schema(float_frame, 'test', 'sqlite', + create_sql = sql.get_schema(float_frame, 'test', con=self.conn, dtype={'b': dtype}) self.assertTrue('CREATE' in create_sql) self.assertTrue('INTEGER' in create_sql) def test_get_schema_keys(self): frame = DataFrame({'Col1': [1.1, 1.2], 'Col2': [2.1, 2.2]}) - create_sql = sql.get_schema(frame, 'test', 'sqlite', - con=self.conn, keys='Col1') + create_sql = sql.get_schema(frame, 'test', con=self.conn, keys='Col1') constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("Col1")' self.assertTrue(constraint_sentence in create_sql) # multiple columns as key (GH10385) - create_sql = sql.get_schema(self.test_frame1, 'test', 'sqlite', + create_sql = sql.get_schema(self.test_frame1, 'test', con=self.conn, keys=['A', 'B']) constraint_sentence = 'CONSTRAINT test_pk PRIMARY KEY ("A", "B")' self.assertTrue(constraint_sentence in create_sql) @@ -1044,8 +1040,8 @@ def test_sql_open_close(self): with tm.ensure_clean() as name: conn = self.connect(name) - sql.to_sql(self.test_frame3, "test_frame3_legacy", conn, - flavor="sqlite", index=False) + sql.to_sql(self.test_frame3, "test_frame3_legacy", + conn, index=False) conn.close() conn = self.connect(name) @@ -1067,12 +1063,11 @@ def test_safe_names_warning(self): df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b ']) # has a space # warns on create table with spaces in names with tm.assert_produces_warning(): - sql.to_sql(df, "test_frame3_legacy", self.conn, - flavor="sqlite", index=False) + sql.to_sql(df, "test_frame3_legacy", self.conn, index=False) def test_get_schema2(self): # without providing a connection object (available for backwards comp) - create_sql = sql.get_schema(self.test_frame1, 'test', 'sqlite') + create_sql = sql.get_schema(self.test_frame1, 'test') self.assertTrue('CREATE' in create_sql) def test_tquery(self): @@ -1098,7 +1093,7 @@ def test_sqlite_type_mapping(self): # Test Timestamp objects (no datetime64 because of timezone) (GH9085) df = DataFrame({'time': to_datetime(['201412120154', '201412110254'], utc=True)}) - db = sql.SQLiteDatabase(self.conn, self.flavor) + db = sql.SQLiteDatabase(self.conn) table = sql.SQLiteTable("test_type", db, frame=df) schema = table.sql_schema() self.assertEqual(self._get_sqlite_column_type(schema, 'time'), @@ -1908,16 +1903,12 @@ def connect(cls): def setUp(self): self.conn = self.connect() - self.pandasSQL = sql.SQLiteDatabase(self.conn, 'sqlite') + self.pandasSQL = sql.SQLiteDatabase(self.conn) self._load_iris_data() self._load_test1_data() - def test_invalid_flavor(self): - self.assertRaises( - NotImplementedError, sql.SQLiteDatabase, self.conn, 'oracle') - def test_read_sql(self): self._read_sql_iris() @@ -1965,7 +1956,7 @@ def test_execute_sql(self): def test_datetime_date(self): # test support for datetime.date df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"]) - df.to_sql('test_date', self.conn, index=False, flavor=self.flavor) + df.to_sql('test_date', self.conn, index=False) res = read_sql_query('SELECT * FROM test_date', self.conn) if self.flavor == 'sqlite': # comes back as strings @@ -1976,7 +1967,7 @@ def test_datetime_date(self): def test_datetime_time(self): # test support for datetime.time, GH #8341 df = DataFrame([time(9, 0, 0), time(9, 1, 30)], columns=["a"]) - df.to_sql('test_time', self.conn, index=False, flavor=self.flavor) + df.to_sql('test_time', self.conn, index=False) res = read_sql_query('SELECT * FROM test_time', self.conn) if self.flavor == 'sqlite': # comes back as strings @@ -2051,130 +2042,22 @@ def test_illegal_names(self): df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) # Raise error on blank - self.assertRaises(ValueError, df.to_sql, "", self.conn, - flavor=self.flavor) + self.assertRaises(ValueError, df.to_sql, "", self.conn) for ndx, weird_name in enumerate( ['test_weird_name]', 'test_weird_name[', 'test_weird_name`', 'test_weird_name"', 'test_weird_name\'', '_b.test_weird_name_01-30', '"_b.test_weird_name_01-30"', '99beginswithnumber', '12345', u'\xe9']): - df.to_sql(weird_name, self.conn, flavor=self.flavor) + df.to_sql(weird_name, self.conn) sql.table_exists(weird_name, self.conn) df2 = DataFrame([[1, 2], [3, 4]], columns=['a', weird_name]) c_tbl = 'test_weird_col_name%d' % ndx - df2.to_sql(c_tbl, self.conn, flavor=self.flavor) + df2.to_sql(c_tbl, self.conn) sql.table_exists(c_tbl, self.conn) -class TestMySQLLegacy(MySQLMixIn, TestSQLiteFallback): - """ - Test the legacy mode against a MySQL database. - - """ - flavor = 'mysql' - - @classmethod - def setUpClass(cls): - cls.setup_driver() - - # test connection - try: - cls.connect() - except cls.driver.err.OperationalError: - raise nose.SkipTest( - "{0} - can't connect to MySQL server".format(cls)) - - @classmethod - def setup_driver(cls): - try: - import pymysql - cls.driver = pymysql - except ImportError: - raise nose.SkipTest('pymysql not installed') - - @classmethod - def connect(cls): - return cls.driver.connect(host='127.0.0.1', user='root', passwd='', - db='pandas_nosetest') - - def _count_rows(self, table_name): - cur = self._get_exec() - cur.execute( - "SELECT count(*) AS count_1 FROM %s" % table_name) - rows = cur.fetchall() - return rows[0][0] - - def setUp(self): - try: - self.conn = self.connect() - except self.driver.err.OperationalError: - raise nose.SkipTest("Can't connect to MySQL server") - - self.pandasSQL = sql.SQLiteDatabase(self.conn, 'mysql') - - self._load_iris_data() - self._load_test1_data() - - def test_a_deprecation(self): - with tm.assert_produces_warning(FutureWarning): - sql.to_sql(self.test_frame1, 'test_frame1', self.conn, - flavor='mysql') - self.assertTrue( - sql.has_table('test_frame1', self.conn, flavor='mysql'), - 'Table not written to DB') - - def _get_index_columns(self, tbl_name): - ixs = sql.read_sql_query( - "SHOW INDEX IN %s" % tbl_name, self.conn) - ix_cols = {} - for ix_name, ix_col in zip(ixs.Key_name, ixs.Column_name): - if ix_name not in ix_cols: - ix_cols[ix_name] = [] - ix_cols[ix_name].append(ix_col) - return list(ix_cols.values()) - - # TODO: cruft? - # def test_to_sql_save_index(self): - # self._to_sql_save_index() - - # for ix_name, ix_col in zip(ixs.Key_name, ixs.Column_name): - # if ix_name not in ix_cols: - # ix_cols[ix_name] = [] - # ix_cols[ix_name].append(ix_col) - # return ix_cols.values() - - def test_to_sql_save_index(self): - self._to_sql_save_index() - - def test_illegal_names(self): - df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) - - # These tables and columns should be ok - for ndx, ok_name in enumerate(['99beginswithnumber', '12345']): - df.to_sql(ok_name, self.conn, flavor=self.flavor, index=False, - if_exists='replace') - df2 = DataFrame([[1, 2], [3, 4]], columns=['a', ok_name]) - - df2.to_sql('test_ok_col_name', self.conn, - flavor=self.flavor, index=False, - if_exists='replace') - - # For MySQL, these should raise ValueError - for ndx, illegal_name in enumerate( - ['test_illegal_name]', 'test_illegal_name[', - 'test_illegal_name`', 'test_illegal_name"', - 'test_illegal_name\'', '']): - self.assertRaises(ValueError, df.to_sql, illegal_name, self.conn, - flavor=self.flavor, index=False) - - df2 = DataFrame([[1, 2], [3, 4]], columns=['a', illegal_name]) - self.assertRaises(ValueError, df2.to_sql, - 'test_illegal_col_name%d' % ndx, - self.conn, flavor=self.flavor, index=False) - - # ----------------------------------------------------------------------------- # -- Old tests from 0.13.1 (before refactor using sqlalchemy) @@ -2228,7 +2111,7 @@ def test_write_row_by_row(self): frame = tm.makeTimeDataFrame() frame.ix[0, 0] = np.nan - create_sql = sql.get_schema(frame, 'test', 'sqlite') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() cur.execute(create_sql) @@ -2247,7 +2130,7 @@ def test_write_row_by_row(self): def test_execute(self): frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'sqlite') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() cur.execute(create_sql) ins = "INSERT INTO test VALUES (?, ?, ?, ?)" @@ -2262,7 +2145,7 @@ def test_execute(self): def test_schema(self): frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'sqlite') + create_sql = sql.get_schema(frame, 'test') lines = create_sql.splitlines() for l in lines: tokens = l.split(' ') @@ -2270,7 +2153,7 @@ def test_schema(self): self.assertTrue(tokens[1] == 'DATETIME') frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'sqlite', keys=['A', 'B'],) + create_sql = sql.get_schema(frame, 'test', keys=['A', 'B']) lines = create_sql.splitlines() self.assertTrue('PRIMARY KEY ("A", "B")' in create_sql) cur = self.conn.cursor() @@ -2425,44 +2308,68 @@ def clean_up(test_table_to_drop): frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='notvalidvalue') clean_up(table_name) # test if_exists='fail' - sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='fail') + sql.to_sql(frame=df_if_exists_1, con=self.conn, + name=table_name, if_exists='fail') self.assertRaises(ValueError, sql.to_sql, frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='fail') # test if_exists='replace' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='sqlite', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) # test if_exists='append' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='sqlite', if_exists='fail', index=False) + if_exists='fail', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='sqlite', if_exists='append', index=False) + if_exists='append', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) +class TestSQLFlavorDeprecation(tm.TestCase): + """ + gh-13611: test that the 'flavor' parameter + is appropriately deprecated by checking the + functions that directly raise the warning + """ + + con = 1234 # don't need real connection for this + funcs = ['SQLiteDatabase', 'pandasSQL_builder'] + + def test_unsupported_flavor(self): + msg = 'is not supported' + + for func in self.funcs: + tm.assertRaisesRegexp(ValueError, msg, getattr(sql, func), + self.con, flavor='mysql') + + def test_deprecated_flavor(self): + for func in self.funcs: + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + getattr(sql, func)(self.con, flavor='sqlite') + + +@unittest.skip("gh-13611: there is no support for MySQL " + "if SQLAlchemy is not installed") class TestXMySQL(MySQLMixIn, tm.TestCase): @classmethod @@ -2531,7 +2438,7 @@ def test_write_row_by_row(self): frame = tm.makeTimeDataFrame() frame.ix[0, 0] = np.nan drop_sql = "DROP TABLE IF EXISTS test" - create_sql = sql.get_schema(frame, 'test', 'mysql') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() cur.execute(drop_sql) cur.execute(create_sql) @@ -2553,7 +2460,7 @@ def test_chunksize_read_type(self): drop_sql = "DROP TABLE IF EXISTS test" cur = self.conn.cursor() cur.execute(drop_sql) - sql.to_sql(frame, name='test', con=self.conn, flavor='mysql') + sql.to_sql(frame, name='test', con=self.conn) query = "select * from test" chunksize = 5 chunk_gen = pd.read_sql_query(sql=query, con=self.conn, @@ -2565,7 +2472,7 @@ def test_execute(self): _skip_if_no_pymysql() frame = tm.makeTimeDataFrame() drop_sql = "DROP TABLE IF EXISTS test" - create_sql = sql.get_schema(frame, 'test', 'mysql') + create_sql = sql.get_schema(frame, 'test') cur = self.conn.cursor() with warnings.catch_warnings(): warnings.filterwarnings("ignore", "Unknown table.*") @@ -2584,7 +2491,7 @@ def test_execute(self): def test_schema(self): _skip_if_no_pymysql() frame = tm.makeTimeDataFrame() - create_sql = sql.get_schema(frame, 'test', 'mysql') + create_sql = sql.get_schema(frame, 'test') lines = create_sql.splitlines() for l in lines: tokens = l.split(' ') @@ -2593,7 +2500,7 @@ def test_schema(self): frame = tm.makeTimeDataFrame() drop_sql = "DROP TABLE IF EXISTS test" - create_sql = sql.get_schema(frame, 'test', 'mysql', keys=['A', 'B'],) + create_sql = sql.get_schema(frame, 'test', keys=['A', 'B']) lines = create_sql.splitlines() self.assertTrue('PRIMARY KEY (`A`, `B`)' in create_sql) cur = self.conn.cursor() @@ -2666,8 +2573,7 @@ def _check_roundtrip(self, frame): with warnings.catch_warnings(): warnings.filterwarnings("ignore", "Unknown table.*") cur.execute(drop_sql) - sql.to_sql(frame, name='test_table', - con=self.conn, flavor='mysql', index=False) + sql.to_sql(frame, name='test_table', con=self.conn, index=False) result = sql.read_sql("select * from test_table", self.conn) # HACK! Change this once indexes are handled properly. @@ -2687,7 +2593,7 @@ def _check_roundtrip(self, frame): warnings.filterwarnings("ignore", "Unknown table.*") cur.execute(drop_sql) sql.to_sql(frame2, name='test_table2', - con=self.conn, flavor='mysql', index=False) + con=self.conn, index=False) result = sql.read_sql("select * from test_table2", self.conn, index_col='Idx') expected = frame.copy() @@ -2707,7 +2613,7 @@ def test_tquery(self): cur = self.conn.cursor() cur.execute(drop_sql) sql.to_sql(frame, name='test_table', - con=self.conn, flavor='mysql', index=False) + con=self.conn, index=False) result = sql.tquery("select A from test_table", self.conn) expected = Series(frame.A.values, frame.index) # not to have name result = Series(result, frame.index) @@ -2733,7 +2639,7 @@ def test_uquery(self): cur = self.conn.cursor() cur.execute(drop_sql) sql.to_sql(frame, name='test_table', - con=self.conn, flavor='mysql', index=False) + con=self.conn, index=False) stmt = 'INSERT INTO test_table VALUES(2.314, -123.1, 1.234, 2.3)' self.assertEqual(sql.uquery(stmt, con=self.conn), 1) @@ -2753,7 +2659,7 @@ def test_keyword_as_column_names(self): _skip_if_no_pymysql() df = DataFrame({'From': np.ones(5)}) sql.to_sql(df, con=self.conn, name='testkeywords', - if_exists='replace', flavor='mysql', index=False) + if_exists='replace', index=False) def test_if_exists(self): _skip_if_no_pymysql() @@ -2776,39 +2682,37 @@ def clean_up(test_table_to_drop): frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='notvalidvalue') clean_up(table_name) # test if_exists='fail' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='fail', index=False) + if_exists='fail', index=False) self.assertRaises(ValueError, sql.to_sql, frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='fail') # test if_exists='replace' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='mysql', if_exists='replace', index=False) + if_exists='replace', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name) # test if_exists='append' sql.to_sql(frame=df_if_exists_1, con=self.conn, name=table_name, - flavor='mysql', if_exists='fail', index=False) + if_exists='fail', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B')]) sql.to_sql(frame=df_if_exists_2, con=self.conn, name=table_name, - flavor='mysql', if_exists='append', index=False) + if_exists='append', index=False) self.assertEqual(sql.tquery(sql_select, con=self.conn), [(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')]) clean_up(table_name)
Deprecated in `0.14.0`, so way, way overdue.
https://api.github.com/repos/pandas-dev/pandas/pulls/13611
2016-07-11T06:56:30Z
2016-07-19T20:31:08Z
2016-07-19T20:31:08Z
2016-07-20T00:46:51Z
TST: add tests for Timestamp.toordinal/fromordinal
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index ce88edcf4249b..31d6393c1c26e 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -255,6 +255,18 @@ def test_constructor_keyword(self): hour=1, minute=2, second=3, microsecond=999999)), repr(Timestamp('2015-11-12 01:02:03.999999'))) + def test_constructor_fromordinal(self): + base = datetime.datetime(2000, 1, 1) + + ts = Timestamp.fromordinal(base.toordinal(), freq='D') + self.assertEqual(base, ts) + self.assertEqual(ts.freq, 'D') + self.assertEqual(base.toordinal(), ts.toordinal()) + + ts = Timestamp.fromordinal(base.toordinal(), tz='US/Eastern') + self.assertEqual(pd.Timestamp('2000-01-01', tz='US/Eastern'), ts) + self.assertEqual(base.toordinal(), ts.toordinal()) + def test_constructor_offset_depr(self): # GH 12160 with tm.assert_produces_warning(FutureWarning, @@ -270,6 +282,21 @@ def test_constructor_offset_depr(self): with tm.assertRaisesRegexp(TypeError, msg): Timestamp('2011-01-01', offset='D', freq='D') + def test_constructor_offset_depr_fromordinal(self): + # GH 12160 + base = datetime.datetime(2000, 1, 1) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + ts = Timestamp.fromordinal(base.toordinal(), offset='D') + self.assertEqual(pd.Timestamp('2000-01-01'), ts) + self.assertEqual(ts.freq, 'D') + self.assertEqual(base.toordinal(), ts.toordinal()) + + msg = "Can only specify freq or offset, not both" + with tm.assertRaisesRegexp(TypeError, msg): + Timestamp.fromordinal(base.toordinal(), offset='D', freq='D') + def test_conversion(self): # GH 9255 ts = Timestamp('2000-01-01') diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 650b4c7979d8d..2af08f2713262 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -235,12 +235,14 @@ class Timestamp(_Timestamp): ---------- ts_input : datetime-like, str, int, float Value to be converted to Timestamp - offset : str, DateOffset + freq : str, DateOffset Offset which Timestamp will have tz : string, pytz.timezone, dateutil.tz.tzfile or None Time zone for time which Timestamp will have. unit : string numpy unit used for conversion, if ts_input is int or float + offset : str, DateOffset + Deprecated, use freq The other two forms mimic the parameters from ``datetime.datetime``. They can be passed by either position or keyword, but not both mixed together. @@ -262,8 +264,21 @@ class Timestamp(_Timestamp): @classmethod def fromordinal(cls, ordinal, freq=None, tz=None, offset=None): - """ passed an ordinal, translate and convert to a ts - note: by definition there cannot be any tz info on the ordinal itself """ + """ + passed an ordinal, translate and convert to a ts + note: by definition there cannot be any tz info on the ordinal itself + + Parameters + ---------- + ordinal : int + date corresponding to a proleptic Gregorian ordinal + freq : str, DateOffset + Offset which Timestamp will have + tz : string, pytz.timezone, dateutil.tz.tzfile or None + Time zone for time which Timestamp will have. + offset : str, DateOffset + Deprecated, use freq + """ return cls(datetime.fromordinal(ordinal), freq=freq, tz=tz, offset=offset) @classmethod
- [x] follow-up for #13593 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` added tests and fixed missed doc change.
https://api.github.com/repos/pandas-dev/pandas/pulls/13610
2016-07-10T22:52:51Z
2016-07-15T00:10:16Z
null
2016-07-15T00:19:57Z
API: Change Period('NAT') to return NaT
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index bef02a06135de..4092694ca6cd1 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -445,6 +445,45 @@ Furthermore: - Passing duplicated ``percentiles`` will now raise a ``ValueError``. - Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`) +.. _whatsnew_0190.api.periodnat: + +``Period('NaT')`` now returns ``pd.NaT`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Previously, ``Period`` has its own ``Period('NaT')`` representation different from ``pd.NaT``. Now ``Period('NaT')`` has been changed to return ``pd.NaT``. (:issue:`12759`) + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.Period('NaT', freq='D') + Out[5]: Period('NaT', 'D') + +New Behavior: + +.. ipython:: python + + pd.Period('NaT') + + +To be compat with ``Period`` addition and subtraction, ``pd.NaT`` now supports addition and subtraction with ``int``. Previously it raises ``ValueError``. + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.NaT + 1 + ... + ValueError: Cannot add integral value to Timestamp without freq. + +New Behavior: + +.. ipython:: python + + pd.NaT + 1 + pd.NaT - 1 + + .. _whatsnew_0190.deprecations: Deprecations diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index af2e295ae0cfc..37f265ede07e7 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -472,7 +472,11 @@ def extract_ordinals(ndarray[object] values, freq): except AttributeError: p = Period(p, freq=freq) - ordinals[i] = p.ordinal + if p is tslib.NaT: + # input may contain NaT-like string + ordinals[i] = tslib.iNaT + else: + ordinals[i] = p.ordinal return ordinals @@ -665,24 +669,8 @@ class IncompatibleFrequency(ValueError): pass -cdef class Period(object): - """ - Represents an period of time +cdef class _Period(object): - Parameters - ---------- - value : Period or compat.string_types, default None - The time period represented (e.g., '4Q2005') - freq : str, default None - One of pandas period strings or corresponding objects - year : int, default None - month : int, default 1 - quarter : int, default None - day : int, default 1 - hour : int, default 0 - minute : int, default 0 - second : int, default 0 - """ cdef public: int64_t ordinal object freq @@ -711,97 +699,22 @@ cdef class Period(object): @classmethod def _from_ordinal(cls, ordinal, freq): """ fast creation from an ordinal and freq that are already validated! """ - self = Period.__new__(cls) - self.ordinal = ordinal - self.freq = cls._maybe_convert_freq(freq) - return self - - def __init__(self, value=None, freq=None, ordinal=None, - year=None, month=1, quarter=None, day=1, - hour=0, minute=0, second=0): - # freq points to a tuple (base, mult); base is one of the defined - # periods such as A, Q, etc. Every five minutes would be, e.g., - # ('T', 5) but may be passed in as a string like '5T' - - # ordinal is the period offset from the gregorian proleptic epoch - - if ordinal is not None and value is not None: - raise ValueError(("Only value or ordinal but not both should be " - "given but not both")) - elif ordinal is not None: - if not lib.is_integer(ordinal): - raise ValueError("Ordinal must be an integer") - if freq is None: - raise ValueError('Must supply freq for ordinal value') - - elif value is None: - if freq is None: - raise ValueError("If value is None, freq cannot be None") - ordinal = _ordinal_from_fields(year, month, quarter, day, - hour, minute, second, freq) - - elif isinstance(value, Period): - other = value - if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): - ordinal = other.ordinal - freq = other.freq - else: - converted = other.asfreq(freq) - ordinal = converted.ordinal - - elif is_null_datetimelike(value) or value in tslib._nat_strings: - ordinal = tslib.iNaT - if freq is None: - raise ValueError("If value is NaT, freq cannot be None " - "because it cannot be inferred") - - elif isinstance(value, compat.string_types) or lib.is_integer(value): - if lib.is_integer(value): - value = str(value) - value = value.upper() - dt, _, reso = parse_time_string(value, freq) - - if freq is None: - try: - freq = frequencies.Resolution.get_freq(reso) - except KeyError: - raise ValueError("Invalid frequency or could not infer: %s" % reso) - - elif isinstance(value, datetime): - dt = value - if freq is None: - raise ValueError('Must supply freq for datetime value') - elif isinstance(value, np.datetime64): - dt = Timestamp(value) - if freq is None: - raise ValueError('Must supply freq for datetime value') - elif isinstance(value, date): - dt = datetime(year=value.year, month=value.month, day=value.day) - if freq is None: - raise ValueError('Must supply freq for datetime value') - else: - msg = "Value must be Period, string, integer, or datetime" - raise ValueError(msg) - - base, mult = frequencies.get_freq_code(freq) - - if ordinal is None: - self.ordinal = get_period_ordinal(dt.year, dt.month, dt.day, - dt.hour, dt.minute, dt.second, - dt.microsecond, 0, base) + if ordinal == tslib.iNaT: + return tslib.NaT else: + self = _Period.__new__(cls) self.ordinal = ordinal - - self.freq = self._maybe_convert_freq(freq) + self.freq = cls._maybe_convert_freq(freq) + return self def __richcmp__(self, other, op): if isinstance(other, Period): if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT: - return _nat_scalar_rules[op] return PyObject_RichCompareBool(self.ordinal, other.ordinal, op) + elif other is tslib.NaT: + return _nat_scalar_rules[op] # index/series like elif hasattr(other, '_typ'): return NotImplemented @@ -824,10 +737,7 @@ cdef class Period(object): offset_nanos = tslib._delta_to_nanoseconds(offset) if nanos % offset_nanos == 0: - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + (nanos // offset_nanos) + ordinal = self.ordinal + (nanos // offset_nanos) return Period(ordinal=ordinal, freq=self.freq) msg = 'Input cannot be converted to Period(freq={0})' raise IncompatibleFrequency(msg.format(self.freqstr)) @@ -835,10 +745,7 @@ cdef class Period(object): freqstr = frequencies.get_standard_freq(other) base = frequencies.get_base_alias(freqstr) if base == self.freq.rule_code: - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + other.n + ordinal = self.ordinal + other.n return Period(ordinal=ordinal, freq=self.freq) msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) @@ -853,10 +760,7 @@ cdef class Period(object): elif other is tslib.NaT: return tslib.NaT elif lib.is_integer(other): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + other * self.freq.n + ordinal = self.ordinal + other * self.freq.n return Period(ordinal=ordinal, freq=self.freq) else: # pragma: no cover return NotImplemented @@ -872,17 +776,12 @@ cdef class Period(object): neg_other = -other return self + neg_other elif lib.is_integer(other): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal - other * self.freq.n + ordinal = self.ordinal - other * self.freq.n return Period(ordinal=ordinal, freq=self.freq) elif isinstance(other, Period): if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT: - return Period(ordinal=tslib.iNaT, freq=self.freq) return self.ordinal - other.ordinal elif getattr(other, '_typ', None) == 'periodindex': return -other.__sub__(self) @@ -914,16 +813,13 @@ cdef class Period(object): base1, mult1 = frequencies.get_freq_code(self.freq) base2, mult2 = frequencies.get_freq_code(freq) - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal + # mult1 can't be negative or 0 + end = how == 'E' + if end: + ordinal = self.ordinal + mult1 - 1 else: - # mult1 can't be negative or 0 - end = how == 'E' - if end: - ordinal = self.ordinal + mult1 - 1 - else: - ordinal = self.ordinal - ordinal = period_asfreq(ordinal, base1, base2, end) + ordinal = self.ordinal + ordinal = period_asfreq(ordinal, base1, base2, end) return Period(ordinal=ordinal, freq=freq) @@ -933,12 +829,9 @@ cdef class Period(object): @property def end_time(self): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - # freq.n can't be negative or 0 - # ordinal = (self + self.freq.n).start_time.value - 1 - ordinal = (self + 1).start_time.value - 1 + # freq.n can't be negative or 0 + # ordinal = (self + self.freq.n).start_time.value - 1 + ordinal = (self + 1).start_time.value - 1 return Timestamp(ordinal) def to_timestamp(self, freq=None, how='start', tz=None): @@ -1199,8 +1092,114 @@ cdef class Period(object): return period_format(self.ordinal, base, fmt) -def _ordinal_from_fields(year, month, quarter, day, hour, minute, - second, freq): +class Period(_Period): + """ + Represents an period of time + + Parameters + ---------- + value : Period or compat.string_types, default None + The time period represented (e.g., '4Q2005') + freq : str, default None + One of pandas period strings or corresponding objects + year : int, default None + month : int, default 1 + quarter : int, default None + day : int, default 1 + hour : int, default 0 + minute : int, default 0 + second : int, default 0 + """ + + def __new__(cls, value=None, freq=None, ordinal=None, + year=None, month=None, quarter=None, day=None, + hour=None, minute=None, second=None): + # freq points to a tuple (base, mult); base is one of the defined + # periods such as A, Q, etc. Every five minutes would be, e.g., + # ('T', 5) but may be passed in as a string like '5T' + + # ordinal is the period offset from the gregorian proleptic epoch + + cdef _Period self + + if ordinal is not None and value is not None: + raise ValueError(("Only value or ordinal but not both should be " + "given but not both")) + elif ordinal is not None: + if not lib.is_integer(ordinal): + raise ValueError("Ordinal must be an integer") + if freq is None: + raise ValueError('Must supply freq for ordinal value') + + elif value is None: + if (year is None and month is None and quarter is None and + day is None and hour is None and minute is None and second is None): + ordinal = tslib.iNaT + else: + if freq is None: + raise ValueError("If value is None, freq cannot be None") + + # set defaults + month = 1 if month is None else month + day = 1 if day is None else day + hour = 0 if hour is None else hour + minute = 0 if minute is None else minute + second = 0 if second is None else second + + ordinal = _ordinal_from_fields(year, month, quarter, day, + hour, minute, second, freq) + + elif isinstance(value, Period): + other = value + if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): + ordinal = other.ordinal + freq = other.freq + else: + converted = other.asfreq(freq) + ordinal = converted.ordinal + + elif is_null_datetimelike(value) or value in tslib._nat_strings: + ordinal = tslib.iNaT + + elif isinstance(value, compat.string_types) or lib.is_integer(value): + if lib.is_integer(value): + value = str(value) + value = value.upper() + dt, _, reso = parse_time_string(value, freq) + + if freq is None: + try: + freq = frequencies.Resolution.get_freq(reso) + except KeyError: + raise ValueError("Invalid frequency or could not infer: %s" % reso) + + elif isinstance(value, datetime): + dt = value + if freq is None: + raise ValueError('Must supply freq for datetime value') + elif isinstance(value, np.datetime64): + dt = Timestamp(value) + if freq is None: + raise ValueError('Must supply freq for datetime value') + elif isinstance(value, date): + dt = datetime(year=value.year, month=value.month, day=value.day) + if freq is None: + raise ValueError('Must supply freq for datetime value') + else: + msg = "Value must be Period, string, integer, or datetime" + raise ValueError(msg) + + if ordinal is None: + base, mult = frequencies.get_freq_code(freq) + ordinal = get_period_ordinal(dt.year, dt.month, dt.day, + dt.hour, dt.minute, dt.second, + dt.microsecond, 0, base) + + return cls._from_ordinal(ordinal, freq) + + +def _ordinal_from_fields(year, month, quarter, day, + hour, minute, second, freq): base, mult = frequencies.get_freq_code(freq) if quarter is not None: year, month = _quarter_to_myear(year, quarter, freq) diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index 9eba481a66685..5c21f71d64660 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -741,14 +741,7 @@ def test_astype(self): result = idx.astype(object) expected = Index([Period('2016-05-16', freq='D')] + [Period(NaT, freq='D')] * 3, dtype='object') - # Hack because of lack of support for Period null checking (GH12759) - tm.assert_index_equal(result[:1], expected[:1]) - result_arr = np.asarray([p.ordinal for p in result], dtype=np.int64) - expected_arr = np.asarray([p.ordinal for p in expected], - dtype=np.int64) - tm.assert_numpy_array_equal(result_arr, expected_arr) - # TODO: When GH12759 is resolved, change the above hack to: - # tm.assert_index_equal(result, expected) # now, it raises. + tm.assert_index_equal(result, expected) result = idx.astype(int) expected = Int64Index([16937] + [-9223372036854775808] * 3, diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 45f634050a5d8..dffb71cff526a 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -92,13 +92,14 @@ def wrapper(self, other): result[mask] = nat_result return result + elif other is tslib.NaT: + result = np.empty(len(self.values), dtype=bool) + result.fill(nat_result) else: other = Period(other, freq=self.freq) func = getattr(self.values, opname) result = func(other.ordinal) - if other.ordinal == tslib.iNaT: - result.fill(nat_result) mask = self.values == tslib.iNaT if mask.any(): result[mask] = nat_result @@ -235,7 +236,7 @@ def _from_arraylike(cls, data, freq, tz): data = _ensure_int64(data) if freq is None: raise ValueError('freq not specified') - data = np.array([Period(x, freq=freq).ordinal for x in data], + data = np.array([Period(x, freq=freq) for x in data], dtype=np.int64) except (TypeError, ValueError): data = _ensure_object(data) @@ -322,15 +323,18 @@ def _na_value(self): return self._box_func(tslib.iNaT) def __contains__(self, key): - if not isinstance(key, Period) or key.freq != self.freq: - if isinstance(key, compat.string_types): - try: - self.get_loc(key) - return True - except Exception: - return False + if isinstance(key, Period): + if key.freq != self.freq: + return False + else: + return key.ordinal in self._engine + else: + try: + self.get_loc(key) + return True + except Exception: + return False return False - return key.ordinal in self._engine def __array_wrap__(self, result, context=None): """ @@ -622,17 +626,13 @@ def _sub_period(self, other): msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if other.ordinal == tslib.iNaT: - new_data = np.empty(len(self)) - new_data.fill(np.nan) - else: - asi8 = self.asi8 - new_data = asi8 - other.ordinal + asi8 = self.asi8 + new_data = asi8 - other.ordinal - if self.hasnans: - mask = asi8 == tslib.iNaT - new_data = new_data.astype(np.float64) - new_data[mask] = np.nan + if self.hasnans: + mask = asi8 == tslib.iNaT + new_data = new_data.astype(np.float64) + new_data[mask] = np.nan # result must be Int64Index or Float64Index return Index(new_data, name=self.name) @@ -740,8 +740,10 @@ def get_loc(self, key, method=None, tolerance=None): # we cannot construct the Period # as we have an invalid type raise KeyError(key) + try: - return Index.get_loc(self, key.ordinal, method, tolerance) + ordinal = tslib.iNaT if key is tslib.NaT else key.ordinal + return Index.get_loc(self, ordinal, method, tolerance) except KeyError: raise KeyError(key) @@ -1044,8 +1046,7 @@ def _get_ordinal_range(start, end, periods, freq, mult=1): if is_start_per and is_end_per and start.freq != end.freq: raise ValueError('Start and end must have same freq') - if ((is_start_per and start.ordinal == tslib.iNaT) or - (is_end_per and end.ordinal == tslib.iNaT)): + if (start is tslib.NaT or end is tslib.NaT): raise ValueError('Start and end must not be NaT') if freq is None: diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 68cea17ba3fc9..958a10c329a46 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -1587,17 +1587,16 @@ def test_asobject_tolist(self): result = idx.asobject self.assertTrue(isinstance(result, Index)) self.assertEqual(result.dtype, object) + tm.assert_index_equal(result, expected) for i in [0, 1, 3]: - self.assertTrue(result[i], expected[i]) - self.assertTrue(result[2].ordinal, pd.tslib.iNaT) - self.assertTrue(result[2].freq, 'D') + self.assertEqual(result[i], expected[i]) + self.assertIs(result[2], pd.NaT) self.assertEqual(result.name, expected.name) result_list = idx.tolist() for i in [0, 1, 3]: - self.assertTrue(result_list[i], expected_list[i]) - self.assertTrue(result_list[2].ordinal, pd.tslib.iNaT) - self.assertTrue(result_list[2].freq, 'D') + self.assertEqual(result_list[i], expected_list[i]) + self.assertIs(result_list[2], pd.NaT) def test_minmax(self): @@ -1623,18 +1622,15 @@ def test_minmax(self): # Return NaT obj = PeriodIndex([], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) obj = PeriodIndex([pd.NaT], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) obj = PeriodIndex([pd.NaT, pd.NaT, pd.NaT], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) def test_numpy_minmax(self): pr = pd.period_range(start='2016-01-15', end='2016-01-20') @@ -1735,9 +1731,9 @@ def test_representation_to_series(self): 2 2013 dtype: object""" - exp6 = """0 2011-01-01 09:00 -1 2012-02-01 10:00 -2 NaT + exp6 = """0 2011-01-01 09:00 +1 2012-02-01 10:00 +2 NaT dtype: object""" exp7 = """0 2013Q1 diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 591fa19aad585..fd5939909746f 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -36,14 +36,17 @@ def test_quarterly_negative_ordinals(self): p = Period(ordinal=-1, freq='Q-DEC') self.assertEqual(p.year, 1969) self.assertEqual(p.quarter, 4) + self.assertIsInstance(p, Period) p = Period(ordinal=-2, freq='Q-DEC') self.assertEqual(p.year, 1969) self.assertEqual(p.quarter, 3) + self.assertIsInstance(p, Period) p = Period(ordinal=-2, freq='M') self.assertEqual(p.year, 1969) self.assertEqual(p.month, 11) + self.assertIsInstance(p, Period) def test_period_cons_quarterly(self): # bugs in scikits.timeseries @@ -67,6 +70,7 @@ def test_period_cons_annual(self): stamp = exp.to_timestamp('D', how='end') + timedelta(days=30) p = Period(stamp, freq=freq) self.assertEqual(p, exp + 1) + self.assertIsInstance(p, Period) def test_period_cons_weekly(self): for num in range(10, 17): @@ -77,34 +81,46 @@ def test_period_cons_weekly(self): result = Period(daystr, freq=freq) expected = Period(daystr, freq='D').asfreq(freq) self.assertEqual(result, expected) + self.assertIsInstance(result, Period) + + def test_period_from_ordinal(self): + p = pd.Period('2011-01', freq='M') + res = pd.Period._from_ordinal(p.ordinal, freq='M') + self.assertEqual(p, res) + self.assertIsInstance(res, Period) def test_period_cons_nat(self): p = Period('NaT', freq='M') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'M') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period('nat', freq='W-SUN') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'W-SUN') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period(tslib.iNaT, freq='D') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'D') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period(tslib.iNaT, freq='3D') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, offsets.Day(3)) - self.assertEqual(p.freqstr, '3D') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) + + p = Period('NaT') + self.assertIs(p, pd.NaT) + + p = Period(tslib.iNaT) + self.assertIs(p, pd.NaT) + + def test_cons_null_like(self): + # check Timestamp compat + self.assertIs(Timestamp('NaT'), pd.NaT) + self.assertIs(Period('NaT'), pd.NaT) + + self.assertIs(Timestamp(None), pd.NaT) + self.assertIs(Period(None), pd.NaT) - self.assertRaises(ValueError, Period, 'NaT') + self.assertIs(Timestamp(float('nan')), pd.NaT) + self.assertIs(Period(float('nan')), pd.NaT) + + self.assertIs(Timestamp(np.nan), pd.NaT) + self.assertIs(Period(np.nan), pd.NaT) def test_period_cons_mult(self): p1 = Period('2011-01', freq='3M') @@ -197,13 +213,6 @@ def test_timestamp_tz_arg_dateutil_from_string(self): freq='M').to_timestamp(tz='dateutil/Europe/Brussels') self.assertEqual(p.tz, gettz('Europe/Brussels')) - def test_timestamp_nat_tz(self): - t = Period('NaT', freq='M').to_timestamp() - self.assertTrue(t is tslib.NaT) - - t = Period('NaT', freq='M').to_timestamp(tz='Asia/Tokyo') - self.assertTrue(t is tslib.NaT) - def test_timestamp_mult(self): p = pd.Period('2011-01', freq='M') self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) @@ -213,12 +222,6 @@ def test_timestamp_mult(self): self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) self.assertEqual(p.to_timestamp(how='E'), pd.Timestamp('2011-03-31')) - def test_timestamp_nat_mult(self): - for freq in ['M', '3M']: - p = pd.Period('NaT', freq=freq) - self.assertTrue(p.to_timestamp(how='S') is pd.NaT) - self.assertTrue(p.to_timestamp(how='E') is pd.NaT) - def test_period_constructor(self): i1 = Period('1/1/2005', freq='M') i2 = Period('Jan 2005') @@ -552,9 +555,6 @@ def _ex(p): result = p.to_timestamp('5S', how='start') self.assertEqual(result, expected) - p = Period('NaT', freq='W') - self.assertTrue(p.to_timestamp() is tslib.NaT) - def test_start_time(self): freq_lst = ['A', 'Q', 'M', 'D', 'H', 'T', 'S'] xp = datetime(2012, 1, 1) @@ -566,9 +566,6 @@ def test_start_time(self): self.assertEqual(Period('2012', freq='W').start_time, datetime(2011, 12, 26)) - p = Period('NaT', freq='W') - self.assertTrue(p.start_time is tslib.NaT) - def test_end_time(self): p = Period('2012', freq='A') @@ -607,9 +604,6 @@ def _ex(*args): xp = _ex(2012, 1, 16) self.assertEqual(xp, p.end_time) - p = Period('NaT', freq='W') - self.assertTrue(p.end_time is tslib.NaT) - def test_anchor_week_end_time(self): def _ex(*args): return Timestamp(Timestamp(datetime(*args)).value - 1) @@ -758,15 +752,14 @@ def test_properties_secondly(self): def test_properties_nat(self): p_nat = Period('NaT', freq='M') t_nat = pd.Timestamp('NaT') + self.assertIs(p_nat, t_nat) + # confirm Period('NaT') work identical with Timestamp('NaT') for f in ['year', 'month', 'day', 'hour', 'minute', 'second', 'week', 'dayofyear', 'quarter', 'days_in_month']: self.assertTrue(np.isnan(getattr(p_nat, f))) self.assertTrue(np.isnan(getattr(t_nat, f))) - for f in ['weekofyear', 'dayofweek', 'weekday', 'qyear']: - self.assertTrue(np.isnan(getattr(p_nat, f))) - def test_pnow(self): dt = datetime.now() @@ -789,7 +782,7 @@ def test_constructor_corner(self): self.assertRaises(ValueError, Period, 1.6, freq='D') self.assertRaises(ValueError, Period, ordinal=1.6, freq='D') self.assertRaises(ValueError, Period, ordinal=2, value=1, freq='D') - self.assertRaises(ValueError, Period) + self.assertIs(Period(None), pd.NaT) self.assertRaises(ValueError, Period, month=1) p = Period('2007-01-01', freq='D') @@ -1526,12 +1519,6 @@ def test_conv_secondly(self): self.assertEqual(ival_S.asfreq('S'), ival_S) - def test_asfreq_nat(self): - p = Period('NaT', freq='A') - result = p.asfreq('M') - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') - def test_asfreq_mult(self): # normal freq to mult freq p = Period(freq='A', year=2007) @@ -1603,21 +1590,6 @@ def test_asfreq_mult(self): self.assertEqual(result.ordinal, expected.ordinal) self.assertEqual(result.freq, expected.freq) - def test_asfreq_mult_nat(self): - # normal freq to mult freq - for p in [Period('NaT', freq='A'), Period('NaT', freq='3A'), - Period('NaT', freq='2M'), Period('NaT', freq='3D')]: - for freq in ['3A', offsets.YearEnd(3)]: - result = p.asfreq(freq) - expected = Period('NaT', freq='3A') - self.assertEqual(result.ordinal, pd.tslib.iNaT) - self.assertEqual(result.freq, expected.freq) - - result = p.asfreq(freq, how='S') - expected = Period('NaT', freq='3A') - self.assertEqual(result.ordinal, pd.tslib.iNaT) - self.assertEqual(result.freq, expected.freq) - class TestPeriodIndex(tm.TestCase): def setUp(self): @@ -1995,6 +1967,19 @@ def test_getitem_datetime(self): rs = ts[dt1:dt4] tm.assert_series_equal(rs, ts) + def test_getitem_nat(self): + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertEqual(idx[0], pd.Period('2011-01', freq='M')) + self.assertIs(idx[1], tslib.NaT) + + s = pd.Series([0, 1, 2], index=idx) + self.assertEqual(s[pd.NaT], 1) + + s = pd.Series(idx, index=idx) + self.assertEqual(s[pd.Period('2011-01', freq='M')], + pd.Period('2011-01', freq='M')) + self.assertIs(s[pd.NaT], tslib.NaT) + def test_slice_with_negative_step(self): ts = Series(np.arange(20), period_range('2014-01', periods=20, freq='M')) @@ -2038,6 +2023,19 @@ def test_contains(self): self.assertFalse(Period('2007-01', freq='D') in rng) self.assertFalse(Period('2007-01', freq='2M') in rng) + def test_contains_nat(self): + idx = period_range('2007-01', freq='M', periods=10) + self.assertFalse(pd.NaT in idx) + self.assertFalse(None in idx) + self.assertFalse(float('nan') in idx) + self.assertFalse(np.nan in idx) + + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertTrue(pd.NaT in idx) + self.assertTrue(None in idx) + self.assertTrue(float('nan') in idx) + self.assertTrue(np.nan in idx) + def test_sub(self): rng = period_range('2007-01', periods=50) @@ -3292,6 +3290,17 @@ def test_get_loc_msg(self): except KeyError as inst: self.assertEqual(inst.args[0], bad_period) + def test_get_loc_nat(self): + didx = DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03']) + pidx = PeriodIndex(['2011-01-01', 'NaT', '2011-01-03'], freq='M') + + # check DatetimeIndex compat + for idx in [didx, pidx]: + self.assertEqual(idx.get_loc(pd.NaT), 1) + self.assertEqual(idx.get_loc(None), 1) + self.assertEqual(idx.get_loc(float('nan')), 1) + self.assertEqual(idx.get_loc(np.nan), 1) + def test_append_concat(self): # #1815 d1 = date_range('12/31/1990', '12/31/1999', freq='A-DEC') @@ -3576,95 +3585,87 @@ def test_add_offset_nat(self): for freq in ['A', '2A', '3A']: p = Period('NaT', freq=freq) for o in [offsets.YearEnd(2)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) + # freq is Tick for freq in ['D', '2D', '3D']: p = Period('NaT', freq=freq) for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), np.timedelta64(3600 * 24, 's'), timedelta(-2), timedelta(hours=48)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23)]: - - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) for freq in ['H', '2H', '3H']: p = Period('NaT', freq=freq) for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), np.timedelta64(3600, 's'), timedelta(minutes=120), timedelta(days=4, minutes=180)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if not isinstance(o, np.timedelta64): - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(3200, 's'), timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) def test_sub_pdnat(self): # GH 13071 @@ -3749,24 +3750,22 @@ def test_sub_offset_nat(self): for freq in ['A', '2A', '3A']: p = Period('NaT', freq=freq) for o in [offsets.YearEnd(2)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) # freq is Tick for freq in ['D', '2D', '3D']: @@ -3774,37 +3773,33 @@ def test_sub_offset_nat(self): for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), np.timedelta64(3600 * 24, 's'), timedelta(-2), timedelta(hours=48)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) for freq in ['H', '2H', '3H']: p = Period('NaT', freq=freq) for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), np.timedelta64(3600, 's'), timedelta(minutes=120), timedelta(days=4, minutes=180)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(3200, 's'), timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) def test_nat_ops(self): for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) - self.assertEqual((p - 1).ordinal, tslib.iNaT) - self.assertEqual((p - Period('2011-01', freq=freq)).ordinal, - tslib.iNaT) - self.assertEqual((Period('2011-01', freq=freq) - p).ordinal, - tslib.iNaT) + self.assertIs(p + 1, tslib.NaT) + self.assertIs(1 + p, tslib.NaT) + self.assertIs(p - 1, tslib.NaT) + self.assertIs(p - Period('2011-01', freq=freq), tslib.NaT) + self.assertIs(Period('2011-01', freq=freq) - p, tslib.NaT) def test_period_ops_offset(self): p = Period('2011-04-01', freq='D') @@ -3830,18 +3825,17 @@ class TestPeriodIndexSeriesMethods(tm.TestCase): def _check(self, values, func, expected): idx = pd.PeriodIndex(values) result = func(idx) - tm.assert_index_equal(result, pd.PeriodIndex(expected)) + if isinstance(expected, pd.Index): + tm.assert_index_equal(result, expected) + else: + # comp op results in bool + tm.assert_numpy_array_equal(result, expected) s = pd.Series(values) result = func(s) - exp = pd.Series(expected) - # Period(NaT) != Period(NaT) - - lmask = result.map(lambda x: x.ordinal != tslib.iNaT) - rmask = exp.map(lambda x: x.ordinal != tslib.iNaT) - tm.assert_series_equal(lmask, rmask) - tm.assert_series_equal(result[lmask], exp[rmask]) + exp = pd.Series(expected, name=values.name) + tm.assert_series_equal(result, exp) def test_pi_ops(self): idx = PeriodIndex(['2011-01', '2011-02', '2011-03', @@ -3962,7 +3956,7 @@ def test_pi_sub_period(self): exp = pd.Index([12, 11, 10, 9], name='idx') tm.assert_index_equal(result, exp) - exp = pd.Index([np.nan, np.nan, np.nan, np.nan], name='idx') + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) @@ -3987,10 +3981,82 @@ def test_pi_sub_period_nat(self): exp = pd.Index([12, np.nan, 10, 9], name='idx') tm.assert_index_equal(result, exp) - exp = pd.Index([np.nan, np.nan, np.nan, np.nan], name='idx') + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) + def test_pi_comp_period(self): + idx = PeriodIndex(['2011-01', '2011-02', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > pd.Period('2011-03', freq='M') + exp = np.array([False, False, False, True], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + def test_pi_comp_period_nat(self): + idx = PeriodIndex(['2011-01', 'NaT', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x == tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: x != tslib.NaT + exp = np.array([True, True, True, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x < pd.Period('2011-03', freq='M') + exp = np.array([True, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: tslib.NaT >= x + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + class TestPeriodRepresentation(tm.TestCase): """ diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index ce88edcf4249b..7e97632ddfef3 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -1197,6 +1197,13 @@ def test_nat_arithmetic(self): self.assertIs(left - right, pd.NaT) self.assertIs(right - left, pd.NaT) + # int addition / subtraction + for (left, right) in [(pd.NaT, 2), (pd.NaT, 0), (pd.NaT, -3)]: + self.assertIs(right + left, pd.NaT) + self.assertIs(left + right, pd.NaT) + self.assertIs(left - right, pd.NaT) + self.assertIs(right - left, pd.NaT) + def test_nat_arithmetic_index(self): # GH 11718 diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 650b4c7979d8d..61955747b6d78 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -1082,7 +1082,10 @@ cdef class _Timestamp(datetime): return Timestamp(self.value + other_int, tz=self.tzinfo, freq=self.freq) elif is_integer_object(other): - if self.freq is None: + if self is NaT: + # to be compat with Period + return NaT + elif self.freq is None: raise ValueError("Cannot add integral value to Timestamp " "without freq.") return Timestamp((self.freq * other).apply(self), freq=self.freq)
- [x] closes #12759, #13582 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry - `pd.Period('NaT', freq='D')` now returns `pd.NaT` rather than its own nat repr. - To be compat with `Period` arithmetic, `pd.NaT` now supports addition / subtraction with `int` (previously `ValueError`). ``` pd.NaT + 1 # pd.NaT ``` Added tests for following: - [x] `Period` and `PeriodIndex`, `Series` ops - [x] add, sub, comp - [x] `PeriodIndex` creation from list-like which contains `Period` and `NaT` (#13430) - [x] `PeriodIndex` boxing - [x] `.to_period` - [x] `__contains__` any NaT-like(`pd.NaT, None, float('nan'), np.nan`)
https://api.github.com/repos/pandas-dev/pandas/pulls/13609
2016-07-10T22:39:40Z
2016-07-15T00:30:34Z
null
2016-07-15T00:36:14Z
GbqConnector should be able to fetch default credentials on Google Compute Engine
diff --git a/doc/source/io.rst b/doc/source/io.rst index 7917e6b4cdfce..477e1a7d5026a 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4475,6 +4475,15 @@ Additional information on service accounts can be found You will need to install an additional dependency: `oauth2client <https://github.com/google/oauth2client>`__. +Authentication via ``application default credentials`` is also possible. This is only valid +if the parameter ``private_key`` is not provided. This method also requires that +the credentials can be fetched from the environment the code is running in. +Otherwise, the OAuth2 client-side authentication is used. +Additional information on +`application default credentials <https://developers.google.com/identity/protocols/application-default-credentials>`__. + +.. versionadded:: 0.19.0 + .. note:: The `'private_key'` parameter can be set to either the file path of the service account key diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index f93e8f4240787..fa1b21116cb4f 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -361,6 +361,8 @@ Google BigQuery Enhancements Other enhancements ^^^^^^^^^^^^^^^^^^ +- The ``.get_credentials()`` method of ``GbqConnector`` can now first try to fetch [the application default credentials](https://developers.google.com/identity/protocols/application-default-credentials). See the :ref:`docs <io.bigquery_authentication>` for more details (:issue:`13577`). + - The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) - ``pd.to_numeric()`` now accepts a ``downcast`` parameter, which will downcast the data if possible to smallest specified numerical dtype (:issue:`13352`) diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 6288fdb609962..326e32c84ebe6 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -160,7 +160,60 @@ def get_credentials(self): if self.private_key: return self.get_service_account_credentials() else: - return self.get_user_account_credentials() + # Try to retrieve Application Default Credentials + credentials = self.get_application_default_credentials() + if not credentials: + credentials = self.get_user_account_credentials() + return credentials + + def get_application_default_credentials(self): + """ + This method tries to retrieve the "default application credentials". + This could be useful for running code on Google Cloud Platform. + + .. versionadded:: 0.19.0 + + Parameters + ---------- + None + + Returns + ------- + - GoogleCredentials, + If the default application credentials can be retrieved + from the environment. The retrieved credentials should also + have access to the project (self.project_id) on BigQuery. + - OR None, + If default application credentials can not be retrieved + from the environment. Or, the retrieved credentials do not + have access to the project (self.project_id) on BigQuery. + """ + import httplib2 + try: + from googleapiclient.discovery import build + except ImportError: + from apiclient.discovery import build + try: + from oauth2client.client import GoogleCredentials + except ImportError: + return None + + try: + credentials = GoogleCredentials.get_application_default() + except: + return None + + http = httplib2.Http() + try: + http = credentials.authorize(http) + bigquery_service = build('bigquery', 'v2', http=http) + # Check if the application has rights to the BigQuery project + jobs = bigquery_service.jobs() + job_data = {'configuration': {'query': {'query': 'SELECT 1'}}} + jobs.insert(projectId=self.project_id, body=job_data).execute() + return credentials + except: + return None def get_user_account_credentials(self): from oauth2client.client import OAuth2WebServerFlow @@ -578,10 +631,16 @@ def read_gbq(query, project_id=None, index_col=None, col_order=None, https://developers.google.com/api-client-library/python/apis/bigquery/v2 Authentication to the Google BigQuery service is via OAuth 2.0. - By default user account credentials are used. You will be asked to - grant permissions for product name 'pandas GBQ'. It is also posible - to authenticate via service account credentials by using - private_key parameter. + - If "private_key" is not provided: + By default "application default credentials" are used. + + .. versionadded:: 0.19.0 + + If default application credentials are not found or are restrictive, + user account credentials are used. In this case, you will be asked to + grant permissions for product name 'pandas GBQ'. + - If "private_key" is provided: + Service account credentials will be used to authenticate. Parameters ---------- @@ -689,10 +748,16 @@ def to_gbq(dataframe, destination_table, project_id, chunksize=10000, https://developers.google.com/api-client-library/python/apis/bigquery/v2 Authentication to the Google BigQuery service is via OAuth 2.0. - By default user account credentials are used. You will be asked to - grant permissions for product name 'pandas GBQ'. It is also posible - to authenticate via service account credentials by using - private_key parameter. + - If "private_key" is not provided: + By default "application default credentials" are used. + + .. versionadded:: 0.19.0 + + If default application credentials are not found or are restrictive, + user account credentials are used. In this case, you will be asked to + grant permissions for product name 'pandas GBQ'. + - If "private_key" is provided: + Service account credentials will be used to authenticate. Parameters ---------- diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 0d8512ffb5524..4b71192c907f8 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -151,6 +151,30 @@ def test_requirements(): raise nose.SkipTest(import_exception) +def _check_if_can_get_correct_default_credentials(): + # Checks if "Application Default Credentials" can be fetched + # from the environment the tests are running in. + # See Issue #13577 + test_requirements() + import httplib2 + try: + from googleapiclient.discovery import build + except ImportError: + from apiclient.discovery import build + try: + from oauth2client.client import GoogleCredentials + credentials = GoogleCredentials.get_application_default() + http = httplib2.Http() + http = credentials.authorize(http) + bigquery_service = build('bigquery', 'v2', http=http) + jobs = bigquery_service.jobs() + job_data = {'configuration': {'query': {'query': 'SELECT 1'}}} + jobs.insert(projectId=PROJECT_ID, body=job_data).execute() + return True + except: + return False + + def clean_gbq_environment(private_key=None): dataset = gbq._Dataset(PROJECT_ID, private_key=private_key) @@ -217,6 +241,21 @@ def test_should_be_able_to_get_results_from_query(self): schema, pages = self.sut.run_query('SELECT 1') self.assertTrue(pages is not None) + def test_get_application_default_credentials_does_not_throw_error(self): + if _check_if_can_get_correct_default_credentials(): + raise nose.SkipTest("Can get default_credentials " + "from the environment!") + credentials = self.sut.get_application_default_credentials() + self.assertIsNone(credentials) + + def test_get_application_default_credentials_returns_credentials(self): + if not _check_if_can_get_correct_default_credentials(): + raise nose.SkipTest("Cannot get default_credentials " + "from the environment!") + from oauth2client.client import GoogleCredentials + credentials = self.sut.get_application_default_credentials() + self.assertTrue(isinstance(credentials, GoogleCredentials)) + class TestGBQConnectorServiceAccountKeyPathIntegration(tm.TestCase): def setUp(self):
- [x] closes #13577 - [x] passed 1 new test - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13608
2016-07-10T20:35:40Z
2016-08-18T10:49:30Z
null
2016-08-18T10:49:51Z
CLN: Fix compile time warnings
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 657de7ec26efc..de2390b9c1259 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -531,3 +531,4 @@ Bug Fixes - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) +- Clean compile time warnings, ``warning: comparison of constant -1 with expression of type 'PANDAS_DATETIMEUNIT' is always true`` (:issue:`13607`) diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c index 3a1d37f86cc28..b633d6cde0820 100644 --- a/pandas/src/datetime/np_datetime_strings.c +++ b/pandas/src/datetime/np_datetime_strings.c @@ -460,7 +460,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -503,7 +503,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -975,7 +975,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -1005,11 +1005,6 @@ get_datetime_iso_8601_strlen(int local, PANDAS_DATETIMEUNIT base) { int len = 0; - /* If no unit is provided, return the maximum length */ - if (base == -1) { - return PANDAS_DATETIME_MAX_ISO8601_STRLEN; - } - switch (base) { /* Generic units can only be used to represent NaT */ /*case PANDAS_FR_GENERIC:*/ @@ -1146,28 +1141,13 @@ make_iso_8601_datetime(pandas_datetimestruct *dts, char *outstr, int outlen, local = 0; } - /* Automatically detect a good unit */ - if (base == -1) { - base = lossless_unit_from_datetimestruct(dts); - /* - * If there's a timezone, use at least minutes precision, - * and never split up hours and minutes by default - */ - if ((base < PANDAS_FR_m && local) || base == PANDAS_FR_h) { - base = PANDAS_FR_m; - } - /* Don't split up dates by default */ - else if (base < PANDAS_FR_D) { - base = PANDAS_FR_D; - } - } /* * Print weeks with the same precision as days. * * TODO: Could print weeks with YYYY-Www format if the week * epoch is a Monday. */ - else if (base == PANDAS_FR_W) { + if (base == PANDAS_FR_W) { base = PANDAS_FR_D; } diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 925c18cd23d8f..1080e9548ba56 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -450,7 +450,7 @@ static void *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue, si static void *PandasDateTimeStructToJSON(pandas_datetimestruct *dts, JSONTypeContext *tc, void *outValue, size_t *_outLen) { - int base = ((PyObjectEncoder*) tc->encoder)->datetimeUnit; + PANDAS_DATETIMEUNIT base = ((PyObjectEncoder*) tc->encoder)->datetimeUnit; if (((PyObjectEncoder*) tc->encoder)->datetimeIso) {
- [x] passes `git diff upstream/master | flake8 --diff` This commit suppresses these warnings warning: comparison of constant -1 with expression\ of type 'PANDAS_DATETIMEUNIT' is always true\ [-Wtautological-constant-out-of-range-compare]
https://api.github.com/repos/pandas-dev/pandas/pulls/13607
2016-07-10T15:35:45Z
2016-07-13T02:17:23Z
null
2016-07-13T02:20:35Z
CLN: remove radd workaround in ops.py
diff --git a/pandas/core/ops.py b/pandas/core/ops.py index f27a83f50e115..34ab3ae6863b5 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -34,7 +34,7 @@ # methods -def _create_methods(arith_method, radd_func, comp_method, bool_method, +def _create_methods(arith_method, comp_method, bool_method, use_numexpr, special=False, default_axis='columns'): # creates actual methods based upon arithmetic, comp and bool method # constructors. @@ -55,14 +55,14 @@ def names(x): return "__%s__" % x else: names = lambda x: x - radd_func = radd_func or operator.add + # Inframe, all special methods have default_axis=None, flex methods have # default_axis set to the default (columns) # yapf: disable new_methods = dict( add=arith_method(operator.add, names('add'), op('+'), default_axis=default_axis), - radd=arith_method(radd_func, names('radd'), op('+'), + radd=arith_method(lambda x, y: y + x, names('radd'), op('+'), default_axis=default_axis), sub=arith_method(operator.sub, names('sub'), op('-'), default_axis=default_axis), @@ -149,7 +149,7 @@ def add_methods(cls, new_methods, force, select, exclude): # ---------------------------------------------------------------------- # Arithmetic -def add_special_arithmetic_methods(cls, arith_method=None, radd_func=None, +def add_special_arithmetic_methods(cls, arith_method=None, comp_method=None, bool_method=None, use_numexpr=True, force=False, select=None, exclude=None): @@ -162,8 +162,6 @@ def add_special_arithmetic_methods(cls, arith_method=None, radd_func=None, arith_method : function (optional) factory for special arithmetic methods, with op string: f(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs) - radd_func : function (optional) - Possible replacement for ``operator.add`` for compatibility comp_method : function, optional, factory for rich comparison - signature: f(op, name, str_rep) use_numexpr : bool, default True @@ -176,12 +174,11 @@ def add_special_arithmetic_methods(cls, arith_method=None, radd_func=None, exclude : iterable of strings (optional) if passed, will not set functions with names in exclude """ - radd_func = radd_func or operator.add # in frame, special methods have default_axis = None, comp methods use # 'columns' - new_methods = _create_methods(arith_method, radd_func, comp_method, + new_methods = _create_methods(arith_method, comp_method, bool_method, use_numexpr, default_axis=None, special=True) @@ -218,7 +215,7 @@ def f(self, other): exclude=exclude) -def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, +def add_flex_arithmetic_methods(cls, flex_arith_method, flex_comp_method=None, flex_bool_method=None, use_numexpr=True, force=False, select=None, exclude=None): @@ -231,9 +228,6 @@ def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, flex_arith_method : function factory for special arithmetic methods, with op string: f(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs) - radd_func : function (optional) - Possible replacement for ``lambda x, y: operator.add(y, x)`` for - compatibility flex_comp_method : function, optional, factory for rich comparison - signature: f(op, name, str_rep) use_numexpr : bool, default True @@ -246,9 +240,8 @@ def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, exclude : iterable of strings (optional) if passed, will not set functions with names in exclude """ - radd_func = radd_func or (lambda x, y: operator.add(y, x)) # in frame, default axis is 'columns', doesn't matter for series and panel - new_methods = _create_methods(flex_arith_method, radd_func, + new_methods = _create_methods(flex_arith_method, flex_comp_method, flex_bool_method, use_numexpr, default_axis='columns', special=False) @@ -858,17 +851,6 @@ def wrapper(self, other): return wrapper -def _radd_compat(left, right): - radd = lambda x, y: y + x - # GH #353, NumPy 1.5.1 workaround - try: - output = radd(left, right) - except TypeError: - raise - - return output - - _op_descriptions = {'add': {'op': '+', 'desc': 'Addition', 'reversed': False, @@ -963,11 +945,9 @@ def flex_wrapper(self, other, level=None, fill_value=None, axis=0): series_flex_funcs = dict(flex_arith_method=_flex_method_SERIES, - radd_func=_radd_compat, flex_comp_method=_comp_method_SERIES) series_special_funcs = dict(arith_method=_arith_method_SERIES, - radd_func=_radd_compat, comp_method=_comp_method_SERIES, bool_method=_bool_method_SERIES) @@ -1209,11 +1189,9 @@ def f(self, other): frame_flex_funcs = dict(flex_arith_method=_arith_method_FRAME, - radd_func=_radd_compat, flex_comp_method=_flex_comp_method_FRAME) frame_special_funcs = dict(arith_method=_arith_method_FRAME, - radd_func=_radd_compat, comp_method=_comp_method_FRAME, bool_method=_arith_method_FRAME) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 519068b97a010..5c7762c56ec6d 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -7,7 +7,6 @@ import numpy as np import warnings -import operator from pandas.compat.numpy import function as nv from pandas.core.common import isnull, _values_from_object, _maybe_match_name @@ -803,7 +802,7 @@ def from_coo(cls, A, dense_index=False): # overwrite basic arithmetic to use SparseSeries version # force methods to overwrite previous definitions. ops.add_special_arithmetic_methods(SparseSeries, _arith_method, - radd_func=operator.add, comp_method=None, + comp_method=None, bool_method=None, use_numexpr=False, force=True) diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index 1e23c87fdb4ca..6ab382beb7973 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -1259,8 +1259,6 @@ def _check_op(arr, op): _check_op(arr, operator.floordiv) def test_series_frame_radd_bug(self): - import operator - # GH 353 vals = Series(tm.rands_array(5, 10)) result = 'foo_' + vals @@ -1273,7 +1271,78 @@ def test_series_frame_radd_bug(self): tm.assert_frame_equal(result, expected) # really raise this time - self.assertRaises(TypeError, operator.add, datetime.now(), self.ts) + with tm.assertRaises(TypeError): + datetime.now() + self.ts + + with tm.assertRaises(TypeError): + self.ts + datetime.now() + + def test_series_radd_more(self): + data = [[1, 2, 3], + [1.1, 2.2, 3.3], + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.NaT], + ['x', 'y', 1]] + + for d in data: + for dtype in [None, object]: + s = Series(d, dtype=dtype) + with tm.assertRaises(TypeError): + 'foo_' + s + + for dtype in [None, object]: + res = 1 + pd.Series([1, 2, 3], dtype=dtype) + exp = pd.Series([2, 3, 4], dtype=dtype) + tm.assert_series_equal(res, exp) + res = pd.Series([1, 2, 3], dtype=dtype) + 1 + tm.assert_series_equal(res, exp) + + res = np.nan + pd.Series([1, 2, 3], dtype=dtype) + exp = pd.Series([np.nan, np.nan, np.nan], dtype=dtype) + tm.assert_series_equal(res, exp) + res = pd.Series([1, 2, 3], dtype=dtype) + np.nan + tm.assert_series_equal(res, exp) + + s = pd.Series([pd.Timedelta('1 days'), pd.Timedelta('2 days'), + pd.Timedelta('3 days')], dtype=dtype) + exp = pd.Series([pd.Timedelta('4 days'), pd.Timedelta('5 days'), + pd.Timedelta('6 days')]) + tm.assert_series_equal(pd.Timedelta('3 days') + s, exp) + tm.assert_series_equal(s + pd.Timedelta('3 days'), exp) + + s = pd.Series(['x', np.nan, 'x']) + tm.assert_series_equal('a' + s, pd.Series(['ax', np.nan, 'ax'])) + tm.assert_series_equal(s + 'a', pd.Series(['xa', np.nan, 'xa'])) + + def test_frame_radd_more(self): + data = [[1, 2, 3], + [1.1, 2.2, 3.3], + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.NaT], + ['x', 'y', 1]] + + for d in data: + for dtype in [None, object]: + s = DataFrame(d, dtype=dtype) + with tm.assertRaises(TypeError): + 'foo_' + s + + for dtype in [None, object]: + res = 1 + pd.DataFrame([1, 2, 3], dtype=dtype) + exp = pd.DataFrame([2, 3, 4], dtype=dtype) + tm.assert_frame_equal(res, exp) + res = pd.DataFrame([1, 2, 3], dtype=dtype) + 1 + tm.assert_frame_equal(res, exp) + + res = np.nan + pd.DataFrame([1, 2, 3], dtype=dtype) + exp = pd.DataFrame([np.nan, np.nan, np.nan], dtype=dtype) + tm.assert_frame_equal(res, exp) + res = pd.DataFrame([1, 2, 3], dtype=dtype) + np.nan + tm.assert_frame_equal(res, exp) + + df = pd.DataFrame(['x', np.nan, 'x']) + tm.assert_frame_equal('a' + df, pd.DataFrame(['ax', np.nan, 'ax'])) + tm.assert_frame_equal(df + 'a', pd.DataFrame(['xa', np.nan, 'xa'])) def test_operators_frame(self): # rpow does not work with DataFrame
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` Remove workaround added in #353. The test added at that time has been passed. - https://github.com/pydata/pandas/blob/master/pandas/tests/series/test_operators.py#L1264
https://api.github.com/repos/pandas-dev/pandas/pulls/13606
2016-07-10T15:31:33Z
2016-07-10T21:16:41Z
null
2016-07-10T21:34:29Z
CLN: Cleanup ops.py
diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 0af7b6d80ce0e..3aaca1eea486e 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -20,7 +20,6 @@ from pandas.compat import bind_method import pandas.core.missing as missing import pandas.algos as _algos -import pandas.core.algorithms as algos from pandas.core.common import (is_list_like, notnull, isnull, _values_from_object, _maybe_match_name, needs_i8_conversion, is_datetimelike_v_numeric, @@ -258,30 +257,87 @@ def add_flex_arithmetic_methods(cls, flex_arith_method, exclude=exclude) -class _TimeOp(object): +class _Op(object): + """ - Wrapper around Series datetime/time/timedelta arithmetic operations. - Generally, you should use classmethod ``maybe_convert_for_time_op`` as an - entry point. + Wrapper around Series arithmetic operations. + Generally, you should use classmethod ``_Op.get_op`` as an entry point. + + This validates and coerces lhs and rhs depending on its dtype and + based on op. See _TimeOp also. + + Parameters + ---------- + left : Series + lhs of op + right : object + rhs of op + name : str + name of op + na_op : callable + a function which wraps op """ - fill_value = iNaT + + fill_value = np.nan wrap_results = staticmethod(lambda x: x) dtype = None def __init__(self, left, right, name, na_op): + self.left = left + self.right = right + + self.name = name + self.na_op = na_op + + self.lvalues = left + self.rvalues = right + + @classmethod + def get_op(cls, left, right, name, na_op): + """ + Get op dispatcher, returns _Op or _TimeOp. + + If ``left`` and ``right`` are appropriate for datetime arithmetic with + operation ``name``, processes them and returns a ``_TimeOp`` object + that stores all the required values. Otherwise, it will generate + either a ``_Op``, indicating that the operation is performed via + normal numpy path. + """ + is_timedelta_lhs = is_timedelta64_dtype(left) + is_datetime_lhs = (is_datetime64_dtype(left) or + is_datetime64tz_dtype(left)) - # need to make sure that we are aligning the data if isinstance(left, ABCSeries) and isinstance(right, ABCSeries): - left, right = left.align(right, copy=False) + # avoid repated alignment + if not left.index.equals(right.index): + left, right = left.align(right, copy=False) + + index, lidx, ridx = left.index.join(right.index, how='outer', + return_indexers=True) + # if DatetimeIndex have different tz, convert to UTC + left.index = index + right.index = index + + if not (is_datetime_lhs or is_timedelta_lhs): + return _Op(left, right, name, na_op) + else: + return _TimeOp(left, right, name, na_op) + + +class _TimeOp(_Op): + """ + Wrapper around Series datetime/time/timedelta arithmetic operations. + Generally, you should use classmethod ``_Op.get_op`` as an entry point. + """ + fill_value = iNaT + + def __init__(self, left, right, name, na_op): + super(_TimeOp, self).__init__(left, right, name, na_op) lvalues = self._convert_to_array(left, name=name) rvalues = self._convert_to_array(right, name=name, other=lvalues) - self.name = name - self.na_op = na_op - # left - self.left = left self.is_offset_lhs = self._is_offset(left) self.is_timedelta_lhs = is_timedelta64_dtype(lvalues) self.is_datetime64_lhs = is_datetime64_dtype(lvalues) @@ -292,7 +348,6 @@ def __init__(self, left, right, name, na_op): self.is_floating_lhs = left.dtype.kind == 'f' # right - self.right = right self.is_offset_rhs = self._is_offset(right) self.is_datetime64_rhs = is_datetime64_dtype(rvalues) self.is_datetime64tz_rhs = is_datetime64tz_dtype(rvalues) @@ -543,26 +598,6 @@ def _is_offset(self, arr_or_obj): else: return False - @classmethod - def maybe_convert_for_time_op(cls, left, right, name, na_op): - """ - if ``left`` and ``right`` are appropriate for datetime arithmetic with - operation ``name``, processes them and returns a ``_TimeOp`` object - that stores all the required values. Otherwise, it will generate - either a ``NotImplementedError`` or ``None``, indicating that the - operation is unsupported for datetimes (e.g., an unsupported r_op) or - that the data is not the right type for time ops. - """ - # decide if we can do it - is_timedelta_lhs = is_timedelta64_dtype(left) - is_datetime_lhs = (is_datetime64_dtype(left) or - is_datetime64tz_dtype(left)) - - if not (is_datetime_lhs or is_timedelta_lhs): - return None - - return cls(left, right, name, na_op) - def _arith_method_SERIES(op, name, str_rep, fill_zeros=None, default_axis=None, **eval_kwargs): @@ -615,53 +650,28 @@ def wrapper(left, right, name=name, na_op=na_op): if isinstance(right, pd.DataFrame): return NotImplemented - time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name, - na_op) + converted = _Op.get_op(left, right, name, na_op) - if time_converted is None: - lvalues, rvalues = left, right - dtype = None - wrap_results = lambda x: x - elif time_converted is NotImplemented: - return NotImplemented - else: - left, right = time_converted.left, time_converted.right - lvalues, rvalues = time_converted.lvalues, time_converted.rvalues - dtype = time_converted.dtype - wrap_results = time_converted.wrap_results - na_op = time_converted.na_op + left, right = converted.left, converted.right + lvalues, rvalues = converted.lvalues, converted.rvalues + dtype = converted.dtype + wrap_results = converted.wrap_results + na_op = converted.na_op if isinstance(rvalues, ABCSeries): - rindex = getattr(rvalues, 'index', rvalues) name = _maybe_match_name(left, rvalues) lvalues = getattr(lvalues, 'values', lvalues) rvalues = getattr(rvalues, 'values', rvalues) - if left.index.equals(rindex): - index = left.index - else: - index, lidx, ridx = left.index.join(rindex, how='outer', - return_indexers=True) - - if lidx is not None: - lvalues = algos.take_1d(lvalues, lidx) - - if ridx is not None: - rvalues = algos.take_1d(rvalues, ridx) - - result = wrap_results(safe_na_op(lvalues, rvalues)) - return left._constructor(result, index=index, - name=name, dtype=dtype) + # _Op aligns left and right else: - # scalars + name = left.name if (hasattr(lvalues, 'values') and not isinstance(lvalues, pd.DatetimeIndex)): lvalues = lvalues.values - result = wrap_results(safe_na_op(lvalues, rvalues)) - return left._constructor(result, - index=left.index, name=left.name, - dtype=dtype) - + result = wrap_results(safe_na_op(lvalues, rvalues)) + return left._constructor(result, index=left.index, + name=name, dtype=dtype) return wrapper @@ -895,6 +905,32 @@ def wrapper(self, other): _op_descriptions[reverse_op]['reverse'] = k +_flex_doc_SERIES = """ +%s of series and other, element-wise (binary operator `%s`). + +Equivalent to ``%s``, but with support to substitute a fill_value for +missing data in one of the inputs. + +Parameters +---------- +other: Series or scalar value +fill_value : None or float value, default None (NaN) + Fill missing (NaN) values with this value. If both Series are + missing, the result will be missing +level : int or name + Broadcast across a level, matching Index values on the + passed MultiIndex level + +Returns +------- +result : Series + +See also +-------- +Series.%s +""" + + def _flex_method_SERIES(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs): op_name = name.replace('__', '') @@ -904,30 +940,8 @@ def _flex_method_SERIES(op, name, str_rep, default_axis=None, fill_zeros=None, else: equiv = 'series ' + op_desc['op'] + ' other' - doc = """ - %s of series and other, element-wise (binary operator `%s`). - - Equivalent to ``%s``, but with support to substitute a fill_value for - missing data in one of the inputs. - - Parameters - ---------- - other: Series or scalar value - fill_value : None or float value, default None (NaN) - Fill missing (NaN) values with this value. If both Series are - missing, the result will be missing - level : int or name - Broadcast across a level, matching Index values on the - passed MultiIndex level - - Returns - ------- - result : Series - - See also - -------- - Series.%s - """ % (op_desc['desc'], op_name, equiv, op_desc['reverse']) + doc = _flex_doc_SERIES % (op_desc['desc'], op_name, equiv, + op_desc['reverse']) @Appender(doc) def flex_wrapper(self, other, level=None, fill_value=None, axis=0): @@ -983,6 +997,75 @@ def flex_wrapper(self, other, level=None, fill_value=None, axis=0): result : DataFrame """ +_flex_doc_FRAME = """ +%s of dataframe and other, element-wise (binary operator `%s`). + +Equivalent to ``%s``, but with support to substitute a fill_value for +missing data in one of the inputs. + +Parameters +---------- +other : Series, DataFrame, or constant +axis : {0, 1, 'index', 'columns'} + For Series input, axis to match Series index on +fill_value : None or float value, default None + Fill missing (NaN) values with this value. If both DataFrame + locations are missing, the result will be missing +level : int or name + Broadcast across a level, matching Index values on the + passed MultiIndex level + +Notes +----- +Mismatched indices will be unioned together + +Returns +------- +result : DataFrame + +See also +-------- +DataFrame.%s +""" + + +def _align_method_FRAME(left, right, axis): + """ convert rhs to meet lhs dims if input is list, tuple or np.ndarray """ + + def to_series(right): + msg = 'Unable to coerce to Series, length must be {0}: given {1}' + if axis is not None and left._get_axis_name(axis) == 'index': + if len(left.index) != len(right): + raise ValueError(msg.format(len(left.index), len(right))) + right = left._constructor_sliced(right, index=left.index) + else: + if len(left.columns) != len(right): + raise ValueError(msg.format(len(left.columns), len(right))) + right = left._constructor_sliced(right, index=left.columns) + return right + + if isinstance(right, (list, tuple)): + right = to_series(right) + + elif isinstance(right, np.ndarray) and right.ndim: # skips np scalar + + if right.ndim == 1: + right = to_series(right) + + elif right.ndim == 2: + if left.shape != right.shape: + msg = ("Unable to coerce to DataFrame, " + "shape must be {0}: given {1}") + raise ValueError(msg.format(left.shape, right.shape)) + + right = left._constructor(right, index=left.index, + columns=left.columns) + else: + msg = 'Unable to coerce to Series/DataFrame, dim must be <= 2: {0}' + raise ValueError(msg.format(right.shape, )) + + return right + def _arith_method_FRAME(op, name, str_rep=None, default_axis='columns', fill_zeros=None, **eval_kwargs): @@ -1027,75 +1110,20 @@ def na_op(x, y): else: equiv = 'dataframe ' + op_desc['op'] + ' other' - doc = """ - %s of dataframe and other, element-wise (binary operator `%s`). - - Equivalent to ``%s``, but with support to substitute a fill_value for - missing data in one of the inputs. - - Parameters - ---------- - other : Series, DataFrame, or constant - axis : {0, 1, 'index', 'columns'} - For Series input, axis to match Series index on - fill_value : None or float value, default None - Fill missing (NaN) values with this value. If both DataFrame - locations are missing, the result will be missing - level : int or name - Broadcast across a level, matching Index values on the - passed MultiIndex level - - Notes - ----- - Mismatched indices will be unioned together - - Returns - ------- - result : DataFrame - - See also - -------- - DataFrame.%s - """ % (op_desc['desc'], op_name, equiv, op_desc['reverse']) + doc = _flex_doc_FRAME % (op_desc['desc'], op_name, equiv, + op_desc['reverse']) else: doc = _arith_doc_FRAME % name @Appender(doc) def f(self, other, axis=default_axis, level=None, fill_value=None): + + other = _align_method_FRAME(self, other, axis) + if isinstance(other, pd.DataFrame): # Another DataFrame return self._combine_frame(other, na_op, fill_value, level) elif isinstance(other, ABCSeries): return self._combine_series(other, na_op, fill_value, axis, level) - elif isinstance(other, (list, tuple)): - if axis is not None and self._get_axis_name(axis) == 'index': - # TODO: Get all of these to use _constructor_sliced - # casted = self._constructor_sliced(other, index=self.index) - casted = pd.Series(other, index=self.index) - else: - # casted = self._constructor_sliced(other, index=self.columns) - casted = pd.Series(other, index=self.columns) - return self._combine_series(casted, na_op, fill_value, axis, level) - elif isinstance(other, np.ndarray) and other.ndim: # skips np scalar - if other.ndim == 1: - if axis is not None and self._get_axis_name(axis) == 'index': - # casted = self._constructor_sliced(other, - # index=self.index) - casted = pd.Series(other, index=self.index) - else: - # casted = self._constructor_sliced(other, - # index=self.columns) - casted = pd.Series(other, index=self.columns) - return self._combine_series(casted, na_op, fill_value, axis, - level) - elif other.ndim == 2: - # casted = self._constructor(other, index=self.index, - # columns=self.columns) - casted = pd.DataFrame(other, index=self.index, - columns=self.columns) - return self._combine_frame(casted, na_op, fill_value, level) - else: - raise ValueError("Incompatible argument shape: %s" % - (other.shape, )) else: if fill_value is not None: self = self.fillna(fill_value) @@ -1135,39 +1163,14 @@ def na_op(x, y): @Appender('Wrapper for flexible comparison methods %s' % name) def f(self, other, axis=default_axis, level=None): + + other = _align_method_FRAME(self, other, axis) + if isinstance(other, pd.DataFrame): # Another DataFrame return self._flex_compare_frame(other, na_op, str_rep, level) elif isinstance(other, ABCSeries): return self._combine_series(other, na_op, None, axis, level) - - elif isinstance(other, (list, tuple)): - if axis is not None and self._get_axis_name(axis) == 'index': - casted = pd.Series(other, index=self.index) - else: - casted = pd.Series(other, index=self.columns) - - return self._combine_series(casted, na_op, None, axis, level) - - elif isinstance(other, np.ndarray): - if other.ndim == 1: - if axis is not None and self._get_axis_name(axis) == 'index': - casted = pd.Series(other, index=self.index) - else: - casted = pd.Series(other, index=self.columns) - - return self._combine_series(casted, na_op, None, axis, level) - - elif other.ndim == 2: - casted = pd.DataFrame(other, index=self.index, - columns=self.columns) - - return self._flex_compare_frame(casted, na_op, str_rep, level) - - else: - raise ValueError("Incompatible argument shape: %s" % - (other.shape, )) - else: return self._combine_const(other, na_op) diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index ee7c296f563f0..e2e0f568e4098 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -417,10 +417,11 @@ def test_arith_flex_frame(self): # ndim >= 3 ndim_5 = np.ones(self.frame.shape + (3, 4, 5)) - with assertRaisesRegexp(ValueError, 'shape'): + msg = "Unable to coerce to Series/DataFrame" + with assertRaisesRegexp(ValueError, msg): f(self.frame, ndim_5) - with assertRaisesRegexp(ValueError, 'shape'): + with assertRaisesRegexp(ValueError, msg): getattr(self.frame, op)(ndim_5) # res_add = self.frame.add(self.frame) @@ -581,8 +582,9 @@ def _check_unaligned_frame(meth, op, df, other): # scalar assert_frame_equal(f(0), o(df, 0)) # NAs + msg = "Unable to coerce to Series/DataFrame" assert_frame_equal(f(np.nan), o(df, np.nan)) - with assertRaisesRegexp(ValueError, 'shape'): + with assertRaisesRegexp(ValueError, msg): f(ndim_5) # Series @@ -662,6 +664,17 @@ def _test_seq(df, idx_ser, col_ser): exp = DataFrame({'col': [False, True, False]}) assert_frame_equal(result, exp) + def test_dti_tz_convert_to_utc(self): + base = pd.DatetimeIndex(['2011-01-01', '2011-01-02', + '2011-01-03'], tz='UTC') + idx1 = base.tz_convert('Asia/Tokyo')[:2] + idx2 = base.tz_convert('US/Eastern')[1:] + + df1 = DataFrame({'A': [1, 2]}, index=idx1) + df2 = DataFrame({'A': [1, 1]}, index=idx2) + exp = DataFrame({'A': [np.nan, 3, np.nan]}, index=base) + assert_frame_equal(df1 + df2, exp) + def test_arith_flex_series(self): df = self.simple @@ -1176,6 +1189,53 @@ def test_inplace_ops_identity(self): assert_frame_equal(df2, expected) self.assertIs(df._data, df2._data) + def test_alignment_non_pandas(self): + index = ['A', 'B', 'C'] + columns = ['X', 'Y', 'Z'] + df = pd.DataFrame(np.random.randn(3, 3), index=index, columns=columns) + + align = pd.core.ops._align_method_FRAME + + for val in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3])]: + + tm.assert_series_equal(align(df, val, 'index'), + Series([1, 2, 3], index=df.index)) + tm.assert_series_equal(align(df, val, 'columns'), + Series([1, 2, 3], index=df.columns)) + + # length mismatch + msg = 'Unable to coerce to Series, length must be 3: given 2' + for val in [[1, 2], (1, 2), np.array([1, 2])]: + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'index') + + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'columns') + + val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + tm.assert_frame_equal(align(df, val, 'index'), + DataFrame(val, index=df.index, + columns=df.columns)) + tm.assert_frame_equal(align(df, val, 'columns'), + DataFrame(val, index=df.index, + columns=df.columns)) + + # shape mismatch + msg = 'Unable to coerce to DataFrame, shape must be' + val = np.array([[1, 2, 3], [4, 5, 6]]) + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'index') + + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'columns') + + val = np.zeros((3, 3, 3)) + with tm.assertRaises(ValueError): + align(df, val, 'index') + with tm.assertRaises(ValueError): + align(df, val, 'columns') + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index 9c401e9ce6da8..5ebe528ff8cab 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -571,11 +571,11 @@ def run_ops(ops, get_ser, test_ser): td2 / td1 # ## datetime64 ### - dt1 = Series([Timestamp('20111230'), Timestamp('20120101'), Timestamp( - '20120103')]) + dt1 = Series([Timestamp('20111230'), Timestamp('20120101'), + Timestamp('20120103')]) dt1.iloc[2] = np.nan - dt2 = Series([Timestamp('20111231'), Timestamp('20120102'), Timestamp( - '20120104')]) + dt2 = Series([Timestamp('20111231'), Timestamp('20120102'), + Timestamp('20120104')]) ops = ['__add__', '__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__', '__radd__', '__rmul__', '__rfloordiv__', '__rtruediv__', '__rdiv__', '__rpow__'] @@ -607,9 +607,10 @@ def run_ops(ops, get_ser, test_ser): ops = ['__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__', '__rmul__', '__rfloordiv__', '__rtruediv__', '__rdiv__', '__rpow__'] - dt1 = Series( - date_range('2000-01-01 09:00:00', periods=5, - tz='US/Eastern'), name='foo') + + tz = 'US/Eastern' + dt1 = Series(date_range('2000-01-01 09:00:00', periods=5, + tz=tz), name='foo') dt2 = dt1.copy() dt2.iloc[2] = np.nan td1 = Series(timedelta_range('1 days 1 min', periods=5, freq='H')) @@ -618,58 +619,48 @@ def run_ops(ops, get_ser, test_ser): run_ops(ops, dt1, td1) result = dt1 + td1[0] - expected = ( - dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt2 + td2[0] - expected = ( - dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) # odd numpy behavior with scalar timedeltas if not _np_version_under1p8: result = td1[0] + dt1 - expected = ( - dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) result = td2[0] + dt2 - expected = ( - dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt1 - td1[0] - expected = ( - dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) self.assertRaises(TypeError, lambda: td1[0] - dt1) result = dt2 - td2[0] - expected = ( - dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) self.assertRaises(TypeError, lambda: td2[0] - dt2) result = dt1 + td1 - expected = ( - dt1.dt.tz_localize(None) + td1).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) + td1).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt2 + td2 - expected = ( - dt2.dt.tz_localize(None) + td2).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) + td2).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt1 - td1 - expected = ( - dt1.dt.tz_localize(None) - td1).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) - td1).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt2 - td2 - expected = ( - dt2.dt.tz_localize(None) - td2).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) - td2).dt.tz_localize(tz) + assert_series_equal(result, exp) self.assertRaises(TypeError, lambda: td1 - dt1) self.assertRaises(TypeError, lambda: td2 - dt2) @@ -1555,3 +1546,12 @@ def test_datetime64_with_index(self): df['expected'] = df['date'] - df.index.to_series() df['result'] = df['date'] - df.index assert_series_equal(df['result'], df['expected'], check_names=False) + + def test_dti_tz_convert_to_utc(self): + base = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], + tz='UTC') + idx1 = base.tz_convert('Asia/Tokyo')[:2] + idx2 = base.tz_convert('US/Eastern')[1:] + + res = Series([1, 2], index=idx1) + Series([1, 1], index=idx2) + assert_series_equal(res, Series([np.nan, 3, np.nan], index=base))
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` Cleanup duplicated code paths before fixing some ops related issues.
https://api.github.com/repos/pandas-dev/pandas/pulls/13605
2016-07-10T15:13:29Z
2016-07-12T10:55:05Z
null
2016-07-12T10:58:33Z
CLN: Removed coerce param in pd.to_timedelta and pd.to_datetime
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 0354a8046e873..7fa9991138fba 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -62,6 +62,7 @@ Deprecations Removal of prior version deprecations/changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +- ``pd.to_datetime`` and ``pd.to_timedelta`` have dropped the ``coerce`` parameter in favor of ``errors`` (:issue:`13602`) diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index ac48fcc2551ea..f640b3974b360 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -5514,22 +5514,6 @@ def test_second(self): class TestDaysInMonth(tm.TestCase): - def test_coerce_deprecation(self): - - # deprecation of coerce - with tm.assert_produces_warning(FutureWarning): - to_datetime('2015-02-29', coerce=True) - with tm.assert_produces_warning(FutureWarning): - self.assertRaises(ValueError, - lambda: to_datetime('2015-02-29', coerce=False)) - - # multiple arguments - for e, c in zip(['raise', 'ignore', 'coerce'], [True, False]): - with tm.assert_produces_warning(FutureWarning): - self.assertRaises(TypeError, - lambda: to_datetime('2015-02-29', errors=e, - coerce=c)) - # tests for issue #10154 def test_day_not_in_month_coerce(self): self.assertTrue(isnull(to_datetime('2015-02-29', errors='coerce'))) diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 2ca3fcea8005b..9bf39652a4e00 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -11,12 +11,9 @@ is_timedelta64_dtype, is_list_like) from pandas.types.generic import ABCSeries, ABCIndexClass -from pandas.util.decorators import deprecate_kwarg -@deprecate_kwarg(old_arg_name='coerce', new_arg_name='errors', - mapping={True: 'coerce', False: 'raise'}) -def to_timedelta(arg, unit='ns', box=True, errors='raise', coerce=None): +def to_timedelta(arg, unit='ns', box=True, errors='raise'): """ Convert argument to timedelta diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index 93d35ff964e69..637e70b76de98 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -16,7 +16,6 @@ from pandas.types.missing import notnull import pandas.compat as compat -from pandas.util.decorators import deprecate_kwarg _DATEUTIL_LEXER_SPLIT = None try: @@ -175,10 +174,8 @@ def _guess_datetime_format_for_array(arr, **kwargs): return _guess_datetime_format(arr[non_nan_elements[0]], **kwargs) -@deprecate_kwarg(old_arg_name='coerce', new_arg_name='errors', - mapping={True: 'coerce', False: 'raise'}) def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, - utc=None, box=True, format=None, exact=True, coerce=None, + utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False): """ Convert argument to datetime.
Deprecated back in `v0.17.0` <a href="https://github.com/pydata/pandas/commit/987b7e7e586b8df1d127406c69e0a9094a1a5322">here</a>. Seems appropriate to carry though now.
https://api.github.com/repos/pandas-dev/pandas/pulls/13602
2016-07-09T21:51:53Z
2016-10-15T19:58:27Z
null
2016-10-15T20:08:03Z
ERR: Fix TimeDelta to Timedelta
diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index e515ba624d203..1586d0385732f 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -137,12 +137,12 @@ def test_construction(self): self.assertRaises(ValueError, lambda: Timedelta('3.1415')) # invalid construction - tm.assertRaisesRegexp(ValueError, "cannot construct a TimeDelta", + tm.assertRaisesRegexp(ValueError, "cannot construct a Timedelta", lambda: Timedelta()) tm.assertRaisesRegexp(ValueError, "unit abbreviation w/o a number", lambda: Timedelta('foo')) tm.assertRaisesRegexp(ValueError, - "cannot construct a TimeDelta from the passed " + "cannot construct a Timedelta from the passed " "arguments, allowed keywords are ", lambda: Timedelta(day=10)) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index df6554fe1d5de..61c0f9c5a093b 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -2615,7 +2615,7 @@ class Timedelta(_Timedelta): if value is None: if not len(kwargs): - raise ValueError("cannot construct a TimeDelta without a value/unit or descriptive keywords (days,seconds....)") + raise ValueError("cannot construct a Timedelta without a value/unit or descriptive keywords (days,seconds....)") def _to_py_int_float(v): if is_integer_object(v): @@ -2630,7 +2630,7 @@ class Timedelta(_Timedelta): nano = kwargs.pop('nanoseconds',0) value = convert_to_timedelta64(timedelta(**kwargs),'ns',False) + nano except TypeError as e: - raise ValueError("cannot construct a TimeDelta from the passed arguments, allowed keywords are " + raise ValueError("cannot construct a Timedelta from the passed arguments, allowed keywords are " "[weeks, days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds]") if isinstance(value, Timedelta):
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13600
2016-07-09T15:29:16Z
2016-07-09T17:01:59Z
2016-07-09T17:01:59Z
2016-07-09T23:47:27Z
CLN: Initialization coincides with mapping, hence with uniqueness check
diff --git a/pandas/index.pyx b/pandas/index.pyx index 71717dd2d771b..bc985100692fc 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -80,7 +80,7 @@ cdef class IndexEngine: cdef: bint unique, monotonic_inc, monotonic_dec - bint initialized, monotonic_check, unique_check + bint initialized, monotonic_check def __init__(self, vgetter, n): self.vgetter = vgetter @@ -91,7 +91,6 @@ cdef class IndexEngine: self.monotonic_check = 0 self.unique = 0 - self.unique_check = 0 self.monotonic_inc = 0 self.monotonic_dec = 0 @@ -211,8 +210,8 @@ cdef class IndexEngine: property is_unique: def __get__(self): - if not self.unique_check: - self._do_unique_check() + if not self.initialized: + self.initialize() return self.unique == 1 @@ -246,9 +245,6 @@ cdef class IndexEngine: cdef _get_index_values(self): return self.vgetter() - cdef inline _do_unique_check(self): - self._ensure_mapping_populated() - def _call_monotonic(self, values): raise NotImplementedError @@ -270,7 +266,6 @@ cdef class IndexEngine: if len(self.mapping) == len(values): self.unique = 1 - self.unique_check = 1 self.initialized = 1
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` Rebased version of https://github.com/pydata/pandas/pull/10229 which was [actually not](https://github.com/pydata/pandas/pull/10229#issuecomment-131470116) fixed by https://github.com/pydata/pandas/pull/10199. Nothing particular relevant, just wanted to delete this branch locally and noticed it still applies: you'll judge what to do of it.
https://api.github.com/repos/pandas-dev/pandas/pulls/13594
2016-07-09T15:07:20Z
2016-07-15T00:21:47Z
null
2016-07-17T07:45:35Z
DEPR: rename Timestamp.offset to .freq
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 40ae38f12fccb..afdc15d5dc3ad 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -439,6 +439,7 @@ Deprecations - ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`) - ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) - top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`) +- ``Timestamp`` ``offset`` option and property has been deprecated in favor of ``freq`` (:issue:`12160`) .. _whatsnew_0190.performance: diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 23aa133125213..ff06a5f212f8b 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -481,12 +481,12 @@ def encode(obj): tz = obj.tzinfo if tz is not None: tz = u(tz.zone) - offset = obj.offset - if offset is not None: - offset = u(offset.freqstr) + freq = obj.freq + if freq is not None: + freq = u(freq.freqstr) return {u'typ': u'timestamp', u'value': obj.value, - u'offset': offset, + u'freq': freq, u'tz': tz} if isinstance(obj, NaTType): return {u'typ': u'nat'} @@ -556,7 +556,8 @@ def decode(obj): if typ is None: return obj elif typ == u'timestamp': - return Timestamp(obj[u'value'], tz=obj[u'tz'], offset=obj[u'offset']) + freq = obj[u'freq'] if 'freq' in obj else obj[u'offset'] + return Timestamp(obj[u'value'], tz=obj[u'tz'], freq=freq) elif typ == u'nat': return NaT elif typ == u'period': diff --git a/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_2.7.12.msgpack b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_2.7.12.msgpack new file mode 100644 index 0000000000000..978c2c5045314 Binary files /dev/null and b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_2.7.12.msgpack differ diff --git a/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_3.5.2.msgpack b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_3.5.2.msgpack new file mode 100644 index 0000000000000..ea8efdc86dd2d Binary files /dev/null and b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_3.5.2.msgpack differ diff --git a/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle index 5ee1f88c93a34..bb237f53476b5 100644 Binary files a/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle and b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle differ diff --git a/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_3.5.2.pickle b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_3.5.2.pickle new file mode 100644 index 0000000000000..db1d17a8b67c3 Binary files /dev/null and b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_3.5.2.pickle differ diff --git a/pandas/io/tests/generate_legacy_storage_files.py b/pandas/io/tests/generate_legacy_storage_files.py index 25fd86d899c08..1910b1c83b12f 100644 --- a/pandas/io/tests/generate_legacy_storage_files.py +++ b/pandas/io/tests/generate_legacy_storage_files.py @@ -5,7 +5,7 @@ SparseSeries, SparseDataFrame, Index, MultiIndex, bdate_range, to_msgpack, date_range, period_range, - Timestamp, Categorical, Period) + Timestamp, NaT, Categorical, Period) from pandas.compat import u import os import sys @@ -140,6 +140,13 @@ def create_data(): int16=Categorical(np.arange(1000)), int32=Categorical(np.arange(10000))) + timestamp = dict(normal=Timestamp('2011-01-01'), + nat=NaT, + tz=Timestamp('2011-01-01', tz='US/Eastern'), + freq=Timestamp('2011-01-01', offset='D'), + both=Timestamp('2011-01-01', tz='Asia/Tokyo', + offset='M')) + return dict(series=series, frame=frame, panel=panel, @@ -149,7 +156,8 @@ def create_data(): sp_series=dict(float=_create_sp_series(), ts=_create_sp_tsseries()), sp_frame=dict(float=_create_sp_frame()), - cat=cat) + cat=cat, + timestamp=timestamp) def create_pickle_data(): diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index ad7d6c3c9f94f..eedc2d5410797 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -8,7 +8,7 @@ from distutils.version import LooseVersion from pandas import compat -from pandas.compat import u +from pandas.compat import u, PY3 from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range, date_range, period_range, Index, Categorical) from pandas.core.common import PerformanceWarning @@ -58,6 +58,19 @@ def check_arbitrary(a, b): assert_series_equal(a, b) elif isinstance(a, Index): assert_index_equal(a, b) + elif isinstance(a, Categorical): + # Temp, + # Categorical.categories is changed from str to bytes in PY3 + # maybe the same as GH 13591 + if PY3 and b.categories.inferred_type == 'string': + pass + else: + tm.assert_categorical_equal(a, b) + elif a is NaT: + tm.assertIs(b, NaT) + elif isinstance(a, Timestamp): + assert a == b + assert a.freq == b.freq else: assert(a == b) @@ -815,8 +828,8 @@ def check_min_structure(self, data): for typ, v in self.minimum_structure.items(): assert typ in data, '"{0}" not found in unpacked data'.format(typ) for kind in v: - assert kind in data[ - typ], '"{0}" not found in data["{1}"]'.format(kind, typ) + msg = '"{0}" not found in data["{1}"]'.format(kind, typ) + assert kind in data[typ], msg def compare(self, vf, version): # GH12277 encoding default used to be latin-1, now utf-8 @@ -839,8 +852,8 @@ def compare(self, vf, version): # use a specific comparator # if available - comparator = getattr( - self, "compare_{typ}_{dt}".format(typ=typ, dt=dt), None) + comp_method = "compare_{typ}_{dt}".format(typ=typ, dt=dt) + comparator = getattr(self, comp_method, None) if comparator is not None: comparator(result, expected, typ, version) else: @@ -872,9 +885,8 @@ def read_msgpacks(self, version): n = 0 for f in os.listdir(pth): # GH12142 0.17 files packed in P2 can't be read in P3 - if (compat.PY3 and - version.startswith('0.17.') and - f.split('.')[-4][-1] == '2'): + if (compat.PY3 and version.startswith('0.17.') and + f.split('.')[-4][-1] == '2'): continue vf = os.path.join(pth, f) try: diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index e337ad4dcfed2..55c14fee9e3ed 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -46,6 +46,12 @@ def compare_element(self, result, expected, typ, version=None): if typ.startswith('sp_'): comparator = getattr(tm, "assert_%s_equal" % typ) comparator(result, expected, exact_indices=False) + elif typ == 'timestamp': + if expected is pd.NaT: + assert result is pd.NaT + else: + tm.assert_equal(result, expected) + tm.assert_equal(result.freq, expected.freq) else: comparator = getattr(tm, "assert_%s_equal" % typ, tm.assert_almost_equal) diff --git a/pandas/lib.pxd b/pandas/lib.pxd index 36c91faa00036..554b0248e97ea 100644 --- a/pandas/lib.pxd +++ b/pandas/lib.pxd @@ -1,3 +1,4 @@ # prototypes for sharing cdef bint is_null_datetimelike(v) +cpdef bint is_period(val) diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index 262e036ff44f1..234ac7ea2c60c 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -33,7 +33,7 @@ def is_bool(object obj): def is_complex(object obj): return util.is_complex_object(obj) -def is_period(object val): +cpdef bint is_period(object val): """ Return a boolean if this is a Period object """ return util.is_period_object(val) @@ -538,9 +538,6 @@ def is_time_array(ndarray[object] values): return False return True -def is_period(object o): - from pandas import Period - return isinstance(o,Period) def is_period_array(ndarray[object] values): cdef Py_ssize_t i, n = len(values) diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index aca0d0dbc107b..af2e295ae0cfc 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -24,7 +24,7 @@ cimport cython from datetime cimport * cimport util cimport lib -from lib cimport is_null_datetimelike +from lib cimport is_null_datetimelike, is_period import lib from pandas import tslib from tslib import Timedelta, Timestamp, iNaT, NaT @@ -484,8 +484,11 @@ def extract_freq(ndarray[object] values): for i in range(n): p = values[i] + try: - return p.freq + # now Timestamp / NaT has freq attr + if is_period(p): + return p.freq except AttributeError: pass diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index b86b248ead290..a6246790f83cb 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -965,7 +965,7 @@ def test_indexing_with_datetime_tz(self): # indexing - fast_xs df = DataFrame({'a': date_range('2014-01-01', periods=10, tz='UTC')}) result = df.iloc[5] - expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', offset='D') + expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', freq='D') self.assertEqual(result, expected) result = df.loc[5] diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index a80a3af56b18f..c632704b7c5eb 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -426,10 +426,10 @@ def test_constructor_with_datetime_tz(self): # indexing result = s.iloc[0] self.assertEqual(result, Timestamp('2013-01-01 00:00:00-0500', - tz='US/Eastern', offset='D')) + tz='US/Eastern', freq='D')) result = s[0] self.assertEqual(result, Timestamp('2013-01-01 00:00:00-0500', - tz='US/Eastern', offset='D')) + tz='US/Eastern', freq='D')) result = s[Series([True, True, False], index=s.index)] assert_series_equal(result, s[0:2]) diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index c4ccef13f2844..1b1db90ea713d 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -2365,7 +2365,7 @@ def test_reset_index_datetime(self): 'a': np.arange(6, dtype='int64')}, columns=['level_0', 'level_1', 'a']) expected['level_1'] = expected['level_1'].apply( - lambda d: pd.Timestamp(d, offset='D', tz=tz)) + lambda d: pd.Timestamp(d, freq='D', tz=tz)) assert_frame_equal(df.reset_index(), expected) def test_reset_index_period(self): diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 83cb768b37aaa..9b36bc5907066 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -558,7 +558,7 @@ def _generate(cls, start, end, periods, name, offset, @property def _box_func(self): - return lambda x: Timestamp(x, offset=self.offset, tz=self.tz) + return lambda x: Timestamp(x, freq=self.offset, tz=self.tz) def _convert_for_op(self, value): """ Convert value to be insertable to ndarray """ @@ -1199,8 +1199,9 @@ def __iter__(self): for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, l) - converted = tslib.ints_to_pydatetime( - data[start_i:end_i], tz=self.tz, offset=self.offset, box=True) + converted = tslib.ints_to_pydatetime(data[start_i:end_i], + tz=self.tz, freq=self.freq, + box=True) for v in converted: yield v diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 360944e355b4d..17b6dd12a5c02 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -124,10 +124,11 @@ def test_minmax(self): def test_numpy_minmax(self): dr = pd.date_range(start='2016-01-15', end='2016-01-20') - self.assertEqual(np.min(dr), Timestamp( - '2016-01-15 00:00:00', offset='D')) - self.assertEqual(np.max(dr), Timestamp( - '2016-01-20 00:00:00', offset='D')) + + self.assertEqual(np.min(dr), + Timestamp('2016-01-15 00:00:00', freq='D')) + self.assertEqual(np.max(dr), + Timestamp('2016-01-20 00:00:00', freq='D')) errmsg = "the 'out' parameter is not supported" tm.assertRaisesRegexp(ValueError, errmsg, np.min, dr, out=0) @@ -148,11 +149,11 @@ def test_round(self): elt = rng[1] expected_rng = DatetimeIndex([ - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 01:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 01:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, freq='30T'), ]) expected_elt = expected_rng[1] @@ -175,10 +176,10 @@ def test_repeat(self): freq='30Min', tz=tz) expected_rng = DatetimeIndex([ - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), ]) tm.assert_index_equal(rng.repeat(reps), expected_rng) @@ -192,10 +193,10 @@ def test_numpy_repeat(self): freq='30Min', tz=tz) expected_rng = DatetimeIndex([ - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), ]) tm.assert_index_equal(np.repeat(rng, reps), expected_rng) diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index c6436163b9edb..c779c698525f5 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -255,6 +255,21 @@ def test_constructor_keyword(self): hour=1, minute=2, second=3, microsecond=999999)), repr(Timestamp('2015-11-12 01:02:03.999999'))) + def test_constructor_offset_depr(self): + # GH 12160 + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + ts = Timestamp('2011-01-01', offset='D') + self.assertEqual(ts.freq, 'D') + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + self.assertEqual(ts.offset, 'D') + + msg = "Can only specify freq or offset, not both" + with tm.assertRaisesRegexp(TypeError, msg): + Timestamp('2011-01-01', freq='D', offset='D') + def test_conversion(self): # GH 9255 ts = Timestamp('2000-01-01') diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 0db4282808a26..1bc935adaed7e 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -63,6 +63,7 @@ from pandas.compat import parse_date, string_types, iteritems, StringIO, callabl import operator import collections +import warnings # initialize numpy import_array() @@ -86,23 +87,24 @@ try: except NameError: # py3 basestring = str -cdef inline object create_timestamp_from_ts(int64_t value, pandas_datetimestruct dts, object tz, object offset): +cdef inline object create_timestamp_from_ts(int64_t value, pandas_datetimestruct dts, + object tz, object freq): cdef _Timestamp ts_base ts_base = _Timestamp.__new__(Timestamp, dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz) - ts_base.value = value - ts_base.offset = offset + ts_base.freq = freq ts_base.nanosecond = dts.ps / 1000 return ts_base -cdef inline object create_datetime_from_ts(int64_t value, pandas_datetimestruct dts, object tz, object offset): +cdef inline object create_datetime_from_ts(int64_t value, pandas_datetimestruct dts, + object tz, object freq): return datetime(dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz) -def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): +def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, freq=None, box=False): # convert an i8 repr to an ndarray of datetimes or Timestamp (if box == True) cdef: @@ -113,9 +115,9 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): ndarray[object] result = np.empty(n, dtype=object) object (*func_create)(int64_t, pandas_datetimestruct, object, object) - if box and util.is_string_object(offset): + if box and util.is_string_object(freq): from pandas.tseries.frequencies import to_offset - offset = to_offset(offset) + freq = to_offset(freq) if box: func_create = create_timestamp_from_ts @@ -130,7 +132,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): result[i] = NaT else: pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts) - result[i] = func_create(value, dts, tz, offset) + result[i] = func_create(value, dts, tz, freq) elif _is_tzlocal(tz) or _is_fixed_offset(tz): for i in range(n): value = arr[i] @@ -138,7 +140,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): result[i] = NaT else: pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts) - dt = create_datetime_from_ts(value, dts, tz, offset) + dt = create_datetime_from_ts(value, dts, tz, freq) dt = dt + tz.utcoffset(dt) if box: dt = Timestamp(dt) @@ -163,7 +165,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): new_tz = tz pandas_datetime_to_datetimestruct(value + deltas[pos], PANDAS_FR_ns, &dts) - result[i] = func_create(value, dts, new_tz, offset) + result[i] = func_create(value, dts, new_tz, freq) else: for i in range(n): @@ -172,7 +174,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): result[i] = NaT else: pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts) - result[i] = func_create(value, dts, None, offset) + result[i] = func_create(value, dts, None, freq) return result @@ -309,11 +311,12 @@ class Timestamp(_Timestamp): def combine(cls, date, time): return cls(datetime.combine(date, time)) - def __new__(cls, - object ts_input=_no_input, object offset=None, tz=None, unit=None, - year=None, month=None, day=None, - hour=None, minute=None, second=None, microsecond=None, - tzinfo=None): + def __new__(cls, object ts_input=_no_input, + object freq=None, tz=None, unit=None, + year=None, month=None, day=None, + hour=None, minute=None, second=None, microsecond=None, + tzinfo=None, + object offset=None): # The parameter list folds together legacy parameter names (the first # four) and positional and keyword parameter names from pydatetime. # @@ -338,15 +341,24 @@ class Timestamp(_Timestamp): cdef _TSObject ts cdef _Timestamp ts_base + if offset is not None: + # deprecate offset kwd in 0.19.0, GH13593 + if freq is not None: + msg = "Can only specify freq or offset, not both" + raise TypeError(msg) + warnings.warn("offset is deprecated. Use freq instead", + FutureWarning) + freq = offset + if ts_input is _no_input: # User passed keyword arguments. return Timestamp(datetime(year, month, day, hour or 0, minute or 0, second or 0, microsecond or 0, tzinfo), tz=tzinfo) - elif is_integer_object(offset): + elif is_integer_object(freq): # User passed positional arguments: # Timestamp(year, month, day[, hour[, minute[, second[, microsecond[, tzinfo]]]]]) - return Timestamp(datetime(ts_input, offset, tz, unit or 0, + return Timestamp(datetime(ts_input, freq, tz, unit or 0, year or 0, month or 0, day or 0, hour), tz=hour) ts = convert_to_tsobject(ts_input, tz, unit, 0, 0) @@ -354,9 +366,9 @@ class Timestamp(_Timestamp): if ts.value == NPY_NAT: return NaT - if util.is_string_object(offset): + if util.is_string_object(freq): from pandas.tseries.frequencies import to_offset - offset = to_offset(offset) + freq = to_offset(freq) # make datetime happy ts_base = _Timestamp.__new__(cls, ts.dts.year, ts.dts.month, @@ -365,7 +377,7 @@ class Timestamp(_Timestamp): # fill out rest of data ts_base.value = ts.value - ts_base.offset = offset + ts_base.freq = freq ts_base.nanosecond = ts.dts.ps / 1000 return ts_base @@ -433,16 +445,18 @@ class Timestamp(_Timestamp): return self.tzinfo @property - def freq(self): - return self.offset + def offset(self): + warnings.warn(".offset is deprecated. Use .freq instead", + FutureWarning) + return self.freq def __setstate__(self, state): self.value = state[0] - self.offset = state[1] + self.freq = state[1] self.tzinfo = state[2] def __reduce__(self): - object_state = self.value, self.offset, self.tzinfo + object_state = self.value, self.freq, self.tzinfo return (Timestamp, object_state) def to_period(self, freq=None): @@ -491,7 +505,7 @@ class Timestamp(_Timestamp): @property def freqstr(self): - return getattr(self.offset, 'freqstr', self.offset) + return getattr(self.freq, 'freqstr', self.freq) @property def is_month_start(self): @@ -602,7 +616,7 @@ class Timestamp(_Timestamp): def replace(self, **kwds): return Timestamp(datetime.replace(self, **kwds), - offset=self.offset) + freq=self.freq) def to_pydatetime(self, warn=True): """ @@ -911,16 +925,6 @@ cdef inline bint _is_multiple(int64_t us, int64_t mult): return us % mult == 0 -def apply_offset(ndarray[object] values, object offset): - cdef: - Py_ssize_t i, n = len(values) - ndarray[int64_t] new_values - object boxed - - result = np.empty(n, dtype='M8[ns]') - new_values = result.view('i8') - - cdef inline bint _cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1: if op == Py_EQ: return lhs == rhs @@ -955,7 +959,7 @@ cdef str _NDIM_STRING = "ndim" cdef class _Timestamp(datetime): cdef readonly: int64_t value, nanosecond - object offset # frequency reference + object freq # frequency reference def __hash__(_Timestamp self): if self.nanosecond: @@ -1029,9 +1033,9 @@ cdef class _Timestamp(datetime): pass tz = ", tz='{0}'".format(zone) if zone is not None else "" - offset = ", offset='{0}'".format(self.offset.freqstr) if self.offset is not None else "" + freq = ", freq='{0}'".format(self.freq.freqstr) if self.freq is not None else "" - return "Timestamp('{stamp}'{tz}{offset})".format(stamp=stamp, tz=tz, offset=offset) + return "Timestamp('{stamp}'{tz}{freq})".format(stamp=stamp, tz=tz, freq=freq) cdef bint _compare_outside_nanorange(_Timestamp self, datetime other, int op) except -1: @@ -1083,17 +1087,17 @@ cdef class _Timestamp(datetime): if is_timedelta64_object(other): other_int = other.astype('timedelta64[ns]').view('i8') - return Timestamp(self.value + other_int, tz=self.tzinfo, offset=self.offset) + return Timestamp(self.value + other_int, tz=self.tzinfo, freq=self.freq) elif is_integer_object(other): - if self.offset is None: + if self.freq is None: raise ValueError("Cannot add integral value to Timestamp " - "without offset.") - return Timestamp((self.offset * other).apply(self), offset=self.offset) + "without freq.") + return Timestamp((self.freq * other).apply(self), offset=self.freq) elif isinstance(other, timedelta) or hasattr(other, 'delta'): nanos = _delta_to_nanoseconds(other) - result = Timestamp(self.value + nanos, tz=self.tzinfo, offset=self.offset) + result = Timestamp(self.value + nanos, tz=self.tzinfo, freq=self.freq) if getattr(other, 'normalize', False): result = Timestamp(normalize_date(result)) return result
- [x] closes #12160 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13593
2016-07-09T15:02:31Z
2016-07-10T21:39:51Z
null
2016-07-10T22:09:47Z
BUG: Series/Index contains NaT with object dtype comparison incorrect
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 40ae38f12fccb..31ba2e1042547 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -520,6 +520,8 @@ Bug Fixes - Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`) - Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) +- Bug in ``Series`` comparison may output incorrect result if rhs contains ``NaT`` (:issue:`9005`) +- Bug in ``Series`` and ``Index`` comparison may output incorrect result if it contains ``NaT`` with ``object`` dtype (:issue:`13592`) - Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`) - Bug in ``Peirod`` and ``Series`` or ``Index`` comparison raises ``TypeError`` (:issue:`13200`) - Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 34ab3ae6863b5..0af7b6d80ce0e 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -27,7 +27,8 @@ is_integer_dtype, is_categorical_dtype, is_object_dtype, is_timedelta64_dtype, is_datetime64_dtype, is_datetime64tz_dtype, - is_bool_dtype, PerformanceWarning, ABCSeries) + is_bool_dtype, PerformanceWarning, + ABCSeries, ABCIndex) # ----------------------------------------------------------------------------- # Functions that add arithmetic methods to objects, given arithmetic factory @@ -664,6 +665,22 @@ def wrapper(left, right, name=name, na_op=na_op): return wrapper +def _comp_method_OBJECT_ARRAY(op, x, y): + if isinstance(y, list): + y = lib.list_to_object_array(y) + if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)): + if not is_object_dtype(y.dtype): + y = y.astype(np.object_) + + if isinstance(y, (ABCSeries, ABCIndex)): + y = y.values + + result = lib.vec_compare(x, y, op) + else: + result = lib.scalar_compare(x, y, op) + return result + + def _comp_method_SERIES(op, name, str_rep, masker=False): """ Wrapper function for Series arithmetic operations, to avoid @@ -680,16 +697,7 @@ def na_op(x, y): return op(y, x) if is_object_dtype(x.dtype): - if isinstance(y, list): - y = lib.list_to_object_array(y) - - if isinstance(y, (np.ndarray, ABCSeries)): - if not is_object_dtype(y.dtype): - result = lib.vec_compare(x, y.astype(np.object_), op) - else: - result = lib.vec_compare(x, y, op) - else: - result = lib.scalar_compare(x, y, op) + result = _comp_method_OBJECT_ARRAY(op, x, y) else: # we want to compare like types @@ -713,12 +721,11 @@ def na_op(x, y): (not isscalar(y) and needs_i8_conversion(y))): if isscalar(y): + mask = isnull(x) y = _index.convert_scalar(x, _values_from_object(y)) else: + mask = isnull(x) | isnull(y) y = y.view('i8') - - mask = isnull(x) - x = x.view('i8') try: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index ad27010714f63..e697dc63c2cdb 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -31,6 +31,7 @@ is_list_like, is_bool_dtype, is_integer_dtype, is_float_dtype, needs_i8_conversion) +from pandas.core.ops import _comp_method_OBJECT_ARRAY from pandas.core.strings import StringAccessorMixin from pandas.core.config import get_option @@ -3182,8 +3183,11 @@ def _evaluate_compare(self, other): if needs_i8_conversion(self) and needs_i8_conversion(other): return self._evaluate_compare(other, op) - func = getattr(self.values, op) - result = func(np.asarray(other)) + if is_object_dtype(self) and self.nlevels == 1: + # don't pass MultiIndex + result = _comp_method_OBJECT_ARRAY(op, self.values, other) + else: + result = op(self.values, np.asarray(other)) # technically we could support bool dtyped Index # for now just return the indexing array directly @@ -3196,12 +3200,12 @@ def _evaluate_compare(self, other): return _evaluate_compare - cls.__eq__ = _make_compare('__eq__') - cls.__ne__ = _make_compare('__ne__') - cls.__lt__ = _make_compare('__lt__') - cls.__gt__ = _make_compare('__gt__') - cls.__le__ = _make_compare('__le__') - cls.__ge__ = _make_compare('__ge__') + cls.__eq__ = _make_compare(operator.eq) + cls.__ne__ = _make_compare(operator.ne) + cls.__lt__ = _make_compare(operator.lt) + cls.__gt__ = _make_compare(operator.gt) + cls.__le__ = _make_compare(operator.le) + cls.__ge__ = _make_compare(operator.ge) @classmethod def _add_numericlike_set_methods_disabled(cls): diff --git a/pandas/lib.pyx b/pandas/lib.pyx index a9c7f93097f1b..7cbb502315b64 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -768,12 +768,12 @@ def scalar_compare(ndarray[object] values, object val, object op): raise ValueError('Unrecognized operator') result = np.empty(n, dtype=bool).view(np.uint8) - isnull_val = _checknull(val) + isnull_val = checknull(val) if flag == cpython.Py_NE: for i in range(n): x = values[i] - if _checknull(x): + if checknull(x): result[i] = True elif isnull_val: result[i] = True @@ -785,7 +785,7 @@ def scalar_compare(ndarray[object] values, object val, object op): elif flag == cpython.Py_EQ: for i in range(n): x = values[i] - if _checknull(x): + if checknull(x): result[i] = False elif isnull_val: result[i] = False @@ -798,7 +798,7 @@ def scalar_compare(ndarray[object] values, object val, object op): else: for i in range(n): x = values[i] - if _checknull(x): + if checknull(x): result[i] = False elif isnull_val: result[i] = False @@ -864,7 +864,7 @@ def vec_compare(ndarray[object] left, ndarray[object] right, object op): x = left[i] y = right[i] - if _checknull(x) or _checknull(y): + if checknull(x) or checknull(y): result[i] = True else: result[i] = cpython.PyObject_RichCompareBool(x, y, flag) @@ -873,7 +873,7 @@ def vec_compare(ndarray[object] left, ndarray[object] right, object op): x = left[i] y = right[i] - if _checknull(x) or _checknull(y): + if checknull(x) or checknull(y): result[i] = False else: result[i] = cpython.PyObject_RichCompareBool(x, y, flag) diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index 6ab382beb7973..9c401e9ce6da8 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -980,24 +980,97 @@ def test_comparison_invalid(self): self.assertRaises(TypeError, lambda: x <= y) def test_more_na_comparisons(self): - left = Series(['a', np.nan, 'c']) - right = Series(['a', np.nan, 'd']) + for dtype in [None, object]: + left = Series(['a', np.nan, 'c'], dtype=dtype) + right = Series(['a', np.nan, 'd'], dtype=dtype) - result = left == right - expected = Series([True, False, False]) - assert_series_equal(result, expected) + result = left == right + expected = Series([True, False, False]) + assert_series_equal(result, expected) - result = left != right - expected = Series([False, True, True]) - assert_series_equal(result, expected) + result = left != right + expected = Series([False, True, True]) + assert_series_equal(result, expected) - result = left == np.nan - expected = Series([False, False, False]) - assert_series_equal(result, expected) + result = left == np.nan + expected = Series([False, False, False]) + assert_series_equal(result, expected) - result = left != np.nan - expected = Series([True, True, True]) - assert_series_equal(result, expected) + result = left != np.nan + expected = Series([True, True, True]) + assert_series_equal(result, expected) + + def test_nat_comparisons(self): + data = [([pd.Timestamp('2011-01-01'), pd.NaT, + pd.Timestamp('2011-01-03')], + [pd.NaT, pd.NaT, pd.Timestamp('2011-01-03')]), + + ([pd.Timedelta('1 days'), pd.NaT, + pd.Timedelta('3 days')], + [pd.NaT, pd.NaT, pd.Timedelta('3 days')]), + + ([pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='M')], + [pd.NaT, pd.NaT, pd.Period('2011-03', freq='M')])] + + # add lhs / rhs switched data + data = data + [(r, l) for l, r in data] + + for l, r in data: + for dtype in [None, object]: + left = Series(l, dtype=dtype) + + # Series, Index + for right in [Series(r, dtype=dtype), Index(r, dtype=dtype)]: + expected = Series([False, False, True]) + assert_series_equal(left == right, expected) + + expected = Series([True, True, False]) + assert_series_equal(left != right, expected) + + expected = Series([False, False, False]) + assert_series_equal(left < right, expected) + + expected = Series([False, False, False]) + assert_series_equal(left > right, expected) + + expected = Series([False, False, True]) + assert_series_equal(left >= right, expected) + + expected = Series([False, False, True]) + assert_series_equal(left <= right, expected) + + def test_nat_comparisons_scalar(self): + data = [[pd.Timestamp('2011-01-01'), pd.NaT, + pd.Timestamp('2011-01-03')], + + [pd.Timedelta('1 days'), pd.NaT, pd.Timedelta('3 days')], + + [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='M')]] + + for l in data: + for dtype in [None, object]: + left = Series(l, dtype=dtype) + + expected = Series([False, False, False]) + assert_series_equal(left == pd.NaT, expected) + assert_series_equal(pd.NaT == left, expected) + + expected = Series([True, True, True]) + assert_series_equal(left != pd.NaT, expected) + assert_series_equal(pd.NaT != left, expected) + + expected = Series([False, False, False]) + assert_series_equal(left < pd.NaT, expected) + assert_series_equal(pd.NaT > left, expected) + assert_series_equal(left <= pd.NaT, expected) + assert_series_equal(pd.NaT >= left, expected) + + assert_series_equal(left > pd.NaT, expected) + assert_series_equal(pd.NaT < left, expected) + assert_series_equal(left >= pd.NaT, expected) + assert_series_equal(pd.NaT <= left, expected) def test_comparison_different_length(self): a = Series(['a', 'b', 'c']) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 2e3d1ace9734c..4bafac873ea09 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -142,7 +142,7 @@ def _evaluate_compare(self, other, op): other = type(self)(other) # compare - result = getattr(self.asi8, op)(other.asi8) + result = op(self.asi8, other.asi8) # technically we could support bool dtyped Index # for now just return the indexing array directly diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 84f357481a28e..af4c46e2d16fa 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -36,7 +36,7 @@ def _td_index_cmp(opname, nat_result=False): def wrapper(self, other): func = getattr(super(TimedeltaIndex, self), opname) - if _is_convertible_to_td(other): + if _is_convertible_to_td(other) or other is tslib.NaT: other = _to_m8(other) result = func(other) if com.isnull(other): diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 360944e355b4d..a03fafdf36adc 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -457,6 +457,32 @@ def test_sub_period(self): with tm.assertRaises(TypeError): p - idx + def test_comp_nat(self): + left = pd.DatetimeIndex([pd.Timestamp('2011-01-01'), pd.NaT, + pd.Timestamp('2011-01-03')]) + right = pd.DatetimeIndex([pd.NaT, pd.NaT, pd.Timestamp('2011-01-03')]) + + for l, r in [(left, right), (left.asobject, right.asobject)]: + result = l == r + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = l != r + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l == pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT == r, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(l != pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT != l, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l < pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT > l, expected) + def test_value_counts_unique(self): # GH 7735 for tz in [None, 'UTC', 'Asia/Tokyo', 'US/Eastern']: @@ -1237,6 +1263,32 @@ def test_addition_ops(self): expected = Timestamp('20130102') self.assertEqual(result, expected) + def test_comp_nat(self): + left = pd.TimedeltaIndex([pd.Timedelta('1 days'), pd.NaT, + pd.Timedelta('3 days')]) + right = pd.TimedeltaIndex([pd.NaT, pd.NaT, pd.Timedelta('3 days')]) + + for l, r in [(left, right), (left.asobject, right.asobject)]: + result = l == r + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = l != r + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l == pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT == r, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(l != pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT != l, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l < pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT > l, expected) + def test_value_counts_unique(self): # GH 7735 @@ -2038,6 +2090,32 @@ def test_sub_isub(self): rng -= 1 tm.assert_index_equal(rng, expected) + def test_comp_nat(self): + left = pd.PeriodIndex([pd.Period('2011-01-01'), pd.NaT, + pd.Period('2011-01-03')]) + right = pd.PeriodIndex([pd.NaT, pd.NaT, pd.Period('2011-01-03')]) + + for l, r in [(left, right), (left.asobject, right.asobject)]: + result = l == r + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = l != r + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l == pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT == r, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(l != pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT != l, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l < pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT > l, expected) + def test_value_counts_unique(self): # GH 7735 idx = pd.period_range('2011-01-01 09:00', freq='H', periods=10)
- [x] closes #9005 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry `Series` and `Index` compares `NaT == NaT` as `True` if it has `object` dtype. ``` pd.Series([pd.NaT]) #0 NaT # dtype: datetime64[ns] # OK pd.Series([pd.NaT]) == pd.NaT #0 False # dtype: bool # NG in object dtype pd.Series([pd.NaT], dtype=object) == pd.NaT #0 True # dtype: bool # OK pd.Index([pd.NaT]) == pd.NaT array([False], dtype=bool) # NG in object dtype pd.Index([pd.NaT], dtype=object) == pd.NaT # array([ True], dtype=bool) ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13592
2016-07-09T12:26:50Z
2016-07-11T01:37:09Z
2016-07-11T01:37:08Z
2016-07-11T02:29:02Z
DEPR: Remove legacy offsets
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 7e832af14c051..f6a1e169afe9d 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -752,7 +752,7 @@ calculate significantly slower and will raise a ``PerformanceWarning`` rng + BQuarterEnd() -.. _timeseries.alias: +.. _timeseries.custombusinessdays: Custom Business Days (Experimental) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -953,6 +953,8 @@ You can use keyword arguments suported by either ``BusinessHour`` and ``CustomBu # Monday is skipped because it's a holiday, business hour starts from 10:00 dt + bhour_mon * 2 +.. _timeseries.alias: + Offset Aliases ~~~~~~~~~~~~~~ @@ -1103,48 +1105,6 @@ it is rolled forward to the next anchor point. pd.Timestamp('2014-01-01') + MonthBegin(n=0) pd.Timestamp('2014-01-31') + MonthEnd(n=0) -.. _timeseries.legacyaliases: - -Legacy Aliases -~~~~~~~~~~~~~~ -Note that prior to v0.8.0, time rules had a slightly different look. These are -deprecated in v0.17.0, and removed in future version. - -.. csv-table:: - :header: "Legacy Time Rule", "Offset Alias" - :widths: 15, 65 - - "WEEKDAY", "B" - "EOM", "BM" - "W\@MON", "W\-MON" - "W\@TUE", "W\-TUE" - "W\@WED", "W\-WED" - "W\@THU", "W\-THU" - "W\@FRI", "W\-FRI" - "W\@SAT", "W\-SAT" - "W\@SUN", "W\-SUN" - "Q\@JAN", "BQ\-JAN" - "Q\@FEB", "BQ\-FEB" - "Q\@MAR", "BQ\-MAR" - "A\@JAN", "BA\-JAN" - "A\@FEB", "BA\-FEB" - "A\@MAR", "BA\-MAR" - "A\@APR", "BA\-APR" - "A\@MAY", "BA\-MAY" - "A\@JUN", "BA\-JUN" - "A\@JUL", "BA\-JUL" - "A\@AUG", "BA\-AUG" - "A\@SEP", "BA\-SEP" - "A\@OCT", "BA\-OCT" - "A\@NOV", "BA\-NOV" - "A\@DEC", "BA\-DEC" - - -As you can see, legacy quarterly and annual frequencies are business quarters -and business year ends. Please also note the legacy time rule for milliseconds -``ms`` versus the new offset alias for month start ``MS``. This means that -offset alias parsing is case sensitive. - .. _timeseries.holiday: Holidays / Holiday Calendars diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index bef02a06135de..38e34d9158717 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -465,6 +465,17 @@ Removal of prior version deprecations/changes - ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`) - ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) +- Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (:issue:`13590`) + + Previous Behavior: + + .. code-block:: ipython + + In [2]: pd.date_range('2016-07-01', freq='W@MON', periods=3) + pandas/tseries/frequencies.py:465: FutureWarning: Freq "W@MON" is deprecated, use "W-MON" as alternative. + Out[2]: DatetimeIndex(['2016-07-04', '2016-07-11', '2016-07-18'], dtype='datetime64[ns]', freq='W-MON') + + Now legacy time rules raises ``ValueError``. For the list of currently supported offsets, see :ref:`here <timeseries.alias>` .. _whatsnew_0190.performance: diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index e2132deb97d64..8b3785d78d260 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -1,5 +1,5 @@ from datetime import timedelta -from pandas.compat import range, long, zip +from pandas.compat import long, zip from pandas import compat import re import warnings @@ -356,34 +356,6 @@ def get_period_alias(offset_str): """ alias to closest period strings BQ->Q etc""" return _offset_to_period_map.get(offset_str, None) -_rule_aliases = { - # Legacy rules that will continue to map to their original values - # essentially for the rest of time - 'WEEKDAY': 'B', - 'EOM': 'BM', - 'W@MON': 'W-MON', - 'W@TUE': 'W-TUE', - 'W@WED': 'W-WED', - 'W@THU': 'W-THU', - 'W@FRI': 'W-FRI', - 'W@SAT': 'W-SAT', - 'W@SUN': 'W-SUN', - 'Q@JAN': 'BQ-JAN', - 'Q@FEB': 'BQ-FEB', - 'Q@MAR': 'BQ-MAR', - 'A@JAN': 'BA-JAN', - 'A@FEB': 'BA-FEB', - 'A@MAR': 'BA-MAR', - 'A@APR': 'BA-APR', - 'A@MAY': 'BA-MAY', - 'A@JUN': 'BA-JUN', - 'A@JUL': 'BA-JUL', - 'A@AUG': 'BA-AUG', - 'A@SEP': 'BA-SEP', - 'A@OCT': 'BA-OCT', - 'A@NOV': 'BA-NOV', - 'A@DEC': 'BA-DEC', -} _lite_rule_alias = { 'W': 'W-SUN', @@ -401,17 +373,6 @@ def get_period_alias(offset_str): 'ns': 'N' } -# TODO: Can this be killed? -for _i, _weekday in enumerate(['MON', 'TUE', 'WED', 'THU', 'FRI']): - for _iweek in range(4): - _name = 'WOM-%d%s' % (_iweek + 1, _weekday) - _rule_aliases[_name.replace('-', '@')] = _name - -# Note that _rule_aliases is not 1:1 (d[BA]==d[A@DEC]), and so traversal -# order matters when constructing an inverse. we pick one. #2331 -# Used in get_legacy_offset_name -_legacy_reverse_map = dict((v, k) for k, v in - reversed(sorted(compat.iteritems(_rule_aliases)))) _name_to_offset_map = {'days': Day(1), 'hours': Hour(1), @@ -422,6 +383,9 @@ def get_period_alias(offset_str): 'nanoseconds': Nano(1)} +_INVALID_FREQ_ERROR = "Invalid frequency: {0}" + + def to_offset(freqstr): """ Return DateOffset object from string representation or @@ -460,7 +424,7 @@ def to_offset(freqstr): else: delta = delta + offset except Exception: - raise ValueError("Could not evaluate %s" % freqstr) + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) else: delta = None @@ -479,10 +443,10 @@ def to_offset(freqstr): else: delta = delta + offset except Exception: - raise ValueError("Could not evaluate %s" % freqstr) + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) if delta is None: - raise ValueError('Unable to understand %s as a frequency' % freqstr) + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) return delta @@ -526,9 +490,6 @@ def get_base_alias(freqstr): _dont_uppercase = set(('MS', 'ms')) -_LEGACY_FREQ_WARNING = 'Freq "{0}" is deprecated, use "{1}" as alternative.' - - def get_offset(name): """ Return DateOffset object associated with rule name @@ -539,27 +500,9 @@ def get_offset(name): """ if name not in _dont_uppercase: name = name.upper() - - if name in _rule_aliases: - new = _rule_aliases[name] - warnings.warn(_LEGACY_FREQ_WARNING.format(name, new), - FutureWarning, stacklevel=2) - name = new - elif name.lower() in _rule_aliases: - new = _rule_aliases[name.lower()] - warnings.warn(_LEGACY_FREQ_WARNING.format(name, new), - FutureWarning, stacklevel=2) - name = new - name = _lite_rule_alias.get(name, name) name = _lite_rule_alias.get(name.lower(), name) - else: - if name in _rule_aliases: - new = _rule_aliases[name] - warnings.warn(_LEGACY_FREQ_WARNING.format(name, new), - FutureWarning, stacklevel=2) - name = new name = _lite_rule_alias.get(name, name) if name not in _offset_map: @@ -571,7 +514,7 @@ def get_offset(name): offset = klass._from_name(*split[1:]) except (ValueError, TypeError, KeyError): # bad prefix or suffix - raise ValueError('Bad rule name requested: %s.' % name) + raise ValueError(_INVALID_FREQ_ERROR.format(name)) # cache _offset_map[name] = offset # do not return cache because it's mutable @@ -595,17 +538,6 @@ def get_offset_name(offset): return offset.freqstr -def get_legacy_offset_name(offset): - """ - Return the pre pandas 0.8.0 name for the date offset - """ - - # This only used in test_timeseries_legacy.py - - name = offset.name - return _legacy_reverse_map.get(name, name) - - def get_standard_freq(freq): """ Return the standardized frequency string @@ -796,36 +728,18 @@ def _period_alias_dictionary(): def _period_str_to_code(freqstr): - # hack - if freqstr in _rule_aliases: - new = _rule_aliases[freqstr] - warnings.warn(_LEGACY_FREQ_WARNING.format(freqstr, new), - FutureWarning, stacklevel=3) - freqstr = new freqstr = _lite_rule_alias.get(freqstr, freqstr) if freqstr not in _dont_uppercase: lower = freqstr.lower() - if lower in _rule_aliases: - new = _rule_aliases[lower] - warnings.warn(_LEGACY_FREQ_WARNING.format(lower, new), - FutureWarning, stacklevel=3) - freqstr = new freqstr = _lite_rule_alias.get(lower, freqstr) + if freqstr not in _dont_uppercase: + freqstr = freqstr.upper() try: - if freqstr not in _dont_uppercase: - freqstr = freqstr.upper() return _period_code_map[freqstr] except KeyError: - try: - alias = _period_alias_dict[freqstr] - warnings.warn(_LEGACY_FREQ_WARNING.format(freqstr, alias), - FutureWarning, stacklevel=3) - except KeyError: - raise ValueError("Unknown freqstr: %s" % freqstr) - - return _period_code_map[alias] + raise ValueError(_INVALID_FREQ_ERROR.format(freqstr)) def infer_freq(index, warn=True): diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 68cea17ba3fc9..ce0f3a8bb6285 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -160,9 +160,11 @@ def test_round(self): tm.assert_index_equal(rng.round(freq='H'), expected_rng) self.assertEqual(elt.round(freq='H'), expected_elt) - msg = "Could not evaluate foo" - tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='foo') - tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with tm.assertRaisesRegexp(ValueError, msg): + rng.round(freq='foo') + with tm.assertRaisesRegexp(ValueError, msg): + elt.round(freq='foo') msg = "<MonthEnd> is a non-fixed frequency" tm.assertRaisesRegexp(ValueError, msg, rng.round, freq='M') @@ -847,9 +849,11 @@ def test_round(self): tm.assert_index_equal(td.round(freq='H'), expected_rng) self.assertEqual(elt.round(freq='H'), expected_elt) - msg = "Could not evaluate foo" - tm.assertRaisesRegexp(ValueError, msg, td.round, freq='foo') - tm.assertRaisesRegexp(ValueError, msg, elt.round, freq='foo') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + td.round(freq='foo') + with tm.assertRaisesRegexp(ValueError, msg): + elt.round(freq='foo') msg = "<MonthEnd> is a non-fixed frequency" tm.assertRaisesRegexp(ValueError, msg, td.round, freq='M') diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py index 1f06b7ad4361b..268933fada7a2 100644 --- a/pandas/tseries/tests/test_frequencies.py +++ b/pandas/tseries/tests/test_frequencies.py @@ -245,10 +245,10 @@ def _assert_depr(freq, expected, aliases): assert isinstance(aliases, list) assert (frequencies._period_str_to_code(freq) == expected) + msg = frequencies._INVALID_FREQ_ERROR for alias in aliases: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - assert (frequencies._period_str_to_code(alias) == expected) + with tm.assertRaisesRegexp(ValueError, msg): + frequencies._period_str_to_code(alias) _assert_depr("M", 3000, ["MTH", "MONTH", "MONTHLY"]) @@ -699,8 +699,9 @@ def test_series(self): s = Series(period_range('2013', periods=10, freq=freq)) self.assertRaises(TypeError, lambda: frequencies.infer_freq(s)) for freq in ['Y']: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): + + msg = frequencies._INVALID_FREQ_ERROR + with tm.assertRaisesRegexp(ValueError, msg): s = Series(period_range('2013', periods=10, freq=freq)) self.assertRaises(TypeError, lambda: frequencies.infer_freq(s)) @@ -715,17 +716,23 @@ def test_series(self): self.assertEqual(inferred, 'D') def test_legacy_offset_warnings(self): - for k, v in compat.iteritems(frequencies._rule_aliases): - with tm.assert_produces_warning(FutureWarning): - result = frequencies.get_offset(k) - exp = frequencies.get_offset(v) - self.assertEqual(result, exp) - - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - idx = date_range('2011-01-01', periods=5, freq=k) - exp = date_range('2011-01-01', periods=5, freq=v) - self.assert_index_equal(idx, exp) + freqs = ['WEEKDAY', 'EOM', 'W@MON', 'W@TUE', 'W@WED', 'W@THU', + 'W@FRI', 'W@SAT', 'W@SUN', 'Q@JAN', 'Q@FEB', 'Q@MAR', + 'A@JAN', 'A@FEB', 'A@MAR', 'A@APR', 'A@MAY', 'A@JUN', + 'A@JUL', 'A@AUG', 'A@SEP', 'A@OCT', 'A@NOV', 'A@DEC', + 'WOM@1MON', 'WOM@2MON', 'WOM@3MON', 'WOM@4MON', + 'WOM@1TUE', 'WOM@2TUE', 'WOM@3TUE', 'WOM@4TUE', + 'WOM@1WED', 'WOM@2WED', 'WOM@3WED', 'WOM@4WED', + 'WOM@1THU', 'WOM@2THU', 'WOM@3THU', 'WOM@4THU' + 'WOM@1FRI', 'WOM@2FRI', 'WOM@3FRI', 'WOM@4FRI'] + + msg = frequencies._INVALID_FREQ_ERROR + for freq in freqs: + with tm.assertRaisesRegexp(ValueError, msg): + frequencies.get_offset(freq) + + with tm.assertRaisesRegexp(ValueError, msg): + date_range('2011-01-01', periods=5, freq=freq) MONTHS = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 5965a661699a6..b31e4d54c551f 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -23,7 +23,7 @@ from pandas.core.series import Series from pandas.tseries.frequencies import (_offset_map, get_freq_code, - _get_freq_str) + _get_freq_str, _INVALID_FREQ_ERROR) from pandas.tseries.index import _to_m8, DatetimeIndex, _daterange_cache from pandas.tseries.tools import parse_time_string, DateParseError import pandas.tseries.offsets as offsets @@ -4531,8 +4531,11 @@ def test_get_offset_name(self): def test_get_offset(): - assertRaisesRegexp(ValueError, "rule.*GIBBERISH", get_offset, 'gibberish') - assertRaisesRegexp(ValueError, "rule.*QS-JAN-B", get_offset, 'QS-JAN-B') + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_offset('gibberish') + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_offset('QS-JAN-B') + pairs = [ ('B', BDay()), ('b', BDay()), ('bm', BMonthEnd()), ('Bm', BMonthEnd()), ('W-MON', Week(weekday=0)), @@ -4558,10 +4561,8 @@ def test_get_offset(): def test_get_offset_legacy(): pairs = [('w@Sat', Week(weekday=5))] for name, expected in pairs: - with tm.assert_produces_warning(FutureWarning): - offset = get_offset(name) - assert offset == expected, ("Expected %r to yield %r (actual: %r)" % - (name, expected, offset)) + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_offset(name) class TestParseTimeString(tm.TestCase): @@ -4595,16 +4596,14 @@ def test_get_standard_freq(): assert fstr == get_standard_freq('1w') assert fstr == get_standard_freq(('W', 1)) - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = get_standard_freq('WeEk') - assert fstr == result + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_standard_freq('WeEk') fstr = get_standard_freq('5Q') assert fstr == get_standard_freq('5q') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - result = get_standard_freq('5QuarTer') - assert fstr == result + with tm.assertRaisesRegexp(ValueError, _INVALID_FREQ_ERROR): + get_standard_freq('5QuarTer') assert fstr == get_standard_freq(('q', 5)) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 591fa19aad585..d47517a076932 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -448,13 +448,16 @@ def test_period_deprecated_freq(self): "L": ["MILLISECOND", "MILLISECONDLY", "millisecond"], "U": ["MICROSECOND", "MICROSECONDLY", "microsecond"], "N": ["NANOSECOND", "NANOSECONDLY", "nanosecond"]} + + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR for exp, freqs in iteritems(cases): for freq in freqs: + with self.assertRaisesRegexp(ValueError, msg): + Period('2016-03-01 09:00', freq=freq) - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - res = pd.Period('2016-03-01 09:00', freq=freq) - self.assertEqual(res, Period('2016-03-01 09:00', freq=exp)) + # check supported freq-aliases still works + p = Period('2016-03-01 09:00', freq=exp) + tm.assertIsInstance(p, Period) def test_repr(self): p = Period('Jan-2000') @@ -665,19 +668,21 @@ def test_properties_weekly(self): def test_properties_weekly_legacy(self): # Test properties on Periods with daily frequency. - with tm.assert_produces_warning(FutureWarning): - w_date = Period(freq='WK', year=2007, month=1, day=7) - # + w_date = Period(freq='W', year=2007, month=1, day=7) self.assertEqual(w_date.year, 2007) self.assertEqual(w_date.quarter, 1) self.assertEqual(w_date.month, 1) self.assertEqual(w_date.week, 1) self.assertEqual((w_date - 1).week, 52) self.assertEqual(w_date.days_in_month, 31) - with tm.assert_produces_warning(FutureWarning): - exp = Period(freq='WK', year=2012, month=2, day=1) + + exp = Period(freq='W', year=2012, month=2, day=1) self.assertEqual(exp.days_in_month, 29) + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK', year=2007, month=1, day=7) + def test_properties_daily(self): # Test properties on Periods with daily frequency. b_date = Period(freq='B', year=2007, month=1, day=1) @@ -826,10 +831,11 @@ def test_asfreq_MS(self): self.assertEqual(initial.asfreq(freq="M", how="S"), Period('2013-01', 'M')) - with self.assertRaisesRegexp(ValueError, "Unknown freqstr"): + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): initial.asfreq(freq="MS", how="S") - with tm.assertRaisesRegexp(ValueError, "Unknown freqstr: MS"): + with tm.assertRaisesRegexp(ValueError, msg): pd.Period('2013-01', 'MS') self.assertTrue(_period_code_map.get("MS") is None) @@ -1129,123 +1135,28 @@ def test_conv_weekly(self): self.assertEqual(ival_W.asfreq('W'), ival_W) + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + ival_W.asfreq('WK') + def test_conv_weekly_legacy(self): # frequency conversion tests: from Weekly Frequency - - with tm.assert_produces_warning(FutureWarning): - ival_W = Period(freq='WK', year=2007, month=1, day=1) - - with tm.assert_produces_warning(FutureWarning): - ival_WSUN = Period(freq='WK', year=2007, month=1, day=7) - with tm.assert_produces_warning(FutureWarning): - ival_WSAT = Period(freq='WK-SAT', year=2007, month=1, day=6) - with tm.assert_produces_warning(FutureWarning): - ival_WFRI = Period(freq='WK-FRI', year=2007, month=1, day=5) - with tm.assert_produces_warning(FutureWarning): - ival_WTHU = Period(freq='WK-THU', year=2007, month=1, day=4) - with tm.assert_produces_warning(FutureWarning): - ival_WWED = Period(freq='WK-WED', year=2007, month=1, day=3) - with tm.assert_produces_warning(FutureWarning): - ival_WTUE = Period(freq='WK-TUE', year=2007, month=1, day=2) - with tm.assert_produces_warning(FutureWarning): - ival_WMON = Period(freq='WK-MON', year=2007, month=1, day=1) - - ival_WSUN_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_WSUN_to_D_end = Period(freq='D', year=2007, month=1, day=7) - ival_WSAT_to_D_start = Period(freq='D', year=2006, month=12, day=31) - ival_WSAT_to_D_end = Period(freq='D', year=2007, month=1, day=6) - ival_WFRI_to_D_start = Period(freq='D', year=2006, month=12, day=30) - ival_WFRI_to_D_end = Period(freq='D', year=2007, month=1, day=5) - ival_WTHU_to_D_start = Period(freq='D', year=2006, month=12, day=29) - ival_WTHU_to_D_end = Period(freq='D', year=2007, month=1, day=4) - ival_WWED_to_D_start = Period(freq='D', year=2006, month=12, day=28) - ival_WWED_to_D_end = Period(freq='D', year=2007, month=1, day=3) - ival_WTUE_to_D_start = Period(freq='D', year=2006, month=12, day=27) - ival_WTUE_to_D_end = Period(freq='D', year=2007, month=1, day=2) - ival_WMON_to_D_start = Period(freq='D', year=2006, month=12, day=26) - ival_WMON_to_D_end = Period(freq='D', year=2007, month=1, day=1) - - with tm.assert_produces_warning(FutureWarning): - ival_W_end_of_year = Period(freq='WK', year=2007, month=12, day=31) - with tm.assert_produces_warning(FutureWarning): - ival_W_end_of_quarter = Period(freq='WK', year=2007, month=3, - day=31) - with tm.assert_produces_warning(FutureWarning): - ival_W_end_of_month = Period(freq='WK', year=2007, month=1, day=31) - ival_W_to_A = Period(freq='A', year=2007) - ival_W_to_Q = Period(freq='Q', year=2007, quarter=1) - ival_W_to_M = Period(freq='M', year=2007, month=1) - - if Period(freq='D', year=2007, month=12, day=31).weekday == 6: - ival_W_to_A_end_of_year = Period(freq='A', year=2007) - else: - ival_W_to_A_end_of_year = Period(freq='A', year=2008) - - if Period(freq='D', year=2007, month=3, day=31).weekday == 6: - ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=1) - else: - ival_W_to_Q_end_of_quarter = Period(freq='Q', year=2007, quarter=2) - - if Period(freq='D', year=2007, month=1, day=31).weekday == 6: - ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=1) - else: - ival_W_to_M_end_of_month = Period(freq='M', year=2007, month=2) - - ival_W_to_B_start = Period(freq='B', year=2007, month=1, day=1) - ival_W_to_B_end = Period(freq='B', year=2007, month=1, day=5) - ival_W_to_D_start = Period(freq='D', year=2007, month=1, day=1) - ival_W_to_D_end = Period(freq='D', year=2007, month=1, day=7) - ival_W_to_H_start = Period(freq='H', year=2007, month=1, day=1, hour=0) - ival_W_to_H_end = Period(freq='H', year=2007, month=1, day=7, hour=23) - ival_W_to_T_start = Period(freq='Min', year=2007, month=1, day=1, - hour=0, minute=0) - ival_W_to_T_end = Period(freq='Min', year=2007, month=1, day=7, - hour=23, minute=59) - ival_W_to_S_start = Period(freq='S', year=2007, month=1, day=1, hour=0, - minute=0, second=0) - ival_W_to_S_end = Period(freq='S', year=2007, month=1, day=7, hour=23, - minute=59, second=59) - - self.assertEqual(ival_W.asfreq('A'), ival_W_to_A) - self.assertEqual(ival_W_end_of_year.asfreq('A'), - ival_W_to_A_end_of_year) - self.assertEqual(ival_W.asfreq('Q'), ival_W_to_Q) - self.assertEqual(ival_W_end_of_quarter.asfreq('Q'), - ival_W_to_Q_end_of_quarter) - self.assertEqual(ival_W.asfreq('M'), ival_W_to_M) - self.assertEqual(ival_W_end_of_month.asfreq('M'), - ival_W_to_M_end_of_month) - - self.assertEqual(ival_W.asfreq('B', 'S'), ival_W_to_B_start) - self.assertEqual(ival_W.asfreq('B', 'E'), ival_W_to_B_end) - - self.assertEqual(ival_W.asfreq('D', 'S'), ival_W_to_D_start) - self.assertEqual(ival_W.asfreq('D', 'E'), ival_W_to_D_end) - - self.assertEqual(ival_WSUN.asfreq('D', 'S'), ival_WSUN_to_D_start) - self.assertEqual(ival_WSUN.asfreq('D', 'E'), ival_WSUN_to_D_end) - self.assertEqual(ival_WSAT.asfreq('D', 'S'), ival_WSAT_to_D_start) - self.assertEqual(ival_WSAT.asfreq('D', 'E'), ival_WSAT_to_D_end) - self.assertEqual(ival_WFRI.asfreq('D', 'S'), ival_WFRI_to_D_start) - self.assertEqual(ival_WFRI.asfreq('D', 'E'), ival_WFRI_to_D_end) - self.assertEqual(ival_WTHU.asfreq('D', 'S'), ival_WTHU_to_D_start) - self.assertEqual(ival_WTHU.asfreq('D', 'E'), ival_WTHU_to_D_end) - self.assertEqual(ival_WWED.asfreq('D', 'S'), ival_WWED_to_D_start) - self.assertEqual(ival_WWED.asfreq('D', 'E'), ival_WWED_to_D_end) - self.assertEqual(ival_WTUE.asfreq('D', 'S'), ival_WTUE_to_D_start) - self.assertEqual(ival_WTUE.asfreq('D', 'E'), ival_WTUE_to_D_end) - self.assertEqual(ival_WMON.asfreq('D', 'S'), ival_WMON_to_D_start) - self.assertEqual(ival_WMON.asfreq('D', 'E'), ival_WMON_to_D_end) - - self.assertEqual(ival_W.asfreq('H', 'S'), ival_W_to_H_start) - self.assertEqual(ival_W.asfreq('H', 'E'), ival_W_to_H_end) - self.assertEqual(ival_W.asfreq('Min', 'S'), ival_W_to_T_start) - self.assertEqual(ival_W.asfreq('Min', 'E'), ival_W_to_T_end) - self.assertEqual(ival_W.asfreq('S', 'S'), ival_W_to_S_start) - self.assertEqual(ival_W.asfreq('S', 'E'), ival_W_to_S_end) - - with tm.assert_produces_warning(FutureWarning): - self.assertEqual(ival_W.asfreq('WK'), ival_W) + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK', year=2007, month=1, day=1) + + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-SAT', year=2007, month=1, day=6) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-FRI', year=2007, month=1, day=5) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-THU', year=2007, month=1, day=4) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-WED', year=2007, month=1, day=3) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-TUE', year=2007, month=1, day=2) + with self.assertRaisesRegexp(ValueError, msg): + Period(freq='WK-MON', year=2007, month=1, day=1) def test_conv_business(self): # frequency conversion tests: from Business Frequency" @@ -2895,11 +2806,14 @@ def test_to_period_monthish(self): prng = rng.to_period() self.assertEqual(prng.freq, 'M') - with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): - rng = date_range('01-Jan-2012', periods=8, freq='EOM') + rng = date_range('01-Jan-2012', periods=8, freq='M') prng = rng.to_period() self.assertEqual(prng.freq, 'M') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + date_range('01-Jan-2012', periods=8, freq='EOM') + def test_multiples(self): result1 = Period('1989', freq='2A') result2 = Period('1989', freq='A') diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index ce88edcf4249b..c9dd126f8abf5 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -1388,16 +1388,14 @@ def _check_round(freq, expected): result = stamp.round(freq=freq) self.assertEqual(result, expected) - for freq, expected in [ - ('D', Timestamp('2000-01-05 00:00:00')), - ('H', Timestamp('2000-01-05 05:00:00')), - ('S', Timestamp('2000-01-05 05:09:15')) - ]: + for freq, expected in [('D', Timestamp('2000-01-05 00:00:00')), + ('H', Timestamp('2000-01-05 05:00:00')), + ('S', Timestamp('2000-01-05 05:09:15'))]: _check_round(freq, expected) - msg = "Could not evaluate" - tm.assertRaisesRegexp(ValueError, msg, - stamp.round, 'foo') + msg = pd.tseries.frequencies._INVALID_FREQ_ERROR + with self.assertRaisesRegexp(ValueError, msg): + stamp.round('foo') class TestTimestampOps(tm.TestCase):
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Follow-up of #10951. Remove legacy offsets deprecated in 0.17.0.
https://api.github.com/repos/pandas-dev/pandas/pulls/13590
2016-07-09T05:56:57Z
2016-07-19T01:30:42Z
null
2016-07-19T02:45:33Z
DOC: Add Fedora and Centos install instructions
diff --git a/doc/source/install.rst b/doc/source/install.rst index b43d2b8aac517..82d2dcd1cc709 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -165,8 +165,9 @@ To install pandas for Python 3 you may need to use the package ``python3-pandas` Debian & Ubuntu, unstable (latest packages), `NeuroDebian <http://neuro.debian.net/index.html#how-to-use-this-repository>`__ , ``sudo apt-get install python-pandas`` Ubuntu, stable, `official Ubuntu repository <http://packages.ubuntu.com/search?keywords=pandas&searchon=names&suite=all&section=all>`__ , ``sudo apt-get install python-pandas`` Ubuntu, unstable (daily builds), `PythonXY PPA <https://code.launchpad.net/~pythonxy/+archive/pythonxy-devel>`__; activate by: ``sudo add-apt-repository ppa:pythonxy/pythonxy-devel && sudo apt-get update``, ``sudo apt-get install python-pandas`` - OpenSuse & Fedora, stable, `OpenSuse Repository <http://software.opensuse.org/package/python-pandas?search_term=pandas>`__ , ``zypper in python-pandas`` - + OpenSuse, stable, `OpenSuse Repository <http://software.opensuse.org/package/python-pandas?search_term=pandas>`__ , ``zypper in python-pandas`` + Fedora, stable, `official Fedora repository <https://admin.fedoraproject.org/pkgdb/package/rpms/python-pandas/>`__ , ``dnf install python-pandas`` + Centos/RHEL, stable, `EPEL repository <https://admin.fedoraproject.org/pkgdb/package/rpms/python-pandas/>`__ , ``yum install python-pandas``
Since Fedora and Centos use a different tools than OpenSuse, and have their own packages in official repositories, it is better to point to them than the one from OpenSuse.
https://api.github.com/repos/pandas-dev/pandas/pulls/13588
2016-07-08T19:24:13Z
2016-07-24T18:13:43Z
2016-07-24T18:13:43Z
2016-07-24T18:13:43Z
RLS: switch master from 0.18.2 to 0.19.0
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index e971f1f28903f..f0e01ddc3fc2d 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -653,7 +653,7 @@ The same applies to ``df.append(df_different)``. Unioning ~~~~~~~~ -.. versionadded:: 0.18.2 +.. versionadded:: 0.19.0 If you want to combine categoricals that do not necessarily have the same categories, the `union_categorical` function will diff --git a/doc/source/merging.rst b/doc/source/merging.rst index b69d0d8ba3015..f14e5741c6e2e 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -1133,7 +1133,7 @@ fill/interpolate missing data: Merging AsOf ~~~~~~~~~~~~ -.. versionadded:: 0.18.2 +.. versionadded:: 0.19.0 A :func:`merge_asof` is similar to an ordered left-join except that we match on nearest key rather than equal keys. For each row in the ``left`` DataFrame, we select the last row in the ``right`` DataFrame whose ``on`` key is less than the left's key. Both DataFrames must be sorted by the key. diff --git a/doc/source/text.rst b/doc/source/text.rst index 3822c713d7f85..3a4a57ff4da95 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -316,7 +316,7 @@ then ``extractall(pat).xs(0, level='match')`` gives the same result as ``Index`` also supports ``.str.extractall``. It returns a ``DataFrame`` which has the same result as a ``Series.str.extractall`` with a default index (starts from 0). -.. versionadded:: 0.18.2 +.. versionadded:: 0.19.0 .. ipython:: python diff --git a/doc/source/whatsnew.rst b/doc/source/whatsnew.rst index 685f1d2086c69..77dc249aeb788 100644 --- a/doc/source/whatsnew.rst +++ b/doc/source/whatsnew.rst @@ -18,7 +18,7 @@ What's New These are new features and improvements of note in each release. -.. include:: whatsnew/v0.18.2.txt +.. include:: whatsnew/v0.19.0.txt .. include:: whatsnew/v0.18.1.txt diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt deleted file mode 100644 index 64644bd9a7a26..0000000000000 --- a/doc/source/whatsnew/v0.18.2.txt +++ /dev/null @@ -1,532 +0,0 @@ -.. _whatsnew_0182: - -v0.18.2 (July ??, 2016) ------------------------ - -This is a minor bug-fix release from 0.18.1 and includes a large number of -bug fixes along with several new features, enhancements, and performance improvements. -We recommend that all users upgrade to this version. - -Highlights include: - -- :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0182.enhancements.asof_merge>` - -.. contents:: What's new in v0.18.2 - :local: - :backlinks: none - -.. _whatsnew_0182.new_features: - -New features -~~~~~~~~~~~~ - -.. _whatsnew_0182.enhancements.asof_merge: - -:func:`merge_asof` for asof-style time-series joining -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A long-time requested feature has been added through the :func:`merge_asof` function, to -support asof style joining of time-series. (:issue:`1870`). Full documentation is -:ref:`here <merging.merge_asof>` - -The :func:`merge_asof` performs an asof merge, which is similar to a left-join -except that we match on nearest key rather than equal keys. - -.. ipython:: python - - left = pd.DataFrame({'a': [1, 5, 10], - 'left_val': ['a', 'b', 'c']}) - right = pd.DataFrame({'a': [1, 2, 3, 6, 7], - 'right_val': [1, 2, 3, 6, 7]}) - - left - right - -We typically want to match exactly when possible, and use the most -recent value otherwise. - -.. ipython:: python - - pd.merge_asof(left, right, on='a') - -We can also match rows ONLY with prior data, and not an exact match. - -.. ipython:: python - - pd.merge_asof(left, right, on='a', allow_exact_matches=False) - - -In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them. -This also illustrates using the ``by`` parameter to group data before merging. - -.. ipython:: python - - trades = pd.DataFrame({ - 'time': pd.to_datetime(['20160525 13:30:00.023', - '20160525 13:30:00.038', - '20160525 13:30:00.048', - '20160525 13:30:00.048', - '20160525 13:30:00.048']), - 'ticker': ['MSFT', 'MSFT', - 'GOOG', 'GOOG', 'AAPL'], - 'price': [51.95, 51.95, - 720.77, 720.92, 98.00], - 'quantity': [75, 155, - 100, 100, 100]}, - columns=['time', 'ticker', 'price', 'quantity']) - - quotes = pd.DataFrame({ - 'time': pd.to_datetime(['20160525 13:30:00.023', - '20160525 13:30:00.023', - '20160525 13:30:00.030', - '20160525 13:30:00.041', - '20160525 13:30:00.048', - '20160525 13:30:00.049', - '20160525 13:30:00.072', - '20160525 13:30:00.075']), - 'ticker': ['GOOG', 'MSFT', 'MSFT', - 'MSFT', 'GOOG', 'AAPL', 'GOOG', - 'MSFT'], - 'bid': [720.50, 51.95, 51.97, 51.99, - 720.50, 97.99, 720.50, 52.01], - 'ask': [720.93, 51.96, 51.98, 52.00, - 720.93, 98.01, 720.88, 52.03]}, - columns=['time', 'ticker', 'bid', 'ask']) - -.. ipython:: python - - trades - quotes - -An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and -in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except -that forward filling happens automatically taking the most recent non-NaN value. - -.. ipython:: python - - pd.merge_asof(trades, quotes, - on='time', - by='ticker') - -This returns a merged DataFrame with the entries in the same order as the original left -passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged. - -.. _whatsnew_0182.enhancements.read_csv_dupe_col_names_support: - -:func:`read_csv` has improved support for duplicate column names -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether -they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`) - -.. ipython :: python - - data = '0,1,2\n3,4,5' - names = ['a', 'b', 'a'] - -Previous behaviour: - -.. code-block:: ipython - - In [2]: pd.read_csv(StringIO(data), names=names) - Out[2]: - a b a - 0 2 1 2 - 1 5 4 5 - -The first 'a' column contains the same data as the second 'a' column, when it should have -contained the array ``[0, 3]``. - -New behaviour: - -.. ipython :: python - - In [2]: pd.read_csv(StringIO(data), names=names) - -.. _whatsnew_0182.enhancements.semi_month_offsets: - -Semi-Month Offsets -^^^^^^^^^^^^^^^^^^ - -Pandas has gained new frequency offsets, ``SemiMonthEnd`` ('SM') and ``SemiMonthBegin`` ('SMS'). -These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. -(:issue:`1543`) - -.. ipython:: python - - from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin - -SemiMonthEnd: - -.. ipython:: python - - Timestamp('2016-01-01') + SemiMonthEnd() - - pd.date_range('2015-01-01', freq='SM', periods=4) - -SemiMonthBegin: - -.. ipython:: python - - Timestamp('2016-01-01') + SemiMonthBegin() - - pd.date_range('2015-01-01', freq='SMS', periods=4) - -Using the anchoring suffix, you can also specify the day of month to use instead of the 15th. - -.. ipython:: python - - pd.date_range('2015-01-01', freq='SMS-16', periods=4) - - pd.date_range('2015-01-01', freq='SM-14', periods=4) - -.. _whatsnew_0182.enhancements.other: - -Other enhancements -^^^^^^^^^^^^^^^^^^ - -- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) - -- ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see :ref:`documentation here <text.extractall>` (:issue:`10008`, :issue:`13156`) -- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`) - - .. ipython:: python - - idx = pd.Index(["a1a2", "b1", "c1"]) - idx.str.extractall("[ab](?P<digit>\d)") - -- ``Timestamp`` s can now accept positional and keyword parameters like :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`) - - .. ipython:: python - - pd.Timestamp(2012, 1, 1) - - pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30) - -- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``decimal`` option (:issue:`12933`) -- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`) -- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`) - -- ``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`) -- ``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`) - - .. ipython:: python - - idx = pd.Index(['a', 'b', 'c']) - idx.where([True, False, True]) - -- ``Categorical.astype()`` now accepts an optional boolean argument ``copy``, effective when dtype is categorical (:issue:`13209`) -- ``DataFrame`` has gained the ``.asof()`` method to return the last non-NaN values according to the selected subset (:issue:`13358`) -- Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`) -- The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`) -- ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) -- A ``union_categorical`` function has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) -- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`) -- ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) - -.. _whatsnew_0182.api: - -API changes -~~~~~~~~~~~ - - -- Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception (:issue:`10001`) -- An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`) -- Calls to ``.sample()`` will respect the random seed set via ``numpy.random.seed(n)`` (:issue:`13161`) -- ``Styler.apply`` is now more strict about the outputs your function must return. For ``axis=0`` or ``axis=1``, the output shape must be identical. For ``axis=None``, the output must be a DataFrame with identical columns and index labels. (:issue:`13222`) - -.. _whatsnew_0182.api.tolist: - -``Series.tolist()`` will now return Python types -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behaviour (:issue:`10904`) - - -.. ipython:: python - - s = pd.Series([1,2,3]) - type(s.tolist()[0]) - -Previous Behavior: - -.. code-block:: ipython - - In [7]: type(s.tolist()[0]) - Out[7]: - <class 'numpy.int64'> - -New Behavior: - -.. ipython:: python - - type(s.tolist()[0]) - -.. _whatsnew_0182.api.promote: - -``Series`` type promotion on assignment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A ``Series`` will now correctly promote its dtype for assignment with incompat values to the current dtype (:issue:`13234`) - - -.. ipython:: python - - s = pd.Series() - -Previous Behavior: - -.. code-block:: ipython - - In [2]: s["a"] = pd.Timestamp("2016-01-01") - - In [3]: s["b"] = 3.0 - TypeError: invalid type promotion - -New Behavior: - -.. ipython:: python - - s["a"] = pd.Timestamp("2016-01-01") - s["b"] = 3.0 - s - s.dtype - -.. _whatsnew_0182.api.to_datetime_coerce: - -``.to_datetime()`` when coercing -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A bug is fixed in ``.to_datetime()`` when passing integers or floats, and no ``unit`` and ``errors='coerce'`` (:issue:`13180`). -Previously if ``.to_datetime()`` encountered mixed integers/floats and strings, but no datetimes with ``errors='coerce'`` it would convert all to ``NaT``. - -Previous Behavior: - -.. code-block:: ipython - - In [2]: pd.to_datetime([1, 'foo'], errors='coerce') - Out[2]: DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None) - -This will now convert integers/floats with the default unit of ``ns``. - -.. ipython:: python - - pd.to_datetime([1, 'foo'], errors='coerce') - -.. _whatsnew_0182.api.merging: - -Merging changes -^^^^^^^^^^^^^^^ - -Merging will now preserve the dtype of the join keys (:issue:`8596`) - -.. ipython:: python - - df1 = pd.DataFrame({'key': [1], 'v1': [10]}) - df1 - df2 = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]}) - df2 - -Previous Behavior: - -.. code-block:: ipython - - In [5]: pd.merge(df1, df2, how='outer') - Out[5]: - key v1 - 0 1.0 10.0 - 1 1.0 20.0 - 2 2.0 30.0 - - In [6]: pd.merge(df1, df2, how='outer').dtypes - Out[6]: - key float64 - v1 float64 - dtype: object - -New Behavior: - -We are able to preserve the join keys - -.. ipython:: python - - pd.merge(df1, df2, how='outer') - pd.merge(df1, df2, how='outer').dtypes - -Of course if you have missing values that are introduced, then the -resulting dtype will be upcast (unchanged from previous). - -.. ipython:: python - - pd.merge(df1, df2, how='outer', on='key') - pd.merge(df1, df2, how='outer', on='key').dtypes - -.. _whatsnew_0182.describe: - -``.describe()`` changes -^^^^^^^^^^^^^^^^^^^^^^^ - -Percentile identifiers in the index of a ``.describe()`` output will now be rounded to the least precision that keeps them distinct (:issue:`13104`) - -.. ipython:: python - - s = pd.Series([0, 1, 2, 3, 4]) - df = pd.DataFrame([0, 1, 2, 3, 4]) - -Previous Behavior: - -The percentiles were rounded to at most one decimal place, which could raise ``ValueError`` for a data frame if the percentiles were duplicated. - -.. code-block:: ipython - - In [3]: s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - Out[3]: - count 5.000000 - mean 2.000000 - std 1.581139 - min 0.000000 - 0.0% 0.000400 - 0.1% 0.002000 - 0.1% 0.004000 - 50% 2.000000 - 99.9% 3.996000 - 100.0% 3.998000 - 100.0% 3.999600 - max 4.000000 - dtype: float64 - - In [4]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - Out[4]: - ... - ValueError: cannot reindex from a duplicate axis - -New Behavior: - -.. ipython:: python - - s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - -Furthermore: - -- Passing duplicated ``percentiles`` will now raise a ``ValueError``. -- Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`) - -.. _whatsnew_0182.api.other: - -Other API changes -^^^^^^^^^^^^^^^^^ - -- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`) -- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`) -- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`) -- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`) -- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`) - -.. _whatsnew_0182.deprecations: - -Deprecations -^^^^^^^^^^^^ - -- ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`) -- ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`) -- ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) -- top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`) - -.. _whatsnew_0182.performance: - -Performance Improvements -~~~~~~~~~~~~~~~~~~~~~~~~ - -- Improved performance of sparse ``IntIndex.intersect`` (:issue:`13082`) -- Improved performance of sparse arithmetic with ``BlockIndex`` when the number of blocks are large, though recommended to use ``IntIndex`` in such cases (:issue:`13082`) -- increased performance of ``DataFrame.quantile()`` as it now operates per-block (:issue:`11623`) - -- Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`) -- Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) - - -.. _whatsnew_0182.bug_fixes: - -Bug Fixes -~~~~~~~~~ - -- Bug in ``io.json.json_normalize()``, where non-ascii keys raised an exception (:issue:`13213`) -- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing may raise ``IndexError`` (:issue:`13144`) -- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing result may have normal ``Index`` (:issue:`13144`) -- Bug in ``SparseDataFrame`` in which ``axis=None`` did not default to ``axis=0`` (:issue:`13048`) -- Bug in ``SparseSeries`` and ``SparseDataFrame`` creation with ``object`` dtype may raise ``TypeError`` (:issue:`11633`) -- Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`) -- Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`) -- Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`) - - -- Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`) -- Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`) - -- Bug in calling ``.memory_usage()`` on object which doesn't implement (:issue:`12924`) - -- Regression in ``Series.quantile`` with nans (also shows up in ``.median()`` and ``.describe()`` ); furthermore now names the ``Series`` with the quantile (:issue:`13098`, :issue:`13146`) - -- Bug in ``SeriesGroupBy.transform`` with datetime values and missing groups (:issue:`13191`) - -- Bug in ``Series.str.extractall()`` with ``str`` index raises ``ValueError`` (:issue:`13156`) -- Bug in ``Series.str.extractall()`` with single group and quantifier (:issue:`13382`) - - -- Bug in ``PeriodIndex`` and ``Period`` subtraction raises ``AttributeError`` (:issue:`13071`) -- Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`) -- Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`) -- Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`) -- Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`) -- Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`) -- Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`) -- Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`) - -- Bug in various index types, which did not propagate the name of passed index (:issue:`12309`) -- Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`) -- Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`) - -- Bug in ``DataFrame.to_csv()`` in which float values were being quoted even though quotations were specified for non-numeric values only (:issue:`12922`, :issue:`13259`) -- Bug in ``MultiIndex`` slicing where extra elements were returned when level is non-unique (:issue:`12896`) -- Bug in ``.str.replace`` does not raise ``TypeError`` for invalid replacement (:issue:`13438`) - - -- Bug in ``pd.read_csv()`` with ``engine='python'`` in which ``NaN`` values weren't being detected after data was converted to numeric values (:issue:`13314`) -- Bug in ``pd.read_csv()`` in which the ``nrows`` argument was not properly validated for both engines (:issue:`10476`) -- Bug in ``pd.read_csv()`` with ``engine='python'`` in which infinities of mixed-case forms were not being interpreted properly (:issue:`13274`) -- Bug in ``pd.read_csv()`` with ``engine='python'`` in which trailing ``NaN`` values were not being parsed (:issue:`13320`) -- Bug in ``pd.read_csv()`` with ``engine='python'`` when reading from a tempfile.TemporaryFile on Windows with Python 3 (:issue:`13398`) -- Bug in ``pd.read_csv()`` that prevents ``usecols`` kwarg from accepting single-byte unicode strings (:issue:`13219`) -- Bug in ``pd.read_csv()`` that prevents ``usecols`` from being an empty set (:issue:`13402`) -- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which null ``quotechar`` was not accepted even though ``quoting`` was specified as ``None`` (:issue:`13411`) -- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which fields were not properly cast to float when quoting was specified as non-numeric (:issue:`13411`) -- Bug in ``pd.pivot_table()`` where ``margins_name`` is ignored when ``aggfunc`` is a list (:issue:`13354`) - - - -- Bug in ``Series`` arithmetic raises ``TypeError`` if it contains datetime-like as ``object`` dtype (:issue:`13043`) - - -- Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`) -- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, `int16`` dtypes (:issue:`13451`) -- Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`) - -- Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) -- Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`) -- Bug in ``Peirod`` and ``Series`` or ``Index`` comparison raises ``TypeError`` (:issue:`13200`) -- Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) -- Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`) - - -- Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`) -- Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) -- Bug in ``groupby(..).nth()`` where the group key is included inconsistently if called after ``.head()/.tail()`` (:issue:`12839`) - -- Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) - - -- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) -- Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) - -- Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 42db0388ca5d9..70d54ea0d364d 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -1,7 +1,7 @@ .. _whatsnew_0190: -v0.19.0 (????, 2016) --------------------- +v0.19.0 (August ??, 2016) +------------------------- This is a major release from 0.18.2 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all @@ -9,75 +9,524 @@ users upgrade to this version. Highlights include: +- :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0190.enhancements.asof_merge>` -Check the :ref:`API Changes <whatsnew_0190.api_breaking>` and :ref:`deprecations <whatsnew_0190.deprecations>` before updating. - -.. contents:: What's new in v0.19.0 +.. contents:: What's new in v0.18.2 :local: :backlinks: none -.. _whatsnew_0190.enhancements: +.. _whatsnew_0190.new_features: New features ~~~~~~~~~~~~ +.. _whatsnew_0190.enhancements.asof_merge: + +:func:`merge_asof` for asof-style time-series joining +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A long-time requested feature has been added through the :func:`merge_asof` function, to +support asof style joining of time-series. (:issue:`1870`). Full documentation is +:ref:`here <merging.merge_asof>` + +The :func:`merge_asof` performs an asof merge, which is similar to a left-join +except that we match on nearest key rather than equal keys. + +.. ipython:: python + + left = pd.DataFrame({'a': [1, 5, 10], + 'left_val': ['a', 'b', 'c']}) + right = pd.DataFrame({'a': [1, 2, 3, 6, 7], + 'right_val': [1, 2, 3, 6, 7]}) + + left + right + +We typically want to match exactly when possible, and use the most +recent value otherwise. + +.. ipython:: python + + pd.merge_asof(left, right, on='a') + +We can also match rows ONLY with prior data, and not an exact match. + +.. ipython:: python + + pd.merge_asof(left, right, on='a', allow_exact_matches=False) + + +In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them. +This also illustrates using the ``by`` parameter to group data before merging. + +.. ipython:: python + + trades = pd.DataFrame({ + 'time': pd.to_datetime(['20160525 13:30:00.023', + '20160525 13:30:00.038', + '20160525 13:30:00.048', + '20160525 13:30:00.048', + '20160525 13:30:00.048']), + 'ticker': ['MSFT', 'MSFT', + 'GOOG', 'GOOG', 'AAPL'], + 'price': [51.95, 51.95, + 720.77, 720.92, 98.00], + 'quantity': [75, 155, + 100, 100, 100]}, + columns=['time', 'ticker', 'price', 'quantity']) + + quotes = pd.DataFrame({ + 'time': pd.to_datetime(['20160525 13:30:00.023', + '20160525 13:30:00.023', + '20160525 13:30:00.030', + '20160525 13:30:00.041', + '20160525 13:30:00.048', + '20160525 13:30:00.049', + '20160525 13:30:00.072', + '20160525 13:30:00.075']), + 'ticker': ['GOOG', 'MSFT', 'MSFT', + 'MSFT', 'GOOG', 'AAPL', 'GOOG', + 'MSFT'], + 'bid': [720.50, 51.95, 51.97, 51.99, + 720.50, 97.99, 720.50, 52.01], + 'ask': [720.93, 51.96, 51.98, 52.00, + 720.93, 98.01, 720.88, 52.03]}, + columns=['time', 'ticker', 'bid', 'ask']) + +.. ipython:: python + + trades + quotes + +An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and +in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except +that forward filling happens automatically taking the most recent non-NaN value. + +.. ipython:: python + + pd.merge_asof(trades, quotes, + on='time', + by='ticker') + +This returns a merged DataFrame with the entries in the same order as the original left +passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged. + +.. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support: + +:func:`read_csv` has improved support for duplicate column names +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +:ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether +they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`) + +.. ipython :: python + + data = '0,1,2\n3,4,5' + names = ['a', 'b', 'a'] + +Previous behaviour: + +.. code-block:: ipython + + In [2]: pd.read_csv(StringIO(data), names=names) + Out[2]: + a b a + 0 2 1 2 + 1 5 4 5 + +The first 'a' column contains the same data as the second 'a' column, when it should have +contained the array ``[0, 3]``. + +New behaviour: + +.. ipython :: python + + In [2]: pd.read_csv(StringIO(data), names=names) + +.. _whatsnew_0190.enhancements.semi_month_offsets: +Semi-Month Offsets +^^^^^^^^^^^^^^^^^^ + +Pandas has gained new frequency offsets, ``SemiMonthEnd`` ('SM') and ``SemiMonthBegin`` ('SMS'). +These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. +(:issue:`1543`) + +.. ipython:: python + + from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin + +SemiMonthEnd: + +.. ipython:: python + + Timestamp('2016-01-01') + SemiMonthEnd() + + pd.date_range('2015-01-01', freq='SM', periods=4) + +SemiMonthBegin: + +.. ipython:: python + Timestamp('2016-01-01') + SemiMonthBegin() + pd.date_range('2015-01-01', freq='SMS', periods=4) + +Using the anchoring suffix, you can also specify the day of month to use instead of the 15th. + +.. ipython:: python + + pd.date_range('2015-01-01', freq='SMS-16', periods=4) + + pd.date_range('2015-01-01', freq='SM-14', periods=4) .. _whatsnew_0190.enhancements.other: Other enhancements ^^^^^^^^^^^^^^^^^^ +- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) + +- ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see :ref:`documentation here <text.extractall>` (:issue:`10008`, :issue:`13156`) +- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`) + + .. ipython:: python + + idx = pd.Index(["a1a2", "b1", "c1"]) + idx.str.extractall("[ab](?P<digit>\d)") + +- ``Timestamp`` s can now accept positional and keyword parameters like :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`) + + .. ipython:: python + pd.Timestamp(2012, 1, 1) + pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30) +- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``decimal`` option (:issue:`12933`) +- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`) +- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`) +- ``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`) +- ``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`) -.. _whatsnew_0190.api_breaking: + .. ipython:: python -Backwards incompatible API changes -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + idx = pd.Index(['a', 'b', 'c']) + idx.where([True, False, True]) + +- ``Categorical.astype()`` now accepts an optional boolean argument ``copy``, effective when dtype is categorical (:issue:`13209`) +- ``DataFrame`` has gained the ``.asof()`` method to return the last non-NaN values according to the selected subset (:issue:`13358`) +- Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`) +- The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`) +- ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) +- A ``union_categorical`` function has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) +- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`) +- ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) .. _whatsnew_0190.api: +API changes +~~~~~~~~~~~ +- Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception (:issue:`10001`) +- An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`) +- Calls to ``.sample()`` will respect the random seed set via ``numpy.random.seed(n)`` (:issue:`13161`) +- ``Styler.apply`` is now more strict about the outputs your function must return. For ``axis=0`` or ``axis=1``, the output shape must be identical. For ``axis=None``, the output must be a DataFrame with identical columns and index labels. (:issue:`13222`) +.. _whatsnew_0190.api.tolist: +``Series.tolist()`` will now return Python types +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Other API Changes -^^^^^^^^^^^^^^^^^ +``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behaviour (:issue:`10904`) -.. _whatsnew_0190.deprecations: -Deprecations -^^^^^^^^^^^^ +.. ipython:: python + + s = pd.Series([1,2,3]) + type(s.tolist()[0]) + +Previous Behavior: + +.. code-block:: ipython + + In [7]: type(s.tolist()[0]) + Out[7]: + <class 'numpy.int64'> + +New Behavior: + +.. ipython:: python + + type(s.tolist()[0]) + +.. _whatsnew_0190.api.promote: + +``Series`` type promotion on assignment +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A ``Series`` will now correctly promote its dtype for assignment with incompat values to the current dtype (:issue:`13234`) + + +.. ipython:: python + + s = pd.Series() +Previous Behavior: +.. code-block:: ipython + In [2]: s["a"] = pd.Timestamp("2016-01-01") + In [3]: s["b"] = 3.0 + TypeError: invalid type promotion -.. _whatsnew_0190.prior_deprecations: +New Behavior: -Removal of prior version deprecations/changes -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. ipython:: python + s["a"] = pd.Timestamp("2016-01-01") + s["b"] = 3.0 + s + s.dtype +.. _whatsnew_0190.api.to_datetime_coerce: +``.to_datetime()`` when coercing +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +A bug is fixed in ``.to_datetime()`` when passing integers or floats, and no ``unit`` and ``errors='coerce'`` (:issue:`13180`). +Previously if ``.to_datetime()`` encountered mixed integers/floats and strings, but no datetimes with ``errors='coerce'`` it would convert all to ``NaT``. + +Previous Behavior: + +.. code-block:: ipython + + In [2]: pd.to_datetime([1, 'foo'], errors='coerce') + Out[2]: DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None) + +This will now convert integers/floats with the default unit of ``ns``. + +.. ipython:: python + + pd.to_datetime([1, 'foo'], errors='coerce') + +.. _whatsnew_0190.api.merging: + +Merging changes +^^^^^^^^^^^^^^^ + +Merging will now preserve the dtype of the join keys (:issue:`8596`) + +.. ipython:: python + + df1 = pd.DataFrame({'key': [1], 'v1': [10]}) + df1 + df2 = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]}) + df2 + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.merge(df1, df2, how='outer') + Out[5]: + key v1 + 0 1.0 10.0 + 1 1.0 20.0 + 2 2.0 30.0 + + In [6]: pd.merge(df1, df2, how='outer').dtypes + Out[6]: + key float64 + v1 float64 + dtype: object + +New Behavior: + +We are able to preserve the join keys + +.. ipython:: python + + pd.merge(df1, df2, how='outer') + pd.merge(df1, df2, how='outer').dtypes + +Of course if you have missing values that are introduced, then the +resulting dtype will be upcast (unchanged from previous). + +.. ipython:: python + + pd.merge(df1, df2, how='outer', on='key') + pd.merge(df1, df2, how='outer', on='key').dtypes + +.. _whatsnew_0190.describe: + +``.describe()`` changes +^^^^^^^^^^^^^^^^^^^^^^^ + +Percentile identifiers in the index of a ``.describe()`` output will now be rounded to the least precision that keeps them distinct (:issue:`13104`) + +.. ipython:: python + + s = pd.Series([0, 1, 2, 3, 4]) + df = pd.DataFrame([0, 1, 2, 3, 4]) + +Previous Behavior: + +The percentiles were rounded to at most one decimal place, which could raise ``ValueError`` for a data frame if the percentiles were duplicated. + +.. code-block:: ipython + + In [3]: s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + Out[3]: + count 5.000000 + mean 2.000000 + std 1.581139 + min 0.000000 + 0.0% 0.000400 + 0.1% 0.002000 + 0.1% 0.004000 + 50% 2.000000 + 99.9% 3.996000 + 100.0% 3.998000 + 100.0% 3.999600 + max 4.000000 + dtype: float64 + + In [4]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + Out[4]: + ... + ValueError: cannot reindex from a duplicate axis + +New Behavior: + +.. ipython:: python + + s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + +Furthermore: + +- Passing duplicated ``percentiles`` will now raise a ``ValueError``. +- Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`) + +.. _whatsnew_0190.api.other: + +Other API changes +^^^^^^^^^^^^^^^^^ + +- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`) +- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`) +- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`) +- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`) +- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`) + +.. _whatsnew_0190.deprecations: + +Deprecations +^^^^^^^^^^^^ + +- ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`) +- ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`) +- ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) +- top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`) .. _whatsnew_0190.performance: Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ +- Improved performance of sparse ``IntIndex.intersect`` (:issue:`13082`) +- Improved performance of sparse arithmetic with ``BlockIndex`` when the number of blocks are large, though recommended to use ``IntIndex`` in such cases (:issue:`13082`) +- increased performance of ``DataFrame.quantile()`` as it now operates per-block (:issue:`11623`) - +- Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`) +- Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) .. _whatsnew_0190.bug_fixes: Bug Fixes ~~~~~~~~~ + +- Bug in ``io.json.json_normalize()``, where non-ascii keys raised an exception (:issue:`13213`) +- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing may raise ``IndexError`` (:issue:`13144`) +- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing result may have normal ``Index`` (:issue:`13144`) +- Bug in ``SparseDataFrame`` in which ``axis=None`` did not default to ``axis=0`` (:issue:`13048`) +- Bug in ``SparseSeries`` and ``SparseDataFrame`` creation with ``object`` dtype may raise ``TypeError`` (:issue:`11633`) +- Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`) +- Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`) +- Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`) + + +- Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`) +- Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`) + +- Bug in calling ``.memory_usage()`` on object which doesn't implement (:issue:`12924`) + +- Regression in ``Series.quantile`` with nans (also shows up in ``.median()`` and ``.describe()`` ); furthermore now names the ``Series`` with the quantile (:issue:`13098`, :issue:`13146`) + +- Bug in ``SeriesGroupBy.transform`` with datetime values and missing groups (:issue:`13191`) + +- Bug in ``Series.str.extractall()`` with ``str`` index raises ``ValueError`` (:issue:`13156`) +- Bug in ``Series.str.extractall()`` with single group and quantifier (:issue:`13382`) + + +- Bug in ``PeriodIndex`` and ``Period`` subtraction raises ``AttributeError`` (:issue:`13071`) +- Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`) +- Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`) +- Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`) +- Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`) +- Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`) +- Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`) +- Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`) + +- Bug in various index types, which did not propagate the name of passed index (:issue:`12309`) +- Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`) +- Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`) + +- Bug in ``DataFrame.to_csv()`` in which float values were being quoted even though quotations were specified for non-numeric values only (:issue:`12922`, :issue:`13259`) +- Bug in ``MultiIndex`` slicing where extra elements were returned when level is non-unique (:issue:`12896`) +- Bug in ``.str.replace`` does not raise ``TypeError`` for invalid replacement (:issue:`13438`) + + +- Bug in ``pd.read_csv()`` with ``engine='python'`` in which ``NaN`` values weren't being detected after data was converted to numeric values (:issue:`13314`) +- Bug in ``pd.read_csv()`` in which the ``nrows`` argument was not properly validated for both engines (:issue:`10476`) +- Bug in ``pd.read_csv()`` with ``engine='python'`` in which infinities of mixed-case forms were not being interpreted properly (:issue:`13274`) +- Bug in ``pd.read_csv()`` with ``engine='python'`` in which trailing ``NaN`` values were not being parsed (:issue:`13320`) +- Bug in ``pd.read_csv()`` with ``engine='python'`` when reading from a tempfile.TemporaryFile on Windows with Python 3 (:issue:`13398`) +- Bug in ``pd.read_csv()`` that prevents ``usecols`` kwarg from accepting single-byte unicode strings (:issue:`13219`) +- Bug in ``pd.read_csv()`` that prevents ``usecols`` from being an empty set (:issue:`13402`) +- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which null ``quotechar`` was not accepted even though ``quoting`` was specified as ``None`` (:issue:`13411`) +- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which fields were not properly cast to float when quoting was specified as non-numeric (:issue:`13411`) +- Bug in ``pd.pivot_table()`` where ``margins_name`` is ignored when ``aggfunc`` is a list (:issue:`13354`) + + + +- Bug in ``Series`` arithmetic raises ``TypeError`` if it contains datetime-like as ``object`` dtype (:issue:`13043`) + + +- Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`) +- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, `int16`` dtypes (:issue:`13451`) +- Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`) + +- Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) +- Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`) +- Bug in ``Peirod`` and ``Series`` or ``Index`` comparison raises ``TypeError`` (:issue:`13200`) +- Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) +- Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`) + + +- Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`) +- Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) +- Bug in ``groupby(..).nth()`` where the group key is included inconsistently if called after ``.head()/.tail()`` (:issue:`12839`) + +- Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) + + +- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) +- Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) + +- Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt new file mode 100644 index 0000000000000..695e917c76ba0 --- /dev/null +++ b/doc/source/whatsnew/v0.20.0.txt @@ -0,0 +1,83 @@ +.. _whatsnew_0200: + +v0.20.0 (????, 2016) +-------------------- + +This is a major release from 0.19 and includes a small number of API changes, several new features, +enhancements, and performance improvements along with a large number of bug fixes. We recommend that all +users upgrade to this version. + +Highlights include: + + +Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations <whatsnew_0200.deprecations>` before updating. + +.. contents:: What's new in v0.19.0 + :local: + :backlinks: none + +.. _whatsnew_0200.enhancements: + +New features +~~~~~~~~~~~~ + + + + + +.. _whatsnew_0200.enhancements.other: + +Other enhancements +^^^^^^^^^^^^^^^^^^ + + + + + + +.. _whatsnew_0200.api_breaking: + +Backwards incompatible API changes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. _whatsnew_0200.api: + + + + + + +Other API Changes +^^^^^^^^^^^^^^^^^ + +.. _whatsnew_0200.deprecations: + +Deprecations +^^^^^^^^^^^^ + + + + + +.. _whatsnew_0200.prior_deprecations: + +Removal of prior version deprecations/changes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + + + + +.. _whatsnew_0200.performance: + +Performance Improvements +~~~~~~~~~~~~~~~~~~~~~~~~ + + + + + +.. _whatsnew_0200.bug_fixes: + +Bug Fixes +~~~~~~~~~ diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index bf6fa35cf255f..7a0743f6b2778 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -286,7 +286,7 @@ def _cast_inplace(terms, acceptable_dtypes, dtype): acceptable_dtypes : list of acceptable numpy.dtype Will not cast if term's dtype in this list. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 dtype : str or numpy.dtype The dtype to cast to. diff --git a/pandas/core/base.py b/pandas/core/base.py index 96732a7140f9e..13a6b4b7b4ce0 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -1001,7 +1001,7 @@ def is_monotonic(self): Return boolean if values in the object are monotonic_increasing - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- @@ -1017,7 +1017,7 @@ def is_monotonic_decreasing(self): Return boolean if values in the object are monotonic_decreasing - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 6dba41a746e19..f4aeaf9184d09 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -348,7 +348,7 @@ def astype(self, dtype, copy=True): If copy is set to False and dtype is categorical, the original object is returned. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 """ if is_categorical_dtype(dtype): diff --git a/pandas/core/generic.py b/pandas/core/generic.py index cc5c45158bf4f..7b271df4085cc 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3642,7 +3642,7 @@ def asof(self, where, subset=None): The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) - .. versionadded:: 0.18.2 For DataFrame + .. versionadded:: 0.19.0 For DataFrame If there is no good value, NaN is returned. diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 96472698ba9d9..ad27010714f63 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -378,7 +378,7 @@ def _shallow_copy_with_infer(self, values=None, **kwargs): def _deepcopy_if_needed(self, orig, copy=False): """ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Make a copy of self if data coincides (in memory) with orig. Subclasses should override this if self._base is not an ndarray. @@ -494,7 +494,7 @@ def repeat(self, n, *args, **kwargs): def where(self, cond, other=None): """ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from @@ -813,7 +813,7 @@ def _to_embed(self, keep_tz=False): satisfied, the original data is used to create a new Index or the original Index is returned. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 """ diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 3b7c660f5faa1..84b8926f4177f 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -313,7 +313,7 @@ def _can_reindex(self, indexer): def where(self, cond, other=None): """ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from diff --git a/pandas/io/html.py b/pandas/io/html.py index 48caaa39dd711..609642e248eda 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -837,7 +837,7 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None, Character to recognize as decimal point (e.g. use ',' for European data). - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index cbe04349b5105..d4ca717ddbc4e 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -276,7 +276,7 @@ def read_hdf(path_or_buf, key=None, **kwargs): path_or_buf : path (string), buffer, or path object (pathlib.Path or py._path.local.LocalPath) to read from - .. versionadded:: 0.18.2 support for pathlib, py.path. + .. versionadded:: 0.19.0 support for pathlib, py.path. key : group identifier in the store. Can be omitted a HDF file contains a single pandas object. diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 4b7162398738e..d65dfc3254465 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -182,7 +182,7 @@ def merge_ordered(left, right, on=None, * outer: use union of keys from both frames (SQL: full outer join) * inner: use intersection of keys from both frames (SQL: inner join) - .. versionadded 0.18.2 + .. versionadded:: 0.19.0 Examples -------- @@ -263,7 +263,7 @@ def merge_asof(left, right, on=None, Optionally perform group-wise merge. This searches for the nearest match on the 'on' key within the same group according to 'by'. - .. versionadded 0.18.2 + .. versionadded:: 0.19.0 Parameters ---------- diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 42631d442a990..2e3d1ace9734c 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -747,7 +747,7 @@ def repeat(self, repeats, *args, **kwargs): def where(self, cond, other=None): """ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 77500081be62c..83cb768b37aaa 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1857,7 +1857,7 @@ def tz_localize(self, tz, ambiguous='raise', errors='raise'): - 'coerce' will return NaT if the timestamp can not be converted into the specified timezone - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 infer_dst : boolean, default False (DEPRECATED) Attempt to infer fall dst-transition hours based on order diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index f4b75ddd72126..d0b1fd746d0d5 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -1258,7 +1258,7 @@ class SemiMonthEnd(SemiMonthOffset): Two DateOffset's per month repeating on the last day of the month and day_of_month. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Parameters ---------- @@ -1317,7 +1317,7 @@ class SemiMonthBegin(SemiMonthOffset): Two DateOffset's per month repeating on the first day of the month and day_of_month. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Parameters ---------- diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 8837881af0b6c..df6554fe1d5de 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -246,7 +246,7 @@ class Timestamp(_Timestamp): :func:`datetime.datetime` Parameters ------------------------------------ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 year : int month : int @@ -539,7 +539,7 @@ class Timestamp(_Timestamp): - 'coerce' will return NaT if the timestamp can not be converted into the specified timezone - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- diff --git a/pandas/types/concat.py b/pandas/types/concat.py index 53db9ddf79a5c..44338f26eb2e8 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -206,7 +206,7 @@ def union_categoricals(to_union): Combine list-like of Categoricals, unioning categories. All must have the same dtype, and none can be ordered. - .. versionadded 0.18.2 + .. versionadded:: 0.19.0 Parameters ----------
xref #13562
https://api.github.com/repos/pandas-dev/pandas/pulls/13586
2016-07-08T10:35:11Z
2016-07-08T15:08:02Z
2016-07-08T15:08:02Z
2016-07-08T15:08:26Z
BUG: groupby apply on selected columns yielding scalar (GH13568)
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 046690e28dba5..4cc16aac15f8b 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -491,6 +491,7 @@ Bug Fixes - Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`) - Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`) - Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`) +- Bug in ``groupby(..).apply(..)`` when the passed function returns scalar values per group (:issue:`13468`). - Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`) - Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`) - Bug in ``.tz_localize`` with ``dateutil.tz.tzlocal`` may return incorrect result (:issue:`13583`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 8d33c27481d93..077acc1e81444 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -3403,11 +3403,14 @@ def first_non_None_value(values): return self._reindex_output(result) + # values are not series or array-like but scalars else: # only coerce dates if we find at least 1 datetime coerce = True if any([isinstance(x, Timestamp) for x in values]) else False - return (Series(values, index=key_index, name=self.name) + # self.name not passed through to Series as the result + # should not take the name of original selection of columns + return (Series(values, index=key_index) ._convert(datetime=True, coerce=coerce)) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index efcba758e3b38..a52f22fe2032a 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2584,6 +2584,16 @@ def test_apply_series_yield_constant(self): result = self.df.groupby(['A', 'B'])['C'].apply(len) self.assertEqual(result.index.names[:2], ('A', 'B')) + def test_apply_frame_yield_constant(self): + # GH13568 + result = self.df.groupby(['A', 'B']).apply(len) + self.assertTrue(isinstance(result, Series)) + self.assertIsNone(result.name) + + result = self.df.groupby(['A', 'B'])[['C', 'D']].apply(len) + self.assertTrue(isinstance(result, Series)) + self.assertIsNone(result.name) + def test_apply_frame_to_series(self): grouped = self.df.groupby(['A', 'B']) result = grouped.apply(len)
- [x] closes #13568 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13585
2016-07-08T09:35:57Z
2016-07-11T15:02:25Z
2016-07-11T15:02:24Z
2016-07-11T15:02:25Z
COMPAT: 32-bit compat fixes mainly in testing
diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c931adc9a31df..1ea567f15cb7f 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3085,7 +3085,7 @@ def reduction(self, f, axis=0, consolidate=True, transposed=False, # compute the orderings of our original data if len(self.blocks) > 1: - indexer = np.empty(len(self.axes[0]), dtype='int64') + indexer = np.empty(len(self.axes[0]), dtype=np.intp) i = 0 for b in self.blocks: for j in b.mgr_locs: diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index 4a664ed3542d7..9eba481a66685 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -534,9 +534,9 @@ def test_get_loc(self): # time indexing idx = pd.date_range('2000-01-01', periods=24, freq='H') tm.assert_numpy_array_equal(idx.get_loc(time(12)), - np.array([12], dtype=np.int64)) + np.array([12]), check_dtype=False) tm.assert_numpy_array_equal(idx.get_loc(time(12, 30)), - np.array([], dtype=np.int64)) + np.array([]), check_dtype=False) with tm.assertRaises(NotImplementedError): idx.get_loc(time(12, 30), method='pad') @@ -587,7 +587,8 @@ def test_time_loc(self): # GH8667 ts = pd.Series(np.random.randn(n), index=idx) i = np.arange(start, n, step) - tm.assert_numpy_array_equal(ts.index.get_loc(key), i) + tm.assert_numpy_array_equal(ts.index.get_loc(key), i, + check_dtype=False) tm.assert_series_equal(ts[key], ts.iloc[i]) left, right = ts.copy(), ts.copy() diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index fb5576bed90b4..e6a8aafc32be4 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -1750,12 +1750,12 @@ def test_reindex_level(self): exp_index2 = self.index.join(idx, level='second', how='left') self.assertTrue(target.equals(exp_index)) - exp_indexer = np.array([0, 2, 4], dtype=np.int64) - tm.assert_numpy_array_equal(indexer, exp_indexer) + exp_indexer = np.array([0, 2, 4]) + tm.assert_numpy_array_equal(indexer, exp_indexer, check_dtype=False) self.assertTrue(target2.equals(exp_index2)) - exp_indexer2 = np.array([0, -1, 0, -1, 0, -1], dtype=np.int64) - tm.assert_numpy_array_equal(indexer2, exp_indexer2) + exp_indexer2 = np.array([0, -1, 0, -1, 0, -1]) + tm.assert_numpy_array_equal(indexer2, exp_indexer2, check_dtype=False) assertRaisesRegexp(TypeError, "Fill method not supported", self.index.reindex, self.index, method='pad', diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 433f0f4bc67f5..0dbff0a028619 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -262,7 +262,7 @@ def test_kurt(self): self.assertTrue((df.kurt() == 0).all()) def test_argsort(self): - self._check_accum_op('argsort') + self._check_accum_op('argsort', check_dtype=False) argsorted = self.ts.argsort() self.assertTrue(issubclass(argsorted.dtype.type, np.integer)) @@ -289,8 +289,10 @@ def test_argsort_stable(self): mexpected = np.argsort(s.values, kind='mergesort') qexpected = np.argsort(s.values, kind='quicksort') - self.assert_series_equal(mindexer, Series(mexpected)) - self.assert_series_equal(qindexer, Series(qexpected)) + self.assert_series_equal(mindexer, Series(mexpected), + check_dtype=False) + self.assert_series_equal(qindexer, Series(qexpected), + check_dtype=False) self.assertFalse(np.array_equal(qindexer, mindexer)) def test_cumsum(self): @@ -487,10 +489,11 @@ def testit(): except ImportError: pass - def _check_accum_op(self, name): + def _check_accum_op(self, name, check_dtype=True): func = getattr(np, name) self.assert_numpy_array_equal(func(self.ts).values, - func(np.array(self.ts))) + func(np.array(self.ts)), + check_dtype=check_dtype) # with missing values ts = self.ts.copy() @@ -499,7 +502,8 @@ def _check_accum_op(self, name): result = func(ts)[1::2] expected = func(np.array(ts.valid())) - self.assert_numpy_array_equal(result.values, expected) + self.assert_numpy_array_equal(result.values, expected, + check_dtype=False) def test_compress(self): cond = [True, False, True, False, False] @@ -1360,13 +1364,13 @@ def test_searchsorted_numeric_dtypes_scalar(self): self.assertEqual(r, e) r = s.searchsorted([30]) - e = np.array([2], dtype=np.int64) + e = np.array([2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_searchsorted_numeric_dtypes_vector(self): s = Series([1, 2, 90, 1000, 3e9]) r = s.searchsorted([91, 2e6]) - e = np.array([3, 4], dtype=np.int64) + e = np.array([3, 4], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_search_sorted_datetime64_scalar(self): @@ -1380,14 +1384,14 @@ def test_search_sorted_datetime64_list(self): s = Series(pd.date_range('20120101', periods=10, freq='2D')) v = [pd.Timestamp('20120102'), pd.Timestamp('20120104')] r = s.searchsorted(v) - e = np.array([1, 2], dtype=np.int64) + e = np.array([1, 2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_searchsorted_sorter(self): # GH8490 s = Series([3, 1, 2]) r = s.searchsorted([0, 3], sorter=np.argsort(s)) - e = np.array([0, 2], dtype=np.int64) + e = np.array([0, 2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_is_unique(self): diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 8af93ad0ecb2e..cb90110c953c1 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -702,12 +702,14 @@ def test_unique_label_indices(): left = unique_label_indices(a) right = np.unique(a, return_index=True)[1] - tm.assert_numpy_array_equal(left, right) + tm.assert_numpy_array_equal(left, right, + check_dtype=False) a[np.random.choice(len(a), 10)] = -1 left = unique_label_indices(a) right = np.unique(a, return_index=True)[1][1:] - tm.assert_numpy_array_equal(left, right) + tm.assert_numpy_array_equal(left, right, + check_dtype=False) def test_rank(): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index cff5bbe14f1eb..90876a4541da6 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -515,17 +515,20 @@ def f(): def test_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) - expected = np.array([2, 4, 1, 3, 0], dtype=np.int64) - tm.assert_numpy_array_equal(c.argsort(ascending=True), expected) + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(c.argsort(ascending=True), expected, + check_dtype=False) expected = expected[::-1] - tm.assert_numpy_array_equal(c.argsort(ascending=False), expected) + tm.assert_numpy_array_equal(c.argsort(ascending=False), expected, + check_dtype=False) def test_numpy_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) - expected = np.array([2, 4, 1, 3, 0], dtype=np.int64) - tm.assert_numpy_array_equal(np.argsort(c), expected) + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(np.argsort(c), expected, + check_dtype=False) msg = "the 'kind' parameter is not supported" tm.assertRaisesRegexp(ValueError, msg, np.argsort, @@ -1505,7 +1508,7 @@ def test_searchsorted(self): # Single item array res = c1.searchsorted(['bread']) chk = s1.searchsorted(['bread']) - exp = np.array([1], dtype=np.int64) + exp = np.array([1], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) @@ -1514,21 +1517,21 @@ def test_searchsorted(self): # np.array.searchsorted() res = c1.searchsorted('bread') chk = s1.searchsorted('bread') - exp = np.array([1], dtype=np.int64) + exp = np.array([1], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) # Searching for a value that is not present in the Categorical res = c1.searchsorted(['bread', 'eggs']) chk = s1.searchsorted(['bread', 'eggs']) - exp = np.array([1, 4], dtype=np.int64) + exp = np.array([1, 4], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) # Searching for a value that is not present, to the right res = c1.searchsorted(['bread', 'eggs'], side='right') chk = s1.searchsorted(['bread', 'eggs'], side='right') - exp = np.array([3, 4], dtype=np.int64) # eggs before milk + exp = np.array([3, 4], dtype=np.intp) # eggs before milk self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) @@ -1538,7 +1541,7 @@ def test_searchsorted(self): chk = s2.searchsorted(['bread', 'eggs'], side='right', sorter=[0, 1, 2, 3, 5, 4]) # eggs after donuts, after switching milk and donuts - exp = np.array([3, 5], dtype=np.int64) + exp = np.array([3, 5], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index d6d601f03d561..efcba758e3b38 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -5934,49 +5934,49 @@ def test_nargsort(self): result = _nargsort(items, kind='mergesort', ascending=True, na_position='last') exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=True, na_position='first' result = _nargsort(items, kind='mergesort', ascending=True, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='last' result = _nargsort(items, kind='mergesort', ascending=False, na_position='last') exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='first' result = _nargsort(items, kind='mergesort', ascending=False, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=True, na_position='last' result = _nargsort(items2, kind='mergesort', ascending=True, na_position='last') exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=True, na_position='first' result = _nargsort(items2, kind='mergesort', ascending=True, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='last' result = _nargsort(items2, kind='mergesort', ascending=False, na_position='last') exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='first' result = _nargsort(items2, kind='mergesort', ascending=False, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) def test_datetime_count(self): df = DataFrame({'a': [1, 2, 3] * 2, diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 4b7162398738e..24f1d099e941a 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -436,7 +436,8 @@ def _merger(x, y): # if we DO have duplicates, then # we cannot guarantee order - sorter = np.concatenate([groupby.indices[g] for g, _ in groupby]) + sorter = com._ensure_platform_int( + np.concatenate([groupby.indices[g] for g, _ in groupby])) if len(result) != len(sorter): if check_duplicates: raise AssertionError("invalid reverse grouping") diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 2505309768997..c8d1bae78dad3 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -91,8 +91,8 @@ def test_cython_left_outer_join(self): exp_rs = exp_rs.take(exp_ri) exp_rs[exp_ri == -1] = -1 - self.assert_numpy_array_equal(ls, exp_ls) - self.assert_numpy_array_equal(rs, exp_rs) + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) def test_cython_right_outer_join(self): left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) @@ -117,8 +117,8 @@ def test_cython_right_outer_join(self): exp_rs = exp_rs.take(exp_ri) exp_rs[exp_ri == -1] = -1 - self.assert_numpy_array_equal(ls, exp_ls) - self.assert_numpy_array_equal(rs, exp_rs) + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) def test_cython_inner_join(self): left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) @@ -141,8 +141,8 @@ def test_cython_inner_join(self): exp_rs = exp_rs.take(exp_ri) exp_rs[exp_ri == -1] = -1 - self.assert_numpy_array_equal(ls, exp_ls) - self.assert_numpy_array_equal(rs, exp_rs) + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) def test_left_outer_join(self): joined_key2 = merge(self.df, self.df2, on='key2') diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py index bb5429b5e8836..16731620a1dcd 100644 --- a/pandas/tools/tests/test_tile.py +++ b/pandas/tools/tests/test_tile.py @@ -19,8 +19,9 @@ class TestCut(tm.TestCase): def test_simple(self): data = np.ones(5) result = cut(data, 4, labels=False) - desired = np.array([1, 1, 1, 1, 1], dtype=np.int64) - tm.assert_numpy_array_equal(result, desired) + desired = np.array([1, 1, 1, 1, 1]) + tm.assert_numpy_array_equal(result, desired, + check_dtype=False) def test_bins(self): data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]) diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 7077a23d5abcb..7eadbfb031222 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -505,7 +505,8 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) self.assertEqual(ordered.freq, idx.freq) ordered, indexer = idx.sort_values(return_indexer=True, @@ -513,7 +514,8 @@ def test_order(self): expected = idx[::-1] self.assert_index_equal(ordered, expected) self.assert_numpy_array_equal(indexer, - np.array([2, 1, 0], dtype=np.int64)) + np.array([2, 1, 0]), + check_dtype=False) self.assertEqual(ordered.freq, expected.freq) self.assertEqual(ordered.freq.n, -1) @@ -550,16 +552,16 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) def test_getitem(self): @@ -1271,7 +1273,8 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) self.assertEqual(ordered.freq, idx.freq) ordered, indexer = idx.sort_values(return_indexer=True, @@ -1309,16 +1312,16 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) def test_getitem(self): @@ -2074,14 +2077,16 @@ def _check_freq(index, expected_index): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) _check_freq(ordered, idx) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, idx[::-1]) self.assert_numpy_array_equal(indexer, - np.array([2, 1, 0], dtype=np.int64)) + np.array([2, 1, 0]), + check_dtype=False) _check_freq(ordered, idx[::-1]) pidx = PeriodIndex(['2011', '2013', '2015', '2012', @@ -2103,16 +2108,17 @@ def _check_freq(index, expected_index): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) _check_freq(ordered, idx) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, + check_dtype=False) _check_freq(ordered, idx) pidx = PeriodIndex(['2011', '2013', 'NaT', '2011'], name='pidx', @@ -2148,7 +2154,8 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) self.assertEqual(ordered.freq, idx.freq) self.assertEqual(ordered.freq, freq) @@ -2157,7 +2164,8 @@ def test_order(self): expected = idx[::-1] self.assert_index_equal(ordered, expected) self.assert_numpy_array_equal(indexer, - np.array([2, 1, 0], dtype=np.int64)) + np.array([2, 1, 0]), + check_dtype=False) self.assertEqual(ordered.freq, expected.freq) self.assertEqual(ordered.freq, freq) @@ -2191,16 +2199,16 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertEqual(ordered.freq, 'D') ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertEqual(ordered.freq, 'D') def test_getitem(self): diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index e515ba624d203..0b7b2b33d3202 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -1547,12 +1547,14 @@ def test_sort_values(self): ordered, dexer = idx.sort_values(return_indexer=True) self.assertTrue(ordered.is_monotonic) self.assert_numpy_array_equal(dexer, - np.array([1, 2, 0], dtype=np.int64)) + np.array([1, 2, 0]), + check_dtype=False) ordered, dexer = idx.sort_values(return_indexer=True, ascending=False) self.assertTrue(ordered[::-1].is_monotonic) self.assert_numpy_array_equal(dexer, - np.array([0, 2, 1], dtype=np.int64)) + np.array([0, 2, 1]), + check_dtype=False) def test_insert(self): diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 8837881af0b6c..9d71024f61bf3 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -3754,11 +3754,11 @@ except: def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): cdef: - ndarray[int64_t] utc_dates, tt, result, trans, deltas, posn + ndarray[int64_t] utc_dates, tt, result, trans, deltas Py_ssize_t i, j, pos, n = len(vals) - int64_t v, offset + ndarray[Py_ssize_t] posn + int64_t v, offset, delta pandas_datetimestruct dts - Py_ssize_t trans_len if not have_pytz: import pytz @@ -3790,7 +3790,6 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): if not len(tt): return vals - trans_len = len(trans) posn = trans.searchsorted(tt, side='right') j = 0 for i in range(n): @@ -3826,18 +3825,19 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): # Convert UTC to other timezone trans, deltas, typ = _get_dst_info(tz2) - trans_len = len(trans) - - # if all NaT, return all NaT - if (utc_dates==NPY_NAT).all(): - return utc_dates # use first non-NaT element # if all-NaT, return all-NaT if (result==NPY_NAT).all(): return result - posn = trans.searchsorted(utc_dates[utc_dates!=NPY_NAT], side='right') + # if all NaT, return all NaT + tt = utc_dates[utc_dates!=NPY_NAT] + if not len(tt): + return utc_dates + + posn = trans.searchsorted(tt, side='right') + j = 0 for i in range(n): v = utc_dates[i]
closes #13566
https://api.github.com/repos/pandas-dev/pandas/pulls/13584
2016-07-08T01:18:22Z
2016-07-09T18:16:40Z
null
2016-07-09T18:16:40Z
BUG: Block/DTI doesnt handle tzlocal properly
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 657de7ec26efc..c19cb33c53b7b 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -480,6 +480,8 @@ Bug Fixes - Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`) - Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`) - Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`) +- Bug in ``.tz_localize`` with ``dateutil.tz.tzlocal`` may return incorrect result (:issue:`13583`) +- Bug in ``DatetimeTZDtype`` dtype with ``dateutil.tz.tzlocal`` cannot be regarded as valid dtype (:issue:`13583`) - Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`) - Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 2505309768997..2eb0c94e6bf85 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1263,6 +1263,18 @@ def test_concat_tz_series_with_datetimelike(self): result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + def test_concat_tz_series_tzlocal(self): + # GH 13583 + tm._skip_if_no_dateutil() + import dateutil + x = [pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2011-02-01', tz=dateutil.tz.tzlocal())] + y = [pd.Timestamp('2012-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2012-02-01', tz=dateutil.tz.tzlocal())] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y)) + self.assertEqual(result.dtype, 'datetime64[ns, tzlocal()]') + def test_concat_period_series(self): x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py index d68ff793c9b6a..71a041d5139a2 100644 --- a/pandas/tseries/tests/test_timezones.py +++ b/pandas/tseries/tests/test_timezones.py @@ -1061,6 +1061,46 @@ def test_tslib_tz_convert_dst(self): self.assert_numpy_array_equal(idx.hour, np.array([4, 4], dtype=np.int32)) + def test_tzlocal(self): + # GH 13583 + ts = Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()) + self.assertEqual(ts.tz, dateutil.tz.tzlocal()) + self.assertTrue("tz='tzlocal()')" in repr(ts)) + + tz = tslib.maybe_get_tz('tzlocal()') + self.assertEqual(tz, dateutil.tz.tzlocal()) + + # get offset using normal datetime for test + offset = dateutil.tz.tzlocal().utcoffset(datetime(2011, 1, 1)) + offset = offset.total_seconds() * 1000000000 + self.assertEqual(ts.value + offset, Timestamp('2011-01-01').value) + + def test_tz_localize_tzlocal(self): + # GH 13583 + offset = dateutil.tz.tzlocal().utcoffset(datetime(2011, 1, 1)) + offset = int(offset.total_seconds() * 1000000000) + + dti = date_range(start='2001-01-01', end='2001-03-01') + dti2 = dti.tz_localize(dateutil.tz.tzlocal()) + tm.assert_numpy_array_equal(dti2.asi8 + offset, dti.asi8) + + dti = date_range(start='2001-01-01', end='2001-03-01', + tz=dateutil.tz.tzlocal()) + dti2 = dti.tz_localize(None) + tm.assert_numpy_array_equal(dti2.asi8 - offset, dti.asi8) + + def test_tz_convert_tzlocal(self): + # GH 13583 + # tz_convert doesn't affect to internal + dti = date_range(start='2001-01-01', end='2001-03-01', tz='UTC') + dti2 = dti.tz_convert(dateutil.tz.tzlocal()) + tm.assert_numpy_array_equal(dti2.asi8, dti.asi8) + + dti = date_range(start='2001-01-01', end='2001-03-01', + tz=dateutil.tz.tzlocal()) + dti2 = dti.tz_convert(None) + tm.assert_numpy_array_equal(dti2.asi8, dti.asi8) + class TestTimeZoneCacheKey(tm.TestCase): def test_cache_keys_are_distinct_for_pytz_vs_dateutil(self): diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index df6554fe1d5de..cd2f8a5267e37 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -1591,7 +1591,9 @@ cpdef inline object maybe_get_tz(object tz): Otherwise, just return tz. """ if isinstance(tz, string_types): - if tz.startswith('dateutil/'): + if tz == 'tzlocal()': + tz = _dateutil_tzlocal() + elif tz.startswith('dateutil/'): zone = tz[9:] tz = _dateutil_gettz(zone) # On Python 3 on Windows, the filename is not always set correctly. @@ -3767,7 +3769,6 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): return np.array([], dtype=np.int64) # Convert to UTC - if _get_zone(tz1) != 'UTC': utc_dates = np.empty(n, dtype=np.int64) if _is_tzlocal(tz1): @@ -3822,7 +3823,7 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): dts.min, dts.sec, dts.us, tz2) delta = int(total_seconds(_get_utcoffset(tz2, dt))) * 1000000000 result[i] = v + delta - return result + return result # Convert UTC to other timezone trans, deltas, typ = _get_dst_info(tz2)
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Unable to concatenate `Series` with `tzlocal` ``` s = pd.Series([pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal())]) s #0 2011-01-01 00:00:00+09:00 # dtype: datetime64[ns, tzlocal()] pd.concat([s, s]) # UnknownTimeZoneError: 'tzlocal()' ``` It is caused by 2 issues: #### 1. `tslib.maybe_get_tz` can't handle `tzlocal`. ``` pd.tslib.maybe_get_tz('tzlocal()') # UnknownTimeZoneError: 'tzlocal()' ``` #### 2. `DatetimeIndex.tz_localize` may return incorrect result when `tzlocal` is used. it's internally used in `conat`. ``` pd.DatetimeIndex(['2011-01-01', '2011-01-02'], tz=dateutil.tz.tzlocal()).tz_localize(None) # DatetimeIndex(['2011-01-01', '1970-01-01'], dtype='datetime64[ns]', freq=None) ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13583
2016-07-07T23:02:00Z
2016-07-10T22:05:03Z
null
2016-07-10T22:10:04Z
BUG: DatetimeIndex - Period shows ununderstandable error
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 657de7ec26efc..6a1d450cf083f 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -473,7 +473,7 @@ Bug Fixes - Bug in ``Series.str.extractall()`` with ``str`` index raises ``ValueError`` (:issue:`13156`) - Bug in ``Series.str.extractall()`` with single group and quantifier (:issue:`13382`) - +- Bug in ``DatetimeIndex`` and ``Period`` subtraction raises ``ValueError`` or ``AttributeError`` rather than ``TypeError`` (:issue:`13078`) - Bug in ``PeriodIndex`` and ``Period`` subtraction raises ``AttributeError`` (:issue:`13071`) - Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`) - Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`) diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 7077a23d5abcb..4ed42d044c0a9 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -443,6 +443,20 @@ def test_sub_isub(self): rng -= 1 tm.assert_index_equal(rng, expected) + def test_sub_period(self): + # GH 13078 + # not supported, check TypeError + p = pd.Period('2011-01-01', freq='D') + + for freq in [None, 'D']: + idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02'], freq=freq) + + with tm.assertRaises(TypeError): + idx - p + + with tm.assertRaises(TypeError): + p - idx + def test_value_counts_unique(self): # GH 7735 for tz in [None, 'UTC', 'Asia/Tokyo', 'US/Eastern']: @@ -1157,6 +1171,20 @@ def test_dti_tdi_numeric_ops(self): expected = DatetimeIndex(['20121231', pd.NaT, '20130101']) tm.assert_index_equal(result, expected) + def test_sub_period(self): + # GH 13078 + # not supported, check TypeError + p = pd.Period('2011-01-01', freq='D') + + for freq in [None, 'H']: + idx = pd.TimedeltaIndex(['1 hours', '2 hours'], freq=freq) + + with tm.assertRaises(TypeError): + idx - p + + with tm.assertRaises(TypeError): + p - idx + def test_addition_ops(self): # with datetimes/timedelta and tdi/dti
- [x] closes #13078 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13581
2016-07-07T20:55:22Z
2016-07-10T21:02:56Z
null
2016-07-10T21:35:00Z
CLN: move plotting funcs to pd.plotting
diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 2b2012dbf0b8a..39a38a82ab997 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -152,7 +152,7 @@ You can also create these other plots using the methods ``DataFrame.plot.<kind>` In addition to these ``kind`` s, there are the :ref:`DataFrame.hist() <visualization.hist>`, and :ref:`DataFrame.boxplot() <visualization.box>` methods, which use a separate interface. -Finally, there are several :ref:`plotting functions <visualization.tools>` in ``pandas.tools.plotting`` +Finally, there are several :ref:`plotting functions <visualization.tools>` in ``pandas.plotting`` that take a :class:`Series` or :class:`DataFrame` as an argument. These include @@ -823,7 +823,7 @@ before plotting. Plotting Tools -------------- -These functions can be imported from ``pandas.tools.plotting`` +These functions can be imported from ``pandas.plotting`` and take a :class:`Series` or :class:`DataFrame` as an argument. .. _visualization.scatter_matrix: @@ -834,7 +834,7 @@ Scatter Matrix Plot .. versionadded:: 0.7.3 You can create a scatter plot matrix using the -``scatter_matrix`` method in ``pandas.tools.plotting``: +``scatter_matrix`` method in ``pandas.plotting``: .. ipython:: python :suppress: @@ -843,7 +843,7 @@ You can create a scatter plot matrix using the .. ipython:: python - from pandas.tools.plotting import scatter_matrix + from pandas.plotting import scatter_matrix df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd']) @savefig scatter_matrix_kde.png @@ -896,7 +896,7 @@ of the same class will usually be closer together and form larger structures. .. ipython:: python - from pandas.tools.plotting import andrews_curves + from pandas.plotting import andrews_curves data = pd.read_csv('data/iris.data') @@ -918,7 +918,7 @@ represents one data point. Points that tend to cluster will appear closer togeth .. ipython:: python - from pandas.tools.plotting import parallel_coordinates + from pandas.plotting import parallel_coordinates data = pd.read_csv('data/iris.data') @@ -948,7 +948,7 @@ implies that the underlying data are not random. .. ipython:: python - from pandas.tools.plotting import lag_plot + from pandas.plotting import lag_plot plt.figure() @@ -983,7 +983,7 @@ confidence band. .. ipython:: python - from pandas.tools.plotting import autocorrelation_plot + from pandas.plotting import autocorrelation_plot plt.figure() @@ -1016,7 +1016,7 @@ are what constitutes the bootstrap plot. .. ipython:: python - from pandas.tools.plotting import bootstrap_plot + from pandas.plotting import bootstrap_plot data = pd.Series(np.random.rand(1000)) @@ -1048,7 +1048,7 @@ be colored differently. .. ipython:: python - from pandas.tools.plotting import radviz + from pandas.plotting import radviz data = pd.read_csv('data/iris.data') @@ -1438,11 +1438,11 @@ Also, you can pass different :class:`DataFrame` or :class:`Series` for ``table`` plt.close('all') -Finally, there is a helper function ``pandas.tools.plotting.table`` to create a table from :class:`DataFrame` and :class:`Series`, and add it to an ``matplotlib.Axes``. This function can accept keywords which matplotlib table has. +Finally, there is a helper function ``pandas.plotting.table`` to create a table from :class:`DataFrame` and :class:`Series`, and add it to an ``matplotlib.Axes``. This function can accept keywords which matplotlib table has. .. ipython:: python - from pandas.tools.plotting import table + from pandas.plotting import table fig, ax = plt.subplots(1, 1) table(ax, np.round(df.describe(), 2), diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 798151971363e..560b54f7b34b8 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -11,6 +11,7 @@ Highlights include: - Building pandas for development now requires ``cython >= 0.23`` (:issue:`14831`) - The ``.ix`` indexer has been deprecated, see :ref:`here <whatsnew_0200.api_breaking.deprecate_ix>` +- The ``pandas.tools.plotting`` module has been deprecated, moved to ``pandas.plotting``. See :ref:`here <whatsnew_0200.api_breaking.plotting>` (:issue:`12548`) Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations <whatsnew_0200.deprecations>` before updating. @@ -194,6 +195,31 @@ Using ``.iloc``. Here we will get the location of the 'A' column, then use *posi df.iloc[[0, 2], df.columns.get_loc('A')] +.. _whatsnew_0200.api_breaking.deprecate_plotting + +Deprecate .plotting +^^^^^^^^^^^^^^^^^^^ + +``pandas.tools.plotting`` module has been deprecated, moving directory under the +top namespace ``pandas.plotting``. All the public plotting functions should be available +from ``pandas.plotting``. + +Also, ``scatter_matrix`` function imported directly under ``pandas`` namespace is also deprecated. +Users shoud use ``pandas.plotting.scatter_matrix`` instead. + +Previous script: + +.. code-block:: python + + pd.tools.plotting.scatter_matrix(df) + pd.scatter_matrix(df) + +Should be changed to: + +.. code-block:: python + + pd.plotting.scatter_matrix(df) + .. _whatsnew_0200.api_breaking.index_map Map on Index types now return other Index types diff --git a/pandas/__init__.py b/pandas/__init__.py index 2d91c97144e3c..e59b4b80eb20d 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -46,7 +46,15 @@ from pandas.tools.merge import (merge, concat, ordered_merge, merge_ordered, merge_asof) from pandas.tools.pivot import pivot_table, crosstab -from pandas.tools.plotting import scatter_matrix, plot_params + +# deprecate tools.plotting, and scatter_matrix on the top namespace +import pandas.tools.plotting +from pandas.plotting import plot_params +# do not import deprecate to top namespace +scatter_matrix = pandas.util.decorators.deprecate( + 'pandas.scatter_matrix', pandas.plotting.scatter_matrix, + 'pandas.plotting.scatter_matrix') + from pandas.tools.tile import cut, qcut from pandas.tools.util import to_numeric from pandas.core.reshape import melt diff --git a/pandas/api/tests/test_api.py b/pandas/api/tests/test_api.py index 78dfe46914200..2796b47a7d44d 100644 --- a/pandas/api/tests/test_api.py +++ b/pandas/api/tests/test_api.py @@ -36,7 +36,7 @@ class TestPDApi(Base, tm.TestCase): lib = ['api', 'compat', 'computation', 'core', 'indexes', 'formats', 'pandas', 'test', 'tools', 'tseries', - 'types', 'util', 'options', 'io'] + 'types', 'util', 'options', 'io', 'plotting'] # top-level packages that are c-imports, should rename to _* # to avoid naming conflicts diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index fe47391c9ff81..52cd20acee53c 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -257,7 +257,7 @@ def mpl_style_cb(key): stacklevel=5) import sys - from pandas.tools.plotting import mpl_stylesheet + from pandas.plotting.style import mpl_stylesheet global style_backup val = cf.get_option(key) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index ef8b7a0d16bdc..0f5e5aae4aa2d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -90,7 +90,7 @@ import pandas.core.ops as ops import pandas.formats.format as fmt from pandas.formats.printing import pprint_thing -import pandas.tools.plotting as gfx +import pandas.plotting.core as gfx import pandas.lib as lib import pandas.algos as _algos @@ -5721,7 +5721,7 @@ def _put_str(s, space): @Appender(_shared_docs['boxplot'] % _shared_doc_kwargs) def boxplot(self, column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, **kwds): - import pandas.tools.plotting as plots + import pandas.plotting as plots import matplotlib.pyplot as plt ax = plots.boxplot(self, column=column, by=by, ax=ax, fontsize=fontsize, grid=grid, rot=rot, figsize=figsize, layout=layout, diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index fc9cd894162e4..7eced3c82c0e6 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -3997,7 +3997,7 @@ def count(self): return self._wrap_agged_blocks(data.items, list(blk)) -from pandas.tools.plotting import boxplot_frame_groupby # noqa +from pandas.plotting.core import boxplot_frame_groupby # noqa DataFrameGroupBy.boxplot = boxplot_frame_groupby diff --git a/pandas/core/series.py b/pandas/core/series.py index 9845e1cd4ad47..12da40d337266 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2997,7 +2997,7 @@ def __init__(self, *args, **kwargs): # ---------------------------------------------------------------------- # Add plotting methods to Series -import pandas.tools.plotting as _gfx # noqa +import pandas.plotting.core as _gfx # noqa Series.plot = base.AccessorProperty(_gfx.SeriesPlotMethods, _gfx.SeriesPlotMethods) diff --git a/pandas/plotting/__init__.py b/pandas/plotting/__init__.py new file mode 100644 index 0000000000000..374276ddc8b56 --- /dev/null +++ b/pandas/plotting/__init__.py @@ -0,0 +1 @@ +from pandas.plotting.api import * # noqa diff --git a/pandas/plotting/api.py b/pandas/plotting/api.py new file mode 100644 index 0000000000000..f1df2e4426151 --- /dev/null +++ b/pandas/plotting/api.py @@ -0,0 +1,20 @@ +""" +Plotting api +""" + +# flake8: noqa + +try: # mpl optional + from pandas.plotting import converter + converter.register() # needs to override so set_xlim works with str/number +except ImportError: + pass + +from pandas.plotting.misc import (scatter_matrix, radviz, + andrews_curves, bootstrap_plot, + parallel_coordinates, lag_plot, + autocorrelation_plot) +from pandas.plotting.core import (boxplot, scatter_plot, grouped_hist, + hist_frame, hist_series) +from pandas.plotting.style import plot_params +from pandas.plotting.tools import table diff --git a/pandas/plotting/compat.py b/pandas/plotting/compat.py new file mode 100644 index 0000000000000..3191972d78dee --- /dev/null +++ b/pandas/plotting/compat.py @@ -0,0 +1,51 @@ +# being a bit too dynamic +# pylint: disable=E1101 +from __future__ import division + +from distutils.version import LooseVersion + + +def _mpl_le_1_2_1(): + try: + import matplotlib as mpl + return (str(mpl.__version__) <= LooseVersion('1.2.1') and + str(mpl.__version__)[0] != '0') + except ImportError: + return False + + +def _mpl_ge_1_3_1(): + try: + import matplotlib + # The or v[0] == '0' is because their versioneer is + # messed up on dev + return (matplotlib.__version__ >= LooseVersion('1.3.1') or + matplotlib.__version__[0] == '0') + except ImportError: + return False + + +def _mpl_ge_1_4_0(): + try: + import matplotlib + return (matplotlib.__version__ >= LooseVersion('1.4') or + matplotlib.__version__[0] == '0') + except ImportError: + return False + + +def _mpl_ge_1_5_0(): + try: + import matplotlib + return (matplotlib.__version__ >= LooseVersion('1.5') or + matplotlib.__version__[0] == '0') + except ImportError: + return False + + +def _mpl_ge_2_0_0(): + try: + import matplotlib + return matplotlib.__version__ >= LooseVersion('2.0') + except ImportError: + return False diff --git a/pandas/plotting/converter.py b/pandas/plotting/converter.py new file mode 100644 index 0000000000000..95ff9578fa3ee --- /dev/null +++ b/pandas/plotting/converter.py @@ -0,0 +1,1002 @@ +from datetime import datetime, timedelta +import datetime as pydt +import numpy as np + +from dateutil.relativedelta import relativedelta + +import matplotlib.units as units +import matplotlib.dates as dates + +from matplotlib.ticker import Formatter, AutoLocator, Locator +from matplotlib.transforms import nonsingular + + +from pandas.types.common import (is_float, is_integer, + is_integer_dtype, + is_float_dtype, + is_datetime64_ns_dtype, + is_period_arraylike, + ) + +from pandas.compat import lrange +import pandas.compat as compat +import pandas.lib as lib +import pandas.core.common as com +from pandas.core.index import Index + +from pandas.core.series import Series +from pandas.tseries.index import date_range +import pandas.tseries.tools as tools +import pandas.tseries.frequencies as frequencies +from pandas.tseries.frequencies import FreqGroup +from pandas.tseries.period import Period, PeriodIndex + +# constants +HOURS_PER_DAY = 24. +MIN_PER_HOUR = 60. +SEC_PER_MIN = 60. + +SEC_PER_HOUR = SEC_PER_MIN * MIN_PER_HOUR +SEC_PER_DAY = SEC_PER_HOUR * HOURS_PER_DAY + +MUSEC_PER_DAY = 1e6 * SEC_PER_DAY + + +def _mpl_le_2_0_0(): + try: + import matplotlib + return matplotlib.compare_versions('2.0.0', matplotlib.__version__) + except ImportError: + return False + + +def register(): + units.registry[lib.Timestamp] = DatetimeConverter() + units.registry[Period] = PeriodConverter() + units.registry[pydt.datetime] = DatetimeConverter() + units.registry[pydt.date] = DatetimeConverter() + units.registry[pydt.time] = TimeConverter() + units.registry[np.datetime64] = DatetimeConverter() + + +def _to_ordinalf(tm): + tot_sec = (tm.hour * 3600 + tm.minute * 60 + tm.second + + float(tm.microsecond / 1e6)) + return tot_sec + + +def time2num(d): + if isinstance(d, compat.string_types): + parsed = tools.to_datetime(d) + if not isinstance(parsed, datetime): + raise ValueError('Could not parse time %s' % d) + return _to_ordinalf(parsed.time()) + if isinstance(d, pydt.time): + return _to_ordinalf(d) + return d + + +class TimeConverter(units.ConversionInterface): + + @staticmethod + def convert(value, unit, axis): + valid_types = (str, pydt.time) + if (isinstance(value, valid_types) or is_integer(value) or + is_float(value)): + return time2num(value) + if isinstance(value, Index): + return value.map(time2num) + if isinstance(value, (list, tuple, np.ndarray, Index)): + return [time2num(x) for x in value] + return value + + @staticmethod + def axisinfo(unit, axis): + if unit != 'time': + return None + + majloc = AutoLocator() + majfmt = TimeFormatter(majloc) + return units.AxisInfo(majloc=majloc, majfmt=majfmt, label='time') + + @staticmethod + def default_units(x, axis): + return 'time' + + +# time formatter +class TimeFormatter(Formatter): + + def __init__(self, locs): + self.locs = locs + + def __call__(self, x, pos=0): + fmt = '%H:%M:%S' + s = int(x) + ms = int((x - s) * 1e3) + us = int((x - s) * 1e6 - ms) + m, s = divmod(s, 60) + h, m = divmod(m, 60) + _, h = divmod(h, 24) + if us != 0: + fmt += '.%6f' + elif ms != 0: + fmt += '.%3f' + + return pydt.time(h, m, s, us).strftime(fmt) + + +# Period Conversion + + +class PeriodConverter(dates.DateConverter): + + @staticmethod + def convert(values, units, axis): + if not hasattr(axis, 'freq'): + raise TypeError('Axis must have `freq` set to convert to Periods') + valid_types = (compat.string_types, datetime, + Period, pydt.date, pydt.time) + if (isinstance(values, valid_types) or is_integer(values) or + is_float(values)): + return get_datevalue(values, axis.freq) + if isinstance(values, PeriodIndex): + return values.asfreq(axis.freq)._values + if isinstance(values, Index): + return values.map(lambda x: get_datevalue(x, axis.freq)) + if is_period_arraylike(values): + return PeriodIndex(values, freq=axis.freq)._values + if isinstance(values, (list, tuple, np.ndarray, Index)): + return [get_datevalue(x, axis.freq) for x in values] + return values + + +def get_datevalue(date, freq): + if isinstance(date, Period): + return date.asfreq(freq).ordinal + elif isinstance(date, (compat.string_types, datetime, + pydt.date, pydt.time)): + return Period(date, freq).ordinal + elif (is_integer(date) or is_float(date) or + (isinstance(date, (np.ndarray, Index)) and (date.size == 1))): + return date + elif date is None: + return None + raise ValueError("Unrecognizable date '%s'" % date) + + +def _dt_to_float_ordinal(dt): + """ + Convert :mod:`datetime` to the Gregorian date as UTC float days, + preserving hours, minutes, seconds and microseconds. Return value + is a :func:`float`. + """ + if (isinstance(dt, (np.ndarray, Index, Series) + ) and is_datetime64_ns_dtype(dt)): + base = dates.epoch2num(dt.asi8 / 1.0E9) + else: + base = dates.date2num(dt) + return base + + +# Datetime Conversion +class DatetimeConverter(dates.DateConverter): + + @staticmethod + def convert(values, unit, axis): + def try_parse(values): + try: + return _dt_to_float_ordinal(tools.to_datetime(values)) + except Exception: + return values + + if isinstance(values, (datetime, pydt.date)): + return _dt_to_float_ordinal(values) + elif isinstance(values, np.datetime64): + return _dt_to_float_ordinal(lib.Timestamp(values)) + elif isinstance(values, pydt.time): + return dates.date2num(values) + elif (is_integer(values) or is_float(values)): + return values + elif isinstance(values, compat.string_types): + return try_parse(values) + elif isinstance(values, (list, tuple, np.ndarray, Index)): + if isinstance(values, Index): + values = values.values + if not isinstance(values, np.ndarray): + values = com._asarray_tuplesafe(values) + + if is_integer_dtype(values) or is_float_dtype(values): + return values + + try: + values = tools.to_datetime(values) + if isinstance(values, Index): + values = _dt_to_float_ordinal(values) + else: + values = [_dt_to_float_ordinal(x) for x in values] + except Exception: + values = _dt_to_float_ordinal(values) + + return values + + @staticmethod + def axisinfo(unit, axis): + """ + Return the :class:`~matplotlib.units.AxisInfo` for *unit*. + + *unit* is a tzinfo instance or None. + The *axis* argument is required but not used. + """ + tz = unit + + majloc = PandasAutoDateLocator(tz=tz) + majfmt = PandasAutoDateFormatter(majloc, tz=tz) + datemin = pydt.date(2000, 1, 1) + datemax = pydt.date(2010, 1, 1) + + return units.AxisInfo(majloc=majloc, majfmt=majfmt, label='', + default_limits=(datemin, datemax)) + + +class PandasAutoDateFormatter(dates.AutoDateFormatter): + + def __init__(self, locator, tz=None, defaultfmt='%Y-%m-%d'): + dates.AutoDateFormatter.__init__(self, locator, tz, defaultfmt) + # matplotlib.dates._UTC has no _utcoffset called by pandas + if self._tz is dates.UTC: + self._tz._utcoffset = self._tz.utcoffset(None) + + # For mpl > 2.0 the format strings are controlled via rcparams + # so do not mess with them. For mpl < 2.0 change the second + # break point and add a musec break point + if _mpl_le_2_0_0(): + self.scaled[1. / SEC_PER_DAY] = '%H:%M:%S' + self.scaled[1. / MUSEC_PER_DAY] = '%H:%M:%S.%f' + + +class PandasAutoDateLocator(dates.AutoDateLocator): + + def get_locator(self, dmin, dmax): + 'Pick the best locator based on a distance.' + delta = relativedelta(dmax, dmin) + + num_days = ((delta.years * 12.0) + delta.months * 31.0) + delta.days + num_sec = (delta.hours * 60.0 + delta.minutes) * 60.0 + delta.seconds + tot_sec = num_days * 86400. + num_sec + + if abs(tot_sec) < self.minticks: + self._freq = -1 + locator = MilliSecondLocator(self.tz) + locator.set_axis(self.axis) + + locator.set_view_interval(*self.axis.get_view_interval()) + locator.set_data_interval(*self.axis.get_data_interval()) + return locator + + return dates.AutoDateLocator.get_locator(self, dmin, dmax) + + def _get_unit(self): + return MilliSecondLocator.get_unit_generic(self._freq) + + +class MilliSecondLocator(dates.DateLocator): + + UNIT = 1. / (24 * 3600 * 1000) + + def __init__(self, tz): + dates.DateLocator.__init__(self, tz) + self._interval = 1. + + def _get_unit(self): + return self.get_unit_generic(-1) + + @staticmethod + def get_unit_generic(freq): + unit = dates.RRuleLocator.get_unit_generic(freq) + if unit < 0: + return MilliSecondLocator.UNIT + return unit + + def __call__(self): + # if no data have been set, this will tank with a ValueError + try: + dmin, dmax = self.viewlim_to_dt() + except ValueError: + return [] + + if dmin > dmax: + dmax, dmin = dmin, dmax + # We need to cap at the endpoints of valid datetime + + # TODO(wesm) unused? + # delta = relativedelta(dmax, dmin) + # try: + # start = dmin - delta + # except ValueError: + # start = _from_ordinal(1.0) + + # try: + # stop = dmax + delta + # except ValueError: + # # The magic number! + # stop = _from_ordinal(3652059.9999999) + + nmax, nmin = dates.date2num((dmax, dmin)) + + num = (nmax - nmin) * 86400 * 1000 + max_millis_ticks = 6 + for interval in [1, 10, 50, 100, 200, 500]: + if num <= interval * (max_millis_ticks - 1): + self._interval = interval + break + else: + # We went through the whole loop without breaking, default to 1 + self._interval = 1000. + + estimate = (nmax - nmin) / (self._get_unit() * self._get_interval()) + + if estimate > self.MAXTICKS * 2: + raise RuntimeError(('MillisecondLocator estimated to generate %d ' + 'ticks from %s to %s: exceeds Locator.MAXTICKS' + '* 2 (%d) ') % + (estimate, dmin, dmax, self.MAXTICKS * 2)) + + freq = '%dL' % self._get_interval() + tz = self.tz.tzname(None) + st = _from_ordinal(dates.date2num(dmin)) # strip tz + ed = _from_ordinal(dates.date2num(dmax)) + all_dates = date_range(start=st, end=ed, freq=freq, tz=tz).asobject + + try: + if len(all_dates) > 0: + locs = self.raise_if_exceeds(dates.date2num(all_dates)) + return locs + except Exception: # pragma: no cover + pass + + lims = dates.date2num([dmin, dmax]) + return lims + + def _get_interval(self): + return self._interval + + def autoscale(self): + """ + Set the view limits to include the data range. + """ + dmin, dmax = self.datalim_to_dt() + if dmin > dmax: + dmax, dmin = dmin, dmax + + # We need to cap at the endpoints of valid datetime + + # TODO(wesm): unused? + + # delta = relativedelta(dmax, dmin) + # try: + # start = dmin - delta + # except ValueError: + # start = _from_ordinal(1.0) + + # try: + # stop = dmax + delta + # except ValueError: + # # The magic number! + # stop = _from_ordinal(3652059.9999999) + + dmin, dmax = self.datalim_to_dt() + + vmin = dates.date2num(dmin) + vmax = dates.date2num(dmax) + + return self.nonsingular(vmin, vmax) + + +def _from_ordinal(x, tz=None): + ix = int(x) + dt = datetime.fromordinal(ix) + remainder = float(x) - ix + hour, remainder = divmod(24 * remainder, 1) + minute, remainder = divmod(60 * remainder, 1) + second, remainder = divmod(60 * remainder, 1) + microsecond = int(1e6 * remainder) + if microsecond < 10: + microsecond = 0 # compensate for rounding errors + dt = datetime(dt.year, dt.month, dt.day, int(hour), int(minute), + int(second), microsecond) + if tz is not None: + dt = dt.astimezone(tz) + + if microsecond > 999990: # compensate for rounding errors + dt += timedelta(microseconds=1e6 - microsecond) + + return dt + +# Fixed frequency dynamic tick locators and formatters + +# ------------------------------------------------------------------------- +# --- Locators --- +# ------------------------------------------------------------------------- + + +def _get_default_annual_spacing(nyears): + """ + Returns a default spacing between consecutive ticks for annual data. + """ + if nyears < 11: + (min_spacing, maj_spacing) = (1, 1) + elif nyears < 20: + (min_spacing, maj_spacing) = (1, 2) + elif nyears < 50: + (min_spacing, maj_spacing) = (1, 5) + elif nyears < 100: + (min_spacing, maj_spacing) = (5, 10) + elif nyears < 200: + (min_spacing, maj_spacing) = (5, 25) + elif nyears < 600: + (min_spacing, maj_spacing) = (10, 50) + else: + factor = nyears // 1000 + 1 + (min_spacing, maj_spacing) = (factor * 20, factor * 100) + return (min_spacing, maj_spacing) + + +def period_break(dates, period): + """ + Returns the indices where the given period changes. + + Parameters + ---------- + dates : PeriodIndex + Array of intervals to monitor. + period : string + Name of the period to monitor. + """ + current = getattr(dates, period) + previous = getattr(dates - 1, period) + return (current - previous).nonzero()[0] + + +def has_level_label(label_flags, vmin): + """ + Returns true if the ``label_flags`` indicate there is at least one label + for this level. + + if the minimum view limit is not an exact integer, then the first tick + label won't be shown, so we must adjust for that. + """ + if label_flags.size == 0 or (label_flags.size == 1 and + label_flags[0] == 0 and + vmin % 1 > 0.0): + return False + else: + return True + + +def _daily_finder(vmin, vmax, freq): + periodsperday = -1 + + if freq >= FreqGroup.FR_HR: + if freq == FreqGroup.FR_NS: + periodsperday = 24 * 60 * 60 * 1000000000 + elif freq == FreqGroup.FR_US: + periodsperday = 24 * 60 * 60 * 1000000 + elif freq == FreqGroup.FR_MS: + periodsperday = 24 * 60 * 60 * 1000 + elif freq == FreqGroup.FR_SEC: + periodsperday = 24 * 60 * 60 + elif freq == FreqGroup.FR_MIN: + periodsperday = 24 * 60 + elif freq == FreqGroup.FR_HR: + periodsperday = 24 + else: # pragma: no cover + raise ValueError("unexpected frequency: %s" % freq) + periodsperyear = 365 * periodsperday + periodspermonth = 28 * periodsperday + + elif freq == FreqGroup.FR_BUS: + periodsperyear = 261 + periodspermonth = 19 + elif freq == FreqGroup.FR_DAY: + periodsperyear = 365 + periodspermonth = 28 + elif frequencies.get_freq_group(freq) == FreqGroup.FR_WK: + periodsperyear = 52 + periodspermonth = 3 + else: # pragma: no cover + raise ValueError("unexpected frequency") + + # save this for later usage + vmin_orig = vmin + + (vmin, vmax) = (Period(ordinal=int(vmin), freq=freq), + Period(ordinal=int(vmax), freq=freq)) + span = vmax.ordinal - vmin.ordinal + 1 + dates_ = PeriodIndex(start=vmin, end=vmax, freq=freq) + # Initialize the output + info = np.zeros(span, + dtype=[('val', np.int64), ('maj', bool), + ('min', bool), ('fmt', '|S20')]) + info['val'][:] = dates_._values + info['fmt'][:] = '' + info['maj'][[0, -1]] = True + # .. and set some shortcuts + info_maj = info['maj'] + info_min = info['min'] + info_fmt = info['fmt'] + + def first_label(label_flags): + if (label_flags[0] == 0) and (label_flags.size > 1) and \ + ((vmin_orig % 1) > 0.0): + return label_flags[1] + else: + return label_flags[0] + + # Case 1. Less than a month + if span <= periodspermonth: + day_start = period_break(dates_, 'day') + month_start = period_break(dates_, 'month') + + def _hour_finder(label_interval, force_year_start): + _hour = dates_.hour + _prev_hour = (dates_ - 1).hour + hour_start = (_hour - _prev_hour) != 0 + info_maj[day_start] = True + info_min[hour_start & (_hour % label_interval == 0)] = True + year_start = period_break(dates_, 'year') + info_fmt[hour_start & (_hour % label_interval == 0)] = '%H:%M' + info_fmt[day_start] = '%H:%M\n%d-%b' + info_fmt[year_start] = '%H:%M\n%d-%b\n%Y' + if force_year_start and not has_level_label(year_start, vmin_orig): + info_fmt[first_label(day_start)] = '%H:%M\n%d-%b\n%Y' + + def _minute_finder(label_interval): + hour_start = period_break(dates_, 'hour') + _minute = dates_.minute + _prev_minute = (dates_ - 1).minute + minute_start = (_minute - _prev_minute) != 0 + info_maj[hour_start] = True + info_min[minute_start & (_minute % label_interval == 0)] = True + year_start = period_break(dates_, 'year') + info_fmt = info['fmt'] + info_fmt[minute_start & (_minute % label_interval == 0)] = '%H:%M' + info_fmt[day_start] = '%H:%M\n%d-%b' + info_fmt[year_start] = '%H:%M\n%d-%b\n%Y' + + def _second_finder(label_interval): + minute_start = period_break(dates_, 'minute') + _second = dates_.second + _prev_second = (dates_ - 1).second + second_start = (_second - _prev_second) != 0 + info['maj'][minute_start] = True + info['min'][second_start & (_second % label_interval == 0)] = True + year_start = period_break(dates_, 'year') + info_fmt = info['fmt'] + info_fmt[second_start & (_second % + label_interval == 0)] = '%H:%M:%S' + info_fmt[day_start] = '%H:%M:%S\n%d-%b' + info_fmt[year_start] = '%H:%M:%S\n%d-%b\n%Y' + + if span < periodsperday / 12000.0: + _second_finder(1) + elif span < periodsperday / 6000.0: + _second_finder(2) + elif span < periodsperday / 2400.0: + _second_finder(5) + elif span < periodsperday / 1200.0: + _second_finder(10) + elif span < periodsperday / 800.0: + _second_finder(15) + elif span < periodsperday / 400.0: + _second_finder(30) + elif span < periodsperday / 150.0: + _minute_finder(1) + elif span < periodsperday / 70.0: + _minute_finder(2) + elif span < periodsperday / 24.0: + _minute_finder(5) + elif span < periodsperday / 12.0: + _minute_finder(15) + elif span < periodsperday / 6.0: + _minute_finder(30) + elif span < periodsperday / 2.5: + _hour_finder(1, False) + elif span < periodsperday / 1.5: + _hour_finder(2, False) + elif span < periodsperday * 1.25: + _hour_finder(3, False) + elif span < periodsperday * 2.5: + _hour_finder(6, True) + elif span < periodsperday * 4: + _hour_finder(12, True) + else: + info_maj[month_start] = True + info_min[day_start] = True + year_start = period_break(dates_, 'year') + info_fmt = info['fmt'] + info_fmt[day_start] = '%d' + info_fmt[month_start] = '%d\n%b' + info_fmt[year_start] = '%d\n%b\n%Y' + if not has_level_label(year_start, vmin_orig): + if not has_level_label(month_start, vmin_orig): + info_fmt[first_label(day_start)] = '%d\n%b\n%Y' + else: + info_fmt[first_label(month_start)] = '%d\n%b\n%Y' + + # Case 2. Less than three months + elif span <= periodsperyear // 4: + month_start = period_break(dates_, 'month') + info_maj[month_start] = True + if freq < FreqGroup.FR_HR: + info['min'] = True + else: + day_start = period_break(dates_, 'day') + info['min'][day_start] = True + week_start = period_break(dates_, 'week') + year_start = period_break(dates_, 'year') + info_fmt[week_start] = '%d' + info_fmt[month_start] = '\n\n%b' + info_fmt[year_start] = '\n\n%b\n%Y' + if not has_level_label(year_start, vmin_orig): + if not has_level_label(month_start, vmin_orig): + info_fmt[first_label(week_start)] = '\n\n%b\n%Y' + else: + info_fmt[first_label(month_start)] = '\n\n%b\n%Y' + # Case 3. Less than 14 months ............... + elif span <= 1.15 * periodsperyear: + year_start = period_break(dates_, 'year') + month_start = period_break(dates_, 'month') + week_start = period_break(dates_, 'week') + info_maj[month_start] = True + info_min[week_start] = True + info_min[year_start] = False + info_min[month_start] = False + info_fmt[month_start] = '%b' + info_fmt[year_start] = '%b\n%Y' + if not has_level_label(year_start, vmin_orig): + info_fmt[first_label(month_start)] = '%b\n%Y' + # Case 4. Less than 2.5 years ............... + elif span <= 2.5 * periodsperyear: + year_start = period_break(dates_, 'year') + quarter_start = period_break(dates_, 'quarter') + month_start = period_break(dates_, 'month') + info_maj[quarter_start] = True + info_min[month_start] = True + info_fmt[quarter_start] = '%b' + info_fmt[year_start] = '%b\n%Y' + # Case 4. Less than 4 years ................. + elif span <= 4 * periodsperyear: + year_start = period_break(dates_, 'year') + month_start = period_break(dates_, 'month') + info_maj[year_start] = True + info_min[month_start] = True + info_min[year_start] = False + + month_break = dates_[month_start].month + jan_or_jul = month_start[(month_break == 1) | (month_break == 7)] + info_fmt[jan_or_jul] = '%b' + info_fmt[year_start] = '%b\n%Y' + # Case 5. Less than 11 years ................ + elif span <= 11 * periodsperyear: + year_start = period_break(dates_, 'year') + quarter_start = period_break(dates_, 'quarter') + info_maj[year_start] = True + info_min[quarter_start] = True + info_min[year_start] = False + info_fmt[year_start] = '%Y' + # Case 6. More than 12 years ................ + else: + year_start = period_break(dates_, 'year') + year_break = dates_[year_start].year + nyears = span / periodsperyear + (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) + major_idx = year_start[(year_break % maj_anndef == 0)] + info_maj[major_idx] = True + minor_idx = year_start[(year_break % min_anndef == 0)] + info_min[minor_idx] = True + info_fmt[major_idx] = '%Y' + + return info + + +def _monthly_finder(vmin, vmax, freq): + periodsperyear = 12 + + vmin_orig = vmin + (vmin, vmax) = (int(vmin), int(vmax)) + span = vmax - vmin + 1 + + # Initialize the output + info = np.zeros(span, + dtype=[('val', int), ('maj', bool), ('min', bool), + ('fmt', '|S8')]) + info['val'] = np.arange(vmin, vmax + 1) + dates_ = info['val'] + info['fmt'] = '' + year_start = (dates_ % 12 == 0).nonzero()[0] + info_maj = info['maj'] + info_fmt = info['fmt'] + + if span <= 1.15 * periodsperyear: + info_maj[year_start] = True + info['min'] = True + + info_fmt[:] = '%b' + info_fmt[year_start] = '%b\n%Y' + + if not has_level_label(year_start, vmin_orig): + if dates_.size > 1: + idx = 1 + else: + idx = 0 + info_fmt[idx] = '%b\n%Y' + + elif span <= 2.5 * periodsperyear: + quarter_start = (dates_ % 3 == 0).nonzero() + info_maj[year_start] = True + # TODO: Check the following : is it really info['fmt'] ? + info['fmt'][quarter_start] = True + info['min'] = True + + info_fmt[quarter_start] = '%b' + info_fmt[year_start] = '%b\n%Y' + + elif span <= 4 * periodsperyear: + info_maj[year_start] = True + info['min'] = True + + jan_or_jul = (dates_ % 12 == 0) | (dates_ % 12 == 6) + info_fmt[jan_or_jul] = '%b' + info_fmt[year_start] = '%b\n%Y' + + elif span <= 11 * periodsperyear: + quarter_start = (dates_ % 3 == 0).nonzero() + info_maj[year_start] = True + info['min'][quarter_start] = True + + info_fmt[year_start] = '%Y' + + else: + nyears = span / periodsperyear + (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) + years = dates_[year_start] // 12 + 1 + major_idx = year_start[(years % maj_anndef == 0)] + info_maj[major_idx] = True + info['min'][year_start[(years % min_anndef == 0)]] = True + + info_fmt[major_idx] = '%Y' + + return info + + +def _quarterly_finder(vmin, vmax, freq): + periodsperyear = 4 + vmin_orig = vmin + (vmin, vmax) = (int(vmin), int(vmax)) + span = vmax - vmin + 1 + + info = np.zeros(span, + dtype=[('val', int), ('maj', bool), ('min', bool), + ('fmt', '|S8')]) + info['val'] = np.arange(vmin, vmax + 1) + info['fmt'] = '' + dates_ = info['val'] + info_maj = info['maj'] + info_fmt = info['fmt'] + year_start = (dates_ % 4 == 0).nonzero()[0] + + if span <= 3.5 * periodsperyear: + info_maj[year_start] = True + info['min'] = True + + info_fmt[:] = 'Q%q' + info_fmt[year_start] = 'Q%q\n%F' + if not has_level_label(year_start, vmin_orig): + if dates_.size > 1: + idx = 1 + else: + idx = 0 + info_fmt[idx] = 'Q%q\n%F' + + elif span <= 11 * periodsperyear: + info_maj[year_start] = True + info['min'] = True + info_fmt[year_start] = '%F' + + else: + years = dates_[year_start] // 4 + 1 + nyears = span / periodsperyear + (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) + major_idx = year_start[(years % maj_anndef == 0)] + info_maj[major_idx] = True + info['min'][year_start[(years % min_anndef == 0)]] = True + info_fmt[major_idx] = '%F' + + return info + + +def _annual_finder(vmin, vmax, freq): + (vmin, vmax) = (int(vmin), int(vmax + 1)) + span = vmax - vmin + 1 + + info = np.zeros(span, + dtype=[('val', int), ('maj', bool), ('min', bool), + ('fmt', '|S8')]) + info['val'] = np.arange(vmin, vmax + 1) + info['fmt'] = '' + dates_ = info['val'] + + (min_anndef, maj_anndef) = _get_default_annual_spacing(span) + major_idx = dates_ % maj_anndef == 0 + info['maj'][major_idx] = True + info['min'][(dates_ % min_anndef == 0)] = True + info['fmt'][major_idx] = '%Y' + + return info + + +def get_finder(freq): + if isinstance(freq, compat.string_types): + freq = frequencies.get_freq(freq) + fgroup = frequencies.get_freq_group(freq) + + if fgroup == FreqGroup.FR_ANN: + return _annual_finder + elif fgroup == FreqGroup.FR_QTR: + return _quarterly_finder + elif freq == FreqGroup.FR_MTH: + return _monthly_finder + elif ((freq >= FreqGroup.FR_BUS) or fgroup == FreqGroup.FR_WK): + return _daily_finder + else: # pragma: no cover + errmsg = "Unsupported frequency: %s" % (freq) + raise NotImplementedError(errmsg) + + +class TimeSeries_DateLocator(Locator): + """ + Locates the ticks along an axis controlled by a :class:`Series`. + + Parameters + ---------- + freq : {var} + Valid frequency specifier. + minor_locator : {False, True}, optional + Whether the locator is for minor ticks (True) or not. + dynamic_mode : {True, False}, optional + Whether the locator should work in dynamic mode. + base : {int}, optional + quarter : {int}, optional + month : {int}, optional + day : {int}, optional + """ + + def __init__(self, freq, minor_locator=False, dynamic_mode=True, + base=1, quarter=1, month=1, day=1, plot_obj=None): + if isinstance(freq, compat.string_types): + freq = frequencies.get_freq(freq) + self.freq = freq + self.base = base + (self.quarter, self.month, self.day) = (quarter, month, day) + self.isminor = minor_locator + self.isdynamic = dynamic_mode + self.offset = 0 + self.plot_obj = plot_obj + self.finder = get_finder(freq) + + def _get_default_locs(self, vmin, vmax): + "Returns the default locations of ticks." + + if self.plot_obj.date_axis_info is None: + self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq) + + locator = self.plot_obj.date_axis_info + + if self.isminor: + return np.compress(locator['min'], locator['val']) + return np.compress(locator['maj'], locator['val']) + + def __call__(self): + 'Return the locations of the ticks.' + # axis calls Locator.set_axis inside set_m<xxxx>_formatter + vi = tuple(self.axis.get_view_interval()) + if vi != self.plot_obj.view_interval: + self.plot_obj.date_axis_info = None + self.plot_obj.view_interval = vi + vmin, vmax = vi + if vmax < vmin: + vmin, vmax = vmax, vmin + if self.isdynamic: + locs = self._get_default_locs(vmin, vmax) + else: # pragma: no cover + base = self.base + (d, m) = divmod(vmin, base) + vmin = (d + 1) * base + locs = lrange(vmin, vmax + 1, base) + return locs + + def autoscale(self): + """ + Sets the view limits to the nearest multiples of base that contain the + data. + """ + # requires matplotlib >= 0.98.0 + (vmin, vmax) = self.axis.get_data_interval() + + locs = self._get_default_locs(vmin, vmax) + (vmin, vmax) = locs[[0, -1]] + if vmin == vmax: + vmin -= 1 + vmax += 1 + return nonsingular(vmin, vmax) + +# ------------------------------------------------------------------------- +# --- Formatter --- +# ------------------------------------------------------------------------- + + +class TimeSeries_DateFormatter(Formatter): + """ + Formats the ticks along an axis controlled by a :class:`PeriodIndex`. + + Parameters + ---------- + freq : {int, string} + Valid frequency specifier. + minor_locator : {False, True} + Whether the current formatter should apply to minor ticks (True) or + major ticks (False). + dynamic_mode : {True, False} + Whether the formatter works in dynamic mode or not. + """ + + def __init__(self, freq, minor_locator=False, dynamic_mode=True, + plot_obj=None): + if isinstance(freq, compat.string_types): + freq = frequencies.get_freq(freq) + self.format = None + self.freq = freq + self.locs = [] + self.formatdict = None + self.isminor = minor_locator + self.isdynamic = dynamic_mode + self.offset = 0 + self.plot_obj = plot_obj + self.finder = get_finder(freq) + + def _set_default_format(self, vmin, vmax): + "Returns the default ticks spacing." + + if self.plot_obj.date_axis_info is None: + self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq) + info = self.plot_obj.date_axis_info + + if self.isminor: + format = np.compress(info['min'] & np.logical_not(info['maj']), + info) + else: + format = np.compress(info['maj'], info) + self.formatdict = dict([(x, f) for (x, _, _, f) in format]) + return self.formatdict + + def set_locs(self, locs): + 'Sets the locations of the ticks' + # don't actually use the locs. This is just needed to work with + # matplotlib. Force to use vmin, vmax + self.locs = locs + + (vmin, vmax) = vi = tuple(self.axis.get_view_interval()) + if vi != self.plot_obj.view_interval: + self.plot_obj.date_axis_info = None + self.plot_obj.view_interval = vi + if vmax < vmin: + (vmin, vmax) = (vmax, vmin) + self._set_default_format(vmin, vmax) + + def __call__(self, x, pos=0): + if self.formatdict is None: + return '' + else: + fmt = self.formatdict.pop(x, '') + return Period(ordinal=int(x), freq=self.freq).strftime(fmt) diff --git a/pandas/plotting/core.py b/pandas/plotting/core.py new file mode 100644 index 0000000000000..5cf403eeb4113 --- /dev/null +++ b/pandas/plotting/core.py @@ -0,0 +1,2854 @@ +# being a bit too dynamic +# pylint: disable=E1101 +from __future__ import division + +import warnings +import re +from collections import namedtuple +from distutils.version import LooseVersion + +import numpy as np + +from pandas.util.decorators import cache_readonly +from pandas.core.base import PandasObject +from pandas.types.common import (is_list_like, + is_integer, + is_number, + is_hashable, + is_iterator) +from pandas.core.common import AbstractMethodError, isnull, _try_sort +from pandas.core.generic import _shared_docs, _shared_doc_kwargs +from pandas.core.index import Index, MultiIndex +from pandas.core.series import Series, remove_na +from pandas.tseries.period import PeriodIndex +from pandas.compat import range, lrange, map, zip, string_types +import pandas.compat as compat +from pandas.formats.printing import pprint_thing +from pandas.util.decorators import Appender + +from pandas.plotting.compat import (_mpl_ge_1_3_1, + _mpl_ge_1_5_0) +from pandas.plotting.style import (mpl_stylesheet, plot_params, + _get_standard_colors) +from pandas.plotting.tools import (_subplots, _flatten, table, + _handle_shared_axes, _get_all_lines, + _get_xlim, _set_ticks_props, + format_date_labels) + + +if _mpl_ge_1_5_0(): + # Compat with mp 1.5, which uses cycler. + import cycler + colors = mpl_stylesheet.pop('axes.color_cycle') + mpl_stylesheet['axes.prop_cycle'] = cycler.cycler('color', colors) + + +def _get_standard_kind(kind): + return {'density': 'kde'}.get(kind, kind) + + +def _gca(): + import matplotlib.pyplot as plt + return plt.gca() + + +def _gcf(): + import matplotlib.pyplot as plt + return plt.gcf() + + +class MPLPlot(object): + """ + Base class for assembling a pandas plot using matplotlib + + Parameters + ---------- + data : + + """ + + @property + def _kind(self): + """Specify kind str. Must be overridden in child class""" + raise NotImplementedError + + _layout_type = 'vertical' + _default_rot = 0 + orientation = None + _pop_attributes = ['label', 'style', 'logy', 'logx', 'loglog', + 'mark_right', 'stacked'] + _attr_defaults = {'logy': False, 'logx': False, 'loglog': False, + 'mark_right': True, 'stacked': False} + + def __init__(self, data, kind=None, by=None, subplots=False, sharex=None, + sharey=False, use_index=True, + figsize=None, grid=None, legend=True, rot=None, + ax=None, fig=None, title=None, xlim=None, ylim=None, + xticks=None, yticks=None, + sort_columns=False, fontsize=None, + secondary_y=False, colormap=None, + table=False, layout=None, **kwds): + + self.data = data + self.by = by + + self.kind = kind + + self.sort_columns = sort_columns + + self.subplots = subplots + + if sharex is None: + if ax is None: + self.sharex = True + else: + # if we get an axis, the users should do the visibility + # setting... + self.sharex = False + else: + self.sharex = sharex + + self.sharey = sharey + self.figsize = figsize + self.layout = layout + + self.xticks = xticks + self.yticks = yticks + self.xlim = xlim + self.ylim = ylim + self.title = title + self.use_index = use_index + + self.fontsize = fontsize + + if rot is not None: + self.rot = rot + # need to know for format_date_labels since it's rotated to 30 by + # default + self._rot_set = True + else: + self._rot_set = False + self.rot = self._default_rot + + if grid is None: + grid = False if secondary_y else self.plt.rcParams['axes.grid'] + + self.grid = grid + self.legend = legend + self.legend_handles = [] + self.legend_labels = [] + + for attr in self._pop_attributes: + value = kwds.pop(attr, self._attr_defaults.get(attr, None)) + setattr(self, attr, value) + + self.ax = ax + self.fig = fig + self.axes = None + + # parse errorbar input if given + xerr = kwds.pop('xerr', None) + yerr = kwds.pop('yerr', None) + self.errors = {} + for kw, err in zip(['xerr', 'yerr'], [xerr, yerr]): + self.errors[kw] = self._parse_errorbars(kw, err) + + if not isinstance(secondary_y, (bool, tuple, list, np.ndarray, Index)): + secondary_y = [secondary_y] + self.secondary_y = secondary_y + + # ugly TypeError if user passes matplotlib's `cmap` name. + # Probably better to accept either. + if 'cmap' in kwds and colormap: + raise TypeError("Only specify one of `cmap` and `colormap`.") + elif 'cmap' in kwds: + self.colormap = kwds.pop('cmap') + else: + self.colormap = colormap + + self.table = table + + self.kwds = kwds + + self._validate_color_args() + + def _validate_color_args(self): + if 'color' not in self.kwds and 'colors' in self.kwds: + warnings.warn(("'colors' is being deprecated. Please use 'color'" + "instead of 'colors'")) + colors = self.kwds.pop('colors') + self.kwds['color'] = colors + + if ('color' in self.kwds and self.nseries == 1): + # support series.plot(color='green') + self.kwds['color'] = [self.kwds['color']] + + if ('color' in self.kwds or 'colors' in self.kwds) and \ + self.colormap is not None: + warnings.warn("'color' and 'colormap' cannot be used " + "simultaneously. Using 'color'") + + if 'color' in self.kwds and self.style is not None: + if is_list_like(self.style): + styles = self.style + else: + styles = [self.style] + # need only a single match + for s in styles: + if re.match('^[a-z]+?', s) is not None: + raise ValueError( + "Cannot pass 'style' string with a color " + "symbol and 'color' keyword argument. Please" + " use one or the other or pass 'style' " + "without a color symbol") + + def _iter_data(self, data=None, keep_index=False, fillna=None): + if data is None: + data = self.data + if fillna is not None: + data = data.fillna(fillna) + + # TODO: unused? + # if self.sort_columns: + # columns = _try_sort(data.columns) + # else: + # columns = data.columns + + for col, values in data.iteritems(): + if keep_index is True: + yield col, values + else: + yield col, values.values + + @property + def nseries(self): + if self.data.ndim == 1: + return 1 + else: + return self.data.shape[1] + + def draw(self): + self.plt.draw_if_interactive() + + def generate(self): + self._args_adjust() + self._compute_plot_data() + self._setup_subplots() + self._make_plot() + self._add_table() + self._make_legend() + self._adorn_subplots() + + for ax in self.axes: + self._post_plot_logic_common(ax, self.data) + self._post_plot_logic(ax, self.data) + + def _args_adjust(self): + pass + + def _has_plotted_object(self, ax): + """check whether ax has data""" + return (len(ax.lines) != 0 or + len(ax.artists) != 0 or + len(ax.containers) != 0) + + def _maybe_right_yaxis(self, ax, axes_num): + if not self.on_right(axes_num): + # secondary axes may be passed via ax kw + return self._get_ax_layer(ax) + + if hasattr(ax, 'right_ax'): + # if it has right_ax proparty, ``ax`` must be left axes + return ax.right_ax + elif hasattr(ax, 'left_ax'): + # if it has left_ax proparty, ``ax`` must be right axes + return ax + else: + # otherwise, create twin axes + orig_ax, new_ax = ax, ax.twinx() + # TODO: use Matplotlib public API when available + new_ax._get_lines = orig_ax._get_lines + new_ax._get_patches_for_fill = orig_ax._get_patches_for_fill + orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax + + if not self._has_plotted_object(orig_ax): # no data on left y + orig_ax.get_yaxis().set_visible(False) + return new_ax + + def _setup_subplots(self): + if self.subplots: + fig, axes = _subplots(naxes=self.nseries, + sharex=self.sharex, sharey=self.sharey, + figsize=self.figsize, ax=self.ax, + layout=self.layout, + layout_type=self._layout_type) + else: + if self.ax is None: + fig = self.plt.figure(figsize=self.figsize) + axes = fig.add_subplot(111) + else: + fig = self.ax.get_figure() + if self.figsize is not None: + fig.set_size_inches(self.figsize) + axes = self.ax + + axes = _flatten(axes) + + if self.logx or self.loglog: + [a.set_xscale('log') for a in axes] + if self.logy or self.loglog: + [a.set_yscale('log') for a in axes] + + self.fig = fig + self.axes = axes + + @property + def result(self): + """ + Return result axes + """ + if self.subplots: + if self.layout is not None and not is_list_like(self.ax): + return self.axes.reshape(*self.layout) + else: + return self.axes + else: + sec_true = isinstance(self.secondary_y, bool) and self.secondary_y + all_sec = (is_list_like(self.secondary_y) and + len(self.secondary_y) == self.nseries) + if (sec_true or all_sec): + # if all data is plotted on secondary, return right axes + return self._get_ax_layer(self.axes[0], primary=False) + else: + return self.axes[0] + + def _compute_plot_data(self): + data = self.data + + if isinstance(data, Series): + label = self.label + if label is None and data.name is None: + label = 'None' + data = data.to_frame(name=label) + + numeric_data = data._convert(datetime=True)._get_numeric_data() + + try: + is_empty = numeric_data.empty + except AttributeError: + is_empty = not len(numeric_data) + + # no empty frames or series allowed + if is_empty: + raise TypeError('Empty {0!r}: no numeric data to ' + 'plot'.format(numeric_data.__class__.__name__)) + + self.data = numeric_data + + def _make_plot(self): + raise AbstractMethodError(self) + + def _add_table(self): + if self.table is False: + return + elif self.table is True: + data = self.data.transpose() + else: + data = self.table + ax = self._get_ax(0) + table(ax, data) + + def _post_plot_logic_common(self, ax, data): + """Common post process for each axes""" + labels = [pprint_thing(key) for key in data.index] + labels = dict(zip(range(len(data.index)), labels)) + + if self.orientation == 'vertical' or self.orientation is None: + if self._need_to_set_index: + xticklabels = [labels.get(x, '') for x in ax.get_xticks()] + ax.set_xticklabels(xticklabels) + self._apply_axis_properties(ax.xaxis, rot=self.rot, + fontsize=self.fontsize) + self._apply_axis_properties(ax.yaxis, fontsize=self.fontsize) + elif self.orientation == 'horizontal': + if self._need_to_set_index: + yticklabels = [labels.get(y, '') for y in ax.get_yticks()] + ax.set_yticklabels(yticklabels) + self._apply_axis_properties(ax.yaxis, rot=self.rot, + fontsize=self.fontsize) + self._apply_axis_properties(ax.xaxis, fontsize=self.fontsize) + else: # pragma no cover + raise ValueError + + def _post_plot_logic(self, ax, data): + """Post process for each axes. Overridden in child classes""" + pass + + def _adorn_subplots(self): + """Common post process unrelated to data""" + if len(self.axes) > 0: + all_axes = self._get_subplots() + nrows, ncols = self._get_axes_layout() + _handle_shared_axes(axarr=all_axes, nplots=len(all_axes), + naxes=nrows * ncols, nrows=nrows, + ncols=ncols, sharex=self.sharex, + sharey=self.sharey) + + for ax in self.axes: + if self.yticks is not None: + ax.set_yticks(self.yticks) + + if self.xticks is not None: + ax.set_xticks(self.xticks) + + if self.ylim is not None: + ax.set_ylim(self.ylim) + + if self.xlim is not None: + ax.set_xlim(self.xlim) + + ax.grid(self.grid) + + if self.title: + if self.subplots: + if is_list_like(self.title): + if len(self.title) != self.nseries: + msg = ('The length of `title` must equal the number ' + 'of columns if using `title` of type `list` ' + 'and `subplots=True`.\n' + 'length of title = {}\n' + 'number of columns = {}').format( + len(self.title), self.nseries) + raise ValueError(msg) + + for (ax, title) in zip(self.axes, self.title): + ax.set_title(title) + else: + self.fig.suptitle(self.title) + else: + if is_list_like(self.title): + msg = ('Using `title` of type `list` is not supported ' + 'unless `subplots=True` is passed') + raise ValueError(msg) + self.axes[0].set_title(self.title) + + def _apply_axis_properties(self, axis, rot=None, fontsize=None): + labels = axis.get_majorticklabels() + axis.get_minorticklabels() + for label in labels: + if rot is not None: + label.set_rotation(rot) + if fontsize is not None: + label.set_fontsize(fontsize) + + @property + def legend_title(self): + if not isinstance(self.data.columns, MultiIndex): + name = self.data.columns.name + if name is not None: + name = pprint_thing(name) + return name + else: + stringified = map(pprint_thing, + self.data.columns.names) + return ','.join(stringified) + + def _add_legend_handle(self, handle, label, index=None): + if label is not None: + if self.mark_right and index is not None: + if self.on_right(index): + label = label + ' (right)' + self.legend_handles.append(handle) + self.legend_labels.append(label) + + def _make_legend(self): + ax, leg = self._get_ax_legend(self.axes[0]) + + handles = [] + labels = [] + title = '' + + if not self.subplots: + if leg is not None: + title = leg.get_title().get_text() + handles = leg.legendHandles + labels = [x.get_text() for x in leg.get_texts()] + + if self.legend: + if self.legend == 'reverse': + self.legend_handles = reversed(self.legend_handles) + self.legend_labels = reversed(self.legend_labels) + + handles += self.legend_handles + labels += self.legend_labels + if self.legend_title is not None: + title = self.legend_title + + if len(handles) > 0: + ax.legend(handles, labels, loc='best', title=title) + + elif self.subplots and self.legend: + for ax in self.axes: + if ax.get_visible(): + ax.legend(loc='best') + + def _get_ax_legend(self, ax): + leg = ax.get_legend() + other_ax = (getattr(ax, 'left_ax', None) or + getattr(ax, 'right_ax', None)) + other_leg = None + if other_ax is not None: + other_leg = other_ax.get_legend() + if leg is None and other_leg is not None: + leg = other_leg + ax = other_ax + return ax, leg + + @cache_readonly + def plt(self): + import matplotlib.pyplot as plt + return plt + + @staticmethod + def mpl_ge_1_3_1(): + return _mpl_ge_1_3_1() + + @staticmethod + def mpl_ge_1_5_0(): + return _mpl_ge_1_5_0() + + _need_to_set_index = False + + def _get_xticks(self, convert_period=False): + index = self.data.index + is_datetype = index.inferred_type in ('datetime', 'date', + 'datetime64', 'time') + + if self.use_index: + if convert_period and isinstance(index, PeriodIndex): + self.data = self.data.reindex(index=index.sort_values()) + x = self.data.index.to_timestamp()._mpl_repr() + elif index.is_numeric(): + """ + Matplotlib supports numeric values or datetime objects as + xaxis values. Taking LBYL approach here, by the time + matplotlib raises exception when using non numeric/datetime + values for xaxis, several actions are already taken by plt. + """ + x = index._mpl_repr() + elif is_datetype: + self.data = self.data.sort_index() + x = self.data.index._mpl_repr() + else: + self._need_to_set_index = True + x = lrange(len(index)) + else: + x = lrange(len(index)) + + return x + + @classmethod + def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds): + mask = isnull(y) + if mask.any(): + y = np.ma.array(y) + y = np.ma.masked_where(mask, y) + + if isinstance(x, Index): + x = x._mpl_repr() + + if is_errorbar: + if 'xerr' in kwds: + kwds['xerr'] = np.array(kwds.get('xerr')) + if 'yerr' in kwds: + kwds['yerr'] = np.array(kwds.get('yerr')) + return ax.errorbar(x, y, **kwds) + else: + # prevent style kwarg from going to errorbar, where it is + # unsupported + if style is not None: + args = (x, y, style) + else: + args = (x, y) + return ax.plot(*args, **kwds) + + def _get_index_name(self): + if isinstance(self.data.index, MultiIndex): + name = self.data.index.names + if any(x is not None for x in name): + name = ','.join([pprint_thing(x) for x in name]) + else: + name = None + else: + name = self.data.index.name + if name is not None: + name = pprint_thing(name) + + return name + + @classmethod + def _get_ax_layer(cls, ax, primary=True): + """get left (primary) or right (secondary) axes""" + if primary: + return getattr(ax, 'left_ax', ax) + else: + return getattr(ax, 'right_ax', ax) + + def _get_ax(self, i): + # get the twinx ax if appropriate + if self.subplots: + ax = self.axes[i] + ax = self._maybe_right_yaxis(ax, i) + self.axes[i] = ax + else: + ax = self.axes[0] + ax = self._maybe_right_yaxis(ax, i) + + ax.get_yaxis().set_visible(True) + return ax + + def on_right(self, i): + if isinstance(self.secondary_y, bool): + return self.secondary_y + + if isinstance(self.secondary_y, (tuple, list, np.ndarray, Index)): + return self.data.columns[i] in self.secondary_y + + def _apply_style_colors(self, colors, kwds, col_num, label): + """ + Manage style and color based on column number and its label. + Returns tuple of appropriate style and kwds which "color" may be added. + """ + style = None + if self.style is not None: + if isinstance(self.style, list): + try: + style = self.style[col_num] + except IndexError: + pass + elif isinstance(self.style, dict): + style = self.style.get(label, style) + else: + style = self.style + + has_color = 'color' in kwds or self.colormap is not None + nocolor_style = style is None or re.match('[a-z]+', style) is None + if (has_color or self.subplots) and nocolor_style: + kwds['color'] = colors[col_num % len(colors)] + return style, kwds + + def _get_colors(self, num_colors=None, color_kwds='color'): + if num_colors is None: + num_colors = self.nseries + + return _get_standard_colors(num_colors=num_colors, + colormap=self.colormap, + color=self.kwds.get(color_kwds)) + + def _parse_errorbars(self, label, err): + """ + Look for error keyword arguments and return the actual errorbar data + or return the error DataFrame/dict + + Error bars can be specified in several ways: + Series: the user provides a pandas.Series object of the same + length as the data + ndarray: provides a np.ndarray of the same length as the data + DataFrame/dict: error values are paired with keys matching the + key in the plotted DataFrame + str: the name of the column within the plotted DataFrame + """ + + if err is None: + return None + + from pandas import DataFrame, Series + + def match_labels(data, e): + e = e.reindex_axis(data.index) + return e + + # key-matched DataFrame + if isinstance(err, DataFrame): + + err = match_labels(self.data, err) + # key-matched dict + elif isinstance(err, dict): + pass + + # Series of error values + elif isinstance(err, Series): + # broadcast error series across data + err = match_labels(self.data, err) + err = np.atleast_2d(err) + err = np.tile(err, (self.nseries, 1)) + + # errors are a column in the dataframe + elif isinstance(err, string_types): + evalues = self.data[err].values + self.data = self.data[self.data.columns.drop(err)] + err = np.atleast_2d(evalues) + err = np.tile(err, (self.nseries, 1)) + + elif is_list_like(err): + if is_iterator(err): + err = np.atleast_2d(list(err)) + else: + # raw error values + err = np.atleast_2d(err) + + err_shape = err.shape + + # asymmetrical error bars + if err.ndim == 3: + if (err_shape[0] != self.nseries) or \ + (err_shape[1] != 2) or \ + (err_shape[2] != len(self.data)): + msg = "Asymmetrical error bars should be provided " + \ + "with the shape (%u, 2, %u)" % \ + (self.nseries, len(self.data)) + raise ValueError(msg) + + # broadcast errors to each data series + if len(err) == 1: + err = np.tile(err, (self.nseries, 1)) + + elif is_number(err): + err = np.tile([err], (self.nseries, len(self.data))) + + else: + msg = "No valid %s detected" % label + raise ValueError(msg) + + return err + + def _get_errorbars(self, label=None, index=None, xerr=True, yerr=True): + from pandas import DataFrame + errors = {} + + for kw, flag in zip(['xerr', 'yerr'], [xerr, yerr]): + if flag: + err = self.errors[kw] + # user provided label-matched dataframe of errors + if isinstance(err, (DataFrame, dict)): + if label is not None and label in err.keys(): + err = err[label] + else: + err = None + elif index is not None and err is not None: + err = err[index] + + if err is not None: + errors[kw] = err + return errors + + def _get_subplots(self): + from matplotlib.axes import Subplot + return [ax for ax in self.axes[0].get_figure().get_axes() + if isinstance(ax, Subplot)] + + def _get_axes_layout(self): + axes = self._get_subplots() + x_set = set() + y_set = set() + for ax in axes: + # check axes coordinates to estimate layout + points = ax.get_position().get_points() + x_set.add(points[0][0]) + y_set.add(points[0][1]) + return (len(y_set), len(x_set)) + + +class PlanePlot(MPLPlot): + """ + Abstract class for plotting on plane, currently scatter and hexbin. + """ + + _layout_type = 'single' + + def __init__(self, data, x, y, **kwargs): + MPLPlot.__init__(self, data, **kwargs) + if x is None or y is None: + raise ValueError(self._kind + ' requires and x and y column') + if is_integer(x) and not self.data.columns.holds_integer(): + x = self.data.columns[x] + if is_integer(y) and not self.data.columns.holds_integer(): + y = self.data.columns[y] + self.x = x + self.y = y + + @property + def nseries(self): + return 1 + + def _post_plot_logic(self, ax, data): + x, y = self.x, self.y + ax.set_ylabel(pprint_thing(y)) + ax.set_xlabel(pprint_thing(x)) + + +class ScatterPlot(PlanePlot): + _kind = 'scatter' + + def __init__(self, data, x, y, s=None, c=None, **kwargs): + if s is None: + # hide the matplotlib default for size, in case we want to change + # the handling of this argument later + s = 20 + super(ScatterPlot, self).__init__(data, x, y, s=s, **kwargs) + if is_integer(c) and not self.data.columns.holds_integer(): + c = self.data.columns[c] + self.c = c + + def _make_plot(self): + x, y, c, data = self.x, self.y, self.c, self.data + ax = self.axes[0] + + c_is_column = is_hashable(c) and c in self.data.columns + + # plot a colorbar only if a colormap is provided or necessary + cb = self.kwds.pop('colorbar', self.colormap or c_is_column) + + # pandas uses colormap, matplotlib uses cmap. + cmap = self.colormap or 'Greys' + cmap = self.plt.cm.get_cmap(cmap) + color = self.kwds.pop("color", None) + if c is not None and color is not None: + raise TypeError('Specify exactly one of `c` and `color`') + elif c is None and color is None: + c_values = self.plt.rcParams['patch.facecolor'] + elif color is not None: + c_values = color + elif c_is_column: + c_values = self.data[c].values + else: + c_values = c + + if self.legend and hasattr(self, 'label'): + label = self.label + else: + label = None + scatter = ax.scatter(data[x].values, data[y].values, c=c_values, + label=label, cmap=cmap, **self.kwds) + if cb: + img = ax.collections[0] + kws = dict(ax=ax) + if self.mpl_ge_1_3_1(): + kws['label'] = c if c_is_column else '' + self.fig.colorbar(img, **kws) + + if label is not None: + self._add_legend_handle(scatter, label) + else: + self.legend = False + + errors_x = self._get_errorbars(label=x, index=0, yerr=False) + errors_y = self._get_errorbars(label=y, index=0, xerr=False) + if len(errors_x) > 0 or len(errors_y) > 0: + err_kwds = dict(errors_x, **errors_y) + err_kwds['ecolor'] = scatter.get_facecolor()[0] + ax.errorbar(data[x].values, data[y].values, + linestyle='none', **err_kwds) + + +class HexBinPlot(PlanePlot): + _kind = 'hexbin' + + def __init__(self, data, x, y, C=None, **kwargs): + super(HexBinPlot, self).__init__(data, x, y, **kwargs) + if is_integer(C) and not self.data.columns.holds_integer(): + C = self.data.columns[C] + self.C = C + + def _make_plot(self): + x, y, data, C = self.x, self.y, self.data, self.C + ax = self.axes[0] + # pandas uses colormap, matplotlib uses cmap. + cmap = self.colormap or 'BuGn' + cmap = self.plt.cm.get_cmap(cmap) + cb = self.kwds.pop('colorbar', True) + + if C is None: + c_values = None + else: + c_values = data[C].values + + ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap, + **self.kwds) + if cb: + img = ax.collections[0] + self.fig.colorbar(img, ax=ax) + + def _make_legend(self): + pass + + +class LinePlot(MPLPlot): + _kind = 'line' + _default_rot = 0 + orientation = 'vertical' + + def __init__(self, data, **kwargs): + MPLPlot.__init__(self, data, **kwargs) + if self.stacked: + self.data = self.data.fillna(value=0) + self.x_compat = plot_params['x_compat'] + if 'x_compat' in self.kwds: + self.x_compat = bool(self.kwds.pop('x_compat')) + + def _is_ts_plot(self): + # this is slightly deceptive + return not self.x_compat and self.use_index and self._use_dynamic_x() + + def _use_dynamic_x(self): + from pandas.plotting.timeseries import _use_dynamic_x + return _use_dynamic_x(self._get_ax(0), self.data) + + def _make_plot(self): + if self._is_ts_plot(): + from pandas.plotting.timeseries import _maybe_convert_index + data = _maybe_convert_index(self._get_ax(0), self.data) + + x = data.index # dummy, not used + plotf = self._ts_plot + it = self._iter_data(data=data, keep_index=True) + else: + x = self._get_xticks(convert_period=True) + plotf = self._plot + it = self._iter_data() + + stacking_id = self._get_stacking_id() + is_errorbar = any(e is not None for e in self.errors.values()) + + colors = self._get_colors() + for i, (label, y) in enumerate(it): + ax = self._get_ax(i) + kwds = self.kwds.copy() + style, kwds = self._apply_style_colors(colors, kwds, i, label) + + errors = self._get_errorbars(label=label, index=i) + kwds = dict(kwds, **errors) + + label = pprint_thing(label) # .encode('utf-8') + kwds['label'] = label + + newlines = plotf(ax, x, y, style=style, column_num=i, + stacking_id=stacking_id, + is_errorbar=is_errorbar, + **kwds) + self._add_legend_handle(newlines[0], label, index=i) + + lines = _get_all_lines(ax) + left, right = _get_xlim(lines) + ax.set_xlim(left, right) + + @classmethod + def _plot(cls, ax, x, y, style=None, column_num=None, + stacking_id=None, **kwds): + # column_num is used to get the target column from protf in line and + # area plots + if column_num == 0: + cls._initialize_stacker(ax, stacking_id, len(y)) + y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label']) + lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds) + cls._update_stacker(ax, stacking_id, y) + return lines + + @classmethod + def _ts_plot(cls, ax, x, data, style=None, **kwds): + from pandas.plotting.timeseries import (_maybe_resample, + _decorate_axes, + format_dateaxis) + # accept x to be consistent with normal plot func, + # x is not passed to tsplot as it uses data.index as x coordinate + # column_num must be in kwds for stacking purpose + freq, data = _maybe_resample(data, ax, kwds) + + # Set ax with freq info + _decorate_axes(ax, freq, kwds) + # digging deeper + if hasattr(ax, 'left_ax'): + _decorate_axes(ax.left_ax, freq, kwds) + if hasattr(ax, 'right_ax'): + _decorate_axes(ax.right_ax, freq, kwds) + ax._plot_data.append((data, cls._kind, kwds)) + + lines = cls._plot(ax, data.index, data.values, style=style, **kwds) + # set date formatter, locators and rescale limits + format_dateaxis(ax, ax.freq) + return lines + + def _get_stacking_id(self): + if self.stacked: + return id(self.data) + else: + return None + + @classmethod + def _initialize_stacker(cls, ax, stacking_id, n): + if stacking_id is None: + return + if not hasattr(ax, '_stacker_pos_prior'): + ax._stacker_pos_prior = {} + if not hasattr(ax, '_stacker_neg_prior'): + ax._stacker_neg_prior = {} + ax._stacker_pos_prior[stacking_id] = np.zeros(n) + ax._stacker_neg_prior[stacking_id] = np.zeros(n) + + @classmethod + def _get_stacked_values(cls, ax, stacking_id, values, label): + if stacking_id is None: + return values + if not hasattr(ax, '_stacker_pos_prior'): + # stacker may not be initialized for subplots + cls._initialize_stacker(ax, stacking_id, len(values)) + + if (values >= 0).all(): + return ax._stacker_pos_prior[stacking_id] + values + elif (values <= 0).all(): + return ax._stacker_neg_prior[stacking_id] + values + + raise ValueError('When stacked is True, each column must be either ' + 'all positive or negative.' + '{0} contains both positive and negative values' + .format(label)) + + @classmethod + def _update_stacker(cls, ax, stacking_id, values): + if stacking_id is None: + return + if (values >= 0).all(): + ax._stacker_pos_prior[stacking_id] += values + elif (values <= 0).all(): + ax._stacker_neg_prior[stacking_id] += values + + def _post_plot_logic(self, ax, data): + condition = (not self._use_dynamic_x() and + data.index.is_all_dates and + not self.subplots or + (self.subplots and self.sharex)) + + index_name = self._get_index_name() + + if condition: + # irregular TS rotated 30 deg. by default + # probably a better place to check / set this. + if not self._rot_set: + self.rot = 30 + format_date_labels(ax, rot=self.rot) + + if index_name is not None and self.use_index: + ax.set_xlabel(index_name) + + +class AreaPlot(LinePlot): + _kind = 'area' + + def __init__(self, data, **kwargs): + kwargs.setdefault('stacked', True) + data = data.fillna(value=0) + LinePlot.__init__(self, data, **kwargs) + + if not self.stacked: + # use smaller alpha to distinguish overlap + self.kwds.setdefault('alpha', 0.5) + + if self.logy or self.loglog: + raise ValueError("Log-y scales are not supported in area plot") + + @classmethod + def _plot(cls, ax, x, y, style=None, column_num=None, + stacking_id=None, is_errorbar=False, **kwds): + + if column_num == 0: + cls._initialize_stacker(ax, stacking_id, len(y)) + y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label']) + + # need to remove label, because subplots uses mpl legend as it is + line_kwds = kwds.copy() + if cls.mpl_ge_1_5_0(): + line_kwds.pop('label') + lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds) + + # get data from the line to get coordinates for fill_between + xdata, y_values = lines[0].get_data(orig=False) + + # unable to use ``_get_stacked_values`` here to get starting point + if stacking_id is None: + start = np.zeros(len(y)) + elif (y >= 0).all(): + start = ax._stacker_pos_prior[stacking_id] + elif (y <= 0).all(): + start = ax._stacker_neg_prior[stacking_id] + else: + start = np.zeros(len(y)) + + if 'color' not in kwds: + kwds['color'] = lines[0].get_color() + + rect = ax.fill_between(xdata, start, y_values, **kwds) + cls._update_stacker(ax, stacking_id, y) + + # LinePlot expects list of artists + res = [rect] if cls.mpl_ge_1_5_0() else lines + return res + + def _add_legend_handle(self, handle, label, index=None): + if not self.mpl_ge_1_5_0(): + from matplotlib.patches import Rectangle + # Because fill_between isn't supported in legend, + # specifically add Rectangle handle here + alpha = self.kwds.get('alpha', None) + handle = Rectangle((0, 0), 1, 1, fc=handle.get_color(), + alpha=alpha) + LinePlot._add_legend_handle(self, handle, label, index=index) + + def _post_plot_logic(self, ax, data): + LinePlot._post_plot_logic(self, ax, data) + + if self.ylim is None: + if (data >= 0).all().all(): + ax.set_ylim(0, None) + elif (data <= 0).all().all(): + ax.set_ylim(None, 0) + + +class BarPlot(MPLPlot): + _kind = 'bar' + _default_rot = 90 + orientation = 'vertical' + + def __init__(self, data, **kwargs): + self.bar_width = kwargs.pop('width', 0.5) + pos = kwargs.pop('position', 0.5) + kwargs.setdefault('align', 'center') + self.tick_pos = np.arange(len(data)) + + self.bottom = kwargs.pop('bottom', 0) + self.left = kwargs.pop('left', 0) + + self.log = kwargs.pop('log', False) + MPLPlot.__init__(self, data, **kwargs) + + if self.stacked or self.subplots: + self.tickoffset = self.bar_width * pos + if kwargs['align'] == 'edge': + self.lim_offset = self.bar_width / 2 + else: + self.lim_offset = 0 + else: + if kwargs['align'] == 'edge': + w = self.bar_width / self.nseries + self.tickoffset = self.bar_width * (pos - 0.5) + w * 0.5 + self.lim_offset = w * 0.5 + else: + self.tickoffset = self.bar_width * pos + self.lim_offset = 0 + + self.ax_pos = self.tick_pos - self.tickoffset + + def _args_adjust(self): + if is_list_like(self.bottom): + self.bottom = np.array(self.bottom) + if is_list_like(self.left): + self.left = np.array(self.left) + + @classmethod + def _plot(cls, ax, x, y, w, start=0, log=False, **kwds): + return ax.bar(x, y, w, bottom=start, log=log, **kwds) + + @property + def _start_base(self): + return self.bottom + + def _make_plot(self): + import matplotlib as mpl + + colors = self._get_colors() + ncolors = len(colors) + + pos_prior = neg_prior = np.zeros(len(self.data)) + K = self.nseries + + for i, (label, y) in enumerate(self._iter_data(fillna=0)): + ax = self._get_ax(i) + kwds = self.kwds.copy() + kwds['color'] = colors[i % ncolors] + + errors = self._get_errorbars(label=label, index=i) + kwds = dict(kwds, **errors) + + label = pprint_thing(label) + + if (('yerr' in kwds) or ('xerr' in kwds)) \ + and (kwds.get('ecolor') is None): + kwds['ecolor'] = mpl.rcParams['xtick.color'] + + start = 0 + if self.log and (y >= 1).all(): + start = 1 + start = start + self._start_base + + if self.subplots: + w = self.bar_width / 2 + rect = self._plot(ax, self.ax_pos + w, y, self.bar_width, + start=start, label=label, + log=self.log, **kwds) + ax.set_title(label) + elif self.stacked: + mask = y > 0 + start = np.where(mask, pos_prior, neg_prior) + self._start_base + w = self.bar_width / 2 + rect = self._plot(ax, self.ax_pos + w, y, self.bar_width, + start=start, label=label, + log=self.log, **kwds) + pos_prior = pos_prior + np.where(mask, y, 0) + neg_prior = neg_prior + np.where(mask, 0, y) + else: + w = self.bar_width / K + rect = self._plot(ax, self.ax_pos + (i + 0.5) * w, y, w, + start=start, label=label, + log=self.log, **kwds) + self._add_legend_handle(rect, label, index=i) + + def _post_plot_logic(self, ax, data): + if self.use_index: + str_index = [pprint_thing(key) for key in data.index] + else: + str_index = [pprint_thing(key) for key in range(data.shape[0])] + name = self._get_index_name() + + s_edge = self.ax_pos[0] - 0.25 + self.lim_offset + e_edge = self.ax_pos[-1] + 0.25 + self.bar_width + self.lim_offset + + self._decorate_ticks(ax, name, str_index, s_edge, e_edge) + + def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge): + ax.set_xlim((start_edge, end_edge)) + ax.set_xticks(self.tick_pos) + ax.set_xticklabels(ticklabels) + if name is not None and self.use_index: + ax.set_xlabel(name) + + +class BarhPlot(BarPlot): + _kind = 'barh' + _default_rot = 0 + orientation = 'horizontal' + + @property + def _start_base(self): + return self.left + + @classmethod + def _plot(cls, ax, x, y, w, start=0, log=False, **kwds): + return ax.barh(x, y, w, left=start, log=log, **kwds) + + def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge): + # horizontal bars + ax.set_ylim((start_edge, end_edge)) + ax.set_yticks(self.tick_pos) + ax.set_yticklabels(ticklabels) + if name is not None and self.use_index: + ax.set_ylabel(name) + + +class HistPlot(LinePlot): + _kind = 'hist' + + def __init__(self, data, bins=10, bottom=0, **kwargs): + self.bins = bins # use mpl default + self.bottom = bottom + # Do not call LinePlot.__init__ which may fill nan + MPLPlot.__init__(self, data, **kwargs) + + def _args_adjust(self): + if is_integer(self.bins): + # create common bin edge + values = (self.data._convert(datetime=True)._get_numeric_data()) + values = np.ravel(values) + values = values[~isnull(values)] + + hist, self.bins = np.histogram( + values, bins=self.bins, + range=self.kwds.get('range', None), + weights=self.kwds.get('weights', None)) + + if is_list_like(self.bottom): + self.bottom = np.array(self.bottom) + + @classmethod + def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, + stacking_id=None, **kwds): + if column_num == 0: + cls._initialize_stacker(ax, stacking_id, len(bins) - 1) + y = y[~isnull(y)] + + base = np.zeros(len(bins) - 1) + bottom = bottom + \ + cls._get_stacked_values(ax, stacking_id, base, kwds['label']) + # ignore style + n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) + cls._update_stacker(ax, stacking_id, n) + return patches + + def _make_plot(self): + colors = self._get_colors() + stacking_id = self._get_stacking_id() + + for i, (label, y) in enumerate(self._iter_data()): + ax = self._get_ax(i) + + kwds = self.kwds.copy() + + label = pprint_thing(label) + kwds['label'] = label + + style, kwds = self._apply_style_colors(colors, kwds, i, label) + if style is not None: + kwds['style'] = style + + kwds = self._make_plot_keywords(kwds, y) + artists = self._plot(ax, y, column_num=i, + stacking_id=stacking_id, **kwds) + self._add_legend_handle(artists[0], label, index=i) + + def _make_plot_keywords(self, kwds, y): + """merge BoxPlot/KdePlot properties to passed kwds""" + # y is required for KdePlot + kwds['bottom'] = self.bottom + kwds['bins'] = self.bins + return kwds + + def _post_plot_logic(self, ax, data): + if self.orientation == 'horizontal': + ax.set_xlabel('Frequency') + else: + ax.set_ylabel('Frequency') + + @property + def orientation(self): + if self.kwds.get('orientation', None) == 'horizontal': + return 'horizontal' + else: + return 'vertical' + + +class KdePlot(HistPlot): + _kind = 'kde' + orientation = 'vertical' + + def __init__(self, data, bw_method=None, ind=None, **kwargs): + MPLPlot.__init__(self, data, **kwargs) + self.bw_method = bw_method + self.ind = ind + + def _args_adjust(self): + pass + + def _get_ind(self, y): + if self.ind is None: + # np.nanmax() and np.nanmin() ignores the missing values + sample_range = np.nanmax(y) - np.nanmin(y) + ind = np.linspace(np.nanmin(y) - 0.5 * sample_range, + np.nanmax(y) + 0.5 * sample_range, 1000) + else: + ind = self.ind + return ind + + @classmethod + def _plot(cls, ax, y, style=None, bw_method=None, ind=None, + column_num=None, stacking_id=None, **kwds): + from scipy.stats import gaussian_kde + from scipy import __version__ as spv + + y = remove_na(y) + + if LooseVersion(spv) >= '0.11.0': + gkde = gaussian_kde(y, bw_method=bw_method) + else: + gkde = gaussian_kde(y) + if bw_method is not None: + msg = ('bw_method was added in Scipy 0.11.0.' + + ' Scipy version in use is %s.' % spv) + warnings.warn(msg) + + y = gkde.evaluate(ind) + lines = MPLPlot._plot(ax, ind, y, style=style, **kwds) + return lines + + def _make_plot_keywords(self, kwds, y): + kwds['bw_method'] = self.bw_method + kwds['ind'] = self._get_ind(y) + return kwds + + def _post_plot_logic(self, ax, data): + ax.set_ylabel('Density') + + +class PiePlot(MPLPlot): + _kind = 'pie' + _layout_type = 'horizontal' + + def __init__(self, data, kind=None, **kwargs): + data = data.fillna(value=0) + if (data < 0).any().any(): + raise ValueError("{0} doesn't allow negative values".format(kind)) + MPLPlot.__init__(self, data, kind=kind, **kwargs) + + def _args_adjust(self): + self.grid = False + self.logy = False + self.logx = False + self.loglog = False + + def _validate_color_args(self): + pass + + def _make_plot(self): + colors = self._get_colors( + num_colors=len(self.data), color_kwds='colors') + self.kwds.setdefault('colors', colors) + + for i, (label, y) in enumerate(self._iter_data()): + ax = self._get_ax(i) + if label is not None: + label = pprint_thing(label) + ax.set_ylabel(label) + + kwds = self.kwds.copy() + + def blank_labeler(label, value): + if value == 0: + return '' + else: + return label + + idx = [pprint_thing(v) for v in self.data.index] + labels = kwds.pop('labels', idx) + # labels is used for each wedge's labels + # Blank out labels for values of 0 so they don't overlap + # with nonzero wedges + if labels is not None: + blabels = [blank_labeler(l, value) for + l, value in zip(labels, y)] + else: + blabels = None + results = ax.pie(y, labels=blabels, **kwds) + + if kwds.get('autopct', None) is not None: + patches, texts, autotexts = results + else: + patches, texts = results + autotexts = [] + + if self.fontsize is not None: + for t in texts + autotexts: + t.set_fontsize(self.fontsize) + + # leglabels is used for legend labels + leglabels = labels if labels is not None else idx + for p, l in zip(patches, leglabels): + self._add_legend_handle(p, l) + + +class BoxPlot(LinePlot): + _kind = 'box' + _layout_type = 'horizontal' + + _valid_return_types = (None, 'axes', 'dict', 'both') + # namedtuple to hold results + BP = namedtuple("Boxplot", ['ax', 'lines']) + + def __init__(self, data, return_type='axes', **kwargs): + # Do not call LinePlot.__init__ which may fill nan + if return_type not in self._valid_return_types: + raise ValueError( + "return_type must be {None, 'axes', 'dict', 'both'}") + + self.return_type = return_type + MPLPlot.__init__(self, data, **kwargs) + + def _args_adjust(self): + if self.subplots: + # Disable label ax sharing. Otherwise, all subplots shows last + # column label + if self.orientation == 'vertical': + self.sharex = False + else: + self.sharey = False + + @classmethod + def _plot(cls, ax, y, column_num=None, return_type='axes', **kwds): + if y.ndim == 2: + y = [remove_na(v) for v in y] + # Boxplot fails with empty arrays, so need to add a NaN + # if any cols are empty + # GH 8181 + y = [v if v.size > 0 else np.array([np.nan]) for v in y] + else: + y = remove_na(y) + bp = ax.boxplot(y, **kwds) + + if return_type == 'dict': + return bp, bp + elif return_type == 'both': + return cls.BP(ax=ax, lines=bp), bp + else: + return ax, bp + + def _validate_color_args(self): + if 'color' in self.kwds: + if self.colormap is not None: + warnings.warn("'color' and 'colormap' cannot be used " + "simultaneously. Using 'color'") + self.color = self.kwds.pop('color') + + if isinstance(self.color, dict): + valid_keys = ['boxes', 'whiskers', 'medians', 'caps'] + for key, values in compat.iteritems(self.color): + if key not in valid_keys: + raise ValueError("color dict contains invalid " + "key '{0}' " + "The key must be either {1}" + .format(key, valid_keys)) + else: + self.color = None + + # get standard colors for default + colors = _get_standard_colors(num_colors=3, + colormap=self.colormap, + color=None) + # use 2 colors by default, for box/whisker and median + # flier colors isn't needed here + # because it can be specified by ``sym`` kw + self._boxes_c = colors[0] + self._whiskers_c = colors[0] + self._medians_c = colors[2] + self._caps_c = 'k' # mpl default + + def _get_colors(self, num_colors=None, color_kwds='color'): + pass + + def maybe_color_bp(self, bp): + if isinstance(self.color, dict): + boxes = self.color.get('boxes', self._boxes_c) + whiskers = self.color.get('whiskers', self._whiskers_c) + medians = self.color.get('medians', self._medians_c) + caps = self.color.get('caps', self._caps_c) + else: + # Other types are forwarded to matplotlib + # If None, use default colors + boxes = self.color or self._boxes_c + whiskers = self.color or self._whiskers_c + medians = self.color or self._medians_c + caps = self.color or self._caps_c + + from matplotlib.artist import setp + setp(bp['boxes'], color=boxes, alpha=1) + setp(bp['whiskers'], color=whiskers, alpha=1) + setp(bp['medians'], color=medians, alpha=1) + setp(bp['caps'], color=caps, alpha=1) + + def _make_plot(self): + if self.subplots: + self._return_obj = Series() + + for i, (label, y) in enumerate(self._iter_data()): + ax = self._get_ax(i) + kwds = self.kwds.copy() + + ret, bp = self._plot(ax, y, column_num=i, + return_type=self.return_type, **kwds) + self.maybe_color_bp(bp) + self._return_obj[label] = ret + + label = [pprint_thing(label)] + self._set_ticklabels(ax, label) + else: + y = self.data.values.T + ax = self._get_ax(0) + kwds = self.kwds.copy() + + ret, bp = self._plot(ax, y, column_num=0, + return_type=self.return_type, **kwds) + self.maybe_color_bp(bp) + self._return_obj = ret + + labels = [l for l, _ in self._iter_data()] + labels = [pprint_thing(l) for l in labels] + if not self.use_index: + labels = [pprint_thing(key) for key in range(len(labels))] + self._set_ticklabels(ax, labels) + + def _set_ticklabels(self, ax, labels): + if self.orientation == 'vertical': + ax.set_xticklabels(labels) + else: + ax.set_yticklabels(labels) + + def _make_legend(self): + pass + + def _post_plot_logic(self, ax, data): + pass + + @property + def orientation(self): + if self.kwds.get('vert', True): + return 'vertical' + else: + return 'horizontal' + + @property + def result(self): + if self.return_type is None: + return super(BoxPlot, self).result + else: + return self._return_obj + + +# kinds supported by both dataframe and series +_common_kinds = ['line', 'bar', 'barh', + 'kde', 'density', 'area', 'hist', 'box'] +# kinds supported by dataframe +_dataframe_kinds = ['scatter', 'hexbin'] +# kinds supported only by series or dataframe single column +_series_kinds = ['pie'] +_all_kinds = _common_kinds + _dataframe_kinds + _series_kinds + +_klasses = [LinePlot, BarPlot, BarhPlot, KdePlot, HistPlot, BoxPlot, + ScatterPlot, HexBinPlot, AreaPlot, PiePlot] + +_plot_klass = {} +for klass in _klasses: + _plot_klass[klass._kind] = klass + + +def _plot(data, x=None, y=None, subplots=False, + ax=None, kind='line', **kwds): + kind = _get_standard_kind(kind.lower().strip()) + if kind in _all_kinds: + klass = _plot_klass[kind] + else: + raise ValueError("%r is not a valid plot kind" % kind) + + from pandas import DataFrame + if kind in _dataframe_kinds: + if isinstance(data, DataFrame): + plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax, + kind=kind, **kwds) + else: + raise ValueError("plot kind %r can only be used for data frames" + % kind) + + elif kind in _series_kinds: + if isinstance(data, DataFrame): + if y is None and subplots is False: + msg = "{0} requires either y column or 'subplots=True'" + raise ValueError(msg.format(kind)) + elif y is not None: + if is_integer(y) and not data.columns.holds_integer(): + y = data.columns[y] + # converted to series actually. copy to not modify + data = data[y].copy() + data.index.name = y + plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds) + else: + if isinstance(data, DataFrame): + if x is not None: + if is_integer(x) and not data.columns.holds_integer(): + x = data.columns[x] + data = data.set_index(x) + + if y is not None: + if is_integer(y) and not data.columns.holds_integer(): + y = data.columns[y] + label = kwds['label'] if 'label' in kwds else y + series = data[y].copy() # Don't modify + series.name = label + + for kw in ['xerr', 'yerr']: + if (kw in kwds) and \ + (isinstance(kwds[kw], string_types) or + is_integer(kwds[kw])): + try: + kwds[kw] = data[kwds[kw]] + except (IndexError, KeyError, TypeError): + pass + data = series + plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds) + + plot_obj.generate() + plot_obj.draw() + return plot_obj.result + + +df_kind = """- 'scatter' : scatter plot + - 'hexbin' : hexbin plot""" +series_kind = "" + +df_coord = """x : label or position, default None + y : label or position, default None + Allows plotting of one column versus another""" +series_coord = "" + +df_unique = """stacked : boolean, default False in line and + bar plots, and True in area plot. If True, create stacked plot. + sort_columns : boolean, default False + Sort column names to determine plot ordering + secondary_y : boolean or sequence, default False + Whether to plot on the secondary y-axis + If a list/tuple, which columns to plot on secondary y-axis""" +series_unique = """label : label argument to provide to plot + secondary_y : boolean or sequence of ints, default False + If True then y-axis will be on the right""" + +df_ax = """ax : matplotlib axes object, default None + subplots : boolean, default False + Make separate subplots for each column + sharex : boolean, default True if ax is None else False + In case subplots=True, share x axis and set some x axis labels to + invisible; defaults to True if ax is None otherwise False if an ax + is passed in; Be aware, that passing in both an ax and sharex=True + will alter all x axis labels for all axis in a figure! + sharey : boolean, default False + In case subplots=True, share y axis and set some y axis labels to + invisible + layout : tuple (optional) + (rows, columns) for the layout of subplots""" +series_ax = """ax : matplotlib axes object + If not passed, uses gca()""" + +df_note = """- If `kind` = 'scatter' and the argument `c` is the name of a dataframe + column, the values of that column are used to color each point. + - If `kind` = 'hexbin', you can control the size of the bins with the + `gridsize` argument. By default, a histogram of the counts around each + `(x, y)` point is computed. You can specify alternative aggregations + by passing values to the `C` and `reduce_C_function` arguments. + `C` specifies the value at each `(x, y)` point and `reduce_C_function` + is a function of one argument that reduces all the values in a bin to + a single number (e.g. `mean`, `max`, `sum`, `std`).""" +series_note = "" + +_shared_doc_df_kwargs = dict(klass='DataFrame', klass_obj='df', + klass_kind=df_kind, klass_coord=df_coord, + klass_ax=df_ax, klass_unique=df_unique, + klass_note=df_note) +_shared_doc_series_kwargs = dict(klass='Series', klass_obj='s', + klass_kind=series_kind, + klass_coord=series_coord, klass_ax=series_ax, + klass_unique=series_unique, + klass_note=series_note) + +_shared_docs['plot'] = """ + Make plots of %(klass)s using matplotlib / pylab. + + *New in version 0.17.0:* Each plot kind has a corresponding method on the + ``%(klass)s.plot`` accessor: + ``%(klass_obj)s.plot(kind='line')`` is equivalent to + ``%(klass_obj)s.plot.line()``. + + Parameters + ---------- + data : %(klass)s + %(klass_coord)s + kind : str + - 'line' : line plot (default) + - 'bar' : vertical bar plot + - 'barh' : horizontal bar plot + - 'hist' : histogram + - 'box' : boxplot + - 'kde' : Kernel Density Estimation plot + - 'density' : same as 'kde' + - 'area' : area plot + - 'pie' : pie plot + %(klass_kind)s + %(klass_ax)s + figsize : a tuple (width, height) in inches + use_index : boolean, default True + Use index as ticks for x axis + title : string or list + Title to use for the plot. If a string is passed, print the string at + the top of the figure. If a list is passed and `subplots` is True, + print each item in the list above the corresponding subplot. + grid : boolean, default None (matlab style default) + Axis grid lines + legend : False/True/'reverse' + Place legend on axis subplots + style : list or dict + matplotlib line style per column + logx : boolean, default False + Use log scaling on x axis + logy : boolean, default False + Use log scaling on y axis + loglog : boolean, default False + Use log scaling on both x and y axes + xticks : sequence + Values to use for the xticks + yticks : sequence + Values to use for the yticks + xlim : 2-tuple/list + ylim : 2-tuple/list + rot : int, default None + Rotation for ticks (xticks for vertical, yticks for horizontal plots) + fontsize : int, default None + Font size for xticks and yticks + colormap : str or matplotlib colormap object, default None + Colormap to select colors from. If string, load colormap with that name + from matplotlib. + colorbar : boolean, optional + If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots) + position : float + Specify relative alignments for bar plot layout. + From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center) + layout : tuple (optional) + (rows, columns) for the layout of the plot + table : boolean, Series or DataFrame, default False + If True, draw a table using the data in the DataFrame and the data will + be transposed to meet matplotlib's default layout. + If a Series or DataFrame is passed, use passed data to draw a table. + yerr : DataFrame, Series, array-like, dict and str + See :ref:`Plotting with Error Bars <visualization.errorbars>` for + detail. + xerr : same types as yerr. + %(klass_unique)s + mark_right : boolean, default True + When using a secondary_y axis, automatically mark the column + labels with "(right)" in the legend + kwds : keywords + Options to pass to matplotlib plotting method + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + + Notes + ----- + + - See matplotlib documentation online for more on this subject + - If `kind` = 'bar' or 'barh', you can specify relative alignments + for bar plot layout by `position` keyword. + From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center) + %(klass_note)s + + """ + + +@Appender(_shared_docs['plot'] % _shared_doc_df_kwargs) +def plot_frame(data, x=None, y=None, kind='line', ax=None, + subplots=False, sharex=None, sharey=False, layout=None, + figsize=None, use_index=True, title=None, grid=None, + legend=True, style=None, logx=False, logy=False, loglog=False, + xticks=None, yticks=None, xlim=None, ylim=None, + rot=None, fontsize=None, colormap=None, table=False, + yerr=None, xerr=None, + secondary_y=False, sort_columns=False, + **kwds): + return _plot(data, kind=kind, x=x, y=y, ax=ax, + subplots=subplots, sharex=sharex, sharey=sharey, + layout=layout, figsize=figsize, use_index=use_index, + title=title, grid=grid, legend=legend, + style=style, logx=logx, logy=logy, loglog=loglog, + xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim, + rot=rot, fontsize=fontsize, colormap=colormap, table=table, + yerr=yerr, xerr=xerr, + secondary_y=secondary_y, sort_columns=sort_columns, + **kwds) + + +@Appender(_shared_docs['plot'] % _shared_doc_series_kwargs) +def plot_series(data, kind='line', ax=None, # Series unique + figsize=None, use_index=True, title=None, grid=None, + legend=False, style=None, logx=False, logy=False, loglog=False, + xticks=None, yticks=None, xlim=None, ylim=None, + rot=None, fontsize=None, colormap=None, table=False, + yerr=None, xerr=None, + label=None, secondary_y=False, # Series unique + **kwds): + + import matplotlib.pyplot as plt + """ + If no axes is specified, check whether there are existing figures + If there is no existing figures, _gca() will + create a figure with the default figsize, causing the figsize=parameter to + be ignored. + """ + if ax is None and len(plt.get_fignums()) > 0: + ax = _gca() + ax = MPLPlot._get_ax_layer(ax) + return _plot(data, kind=kind, ax=ax, + figsize=figsize, use_index=use_index, title=title, + grid=grid, legend=legend, + style=style, logx=logx, logy=logy, loglog=loglog, + xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim, + rot=rot, fontsize=fontsize, colormap=colormap, table=table, + yerr=yerr, xerr=xerr, + label=label, secondary_y=secondary_y, + **kwds) + + +_shared_docs['boxplot'] = """ + Make a box plot from DataFrame column optionally grouped by some columns or + other inputs + + Parameters + ---------- + data : the pandas object holding the data + column : column name or list of names, or vector + Can be any valid input to groupby + by : string or sequence + Column in the DataFrame to group by + ax : Matplotlib axes object, optional + fontsize : int or string + rot : label rotation angle + figsize : A tuple (width, height) in inches + grid : Setting this to True will show the grid + layout : tuple (optional) + (rows, columns) for the layout of the plot + return_type : {None, 'axes', 'dict', 'both'}, default None + The kind of object to return. The default is ``axes`` + 'axes' returns the matplotlib axes the boxplot is drawn on; + 'dict' returns a dictionary whose values are the matplotlib + Lines of the boxplot; + 'both' returns a namedtuple with the axes and dict. + + When grouping with ``by``, a Series mapping columns to ``return_type`` + is returned, unless ``return_type`` is None, in which case a NumPy + array of axes is returned with the same shape as ``layout``. + See the prose documentation for more. + + kwds : other plotting keyword arguments to be passed to matplotlib boxplot + function + + Returns + ------- + lines : dict + ax : matplotlib Axes + (ax, lines): namedtuple + + Notes + ----- + Use ``return_type='dict'`` when you want to tweak the appearance + of the lines after plotting. In this case a dict containing the Lines + making up the boxes, caps, fliers, medians, and whiskers is returned. + """ + + +@Appender(_shared_docs['boxplot'] % _shared_doc_kwargs) +def boxplot(data, column=None, by=None, ax=None, fontsize=None, + rot=0, grid=True, figsize=None, layout=None, return_type=None, + **kwds): + + # validate return_type: + if return_type not in BoxPlot._valid_return_types: + raise ValueError("return_type must be {'axes', 'dict', 'both'}") + + from pandas import Series, DataFrame + if isinstance(data, Series): + data = DataFrame({'x': data}) + column = 'x' + + def _get_colors(): + return _get_standard_colors(color=kwds.get('color'), num_colors=1) + + def maybe_color_bp(bp): + if 'color' not in kwds: + from matplotlib.artist import setp + setp(bp['boxes'], color=colors[0], alpha=1) + setp(bp['whiskers'], color=colors[0], alpha=1) + setp(bp['medians'], color=colors[2], alpha=1) + + def plot_group(keys, values, ax): + keys = [pprint_thing(x) for x in keys] + values = [remove_na(v) for v in values] + bp = ax.boxplot(values, **kwds) + if fontsize is not None: + ax.tick_params(axis='both', labelsize=fontsize) + if kwds.get('vert', 1): + ax.set_xticklabels(keys, rotation=rot) + else: + ax.set_yticklabels(keys, rotation=rot) + maybe_color_bp(bp) + + # Return axes in multiplot case, maybe revisit later # 985 + if return_type == 'dict': + return bp + elif return_type == 'both': + return BoxPlot.BP(ax=ax, lines=bp) + else: + return ax + + colors = _get_colors() + if column is None: + columns = None + else: + if isinstance(column, (list, tuple)): + columns = column + else: + columns = [column] + + if by is not None: + # Prefer array return type for 2-D plots to match the subplot layout + # https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580 + result = _grouped_plot_by_column(plot_group, data, columns=columns, + by=by, grid=grid, figsize=figsize, + ax=ax, layout=layout, + return_type=return_type) + else: + if return_type is None: + return_type = 'axes' + if layout is not None: + raise ValueError("The 'layout' keyword is not supported when " + "'by' is None") + + if ax is None: + ax = _gca() + data = data._get_numeric_data() + if columns is None: + columns = data.columns + else: + data = data[columns] + + result = plot_group(columns, data.values.T, ax) + ax.grid(grid) + + return result + + +def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False, + **kwargs): + """ + Make a scatter plot from two DataFrame columns + + Parameters + ---------- + data : DataFrame + x : Column name for the x-axis values + y : Column name for the y-axis values + ax : Matplotlib axis object + figsize : A tuple (width, height) in inches + grid : Setting this to True will show the grid + kwargs : other plotting keyword arguments + To be passed to scatter function + + Returns + ------- + fig : matplotlib.Figure + """ + import matplotlib.pyplot as plt + + # workaround because `c='b'` is hardcoded in matplotlibs scatter method + kwargs.setdefault('c', plt.rcParams['patch.facecolor']) + + def plot_group(group, ax): + xvals = group[x].values + yvals = group[y].values + ax.scatter(xvals, yvals, **kwargs) + ax.grid(grid) + + if by is not None: + fig = _grouped_plot(plot_group, data, by=by, figsize=figsize, ax=ax) + else: + if ax is None: + fig = plt.figure() + ax = fig.add_subplot(111) + else: + fig = ax.get_figure() + plot_group(data, ax) + ax.set_ylabel(pprint_thing(y)) + ax.set_xlabel(pprint_thing(x)) + + ax.grid(grid) + + return fig + + +def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, + xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, + sharey=False, figsize=None, layout=None, bins=10, **kwds): + """ + Draw histogram of the DataFrame's series using matplotlib / pylab. + + Parameters + ---------- + data : DataFrame + column : string or sequence + If passed, will be used to limit data to a subset of columns + by : object, optional + If passed, then used to form histograms for separate groups + grid : boolean, default True + Whether to show axis grid lines + xlabelsize : int, default None + If specified changes the x-axis label size + xrot : float, default None + rotation of x axis labels + ylabelsize : int, default None + If specified changes the y-axis label size + yrot : float, default None + rotation of y axis labels + ax : matplotlib axes object, default None + sharex : boolean, default True if ax is None else False + In case subplots=True, share x axis and set some x axis labels to + invisible; defaults to True if ax is None otherwise False if an ax + is passed in; Be aware, that passing in both an ax and sharex=True + will alter all x axis labels for all subplots in a figure! + sharey : boolean, default False + In case subplots=True, share y axis and set some y axis labels to + invisible + figsize : tuple + The size of the figure to create in inches by default + layout : tuple, optional + Tuple of (rows, columns) for the layout of the histograms + bins : integer, default 10 + Number of histogram bins to be used + kwds : other plotting keyword arguments + To be passed to hist function + """ + + if by is not None: + axes = grouped_hist(data, column=column, by=by, ax=ax, grid=grid, + figsize=figsize, sharex=sharex, sharey=sharey, + layout=layout, bins=bins, xlabelsize=xlabelsize, + xrot=xrot, ylabelsize=ylabelsize, + yrot=yrot, **kwds) + return axes + + if column is not None: + if not isinstance(column, (list, np.ndarray, Index)): + column = [column] + data = data[column] + data = data._get_numeric_data() + naxes = len(data.columns) + + fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False, + sharex=sharex, sharey=sharey, figsize=figsize, + layout=layout) + _axes = _flatten(axes) + + for i, col in enumerate(_try_sort(data.columns)): + ax = _axes[i] + ax.hist(data[col].dropna().values, bins=bins, **kwds) + ax.set_title(col) + ax.grid(grid) + + _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, + ylabelsize=ylabelsize, yrot=yrot) + fig.subplots_adjust(wspace=0.3, hspace=0.3) + + return axes + + +def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, + xrot=None, ylabelsize=None, yrot=None, figsize=None, + bins=10, **kwds): + """ + Draw histogram of the input series using matplotlib + + Parameters + ---------- + by : object, optional + If passed, then used to form histograms for separate groups + ax : matplotlib axis object + If not passed, uses gca() + grid : boolean, default True + Whether to show axis grid lines + xlabelsize : int, default None + If specified changes the x-axis label size + xrot : float, default None + rotation of x axis labels + ylabelsize : int, default None + If specified changes the y-axis label size + yrot : float, default None + rotation of y axis labels + figsize : tuple, default None + figure size in inches by default + bins: integer, default 10 + Number of histogram bins to be used + kwds : keywords + To be passed to the actual plotting function + + Notes + ----- + See matplotlib documentation online for more on this + + """ + import matplotlib.pyplot as plt + + if by is None: + if kwds.get('layout', None) is not None: + raise ValueError("The 'layout' keyword is not supported when " + "'by' is None") + # hack until the plotting interface is a bit more unified + fig = kwds.pop('figure', plt.gcf() if plt.get_fignums() else + plt.figure(figsize=figsize)) + if (figsize is not None and tuple(figsize) != + tuple(fig.get_size_inches())): + fig.set_size_inches(*figsize, forward=True) + if ax is None: + ax = fig.gca() + elif ax.get_figure() != fig: + raise AssertionError('passed axis not bound to passed figure') + values = self.dropna().values + + ax.hist(values, bins=bins, **kwds) + ax.grid(grid) + axes = np.array([ax]) + + _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, + ylabelsize=ylabelsize, yrot=yrot) + + else: + if 'figure' in kwds: + raise ValueError("Cannot pass 'figure' when using the " + "'by' argument, since a new 'Figure' instance " + "will be created") + axes = grouped_hist(self, by=by, ax=ax, grid=grid, figsize=figsize, + bins=bins, xlabelsize=xlabelsize, xrot=xrot, + ylabelsize=ylabelsize, yrot=yrot, **kwds) + + if hasattr(axes, 'ndim'): + if axes.ndim == 1 and len(axes) == 1: + return axes[0] + return axes + + +def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, + layout=None, sharex=False, sharey=False, rot=90, grid=True, + xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, + **kwargs): + """ + Grouped histogram + + Parameters + ---------- + data: Series/DataFrame + column: object, optional + by: object, optional + ax: axes, optional + bins: int, default 50 + figsize: tuple, optional + layout: optional + sharex: boolean, default False + sharey: boolean, default False + rot: int, default 90 + grid: bool, default True + kwargs: dict, keyword arguments passed to matplotlib.Axes.hist + + Returns + ------- + axes: collection of Matplotlib Axes + """ + def plot_group(group, ax): + ax.hist(group.dropna().values, bins=bins, **kwargs) + + xrot = xrot or rot + + fig, axes = _grouped_plot(plot_group, data, column=column, + by=by, sharex=sharex, sharey=sharey, ax=ax, + figsize=figsize, layout=layout, rot=rot) + + _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, + ylabelsize=ylabelsize, yrot=yrot) + + fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, + hspace=0.5, wspace=0.3) + return axes + + +def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None, + rot=0, grid=True, ax=None, figsize=None, + layout=None, **kwds): + """ + Make box plots from DataFrameGroupBy data. + + Parameters + ---------- + grouped : Grouped DataFrame + subplots : + * ``False`` - no subplots will be used + * ``True`` - create a subplot for each group + column : column name or list of names, or vector + Can be any valid input to groupby + fontsize : int or string + rot : label rotation angle + grid : Setting this to True will show the grid + ax : Matplotlib axis object, default None + figsize : A tuple (width, height) in inches + layout : tuple (optional) + (rows, columns) for the layout of the plot + kwds : other plotting keyword arguments to be passed to matplotlib boxplot + function + + Returns + ------- + dict of key/value = group key/DataFrame.boxplot return value + or DataFrame.boxplot return value in case subplots=figures=False + + Examples + -------- + >>> import pandas + >>> import numpy as np + >>> import itertools + >>> + >>> tuples = [t for t in itertools.product(range(1000), range(4))] + >>> index = pandas.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1']) + >>> data = np.random.randn(len(index),4) + >>> df = pandas.DataFrame(data, columns=list('ABCD'), index=index) + >>> + >>> grouped = df.groupby(level='lvl1') + >>> boxplot_frame_groupby(grouped) + >>> + >>> grouped = df.unstack(level='lvl1').groupby(level=0, axis=1) + >>> boxplot_frame_groupby(grouped, subplots=False) + """ + if subplots is True: + naxes = len(grouped) + fig, axes = _subplots(naxes=naxes, squeeze=False, + ax=ax, sharex=False, sharey=True, + figsize=figsize, layout=layout) + axes = _flatten(axes) + + ret = Series() + for (key, group), ax in zip(grouped, axes): + d = group.boxplot(ax=ax, column=column, fontsize=fontsize, + rot=rot, grid=grid, **kwds) + ax.set_title(pprint_thing(key)) + ret.loc[key] = d + fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, + right=0.9, wspace=0.2) + else: + from pandas.tools.merge import concat + keys, frames = zip(*grouped) + if grouped.axis == 0: + df = concat(frames, keys=keys, axis=1) + else: + if len(frames) > 1: + df = frames[0].join(frames[1::]) + else: + df = frames[0] + ret = df.boxplot(column=column, fontsize=fontsize, rot=rot, + grid=grid, ax=ax, figsize=figsize, + layout=layout, **kwds) + return ret + + +def _grouped_plot(plotf, data, column=None, by=None, numeric_only=True, + figsize=None, sharex=True, sharey=True, layout=None, + rot=0, ax=None, **kwargs): + from pandas import DataFrame + + if figsize == 'default': + # allowed to specify mpl default with 'default' + warnings.warn("figsize='default' is deprecated. Specify figure" + "size by tuple instead", FutureWarning, stacklevel=4) + figsize = None + + grouped = data.groupby(by) + if column is not None: + grouped = grouped[column] + + naxes = len(grouped) + fig, axes = _subplots(naxes=naxes, figsize=figsize, + sharex=sharex, sharey=sharey, ax=ax, + layout=layout) + + _axes = _flatten(axes) + + for i, (key, group) in enumerate(grouped): + ax = _axes[i] + if numeric_only and isinstance(group, DataFrame): + group = group._get_numeric_data() + plotf(group, ax, **kwargs) + ax.set_title(pprint_thing(key)) + + return fig, axes + + +def _grouped_plot_by_column(plotf, data, columns=None, by=None, + numeric_only=True, grid=False, + figsize=None, ax=None, layout=None, + return_type=None, **kwargs): + grouped = data.groupby(by) + if columns is None: + if not isinstance(by, (list, tuple)): + by = [by] + columns = data._get_numeric_data().columns.difference(by) + naxes = len(columns) + fig, axes = _subplots(naxes=naxes, sharex=True, sharey=True, + figsize=figsize, ax=ax, layout=layout) + + _axes = _flatten(axes) + + result = Series() + ax_values = [] + + for i, col in enumerate(columns): + ax = _axes[i] + gp_col = grouped[col] + keys, values = zip(*gp_col) + re_plotf = plotf(keys, values, ax, **kwargs) + ax.set_title(col) + ax.set_xlabel(pprint_thing(by)) + ax_values.append(re_plotf) + ax.grid(grid) + + result = Series(ax_values, index=columns) + + # Return axes in multiplot case, maybe revisit later # 985 + if return_type is None: + result = axes + + byline = by[0] if len(by) == 1 else by + fig.suptitle('Boxplot grouped by %s' % byline) + fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2) + + return result + + +class BasePlotMethods(PandasObject): + + def __init__(self, data): + self._data = data + + def __call__(self, *args, **kwargs): + raise NotImplementedError + + +class SeriesPlotMethods(BasePlotMethods): + """Series plotting accessor and method + + Examples + -------- + >>> s.plot.line() + >>> s.plot.bar() + >>> s.plot.hist() + + Plotting methods can also be accessed by calling the accessor as a method + with the ``kind`` argument: + ``s.plot(kind='line')`` is equivalent to ``s.plot.line()`` + """ + + def __call__(self, kind='line', ax=None, + figsize=None, use_index=True, title=None, grid=None, + legend=False, style=None, logx=False, logy=False, + loglog=False, xticks=None, yticks=None, + xlim=None, ylim=None, + rot=None, fontsize=None, colormap=None, table=False, + yerr=None, xerr=None, + label=None, secondary_y=False, **kwds): + return plot_series(self._data, kind=kind, ax=ax, figsize=figsize, + use_index=use_index, title=title, grid=grid, + legend=legend, style=style, logx=logx, logy=logy, + loglog=loglog, xticks=xticks, yticks=yticks, + xlim=xlim, ylim=ylim, rot=rot, fontsize=fontsize, + colormap=colormap, table=table, yerr=yerr, + xerr=xerr, label=label, secondary_y=secondary_y, + **kwds) + __call__.__doc__ = plot_series.__doc__ + + def line(self, **kwds): + """ + Line plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='line', **kwds) + + def bar(self, **kwds): + """ + Vertical bar plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='bar', **kwds) + + def barh(self, **kwds): + """ + Horizontal bar plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='barh', **kwds) + + def box(self, **kwds): + """ + Boxplot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='box', **kwds) + + def hist(self, bins=10, **kwds): + """ + Histogram + + .. versionadded:: 0.17.0 + + Parameters + ---------- + bins: integer, default 10 + Number of histogram bins to be used + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='hist', bins=bins, **kwds) + + def kde(self, **kwds): + """ + Kernel Density Estimate plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='kde', **kwds) + + density = kde + + def area(self, **kwds): + """ + Area plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='area', **kwds) + + def pie(self, **kwds): + """ + Pie chart + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='pie', **kwds) + + +class FramePlotMethods(BasePlotMethods): + """DataFrame plotting accessor and method + + Examples + -------- + >>> df.plot.line() + >>> df.plot.scatter('x', 'y') + >>> df.plot.hexbin() + + These plotting methods can also be accessed by calling the accessor as a + method with the ``kind`` argument: + ``df.plot(kind='line')`` is equivalent to ``df.plot.line()`` + """ + + def __call__(self, x=None, y=None, kind='line', ax=None, + subplots=False, sharex=None, sharey=False, layout=None, + figsize=None, use_index=True, title=None, grid=None, + legend=True, style=None, logx=False, logy=False, loglog=False, + xticks=None, yticks=None, xlim=None, ylim=None, + rot=None, fontsize=None, colormap=None, table=False, + yerr=None, xerr=None, + secondary_y=False, sort_columns=False, **kwds): + return plot_frame(self._data, kind=kind, x=x, y=y, ax=ax, + subplots=subplots, sharex=sharex, sharey=sharey, + layout=layout, figsize=figsize, use_index=use_index, + title=title, grid=grid, legend=legend, style=style, + logx=logx, logy=logy, loglog=loglog, xticks=xticks, + yticks=yticks, xlim=xlim, ylim=ylim, rot=rot, + fontsize=fontsize, colormap=colormap, table=table, + yerr=yerr, xerr=xerr, secondary_y=secondary_y, + sort_columns=sort_columns, **kwds) + __call__.__doc__ = plot_frame.__doc__ + + def line(self, x=None, y=None, **kwds): + """ + Line plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + x, y : label or position, optional + Coordinates for each point. + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='line', x=x, y=y, **kwds) + + def bar(self, x=None, y=None, **kwds): + """ + Vertical bar plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + x, y : label or position, optional + Coordinates for each point. + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='bar', x=x, y=y, **kwds) + + def barh(self, x=None, y=None, **kwds): + """ + Horizontal bar plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + x, y : label or position, optional + Coordinates for each point. + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='barh', x=x, y=y, **kwds) + + def box(self, by=None, **kwds): + """ + Boxplot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + by : string or sequence + Column in the DataFrame to group by. + \*\*kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='box', by=by, **kwds) + + def hist(self, by=None, bins=10, **kwds): + """ + Histogram + + .. versionadded:: 0.17.0 + + Parameters + ---------- + by : string or sequence + Column in the DataFrame to group by. + bins: integer, default 10 + Number of histogram bins to be used + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='hist', by=by, bins=bins, **kwds) + + def kde(self, **kwds): + """ + Kernel Density Estimate plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='kde', **kwds) + + density = kde + + def area(self, x=None, y=None, **kwds): + """ + Area plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + x, y : label or position, optional + Coordinates for each point. + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='area', x=x, y=y, **kwds) + + def pie(self, y=None, **kwds): + """ + Pie chart + + .. versionadded:: 0.17.0 + + Parameters + ---------- + y : label or position, optional + Column to plot. + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='pie', y=y, **kwds) + + def scatter(self, x, y, s=None, c=None, **kwds): + """ + Scatter plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + x, y : label or position, optional + Coordinates for each point. + s : scalar or array_like, optional + Size of each point. + c : label or position, optional + Color of each point. + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + return self(kind='scatter', x=x, y=y, c=c, s=s, **kwds) + + def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None, + **kwds): + """ + Hexbin plot + + .. versionadded:: 0.17.0 + + Parameters + ---------- + x, y : label or position, optional + Coordinates for each point. + C : label or position, optional + The value at each `(x, y)` point. + reduce_C_function : callable, optional + Function of one argument that reduces all the values in a bin to + a single number (e.g. `mean`, `max`, `sum`, `std`). + gridsize : int, optional + Number of bins. + **kwds : optional + Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. + + Returns + ------- + axes : matplotlib.AxesSubplot or np.array of them + """ + if reduce_C_function is not None: + kwds['reduce_C_function'] = reduce_C_function + if gridsize is not None: + kwds['gridsize'] = gridsize + return self(kind='hexbin', x=x, y=y, C=C, **kwds) + + +if __name__ == '__main__': + # import pandas.rpy.common as com + # sales = com.load_data('sanfrancisco.home.sales', package='nutshell') + # top10 = sales['zip'].value_counts()[:10].index + # sales2 = sales[sales.zip.isin(top10)] + # _ = scatter_plot(sales2, 'squarefeet', 'price', by='zip') + + # plt.show() + + import matplotlib.pyplot as plt + + import pandas.tools.plotting as plots + import pandas.core.frame as fr + reload(plots) # noqa + reload(fr) # noqa + from pandas.core.frame import DataFrame + + data = DataFrame([[3, 6, -5], [4, 8, 2], [4, 9, -6], + [4, 9, -3], [2, 5, -1]], + columns=['A', 'B', 'C']) + data.plot(kind='barh', stacked=True) + + plt.show() diff --git a/pandas/plotting/misc.py b/pandas/plotting/misc.py new file mode 100644 index 0000000000000..9be87f6dfa6c0 --- /dev/null +++ b/pandas/plotting/misc.py @@ -0,0 +1,565 @@ +# being a bit too dynamic +# pylint: disable=E1101 +from __future__ import division + +import numpy as np + +from pandas.util.decorators import deprecate_kwarg +from pandas.types.missing import notnull +from pandas.compat import range, lrange, lmap, zip +from pandas.formats.printing import pprint_thing + + +from pandas.plotting.style import _get_standard_colors +from pandas.plotting.tools import _subplots, _set_ticks_props + + +def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, + diagonal='hist', marker='.', density_kwds=None, + hist_kwds=None, range_padding=0.05, **kwds): + """ + Draw a matrix of scatter plots. + + Parameters + ---------- + frame : DataFrame + alpha : float, optional + amount of transparency applied + figsize : (float,float), optional + a tuple (width, height) in inches + ax : Matplotlib axis object, optional + grid : bool, optional + setting this to True will show the grid + diagonal : {'hist', 'kde'} + pick between 'kde' and 'hist' for + either Kernel Density Estimation or Histogram + plot in the diagonal + marker : str, optional + Matplotlib marker type, default '.' + hist_kwds : other plotting keyword arguments + To be passed to hist function + density_kwds : other plotting keyword arguments + To be passed to kernel density estimate plot + range_padding : float, optional + relative extension of axis range in x and y + with respect to (x_max - x_min) or (y_max - y_min), + default 0.05 + kwds : other plotting keyword arguments + To be passed to scatter function + + Examples + -------- + >>> df = DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D']) + >>> scatter_matrix(df, alpha=0.2) + """ + import matplotlib.pyplot as plt + + df = frame._get_numeric_data() + n = df.columns.size + naxes = n * n + fig, axes = _subplots(naxes=naxes, figsize=figsize, ax=ax, + squeeze=False) + + # no gaps between subplots + fig.subplots_adjust(wspace=0, hspace=0) + + mask = notnull(df) + + marker = _get_marker_compat(marker) + + hist_kwds = hist_kwds or {} + density_kwds = density_kwds or {} + + # workaround because `c='b'` is hardcoded in matplotlibs scatter method + kwds.setdefault('c', plt.rcParams['patch.facecolor']) + + boundaries_list = [] + for a in df.columns: + values = df[a].values[mask[a].values] + rmin_, rmax_ = np.min(values), np.max(values) + rdelta_ext = (rmax_ - rmin_) * range_padding / 2. + boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext)) + + for i, a in zip(lrange(n), df.columns): + for j, b in zip(lrange(n), df.columns): + ax = axes[i, j] + + if i == j: + values = df[a].values[mask[a].values] + + # Deal with the diagonal by drawing a histogram there. + if diagonal == 'hist': + ax.hist(values, **hist_kwds) + + elif diagonal in ('kde', 'density'): + from scipy.stats import gaussian_kde + y = values + gkde = gaussian_kde(y) + ind = np.linspace(y.min(), y.max(), 1000) + ax.plot(ind, gkde.evaluate(ind), **density_kwds) + + ax.set_xlim(boundaries_list[i]) + + else: + common = (mask[a] & mask[b]).values + + ax.scatter(df[b][common], df[a][common], + marker=marker, alpha=alpha, **kwds) + + ax.set_xlim(boundaries_list[j]) + ax.set_ylim(boundaries_list[i]) + + ax.set_xlabel(b) + ax.set_ylabel(a) + + if j != 0: + ax.yaxis.set_visible(False) + if i != n - 1: + ax.xaxis.set_visible(False) + + if len(df.columns) > 1: + lim1 = boundaries_list[0] + locs = axes[0][1].yaxis.get_majorticklocs() + locs = locs[(lim1[0] <= locs) & (locs <= lim1[1])] + adj = (locs - lim1[0]) / (lim1[1] - lim1[0]) + + lim0 = axes[0][0].get_ylim() + adj = adj * (lim0[1] - lim0[0]) + lim0[0] + axes[0][0].yaxis.set_ticks(adj) + + if np.all(locs == locs.astype(int)): + # if all ticks are int + locs = locs.astype(int) + axes[0][0].yaxis.set_ticklabels(locs) + + _set_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) + + return axes + + +def _get_marker_compat(marker): + import matplotlib.lines as mlines + import matplotlib as mpl + if mpl.__version__ < '1.1.0' and marker == '.': + return 'o' + if marker not in mlines.lineMarkers: + return 'o' + return marker + + +def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds): + """RadViz - a multivariate data visualization algorithm + + Parameters: + ----------- + frame: DataFrame + class_column: str + Column name containing class names + ax: Matplotlib axis object, optional + color: list or tuple, optional + Colors to use for the different classes + colormap : str or matplotlib colormap object, default None + Colormap to select colors from. If string, load colormap with that name + from matplotlib. + kwds: keywords + Options to pass to matplotlib scatter plotting method + + Returns: + -------- + ax: Matplotlib axis object + """ + import matplotlib.pyplot as plt + import matplotlib.patches as patches + + def normalize(series): + a = min(series) + b = max(series) + return (series - a) / (b - a) + + n = len(frame) + classes = frame[class_column].drop_duplicates() + class_col = frame[class_column] + df = frame.drop(class_column, axis=1).apply(normalize) + + if ax is None: + ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1]) + + to_plot = {} + colors = _get_standard_colors(num_colors=len(classes), colormap=colormap, + color_type='random', color=color) + + for kls in classes: + to_plot[kls] = [[], []] + + m = len(frame.columns) - 1 + s = np.array([(np.cos(t), np.sin(t)) + for t in [2.0 * np.pi * (i / float(m)) + for i in range(m)]]) + + for i in range(n): + row = df.iloc[i].values + row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1) + y = (s * row_).sum(axis=0) / row.sum() + kls = class_col.iat[i] + to_plot[kls][0].append(y[0]) + to_plot[kls][1].append(y[1]) + + for i, kls in enumerate(classes): + ax.scatter(to_plot[kls][0], to_plot[kls][1], color=colors[i], + label=pprint_thing(kls), **kwds) + ax.legend() + + ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none')) + + for xy, name in zip(s, df.columns): + + ax.add_patch(patches.Circle(xy, radius=0.025, facecolor='gray')) + + if xy[0] < 0.0 and xy[1] < 0.0: + ax.text(xy[0] - 0.025, xy[1] - 0.025, name, + ha='right', va='top', size='small') + elif xy[0] < 0.0 and xy[1] >= 0.0: + ax.text(xy[0] - 0.025, xy[1] + 0.025, name, + ha='right', va='bottom', size='small') + elif xy[0] >= 0.0 and xy[1] < 0.0: + ax.text(xy[0] + 0.025, xy[1] - 0.025, name, + ha='left', va='top', size='small') + elif xy[0] >= 0.0 and xy[1] >= 0.0: + ax.text(xy[0] + 0.025, xy[1] + 0.025, name, + ha='left', va='bottom', size='small') + + ax.axis('equal') + return ax + + +@deprecate_kwarg(old_arg_name='data', new_arg_name='frame') +def andrews_curves(frame, class_column, ax=None, samples=200, color=None, + colormap=None, **kwds): + """ + Generates a matplotlib plot of Andrews curves, for visualising clusters of + multivariate data. + + Andrews curves have the functional form: + + f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) + + x_4 sin(2t) + x_5 cos(2t) + ... + + Where x coefficients correspond to the values of each dimension and t is + linearly spaced between -pi and +pi. Each row of frame then corresponds to + a single curve. + + Parameters: + ----------- + frame : DataFrame + Data to be plotted, preferably normalized to (0.0, 1.0) + class_column : Name of the column containing class names + ax : matplotlib axes object, default None + samples : Number of points to plot in each curve + color: list or tuple, optional + Colors to use for the different classes + colormap : str or matplotlib colormap object, default None + Colormap to select colors from. If string, load colormap with that name + from matplotlib. + kwds: keywords + Options to pass to matplotlib plotting method + + Returns: + -------- + ax: Matplotlib axis object + + """ + from math import sqrt, pi + import matplotlib.pyplot as plt + + def function(amplitudes): + def f(t): + x1 = amplitudes[0] + result = x1 / sqrt(2.0) + + # Take the rest of the coefficients and resize them + # appropriately. Take a copy of amplitudes as otherwise numpy + # deletes the element from amplitudes itself. + coeffs = np.delete(np.copy(amplitudes), 0) + coeffs.resize(int((coeffs.size + 1) / 2), 2) + + # Generate the harmonics and arguments for the sin and cos + # functions. + harmonics = np.arange(0, coeffs.shape[0]) + 1 + trig_args = np.outer(harmonics, t) + + result += np.sum(coeffs[:, 0, np.newaxis] * np.sin(trig_args) + + coeffs[:, 1, np.newaxis] * np.cos(trig_args), + axis=0) + return result + return f + + n = len(frame) + class_col = frame[class_column] + classes = frame[class_column].drop_duplicates() + df = frame.drop(class_column, axis=1) + t = np.linspace(-pi, pi, samples) + used_legends = set([]) + + color_values = _get_standard_colors(num_colors=len(classes), + colormap=colormap, color_type='random', + color=color) + colors = dict(zip(classes, color_values)) + if ax is None: + ax = plt.gca(xlim=(-pi, pi)) + for i in range(n): + row = df.iloc[i].values + f = function(row) + y = f(t) + kls = class_col.iat[i] + label = pprint_thing(kls) + if label not in used_legends: + used_legends.add(label) + ax.plot(t, y, color=colors[kls], label=label, **kwds) + else: + ax.plot(t, y, color=colors[kls], **kwds) + + ax.legend(loc='upper right') + ax.grid() + return ax + + +def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds): + """Bootstrap plot. + + Parameters: + ----------- + series: Time series + fig: matplotlib figure object, optional + size: number of data points to consider during each sampling + samples: number of times the bootstrap procedure is performed + kwds: optional keyword arguments for plotting commands, must be accepted + by both hist and plot + + Returns: + -------- + fig: matplotlib figure + """ + import random + import matplotlib.pyplot as plt + + # random.sample(ndarray, int) fails on python 3.3, sigh + data = list(series.values) + samplings = [random.sample(data, size) for _ in range(samples)] + + means = np.array([np.mean(sampling) for sampling in samplings]) + medians = np.array([np.median(sampling) for sampling in samplings]) + midranges = np.array([(min(sampling) + max(sampling)) * 0.5 + for sampling in samplings]) + if fig is None: + fig = plt.figure() + x = lrange(samples) + axes = [] + ax1 = fig.add_subplot(2, 3, 1) + ax1.set_xlabel("Sample") + axes.append(ax1) + ax1.plot(x, means, **kwds) + ax2 = fig.add_subplot(2, 3, 2) + ax2.set_xlabel("Sample") + axes.append(ax2) + ax2.plot(x, medians, **kwds) + ax3 = fig.add_subplot(2, 3, 3) + ax3.set_xlabel("Sample") + axes.append(ax3) + ax3.plot(x, midranges, **kwds) + ax4 = fig.add_subplot(2, 3, 4) + ax4.set_xlabel("Mean") + axes.append(ax4) + ax4.hist(means, **kwds) + ax5 = fig.add_subplot(2, 3, 5) + ax5.set_xlabel("Median") + axes.append(ax5) + ax5.hist(medians, **kwds) + ax6 = fig.add_subplot(2, 3, 6) + ax6.set_xlabel("Midrange") + axes.append(ax6) + ax6.hist(midranges, **kwds) + for axis in axes: + plt.setp(axis.get_xticklabels(), fontsize=8) + plt.setp(axis.get_yticklabels(), fontsize=8) + return fig + + +@deprecate_kwarg(old_arg_name='colors', new_arg_name='color') +@deprecate_kwarg(old_arg_name='data', new_arg_name='frame', stacklevel=3) +def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None, + use_columns=False, xticks=None, colormap=None, + axvlines=True, axvlines_kwds=None, **kwds): + """Parallel coordinates plotting. + + Parameters + ---------- + frame: DataFrame + class_column: str + Column name containing class names + cols: list, optional + A list of column names to use + ax: matplotlib.axis, optional + matplotlib axis object + color: list or tuple, optional + Colors to use for the different classes + use_columns: bool, optional + If true, columns will be used as xticks + xticks: list or tuple, optional + A list of values to use for xticks + colormap: str or matplotlib colormap, default None + Colormap to use for line colors. + axvlines: bool, optional + If true, vertical lines will be added at each xtick + axvlines_kwds: keywords, optional + Options to be passed to axvline method for vertical lines + kwds: keywords + Options to pass to matplotlib plotting method + + Returns + ------- + ax: matplotlib axis object + + Examples + -------- + >>> from pandas import read_csv + >>> from pandas.tools.plotting import parallel_coordinates + >>> from matplotlib import pyplot as plt + >>> df = read_csv('https://raw.github.com/pandas-dev/pandas/master' + '/pandas/tests/data/iris.csv') + >>> parallel_coordinates(df, 'Name', color=('#556270', + '#4ECDC4', '#C7F464')) + >>> plt.show() + """ + if axvlines_kwds is None: + axvlines_kwds = {'linewidth': 1, 'color': 'black'} + import matplotlib.pyplot as plt + + n = len(frame) + classes = frame[class_column].drop_duplicates() + class_col = frame[class_column] + + if cols is None: + df = frame.drop(class_column, axis=1) + else: + df = frame[cols] + + used_legends = set([]) + + ncols = len(df.columns) + + # determine values to use for xticks + if use_columns is True: + if not np.all(np.isreal(list(df.columns))): + raise ValueError('Columns must be numeric to be used as xticks') + x = df.columns + elif xticks is not None: + if not np.all(np.isreal(xticks)): + raise ValueError('xticks specified must be numeric') + elif len(xticks) != ncols: + raise ValueError('Length of xticks must match number of columns') + x = xticks + else: + x = lrange(ncols) + + if ax is None: + ax = plt.gca() + + color_values = _get_standard_colors(num_colors=len(classes), + colormap=colormap, color_type='random', + color=color) + + colors = dict(zip(classes, color_values)) + + for i in range(n): + y = df.iloc[i].values + kls = class_col.iat[i] + label = pprint_thing(kls) + if label not in used_legends: + used_legends.add(label) + ax.plot(x, y, color=colors[kls], label=label, **kwds) + else: + ax.plot(x, y, color=colors[kls], **kwds) + + if axvlines: + for i in x: + ax.axvline(i, **axvlines_kwds) + + ax.set_xticks(x) + ax.set_xticklabels(df.columns) + ax.set_xlim(x[0], x[-1]) + ax.legend(loc='upper right') + ax.grid() + return ax + + +def lag_plot(series, lag=1, ax=None, **kwds): + """Lag plot for time series. + + Parameters: + ----------- + series: Time series + lag: lag of the scatter plot, default 1 + ax: Matplotlib axis object, optional + kwds: Matplotlib scatter method keyword arguments, optional + + Returns: + -------- + ax: Matplotlib axis object + """ + import matplotlib.pyplot as plt + + # workaround because `c='b'` is hardcoded in matplotlibs scatter method + kwds.setdefault('c', plt.rcParams['patch.facecolor']) + + data = series.values + y1 = data[:-lag] + y2 = data[lag:] + if ax is None: + ax = plt.gca() + ax.set_xlabel("y(t)") + ax.set_ylabel("y(t + %s)" % lag) + ax.scatter(y1, y2, **kwds) + return ax + + +def autocorrelation_plot(series, ax=None, **kwds): + """Autocorrelation plot for time series. + + Parameters: + ----------- + series: Time series + ax: Matplotlib axis object, optional + kwds : keywords + Options to pass to matplotlib plotting method + + Returns: + ----------- + ax: Matplotlib axis object + """ + import matplotlib.pyplot as plt + n = len(series) + data = np.asarray(series) + if ax is None: + ax = plt.gca(xlim=(1, n), ylim=(-1.0, 1.0)) + mean = np.mean(data) + c0 = np.sum((data - mean) ** 2) / float(n) + + def r(h): + return ((data[:n - h] - mean) * + (data[h:] - mean)).sum() / float(n) / c0 + x = np.arange(n) + 1 + y = lmap(r, x) + z95 = 1.959963984540054 + z99 = 2.5758293035489004 + ax.axhline(y=z99 / np.sqrt(n), linestyle='--', color='grey') + ax.axhline(y=z95 / np.sqrt(n), color='grey') + ax.axhline(y=0.0, color='black') + ax.axhline(y=-z95 / np.sqrt(n), color='grey') + ax.axhline(y=-z99 / np.sqrt(n), linestyle='--', color='grey') + ax.set_xlabel("Lag") + ax.set_ylabel("Autocorrelation") + ax.plot(x, y, **kwds) + if 'label' in kwds: + ax.legend() + ax.grid() + return ax diff --git a/pandas/plotting/style.py b/pandas/plotting/style.py new file mode 100644 index 0000000000000..18c80cc937f66 --- /dev/null +++ b/pandas/plotting/style.py @@ -0,0 +1,221 @@ +# being a bit too dynamic +# pylint: disable=E1101 +from __future__ import division + +import warnings +from contextlib import contextmanager + +import numpy as np + +from pandas.types.common import is_list_like +from pandas.compat import range, lrange, lmap +import pandas.compat as compat + + +# Extracted from https://gist.github.com/huyng/816622 +# this is the rcParams set when setting display.with_mpl_style +# to True. +mpl_stylesheet = { + 'axes.axisbelow': True, + 'axes.color_cycle': ['#348ABD', + '#7A68A6', + '#A60628', + '#467821', + '#CF4457', + '#188487', + '#E24A33'], + 'axes.edgecolor': '#bcbcbc', + 'axes.facecolor': '#eeeeee', + 'axes.grid': True, + 'axes.labelcolor': '#555555', + 'axes.labelsize': 'large', + 'axes.linewidth': 1.0, + 'axes.titlesize': 'x-large', + 'figure.edgecolor': 'white', + 'figure.facecolor': 'white', + 'figure.figsize': (6.0, 4.0), + 'figure.subplot.hspace': 0.5, + 'font.family': 'monospace', + 'font.monospace': ['Andale Mono', + 'Nimbus Mono L', + 'Courier New', + 'Courier', + 'Fixed', + 'Terminal', + 'monospace'], + 'font.size': 10, + 'interactive': True, + 'keymap.all_axes': ['a'], + 'keymap.back': ['left', 'c', 'backspace'], + 'keymap.forward': ['right', 'v'], + 'keymap.fullscreen': ['f'], + 'keymap.grid': ['g'], + 'keymap.home': ['h', 'r', 'home'], + 'keymap.pan': ['p'], + 'keymap.save': ['s'], + 'keymap.xscale': ['L', 'k'], + 'keymap.yscale': ['l'], + 'keymap.zoom': ['o'], + 'legend.fancybox': True, + 'lines.antialiased': True, + 'lines.linewidth': 1.0, + 'patch.antialiased': True, + 'patch.edgecolor': '#EEEEEE', + 'patch.facecolor': '#348ABD', + 'patch.linewidth': 0.5, + 'toolbar': 'toolbar2', + 'xtick.color': '#555555', + 'xtick.direction': 'in', + 'xtick.major.pad': 6.0, + 'xtick.major.size': 0.0, + 'xtick.minor.pad': 6.0, + 'xtick.minor.size': 0.0, + 'ytick.color': '#555555', + 'ytick.direction': 'in', + 'ytick.major.pad': 6.0, + 'ytick.major.size': 0.0, + 'ytick.minor.pad': 6.0, + 'ytick.minor.size': 0.0 +} + + +def _get_standard_colors(num_colors=None, colormap=None, color_type='default', + color=None): + import matplotlib.pyplot as plt + + if color is None and colormap is not None: + if isinstance(colormap, compat.string_types): + import matplotlib.cm as cm + cmap = colormap + colormap = cm.get_cmap(colormap) + if colormap is None: + raise ValueError("Colormap {0} is not recognized".format(cmap)) + colors = lmap(colormap, np.linspace(0, 1, num=num_colors)) + elif color is not None: + if colormap is not None: + warnings.warn("'color' and 'colormap' cannot be used " + "simultaneously. Using 'color'") + colors = list(color) if is_list_like(color) else color + else: + if color_type == 'default': + # need to call list() on the result to copy so we don't + # modify the global rcParams below + try: + colors = [c['color'] + for c in list(plt.rcParams['axes.prop_cycle'])] + except KeyError: + colors = list(plt.rcParams.get('axes.color_cycle', + list('bgrcmyk'))) + if isinstance(colors, compat.string_types): + colors = list(colors) + elif color_type == 'random': + import random + + def random_color(column): + random.seed(column) + return [random.random() for _ in range(3)] + + colors = lmap(random_color, lrange(num_colors)) + else: + raise ValueError("color_type must be either 'default' or 'random'") + + if isinstance(colors, compat.string_types): + import matplotlib.colors + conv = matplotlib.colors.ColorConverter() + + def _maybe_valid_colors(colors): + try: + [conv.to_rgba(c) for c in colors] + return True + except ValueError: + return False + + # check whether the string can be convertable to single color + maybe_single_color = _maybe_valid_colors([colors]) + # check whether each character can be convertable to colors + maybe_color_cycle = _maybe_valid_colors(list(colors)) + if maybe_single_color and maybe_color_cycle and len(colors) > 1: + msg = ("'{0}' can be parsed as both single color and " + "color cycle. Specify each color using a list " + "like ['{0}'] or {1}") + raise ValueError(msg.format(colors, list(colors))) + elif maybe_single_color: + colors = [colors] + else: + # ``colors`` is regarded as color cycle. + # mpl will raise error any of them is invalid + pass + + if len(colors) != num_colors: + multiple = num_colors // len(colors) - 1 + mod = num_colors % len(colors) + + colors += multiple * colors + colors += colors[:mod] + + return colors + + +class _Options(dict): + """ + Stores pandas plotting options. + Allows for parameter aliasing so you can just use parameter names that are + the same as the plot function parameters, but is stored in a canonical + format that makes it easy to breakdown into groups later + """ + + # alias so the names are same as plotting method parameter names + _ALIASES = {'x_compat': 'xaxis.compat'} + _DEFAULT_KEYS = ['xaxis.compat'] + + def __init__(self): + self['xaxis.compat'] = False + + def __getitem__(self, key): + key = self._get_canonical_key(key) + if key not in self: + raise ValueError('%s is not a valid pandas plotting option' % key) + return super(_Options, self).__getitem__(key) + + def __setitem__(self, key, value): + key = self._get_canonical_key(key) + return super(_Options, self).__setitem__(key, value) + + def __delitem__(self, key): + key = self._get_canonical_key(key) + if key in self._DEFAULT_KEYS: + raise ValueError('Cannot remove default parameter %s' % key) + return super(_Options, self).__delitem__(key) + + def __contains__(self, key): + key = self._get_canonical_key(key) + return super(_Options, self).__contains__(key) + + def reset(self): + """ + Reset the option store to its initial state + + Returns + ------- + None + """ + self.__init__() + + def _get_canonical_key(self, key): + return self._ALIASES.get(key, key) + + @contextmanager + def use(self, key, value): + """ + Temporarily set a parameter value using the with statement. + Aliasing allowed. + """ + old_value = self[key] + try: + self[key] = value + yield self + finally: + self[key] = old_value + + +plot_params = _Options() diff --git a/pandas/tests/plotting/__init__.py b/pandas/plotting/tests/__init__.py similarity index 100% rename from pandas/tests/plotting/__init__.py rename to pandas/plotting/tests/__init__.py diff --git a/pandas/tests/plotting/common.py b/pandas/plotting/tests/common.py similarity index 97% rename from pandas/tests/plotting/common.py rename to pandas/plotting/tests/common.py index 9fe1d7cacd38f..0b32e20d0fe97 100644 --- a/pandas/tests/plotting/common.py +++ b/pandas/plotting/tests/common.py @@ -16,7 +16,7 @@ import numpy as np from numpy import random -import pandas.tools.plotting as plotting +import pandas.plotting as plotting """ @@ -48,11 +48,11 @@ def setUp(self): import matplotlib as mpl mpl.rcdefaults() - self.mpl_le_1_2_1 = plotting._mpl_le_1_2_1() - self.mpl_ge_1_3_1 = plotting._mpl_ge_1_3_1() - self.mpl_ge_1_4_0 = plotting._mpl_ge_1_4_0() - self.mpl_ge_1_5_0 = plotting._mpl_ge_1_5_0() - self.mpl_ge_2_0_0 = plotting._mpl_ge_2_0_0() + self.mpl_le_1_2_1 = plotting.compat._mpl_le_1_2_1() + self.mpl_ge_1_3_1 = plotting.compat._mpl_ge_1_3_1() + self.mpl_ge_1_4_0 = plotting.compat._mpl_ge_1_4_0() + self.mpl_ge_1_5_0 = plotting.compat._mpl_ge_1_5_0() + self.mpl_ge_2_0_0 = plotting.compat._mpl_ge_2_0_0() if self.mpl_ge_1_4_0: self.bp_n_objects = 7 @@ -72,7 +72,8 @@ def setUp(self): self.default_tick_position = 'left' if self.mpl_ge_2_0_0 else 'default' # common test data from pandas import read_csv - path = os.path.join(os.path.dirname(curpath()), 'data', 'iris.csv') + base = os.path.join(os.path.dirname(curpath()), os.pardir) + path = os.path.join(base, 'tests', 'data', 'iris.csv') self.iris = read_csv(path) n = 100 @@ -352,7 +353,7 @@ def _check_axes_shape(self, axes, axes_num=None, layout=None, self.assertTrue(len(ax.get_children()) > 0) if layout is not None: - result = self._get_axes_layout(plotting._flatten(axes)) + result = self._get_axes_layout(plotting.tools._flatten(axes)) self.assertEqual(result, layout) self.assert_numpy_array_equal( @@ -378,7 +379,7 @@ def _flatten_visible(self, axes): axes : matplotlib Axes object, or its list-like """ - axes = plotting._flatten(axes) + axes = plotting.tools._flatten(axes) axes = [ax for ax in axes if ax.get_visible()] return axes diff --git a/pandas/tests/plotting/test_boxplot_method.py b/pandas/plotting/tests/test_boxplot_method.py similarity index 99% rename from pandas/tests/plotting/test_boxplot_method.py rename to pandas/plotting/tests/test_boxplot_method.py index 289d48ba6d4cc..993daaf91ef72 100644 --- a/pandas/tests/plotting/test_boxplot_method.py +++ b/pandas/plotting/tests/test_boxplot_method.py @@ -14,9 +14,9 @@ from numpy import random from numpy.random import randn -import pandas.tools.plotting as plotting +import pandas.plotting as plotting -from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works) +from pandas.plotting.tests.common import (TestPlotBase, _check_plot_works) """ Test cases for .boxplot method """ diff --git a/pandas/tseries/tests/test_converter.py b/pandas/plotting/tests/test_converter.py similarity index 99% rename from pandas/tseries/tests/test_converter.py rename to pandas/plotting/tests/test_converter.py index f6cf11c871bba..fcffc1380f914 100644 --- a/pandas/tseries/tests/test_converter.py +++ b/pandas/plotting/tests/test_converter.py @@ -10,7 +10,7 @@ from pandas.compat.numpy import np_datetime64_compat try: - import pandas.tseries.converter as converter + import pandas.plotting.converter as converter except ImportError: raise nose.SkipTest("no pandas.tseries.converter, skipping") diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/plotting/tests/test_datetimelike.py similarity index 99% rename from pandas/tests/plotting/test_datetimelike.py rename to pandas/plotting/tests/test_datetimelike.py index 6486c8aa21c1b..a36cbd228b79c 100644 --- a/pandas/tests/plotting/test_datetimelike.py +++ b/pandas/plotting/tests/test_datetimelike.py @@ -14,7 +14,7 @@ from pandas.util.testing import assert_series_equal, ensure_clean, slow import pandas.util.testing as tm -from pandas.tests.plotting.common import (TestPlotBase, +from pandas.plotting.tests.common import (TestPlotBase, _skip_if_no_scipy_gaussian_kde) @@ -144,7 +144,7 @@ def test_high_freq(self): _check_plot_works(ser.plot) def test_get_datevalue(self): - from pandas.tseries.converter import get_datevalue + from pandas.plotting.converter import get_datevalue self.assertIsNone(get_datevalue(None, 'D')) self.assertEqual(get_datevalue(1987, 'A'), 1987) self.assertEqual(get_datevalue(Period(1987, 'A'), 'M'), @@ -243,7 +243,7 @@ def test_plot_multiple_inferred_freq(self): @slow def test_uhf(self): - import pandas.tseries.converter as conv + import pandas.plotting.converter as conv import matplotlib.pyplot as plt fig = plt.gcf() plt.clf() @@ -387,7 +387,7 @@ def _test(ax): _test(ax) def test_get_finder(self): - import pandas.tseries.converter as conv + import pandas.plotting.converter as conv self.assertEqual(conv.get_finder('B'), conv._daily_finder) self.assertEqual(conv.get_finder('D'), conv._daily_finder) diff --git a/pandas/plotting/tests/test_deprecated.py b/pandas/plotting/tests/test_deprecated.py new file mode 100644 index 0000000000000..59434ec813f2b --- /dev/null +++ b/pandas/plotting/tests/test_deprecated.py @@ -0,0 +1,68 @@ +# coding: utf-8 + +import nose +import string + +import pandas as pd +import pandas.util.testing as tm +from pandas.util.testing import slow + +import numpy as np +from numpy.random import randn + +import pandas.tools.plotting as plotting + +from pandas.plotting.tests.common import TestPlotBase + + +""" +Test cases for plot functions imported from deprecated +pandas.tools.plotting +""" + + +@tm.mplskip +class TestDeprecatedNameSpace(TestPlotBase): + + @slow + def test_scatter_plot_legacy(self): + tm._skip_if_no_scipy() + + df = pd.DataFrame(randn(100, 2)) + + with tm.assert_produces_warning(FutureWarning): + plotting.scatter_matrix(df) + + with tm.assert_produces_warning(FutureWarning): + pd.scatter_matrix(df) + + @slow + def test_boxplot_deprecated(self): + df = pd.DataFrame(randn(6, 4), + index=list(string.ascii_letters[:6]), + columns=['one', 'two', 'three', 'four']) + df['indic'] = ['foo', 'bar'] * 3 + + with tm.assert_produces_warning(FutureWarning): + plotting.boxplot(df, column=['one', 'two'], + by='indic') + + @slow + def test_grouped_hist_legacy(self): + df = pd.DataFrame(randn(500, 2), columns=['A', 'B']) + df['C'] = np.random.randint(0, 4, 500) + df['D'] = ['X'] * 500 + + with tm.assert_produces_warning(FutureWarning): + plotting.grouped_hist(df.A, by=df.C) + + @slow + def test_radviz_deprecated(self): + df = self.iris + with tm.assert_produces_warning(FutureWarning): + plotting.radviz(frame=df, class_column='Name') + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/plotting/test_frame.py b/pandas/plotting/tests/test_frame.py similarity index 99% rename from pandas/tests/plotting/test_frame.py rename to pandas/plotting/tests/test_frame.py index fba554b03f191..79f9176fc58bf 100644 --- a/pandas/tests/plotting/test_frame.py +++ b/pandas/plotting/tests/test_frame.py @@ -20,8 +20,8 @@ import numpy as np from numpy.random import rand, randn -import pandas.tools.plotting as plotting -from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works, +import pandas.plotting as plotting +from pandas.plotting.tests.common import (TestPlotBase, _check_plot_works, _skip_if_no_scipy_gaussian_kde, _ok_for_gaussian_kde) @@ -1967,7 +1967,7 @@ def test_unordered_ts(self): def test_kind_both_ways(self): df = DataFrame({'x': [1, 2, 3]}) - for kind in plotting._common_kinds: + for kind in plotting.core._common_kinds: if not _ok_for_gaussian_kde(kind): continue df.plot(kind=kind) @@ -1978,7 +1978,7 @@ def test_kind_both_ways(self): def test_all_invalid_plot_data(self): df = DataFrame(list('abcd')) - for kind in plotting._common_kinds: + for kind in plotting.core._common_kinds: if not _ok_for_gaussian_kde(kind): continue with tm.assertRaises(TypeError): @@ -1989,7 +1989,7 @@ def test_partially_invalid_plot_data(self): with tm.RNGContext(42): df = DataFrame(randn(10, 2), dtype=object) df[np.random.rand(df.shape[0]) > 0.5] = 'a' - for kind in plotting._common_kinds: + for kind in plotting.core._common_kinds: if not _ok_for_gaussian_kde(kind): continue with tm.assertRaises(TypeError): @@ -2442,7 +2442,7 @@ def test_memory_leak(self): import gc results = {} - for kind in plotting._plot_klass.keys(): + for kind in plotting.core._plot_klass.keys(): if not _ok_for_gaussian_kde(kind): continue args = {} @@ -2641,7 +2641,7 @@ def test_df_grid_settings(self): # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 self._check_grid_settings( DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4]}), - plotting._dataframe_kinds, kws={'x': 'a', 'y': 'b'}) + plotting.core._dataframe_kinds, kws={'x': 'a', 'y': 'b'}) def test_option_mpl_style(self): with tm.assert_produces_warning(FutureWarning, diff --git a/pandas/tests/plotting/test_groupby.py b/pandas/plotting/tests/test_groupby.py similarity index 97% rename from pandas/tests/plotting/test_groupby.py rename to pandas/plotting/tests/test_groupby.py index 3c682fbfbb89e..fc2ba897c396d 100644 --- a/pandas/tests/plotting/test_groupby.py +++ b/pandas/plotting/tests/test_groupby.py @@ -7,7 +7,7 @@ import numpy as np -from pandas.tests.plotting.common import TestPlotBase +from pandas.plotting.tests.common import TestPlotBase """ Test cases for GroupBy.plot """ diff --git a/pandas/tests/plotting/test_hist_method.py b/pandas/plotting/tests/test_hist_method.py similarity index 99% rename from pandas/tests/plotting/test_hist_method.py rename to pandas/plotting/tests/test_hist_method.py index bde5544390b85..3bab4570e6d88 100644 --- a/pandas/tests/plotting/test_hist_method.py +++ b/pandas/plotting/tests/test_hist_method.py @@ -9,8 +9,8 @@ import numpy as np from numpy.random import randn -import pandas.tools.plotting as plotting -from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works) +import pandas.plotting as plotting +from pandas.plotting.tests.common import (TestPlotBase, _check_plot_works) """ Test cases for .hist method """ diff --git a/pandas/tests/plotting/test_misc.py b/pandas/plotting/tests/test_misc.py similarity index 96% rename from pandas/tests/plotting/test_misc.py rename to pandas/plotting/tests/test_misc.py index 2650ce2879db7..1fb279e814a9f 100644 --- a/pandas/tests/plotting/test_misc.py +++ b/pandas/plotting/tests/test_misc.py @@ -11,8 +11,8 @@ from numpy import random from numpy.random import randn -import pandas.tools.plotting as plotting -from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works, +import pandas.plotting as plotting +from pandas.plotting.tests.common import (TestPlotBase, _check_plot_works, _ok_for_gaussian_kde) """ Test cases for misc plot functions """ @@ -30,7 +30,7 @@ def setUp(self): @slow def test_autocorrelation_plot(self): - from pandas.tools.plotting import autocorrelation_plot + from pandas.plotting import autocorrelation_plot _check_plot_works(autocorrelation_plot, series=self.ts) _check_plot_works(autocorrelation_plot, series=self.ts.values) @@ -39,13 +39,13 @@ def test_autocorrelation_plot(self): @slow def test_lag_plot(self): - from pandas.tools.plotting import lag_plot + from pandas.plotting import lag_plot _check_plot_works(lag_plot, series=self.ts) _check_plot_works(lag_plot, series=self.ts, lag=5) @slow def test_bootstrap_plot(self): - from pandas.tools.plotting import bootstrap_plot + from pandas.plotting import bootstrap_plot _check_plot_works(bootstrap_plot, series=self.ts, size=10) @@ -124,7 +124,7 @@ def test_scatter_matrix_axis(self): @slow def test_andrews_curves(self): - from pandas.tools.plotting import andrews_curves + from pandas.plotting import andrews_curves from matplotlib import cm df = self.iris @@ -189,7 +189,7 @@ def test_andrews_curves(self): @slow def test_parallel_coordinates(self): - from pandas.tools.plotting import parallel_coordinates + from pandas.plotting import parallel_coordinates from matplotlib import cm df = self.iris @@ -237,7 +237,7 @@ def test_parallel_coordinates(self): @slow def test_radviz(self): - from pandas.tools.plotting import radviz + from pandas.plotting import radviz from matplotlib import cm df = self.iris diff --git a/pandas/tests/plotting/test_series.py b/pandas/plotting/tests/test_series.py similarity index 95% rename from pandas/tests/plotting/test_series.py rename to pandas/plotting/tests/test_series.py index f668c46a15173..b154ece2eadb0 100644 --- a/pandas/tests/plotting/test_series.py +++ b/pandas/plotting/tests/test_series.py @@ -14,8 +14,8 @@ import numpy as np from numpy.random import randn -import pandas.tools.plotting as plotting -from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works, +import pandas.plotting as plotting +from pandas.plotting.tests.common import (TestPlotBase, _check_plot_works, _skip_if_no_scipy_gaussian_kde, _ok_for_gaussian_kde) @@ -623,7 +623,9 @@ def test_boxplot_series(self): @slow def test_kind_both_ways(self): s = Series(range(3)) - for kind in plotting._common_kinds + plotting._series_kinds: + kinds = (plotting.core._common_kinds + + plotting.core._series_kinds) + for kind in kinds: if not _ok_for_gaussian_kde(kind): continue s.plot(kind=kind) @@ -632,7 +634,7 @@ def test_kind_both_ways(self): @slow def test_invalid_plot_data(self): s = Series(list('abcd')) - for kind in plotting._common_kinds: + for kind in plotting.core._common_kinds: if not _ok_for_gaussian_kde(kind): continue with tm.assertRaises(TypeError): @@ -641,14 +643,14 @@ def test_invalid_plot_data(self): @slow def test_valid_object_plot(self): s = Series(lrange(10), dtype=object) - for kind in plotting._common_kinds: + for kind in plotting.core._common_kinds: if not _ok_for_gaussian_kde(kind): continue _check_plot_works(s.plot, kind=kind) def test_partially_invalid_plot_data(self): s = Series(['a', 'b', 1.0, 2]) - for kind in plotting._common_kinds: + for kind in plotting.core._common_kinds: if not _ok_for_gaussian_kde(kind): continue with tm.assertRaises(TypeError): @@ -719,54 +721,57 @@ def test_table(self): def test_series_grid_settings(self): # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 self._check_grid_settings(Series([1, 2, 3]), - plotting._series_kinds + - plotting._common_kinds) + plotting.core._series_kinds + + plotting.core._common_kinds) @slow def test_standard_colors(self): + from pandas.plotting.style import _get_standard_colors + for c in ['r', 'red', 'green', '#FF0000']: - result = plotting._get_standard_colors(1, color=c) + result = _get_standard_colors(1, color=c) self.assertEqual(result, [c]) - result = plotting._get_standard_colors(1, color=[c]) + result = _get_standard_colors(1, color=[c]) self.assertEqual(result, [c]) - result = plotting._get_standard_colors(3, color=c) + result = _get_standard_colors(3, color=c) self.assertEqual(result, [c] * 3) - result = plotting._get_standard_colors(3, color=[c]) + result = _get_standard_colors(3, color=[c]) self.assertEqual(result, [c] * 3) @slow def test_standard_colors_all(self): import matplotlib.colors as colors + from pandas.plotting.style import _get_standard_colors # multiple colors like mediumaquamarine for c in colors.cnames: - result = plotting._get_standard_colors(num_colors=1, color=c) + result = _get_standard_colors(num_colors=1, color=c) self.assertEqual(result, [c]) - result = plotting._get_standard_colors(num_colors=1, color=[c]) + result = _get_standard_colors(num_colors=1, color=[c]) self.assertEqual(result, [c]) - result = plotting._get_standard_colors(num_colors=3, color=c) + result = _get_standard_colors(num_colors=3, color=c) self.assertEqual(result, [c] * 3) - result = plotting._get_standard_colors(num_colors=3, color=[c]) + result = _get_standard_colors(num_colors=3, color=[c]) self.assertEqual(result, [c] * 3) # single letter colors like k for c in colors.ColorConverter.colors: - result = plotting._get_standard_colors(num_colors=1, color=c) + result = _get_standard_colors(num_colors=1, color=c) self.assertEqual(result, [c]) - result = plotting._get_standard_colors(num_colors=1, color=[c]) + result = _get_standard_colors(num_colors=1, color=[c]) self.assertEqual(result, [c]) - result = plotting._get_standard_colors(num_colors=3, color=c) + result = _get_standard_colors(num_colors=3, color=c) self.assertEqual(result, [c] * 3) - result = plotting._get_standard_colors(num_colors=3, color=[c]) + result = _get_standard_colors(num_colors=3, color=[c]) self.assertEqual(result, [c] * 3) def test_series_plot_color_kwargs(self): diff --git a/pandas/plotting/timeseries.py b/pandas/plotting/timeseries.py new file mode 100644 index 0000000000000..3f1cea1fb42a7 --- /dev/null +++ b/pandas/plotting/timeseries.py @@ -0,0 +1,308 @@ +# TODO: Use the fact that axis can have units to simplify the process + +import numpy as np + +from matplotlib import pylab +from pandas.tseries.period import Period +from pandas.tseries.offsets import DateOffset +import pandas.tseries.frequencies as frequencies +from pandas.tseries.index import DatetimeIndex +from pandas.formats.printing import pprint_thing +import pandas.compat as compat + +from pandas.plotting.converter import (TimeSeries_DateLocator, + TimeSeries_DateFormatter) + +# --------------------------------------------------------------------- +# Plotting functions and monkey patches + + +def tsplot(series, plotf, ax=None, **kwargs): + """ + Plots a Series on the given Matplotlib axes or the current axes + + Parameters + ---------- + axes : Axes + series : Series + + Notes + _____ + Supports same kwargs as Axes.plot + + """ + # Used inferred freq is possible, need a test case for inferred + if ax is None: + import matplotlib.pyplot as plt + ax = plt.gca() + + freq, series = _maybe_resample(series, ax, kwargs) + + # Set ax with freq info + _decorate_axes(ax, freq, kwargs) + ax._plot_data.append((series, plotf, kwargs)) + lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs) + + # set date formatter, locators and rescale limits + format_dateaxis(ax, ax.freq) + return lines + + +def _maybe_resample(series, ax, kwargs): + # resample against axes freq if necessary + freq, ax_freq = _get_freq(ax, series) + + if freq is None: # pragma: no cover + raise ValueError('Cannot use dynamic axis without frequency info') + + # Convert DatetimeIndex to PeriodIndex + if isinstance(series.index, DatetimeIndex): + series = series.to_period(freq=freq) + + if ax_freq is not None and freq != ax_freq: + if frequencies.is_superperiod(freq, ax_freq): # upsample input + series = series.copy() + series.index = series.index.asfreq(ax_freq, how='s') + freq = ax_freq + elif _is_sup(freq, ax_freq): # one is weekly + how = kwargs.pop('how', 'last') + series = getattr(series.resample('D'), how)().dropna() + series = getattr(series.resample(ax_freq), how)().dropna() + freq = ax_freq + elif frequencies.is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq): + _upsample_others(ax, freq, kwargs) + ax_freq = freq + else: # pragma: no cover + raise ValueError('Incompatible frequency conversion') + return freq, series + + +def _is_sub(f1, f2): + return ((f1.startswith('W') and frequencies.is_subperiod('D', f2)) or + (f2.startswith('W') and frequencies.is_subperiod(f1, 'D'))) + + +def _is_sup(f1, f2): + return ((f1.startswith('W') and frequencies.is_superperiod('D', f2)) or + (f2.startswith('W') and frequencies.is_superperiod(f1, 'D'))) + + +def _upsample_others(ax, freq, kwargs): + legend = ax.get_legend() + lines, labels = _replot_ax(ax, freq, kwargs) + _replot_ax(ax, freq, kwargs) + + other_ax = None + if hasattr(ax, 'left_ax'): + other_ax = ax.left_ax + if hasattr(ax, 'right_ax'): + other_ax = ax.right_ax + + if other_ax is not None: + rlines, rlabels = _replot_ax(other_ax, freq, kwargs) + lines.extend(rlines) + labels.extend(rlabels) + + if (legend is not None and kwargs.get('legend', True) and + len(lines) > 0): + title = legend.get_title().get_text() + if title == 'None': + title = None + ax.legend(lines, labels, loc='best', title=title) + + +def _replot_ax(ax, freq, kwargs): + data = getattr(ax, '_plot_data', None) + + # clear current axes and data + ax._plot_data = [] + ax.clear() + + _decorate_axes(ax, freq, kwargs) + + lines = [] + labels = [] + if data is not None: + for series, plotf, kwds in data: + series = series.copy() + idx = series.index.asfreq(freq, how='S') + series.index = idx + ax._plot_data.append((series, plotf, kwds)) + + # for tsplot + if isinstance(plotf, compat.string_types): + from pandas.plotting.core import _plot_klass + plotf = _plot_klass[plotf]._plot + + lines.append(plotf(ax, series.index._mpl_repr(), + series.values, **kwds)[0]) + labels.append(pprint_thing(series.name)) + + return lines, labels + + +def _decorate_axes(ax, freq, kwargs): + """Initialize axes for time-series plotting""" + if not hasattr(ax, '_plot_data'): + ax._plot_data = [] + + ax.freq = freq + xaxis = ax.get_xaxis() + xaxis.freq = freq + if not hasattr(ax, 'legendlabels'): + ax.legendlabels = [kwargs.get('label', None)] + else: + ax.legendlabels.append(kwargs.get('label', None)) + ax.view_interval = None + ax.date_axis_info = None + + +def _get_ax_freq(ax): + """ + Get the freq attribute of the ax object if set. + Also checks shared axes (eg when using secondary yaxis, sharex=True + or twinx) + """ + ax_freq = getattr(ax, 'freq', None) + if ax_freq is None: + # check for left/right ax in case of secondary yaxis + if hasattr(ax, 'left_ax'): + ax_freq = getattr(ax.left_ax, 'freq', None) + elif hasattr(ax, 'right_ax'): + ax_freq = getattr(ax.right_ax, 'freq', None) + if ax_freq is None: + # check if a shared ax (sharex/twinx) has already freq set + shared_axes = ax.get_shared_x_axes().get_siblings(ax) + if len(shared_axes) > 1: + for shared_ax in shared_axes: + ax_freq = getattr(shared_ax, 'freq', None) + if ax_freq is not None: + break + return ax_freq + + +def _get_freq(ax, series): + # get frequency from data + freq = getattr(series.index, 'freq', None) + if freq is None: + freq = getattr(series.index, 'inferred_freq', None) + + ax_freq = _get_ax_freq(ax) + + # use axes freq if no data freq + if freq is None: + freq = ax_freq + + # get the period frequency + if isinstance(freq, DateOffset): + freq = freq.rule_code + else: + freq = frequencies.get_base_alias(freq) + + freq = frequencies.get_period_alias(freq) + return freq, ax_freq + + +def _use_dynamic_x(ax, data): + freq = _get_index_freq(data) + ax_freq = _get_ax_freq(ax) + + if freq is None: # convert irregular if axes has freq info + freq = ax_freq + else: # do not use tsplot if irregular was plotted first + if (ax_freq is None) and (len(ax.get_lines()) > 0): + return False + + if freq is None: + return False + + if isinstance(freq, DateOffset): + freq = freq.rule_code + else: + freq = frequencies.get_base_alias(freq) + freq = frequencies.get_period_alias(freq) + + if freq is None: + return False + + # hack this for 0.10.1, creating more technical debt...sigh + if isinstance(data.index, DatetimeIndex): + base = frequencies.get_freq(freq) + x = data.index + if (base <= frequencies.FreqGroup.FR_DAY): + return x[:1].is_normalized + return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0] + return True + + +def _get_index_freq(data): + freq = getattr(data.index, 'freq', None) + if freq is None: + freq = getattr(data.index, 'inferred_freq', None) + if freq == 'B': + weekdays = np.unique(data.index.dayofweek) + if (5 in weekdays) or (6 in weekdays): + freq = None + return freq + + +def _maybe_convert_index(ax, data): + # tsplot converts automatically, but don't want to convert index + # over and over for DataFrames + if isinstance(data.index, DatetimeIndex): + freq = getattr(data.index, 'freq', None) + + if freq is None: + freq = getattr(data.index, 'inferred_freq', None) + if isinstance(freq, DateOffset): + freq = freq.rule_code + + if freq is None: + freq = _get_ax_freq(ax) + + if freq is None: + raise ValueError('Could not get frequency alias for plotting') + + freq = frequencies.get_base_alias(freq) + freq = frequencies.get_period_alias(freq) + + data = data.to_period(freq=freq) + return data + + +# Patch methods for subplot. Only format_dateaxis is currently used. +# Do we need the rest for convenience? + + +def format_dateaxis(subplot, freq): + """ + Pretty-formats the date axis (x-axis). + + Major and minor ticks are automatically set for the frequency of the + current underlying series. As the dynamic mode is activated by + default, changing the limits of the x axis will intelligently change + the positions of the ticks. + """ + majlocator = TimeSeries_DateLocator(freq, dynamic_mode=True, + minor_locator=False, + plot_obj=subplot) + minlocator = TimeSeries_DateLocator(freq, dynamic_mode=True, + minor_locator=True, + plot_obj=subplot) + subplot.xaxis.set_major_locator(majlocator) + subplot.xaxis.set_minor_locator(minlocator) + + majformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True, + minor_locator=False, + plot_obj=subplot) + minformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True, + minor_locator=True, + plot_obj=subplot) + subplot.xaxis.set_major_formatter(majformatter) + subplot.xaxis.set_minor_formatter(minformatter) + + # x and y coord info + subplot.format_coord = lambda t, y: ( + "t = {0} y = {1:8f}".format(Period(ordinal=int(t), freq=freq), y)) + + pylab.draw_if_interactive() diff --git a/pandas/plotting/tools.py b/pandas/plotting/tools.py new file mode 100644 index 0000000000000..720f776279869 --- /dev/null +++ b/pandas/plotting/tools.py @@ -0,0 +1,383 @@ +# being a bit too dynamic +# pylint: disable=E1101 +from __future__ import division + +import warnings +from math import ceil + +import numpy as np + +from pandas.types.common import is_list_like +from pandas.core.index import Index +from pandas.core.series import Series +from pandas.compat import range + + +def format_date_labels(ax, rot): + # mini version of autofmt_xdate + try: + for label in ax.get_xticklabels(): + label.set_ha('right') + label.set_rotation(rot) + fig = ax.get_figure() + fig.subplots_adjust(bottom=0.2) + except Exception: # pragma: no cover + pass + + +def table(ax, data, rowLabels=None, colLabels=None, + **kwargs): + """ + Helper function to convert DataFrame and Series to matplotlib.table + + Parameters + ---------- + `ax`: Matplotlib axes object + `data`: DataFrame or Series + data for table contents + `kwargs`: keywords, optional + keyword arguments which passed to matplotlib.table.table. + If `rowLabels` or `colLabels` is not specified, data index or column + name will be used. + + Returns + ------- + matplotlib table object + """ + from pandas import DataFrame + if isinstance(data, Series): + data = DataFrame(data, columns=[data.name]) + elif isinstance(data, DataFrame): + pass + else: + raise ValueError('Input data must be DataFrame or Series') + + if rowLabels is None: + rowLabels = data.index + + if colLabels is None: + colLabels = data.columns + + cellText = data.values + + import matplotlib.table + table = matplotlib.table.table(ax, cellText=cellText, + rowLabels=rowLabels, + colLabels=colLabels, **kwargs) + return table + + +def _get_layout(nplots, layout=None, layout_type='box'): + if layout is not None: + if not isinstance(layout, (tuple, list)) or len(layout) != 2: + raise ValueError('Layout must be a tuple of (rows, columns)') + + nrows, ncols = layout + + # Python 2 compat + ceil_ = lambda x: int(ceil(x)) + if nrows == -1 and ncols > 0: + layout = nrows, ncols = (ceil_(float(nplots) / ncols), ncols) + elif ncols == -1 and nrows > 0: + layout = nrows, ncols = (nrows, ceil_(float(nplots) / nrows)) + elif ncols <= 0 and nrows <= 0: + msg = "At least one dimension of layout must be positive" + raise ValueError(msg) + + if nrows * ncols < nplots: + raise ValueError('Layout of %sx%s must be larger than ' + 'required size %s' % (nrows, ncols, nplots)) + + return layout + + if layout_type == 'single': + return (1, 1) + elif layout_type == 'horizontal': + return (1, nplots) + elif layout_type == 'vertical': + return (nplots, 1) + + layouts = {1: (1, 1), 2: (1, 2), 3: (2, 2), 4: (2, 2)} + try: + return layouts[nplots] + except KeyError: + k = 1 + while k ** 2 < nplots: + k += 1 + + if (k - 1) * k >= nplots: + return k, (k - 1) + else: + return k, k + +# copied from matplotlib/pyplot.py and modified for pandas.plotting + + +def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True, + subplot_kw=None, ax=None, layout=None, layout_type='box', + **fig_kw): + """Create a figure with a set of subplots already made. + + This utility wrapper makes it convenient to create common layouts of + subplots, including the enclosing figure object, in a single call. + + Keyword arguments: + + naxes : int + Number of required axes. Exceeded axes are set invisible. Default is + nrows * ncols. + + sharex : bool + If True, the X axis will be shared amongst all subplots. + + sharey : bool + If True, the Y axis will be shared amongst all subplots. + + squeeze : bool + + If True, extra dimensions are squeezed out from the returned axis object: + - if only one subplot is constructed (nrows=ncols=1), the resulting + single Axis object is returned as a scalar. + - for Nx1 or 1xN subplots, the returned object is a 1-d numpy object + array of Axis objects are returned as numpy 1-d arrays. + - for NxM subplots with N>1 and M>1 are returned as a 2d array. + + If False, no squeezing at all is done: the returned axis object is always + a 2-d array containing Axis instances, even if it ends up being 1x1. + + subplot_kw : dict + Dict with keywords passed to the add_subplot() call used to create each + subplots. + + ax : Matplotlib axis object, optional + + layout : tuple + Number of rows and columns of the subplot grid. + If not specified, calculated from naxes and layout_type + + layout_type : {'box', 'horziontal', 'vertical'}, default 'box' + Specify how to layout the subplot grid. + + fig_kw : Other keyword arguments to be passed to the figure() call. + Note that all keywords not recognized above will be + automatically included here. + + Returns: + + fig, ax : tuple + - fig is the Matplotlib Figure object + - ax can be either a single axis object or an array of axis objects if + more than one subplot was created. The dimensions of the resulting array + can be controlled with the squeeze keyword, see above. + + **Examples:** + + x = np.linspace(0, 2*np.pi, 400) + y = np.sin(x**2) + + # Just a figure and one subplot + f, ax = plt.subplots() + ax.plot(x, y) + ax.set_title('Simple plot') + + # Two subplots, unpack the output array immediately + f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) + ax1.plot(x, y) + ax1.set_title('Sharing Y axis') + ax2.scatter(x, y) + + # Four polar axes + plt.subplots(2, 2, subplot_kw=dict(polar=True)) + """ + import matplotlib.pyplot as plt + + if subplot_kw is None: + subplot_kw = {} + + if ax is None: + fig = plt.figure(**fig_kw) + else: + if is_list_like(ax): + ax = _flatten(ax) + if layout is not None: + warnings.warn("When passing multiple axes, layout keyword is " + "ignored", UserWarning) + if sharex or sharey: + warnings.warn("When passing multiple axes, sharex and sharey " + "are ignored. These settings must be specified " + "when creating axes", UserWarning, + stacklevel=4) + if len(ax) == naxes: + fig = ax[0].get_figure() + return fig, ax + else: + raise ValueError("The number of passed axes must be {0}, the " + "same as the output plot".format(naxes)) + + fig = ax.get_figure() + # if ax is passed and a number of subplots is 1, return ax as it is + if naxes == 1: + if squeeze: + return fig, ax + else: + return fig, _flatten(ax) + else: + warnings.warn("To output multiple subplots, the figure containing " + "the passed axes is being cleared", UserWarning, + stacklevel=4) + fig.clear() + + nrows, ncols = _get_layout(naxes, layout=layout, layout_type=layout_type) + nplots = nrows * ncols + + # Create empty object array to hold all axes. It's easiest to make it 1-d + # so we can just append subplots upon creation, and then + axarr = np.empty(nplots, dtype=object) + + # Create first subplot separately, so we can share it if requested + ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw) + + if sharex: + subplot_kw['sharex'] = ax0 + if sharey: + subplot_kw['sharey'] = ax0 + axarr[0] = ax0 + + # Note off-by-one counting because add_subplot uses the MATLAB 1-based + # convention. + for i in range(1, nplots): + kwds = subplot_kw.copy() + # Set sharex and sharey to None for blank/dummy axes, these can + # interfere with proper axis limits on the visible axes if + # they share axes e.g. issue #7528 + if i >= naxes: + kwds['sharex'] = None + kwds['sharey'] = None + ax = fig.add_subplot(nrows, ncols, i + 1, **kwds) + axarr[i] = ax + + if naxes != nplots: + for ax in axarr[naxes:]: + ax.set_visible(False) + + _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey) + + if squeeze: + # Reshape the array to have the final desired dimension (nrow,ncol), + # though discarding unneeded dimensions that equal 1. If we only have + # one subplot, just return it instead of a 1-element array. + if nplots == 1: + axes = axarr[0] + else: + axes = axarr.reshape(nrows, ncols).squeeze() + else: + # returned axis array will be always 2-d, even if nrows=ncols=1 + axes = axarr.reshape(nrows, ncols) + + return fig, axes + + +def _remove_labels_from_axis(axis): + for t in axis.get_majorticklabels(): + t.set_visible(False) + + try: + # set_visible will not be effective if + # minor axis has NullLocator and NullFormattor (default) + import matplotlib.ticker as ticker + if isinstance(axis.get_minor_locator(), ticker.NullLocator): + axis.set_minor_locator(ticker.AutoLocator()) + if isinstance(axis.get_minor_formatter(), ticker.NullFormatter): + axis.set_minor_formatter(ticker.FormatStrFormatter('')) + for t in axis.get_minorticklabels(): + t.set_visible(False) + except Exception: # pragma no cover + raise + axis.get_label().set_visible(False) + + +def _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey): + if nplots > 1: + + if nrows > 1: + try: + # first find out the ax layout, + # so that we can correctly handle 'gaps" + layout = np.zeros((nrows + 1, ncols + 1), dtype=np.bool) + for ax in axarr: + layout[ax.rowNum, ax.colNum] = ax.get_visible() + + for ax in axarr: + # only the last row of subplots should get x labels -> all + # other off layout handles the case that the subplot is + # the last in the column, because below is no subplot/gap. + if not layout[ax.rowNum + 1, ax.colNum]: + continue + if sharex or len(ax.get_shared_x_axes() + .get_siblings(ax)) > 1: + _remove_labels_from_axis(ax.xaxis) + + except IndexError: + # if gridspec is used, ax.rowNum and ax.colNum may different + # from layout shape. in this case, use last_row logic + for ax in axarr: + if ax.is_last_row(): + continue + if sharex or len(ax.get_shared_x_axes() + .get_siblings(ax)) > 1: + _remove_labels_from_axis(ax.xaxis) + + if ncols > 1: + for ax in axarr: + # only the first column should get y labels -> set all other to + # off as we only have labels in teh first column and we always + # have a subplot there, we can skip the layout test + if ax.is_first_col(): + continue + if sharey or len(ax.get_shared_y_axes().get_siblings(ax)) > 1: + _remove_labels_from_axis(ax.yaxis) + + +def _flatten(axes): + if not is_list_like(axes): + return np.array([axes]) + elif isinstance(axes, (np.ndarray, Index)): + return axes.ravel() + return np.array(axes) + + +def _get_all_lines(ax): + lines = ax.get_lines() + + if hasattr(ax, 'right_ax'): + lines += ax.right_ax.get_lines() + + if hasattr(ax, 'left_ax'): + lines += ax.left_ax.get_lines() + + return lines + + +def _get_xlim(lines): + left, right = np.inf, -np.inf + for l in lines: + x = l.get_xdata(orig=False) + left = min(x[0], left) + right = max(x[-1], right) + return left, right + + +def _set_ticks_props(axes, xlabelsize=None, xrot=None, + ylabelsize=None, yrot=None): + import matplotlib.pyplot as plt + + for ax in _flatten(axes): + if xlabelsize is not None: + plt.setp(ax.get_xticklabels(), fontsize=xlabelsize) + if xrot is not None: + plt.setp(ax.get_xticklabels(), rotation=xrot) + if ylabelsize is not None: + plt.setp(ax.get_yticklabels(), fontsize=ylabelsize) + if yrot is not None: + plt.setp(ax.get_yticklabels(), rotation=yrot) + return axes diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 012d67d29cc3f..c7d17760bbdbb 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -1,4030 +1,21 @@ -# being a bit too dynamic -# pylint: disable=E1101 -from __future__ import division - +import sys import warnings -import re -from math import ceil -from collections import namedtuple -from contextlib import contextmanager -from distutils.version import LooseVersion - -import numpy as np - -from pandas.types.common import (is_list_like, - is_integer, - is_number, - is_hashable, - is_iterator) -from pandas.types.missing import isnull, notnull - -from pandas.util.decorators import cache_readonly, deprecate_kwarg -from pandas.core.base import PandasObject - -from pandas.core.common import AbstractMethodError, _try_sort -from pandas.core.generic import _shared_docs, _shared_doc_kwargs -from pandas.core.index import Index, MultiIndex -from pandas.core.series import Series, remove_na -from pandas.tseries.period import PeriodIndex -from pandas.compat import range, lrange, lmap, map, zip, string_types -import pandas.compat as compat -from pandas.formats.printing import pprint_thing -from pandas.util.decorators import Appender -try: # mpl optional - import pandas.tseries.converter as conv - conv.register() # needs to override so set_xlim works with str/number -except ImportError: - pass - - -# Extracted from https://gist.github.com/huyng/816622 -# this is the rcParams set when setting display.with_mpl_style -# to True. -mpl_stylesheet = { - 'axes.axisbelow': True, - 'axes.color_cycle': ['#348ABD', - '#7A68A6', - '#A60628', - '#467821', - '#CF4457', - '#188487', - '#E24A33'], - 'axes.edgecolor': '#bcbcbc', - 'axes.facecolor': '#eeeeee', - 'axes.grid': True, - 'axes.labelcolor': '#555555', - 'axes.labelsize': 'large', - 'axes.linewidth': 1.0, - 'axes.titlesize': 'x-large', - 'figure.edgecolor': 'white', - 'figure.facecolor': 'white', - 'figure.figsize': (6.0, 4.0), - 'figure.subplot.hspace': 0.5, - 'font.family': 'monospace', - 'font.monospace': ['Andale Mono', - 'Nimbus Mono L', - 'Courier New', - 'Courier', - 'Fixed', - 'Terminal', - 'monospace'], - 'font.size': 10, - 'interactive': True, - 'keymap.all_axes': ['a'], - 'keymap.back': ['left', 'c', 'backspace'], - 'keymap.forward': ['right', 'v'], - 'keymap.fullscreen': ['f'], - 'keymap.grid': ['g'], - 'keymap.home': ['h', 'r', 'home'], - 'keymap.pan': ['p'], - 'keymap.save': ['s'], - 'keymap.xscale': ['L', 'k'], - 'keymap.yscale': ['l'], - 'keymap.zoom': ['o'], - 'legend.fancybox': True, - 'lines.antialiased': True, - 'lines.linewidth': 1.0, - 'patch.antialiased': True, - 'patch.edgecolor': '#EEEEEE', - 'patch.facecolor': '#348ABD', - 'patch.linewidth': 0.5, - 'toolbar': 'toolbar2', - 'xtick.color': '#555555', - 'xtick.direction': 'in', - 'xtick.major.pad': 6.0, - 'xtick.major.size': 0.0, - 'xtick.minor.pad': 6.0, - 'xtick.minor.size': 0.0, - 'ytick.color': '#555555', - 'ytick.direction': 'in', - 'ytick.major.pad': 6.0, - 'ytick.major.size': 0.0, - 'ytick.minor.pad': 6.0, - 'ytick.minor.size': 0.0 -} - - -def _mpl_le_1_2_1(): - try: - import matplotlib as mpl - return (str(mpl.__version__) <= LooseVersion('1.2.1') and - str(mpl.__version__)[0] != '0') - except ImportError: - return False - - -def _mpl_ge_1_3_1(): - try: - import matplotlib - # The or v[0] == '0' is because their versioneer is - # messed up on dev - return (matplotlib.__version__ >= LooseVersion('1.3.1') or - matplotlib.__version__[0] == '0') - except ImportError: - return False - - -def _mpl_ge_1_4_0(): - try: - import matplotlib - return (matplotlib.__version__ >= LooseVersion('1.4') or - matplotlib.__version__[0] == '0') - except ImportError: - return False - - -def _mpl_ge_1_5_0(): - try: - import matplotlib - return (matplotlib.__version__ >= LooseVersion('1.5') or - matplotlib.__version__[0] == '0') - except ImportError: - return False - - -def _mpl_ge_2_0_0(): - try: - import matplotlib - return matplotlib.__version__ >= LooseVersion('2.0') - except ImportError: - return False - -if _mpl_ge_1_5_0(): - # Compat with mp 1.5, which uses cycler. - import cycler - colors = mpl_stylesheet.pop('axes.color_cycle') - mpl_stylesheet['axes.prop_cycle'] = cycler.cycler('color', colors) - - -def _get_standard_kind(kind): - return {'density': 'kde'}.get(kind, kind) - - -def _get_standard_colors(num_colors=None, colormap=None, color_type='default', - color=None): - import matplotlib.pyplot as plt - - if color is None and colormap is not None: - if isinstance(colormap, compat.string_types): - import matplotlib.cm as cm - cmap = colormap - colormap = cm.get_cmap(colormap) - if colormap is None: - raise ValueError("Colormap {0} is not recognized".format(cmap)) - colors = lmap(colormap, np.linspace(0, 1, num=num_colors)) - elif color is not None: - if colormap is not None: - warnings.warn("'color' and 'colormap' cannot be used " - "simultaneously. Using 'color'") - colors = list(color) if is_list_like(color) else color - else: - if color_type == 'default': - # need to call list() on the result to copy so we don't - # modify the global rcParams below - try: - colors = [c['color'] - for c in list(plt.rcParams['axes.prop_cycle'])] - except KeyError: - colors = list(plt.rcParams.get('axes.color_cycle', - list('bgrcmyk'))) - if isinstance(colors, compat.string_types): - colors = list(colors) - elif color_type == 'random': - import random - - def random_color(column): - random.seed(column) - return [random.random() for _ in range(3)] - - colors = lmap(random_color, lrange(num_colors)) - else: - raise ValueError("color_type must be either 'default' or 'random'") - - if isinstance(colors, compat.string_types): - import matplotlib.colors - conv = matplotlib.colors.ColorConverter() - - def _maybe_valid_colors(colors): - try: - [conv.to_rgba(c) for c in colors] - return True - except ValueError: - return False - - # check whether the string can be convertable to single color - maybe_single_color = _maybe_valid_colors([colors]) - # check whether each character can be convertable to colors - maybe_color_cycle = _maybe_valid_colors(list(colors)) - if maybe_single_color and maybe_color_cycle and len(colors) > 1: - msg = ("'{0}' can be parsed as both single color and " - "color cycle. Specify each color using a list " - "like ['{0}'] or {1}") - raise ValueError(msg.format(colors, list(colors))) - elif maybe_single_color: - colors = [colors] - else: - # ``colors`` is regarded as color cycle. - # mpl will raise error any of them is invalid - pass - - if len(colors) != num_colors: - multiple = num_colors // len(colors) - 1 - mod = num_colors % len(colors) - - colors += multiple * colors - colors += colors[:mod] - - return colors - - -class _Options(dict): - """ - Stores pandas plotting options. - Allows for parameter aliasing so you can just use parameter names that are - the same as the plot function parameters, but is stored in a canonical - format that makes it easy to breakdown into groups later - """ - - # alias so the names are same as plotting method parameter names - _ALIASES = {'x_compat': 'xaxis.compat'} - _DEFAULT_KEYS = ['xaxis.compat'] - - def __init__(self): - self['xaxis.compat'] = False - - def __getitem__(self, key): - key = self._get_canonical_key(key) - if key not in self: - raise ValueError('%s is not a valid pandas plotting option' % key) - return super(_Options, self).__getitem__(key) - - def __setitem__(self, key, value): - key = self._get_canonical_key(key) - return super(_Options, self).__setitem__(key, value) - - def __delitem__(self, key): - key = self._get_canonical_key(key) - if key in self._DEFAULT_KEYS: - raise ValueError('Cannot remove default parameter %s' % key) - return super(_Options, self).__delitem__(key) - - def __contains__(self, key): - key = self._get_canonical_key(key) - return super(_Options, self).__contains__(key) - - def reset(self): - """ - Reset the option store to its initial state - - Returns - ------- - None - """ - self.__init__() - - def _get_canonical_key(self, key): - return self._ALIASES.get(key, key) - - @contextmanager - def use(self, key, value): - """ - Temporarily set a parameter value using the with statement. - Aliasing allowed. - """ - old_value = self[key] - try: - self[key] = value - yield self - finally: - self[key] = old_value - - -plot_params = _Options() - - -def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, - diagonal='hist', marker='.', density_kwds=None, - hist_kwds=None, range_padding=0.05, **kwds): - """ - Draw a matrix of scatter plots. - - Parameters - ---------- - frame : DataFrame - alpha : float, optional - amount of transparency applied - figsize : (float,float), optional - a tuple (width, height) in inches - ax : Matplotlib axis object, optional - grid : bool, optional - setting this to True will show the grid - diagonal : {'hist', 'kde'} - pick between 'kde' and 'hist' for - either Kernel Density Estimation or Histogram - plot in the diagonal - marker : str, optional - Matplotlib marker type, default '.' - hist_kwds : other plotting keyword arguments - To be passed to hist function - density_kwds : other plotting keyword arguments - To be passed to kernel density estimate plot - range_padding : float, optional - relative extension of axis range in x and y - with respect to (x_max - x_min) or (y_max - y_min), - default 0.05 - kwds : other plotting keyword arguments - To be passed to scatter function - - Examples - -------- - >>> df = DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D']) - >>> scatter_matrix(df, alpha=0.2) - """ - import matplotlib.pyplot as plt - - df = frame._get_numeric_data() - n = df.columns.size - naxes = n * n - fig, axes = _subplots(naxes=naxes, figsize=figsize, ax=ax, - squeeze=False) - - # no gaps between subplots - fig.subplots_adjust(wspace=0, hspace=0) - - mask = notnull(df) - - marker = _get_marker_compat(marker) - - hist_kwds = hist_kwds or {} - density_kwds = density_kwds or {} - - # workaround because `c='b'` is hardcoded in matplotlibs scatter method - kwds.setdefault('c', plt.rcParams['patch.facecolor']) - - boundaries_list = [] - for a in df.columns: - values = df[a].values[mask[a].values] - rmin_, rmax_ = np.min(values), np.max(values) - rdelta_ext = (rmax_ - rmin_) * range_padding / 2. - boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext)) - - for i, a in zip(lrange(n), df.columns): - for j, b in zip(lrange(n), df.columns): - ax = axes[i, j] - - if i == j: - values = df[a].values[mask[a].values] - - # Deal with the diagonal by drawing a histogram there. - if diagonal == 'hist': - ax.hist(values, **hist_kwds) - - elif diagonal in ('kde', 'density'): - from scipy.stats import gaussian_kde - y = values - gkde = gaussian_kde(y) - ind = np.linspace(y.min(), y.max(), 1000) - ax.plot(ind, gkde.evaluate(ind), **density_kwds) - - ax.set_xlim(boundaries_list[i]) - - else: - common = (mask[a] & mask[b]).values - - ax.scatter(df[b][common], df[a][common], - marker=marker, alpha=alpha, **kwds) - - ax.set_xlim(boundaries_list[j]) - ax.set_ylim(boundaries_list[i]) - - ax.set_xlabel(b) - ax.set_ylabel(a) - - if j != 0: - ax.yaxis.set_visible(False) - if i != n - 1: - ax.xaxis.set_visible(False) - - if len(df.columns) > 1: - lim1 = boundaries_list[0] - locs = axes[0][1].yaxis.get_majorticklocs() - locs = locs[(lim1[0] <= locs) & (locs <= lim1[1])] - adj = (locs - lim1[0]) / (lim1[1] - lim1[0]) - - lim0 = axes[0][0].get_ylim() - adj = adj * (lim0[1] - lim0[0]) + lim0[0] - axes[0][0].yaxis.set_ticks(adj) - - if np.all(locs == locs.astype(int)): - # if all ticks are int - locs = locs.astype(int) - axes[0][0].yaxis.set_ticklabels(locs) - - _set_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) - - return axes - - -def _gca(): - import matplotlib.pyplot as plt - return plt.gca() - - -def _gcf(): - import matplotlib.pyplot as plt - return plt.gcf() - - -def _get_marker_compat(marker): - import matplotlib.lines as mlines - import matplotlib as mpl - if mpl.__version__ < '1.1.0' and marker == '.': - return 'o' - if marker not in mlines.lineMarkers: - return 'o' - return marker - - -def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds): - """RadViz - a multivariate data visualization algorithm - - Parameters: - ----------- - frame: DataFrame - class_column: str - Column name containing class names - ax: Matplotlib axis object, optional - color: list or tuple, optional - Colors to use for the different classes - colormap : str or matplotlib colormap object, default None - Colormap to select colors from. If string, load colormap with that name - from matplotlib. - kwds: keywords - Options to pass to matplotlib scatter plotting method - - Returns: - -------- - ax: Matplotlib axis object - """ - import matplotlib.pyplot as plt - import matplotlib.patches as patches - - def normalize(series): - a = min(series) - b = max(series) - return (series - a) / (b - a) - - n = len(frame) - classes = frame[class_column].drop_duplicates() - class_col = frame[class_column] - df = frame.drop(class_column, axis=1).apply(normalize) - - if ax is None: - ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1]) - - to_plot = {} - colors = _get_standard_colors(num_colors=len(classes), colormap=colormap, - color_type='random', color=color) - - for kls in classes: - to_plot[kls] = [[], []] - - m = len(frame.columns) - 1 - s = np.array([(np.cos(t), np.sin(t)) - for t in [2.0 * np.pi * (i / float(m)) - for i in range(m)]]) - - for i in range(n): - row = df.iloc[i].values - row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1) - y = (s * row_).sum(axis=0) / row.sum() - kls = class_col.iat[i] - to_plot[kls][0].append(y[0]) - to_plot[kls][1].append(y[1]) - - for i, kls in enumerate(classes): - ax.scatter(to_plot[kls][0], to_plot[kls][1], color=colors[i], - label=pprint_thing(kls), **kwds) - ax.legend() - - ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none')) - - for xy, name in zip(s, df.columns): - - ax.add_patch(patches.Circle(xy, radius=0.025, facecolor='gray')) - - if xy[0] < 0.0 and xy[1] < 0.0: - ax.text(xy[0] - 0.025, xy[1] - 0.025, name, - ha='right', va='top', size='small') - elif xy[0] < 0.0 and xy[1] >= 0.0: - ax.text(xy[0] - 0.025, xy[1] + 0.025, name, - ha='right', va='bottom', size='small') - elif xy[0] >= 0.0 and xy[1] < 0.0: - ax.text(xy[0] + 0.025, xy[1] - 0.025, name, - ha='left', va='top', size='small') - elif xy[0] >= 0.0 and xy[1] >= 0.0: - ax.text(xy[0] + 0.025, xy[1] + 0.025, name, - ha='left', va='bottom', size='small') - - ax.axis('equal') - return ax - - -@deprecate_kwarg(old_arg_name='data', new_arg_name='frame') -def andrews_curves(frame, class_column, ax=None, samples=200, color=None, - colormap=None, **kwds): - """ - Generates a matplotlib plot of Andrews curves, for visualising clusters of - multivariate data. - - Andrews curves have the functional form: - - f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) + - x_4 sin(2t) + x_5 cos(2t) + ... - - Where x coefficients correspond to the values of each dimension and t is - linearly spaced between -pi and +pi. Each row of frame then corresponds to - a single curve. - - Parameters: - ----------- - frame : DataFrame - Data to be plotted, preferably normalized to (0.0, 1.0) - class_column : Name of the column containing class names - ax : matplotlib axes object, default None - samples : Number of points to plot in each curve - color: list or tuple, optional - Colors to use for the different classes - colormap : str or matplotlib colormap object, default None - Colormap to select colors from. If string, load colormap with that name - from matplotlib. - kwds: keywords - Options to pass to matplotlib plotting method - - Returns: - -------- - ax: Matplotlib axis object - - """ - from math import sqrt, pi - import matplotlib.pyplot as plt - - def function(amplitudes): - def f(t): - x1 = amplitudes[0] - result = x1 / sqrt(2.0) - - # Take the rest of the coefficients and resize them - # appropriately. Take a copy of amplitudes as otherwise numpy - # deletes the element from amplitudes itself. - coeffs = np.delete(np.copy(amplitudes), 0) - coeffs.resize(int((coeffs.size + 1) / 2), 2) - - # Generate the harmonics and arguments for the sin and cos - # functions. - harmonics = np.arange(0, coeffs.shape[0]) + 1 - trig_args = np.outer(harmonics, t) - - result += np.sum(coeffs[:, 0, np.newaxis] * np.sin(trig_args) + - coeffs[:, 1, np.newaxis] * np.cos(trig_args), - axis=0) - return result - return f - - n = len(frame) - class_col = frame[class_column] - classes = frame[class_column].drop_duplicates() - df = frame.drop(class_column, axis=1) - t = np.linspace(-pi, pi, samples) - used_legends = set([]) - - color_values = _get_standard_colors(num_colors=len(classes), - colormap=colormap, color_type='random', - color=color) - colors = dict(zip(classes, color_values)) - if ax is None: - ax = plt.gca(xlim=(-pi, pi)) - for i in range(n): - row = df.iloc[i].values - f = function(row) - y = f(t) - kls = class_col.iat[i] - label = pprint_thing(kls) - if label not in used_legends: - used_legends.add(label) - ax.plot(t, y, color=colors[kls], label=label, **kwds) - else: - ax.plot(t, y, color=colors[kls], **kwds) - - ax.legend(loc='upper right') - ax.grid() - return ax - - -def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds): - """Bootstrap plot. - - Parameters: - ----------- - series: Time series - fig: matplotlib figure object, optional - size: number of data points to consider during each sampling - samples: number of times the bootstrap procedure is performed - kwds: optional keyword arguments for plotting commands, must be accepted - by both hist and plot - - Returns: - -------- - fig: matplotlib figure - """ - import random - import matplotlib.pyplot as plt - - # random.sample(ndarray, int) fails on python 3.3, sigh - data = list(series.values) - samplings = [random.sample(data, size) for _ in range(samples)] - - means = np.array([np.mean(sampling) for sampling in samplings]) - medians = np.array([np.median(sampling) for sampling in samplings]) - midranges = np.array([(min(sampling) + max(sampling)) * 0.5 - for sampling in samplings]) - if fig is None: - fig = plt.figure() - x = lrange(samples) - axes = [] - ax1 = fig.add_subplot(2, 3, 1) - ax1.set_xlabel("Sample") - axes.append(ax1) - ax1.plot(x, means, **kwds) - ax2 = fig.add_subplot(2, 3, 2) - ax2.set_xlabel("Sample") - axes.append(ax2) - ax2.plot(x, medians, **kwds) - ax3 = fig.add_subplot(2, 3, 3) - ax3.set_xlabel("Sample") - axes.append(ax3) - ax3.plot(x, midranges, **kwds) - ax4 = fig.add_subplot(2, 3, 4) - ax4.set_xlabel("Mean") - axes.append(ax4) - ax4.hist(means, **kwds) - ax5 = fig.add_subplot(2, 3, 5) - ax5.set_xlabel("Median") - axes.append(ax5) - ax5.hist(medians, **kwds) - ax6 = fig.add_subplot(2, 3, 6) - ax6.set_xlabel("Midrange") - axes.append(ax6) - ax6.hist(midranges, **kwds) - for axis in axes: - plt.setp(axis.get_xticklabels(), fontsize=8) - plt.setp(axis.get_yticklabels(), fontsize=8) - return fig - - -@deprecate_kwarg(old_arg_name='colors', new_arg_name='color') -@deprecate_kwarg(old_arg_name='data', new_arg_name='frame', stacklevel=3) -def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None, - use_columns=False, xticks=None, colormap=None, - axvlines=True, axvlines_kwds=None, **kwds): - """Parallel coordinates plotting. - - Parameters - ---------- - frame: DataFrame - class_column: str - Column name containing class names - cols: list, optional - A list of column names to use - ax: matplotlib.axis, optional - matplotlib axis object - color: list or tuple, optional - Colors to use for the different classes - use_columns: bool, optional - If true, columns will be used as xticks - xticks: list or tuple, optional - A list of values to use for xticks - colormap: str or matplotlib colormap, default None - Colormap to use for line colors. - axvlines: bool, optional - If true, vertical lines will be added at each xtick - axvlines_kwds: keywords, optional - Options to be passed to axvline method for vertical lines - kwds: keywords - Options to pass to matplotlib plotting method - - Returns - ------- - ax: matplotlib axis object - - Examples - -------- - >>> from pandas import read_csv - >>> from pandas.tools.plotting import parallel_coordinates - >>> from matplotlib import pyplot as plt - >>> df = read_csv('https://raw.github.com/pandas-dev/pandas/master' - '/pandas/tests/data/iris.csv') - >>> parallel_coordinates(df, 'Name', color=('#556270', - '#4ECDC4', '#C7F464')) - >>> plt.show() - """ - if axvlines_kwds is None: - axvlines_kwds = {'linewidth': 1, 'color': 'black'} - import matplotlib.pyplot as plt - - n = len(frame) - classes = frame[class_column].drop_duplicates() - class_col = frame[class_column] - - if cols is None: - df = frame.drop(class_column, axis=1) - else: - df = frame[cols] - - used_legends = set([]) - - ncols = len(df.columns) - - # determine values to use for xticks - if use_columns is True: - if not np.all(np.isreal(list(df.columns))): - raise ValueError('Columns must be numeric to be used as xticks') - x = df.columns - elif xticks is not None: - if not np.all(np.isreal(xticks)): - raise ValueError('xticks specified must be numeric') - elif len(xticks) != ncols: - raise ValueError('Length of xticks must match number of columns') - x = xticks - else: - x = lrange(ncols) - - if ax is None: - ax = plt.gca() - - color_values = _get_standard_colors(num_colors=len(classes), - colormap=colormap, color_type='random', - color=color) - - colors = dict(zip(classes, color_values)) - - for i in range(n): - y = df.iloc[i].values - kls = class_col.iat[i] - label = pprint_thing(kls) - if label not in used_legends: - used_legends.add(label) - ax.plot(x, y, color=colors[kls], label=label, **kwds) - else: - ax.plot(x, y, color=colors[kls], **kwds) - - if axvlines: - for i in x: - ax.axvline(i, **axvlines_kwds) - - ax.set_xticks(x) - ax.set_xticklabels(df.columns) - ax.set_xlim(x[0], x[-1]) - ax.legend(loc='upper right') - ax.grid() - return ax - - -def lag_plot(series, lag=1, ax=None, **kwds): - """Lag plot for time series. - - Parameters: - ----------- - series: Time series - lag: lag of the scatter plot, default 1 - ax: Matplotlib axis object, optional - kwds: Matplotlib scatter method keyword arguments, optional - - Returns: - -------- - ax: Matplotlib axis object - """ - import matplotlib.pyplot as plt - - # workaround because `c='b'` is hardcoded in matplotlibs scatter method - kwds.setdefault('c', plt.rcParams['patch.facecolor']) - - data = series.values - y1 = data[:-lag] - y2 = data[lag:] - if ax is None: - ax = plt.gca() - ax.set_xlabel("y(t)") - ax.set_ylabel("y(t + %s)" % lag) - ax.scatter(y1, y2, **kwds) - return ax - - -def autocorrelation_plot(series, ax=None, **kwds): - """Autocorrelation plot for time series. - - Parameters: - ----------- - series: Time series - ax: Matplotlib axis object, optional - kwds : keywords - Options to pass to matplotlib plotting method - - Returns: - ----------- - ax: Matplotlib axis object - """ - import matplotlib.pyplot as plt - n = len(series) - data = np.asarray(series) - if ax is None: - ax = plt.gca(xlim=(1, n), ylim=(-1.0, 1.0)) - mean = np.mean(data) - c0 = np.sum((data - mean) ** 2) / float(n) - - def r(h): - return ((data[:n - h] - mean) * - (data[h:] - mean)).sum() / float(n) / c0 - x = np.arange(n) + 1 - y = lmap(r, x) - z95 = 1.959963984540054 - z99 = 2.5758293035489004 - ax.axhline(y=z99 / np.sqrt(n), linestyle='--', color='grey') - ax.axhline(y=z95 / np.sqrt(n), color='grey') - ax.axhline(y=0.0, color='black') - ax.axhline(y=-z95 / np.sqrt(n), color='grey') - ax.axhline(y=-z99 / np.sqrt(n), linestyle='--', color='grey') - ax.set_xlabel("Lag") - ax.set_ylabel("Autocorrelation") - ax.plot(x, y, **kwds) - if 'label' in kwds: - ax.legend() - ax.grid() - return ax - - -class MPLPlot(object): - """ - Base class for assembling a pandas plot using matplotlib - - Parameters - ---------- - data : - - """ - - @property - def _kind(self): - """Specify kind str. Must be overridden in child class""" - raise NotImplementedError - - _layout_type = 'vertical' - _default_rot = 0 - orientation = None - _pop_attributes = ['label', 'style', 'logy', 'logx', 'loglog', - 'mark_right', 'stacked'] - _attr_defaults = {'logy': False, 'logx': False, 'loglog': False, - 'mark_right': True, 'stacked': False} - - def __init__(self, data, kind=None, by=None, subplots=False, sharex=None, - sharey=False, use_index=True, - figsize=None, grid=None, legend=True, rot=None, - ax=None, fig=None, title=None, xlim=None, ylim=None, - xticks=None, yticks=None, - sort_columns=False, fontsize=None, - secondary_y=False, colormap=None, - table=False, layout=None, **kwds): - - self.data = data - self.by = by - - self.kind = kind - - self.sort_columns = sort_columns - - self.subplots = subplots - - if sharex is None: - if ax is None: - self.sharex = True - else: - # if we get an axis, the users should do the visibility - # setting... - self.sharex = False - else: - self.sharex = sharex - - self.sharey = sharey - self.figsize = figsize - self.layout = layout - - self.xticks = xticks - self.yticks = yticks - self.xlim = xlim - self.ylim = ylim - self.title = title - self.use_index = use_index - - self.fontsize = fontsize - - if rot is not None: - self.rot = rot - # need to know for format_date_labels since it's rotated to 30 by - # default - self._rot_set = True - else: - self._rot_set = False - self.rot = self._default_rot - - if grid is None: - grid = False if secondary_y else self.plt.rcParams['axes.grid'] - - self.grid = grid - self.legend = legend - self.legend_handles = [] - self.legend_labels = [] - - for attr in self._pop_attributes: - value = kwds.pop(attr, self._attr_defaults.get(attr, None)) - setattr(self, attr, value) - - self.ax = ax - self.fig = fig - self.axes = None - - # parse errorbar input if given - xerr = kwds.pop('xerr', None) - yerr = kwds.pop('yerr', None) - self.errors = {} - for kw, err in zip(['xerr', 'yerr'], [xerr, yerr]): - self.errors[kw] = self._parse_errorbars(kw, err) - - if not isinstance(secondary_y, (bool, tuple, list, np.ndarray, Index)): - secondary_y = [secondary_y] - self.secondary_y = secondary_y - - # ugly TypeError if user passes matplotlib's `cmap` name. - # Probably better to accept either. - if 'cmap' in kwds and colormap: - raise TypeError("Only specify one of `cmap` and `colormap`.") - elif 'cmap' in kwds: - self.colormap = kwds.pop('cmap') - else: - self.colormap = colormap - - self.table = table - - self.kwds = kwds - - self._validate_color_args() - - def _validate_color_args(self): - if 'color' not in self.kwds and 'colors' in self.kwds: - warnings.warn(("'colors' is being deprecated. Please use 'color'" - "instead of 'colors'")) - colors = self.kwds.pop('colors') - self.kwds['color'] = colors - - if ('color' in self.kwds and self.nseries == 1): - # support series.plot(color='green') - self.kwds['color'] = [self.kwds['color']] - - if ('color' in self.kwds or 'colors' in self.kwds) and \ - self.colormap is not None: - warnings.warn("'color' and 'colormap' cannot be used " - "simultaneously. Using 'color'") - - if 'color' in self.kwds and self.style is not None: - if is_list_like(self.style): - styles = self.style - else: - styles = [self.style] - # need only a single match - for s in styles: - if re.match('^[a-z]+?', s) is not None: - raise ValueError( - "Cannot pass 'style' string with a color " - "symbol and 'color' keyword argument. Please" - " use one or the other or pass 'style' " - "without a color symbol") - - def _iter_data(self, data=None, keep_index=False, fillna=None): - if data is None: - data = self.data - if fillna is not None: - data = data.fillna(fillna) - - # TODO: unused? - # if self.sort_columns: - # columns = _try_sort(data.columns) - # else: - # columns = data.columns - - for col, values in data.iteritems(): - if keep_index is True: - yield col, values - else: - yield col, values.values - - @property - def nseries(self): - if self.data.ndim == 1: - return 1 - else: - return self.data.shape[1] - - def draw(self): - self.plt.draw_if_interactive() - - def generate(self): - self._args_adjust() - self._compute_plot_data() - self._setup_subplots() - self._make_plot() - self._add_table() - self._make_legend() - self._adorn_subplots() - - for ax in self.axes: - self._post_plot_logic_common(ax, self.data) - self._post_plot_logic(ax, self.data) - - def _args_adjust(self): - pass - - def _has_plotted_object(self, ax): - """check whether ax has data""" - return (len(ax.lines) != 0 or - len(ax.artists) != 0 or - len(ax.containers) != 0) - - def _maybe_right_yaxis(self, ax, axes_num): - if not self.on_right(axes_num): - # secondary axes may be passed via ax kw - return self._get_ax_layer(ax) - - if hasattr(ax, 'right_ax'): - # if it has right_ax proparty, ``ax`` must be left axes - return ax.right_ax - elif hasattr(ax, 'left_ax'): - # if it has left_ax proparty, ``ax`` must be right axes - return ax - else: - # otherwise, create twin axes - orig_ax, new_ax = ax, ax.twinx() - # TODO: use Matplotlib public API when available - new_ax._get_lines = orig_ax._get_lines - new_ax._get_patches_for_fill = orig_ax._get_patches_for_fill - orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax - - if not self._has_plotted_object(orig_ax): # no data on left y - orig_ax.get_yaxis().set_visible(False) - return new_ax - - def _setup_subplots(self): - if self.subplots: - fig, axes = _subplots(naxes=self.nseries, - sharex=self.sharex, sharey=self.sharey, - figsize=self.figsize, ax=self.ax, - layout=self.layout, - layout_type=self._layout_type) - else: - if self.ax is None: - fig = self.plt.figure(figsize=self.figsize) - axes = fig.add_subplot(111) - else: - fig = self.ax.get_figure() - if self.figsize is not None: - fig.set_size_inches(self.figsize) - axes = self.ax - - axes = _flatten(axes) - - if self.logx or self.loglog: - [a.set_xscale('log') for a in axes] - if self.logy or self.loglog: - [a.set_yscale('log') for a in axes] - - self.fig = fig - self.axes = axes - - @property - def result(self): - """ - Return result axes - """ - if self.subplots: - if self.layout is not None and not is_list_like(self.ax): - return self.axes.reshape(*self.layout) - else: - return self.axes - else: - sec_true = isinstance(self.secondary_y, bool) and self.secondary_y - all_sec = (is_list_like(self.secondary_y) and - len(self.secondary_y) == self.nseries) - if (sec_true or all_sec): - # if all data is plotted on secondary, return right axes - return self._get_ax_layer(self.axes[0], primary=False) - else: - return self.axes[0] - - def _compute_plot_data(self): - data = self.data - - if isinstance(data, Series): - label = self.label - if label is None and data.name is None: - label = 'None' - data = data.to_frame(name=label) - - numeric_data = data._convert(datetime=True)._get_numeric_data() - - try: - is_empty = numeric_data.empty - except AttributeError: - is_empty = not len(numeric_data) - - # no empty frames or series allowed - if is_empty: - raise TypeError('Empty {0!r}: no numeric data to ' - 'plot'.format(numeric_data.__class__.__name__)) - - self.data = numeric_data - - def _make_plot(self): - raise AbstractMethodError(self) - - def _add_table(self): - if self.table is False: - return - elif self.table is True: - data = self.data.transpose() - else: - data = self.table - ax = self._get_ax(0) - table(ax, data) - - def _post_plot_logic_common(self, ax, data): - """Common post process for each axes""" - labels = [pprint_thing(key) for key in data.index] - labels = dict(zip(range(len(data.index)), labels)) - - if self.orientation == 'vertical' or self.orientation is None: - if self._need_to_set_index: - xticklabels = [labels.get(x, '') for x in ax.get_xticks()] - ax.set_xticklabels(xticklabels) - self._apply_axis_properties(ax.xaxis, rot=self.rot, - fontsize=self.fontsize) - self._apply_axis_properties(ax.yaxis, fontsize=self.fontsize) - elif self.orientation == 'horizontal': - if self._need_to_set_index: - yticklabels = [labels.get(y, '') for y in ax.get_yticks()] - ax.set_yticklabels(yticklabels) - self._apply_axis_properties(ax.yaxis, rot=self.rot, - fontsize=self.fontsize) - self._apply_axis_properties(ax.xaxis, fontsize=self.fontsize) - else: # pragma no cover - raise ValueError - - def _post_plot_logic(self, ax, data): - """Post process for each axes. Overridden in child classes""" - pass - - def _adorn_subplots(self): - """Common post process unrelated to data""" - if len(self.axes) > 0: - all_axes = self._get_subplots() - nrows, ncols = self._get_axes_layout() - _handle_shared_axes(axarr=all_axes, nplots=len(all_axes), - naxes=nrows * ncols, nrows=nrows, - ncols=ncols, sharex=self.sharex, - sharey=self.sharey) - - for ax in self.axes: - if self.yticks is not None: - ax.set_yticks(self.yticks) - - if self.xticks is not None: - ax.set_xticks(self.xticks) - - if self.ylim is not None: - ax.set_ylim(self.ylim) - - if self.xlim is not None: - ax.set_xlim(self.xlim) - - ax.grid(self.grid) - - if self.title: - if self.subplots: - if is_list_like(self.title): - if len(self.title) != self.nseries: - msg = ('The length of `title` must equal the number ' - 'of columns if using `title` of type `list` ' - 'and `subplots=True`.\n' - 'length of title = {}\n' - 'number of columns = {}').format( - len(self.title), self.nseries) - raise ValueError(msg) - - for (ax, title) in zip(self.axes, self.title): - ax.set_title(title) - else: - self.fig.suptitle(self.title) - else: - if is_list_like(self.title): - msg = ('Using `title` of type `list` is not supported ' - 'unless `subplots=True` is passed') - raise ValueError(msg) - self.axes[0].set_title(self.title) - - def _apply_axis_properties(self, axis, rot=None, fontsize=None): - labels = axis.get_majorticklabels() + axis.get_minorticklabels() - for label in labels: - if rot is not None: - label.set_rotation(rot) - if fontsize is not None: - label.set_fontsize(fontsize) - - @property - def legend_title(self): - if not isinstance(self.data.columns, MultiIndex): - name = self.data.columns.name - if name is not None: - name = pprint_thing(name) - return name - else: - stringified = map(pprint_thing, - self.data.columns.names) - return ','.join(stringified) - - def _add_legend_handle(self, handle, label, index=None): - if label is not None: - if self.mark_right and index is not None: - if self.on_right(index): - label = label + ' (right)' - self.legend_handles.append(handle) - self.legend_labels.append(label) - - def _make_legend(self): - ax, leg = self._get_ax_legend(self.axes[0]) - - handles = [] - labels = [] - title = '' - - if not self.subplots: - if leg is not None: - title = leg.get_title().get_text() - handles = leg.legendHandles - labels = [x.get_text() for x in leg.get_texts()] - - if self.legend: - if self.legend == 'reverse': - self.legend_handles = reversed(self.legend_handles) - self.legend_labels = reversed(self.legend_labels) - - handles += self.legend_handles - labels += self.legend_labels - if self.legend_title is not None: - title = self.legend_title - - if len(handles) > 0: - ax.legend(handles, labels, loc='best', title=title) - - elif self.subplots and self.legend: - for ax in self.axes: - if ax.get_visible(): - ax.legend(loc='best') - - def _get_ax_legend(self, ax): - leg = ax.get_legend() - other_ax = (getattr(ax, 'left_ax', None) or - getattr(ax, 'right_ax', None)) - other_leg = None - if other_ax is not None: - other_leg = other_ax.get_legend() - if leg is None and other_leg is not None: - leg = other_leg - ax = other_ax - return ax, leg - - @cache_readonly - def plt(self): - import matplotlib.pyplot as plt - return plt - - @staticmethod - def mpl_ge_1_3_1(): - return _mpl_ge_1_3_1() - - @staticmethod - def mpl_ge_1_5_0(): - return _mpl_ge_1_5_0() - - _need_to_set_index = False - - def _get_xticks(self, convert_period=False): - index = self.data.index - is_datetype = index.inferred_type in ('datetime', 'date', - 'datetime64', 'time') - - if self.use_index: - if convert_period and isinstance(index, PeriodIndex): - self.data = self.data.reindex(index=index.sort_values()) - x = self.data.index.to_timestamp()._mpl_repr() - elif index.is_numeric(): - """ - Matplotlib supports numeric values or datetime objects as - xaxis values. Taking LBYL approach here, by the time - matplotlib raises exception when using non numeric/datetime - values for xaxis, several actions are already taken by plt. - """ - x = index._mpl_repr() - elif is_datetype: - self.data = self.data.sort_index() - x = self.data.index._mpl_repr() - else: - self._need_to_set_index = True - x = lrange(len(index)) - else: - x = lrange(len(index)) - - return x - - @classmethod - def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds): - mask = isnull(y) - if mask.any(): - y = np.ma.array(y) - y = np.ma.masked_where(mask, y) - - if isinstance(x, Index): - x = x._mpl_repr() - - if is_errorbar: - if 'xerr' in kwds: - kwds['xerr'] = np.array(kwds.get('xerr')) - if 'yerr' in kwds: - kwds['yerr'] = np.array(kwds.get('yerr')) - return ax.errorbar(x, y, **kwds) - else: - # prevent style kwarg from going to errorbar, where it is - # unsupported - if style is not None: - args = (x, y, style) - else: - args = (x, y) - return ax.plot(*args, **kwds) - - def _get_index_name(self): - if isinstance(self.data.index, MultiIndex): - name = self.data.index.names - if any(x is not None for x in name): - name = ','.join([pprint_thing(x) for x in name]) - else: - name = None - else: - name = self.data.index.name - if name is not None: - name = pprint_thing(name) - - return name - - @classmethod - def _get_ax_layer(cls, ax, primary=True): - """get left (primary) or right (secondary) axes""" - if primary: - return getattr(ax, 'left_ax', ax) - else: - return getattr(ax, 'right_ax', ax) - - def _get_ax(self, i): - # get the twinx ax if appropriate - if self.subplots: - ax = self.axes[i] - ax = self._maybe_right_yaxis(ax, i) - self.axes[i] = ax - else: - ax = self.axes[0] - ax = self._maybe_right_yaxis(ax, i) - - ax.get_yaxis().set_visible(True) - return ax - - def on_right(self, i): - if isinstance(self.secondary_y, bool): - return self.secondary_y - - if isinstance(self.secondary_y, (tuple, list, np.ndarray, Index)): - return self.data.columns[i] in self.secondary_y - - def _apply_style_colors(self, colors, kwds, col_num, label): - """ - Manage style and color based on column number and its label. - Returns tuple of appropriate style and kwds which "color" may be added. - """ - style = None - if self.style is not None: - if isinstance(self.style, list): - try: - style = self.style[col_num] - except IndexError: - pass - elif isinstance(self.style, dict): - style = self.style.get(label, style) - else: - style = self.style - - has_color = 'color' in kwds or self.colormap is not None - nocolor_style = style is None or re.match('[a-z]+', style) is None - if (has_color or self.subplots) and nocolor_style: - kwds['color'] = colors[col_num % len(colors)] - return style, kwds - - def _get_colors(self, num_colors=None, color_kwds='color'): - if num_colors is None: - num_colors = self.nseries - - return _get_standard_colors(num_colors=num_colors, - colormap=self.colormap, - color=self.kwds.get(color_kwds)) - - def _parse_errorbars(self, label, err): - """ - Look for error keyword arguments and return the actual errorbar data - or return the error DataFrame/dict - - Error bars can be specified in several ways: - Series: the user provides a pandas.Series object of the same - length as the data - ndarray: provides a np.ndarray of the same length as the data - DataFrame/dict: error values are paired with keys matching the - key in the plotted DataFrame - str: the name of the column within the plotted DataFrame - """ - - if err is None: - return None - - from pandas import DataFrame, Series - - def match_labels(data, e): - e = e.reindex_axis(data.index) - return e - - # key-matched DataFrame - if isinstance(err, DataFrame): - - err = match_labels(self.data, err) - # key-matched dict - elif isinstance(err, dict): - pass - - # Series of error values - elif isinstance(err, Series): - # broadcast error series across data - err = match_labels(self.data, err) - err = np.atleast_2d(err) - err = np.tile(err, (self.nseries, 1)) - - # errors are a column in the dataframe - elif isinstance(err, string_types): - evalues = self.data[err].values - self.data = self.data[self.data.columns.drop(err)] - err = np.atleast_2d(evalues) - err = np.tile(err, (self.nseries, 1)) - - elif is_list_like(err): - if is_iterator(err): - err = np.atleast_2d(list(err)) - else: - # raw error values - err = np.atleast_2d(err) - - err_shape = err.shape - - # asymmetrical error bars - if err.ndim == 3: - if (err_shape[0] != self.nseries) or \ - (err_shape[1] != 2) or \ - (err_shape[2] != len(self.data)): - msg = "Asymmetrical error bars should be provided " + \ - "with the shape (%u, 2, %u)" % \ - (self.nseries, len(self.data)) - raise ValueError(msg) - - # broadcast errors to each data series - if len(err) == 1: - err = np.tile(err, (self.nseries, 1)) - - elif is_number(err): - err = np.tile([err], (self.nseries, len(self.data))) - - else: - msg = "No valid %s detected" % label - raise ValueError(msg) - - return err - - def _get_errorbars(self, label=None, index=None, xerr=True, yerr=True): - from pandas import DataFrame - errors = {} - - for kw, flag in zip(['xerr', 'yerr'], [xerr, yerr]): - if flag: - err = self.errors[kw] - # user provided label-matched dataframe of errors - if isinstance(err, (DataFrame, dict)): - if label is not None and label in err.keys(): - err = err[label] - else: - err = None - elif index is not None and err is not None: - err = err[index] - - if err is not None: - errors[kw] = err - return errors - - def _get_subplots(self): - from matplotlib.axes import Subplot - return [ax for ax in self.axes[0].get_figure().get_axes() - if isinstance(ax, Subplot)] - - def _get_axes_layout(self): - axes = self._get_subplots() - x_set = set() - y_set = set() - for ax in axes: - # check axes coordinates to estimate layout - points = ax.get_position().get_points() - x_set.add(points[0][0]) - y_set.add(points[0][1]) - return (len(y_set), len(x_set)) - - -class PlanePlot(MPLPlot): - """ - Abstract class for plotting on plane, currently scatter and hexbin. - """ - - _layout_type = 'single' - - def __init__(self, data, x, y, **kwargs): - MPLPlot.__init__(self, data, **kwargs) - if x is None or y is None: - raise ValueError(self._kind + ' requires and x and y column') - if is_integer(x) and not self.data.columns.holds_integer(): - x = self.data.columns[x] - if is_integer(y) and not self.data.columns.holds_integer(): - y = self.data.columns[y] - self.x = x - self.y = y - - @property - def nseries(self): - return 1 - - def _post_plot_logic(self, ax, data): - x, y = self.x, self.y - ax.set_ylabel(pprint_thing(y)) - ax.set_xlabel(pprint_thing(x)) - - -class ScatterPlot(PlanePlot): - _kind = 'scatter' - - def __init__(self, data, x, y, s=None, c=None, **kwargs): - if s is None: - # hide the matplotlib default for size, in case we want to change - # the handling of this argument later - s = 20 - super(ScatterPlot, self).__init__(data, x, y, s=s, **kwargs) - if is_integer(c) and not self.data.columns.holds_integer(): - c = self.data.columns[c] - self.c = c - - def _make_plot(self): - x, y, c, data = self.x, self.y, self.c, self.data - ax = self.axes[0] - - c_is_column = is_hashable(c) and c in self.data.columns - - # plot a colorbar only if a colormap is provided or necessary - cb = self.kwds.pop('colorbar', self.colormap or c_is_column) - - # pandas uses colormap, matplotlib uses cmap. - cmap = self.colormap or 'Greys' - cmap = self.plt.cm.get_cmap(cmap) - color = self.kwds.pop("color", None) - if c is not None and color is not None: - raise TypeError('Specify exactly one of `c` and `color`') - elif c is None and color is None: - c_values = self.plt.rcParams['patch.facecolor'] - elif color is not None: - c_values = color - elif c_is_column: - c_values = self.data[c].values - else: - c_values = c - - if self.legend and hasattr(self, 'label'): - label = self.label - else: - label = None - scatter = ax.scatter(data[x].values, data[y].values, c=c_values, - label=label, cmap=cmap, **self.kwds) - if cb: - img = ax.collections[0] - kws = dict(ax=ax) - if self.mpl_ge_1_3_1(): - kws['label'] = c if c_is_column else '' - self.fig.colorbar(img, **kws) - - if label is not None: - self._add_legend_handle(scatter, label) - else: - self.legend = False - - errors_x = self._get_errorbars(label=x, index=0, yerr=False) - errors_y = self._get_errorbars(label=y, index=0, xerr=False) - if len(errors_x) > 0 or len(errors_y) > 0: - err_kwds = dict(errors_x, **errors_y) - err_kwds['ecolor'] = scatter.get_facecolor()[0] - ax.errorbar(data[x].values, data[y].values, - linestyle='none', **err_kwds) - - -class HexBinPlot(PlanePlot): - _kind = 'hexbin' - - def __init__(self, data, x, y, C=None, **kwargs): - super(HexBinPlot, self).__init__(data, x, y, **kwargs) - if is_integer(C) and not self.data.columns.holds_integer(): - C = self.data.columns[C] - self.C = C - - def _make_plot(self): - x, y, data, C = self.x, self.y, self.data, self.C - ax = self.axes[0] - # pandas uses colormap, matplotlib uses cmap. - cmap = self.colormap or 'BuGn' - cmap = self.plt.cm.get_cmap(cmap) - cb = self.kwds.pop('colorbar', True) - - if C is None: - c_values = None - else: - c_values = data[C].values - - ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap, - **self.kwds) - if cb: - img = ax.collections[0] - self.fig.colorbar(img, ax=ax) - - def _make_legend(self): - pass - - -class LinePlot(MPLPlot): - _kind = 'line' - _default_rot = 0 - orientation = 'vertical' - - def __init__(self, data, **kwargs): - MPLPlot.__init__(self, data, **kwargs) - if self.stacked: - self.data = self.data.fillna(value=0) - self.x_compat = plot_params['x_compat'] - if 'x_compat' in self.kwds: - self.x_compat = bool(self.kwds.pop('x_compat')) - - def _is_ts_plot(self): - # this is slightly deceptive - return not self.x_compat and self.use_index and self._use_dynamic_x() - - def _use_dynamic_x(self): - from pandas.tseries.plotting import _use_dynamic_x - return _use_dynamic_x(self._get_ax(0), self.data) - - def _make_plot(self): - if self._is_ts_plot(): - from pandas.tseries.plotting import _maybe_convert_index - data = _maybe_convert_index(self._get_ax(0), self.data) - - x = data.index # dummy, not used - plotf = self._ts_plot - it = self._iter_data(data=data, keep_index=True) - else: - x = self._get_xticks(convert_period=True) - plotf = self._plot - it = self._iter_data() - - stacking_id = self._get_stacking_id() - is_errorbar = any(e is not None for e in self.errors.values()) - - colors = self._get_colors() - for i, (label, y) in enumerate(it): - ax = self._get_ax(i) - kwds = self.kwds.copy() - style, kwds = self._apply_style_colors(colors, kwds, i, label) - - errors = self._get_errorbars(label=label, index=i) - kwds = dict(kwds, **errors) - - label = pprint_thing(label) # .encode('utf-8') - kwds['label'] = label - - newlines = plotf(ax, x, y, style=style, column_num=i, - stacking_id=stacking_id, - is_errorbar=is_errorbar, - **kwds) - self._add_legend_handle(newlines[0], label, index=i) - - lines = _get_all_lines(ax) - left, right = _get_xlim(lines) - ax.set_xlim(left, right) - - @classmethod - def _plot(cls, ax, x, y, style=None, column_num=None, - stacking_id=None, **kwds): - # column_num is used to get the target column from protf in line and - # area plots - if column_num == 0: - cls._initialize_stacker(ax, stacking_id, len(y)) - y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label']) - lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds) - cls._update_stacker(ax, stacking_id, y) - return lines - - @classmethod - def _ts_plot(cls, ax, x, data, style=None, **kwds): - from pandas.tseries.plotting import (_maybe_resample, - _decorate_axes, - format_dateaxis) - # accept x to be consistent with normal plot func, - # x is not passed to tsplot as it uses data.index as x coordinate - # column_num must be in kwds for stacking purpose - freq, data = _maybe_resample(data, ax, kwds) - - # Set ax with freq info - _decorate_axes(ax, freq, kwds) - # digging deeper - if hasattr(ax, 'left_ax'): - _decorate_axes(ax.left_ax, freq, kwds) - if hasattr(ax, 'right_ax'): - _decorate_axes(ax.right_ax, freq, kwds) - ax._plot_data.append((data, cls._kind, kwds)) - - lines = cls._plot(ax, data.index, data.values, style=style, **kwds) - # set date formatter, locators and rescale limits - format_dateaxis(ax, ax.freq) - return lines - - def _get_stacking_id(self): - if self.stacked: - return id(self.data) - else: - return None - - @classmethod - def _initialize_stacker(cls, ax, stacking_id, n): - if stacking_id is None: - return - if not hasattr(ax, '_stacker_pos_prior'): - ax._stacker_pos_prior = {} - if not hasattr(ax, '_stacker_neg_prior'): - ax._stacker_neg_prior = {} - ax._stacker_pos_prior[stacking_id] = np.zeros(n) - ax._stacker_neg_prior[stacking_id] = np.zeros(n) - - @classmethod - def _get_stacked_values(cls, ax, stacking_id, values, label): - if stacking_id is None: - return values - if not hasattr(ax, '_stacker_pos_prior'): - # stacker may not be initialized for subplots - cls._initialize_stacker(ax, stacking_id, len(values)) - - if (values >= 0).all(): - return ax._stacker_pos_prior[stacking_id] + values - elif (values <= 0).all(): - return ax._stacker_neg_prior[stacking_id] + values - - raise ValueError('When stacked is True, each column must be either ' - 'all positive or negative.' - '{0} contains both positive and negative values' - .format(label)) - - @classmethod - def _update_stacker(cls, ax, stacking_id, values): - if stacking_id is None: - return - if (values >= 0).all(): - ax._stacker_pos_prior[stacking_id] += values - elif (values <= 0).all(): - ax._stacker_neg_prior[stacking_id] += values - - def _post_plot_logic(self, ax, data): - condition = (not self._use_dynamic_x() and - data.index.is_all_dates and - not self.subplots or - (self.subplots and self.sharex)) - - index_name = self._get_index_name() - - if condition: - # irregular TS rotated 30 deg. by default - # probably a better place to check / set this. - if not self._rot_set: - self.rot = 30 - format_date_labels(ax, rot=self.rot) - - if index_name is not None and self.use_index: - ax.set_xlabel(index_name) - - -class AreaPlot(LinePlot): - _kind = 'area' - - def __init__(self, data, **kwargs): - kwargs.setdefault('stacked', True) - data = data.fillna(value=0) - LinePlot.__init__(self, data, **kwargs) - - if not self.stacked: - # use smaller alpha to distinguish overlap - self.kwds.setdefault('alpha', 0.5) - - if self.logy or self.loglog: - raise ValueError("Log-y scales are not supported in area plot") - - @classmethod - def _plot(cls, ax, x, y, style=None, column_num=None, - stacking_id=None, is_errorbar=False, **kwds): - - if column_num == 0: - cls._initialize_stacker(ax, stacking_id, len(y)) - y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label']) - - # need to remove label, because subplots uses mpl legend as it is - line_kwds = kwds.copy() - if cls.mpl_ge_1_5_0(): - line_kwds.pop('label') - lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds) - - # get data from the line to get coordinates for fill_between - xdata, y_values = lines[0].get_data(orig=False) - - # unable to use ``_get_stacked_values`` here to get starting point - if stacking_id is None: - start = np.zeros(len(y)) - elif (y >= 0).all(): - start = ax._stacker_pos_prior[stacking_id] - elif (y <= 0).all(): - start = ax._stacker_neg_prior[stacking_id] - else: - start = np.zeros(len(y)) - - if 'color' not in kwds: - kwds['color'] = lines[0].get_color() - - rect = ax.fill_between(xdata, start, y_values, **kwds) - cls._update_stacker(ax, stacking_id, y) - - # LinePlot expects list of artists - res = [rect] if cls.mpl_ge_1_5_0() else lines - return res - - def _add_legend_handle(self, handle, label, index=None): - if not self.mpl_ge_1_5_0(): - from matplotlib.patches import Rectangle - # Because fill_between isn't supported in legend, - # specifically add Rectangle handle here - alpha = self.kwds.get('alpha', None) - handle = Rectangle((0, 0), 1, 1, fc=handle.get_color(), - alpha=alpha) - LinePlot._add_legend_handle(self, handle, label, index=index) - - def _post_plot_logic(self, ax, data): - LinePlot._post_plot_logic(self, ax, data) - - if self.ylim is None: - if (data >= 0).all().all(): - ax.set_ylim(0, None) - elif (data <= 0).all().all(): - ax.set_ylim(None, 0) - - -class BarPlot(MPLPlot): - _kind = 'bar' - _default_rot = 90 - orientation = 'vertical' - - def __init__(self, data, **kwargs): - self.bar_width = kwargs.pop('width', 0.5) - pos = kwargs.pop('position', 0.5) - kwargs.setdefault('align', 'center') - self.tick_pos = np.arange(len(data)) - - self.bottom = kwargs.pop('bottom', 0) - self.left = kwargs.pop('left', 0) - - self.log = kwargs.pop('log', False) - MPLPlot.__init__(self, data, **kwargs) - - if self.stacked or self.subplots: - self.tickoffset = self.bar_width * pos - if kwargs['align'] == 'edge': - self.lim_offset = self.bar_width / 2 - else: - self.lim_offset = 0 - else: - if kwargs['align'] == 'edge': - w = self.bar_width / self.nseries - self.tickoffset = self.bar_width * (pos - 0.5) + w * 0.5 - self.lim_offset = w * 0.5 - else: - self.tickoffset = self.bar_width * pos - self.lim_offset = 0 - - self.ax_pos = self.tick_pos - self.tickoffset - - def _args_adjust(self): - if is_list_like(self.bottom): - self.bottom = np.array(self.bottom) - if is_list_like(self.left): - self.left = np.array(self.left) - - @classmethod - def _plot(cls, ax, x, y, w, start=0, log=False, **kwds): - return ax.bar(x, y, w, bottom=start, log=log, **kwds) - - @property - def _start_base(self): - return self.bottom - - def _make_plot(self): - import matplotlib as mpl - - colors = self._get_colors() - ncolors = len(colors) - - pos_prior = neg_prior = np.zeros(len(self.data)) - K = self.nseries - - for i, (label, y) in enumerate(self._iter_data(fillna=0)): - ax = self._get_ax(i) - kwds = self.kwds.copy() - kwds['color'] = colors[i % ncolors] - - errors = self._get_errorbars(label=label, index=i) - kwds = dict(kwds, **errors) - - label = pprint_thing(label) - - if (('yerr' in kwds) or ('xerr' in kwds)) \ - and (kwds.get('ecolor') is None): - kwds['ecolor'] = mpl.rcParams['xtick.color'] - - start = 0 - if self.log and (y >= 1).all(): - start = 1 - start = start + self._start_base - - if self.subplots: - w = self.bar_width / 2 - rect = self._plot(ax, self.ax_pos + w, y, self.bar_width, - start=start, label=label, - log=self.log, **kwds) - ax.set_title(label) - elif self.stacked: - mask = y > 0 - start = np.where(mask, pos_prior, neg_prior) + self._start_base - w = self.bar_width / 2 - rect = self._plot(ax, self.ax_pos + w, y, self.bar_width, - start=start, label=label, - log=self.log, **kwds) - pos_prior = pos_prior + np.where(mask, y, 0) - neg_prior = neg_prior + np.where(mask, 0, y) - else: - w = self.bar_width / K - rect = self._plot(ax, self.ax_pos + (i + 0.5) * w, y, w, - start=start, label=label, - log=self.log, **kwds) - self._add_legend_handle(rect, label, index=i) - - def _post_plot_logic(self, ax, data): - if self.use_index: - str_index = [pprint_thing(key) for key in data.index] - else: - str_index = [pprint_thing(key) for key in range(data.shape[0])] - name = self._get_index_name() - - s_edge = self.ax_pos[0] - 0.25 + self.lim_offset - e_edge = self.ax_pos[-1] + 0.25 + self.bar_width + self.lim_offset - - self._decorate_ticks(ax, name, str_index, s_edge, e_edge) - - def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge): - ax.set_xlim((start_edge, end_edge)) - ax.set_xticks(self.tick_pos) - ax.set_xticklabels(ticklabels) - if name is not None and self.use_index: - ax.set_xlabel(name) - - -class BarhPlot(BarPlot): - _kind = 'barh' - _default_rot = 0 - orientation = 'horizontal' - - @property - def _start_base(self): - return self.left - - @classmethod - def _plot(cls, ax, x, y, w, start=0, log=False, **kwds): - return ax.barh(x, y, w, left=start, log=log, **kwds) - - def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge): - # horizontal bars - ax.set_ylim((start_edge, end_edge)) - ax.set_yticks(self.tick_pos) - ax.set_yticklabels(ticklabels) - if name is not None and self.use_index: - ax.set_ylabel(name) - - -class HistPlot(LinePlot): - _kind = 'hist' - - def __init__(self, data, bins=10, bottom=0, **kwargs): - self.bins = bins # use mpl default - self.bottom = bottom - # Do not call LinePlot.__init__ which may fill nan - MPLPlot.__init__(self, data, **kwargs) - - def _args_adjust(self): - if is_integer(self.bins): - # create common bin edge - values = (self.data._convert(datetime=True)._get_numeric_data()) - values = np.ravel(values) - values = values[~isnull(values)] - - hist, self.bins = np.histogram( - values, bins=self.bins, - range=self.kwds.get('range', None), - weights=self.kwds.get('weights', None)) - - if is_list_like(self.bottom): - self.bottom = np.array(self.bottom) - - @classmethod - def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, - stacking_id=None, **kwds): - if column_num == 0: - cls._initialize_stacker(ax, stacking_id, len(bins) - 1) - y = y[~isnull(y)] - - base = np.zeros(len(bins) - 1) - bottom = bottom + \ - cls._get_stacked_values(ax, stacking_id, base, kwds['label']) - # ignore style - n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) - cls._update_stacker(ax, stacking_id, n) - return patches - - def _make_plot(self): - colors = self._get_colors() - stacking_id = self._get_stacking_id() - - for i, (label, y) in enumerate(self._iter_data()): - ax = self._get_ax(i) - - kwds = self.kwds.copy() - - label = pprint_thing(label) - kwds['label'] = label - - style, kwds = self._apply_style_colors(colors, kwds, i, label) - if style is not None: - kwds['style'] = style - - kwds = self._make_plot_keywords(kwds, y) - artists = self._plot(ax, y, column_num=i, - stacking_id=stacking_id, **kwds) - self._add_legend_handle(artists[0], label, index=i) - - def _make_plot_keywords(self, kwds, y): - """merge BoxPlot/KdePlot properties to passed kwds""" - # y is required for KdePlot - kwds['bottom'] = self.bottom - kwds['bins'] = self.bins - return kwds - - def _post_plot_logic(self, ax, data): - if self.orientation == 'horizontal': - ax.set_xlabel('Frequency') - else: - ax.set_ylabel('Frequency') - - @property - def orientation(self): - if self.kwds.get('orientation', None) == 'horizontal': - return 'horizontal' - else: - return 'vertical' - - -class KdePlot(HistPlot): - _kind = 'kde' - orientation = 'vertical' - - def __init__(self, data, bw_method=None, ind=None, **kwargs): - MPLPlot.__init__(self, data, **kwargs) - self.bw_method = bw_method - self.ind = ind - - def _args_adjust(self): - pass - - def _get_ind(self, y): - if self.ind is None: - # np.nanmax() and np.nanmin() ignores the missing values - sample_range = np.nanmax(y) - np.nanmin(y) - ind = np.linspace(np.nanmin(y) - 0.5 * sample_range, - np.nanmax(y) + 0.5 * sample_range, 1000) - else: - ind = self.ind - return ind - - @classmethod - def _plot(cls, ax, y, style=None, bw_method=None, ind=None, - column_num=None, stacking_id=None, **kwds): - from scipy.stats import gaussian_kde - from scipy import __version__ as spv - - y = remove_na(y) - - if LooseVersion(spv) >= '0.11.0': - gkde = gaussian_kde(y, bw_method=bw_method) - else: - gkde = gaussian_kde(y) - if bw_method is not None: - msg = ('bw_method was added in Scipy 0.11.0.' + - ' Scipy version in use is %s.' % spv) - warnings.warn(msg) - - y = gkde.evaluate(ind) - lines = MPLPlot._plot(ax, ind, y, style=style, **kwds) - return lines - - def _make_plot_keywords(self, kwds, y): - kwds['bw_method'] = self.bw_method - kwds['ind'] = self._get_ind(y) - return kwds - - def _post_plot_logic(self, ax, data): - ax.set_ylabel('Density') - - -class PiePlot(MPLPlot): - _kind = 'pie' - _layout_type = 'horizontal' - - def __init__(self, data, kind=None, **kwargs): - data = data.fillna(value=0) - if (data < 0).any().any(): - raise ValueError("{0} doesn't allow negative values".format(kind)) - MPLPlot.__init__(self, data, kind=kind, **kwargs) - - def _args_adjust(self): - self.grid = False - self.logy = False - self.logx = False - self.loglog = False - - def _validate_color_args(self): - pass - - def _make_plot(self): - colors = self._get_colors( - num_colors=len(self.data), color_kwds='colors') - self.kwds.setdefault('colors', colors) - - for i, (label, y) in enumerate(self._iter_data()): - ax = self._get_ax(i) - if label is not None: - label = pprint_thing(label) - ax.set_ylabel(label) - - kwds = self.kwds.copy() - - def blank_labeler(label, value): - if value == 0: - return '' - else: - return label - - idx = [pprint_thing(v) for v in self.data.index] - labels = kwds.pop('labels', idx) - # labels is used for each wedge's labels - # Blank out labels for values of 0 so they don't overlap - # with nonzero wedges - if labels is not None: - blabels = [blank_labeler(l, value) for - l, value in zip(labels, y)] - else: - blabels = None - results = ax.pie(y, labels=blabels, **kwds) - - if kwds.get('autopct', None) is not None: - patches, texts, autotexts = results - else: - patches, texts = results - autotexts = [] - - if self.fontsize is not None: - for t in texts + autotexts: - t.set_fontsize(self.fontsize) - - # leglabels is used for legend labels - leglabels = labels if labels is not None else idx - for p, l in zip(patches, leglabels): - self._add_legend_handle(p, l) - - -class BoxPlot(LinePlot): - _kind = 'box' - _layout_type = 'horizontal' - - _valid_return_types = (None, 'axes', 'dict', 'both') - # namedtuple to hold results - BP = namedtuple("Boxplot", ['ax', 'lines']) - - def __init__(self, data, return_type='axes', **kwargs): - # Do not call LinePlot.__init__ which may fill nan - if return_type not in self._valid_return_types: - raise ValueError( - "return_type must be {None, 'axes', 'dict', 'both'}") - - self.return_type = return_type - MPLPlot.__init__(self, data, **kwargs) - - def _args_adjust(self): - if self.subplots: - # Disable label ax sharing. Otherwise, all subplots shows last - # column label - if self.orientation == 'vertical': - self.sharex = False - else: - self.sharey = False - - @classmethod - def _plot(cls, ax, y, column_num=None, return_type='axes', **kwds): - if y.ndim == 2: - y = [remove_na(v) for v in y] - # Boxplot fails with empty arrays, so need to add a NaN - # if any cols are empty - # GH 8181 - y = [v if v.size > 0 else np.array([np.nan]) for v in y] - else: - y = remove_na(y) - bp = ax.boxplot(y, **kwds) - - if return_type == 'dict': - return bp, bp - elif return_type == 'both': - return cls.BP(ax=ax, lines=bp), bp - else: - return ax, bp - - def _validate_color_args(self): - if 'color' in self.kwds: - if self.colormap is not None: - warnings.warn("'color' and 'colormap' cannot be used " - "simultaneously. Using 'color'") - self.color = self.kwds.pop('color') - - if isinstance(self.color, dict): - valid_keys = ['boxes', 'whiskers', 'medians', 'caps'] - for key, values in compat.iteritems(self.color): - if key not in valid_keys: - raise ValueError("color dict contains invalid " - "key '{0}' " - "The key must be either {1}" - .format(key, valid_keys)) - else: - self.color = None - - # get standard colors for default - colors = _get_standard_colors(num_colors=3, - colormap=self.colormap, - color=None) - # use 2 colors by default, for box/whisker and median - # flier colors isn't needed here - # because it can be specified by ``sym`` kw - self._boxes_c = colors[0] - self._whiskers_c = colors[0] - self._medians_c = colors[2] - self._caps_c = 'k' # mpl default - - def _get_colors(self, num_colors=None, color_kwds='color'): - pass - - def maybe_color_bp(self, bp): - if isinstance(self.color, dict): - boxes = self.color.get('boxes', self._boxes_c) - whiskers = self.color.get('whiskers', self._whiskers_c) - medians = self.color.get('medians', self._medians_c) - caps = self.color.get('caps', self._caps_c) - else: - # Other types are forwarded to matplotlib - # If None, use default colors - boxes = self.color or self._boxes_c - whiskers = self.color or self._whiskers_c - medians = self.color or self._medians_c - caps = self.color or self._caps_c - - from matplotlib.artist import setp - setp(bp['boxes'], color=boxes, alpha=1) - setp(bp['whiskers'], color=whiskers, alpha=1) - setp(bp['medians'], color=medians, alpha=1) - setp(bp['caps'], color=caps, alpha=1) - - def _make_plot(self): - if self.subplots: - self._return_obj = Series() - - for i, (label, y) in enumerate(self._iter_data()): - ax = self._get_ax(i) - kwds = self.kwds.copy() - - ret, bp = self._plot(ax, y, column_num=i, - return_type=self.return_type, **kwds) - self.maybe_color_bp(bp) - self._return_obj[label] = ret - - label = [pprint_thing(label)] - self._set_ticklabels(ax, label) - else: - y = self.data.values.T - ax = self._get_ax(0) - kwds = self.kwds.copy() - - ret, bp = self._plot(ax, y, column_num=0, - return_type=self.return_type, **kwds) - self.maybe_color_bp(bp) - self._return_obj = ret - - labels = [l for l, _ in self._iter_data()] - labels = [pprint_thing(l) for l in labels] - if not self.use_index: - labels = [pprint_thing(key) for key in range(len(labels))] - self._set_ticklabels(ax, labels) - - def _set_ticklabels(self, ax, labels): - if self.orientation == 'vertical': - ax.set_xticklabels(labels) - else: - ax.set_yticklabels(labels) - - def _make_legend(self): - pass - - def _post_plot_logic(self, ax, data): - pass - - @property - def orientation(self): - if self.kwds.get('vert', True): - return 'vertical' - else: - return 'horizontal' - - @property - def result(self): - if self.return_type is None: - return super(BoxPlot, self).result - else: - return self._return_obj - - -# kinds supported by both dataframe and series -_common_kinds = ['line', 'bar', 'barh', - 'kde', 'density', 'area', 'hist', 'box'] -# kinds supported by dataframe -_dataframe_kinds = ['scatter', 'hexbin'] -# kinds supported only by series or dataframe single column -_series_kinds = ['pie'] -_all_kinds = _common_kinds + _dataframe_kinds + _series_kinds - -_klasses = [LinePlot, BarPlot, BarhPlot, KdePlot, HistPlot, BoxPlot, - ScatterPlot, HexBinPlot, AreaPlot, PiePlot] - -_plot_klass = {} -for klass in _klasses: - _plot_klass[klass._kind] = klass - - -def _plot(data, x=None, y=None, subplots=False, - ax=None, kind='line', **kwds): - kind = _get_standard_kind(kind.lower().strip()) - if kind in _all_kinds: - klass = _plot_klass[kind] - else: - raise ValueError("%r is not a valid plot kind" % kind) - - from pandas import DataFrame - if kind in _dataframe_kinds: - if isinstance(data, DataFrame): - plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax, - kind=kind, **kwds) - else: - raise ValueError("plot kind %r can only be used for data frames" - % kind) - - elif kind in _series_kinds: - if isinstance(data, DataFrame): - if y is None and subplots is False: - msg = "{0} requires either y column or 'subplots=True'" - raise ValueError(msg.format(kind)) - elif y is not None: - if is_integer(y) and not data.columns.holds_integer(): - y = data.columns[y] - # converted to series actually. copy to not modify - data = data[y].copy() - data.index.name = y - plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds) - else: - if isinstance(data, DataFrame): - if x is not None: - if is_integer(x) and not data.columns.holds_integer(): - x = data.columns[x] - data = data.set_index(x) - - if y is not None: - if is_integer(y) and not data.columns.holds_integer(): - y = data.columns[y] - label = kwds['label'] if 'label' in kwds else y - series = data[y].copy() # Don't modify - series.name = label - - for kw in ['xerr', 'yerr']: - if (kw in kwds) and \ - (isinstance(kwds[kw], string_types) or - is_integer(kwds[kw])): - try: - kwds[kw] = data[kwds[kw]] - except (IndexError, KeyError, TypeError): - pass - data = series - plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds) - - plot_obj.generate() - plot_obj.draw() - return plot_obj.result - - -df_kind = """- 'scatter' : scatter plot - - 'hexbin' : hexbin plot""" -series_kind = "" - -df_coord = """x : label or position, default None - y : label or position, default None - Allows plotting of one column versus another""" -series_coord = "" - -df_unique = """stacked : boolean, default False in line and - bar plots, and True in area plot. If True, create stacked plot. - sort_columns : boolean, default False - Sort column names to determine plot ordering - secondary_y : boolean or sequence, default False - Whether to plot on the secondary y-axis - If a list/tuple, which columns to plot on secondary y-axis""" -series_unique = """label : label argument to provide to plot - secondary_y : boolean or sequence of ints, default False - If True then y-axis will be on the right""" - -df_ax = """ax : matplotlib axes object, default None - subplots : boolean, default False - Make separate subplots for each column - sharex : boolean, default True if ax is None else False - In case subplots=True, share x axis and set some x axis labels to - invisible; defaults to True if ax is None otherwise False if an ax - is passed in; Be aware, that passing in both an ax and sharex=True - will alter all x axis labels for all axis in a figure! - sharey : boolean, default False - In case subplots=True, share y axis and set some y axis labels to - invisible - layout : tuple (optional) - (rows, columns) for the layout of subplots""" -series_ax = """ax : matplotlib axes object - If not passed, uses gca()""" - -df_note = """- If `kind` = 'scatter' and the argument `c` is the name of a dataframe - column, the values of that column are used to color each point. - - If `kind` = 'hexbin', you can control the size of the bins with the - `gridsize` argument. By default, a histogram of the counts around each - `(x, y)` point is computed. You can specify alternative aggregations - by passing values to the `C` and `reduce_C_function` arguments. - `C` specifies the value at each `(x, y)` point and `reduce_C_function` - is a function of one argument that reduces all the values in a bin to - a single number (e.g. `mean`, `max`, `sum`, `std`).""" -series_note = "" - -_shared_doc_df_kwargs = dict(klass='DataFrame', klass_obj='df', - klass_kind=df_kind, klass_coord=df_coord, - klass_ax=df_ax, klass_unique=df_unique, - klass_note=df_note) -_shared_doc_series_kwargs = dict(klass='Series', klass_obj='s', - klass_kind=series_kind, - klass_coord=series_coord, klass_ax=series_ax, - klass_unique=series_unique, - klass_note=series_note) - -_shared_docs['plot'] = """ - Make plots of %(klass)s using matplotlib / pylab. - - *New in version 0.17.0:* Each plot kind has a corresponding method on the - ``%(klass)s.plot`` accessor: - ``%(klass_obj)s.plot(kind='line')`` is equivalent to - ``%(klass_obj)s.plot.line()``. - - Parameters - ---------- - data : %(klass)s - %(klass_coord)s - kind : str - - 'line' : line plot (default) - - 'bar' : vertical bar plot - - 'barh' : horizontal bar plot - - 'hist' : histogram - - 'box' : boxplot - - 'kde' : Kernel Density Estimation plot - - 'density' : same as 'kde' - - 'area' : area plot - - 'pie' : pie plot - %(klass_kind)s - %(klass_ax)s - figsize : a tuple (width, height) in inches - use_index : boolean, default True - Use index as ticks for x axis - title : string or list - Title to use for the plot. If a string is passed, print the string at - the top of the figure. If a list is passed and `subplots` is True, - print each item in the list above the corresponding subplot. - grid : boolean, default None (matlab style default) - Axis grid lines - legend : False/True/'reverse' - Place legend on axis subplots - style : list or dict - matplotlib line style per column - logx : boolean, default False - Use log scaling on x axis - logy : boolean, default False - Use log scaling on y axis - loglog : boolean, default False - Use log scaling on both x and y axes - xticks : sequence - Values to use for the xticks - yticks : sequence - Values to use for the yticks - xlim : 2-tuple/list - ylim : 2-tuple/list - rot : int, default None - Rotation for ticks (xticks for vertical, yticks for horizontal plots) - fontsize : int, default None - Font size for xticks and yticks - colormap : str or matplotlib colormap object, default None - Colormap to select colors from. If string, load colormap with that name - from matplotlib. - colorbar : boolean, optional - If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots) - position : float - Specify relative alignments for bar plot layout. - From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center) - layout : tuple (optional) - (rows, columns) for the layout of the plot - table : boolean, Series or DataFrame, default False - If True, draw a table using the data in the DataFrame and the data will - be transposed to meet matplotlib's default layout. - If a Series or DataFrame is passed, use passed data to draw a table. - yerr : DataFrame, Series, array-like, dict and str - See :ref:`Plotting with Error Bars <visualization.errorbars>` for - detail. - xerr : same types as yerr. - %(klass_unique)s - mark_right : boolean, default True - When using a secondary_y axis, automatically mark the column - labels with "(right)" in the legend - kwds : keywords - Options to pass to matplotlib plotting method - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - - Notes - ----- - - - See matplotlib documentation online for more on this subject - - If `kind` = 'bar' or 'barh', you can specify relative alignments - for bar plot layout by `position` keyword. - From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center) - %(klass_note)s - - """ - - -@Appender(_shared_docs['plot'] % _shared_doc_df_kwargs) -def plot_frame(data, x=None, y=None, kind='line', ax=None, - subplots=False, sharex=None, sharey=False, layout=None, - figsize=None, use_index=True, title=None, grid=None, - legend=True, style=None, logx=False, logy=False, loglog=False, - xticks=None, yticks=None, xlim=None, ylim=None, - rot=None, fontsize=None, colormap=None, table=False, - yerr=None, xerr=None, - secondary_y=False, sort_columns=False, - **kwds): - return _plot(data, kind=kind, x=x, y=y, ax=ax, - subplots=subplots, sharex=sharex, sharey=sharey, - layout=layout, figsize=figsize, use_index=use_index, - title=title, grid=grid, legend=legend, - style=style, logx=logx, logy=logy, loglog=loglog, - xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim, - rot=rot, fontsize=fontsize, colormap=colormap, table=table, - yerr=yerr, xerr=xerr, - secondary_y=secondary_y, sort_columns=sort_columns, - **kwds) - - -@Appender(_shared_docs['plot'] % _shared_doc_series_kwargs) -def plot_series(data, kind='line', ax=None, # Series unique - figsize=None, use_index=True, title=None, grid=None, - legend=False, style=None, logx=False, logy=False, loglog=False, - xticks=None, yticks=None, xlim=None, ylim=None, - rot=None, fontsize=None, colormap=None, table=False, - yerr=None, xerr=None, - label=None, secondary_y=False, # Series unique - **kwds): - - import matplotlib.pyplot as plt - """ - If no axes is specified, check whether there are existing figures - If there is no existing figures, _gca() will - create a figure with the default figsize, causing the figsize=parameter to - be ignored. - """ - if ax is None and len(plt.get_fignums()) > 0: - ax = _gca() - ax = MPLPlot._get_ax_layer(ax) - return _plot(data, kind=kind, ax=ax, - figsize=figsize, use_index=use_index, title=title, - grid=grid, legend=legend, - style=style, logx=logx, logy=logy, loglog=loglog, - xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim, - rot=rot, fontsize=fontsize, colormap=colormap, table=table, - yerr=yerr, xerr=xerr, - label=label, secondary_y=secondary_y, - **kwds) - - -_shared_docs['boxplot'] = """ - Make a box plot from DataFrame column optionally grouped by some columns or - other inputs - - Parameters - ---------- - data : the pandas object holding the data - column : column name or list of names, or vector - Can be any valid input to groupby - by : string or sequence - Column in the DataFrame to group by - ax : Matplotlib axes object, optional - fontsize : int or string - rot : label rotation angle - figsize : A tuple (width, height) in inches - grid : Setting this to True will show the grid - layout : tuple (optional) - (rows, columns) for the layout of the plot - return_type : {None, 'axes', 'dict', 'both'}, default None - The kind of object to return. The default is ``axes`` - 'axes' returns the matplotlib axes the boxplot is drawn on; - 'dict' returns a dictionary whose values are the matplotlib - Lines of the boxplot; - 'both' returns a namedtuple with the axes and dict. - - When grouping with ``by``, a Series mapping columns to ``return_type`` - is returned, unless ``return_type`` is None, in which case a NumPy - array of axes is returned with the same shape as ``layout``. - See the prose documentation for more. - - kwds : other plotting keyword arguments to be passed to matplotlib boxplot - function - - Returns - ------- - lines : dict - ax : matplotlib Axes - (ax, lines): namedtuple - - Notes - ----- - Use ``return_type='dict'`` when you want to tweak the appearance - of the lines after plotting. In this case a dict containing the Lines - making up the boxes, caps, fliers, medians, and whiskers is returned. - """ - - -@Appender(_shared_docs['boxplot'] % _shared_doc_kwargs) -def boxplot(data, column=None, by=None, ax=None, fontsize=None, - rot=0, grid=True, figsize=None, layout=None, return_type=None, - **kwds): - - # validate return_type: - if return_type not in BoxPlot._valid_return_types: - raise ValueError("return_type must be {'axes', 'dict', 'both'}") - - from pandas import Series, DataFrame - if isinstance(data, Series): - data = DataFrame({'x': data}) - column = 'x' - - def _get_colors(): - return _get_standard_colors(color=kwds.get('color'), num_colors=1) - - def maybe_color_bp(bp): - if 'color' not in kwds: - from matplotlib.artist import setp - setp(bp['boxes'], color=colors[0], alpha=1) - setp(bp['whiskers'], color=colors[0], alpha=1) - setp(bp['medians'], color=colors[2], alpha=1) - - def plot_group(keys, values, ax): - keys = [pprint_thing(x) for x in keys] - values = [remove_na(v) for v in values] - bp = ax.boxplot(values, **kwds) - if fontsize is not None: - ax.tick_params(axis='both', labelsize=fontsize) - if kwds.get('vert', 1): - ax.set_xticklabels(keys, rotation=rot) - else: - ax.set_yticklabels(keys, rotation=rot) - maybe_color_bp(bp) - - # Return axes in multiplot case, maybe revisit later # 985 - if return_type == 'dict': - return bp - elif return_type == 'both': - return BoxPlot.BP(ax=ax, lines=bp) - else: - return ax - - colors = _get_colors() - if column is None: - columns = None - else: - if isinstance(column, (list, tuple)): - columns = column - else: - columns = [column] - - if by is not None: - # Prefer array return type for 2-D plots to match the subplot layout - # https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580 - result = _grouped_plot_by_column(plot_group, data, columns=columns, - by=by, grid=grid, figsize=figsize, - ax=ax, layout=layout, - return_type=return_type) - else: - if return_type is None: - return_type = 'axes' - if layout is not None: - raise ValueError("The 'layout' keyword is not supported when " - "'by' is None") - - if ax is None: - ax = _gca() - data = data._get_numeric_data() - if columns is None: - columns = data.columns - else: - data = data[columns] - - result = plot_group(columns, data.values.T, ax) - ax.grid(grid) - - return result - - -def format_date_labels(ax, rot): - # mini version of autofmt_xdate - try: - for label in ax.get_xticklabels(): - label.set_ha('right') - label.set_rotation(rot) - fig = ax.get_figure() - fig.subplots_adjust(bottom=0.2) - except Exception: # pragma: no cover - pass - - -def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False, - **kwargs): - """ - Make a scatter plot from two DataFrame columns - - Parameters - ---------- - data : DataFrame - x : Column name for the x-axis values - y : Column name for the y-axis values - ax : Matplotlib axis object - figsize : A tuple (width, height) in inches - grid : Setting this to True will show the grid - kwargs : other plotting keyword arguments - To be passed to scatter function - - Returns - ------- - fig : matplotlib.Figure - """ - import matplotlib.pyplot as plt - - # workaround because `c='b'` is hardcoded in matplotlibs scatter method - kwargs.setdefault('c', plt.rcParams['patch.facecolor']) - - def plot_group(group, ax): - xvals = group[x].values - yvals = group[y].values - ax.scatter(xvals, yvals, **kwargs) - ax.grid(grid) - - if by is not None: - fig = _grouped_plot(plot_group, data, by=by, figsize=figsize, ax=ax) - else: - if ax is None: - fig = plt.figure() - ax = fig.add_subplot(111) - else: - fig = ax.get_figure() - plot_group(data, ax) - ax.set_ylabel(pprint_thing(y)) - ax.set_xlabel(pprint_thing(x)) - - ax.grid(grid) - - return fig - - -def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, - xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, - sharey=False, figsize=None, layout=None, bins=10, **kwds): - """ - Draw histogram of the DataFrame's series using matplotlib / pylab. - - Parameters - ---------- - data : DataFrame - column : string or sequence - If passed, will be used to limit data to a subset of columns - by : object, optional - If passed, then used to form histograms for separate groups - grid : boolean, default True - Whether to show axis grid lines - xlabelsize : int, default None - If specified changes the x-axis label size - xrot : float, default None - rotation of x axis labels - ylabelsize : int, default None - If specified changes the y-axis label size - yrot : float, default None - rotation of y axis labels - ax : matplotlib axes object, default None - sharex : boolean, default True if ax is None else False - In case subplots=True, share x axis and set some x axis labels to - invisible; defaults to True if ax is None otherwise False if an ax - is passed in; Be aware, that passing in both an ax and sharex=True - will alter all x axis labels for all subplots in a figure! - sharey : boolean, default False - In case subplots=True, share y axis and set some y axis labels to - invisible - figsize : tuple - The size of the figure to create in inches by default - layout : tuple, optional - Tuple of (rows, columns) for the layout of the histograms - bins : integer, default 10 - Number of histogram bins to be used - kwds : other plotting keyword arguments - To be passed to hist function - """ - - if by is not None: - axes = grouped_hist(data, column=column, by=by, ax=ax, grid=grid, - figsize=figsize, sharex=sharex, sharey=sharey, - layout=layout, bins=bins, xlabelsize=xlabelsize, - xrot=xrot, ylabelsize=ylabelsize, - yrot=yrot, **kwds) - return axes - - if column is not None: - if not isinstance(column, (list, np.ndarray, Index)): - column = [column] - data = data[column] - data = data._get_numeric_data() - naxes = len(data.columns) - - fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False, - sharex=sharex, sharey=sharey, figsize=figsize, - layout=layout) - _axes = _flatten(axes) - - for i, col in enumerate(_try_sort(data.columns)): - ax = _axes[i] - ax.hist(data[col].dropna().values, bins=bins, **kwds) - ax.set_title(col) - ax.grid(grid) - - _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, - ylabelsize=ylabelsize, yrot=yrot) - fig.subplots_adjust(wspace=0.3, hspace=0.3) - - return axes - - -def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, - xrot=None, ylabelsize=None, yrot=None, figsize=None, - bins=10, **kwds): - """ - Draw histogram of the input series using matplotlib - - Parameters - ---------- - by : object, optional - If passed, then used to form histograms for separate groups - ax : matplotlib axis object - If not passed, uses gca() - grid : boolean, default True - Whether to show axis grid lines - xlabelsize : int, default None - If specified changes the x-axis label size - xrot : float, default None - rotation of x axis labels - ylabelsize : int, default None - If specified changes the y-axis label size - yrot : float, default None - rotation of y axis labels - figsize : tuple, default None - figure size in inches by default - bins: integer, default 10 - Number of histogram bins to be used - kwds : keywords - To be passed to the actual plotting function - - Notes - ----- - See matplotlib documentation online for more on this - - """ - import matplotlib.pyplot as plt - - if by is None: - if kwds.get('layout', None) is not None: - raise ValueError("The 'layout' keyword is not supported when " - "'by' is None") - # hack until the plotting interface is a bit more unified - fig = kwds.pop('figure', plt.gcf() if plt.get_fignums() else - plt.figure(figsize=figsize)) - if (figsize is not None and tuple(figsize) != - tuple(fig.get_size_inches())): - fig.set_size_inches(*figsize, forward=True) - if ax is None: - ax = fig.gca() - elif ax.get_figure() != fig: - raise AssertionError('passed axis not bound to passed figure') - values = self.dropna().values - - ax.hist(values, bins=bins, **kwds) - ax.grid(grid) - axes = np.array([ax]) - - _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, - ylabelsize=ylabelsize, yrot=yrot) - - else: - if 'figure' in kwds: - raise ValueError("Cannot pass 'figure' when using the " - "'by' argument, since a new 'Figure' instance " - "will be created") - axes = grouped_hist(self, by=by, ax=ax, grid=grid, figsize=figsize, - bins=bins, xlabelsize=xlabelsize, xrot=xrot, - ylabelsize=ylabelsize, yrot=yrot, **kwds) - - if hasattr(axes, 'ndim'): - if axes.ndim == 1 and len(axes) == 1: - return axes[0] - return axes - - -def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, - layout=None, sharex=False, sharey=False, rot=90, grid=True, - xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, - **kwargs): - """ - Grouped histogram - - Parameters - ---------- - data: Series/DataFrame - column: object, optional - by: object, optional - ax: axes, optional - bins: int, default 50 - figsize: tuple, optional - layout: optional - sharex: boolean, default False - sharey: boolean, default False - rot: int, default 90 - grid: bool, default True - kwargs: dict, keyword arguments passed to matplotlib.Axes.hist - - Returns - ------- - axes: collection of Matplotlib Axes - """ - def plot_group(group, ax): - ax.hist(group.dropna().values, bins=bins, **kwargs) - - xrot = xrot or rot - - fig, axes = _grouped_plot(plot_group, data, column=column, - by=by, sharex=sharex, sharey=sharey, ax=ax, - figsize=figsize, layout=layout, rot=rot) - - _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, - ylabelsize=ylabelsize, yrot=yrot) - - fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, - hspace=0.5, wspace=0.3) - return axes - - -def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None, - rot=0, grid=True, ax=None, figsize=None, - layout=None, **kwds): - """ - Make box plots from DataFrameGroupBy data. - - Parameters - ---------- - grouped : Grouped DataFrame - subplots : - * ``False`` - no subplots will be used - * ``True`` - create a subplot for each group - column : column name or list of names, or vector - Can be any valid input to groupby - fontsize : int or string - rot : label rotation angle - grid : Setting this to True will show the grid - ax : Matplotlib axis object, default None - figsize : A tuple (width, height) in inches - layout : tuple (optional) - (rows, columns) for the layout of the plot - kwds : other plotting keyword arguments to be passed to matplotlib boxplot - function - - Returns - ------- - dict of key/value = group key/DataFrame.boxplot return value - or DataFrame.boxplot return value in case subplots=figures=False - - Examples - -------- - >>> import pandas - >>> import numpy as np - >>> import itertools - >>> - >>> tuples = [t for t in itertools.product(range(1000), range(4))] - >>> index = pandas.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1']) - >>> data = np.random.randn(len(index),4) - >>> df = pandas.DataFrame(data, columns=list('ABCD'), index=index) - >>> - >>> grouped = df.groupby(level='lvl1') - >>> boxplot_frame_groupby(grouped) - >>> - >>> grouped = df.unstack(level='lvl1').groupby(level=0, axis=1) - >>> boxplot_frame_groupby(grouped, subplots=False) - """ - if subplots is True: - naxes = len(grouped) - fig, axes = _subplots(naxes=naxes, squeeze=False, - ax=ax, sharex=False, sharey=True, - figsize=figsize, layout=layout) - axes = _flatten(axes) - - ret = Series() - for (key, group), ax in zip(grouped, axes): - d = group.boxplot(ax=ax, column=column, fontsize=fontsize, - rot=rot, grid=grid, **kwds) - ax.set_title(pprint_thing(key)) - ret.loc[key] = d - fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, - right=0.9, wspace=0.2) - else: - from pandas.tools.merge import concat - keys, frames = zip(*grouped) - if grouped.axis == 0: - df = concat(frames, keys=keys, axis=1) - else: - if len(frames) > 1: - df = frames[0].join(frames[1::]) - else: - df = frames[0] - ret = df.boxplot(column=column, fontsize=fontsize, rot=rot, - grid=grid, ax=ax, figsize=figsize, - layout=layout, **kwds) - return ret - - -def _grouped_plot(plotf, data, column=None, by=None, numeric_only=True, - figsize=None, sharex=True, sharey=True, layout=None, - rot=0, ax=None, **kwargs): - from pandas import DataFrame - - if figsize == 'default': - # allowed to specify mpl default with 'default' - warnings.warn("figsize='default' is deprecated. Specify figure" - "size by tuple instead", FutureWarning, stacklevel=4) - figsize = None - - grouped = data.groupby(by) - if column is not None: - grouped = grouped[column] - - naxes = len(grouped) - fig, axes = _subplots(naxes=naxes, figsize=figsize, - sharex=sharex, sharey=sharey, ax=ax, - layout=layout) - - _axes = _flatten(axes) - - for i, (key, group) in enumerate(grouped): - ax = _axes[i] - if numeric_only and isinstance(group, DataFrame): - group = group._get_numeric_data() - plotf(group, ax, **kwargs) - ax.set_title(pprint_thing(key)) - - return fig, axes - - -def _grouped_plot_by_column(plotf, data, columns=None, by=None, - numeric_only=True, grid=False, - figsize=None, ax=None, layout=None, - return_type=None, **kwargs): - grouped = data.groupby(by) - if columns is None: - if not isinstance(by, (list, tuple)): - by = [by] - columns = data._get_numeric_data().columns.difference(by) - naxes = len(columns) - fig, axes = _subplots(naxes=naxes, sharex=True, sharey=True, - figsize=figsize, ax=ax, layout=layout) - - _axes = _flatten(axes) - - result = Series() - ax_values = [] - - for i, col in enumerate(columns): - ax = _axes[i] - gp_col = grouped[col] - keys, values = zip(*gp_col) - re_plotf = plotf(keys, values, ax, **kwargs) - ax.set_title(col) - ax.set_xlabel(pprint_thing(by)) - ax_values.append(re_plotf) - ax.grid(grid) - - result = Series(ax_values, index=columns) - - # Return axes in multiplot case, maybe revisit later # 985 - if return_type is None: - result = axes - - byline = by[0] if len(by) == 1 else by - fig.suptitle('Boxplot grouped by %s' % byline) - fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2) - - return result - - -def table(ax, data, rowLabels=None, colLabels=None, - **kwargs): - """ - Helper function to convert DataFrame and Series to matplotlib.table - - Parameters - ---------- - `ax`: Matplotlib axes object - `data`: DataFrame or Series - data for table contents - `kwargs`: keywords, optional - keyword arguments which passed to matplotlib.table.table. - If `rowLabels` or `colLabels` is not specified, data index or column - name will be used. - - Returns - ------- - matplotlib table object - """ - from pandas import DataFrame - if isinstance(data, Series): - data = DataFrame(data, columns=[data.name]) - elif isinstance(data, DataFrame): - pass - else: - raise ValueError('Input data must be DataFrame or Series') - - if rowLabels is None: - rowLabels = data.index - - if colLabels is None: - colLabels = data.columns - - cellText = data.values - - import matplotlib.table - table = matplotlib.table.table(ax, cellText=cellText, - rowLabels=rowLabels, - colLabels=colLabels, **kwargs) - return table - - -def _get_layout(nplots, layout=None, layout_type='box'): - if layout is not None: - if not isinstance(layout, (tuple, list)) or len(layout) != 2: - raise ValueError('Layout must be a tuple of (rows, columns)') - - nrows, ncols = layout - - # Python 2 compat - ceil_ = lambda x: int(ceil(x)) - if nrows == -1 and ncols > 0: - layout = nrows, ncols = (ceil_(float(nplots) / ncols), ncols) - elif ncols == -1 and nrows > 0: - layout = nrows, ncols = (nrows, ceil_(float(nplots) / nrows)) - elif ncols <= 0 and nrows <= 0: - msg = "At least one dimension of layout must be positive" - raise ValueError(msg) - - if nrows * ncols < nplots: - raise ValueError('Layout of %sx%s must be larger than ' - 'required size %s' % (nrows, ncols, nplots)) - - return layout - - if layout_type == 'single': - return (1, 1) - elif layout_type == 'horizontal': - return (1, nplots) - elif layout_type == 'vertical': - return (nplots, 1) - - layouts = {1: (1, 1), 2: (1, 2), 3: (2, 2), 4: (2, 2)} - try: - return layouts[nplots] - except KeyError: - k = 1 - while k ** 2 < nplots: - k += 1 - - if (k - 1) * k >= nplots: - return k, (k - 1) - else: - return k, k - -# copied from matplotlib/pyplot.py and modified for pandas.plotting - - -def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True, - subplot_kw=None, ax=None, layout=None, layout_type='box', - **fig_kw): - """Create a figure with a set of subplots already made. - - This utility wrapper makes it convenient to create common layouts of - subplots, including the enclosing figure object, in a single call. - - Keyword arguments: - - naxes : int - Number of required axes. Exceeded axes are set invisible. Default is - nrows * ncols. - - sharex : bool - If True, the X axis will be shared amongst all subplots. - - sharey : bool - If True, the Y axis will be shared amongst all subplots. - - squeeze : bool - - If True, extra dimensions are squeezed out from the returned axis object: - - if only one subplot is constructed (nrows=ncols=1), the resulting - single Axis object is returned as a scalar. - - for Nx1 or 1xN subplots, the returned object is a 1-d numpy object - array of Axis objects are returned as numpy 1-d arrays. - - for NxM subplots with N>1 and M>1 are returned as a 2d array. - - If False, no squeezing at all is done: the returned axis object is always - a 2-d array containing Axis instances, even if it ends up being 1x1. - - subplot_kw : dict - Dict with keywords passed to the add_subplot() call used to create each - subplots. - - ax : Matplotlib axis object, optional - - layout : tuple - Number of rows and columns of the subplot grid. - If not specified, calculated from naxes and layout_type - - layout_type : {'box', 'horziontal', 'vertical'}, default 'box' - Specify how to layout the subplot grid. - - fig_kw : Other keyword arguments to be passed to the figure() call. - Note that all keywords not recognized above will be - automatically included here. - - Returns: - - fig, ax : tuple - - fig is the Matplotlib Figure object - - ax can be either a single axis object or an array of axis objects if - more than one subplot was created. The dimensions of the resulting array - can be controlled with the squeeze keyword, see above. - - **Examples:** - - x = np.linspace(0, 2*np.pi, 400) - y = np.sin(x**2) - - # Just a figure and one subplot - f, ax = plt.subplots() - ax.plot(x, y) - ax.set_title('Simple plot') - - # Two subplots, unpack the output array immediately - f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) - ax1.plot(x, y) - ax1.set_title('Sharing Y axis') - ax2.scatter(x, y) - - # Four polar axes - plt.subplots(2, 2, subplot_kw=dict(polar=True)) - """ - import matplotlib.pyplot as plt - - if subplot_kw is None: - subplot_kw = {} - - if ax is None: - fig = plt.figure(**fig_kw) - else: - if is_list_like(ax): - ax = _flatten(ax) - if layout is not None: - warnings.warn("When passing multiple axes, layout keyword is " - "ignored", UserWarning) - if sharex or sharey: - warnings.warn("When passing multiple axes, sharex and sharey " - "are ignored. These settings must be specified " - "when creating axes", UserWarning, - stacklevel=4) - if len(ax) == naxes: - fig = ax[0].get_figure() - return fig, ax - else: - raise ValueError("The number of passed axes must be {0}, the " - "same as the output plot".format(naxes)) - - fig = ax.get_figure() - # if ax is passed and a number of subplots is 1, return ax as it is - if naxes == 1: - if squeeze: - return fig, ax - else: - return fig, _flatten(ax) - else: - warnings.warn("To output multiple subplots, the figure containing " - "the passed axes is being cleared", UserWarning, - stacklevel=4) - fig.clear() - - nrows, ncols = _get_layout(naxes, layout=layout, layout_type=layout_type) - nplots = nrows * ncols - - # Create empty object array to hold all axes. It's easiest to make it 1-d - # so we can just append subplots upon creation, and then - axarr = np.empty(nplots, dtype=object) - - # Create first subplot separately, so we can share it if requested - ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw) - - if sharex: - subplot_kw['sharex'] = ax0 - if sharey: - subplot_kw['sharey'] = ax0 - axarr[0] = ax0 - - # Note off-by-one counting because add_subplot uses the MATLAB 1-based - # convention. - for i in range(1, nplots): - kwds = subplot_kw.copy() - # Set sharex and sharey to None for blank/dummy axes, these can - # interfere with proper axis limits on the visible axes if - # they share axes e.g. issue #7528 - if i >= naxes: - kwds['sharex'] = None - kwds['sharey'] = None - ax = fig.add_subplot(nrows, ncols, i + 1, **kwds) - axarr[i] = ax - - if naxes != nplots: - for ax in axarr[naxes:]: - ax.set_visible(False) - - _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey) - - if squeeze: - # Reshape the array to have the final desired dimension (nrow,ncol), - # though discarding unneeded dimensions that equal 1. If we only have - # one subplot, just return it instead of a 1-element array. - if nplots == 1: - axes = axarr[0] - else: - axes = axarr.reshape(nrows, ncols).squeeze() - else: - # returned axis array will be always 2-d, even if nrows=ncols=1 - axes = axarr.reshape(nrows, ncols) - - return fig, axes - - -def _remove_labels_from_axis(axis): - for t in axis.get_majorticklabels(): - t.set_visible(False) - - try: - # set_visible will not be effective if - # minor axis has NullLocator and NullFormattor (default) - import matplotlib.ticker as ticker - if isinstance(axis.get_minor_locator(), ticker.NullLocator): - axis.set_minor_locator(ticker.AutoLocator()) - if isinstance(axis.get_minor_formatter(), ticker.NullFormatter): - axis.set_minor_formatter(ticker.FormatStrFormatter('')) - for t in axis.get_minorticklabels(): - t.set_visible(False) - except Exception: # pragma no cover - raise - axis.get_label().set_visible(False) - - -def _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey): - if nplots > 1: - - if nrows > 1: - try: - # first find out the ax layout, - # so that we can correctly handle 'gaps" - layout = np.zeros((nrows + 1, ncols + 1), dtype=np.bool) - for ax in axarr: - layout[ax.rowNum, ax.colNum] = ax.get_visible() - - for ax in axarr: - # only the last row of subplots should get x labels -> all - # other off layout handles the case that the subplot is - # the last in the column, because below is no subplot/gap. - if not layout[ax.rowNum + 1, ax.colNum]: - continue - if sharex or len(ax.get_shared_x_axes() - .get_siblings(ax)) > 1: - _remove_labels_from_axis(ax.xaxis) - - except IndexError: - # if gridspec is used, ax.rowNum and ax.colNum may different - # from layout shape. in this case, use last_row logic - for ax in axarr: - if ax.is_last_row(): - continue - if sharex or len(ax.get_shared_x_axes() - .get_siblings(ax)) > 1: - _remove_labels_from_axis(ax.xaxis) - - if ncols > 1: - for ax in axarr: - # only the first column should get y labels -> set all other to - # off as we only have labels in teh first column and we always - # have a subplot there, we can skip the layout test - if ax.is_first_col(): - continue - if sharey or len(ax.get_shared_y_axes().get_siblings(ax)) > 1: - _remove_labels_from_axis(ax.yaxis) - - -def _flatten(axes): - if not is_list_like(axes): - return np.array([axes]) - elif isinstance(axes, (np.ndarray, Index)): - return axes.ravel() - return np.array(axes) - - -def _get_all_lines(ax): - lines = ax.get_lines() - - if hasattr(ax, 'right_ax'): - lines += ax.right_ax.get_lines() - - if hasattr(ax, 'left_ax'): - lines += ax.left_ax.get_lines() - - return lines - - -def _get_xlim(lines): - left, right = np.inf, -np.inf - for l in lines: - x = l.get_xdata(orig=False) - left = min(x[0], left) - right = max(x[-1], right) - return left, right - - -def _set_ticks_props(axes, xlabelsize=None, xrot=None, - ylabelsize=None, yrot=None): - import matplotlib.pyplot as plt - - for ax in _flatten(axes): - if xlabelsize is not None: - plt.setp(ax.get_xticklabels(), fontsize=xlabelsize) - if xrot is not None: - plt.setp(ax.get_xticklabels(), rotation=xrot) - if ylabelsize is not None: - plt.setp(ax.get_yticklabels(), fontsize=ylabelsize) - if yrot is not None: - plt.setp(ax.get_yticklabels(), rotation=yrot) - return axes - - -class BasePlotMethods(PandasObject): - - def __init__(self, data): - self._data = data - - def __call__(self, *args, **kwargs): - raise NotImplementedError - - -class SeriesPlotMethods(BasePlotMethods): - """Series plotting accessor and method - - Examples - -------- - >>> s.plot.line() - >>> s.plot.bar() - >>> s.plot.hist() - - Plotting methods can also be accessed by calling the accessor as a method - with the ``kind`` argument: - ``s.plot(kind='line')`` is equivalent to ``s.plot.line()`` - """ - - def __call__(self, kind='line', ax=None, - figsize=None, use_index=True, title=None, grid=None, - legend=False, style=None, logx=False, logy=False, - loglog=False, xticks=None, yticks=None, - xlim=None, ylim=None, - rot=None, fontsize=None, colormap=None, table=False, - yerr=None, xerr=None, - label=None, secondary_y=False, **kwds): - return plot_series(self._data, kind=kind, ax=ax, figsize=figsize, - use_index=use_index, title=title, grid=grid, - legend=legend, style=style, logx=logx, logy=logy, - loglog=loglog, xticks=xticks, yticks=yticks, - xlim=xlim, ylim=ylim, rot=rot, fontsize=fontsize, - colormap=colormap, table=table, yerr=yerr, - xerr=xerr, label=label, secondary_y=secondary_y, - **kwds) - __call__.__doc__ = plot_series.__doc__ - - def line(self, **kwds): - """ - Line plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='line', **kwds) - - def bar(self, **kwds): - """ - Vertical bar plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='bar', **kwds) - - def barh(self, **kwds): - """ - Horizontal bar plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='barh', **kwds) - - def box(self, **kwds): - """ - Boxplot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='box', **kwds) - - def hist(self, bins=10, **kwds): - """ - Histogram - - .. versionadded:: 0.17.0 - - Parameters - ---------- - bins: integer, default 10 - Number of histogram bins to be used - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='hist', bins=bins, **kwds) - - def kde(self, **kwds): - """ - Kernel Density Estimate plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='kde', **kwds) - - density = kde - - def area(self, **kwds): - """ - Area plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='area', **kwds) - - def pie(self, **kwds): - """ - Pie chart - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.Series.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='pie', **kwds) - - -class FramePlotMethods(BasePlotMethods): - """DataFrame plotting accessor and method - - Examples - -------- - >>> df.plot.line() - >>> df.plot.scatter('x', 'y') - >>> df.plot.hexbin() - - These plotting methods can also be accessed by calling the accessor as a - method with the ``kind`` argument: - ``df.plot(kind='line')`` is equivalent to ``df.plot.line()`` - """ - - def __call__(self, x=None, y=None, kind='line', ax=None, - subplots=False, sharex=None, sharey=False, layout=None, - figsize=None, use_index=True, title=None, grid=None, - legend=True, style=None, logx=False, logy=False, loglog=False, - xticks=None, yticks=None, xlim=None, ylim=None, - rot=None, fontsize=None, colormap=None, table=False, - yerr=None, xerr=None, - secondary_y=False, sort_columns=False, **kwds): - return plot_frame(self._data, kind=kind, x=x, y=y, ax=ax, - subplots=subplots, sharex=sharex, sharey=sharey, - layout=layout, figsize=figsize, use_index=use_index, - title=title, grid=grid, legend=legend, style=style, - logx=logx, logy=logy, loglog=loglog, xticks=xticks, - yticks=yticks, xlim=xlim, ylim=ylim, rot=rot, - fontsize=fontsize, colormap=colormap, table=table, - yerr=yerr, xerr=xerr, secondary_y=secondary_y, - sort_columns=sort_columns, **kwds) - __call__.__doc__ = plot_frame.__doc__ - - def line(self, x=None, y=None, **kwds): - """ - Line plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - x, y : label or position, optional - Coordinates for each point. - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='line', x=x, y=y, **kwds) - - def bar(self, x=None, y=None, **kwds): - """ - Vertical bar plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - x, y : label or position, optional - Coordinates for each point. - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='bar', x=x, y=y, **kwds) - - def barh(self, x=None, y=None, **kwds): - """ - Horizontal bar plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - x, y : label or position, optional - Coordinates for each point. - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='barh', x=x, y=y, **kwds) - - def box(self, by=None, **kwds): - """ - Boxplot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - by : string or sequence - Column in the DataFrame to group by. - \*\*kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='box', by=by, **kwds) - - def hist(self, by=None, bins=10, **kwds): - """ - Histogram - - .. versionadded:: 0.17.0 - - Parameters - ---------- - by : string or sequence - Column in the DataFrame to group by. - bins: integer, default 10 - Number of histogram bins to be used - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='hist', by=by, bins=bins, **kwds) - - def kde(self, **kwds): - """ - Kernel Density Estimate plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='kde', **kwds) - - density = kde - - def area(self, x=None, y=None, **kwds): - """ - Area plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - x, y : label or position, optional - Coordinates for each point. - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='area', x=x, y=y, **kwds) - - def pie(self, y=None, **kwds): - """ - Pie chart - - .. versionadded:: 0.17.0 - - Parameters - ---------- - y : label or position, optional - Column to plot. - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='pie', y=y, **kwds) - - def scatter(self, x, y, s=None, c=None, **kwds): - """ - Scatter plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - x, y : label or position, optional - Coordinates for each point. - s : scalar or array_like, optional - Size of each point. - c : label or position, optional - Color of each point. - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - return self(kind='scatter', x=x, y=y, c=c, s=s, **kwds) - - def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None, - **kwds): - """ - Hexbin plot - - .. versionadded:: 0.17.0 - - Parameters - ---------- - x, y : label or position, optional - Coordinates for each point. - C : label or position, optional - The value at each `(x, y)` point. - reduce_C_function : callable, optional - Function of one argument that reduces all the values in a bin to - a single number (e.g. `mean`, `max`, `sum`, `std`). - gridsize : int, optional - Number of bins. - **kwds : optional - Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`. - - Returns - ------- - axes : matplotlib.AxesSubplot or np.array of them - """ - if reduce_C_function is not None: - kwds['reduce_C_function'] = reduce_C_function - if gridsize is not None: - kwds['gridsize'] = gridsize - return self(kind='hexbin', x=x, y=y, C=C, **kwds) - - -if __name__ == '__main__': - # import pandas.rpy.common as com - # sales = com.load_data('sanfrancisco.home.sales', package='nutshell') - # top10 = sales['zip'].value_counts()[:10].index - # sales2 = sales[sales.zip.isin(top10)] - # _ = scatter_plot(sales2, 'squarefeet', 'price', by='zip') - # plt.show() +import pandas.plotting.api as api - import matplotlib.pyplot as plt +# back-compat of public API +# deprecate these functions +m = sys.modules['pandas.tools.plotting'] +for t in [t for t in dir(api) if not t.startswith('_')]: - import pandas.tools.plotting as plots - import pandas.core.frame as fr - reload(plots) # noqa - reload(fr) # noqa - from pandas.core.frame import DataFrame + def outer(t=t): - data = DataFrame([[3, 6, -5], [4, 8, 2], [4, 9, -6], - [4, 9, -3], [2, 5, -1]], - columns=['A', 'B', 'C']) - data.plot(kind='barh', stacked=True) + def wrapper(*args, **kwargs): + warnings.warn("pandas.tools.plotting.{t} is deprecated. " + "import from the " + "pandas.plotting.{t} instead".format(t=t), + FutureWarning, stacklevel=2) + return getattr(api, t)(*args, **kwargs) + return wrapper - plt.show() + setattr(m, t, outer(t)) diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py index 95ff9578fa3ee..a93515b110cf4 100644 --- a/pandas/tseries/converter.py +++ b/pandas/tseries/converter.py @@ -1,1002 +1,11 @@ -from datetime import datetime, timedelta -import datetime as pydt -import numpy as np - -from dateutil.relativedelta import relativedelta - -import matplotlib.units as units -import matplotlib.dates as dates - -from matplotlib.ticker import Formatter, AutoLocator, Locator -from matplotlib.transforms import nonsingular - - -from pandas.types.common import (is_float, is_integer, - is_integer_dtype, - is_float_dtype, - is_datetime64_ns_dtype, - is_period_arraylike, - ) - -from pandas.compat import lrange -import pandas.compat as compat -import pandas.lib as lib -import pandas.core.common as com -from pandas.core.index import Index - -from pandas.core.series import Series -from pandas.tseries.index import date_range -import pandas.tseries.tools as tools -import pandas.tseries.frequencies as frequencies -from pandas.tseries.frequencies import FreqGroup -from pandas.tseries.period import Period, PeriodIndex - -# constants -HOURS_PER_DAY = 24. -MIN_PER_HOUR = 60. -SEC_PER_MIN = 60. - -SEC_PER_HOUR = SEC_PER_MIN * MIN_PER_HOUR -SEC_PER_DAY = SEC_PER_HOUR * HOURS_PER_DAY - -MUSEC_PER_DAY = 1e6 * SEC_PER_DAY - - -def _mpl_le_2_0_0(): - try: - import matplotlib - return matplotlib.compare_versions('2.0.0', matplotlib.__version__) - except ImportError: - return False - - -def register(): - units.registry[lib.Timestamp] = DatetimeConverter() - units.registry[Period] = PeriodConverter() - units.registry[pydt.datetime] = DatetimeConverter() - units.registry[pydt.date] = DatetimeConverter() - units.registry[pydt.time] = TimeConverter() - units.registry[np.datetime64] = DatetimeConverter() - - -def _to_ordinalf(tm): - tot_sec = (tm.hour * 3600 + tm.minute * 60 + tm.second + - float(tm.microsecond / 1e6)) - return tot_sec - - -def time2num(d): - if isinstance(d, compat.string_types): - parsed = tools.to_datetime(d) - if not isinstance(parsed, datetime): - raise ValueError('Could not parse time %s' % d) - return _to_ordinalf(parsed.time()) - if isinstance(d, pydt.time): - return _to_ordinalf(d) - return d - - -class TimeConverter(units.ConversionInterface): - - @staticmethod - def convert(value, unit, axis): - valid_types = (str, pydt.time) - if (isinstance(value, valid_types) or is_integer(value) or - is_float(value)): - return time2num(value) - if isinstance(value, Index): - return value.map(time2num) - if isinstance(value, (list, tuple, np.ndarray, Index)): - return [time2num(x) for x in value] - return value - - @staticmethod - def axisinfo(unit, axis): - if unit != 'time': - return None - - majloc = AutoLocator() - majfmt = TimeFormatter(majloc) - return units.AxisInfo(majloc=majloc, majfmt=majfmt, label='time') - - @staticmethod - def default_units(x, axis): - return 'time' - - -# time formatter -class TimeFormatter(Formatter): - - def __init__(self, locs): - self.locs = locs - - def __call__(self, x, pos=0): - fmt = '%H:%M:%S' - s = int(x) - ms = int((x - s) * 1e3) - us = int((x - s) * 1e6 - ms) - m, s = divmod(s, 60) - h, m = divmod(m, 60) - _, h = divmod(h, 24) - if us != 0: - fmt += '.%6f' - elif ms != 0: - fmt += '.%3f' - - return pydt.time(h, m, s, us).strftime(fmt) - - -# Period Conversion - - -class PeriodConverter(dates.DateConverter): - - @staticmethod - def convert(values, units, axis): - if not hasattr(axis, 'freq'): - raise TypeError('Axis must have `freq` set to convert to Periods') - valid_types = (compat.string_types, datetime, - Period, pydt.date, pydt.time) - if (isinstance(values, valid_types) or is_integer(values) or - is_float(values)): - return get_datevalue(values, axis.freq) - if isinstance(values, PeriodIndex): - return values.asfreq(axis.freq)._values - if isinstance(values, Index): - return values.map(lambda x: get_datevalue(x, axis.freq)) - if is_period_arraylike(values): - return PeriodIndex(values, freq=axis.freq)._values - if isinstance(values, (list, tuple, np.ndarray, Index)): - return [get_datevalue(x, axis.freq) for x in values] - return values - - -def get_datevalue(date, freq): - if isinstance(date, Period): - return date.asfreq(freq).ordinal - elif isinstance(date, (compat.string_types, datetime, - pydt.date, pydt.time)): - return Period(date, freq).ordinal - elif (is_integer(date) or is_float(date) or - (isinstance(date, (np.ndarray, Index)) and (date.size == 1))): - return date - elif date is None: - return None - raise ValueError("Unrecognizable date '%s'" % date) - - -def _dt_to_float_ordinal(dt): - """ - Convert :mod:`datetime` to the Gregorian date as UTC float days, - preserving hours, minutes, seconds and microseconds. Return value - is a :func:`float`. - """ - if (isinstance(dt, (np.ndarray, Index, Series) - ) and is_datetime64_ns_dtype(dt)): - base = dates.epoch2num(dt.asi8 / 1.0E9) - else: - base = dates.date2num(dt) - return base - - -# Datetime Conversion -class DatetimeConverter(dates.DateConverter): - - @staticmethod - def convert(values, unit, axis): - def try_parse(values): - try: - return _dt_to_float_ordinal(tools.to_datetime(values)) - except Exception: - return values - - if isinstance(values, (datetime, pydt.date)): - return _dt_to_float_ordinal(values) - elif isinstance(values, np.datetime64): - return _dt_to_float_ordinal(lib.Timestamp(values)) - elif isinstance(values, pydt.time): - return dates.date2num(values) - elif (is_integer(values) or is_float(values)): - return values - elif isinstance(values, compat.string_types): - return try_parse(values) - elif isinstance(values, (list, tuple, np.ndarray, Index)): - if isinstance(values, Index): - values = values.values - if not isinstance(values, np.ndarray): - values = com._asarray_tuplesafe(values) - - if is_integer_dtype(values) or is_float_dtype(values): - return values - - try: - values = tools.to_datetime(values) - if isinstance(values, Index): - values = _dt_to_float_ordinal(values) - else: - values = [_dt_to_float_ordinal(x) for x in values] - except Exception: - values = _dt_to_float_ordinal(values) - - return values - - @staticmethod - def axisinfo(unit, axis): - """ - Return the :class:`~matplotlib.units.AxisInfo` for *unit*. - - *unit* is a tzinfo instance or None. - The *axis* argument is required but not used. - """ - tz = unit - - majloc = PandasAutoDateLocator(tz=tz) - majfmt = PandasAutoDateFormatter(majloc, tz=tz) - datemin = pydt.date(2000, 1, 1) - datemax = pydt.date(2010, 1, 1) - - return units.AxisInfo(majloc=majloc, majfmt=majfmt, label='', - default_limits=(datemin, datemax)) - - -class PandasAutoDateFormatter(dates.AutoDateFormatter): - - def __init__(self, locator, tz=None, defaultfmt='%Y-%m-%d'): - dates.AutoDateFormatter.__init__(self, locator, tz, defaultfmt) - # matplotlib.dates._UTC has no _utcoffset called by pandas - if self._tz is dates.UTC: - self._tz._utcoffset = self._tz.utcoffset(None) - - # For mpl > 2.0 the format strings are controlled via rcparams - # so do not mess with them. For mpl < 2.0 change the second - # break point and add a musec break point - if _mpl_le_2_0_0(): - self.scaled[1. / SEC_PER_DAY] = '%H:%M:%S' - self.scaled[1. / MUSEC_PER_DAY] = '%H:%M:%S.%f' - - -class PandasAutoDateLocator(dates.AutoDateLocator): - - def get_locator(self, dmin, dmax): - 'Pick the best locator based on a distance.' - delta = relativedelta(dmax, dmin) - - num_days = ((delta.years * 12.0) + delta.months * 31.0) + delta.days - num_sec = (delta.hours * 60.0 + delta.minutes) * 60.0 + delta.seconds - tot_sec = num_days * 86400. + num_sec - - if abs(tot_sec) < self.minticks: - self._freq = -1 - locator = MilliSecondLocator(self.tz) - locator.set_axis(self.axis) - - locator.set_view_interval(*self.axis.get_view_interval()) - locator.set_data_interval(*self.axis.get_data_interval()) - return locator - - return dates.AutoDateLocator.get_locator(self, dmin, dmax) - - def _get_unit(self): - return MilliSecondLocator.get_unit_generic(self._freq) - - -class MilliSecondLocator(dates.DateLocator): - - UNIT = 1. / (24 * 3600 * 1000) - - def __init__(self, tz): - dates.DateLocator.__init__(self, tz) - self._interval = 1. - - def _get_unit(self): - return self.get_unit_generic(-1) - - @staticmethod - def get_unit_generic(freq): - unit = dates.RRuleLocator.get_unit_generic(freq) - if unit < 0: - return MilliSecondLocator.UNIT - return unit - - def __call__(self): - # if no data have been set, this will tank with a ValueError - try: - dmin, dmax = self.viewlim_to_dt() - except ValueError: - return [] - - if dmin > dmax: - dmax, dmin = dmin, dmax - # We need to cap at the endpoints of valid datetime - - # TODO(wesm) unused? - # delta = relativedelta(dmax, dmin) - # try: - # start = dmin - delta - # except ValueError: - # start = _from_ordinal(1.0) - - # try: - # stop = dmax + delta - # except ValueError: - # # The magic number! - # stop = _from_ordinal(3652059.9999999) - - nmax, nmin = dates.date2num((dmax, dmin)) - - num = (nmax - nmin) * 86400 * 1000 - max_millis_ticks = 6 - for interval in [1, 10, 50, 100, 200, 500]: - if num <= interval * (max_millis_ticks - 1): - self._interval = interval - break - else: - # We went through the whole loop without breaking, default to 1 - self._interval = 1000. - - estimate = (nmax - nmin) / (self._get_unit() * self._get_interval()) - - if estimate > self.MAXTICKS * 2: - raise RuntimeError(('MillisecondLocator estimated to generate %d ' - 'ticks from %s to %s: exceeds Locator.MAXTICKS' - '* 2 (%d) ') % - (estimate, dmin, dmax, self.MAXTICKS * 2)) - - freq = '%dL' % self._get_interval() - tz = self.tz.tzname(None) - st = _from_ordinal(dates.date2num(dmin)) # strip tz - ed = _from_ordinal(dates.date2num(dmax)) - all_dates = date_range(start=st, end=ed, freq=freq, tz=tz).asobject - - try: - if len(all_dates) > 0: - locs = self.raise_if_exceeds(dates.date2num(all_dates)) - return locs - except Exception: # pragma: no cover - pass - - lims = dates.date2num([dmin, dmax]) - return lims - - def _get_interval(self): - return self._interval - - def autoscale(self): - """ - Set the view limits to include the data range. - """ - dmin, dmax = self.datalim_to_dt() - if dmin > dmax: - dmax, dmin = dmin, dmax - - # We need to cap at the endpoints of valid datetime - - # TODO(wesm): unused? - - # delta = relativedelta(dmax, dmin) - # try: - # start = dmin - delta - # except ValueError: - # start = _from_ordinal(1.0) - - # try: - # stop = dmax + delta - # except ValueError: - # # The magic number! - # stop = _from_ordinal(3652059.9999999) - - dmin, dmax = self.datalim_to_dt() - - vmin = dates.date2num(dmin) - vmax = dates.date2num(dmax) - - return self.nonsingular(vmin, vmax) - - -def _from_ordinal(x, tz=None): - ix = int(x) - dt = datetime.fromordinal(ix) - remainder = float(x) - ix - hour, remainder = divmod(24 * remainder, 1) - minute, remainder = divmod(60 * remainder, 1) - second, remainder = divmod(60 * remainder, 1) - microsecond = int(1e6 * remainder) - if microsecond < 10: - microsecond = 0 # compensate for rounding errors - dt = datetime(dt.year, dt.month, dt.day, int(hour), int(minute), - int(second), microsecond) - if tz is not None: - dt = dt.astimezone(tz) - - if microsecond > 999990: # compensate for rounding errors - dt += timedelta(microseconds=1e6 - microsecond) - - return dt - -# Fixed frequency dynamic tick locators and formatters - -# ------------------------------------------------------------------------- -# --- Locators --- -# ------------------------------------------------------------------------- - - -def _get_default_annual_spacing(nyears): - """ - Returns a default spacing between consecutive ticks for annual data. - """ - if nyears < 11: - (min_spacing, maj_spacing) = (1, 1) - elif nyears < 20: - (min_spacing, maj_spacing) = (1, 2) - elif nyears < 50: - (min_spacing, maj_spacing) = (1, 5) - elif nyears < 100: - (min_spacing, maj_spacing) = (5, 10) - elif nyears < 200: - (min_spacing, maj_spacing) = (5, 25) - elif nyears < 600: - (min_spacing, maj_spacing) = (10, 50) - else: - factor = nyears // 1000 + 1 - (min_spacing, maj_spacing) = (factor * 20, factor * 100) - return (min_spacing, maj_spacing) - - -def period_break(dates, period): - """ - Returns the indices where the given period changes. - - Parameters - ---------- - dates : PeriodIndex - Array of intervals to monitor. - period : string - Name of the period to monitor. - """ - current = getattr(dates, period) - previous = getattr(dates - 1, period) - return (current - previous).nonzero()[0] - - -def has_level_label(label_flags, vmin): - """ - Returns true if the ``label_flags`` indicate there is at least one label - for this level. - - if the minimum view limit is not an exact integer, then the first tick - label won't be shown, so we must adjust for that. - """ - if label_flags.size == 0 or (label_flags.size == 1 and - label_flags[0] == 0 and - vmin % 1 > 0.0): - return False - else: - return True - - -def _daily_finder(vmin, vmax, freq): - periodsperday = -1 - - if freq >= FreqGroup.FR_HR: - if freq == FreqGroup.FR_NS: - periodsperday = 24 * 60 * 60 * 1000000000 - elif freq == FreqGroup.FR_US: - periodsperday = 24 * 60 * 60 * 1000000 - elif freq == FreqGroup.FR_MS: - periodsperday = 24 * 60 * 60 * 1000 - elif freq == FreqGroup.FR_SEC: - periodsperday = 24 * 60 * 60 - elif freq == FreqGroup.FR_MIN: - periodsperday = 24 * 60 - elif freq == FreqGroup.FR_HR: - periodsperday = 24 - else: # pragma: no cover - raise ValueError("unexpected frequency: %s" % freq) - periodsperyear = 365 * periodsperday - periodspermonth = 28 * periodsperday - - elif freq == FreqGroup.FR_BUS: - periodsperyear = 261 - periodspermonth = 19 - elif freq == FreqGroup.FR_DAY: - periodsperyear = 365 - periodspermonth = 28 - elif frequencies.get_freq_group(freq) == FreqGroup.FR_WK: - periodsperyear = 52 - periodspermonth = 3 - else: # pragma: no cover - raise ValueError("unexpected frequency") - - # save this for later usage - vmin_orig = vmin - - (vmin, vmax) = (Period(ordinal=int(vmin), freq=freq), - Period(ordinal=int(vmax), freq=freq)) - span = vmax.ordinal - vmin.ordinal + 1 - dates_ = PeriodIndex(start=vmin, end=vmax, freq=freq) - # Initialize the output - info = np.zeros(span, - dtype=[('val', np.int64), ('maj', bool), - ('min', bool), ('fmt', '|S20')]) - info['val'][:] = dates_._values - info['fmt'][:] = '' - info['maj'][[0, -1]] = True - # .. and set some shortcuts - info_maj = info['maj'] - info_min = info['min'] - info_fmt = info['fmt'] - - def first_label(label_flags): - if (label_flags[0] == 0) and (label_flags.size > 1) and \ - ((vmin_orig % 1) > 0.0): - return label_flags[1] - else: - return label_flags[0] - - # Case 1. Less than a month - if span <= periodspermonth: - day_start = period_break(dates_, 'day') - month_start = period_break(dates_, 'month') - - def _hour_finder(label_interval, force_year_start): - _hour = dates_.hour - _prev_hour = (dates_ - 1).hour - hour_start = (_hour - _prev_hour) != 0 - info_maj[day_start] = True - info_min[hour_start & (_hour % label_interval == 0)] = True - year_start = period_break(dates_, 'year') - info_fmt[hour_start & (_hour % label_interval == 0)] = '%H:%M' - info_fmt[day_start] = '%H:%M\n%d-%b' - info_fmt[year_start] = '%H:%M\n%d-%b\n%Y' - if force_year_start and not has_level_label(year_start, vmin_orig): - info_fmt[first_label(day_start)] = '%H:%M\n%d-%b\n%Y' - - def _minute_finder(label_interval): - hour_start = period_break(dates_, 'hour') - _minute = dates_.minute - _prev_minute = (dates_ - 1).minute - minute_start = (_minute - _prev_minute) != 0 - info_maj[hour_start] = True - info_min[minute_start & (_minute % label_interval == 0)] = True - year_start = period_break(dates_, 'year') - info_fmt = info['fmt'] - info_fmt[minute_start & (_minute % label_interval == 0)] = '%H:%M' - info_fmt[day_start] = '%H:%M\n%d-%b' - info_fmt[year_start] = '%H:%M\n%d-%b\n%Y' - - def _second_finder(label_interval): - minute_start = period_break(dates_, 'minute') - _second = dates_.second - _prev_second = (dates_ - 1).second - second_start = (_second - _prev_second) != 0 - info['maj'][minute_start] = True - info['min'][second_start & (_second % label_interval == 0)] = True - year_start = period_break(dates_, 'year') - info_fmt = info['fmt'] - info_fmt[second_start & (_second % - label_interval == 0)] = '%H:%M:%S' - info_fmt[day_start] = '%H:%M:%S\n%d-%b' - info_fmt[year_start] = '%H:%M:%S\n%d-%b\n%Y' - - if span < periodsperday / 12000.0: - _second_finder(1) - elif span < periodsperday / 6000.0: - _second_finder(2) - elif span < periodsperday / 2400.0: - _second_finder(5) - elif span < periodsperday / 1200.0: - _second_finder(10) - elif span < periodsperday / 800.0: - _second_finder(15) - elif span < periodsperday / 400.0: - _second_finder(30) - elif span < periodsperday / 150.0: - _minute_finder(1) - elif span < periodsperday / 70.0: - _minute_finder(2) - elif span < periodsperday / 24.0: - _minute_finder(5) - elif span < periodsperday / 12.0: - _minute_finder(15) - elif span < periodsperday / 6.0: - _minute_finder(30) - elif span < periodsperday / 2.5: - _hour_finder(1, False) - elif span < periodsperday / 1.5: - _hour_finder(2, False) - elif span < periodsperday * 1.25: - _hour_finder(3, False) - elif span < periodsperday * 2.5: - _hour_finder(6, True) - elif span < periodsperday * 4: - _hour_finder(12, True) - else: - info_maj[month_start] = True - info_min[day_start] = True - year_start = period_break(dates_, 'year') - info_fmt = info['fmt'] - info_fmt[day_start] = '%d' - info_fmt[month_start] = '%d\n%b' - info_fmt[year_start] = '%d\n%b\n%Y' - if not has_level_label(year_start, vmin_orig): - if not has_level_label(month_start, vmin_orig): - info_fmt[first_label(day_start)] = '%d\n%b\n%Y' - else: - info_fmt[first_label(month_start)] = '%d\n%b\n%Y' - - # Case 2. Less than three months - elif span <= periodsperyear // 4: - month_start = period_break(dates_, 'month') - info_maj[month_start] = True - if freq < FreqGroup.FR_HR: - info['min'] = True - else: - day_start = period_break(dates_, 'day') - info['min'][day_start] = True - week_start = period_break(dates_, 'week') - year_start = period_break(dates_, 'year') - info_fmt[week_start] = '%d' - info_fmt[month_start] = '\n\n%b' - info_fmt[year_start] = '\n\n%b\n%Y' - if not has_level_label(year_start, vmin_orig): - if not has_level_label(month_start, vmin_orig): - info_fmt[first_label(week_start)] = '\n\n%b\n%Y' - else: - info_fmt[first_label(month_start)] = '\n\n%b\n%Y' - # Case 3. Less than 14 months ............... - elif span <= 1.15 * periodsperyear: - year_start = period_break(dates_, 'year') - month_start = period_break(dates_, 'month') - week_start = period_break(dates_, 'week') - info_maj[month_start] = True - info_min[week_start] = True - info_min[year_start] = False - info_min[month_start] = False - info_fmt[month_start] = '%b' - info_fmt[year_start] = '%b\n%Y' - if not has_level_label(year_start, vmin_orig): - info_fmt[first_label(month_start)] = '%b\n%Y' - # Case 4. Less than 2.5 years ............... - elif span <= 2.5 * periodsperyear: - year_start = period_break(dates_, 'year') - quarter_start = period_break(dates_, 'quarter') - month_start = period_break(dates_, 'month') - info_maj[quarter_start] = True - info_min[month_start] = True - info_fmt[quarter_start] = '%b' - info_fmt[year_start] = '%b\n%Y' - # Case 4. Less than 4 years ................. - elif span <= 4 * periodsperyear: - year_start = period_break(dates_, 'year') - month_start = period_break(dates_, 'month') - info_maj[year_start] = True - info_min[month_start] = True - info_min[year_start] = False - - month_break = dates_[month_start].month - jan_or_jul = month_start[(month_break == 1) | (month_break == 7)] - info_fmt[jan_or_jul] = '%b' - info_fmt[year_start] = '%b\n%Y' - # Case 5. Less than 11 years ................ - elif span <= 11 * periodsperyear: - year_start = period_break(dates_, 'year') - quarter_start = period_break(dates_, 'quarter') - info_maj[year_start] = True - info_min[quarter_start] = True - info_min[year_start] = False - info_fmt[year_start] = '%Y' - # Case 6. More than 12 years ................ - else: - year_start = period_break(dates_, 'year') - year_break = dates_[year_start].year - nyears = span / periodsperyear - (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) - major_idx = year_start[(year_break % maj_anndef == 0)] - info_maj[major_idx] = True - minor_idx = year_start[(year_break % min_anndef == 0)] - info_min[minor_idx] = True - info_fmt[major_idx] = '%Y' - - return info - - -def _monthly_finder(vmin, vmax, freq): - periodsperyear = 12 - - vmin_orig = vmin - (vmin, vmax) = (int(vmin), int(vmax)) - span = vmax - vmin + 1 - - # Initialize the output - info = np.zeros(span, - dtype=[('val', int), ('maj', bool), ('min', bool), - ('fmt', '|S8')]) - info['val'] = np.arange(vmin, vmax + 1) - dates_ = info['val'] - info['fmt'] = '' - year_start = (dates_ % 12 == 0).nonzero()[0] - info_maj = info['maj'] - info_fmt = info['fmt'] - - if span <= 1.15 * periodsperyear: - info_maj[year_start] = True - info['min'] = True - - info_fmt[:] = '%b' - info_fmt[year_start] = '%b\n%Y' - - if not has_level_label(year_start, vmin_orig): - if dates_.size > 1: - idx = 1 - else: - idx = 0 - info_fmt[idx] = '%b\n%Y' - - elif span <= 2.5 * periodsperyear: - quarter_start = (dates_ % 3 == 0).nonzero() - info_maj[year_start] = True - # TODO: Check the following : is it really info['fmt'] ? - info['fmt'][quarter_start] = True - info['min'] = True - - info_fmt[quarter_start] = '%b' - info_fmt[year_start] = '%b\n%Y' - - elif span <= 4 * periodsperyear: - info_maj[year_start] = True - info['min'] = True - - jan_or_jul = (dates_ % 12 == 0) | (dates_ % 12 == 6) - info_fmt[jan_or_jul] = '%b' - info_fmt[year_start] = '%b\n%Y' - - elif span <= 11 * periodsperyear: - quarter_start = (dates_ % 3 == 0).nonzero() - info_maj[year_start] = True - info['min'][quarter_start] = True - - info_fmt[year_start] = '%Y' - - else: - nyears = span / periodsperyear - (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) - years = dates_[year_start] // 12 + 1 - major_idx = year_start[(years % maj_anndef == 0)] - info_maj[major_idx] = True - info['min'][year_start[(years % min_anndef == 0)]] = True - - info_fmt[major_idx] = '%Y' - - return info - - -def _quarterly_finder(vmin, vmax, freq): - periodsperyear = 4 - vmin_orig = vmin - (vmin, vmax) = (int(vmin), int(vmax)) - span = vmax - vmin + 1 - - info = np.zeros(span, - dtype=[('val', int), ('maj', bool), ('min', bool), - ('fmt', '|S8')]) - info['val'] = np.arange(vmin, vmax + 1) - info['fmt'] = '' - dates_ = info['val'] - info_maj = info['maj'] - info_fmt = info['fmt'] - year_start = (dates_ % 4 == 0).nonzero()[0] - - if span <= 3.5 * periodsperyear: - info_maj[year_start] = True - info['min'] = True - - info_fmt[:] = 'Q%q' - info_fmt[year_start] = 'Q%q\n%F' - if not has_level_label(year_start, vmin_orig): - if dates_.size > 1: - idx = 1 - else: - idx = 0 - info_fmt[idx] = 'Q%q\n%F' - - elif span <= 11 * periodsperyear: - info_maj[year_start] = True - info['min'] = True - info_fmt[year_start] = '%F' - - else: - years = dates_[year_start] // 4 + 1 - nyears = span / periodsperyear - (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) - major_idx = year_start[(years % maj_anndef == 0)] - info_maj[major_idx] = True - info['min'][year_start[(years % min_anndef == 0)]] = True - info_fmt[major_idx] = '%F' - - return info - - -def _annual_finder(vmin, vmax, freq): - (vmin, vmax) = (int(vmin), int(vmax + 1)) - span = vmax - vmin + 1 - - info = np.zeros(span, - dtype=[('val', int), ('maj', bool), ('min', bool), - ('fmt', '|S8')]) - info['val'] = np.arange(vmin, vmax + 1) - info['fmt'] = '' - dates_ = info['val'] - - (min_anndef, maj_anndef) = _get_default_annual_spacing(span) - major_idx = dates_ % maj_anndef == 0 - info['maj'][major_idx] = True - info['min'][(dates_ % min_anndef == 0)] = True - info['fmt'][major_idx] = '%Y' - - return info - - -def get_finder(freq): - if isinstance(freq, compat.string_types): - freq = frequencies.get_freq(freq) - fgroup = frequencies.get_freq_group(freq) - - if fgroup == FreqGroup.FR_ANN: - return _annual_finder - elif fgroup == FreqGroup.FR_QTR: - return _quarterly_finder - elif freq == FreqGroup.FR_MTH: - return _monthly_finder - elif ((freq >= FreqGroup.FR_BUS) or fgroup == FreqGroup.FR_WK): - return _daily_finder - else: # pragma: no cover - errmsg = "Unsupported frequency: %s" % (freq) - raise NotImplementedError(errmsg) - - -class TimeSeries_DateLocator(Locator): - """ - Locates the ticks along an axis controlled by a :class:`Series`. - - Parameters - ---------- - freq : {var} - Valid frequency specifier. - minor_locator : {False, True}, optional - Whether the locator is for minor ticks (True) or not. - dynamic_mode : {True, False}, optional - Whether the locator should work in dynamic mode. - base : {int}, optional - quarter : {int}, optional - month : {int}, optional - day : {int}, optional - """ - - def __init__(self, freq, minor_locator=False, dynamic_mode=True, - base=1, quarter=1, month=1, day=1, plot_obj=None): - if isinstance(freq, compat.string_types): - freq = frequencies.get_freq(freq) - self.freq = freq - self.base = base - (self.quarter, self.month, self.day) = (quarter, month, day) - self.isminor = minor_locator - self.isdynamic = dynamic_mode - self.offset = 0 - self.plot_obj = plot_obj - self.finder = get_finder(freq) - - def _get_default_locs(self, vmin, vmax): - "Returns the default locations of ticks." - - if self.plot_obj.date_axis_info is None: - self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq) - - locator = self.plot_obj.date_axis_info - - if self.isminor: - return np.compress(locator['min'], locator['val']) - return np.compress(locator['maj'], locator['val']) - - def __call__(self): - 'Return the locations of the ticks.' - # axis calls Locator.set_axis inside set_m<xxxx>_formatter - vi = tuple(self.axis.get_view_interval()) - if vi != self.plot_obj.view_interval: - self.plot_obj.date_axis_info = None - self.plot_obj.view_interval = vi - vmin, vmax = vi - if vmax < vmin: - vmin, vmax = vmax, vmin - if self.isdynamic: - locs = self._get_default_locs(vmin, vmax) - else: # pragma: no cover - base = self.base - (d, m) = divmod(vmin, base) - vmin = (d + 1) * base - locs = lrange(vmin, vmax + 1, base) - return locs - - def autoscale(self): - """ - Sets the view limits to the nearest multiples of base that contain the - data. - """ - # requires matplotlib >= 0.98.0 - (vmin, vmax) = self.axis.get_data_interval() - - locs = self._get_default_locs(vmin, vmax) - (vmin, vmax) = locs[[0, -1]] - if vmin == vmax: - vmin -= 1 - vmax += 1 - return nonsingular(vmin, vmax) - -# ------------------------------------------------------------------------- -# --- Formatter --- -# ------------------------------------------------------------------------- - - -class TimeSeries_DateFormatter(Formatter): - """ - Formats the ticks along an axis controlled by a :class:`PeriodIndex`. - - Parameters - ---------- - freq : {int, string} - Valid frequency specifier. - minor_locator : {False, True} - Whether the current formatter should apply to minor ticks (True) or - major ticks (False). - dynamic_mode : {True, False} - Whether the formatter works in dynamic mode or not. - """ - - def __init__(self, freq, minor_locator=False, dynamic_mode=True, - plot_obj=None): - if isinstance(freq, compat.string_types): - freq = frequencies.get_freq(freq) - self.format = None - self.freq = freq - self.locs = [] - self.formatdict = None - self.isminor = minor_locator - self.isdynamic = dynamic_mode - self.offset = 0 - self.plot_obj = plot_obj - self.finder = get_finder(freq) - - def _set_default_format(self, vmin, vmax): - "Returns the default ticks spacing." - - if self.plot_obj.date_axis_info is None: - self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq) - info = self.plot_obj.date_axis_info - - if self.isminor: - format = np.compress(info['min'] & np.logical_not(info['maj']), - info) - else: - format = np.compress(info['maj'], info) - self.formatdict = dict([(x, f) for (x, _, _, f) in format]) - return self.formatdict - - def set_locs(self, locs): - 'Sets the locations of the ticks' - # don't actually use the locs. This is just needed to work with - # matplotlib. Force to use vmin, vmax - self.locs = locs - - (vmin, vmax) = vi = tuple(self.axis.get_view_interval()) - if vi != self.plot_obj.view_interval: - self.plot_obj.date_axis_info = None - self.plot_obj.view_interval = vi - if vmax < vmin: - (vmin, vmax) = (vmax, vmin) - self._set_default_format(vmin, vmax) - - def __call__(self, x, pos=0): - if self.formatdict is None: - return '' - else: - fmt = self.formatdict.pop(x, '') - return Period(ordinal=int(x), freq=self.freq).strftime(fmt) +# flake8: noqa + +from pandas.plotting.converter import (register, time2num, + TimeConverter, TimeFormatter, + PeriodConverter, get_datevalue, + DatetimeConverter, + PandasAutoDateFormatter, + PandasAutoDateLocator, + MilliSecondLocator, get_finder, + TimeSeries_DateLocator, + TimeSeries_DateFormatter) diff --git a/pandas/tseries/plotting.py b/pandas/tseries/plotting.py index 89aecf2acc07e..6ecada90665cd 100644 --- a/pandas/tseries/plotting.py +++ b/pandas/tseries/plotting.py @@ -1,313 +1,3 @@ -""" -Period formatters and locators adapted from scikits.timeseries by -Pierre GF Gerard-Marchant & Matt Knox -""" +# flake8: noqa -# TODO: Use the fact that axis can have units to simplify the process - -import numpy as np - -from matplotlib import pylab -from pandas.tseries.period import Period -from pandas.tseries.offsets import DateOffset -import pandas.tseries.frequencies as frequencies -from pandas.tseries.index import DatetimeIndex -from pandas.formats.printing import pprint_thing -import pandas.compat as compat - -from pandas.tseries.converter import (TimeSeries_DateLocator, - TimeSeries_DateFormatter) - -# --------------------------------------------------------------------- -# Plotting functions and monkey patches - - -def tsplot(series, plotf, ax=None, **kwargs): - """ - Plots a Series on the given Matplotlib axes or the current axes - - Parameters - ---------- - axes : Axes - series : Series - - Notes - _____ - Supports same kwargs as Axes.plot - - """ - # Used inferred freq is possible, need a test case for inferred - if ax is None: - import matplotlib.pyplot as plt - ax = plt.gca() - - freq, series = _maybe_resample(series, ax, kwargs) - - # Set ax with freq info - _decorate_axes(ax, freq, kwargs) - ax._plot_data.append((series, plotf, kwargs)) - lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs) - - # set date formatter, locators and rescale limits - format_dateaxis(ax, ax.freq) - return lines - - -def _maybe_resample(series, ax, kwargs): - # resample against axes freq if necessary - freq, ax_freq = _get_freq(ax, series) - - if freq is None: # pragma: no cover - raise ValueError('Cannot use dynamic axis without frequency info') - - # Convert DatetimeIndex to PeriodIndex - if isinstance(series.index, DatetimeIndex): - series = series.to_period(freq=freq) - - if ax_freq is not None and freq != ax_freq: - if frequencies.is_superperiod(freq, ax_freq): # upsample input - series = series.copy() - series.index = series.index.asfreq(ax_freq, how='s') - freq = ax_freq - elif _is_sup(freq, ax_freq): # one is weekly - how = kwargs.pop('how', 'last') - series = getattr(series.resample('D'), how)().dropna() - series = getattr(series.resample(ax_freq), how)().dropna() - freq = ax_freq - elif frequencies.is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq): - _upsample_others(ax, freq, kwargs) - ax_freq = freq - else: # pragma: no cover - raise ValueError('Incompatible frequency conversion') - return freq, series - - -def _is_sub(f1, f2): - return ((f1.startswith('W') and frequencies.is_subperiod('D', f2)) or - (f2.startswith('W') and frequencies.is_subperiod(f1, 'D'))) - - -def _is_sup(f1, f2): - return ((f1.startswith('W') and frequencies.is_superperiod('D', f2)) or - (f2.startswith('W') and frequencies.is_superperiod(f1, 'D'))) - - -def _upsample_others(ax, freq, kwargs): - legend = ax.get_legend() - lines, labels = _replot_ax(ax, freq, kwargs) - _replot_ax(ax, freq, kwargs) - - other_ax = None - if hasattr(ax, 'left_ax'): - other_ax = ax.left_ax - if hasattr(ax, 'right_ax'): - other_ax = ax.right_ax - - if other_ax is not None: - rlines, rlabels = _replot_ax(other_ax, freq, kwargs) - lines.extend(rlines) - labels.extend(rlabels) - - if (legend is not None and kwargs.get('legend', True) and - len(lines) > 0): - title = legend.get_title().get_text() - if title == 'None': - title = None - ax.legend(lines, labels, loc='best', title=title) - - -def _replot_ax(ax, freq, kwargs): - data = getattr(ax, '_plot_data', None) - - # clear current axes and data - ax._plot_data = [] - ax.clear() - - _decorate_axes(ax, freq, kwargs) - - lines = [] - labels = [] - if data is not None: - for series, plotf, kwds in data: - series = series.copy() - idx = series.index.asfreq(freq, how='S') - series.index = idx - ax._plot_data.append((series, plotf, kwds)) - - # for tsplot - if isinstance(plotf, compat.string_types): - from pandas.tools.plotting import _plot_klass - plotf = _plot_klass[plotf]._plot - - lines.append(plotf(ax, series.index._mpl_repr(), - series.values, **kwds)[0]) - labels.append(pprint_thing(series.name)) - - return lines, labels - - -def _decorate_axes(ax, freq, kwargs): - """Initialize axes for time-series plotting""" - if not hasattr(ax, '_plot_data'): - ax._plot_data = [] - - ax.freq = freq - xaxis = ax.get_xaxis() - xaxis.freq = freq - if not hasattr(ax, 'legendlabels'): - ax.legendlabels = [kwargs.get('label', None)] - else: - ax.legendlabels.append(kwargs.get('label', None)) - ax.view_interval = None - ax.date_axis_info = None - - -def _get_ax_freq(ax): - """ - Get the freq attribute of the ax object if set. - Also checks shared axes (eg when using secondary yaxis, sharex=True - or twinx) - """ - ax_freq = getattr(ax, 'freq', None) - if ax_freq is None: - # check for left/right ax in case of secondary yaxis - if hasattr(ax, 'left_ax'): - ax_freq = getattr(ax.left_ax, 'freq', None) - elif hasattr(ax, 'right_ax'): - ax_freq = getattr(ax.right_ax, 'freq', None) - if ax_freq is None: - # check if a shared ax (sharex/twinx) has already freq set - shared_axes = ax.get_shared_x_axes().get_siblings(ax) - if len(shared_axes) > 1: - for shared_ax in shared_axes: - ax_freq = getattr(shared_ax, 'freq', None) - if ax_freq is not None: - break - return ax_freq - - -def _get_freq(ax, series): - # get frequency from data - freq = getattr(series.index, 'freq', None) - if freq is None: - freq = getattr(series.index, 'inferred_freq', None) - - ax_freq = _get_ax_freq(ax) - - # use axes freq if no data freq - if freq is None: - freq = ax_freq - - # get the period frequency - if isinstance(freq, DateOffset): - freq = freq.rule_code - else: - freq = frequencies.get_base_alias(freq) - - freq = frequencies.get_period_alias(freq) - return freq, ax_freq - - -def _use_dynamic_x(ax, data): - freq = _get_index_freq(data) - ax_freq = _get_ax_freq(ax) - - if freq is None: # convert irregular if axes has freq info - freq = ax_freq - else: # do not use tsplot if irregular was plotted first - if (ax_freq is None) and (len(ax.get_lines()) > 0): - return False - - if freq is None: - return False - - if isinstance(freq, DateOffset): - freq = freq.rule_code - else: - freq = frequencies.get_base_alias(freq) - freq = frequencies.get_period_alias(freq) - - if freq is None: - return False - - # hack this for 0.10.1, creating more technical debt...sigh - if isinstance(data.index, DatetimeIndex): - base = frequencies.get_freq(freq) - x = data.index - if (base <= frequencies.FreqGroup.FR_DAY): - return x[:1].is_normalized - return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0] - return True - - -def _get_index_freq(data): - freq = getattr(data.index, 'freq', None) - if freq is None: - freq = getattr(data.index, 'inferred_freq', None) - if freq == 'B': - weekdays = np.unique(data.index.dayofweek) - if (5 in weekdays) or (6 in weekdays): - freq = None - return freq - - -def _maybe_convert_index(ax, data): - # tsplot converts automatically, but don't want to convert index - # over and over for DataFrames - if isinstance(data.index, DatetimeIndex): - freq = getattr(data.index, 'freq', None) - - if freq is None: - freq = getattr(data.index, 'inferred_freq', None) - if isinstance(freq, DateOffset): - freq = freq.rule_code - - if freq is None: - freq = _get_ax_freq(ax) - - if freq is None: - raise ValueError('Could not get frequency alias for plotting') - - freq = frequencies.get_base_alias(freq) - freq = frequencies.get_period_alias(freq) - - data = data.to_period(freq=freq) - return data - - -# Patch methods for subplot. Only format_dateaxis is currently used. -# Do we need the rest for convenience? - - -def format_dateaxis(subplot, freq): - """ - Pretty-formats the date axis (x-axis). - - Major and minor ticks are automatically set for the frequency of the - current underlying series. As the dynamic mode is activated by - default, changing the limits of the x axis will intelligently change - the positions of the ticks. - """ - majlocator = TimeSeries_DateLocator(freq, dynamic_mode=True, - minor_locator=False, - plot_obj=subplot) - minlocator = TimeSeries_DateLocator(freq, dynamic_mode=True, - minor_locator=True, - plot_obj=subplot) - subplot.xaxis.set_major_locator(majlocator) - subplot.xaxis.set_minor_locator(minlocator) - - majformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True, - minor_locator=False, - plot_obj=subplot) - minformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True, - minor_locator=True, - plot_obj=subplot) - subplot.xaxis.set_major_formatter(majformatter) - subplot.xaxis.set_minor_formatter(minformatter) - - # x and y coord info - subplot.format_coord = lambda t, y: ( - "t = {0} y = {1:8f}".format(Period(ordinal=int(t), freq=freq), y)) - - pylab.draw_if_interactive() +from pandas.plotting.timeseries import tsplot diff --git a/pandas/util/doctools.py b/pandas/util/doctools.py index 62dcba1405581..ff7236b4b78d3 100644 --- a/pandas/util/doctools.py +++ b/pandas/util/doctools.py @@ -131,7 +131,7 @@ def _make_table(self, ax, df, title, height=None): ax.set_visible(False) return - import pandas.tools.plotting as plotting + import pandas.plotting as plotting idx_nlevels = df.index.nlevels col_nlevels = df.columns.nlevels diff --git a/setup.py b/setup.py index a53464f8f7987..d1ca75965da04 100755 --- a/setup.py +++ b/setup.py @@ -644,7 +644,6 @@ def pxd(name): 'pandas.tests.formats', 'pandas.tests.types', 'pandas.tests.test_msgpack', - 'pandas.tests.plotting', 'pandas.tools', 'pandas.tools.tests', 'pandas.tseries', @@ -656,7 +655,9 @@ def pxd(name): 'pandas.io.tests.sas', 'pandas.stats.tests', 'pandas.msgpack', - 'pandas.util.clipboard' + 'pandas.util.clipboard', + 'pandas.plotting', + 'pandas.plotting.tests' ], package_data={'pandas.io': ['tests/data/legacy_hdf/*.h5', 'tests/data/legacy_pickle/*/*.pickle',
Rebased in #16005 --- - [x] closes #12548 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry cleanup `tools/plotting` as `plotting` subpackage. Because the differences are being huge, would like to split 3 part if base direction is OK: ~~1. Move plotting related tests under `pandas/tests/plotting` splitting to `Series`, `DataFrame`, etc. (0.18.2)~~ (#13621) 2. Create `pd.plotting` and move related files (0.20.0) 3. Split / refactor `pd.plotting` to use small utility functions / property control classes. Add tests. (0.20.0)
https://api.github.com/repos/pandas-dev/pandas/pulls/13579
2016-07-07T14:51:30Z
2017-04-14T23:54:36Z
null
2023-05-11T01:13:46Z
Default credentials for Google Compute Engine (#13577)
diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 64644bd9a7a26..ac5fceb672809 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -223,6 +223,7 @@ Other enhancements - A ``union_categorical`` function has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) - ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`) - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) +- The ``.get_credentials()`` method of ``GbqConnector`` can now first try to fetch the default credentials for Google Compute Engine without the need to run ``OAuth2WebServerFlow`` - if private_key is not provided (:issue:`13577`) .. _whatsnew_0182.api: diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 140f5cc6bb6e3..c26d8431ffe57 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -159,7 +159,33 @@ def get_credentials(self): if self.private_key: return self.get_service_account_credentials() else: - return self.get_user_account_credentials() + # Try to retrieve Application Default Credentials + credentials = self.get_application_default_credentials() + if not credentials: + credentials = self.get_user_account_credentials() + return credentials + + def get_application_default_credentials(self): + from oauth2client.client import GoogleCredentials + from oauth2client.client import AccessTokenRefreshError + from oauth2client.client import ApplicationDefaultCredentialsError + from apiclient.discovery import build + from apiclient.errors import HttpError + + credentials = None + try: + credentials = GoogleCredentials.get_application_default() + except ApplicationDefaultCredentialsError: + return None + # Check if the application has rights to the BigQuery project + bigquery_service = build('bigquery', 'v2', credentials=credentials) + job_collection = bigquery_service.jobs() + job_data = {'configuration': {'query': {'query': 'SELECT 1'}}} + try: + job_collection.insert(projectId=self.project_id, body=job_data).execute() + except (AccessTokenRefreshError, HttpError): + return None + return credentials def get_user_account_credentials(self): from oauth2client.client import OAuth2WebServerFlow @@ -576,7 +602,9 @@ def read_gbq(query, project_id=None, index_col=None, col_order=None, https://developers.google.com/api-client-library/python/apis/bigquery/v2 Authentication to the Google BigQuery service is via OAuth 2.0. - By default user account credentials are used. You will be asked to + By default "application default credentials" are used. + If default application credentials are not found or are restrictive - + User account credentials are used. You will be asked to grant permissions for product name 'pandas GBQ'. It is also posible to authenticate via service account credentials by using private_key parameter. @@ -672,7 +700,9 @@ def to_gbq(dataframe, destination_table, project_id, chunksize=10000, https://developers.google.com/api-client-library/python/apis/bigquery/v2 Authentication to the Google BigQuery service is via OAuth 2.0. - By default user account credentials are used. You will be asked to + By default "application default credentials" are used. + If default application credentials are not found or are restrictive - + User account credentials are used. You will be asked to grant permissions for product name 'pandas GBQ'. It is also posible to authenticate via service account credentials by using private_key parameter. diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 278c5d7215624..0cbf682480678 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -80,8 +80,10 @@ def _test_imports(): from apiclient.discovery import build # noqa from apiclient.errors import HttpError # noqa + from oauth2client.client import GoogleCredentials # noqa from oauth2client.client import OAuth2WebServerFlow # noqa from oauth2client.client import AccessTokenRefreshError # noqa + from oauth2client.client import ApplicationDefaultCredentialsError # noqa from oauth2client.file import Storage # noqa from oauth2client.tools import run_flow # noqa
- [x] closes #13577 - [x] passed all existing tests for GbqConnector - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13578
2016-07-07T09:20:00Z
2016-07-10T20:18:41Z
null
2023-05-11T01:13:46Z
ENH: Adding additional keywords to read_html for #13461
diff --git a/doc/source/io.rst b/doc/source/io.rst index da0444a8b8df9..113afa32d182e 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1959,6 +1959,35 @@ Specify an HTML attribute dfs2 = read_html(url, attrs={'class': 'sortable'}) print(np.array_equal(dfs1[0], dfs2[0])) # Should be True +Specify values that should be converted to NaN + +.. code-block:: python + + dfs = read_html(url, na_values=['No Acquirer']) + +.. versionadded:: 0.19 + +Specify whether to keep the default set of NaN values + +.. code-block:: python + + dfs = read_html(url, keep_default_na=False) + +.. versionadded:: 0.19 + +Specify converters for columns. This is useful for numerical text data that has +leading zeros. By default columns that are numerical are cast to numeric +types and the leading zeros are lost. To avoid this, we can convert these +columns to strings. + +.. code-block:: python + + url_mcc = 'https://en.wikipedia.org/wiki/Mobile_country_code' + dfs = read_html(url_mcc, match='Telekom Albania', header=0, converters={'MNC': + str}) + +.. versionadded:: 0.19 + Use some combination of the above .. code-block:: python diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 657de7ec26efc..351b0ba9b2906 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -207,6 +207,8 @@ Other enhancements - The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`) - The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`) +- The ``pd.read_html()`` has gained support for the ``na_values``, ``converters``, ``keep_default_na`` options (:issue:`13461`) + - ``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`) - ``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`) diff --git a/pandas/io/html.py b/pandas/io/html.py index 609642e248eda..79f0f326c4dd7 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -611,10 +611,10 @@ def _expand_elements(body): body[ind] += empty * (lens_max - length) -def _data_to_frame(data, header, index_col, skiprows, - parse_dates, tupleize_cols, thousands, - decimal): - head, body, foot = data +def _data_to_frame(**kwargs): + head, body, foot = kwargs.pop('data') + header = kwargs.pop('header') + kwargs['skiprows'] = _get_skiprows(kwargs['skiprows']) if head: body = [head] + body @@ -628,10 +628,7 @@ def _data_to_frame(data, header, index_col, skiprows, # fill out elements of body that are "ragged" _expand_elements(body) - tp = TextParser(body, header=header, index_col=index_col, - skiprows=_get_skiprows(skiprows), - parse_dates=parse_dates, tupleize_cols=tupleize_cols, - thousands=thousands, decimal=decimal) + tp = TextParser(body, header=header, **kwargs) df = tp.read() return df @@ -716,9 +713,7 @@ def _validate_flavor(flavor): return flavor -def _parse(flavor, io, match, header, index_col, skiprows, - parse_dates, tupleize_cols, thousands, attrs, encoding, - decimal): +def _parse(flavor, io, match, attrs, encoding, **kwargs): flavor = _validate_flavor(flavor) compiled_match = re.compile(match) # you can pass a compiled regex here @@ -740,15 +735,7 @@ def _parse(flavor, io, match, header, index_col, skiprows, ret = [] for table in tables: try: - ret.append(_data_to_frame(data=table, - header=header, - index_col=index_col, - skiprows=skiprows, - parse_dates=parse_dates, - tupleize_cols=tupleize_cols, - thousands=thousands, - decimal=decimal - )) + ret.append(_data_to_frame(data=table, **kwargs)) except EmptyDataError: # empty table continue return ret @@ -757,7 +744,8 @@ def _parse(flavor, io, match, header, index_col, skiprows, def read_html(io, match='.+', flavor=None, header=None, index_col=None, skiprows=None, attrs=None, parse_dates=False, tupleize_cols=False, thousands=',', encoding=None, - decimal='.'): + decimal='.', converters=None, na_values=None, + keep_default_na=True): r"""Read HTML tables into a ``list`` of ``DataFrame`` objects. Parameters @@ -839,6 +827,25 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None, .. versionadded:: 0.19.0 + converters : dict, default None + Dict of functions for converting values in certain columns. Keys can + either be integers or column labels, values are functions that take one + input argument, the cell (not column) content, and return the + transformed content. + + .. versionadded:: 0.19.0 + + na_values : iterable, default None + Custom NA values + + .. versionadded:: 0.19.0 + + keep_default_na : bool, default True + If na_values are specified and keep_default_na is False the default NaN + values are overridden, otherwise they're appended to + + .. versionadded:: 0.19.0 + Returns ------- dfs : list of DataFrames @@ -881,6 +888,9 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None, raise ValueError('cannot skip rows starting from the end of the ' 'data (you passed a negative value)') _validate_header_arg(header) - return _parse(flavor, io, match, header, index_col, skiprows, - parse_dates, tupleize_cols, thousands, attrs, encoding, - decimal) + return _parse(flavor=flavor, io=io, match=match, header=header, + index_col=index_col, skiprows=skiprows, + parse_dates=parse_dates, tupleize_cols=tupleize_cols, + thousands=thousands, attrs=attrs, encoding=encoding, + decimal=decimal, converters=converters, na_values=na_values, + keep_default_na=keep_default_na) diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py index 5a95fe7727df0..7b4e775db9476 100644 --- a/pandas/io/tests/test_html.py +++ b/pandas/io/tests/test_html.py @@ -694,6 +694,72 @@ def test_bool_header_arg(self): with tm.assertRaises(TypeError): read_html(self.spam_data, header=arg) + def test_converters(self): + # GH 13461 + html_data = """<table> + <thead> + <th>a</th> + </tr> + </thead> + <tbody> + <tr> + <td> 0.763</td> + </tr> + <tr> + <td> 0.244</td> + </tr> + </tbody> + </table>""" + + expected_df = DataFrame({'a': ['0.763', '0.244']}) + html_df = read_html(html_data, converters={'a': str})[0] + tm.assert_frame_equal(expected_df, html_df) + + def test_na_values(self): + # GH 13461 + html_data = """<table> + <thead> + <th>a</th> + </tr> + </thead> + <tbody> + <tr> + <td> 0.763</td> + </tr> + <tr> + <td> 0.244</td> + </tr> + </tbody> + </table>""" + + expected_df = DataFrame({'a': [0.763, np.nan]}) + html_df = read_html(html_data, na_values=[0.244])[0] + tm.assert_frame_equal(expected_df, html_df) + + def test_keep_default_na(self): + html_data = """<table> + <thead> + <th>a</th> + </tr> + </thead> + <tbody> + <tr> + <td> N/A</td> + </tr> + <tr> + <td> NA</td> + </tr> + </tbody> + </table>""" + + expected_df = DataFrame({'a': ['N/A', 'NA']}) + html_df = read_html(html_data, keep_default_na=False)[0] + tm.assert_frame_equal(expected_df, html_df) + + expected_df = DataFrame({'a': [np.nan, np.nan]}) + html_df = read_html(html_data, keep_default_na=True)[0] + tm.assert_frame_equal(expected_df, html_df) + def _lang_enc(filename): return os.path.splitext(os.path.basename(filename))[0].split('_')
- [x] closes #13461 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13575
2016-07-07T06:47:15Z
2016-07-21T14:16:50Z
2016-07-21T14:16:49Z
2016-07-21T14:21:08Z
Fix bug in contains when looking up a string in a non-monotonic datet…
diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json index 7b9fe353df2e3..f5fa849464881 100644 --- a/asv_bench/asv.conf.json +++ b/asv_bench/asv.conf.json @@ -77,11 +77,11 @@ // On conda install pytables, otherwise tables {"environment_type": "conda", "tables": ""}, {"environment_type": "conda", "pytables": null}, - {"environment_type": "virtualenv", "tables": null}, - {"environment_type": "virtualenv", "pytables": ""}, + {"environment_type": "(?!conda).*", "tables": null}, + {"environment_type": "(?!conda).*", "pytables": ""}, // On conda&win32, install libpython {"sys_platform": "(?!win32).*", "libpython": ""}, - {"sys_platform": "win32", "libpython": null}, + {"environment_type": "conda", "sys_platform": "win32", "libpython": null}, {"environment_type": "(?!conda).*", "libpython": ""} ], "include": [], diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py index 53d37a8161f43..094ae23a92fad 100644 --- a/asv_bench/benchmarks/indexing.py +++ b/asv_bench/benchmarks/indexing.py @@ -19,24 +19,6 @@ def time_dataframe_getitem_scalar(self): self.df[self.col][self.idx] -class datamatrix_getitem_scalar(object): - goal_time = 0.2 - - def setup(self): - try: - self.klass = DataMatrix - except: - self.klass = DataFrame - self.index = tm.makeStringIndex(1000) - self.columns = tm.makeStringIndex(30) - self.df = self.klass(np.random.rand(1000, 30), index=self.index, columns=self.columns) - self.idx = self.index[100] - self.col = self.columns[10] - - def time_datamatrix_getitem_scalar(self): - self.df[self.col][self.idx] - - class series_get_value(object): goal_time = 0.2 @@ -498,5 +480,3 @@ def setup(self): def time_float_loc(self): self.ind.get_loc(0) - - diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py index 3fceed087facb..ee9d3104be4b1 100644 --- a/asv_bench/benchmarks/inference.py +++ b/asv_bench/benchmarks/inference.py @@ -135,4 +135,23 @@ def setup(self): self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) def time_dtype_infer_uint32(self): - (self.df_uint32['A'] + self.df_uint32['B']) \ No newline at end of file + (self.df_uint32['A'] + self.df_uint32['B']) + + +class to_numeric(object): + N = 500000 + + param_names = ['data', 'downcast'] + params = [ + [(['1'] * (N / 2)) + ([2] * (N / 2)), + (['-1'] * (N / 2)) + ([2] * (N / 2)), + np.repeat(np.array(['1970-01-01', '1970-01-02'], + dtype='datetime64[D]'), N), + (['1.1'] * (N / 2)) + ([2] * (N / 2)), + ([1] * (N / 2)) + ([2] * (N / 2)), + np.repeat(np.int32(1), N)], + [None, 'integer', 'signed', 'unsigned', 'float'], + ] + + def time_to_numeric(self, data, downcast): + pd.to_numeric(data, downcast=downcast) diff --git a/asv_bench/benchmarks/join_merge.py b/asv_bench/benchmarks/join_merge.py index 39ebd9cb1cb73..dcd07911f2ff0 100644 --- a/asv_bench/benchmarks/join_merge.py +++ b/asv_bench/benchmarks/join_merge.py @@ -179,10 +179,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -210,10 +206,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -241,10 +233,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) @@ -272,10 +260,6 @@ def setup(self): self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) except: pass - try: - self.DataFrame = DataMatrix - except: - pass self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) diff --git a/ci/lint.sh b/ci/lint.sh index a4c960084040f..9f582f72fcdd7 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -8,7 +8,7 @@ RET=0 if [ "$LINT" ]; then echo "Linting" - for path in 'core' 'indexes' 'types' 'formats' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' + for path in 'api' 'core' 'indexes' 'types' 'formats' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' do echo "linting -> pandas/$path" flake8 pandas/$path --filename '*.py' diff --git a/ci/requirements-2.7_DOC_BUILD.run b/ci/requirements-2.7_DOC_BUILD.run index b87a41df4191d..cde0719aa027e 100644 --- a/ci/requirements-2.7_DOC_BUILD.run +++ b/ci/requirements-2.7_DOC_BUILD.run @@ -1,4 +1,4 @@ -ipython +ipython=4.2.0 ipykernel sphinx nbconvert diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 8145e9536a82a..63a7c8fded2db 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -1754,39 +1754,93 @@ Convert a subset of columns to a specified type using :meth:`~DataFrame.astype` object conversion ~~~~~~~~~~~~~~~~~ -:meth:`~DataFrame.convert_objects` is a method to try to force conversion of types from the ``object`` dtype to other types. -To force conversion of specific types that are *number like*, e.g. could be a string that represents a number, -pass ``convert_numeric=True``. This will force strings and numbers alike to be numbers if possible, otherwise -they will be set to ``np.nan``. +pandas offers various functions to try to force conversion of types from the ``object`` dtype to other types. +The following functions are available for one dimensional object arrays or scalars: + +- :meth:`~pandas.to_numeric` (conversion to numeric dtypes) + + .. ipython:: python + + m = ['1.1', 2, 3] + pd.to_numeric(m) + +- :meth:`~pandas.to_datetime` (conversion to datetime objects) + + .. ipython:: python + + import datetime + m = ['2016-07-09', datetime.datetime(2016, 3, 2)] + pd.to_datetime(m) + +- :meth:`~pandas.to_timedelta` (conversion to timedelta objects) + + .. ipython:: python + + m = ['5us', pd.Timedelta('1day')] + pd.to_timedelta(m) + +To force a conversion, we can pass in an ``errors`` argument, which specifies how pandas should deal with elements +that cannot be converted to desired dtype or object. By default, ``errors='raise'``, meaning that any errors encountered +will be raised during the conversion process. However, if ``errors='coerce'``, these errors will be ignored and pandas +will convert problematic elements to ``pd.NaT`` (for datetime and timedelta) or ``np.nan`` (for numeric). This might be +useful if you are reading in data which is mostly of the desired dtype (e.g. numeric, datetime), but occasionally has +non-conforming elements intermixed that you want to represent as missing: .. ipython:: python - :okwarning: - df3['D'] = '1.' - df3['E'] = '1' - df3.convert_objects(convert_numeric=True).dtypes + import datetime + m = ['apple', datetime.datetime(2016, 3, 2)] + pd.to_datetime(m, errors='coerce') - # same, but specific dtype conversion - df3['D'] = df3['D'].astype('float16') - df3['E'] = df3['E'].astype('int32') - df3.dtypes + m = ['apple', 2, 3] + pd.to_numeric(m, errors='coerce') + + m = ['apple', pd.Timedelta('1day')] + pd.to_timedelta(m, errors='coerce') -To force conversion to ``datetime64[ns]``, pass ``convert_dates='coerce'``. -This will convert any datetime-like object to dates, forcing other values to ``NaT``. -This might be useful if you are reading in data which is mostly dates, -but occasionally has non-dates intermixed and you want to represent as missing. +The ``errors`` parameter has a third option of ``errors='ignore'``, which will simply return the passed in data if it +encounters any errors with the conversion to a desired data type: .. ipython:: python - import datetime - s = pd.Series([datetime.datetime(2001,1,1,0,0), - 'foo', 1.0, 1, pd.Timestamp('20010104'), - '20010105'], dtype='O') - s - pd.to_datetime(s, errors='coerce') + import datetime + m = ['apple', datetime.datetime(2016, 3, 2)] + pd.to_datetime(m, errors='ignore') + + m = ['apple', 2, 3] + pd.to_numeric(m, errors='ignore') + + m = ['apple', pd.Timedelta('1day')] + pd.to_timedelta(m, errors='ignore') + +In addition to object conversion, :meth:`~pandas.to_numeric` provides another argument ``downcast``, which gives the +option of downcasting the newly (or already) numeric data to a smaller dtype, which can conserve memory: + +.. ipython:: python + + m = ['1', 2, 3] + pd.to_numeric(m, downcast='integer') # smallest signed int dtype + pd.to_numeric(m, downcast='signed') # same as 'integer' + pd.to_numeric(m, downcast='unsigned') # smallest unsigned int dtype + pd.to_numeric(m, downcast='float') # smallest float dtype + +As these methods apply only to one-dimensional arrays, lists or scalars; they cannot be used directly on multi-dimensional objects such +as DataFrames. However, with :meth:`~pandas.DataFrame.apply`, we can "apply" the function over each column efficiently: -In addition, :meth:`~DataFrame.convert_objects` will attempt the *soft* conversion of any *object* dtypes, meaning that if all -the objects in a Series are of the same type, the Series will have that dtype. +.. ipython:: python + + import datetime + df = pd.DataFrame([['2016-07-09', datetime.datetime(2016, 3, 2)]] * 2, dtype='O') + df + df.apply(pd.to_datetime) + + df = pd.DataFrame([['1.1', 2, 3]] * 2, dtype='O') + df + df.apply(pd.to_numeric) + + df = pd.DataFrame([['5us', pd.Timedelta('1day')]] * 2, dtype='O') + df + df.apply(pd.to_timedelta) gotchas ~~~~~~~ diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index e971f1f28903f..f0e01ddc3fc2d 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -653,7 +653,7 @@ The same applies to ``df.append(df_different)``. Unioning ~~~~~~~~ -.. versionadded:: 0.18.2 +.. versionadded:: 0.19.0 If you want to combine categoricals that do not necessarily have the same categories, the `union_categorical` function will diff --git a/doc/source/merging.rst b/doc/source/merging.rst index b69d0d8ba3015..f14e5741c6e2e 100644 --- a/doc/source/merging.rst +++ b/doc/source/merging.rst @@ -1133,7 +1133,7 @@ fill/interpolate missing data: Merging AsOf ~~~~~~~~~~~~ -.. versionadded:: 0.18.2 +.. versionadded:: 0.19.0 A :func:`merge_asof` is similar to an ordered left-join except that we match on nearest key rather than equal keys. For each row in the ``left`` DataFrame, we select the last row in the ``right`` DataFrame whose ``on`` key is less than the left's key. Both DataFrames must be sorted by the key. diff --git a/doc/source/text.rst b/doc/source/text.rst index 3822c713d7f85..3a4a57ff4da95 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -316,7 +316,7 @@ then ``extractall(pat).xs(0, level='match')`` gives the same result as ``Index`` also supports ``.str.extractall``. It returns a ``DataFrame`` which has the same result as a ``Series.str.extractall`` with a default index (starts from 0). -.. versionadded:: 0.18.2 +.. versionadded:: 0.19.0 .. ipython:: python diff --git a/doc/source/whatsnew.rst b/doc/source/whatsnew.rst index 685f1d2086c69..77dc249aeb788 100644 --- a/doc/source/whatsnew.rst +++ b/doc/source/whatsnew.rst @@ -18,7 +18,7 @@ What's New These are new features and improvements of note in each release. -.. include:: whatsnew/v0.18.2.txt +.. include:: whatsnew/v0.19.0.txt .. include:: whatsnew/v0.18.1.txt diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt deleted file mode 100644 index be1f745537d05..0000000000000 --- a/doc/source/whatsnew/v0.18.2.txt +++ /dev/null @@ -1,530 +0,0 @@ -.. _whatsnew_0182: - -v0.18.2 (July ??, 2016) ------------------------ - -This is a minor bug-fix release from 0.18.1 and includes a large number of -bug fixes along with several new features, enhancements, and performance improvements. -We recommend that all users upgrade to this version. - -Highlights include: - -- :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0182.enhancements.asof_merge>` - -.. contents:: What's new in v0.18.2 - :local: - :backlinks: none - -.. _whatsnew_0182.new_features: - -New features -~~~~~~~~~~~~ - -.. _whatsnew_0182.enhancements.asof_merge: - -:func:`merge_asof` for asof-style time-series joining -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A long-time requested feature has been added through the :func:`merge_asof` function, to -support asof style joining of time-series. (:issue:`1870`). Full documentation is -:ref:`here <merging.merge_asof>` - -The :func:`merge_asof` performs an asof merge, which is similar to a left-join -except that we match on nearest key rather than equal keys. - -.. ipython:: python - - left = pd.DataFrame({'a': [1, 5, 10], - 'left_val': ['a', 'b', 'c']}) - right = pd.DataFrame({'a': [1, 2, 3, 6, 7], - 'right_val': [1, 2, 3, 6, 7]}) - - left - right - -We typically want to match exactly when possible, and use the most -recent value otherwise. - -.. ipython:: python - - pd.merge_asof(left, right, on='a') - -We can also match rows ONLY with prior data, and not an exact match. - -.. ipython:: python - - pd.merge_asof(left, right, on='a', allow_exact_matches=False) - - -In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them. -This also illustrates using the ``by`` parameter to group data before merging. - -.. ipython:: python - - trades = pd.DataFrame({ - 'time': pd.to_datetime(['20160525 13:30:00.023', - '20160525 13:30:00.038', - '20160525 13:30:00.048', - '20160525 13:30:00.048', - '20160525 13:30:00.048']), - 'ticker': ['MSFT', 'MSFT', - 'GOOG', 'GOOG', 'AAPL'], - 'price': [51.95, 51.95, - 720.77, 720.92, 98.00], - 'quantity': [75, 155, - 100, 100, 100]}, - columns=['time', 'ticker', 'price', 'quantity']) - - quotes = pd.DataFrame({ - 'time': pd.to_datetime(['20160525 13:30:00.023', - '20160525 13:30:00.023', - '20160525 13:30:00.030', - '20160525 13:30:00.041', - '20160525 13:30:00.048', - '20160525 13:30:00.049', - '20160525 13:30:00.072', - '20160525 13:30:00.075']), - 'ticker': ['GOOG', 'MSFT', 'MSFT', - 'MSFT', 'GOOG', 'AAPL', 'GOOG', - 'MSFT'], - 'bid': [720.50, 51.95, 51.97, 51.99, - 720.50, 97.99, 720.50, 52.01], - 'ask': [720.93, 51.96, 51.98, 52.00, - 720.93, 98.01, 720.88, 52.03]}, - columns=['time', 'ticker', 'bid', 'ask']) - -.. ipython:: python - - trades - quotes - -An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and -in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except -that forward filling happens automatically taking the most recent non-NaN value. - -.. ipython:: python - - pd.merge_asof(trades, quotes, - on='time', - by='ticker') - -This returns a merged DataFrame with the entries in the same order as the original left -passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged. - -.. _whatsnew_0182.enhancements.read_csv_dupe_col_names_support: - -:func:`read_csv` has improved support for duplicate column names -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether -they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`) - -.. ipython :: python - - data = '0,1,2\n3,4,5' - names = ['a', 'b', 'a'] - -Previous behaviour: - -.. code-block:: ipython - - In [2]: pd.read_csv(StringIO(data), names=names) - Out[2]: - a b a - 0 2 1 2 - 1 5 4 5 - -The first 'a' column contains the same data as the second 'a' column, when it should have -contained the array ``[0, 3]``. - -New behaviour: - -.. ipython :: python - - In [2]: pd.read_csv(StringIO(data), names=names) - -.. _whatsnew_0182.enhancements.semi_month_offsets: - -Semi-Month Offsets -^^^^^^^^^^^^^^^^^^ - -Pandas has gained new frequency offsets, ``SemiMonthEnd`` ('SM') and ``SemiMonthBegin`` ('SMS'). -These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. -(:issue:`1543`) - -.. ipython:: python - - from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin - -SemiMonthEnd: - -.. ipython:: python - - Timestamp('2016-01-01') + SemiMonthEnd() - - pd.date_range('2015-01-01', freq='SM', periods=4) - -SemiMonthBegin: - -.. ipython:: python - - Timestamp('2016-01-01') + SemiMonthBegin() - - pd.date_range('2015-01-01', freq='SMS', periods=4) - -Using the anchoring suffix, you can also specify the day of month to use instead of the 15th. - -.. ipython:: python - - pd.date_range('2015-01-01', freq='SMS-16', periods=4) - - pd.date_range('2015-01-01', freq='SM-14', periods=4) - -.. _whatsnew_0182.enhancements.other: - -Other enhancements -^^^^^^^^^^^^^^^^^^ - -- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) - -- ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see :ref:`documentation here <text.extractall>` (:issue:`10008`, :issue:`13156`) -- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`) - - .. ipython:: python - - idx = pd.Index(["a1a2", "b1", "c1"]) - idx.str.extractall("[ab](?P<digit>\d)") - -- ``Timestamp`` s can now accept positional and keyword parameters like :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`) - - .. ipython:: python - - pd.Timestamp(2012, 1, 1) - - pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30) - -- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``decimal`` option (:issue:`12933`) -- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`) -- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`) - -- ``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`) -- ``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`) - - .. ipython:: python - - idx = pd.Index(['a', 'b', 'c']) - idx.where([True, False, True]) - -- ``Categorical.astype()`` now accepts an optional boolean argument ``copy``, effective when dtype is categorical (:issue:`13209`) -- ``DataFrame`` has gained the ``.asof()`` method to return the last non-NaN values according to the selected subset (:issue:`13358`) -- Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`) -- The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`) -- ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) -- A ``union_categorical`` function has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) -- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`) -- ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) - -.. _whatsnew_0182.api: - -API changes -~~~~~~~~~~~ - - -- Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception (:issue:`10001`) -- An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`) -- Calls to ``.sample()`` will respect the random seed set via ``numpy.random.seed(n)`` (:issue:`13161`) -- ``Styler.apply`` is now more strict about the outputs your function must return. For ``axis=0`` or ``axis=1``, the output shape must be identical. For ``axis=None``, the output must be a DataFrame with identical columns and index labels. (:issue:`13222`) - -.. _whatsnew_0182.api.tolist: - -``Series.tolist()`` will now return Python types -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behaviour (:issue:`10904`) - - -.. ipython:: python - - s = pd.Series([1,2,3]) - type(s.tolist()[0]) - -Previous Behavior: - -.. code-block:: ipython - - In [7]: type(s.tolist()[0]) - Out[7]: - <class 'numpy.int64'> - -New Behavior: - -.. ipython:: python - - type(s.tolist()[0]) - -.. _whatsnew_0182.api.promote: - -``Series`` type promotion on assignment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A ``Series`` will now correctly promote its dtype for assignment with incompat values to the current dtype (:issue:`13234`) - - -.. ipython:: python - - s = pd.Series() - -Previous Behavior: - -.. code-block:: ipython - - In [2]: s["a"] = pd.Timestamp("2016-01-01") - - In [3]: s["b"] = 3.0 - TypeError: invalid type promotion - -New Behavior: - -.. ipython:: python - - s["a"] = pd.Timestamp("2016-01-01") - s["b"] = 3.0 - s - s.dtype - -.. _whatsnew_0182.api.to_datetime_coerce: - -``.to_datetime()`` when coercing -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A bug is fixed in ``.to_datetime()`` when passing integers or floats, and no ``unit`` and ``errors='coerce'`` (:issue:`13180`). -Previously if ``.to_datetime()`` encountered mixed integers/floats and strings, but no datetimes with ``errors='coerce'`` it would convert all to ``NaT``. - -Previous Behavior: - -.. code-block:: ipython - - In [2]: pd.to_datetime([1, 'foo'], errors='coerce') - Out[2]: DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None) - -This will now convert integers/floats with the default unit of ``ns``. - -.. ipython:: python - - pd.to_datetime([1, 'foo'], errors='coerce') - -.. _whatsnew_0182.api.merging: - -Merging changes -^^^^^^^^^^^^^^^ - -Merging will now preserve the dtype of the join keys (:issue:`8596`) - -.. ipython:: python - - df1 = pd.DataFrame({'key': [1], 'v1': [10]}) - df1 - df2 = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]}) - df2 - -Previous Behavior: - -.. code-block:: ipython - - In [5]: pd.merge(df1, df2, how='outer') - Out[5]: - key v1 - 0 1.0 10.0 - 1 1.0 20.0 - 2 2.0 30.0 - - In [6]: pd.merge(df1, df2, how='outer').dtypes - Out[6]: - key float64 - v1 float64 - dtype: object - -New Behavior: - -We are able to preserve the join keys - -.. ipython:: python - - pd.merge(df1, df2, how='outer') - pd.merge(df1, df2, how='outer').dtypes - -Of course if you have missing values that are introduced, then the -resulting dtype will be upcast (unchanged from previous). - -.. ipython:: python - - pd.merge(df1, df2, how='outer', on='key') - pd.merge(df1, df2, how='outer', on='key').dtypes - -.. _whatsnew_0182.describe: - -``.describe()`` changes -^^^^^^^^^^^^^^^^^^^^^^^ - -Percentile identifiers in the index of a ``.describe()`` output will now be rounded to the least precision that keeps them distinct (:issue:`13104`) - -.. ipython:: python - - s = pd.Series([0, 1, 2, 3, 4]) - df = pd.DataFrame([0, 1, 2, 3, 4]) - -Previous Behavior: - -The percentiles were rounded to at most one decimal place, which could raise ``ValueError`` for a data frame if the percentiles were duplicated. - -.. code-block:: ipython - - In [3]: s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - Out[3]: - count 5.000000 - mean 2.000000 - std 1.581139 - min 0.000000 - 0.0% 0.000400 - 0.1% 0.002000 - 0.1% 0.004000 - 50% 2.000000 - 99.9% 3.996000 - 100.0% 3.998000 - 100.0% 3.999600 - max 4.000000 - dtype: float64 - - In [4]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - Out[4]: - ... - ValueError: cannot reindex from a duplicate axis - -New Behavior: - -.. ipython:: python - - s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) - -Furthermore: - -- Passing duplicated ``percentiles`` will now raise a ``ValueError``. -- Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`) - -.. _whatsnew_0182.api.other: - -Other API changes -^^^^^^^^^^^^^^^^^ - -- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`) -- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`) -- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`) -- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`) -- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`) - -.. _whatsnew_0182.deprecations: - -Deprecations -^^^^^^^^^^^^ - -- ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`) -- ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`) -- ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) -- top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`) - -.. _whatsnew_0182.performance: - -Performance Improvements -~~~~~~~~~~~~~~~~~~~~~~~~ - -- Improved performance of sparse ``IntIndex.intersect`` (:issue:`13082`) -- Improved performance of sparse arithmetic with ``BlockIndex`` when the number of blocks are large, though recommended to use ``IntIndex`` in such cases (:issue:`13082`) -- increased performance of ``DataFrame.quantile()`` as it now operates per-block (:issue:`11623`) - -- Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`) -- Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) - - -.. _whatsnew_0182.bug_fixes: - -Bug Fixes -~~~~~~~~~ - -- Bug in ``io.json.json_normalize()``, where non-ascii keys raised an exception (:issue:`13213`) -- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing may raise ``IndexError`` (:issue:`13144`) -- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing result may have normal ``Index`` (:issue:`13144`) -- Bug in ``SparseDataFrame`` in which ``axis=None`` did not default to ``axis=0`` (:issue:`13048`) -- Bug in ``SparseSeries`` and ``SparseDataFrame`` creation with ``object`` dtype may raise ``TypeError`` (:issue:`11633`) -- Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`) -- Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`) -- Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`) - - -- Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`) -- Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`) - -- Bug in calling ``.memory_usage()`` on object which doesn't implement (:issue:`12924`) - -- Regression in ``Series.quantile`` with nans (also shows up in ``.median()`` and ``.describe()`` ); furthermore now names the ``Series`` with the quantile (:issue:`13098`, :issue:`13146`) - -- Bug in ``SeriesGroupBy.transform`` with datetime values and missing groups (:issue:`13191`) - -- Bug in ``Series.str.extractall()`` with ``str`` index raises ``ValueError`` (:issue:`13156`) -- Bug in ``Series.str.extractall()`` with single group and quantifier (:issue:`13382`) - - -- Bug in ``PeriodIndex`` and ``Period`` subtraction raises ``AttributeError`` (:issue:`13071`) -- Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`) -- Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`) -- Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`) -- Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`) -- Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`) -- Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`) -- Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`) - -- Bug in various index types, which did not propagate the name of passed index (:issue:`12309`) -- Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`) -- Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`) - -- Bug in ``DataFrame.to_csv()`` in which float values were being quoted even though quotations were specified for non-numeric values only (:issue:`12922`, :issue:`13259`) -- Bug in ``MultiIndex`` slicing where extra elements were returned when level is non-unique (:issue:`12896`) -- Bug in ``.str.replace`` does not raise ``TypeError`` for invalid replacement (:issue:`13438`) - - -- Bug in ``pd.read_csv()`` with ``engine='python'`` in which ``NaN`` values weren't being detected after data was converted to numeric values (:issue:`13314`) -- Bug in ``pd.read_csv()`` in which the ``nrows`` argument was not properly validated for both engines (:issue:`10476`) -- Bug in ``pd.read_csv()`` with ``engine='python'`` in which infinities of mixed-case forms were not being interpreted properly (:issue:`13274`) -- Bug in ``pd.read_csv()`` with ``engine='python'`` in which trailing ``NaN`` values were not being parsed (:issue:`13320`) -- Bug in ``pd.read_csv()`` with ``engine='python'`` when reading from a tempfile.TemporaryFile on Windows with Python 3 (:issue:`13398`) -- Bug in ``pd.read_csv()`` that prevents ``usecols`` kwarg from accepting single-byte unicode strings (:issue:`13219`) -- Bug in ``pd.read_csv()`` that prevents ``usecols`` from being an empty set (:issue:`13402`) -- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which null ``quotechar`` was not accepted even though ``quoting`` was specified as ``None`` (:issue:`13411`) -- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which fields were not properly cast to float when quoting was specified as non-numeric (:issue:`13411`) -- Bug in ``pd.pivot_table()`` where ``margins_name`` is ignored when ``aggfunc`` is a list (:issue:`13354`) - - - -- Bug in ``Series`` arithmetic raises ``TypeError`` if it contains datetime-like as ``object`` dtype (:issue:`13043`) - - -- Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`) -- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, `int16`` dtypes (:issue:`13451`) -- Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`) - -- Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) -- Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`) -- Bug in ``Peirod`` and ``Series`` or ``Index`` comparison raises ``TypeError`` (:issue:`13200`) -- Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) -- Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`) - - -- Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`) -- Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) - - -- Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) - - -- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) -- Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 42db0388ca5d9..0b9695125c0a9 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -1,7 +1,7 @@ .. _whatsnew_0190: -v0.19.0 (????, 2016) --------------------- +v0.19.0 (August ??, 2016) +------------------------- This is a major release from 0.18.2 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all @@ -9,54 +9,494 @@ users upgrade to this version. Highlights include: +- :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0190.enhancements.asof_merge>` +- pandas development api, see :ref:`here <whatsnew_0190.dev_api>` -Check the :ref:`API Changes <whatsnew_0190.api_breaking>` and :ref:`deprecations <whatsnew_0190.deprecations>` before updating. - -.. contents:: What's new in v0.19.0 +.. contents:: What's new in v0.18.2 :local: :backlinks: none -.. _whatsnew_0190.enhancements: +.. _whatsnew_0190.new_features: New features ~~~~~~~~~~~~ +.. _whatsnew_0190.dev_api: + +pandas development API +^^^^^^^^^^^^^^^^^^^^^^ + +As part of making pandas APi more uniform and accessible in the future, we have created a standard +sub-package of pandas, ``pandas.api`` to hold public API's. We are starting by exposing type +introspection functions in ``pandas.api.types``. More sub-packages and officially sanctioned API's +will be published in future versions of pandas. + +The following are now part of this API: + +.. ipython:: python + + import pprint + from pandas.api import types + funcs = [ f for f in dir(types) if not f.startswith('_') ] + pprint.pprint(funcs) + +.. _whatsnew_0190.enhancements.asof_merge: + +:func:`merge_asof` for asof-style time-series joining +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A long-time requested feature has been added through the :func:`merge_asof` function, to +support asof style joining of time-series. (:issue:`1870`). Full documentation is +:ref:`here <merging.merge_asof>` + +The :func:`merge_asof` performs an asof merge, which is similar to a left-join +except that we match on nearest key rather than equal keys. + +.. ipython:: python + + left = pd.DataFrame({'a': [1, 5, 10], + 'left_val': ['a', 'b', 'c']}) + right = pd.DataFrame({'a': [1, 2, 3, 6, 7], + 'right_val': [1, 2, 3, 6, 7]}) + + left + right + +We typically want to match exactly when possible, and use the most +recent value otherwise. + +.. ipython:: python + + pd.merge_asof(left, right, on='a') + +We can also match rows ONLY with prior data, and not an exact match. + +.. ipython:: python + + pd.merge_asof(left, right, on='a', allow_exact_matches=False) + + +In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them. +This also illustrates using the ``by`` parameter to group data before merging. + +.. ipython:: python + + trades = pd.DataFrame({ + 'time': pd.to_datetime(['20160525 13:30:00.023', + '20160525 13:30:00.038', + '20160525 13:30:00.048', + '20160525 13:30:00.048', + '20160525 13:30:00.048']), + 'ticker': ['MSFT', 'MSFT', + 'GOOG', 'GOOG', 'AAPL'], + 'price': [51.95, 51.95, + 720.77, 720.92, 98.00], + 'quantity': [75, 155, + 100, 100, 100]}, + columns=['time', 'ticker', 'price', 'quantity']) + + quotes = pd.DataFrame({ + 'time': pd.to_datetime(['20160525 13:30:00.023', + '20160525 13:30:00.023', + '20160525 13:30:00.030', + '20160525 13:30:00.041', + '20160525 13:30:00.048', + '20160525 13:30:00.049', + '20160525 13:30:00.072', + '20160525 13:30:00.075']), + 'ticker': ['GOOG', 'MSFT', 'MSFT', + 'MSFT', 'GOOG', 'AAPL', 'GOOG', + 'MSFT'], + 'bid': [720.50, 51.95, 51.97, 51.99, + 720.50, 97.99, 720.50, 52.01], + 'ask': [720.93, 51.96, 51.98, 52.00, + 720.93, 98.01, 720.88, 52.03]}, + columns=['time', 'ticker', 'bid', 'ask']) + +.. ipython:: python + + trades + quotes + +An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and +in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except +that forward filling happens automatically taking the most recent non-NaN value. + +.. ipython:: python + + pd.merge_asof(trades, quotes, + on='time', + by='ticker') + +This returns a merged DataFrame with the entries in the same order as the original left +passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged. + +.. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support: + +:func:`read_csv` has improved support for duplicate column names +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +:ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether +they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`) + +.. ipython :: python + + data = '0,1,2\n3,4,5' + names = ['a', 'b', 'a'] + +Previous behaviour: + +.. code-block:: ipython + + In [2]: pd.read_csv(StringIO(data), names=names) + Out[2]: + a b a + 0 2 1 2 + 1 5 4 5 +The first 'a' column contains the same data as the second 'a' column, when it should have +contained the array ``[0, 3]``. +New behaviour: +.. ipython :: python + + In [2]: pd.read_csv(StringIO(data), names=names) + +.. _whatsnew_0190.enhancements.semi_month_offsets: + +Semi-Month Offsets +^^^^^^^^^^^^^^^^^^ + +Pandas has gained new frequency offsets, ``SemiMonthEnd`` ('SM') and ``SemiMonthBegin`` ('SMS'). +These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. +(:issue:`1543`) + +.. ipython:: python + + from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin + +SemiMonthEnd: + +.. ipython:: python + + Timestamp('2016-01-01') + SemiMonthEnd() + + pd.date_range('2015-01-01', freq='SM', periods=4) + +SemiMonthBegin: + +.. ipython:: python + + Timestamp('2016-01-01') + SemiMonthBegin() + + pd.date_range('2015-01-01', freq='SMS', periods=4) + +Using the anchoring suffix, you can also specify the day of month to use instead of the 15th. + +.. ipython:: python + + pd.date_range('2015-01-01', freq='SMS-16', periods=4) + + pd.date_range('2015-01-01', freq='SM-14', periods=4) .. _whatsnew_0190.enhancements.other: Other enhancements ^^^^^^^^^^^^^^^^^^ +- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) +- ``pd.to_numeric()`` now accepts a ``downcast`` parameter, which will downcast the data if possible to smallest specified numerical dtype (:issue:`13352`) + .. ipython:: python + s = ['1', 2, 3] + pd.to_numeric(s, downcast='unsigned') + pd.to_numeric(s, downcast='integer') +- ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, the see :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`) +- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`) + .. ipython:: python -.. _whatsnew_0190.api_breaking: + idx = pd.Index(["a1a2", "b1", "c1"]) + idx.str.extractall("[ab](?P<digit>\d)") -Backwards incompatible API changes -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +- ``Timestamp`` can now accept positional and keyword parameters similar to :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`) + + .. ipython:: python + + pd.Timestamp(2012, 1, 1) + + pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30) + +- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``decimal`` option (:issue:`12933`) +- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`) +- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`) + +- ``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`) +- ``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`) + + .. ipython:: python + + idx = pd.Index(['a', 'b', 'c']) + idx.where([True, False, True]) + +- ``Categorical.astype()`` now accepts an optional boolean argument ``copy``, effective when dtype is categorical (:issue:`13209`) +- ``DataFrame`` has gained the ``.asof()`` method to return the last non-NaN values according to the selected subset (:issue:`13358`) +- Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`) +- The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`) +- ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) +- A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`) +- ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`) .. _whatsnew_0190.api: +API changes +~~~~~~~~~~~ + + +- ``Index.reshape`` will raise a ``NotImplementedError`` exception when called (:issue: `12882`) +- Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception (:issue:`10001`) +- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`) +- An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`) +- Calls to ``.sample()`` will respect the random seed set via ``numpy.random.seed(n)`` (:issue:`13161`) +- ``Styler.apply`` is now more strict about the outputs your function must return. For ``axis=0`` or ``axis=1``, the output shape must be identical. For ``axis=None``, the output must be a DataFrame with identical columns and index labels. (:issue:`13222`) +- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`) +- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`) +- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`) +- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`) +- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`) + + +.. _whatsnew_0190.api.tolist: + +``Series.tolist()`` will now return Python types +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behaviour (:issue:`10904`) + + +.. ipython:: python + + s = pd.Series([1,2,3]) + type(s.tolist()[0]) + +Previous Behavior: + +.. code-block:: ipython + In [7]: type(s.tolist()[0]) + Out[7]: + <class 'numpy.int64'> +New Behavior: +.. ipython:: python + type(s.tolist()[0]) + +.. _whatsnew_0190.api.promote: + +``Series`` type promotion on assignment +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A ``Series`` will now correctly promote its dtype for assignment with incompat values to the current dtype (:issue:`13234`) + + +.. ipython:: python + + s = pd.Series() + +Previous Behavior: + +.. code-block:: ipython + + In [2]: s["a"] = pd.Timestamp("2016-01-01") + + In [3]: s["b"] = 3.0 + TypeError: invalid type promotion + +New Behavior: + +.. ipython:: python + + s["a"] = pd.Timestamp("2016-01-01") + s["b"] = 3.0 + s + s.dtype + +.. _whatsnew_0190.api.to_datetime_coerce: + +``.to_datetime()`` when coercing +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A bug is fixed in ``.to_datetime()`` when passing integers or floats, and no ``unit`` and ``errors='coerce'`` (:issue:`13180`). +Previously if ``.to_datetime()`` encountered mixed integers/floats and strings, but no datetimes with ``errors='coerce'`` it would convert all to ``NaT``. + +Previous Behavior: + +.. code-block:: ipython + + In [2]: pd.to_datetime([1, 'foo'], errors='coerce') + Out[2]: DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None) + +This will now convert integers/floats with the default unit of ``ns``. + +.. ipython:: python + + pd.to_datetime([1, 'foo'], errors='coerce') + +.. _whatsnew_0190.api.merging: + +Merging changes +^^^^^^^^^^^^^^^ + +Merging will now preserve the dtype of the join keys (:issue:`8596`) + +.. ipython:: python + + df1 = pd.DataFrame({'key': [1], 'v1': [10]}) + df1 + df2 = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]}) + df2 + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.merge(df1, df2, how='outer') + Out[5]: + key v1 + 0 1.0 10.0 + 1 1.0 20.0 + 2 2.0 30.0 + + In [6]: pd.merge(df1, df2, how='outer').dtypes + Out[6]: + key float64 + v1 float64 + dtype: object + +New Behavior: + +We are able to preserve the join keys + +.. ipython:: python + + pd.merge(df1, df2, how='outer') + pd.merge(df1, df2, how='outer').dtypes + +Of course if you have missing values that are introduced, then the +resulting dtype will be upcast, which is unchanged from previous. + +.. ipython:: python + + pd.merge(df1, df2, how='outer', on='key') + pd.merge(df1, df2, how='outer', on='key').dtypes + +.. _whatsnew_0190.describe: + +``.describe()`` changes +^^^^^^^^^^^^^^^^^^^^^^^ + +Percentile identifiers in the index of a ``.describe()`` output will now be rounded to the least precision that keeps them distinct (:issue:`13104`) + +.. ipython:: python + + s = pd.Series([0, 1, 2, 3, 4]) + df = pd.DataFrame([0, 1, 2, 3, 4]) + +Previous Behavior: + +The percentiles were rounded to at most one decimal place, which could raise ``ValueError`` for a data frame if the percentiles were duplicated. + +.. code-block:: ipython + + In [3]: s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + Out[3]: + count 5.000000 + mean 2.000000 + std 1.581139 + min 0.000000 + 0.0% 0.000400 + 0.1% 0.002000 + 0.1% 0.004000 + 50% 2.000000 + 99.9% 3.996000 + 100.0% 3.998000 + 100.0% 3.999600 + max 4.000000 + dtype: float64 + + In [4]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + Out[4]: + ... + ValueError: cannot reindex from a duplicate axis + +New Behavior: + +.. ipython:: python + + s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999]) + +Furthermore: + +- Passing duplicated ``percentiles`` will now raise a ``ValueError``. +- Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`) + +.. _whatsnew_0190.api.periodnat: + +``Period('NaT')`` now returns ``pd.NaT`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Previously, ``Period`` has its own ``Period('NaT')`` representation different from ``pd.NaT``. Now ``Period('NaT')`` has been changed to return ``pd.NaT``. (:issue:`12759`, :issue:`13582`) + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.Period('NaT', freq='D') + Out[5]: Period('NaT', 'D') + +New Behavior: + +.. ipython:: python + + pd.Period('NaT') + + +To be compat with ``Period`` addition and subtraction, ``pd.NaT`` now supports addition and subtraction with ``int``. Previously it raises ``ValueError``. + +Previous Behavior: + +.. code-block:: ipython + + In [5]: pd.NaT + 1 + ... + ValueError: Cannot add integral value to Timestamp without freq. + +New Behavior: + +.. ipython:: python + + pd.NaT + 1 + pd.NaT - 1 -Other API Changes -^^^^^^^^^^^^^^^^^ .. _whatsnew_0190.deprecations: Deprecations ^^^^^^^^^^^^ +- ``Categorical.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`) +- ``Series.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`) - - +- ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`) +- ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`) +- ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`) +- top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`) +- ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`) .. _whatsnew_0190.prior_deprecations: @@ -64,8 +504,9 @@ Deprecations Removal of prior version deprecations/changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - +- ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`) +- ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`) +- ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`) .. _whatsnew_0190.performance: @@ -73,11 +514,103 @@ Removal of prior version deprecations/changes Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ +- Improved performance of sparse ``IntIndex.intersect`` (:issue:`13082`) +- Improved performance of sparse arithmetic with ``BlockIndex`` when the number of blocks are large, though recommended to use ``IntIndex`` in such cases (:issue:`13082`) +- increased performance of ``DataFrame.quantile()`` as it now operates per-block (:issue:`11623`) - +- Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`) +- Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) .. _whatsnew_0190.bug_fixes: Bug Fixes ~~~~~~~~~ + +- Bug in ``io.json.json_normalize()``, where non-ascii keys raised an exception (:issue:`13213`) +- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing may raise ``IndexError`` (:issue:`13144`) +- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing result may have normal ``Index`` (:issue:`13144`) +- Bug in ``SparseDataFrame`` in which ``axis=None`` did not default to ``axis=0`` (:issue:`13048`) +- Bug in ``SparseSeries`` and ``SparseDataFrame`` creation with ``object`` dtype may raise ``TypeError`` (:issue:`11633`) +- Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`) +- Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`) +- Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`) +- Bug in ``Series`` construction from a tuple of integers on windows not returning default dtype (int64) (:issue:`13646`) + +- Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`) +- Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`) + +- Bug in calling ``.memory_usage()`` on object which doesn't implement (:issue:`12924`) + +- Regression in ``Series.quantile`` with nans (also shows up in ``.median()`` and ``.describe()`` ); furthermore now names the ``Series`` with the quantile (:issue:`13098`, :issue:`13146`) + +- Bug in ``SeriesGroupBy.transform`` with datetime values and missing groups (:issue:`13191`) + +- Bug in ``Series.str.extractall()`` with ``str`` index raises ``ValueError`` (:issue:`13156`) +- Bug in ``Series.str.extractall()`` with single group and quantifier (:issue:`13382`) +- Bug in ``DatetimeIndex`` and ``Period`` subtraction raises ``ValueError`` or ``AttributeError`` rather than ``TypeError`` (:issue:`13078`) +- Bug in ``Index`` and ``Series`` created with ``NaN`` and ``NaT`` mixed data may not have ``datetime64`` dtype (:issue:`13324`) +- Bug in ``Index`` and ``Series`` may ignore ``np.datetime64('nat')`` and ``np.timdelta64('nat')`` to infer dtype (:issue:`13324`) +- Bug in ``PeriodIndex`` and ``Period`` subtraction raises ``AttributeError`` (:issue:`13071`) +- Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`) +- Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`) +- Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`) +- Bug in ``groupby(..).apply(..)`` when the passed function returns scalar values per group (:issue:`13468`). +- Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`) +- Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`) +- Bug in ``.tz_localize`` with ``dateutil.tz.tzlocal`` may return incorrect result (:issue:`13583`) +- Bug in ``DatetimeTZDtype`` dtype with ``dateutil.tz.tzlocal`` cannot be regarded as valid dtype (:issue:`13583`) +- Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`) +- Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`) + +- Bug in various index types, which did not propagate the name of passed index (:issue:`12309`) +- Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`) +- Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`) + +- Bug in ``DataFrame.to_csv()`` in which float values were being quoted even though quotations were specified for non-numeric values only (:issue:`12922`, :issue:`13259`) +- Bug in ``MultiIndex`` slicing where extra elements were returned when level is non-unique (:issue:`12896`) +- Bug in ``.str.replace`` does not raise ``TypeError`` for invalid replacement (:issue:`13438`) + + +- Bug in ``pd.read_csv()`` with ``engine='python'`` in which ``NaN`` values weren't being detected after data was converted to numeric values (:issue:`13314`) +- Bug in ``pd.read_csv()`` in which the ``nrows`` argument was not properly validated for both engines (:issue:`10476`) +- Bug in ``pd.read_csv()`` with ``engine='python'`` in which infinities of mixed-case forms were not being interpreted properly (:issue:`13274`) +- Bug in ``pd.read_csv()`` with ``engine='python'`` in which trailing ``NaN`` values were not being parsed (:issue:`13320`) +- Bug in ``pd.read_csv()`` with ``engine='python'`` when reading from a ``tempfile.TemporaryFile`` on Windows with Python 3 (:issue:`13398`) +- Bug in ``pd.read_csv()`` that prevents ``usecols`` kwarg from accepting single-byte unicode strings (:issue:`13219`) +- Bug in ``pd.read_csv()`` that prevents ``usecols`` from being an empty set (:issue:`13402`) +- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which null ``quotechar`` was not accepted even though ``quoting`` was specified as ``None`` (:issue:`13411`) +- Bug in ``pd.read_csv()`` with ``engine=='c'`` in which fields were not properly cast to float when quoting was specified as non-numeric (:issue:`13411`) +- Bug in ``pd.pivot_table()`` where ``margins_name`` is ignored when ``aggfunc`` is a list (:issue:`13354`) + + + +- Bug in ``Series`` arithmetic raises ``TypeError`` if it contains datetime-like as ``object`` dtype (:issue:`13043`) + + +- Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`) +- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, and ``int16`` dtypes (:issue:`13451`) +- Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`) + +- Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`) +- Bug in ``Series`` comparison may output incorrect result if rhs contains ``NaT`` (:issue:`9005`) +- Bug in ``Series`` and ``Index`` comparison may output incorrect result if it contains ``NaT`` with ``object`` dtype (:issue:`13592`) +- Bug in ``Period`` addition raises ``TypeError`` if ``Period`` is on right hand side (:issue:`13069`) +- Bug in ``Peirod`` and ``Series`` or ``Index`` comparison raises ``TypeError`` (:issue:`13200`) +- Bug in ``pd.set_eng_float_format()`` that would prevent NaN's from formatting (:issue:`11981`) +- Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`) +- Clean some compile time warnings in datetime parsing (:issue:`13607`) + + +- Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`) +- Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`) +- Bug in ``groupby(..).nth()`` where the group key is included inconsistently if called after ``.head()/.tail()`` (:issue:`12839`) +- Bug in ``.to_html``, ``.to_latex`` and ``.to_string`` silently ignore custom datetime formatter passed through the ``formatters`` key word (:issue:`10690`) + +- Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) +- Bug in invalid ``Timedelta`` arithmetic and comparison may raise ``ValueError`` rather than ``TypeError`` (:issue:`13624`) + +- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) +- Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) + +- Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt new file mode 100644 index 0000000000000..695e917c76ba0 --- /dev/null +++ b/doc/source/whatsnew/v0.20.0.txt @@ -0,0 +1,83 @@ +.. _whatsnew_0200: + +v0.20.0 (????, 2016) +-------------------- + +This is a major release from 0.19 and includes a small number of API changes, several new features, +enhancements, and performance improvements along with a large number of bug fixes. We recommend that all +users upgrade to this version. + +Highlights include: + + +Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations <whatsnew_0200.deprecations>` before updating. + +.. contents:: What's new in v0.19.0 + :local: + :backlinks: none + +.. _whatsnew_0200.enhancements: + +New features +~~~~~~~~~~~~ + + + + + +.. _whatsnew_0200.enhancements.other: + +Other enhancements +^^^^^^^^^^^^^^^^^^ + + + + + + +.. _whatsnew_0200.api_breaking: + +Backwards incompatible API changes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. _whatsnew_0200.api: + + + + + + +Other API Changes +^^^^^^^^^^^^^^^^^ + +.. _whatsnew_0200.deprecations: + +Deprecations +^^^^^^^^^^^^ + + + + + +.. _whatsnew_0200.prior_deprecations: + +Removal of prior version deprecations/changes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + + + + +.. _whatsnew_0200.performance: + +Performance Improvements +~~~~~~~~~~~~~~~~~~~~~~~~ + + + + + +.. _whatsnew_0200.bug_fixes: + +Bug Fixes +~~~~~~~~~ diff --git a/pandas/__init__.py b/pandas/__init__.py index 350898c9925e7..2d91c97144e3c 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -16,7 +16,7 @@ if missing_dependencies: raise ImportError("Missing required dependencies {0}".format(missing_dependencies)) - +del hard_dependencies, dependency, missing_dependencies # numpy compat from pandas.compat.numpy import * diff --git a/pandas/api/__init__.py b/pandas/api/__init__.py new file mode 100644 index 0000000000000..fcbf42f6dabc4 --- /dev/null +++ b/pandas/api/__init__.py @@ -0,0 +1 @@ +""" public toolkit API """ diff --git a/pandas/api/tests/__init__.py b/pandas/api/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/api/tests/test_api.py b/pandas/api/tests/test_api.py new file mode 100644 index 0000000000000..3f6c97441d659 --- /dev/null +++ b/pandas/api/tests/test_api.py @@ -0,0 +1,213 @@ +# -*- coding: utf-8 -*- + +import pandas as pd +from pandas.core import common as com +from pandas import api +from pandas.api import types +from pandas.util import testing as tm + +_multiprocess_can_split_ = True + + +class Base(object): + + def check(self, namespace, expected, ignored=None): + # see which names are in the namespace, minus optional + # ignored ones + # compare vs the expected + + result = sorted([f for f in dir(namespace) if not f.startswith('_')]) + if ignored is not None: + result = sorted(list(set(result) - set(ignored))) + + expected = sorted(expected) + tm.assert_almost_equal(result, expected) + + +class TestPDApi(Base, tm.TestCase): + + # these are optionally imported based on testing + # & need to be ignored + ignored = ['tests', 'rpy', 'sandbox', 'locale'] + + # top-level sub-packages + lib = ['api', 'compat', 'computation', 'core', + 'indexes', 'formats', 'pandas', + 'test', 'tools', 'tseries', + 'types', 'util', 'options', 'io'] + + # top-level packages that are c-imports, should rename to _* + # to avoid naming conflicts + lib_to_rename = ['algos', 'hashtable', 'tslib', 'msgpack', 'sparse', + 'json', 'lib', 'index', 'parser'] + + # these are already deprecated; awaiting removal + deprecated_modules = ['ols', 'stats'] + + # misc + misc = ['IndexSlice', 'NaT'] + + # top-level classes + classes = ['Categorical', 'CategoricalIndex', 'DataFrame', 'DateOffset', + 'DatetimeIndex', 'ExcelFile', 'ExcelWriter', 'Float64Index', + 'Grouper', 'HDFStore', 'Index', 'Int64Index', 'MultiIndex', + 'Period', 'PeriodIndex', 'RangeIndex', + 'Series', 'SparseArray', 'SparseDataFrame', + 'SparseSeries', 'TimeGrouper', 'Timedelta', + 'TimedeltaIndex', 'Timestamp'] + + # these are already deprecated; awaiting removal + deprecated_classes = ['SparsePanel', 'TimeSeries', 'WidePanel', + 'SparseTimeSeries'] + + # these should be deperecated in the future + deprecated_classes_in_future = ['Panel', 'Panel4D', + 'SparseList', 'Term'] + + # these should be removed from top-level namespace + remove_classes_from_top_level_namespace = ['Expr'] + + # external modules exposed in pandas namespace + modules = ['np', 'datetime', 'datetools'] + + # top-level functions + funcs = ['bdate_range', 'concat', 'crosstab', 'cut', + 'date_range', 'eval', + 'factorize', 'get_dummies', 'get_store', + 'infer_freq', 'isnull', 'lreshape', + 'match', 'melt', 'notnull', 'offsets', + 'merge', 'merge_ordered', 'merge_asof', + 'period_range', + 'pivot', 'pivot_table', 'plot_params', 'qcut', + 'scatter_matrix', + 'show_versions', 'timedelta_range', 'unique', + 'value_counts', 'wide_to_long'] + + # top-level option funcs + funcs_option = ['reset_option', 'describe_option', 'get_option', + 'option_context', 'set_option', + 'set_eng_float_format'] + + # top-level read_* funcs + funcs_read = ['read_clipboard', 'read_csv', 'read_excel', 'read_fwf', + 'read_gbq', 'read_hdf', 'read_html', 'read_json', + 'read_msgpack', 'read_pickle', 'read_sas', 'read_sql', + 'read_sql_query', 'read_sql_table', 'read_stata', + 'read_table'] + + # top-level to_* funcs + funcs_to = ['to_datetime', 'to_msgpack', + 'to_numeric', 'to_pickle', 'to_timedelta'] + + # these should be deperecated in the future + deprecated_funcs_in_future = ['pnow', 'groupby', 'info'] + + # these are already deprecated; awaiting removal + deprecated_funcs = ['ewma', 'ewmcorr', 'ewmcov', 'ewmstd', 'ewmvar', + 'ewmvol', 'expanding_apply', 'expanding_corr', + 'expanding_count', 'expanding_cov', 'expanding_kurt', + 'expanding_max', 'expanding_mean', 'expanding_median', + 'expanding_min', 'expanding_quantile', + 'expanding_skew', 'expanding_std', 'expanding_sum', + 'expanding_var', 'fama_macbeth', 'rolling_apply', + 'rolling_corr', 'rolling_count', 'rolling_cov', + 'rolling_kurt', 'rolling_max', 'rolling_mean', + 'rolling_median', 'rolling_min', 'rolling_quantile', + 'rolling_skew', 'rolling_std', 'rolling_sum', + 'rolling_var', 'rolling_window', 'ordered_merge'] + + def test_api(self): + + self.check(pd, + self.lib + self.lib_to_rename + self.misc + + self.modules + self.deprecated_modules + + self.classes + self.deprecated_classes + + self.deprecated_classes_in_future + + self.remove_classes_from_top_level_namespace + + self.funcs + self.funcs_option + + self.funcs_read + self.funcs_to + + self.deprecated_funcs + + self.deprecated_funcs_in_future, + self.ignored) + + +class TestApi(Base, tm.TestCase): + + allowed = ['tests', 'types'] + + def test_api(self): + + self.check(api, self.allowed) + + +class TestTypes(Base, tm.TestCase): + + allowed = ['is_any_int_dtype', 'is_bool', 'is_bool_dtype', + 'is_categorical', 'is_categorical_dtype', 'is_complex', + 'is_complex_dtype', 'is_datetime64_any_dtype', + 'is_datetime64_dtype', 'is_datetime64_ns_dtype', + 'is_datetime64tz_dtype', 'is_datetimetz', 'is_dtype_equal', + 'is_extension_type', 'is_float', 'is_float_dtype', + 'is_floating_dtype', 'is_int64_dtype', 'is_integer', + 'is_integer_dtype', 'is_number', 'is_numeric_dtype', + 'is_object_dtype', 'is_scalar', 'is_sparse', + 'is_string_dtype', 'is_timedelta64_dtype', + 'is_timedelta64_ns_dtype', + 'is_re', 'is_re_compilable', + 'is_dict_like', 'is_iterator', + 'is_list_like', 'is_hashable', + 'is_named_tuple', 'is_sequence', + 'pandas_dtype'] + + def test_types(self): + + self.check(types, self.allowed) + + def check_deprecation(self, fold, fnew): + with tm.assert_produces_warning(FutureWarning): + try: + result = fold('foo') + expected = fnew('foo') + self.assertEqual(result, expected) + except TypeError: + self.assertRaises(TypeError, + lambda: fnew('foo')) + except AttributeError: + self.assertRaises(AttributeError, + lambda: fnew('foo')) + + def test_deprecation_core_common(self): + + # test that we are in fact deprecating + # the pandas.core.common introspectors + for t in self.allowed: + self.check_deprecation(getattr(com, t), getattr(types, t)) + + def test_deprecation_core_common_moved(self): + + # these are in pandas.types.common + l = ['is_datetime_arraylike', + 'is_datetime_or_timedelta_dtype', + 'is_datetimelike', + 'is_datetimelike_v_numeric', + 'is_datetimelike_v_object', + 'is_datetimetz', + 'is_int_or_datetime_dtype', + 'is_period_arraylike', + 'is_string_like', + 'is_string_like_dtype'] + + from pandas.types import common as c + for t in l: + self.check_deprecation(getattr(com, t), getattr(c, t)) + + def test_removed_from_core_common(self): + + for t in ['is_null_datelike_scalar', + 'ensure_float']: + self.assertRaises(AttributeError, lambda: getattr(com, t)) + +if __name__ == '__main__': + import nose + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/api/types/__init__.py b/pandas/api/types/__init__.py new file mode 100644 index 0000000000000..ee217543f0420 --- /dev/null +++ b/pandas/api/types/__init__.py @@ -0,0 +1,4 @@ +""" public toolkit API """ + +from pandas.types.api import * # noqa +del np # noqa diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index 15bf6d31b7109..adc17c7514832 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -21,7 +21,8 @@ from numpy import ndarray from pandas.util.validators import (validate_args, validate_kwargs, validate_args_and_kwargs) -from pandas.core.common import is_bool, is_integer, UnsupportedFunctionCall +from pandas.core.common import UnsupportedFunctionCall +from pandas.types.common import is_integer, is_bool from pandas.compat import OrderedDict diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index bf6fa35cf255f..96a04cff9372e 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -7,11 +7,11 @@ import numpy as np +from pandas.types.common import is_list_like, is_scalar import pandas as pd from pandas.compat import PY3, string_types, text_type import pandas.core.common as com from pandas.formats.printing import pprint_thing, pprint_thing_encoded -import pandas.lib as lib from pandas.core.base import StringMixin from pandas.computation.common import _ensure_decoded, _result_type_many from pandas.computation.scope import _DEFAULT_GLOBALS @@ -100,7 +100,7 @@ def update(self, value): @property def isscalar(self): - return lib.isscalar(self._value) + return is_scalar(self._value) @property def type(self): @@ -229,7 +229,7 @@ def _in(x, y): try: return x.isin(y) except AttributeError: - if com.is_list_like(x): + if is_list_like(x): try: return y.isin(x) except AttributeError: @@ -244,7 +244,7 @@ def _not_in(x, y): try: return ~x.isin(y) except AttributeError: - if com.is_list_like(x): + if is_list_like(x): try: return ~y.isin(x) except AttributeError: @@ -286,7 +286,7 @@ def _cast_inplace(terms, acceptable_dtypes, dtype): acceptable_dtypes : list of acceptable numpy.dtype Will not cast if term's dtype in this list. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 dtype : str or numpy.dtype The dtype to cast to. diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py index d6d55d15fec30..e375716b0d606 100644 --- a/pandas/computation/pytables.py +++ b/pandas/computation/pytables.py @@ -7,6 +7,8 @@ from datetime import datetime, timedelta import numpy as np import pandas as pd + +from pandas.types.common import is_list_like import pandas.core.common as com from pandas.compat import u, string_types, DeepChainMap from pandas.core.base import StringMixin @@ -127,7 +129,7 @@ def pr(left, right): def conform(self, rhs): """ inplace conform rhs """ - if not com.is_list_like(rhs): + if not is_list_like(rhs): rhs = [rhs] if isinstance(rhs, np.ndarray): rhs = rhs.ravel() diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index 5019dd392a567..066df0521fef6 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -13,6 +13,7 @@ from numpy.random import randn, rand, randint import numpy as np +from pandas.types.common import is_list_like, is_scalar import pandas as pd from pandas.core import common as com from pandas import DataFrame, Series, Panel, date_range @@ -200,7 +201,7 @@ def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2): ex = '(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)'.format(cmp1=cmp1, binop=binop, cmp2=cmp2) - scalar_with_in_notin = (lib.isscalar(rhs) and (cmp1 in skip_these or + scalar_with_in_notin = (is_scalar(rhs) and (cmp1 in skip_these or cmp2 in skip_these)) if scalar_with_in_notin: with tm.assertRaises(TypeError): @@ -253,7 +254,7 @@ def check_operands(left, right, cmp_op): def check_simple_cmp_op(self, lhs, cmp1, rhs): ex = 'lhs {0} rhs'.format(cmp1) - if cmp1 in ('in', 'not in') and not com.is_list_like(rhs): + if cmp1 in ('in', 'not in') and not is_list_like(rhs): self.assertRaises(TypeError, pd.eval, ex, engine=self.engine, parser=self.parser, local_dict={'lhs': lhs, 'rhs': rhs}) @@ -331,7 +332,7 @@ def check_pow(self, lhs, arith1, rhs): expected = self.get_expected_pow_result(lhs, rhs) result = pd.eval(ex, engine=self.engine, parser=self.parser) - if (lib.isscalar(lhs) and lib.isscalar(rhs) and + if (is_scalar(lhs) and is_scalar(rhs) and _is_py3_complex_incompat(result, expected)): self.assertRaises(AssertionError, tm.assert_numpy_array_equal, result, expected) @@ -364,16 +365,16 @@ def check_compound_invert_op(self, lhs, cmp1, rhs): skip_these = 'in', 'not in' ex = '~(lhs {0} rhs)'.format(cmp1) - if lib.isscalar(rhs) and cmp1 in skip_these: + if is_scalar(rhs) and cmp1 in skip_these: self.assertRaises(TypeError, pd.eval, ex, engine=self.engine, parser=self.parser, local_dict={'lhs': lhs, 'rhs': rhs}) else: # compound - if lib.isscalar(lhs) and lib.isscalar(rhs): + if is_scalar(lhs) and is_scalar(rhs): lhs, rhs = map(lambda x: np.array([x]), (lhs, rhs)) expected = _eval_single_bin(lhs, cmp1, rhs, self.engine) - if lib.isscalar(expected): + if is_scalar(expected): expected = not expected else: expected = ~expected @@ -643,17 +644,17 @@ def test_identical(self): x = 1 result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, 1) - self.assertTrue(lib.isscalar(result)) + self.assertTrue(is_scalar(result)) x = 1.5 result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, 1.5) - self.assertTrue(lib.isscalar(result)) + self.assertTrue(is_scalar(result)) x = False result = pd.eval('x', engine=self.engine, parser=self.parser) self.assertEqual(result, False) - self.assertTrue(lib.isscalar(result)) + self.assertTrue(is_scalar(result)) x = np.array([1]) result = pd.eval('x', engine=self.engine, parser=self.parser) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 4b40bce79cbb5..c3ba734353a8d 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -7,10 +7,31 @@ import numpy as np from pandas import compat, lib, tslib, _np_version_under1p8 +from pandas.types.cast import _maybe_promote +from pandas.types.generic import ABCPeriodIndex, ABCDatetimeIndex +from pandas.types.common import (is_integer_dtype, + is_int64_dtype, + is_categorical_dtype, + is_extension_type, + is_datetimetz, + is_period_arraylike, + is_datetime_or_timedelta_dtype, + is_float_dtype, + needs_i8_conversion, + is_categorical, + is_datetime64_dtype, + is_timedelta64_dtype, + is_scalar, + _ensure_platform_int, + _ensure_object, + _ensure_float64, + _ensure_int64, + is_list_like) +from pandas.types.missing import isnull + import pandas.core.common as com import pandas.algos as algos import pandas.hashtable as htable -from pandas.types import api as gt from pandas.compat import string_types from pandas.tslib import iNaT @@ -105,12 +126,12 @@ def isin(comps, values): boolean array same length as comps """ - if not com.is_list_like(comps): + if not is_list_like(comps): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(comps).__name__)) comps = np.asarray(comps) - if not com.is_list_like(values): + if not is_list_like(values): raise TypeError("only list-like objects are allowed to be passed" " to isin(), you passed a " "[{0}]".format(type(values).__name__)) @@ -126,15 +147,15 @@ def isin(comps, values): f = lambda x, y: lib.ismember_int64(x, set(y)) # may need i8 conversion for proper membership testing - if com.is_datetime64_dtype(comps): + if is_datetime64_dtype(comps): from pandas.tseries.tools import to_datetime values = to_datetime(values)._values.view('i8') comps = comps.view('i8') - elif com.is_timedelta64_dtype(comps): + elif is_timedelta64_dtype(comps): from pandas.tseries.timedeltas import to_timedelta values = to_timedelta(values)._values.view('i8') comps = comps.view('i8') - elif com.is_int64_dtype(comps): + elif is_int64_dtype(comps): pass else: f = lambda x, y: lib.ismember(x, set(values)) @@ -171,20 +192,20 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): vals = np.asarray(values) # localize to UTC - is_datetimetz = com.is_datetimetz(values) - if is_datetimetz: + is_datetimetz_type = is_datetimetz(values) + if is_datetimetz_type: values = DatetimeIndex(values) vals = values.tz_localize(None) - is_datetime = com.is_datetime64_dtype(vals) - is_timedelta = com.is_timedelta64_dtype(vals) + is_datetime = is_datetime64_dtype(vals) + is_timedelta = is_timedelta64_dtype(vals) (hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables) table = hash_klass(size_hint or len(vals)) uniques = vec_klass() labels = table.get_labels(vals, uniques, 0, na_sentinel, True) - labels = com._ensure_platform_int(labels) + labels = _ensure_platform_int(labels) uniques = uniques.to_array() @@ -194,7 +215,7 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): except: # unorderable in py3 if mixed str/int t = hash_klass(len(uniques)) - t.map_locations(com._ensure_object(uniques)) + t.map_locations(_ensure_object(uniques)) # order ints before strings ordered = np.concatenate([ @@ -202,8 +223,8 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): dtype=object)) for f in [lambda x: not isinstance(x, string_types), lambda x: isinstance(x, string_types)]]) - sorter = com._ensure_platform_int(t.lookup( - com._ensure_object(ordered))) + sorter = _ensure_platform_int(t.lookup( + _ensure_object(ordered))) reverse_indexer = np.empty(len(sorter), dtype=np.int_) reverse_indexer.put(sorter, np.arange(len(sorter))) @@ -214,7 +235,7 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): uniques = uniques.take(sorter) - if is_datetimetz: + if is_datetimetz_type: # reset tz uniques = DatetimeIndex(uniques.astype('M8[ns]')).tz_localize( @@ -267,7 +288,7 @@ def value_counts(values, sort=True, ascending=False, normalize=False, raise TypeError("bins argument only works with numeric data.") values = cat.codes - if com.is_extension_type(values) and not com.is_datetimetz(values): + if is_extension_type(values) and not is_datetimetz(values): # handle Categorical and sparse, # datetime tz can be handeled in ndarray path result = Series(values).values.value_counts(dropna=dropna) @@ -298,9 +319,9 @@ def value_counts(values, sort=True, ascending=False, normalize=False, def _value_counts_arraylike(values, dropna=True): - is_datetimetz = com.is_datetimetz(values) - is_period = (isinstance(values, gt.ABCPeriodIndex) or - com.is_period_arraylike(values)) + is_datetimetz_type = is_datetimetz(values) + is_period = (isinstance(values, ABCPeriodIndex) or + is_period_arraylike(values)) orig = values @@ -308,7 +329,7 @@ def _value_counts_arraylike(values, dropna=True): values = Series(values).values dtype = values.dtype - if com.is_datetime_or_timedelta_dtype(dtype) or is_period: + if is_datetime_or_timedelta_dtype(dtype) or is_period: from pandas.tseries.index import DatetimeIndex from pandas.tseries.period import PeriodIndex @@ -327,8 +348,8 @@ def _value_counts_arraylike(values, dropna=True): keys = keys.astype(dtype) # dtype handling - if is_datetimetz: - if isinstance(orig, gt.ABCDatetimeIndex): + if is_datetimetz_type: + if isinstance(orig, ABCDatetimeIndex): tz = orig.tz else: tz = orig.dt.tz @@ -336,15 +357,15 @@ def _value_counts_arraylike(values, dropna=True): if is_period: keys = PeriodIndex._simple_new(keys, freq=freq) - elif com.is_integer_dtype(dtype): - values = com._ensure_int64(values) + elif is_integer_dtype(dtype): + values = _ensure_int64(values) keys, counts = htable.value_count_scalar64(values, dropna) - elif com.is_float_dtype(dtype): - values = com._ensure_float64(values) + elif is_float_dtype(dtype): + values = _ensure_float64(values) keys, counts = htable.value_count_scalar64(values, dropna) else: - values = com._ensure_object(values) - mask = com.isnull(values) + values = _ensure_object(values) + mask = isnull(values) keys, counts = htable.value_count_object(values, mask) if not dropna and mask.any(): keys = np.insert(keys, 0, np.NaN) @@ -366,8 +387,8 @@ def mode(values): constructor = Series dtype = values.dtype - if com.is_integer_dtype(values): - values = com._ensure_int64(values) + if is_integer_dtype(values): + values = _ensure_int64(values) result = constructor(sorted(htable.mode_int64(values)), dtype=dtype) elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)): @@ -375,11 +396,11 @@ def mode(values): values = values.view(np.int64) result = constructor(sorted(htable.mode_int64(values)), dtype=dtype) - elif com.is_categorical_dtype(values): + elif is_categorical_dtype(values): result = constructor(values.mode()) else: - mask = com.isnull(values) - values = com._ensure_object(values) + mask = isnull(values) + values = _ensure_object(values) res = htable.mode_object(values, mask) try: res = sorted(res) @@ -459,7 +480,7 @@ def quantile(x, q, interpolation_method='fraction'): """ x = np.asarray(x) - mask = com.isnull(x) + mask = isnull(x) x = x[~mask] @@ -486,7 +507,7 @@ def _get_score(at): return score - if lib.isscalar(q): + if is_scalar(q): return _get_score(q) else: q = np.asarray(q, np.float64) @@ -593,18 +614,18 @@ def _hashtable_algo(f, dtype, return_dtype=None): """ f(HashTable, type_caster) -> result """ - if com.is_float_dtype(dtype): - return f(htable.Float64HashTable, com._ensure_float64) - elif com.is_integer_dtype(dtype): - return f(htable.Int64HashTable, com._ensure_int64) - elif com.is_datetime64_dtype(dtype): + if is_float_dtype(dtype): + return f(htable.Float64HashTable, _ensure_float64) + elif is_integer_dtype(dtype): + return f(htable.Int64HashTable, _ensure_int64) + elif is_datetime64_dtype(dtype): return_dtype = return_dtype or 'M8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) - elif com.is_timedelta64_dtype(dtype): + return f(htable.Int64HashTable, _ensure_int64).view(return_dtype) + elif is_timedelta64_dtype(dtype): return_dtype = return_dtype or 'm8[ns]' - return f(htable.Int64HashTable, com._ensure_int64).view(return_dtype) + return f(htable.Int64HashTable, _ensure_int64).view(return_dtype) else: - return f(htable.PyObjectHashTable, com._ensure_object) + return f(htable.PyObjectHashTable, _ensure_object) _hashtables = { 'float64': (htable.Float64HashTable, htable.Float64Vector), @@ -614,20 +635,20 @@ def _hashtable_algo(f, dtype, return_dtype=None): def _get_data_algo(values, func_map): - if com.is_float_dtype(values): + if is_float_dtype(values): f = func_map['float64'] - values = com._ensure_float64(values) + values = _ensure_float64(values) - elif com.needs_i8_conversion(values): + elif needs_i8_conversion(values): f = func_map['int64'] values = values.view('i8') - elif com.is_integer_dtype(values): + elif is_integer_dtype(values): f = func_map['int64'] - values = com._ensure_int64(values) + values = _ensure_int64(values) else: f = func_map['generic'] - values = com._ensure_object(values) + values = _ensure_object(values) return f, values @@ -689,7 +710,7 @@ def _take_nd_generic(arr, indexer, out, axis, fill_value, mask_info): if arr.dtype != out.dtype: arr = arr.astype(out.dtype) if arr.shape[axis] > 0: - arr.take(com._ensure_platform_int(indexer), axis=axis, out=out) + arr.take(_ensure_platform_int(indexer), axis=axis, out=out) if needs_masking: outindexer = [slice(None)] * arr.ndim outindexer[axis] = mask @@ -830,7 +851,7 @@ def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis=0, mask_info=None): return func def func(arr, indexer, out, fill_value=np.nan): - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) _take_nd_generic(arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info) @@ -854,7 +875,7 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, out : ndarray or None, default None Optional output array, must be appropriate type to hold input and fill_value together, if indexer has any -1 value entries; call - common._maybe_promote to determine this type for any fill_value + _maybe_promote to determine this type for any fill_value fill_value : any, default np.nan Fill value to replace -1 values with mask_info : tuple of (ndarray, boolean) @@ -868,24 +889,24 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, """ # dispatch to internal type takes - if com.is_categorical(arr): + if is_categorical(arr): return arr.take_nd(indexer, fill_value=fill_value, allow_fill=allow_fill) - elif com.is_datetimetz(arr): + elif is_datetimetz(arr): return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) if indexer is None: indexer = np.arange(arr.shape[axis], dtype=np.int64) dtype, fill_value = arr.dtype, arr.dtype.type() else: - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) if not allow_fill: dtype, fill_value = arr.dtype, arr.dtype.type() mask_info = None, False else: # check for promotion based on types only (do this first because # it's faster than computing a mask) - dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + dtype, fill_value = _maybe_promote(arr.dtype, fill_value) if dtype != arr.dtype and (out is None or out.dtype != dtype): # check if promotion is actually required based on indexer if mask_info is not None: @@ -931,7 +952,7 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info) - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) func(arr, indexer, out, fill_value) if flip_order: @@ -957,11 +978,11 @@ def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, if row_idx is None: row_idx = np.arange(arr.shape[0], dtype=np.int64) else: - row_idx = com._ensure_int64(row_idx) + row_idx = _ensure_int64(row_idx) if col_idx is None: col_idx = np.arange(arr.shape[1], dtype=np.int64) else: - col_idx = com._ensure_int64(col_idx) + col_idx = _ensure_int64(col_idx) indexer = row_idx, col_idx if not allow_fill: dtype, fill_value = arr.dtype, arr.dtype.type() @@ -969,7 +990,7 @@ def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, else: # check for promotion based on types only (do this first because # it's faster than computing a mask) - dtype, fill_value = com._maybe_promote(arr.dtype, fill_value) + dtype, fill_value = _maybe_promote(arr.dtype, fill_value) if dtype != arr.dtype and (out is None or out.dtype != dtype): # check if promotion is actually required based on indexer if mask_info is not None: @@ -1032,7 +1053,7 @@ def diff(arr, n, axis=0): na = np.nan dtype = arr.dtype is_timedelta = False - if com.needs_i8_conversion(arr): + if needs_i8_conversion(arr): dtype = np.float64 arr = arr.view('i8') na = tslib.iNaT diff --git a/pandas/core/api.py b/pandas/core/api.py index 0a6992bfebd70..579f21eb4ada8 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -5,7 +5,7 @@ import numpy as np from pandas.core.algorithms import factorize, match, unique, value_counts -from pandas.core.common import isnull, notnull +from pandas.types.missing import isnull, notnull from pandas.core.categorical import Categorical from pandas.core.groupby import Grouper from pandas.formats.format import set_eng_float_format diff --git a/pandas/core/base.py b/pandas/core/base.py index 96732a7140f9e..a0dfebdfde356 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -4,6 +4,12 @@ from pandas import compat from pandas.compat import builtins import numpy as np + +from pandas.types.missing import isnull +from pandas.types.generic import ABCDataFrame, ABCSeries, ABCIndex +from pandas.types.common import (_ensure_object, is_object_dtype, + is_list_like, is_scalar) + from pandas.core import common as com import pandas.core.nanops as nanops import pandas.lib as lib @@ -11,7 +17,6 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) from pandas.core.common import AbstractMethodError -from pandas.types import api as gt from pandas.formats.printing import pprint_thing _shared_docs = dict() @@ -121,7 +126,7 @@ def __sizeof__(self): """ if hasattr(self, 'memory_usage'): mem = self.memory_usage(deep=True) - if not lib.isscalar(mem): + if not is_scalar(mem): mem = mem.sum() return int(mem) @@ -293,15 +298,15 @@ def name(self): @property def _selection_list(self): - if not isinstance(self._selection, (list, tuple, gt.ABCSeries, - gt.ABCIndex, np.ndarray)): + if not isinstance(self._selection, (list, tuple, ABCSeries, + ABCIndex, np.ndarray)): return [self._selection] return self._selection @cache_readonly def _selected_obj(self): - if self._selection is None or isinstance(self.obj, gt.ABCSeries): + if self._selection is None or isinstance(self.obj, ABCSeries): return self.obj else: return self.obj[self._selection] @@ -313,7 +318,7 @@ def ndim(self): @cache_readonly def _obj_with_exclusions(self): if self._selection is not None and isinstance(self.obj, - gt.ABCDataFrame): + ABCDataFrame): return self.obj.reindex(columns=self._selection_list) if len(self.exclusions) > 0: @@ -325,7 +330,7 @@ def __getitem__(self, key): if self._selection is not None: raise Exception('Column(s) %s already selected' % self._selection) - if isinstance(key, (list, tuple, gt.ABCSeries, gt.ABCIndex, + if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)): if len(self.obj.columns.intersection(key)) != len(key): bad_keys = list(set(key).difference(self.obj.columns)) @@ -553,7 +558,7 @@ def _agg(arg, func): if isinstance(result, list): result = concat(result, keys=keys, axis=1) elif isinstance(list(compat.itervalues(result))[0], - gt.ABCDataFrame): + ABCDataFrame): result = concat([result[k] for k in keys], keys=keys, axis=1) else: from pandas import DataFrame @@ -682,7 +687,7 @@ def _gotitem(self, key, ndim, subset=None): **kwargs) self._reset_cache() if subset.ndim == 2: - if lib.isscalar(key) and key in subset or com.is_list_like(key): + if is_scalar(key) and key in subset or is_list_like(key): self._selection = key return self @@ -903,7 +908,7 @@ def argmin(self, axis=None): @cache_readonly def hasnans(self): """ return if I have any nans; enables various perf speedups """ - return com.isnull(self).any() + return isnull(self).any() def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): @@ -980,7 +985,7 @@ def nunique(self, dropna=True): """ uniqs = self.unique() n = len(uniqs) - if dropna and com.isnull(uniqs).any(): + if dropna and isnull(uniqs).any(): n -= 1 return n @@ -1001,7 +1006,7 @@ def is_monotonic(self): Return boolean if values in the object are monotonic_increasing - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- @@ -1017,7 +1022,7 @@ def is_monotonic_decreasing(self): Return boolean if values in the object are monotonic_decreasing - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- @@ -1053,7 +1058,7 @@ def memory_usage(self, deep=False): return self.values.memory_usage(deep=deep) v = self.values.nbytes - if deep and com.is_object_dtype(self): + if deep and is_object_dtype(self): v += lib.memory_usage_of_objects(self.values) return v @@ -1195,7 +1200,7 @@ def drop_duplicates(self, keep='first', inplace=False): False: 'first'}) @Appender(_shared_docs['duplicated'] % _indexops_doc_kwargs) def duplicated(self, keep='first'): - keys = com._values_from_object(com._ensure_object(self.values)) + keys = com._values_from_object(_ensure_object(self.values)) duplicated = lib.duplicated(keys, keep=keep) try: return self._constructor(duplicated, diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 6dba41a746e19..a26cc5125db78 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -7,6 +7,22 @@ from pandas import compat, lib from pandas.compat import u +from pandas.types.generic import ABCSeries, ABCIndexClass, ABCCategoricalIndex +from pandas.types.missing import isnull, notnull +from pandas.types.cast import (_possibly_infer_to_datetimelike, + _coerce_indexer_dtype) +from pandas.types.dtypes import CategoricalDtype +from pandas.types.common import (_ensure_int64, + _ensure_object, + _ensure_platform_int, + is_dtype_equal, + is_datetimelike, + is_categorical_dtype, + is_integer_dtype, is_bool, + is_list_like, is_sequence, + is_scalar) +from pandas.core.common import is_null_slice + from pandas.core.algorithms import factorize, take_1d from pandas.core.base import (PandasObject, PandasDelegate, NoNewAttributesMixin, _shared_docs) @@ -16,13 +32,6 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) -from pandas.core.common import ( - ABCSeries, ABCIndexClass, ABCCategoricalIndex, isnull, notnull, - is_dtype_equal, is_categorical_dtype, is_integer_dtype, - _possibly_infer_to_datetimelike, is_list_like, - is_sequence, is_null_slice, is_bool, _ensure_object, _ensure_int64, - _coerce_indexer_dtype) -from pandas.types.api import CategoricalDtype from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option @@ -64,7 +73,7 @@ def f(self, other): # With cat[0], for example, being ``np.int64(1)`` by the time it gets # into this function would become ``np.array(1)``. other = lib.item_from_zerodim(other) - if lib.isscalar(other): + if is_scalar(other): if other in self.categories: i = self.categories.get_loc(other) return getattr(self._codes, op)(i) @@ -219,8 +228,8 @@ class Categorical(PandasObject): __array_priority__ = 1000 _typ = 'categorical' - def __init__(self, values, categories=None, ordered=False, name=None, - fastpath=False, levels=None): + def __init__(self, values, categories=None, ordered=False, + name=None, fastpath=False): if fastpath: # fast path @@ -236,17 +245,6 @@ def __init__(self, values, categories=None, ordered=False, name=None, "name=\"something\")'") warn(msg, UserWarning, stacklevel=2) - # TODO: Remove after deprecation period in 2017/ after 0.18 - if levels is not None: - warn("Creating a 'Categorical' with 'levels' is deprecated, use " - "'categories' instead", FutureWarning, stacklevel=2) - if categories is None: - categories = levels - else: - raise ValueError("Cannot pass in both 'categories' and " - "(deprecated) 'levels', use only " - "'categories'", stacklevel=2) - # sanitize input if is_categorical_dtype(values): @@ -348,7 +346,7 @@ def astype(self, dtype, copy=True): If copy is set to False and dtype is categorical, the original object is returned. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 """ if is_categorical_dtype(dtype): @@ -374,11 +372,28 @@ def itemsize(self): def reshape(self, new_shape, *args, **kwargs): """ - An ndarray-compatible method that returns - `self` because categorical instances cannot - actually be reshaped. + DEPRECATED: calling this method will raise an error in a + future release. + + An ndarray-compatible method that returns `self` because + `Categorical` instances cannot actually be reshaped. + + Parameters + ---------- + new_shape : int or tuple of ints + A 1-D array of integers that correspond to the new + shape of the `Categorical`. For more information on + the parameter, please refer to `np.reshape`. """ + warn("reshape is deprecated and will raise " + "in a subsequent release", FutureWarning, stacklevel=2) + nv.validate_reshape(args, kwargs) + + # while the 'new_shape' parameter has no effect, + # we should still enforce valid shape parameters + np.reshape(self.codes, new_shape) + return self @property @@ -554,21 +569,6 @@ def _get_categories(self): categories = property(fget=_get_categories, fset=_set_categories, doc=_categories_doc) - def _set_levels(self, levels): - """ set new levels (deprecated, use "categories") """ - warn("Assigning to 'levels' is deprecated, use 'categories'", - FutureWarning, stacklevel=2) - self.categories = levels - - def _get_levels(self): - """ Gets the levels (deprecated, use "categories") """ - warn("Accessing 'levels' is deprecated, use 'categories'", - FutureWarning, stacklevel=2) - return self.categories - - # TODO: Remove after deprecation period in 2017/ after 0.18 - levels = property(fget=_get_levels, fset=_set_levels) - _ordered = None def _set_ordered(self, value): @@ -968,7 +968,7 @@ def shift(self, periods): if codes.ndim > 1: raise NotImplementedError("Categorical with ndim > 1.") if np.prod(codes.shape) and (periods != 0): - codes = np.roll(codes, com._ensure_platform_int(periods), axis=0) + codes = np.roll(codes, _ensure_platform_int(periods), axis=0) if periods > 0: codes[:periods] = -1 else: @@ -1148,7 +1148,7 @@ def value_counts(self, dropna=True): counts : Series """ from numpy import bincount - from pandas.core.common import isnull + from pandas.types.missing import isnull from pandas.core.series import Series from pandas.core.index import CategoricalIndex @@ -1182,7 +1182,7 @@ def get_values(self): Index if datetime / periods """ # if we are a datetime and period index, return Index to keep metadata - if com.is_datetimelike(self.categories): + if is_datetimelike(self.categories): return self.categories.take(self._codes, fill_value=np.nan) return np.array(self) @@ -1933,7 +1933,7 @@ def _convert_to_list_like(list_like): if (is_sequence(list_like) or isinstance(list_like, tuple) or isinstance(list_like, types.GeneratorType)): return list(list_like) - elif lib.isscalar(list_like): + elif is_scalar(list_like): return [list_like] else: # is this reached? diff --git a/pandas/core/common.py b/pandas/core/common.py index 28bae362a3411..99dd2e9f5b8a9 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2,23 +2,66 @@ Misc tools for implementing data structures """ -import re -import collections -import numbers +import sys +import warnings from datetime import datetime, timedelta from functools import partial import numpy as np -import pandas as pd -import pandas.algos as algos import pandas.lib as lib import pandas.tslib as tslib from pandas import compat -from pandas.compat import (long, zip, map, string_types, - iteritems) -from pandas.types import api as gt -from pandas.types.api import * # noqa +from pandas.compat import long, zip, iteritems from pandas.core.config import get_option +from pandas.types.generic import ABCSeries +from pandas.types.common import _NS_DTYPE, is_integer +from pandas.types.inference import _iterable_not_string +from pandas.types.missing import isnull +from pandas.api import types +from pandas.types import common + +# back-compat of public API +# deprecate these functions +m = sys.modules['pandas.core.common'] +for t in [t for t in dir(types) if not t.startswith('_')]: + + def outer(t=t): + + def wrapper(*args, **kwargs): + warnings.warn("pandas.core.common.{t} is deprecated. " + "import from the public API: " + "pandas.api.types.{t} instead".format(t=t), + FutureWarning, stacklevel=2) + return getattr(types, t)(*args, **kwargs) + return wrapper + + setattr(m, t, outer(t)) + +# back-compat for non-public functions +# deprecate these functions +for t in ['is_datetime_arraylike', + 'is_datetime_or_timedelta_dtype', + 'is_datetimelike', + 'is_datetimelike_v_numeric', + 'is_datetimelike_v_object', + 'is_datetimetz', + 'is_int_or_datetime_dtype', + 'is_period_arraylike', + 'is_string_like', + 'is_string_like_dtype']: + + def outer(t=t): + + def wrapper(*args, **kwargs): + warnings.warn("pandas.core.common.{t} is deprecated. " + "These are not longer public API functions, " + "but can be imported from " + "pandas.types.common.{t} instead".format(t=t), + FutureWarning, stacklevel=2) + return getattr(common, t)(*args, **kwargs) + return wrapper + + setattr(m, t, outer(t)) class PandasError(Exception): @@ -58,322 +101,6 @@ def __str__(self): self.class_instance.__class__.__name__) -_POSSIBLY_CAST_DTYPES = set([np.dtype(t).name - for t in ['O', 'int8', 'uint8', 'int16', 'uint16', - 'int32', 'uint32', 'int64', 'uint64']]) - -_NS_DTYPE = np.dtype('M8[ns]') -_TD_DTYPE = np.dtype('m8[ns]') -_INT64_DTYPE = np.dtype(np.int64) -_DATELIKE_DTYPES = set([np.dtype(t) - for t in ['M8[ns]', '<M8[ns]', '>M8[ns]', - 'm8[ns]', '<m8[ns]', '>m8[ns]']]) -_int8_max = np.iinfo(np.int8).max -_int16_max = np.iinfo(np.int16).max -_int32_max = np.iinfo(np.int32).max -_int64_max = np.iinfo(np.int64).max - - -def isnull(obj): - """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) - - Parameters - ---------- - arr : ndarray or object value - Object to check for null-ness - - Returns - ------- - isnulled : array-like of bool or bool - Array or bool indicating whether an object is null or if an array is - given which of the element is null. - - See also - -------- - pandas.notnull: boolean inverse of pandas.isnull - """ - return _isnull(obj) - - -def _isnull_new(obj): - if lib.isscalar(obj): - return lib.checknull(obj) - # hack (for now) because MI registers as ndarray - elif isinstance(obj, pd.MultiIndex): - raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): - return _isnull_ndarraylike(obj) - elif isinstance(obj, gt.ABCGeneric): - return obj._constructor(obj._data.isnull(func=isnull)) - elif isinstance(obj, list) or hasattr(obj, '__array__'): - return _isnull_ndarraylike(np.asarray(obj)) - else: - return obj is None - - -def _isnull_old(obj): - """Detect missing values. Treat None, NaN, INF, -INF as null. - - Parameters - ---------- - arr: ndarray or object value - - Returns - ------- - boolean ndarray or boolean - """ - if lib.isscalar(obj): - return lib.checknull_old(obj) - # hack (for now) because MI registers as ndarray - elif isinstance(obj, pd.MultiIndex): - raise NotImplementedError("isnull is not defined for MultiIndex") - elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)): - return _isnull_ndarraylike_old(obj) - elif isinstance(obj, gt.ABCGeneric): - return obj._constructor(obj._data.isnull(func=_isnull_old)) - elif isinstance(obj, list) or hasattr(obj, '__array__'): - return _isnull_ndarraylike_old(np.asarray(obj)) - else: - return obj is None - - -_isnull = _isnull_new - - -def _use_inf_as_null(key): - """Option change callback for null/inf behaviour - Choose which replacement for numpy.isnan / ~numpy.isfinite is used. - - Parameters - ---------- - flag: bool - True means treat None, NaN, INF, -INF as null (old way), - False means None and NaN are null, but INF, -INF are not null - (new way). - - Notes - ----- - This approach to setting global module values is discussed and - approved here: - - * http://stackoverflow.com/questions/4859217/ - programmatically-creating-variables-in-python/4859312#4859312 - """ - flag = get_option(key) - if flag: - globals()['_isnull'] = _isnull_old - else: - globals()['_isnull'] = _isnull_new - - -def _isnull_ndarraylike(obj): - - values = getattr(obj, 'values', obj) - dtype = values.dtype - - if is_string_dtype(dtype): - if is_categorical_dtype(values): - from pandas import Categorical - if not isinstance(values, Categorical): - values = values.values - result = values.isnull() - else: - - # Working around NumPy ticket 1542 - shape = values.shape - - if is_string_like_dtype(dtype): - result = np.zeros(values.shape, dtype=bool) - else: - result = np.empty(shape, dtype=bool) - vec = lib.isnullobj(values.ravel()) - result[...] = vec.reshape(shape) - - elif is_datetimelike(obj): - # this is the NaT pattern - result = values.view('i8') == tslib.iNaT - else: - result = np.isnan(values) - - # box - if isinstance(obj, gt.ABCSeries): - from pandas import Series - result = Series(result, index=obj.index, name=obj.name, copy=False) - - return result - - -def _isnull_ndarraylike_old(obj): - values = getattr(obj, 'values', obj) - dtype = values.dtype - - if is_string_dtype(dtype): - # Working around NumPy ticket 1542 - shape = values.shape - - if is_string_like_dtype(dtype): - result = np.zeros(values.shape, dtype=bool) - else: - result = np.empty(shape, dtype=bool) - vec = lib.isnullobj_old(values.ravel()) - result[:] = vec.reshape(shape) - - elif dtype in _DATELIKE_DTYPES: - # this is the NaT pattern - result = values.view('i8') == tslib.iNaT - else: - result = ~np.isfinite(values) - - # box - if isinstance(obj, gt.ABCSeries): - from pandas import Series - result = Series(result, index=obj.index, name=obj.name, copy=False) - - return result - - -def notnull(obj): - """Replacement for numpy.isfinite / ~numpy.isnan which is suitable for use - on object arrays. - - Parameters - ---------- - arr : ndarray or object value - Object to check for *not*-null-ness - - Returns - ------- - isnulled : array-like of bool or bool - Array or bool indicating whether an object is *not* null or if an array - is given which of the element is *not* null. - - See also - -------- - pandas.isnull : boolean inverse of pandas.notnull - """ - res = isnull(obj) - if lib.isscalar(res): - return not res - return ~res - - -def is_null_datelike_scalar(other): - """ test whether the object is a null datelike, e.g. Nat - but guard against passing a non-scalar """ - if other is pd.NaT or other is None: - return True - elif lib.isscalar(other): - - # a timedelta - if hasattr(other, 'dtype'): - return other.view('i8') == tslib.iNaT - elif is_integer(other) and other == tslib.iNaT: - return True - return isnull(other) - return False - - -def array_equivalent(left, right, strict_nan=False): - """ - True if two arrays, left and right, have equal non-NaN elements, and NaNs - in corresponding locations. False otherwise. It is assumed that left and - right are NumPy arrays of the same dtype. The behavior of this function - (particularly with respect to NaNs) is not defined if the dtypes are - different. - - Parameters - ---------- - left, right : ndarrays - strict_nan : bool, default False - If True, consider NaN and None to be different. - - Returns - ------- - b : bool - Returns True if the arrays are equivalent. - - Examples - -------- - >>> array_equivalent( - ... np.array([1, 2, np.nan]), - ... np.array([1, 2, np.nan])) - True - >>> array_equivalent( - ... np.array([1, np.nan, 2]), - ... np.array([1, 2, np.nan])) - False - """ - - left, right = np.asarray(left), np.asarray(right) - - # shape compat - if left.shape != right.shape: - return False - - # Object arrays can contain None, NaN and NaT. - # string dtypes must be come to this path for NumPy 1.7.1 compat - if is_string_dtype(left) or is_string_dtype(right): - - if not strict_nan: - # pd.isnull considers NaN and None to be equivalent. - return lib.array_equivalent_object(_ensure_object(left.ravel()), - _ensure_object(right.ravel())) - - for left_value, right_value in zip(left, right): - if left_value is tslib.NaT and right_value is not tslib.NaT: - return False - - elif isinstance(left_value, float) and np.isnan(left_value): - if (not isinstance(right_value, float) or - not np.isnan(right_value)): - return False - else: - if left_value != right_value: - return False - return True - - # NaNs can occur in float and complex arrays. - if is_float_dtype(left) or is_complex_dtype(left): - return ((left == right) | (np.isnan(left) & np.isnan(right))).all() - - # numpy will will not allow this type of datetimelike vs integer comparison - elif is_datetimelike_v_numeric(left, right): - return False - - # M8/m8 - elif needs_i8_conversion(left) and needs_i8_conversion(right): - if not is_dtype_equal(left.dtype, right.dtype): - return False - - left = left.view('i8') - right = right.view('i8') - - # NaNs cannot occur otherwise. - try: - return np.array_equal(left, right) - except AttributeError: - # see gh-13388 - # - # NumPy v1.7.1 has a bug in its array_equal - # function that prevents it from correctly - # comparing two arrays with complex dtypes. - # This bug is corrected in v1.8.0, so remove - # this try-except block as soon as we stop - # supporting NumPy versions < 1.8.0 - if not is_dtype_equal(left.dtype, right.dtype): - return False - - left = left.tolist() - right = right.tolist() - - return left == right - - -def _iterable_not_string(x): - return (isinstance(x, collections.Iterable) and - not isinstance(x, compat.string_types)) - - def flatten(l): """Flatten an arbitrarily nested sequence. @@ -398,510 +125,6 @@ def flatten(l): yield el -def _coerce_indexer_dtype(indexer, categories): - """ coerce the indexer input array to the smallest dtype possible """ - l = len(categories) - if l < _int8_max: - return _ensure_int8(indexer) - elif l < _int16_max: - return _ensure_int16(indexer) - elif l < _int32_max: - return _ensure_int32(indexer) - return _ensure_int64(indexer) - - -def _coerce_to_dtypes(result, dtypes): - """ given a dtypes and a result set, coerce the result elements to the - dtypes - """ - if len(result) != len(dtypes): - raise AssertionError("_coerce_to_dtypes requires equal len arrays") - - from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type - - def conv(r, dtype): - try: - if isnull(r): - pass - elif dtype == _NS_DTYPE: - r = lib.Timestamp(r) - elif dtype == _TD_DTYPE: - r = _coerce_scalar_to_timedelta_type(r) - elif dtype == np.bool_: - # messy. non 0/1 integers do not get converted. - if is_integer(r) and r not in [0, 1]: - return int(r) - r = bool(r) - elif dtype.kind == 'f': - r = float(r) - elif dtype.kind == 'i': - r = int(r) - except: - pass - - return r - - return [conv(r, dtype) for r, dtype in zip(result, dtypes)] - - -def _infer_fill_value(val): - """ - infer the fill value for the nan/NaT from the provided - scalar/ndarray/list-like if we are a NaT, return the correct dtyped - element to provide proper block construction - """ - - if not is_list_like(val): - val = [val] - val = np.array(val, copy=False) - if is_datetimelike(val): - return np.array('NaT', dtype=val.dtype) - elif is_object_dtype(val.dtype): - dtype = lib.infer_dtype(_ensure_object(val)) - if dtype in ['datetime', 'datetime64']: - return np.array('NaT', dtype=_NS_DTYPE) - elif dtype in ['timedelta', 'timedelta64']: - return np.array('NaT', dtype=_TD_DTYPE) - return np.nan - - -def _infer_dtype_from_scalar(val): - """ interpret the dtype from a scalar """ - - dtype = np.object_ - - # a 1-element ndarray - if isinstance(val, np.ndarray): - if val.ndim != 0: - raise ValueError( - "invalid ndarray passed to _infer_dtype_from_scalar") - - dtype = val.dtype - val = val.item() - - elif isinstance(val, compat.string_types): - - # If we create an empty array using a string to infer - # the dtype, NumPy will only allocate one character per entry - # so this is kind of bad. Alternately we could use np.repeat - # instead of np.empty (but then you still don't want things - # coming out as np.str_! - - dtype = np.object_ - - elif isinstance(val, (np.datetime64, - datetime)) and getattr(val, 'tzinfo', None) is None: - val = lib.Timestamp(val).value - dtype = np.dtype('M8[ns]') - - elif isinstance(val, (np.timedelta64, timedelta)): - val = lib.Timedelta(val).value - dtype = np.dtype('m8[ns]') - - elif is_bool(val): - dtype = np.bool_ - - elif is_integer(val): - if isinstance(val, np.integer): - dtype = type(val) - else: - dtype = np.int64 - - elif is_float(val): - if isinstance(val, np.floating): - dtype = type(val) - else: - dtype = np.float64 - - elif is_complex(val): - dtype = np.complex_ - - return dtype, val - - -def _is_na_compat(arr, fill_value=np.nan): - """ - Parameters - ---------- - arr: a numpy array - fill_value: fill value, default to np.nan - - Returns - ------- - True if we can fill using this fill_value - """ - dtype = arr.dtype - if isnull(fill_value): - return not (is_bool_dtype(dtype) or - is_integer_dtype(dtype)) - return True - - -def _maybe_fill(arr, fill_value=np.nan): - """ - if we have a compatiable fill_value and arr dtype, then fill - """ - if _is_na_compat(arr, fill_value): - arr.fill(fill_value) - return arr - - -def _maybe_promote(dtype, fill_value=np.nan): - - # if we passed an array here, determine the fill value by dtype - if isinstance(fill_value, np.ndarray): - if issubclass(fill_value.dtype.type, (np.datetime64, np.timedelta64)): - fill_value = tslib.iNaT - else: - - # we need to change to object type as our - # fill_value is of object type - if fill_value.dtype == np.object_: - dtype = np.dtype(np.object_) - fill_value = np.nan - - # returns tuple of (dtype, fill_value) - if issubclass(dtype.type, (np.datetime64, np.timedelta64)): - # for now: refuse to upcast datetime64 - # (this is because datetime64 will not implicitly upconvert - # to object correctly as of numpy 1.6.1) - if isnull(fill_value): - fill_value = tslib.iNaT - else: - if issubclass(dtype.type, np.datetime64): - try: - fill_value = lib.Timestamp(fill_value).value - except: - # the proper thing to do here would probably be to upcast - # to object (but numpy 1.6.1 doesn't do this properly) - fill_value = tslib.iNaT - elif issubclass(dtype.type, np.timedelta64): - try: - fill_value = lib.Timedelta(fill_value).value - except: - # as for datetimes, cannot upcast to object - fill_value = tslib.iNaT - else: - fill_value = tslib.iNaT - elif is_datetimetz(dtype): - if isnull(fill_value): - fill_value = tslib.iNaT - elif is_float(fill_value): - if issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif issubclass(dtype.type, np.integer): - dtype = np.float64 - elif is_bool(fill_value): - if not issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif is_integer(fill_value): - if issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif issubclass(dtype.type, np.integer): - # upcast to prevent overflow - arr = np.asarray(fill_value) - if arr != arr.astype(dtype): - dtype = arr.dtype - elif is_complex(fill_value): - if issubclass(dtype.type, np.bool_): - dtype = np.object_ - elif issubclass(dtype.type, (np.integer, np.floating)): - dtype = np.complex128 - elif fill_value is None: - if is_float_dtype(dtype) or is_complex_dtype(dtype): - fill_value = np.nan - elif is_integer_dtype(dtype): - dtype = np.float64 - fill_value = np.nan - elif is_datetime_or_timedelta_dtype(dtype): - fill_value = tslib.iNaT - else: - dtype = np.object_ - else: - dtype = np.object_ - - # in case we have a string that looked like a number - if is_categorical_dtype(dtype): - pass - elif is_datetimetz(dtype): - pass - elif issubclass(np.dtype(dtype).type, compat.string_types): - dtype = np.object_ - - return dtype, fill_value - - -def _maybe_upcast_putmask(result, mask, other): - """ - A safe version of putmask that potentially upcasts the result - - Parameters - ---------- - result : ndarray - The destination array. This will be mutated in-place if no upcasting is - necessary. - mask : boolean ndarray - other : ndarray or scalar - The source array or value - - Returns - ------- - result : ndarray - changed : boolean - Set to true if the result array was upcasted - """ - - if mask.any(): - # Two conversions for date-like dtypes that can't be done automatically - # in np.place: - # NaN -> NaT - # integer or integer array -> date-like array - if result.dtype in _DATELIKE_DTYPES: - if lib.isscalar(other): - if isnull(other): - other = result.dtype.type('nat') - elif is_integer(other): - other = np.array(other, dtype=result.dtype) - elif is_integer_dtype(other): - other = np.array(other, dtype=result.dtype) - - def changeit(): - - # try to directly set by expanding our array to full - # length of the boolean - try: - om = other[mask] - om_at = om.astype(result.dtype) - if (om == om_at).all(): - new_result = result.values.copy() - new_result[mask] = om_at - result[:] = new_result - return result, False - except: - pass - - # we are forced to change the dtype of the result as the input - # isn't compatible - r, _ = _maybe_upcast(result, fill_value=other, copy=True) - np.place(r, mask, other) - - return r, True - - # we want to decide whether place will work - # if we have nans in the False portion of our mask then we need to - # upcast (possibly), otherwise we DON't want to upcast (e.g. if we - # have values, say integers, in the success portion then it's ok to not - # upcast) - new_dtype, _ = _maybe_promote(result.dtype, other) - if new_dtype != result.dtype: - - # we have a scalar or len 0 ndarray - # and its nan and we are changing some values - if (lib.isscalar(other) or - (isinstance(other, np.ndarray) and other.ndim < 1)): - if isnull(other): - return changeit() - - # we have an ndarray and the masking has nans in it - else: - - if isnull(other[mask]).any(): - return changeit() - - try: - np.place(result, mask, other) - except: - return changeit() - - return result, False - - -def _maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): - """ provide explict type promotion and coercion - - Parameters - ---------- - values : the ndarray that we want to maybe upcast - fill_value : what we want to fill with - dtype : if None, then use the dtype of the values, else coerce to this type - copy : if True always make a copy even if no upcast is required - """ - - if is_extension_type(values): - if copy: - values = values.copy() - else: - if dtype is None: - dtype = values.dtype - new_dtype, fill_value = _maybe_promote(dtype, fill_value) - if new_dtype != values.dtype: - values = values.astype(new_dtype) - elif copy: - values = values.copy() - - return values, fill_value - - -def _possibly_cast_item(obj, item, dtype): - chunk = obj[item] - - if chunk.values.dtype != dtype: - if dtype in (np.object_, np.bool_): - obj[item] = chunk.astype(np.object_) - elif not issubclass(dtype, (np.integer, np.bool_)): # pragma: no cover - raise ValueError("Unexpected dtype encountered: %s" % dtype) - - -def _possibly_downcast_to_dtype(result, dtype): - """ try to cast to the specified dtype (e.g. convert back to bool/int - or could be an astype of float64->float32 - """ - - if lib.isscalar(result): - return result - - def trans(x): - return x - - if isinstance(dtype, compat.string_types): - if dtype == 'infer': - inferred_type = lib.infer_dtype(_ensure_object(result.ravel())) - if inferred_type == 'boolean': - dtype = 'bool' - elif inferred_type == 'integer': - dtype = 'int64' - elif inferred_type == 'datetime64': - dtype = 'datetime64[ns]' - elif inferred_type == 'timedelta64': - dtype = 'timedelta64[ns]' - - # try to upcast here - elif inferred_type == 'floating': - dtype = 'int64' - if issubclass(result.dtype.type, np.number): - - def trans(x): # noqa - return x.round() - else: - dtype = 'object' - - if isinstance(dtype, compat.string_types): - dtype = np.dtype(dtype) - - try: - - # don't allow upcasts here (except if empty) - if dtype.kind == result.dtype.kind: - if (result.dtype.itemsize <= dtype.itemsize and - np.prod(result.shape)): - return result - - if issubclass(dtype.type, np.floating): - return result.astype(dtype) - elif dtype == np.bool_ or issubclass(dtype.type, np.integer): - - # if we don't have any elements, just astype it - if not np.prod(result.shape): - return trans(result).astype(dtype) - - # do a test on the first element, if it fails then we are done - r = result.ravel() - arr = np.array([r[0]]) - - # if we have any nulls, then we are done - if isnull(arr).any() or not np.allclose(arr, - trans(arr).astype(dtype)): - return result - - # a comparable, e.g. a Decimal may slip in here - elif not isinstance(r[0], (np.integer, np.floating, np.bool, int, - float, bool)): - return result - - if (issubclass(result.dtype.type, (np.object_, np.number)) and - notnull(result).all()): - new_result = trans(result).astype(dtype) - try: - if np.allclose(new_result, result): - return new_result - except: - - # comparison of an object dtype with a number type could - # hit here - if (new_result == result).all(): - return new_result - - # a datetimelike - elif dtype.kind in ['M', 'm'] and result.dtype.kind in ['i']: - try: - result = result.astype(dtype) - except: - if dtype.tz: - # convert to datetime and change timezone - result = pd.to_datetime(result).tz_localize(dtype.tz) - - except: - pass - - return result - - -def _maybe_convert_string_to_object(values): - """ - - Convert string-like and string-like array to convert object dtype. - This is to avoid numpy to handle the array as str dtype. - """ - if isinstance(values, string_types): - values = np.array([values], dtype=object) - elif (isinstance(values, np.ndarray) and - issubclass(values.dtype.type, (np.string_, np.unicode_))): - values = values.astype(object) - return values - - -def _maybe_convert_scalar(values): - """ - Convert a python scalar to the appropriate numpy dtype if possible - This avoids numpy directly converting according to platform preferences - """ - if lib.isscalar(values): - dtype, values = _infer_dtype_from_scalar(values) - try: - values = dtype(values) - except TypeError: - pass - return values - - -def _lcd_dtypes(a_dtype, b_dtype): - """ return the lcd dtype to hold these types """ - - if is_datetime64_dtype(a_dtype) or is_datetime64_dtype(b_dtype): - return _NS_DTYPE - elif is_timedelta64_dtype(a_dtype) or is_timedelta64_dtype(b_dtype): - return _TD_DTYPE - elif is_complex_dtype(a_dtype): - if is_complex_dtype(b_dtype): - return a_dtype - return np.float64 - elif is_integer_dtype(a_dtype): - if is_integer_dtype(b_dtype): - if a_dtype.itemsize == b_dtype.itemsize: - return a_dtype - return np.int64 - return np.float64 - elif is_float_dtype(a_dtype): - if is_float_dtype(b_dtype): - if a_dtype.itemsize == b_dtype.itemsize: - return a_dtype - else: - return np.float64 - elif is_integer(b_dtype): - return np.float64 - return np.object - - def _consensus_name_attr(objs): name = objs[0].name for obj in objs[1:]: @@ -909,66 +132,20 @@ def _consensus_name_attr(objs): return None return name -# ---------------------------------------------------------------------- -# Lots of little utilities - - -def _validate_date_like_dtype(dtype): - try: - typ = np.datetime_data(dtype)[0] - except ValueError as e: - raise TypeError('%s' % e) - if typ != 'generic' and typ != 'ns': - raise ValueError('%r is too specific of a frequency, try passing %r' % - (dtype.name, dtype.type.__name__)) - - -def _invalidate_string_dtypes(dtype_set): - """Change string like dtypes to object for - ``DataFrame.select_dtypes()``. - """ - non_string_dtypes = dtype_set - _string_dtypes - if non_string_dtypes != dtype_set: - raise TypeError("string dtypes are not allowed, use 'object' instead") - - -def _get_dtype_from_object(dtype): - """Get a numpy dtype.type-style object. This handles the datetime64[ns] - and datetime64[ns, TZ] compat - - Notes - ----- - If nothing can be found, returns ``object``. - """ - # type object from a dtype - if isinstance(dtype, type) and issubclass(dtype, np.generic): - return dtype - elif is_categorical(dtype): - return gt.CategoricalDtype().type - elif is_datetimetz(dtype): - return gt.DatetimeTZDtype(dtype).type - elif isinstance(dtype, np.dtype): # dtype object - try: - _validate_date_like_dtype(dtype) - except TypeError: - # should still pass if we don't have a datelike - pass - return dtype.type - elif isinstance(dtype, compat.string_types): - if dtype == 'datetime' or dtype == 'timedelta': - dtype += '64' - - try: - return _get_dtype_from_object(getattr(np, dtype)) - except (AttributeError, TypeError): - # handles cases like _get_dtype(int) - # i.e., python objects that are valid dtypes (unlike user-defined - # types, in general) - # TypeError handles the float16 typecode of 'e' - # further handle internal types - pass - return _get_dtype_from_object(np.dtype(dtype)) +def _maybe_match_name(a, b): + a_has = hasattr(a, 'name') + b_has = hasattr(b, 'name') + if a_has and b_has: + if a.name == b.name: + return a.name + else: + return None + elif a_has: + return a.name + elif b_has: + return b.name + return None def _get_info_slice(obj, indexer): @@ -1005,225 +182,8 @@ def _maybe_box_datetimelike(value): _values_from_object = lib.values_from_object -def _possibly_castable(arr): - # return False to force a non-fastpath - - # check datetime64[ns]/timedelta64[ns] are valid - # otherwise try to coerce - kind = arr.dtype.kind - if kind == 'M' or kind == 'm': - return arr.dtype in _DATELIKE_DTYPES - - return arr.dtype.name not in _POSSIBLY_CAST_DTYPES - - -def _possibly_convert_platform(values): - """ try to do platform conversion, allow ndarray or list here """ - - if isinstance(values, (list, tuple)): - values = lib.list_to_object_array(values) - if getattr(values, 'dtype', None) == np.object_: - if hasattr(values, '_values'): - values = values._values - values = lib.maybe_convert_objects(values) - - return values - - -def _possibly_cast_to_datetime(value, dtype, errors='raise'): - """ try to cast the array/value to a datetimelike dtype, converting float - nan to iNaT - """ - from pandas.tseries.timedeltas import to_timedelta - from pandas.tseries.tools import to_datetime - - if dtype is not None: - if isinstance(dtype, compat.string_types): - dtype = np.dtype(dtype) - - is_datetime64 = is_datetime64_dtype(dtype) - is_datetime64tz = is_datetime64tz_dtype(dtype) - is_timedelta64 = is_timedelta64_dtype(dtype) - - if is_datetime64 or is_datetime64tz or is_timedelta64: - - # force the dtype if needed - if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE): - if dtype.name == 'datetime64[ns]': - dtype = _NS_DTYPE - else: - raise TypeError("cannot convert datetimelike to " - "dtype [%s]" % dtype) - elif is_datetime64tz: - - # our NaT doesn't support tz's - # this will coerce to DatetimeIndex with - # a matching dtype below - if lib.isscalar(value) and isnull(value): - value = [value] - - elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE): - if dtype.name == 'timedelta64[ns]': - dtype = _TD_DTYPE - else: - raise TypeError("cannot convert timedeltalike to " - "dtype [%s]" % dtype) - - if lib.isscalar(value): - if value == tslib.iNaT or isnull(value): - value = tslib.iNaT - else: - value = np.array(value, copy=False) - - # have a scalar array-like (e.g. NaT) - if value.ndim == 0: - value = tslib.iNaT - - # we have an array of datetime or timedeltas & nulls - elif np.prod(value.shape) or not is_dtype_equal(value.dtype, - dtype): - try: - if is_datetime64: - value = to_datetime(value, errors=errors)._values - elif is_datetime64tz: - # input has to be UTC at this point, so just - # localize - value = to_datetime( - value, - errors=errors).tz_localize(dtype.tz) - elif is_timedelta64: - value = to_timedelta(value, errors=errors)._values - except (AttributeError, ValueError, TypeError): - pass - - # coerce datetimelike to object - elif is_datetime64_dtype(value) and not is_datetime64_dtype(dtype): - if is_object_dtype(dtype): - ints = np.asarray(value).view('i8') - return tslib.ints_to_pydatetime(ints) - - # we have a non-castable dtype that was passed - raise TypeError('Cannot cast datetime64 to %s' % dtype) - - else: - - is_array = isinstance(value, np.ndarray) - - # catch a datetime/timedelta that is not of ns variety - # and no coercion specified - if is_array and value.dtype.kind in ['M', 'm']: - dtype = value.dtype - - if dtype.kind == 'M' and dtype != _NS_DTYPE: - value = value.astype(_NS_DTYPE) - - elif dtype.kind == 'm' and dtype != _TD_DTYPE: - value = to_timedelta(value) - - # only do this if we have an array and the dtype of the array is not - # setup already we are not an integer/object, so don't bother with this - # conversion - elif not (is_array and not (issubclass(value.dtype.type, np.integer) or - value.dtype == np.object_)): - value = _possibly_infer_to_datetimelike(value) - - return value - - -def _possibly_infer_to_datetimelike(value, convert_dates=False): - """ - we might have an array (or single object) that is datetime like, - and no dtype is passed don't change the value unless we find a - datetime/timedelta set - - this is pretty strict in that a datetime/timedelta is REQUIRED - in addition to possible nulls/string likes - - ONLY strings are NOT datetimelike - - Parameters - ---------- - value : np.array / Series / Index / list-like - convert_dates : boolean, default False - if True try really hard to convert dates (such as datetime.date), other - leave inferred dtype 'date' alone - - """ - - if isinstance(value, (gt.ABCDatetimeIndex, gt.ABCPeriodIndex)): - return value - elif isinstance(value, gt.ABCSeries): - if isinstance(value._values, gt.ABCDatetimeIndex): - return value._values - - v = value - if not is_list_like(v): - v = [v] - v = np.array(v, copy=False) - shape = v.shape - if not v.ndim == 1: - v = v.ravel() - - if len(v): - - def _try_datetime(v): - # safe coerce to datetime64 - try: - v = tslib.array_to_datetime(v, errors='raise') - except ValueError: - - # we might have a sequence of the same-datetimes with tz's - # if so coerce to a DatetimeIndex; if they are not the same, - # then these stay as object dtype - try: - from pandas import to_datetime - return to_datetime(v) - except: - pass - - except: - pass - - return v.reshape(shape) - - def _try_timedelta(v): - # safe coerce to timedelta64 - - # will try first with a string & object conversion - from pandas.tseries.timedeltas import to_timedelta - try: - return to_timedelta(v)._values.reshape(shape) - except: - return v - - # do a quick inference for perf - sample = v[:min(3, len(v))] - inferred_type = lib.infer_dtype(sample) - - if (inferred_type in ['datetime', 'datetime64'] or - (convert_dates and inferred_type in ['date'])): - value = _try_datetime(v) - elif inferred_type in ['timedelta', 'timedelta64']: - value = _try_timedelta(v) - - # It's possible to have nulls intermixed within the datetime or - # timedelta. These will in general have an inferred_type of 'mixed', - # so have to try both datetime and timedelta. - - # try timedelta first to avoid spurious datetime conversions - # e.g. '00:00:01' is a timedelta but technically is also a datetime - elif inferred_type in ['mixed']: - - if lib.is_possible_datetimelike_array(_ensure_object(v)): - value = _try_timedelta(v) - if lib.infer_dtype(value) in ['mixed']: - value = _try_datetime(v) - - return value - - def is_bool_indexer(key): - if isinstance(key, (gt.ABCSeries, np.ndarray)): + if isinstance(key, (ABCSeries, np.ndarray)): if key.dtype == np.object_: key = np.asarray(_values_from_object(key)) @@ -1250,12 +210,6 @@ def _default_index(n): return RangeIndex(0, n, name=None) -def ensure_float(arr): - if issubclass(arr.dtype.type, (np.integer, np.bool_)): - arr = arr.astype(float) - return arr - - def _mut_exclusive(**kwargs): item1, item2 = kwargs.items() label1, val1 = item1 @@ -1287,6 +241,10 @@ def _all_not_none(*args): return True +def _count_not_none(*args): + return sum(x is not None for x in args) + + def _try_sort(iterable): listed = list(iterable) try: @@ -1295,10 +253,6 @@ def _try_sort(iterable): return listed -def _count_not_none(*args): - return sum(x is not None for x in args) - - def iterpairs(seq): """ Parameters @@ -1451,349 +405,6 @@ def _maybe_make_list(obj): return [obj] return obj -# TYPE TESTING - -is_bool = lib.is_bool - -is_integer = lib.is_integer - -is_float = lib.is_float - -is_complex = lib.is_complex - - -def is_string_like(obj): - return isinstance(obj, (compat.text_type, compat.string_types)) - - -def is_iterator(obj): - # python 3 generators have __next__ instead of next - return hasattr(obj, 'next') or hasattr(obj, '__next__') - - -def is_number(obj): - return isinstance(obj, (numbers.Number, np.number)) - - -def is_period_arraylike(arr): - """ return if we are period arraylike / PeriodIndex """ - if isinstance(arr, pd.PeriodIndex): - return True - elif isinstance(arr, (np.ndarray, gt.ABCSeries)): - return arr.dtype == object and lib.infer_dtype(arr) == 'period' - return getattr(arr, 'inferred_type', None) == 'period' - - -def is_datetime_arraylike(arr): - """ return if we are datetime arraylike / DatetimeIndex """ - if isinstance(arr, gt.ABCDatetimeIndex): - return True - elif isinstance(arr, (np.ndarray, gt.ABCSeries)): - return arr.dtype == object and lib.infer_dtype(arr) == 'datetime' - return getattr(arr, 'inferred_type', None) == 'datetime' - - -def is_datetimelike(arr): - return (arr.dtype in _DATELIKE_DTYPES or - isinstance(arr, gt.ABCPeriodIndex) or - is_datetimetz(arr)) - - -def _coerce_to_dtype(dtype): - """ coerce a string / np.dtype to a dtype """ - if is_categorical_dtype(dtype): - dtype = gt.CategoricalDtype() - elif is_datetime64tz_dtype(dtype): - dtype = gt.DatetimeTZDtype(dtype) - else: - dtype = np.dtype(dtype) - return dtype - - -def _get_dtype(arr_or_dtype): - if isinstance(arr_or_dtype, np.dtype): - return arr_or_dtype - elif isinstance(arr_or_dtype, type): - return np.dtype(arr_or_dtype) - elif isinstance(arr_or_dtype, gt.CategoricalDtype): - return arr_or_dtype - elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): - return arr_or_dtype - elif isinstance(arr_or_dtype, compat.string_types): - if is_categorical_dtype(arr_or_dtype): - return gt.CategoricalDtype.construct_from_string(arr_or_dtype) - elif is_datetime64tz_dtype(arr_or_dtype): - return gt.DatetimeTZDtype.construct_from_string(arr_or_dtype) - - if hasattr(arr_or_dtype, 'dtype'): - arr_or_dtype = arr_or_dtype.dtype - return np.dtype(arr_or_dtype) - - -def _get_dtype_type(arr_or_dtype): - if isinstance(arr_or_dtype, np.dtype): - return arr_or_dtype.type - elif isinstance(arr_or_dtype, type): - return np.dtype(arr_or_dtype).type - elif isinstance(arr_or_dtype, gt.CategoricalDtype): - return gt.CategoricalDtypeType - elif isinstance(arr_or_dtype, gt.DatetimeTZDtype): - return gt.DatetimeTZDtypeType - elif isinstance(arr_or_dtype, compat.string_types): - if is_categorical_dtype(arr_or_dtype): - return gt.CategoricalDtypeType - elif is_datetime64tz_dtype(arr_or_dtype): - return gt.DatetimeTZDtypeType - return _get_dtype_type(np.dtype(arr_or_dtype)) - try: - return arr_or_dtype.dtype.type - except AttributeError: - return type(None) - - -def is_dtype_equal(source, target): - """ return a boolean if the dtypes are equal """ - try: - source = _get_dtype(source) - target = _get_dtype(target) - return source == target - except (TypeError, AttributeError): - - # invalid comparison - # object == category will hit this - return False - - -def is_any_int_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.integer) - - -def is_integer_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return (issubclass(tipo, np.integer) and - not issubclass(tipo, (np.datetime64, np.timedelta64))) - - -def is_int64_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.int64) - - -def is_int_or_datetime_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return (issubclass(tipo, np.integer) or - issubclass(tipo, (np.datetime64, np.timedelta64))) - - -def is_datetime64_dtype(arr_or_dtype): - try: - tipo = _get_dtype_type(arr_or_dtype) - except TypeError: - return False - return issubclass(tipo, np.datetime64) - - -def is_datetime64tz_dtype(arr_or_dtype): - return gt.DatetimeTZDtype.is_dtype(arr_or_dtype) - - -def is_datetime64_any_dtype(arr_or_dtype): - return (is_datetime64_dtype(arr_or_dtype) or - is_datetime64tz_dtype(arr_or_dtype)) - - -def is_datetime64_ns_dtype(arr_or_dtype): - try: - tipo = _get_dtype(arr_or_dtype) - except TypeError: - return False - return tipo == _NS_DTYPE - - -def is_timedelta64_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.timedelta64) - - -def is_timedelta64_ns_dtype(arr_or_dtype): - tipo = _get_dtype(arr_or_dtype) - return tipo == _TD_DTYPE - - -def is_datetime_or_timedelta_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, (np.datetime64, np.timedelta64)) - - -def is_numeric_v_string_like(a, b): - """ - numpy doesn't like to compare numeric arrays vs scalar string-likes - - return a boolean result if this is the case for a,b or b,a - - """ - is_a_array = isinstance(a, np.ndarray) - is_b_array = isinstance(b, np.ndarray) - - is_a_numeric_array = is_a_array and is_numeric_dtype(a) - is_b_numeric_array = is_b_array and is_numeric_dtype(b) - is_a_string_array = is_a_array and is_string_like_dtype(a) - is_b_string_array = is_b_array and is_string_like_dtype(b) - - is_a_scalar_string_like = not is_a_array and is_string_like(a) - is_b_scalar_string_like = not is_b_array and is_string_like(b) - - return ((is_a_numeric_array and is_b_scalar_string_like) or - (is_b_numeric_array and is_a_scalar_string_like) or - (is_a_numeric_array and is_b_string_array) or - (is_b_numeric_array and is_a_string_array)) - - -def is_datetimelike_v_numeric(a, b): - # return if we have an i8 convertible and numeric comparison - if not hasattr(a, 'dtype'): - a = np.asarray(a) - if not hasattr(b, 'dtype'): - b = np.asarray(b) - - def is_numeric(x): - return is_integer_dtype(x) or is_float_dtype(x) - - is_datetimelike = needs_i8_conversion - return ((is_datetimelike(a) and is_numeric(b)) or - (is_datetimelike(b) and is_numeric(a))) - - -def is_datetimelike_v_object(a, b): - # return if we have an i8 convertible and object comparsion - if not hasattr(a, 'dtype'): - a = np.asarray(a) - if not hasattr(b, 'dtype'): - b = np.asarray(b) - - def f(x): - return is_object_dtype(x) - - def is_object(x): - return is_integer_dtype(x) or is_float_dtype(x) - - is_datetimelike = needs_i8_conversion - return ((is_datetimelike(a) and is_object(b)) or - (is_datetimelike(b) and is_object(a))) - - -def needs_i8_conversion(arr_or_dtype): - return (is_datetime_or_timedelta_dtype(arr_or_dtype) or - is_datetime64tz_dtype(arr_or_dtype)) - - -def is_numeric_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return (issubclass(tipo, (np.number, np.bool_)) and - not issubclass(tipo, (np.datetime64, np.timedelta64))) - - -def is_string_dtype(arr_or_dtype): - dtype = _get_dtype(arr_or_dtype) - return dtype.kind in ('O', 'S', 'U') - - -def is_string_like_dtype(arr_or_dtype): - # exclude object as its a mixed dtype - dtype = _get_dtype(arr_or_dtype) - return dtype.kind in ('S', 'U') - - -def is_float_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.floating) - - -def is_floating_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return isinstance(tipo, np.floating) - - -def is_bool_dtype(arr_or_dtype): - try: - tipo = _get_dtype_type(arr_or_dtype) - except ValueError: - # this isn't even a dtype - return False - return issubclass(tipo, np.bool_) - - -def is_sparse(array): - """ return if we are a sparse array """ - return isinstance(array, (gt.ABCSparseArray, gt.ABCSparseSeries)) - - -def is_datetimetz(array): - """ return if we are a datetime with tz array """ - return ((isinstance(array, gt.ABCDatetimeIndex) and - getattr(array, 'tz', None) is not None) or - is_datetime64tz_dtype(array)) - - -def is_extension_type(value): - """ - if we are a klass that is preserved by the internals - these are internal klasses that we represent (and don't use a np.array) - """ - if is_categorical(value): - return True - elif is_sparse(value): - return True - elif is_datetimetz(value): - return True - return False - - -def is_categorical(array): - """ return if we are a categorical possibility """ - return isinstance(array, gt.ABCCategorical) or is_categorical_dtype(array) - - -def is_categorical_dtype(arr_or_dtype): - return gt.CategoricalDtype.is_dtype(arr_or_dtype) - - -def is_complex_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.complexfloating) - - -def is_object_dtype(arr_or_dtype): - tipo = _get_dtype_type(arr_or_dtype) - return issubclass(tipo, np.object_) - - -def is_re(obj): - return isinstance(obj, re._pattern_type) - - -def is_re_compilable(obj): - try: - re.compile(obj) - except TypeError: - return False - else: - return True - - -def is_list_like(arg): - return (hasattr(arg, '__iter__') and - not isinstance(arg, compat.string_and_binary_types)) - - -def is_dict_like(arg): - return hasattr(arg, '__getitem__') and hasattr(arg, 'keys') - - -def is_named_tuple(arg): - return isinstance(arg, tuple) and hasattr(arg, '_fields') - def is_null_slice(obj): """ we have a null slice """ @@ -1807,47 +418,6 @@ def is_full_slice(obj, l): obj.step is None) -def is_hashable(arg): - """Return True if hash(arg) will succeed, False otherwise. - - Some types will pass a test against collections.Hashable but fail when they - are actually hashed with hash(). - - Distinguish between these and other types by trying the call to hash() and - seeing if they raise TypeError. - - Examples - -------- - >>> a = ([],) - >>> isinstance(a, collections.Hashable) - True - >>> is_hashable(a) - False - """ - # unfortunately, we can't use isinstance(arg, collections.Hashable), which - # can be faster than calling hash, because numpy scalars on Python 3 fail - # this test - - # reconsider this decision once this numpy bug is fixed: - # https://github.com/numpy/numpy/issues/5562 - - try: - hash(arg) - except TypeError: - return False - else: - return True - - -def is_sequence(x): - try: - iter(x) - len(x) # it has a length - return not isinstance(x, compat.string_and_binary_types) - except (TypeError, AttributeError): - return False - - def _get_callable_name(obj): # typical case has name if hasattr(obj, '__name__'): @@ -1875,74 +445,6 @@ def _apply_if_callable(maybe_callable, obj, **kwargs): return maybe_callable -_string_dtypes = frozenset(map(_get_dtype_from_object, (compat.binary_type, - compat.text_type))) - -_ensure_float64 = algos.ensure_float64 -_ensure_float32 = algos.ensure_float32 -_ensure_int64 = algos.ensure_int64 -_ensure_int32 = algos.ensure_int32 -_ensure_int16 = algos.ensure_int16 -_ensure_int8 = algos.ensure_int8 -_ensure_platform_int = algos.ensure_platform_int -_ensure_object = algos.ensure_object - - -def _astype_nansafe(arr, dtype, copy=True): - """ return a view if copy is False, but - need to be very careful as the result shape could change! """ - if not isinstance(dtype, np.dtype): - dtype = _coerce_to_dtype(dtype) - - if issubclass(dtype.type, compat.text_type): - # in Py3 that's str, in Py2 that's unicode - return lib.astype_unicode(arr.ravel()).reshape(arr.shape) - elif issubclass(dtype.type, compat.string_types): - return lib.astype_str(arr.ravel()).reshape(arr.shape) - elif is_datetime64_dtype(arr): - if dtype == object: - return tslib.ints_to_pydatetime(arr.view(np.int64)) - elif dtype == np.int64: - return arr.view(dtype) - elif dtype != _NS_DTYPE: - raise TypeError("cannot astype a datetimelike from [%s] to [%s]" % - (arr.dtype, dtype)) - return arr.astype(_NS_DTYPE) - elif is_timedelta64_dtype(arr): - if dtype == np.int64: - return arr.view(dtype) - elif dtype == object: - return tslib.ints_to_pytimedelta(arr.view(np.int64)) - - # in py3, timedelta64[ns] are int64 - elif ((compat.PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE]) or - (not compat.PY3 and dtype != _TD_DTYPE)): - - # allow frequency conversions - if dtype.kind == 'm': - mask = isnull(arr) - result = arr.astype(dtype).astype(np.float64) - result[mask] = np.nan - return result - - raise TypeError("cannot astype a timedelta from [%s] to [%s]" % - (arr.dtype, dtype)) - - return arr.astype(_TD_DTYPE) - elif (np.issubdtype(arr.dtype, np.floating) and - np.issubdtype(dtype, np.integer)): - - if np.isnan(arr).any(): - raise ValueError('Cannot convert NA to integer') - elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer): - # work around NumPy brokenness, #1987 - return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape) - - if copy: - return arr.astype(dtype) - return arr.view(dtype) - - def _all_none(*args): for arg in args: if arg is not None: @@ -1988,6 +490,9 @@ class Sentinel(object): return Sentinel() +# ---------------------------------------------------------------------- +# Detect our environment + def in_interactive_session(): """ check if we're running in an interactive shell @@ -2055,21 +560,6 @@ def in_ipython_frontend(): return False -def _maybe_match_name(a, b): - a_has = hasattr(a, 'name') - b_has = hasattr(b, 'name') - if a_has and b_has: - if a.name == b.name: - return a.name - else: - return None - elif a_has: - return a.name - elif b_has: - return b.name - return None - - def _random_state(state=None): """ Helper function for processing random_state arguments. diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 3ca2c6cd014bc..5cbc968f06fa7 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -366,7 +366,7 @@ def mpl_style_cb(key): def use_inf_as_null_cb(key): - from pandas.core.common import _use_inf_as_null + from pandas.types.missing import _use_inf_as_null _use_inf_as_null(key) with cf.config_prefix('mode'): diff --git a/pandas/core/convert.py b/pandas/core/convert.py deleted file mode 100644 index 7f4fe73c688f8..0000000000000 --- a/pandas/core/convert.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Functions for converting object to other types -""" - -import numpy as np - -import pandas as pd -from pandas.core.common import (_possibly_cast_to_datetime, is_object_dtype, - isnull) -import pandas.lib as lib - - -# TODO: Remove in 0.18 or 2017, which ever is sooner -def _possibly_convert_objects(values, convert_dates=True, convert_numeric=True, - convert_timedeltas=True, copy=True): - """ if we have an object dtype, try to coerce dates and/or numbers """ - - # if we have passed in a list or scalar - if isinstance(values, (list, tuple)): - values = np.array(values, dtype=np.object_) - if not hasattr(values, 'dtype'): - values = np.array([values], dtype=np.object_) - - # convert dates - if convert_dates and values.dtype == np.object_: - - # we take an aggressive stance and convert to datetime64[ns] - if convert_dates == 'coerce': - new_values = _possibly_cast_to_datetime(values, 'M8[ns]', - errors='coerce') - - # if we are all nans then leave me alone - if not isnull(new_values).all(): - values = new_values - - else: - values = lib.maybe_convert_objects(values, - convert_datetime=convert_dates) - - # convert timedeltas - if convert_timedeltas and values.dtype == np.object_: - - if convert_timedeltas == 'coerce': - from pandas.tseries.timedeltas import to_timedelta - new_values = to_timedelta(values, coerce=True) - - # if we are all nans then leave me alone - if not isnull(new_values).all(): - values = new_values - - else: - values = lib.maybe_convert_objects( - values, convert_timedelta=convert_timedeltas) - - # convert to numeric - if values.dtype == np.object_: - if convert_numeric: - try: - new_values = lib.maybe_convert_numeric(values, set(), - coerce_numeric=True) - - # if we are all nans then leave me alone - if not isnull(new_values).all(): - values = new_values - - except: - pass - else: - # soft-conversion - values = lib.maybe_convert_objects(values) - - values = values.copy() if copy else values - - return values - - -def _soft_convert_objects(values, datetime=True, numeric=True, timedelta=True, - coerce=False, copy=True): - """ if we have an object dtype, try to coerce dates and/or numbers """ - - conversion_count = sum((datetime, numeric, timedelta)) - if conversion_count == 0: - raise ValueError('At least one of datetime, numeric or timedelta must ' - 'be True.') - elif conversion_count > 1 and coerce: - raise ValueError("Only one of 'datetime', 'numeric' or " - "'timedelta' can be True when when coerce=True.") - - if isinstance(values, (list, tuple)): - # List or scalar - values = np.array(values, dtype=np.object_) - elif not hasattr(values, 'dtype'): - values = np.array([values], dtype=np.object_) - elif not is_object_dtype(values.dtype): - # If not object, do not attempt conversion - values = values.copy() if copy else values - return values - - # If 1 flag is coerce, ensure 2 others are False - if coerce: - # Immediate return if coerce - if datetime: - return pd.to_datetime(values, errors='coerce', box=False) - elif timedelta: - return pd.to_timedelta(values, errors='coerce', box=False) - elif numeric: - return pd.to_numeric(values, errors='coerce') - - # Soft conversions - if datetime: - values = lib.maybe_convert_objects(values, convert_datetime=datetime) - - if timedelta and is_object_dtype(values.dtype): - # Object check to ensure only run if previous did not convert - values = lib.maybe_convert_objects(values, convert_timedelta=timedelta) - - if numeric and is_object_dtype(values.dtype): - try: - converted = lib.maybe_convert_numeric(values, set(), - coerce_numeric=True) - # If all NaNs, then do not-alter - values = converted if not isnull(converted).all() else values - values = values.copy() if copy else values - except: - pass - - return values diff --git a/pandas/core/frame.py b/pandas/core/frame.py index e804271d8afa9..334526b424be5 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -23,12 +23,43 @@ import numpy as np import numpy.ma as ma -from pandas.core.common import ( - isnull, notnull, PandasError, _try_sort, _default_index, _maybe_upcast, - is_sequence, _infer_dtype_from_scalar, _values_from_object, is_list_like, - _maybe_box_datetimelike, is_categorical_dtype, is_object_dtype, - is_extension_type, is_datetimetz, _possibly_infer_to_datetimelike, - _dict_compat) +from pandas.types.cast import (_maybe_upcast, + _infer_dtype_from_scalar, + _possibly_cast_to_datetime, + _possibly_infer_to_datetimelike, + _possibly_convert_platform, + _possibly_downcast_to_dtype, + _invalidate_string_dtypes, + _coerce_to_dtypes, + _maybe_upcast_putmask) +from pandas.types.common import (is_categorical_dtype, + is_object_dtype, + is_extension_type, + is_datetimetz, + is_datetime64_dtype, + is_bool_dtype, + is_integer_dtype, + is_float_dtype, + is_integer, + is_scalar, + needs_i8_conversion, + _get_dtype_from_object, + _lcd_dtypes, + _ensure_float, + _ensure_float64, + _ensure_int64, + _ensure_platform_int, + is_list_like, + is_iterator, + is_sequence, + is_named_tuple) +from pandas.types.missing import isnull, notnull + +from pandas.core.common import (PandasError, _try_sort, + _default_index, + _values_from_object, + _maybe_box_datetimelike, + _dict_compat) from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import (maybe_droplevels, convert_to_index_sliceable, @@ -68,8 +99,12 @@ # --------------------------------------------------------------------- # Docstring templates -_shared_doc_kwargs = dict(axes='index, columns', klass='DataFrame', - axes_single_arg="{0, 1, 'index', 'columns'}") +_shared_doc_kwargs = dict( + axes='index, columns', klass='DataFrame', + axes_single_arg="{0, 1, 'index', 'columns'}", + optional_by=""" + by : str or list of str + Name or list of names which refer to the axis items.""") _numeric_only_doc = """numeric_only : boolean, default None Include only float, int, boolean data. If None, will attempt to use @@ -264,7 +299,7 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, data = list(data) if len(data) > 0: if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1: - if com.is_named_tuple(data[0]) and columns is None: + if is_named_tuple(data[0]) and columns is None: columns = data[0]._fields arrays, columns = _to_arrays(data, columns, dtype=dtype) columns = _ensure_index(columns) @@ -814,7 +849,6 @@ def from_dict(cls, data, orient='columns', dtype=None): return cls(data, index=index, columns=columns, dtype=dtype) - @deprecate_kwarg(old_arg_name='outtype', new_arg_name='orient') def to_dict(self, orient='dict'): """Convert DataFrame to dictionary. @@ -937,7 +971,7 @@ def from_records(cls, data, index=None, exclude=None, columns=None, if columns is not None: columns = _ensure_index(columns) - if com.is_iterator(data): + if is_iterator(data): if nrows == 0: return cls() @@ -1048,7 +1082,7 @@ def to_records(self, index=True, convert_datetime64=True): y : recarray """ if index: - if com.is_datetime64_dtype(self.index) and convert_datetime64: + if is_datetime64_dtype(self.index) and convert_datetime64: ix_vals = [self.index.to_pydatetime()] else: if isinstance(self.index, MultiIndex): @@ -1342,7 +1376,6 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None, cols=columns, header=header, index=index, index_label=index_label, mode=mode, chunksize=chunksize, quotechar=quotechar, - engine=kwds.get("engine"), tupleize_cols=tupleize_cols, date_format=date_format, doublequote=doublequote, @@ -1918,7 +1951,7 @@ def _ixs(self, i, axis=0): copy = True else: new_values = self._data.fast_xs(i) - if lib.isscalar(new_values): + if is_scalar(new_values): return new_values # if we are a copy, mark as such @@ -2070,7 +2103,7 @@ def _getitem_multilevel(self, key): return self._get_item_cache(key) def _getitem_frame(self, key): - if key.values.size and not com.is_bool_dtype(key.values): + if key.values.size and not is_bool_dtype(key.values): raise ValueError('Must pass DataFrame with boolean values only') return self.where(key) @@ -2287,7 +2320,7 @@ def select_dtypes(self, include=None, exclude=None): 5 False """ include, exclude = include or (), exclude or () - if not (com.is_list_like(include) and com.is_list_like(exclude)): + if not (is_list_like(include) and is_list_like(exclude)): raise TypeError('include and exclude must both be non-string' ' sequences') selection = tuple(map(frozenset, (include, exclude))) @@ -2298,9 +2331,9 @@ def select_dtypes(self, include=None, exclude=None): # convert the myriad valid dtypes object to a single representation include, exclude = map( - lambda x: frozenset(map(com._get_dtype_from_object, x)), selection) + lambda x: frozenset(map(_get_dtype_from_object, x)), selection) for dtypes in (include, exclude): - com._invalidate_string_dtypes(dtypes) + _invalidate_string_dtypes(dtypes) # can't both include AND exclude! if not include.isdisjoint(exclude): @@ -2390,7 +2423,7 @@ def _setitem_array(self, key, value): def _setitem_frame(self, key, value): # support boolean setting with DataFrame input, e.g. # df[df > df2] = 0 - if key.values.size and not com.is_bool_dtype(key.values): + if key.values.size and not is_bool_dtype(key.values): raise TypeError('Must pass DataFrame with boolean values only') self._check_inplace_setting(value) @@ -2584,7 +2617,7 @@ def reindexer(value): value = _sanitize_index(value, self.index, copy=False) if not isinstance(value, (np.ndarray, Index)): if isinstance(value, list) and len(value) > 0: - value = com._possibly_convert_platform(value) + value = _possibly_convert_platform(value) else: value = com._asarray_tuplesafe(value) elif value.ndim == 2: @@ -2600,7 +2633,7 @@ def reindexer(value): # upcast the scalar dtype, value = _infer_dtype_from_scalar(value) value = np.repeat(value, len(self.index)).astype(dtype) - value = com._possibly_cast_to_datetime(value, dtype) + value = _possibly_cast_to_datetime(value, dtype) # return internal types directly if is_extension_type(value): @@ -2914,8 +2947,8 @@ def _maybe_casted_values(index, labels=None): mask = labels == -1 values = values.take(labels) if mask.any(): - values, changed = com._maybe_upcast_putmask(values, mask, - np.nan) + values, changed = _maybe_upcast_putmask(values, mask, + np.nan) return values new_index = _default_index(len(new_obj)) @@ -3129,14 +3162,14 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False, raise ValueError('When sorting by column, axis must be 0 (rows)') if not isinstance(by, list): by = [by] - if com.is_sequence(ascending) and len(by) != len(ascending): + if is_sequence(ascending) and len(by) != len(ascending): raise ValueError('Length of ascending (%d) != length of by (%d)' % (len(ascending), len(by))) if len(by) > 1: from pandas.core.groupby import _lexsort_indexer def trans(v): - if com.needs_i8_conversion(v): + if needs_i8_conversion(v): return v.view('i8') return v @@ -3149,7 +3182,7 @@ def trans(v): keys.append(trans(k)) indexer = _lexsort_indexer(keys, orders=ascending, na_position=na_position) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) else: from pandas.core.groupby import _nargsort @@ -3318,7 +3351,7 @@ def sortlevel(self, level=0, axis=0, ascending=True, inplace=False, inplace=inplace, sort_remaining=sort_remaining) def _nsorted(self, columns, n, method, keep): - if not com.is_list_like(columns): + if not is_list_like(columns): columns = [columns] columns = list(columns) ser = getattr(self[columns[0]], method)(n, keep=keep) @@ -3656,28 +3689,28 @@ def combine(self, other, func, fill_value=None, overwrite=True): # if we have different dtypes, possibily promote new_dtype = this_dtype if this_dtype != other_dtype: - new_dtype = com._lcd_dtypes(this_dtype, other_dtype) + new_dtype = _lcd_dtypes(this_dtype, other_dtype) series = series.astype(new_dtype) otherSeries = otherSeries.astype(new_dtype) # see if we need to be represented as i8 (datetimelike) # try to keep us at this dtype - needs_i8_conversion = com.needs_i8_conversion(new_dtype) - if needs_i8_conversion: + needs_i8_conversion_i = needs_i8_conversion(new_dtype) + if needs_i8_conversion_i: this_dtype = new_dtype arr = func(series, otherSeries, True) else: arr = func(series, otherSeries) if do_fill: - arr = com.ensure_float(arr) + arr = _ensure_float(arr) arr[this_mask & other_mask] = NA # try to downcast back to the original dtype - if needs_i8_conversion: - arr = com._possibly_cast_to_datetime(arr, this_dtype) + if needs_i8_conversion_i: + arr = _possibly_cast_to_datetime(arr, this_dtype) else: - arr = com._possibly_downcast_to_dtype(arr, this_dtype) + arr = _possibly_downcast_to_dtype(arr, this_dtype) result[col] = arr @@ -4579,7 +4612,7 @@ def _dict_round(df, decimals): yield vals def _series_round(s, decimals): - if com.is_integer_dtype(s) or com.is_float_dtype(s): + if is_integer_dtype(s) or is_float_dtype(s): return s.round(decimals) return s @@ -4590,7 +4623,7 @@ def _series_round(s, decimals): if not decimals.index.is_unique: raise ValueError("Index of decimals must be unique") new_cols = [col for col in _dict_round(self, decimals)] - elif com.is_integer(decimals): + elif is_integer(decimals): # Dispatch to Series.round new_cols = [_series_round(v, decimals) for _, v in self.iteritems()] @@ -4632,14 +4665,14 @@ def corr(self, method='pearson', min_periods=1): mat = numeric_df.values if method == 'pearson': - correl = _algos.nancorr(com._ensure_float64(mat), minp=min_periods) + correl = _algos.nancorr(_ensure_float64(mat), minp=min_periods) elif method == 'spearman': - correl = _algos.nancorr_spearman(com._ensure_float64(mat), + correl = _algos.nancorr_spearman(_ensure_float64(mat), minp=min_periods) else: if min_periods is None: min_periods = 1 - mat = com._ensure_float64(mat).T + mat = _ensure_float64(mat).T corrf = nanops.get_corr_func(method) K = len(cols) correl = np.empty((K, K), dtype=float) @@ -4694,7 +4727,7 @@ def cov(self, min_periods=None): baseCov = np.cov(mat.T) baseCov = baseCov.reshape((len(cols), len(cols))) else: - baseCov = _algos.nancorr(com._ensure_float64(mat), cov=True, + baseCov = _algos.nancorr(_ensure_float64(mat), cov=True, minp=min_periods) return self._constructor(baseCov, index=cols, columns=cols) @@ -4823,7 +4856,7 @@ def _count_level(self, level, axis=0, numeric_only=False): level = count_axis._get_level_number(level) level_index = count_axis.levels[level] - labels = com._ensure_int64(count_axis.labels[level]) + labels = _ensure_int64(count_axis.labels[level]) counts = lib.count_level_2d(mask, labels, len(level_index), axis=0) result = DataFrame(counts, index=level_index, columns=agg_axis) @@ -4904,7 +4937,7 @@ def f(x): # try to coerce to the original dtypes item by item if we can if axis == 0: - result = com._coerce_to_dtypes(result, self.dtypes) + result = _coerce_to_dtypes(result, self.dtypes) return Series(result, index=labels) @@ -5374,13 +5407,13 @@ def _prep_ndarray(values, copy=True): return np.empty((0, 0), dtype=object) def convert(v): - return com._possibly_convert_platform(v) + return _possibly_convert_platform(v) # we could have a 1-dim or 2-dim list here # this is equiv of np.asarray, but does object conversion # and platform dtype preservation try: - if com.is_list_like(values[0]) or hasattr(values[0], 'len'): + if is_list_like(values[0]) or hasattr(values[0], 'len'): values = np.array([convert(v) for v in values]) else: values = convert(values) @@ -5568,7 +5601,7 @@ def _convert_object_array(content, columns, coerce_float=False, dtype=None): def convert(arr): if dtype != object and dtype != np.object: arr = lib.maybe_convert_objects(arr, try_float=coerce_float) - arr = com._possibly_cast_to_datetime(arr, dtype) + arr = _possibly_cast_to_datetime(arr, dtype) return arr arrays = [convert(arr) for arr in content] diff --git a/pandas/core/generic.py b/pandas/core/generic.py index cc5c45158bf4f..d6e6f571be53a 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -8,6 +8,29 @@ import pandas.lib as lib import pandas as pd + + +from pandas.types.common import (_coerce_to_dtype, + _ensure_int64, + needs_i8_conversion, + is_scalar, + is_integer, is_bool, + is_bool_dtype, + is_numeric_dtype, + is_datetime64_dtype, + is_timedelta64_dtype, + is_list_like, + is_dict_like, + is_re_compilable) +from pandas.types.cast import _maybe_promote, _maybe_upcast_putmask +from pandas.types.missing import isnull, notnull +from pandas.types.generic import ABCSeries, ABCPanel + +from pandas.core.common import (_values_from_object, + _maybe_box_datetimelike, + SettingWithCopyError, SettingWithCopyWarning, + AbstractMethodError) + from pandas.core.base import PandasObject from pandas.core.index import (Index, MultiIndex, _ensure_index, InvalidIndexError) @@ -25,11 +48,6 @@ from pandas.compat.numpy import function as nv from pandas.compat import (map, zip, lrange, string_types, isidentifier, set_function_name) -from pandas.core.common import (isnull, notnull, is_list_like, - _values_from_object, _maybe_promote, - _maybe_box_datetimelike, ABCSeries, - SettingWithCopyError, SettingWithCopyWarning, - AbstractMethodError) import pandas.core.nanops as nanops from pandas.util.decorators import Appender, Substitution, deprecate_kwarg from pandas.core import config @@ -37,14 +55,13 @@ # goal is to be able to define the docs close to function, while still being # able to share _shared_docs = dict() -_shared_doc_kwargs = dict(axes='keywords for axes', klass='NDFrame', - axes_single_arg='int or labels for object', - args_transpose='axes to permute (int or label for' - ' object)') - - -def is_dictlike(x): - return isinstance(x, (dict, com.ABCSeries)) +_shared_doc_kwargs = dict( + axes='keywords for axes', klass='NDFrame', + axes_single_arg='int or labels for object', + args_transpose='axes to permute (int or label for object)', + optional_by=""" + by : str or list of str + Name or list of names which refer to the axis items.""") def _single_replace(self, to_replace, method, inplace, limit): @@ -113,7 +130,7 @@ def _validate_dtype(self, dtype): """ validate the passed dtype """ if dtype is not None: - dtype = com._coerce_to_dtype(dtype) + dtype = _coerce_to_dtype(dtype) # a compound dtype if dtype.kind == 'V': @@ -307,7 +324,7 @@ def _from_axes(cls, data, axes, **kwargs): def _get_axis_number(self, axis): axis = self._AXIS_ALIASES.get(axis, axis) - if com.is_integer(axis): + if is_integer(axis): if axis in self._AXIS_NAMES: return axis else: @@ -714,8 +731,8 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): 1 2 5 2 3 6 """ - non_mapper = lib.isscalar(mapper) or (com.is_list_like(mapper) and not - com.is_dict_like(mapper)) + non_mapper = is_scalar(mapper) or (is_list_like(mapper) and not + is_dict_like(mapper)) if non_mapper: return self._set_axis_name(mapper, axis=axis) else: @@ -909,7 +926,7 @@ def bool(self): v = self.squeeze() if isinstance(v, (bool, np.bool_)): return bool(v) - elif lib.isscalar(v): + elif is_scalar(v): raise ValueError("bool cannot act on a non-boolean single element " "{0}".format(self.__class__.__name__)) @@ -1761,10 +1778,10 @@ def xs(self, key, axis=0, level=None, copy=None, drop_level=True): else: return self.take(loc, axis=axis, convert=True) - if not lib.isscalar(loc): + if not is_scalar(loc): new_index = self.index[loc] - if lib.isscalar(loc): + if is_scalar(loc): new_values = self._data.fast_xs(loc) # may need to box a datelike-scalar @@ -1961,21 +1978,20 @@ def add_suffix(self, suffix): .. versionadded:: 0.17.0 Parameters - ---------- - by : string name or list of names which refer to the axis items - axis : %(axes)s to direct sorting - ascending : bool or list of bool + ----------%(optional_by)s + axis : %(axes)s to direct sorting, default 0 + ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. - inplace : bool + inplace : bool, default False if True, perform operation in-place - kind : {`quicksort`, `mergesort`, `heapsort`} + kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See also ndarray.np.sort for more information. `mergesort` is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label. - na_position : {'first', 'last'} + na_position : {'first', 'last'}, default 'last' `first` puts NaNs at the beginning, `last` puts NaNs at the end Returns @@ -1997,16 +2013,16 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False, if not None, sort on values in specified index level(s) ascending : boolean, default True Sort ascending vs. descending - inplace : bool + inplace : bool, default False if True, perform operation in-place - kind : {`quicksort`, `mergesort`, `heapsort`} + kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See also ndarray.np.sort for more information. `mergesort` is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label. - na_position : {'first', 'last'} + na_position : {'first', 'last'}, default 'last' `first` puts NaNs at the beginning, `last` puts NaNs at the end - sort_remaining : bool + sort_remaining : bool, default True if true and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level @@ -2338,7 +2354,7 @@ def _reindex_with_indexers(self, reindexers, fill_value=np.nan, copy=False, index = _ensure_index(index) if indexer is not None: - indexer = com._ensure_int64(indexer) + indexer = _ensure_int64(indexer) # TODO: speed up on homogeneous DataFrame objects new_data = new_data.reindex_indexer(index, indexer, axis=baxis, @@ -3200,10 +3216,10 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, return self if self.ndim == 1: - if isinstance(value, (dict, com.ABCSeries)): + if isinstance(value, (dict, ABCSeries)): from pandas import Series value = Series(value) - elif not com.is_list_like(value): + elif not is_list_like(value): pass else: raise ValueError("invalid fill value with a %s" % @@ -3213,7 +3229,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, inplace=inplace, downcast=downcast) - elif isinstance(value, (dict, com.ABCSeries)): + elif isinstance(value, (dict, ABCSeries)): if axis == 1: raise NotImplementedError('Currently only can fill ' 'with dict/Series column ' @@ -3226,7 +3242,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, obj = result[k] obj.fillna(v, limit=limit, inplace=True) return result - elif not com.is_list_like(value): + elif not is_list_like(value): new_data = self._data.fillna(value=value, limit=limit, inplace=inplace, downcast=downcast) @@ -3352,7 +3368,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, and play with this method to gain intuition about how it works. """ - if not com.is_bool(regex) and to_replace is not None: + if not is_bool(regex) and to_replace is not None: raise AssertionError("'to_replace' must be 'None' if 'regex' is " "not a bool") if axis is not None: @@ -3365,15 +3381,15 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, if value is None: # passing a single value that is scalar like # when value is None (GH5319), for compat - if not is_dictlike(to_replace) and not is_dictlike(regex): + if not is_dict_like(to_replace) and not is_dict_like(regex): to_replace = [to_replace] if isinstance(to_replace, (tuple, list)): return _single_replace(self, to_replace, method, inplace, limit) - if not is_dictlike(to_replace): - if not is_dictlike(regex): + if not is_dict_like(to_replace): + if not is_dict_like(regex): raise TypeError('If "to_replace" and "value" are both None' ' and "to_replace" is not a list, then ' 'regex must be a mapping') @@ -3383,7 +3399,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, items = list(compat.iteritems(to_replace)) keys, values = zip(*items) - are_mappings = [is_dictlike(v) for v in values] + are_mappings = [is_dict_like(v) for v in values] if any(are_mappings): if not all(are_mappings): @@ -3416,8 +3432,8 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, return self new_data = self._data - if is_dictlike(to_replace): - if is_dictlike(value): # {'A' : NA} -> {'A' : 0} + if is_dict_like(to_replace): + if is_dict_like(value): # {'A' : NA} -> {'A' : 0} res = self if inplace else self.copy() for c, src in compat.iteritems(to_replace): if c in value and c in self: @@ -3427,7 +3443,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, return None if inplace else res # {'A': NA} -> 0 - elif not com.is_list_like(value): + elif not is_list_like(value): for k, src in compat.iteritems(to_replace): if k in self: new_data = new_data.replace(to_replace=src, @@ -3439,8 +3455,8 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, raise TypeError('value argument must be scalar, dict, or ' 'Series') - elif com.is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] - if com.is_list_like(value): + elif is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] + if is_list_like(value): if len(to_replace) != len(value): raise ValueError('Replacement lists must match ' 'in length. Expecting %d got %d ' % @@ -3456,8 +3472,8 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, value=value, inplace=inplace, regex=regex) elif to_replace is None: - if not (com.is_re_compilable(regex) or - com.is_list_like(regex) or is_dictlike(regex)): + if not (is_re_compilable(regex) or + is_list_like(regex) or is_dict_like(regex)): raise TypeError("'regex' must be a string or a compiled " "regular expression or a list or dict of " "strings or regular expressions, you " @@ -3468,7 +3484,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, else: # dest iterable dict-like - if is_dictlike(value): # NA -> {'A' : 0, 'B' : -1} + if is_dict_like(value): # NA -> {'A' : 0, 'B' : -1} new_data = self._data for k, v in compat.iteritems(value): @@ -3478,7 +3494,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, inplace=inplace, regex=regex) - elif not com.is_list_like(value): # NA -> 0 + elif not is_list_like(value): # NA -> 0 new_data = self._data.replace(to_replace=to_replace, value=value, inplace=inplace, regex=regex) @@ -3642,7 +3658,7 @@ def asof(self, where, subset=None): The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) - .. versionadded:: 0.18.2 For DataFrame + .. versionadded:: 0.19.0 For DataFrame If there is no good value, NaN is returned. @@ -3790,14 +3806,14 @@ def clip(self, lower=None, upper=None, axis=None, *args, **kwargs): 3 0.230930 0.000000 4 1.100000 0.570967 """ - if isinstance(self, com.ABCPanel): + if isinstance(self, ABCPanel): raise NotImplementedError("clip is not supported yet for panels") axis = nv.validate_clip_with_axis(axis, args, kwargs) # GH 2747 (arguments were reversed) if lower is not None and upper is not None: - if lib.isscalar(lower) and lib.isscalar(upper): + if is_scalar(lower) and is_scalar(upper): lower, upper = min(lower, upper), max(lower, upper) result = self @@ -3914,16 +3930,20 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, def asfreq(self, freq, method=None, how=None, normalize=False): """ - Convert all TimeSeries inside to specified frequency using DateOffset - objects. Optionally provide fill method to pad/backfill missing values. + Convert TimeSeries to specified frequency. + + Optionally provide filling method to pad/backfill missing values. Parameters ---------- freq : DateOffset object, or string - method : {'backfill', 'bfill', 'pad', 'ffill', None} - Method to use for filling holes in reindexed Series - pad / ffill: propagate last valid observation forward to next valid - backfill / bfill: use NEXT valid observation to fill method + method : {'backfill'/'bfill', 'pad'/'ffill'}, default None + Method to use for filling holes in reindexed Series (note this + does not fill NaNs that already were present): + + * 'pad' / 'ffill': propagate last valid observation forward to next + valid + * 'backfill' / 'bfill': use NEXT valid observation to fill how : {'start', 'end'}, default end For PeriodIndex only, see PeriodIndex.asfreq normalize : bool, default False @@ -4479,10 +4499,12 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, new_other = np.array(other, dtype=self.dtype) except ValueError: new_other = np.array(other) + except TypeError: + new_other = other # we can end up comparing integers and m8[ns] # which is a numpy no no - is_i8 = com.needs_i8_conversion(self.dtype) + is_i8 = needs_i8_conversion(self.dtype) if is_i8: matches = False else: @@ -4491,7 +4513,7 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, if matches is False or not matches.all(): # coerce other to a common dtype if we can - if com.needs_i8_conversion(self.dtype): + if needs_i8_conversion(self.dtype): try: other = np.array(other, dtype=self.dtype) except: @@ -4544,7 +4566,7 @@ def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, dtype, fill_value = _maybe_promote(other.dtype) new_other = np.empty(len(icond), dtype=dtype) new_other.fill(fill_value) - com._maybe_upcast_putmask(new_other, icond, other) + _maybe_upcast_putmask(new_other, icond, other) other = new_other else: @@ -5052,7 +5074,7 @@ def describe_categorical_1d(data): if result[1] > 0: top, freq = objcounts.index[0], objcounts.iloc[0] - if com.is_datetime64_dtype(data): + if is_datetime64_dtype(data): asint = data.dropna().values.view('i8') names += ['top', 'freq', 'first', 'last'] result += [lib.Timestamp(top), freq, @@ -5065,11 +5087,11 @@ def describe_categorical_1d(data): return pd.Series(result, index=names, name=data.name) def describe_1d(data): - if com.is_bool_dtype(data): + if is_bool_dtype(data): return describe_categorical_1d(data) - elif com.is_numeric_dtype(data): + elif is_numeric_dtype(data): return describe_numeric_1d(data) - elif com.is_timedelta64_dtype(data): + elif is_timedelta64_dtype(data): return describe_numeric_1d(data) else: return describe_categorical_1d(data) @@ -5156,7 +5178,7 @@ def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, rs = (data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1) if freq is None: - mask = com.isnull(_values_from_object(self)) + mask = isnull(_values_from_object(self)) np.putmask(rs.values, mask, np.nan) return rs diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 04e4db9d1fdc6..6179857978b7b 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -13,6 +13,25 @@ from pandas import compat from pandas.compat.numpy import function as nv from pandas.compat.numpy import _np_version_under1p8 + +from pandas.types.common import (_DATELIKE_DTYPES, + is_numeric_dtype, + is_timedelta64_dtype, is_datetime64_dtype, + is_categorical_dtype, + is_datetime_or_timedelta_dtype, + is_bool, is_integer_dtype, + is_complex_dtype, + is_bool_dtype, + is_scalar, + _ensure_float64, + _ensure_platform_int, + _ensure_int64, + _ensure_object, + _ensure_float) +from pandas.types.cast import _possibly_downcast_to_dtype +from pandas.types.missing import isnull, notnull, _maybe_fill + +from pandas.core.common import _values_from_object, AbstractMethodError from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.categorical import Categorical @@ -30,14 +49,7 @@ import pandas.core.algorithms as algos import pandas.core.common as com -from pandas.core.common import(_possibly_downcast_to_dtype, isnull, - notnull, _DATELIKE_DTYPES, is_numeric_dtype, - is_timedelta64_dtype, is_datetime64_dtype, - is_categorical_dtype, _values_from_object, - is_datetime_or_timedelta_dtype, is_bool, - is_bool_dtype, AbstractMethodError, - _maybe_fill) -from pandas.core.config import option_context, is_callable +from pandas.core.config import option_context import pandas.lib as lib from pandas.lib import Timestamp import pandas.tslib as tslib @@ -95,7 +107,7 @@ def _groupby_function(name, alias, npfunc, numeric_only=True, @Appender(_doc_template) @Appender(_local_template) def f(self): - self._set_selection_from_grouper() + self._set_group_selection() try: return self._cython_agg_general(alias, numeric_only=numeric_only) except AssertionError as e: @@ -457,8 +469,21 @@ def _selected_obj(self): else: return self.obj[self._selection] - def _set_selection_from_grouper(self): - """ we may need create a selection if we have non-level groupers """ + def _reset_group_selection(self): + """ + Clear group based selection. Used for methods needing to return info on + each group regardless of whether a group selection was previously set. + """ + if self._group_selection is not None: + self._group_selection = None + # GH12839 clear cached selection too when changing group selection + self._reset_cache('_selected_obj') + + def _set_group_selection(self): + """ + Create group based selection. Used when selection is not passed + directly but instead via a grouper. + """ grp = self.grouper if self.as_index and getattr(grp, 'groupings', None) is not None and \ self.obj.ndim > 1: @@ -468,6 +493,8 @@ def _set_selection_from_grouper(self): if len(groupers): self._group_selection = ax.difference(Index(groupers)).tolist() + # GH12839 clear selected obj cache when group selection changes + self._reset_cache('_selected_obj') def _set_result_index_ordered(self, result): # set the result index on the passed values object and @@ -511,7 +538,7 @@ def _make_wrapper(self, name): # need to setup the selection # as are not passed directly but in the grouper - self._set_selection_from_grouper() + self._set_group_selection() f = getattr(self._selected_obj, name) if not isinstance(f, types.MethodType): @@ -647,7 +674,7 @@ def apply(self, func, *args, **kwargs): # resolve functions to their callable functions prior, this # wouldn't be needed if args or kwargs: - if is_callable(func): + if callable(func): @wraps(func) def f(g): @@ -737,7 +764,7 @@ def _try_cast(self, result, obj): else: dtype = obj.dtype - if not lib.isscalar(result): + if not is_scalar(result): result = _possibly_downcast_to_dtype(result, dtype) return result @@ -802,7 +829,7 @@ def _python_agg_general(self, func, *args, **kwargs): # since we are masking, make sure that we have a float object values = result if is_numeric_dtype(values.dtype): - values = com.ensure_float(values) + values = _ensure_float(values) output[name] = self._try_cast(values[mask], result) @@ -979,7 +1006,7 @@ def mean(self, *args, **kwargs): except GroupByError: raise except Exception: # pragma: no cover - self._set_selection_from_grouper() + self._set_group_selection() f = lambda x: x.mean(axis=self.axis) return self._python_agg_general(f) @@ -997,7 +1024,7 @@ def median(self): raise except Exception: # pragma: no cover - self._set_selection_from_grouper() + self._set_group_selection() def f(x): if isinstance(x, np.ndarray): @@ -1040,7 +1067,7 @@ def var(self, ddof=1, *args, **kwargs): if ddof == 1: return self._cython_agg_general('var') else: - self._set_selection_from_grouper() + self._set_group_selection() f = lambda x: x.var(ddof=ddof) return self._python_agg_general(f) @@ -1217,7 +1244,7 @@ def nth(self, n, dropna=None): raise TypeError("n needs to be an int or a list/set/tuple of ints") nth_values = np.array(nth_values, dtype=np.intp) - self._set_selection_from_grouper() + self._set_group_selection() if not dropna: mask = np.in1d(self._cumcount_array(), nth_values) | \ @@ -1325,7 +1352,7 @@ def cumcount(self, ascending=True): dtype: int64 """ - self._set_selection_from_grouper() + self._set_group_selection() index = self._selected_obj.index cumcounts = self._cumcount_array(ascending=ascending) @@ -1403,6 +1430,7 @@ def head(self, n=5): 0 1 2 2 5 6 """ + self._reset_group_selection() mask = self._cumcount_array() < n return self._selected_obj[mask] @@ -1429,6 +1457,7 @@ def tail(self, n=5): 0 a 1 2 b 1 """ + self._reset_group_selection() mask = self._cumcount_array(ascending=False) < n return self._selected_obj[mask] @@ -1578,7 +1607,7 @@ def size(self): """ ids, _, ngroup = self.group_info - ids = com._ensure_platform_int(ids) + ids = _ensure_platform_int(ids) out = np.bincount(ids[ids != -1], minlength=ngroup or None) return Series(out, index=self.result_index, dtype='int64') @@ -1614,7 +1643,7 @@ def group_info(self): comp_ids, obs_group_ids = self._get_compressed_labels() ngroups = len(obs_group_ids) - comp_ids = com._ensure_int64(comp_ids) + comp_ids = _ensure_int64(comp_ids) return comp_ids, obs_group_ids, ngroups def _get_compressed_labels(self): @@ -1654,7 +1683,7 @@ def get_group_levels(self): name_list = [] for ping, labels in zip(self.groupings, self.recons_labels): - labels = com._ensure_platform_int(labels) + labels = _ensure_platform_int(labels) levels = ping.group_index.take(labels) name_list.append(levels) @@ -1763,11 +1792,11 @@ def _cython_operation(self, kind, values, how, axis): values = values.view('int64') is_numeric = True elif is_bool_dtype(values.dtype): - values = _algos.ensure_float64(values) - elif com.is_integer_dtype(values): + values = _ensure_float64(values) + elif is_integer_dtype(values): values = values.astype('int64', copy=False) - elif is_numeric and not com.is_complex_dtype(values): - values = _algos.ensure_float64(values) + elif is_numeric and not is_complex_dtype(values): + values = _ensure_float64(values) else: values = values.astype(object) @@ -1776,7 +1805,7 @@ def _cython_operation(self, kind, values, how, axis): kind, how, values, is_numeric) except NotImplementedError: if is_numeric: - values = _algos.ensure_float64(values) + values = _ensure_float64(values) func, dtype_str = self._get_cython_function( kind, how, values, is_numeric) else: @@ -1804,7 +1833,7 @@ def _cython_operation(self, kind, values, how, axis): result = self._transform( result, accum, values, labels, func, is_numeric) - if com.is_integer_dtype(result): + if is_integer_dtype(result): if len(result[result == tslib.iNaT]) > 0: result = result.astype('float64') result[result == tslib.iNaT] = np.nan @@ -1817,7 +1846,7 @@ def _cython_operation(self, kind, values, how, axis): result, (counts > 0).view(np.uint8)) except ValueError: result = lib.row_bool_subset_object( - com._ensure_object(result), + _ensure_object(result), (counts > 0).view(np.uint8)) else: result = result[counts > 0] @@ -1979,7 +2008,7 @@ def generate_bins_generic(values, binner, closed): class BinGrouper(BaseGrouper): def __init__(self, bins, binlabels, filter_empty=False, mutated=False): - self.bins = com._ensure_int64(bins) + self.bins = _ensure_int64(bins) self.binlabels = _ensure_index(binlabels) self._filter_empty_groups = filter_empty self.mutated = mutated @@ -2044,7 +2073,7 @@ def group_info(self): obs_group_ids = np.arange(ngroups) rep = np.diff(np.r_[0, self.bins]) - rep = com._ensure_platform_int(rep) + rep = _ensure_platform_int(rep) if ngroups == len(self.bins): comp_ids = np.repeat(np.arange(ngroups), rep) else: @@ -2432,7 +2461,7 @@ def is_in_obj(gpr): def _is_label_like(val): return (isinstance(val, compat.string_types) or - (val is not None and lib.isscalar(val))) + (val is not None and is_scalar(val))) def _convert_grouper(axis, grouper): @@ -2654,7 +2683,7 @@ def _aggregate_multiple_funcs(self, arg, _level): results[name] = obj.aggregate(func) if isinstance(list(compat.itervalues(results))[0], - com.ABCDataFrame): + DataFrame): # let higher level handle if _level: @@ -2853,9 +2882,9 @@ def nunique(self, dropna=True): 'val.dtype must be object, got %s' % val.dtype val, _ = algos.factorize(val, sort=False) sorter = np.lexsort((val, ids)) - isnull = lambda a: a == -1 + _isnull = lambda a: a == -1 else: - isnull = com.isnull + _isnull = isnull ids, val = ids[sorter], val[sorter] @@ -2865,7 +2894,7 @@ def nunique(self, dropna=True): inc = np.r_[1, val[1:] != val[:-1]] # 1st item of each group is a new unique observation - mask = isnull(val) + mask = _isnull(val) if dropna: inc[idx] = 1 inc[mask] = 0 @@ -2981,8 +3010,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False, mi = MultiIndex(levels=levels, labels=labels, names=names, verify_integrity=False) - if com.is_integer_dtype(out): - out = com._ensure_int64(out) + if is_integer_dtype(out): + out = _ensure_int64(out) return Series(out, index=mi, name=self.name) # for compat. with algos.value_counts need to ensure every @@ -3012,8 +3041,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False, mi = MultiIndex(levels=levels, labels=labels, names=names, verify_integrity=False) - if com.is_integer_dtype(out): - out = com._ensure_int64(out) + if is_integer_dtype(out): + out = _ensure_int64(out) return Series(out, index=mi, name=self.name) def count(self): @@ -3022,7 +3051,7 @@ def count(self): val = self.obj.get_values() mask = (ids != -1) & ~isnull(val) - ids = com._ensure_platform_int(ids) + ids = _ensure_platform_int(ids) out = np.bincount(ids[mask], minlength=ngroups or None) return Series(out, @@ -3386,11 +3415,14 @@ def first_non_None_value(values): return self._reindex_output(result) + # values are not series or array-like but scalars else: # only coerce dates if we find at least 1 datetime coerce = True if any([isinstance(x, Timestamp) for x in values]) else False - return (Series(values, index=key_index, name=self.name) + # self.name not passed through to Series as the result + # should not take the name of original selection of columns + return (Series(values, index=key_index) ._convert(datetime=True, coerce=coerce)) @@ -3596,7 +3628,7 @@ def filter(self, func, dropna=True, *args, **kwargs): # noqa pass # interpret the result of the filter - if is_bool(res) or (lib.isscalar(res) and isnull(res)): + if is_bool(res) or (is_scalar(res) and isnull(res)): if res and notnull(res): indices.append(self._get_index(name)) else: @@ -3793,7 +3825,7 @@ def count(self): """ Compute count of group, excluding missing values """ from functools import partial from pandas.lib import count_level_2d - from pandas.core.common import _isnull_ndarraylike as isnull + from pandas.types.missing import _isnull_ndarraylike as isnull data, _ = self._get_data_to_aggregate() ids, _, ngroups = self.grouper.group_info @@ -3914,7 +3946,7 @@ class DataSplitter(object): def __init__(self, data, labels, ngroups, axis=0): self.data = data - self.labels = com._ensure_int64(labels) + self.labels = _ensure_int64(labels) self.ngroups = ngroups self.axis = axis @@ -4095,7 +4127,7 @@ def loop(labels, shape): def maybe_lift(lab, size): # pormote nan values return (lab + 1, size + 1) if (lab == -1).any() else (lab, size) - labels = map(com._ensure_int64, labels) + labels = map(_ensure_int64, labels) if not xnull: labels, shape = map(list, zip(*map(maybe_lift, labels, shape))) @@ -4311,9 +4343,9 @@ def _get_group_index_sorter(group_index, ngroups): alpha = 0.0 # taking complexities literally; there may be beta = 1.0 # some room for fine-tuning these parameters if alpha + beta * ngroups < count * np.log(count): - sorter, _ = _algos.groupsort_indexer(com._ensure_int64(group_index), + sorter, _ = _algos.groupsort_indexer(_ensure_int64(group_index), ngroups) - return com._ensure_platform_int(sorter) + return _ensure_platform_int(sorter) else: return group_index.argsort(kind='mergesort') @@ -4328,7 +4360,7 @@ def _compress_group_index(group_index, sort=True): size_hint = min(len(group_index), _hash._SIZE_HINT_LIMIT) table = _hash.Int64HashTable(size_hint) - group_index = com._ensure_int64(group_index) + group_index = _ensure_int64(group_index) # note, group labels come out ascending (ie, 1,2,3 etc) comp_ids, obs_group_ids = table.get_labels_groupby(group_index) @@ -4370,7 +4402,7 @@ def _groupby_indices(values): _, counts = _hash.value_count_scalar64(codes, False) else: reverse, codes, counts = _algos.group_labels( - _values_from_object(com._ensure_object(values))) + _values_from_object(_ensure_object(values))) return _algos.groupby_indices(reverse, codes, counts) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 9485f50ed07f1..0cba8308c1c53 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1,17 +1,24 @@ # pylint: disable=W0223 -from pandas.core.index import Index, MultiIndex +import numpy as np from pandas.compat import range, zip import pandas.compat as compat +from pandas.types.generic import ABCDataFrame, ABCPanel, ABCSeries +from pandas.types.common import (is_integer_dtype, + is_integer, is_float, + is_categorical_dtype, + is_list_like, + is_sequence, + is_scalar, + _ensure_platform_int) +from pandas.types.missing import isnull, _infer_fill_value + +from pandas.core.index import Index, MultiIndex + import pandas.core.common as com -import pandas.lib as lib -from pandas.core.common import (is_bool_indexer, is_integer_dtype, - _asarray_tuplesafe, is_list_like, isnull, - is_null_slice, is_full_slice, ABCSeries, - ABCDataFrame, ABCPanel, is_float, - _values_from_object, _infer_fill_value, - is_integer) -import numpy as np +from pandas.core.common import (is_bool_indexer, _asarray_tuplesafe, + is_null_slice, is_full_slice, + _values_from_object) # the supported indexers @@ -67,7 +74,7 @@ def __getitem__(self, key): key = tuple(com._apply_if_callable(x, self.obj) for x in key) try: values = self.obj.get_value(*key) - if lib.isscalar(values): + if is_scalar(values): return values except Exception: pass @@ -625,7 +632,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False): # we have a frame, with multiple indexers on both axes; and a # series, so need to broadcast (see GH5206) if (sum_aligners == self.ndim and - all([com.is_sequence(_) for _ in indexer])): + all([is_sequence(_) for _ in indexer])): ser = ser.reindex(obj.axes[0][indexer[0]], copy=True)._values # single indexer @@ -639,7 +646,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False): ax = obj.axes[i] # multiple aligners (or null slices) - if com.is_sequence(idx) or isinstance(idx, slice): + if is_sequence(idx) or isinstance(idx, slice): if single_aligner and is_null_slice(idx): continue new_ix = ax[idx] @@ -685,7 +692,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False): return ser - elif lib.isscalar(indexer): + elif is_scalar(indexer): ax = self.obj._get_axis(1) if ser.index.equals(ax): @@ -710,7 +717,7 @@ def _align_frame(self, indexer, df): sindexers = [] for i, ix in enumerate(indexer): ax = self.obj.axes[i] - if com.is_sequence(ix) or isinstance(ix, slice): + if is_sequence(ix) or isinstance(ix, slice): if idx is None: idx = ax[ix].ravel() elif cols is None: @@ -761,7 +768,7 @@ def _align_frame(self, indexer, df): val = df.reindex(index=ax)._values return val - elif lib.isscalar(indexer) and is_panel: + elif is_scalar(indexer) and is_panel: idx = self.obj.axes[1] cols = self.obj.axes[2] @@ -857,7 +864,7 @@ def _convert_for_reindex(self, key, axis=0): keyarr = _asarray_tuplesafe(key) if is_integer_dtype(keyarr) and not labels.is_integer(): - keyarr = com._ensure_platform_int(keyarr) + keyarr = _ensure_platform_int(keyarr) return labels.take(keyarr) return keyarr @@ -968,7 +975,7 @@ def _getitem_nested_tuple(self, tup): axis += 1 # if we have a scalar, we are done - if lib.isscalar(obj) or not hasattr(obj, 'ndim'): + if is_scalar(obj) or not hasattr(obj, 'ndim'): break # has the dim of the obj changed? @@ -1038,7 +1045,7 @@ def _getitem_iterable(self, key, axis=0): # asarray can be unsafe, NumPy strings are weird keyarr = _asarray_tuplesafe(key) - if com.is_categorical_dtype(labels): + if is_categorical_dtype(labels): keyarr = labels._shallow_copy(keyarr) # have the index handle the indexer and possibly return @@ -1799,7 +1806,7 @@ def check_bool_indexer(ax, key): result = key if isinstance(key, ABCSeries) and not key.index.equals(ax): result = result.reindex(ax) - mask = com.isnull(result._values) + mask = isnull(result._values) if mask.any(): raise IndexingError('Unalignable boolean Series key provided') @@ -1941,9 +1948,9 @@ def _non_reducing_slice(slice_): def pred(part): # true when slice does *not* reduce - return isinstance(part, slice) or com.is_list_like(part) + return isinstance(part, slice) or is_list_like(part) - if not com.is_list_like(slice_): + if not is_list_like(slice_): if not isinstance(slice_, slice): # a 1-d slice, like df.loc[1] slice_ = [[slice_]] diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c931adc9a31df..ff12cfddbe9cd 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -10,29 +10,48 @@ from pandas.core.base import PandasObject -from pandas.core.common import (_possibly_downcast_to_dtype, isnull, _NS_DTYPE, - _TD_DTYPE, ABCSeries, is_list_like, - _infer_dtype_from_scalar, is_null_slice, - is_dtype_equal, is_null_datelike_scalar, - _maybe_promote, is_timedelta64_dtype, - is_datetime64_dtype, is_datetimetz, is_sparse, - array_equivalent, _is_na_compat, - _maybe_convert_string_to_object, - _maybe_convert_scalar, - is_categorical, is_datetimelike_v_numeric, - is_numeric_v_string_like, is_extension_type) +from pandas.types.dtypes import DatetimeTZDtype, CategoricalDtype +from pandas.types.common import (_TD_DTYPE, _NS_DTYPE, + _ensure_int64, _ensure_platform_int, + is_integer, + is_dtype_equal, + is_timedelta64_dtype, + is_datetime64_dtype, is_datetimetz, is_sparse, + is_categorical, is_categorical_dtype, + is_integer_dtype, + is_datetime64tz_dtype, + is_object_dtype, + is_datetimelike_v_numeric, + is_numeric_v_string_like, is_extension_type, + is_list_like, + is_re, + is_re_compilable, + is_scalar, + _get_dtype) +from pandas.types.cast import (_possibly_downcast_to_dtype, + _maybe_convert_string_to_object, + _maybe_upcast, + _maybe_convert_scalar, _maybe_promote, + _infer_dtype_from_scalar, + _soft_convert_objects, + _possibly_convert_objects, + _astype_nansafe) +from pandas.types.missing import (isnull, array_equivalent, + _is_na_compat, + is_null_datelike_scalar) +import pandas.types.concat as _concat + +from pandas.types.generic import ABCSeries +from pandas.core.common import is_null_slice import pandas.core.algorithms as algos -from pandas.types.api import DatetimeTZDtype from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import maybe_convert_indices, length_of_indexer from pandas.core.categorical import Categorical, maybe_to_categorical from pandas.tseries.index import DatetimeIndex from pandas.formats.printing import pprint_thing -import pandas.core.common as com -import pandas.types.concat as _concat + import pandas.core.missing as missing -import pandas.core.convert as convert from pandas.sparse.array import _maybe_to_sparse, SparseArray import pandas.lib as lib import pandas.tslib as tslib @@ -112,8 +131,8 @@ def is_categorical_astype(self, dtype): validate that we have a astypeable to categorical, returns a boolean if we are a categorical """ - if com.is_categorical_dtype(dtype): - if dtype == com.CategoricalDtype(): + if is_categorical_dtype(dtype): + if dtype == CategoricalDtype(): return True # this is a pd.Categorical, but is not @@ -137,7 +156,7 @@ def get_values(self, dtype=None): return an internal format, currently just the ndarray this is often overriden to handle to_dense like operations """ - if com.is_object_dtype(dtype): + if is_object_dtype(dtype): return self.values.astype(object) return self.values @@ -481,7 +500,7 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, values = self.get_values(dtype=dtype) # _astype_nansafe works fine with 1-d only - values = com._astype_nansafe(values.ravel(), dtype, copy=True) + values = _astype_nansafe(values.ravel(), dtype, copy=True) values = values.reshape(self.shape) newb = make_block(values, placement=self.mgr_locs, dtype=dtype, @@ -651,7 +670,7 @@ def setitem(self, indexer, value, mgr=None): # cast the values to a type that can hold nan (if necessary) if not self._can_hold_element(value): - dtype, _ = com._maybe_promote(arr_value.dtype) + dtype, _ = _maybe_promote(arr_value.dtype) values = values.astype(dtype) transf = (lambda x: x.T) if self.ndim == 2 else (lambda x: x) @@ -684,7 +703,7 @@ def _is_scalar_indexer(indexer): if arr_value.ndim == 1: if not isinstance(indexer, tuple): indexer = tuple([indexer]) - return all([lib.isscalar(idx) for idx in indexer]) + return all([is_scalar(idx) for idx in indexer]) return False def _is_empty_indexer(indexer): @@ -724,7 +743,7 @@ def _is_empty_indexer(indexer): if hasattr(value, 'dtype') and is_dtype_equal(values.dtype, value.dtype): dtype = value.dtype - elif lib.isscalar(value): + elif is_scalar(value): dtype, _ = _infer_dtype_from_scalar(value) else: dtype = 'infer' @@ -838,7 +857,7 @@ def putmask(self, mask, new, align=True, inplace=False, axis=0, n = np.array(new) # type of the new block - dtype, _ = com._maybe_promote(n.dtype) + dtype, _ = _maybe_promote(n.dtype) # we need to explicitly astype here to make a copy n = n.astype(dtype) @@ -1027,7 +1046,7 @@ def shift(self, periods, axis=0, mgr=None): # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also - new_values, fill_value = com._maybe_upcast(self.values) + new_values, fill_value = _maybe_upcast(self.values) # make sure array sent to np.roll is c_contiguous f_ordered = new_values.flags.f_contiguous @@ -1036,7 +1055,7 @@ def shift(self, periods, axis=0, mgr=None): axis = new_values.ndim - axis - 1 if np.prod(new_values.shape): - new_values = np.roll(new_values, com._ensure_platform_int(periods), + new_values = np.roll(new_values, _ensure_platform_int(periods), axis=axis) axis_indexer = [slice(None)] * self.ndim @@ -1306,7 +1325,7 @@ def quantile(self, qs, interpolation='linear', axis=0, mgr=None): from pandas import Float64Index is_empty = values.shape[axis] == 0 - if com.is_list_like(qs): + if is_list_like(qs): ax = Float64Index(qs) if is_empty: @@ -1350,7 +1369,7 @@ def quantile(self, qs, interpolation='linear', axis=0, mgr=None): ndim = getattr(result, 'ndim', None) or 0 result = self._try_coerce_result(result) - if lib.isscalar(result): + if is_scalar(result): return ax, self.make_block_scalar(result) return ax, make_block(result, placement=np.arange(len(result)), @@ -1591,7 +1610,7 @@ def _can_hold_element(self, element): tipo = element.dtype.type return (issubclass(tipo, np.integer) and not issubclass(tipo, (np.datetime64, np.timedelta64))) - return com.is_integer(element) + return is_integer(element) def _try_cast(self, element): try: @@ -1600,7 +1619,7 @@ def _try_cast(self, element): return element def should_store(self, value): - return com.is_integer_dtype(value) and value.dtype == self.dtype + return is_integer_dtype(value) and value.dtype == self.dtype class DatetimeLikeBlockMixin(object): @@ -1621,7 +1640,7 @@ def get_values(self, dtype=None): """ return object dtype as boxed values, such as Timestamps/Timedelta """ - if com.is_object_dtype(dtype): + if is_object_dtype(dtype): return lib.map_infer(self.values.ravel(), self._box_func).reshape(self.values.shape) return self.values @@ -1641,7 +1660,7 @@ def fillna(self, value, **kwargs): # allow filling with integers to be # interpreted as seconds - if not isinstance(value, np.timedelta64) and com.is_integer(value): + if not isinstance(value, np.timedelta64) and is_integer(value): value = Timedelta(value, unit='s') return super(TimeDeltaBlock, self).fillna(value, **kwargs) @@ -1795,10 +1814,10 @@ def convert(self, *args, **kwargs): new_style |= kw in kwargs if new_style: - fn = convert._soft_convert_objects + fn = _soft_convert_objects fn_inputs = new_inputs else: - fn = convert._possibly_convert_objects + fn = _possibly_convert_objects fn_inputs = ['convert_dates', 'convert_numeric', 'convert_timedeltas'] fn_inputs += ['copy'] @@ -1820,7 +1839,7 @@ def convert(self, *args, **kwargs): try: values = values.reshape(shape) values = _block_shape(values, ndim=self.ndim) - except AttributeError: + except (AttributeError, NotImplementedError): pass newb = make_block(values, ndim=self.ndim, placement=[rl]) blocks.append(newb) @@ -1884,15 +1903,15 @@ def should_store(self, value): def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mgr=None): - to_rep_is_list = com.is_list_like(to_replace) - value_is_list = com.is_list_like(value) + to_rep_is_list = is_list_like(to_replace) + value_is_list = is_list_like(value) both_lists = to_rep_is_list and value_is_list either_list = to_rep_is_list or value_is_list result_blocks = [] blocks = [self] - if not either_list and com.is_re(to_replace): + if not either_list and is_re(to_replace): return self._replace_single(to_replace, value, inplace=inplace, filter=filter, regex=True, convert=convert, mgr=mgr) @@ -1930,10 +1949,10 @@ def replace(self, to_replace, value, inplace=False, filter=None, def _replace_single(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mgr=None): # to_replace is regex compilable - to_rep_re = regex and com.is_re_compilable(to_replace) + to_rep_re = regex and is_re_compilable(to_replace) # regex is regex compilable - regex_re = com.is_re_compilable(regex) + regex_re = is_re_compilable(regex) # only one will survive if to_rep_re and regex_re: @@ -2046,7 +2065,7 @@ def _try_coerce_result(self, result): # GH12564: CategoricalBlock is 1-dim only # while returned results could be any dim - if ((not com.is_categorical_dtype(result)) and + if ((not is_categorical_dtype(result)) and isinstance(result, np.ndarray)): result = _block_shape(result, ndim=self.ndim) @@ -2151,7 +2170,7 @@ def _astype(self, dtype, mgr=None, **kwargs): """ # if we are passed a datetime64[ns, tz] - if com.is_datetime64tz_dtype(dtype): + if is_datetime64tz_dtype(dtype): dtype = DatetimeTZDtype(dtype) values = self.values @@ -2167,7 +2186,7 @@ def _can_hold_element(self, element): if is_list_like(element): element = np.array(element) return element.dtype == _NS_DTYPE or element.dtype == np.int64 - return (com.is_integer(element) or isinstance(element, datetime) or + return (is_integer(element) or isinstance(element, datetime) or isnull(element)) def _try_cast(self, element): @@ -2209,7 +2228,7 @@ def _try_coerce_args(self, values, other): "naive Block") other_mask = isnull(other) other = other.asm8.view('i8') - elif hasattr(other, 'dtype') and com.is_integer_dtype(other): + elif hasattr(other, 'dtype') and is_integer_dtype(other): other = other.view('i8') else: try: @@ -2315,7 +2334,7 @@ def external_values(self): def get_values(self, dtype=None): # return object dtype as Timestamps with the zones - if com.is_object_dtype(dtype): + if is_object_dtype(dtype): f = lambda x: lib.Timestamp(x, tz=self.values.tz) return lib.map_infer( self.values.ravel(), f).reshape(self.values.shape) @@ -2561,7 +2580,7 @@ def shift(self, periods, axis=0, mgr=None): new_values = self.values.to_dense().take(indexer) # convert integer to float if necessary. need to do a lot more than # that, handle boolean etc also - new_values, fill_value = com._maybe_upcast(new_values) + new_values, fill_value = _maybe_upcast(new_values) if periods > 0: new_values[:periods] = fill_value else: @@ -3085,7 +3104,7 @@ def reduction(self, f, axis=0, consolidate=True, transposed=False, # compute the orderings of our original data if len(self.blocks) > 1: - indexer = np.empty(len(self.axes[0]), dtype='int64') + indexer = np.empty(len(self.axes[0]), dtype=np.intp) i = 0 for b in self.blocks: for j in b.mgr_locs: @@ -3491,7 +3510,7 @@ def get(self, item, fastpath=True): indexer = np.arange(len(self.items))[isnull(self.items)] # allow a single nan location indexer - if not lib.isscalar(indexer): + if not is_scalar(indexer): if len(indexer) == 1: loc = indexer.item() else: @@ -3597,7 +3616,7 @@ def value_getitem(placement): return value else: if value.ndim == self.ndim - 1: - value = value.reshape((1,) + value.shape) + value = _safe_reshape(value, (1,) + value.shape) def value_getitem(placement): return value @@ -3823,7 +3842,7 @@ def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None): return [blk.getitem_block(slobj, new_mgr_locs=slice(0, sllen))] elif not allow_fill or self.ndim == 1: if allow_fill and fill_tuple[0] is None: - _, fill_value = com._maybe_promote(blk.dtype) + _, fill_value = _maybe_promote(blk.dtype) fill_tuple = (fill_value, ) return [blk.take_nd(slobj, axis=0, @@ -3881,7 +3900,7 @@ def _make_na_block(self, placement, fill_value=None): block_shape = list(self.shape) block_shape[0] = len(placement) - dtype, fill_value = com._infer_dtype_from_scalar(fill_value) + dtype, fill_value = _infer_dtype_from_scalar(fill_value) block_values = np.empty(block_shape, dtype=dtype) block_values.fill(fill_value) return make_block(block_values, placement=placement) @@ -4560,7 +4579,7 @@ def _possibly_compare(a, b, op): else: result = op(a, b) - if lib.isscalar(result) and (is_a_array or is_b_array): + if is_scalar(result) and (is_a_array or is_b_array): type_names = [type(a).__name__, type(b).__name__] if is_a_array: @@ -4611,7 +4630,7 @@ def _factor_indexer(shape, labels): expanded label indexer """ mult = np.array(shape)[::-1].cumprod()[::-1] - return com._ensure_platform_int( + return _ensure_platform_int( np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T) @@ -4631,7 +4650,7 @@ def _get_blkno_placements(blknos, blk_count, group=True): """ - blknos = com._ensure_int64(blknos) + blknos = _ensure_int64(blknos) # FIXME: blk_count is unused, but it may avoid the use of dicts in cython for blkno, indexer in lib.get_blkno_indexers(blknos, group): @@ -4667,6 +4686,28 @@ def rrenamer(x): _transform_index(right, rrenamer)) +def _safe_reshape(arr, new_shape): + """ + If possible, reshape `arr` to have shape `new_shape`, + with a couple of exceptions (see gh-13012): + + 1) If `arr` is a Categorical or Index, `arr` will be + returned as is. + 2) If `arr` is a Series, the `_values` attribute will + be reshaped and returned. + + Parameters + ---------- + arr : array-like, object to be reshaped + new_shape : int or tuple of ints, the new shape + """ + if isinstance(arr, ABCSeries): + arr = arr._values + if not isinstance(arr, Categorical): + arr = arr.reshape(new_shape) + return arr + + def _transform_index(index, func): """ Apply function to all values found in index. @@ -4721,7 +4762,7 @@ def _putmask_smart(v, m, n): pass # change the dtype - dtype, _ = com._maybe_promote(n.dtype) + dtype, _ = _maybe_promote(n.dtype) nv = v.astype(dtype) try: nv[m] = n[m] @@ -4787,9 +4828,9 @@ def get_empty_dtype_and_na(join_units): if dtype is None: continue - if com.is_categorical_dtype(dtype): + if is_categorical_dtype(dtype): upcast_cls = 'category' - elif com.is_datetimetz(dtype): + elif is_datetimetz(dtype): upcast_cls = 'datetimetz' elif issubclass(dtype.type, np.bool_): upcast_cls = 'bool' @@ -5062,8 +5103,8 @@ def dtype(self): if not self.needs_filling: return self.block.dtype else: - return com._get_dtype(com._maybe_promote(self.block.dtype, - self.block.fill_value)[0]) + return _get_dtype(_maybe_promote(self.block.dtype, + self.block.fill_value)[0]) return self._dtype diff --git a/pandas/core/missing.py b/pandas/core/missing.py index 911fcaf529f98..b847415f274db 100644 --- a/pandas/core/missing.py +++ b/pandas/core/missing.py @@ -5,10 +5,15 @@ import numpy as np from distutils.version import LooseVersion -import pandas.core.common as com import pandas.algos as algos import pandas.lib as lib from pandas.compat import range, string_types +from pandas.types.common import (is_numeric_v_string_like, + is_float_dtype, is_datetime64_dtype, + is_integer_dtype, _ensure_float64, + is_scalar, + _DATELIKE_DTYPES) +from pandas.types.missing import isnull def mask_missing(arr, values_to_mask): @@ -24,7 +29,7 @@ def mask_missing(arr, values_to_mask): except Exception: values_to_mask = np.array(values_to_mask, dtype=object) - na_mask = com.isnull(values_to_mask) + na_mask = isnull(values_to_mask) nonna = values_to_mask[~na_mask] mask = None @@ -32,28 +37,28 @@ def mask_missing(arr, values_to_mask): if mask is None: # numpy elementwise comparison warning - if com.is_numeric_v_string_like(arr, x): + if is_numeric_v_string_like(arr, x): mask = False else: mask = arr == x # if x is a string and arr is not, then we get False and we must # expand the mask to size arr.shape - if lib.isscalar(mask): + if is_scalar(mask): mask = np.zeros(arr.shape, dtype=bool) else: # numpy elementwise comparison warning - if com.is_numeric_v_string_like(arr, x): + if is_numeric_v_string_like(arr, x): mask |= False else: mask |= arr == x if na_mask.any(): if mask is None: - mask = com.isnull(arr) + mask = isnull(arr) else: - mask |= com.isnull(arr) + mask |= isnull(arr) return mask @@ -110,7 +115,7 @@ def interpolate_1d(xvalues, yvalues, method='linear', limit=None, """ # Treat the original, non-scipy methods first. - invalid = com.isnull(yvalues) + invalid = isnull(yvalues) valid = ~invalid if not valid.any(): @@ -442,12 +447,12 @@ def pad_1d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'pad_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _pad_1d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.pad_inplace_float64 elif values.dtype == np.object_: _method = algos.pad_inplace_object @@ -456,7 +461,7 @@ def pad_1d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for pad_1d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) _method(values, mask, limit=limit) return values @@ -467,12 +472,12 @@ def backfill_1d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'backfill_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _backfill_1d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.backfill_inplace_float64 elif values.dtype == np.object_: _method = algos.backfill_inplace_object @@ -481,7 +486,7 @@ def backfill_1d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for backfill_1d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) _method(values, mask, limit=limit) @@ -493,12 +498,12 @@ def pad_2d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'pad_2d_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _pad_2d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.pad_2d_inplace_float64 elif values.dtype == np.object_: _method = algos.pad_2d_inplace_object @@ -507,7 +512,7 @@ def pad_2d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for pad_2d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) if np.all(values.shape): @@ -523,12 +528,12 @@ def backfill_2d(values, limit=None, mask=None, dtype=None): if dtype is None: dtype = values.dtype _method = None - if com.is_float_dtype(values): + if is_float_dtype(values): _method = getattr(algos, 'backfill_2d_inplace_%s' % dtype.name, None) - elif dtype in com._DATELIKE_DTYPES or com.is_datetime64_dtype(values): + elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values): _method = _backfill_2d_datetime - elif com.is_integer_dtype(values): - values = com._ensure_float64(values) + elif is_integer_dtype(values): + values = _ensure_float64(values) _method = algos.backfill_2d_inplace_float64 elif values.dtype == np.object_: _method = algos.backfill_2d_inplace_object @@ -537,7 +542,7 @@ def backfill_2d(values, limit=None, mask=None, dtype=None): raise ValueError('Invalid dtype for backfill_2d [%s]' % dtype.name) if mask is None: - mask = com.isnull(values) + mask = isnull(values) mask = mask.view(np.uint8) if np.all(values.shape): @@ -570,22 +575,22 @@ def fill_zeros(result, x, y, name, fill): mask the nan's from x """ - if fill is None or com.is_float_dtype(result): + if fill is None or is_float_dtype(result): return result if name.startswith(('r', '__r')): x, y = y, x - is_typed_variable = (hasattr(y, 'dtype') or hasattr(y, 'type')) - is_scalar = lib.isscalar(y) + is_variable_type = (hasattr(y, 'dtype') or hasattr(y, 'type')) + is_scalar_type = is_scalar(y) - if not is_typed_variable and not is_scalar: + if not is_variable_type and not is_scalar_type: return result - if is_scalar: + if is_scalar_type: y = np.array(y) - if com.is_integer_dtype(y): + if is_integer_dtype(y): if (y == 0).any(): diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index f390e3f04a6c3..7b89373dda7ba 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -11,16 +11,19 @@ import pandas.hashtable as _hash from pandas import compat, lib, algos, tslib -from pandas.core.common import (isnull, notnull, _values_from_object, - _maybe_upcast_putmask, _ensure_float64, - _ensure_int64, _ensure_object, is_float, - is_integer, is_complex, is_float_dtype, - is_complex_dtype, is_integer_dtype, - is_bool_dtype, is_object_dtype, - is_datetime64_dtype, is_timedelta64_dtype, - is_datetime_or_timedelta_dtype, _get_dtype, - is_int_or_datetime_dtype, is_any_int_dtype, - _int64_max) +from pandas.types.common import (_ensure_int64, _ensure_object, + _ensure_float64, _get_dtype, + is_float, is_scalar, + is_integer, is_complex, is_float_dtype, + is_complex_dtype, is_integer_dtype, + is_bool_dtype, is_object_dtype, + is_datetime64_dtype, is_timedelta64_dtype, + is_datetime_or_timedelta_dtype, + is_int_or_datetime_dtype, is_any_int_dtype) +from pandas.types.cast import _int64_max, _maybe_upcast_putmask +from pandas.types.missing import isnull, notnull + +from pandas.core.common import _values_from_object class disallow(object): @@ -351,7 +354,7 @@ def _get_counts_nanvar(mask, axis, ddof, dtype=float): d = count - dtype.type(ddof) # always return NaN, never inf - if lib.isscalar(count): + if is_scalar(count): if count <= ddof: count = np.nan d = np.nan @@ -623,7 +626,7 @@ def _get_counts(mask, axis, dtype=float): return dtype.type(mask.size - mask.sum()) count = mask.shape[axis] - mask.sum(axis) - if lib.isscalar(count): + if is_scalar(count): return dtype.type(count) try: return count.astype(dtype) diff --git a/pandas/core/ops.py b/pandas/core/ops.py index f27a83f50e115..d76f011df3dd8 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -13,28 +13,32 @@ from pandas import compat, lib, tslib import pandas.index as _index from pandas.util.decorators import Appender -import pandas.core.common as com import pandas.computation.expressions as expressions from pandas.lib import isscalar from pandas.tslib import iNaT from pandas.compat import bind_method import pandas.core.missing as missing import pandas.algos as _algos -import pandas.core.algorithms as algos -from pandas.core.common import (is_list_like, notnull, isnull, - _values_from_object, _maybe_match_name, - needs_i8_conversion, is_datetimelike_v_numeric, - is_integer_dtype, is_categorical_dtype, - is_object_dtype, is_timedelta64_dtype, - is_datetime64_dtype, is_datetime64tz_dtype, - is_bool_dtype, PerformanceWarning, ABCSeries) +from pandas.core.common import (_values_from_object, _maybe_match_name, + PerformanceWarning) +from pandas.types.missing import notnull, isnull +from pandas.types.common import (needs_i8_conversion, + is_datetimelike_v_numeric, + is_integer_dtype, is_categorical_dtype, + is_object_dtype, is_timedelta64_dtype, + is_datetime64_dtype, is_datetime64tz_dtype, + is_bool_dtype, is_datetimetz, + is_list_like, + _ensure_object) +from pandas.types.cast import _maybe_upcast_putmask +from pandas.types.generic import ABCSeries, ABCIndex # ----------------------------------------------------------------------------- # Functions that add arithmetic methods to objects, given arithmetic factory # methods -def _create_methods(arith_method, radd_func, comp_method, bool_method, +def _create_methods(arith_method, comp_method, bool_method, use_numexpr, special=False, default_axis='columns'): # creates actual methods based upon arithmetic, comp and bool method # constructors. @@ -55,14 +59,14 @@ def names(x): return "__%s__" % x else: names = lambda x: x - radd_func = radd_func or operator.add + # Inframe, all special methods have default_axis=None, flex methods have # default_axis set to the default (columns) # yapf: disable new_methods = dict( add=arith_method(operator.add, names('add'), op('+'), default_axis=default_axis), - radd=arith_method(radd_func, names('radd'), op('+'), + radd=arith_method(lambda x, y: y + x, names('radd'), op('+'), default_axis=default_axis), sub=arith_method(operator.sub, names('sub'), op('-'), default_axis=default_axis), @@ -149,7 +153,7 @@ def add_methods(cls, new_methods, force, select, exclude): # ---------------------------------------------------------------------- # Arithmetic -def add_special_arithmetic_methods(cls, arith_method=None, radd_func=None, +def add_special_arithmetic_methods(cls, arith_method=None, comp_method=None, bool_method=None, use_numexpr=True, force=False, select=None, exclude=None): @@ -162,8 +166,6 @@ def add_special_arithmetic_methods(cls, arith_method=None, radd_func=None, arith_method : function (optional) factory for special arithmetic methods, with op string: f(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs) - radd_func : function (optional) - Possible replacement for ``operator.add`` for compatibility comp_method : function, optional, factory for rich comparison - signature: f(op, name, str_rep) use_numexpr : bool, default True @@ -176,12 +178,11 @@ def add_special_arithmetic_methods(cls, arith_method=None, radd_func=None, exclude : iterable of strings (optional) if passed, will not set functions with names in exclude """ - radd_func = radd_func or operator.add # in frame, special methods have default_axis = None, comp methods use # 'columns' - new_methods = _create_methods(arith_method, radd_func, comp_method, + new_methods = _create_methods(arith_method, comp_method, bool_method, use_numexpr, default_axis=None, special=True) @@ -218,7 +219,7 @@ def f(self, other): exclude=exclude) -def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, +def add_flex_arithmetic_methods(cls, flex_arith_method, flex_comp_method=None, flex_bool_method=None, use_numexpr=True, force=False, select=None, exclude=None): @@ -231,9 +232,6 @@ def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, flex_arith_method : function factory for special arithmetic methods, with op string: f(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs) - radd_func : function (optional) - Possible replacement for ``lambda x, y: operator.add(y, x)`` for - compatibility flex_comp_method : function, optional, factory for rich comparison - signature: f(op, name, str_rep) use_numexpr : bool, default True @@ -246,9 +244,8 @@ def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, exclude : iterable of strings (optional) if passed, will not set functions with names in exclude """ - radd_func = radd_func or (lambda x, y: operator.add(y, x)) # in frame, default axis is 'columns', doesn't matter for series and panel - new_methods = _create_methods(flex_arith_method, radd_func, + new_methods = _create_methods(flex_arith_method, flex_comp_method, flex_bool_method, use_numexpr, default_axis='columns', special=False) @@ -264,30 +261,87 @@ def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, exclude=exclude) -class _TimeOp(object): +class _Op(object): + """ - Wrapper around Series datetime/time/timedelta arithmetic operations. - Generally, you should use classmethod ``maybe_convert_for_time_op`` as an - entry point. + Wrapper around Series arithmetic operations. + Generally, you should use classmethod ``_Op.get_op`` as an entry point. + + This validates and coerces lhs and rhs depending on its dtype and + based on op. See _TimeOp also. + + Parameters + ---------- + left : Series + lhs of op + right : object + rhs of op + name : str + name of op + na_op : callable + a function which wraps op """ - fill_value = iNaT + + fill_value = np.nan wrap_results = staticmethod(lambda x: x) dtype = None def __init__(self, left, right, name, na_op): + self.left = left + self.right = right + + self.name = name + self.na_op = na_op + + self.lvalues = left + self.rvalues = right + + @classmethod + def get_op(cls, left, right, name, na_op): + """ + Get op dispatcher, returns _Op or _TimeOp. + + If ``left`` and ``right`` are appropriate for datetime arithmetic with + operation ``name``, processes them and returns a ``_TimeOp`` object + that stores all the required values. Otherwise, it will generate + either a ``_Op``, indicating that the operation is performed via + normal numpy path. + """ + is_timedelta_lhs = is_timedelta64_dtype(left) + is_datetime_lhs = (is_datetime64_dtype(left) or + is_datetime64tz_dtype(left)) - # need to make sure that we are aligning the data if isinstance(left, ABCSeries) and isinstance(right, ABCSeries): - left, right = left.align(right, copy=False) + # avoid repated alignment + if not left.index.equals(right.index): + left, right = left.align(right, copy=False) + + index, lidx, ridx = left.index.join(right.index, how='outer', + return_indexers=True) + # if DatetimeIndex have different tz, convert to UTC + left.index = index + right.index = index + + if not (is_datetime_lhs or is_timedelta_lhs): + return _Op(left, right, name, na_op) + else: + return _TimeOp(left, right, name, na_op) + + +class _TimeOp(_Op): + """ + Wrapper around Series datetime/time/timedelta arithmetic operations. + Generally, you should use classmethod ``_Op.get_op`` as an entry point. + """ + fill_value = iNaT + + def __init__(self, left, right, name, na_op): + super(_TimeOp, self).__init__(left, right, name, na_op) lvalues = self._convert_to_array(left, name=name) rvalues = self._convert_to_array(right, name=name, other=lvalues) - self.name = name - self.na_op = na_op - # left - self.left = left self.is_offset_lhs = self._is_offset(left) self.is_timedelta_lhs = is_timedelta64_dtype(lvalues) self.is_datetime64_lhs = is_datetime64_dtype(lvalues) @@ -298,7 +352,6 @@ def __init__(self, left, right, name, na_op): self.is_floating_lhs = left.dtype.kind == 'f' # right - self.right = right self.is_offset_rhs = self._is_offset(right) self.is_datetime64_rhs = is_datetime64_dtype(rvalues) self.is_datetime64tz_rhs = is_datetime64tz_dtype(rvalues) @@ -397,7 +450,7 @@ def _convert_to_array(self, values, name=None, other=None): supplied_dtype = values.dtype inferred_type = supplied_dtype or lib.infer_dtype(values) if (inferred_type in ('datetime64', 'datetime', 'date', 'time') or - com.is_datetimetz(inferred_type)): + is_datetimetz(inferred_type)): # if we have a other of timedelta, but use pd.NaT here we # we are in the wrong path if (supplied_dtype is None and other is not None and @@ -414,7 +467,7 @@ def _convert_to_array(self, values, name=None, other=None): hasattr(ovalues, 'tz')): values = pd.DatetimeIndex(values) # datetime array with tz - elif com.is_datetimetz(values): + elif is_datetimetz(values): if isinstance(values, ABCSeries): values = values._values elif not (isinstance(values, (np.ndarray, ABCSeries)) and @@ -549,26 +602,6 @@ def _is_offset(self, arr_or_obj): else: return False - @classmethod - def maybe_convert_for_time_op(cls, left, right, name, na_op): - """ - if ``left`` and ``right`` are appropriate for datetime arithmetic with - operation ``name``, processes them and returns a ``_TimeOp`` object - that stores all the required values. Otherwise, it will generate - either a ``NotImplementedError`` or ``None``, indicating that the - operation is unsupported for datetimes (e.g., an unsupported r_op) or - that the data is not the right type for time ops. - """ - # decide if we can do it - is_timedelta_lhs = is_timedelta64_dtype(left) - is_datetime_lhs = (is_datetime64_dtype(left) or - is_datetime64tz_dtype(left)) - - if not (is_datetime_lhs or is_timedelta_lhs): - return None - - return cls(left, right, name, na_op) - def _arith_method_SERIES(op, name, str_rep, fill_zeros=None, default_axis=None, **eval_kwargs): @@ -596,7 +629,7 @@ def na_op(x, y): "{op}".format(typ=type(x).__name__, op=str_rep)) - result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) + result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result @@ -621,56 +654,47 @@ def wrapper(left, right, name=name, na_op=na_op): if isinstance(right, pd.DataFrame): return NotImplemented - time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name, - na_op) + converted = _Op.get_op(left, right, name, na_op) - if time_converted is None: - lvalues, rvalues = left, right - dtype = None - wrap_results = lambda x: x - elif time_converted is NotImplemented: - return NotImplemented - else: - left, right = time_converted.left, time_converted.right - lvalues, rvalues = time_converted.lvalues, time_converted.rvalues - dtype = time_converted.dtype - wrap_results = time_converted.wrap_results - na_op = time_converted.na_op + left, right = converted.left, converted.right + lvalues, rvalues = converted.lvalues, converted.rvalues + dtype = converted.dtype + wrap_results = converted.wrap_results + na_op = converted.na_op if isinstance(rvalues, ABCSeries): - rindex = getattr(rvalues, 'index', rvalues) name = _maybe_match_name(left, rvalues) lvalues = getattr(lvalues, 'values', lvalues) rvalues = getattr(rvalues, 'values', rvalues) - if left.index.equals(rindex): - index = left.index - else: - index, lidx, ridx = left.index.join(rindex, how='outer', - return_indexers=True) - - if lidx is not None: - lvalues = algos.take_1d(lvalues, lidx) - - if ridx is not None: - rvalues = algos.take_1d(rvalues, ridx) - - result = wrap_results(safe_na_op(lvalues, rvalues)) - return left._constructor(result, index=index, - name=name, dtype=dtype) + # _Op aligns left and right else: - # scalars + name = left.name if (hasattr(lvalues, 'values') and not isinstance(lvalues, pd.DatetimeIndex)): lvalues = lvalues.values - result = wrap_results(safe_na_op(lvalues, rvalues)) - return left._constructor(result, - index=left.index, name=left.name, - dtype=dtype) - + result = wrap_results(safe_na_op(lvalues, rvalues)) + return left._constructor(result, index=left.index, + name=name, dtype=dtype) return wrapper +def _comp_method_OBJECT_ARRAY(op, x, y): + if isinstance(y, list): + y = lib.list_to_object_array(y) + if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)): + if not is_object_dtype(y.dtype): + y = y.astype(np.object_) + + if isinstance(y, (ABCSeries, ABCIndex)): + y = y.values + + result = lib.vec_compare(x, y, op) + else: + result = lib.scalar_compare(x, y, op) + return result + + def _comp_method_SERIES(op, name, str_rep, masker=False): """ Wrapper function for Series arithmetic operations, to avoid @@ -687,16 +711,7 @@ def na_op(x, y): return op(y, x) if is_object_dtype(x.dtype): - if isinstance(y, list): - y = lib.list_to_object_array(y) - - if isinstance(y, (np.ndarray, ABCSeries)): - if not is_object_dtype(y.dtype): - result = lib.vec_compare(x, y.astype(np.object_), op) - else: - result = lib.vec_compare(x, y, op) - else: - result = lib.scalar_compare(x, y, op) + result = _comp_method_OBJECT_ARRAY(op, x, y) else: # we want to compare like types @@ -720,12 +735,11 @@ def na_op(x, y): (not isscalar(y) and needs_i8_conversion(y))): if isscalar(y): + mask = isnull(x) y = _index.convert_scalar(x, _values_from_object(y)) else: + mask = isnull(x) | isnull(y) y = y.view('i8') - - mask = isnull(x) - x = x.view('i8') try: @@ -810,8 +824,8 @@ def na_op(x, y): if (is_bool_dtype(x.dtype) and is_bool_dtype(y.dtype)): result = op(x, y) # when would this be hit? else: - x = com._ensure_object(x) - y = com._ensure_object(y) + x = _ensure_object(x) + y = _ensure_object(y) result = lib.vec_binop(x, y, op) else: try: @@ -858,17 +872,6 @@ def wrapper(self, other): return wrapper -def _radd_compat(left, right): - radd = lambda x, y: y + x - # GH #353, NumPy 1.5.1 workaround - try: - output = radd(left, right) - except TypeError: - raise - - return output - - _op_descriptions = {'add': {'op': '+', 'desc': 'Addition', 'reversed': False, @@ -906,6 +909,32 @@ def _radd_compat(left, right): _op_descriptions[reverse_op]['reverse'] = k +_flex_doc_SERIES = """ +%s of series and other, element-wise (binary operator `%s`). + +Equivalent to ``%s``, but with support to substitute a fill_value for +missing data in one of the inputs. + +Parameters +---------- +other: Series or scalar value +fill_value : None or float value, default None (NaN) + Fill missing (NaN) values with this value. If both Series are + missing, the result will be missing +level : int or name + Broadcast across a level, matching Index values on the + passed MultiIndex level + +Returns +------- +result : Series + +See also +-------- +Series.%s +""" + + def _flex_method_SERIES(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs): op_name = name.replace('__', '') @@ -915,30 +944,8 @@ def _flex_method_SERIES(op, name, str_rep, default_axis=None, fill_zeros=None, else: equiv = 'series ' + op_desc['op'] + ' other' - doc = """ - %s of series and other, element-wise (binary operator `%s`). - - Equivalent to ``%s``, but with support to substitute a fill_value for - missing data in one of the inputs. - - Parameters - ---------- - other: Series or scalar value - fill_value : None or float value, default None (NaN) - Fill missing (NaN) values with this value. If both Series are - missing, the result will be missing - level : int or name - Broadcast across a level, matching Index values on the - passed MultiIndex level - - Returns - ------- - result : Series - - See also - -------- - Series.%s - """ % (op_desc['desc'], op_name, equiv, op_desc['reverse']) + doc = _flex_doc_SERIES % (op_desc['desc'], op_name, equiv, + op_desc['reverse']) @Appender(doc) def flex_wrapper(self, other, level=None, fill_value=None, axis=0): @@ -963,11 +970,9 @@ def flex_wrapper(self, other, level=None, fill_value=None, axis=0): series_flex_funcs = dict(flex_arith_method=_flex_method_SERIES, - radd_func=_radd_compat, flex_comp_method=_comp_method_SERIES) series_special_funcs = dict(arith_method=_arith_method_SERIES, - radd_func=_radd_compat, comp_method=_comp_method_SERIES, bool_method=_bool_method_SERIES) @@ -996,6 +1001,75 @@ def flex_wrapper(self, other, level=None, fill_value=None, axis=0): result : DataFrame """ +_flex_doc_FRAME = """ +%s of dataframe and other, element-wise (binary operator `%s`). + +Equivalent to ``%s``, but with support to substitute a fill_value for +missing data in one of the inputs. + +Parameters +---------- +other : Series, DataFrame, or constant +axis : {0, 1, 'index', 'columns'} + For Series input, axis to match Series index on +fill_value : None or float value, default None + Fill missing (NaN) values with this value. If both DataFrame + locations are missing, the result will be missing +level : int or name + Broadcast across a level, matching Index values on the + passed MultiIndex level + +Notes +----- +Mismatched indices will be unioned together + +Returns +------- +result : DataFrame + +See also +-------- +DataFrame.%s +""" + + +def _align_method_FRAME(left, right, axis): + """ convert rhs to meet lhs dims if input is list, tuple or np.ndarray """ + + def to_series(right): + msg = 'Unable to coerce to Series, length must be {0}: given {1}' + if axis is not None and left._get_axis_name(axis) == 'index': + if len(left.index) != len(right): + raise ValueError(msg.format(len(left.index), len(right))) + right = left._constructor_sliced(right, index=left.index) + else: + if len(left.columns) != len(right): + raise ValueError(msg.format(len(left.columns), len(right))) + right = left._constructor_sliced(right, index=left.columns) + return right + + if isinstance(right, (list, tuple)): + right = to_series(right) + + elif isinstance(right, np.ndarray) and right.ndim: # skips np scalar + + if right.ndim == 1: + right = to_series(right) + + elif right.ndim == 2: + if left.shape != right.shape: + msg = ("Unable to coerce to DataFrame, " + "shape must be {0}: given {1}") + raise ValueError(msg.format(left.shape, right.shape)) + + right = left._constructor(right, index=left.index, + columns=left.columns) + else: + msg = 'Unable to coerce to Series/DataFrame, dim must be <= 2: {0}' + raise ValueError(msg.format(right.shape, )) + + return right + def _arith_method_FRAME(op, name, str_rep=None, default_axis='columns', fill_zeros=None, **eval_kwargs): @@ -1025,7 +1099,7 @@ def na_op(x, y): "objects of type {x} and {y}".format( op=name, x=type(x), y=type(y))) - result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) + result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = result.reshape(x.shape) result = missing.fill_zeros(result, x, y, name, fill_zeros) @@ -1040,75 +1114,20 @@ def na_op(x, y): else: equiv = 'dataframe ' + op_desc['op'] + ' other' - doc = """ - %s of dataframe and other, element-wise (binary operator `%s`). - - Equivalent to ``%s``, but with support to substitute a fill_value for - missing data in one of the inputs. - - Parameters - ---------- - other : Series, DataFrame, or constant - axis : {0, 1, 'index', 'columns'} - For Series input, axis to match Series index on - fill_value : None or float value, default None - Fill missing (NaN) values with this value. If both DataFrame - locations are missing, the result will be missing - level : int or name - Broadcast across a level, matching Index values on the - passed MultiIndex level - - Notes - ----- - Mismatched indices will be unioned together - - Returns - ------- - result : DataFrame - - See also - -------- - DataFrame.%s - """ % (op_desc['desc'], op_name, equiv, op_desc['reverse']) + doc = _flex_doc_FRAME % (op_desc['desc'], op_name, equiv, + op_desc['reverse']) else: doc = _arith_doc_FRAME % name @Appender(doc) def f(self, other, axis=default_axis, level=None, fill_value=None): + + other = _align_method_FRAME(self, other, axis) + if isinstance(other, pd.DataFrame): # Another DataFrame return self._combine_frame(other, na_op, fill_value, level) elif isinstance(other, ABCSeries): return self._combine_series(other, na_op, fill_value, axis, level) - elif isinstance(other, (list, tuple)): - if axis is not None and self._get_axis_name(axis) == 'index': - # TODO: Get all of these to use _constructor_sliced - # casted = self._constructor_sliced(other, index=self.index) - casted = pd.Series(other, index=self.index) - else: - # casted = self._constructor_sliced(other, index=self.columns) - casted = pd.Series(other, index=self.columns) - return self._combine_series(casted, na_op, fill_value, axis, level) - elif isinstance(other, np.ndarray) and other.ndim: # skips np scalar - if other.ndim == 1: - if axis is not None and self._get_axis_name(axis) == 'index': - # casted = self._constructor_sliced(other, - # index=self.index) - casted = pd.Series(other, index=self.index) - else: - # casted = self._constructor_sliced(other, - # index=self.columns) - casted = pd.Series(other, index=self.columns) - return self._combine_series(casted, na_op, fill_value, axis, - level) - elif other.ndim == 2: - # casted = self._constructor(other, index=self.index, - # columns=self.columns) - casted = pd.DataFrame(other, index=self.index, - columns=self.columns) - return self._combine_frame(casted, na_op, fill_value, level) - else: - raise ValueError("Incompatible argument shape: %s" % - (other.shape, )) else: if fill_value is not None: self = self.fillna(fill_value) @@ -1148,39 +1167,14 @@ def na_op(x, y): @Appender('Wrapper for flexible comparison methods %s' % name) def f(self, other, axis=default_axis, level=None): + + other = _align_method_FRAME(self, other, axis) + if isinstance(other, pd.DataFrame): # Another DataFrame return self._flex_compare_frame(other, na_op, str_rep, level) elif isinstance(other, ABCSeries): return self._combine_series(other, na_op, None, axis, level) - - elif isinstance(other, (list, tuple)): - if axis is not None and self._get_axis_name(axis) == 'index': - casted = pd.Series(other, index=self.index) - else: - casted = pd.Series(other, index=self.columns) - - return self._combine_series(casted, na_op, None, axis, level) - - elif isinstance(other, np.ndarray): - if other.ndim == 1: - if axis is not None and self._get_axis_name(axis) == 'index': - casted = pd.Series(other, index=self.index) - else: - casted = pd.Series(other, index=self.columns) - - return self._combine_series(casted, na_op, None, axis, level) - - elif other.ndim == 2: - casted = pd.DataFrame(other, index=self.index, - columns=self.columns) - - return self._flex_compare_frame(casted, na_op, str_rep, level) - - else: - raise ValueError("Incompatible argument shape: %s" % - (other.shape, )) - else: return self._combine_const(other, na_op) @@ -1209,11 +1203,9 @@ def f(self, other): frame_flex_funcs = dict(flex_arith_method=_arith_method_FRAME, - radd_func=_radd_compat, flex_comp_method=_flex_comp_method_FRAME) frame_special_funcs = dict(arith_method=_arith_method_FRAME, - radd_func=_radd_compat, comp_method=_comp_method_FRAME, bool_method=_arith_method_FRAME) @@ -1232,7 +1224,7 @@ def na_op(x, y): result = np.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) - result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) + result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 7d0bedcc2b381..4d61563cccce5 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -8,17 +8,21 @@ import numpy as np +from pandas.types.cast import (_infer_dtype_from_scalar, + _possibly_cast_item) +from pandas.types.common import (is_integer, is_list_like, + is_string_like, is_scalar) +from pandas.types.missing import notnull + import pandas.computation.expressions as expressions import pandas.core.common as com import pandas.core.ops as ops import pandas.core.missing as missing from pandas import compat -from pandas import lib from pandas.compat import (map, zip, range, u, OrderedDict, OrderedDefaultdict) from pandas.compat.numpy import function as nv from pandas.core.categorical import Categorical -from pandas.core.common import (PandasError, _try_sort, _default_index, - _infer_dtype_from_scalar, is_list_like) +from pandas.core.common import PandasError, _try_sort, _default_index from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import (Index, MultiIndex, _ensure_index, @@ -168,7 +172,7 @@ def _init_data(self, data, copy, dtype, **kwargs): mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy) copy = False dtype = None - elif lib.isscalar(data) and all(x is not None for x in passed_axes): + elif is_scalar(data) and all(x is not None for x in passed_axes): if dtype is None: dtype, data = _infer_dtype_from_scalar(data) values = np.empty([len(x) for x in passed_axes], dtype=dtype) @@ -552,7 +556,7 @@ def set_value(self, *args, **kwargs): made_bigger = not np.array_equal(axes[0], self._info_axis) # how to make this logic simpler? if made_bigger: - com._possibly_cast_item(result, args[0], likely_dtype) + _possibly_cast_item(result, args[0], likely_dtype) return result.set_value(*args) @@ -582,7 +586,7 @@ def __setitem__(self, key, value): 'object was {1}'.format( shape[1:], tuple(map(int, value.shape)))) mat = np.asarray(value) - elif lib.isscalar(value): + elif is_scalar(value): dtype, value = _infer_dtype_from_scalar(value) mat = np.empty(shape[1:], dtype=dtype) mat.fill(value) @@ -653,7 +657,7 @@ def round(self, decimals=0, *args, **kwargs): """ nv.validate_round(args, kwargs) - if com.is_integer(decimals): + if is_integer(decimals): result = np.apply_along_axis(np.round, 0, self.values) return self._wrap_result(result, axis=0) raise TypeError("decimals must be an integer") @@ -687,7 +691,7 @@ def dropna(self, axis=0, how='any', inplace=False): axis = self._get_axis_number(axis) values = self.values - mask = com.notnull(values) + mask = notnull(values) for ax in reversed(sorted(set(range(self._AXIS_LEN)) - set([axis]))): mask = mask.sum(ax) @@ -711,7 +715,7 @@ def _combine(self, other, func, axis=0): return self._combine_panel(other, func) elif isinstance(other, DataFrame): return self._combine_frame(other, func, axis=axis) - elif lib.isscalar(other): + elif is_scalar(other): return self._combine_const(other, func) else: raise NotImplementedError("%s is not supported in combine " @@ -924,7 +928,7 @@ def to_frame(self, filter_observations=True): if filter_observations: # shaped like the return DataFrame - mask = com.notnull(self.values).all(axis=0) + mask = notnull(self.values).all(axis=0) # size = mask.sum() selector = mask.ravel() else: @@ -1218,7 +1222,7 @@ def transpose(self, *args, **kwargs): # check if a list of axes was passed in instead as a # single *args element if (len(args) == 1 and hasattr(args[0], '__iter__') and - not com.is_string_like(args[0])): + not is_string_like(args[0])): axes = args[0] else: axes = args diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index 8d237016d1b33..4f601a2d377a6 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -6,6 +6,11 @@ import numpy as np +from pandas.types.common import _ensure_platform_int, is_list_like +from pandas.types.cast import _maybe_promote +from pandas.types.missing import notnull +import pandas.types.concat as _concat + from pandas.core.series import Series from pandas.core.frame import DataFrame @@ -14,11 +19,8 @@ from pandas._sparse import IntIndex from pandas.core.categorical import Categorical -from pandas.core.common import notnull, _ensure_platform_int, _maybe_promote from pandas.core.groupby import get_group_index, _compress_group_index -import pandas.core.common as com -import pandas.types.concat as _concat import pandas.core.algorithms as algos import pandas.algos as _algos @@ -1063,7 +1065,7 @@ def check_len(item, name): length_msg = ("Length of '{0}' ({1}) did not match the length of " "the columns being encoded ({2}).") - if com.is_list_like(item): + if is_list_like(item): if not len(item) == len(columns_to_encode): raise ValueError(length_msg.format(name, len(item), len(columns_to_encode))) diff --git a/pandas/core/series.py b/pandas/core/series.py index e2726bef0bd03..3c1f834c3d479 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -13,18 +13,33 @@ import numpy as np import numpy.ma as ma -from pandas.core.common import (isnull, notnull, is_bool_indexer, - _default_index, _maybe_upcast, - _asarray_tuplesafe, _infer_dtype_from_scalar, - is_list_like, _values_from_object, - is_categorical_dtype, - _possibly_cast_to_datetime, - _possibly_castable, _possibly_convert_platform, - _try_sort, is_extension_type, is_datetimetz, - _maybe_match_name, ABCSparseArray, - _coerce_to_dtype, SettingWithCopyError, - _maybe_box_datetimelike, ABCDataFrame, - _dict_compat, is_integer) +from pandas.types.common import (_coerce_to_dtype, is_categorical_dtype, + is_integer, is_integer_dtype, + is_float_dtype, + is_extension_type, is_datetimetz, + is_datetimelike, + is_timedelta64_dtype, + is_list_like, + is_hashable, + is_iterator, + is_dict_like, + is_scalar, + _ensure_platform_int) +from pandas.types.generic import ABCSparseArray, ABCDataFrame +from pandas.types.cast import (_maybe_upcast, _infer_dtype_from_scalar, + _possibly_convert_platform, + _possibly_cast_to_datetime, _possibly_castable) +from pandas.types.missing import isnull, notnull + +from pandas.core.common import (is_bool_indexer, + _default_index, + _asarray_tuplesafe, + _values_from_object, + _try_sort, + _maybe_match_name, + SettingWithCopyError, + _maybe_box_datetimelike, + _dict_compat) from pandas.core.index import (Index, MultiIndex, InvalidIndexError, Float64Index, _ensure_index) from pandas.core.indexing import check_bool_indexer, maybe_convert_indices @@ -62,7 +77,8 @@ axes='index', klass='Series', axes_single_arg="{0, 'index'}", inplace="""inplace : boolean, default False If True, performs operation inplace and returns None.""", - duplicated='Series') + duplicated='Series', + optional_by='') def _coerce_method(converter): @@ -302,7 +318,7 @@ def name(self): @name.setter def name(self, value): - if value is not None and not com.is_hashable(value): + if value is not None and not is_hashable(value): raise TypeError('Series.name must be a hashable type') object.__setattr__(self, '_name', value) @@ -579,7 +595,7 @@ def __getitem__(self, key): try: result = self.index.get_value(self, key) - if not lib.isscalar(result): + if not is_scalar(result): if is_list_like(result) and not isinstance(result, Series): # we need to box if we have a non-unique index here @@ -612,10 +628,10 @@ def __getitem__(self, key): except Exception: raise - if com.is_iterator(key): + if is_iterator(key): key = list(key) - if is_bool_indexer(key): + if com.is_bool_indexer(key): key = check_bool_indexer(self.index, key) return self._get_with(key) @@ -709,9 +725,9 @@ def setitem(key, value): elif key is Ellipsis: self[:] = value return - elif is_bool_indexer(key): + elif com.is_bool_indexer(key): pass - elif com.is_timedelta64_dtype(self.dtype): + elif is_timedelta64_dtype(self.dtype): # reassign a null value to iNaT if isnull(value): value = tslib.iNaT @@ -735,7 +751,7 @@ def setitem(key, value): if 'unorderable' in str(e): # pragma: no cover raise IndexError(key) - if is_bool_indexer(key): + if com.is_bool_indexer(key): key = check_bool_indexer(self.index, key) try: self._where(~key, value, inplace=True) @@ -827,14 +843,22 @@ def repeat(self, reps, *args, **kwargs): def reshape(self, *args, **kwargs): """ - Return the values attribute of `self` with shape `args`. - However, if the specified shape matches exactly the current - shape, `self` is returned for compatibility reasons. + DEPRECATED: calling this method will raise an error in a + future release. Please call ``.values.reshape(...)`` instead. + + return an ndarray with the values shape + if the specified shape matches exactly the current shape, then + return self (for compat) See also -------- numpy.ndarray.reshape """ + warnings.warn("reshape is deprecated and will raise " + "in a subsequent release. Please use " + ".values.reshape(...) instead", FutureWarning, + stacklevel=2) + if len(args) == 1 and hasattr(args[0], '__iter__'): shape = args[0] else: @@ -1059,7 +1083,7 @@ def _get_repr(self, name=False, header=True, index=True, length=True, def __iter__(self): """ provide iteration over the values of the Series box values if necessary """ - if com.is_datetimelike(self): + if is_datetimelike(self): return (_maybe_box_datetimelike(x) for x in self._values) else: return iter(self._values) @@ -1348,7 +1372,7 @@ def quantile(self, q=0.5, interpolation='linear'): result = self._data.quantile(qs=q, interpolation=interpolation) - if com.is_list_like(q): + if is_list_like(q): return self._constructor(result, index=Float64Index(q), name=self.name) @@ -1480,7 +1504,7 @@ def dot(self, other): @Appender(base._shared_docs['searchsorted']) def searchsorted(self, v, side='left', sorter=None): if sorter is not None: - sorter = com._ensure_platform_int(sorter) + sorter = _ensure_platform_int(sorter) return self._values.searchsorted(Series(v)._values, side=side, sorter=sorter) @@ -1726,7 +1750,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, elif isinstance(index, MultiIndex): from pandas.core.groupby import _lexsort_indexer indexer = _lexsort_indexer(index.labels, orders=ascending) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) new_index = index.take(indexer) else: new_index, indexer = index.sort_values(return_indexer=True, @@ -2264,8 +2288,8 @@ def align(self, other, join='outer', axis=None, level=None, copy=True, @Appender(generic._shared_docs['rename'] % _shared_doc_kwargs) def rename(self, index=None, **kwargs): - non_mapping = lib.isscalar(index) or (com.is_list_like(index) and - not com.is_dict_like(index)) + non_mapping = is_scalar(index) or (is_list_like(index) and + not is_dict_like(index)) if non_mapping: return self._set_name(index, inplace=kwargs.get('inplace')) return super(Series, self).rename(index=index, **kwargs) @@ -2344,7 +2368,7 @@ def take(self, indices, axis=0, convert=True, is_copy=False, **kwargs): if convert: indices = maybe_convert_indices(indices, len(self._get_axis(axis))) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) new_index = self.index.take(indices) new_values = self._values.take(indices) return self._constructor(new_values, @@ -2770,7 +2794,7 @@ def _try_cast(arr, take_fast_path): subarr = np.array(data, copy=False) # possibility of nan -> garbage - if com.is_float_dtype(data.dtype) and com.is_integer_dtype(dtype): + if is_float_dtype(data.dtype) and is_integer_dtype(dtype): if not isnull(data).any(): subarr = _try_cast(data, True) elif copy: @@ -2796,7 +2820,7 @@ def _try_cast(arr, take_fast_path): subarr = data.copy() return subarr - elif isinstance(data, list) and len(data) > 0: + elif isinstance(data, (list, tuple)) and len(data) > 0: if dtype is not None: try: subarr = _try_cast(data, False) diff --git a/pandas/core/strings.py b/pandas/core/strings.py index a3f687b7fd73c..6ec28f9735850 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -1,14 +1,19 @@ import numpy as np from pandas.compat import zip -from pandas.core.common import (isnull, notnull, _values_from_object, - is_bool_dtype, - is_list_like, is_categorical_dtype, - is_object_dtype, is_string_like) +from pandas.types.generic import ABCSeries, ABCIndex +from pandas.types.missing import isnull, notnull +from pandas.types.common import (is_bool_dtype, + is_categorical_dtype, + is_object_dtype, + is_string_like, + is_list_like, + is_scalar) +from pandas.core.common import _values_from_object + from pandas.core.algorithms import take_1d import pandas.compat as compat from pandas.core.base import AccessorProperty, NoNewAttributesMixin -from pandas.types import api as gt from pandas.util.decorators import Appender, deprecate_kwarg import re import pandas.lib as lib @@ -152,7 +157,7 @@ def _map(f, arr, na_mask=False, na_value=np.nan, dtype=object): if not len(arr): return np.ndarray(0, dtype=dtype) - if isinstance(arr, gt.ABCSeries): + if isinstance(arr, ABCSeries): arr = arr.values if not isinstance(arr, np.ndarray): arr = np.asarray(arr, dtype=object) @@ -343,7 +348,7 @@ def str_repeat(arr, repeats): ------- repeated : Series/Index of objects """ - if lib.isscalar(repeats): + if is_scalar(repeats): def rep(x): try: @@ -696,7 +701,7 @@ def str_extractall(arr, pat, flags=0): if regex.groups == 0: raise ValueError("pattern contains no capture groups") - if isinstance(arr, gt.ABCIndex): + if isinstance(arr, ABCIndex): arr = arr.to_series().reset_index(drop=True) names = dict(zip(regex.groupindex.values(), regex.groupindex.keys())) @@ -1538,7 +1543,7 @@ def rjust(self, width, fillchar=' '): return self.pad(width, side='left', fillchar=fillchar) def zfill(self, width): - """" + """ Filling left side of strings in the Series/Index with 0. Equivalent to :meth:`str.zfill`. @@ -1820,7 +1825,7 @@ class StringAccessorMixin(object): def _make_str_accessor(self): from pandas.core.index import Index - if (isinstance(self, gt.ABCSeries) and + if (isinstance(self, ABCSeries) and not ((is_categorical_dtype(self.dtype) and is_object_dtype(self.values.categories)) or (is_object_dtype(self.dtype)))): diff --git a/pandas/core/window.py b/pandas/core/window.py index 1e34d18fe3e54..bc4d34529287b 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -11,6 +11,15 @@ import numpy as np from collections import defaultdict +from pandas.types.generic import ABCSeries, ABCDataFrame +from pandas.types.common import (is_integer, + is_bool, + is_float_dtype, + is_integer_dtype, + needs_i8_conversion, + is_timedelta64_dtype, + is_list_like, + _ensure_float64) import pandas as pd from pandas.lib import isscalar from pandas.core.base import (PandasObject, SelectionMixin, @@ -64,10 +73,10 @@ def _constructor(self): return Window def validate(self): - if self.center is not None and not com.is_bool(self.center): + if self.center is not None and not is_bool(self.center): raise ValueError("center must be a boolean") if self.min_periods is not None and not \ - com.is_integer(self.min_periods): + is_integer(self.min_periods): raise ValueError("min_periods must be an integer") def _convert_freq(self, how=None): @@ -75,7 +84,7 @@ def _convert_freq(self, how=None): obj = self._selected_obj if (self.freq is not None and - isinstance(obj, (com.ABCSeries, com.ABCDataFrame))): + isinstance(obj, (ABCSeries, ABCDataFrame))): if how is not None: warnings.warn("The how kw argument is deprecated and removed " "in a future version. You can resample prior " @@ -111,7 +120,7 @@ def _gotitem(self, key, ndim, subset=None): self = self._shallow_copy(subset) self._reset_cache() if subset.ndim == 2: - if isscalar(key) and key in subset or com.is_list_like(key): + if isscalar(key) and key in subset or is_list_like(key): self._selection = key return self @@ -150,11 +159,11 @@ def _prep_values(self, values=None, kill_inf=True, how=None): # GH #12373 : rolling functions error on float32 data # make sure the data is coerced to float64 - if com.is_float_dtype(values.dtype): - values = com._ensure_float64(values) - elif com.is_integer_dtype(values.dtype): - values = com._ensure_float64(values) - elif com.needs_i8_conversion(values.dtype): + if is_float_dtype(values.dtype): + values = _ensure_float64(values) + elif is_integer_dtype(values.dtype): + values = _ensure_float64(values) + elif needs_i8_conversion(values.dtype): raise NotImplementedError("ops for {action} for this " "dtype {dtype} are not " "implemented".format( @@ -162,7 +171,7 @@ def _prep_values(self, values=None, kill_inf=True, how=None): dtype=values.dtype)) else: try: - values = com._ensure_float64(values) + values = _ensure_float64(values) except (ValueError, TypeError): raise TypeError("cannot handle this type -> {0}" "".format(values.dtype)) @@ -184,7 +193,7 @@ def _wrap_result(self, result, block=None, obj=None): # coerce if necessary if block is not None: - if com.is_timedelta64_dtype(block.values.dtype): + if is_timedelta64_dtype(block.values.dtype): result = pd.to_timedelta( result.ravel(), unit='ns').values.reshape(result.shape) @@ -345,7 +354,7 @@ def _prep_window(self, **kwargs): window = self._get_window() if isinstance(window, (list, tuple, np.ndarray)): return com._asarray_tuplesafe(window).astype(float) - elif com.is_integer(window): + elif is_integer(window): import scipy.signal as sig # the below may pop from kwargs @@ -543,7 +552,7 @@ def _apply(self, func, name=None, window=None, center=None, def func(arg, window, min_periods=None): minp = check_minp(min_periods, window) # GH #12373: rolling functions error on float32 data - return cfunc(com._ensure_float64(arg), + return cfunc(_ensure_float64(arg), window, minp, **kwargs) # calculation function @@ -586,7 +595,7 @@ def count(self): results = [] for b in blocks: - if com.needs_i8_conversion(b.values): + if needs_i8_conversion(b.values): result = b.notnull().astype(int) else: try: @@ -850,7 +859,7 @@ class Rolling(_Rolling_and_Expanding): def validate(self): super(Rolling, self).validate() - if not com.is_integer(self.window): + if not is_integer(self.window): raise ValueError("window must be an integer") elif self.window < 0: raise ValueError("window must be non-negative") @@ -1484,7 +1493,7 @@ def _get_center_of_mass(com, span, halflife, alpha): def _offset(window, center): - if not com.is_integer(window): + if not is_integer(window): window = len(window) offset = (window - 1) / 2. if center else 0 try: diff --git a/pandas/formats/format.py b/pandas/formats/format.py index a8e184ce94c89..436a9d5d5d4c8 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -10,8 +10,19 @@ import sys +from pandas.types.missing import isnull, notnull +from pandas.types.common import (is_categorical_dtype, + is_float_dtype, + is_period_arraylike, + is_integer_dtype, + is_datetimetz, + is_integer, + is_float, + is_numeric_dtype, + is_datetime64_dtype, + is_timedelta64_dtype) + from pandas.core.base import PandasObject -from pandas.core.common import isnull, notnull, is_numeric_dtype from pandas.core.index import Index, MultiIndex, _ensure_index from pandas import compat from pandas.compat import (StringIO, lzip, range, map, zip, reduce, u, @@ -30,7 +41,6 @@ import itertools import csv -import warnings common_docstring = """ Parameters @@ -195,7 +205,7 @@ def _get_footer(self): # level infos are added to the end and in a new line, like it is done # for Categoricals - if com.is_categorical_dtype(self.tr_series.dtype): + if is_categorical_dtype(self.tr_series.dtype): level_info = self.tr_series._values._repr_categories_info() if footer: footer += "\n" @@ -317,12 +327,12 @@ def should_show_dimensions(self): def _get_formatter(self, i): if isinstance(self.formatters, (list, tuple)): - if com.is_integer(i): + if is_integer(i): return self.formatters[i] else: return None else: - if com.is_integer(i) and i not in self.columns: + if is_integer(i) and i not in self.columns: i = self.columns[i] return self.formatters.get(i, None) @@ -1326,15 +1336,10 @@ def __init__(self, obj, path_or_buf=None, sep=",", na_rep='', float_format=None, cols=None, header=True, index=True, index_label=None, mode='w', nanRep=None, encoding=None, compression=None, quoting=None, line_terminator='\n', - chunksize=None, engine=None, tupleize_cols=False, - quotechar='"', date_format=None, doublequote=True, - escapechar=None, decimal='.'): - - if engine is not None: - warnings.warn("'engine' keyword is deprecated and will be " - "removed in a future version", FutureWarning, - stacklevel=3) - self.engine = engine # remove for 0.18 + chunksize=None, tupleize_cols=False, quotechar='"', + date_format=None, doublequote=True, escapechar=None, + decimal='.'): + self.obj = obj if path_or_buf is None: @@ -1369,11 +1374,6 @@ def __init__(self, obj, path_or_buf=None, sep=",", na_rep='', self.date_format = date_format - # GH3457 - if not self.obj.columns.is_unique and engine == 'python': - raise NotImplementedError("columns.is_unique == False not " - "supported with engine='python'") - self.tupleize_cols = tupleize_cols self.has_mi_columns = (isinstance(obj.columns, MultiIndex) and not self.tupleize_cols) @@ -1430,108 +1430,6 @@ def __init__(self, obj, path_or_buf=None, sep=",", na_rep='', if not index: self.nlevels = 0 - # original python implem. of df.to_csv - # invoked by df.to_csv(engine=python) - def _helper_csv(self, writer, na_rep=None, cols=None, header=True, - index=True, index_label=None, float_format=None, - date_format=None): - if cols is None: - cols = self.columns - - has_aliases = isinstance(header, (tuple, list, np.ndarray, Index)) - if has_aliases or header: - if index: - # should write something for index label - if index_label is not False: - if index_label is None: - if isinstance(self.obj.index, MultiIndex): - index_label = [] - for i, name in enumerate(self.obj.index.names): - if name is None: - name = '' - index_label.append(name) - else: - index_label = self.obj.index.name - if index_label is None: - index_label = [''] - else: - index_label = [index_label] - elif not isinstance(index_label, - (list, tuple, np.ndarray, Index)): - # given a string for a DF with Index - index_label = [index_label] - - encoded_labels = list(index_label) - else: - encoded_labels = [] - - if has_aliases: - if len(header) != len(cols): - raise ValueError(('Writing %d cols but got %d aliases' - % (len(cols), len(header)))) - else: - write_cols = header - else: - write_cols = cols - encoded_cols = list(write_cols) - - writer.writerow(encoded_labels + encoded_cols) - else: - encoded_cols = list(cols) - writer.writerow(encoded_cols) - - if date_format is None: - date_formatter = lambda x: Timestamp(x)._repr_base - else: - - def strftime_with_nulls(x): - x = Timestamp(x) - if notnull(x): - return x.strftime(date_format) - - date_formatter = lambda x: strftime_with_nulls(x) - - data_index = self.obj.index - - if isinstance(self.obj.index, PeriodIndex): - data_index = self.obj.index.to_timestamp() - - if isinstance(data_index, DatetimeIndex) and date_format is not None: - data_index = Index([date_formatter(x) for x in data_index]) - - values = self.obj.copy() - values.index = data_index - values.columns = values.columns.to_native_types( - na_rep=na_rep, float_format=float_format, date_format=date_format, - quoting=self.quoting) - values = values[cols] - - series = {} - for k, v in compat.iteritems(values._series): - series[k] = v._values - - nlevels = getattr(data_index, 'nlevels', 1) - for j, idx in enumerate(data_index): - row_fields = [] - if index: - if nlevels == 1: - row_fields = [idx] - else: # handle MultiIndex - row_fields = list(idx) - for i, col in enumerate(cols): - val = series[col][j] - if lib.checknull(val): - val = na_rep - - if float_format is not None and com.is_float(val): - val = float_format % val - elif isinstance(val, (np.datetime64, Timestamp)): - val = date_formatter(val) - - row_fields.append(val) - - writer.writerow(row_fields) - def save(self): # create the writer & save if hasattr(self.path_or_buf, 'write'): @@ -1555,17 +1453,7 @@ def save(self): else: self.writer = csv.writer(f, **writer_kwargs) - if self.engine == 'python': - # to be removed in 0.13 - self._helper_csv(self.writer, na_rep=self.na_rep, - float_format=self.float_format, - cols=self.cols, header=self.header, - index=self.index, - index_label=self.index_label, - date_format=self.date_format) - - else: - self._save() + self._save() finally: if close: @@ -1769,7 +1657,7 @@ def __init__(self, df, na_rep='', float_format=None, cols=None, def _format_value(self, val): if lib.checknull(val): val = self.na_rep - elif com.is_float(val): + elif is_float(val): if lib.isposinf_scalar(val): val = self.inf_rep elif lib.isneginf_scalar(val): @@ -1990,19 +1878,19 @@ def get_formatted_cells(self): def format_array(values, formatter, float_format=None, na_rep='NaN', digits=None, space=None, justify='right', decimal='.'): - if com.is_categorical_dtype(values): + if is_categorical_dtype(values): fmt_klass = CategoricalArrayFormatter - elif com.is_float_dtype(values.dtype): + elif is_float_dtype(values.dtype): fmt_klass = FloatArrayFormatter - elif com.is_period_arraylike(values): + elif is_period_arraylike(values): fmt_klass = PeriodArrayFormatter - elif com.is_integer_dtype(values.dtype): + elif is_integer_dtype(values.dtype): fmt_klass = IntArrayFormatter - elif com.is_datetimetz(values): + elif is_datetimetz(values): fmt_klass = Datetime64TZFormatter - elif com.is_datetime64_dtype(values.dtype): + elif is_datetime64_dtype(values.dtype): fmt_klass = Datetime64Formatter - elif com.is_timedelta64_dtype(values.dtype): + elif is_timedelta64_dtype(values.dtype): fmt_klass = Timedelta64Formatter else: fmt_klass = GenericArrayFormatter @@ -2072,14 +1960,14 @@ def _format(x): if isinstance(vals, Index): vals = vals._values - is_float = lib.map_infer(vals, com.is_float) & notnull(vals) - leading_space = is_float.any() + is_float_type = lib.map_infer(vals, is_float) & notnull(vals) + leading_space = is_float_type.any() fmt_values = [] for i, v in enumerate(vals): - if not is_float[i] and leading_space: + if not is_float_type[i] and leading_space: fmt_values.append(' %s' % _format(v)) - elif is_float[i]: + elif is_float_type[i]: fmt_values.append(float_format(v)) else: fmt_values.append(' %s' % _format(v)) @@ -2239,9 +2127,13 @@ def _format_strings(self): """ we by definition have DO NOT have a TZ """ values = self.values + if not isinstance(values, DatetimeIndex): values = DatetimeIndex(values) + if self.formatter is not None and callable(self.formatter): + return [self.formatter(x) for x in values] + fmt_values = format_array_from_datetime( values.asi8.ravel(), format=_get_format_datetime64_from_values(values, diff --git a/pandas/formats/printing.py b/pandas/formats/printing.py index a4eaec8d5334b..37bd4b63d6f7a 100644 --- a/pandas/formats/printing.py +++ b/pandas/formats/printing.py @@ -2,9 +2,9 @@ printing tools """ +from pandas.types.inference import is_sequence from pandas import compat from pandas.compat import u -import pandas.core.common as com from pandas.core.config import get_option @@ -213,7 +213,7 @@ def as_escaped_unicode(thing, escape_chars=escape_chars): _nest_lvl < get_option("display.pprint_nest_depth")): result = _pprint_dict(thing, _nest_lvl, quote_strings=True, max_seq_items=max_seq_items) - elif (com.is_sequence(thing) and + elif (is_sequence(thing) and _nest_lvl < get_option("display.pprint_nest_depth")): result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars, quote_strings=quote_strings, diff --git a/pandas/formats/style.py b/pandas/formats/style.py index 477ecccc03f4f..472fd958d35eb 100644 --- a/pandas/formats/style.py +++ b/pandas/formats/style.py @@ -17,10 +17,11 @@ "or `pip install Jinja2`" raise ImportError(msg) +from pandas.types.common import is_float, is_string_like + import numpy as np import pandas as pd from pandas.compat import lzip, range -import pandas.core.common as com from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice try: import matplotlib.pyplot as plt @@ -153,7 +154,7 @@ def __init__(self, data, precision=None, table_styles=None, uuid=None, # display_funcs maps (row, col) -> formatting function def default_display_func(x): - if com.is_float(x): + if is_float(x): return '{:>.{precision}g}'.format(x, precision=self.precision) else: return x @@ -893,7 +894,7 @@ def _highlight_extrema(data, color='yellow', max_=True): def _maybe_wrap_formatter(formatter): - if com.is_string_like(formatter): + if is_string_like(formatter): return lambda x: formatter.format(x) elif callable(formatter): return formatter diff --git a/pandas/index.pyx b/pandas/index.pyx index 71717dd2d771b..bc985100692fc 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -80,7 +80,7 @@ cdef class IndexEngine: cdef: bint unique, monotonic_inc, monotonic_dec - bint initialized, monotonic_check, unique_check + bint initialized, monotonic_check def __init__(self, vgetter, n): self.vgetter = vgetter @@ -91,7 +91,6 @@ cdef class IndexEngine: self.monotonic_check = 0 self.unique = 0 - self.unique_check = 0 self.monotonic_inc = 0 self.monotonic_dec = 0 @@ -211,8 +210,8 @@ cdef class IndexEngine: property is_unique: def __get__(self): - if not self.unique_check: - self._do_unique_check() + if not self.initialized: + self.initialize() return self.unique == 1 @@ -246,9 +245,6 @@ cdef class IndexEngine: cdef _get_index_values(self): return self.vgetter() - cdef inline _do_unique_check(self): - self._ensure_mapping_populated() - def _call_monotonic(self, values): raise NotImplementedError @@ -270,7 +266,6 @@ cdef class IndexEngine: if len(self.mapping) == len(values): self.unique = 1 - self.unique_check = 1 self.initialized = 1 diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 96472698ba9d9..b013d6ccb0b8e 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -12,6 +12,28 @@ from pandas.compat import range, u from pandas.compat.numpy import function as nv from pandas import compat + + +from pandas.types.generic import ABCSeries, ABCMultiIndex, ABCPeriodIndex +from pandas.types.missing import isnull, array_equivalent +from pandas.types.common import (_ensure_int64, _ensure_object, + _ensure_platform_int, + is_datetimetz, + is_integer, + is_float, + is_dtype_equal, + is_object_dtype, + is_categorical_dtype, + is_bool_dtype, + is_integer_dtype, is_float_dtype, + needs_i8_conversion, + is_iterator, is_list_like, + is_scalar) +from pandas.types.cast import _coerce_indexer_dtype +from pandas.core.common import (is_bool_indexer, + _values_from_object, + _asarray_tuplesafe) + from pandas.core.base import (PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin) import pandas.core.base as base @@ -22,15 +44,7 @@ import pandas.core.missing as missing import pandas.core.algorithms as algos from pandas.formats.printing import pprint_thing -from pandas.core.common import (isnull, array_equivalent, - is_object_dtype, is_datetimetz, ABCSeries, - ABCPeriodIndex, ABCMultiIndex, - _values_from_object, is_float, is_integer, - is_iterator, is_categorical_dtype, - _ensure_object, _ensure_int64, is_bool_indexer, - is_list_like, is_bool_dtype, - is_integer_dtype, is_float_dtype, - needs_i8_conversion) +from pandas.core.ops import _comp_method_OBJECT_ARRAY from pandas.core.strings import StringAccessorMixin from pandas.core.config import get_option @@ -222,7 +236,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data): subarr = data.astype('object') else: - subarr = com._asarray_tuplesafe(data, dtype=object) + subarr = _asarray_tuplesafe(data, dtype=object) # _asarray_tuplesafe does not always copy underlying data, # so need to make sure that this happens @@ -242,8 +256,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, # don't support boolean explicity ATM pass elif inferred != 'string': - if (inferred.startswith('datetime') or - tslib.is_timestamp_array(subarr)): + if inferred.startswith('datetime'): if (lib.is_datetime_with_singletz_array(subarr) or 'tz' in kwargs): @@ -264,7 +277,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, elif hasattr(data, '__array__'): return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs) - elif data is None or lib.isscalar(data): + elif data is None or is_scalar(data): cls._scalar_data_error(data) else: if (tupleize_cols and isinstance(data, list) and data and @@ -284,7 +297,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, # python2 - MultiIndex fails on mixed types pass # other iterable of some kind - subarr = com._asarray_tuplesafe(data, dtype=object) + subarr = _asarray_tuplesafe(data, dtype=object) return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs) """ @@ -378,7 +391,7 @@ def _shallow_copy_with_infer(self, values=None, **kwargs): def _deepcopy_if_needed(self, orig, copy=False): """ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Make a copy of self if data coincides (in memory) with orig. Subclasses should override this if self._base is not an ndarray. @@ -494,7 +507,7 @@ def repeat(self, n, *args, **kwargs): def where(self, cond, other=None): """ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from @@ -539,7 +552,7 @@ def _coerce_to_ndarray(cls, data): """ if not isinstance(data, (np.ndarray, Index)): - if data is None or lib.isscalar(data): + if data is None or is_scalar(data): cls._scalar_data_error(data) # other iterable of some kind @@ -813,7 +826,7 @@ def _to_embed(self, keep_tz=False): satisfied, the original data is used to create a new Index or the original Index is returned. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 """ @@ -841,7 +854,7 @@ def to_datetime(self, dayfirst=False): return DatetimeIndex(self.values) def _assert_can_do_setop(self, other): - if not com.is_list_like(other): + if not is_list_like(other): raise TypeError('Input must be Index or array-like') return True @@ -944,6 +957,16 @@ def rename(self, name, inplace=False): """ return self.set_names([name], inplace=inplace) + def reshape(self, *args, **kwargs): + """ + NOT IMPLEMENTED: do not call this method, as reshaping is not + supported for Index objects and will raise an error. + + Reshape an Index. + """ + raise NotImplementedError("reshaping is not supported " + "for Index objects") + @property def _has_complex_internals(self): # to disable groupby tricks in MultiIndex @@ -1325,7 +1348,7 @@ def __getitem__(self, key): getitem = self._data.__getitem__ promote = self._shallow_copy - if lib.isscalar(key): + if is_scalar(key): return getitem(key) if isinstance(key, slice): @@ -1338,7 +1361,7 @@ def __getitem__(self, key): key = _values_from_object(key) result = getitem(key) - if not lib.isscalar(result): + if not is_scalar(result): return promote(result) else: return result @@ -1426,7 +1449,7 @@ def _ensure_compat_concat(indexes): def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): nv.validate_take(tuple(), kwargs) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) if self._can_hold_na: taken = self._assert_take_fillable(self.values, indices, allow_fill=allow_fill, @@ -1442,7 +1465,7 @@ def take(self, indices, axis=0, allow_fill=True, def _assert_take_fillable(self, values, indices, allow_fill=True, fill_value=None, na_value=np.nan): """ Internal method to handle NA filling of take """ - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) # only fill if we are passing a non-None fill_value if allow_fill and fill_value is not None: @@ -1491,7 +1514,7 @@ def _convert_for_op(self, value): def _assert_can_do_op(self, value): """ Check value is valid for scalar op """ - if not lib.isscalar(value): + if not is_scalar(value): msg = "'value' must be a scalar, passed: {0}" raise TypeError(msg.format(type(value).__name__)) @@ -1706,7 +1729,7 @@ def argsort(self, *args, **kwargs): return result.argsort(*args, **kwargs) def __add__(self, other): - if com.is_list_like(other): + if is_list_like(other): warnings.warn("using '+' to provide set union with Indexes is " "deprecated, use '|' or .union()", FutureWarning, stacklevel=2) @@ -1783,7 +1806,7 @@ def union(self, other): if len(self) == 0: return other._get_consensus_name(self) - if not com.is_dtype_equal(self.dtype, other.dtype): + if not is_dtype_equal(self.dtype, other.dtype): this = self.astype('O') other = other.astype('O') return this.union(other) @@ -1866,7 +1889,7 @@ def intersection(self, other): if self.equals(other): return self._get_consensus_name(other) - if not com.is_dtype_equal(self.dtype, other.dtype): + if not is_dtype_equal(self.dtype, other.dtype): this = self.astype('O') other = other.astype('O') return this.intersection(other) @@ -2028,7 +2051,7 @@ def get_value(self, series, key): # if we have something that is Index-like, then # use this, e.g. DatetimeIndex s = getattr(series, '_values', None) - if isinstance(s, Index) and lib.isscalar(key): + if isinstance(s, Index) and is_scalar(key): try: return s[key] except (IndexError, ValueError): @@ -2061,7 +2084,7 @@ def get_value(self, series, key): raise e1 except TypeError: # python 3 - if lib.isscalar(key): # pragma: no cover + if is_scalar(key): # pragma: no cover raise IndexError(key) raise InvalidIndexError(key) @@ -2137,7 +2160,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): return pself.get_indexer(ptarget, method=method, limit=limit, tolerance=tolerance) - if not com.is_dtype_equal(self.dtype, target.dtype): + if not is_dtype_equal(self.dtype, target.dtype): this = self.astype(object) target = target.astype(object) return this.get_indexer(target, method=method, limit=limit, @@ -2161,7 +2184,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): indexer = self._engine.get_indexer(target._values) - return com._ensure_platform_int(indexer) + return _ensure_platform_int(indexer) def _convert_tolerance(self, tolerance): # override this method on subclasses @@ -2443,7 +2466,7 @@ def _reindex_non_unique(self, target): if len(missing): l = np.arange(len(indexer)) - missing = com._ensure_platform_int(missing) + missing = _ensure_platform_int(missing) missing_labels = target.take(missing) missing_indexer = _ensure_int64(l[~check]) cur_labels = self.take(indexer[check])._values @@ -2541,7 +2564,7 @@ def join(self, other, how='left', level=None, return_indexers=False): result = x, z, y return result - if not com.is_dtype_equal(self.dtype, other.dtype): + if not is_dtype_equal(self.dtype, other.dtype): this = self.astype('O') other = other.astype('O') return this.join(other, how=how, return_indexers=return_indexers) @@ -2637,8 +2660,8 @@ def _join_non_unique(self, other, how='left', return_indexers=False): [other._values], how=how, sort=True) - left_idx = com._ensure_platform_int(left_idx) - right_idx = com._ensure_platform_int(right_idx) + left_idx = _ensure_platform_int(left_idx) + right_idx = _ensure_platform_int(right_idx) join_index = self.values.take(left_idx) mask = left_idx == -1 @@ -2850,9 +2873,9 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): kind=kind) # return a slice - if not lib.isscalar(start_slice): + if not is_scalar(start_slice): raise AssertionError("Start slice bound is non-scalar") - if not lib.isscalar(end_slice): + if not is_scalar(end_slice): raise AssertionError("End slice bound is non-scalar") return slice(start_slice, end_slice, step) @@ -3182,8 +3205,11 @@ def _evaluate_compare(self, other): if needs_i8_conversion(self) and needs_i8_conversion(other): return self._evaluate_compare(other, op) - func = getattr(self.values, op) - result = func(np.asarray(other)) + if is_object_dtype(self) and self.nlevels == 1: + # don't pass MultiIndex + result = _comp_method_OBJECT_ARRAY(op, self.values, other) + else: + result = op(self.values, np.asarray(other)) # technically we could support bool dtyped Index # for now just return the indexing array directly @@ -3196,12 +3222,12 @@ def _evaluate_compare(self, other): return _evaluate_compare - cls.__eq__ = _make_compare('__eq__') - cls.__ne__ = _make_compare('__ne__') - cls.__lt__ = _make_compare('__lt__') - cls.__gt__ = _make_compare('__gt__') - cls.__le__ = _make_compare('__le__') - cls.__ge__ = _make_compare('__ge__') + cls.__eq__ = _make_compare(operator.eq) + cls.__ne__ = _make_compare(operator.ne) + cls.__lt__ = _make_compare(operator.lt) + cls.__gt__ = _make_compare(operator.gt) + cls.__le__ = _make_compare(operator.le) + cls.__ge__ = _make_compare(operator.ge) @classmethod def _add_numericlike_set_methods_disabled(cls): @@ -3480,7 +3506,7 @@ def _get_na_value(dtype): def _ensure_frozen(array_like, categories, copy=False): - array_like = com._coerce_indexer_dtype(array_like, categories) + array_like = _coerce_indexer_dtype(array_like, categories) array_like = array_like.view(FrozenNDArray) if copy: array_like = array_like.copy() diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 3b7c660f5faa1..f1d4fe2f26bdd 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -1,15 +1,21 @@ import numpy as np -import pandas.lib as lib import pandas.index as _index from pandas import compat from pandas.compat.numpy import function as nv +from pandas.types.generic import ABCCategorical, ABCSeries +from pandas.types.common import (is_categorical_dtype, + _ensure_platform_int, + is_list_like, + is_scalar) +from pandas.types.missing import array_equivalent + + from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg) from pandas.core.config import get_option from pandas.indexes.base import Index, _index_shared_docs import pandas.core.base as base -import pandas.core.common as com import pandas.core.missing as missing import pandas.indexes.base as ibase @@ -49,7 +55,7 @@ def __new__(cls, data=None, categories=None, ordered=None, dtype=None, if name is None and hasattr(data, 'name'): name = data.name - if isinstance(data, com.ABCCategorical): + if isinstance(data, ABCCategorical): data = cls._create_categorical(cls, data, categories, ordered) elif isinstance(data, CategoricalIndex): data = data._data @@ -58,7 +64,7 @@ def __new__(cls, data=None, categories=None, ordered=None, dtype=None, # don't allow scalars # if data is None, then categories must be provided - if lib.isscalar(data): + if is_scalar(data): if data is not None or categories is None: cls._scalar_data_error(data) data = [] @@ -116,7 +122,7 @@ def _create_categorical(self, data, categories=None, ordered=None): ------- Categorical """ - if not isinstance(data, com.ABCCategorical): + if not isinstance(data, ABCCategorical): from pandas.core.categorical import Categorical data = Categorical(data, categories=categories, ordered=ordered) else: @@ -164,7 +170,7 @@ def _is_dtype_compat(self, other): ------ TypeError if the dtypes are not compatible """ - if com.is_categorical_dtype(other): + if is_categorical_dtype(other): if isinstance(other, CategoricalIndex): other = other._values if not other.is_dtype_equal(self): @@ -172,7 +178,7 @@ def _is_dtype_compat(self, other): "when appending") else: values = other - if not com.is_list_like(values): + if not is_list_like(values): values = [values] other = CategoricalIndex(self._create_categorical( self, other, categories=self.categories, ordered=self.ordered)) @@ -191,7 +197,7 @@ def equals(self, other): try: other = self._is_dtype_compat(other) - return com.array_equivalent(self._data, other) + return array_equivalent(self._data, other) except (TypeError, ValueError): pass @@ -313,7 +319,7 @@ def _can_reindex(self, indexer): def where(self, cond, other=None): """ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from @@ -360,7 +366,7 @@ def reindex(self, target, method=None, level=None, limit=None, target = ibase._ensure_index(target) - if not com.is_categorical_dtype(target) and not target.is_unique: + if not is_categorical_dtype(target) and not target.is_unique: raise ValueError("cannot reindex with a non-unique indexer") indexer, missing = self.get_indexer_non_unique(np.array(target)) @@ -388,7 +394,7 @@ def reindex(self, target, method=None, level=None, limit=None, # unless we had an inital Categorical to begin with # in which case we are going to conform to the passed Categorical new_target = np.asarray(new_target) - if com.is_categorical_dtype(target): + if is_categorical_dtype(target): new_target = target._shallow_copy(new_target, name=self.name) else: new_target = Index(new_target, name=self.name) @@ -460,7 +466,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): codes = self.categories.get_indexer(target) indexer, _ = self._engine.get_indexer_non_unique(codes) - return com._ensure_platform_int(indexer) + return _ensure_platform_int(indexer) def get_indexer_non_unique(self, target): """ this is the same for a CategoricalIndex for get_indexer; the API @@ -491,7 +497,7 @@ def _convert_list_indexer(self, keyarr, kind=None): def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): nv.validate_take(tuple(), kwargs) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) taken = self._assert_take_fillable(self.codes, indices, allow_fill=allow_fill, fill_value=fill_value, @@ -591,12 +597,12 @@ def _evaluate_compare(self, other): self, other._values, categories=self.categories, ordered=self.ordered) - if isinstance(other, (com.ABCCategorical, np.ndarray, - com.ABCSeries)): + if isinstance(other, (ABCCategorical, np.ndarray, + ABCSeries)): if len(self.values) != len(other): raise ValueError("Lengths must match to compare") - if isinstance(other, com.ABCCategorical): + if isinstance(other, ABCCategorical): if not self.values.is_dtype_equal(other): raise TypeError("categorical index comparisions must " "have the same categories and ordered " @@ -619,7 +625,7 @@ def _delegate_method(self, name, *args, **kwargs): if 'inplace' in kwargs: raise ValueError("cannot use inplace with CategoricalIndex") res = method(*args, **kwargs) - if lib.isscalar(res): + if is_scalar(res): return res return CategoricalIndex(res, name=self.name) diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 05b2045a4850f..365a971f82a3b 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -13,6 +13,21 @@ from pandas.compat import range, zip, lrange, lzip, map from pandas.compat.numpy import function as nv from pandas import compat + + +from pandas.types.common import (_ensure_int64, + _ensure_platform_int, + is_object_dtype, + is_iterator, + is_list_like, + is_scalar) +from pandas.types.missing import isnull, array_equivalent +from pandas.core.common import (_values_from_object, + is_bool_indexer, + is_null_slice, + PerformanceWarning) + + from pandas.core.base import FrozenList import pandas.core.base as base from pandas.util.decorators import (Appender, cache_readonly, @@ -21,13 +36,6 @@ import pandas.core.missing as missing import pandas.core.algorithms as algos from pandas.formats.printing import pprint_thing -from pandas.core.common import (isnull, array_equivalent, - is_object_dtype, - _values_from_object, - is_iterator, - _ensure_int64, is_bool_indexer, - is_list_like, is_null_slice, - PerformanceWarning) from pandas.core.config import get_option @@ -798,7 +806,7 @@ def lexsort_depth(self): else: return 0 - int64_labels = [com._ensure_int64(lab) for lab in self.labels] + int64_labels = [_ensure_int64(lab) for lab in self.labels] for k in range(self.nlevels, 0, -1): if lib.is_lexsorted(int64_labels[:k]): return k @@ -984,7 +992,7 @@ def __setstate__(self, state): self._reset_identity() def __getitem__(self, key): - if lib.isscalar(key): + if is_scalar(key): retval = [] for lev, lab in zip(self.levels, self.labels): if lab[key] == -1: @@ -1011,7 +1019,7 @@ def __getitem__(self, key): def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs): nv.validate_take(tuple(), kwargs) - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) taken = self._assert_take_fillable(self.labels, indices, allow_fill=allow_fill, fill_value=fill_value, @@ -1313,7 +1321,7 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): if not ascending: indexer = indexer[::-1] - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) new_labels = [lab.take(indexer) for lab in self.labels] new_index = MultiIndex(labels=new_labels, levels=self.levels, @@ -1377,7 +1385,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): else: indexer = self_index._engine.get_indexer(target._values) - return com._ensure_platform_int(indexer) + return _ensure_platform_int(indexer) def reindex(self, target, method=None, level=None, limit=None, tolerance=None): @@ -1759,7 +1767,7 @@ def convert_indexer(start, stop, step, indexer=indexer, labels=labels): # selected from pandas import Series mapper = Series(indexer) - indexer = labels.take(com._ensure_platform_int(indexer)) + indexer = labels.take(_ensure_platform_int(indexer)) result = Series(Index(indexer).isin(r).nonzero()[0]) m = result.map(mapper)._values diff --git a/pandas/indexes/numeric.py b/pandas/indexes/numeric.py index 89fc05fdcc5f5..86d22e141f781 100644 --- a/pandas/indexes/numeric.py +++ b/pandas/indexes/numeric.py @@ -3,13 +3,15 @@ import pandas.algos as _algos import pandas.index as _index +from pandas.types.common import (is_dtype_equal, pandas_dtype, + is_float_dtype, is_object_dtype, + is_integer_dtype, is_scalar) +from pandas.types.missing import array_equivalent, isnull +from pandas.core.common import _values_from_object + from pandas import compat from pandas.indexes.base import Index, InvalidIndexError, _index_shared_docs from pandas.util.decorators import Appender, cache_readonly -import pandas.core.common as com -from pandas.core.common import (is_dtype_equal, isnull, pandas_dtype, - is_float_dtype, is_object_dtype, - is_integer_dtype) import pandas.indexes.base as ibase @@ -164,8 +166,8 @@ def equals(self, other): if self.is_(other): return True - return com.array_equivalent(com._values_from_object(self), - com._values_from_object(other)) + return array_equivalent(_values_from_object(self), + _values_from_object(other)) def _wrap_joined_index(self, joined, other): name = self.name if self.name == other.name else None @@ -287,17 +289,17 @@ def _format_native_types(self, na_rep='', float_format=None, decimal='.', def get_value(self, series, key): """ we always want to get an index value, never a value """ - if not lib.isscalar(key): + if not is_scalar(key): raise InvalidIndexError from pandas.core.indexing import maybe_droplevels from pandas.core.series import Series - k = com._values_from_object(key) + k = _values_from_object(key) loc = self.get_loc(k) - new_values = com._values_from_object(series)[loc] + new_values = _values_from_object(series)[loc] - if lib.isscalar(new_values) or new_values is None: + if is_scalar(new_values) or new_values is None: return new_values new_index = self[loc] diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 168143fdea047..f680d2da0161e 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -4,14 +4,16 @@ import numpy as np import pandas.index as _index +from pandas.types.common import (is_integer, + is_scalar, + is_int64_dtype) + from pandas import compat from pandas.compat import lrange, range from pandas.compat.numpy import function as nv from pandas.indexes.base import Index, _index_shared_docs from pandas.util.decorators import Appender, cache_readonly -import pandas.core.common as com import pandas.indexes.base as ibase -import pandas.lib as lib from pandas.indexes.numeric import Int64Index @@ -120,7 +122,7 @@ def _simple_new(cls, start, stop=None, step=None, name=None, result = object.__new__(cls) # handle passed None, non-integers - if start is None or not com.is_integer(start): + if start is None or not is_integer(start): try: return RangeIndex(start, stop, step, name=name, **kwargs) except TypeError: @@ -139,7 +141,7 @@ def _simple_new(cls, start, stop=None, step=None, name=None, @staticmethod def _validate_dtype(dtype): """ require dtype to be None or int64 """ - if not (dtype is None or com.is_int64_dtype(dtype)): + if not (dtype is None or is_int64_dtype(dtype)): raise TypeError('Invalid to pass a non-int64 dtype to RangeIndex') @cache_readonly @@ -448,7 +450,7 @@ def __getitem__(self, key): """ super_getitem = super(RangeIndex, self).__getitem__ - if lib.isscalar(key): + if is_scalar(key): n = int(key) if n != key: return super_getitem(key) @@ -510,7 +512,7 @@ def __getitem__(self, key): return super_getitem(key) def __floordiv__(self, other): - if com.is_integer(other): + if is_integer(other): if (len(self) == 0 or self._start % other == 0 and self._step % other == 0): @@ -560,7 +562,7 @@ def _evaluate_numeric_binop(self, other): # we don't have a representable op # so return a base index - if not com.is_integer(rstep) or not rstep: + if not is_integer(rstep) or not rstep: raise ValueError else: @@ -577,7 +579,7 @@ def _evaluate_numeric_binop(self, other): # for compat with numpy / Int64Index # even if we can represent as a RangeIndex, return # as a Float64Index if we have float-like descriptors - if not all([com.is_integer(x) for x in + if not all([is_integer(x) for x in [rstart, rstop, rstep]]): result = result.astype('float64') diff --git a/pandas/io/common.py b/pandas/io/common.py index 76395928eb011..6f9bddd0fdf9b 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -11,8 +11,8 @@ from pandas.compat import StringIO, BytesIO, string_types, text_type from pandas import compat from pandas.formats.printing import pprint_thing -from pandas.core.common import is_number, AbstractMethodError - +from pandas.core.common import AbstractMethodError +from pandas.types.common import is_number try: import pathlib diff --git a/pandas/io/data.py b/pandas/io/data.py index 5fa440e7bb1ff..68151fbb091fa 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -19,7 +19,9 @@ ) import pandas.compat as compat from pandas import Panel, DataFrame, Series, read_csv, concat, to_datetime, DatetimeIndex, DateOffset -from pandas.core.common import is_list_like, PandasError + +from pandas.types.common import is_list_like +from pandas.core.common import PandasError from pandas.io.common import urlopen, ZipFile, urlencode from pandas.tseries.offsets import MonthEnd from pandas.util.testing import _network_error_classes diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 775465ea9372d..703cdbeaa7a8f 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -10,6 +10,9 @@ import abc import numpy as np +from pandas.types.common import (is_integer, is_float, + is_bool, is_list_like) + from pandas.core.frame import DataFrame from pandas.io.parsers import TextParser from pandas.io.common import (_is_url, _urlopen, _validate_header_arg, @@ -22,7 +25,6 @@ from pandas.formats.printing import pprint_thing import pandas.compat as compat import pandas.compat.openpyxl_compat as openpyxl_compat -import pandas.core.common as com from warnings import warn from distutils.version import LooseVersion @@ -423,17 +425,17 @@ def _parse_cell(cell_contents, cell_typ): output[asheetname] = DataFrame() continue - if com.is_list_like(header) and len(header) == 1: + if is_list_like(header) and len(header) == 1: header = header[0] # forward fill and pull out names for MultiIndex column header_names = None if header is not None: - if com.is_list_like(header): + if is_list_like(header): header_names = [] control_row = [True for x in data[0]] for row in header: - if com.is_integer(skiprows): + if is_integer(skiprows): row += skiprows data[row], control_row = _fill_mi_header( @@ -444,9 +446,9 @@ def _parse_cell(cell_contents, cell_typ): else: data[header] = _trim_excel_header(data[header]) - if com.is_list_like(index_col): + if is_list_like(index_col): # forward fill values for MultiIndex index - if not com.is_list_like(header): + if not is_list_like(header): offset = 1 + header else: offset = 1 + max(header) @@ -459,7 +461,7 @@ def _parse_cell(cell_contents, cell_typ): else: last = data[row][col] - if com.is_list_like(header) and len(header) > 1: + if is_list_like(header) and len(header) > 1: has_index_names = True # GH 12292 : error when read one empty column from excel file @@ -556,21 +558,21 @@ def _pop_header_name(row, index_col): return none_fill(row[0]), row[1:] else: # pop out header name and fill w/ blank - i = index_col if not com.is_list_like(index_col) else max(index_col) + i = index_col if not is_list_like(index_col) else max(index_col) return none_fill(row[i]), row[:i] + [''] + row[i + 1:] def _conv_value(val): # Convert numpy types to Python types for the Excel writers. - if com.is_integer(val): + if is_integer(val): val = int(val) - elif com.is_float(val): + elif is_float(val): val = float(val) - elif com.is_bool(val): + elif is_bool(val): val = bool(val) elif isinstance(val, Period): val = "%s" % val - elif com.is_list_like(val): + elif is_list_like(val): val = str(val) return val diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index e706434f29dc5..140f5cc6bb6e3 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -46,8 +46,12 @@ def _test_google_api_imports(): try: import httplib2 # noqa - from apiclient.discovery import build # noqa - from apiclient.errors import HttpError # noqa + try: + from googleapiclient.discovery import build # noqa + from googleapiclient.errors import HttpError # noqa + except: + from apiclient.discovery import build # noqa + from apiclient.errors import HttpError # noqa from oauth2client.client import AccessTokenRefreshError # noqa from oauth2client.client import OAuth2WebServerFlow # noqa from oauth2client.file import Storage # noqa @@ -266,7 +270,10 @@ def sizeof_fmt(num, suffix='b'): def get_service(self): import httplib2 - from apiclient.discovery import build + try: + from googleapiclient.discovery import build + except: + from apiclient.discovery import build http = httplib2.Http() http = self.credentials.authorize(http) @@ -315,7 +322,10 @@ def process_insert_errors(self, insert_errors): raise StreamingInsertError def run_query(self, query): - from apiclient.errors import HttpError + try: + from googleapiclient.errors import HttpError + except: + from apiclient.errors import HttpError from oauth2client.client import AccessTokenRefreshError _check_google_client_version() @@ -420,7 +430,10 @@ def run_query(self, query): return schema, result_pages def load_data(self, dataframe, dataset_id, table_id, chunksize): - from apiclient.errors import HttpError + try: + from googleapiclient.errors import HttpError + except: + from apiclient.errors import HttpError job_id = uuid.uuid4().hex rows = [] @@ -474,7 +487,10 @@ def load_data(self, dataframe, dataset_id, table_id, chunksize): self._print("\n") def verify_schema(self, dataset_id, table_id, schema): - from apiclient.errors import HttpError + try: + from googleapiclient.errors import HttpError + except: + from apiclient.errors import HttpError try: return (self.service.tables().get( @@ -765,7 +781,10 @@ class _Table(GbqConnector): def __init__(self, project_id, dataset_id, reauth=False, verbose=False, private_key=None): - from apiclient.errors import HttpError + try: + from googleapiclient.errors import HttpError + except: + from apiclient.errors import HttpError self.http_error = HttpError self.dataset_id = dataset_id super(_Table, self).__init__(project_id, reauth, verbose, private_key) @@ -865,7 +884,10 @@ class _Dataset(GbqConnector): def __init__(self, project_id, reauth=False, verbose=False, private_key=None): - from apiclient.errors import HttpError + try: + from googleapiclient.errors import HttpError + except: + from apiclient.errors import HttpError self.http_error = HttpError super(_Dataset, self).__init__(project_id, reauth, verbose, private_key) diff --git a/pandas/io/html.py b/pandas/io/html.py index 48caaa39dd711..e0d84a9617ae4 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -12,12 +12,12 @@ import numpy as np +from pandas.types.common import is_list_like from pandas.io.common import (EmptyDataError, _is_url, urlopen, parse_url, _validate_header_arg) from pandas.io.parsers import TextParser from pandas.compat import (lrange, lmap, u, string_types, iteritems, raise_with_traceback, binary_type) -from pandas.core import common as com from pandas import Series from pandas.core.common import AbstractMethodError from pandas.formats.printing import pprint_thing @@ -107,7 +107,7 @@ def _get_skiprows(skiprows): """ if isinstance(skiprows, slice): return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1) - elif isinstance(skiprows, numbers.Integral) or com.is_list_like(skiprows): + elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows): return skiprows elif skiprows is None: return 0 @@ -837,7 +837,7 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None, Character to recognize as decimal point (e.g. use ',' for European data). - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- diff --git a/pandas/io/packers.py b/pandas/io/packers.py index 23aa133125213..94f390955dddd 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -47,6 +47,10 @@ import numpy as np from pandas import compat from pandas.compat import u, u_safe + +from pandas.types.common import (is_categorical_dtype, is_object_dtype, + needs_i8_conversion, pandas_dtype) + from pandas import (Timestamp, Period, Series, DataFrame, # noqa Index, MultiIndex, Float64Index, Int64Index, Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT, @@ -55,11 +59,9 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame -from pandas.core.common import (PerformanceWarning, - is_categorical_dtype, is_object_dtype, - needs_i8_conversion, pandas_dtype) +from pandas.core.common import PerformanceWarning from pandas.io.common import get_filepath_or_buffer -from pandas.core.internals import BlockManager, make_block +from pandas.core.internals import BlockManager, make_block, _safe_reshape import pandas.core.internals as internals from pandas.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType @@ -481,12 +483,12 @@ def encode(obj): tz = obj.tzinfo if tz is not None: tz = u(tz.zone) - offset = obj.offset - if offset is not None: - offset = u(offset.freqstr) + freq = obj.freq + if freq is not None: + freq = u(freq.freqstr) return {u'typ': u'timestamp', u'value': obj.value, - u'offset': offset, + u'freq': freq, u'tz': tz} if isinstance(obj, NaTType): return {u'typ': u'nat'} @@ -556,7 +558,8 @@ def decode(obj): if typ is None: return obj elif typ == u'timestamp': - return Timestamp(obj[u'value'], tz=obj[u'tz'], offset=obj[u'offset']) + freq = obj[u'freq'] if 'freq' in obj else obj[u'offset'] + return Timestamp(obj[u'value'], tz=obj[u'tz'], freq=freq) elif typ == u'nat': return NaT elif typ == u'period': @@ -619,8 +622,9 @@ def decode(obj): axes = obj[u'axes'] def create_block(b): - values = unconvert(b[u'values'], dtype_for(b[u'dtype']), - b[u'compress']).reshape(b[u'shape']) + values = _safe_reshape(unconvert( + b[u'values'], dtype_for(b[u'dtype']), + b[u'compress']), b[u'shape']) # locs handles duplicate column names, and should be used instead # of items; see GH 9618 diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index dc9455289b757..84ea2a92b8026 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -2,20 +2,22 @@ Module contains tools for processing files into DataFrames or other objects """ from __future__ import print_function -from pandas.compat import (range, lrange, StringIO, lzip, zip, - string_types, map, OrderedDict) -from pandas import compat from collections import defaultdict import re import csv import warnings +import datetime import numpy as np +from pandas import compat +from pandas.compat import range, lrange, StringIO, lzip, zip, string_types, map +from pandas.types.common import (is_integer, _ensure_object, + is_list_like, is_integer_dtype, + is_float, + is_scalar) from pandas.core.index import Index, MultiIndex from pandas.core.frame import DataFrame -import datetime -import pandas.core.common as com from pandas.core.common import AbstractMethodError from pandas.core.config import get_option from pandas.io.date_converters import generic_parser @@ -326,11 +328,11 @@ def _validate_nrows(nrows): msg = "'nrows' must be an integer" if nrows is not None: - if com.is_float(nrows): + if is_float(nrows): if int(nrows) != nrows: raise ValueError(msg) nrows = int(nrows) - elif not com.is_integer(nrows): + elif not is_integer(nrows): raise ValueError(msg) return nrows @@ -869,7 +871,7 @@ def _clean_options(self, options, engine): # handle skiprows; this is internally handled by the # c-engine, so only need for python parsers if engine != 'c': - if com.is_integer(skiprows): + if is_integer(skiprows): skiprows = lrange(skiprows) skiprows = set() if skiprows is None else set(skiprows) @@ -961,7 +963,7 @@ def _validate_parse_dates_arg(parse_dates): "for the 'parse_dates' parameter") if parse_dates is not None: - if lib.isscalar(parse_dates): + if is_scalar(parse_dates): if not lib.is_bool(parse_dates): raise TypeError(msg) @@ -1021,8 +1023,8 @@ def __init__(self, kwds): is_sequence = isinstance(self.index_col, (list, tuple, np.ndarray)) if not (is_sequence and - all(map(com.is_integer, self.index_col)) or - com.is_integer(self.index_col)): + all(map(is_integer, self.index_col)) or + is_integer(self.index_col)): raise ValueError("index_col must only contain row numbers " "when specifying a multi-index header") @@ -1047,7 +1049,7 @@ def _should_parse_dates(self, i): name = self.index_names[i] j = self.index_col[i] - if lib.isscalar(self.parse_dates): + if is_scalar(self.parse_dates): return (j == self.parse_dates) or (name == self.parse_dates) else: return (j in self.parse_dates) or (name in self.parse_dates) @@ -1281,7 +1283,7 @@ def _convert_types(self, values, na_values, try_num_bool=True): mask = lib.ismember(values, na_values) na_count = mask.sum() if na_count > 0: - if com.is_integer_dtype(values): + if is_integer_dtype(values): values = values.astype(np.float64) np.putmask(values, mask, np.nan) return values, na_count @@ -1407,10 +1409,10 @@ def _set_noconvert_columns(self): usecols = self.usecols def _set(x): - if usecols and com.is_integer(x): + if usecols and is_integer(x): x = list(usecols)[x] - if not com.is_integer(x): + if not is_integer(x): x = names.index(x) self._reader.set_noconvert(x) @@ -1790,7 +1792,7 @@ def _set_no_thousands_columns(self): noconvert_columns = set() def _set(x): - if com.is_integer(x): + if is_integer(x): noconvert_columns.add(x) else: noconvert_columns.add(self.columns.index(x)) @@ -1954,7 +1956,7 @@ def _convert_data(self, data): def _to_recarray(self, data, columns): dtypes = [] - o = OrderedDict() + o = compat.OrderedDict() # use the columns to "order" the keys # in the unordered 'data' dictionary @@ -2439,7 +2441,7 @@ def converter(*date_cols): try: return tools._to_datetime( - com._ensure_object(strs), + _ensure_object(strs), utc=None, box=False, dayfirst=dayfirst, @@ -2492,7 +2494,7 @@ def _isindex(colspec): if isinstance(parse_spec, list): # list of column lists for colspec in parse_spec: - if lib.isscalar(colspec): + if is_scalar(colspec): if isinstance(colspec, int) and colspec not in data_dict: colspec = orig_names[colspec] if _isindex(colspec): @@ -2569,7 +2571,7 @@ def _clean_na_values(na_values, keep_default_na=True): (k, _floatify_na_values(v)) for k, v in na_values.items() # noqa ]) else: - if not com.is_list_like(na_values): + if not is_list_like(na_values): na_values = [na_values] na_values = _stringify_na_values(na_values) if keep_default_na: @@ -2622,7 +2624,7 @@ def _get_empty_meta(columns, index_col, index_names, dtype=None): if not isinstance(dtype, dict): dtype = defaultdict(lambda: dtype) # Convert column indexes to column names. - dtype = dict((columns[k] if com.is_integer(k) else k, v) + dtype = dict((columns[k] if is_integer(k) else k, v) for k, v in compat.iteritems(dtype)) if index_col is None or index_col is False: diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py index c19dae7f3545e..2358c296f782e 100644 --- a/pandas/io/pickle.py +++ b/pandas/io/pickle.py @@ -3,7 +3,7 @@ import numpy as np from numpy.lib.format import read_array, write_array from pandas.compat import BytesIO, cPickle as pkl, pickle_compat as pc, PY3 -import pandas.core.common as com +from pandas.types.common import is_datetime64_dtype, _NS_DTYPE def to_pickle(obj, path): @@ -86,7 +86,7 @@ def _unpickle_array(bytes): # All datetimes should be stored as M8[ns]. When unpickling with # numpy1.6, it will read these as M8[us]. So this ensures all # datetime64 types are read as MS[ns] - if com.is_datetime64_dtype(arr): - arr = arr.view(com._NS_DTYPE) + if is_datetime64_dtype(arr): + arr = arr.view(_NS_DTYPE) return arr diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index cbe04349b5105..038ca7ac7775b 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -12,11 +12,21 @@ import warnings import os +from pandas.types.common import (is_list_like, + is_categorical_dtype, + is_timedelta64_dtype, + is_datetime64tz_dtype, + is_datetime64_dtype, + _ensure_object, + _ensure_int64, + _ensure_platform_int) +from pandas.types.missing import array_equivalent + import numpy as np import pandas as pd from pandas import (Series, DataFrame, Panel, Panel4D, Index, - MultiIndex, Int64Index) + MultiIndex, Int64Index, isnull) from pandas.core import config from pandas.io.common import _stringify_path from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel @@ -32,7 +42,6 @@ _block2d_to_blocknd, _factor_indexer, _block_shape) from pandas.core.index import _ensure_index -import pandas.core.common as com from pandas.tools.merge import concat from pandas import compat from pandas.compat import u_safe as u, PY3, range, lrange, string_types, filter @@ -276,7 +285,7 @@ def read_hdf(path_or_buf, key=None, **kwargs): path_or_buf : path (string), buffer, or path object (pathlib.Path or py._path.local.LocalPath) to read from - .. versionadded:: 0.18.2 support for pathlib, py.path. + .. versionadded:: 0.19.0 support for pathlib, py.path. key : group identifier in the store. Can be omitted a HDF file contains a single pandas object. @@ -1677,7 +1686,7 @@ def validate_metadata(self, handler): new_metadata = self.metadata cur_metadata = handler.read_metadata(self.cname) if new_metadata is not None and cur_metadata is not None \ - and not com.array_equivalent(new_metadata, cur_metadata): + and not array_equivalent(new_metadata, cur_metadata): raise ValueError("cannot append a categorical with " "different categories to the existing") @@ -2566,7 +2575,7 @@ def write_array(self, key, value, items=None): empty_array = self._is_empty_array(value.shape) transposed = False - if com.is_categorical_dtype(value): + if is_categorical_dtype(value): raise NotImplementedError('Cannot store a category dtype in ' 'a HDF5 dataset that uses format=' '"fixed". Use format="table".') @@ -2621,12 +2630,12 @@ def write_array(self, key, value, items=None): if empty_array: self.write_array_empty(key, value) else: - if com.is_datetime64_dtype(value.dtype): + if is_datetime64_dtype(value.dtype): self._handle.create_array( self.group, key, value.view('i8')) getattr( self.group, key)._v_attrs.value_type = 'datetime64' - elif com.is_datetime64tz_dtype(value.dtype): + elif is_datetime64tz_dtype(value.dtype): # store as UTC # with a zone self._handle.create_array(self.group, key, @@ -2635,7 +2644,7 @@ def write_array(self, key, value, items=None): node = getattr(self.group, key) node._v_attrs.tz = _get_tz(value.tz) node._v_attrs.value_type = 'datetime64' - elif com.is_timedelta64_dtype(value.dtype): + elif is_timedelta64_dtype(value.dtype): self._handle.create_array( self.group, key, value.view('i8')) getattr( @@ -3756,8 +3765,8 @@ def read(self, where=None, columns=None, **kwargs): if len(unique(key)) == len(key): sorter, _ = algos.groupsort_indexer( - com._ensure_int64(key), np.prod(N)) - sorter = com._ensure_platform_int(sorter) + _ensure_int64(key), np.prod(N)) + sorter = _ensure_platform_int(sorter) # create the objs for c in self.values_axes: @@ -3802,7 +3811,7 @@ def read(self, where=None, columns=None, **kwargs): unique_tuples = _asarray_tuplesafe(unique_tuples) indexer = match(unique_tuples, tuple_index) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) new_index = long_index.take(indexer) new_values = lp.values.take(indexer, axis=0) @@ -3903,7 +3912,7 @@ def write_data(self, chunksize, dropna=False): # figure the mask: only do if we can successfully process this # column, otherwise ignore the mask - mask = com.isnull(a.data).all(axis=0) + mask = isnull(a.data).all(axis=0) if isinstance(mask, np.ndarray): masks.append(mask.astype('u1', copy=False)) @@ -4522,7 +4531,7 @@ def _convert_string_array(data, encoding, itemsize=None): # create the sized dtype if itemsize is None: - itemsize = lib.max_len_string_array(com._ensure_object(data.ravel())) + itemsize = lib.max_len_string_array(_ensure_object(data.ravel())) data = np.asarray(data, dtype="S%d" % itemsize) return data @@ -4551,7 +4560,7 @@ def _unconvert_string_array(data, nan_rep=None, encoding=None): encoding = _ensure_encoding(encoding) if encoding is not None and len(data): - itemsize = lib.max_len_string_array(com._ensure_object(data)) + itemsize = lib.max_len_string_array(_ensure_object(data)) if compat.PY3: dtype = "U{0}".format(itemsize) else: @@ -4619,7 +4628,7 @@ def __init__(self, table, where=None, start=None, stop=None, **kwargs): self.terms = None self.coordinates = None - if com.is_list_like(where): + if is_list_like(where): # see if we have a passed coordinate like try: diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 324988360c9fe..8485a3f13f047 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -13,13 +13,15 @@ import numpy as np import pandas.lib as lib -import pandas.core.common as com +from pandas.types.missing import isnull +from pandas.types.dtypes import DatetimeTZDtype +from pandas.types.common import (is_list_like, + is_datetime64tz_dtype) + from pandas.compat import (lzip, map, zip, raise_with_traceback, string_types, text_type) from pandas.core.api import DataFrame, Series -from pandas.core.common import isnull from pandas.core.base import PandasObject -from pandas.types.api import DatetimeTZDtype from pandas.tseries.tools import to_datetime from contextlib import contextmanager @@ -90,7 +92,7 @@ def _handle_date_column(col, format=None): # parse dates as timestamp format = 's' if format is None else format return to_datetime(col, errors='coerce', unit=format, utc=True) - elif com.is_datetime64tz_dtype(col): + elif is_datetime64tz_dtype(col): # coerce to UTC timezone # GH11216 return (to_datetime(col, errors='coerce') @@ -123,7 +125,7 @@ def _parse_date_columns(data_frame, parse_dates): # we could in theory do a 'nice' conversion from a FixedOffset tz # GH11216 for col_name, df_col in data_frame.iteritems(): - if com.is_datetime64tz_dtype(df_col): + if is_datetime64tz_dtype(df_col): data_frame[col_name] = _handle_date_column(df_col) return data_frame @@ -876,7 +878,7 @@ def _create_table_setup(self): for name, typ, is_index in column_names_and_types] if self.keys is not None: - if not com.is_list_like(self.keys): + if not is_list_like(self.keys): keys = [self.keys] else: keys = self.keys @@ -1465,7 +1467,7 @@ def _create_table_setup(self): for cname, ctype, _ in column_names_and_types] if self.keys is not None and len(self.keys): - if not com.is_list_like(self.keys): + if not is_list_like(self.keys): keys = [self.keys] else: keys = self.keys diff --git a/pandas/io/stata.py b/pandas/io/stata.py index c7390cf240f8a..bd19102c7f18c 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -14,6 +14,10 @@ import sys import struct from dateutil.relativedelta import relativedelta + +from pandas.types.common import (is_categorical_dtype, is_datetime64_dtype, + _ensure_object) + from pandas.core.base import StringMixin from pandas.core.categorical import Categorical from pandas.core.frame import DataFrame @@ -24,7 +28,7 @@ zip, BytesIO from pandas.util.decorators import Appender import pandas as pd -import pandas.core.common as com + from pandas.io.common import get_filepath_or_buffer, BaseIterator from pandas.lib import max_len_string_array, infer_dtype from pandas.tslib import NaT, Timestamp @@ -358,7 +362,7 @@ def _datetime_to_stata_elapsed_vec(dates, fmt): def parse_dates_safe(dates, delta=False, year=False, days=False): d = {} - if com.is_datetime64_dtype(dates.values): + if is_datetime64_dtype(dates.values): if delta: delta = dates - stata_epoch d['delta'] = delta.values.astype( @@ -396,7 +400,7 @@ def parse_dates_safe(dates, delta=False, year=False, days=False): index = dates.index if bad_loc.any(): dates = Series(dates) - if com.is_datetime64_dtype(dates): + if is_datetime64_dtype(dates): dates[bad_loc] = to_datetime(stata_epoch) else: dates[bad_loc] = stata_epoch @@ -1746,7 +1750,7 @@ def _dtype_to_stata_type(dtype, column): elif dtype.type == np.object_: # try to coerce it to the biggest string # not memory efficient, what else could we # do? - itemsize = max_len_string_array(com._ensure_object(column.values)) + itemsize = max_len_string_array(_ensure_object(column.values)) return chr(max(itemsize, 1)) elif dtype == np.float64: return chr(255) @@ -1784,7 +1788,7 @@ def _dtype_to_default_stata_fmt(dtype, column): if not (inferred_dtype in ('string', 'unicode') or len(column) == 0): raise ValueError('Writing general object arrays is not supported') - itemsize = max_len_string_array(com._ensure_object(column.values)) + itemsize = max_len_string_array(_ensure_object(column.values)) if itemsize > 244: raise ValueError(excessive_string_length_error % column.name) return "%" + str(max(itemsize, 1)) + "s" @@ -1880,7 +1884,7 @@ def _prepare_categoricals(self, data): """Check for categorical columns, retain categorical information for Stata file and convert categorical data to int""" - is_cat = [com.is_categorical_dtype(data[col]) for col in data] + is_cat = [is_categorical_dtype(data[col]) for col in data] self._is_col_cat = is_cat self._value_labels = [] if not any(is_cat): diff --git a/pandas/tests/data/categorical_0_14_1.pickle b/pandas/io/tests/data/categorical_0_14_1.pickle similarity index 100% rename from pandas/tests/data/categorical_0_14_1.pickle rename to pandas/io/tests/data/categorical_0_14_1.pickle diff --git a/pandas/tests/data/categorical_0_15_2.pickle b/pandas/io/tests/data/categorical_0_15_2.pickle similarity index 100% rename from pandas/tests/data/categorical_0_15_2.pickle rename to pandas/io/tests/data/categorical_0_15_2.pickle diff --git a/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_2.7.12.msgpack b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_2.7.12.msgpack new file mode 100644 index 0000000000000..978c2c5045314 Binary files /dev/null and b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_2.7.12.msgpack differ diff --git a/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_3.5.2.msgpack b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_3.5.2.msgpack new file mode 100644 index 0000000000000..ea8efdc86dd2d Binary files /dev/null and b/pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_3.5.2.msgpack differ diff --git a/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle index 5ee1f88c93a34..bb237f53476b5 100644 Binary files a/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle and b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle differ diff --git a/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_3.5.2.pickle b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_3.5.2.pickle new file mode 100644 index 0000000000000..db1d17a8b67c3 Binary files /dev/null and b/pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_3.5.2.pickle differ diff --git a/pandas/io/tests/generate_legacy_storage_files.py b/pandas/io/tests/generate_legacy_storage_files.py index 25fd86d899c08..d0365cb2c30b3 100644 --- a/pandas/io/tests/generate_legacy_storage_files.py +++ b/pandas/io/tests/generate_legacy_storage_files.py @@ -5,7 +5,7 @@ SparseSeries, SparseDataFrame, Index, MultiIndex, bdate_range, to_msgpack, date_range, period_range, - Timestamp, Categorical, Period) + Timestamp, NaT, Categorical, Period) from pandas.compat import u import os import sys @@ -140,6 +140,13 @@ def create_data(): int16=Categorical(np.arange(1000)), int32=Categorical(np.arange(10000))) + timestamp = dict(normal=Timestamp('2011-01-01'), + nat=NaT, + tz=Timestamp('2011-01-01', tz='US/Eastern'), + freq=Timestamp('2011-01-01', freq='D'), + both=Timestamp('2011-01-01', tz='Asia/Tokyo', + freq='M')) + return dict(series=series, frame=frame, panel=panel, @@ -149,7 +156,8 @@ def create_data(): sp_series=dict(float=_create_sp_series(), ts=_create_sp_tsseries()), sp_frame=dict(float=_create_sp_frame()), - cat=cat) + cat=cat, + timestamp=timestamp) def create_pickle_data(): diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 5cb681f4d2e7d..278c5d7215624 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -73,8 +73,12 @@ def _test_imports(): if _SETUPTOOLS_INSTALLED: try: - from apiclient.discovery import build # noqa - from apiclient.errors import HttpError # noqa + try: + from googleapiclient.discovery import build # noqa + from googleapiclient.errors import HttpError # noqa + except: + from apiclient.discovery import build # noqa + from apiclient.errors import HttpError # noqa from oauth2client.client import OAuth2WebServerFlow # noqa from oauth2client.client import AccessTokenRefreshError # noqa @@ -280,6 +284,17 @@ class GBQUnitTests(tm.TestCase): def setUp(self): test_requirements() + def test_import_google_api_python_client(self): + if compat.PY2: + with tm.assertRaises(ImportError): + from googleapiclient.discovery import build # noqa + from googleapiclient.errors import HttpError # noqa + from apiclient.discovery import build # noqa + from apiclient.errors import HttpError # noqa + else: + from googleapiclient.discovery import build # noqa + from googleapiclient.errors import HttpError # noqa + def test_should_return_bigquery_integers_as_python_floats(self): result = gbq._parse_entry(1, 'INTEGER') tm.assert_equal(result, float(1)) diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index ad7d6c3c9f94f..0a491a69af8e2 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -8,7 +8,7 @@ from distutils.version import LooseVersion from pandas import compat -from pandas.compat import u +from pandas.compat import u, PY3 from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range, date_range, period_range, Index, Categorical) from pandas.core.common import PerformanceWarning @@ -58,6 +58,19 @@ def check_arbitrary(a, b): assert_series_equal(a, b) elif isinstance(a, Index): assert_index_equal(a, b) + elif isinstance(a, Categorical): + # Temp, + # Categorical.categories is changed from str to bytes in PY3 + # maybe the same as GH 13591 + if PY3 and b.categories.inferred_type == 'string': + pass + else: + tm.assert_categorical_equal(a, b) + elif a is NaT: + assert b is NaT + elif isinstance(a, Timestamp): + assert a == b + assert a.freq == b.freq else: assert(a == b) @@ -815,8 +828,8 @@ def check_min_structure(self, data): for typ, v in self.minimum_structure.items(): assert typ in data, '"{0}" not found in unpacked data'.format(typ) for kind in v: - assert kind in data[ - typ], '"{0}" not found in data["{1}"]'.format(kind, typ) + msg = '"{0}" not found in data["{1}"]'.format(kind, typ) + assert kind in data[typ], msg def compare(self, vf, version): # GH12277 encoding default used to be latin-1, now utf-8 @@ -839,8 +852,8 @@ def compare(self, vf, version): # use a specific comparator # if available - comparator = getattr( - self, "compare_{typ}_{dt}".format(typ=typ, dt=dt), None) + comp_method = "compare_{typ}_{dt}".format(typ=typ, dt=dt) + comparator = getattr(self, comp_method, None) if comparator is not None: comparator(result, expected, typ, version) else: @@ -872,9 +885,8 @@ def read_msgpacks(self, version): n = 0 for f in os.listdir(pth): # GH12142 0.17 files packed in P2 can't be read in P3 - if (compat.PY3 and - version.startswith('0.17.') and - f.split('.')[-4][-1] == '2'): + if (compat.PY3 and version.startswith('0.17.') and + f.split('.')[-4][-1] == '2'): continue vf = os.path.join(pth, f) try: diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py index e337ad4dcfed2..6019144d59698 100644 --- a/pandas/io/tests/test_pickle.py +++ b/pandas/io/tests/test_pickle.py @@ -46,6 +46,12 @@ def compare_element(self, result, expected, typ, version=None): if typ.startswith('sp_'): comparator = getattr(tm, "assert_%s_equal" % typ) comparator(result, expected, exact_indices=False) + elif typ == 'timestamp': + if expected is pd.NaT: + assert result is pd.NaT + else: + tm.assert_equal(result, expected) + tm.assert_equal(result.freq, expected.freq) else: comparator = getattr(tm, "assert_%s_equal" % typ, tm.assert_almost_equal) @@ -225,6 +231,44 @@ def python_unpickler(path): result = python_unpickler(path) self.compare_element(result, expected, typ) + def test_pickle_v0_14_1(self): + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_14_1.pickle') + # This code was executed once on v0.14.1 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + + def test_pickle_v0_15_2(self): + # ordered -> _ordered + # GH 9347 + + # we have the name warning + # 10482 + with tm.assert_produces_warning(UserWarning): + cat = pd.Categorical(values=['a', 'b', 'c'], + categories=['a', 'b', 'c', 'd'], + name='foobar', ordered=False) + pickle_path = os.path.join(tm.get_data_path(), + 'categorical_0_15_2.pickle') + # This code was executed once on v0.15.2 to generate the pickle: + # + # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], + # name='foobar') + # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) + # + tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py index 9a995c17f0445..e5a49c5213a48 100644 --- a/pandas/io/tests/test_sql.py +++ b/pandas/io/tests/test_sql.py @@ -31,11 +31,12 @@ from datetime import datetime, date, time +from pandas.types.common import (is_object_dtype, is_datetime64_dtype, + is_datetime64tz_dtype) from pandas import DataFrame, Series, Index, MultiIndex, isnull, concat from pandas import date_range, to_datetime, to_timedelta, Timestamp import pandas.compat as compat from pandas.compat import StringIO, range, lrange, string_types -from pandas.core import common as com from pandas.core.datetools import format as date_format import pandas.io.sql as sql @@ -1275,7 +1276,7 @@ def test_datetime_with_timezone(self): def check(col): # check that a column is either datetime64[ns] # or datetime64[ns, UTC] - if com.is_datetime64_dtype(col.dtype): + if is_datetime64_dtype(col.dtype): # "2000-01-01 00:00:00-08:00" should convert to # "2000-01-01 08:00:00" @@ -1285,7 +1286,7 @@ def check(col): # "2000-06-01 07:00:00" self.assertEqual(col[1], Timestamp('2000-06-01 07:00:00')) - elif com.is_datetime64tz_dtype(col.dtype): + elif is_datetime64tz_dtype(col.dtype): self.assertTrue(str(col.dt.tz) == 'UTC') # "2000-01-01 00:00:00-08:00" should convert to @@ -1311,9 +1312,9 @@ def check(col): # even with the same versions of psycopg2 & sqlalchemy, possibly a # Postgrsql server version difference col = df.DateColWithTz - self.assertTrue(com.is_object_dtype(col.dtype) or - com.is_datetime64_dtype(col.dtype) or - com.is_datetime64tz_dtype(col.dtype), + self.assertTrue(is_object_dtype(col.dtype) or + is_datetime64_dtype(col.dtype) or + is_datetime64tz_dtype(col.dtype), "DateCol loaded with incorrect type -> {0}" .format(col.dtype)) @@ -1327,7 +1328,7 @@ def check(col): self.conn, chunksize=1)), ignore_index=True) col = df.DateColWithTz - self.assertTrue(com.is_datetime64tz_dtype(col.dtype), + self.assertTrue(is_datetime64tz_dtype(col.dtype), "DateCol loaded with incorrect type -> {0}" .format(col.dtype)) self.assertTrue(str(col.dt.tz) == 'UTC') diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 830c68d62efad..5f45d1b547e62 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -15,7 +15,7 @@ import pandas as pd from pandas.compat import iterkeys from pandas.core.frame import DataFrame, Series -from pandas.core.common import is_categorical_dtype +from pandas.types.common import is_categorical_dtype from pandas.io.parsers import read_csv from pandas.io.stata import (read_stata, StataReader, InvalidColumnName, PossiblePrecisionLoss, StataMissingValue) diff --git a/pandas/lib.pxd b/pandas/lib.pxd index 36c91faa00036..554b0248e97ea 100644 --- a/pandas/lib.pxd +++ b/pandas/lib.pxd @@ -1,3 +1,4 @@ # prototypes for sharing cdef bint is_null_datetimelike(v) +cpdef bint is_period(val) diff --git a/pandas/lib.pyx b/pandas/lib.pyx index a9c7f93097f1b..7cbb502315b64 100644 --- a/pandas/lib.pyx +++ b/pandas/lib.pyx @@ -768,12 +768,12 @@ def scalar_compare(ndarray[object] values, object val, object op): raise ValueError('Unrecognized operator') result = np.empty(n, dtype=bool).view(np.uint8) - isnull_val = _checknull(val) + isnull_val = checknull(val) if flag == cpython.Py_NE: for i in range(n): x = values[i] - if _checknull(x): + if checknull(x): result[i] = True elif isnull_val: result[i] = True @@ -785,7 +785,7 @@ def scalar_compare(ndarray[object] values, object val, object op): elif flag == cpython.Py_EQ: for i in range(n): x = values[i] - if _checknull(x): + if checknull(x): result[i] = False elif isnull_val: result[i] = False @@ -798,7 +798,7 @@ def scalar_compare(ndarray[object] values, object val, object op): else: for i in range(n): x = values[i] - if _checknull(x): + if checknull(x): result[i] = False elif isnull_val: result[i] = False @@ -864,7 +864,7 @@ def vec_compare(ndarray[object] left, ndarray[object] right, object op): x = left[i] y = right[i] - if _checknull(x) or _checknull(y): + if checknull(x) or checknull(y): result[i] = True else: result[i] = cpython.PyObject_RichCompareBool(x, y, flag) @@ -873,7 +873,7 @@ def vec_compare(ndarray[object] left, ndarray[object] right, object op): x = left[i] y = right[i] - if _checknull(x) or _checknull(y): + if checknull(x) or checknull(y): result[i] = False else: result[i] = cpython.PyObject_RichCompareBool(x, y, flag) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 0312fb023f7fd..35233d1b6ba94 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -15,6 +15,14 @@ from pandas.compat import range from pandas.compat.numpy import function as nv +from pandas.types.generic import ABCSparseArray, ABCSparseSeries +from pandas.types.common import (is_float, is_integer, + is_integer_dtype, _ensure_platform_int, + is_list_like, + is_scalar) +from pandas.types.cast import _possibly_convert_platform +from pandas.types.missing import isnull, notnull + from pandas._sparse import SparseIndex, BlockIndex, IntIndex import pandas._sparse as splib import pandas.index as _index @@ -40,13 +48,13 @@ def wrapper(self, other): if len(self) != len(other): raise AssertionError("length mismatch: %d vs. %d" % (len(self), len(other))) - if not isinstance(other, com.ABCSparseArray): + if not isinstance(other, ABCSparseArray): other = SparseArray(other, fill_value=self.fill_value) if name[0] == 'r': return _sparse_array_op(other, self, op, name[1:]) else: return _sparse_array_op(self, other, op, name) - elif lib.isscalar(other): + elif is_scalar(other): new_fill_value = op(np.float64(self.fill_value), np.float64(other)) return _wrap_result(name, op(self.sp_values, other), @@ -120,7 +128,7 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', if index is not None: if data is None: data = np.nan - if not lib.isscalar(data): + if not is_scalar(data): raise Exception("must only pass scalars with an index ") values = np.empty(len(index), dtype='float64') values.fill(data) @@ -177,7 +185,7 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer', @classmethod def _simple_new(cls, data, sp_index, fill_value): - if (com.is_integer_dtype(data) and com.is_float(fill_value) and + if (is_integer_dtype(data) and is_float(fill_value) and sp_index.ngaps > 0): # if float fill_value is being included in dense repr, # convert values to float @@ -288,7 +296,7 @@ def __getitem__(self, key): """ """ - if com.is_integer(key): + if is_integer(key): return self._get_val_at(key) elif isinstance(key, tuple): data_slice = self.values[key] @@ -340,11 +348,11 @@ def take(self, indices, axis=0, allow_fill=True, if axis: raise ValueError("axis must be 0, input was {0}".format(axis)) - if com.is_integer(indices): + if is_integer(indices): # return scalar return self[indices] - indices = com._ensure_platform_int(indices) + indices = _ensure_platform_int(indices) n = len(self) if allow_fill and fill_value is not None: # allow -1 to indicate self.fill_value, @@ -380,7 +388,7 @@ def take(self, indices, axis=0, allow_fill=True, return self._simple_new(new_values, sp_index, self.fill_value) def __setitem__(self, key, value): - # if com.is_integer(key): + # if is_integer(key): # self.values[key] = value # else: # raise Exception("SparseArray does not support seting non-scalars @@ -395,7 +403,7 @@ def __setslice__(self, i, j, value): j = 0 slobj = slice(i, j) # noqa - # if not lib.isscalar(value): + # if not is_scalar(value): # raise Exception("SparseArray does not support seting non-scalars # via slices") @@ -445,12 +453,12 @@ def count(self): @property def _null_fill_value(self): - return com.isnull(self.fill_value) + return isnull(self.fill_value) @property def _valid_sp_values(self): sp_vals = self.sp_values - mask = com.notnull(sp_vals) + mask = notnull(sp_vals) return sp_vals[mask] @Appender(_index_shared_docs['fillna'] % _sparray_doc_kwargs) @@ -466,7 +474,7 @@ def fillna(self, value, downcast=None): fill_value=value) else: new_values = self.sp_values.copy() - new_values[com.isnull(new_values)] = value + new_values[isnull(new_values)] = value return self._simple_new(new_values, self.sp_index, fill_value=self.fill_value) @@ -498,7 +506,7 @@ def cumsum(self, axis=0, *args, **kwargs): nv.validate_cumsum(args, kwargs) # TODO: gh-12855 - return a SparseArray here - if com.notnull(self.fill_value): + if notnull(self.fill_value): return self.to_dense().cumsum() # TODO: what if sp_values contains NaN?? @@ -569,7 +577,7 @@ def _maybe_to_dense(obj): def _maybe_to_sparse(array): - if isinstance(array, com.ABCSparseSeries): + if isinstance(array, ABCSparseSeries): array = SparseArray(array.values, sparse_index=array.sp_index, fill_value=array.fill_value, copy=True) if not isinstance(array, SparseArray): @@ -588,15 +596,15 @@ def _sanitize_values(arr): else: # scalar - if lib.isscalar(arr): + if is_scalar(arr): arr = [arr] # ndarray if isinstance(arr, np.ndarray): pass - elif com.is_list_like(arr) and len(arr) > 0: - arr = com._possibly_convert_platform(arr) + elif is_list_like(arr) and len(arr) > 0: + arr = _possibly_convert_platform(arr) else: arr = np.asarray(arr) @@ -624,8 +632,8 @@ def make_sparse(arr, kind='block', fill_value=nan): if arr.ndim > 1: raise TypeError("expected dimension <= 1 data") - if com.isnull(fill_value): - mask = com.notnull(arr) + if isnull(fill_value): + mask = notnull(arr) else: mask = arr != fill_value diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 52a6e6edf0896..811d8019c7fee 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -10,13 +10,15 @@ from pandas import compat import numpy as np +from pandas.types.missing import isnull, notnull +from pandas.types.common import _ensure_platform_int + +from pandas.core.common import _try_sort from pandas.compat.numpy import function as nv -from pandas.core.common import isnull, _try_sort from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.series import Series from pandas.core.frame import (DataFrame, extract_index, _prep_ndarray, _default_index) -import pandas.core.common as com import pandas.core.algorithms as algos from pandas.core.internals import (BlockManager, create_block_manager_from_arrays) @@ -520,7 +522,7 @@ def _reindex_index(self, index, method, copy, level, fill_value=np.nan, return SparseDataFrame(index=index, columns=self.columns) indexer = self.index.get_indexer(index, method, limit=limit) - indexer = com._ensure_platform_int(indexer) + indexer = _ensure_platform_int(indexer) mask = indexer == -1 need_mask = mask.any() @@ -546,7 +548,7 @@ def _reindex_columns(self, columns, copy, level, fill_value, limit=None, if level is not None: raise TypeError('Reindex by level not supported for sparse') - if com.notnull(fill_value): + if notnull(fill_value): raise NotImplementedError("'fill_value' argument is not supported") if limit: diff --git a/pandas/sparse/list.py b/pandas/sparse/list.py index bc10b73a47723..666dae8071053 100644 --- a/pandas/sparse/list.py +++ b/pandas/sparse/list.py @@ -2,9 +2,9 @@ from pandas.core.base import PandasObject from pandas.formats.printing import pprint_thing +from pandas.types.common import is_scalar from pandas.sparse.array import SparseArray import pandas._sparse as splib -import pandas.lib as lib class SparseList(PandasObject): @@ -121,7 +121,7 @@ def append(self, value): ---------- value: scalar or array-like """ - if lib.isscalar(value): + if is_scalar(value): value = [value] sparr = SparseArray(value, fill_value=self.fill_value) diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index 88f396d20a91e..0996cd3bd826a 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -10,6 +10,7 @@ from pandas import compat import numpy as np +from pandas.types.common import is_list_like, is_scalar from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.frame import DataFrame from pandas.core.panel import Panel @@ -18,7 +19,6 @@ import pandas.core.common as com import pandas.core.ops as ops -import pandas.lib as lib class SparsePanelAxis(object): @@ -186,7 +186,7 @@ def _ixs(self, i, axis=0): key = self._get_axis(axis)[i] # xs cannot handle a non-scalar key, so just reindex here - if com.is_list_like(key): + if is_list_like(key): return self.reindex(**{self._get_axis_name(axis): key}) return self.xs(key, axis=axis) @@ -393,7 +393,7 @@ def _combine(self, other, func, axis=0): return self._combineFrame(other, func, axis=axis) elif isinstance(other, Panel): return self._combinePanel(other, func) - elif lib.isscalar(other): + elif is_scalar(other): new_frames = dict((k, func(v, other)) for k, v in self.iteritems()) return self._new_like(new_frames) diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 519068b97a010..951c2ae0c0d5a 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -7,10 +7,12 @@ import numpy as np import warnings -import operator + +from pandas.types.missing import isnull +from pandas.types.common import is_scalar +from pandas.core.common import _values_from_object, _maybe_match_name from pandas.compat.numpy import function as nv -from pandas.core.common import isnull, _values_from_object, _maybe_match_name from pandas.core.index import Index, _ensure_index, InvalidIndexError from pandas.core.series import Series from pandas.core.frame import DataFrame @@ -19,7 +21,6 @@ import pandas.core.common as com import pandas.core.ops as ops import pandas.index as _index -import pandas.lib as lib from pandas.util.decorators import Appender from pandas.sparse.array import (make_sparse, _sparse_array_op, SparseArray, @@ -55,7 +56,7 @@ def wrapper(self, other): return _sparse_series_op(self, other, op, name) elif isinstance(other, DataFrame): return NotImplemented - elif lib.isscalar(other): + elif is_scalar(other): if isnull(other) or isnull(self.fill_value): new_fill_value = np.nan else: @@ -803,7 +804,7 @@ def from_coo(cls, A, dense_index=False): # overwrite basic arithmetic to use SparseSeries version # force methods to overwrite previous definitions. ops.add_special_arithmetic_methods(SparseSeries, _arith_method, - radd_func=operator.add, comp_method=None, + comp_method=None, bool_method=None, use_numexpr=False, force=True) diff --git a/pandas/src/datetime/np_datetime.c b/pandas/src/datetime/np_datetime.c index c30b404d2b8b2..80703c8b08de6 100644 --- a/pandas/src/datetime/np_datetime.c +++ b/pandas/src/datetime/np_datetime.c @@ -576,7 +576,7 @@ void pandas_datetime_to_datetimestruct(npy_datetime val, PANDAS_DATETIMEUNIT fr, } PANDAS_DATETIMEUNIT get_datetime64_unit(PyObject *obj) { - return ((PyDatetimeScalarObject *) obj)->obmeta.base; + return (PANDAS_DATETIMEUNIT)((PyDatetimeScalarObject *) obj)->obmeta.base; } diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c index 3a1d37f86cc28..b633d6cde0820 100644 --- a/pandas/src/datetime/np_datetime_strings.c +++ b/pandas/src/datetime/np_datetime_strings.c @@ -460,7 +460,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -503,7 +503,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -975,7 +975,7 @@ parse_iso_8601_datetime(char *str, int len, } /* Check the casting rule */ - if (unit != -1 && !can_cast_datetime64_units(bestunit, unit, + if (!can_cast_datetime64_units(bestunit, unit, casting)) { PyErr_Format(PyExc_TypeError, "Cannot parse \"%s\" as unit " "'%s' using casting rule %s", @@ -1005,11 +1005,6 @@ get_datetime_iso_8601_strlen(int local, PANDAS_DATETIMEUNIT base) { int len = 0; - /* If no unit is provided, return the maximum length */ - if (base == -1) { - return PANDAS_DATETIME_MAX_ISO8601_STRLEN; - } - switch (base) { /* Generic units can only be used to represent NaT */ /*case PANDAS_FR_GENERIC:*/ @@ -1146,28 +1141,13 @@ make_iso_8601_datetime(pandas_datetimestruct *dts, char *outstr, int outlen, local = 0; } - /* Automatically detect a good unit */ - if (base == -1) { - base = lossless_unit_from_datetimestruct(dts); - /* - * If there's a timezone, use at least minutes precision, - * and never split up hours and minutes by default - */ - if ((base < PANDAS_FR_m && local) || base == PANDAS_FR_h) { - base = PANDAS_FR_m; - } - /* Don't split up dates by default */ - else if (base < PANDAS_FR_D) { - base = PANDAS_FR_D; - } - } /* * Print weeks with the same precision as days. * * TODO: Could print weeks with YYYY-Www format if the week * epoch is a Monday. */ - else if (base == PANDAS_FR_W) { + if (base == PANDAS_FR_W) { base = PANDAS_FR_D; } diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx index 262e036ff44f1..9f96037c97c62 100644 --- a/pandas/src/inference.pyx +++ b/pandas/src/inference.pyx @@ -33,7 +33,7 @@ def is_bool(object obj): def is_complex(object obj): return util.is_complex_object(obj) -def is_period(object val): +cpdef bint is_period(object val): """ Return a boolean if this is a Period object """ return util.is_period_object(val) @@ -103,6 +103,7 @@ def infer_dtype(object _values): Py_ssize_t i, n object val ndarray values + bint seen_pdnat = False, seen_val = False if isinstance(_values, np.ndarray): values = _values @@ -141,17 +142,34 @@ def infer_dtype(object _values): values = values.ravel() # try to use a valid value - for i in range(n): - val = util.get_value_1d(values, i) - if not is_null_datetimelike(val): - break + for i from 0 <= i < n: + val = util.get_value_1d(values, i) - if util.is_datetime64_object(val) or val is NaT: + # do not use is_nul_datetimelike to keep + # np.datetime64('nat') and np.timedelta64('nat') + if util._checknull(val): + pass + elif val is NaT: + seen_pdnat = True + else: + seen_val = True + break + + # if all values are nan/NaT + if seen_val is False and seen_pdnat is True: + return 'datetime' + # float/object nan is handled in latter logic + + if util.is_datetime64_object(val): if is_datetime64_array(values): return 'datetime64' elif is_timedelta_or_timedelta64_array(values): return 'timedelta' + elif is_timedelta(val): + if is_timedelta_or_timedelta64_array(values): + return 'timedelta' + elif util.is_integer_object(val): # a timedelta will show true here as well if is_timedelta(val): @@ -200,17 +218,15 @@ def infer_dtype(object _values): if is_bytes_array(values): return 'bytes' - elif is_timedelta(val): - if is_timedelta_or_timedelta64_array(values): - return 'timedelta' - elif is_period(val): if is_period_array(values): return 'period' for i in range(n): val = util.get_value_1d(values, i) - if util.is_integer_object(val): + if (util.is_integer_object(val) and + not util.is_timedelta64_object(val) and + not util.is_datetime64_object(val)): return 'mixed-integer' return 'mixed' @@ -237,20 +253,46 @@ def is_possible_datetimelike_array(object arr): return False return seen_datetime or seen_timedelta + cdef inline bint is_null_datetimelike(v): # determine if we have a null for a timedelta/datetime (or integer versions)x if util._checknull(v): return True + elif v is NaT: + return True elif util.is_timedelta64_object(v): return v.view('int64') == iNaT elif util.is_datetime64_object(v): return v.view('int64') == iNaT elif util.is_integer_object(v): return v == iNaT + return False + + +cdef inline bint is_null_datetime64(v): + # determine if we have a null for a datetime (or integer versions)x, + # excluding np.timedelta64('nat') + if util._checknull(v): + return True + elif v is NaT: + return True + elif util.is_datetime64_object(v): + return v.view('int64') == iNaT + return False + + +cdef inline bint is_null_timedelta64(v): + # determine if we have a null for a timedelta (or integer versions)x, + # excluding np.datetime64('nat') + if util._checknull(v): + return True elif v is NaT: return True + elif util.is_timedelta64_object(v): + return v.view('int64') == iNaT return False + cdef inline bint is_datetime(object o): return PyDateTime_Check(o) @@ -420,7 +462,7 @@ def is_datetime_array(ndarray[object] values): # return False for all nulls for i in range(n): v = values[i] - if is_null_datetimelike(v): + if is_null_datetime64(v): # we are a regular null if util._checknull(v): null_count += 1 @@ -437,7 +479,7 @@ def is_datetime64_array(ndarray values): # return False for all nulls for i in range(n): v = values[i] - if is_null_datetimelike(v): + if is_null_datetime64(v): # we are a regular null if util._checknull(v): null_count += 1 @@ -481,7 +523,7 @@ def is_timedelta_array(ndarray values): return False for i in range(n): v = values[i] - if is_null_datetimelike(v): + if is_null_timedelta64(v): # we are a regular null if util._checknull(v): null_count += 1 @@ -496,7 +538,7 @@ def is_timedelta64_array(ndarray values): return False for i in range(n): v = values[i] - if is_null_datetimelike(v): + if is_null_timedelta64(v): # we are a regular null if util._checknull(v): null_count += 1 @@ -512,7 +554,7 @@ def is_timedelta_or_timedelta64_array(ndarray values): return False for i in range(n): v = values[i] - if is_null_datetimelike(v): + if is_null_timedelta64(v): # we are a regular null if util._checknull(v): null_count += 1 @@ -538,9 +580,6 @@ def is_time_array(ndarray[object] values): return False return True -def is_period(object o): - from pandas import Period - return isinstance(o,Period) def is_period_array(ndarray[object] values): cdef Py_ssize_t i, n = len(values) diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index aca0d0dbc107b..37f265ede07e7 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -24,7 +24,7 @@ cimport cython from datetime cimport * cimport util cimport lib -from lib cimport is_null_datetimelike +from lib cimport is_null_datetimelike, is_period import lib from pandas import tslib from tslib import Timedelta, Timestamp, iNaT, NaT @@ -472,7 +472,11 @@ def extract_ordinals(ndarray[object] values, freq): except AttributeError: p = Period(p, freq=freq) - ordinals[i] = p.ordinal + if p is tslib.NaT: + # input may contain NaT-like string + ordinals[i] = tslib.iNaT + else: + ordinals[i] = p.ordinal return ordinals @@ -484,8 +488,11 @@ def extract_freq(ndarray[object] values): for i in range(n): p = values[i] + try: - return p.freq + # now Timestamp / NaT has freq attr + if is_period(p): + return p.freq except AttributeError: pass @@ -662,24 +669,8 @@ class IncompatibleFrequency(ValueError): pass -cdef class Period(object): - """ - Represents an period of time +cdef class _Period(object): - Parameters - ---------- - value : Period or compat.string_types, default None - The time period represented (e.g., '4Q2005') - freq : str, default None - One of pandas period strings or corresponding objects - year : int, default None - month : int, default 1 - quarter : int, default None - day : int, default 1 - hour : int, default 0 - minute : int, default 0 - second : int, default 0 - """ cdef public: int64_t ordinal object freq @@ -708,97 +699,22 @@ cdef class Period(object): @classmethod def _from_ordinal(cls, ordinal, freq): """ fast creation from an ordinal and freq that are already validated! """ - self = Period.__new__(cls) - self.ordinal = ordinal - self.freq = cls._maybe_convert_freq(freq) - return self - - def __init__(self, value=None, freq=None, ordinal=None, - year=None, month=1, quarter=None, day=1, - hour=0, minute=0, second=0): - # freq points to a tuple (base, mult); base is one of the defined - # periods such as A, Q, etc. Every five minutes would be, e.g., - # ('T', 5) but may be passed in as a string like '5T' - - # ordinal is the period offset from the gregorian proleptic epoch - - if ordinal is not None and value is not None: - raise ValueError(("Only value or ordinal but not both should be " - "given but not both")) - elif ordinal is not None: - if not lib.is_integer(ordinal): - raise ValueError("Ordinal must be an integer") - if freq is None: - raise ValueError('Must supply freq for ordinal value') - - elif value is None: - if freq is None: - raise ValueError("If value is None, freq cannot be None") - ordinal = _ordinal_from_fields(year, month, quarter, day, - hour, minute, second, freq) - - elif isinstance(value, Period): - other = value - if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): - ordinal = other.ordinal - freq = other.freq - else: - converted = other.asfreq(freq) - ordinal = converted.ordinal - - elif is_null_datetimelike(value) or value in tslib._nat_strings: - ordinal = tslib.iNaT - if freq is None: - raise ValueError("If value is NaT, freq cannot be None " - "because it cannot be inferred") - - elif isinstance(value, compat.string_types) or lib.is_integer(value): - if lib.is_integer(value): - value = str(value) - value = value.upper() - dt, _, reso = parse_time_string(value, freq) - - if freq is None: - try: - freq = frequencies.Resolution.get_freq(reso) - except KeyError: - raise ValueError("Invalid frequency or could not infer: %s" % reso) - - elif isinstance(value, datetime): - dt = value - if freq is None: - raise ValueError('Must supply freq for datetime value') - elif isinstance(value, np.datetime64): - dt = Timestamp(value) - if freq is None: - raise ValueError('Must supply freq for datetime value') - elif isinstance(value, date): - dt = datetime(year=value.year, month=value.month, day=value.day) - if freq is None: - raise ValueError('Must supply freq for datetime value') - else: - msg = "Value must be Period, string, integer, or datetime" - raise ValueError(msg) - - base, mult = frequencies.get_freq_code(freq) - - if ordinal is None: - self.ordinal = get_period_ordinal(dt.year, dt.month, dt.day, - dt.hour, dt.minute, dt.second, - dt.microsecond, 0, base) + if ordinal == tslib.iNaT: + return tslib.NaT else: + self = _Period.__new__(cls) self.ordinal = ordinal - - self.freq = self._maybe_convert_freq(freq) + self.freq = cls._maybe_convert_freq(freq) + return self def __richcmp__(self, other, op): if isinstance(other, Period): if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT: - return _nat_scalar_rules[op] return PyObject_RichCompareBool(self.ordinal, other.ordinal, op) + elif other is tslib.NaT: + return _nat_scalar_rules[op] # index/series like elif hasattr(other, '_typ'): return NotImplemented @@ -821,10 +737,7 @@ cdef class Period(object): offset_nanos = tslib._delta_to_nanoseconds(offset) if nanos % offset_nanos == 0: - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + (nanos // offset_nanos) + ordinal = self.ordinal + (nanos // offset_nanos) return Period(ordinal=ordinal, freq=self.freq) msg = 'Input cannot be converted to Period(freq={0})' raise IncompatibleFrequency(msg.format(self.freqstr)) @@ -832,10 +745,7 @@ cdef class Period(object): freqstr = frequencies.get_standard_freq(other) base = frequencies.get_base_alias(freqstr) if base == self.freq.rule_code: - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + other.n + ordinal = self.ordinal + other.n return Period(ordinal=ordinal, freq=self.freq) msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) @@ -850,10 +760,7 @@ cdef class Period(object): elif other is tslib.NaT: return tslib.NaT elif lib.is_integer(other): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal + other * self.freq.n + ordinal = self.ordinal + other * self.freq.n return Period(ordinal=ordinal, freq=self.freq) else: # pragma: no cover return NotImplemented @@ -869,17 +776,12 @@ cdef class Period(object): neg_other = -other return self + neg_other elif lib.is_integer(other): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - ordinal = self.ordinal - other * self.freq.n + ordinal = self.ordinal - other * self.freq.n return Period(ordinal=ordinal, freq=self.freq) elif isinstance(other, Period): if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT: - return Period(ordinal=tslib.iNaT, freq=self.freq) return self.ordinal - other.ordinal elif getattr(other, '_typ', None) == 'periodindex': return -other.__sub__(self) @@ -911,16 +813,13 @@ cdef class Period(object): base1, mult1 = frequencies.get_freq_code(self.freq) base2, mult2 = frequencies.get_freq_code(freq) - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal + # mult1 can't be negative or 0 + end = how == 'E' + if end: + ordinal = self.ordinal + mult1 - 1 else: - # mult1 can't be negative or 0 - end = how == 'E' - if end: - ordinal = self.ordinal + mult1 - 1 - else: - ordinal = self.ordinal - ordinal = period_asfreq(ordinal, base1, base2, end) + ordinal = self.ordinal + ordinal = period_asfreq(ordinal, base1, base2, end) return Period(ordinal=ordinal, freq=freq) @@ -930,12 +829,9 @@ cdef class Period(object): @property def end_time(self): - if self.ordinal == tslib.iNaT: - ordinal = self.ordinal - else: - # freq.n can't be negative or 0 - # ordinal = (self + self.freq.n).start_time.value - 1 - ordinal = (self + 1).start_time.value - 1 + # freq.n can't be negative or 0 + # ordinal = (self + self.freq.n).start_time.value - 1 + ordinal = (self + 1).start_time.value - 1 return Timestamp(ordinal) def to_timestamp(self, freq=None, how='start', tz=None): @@ -1196,8 +1092,114 @@ cdef class Period(object): return period_format(self.ordinal, base, fmt) -def _ordinal_from_fields(year, month, quarter, day, hour, minute, - second, freq): +class Period(_Period): + """ + Represents an period of time + + Parameters + ---------- + value : Period or compat.string_types, default None + The time period represented (e.g., '4Q2005') + freq : str, default None + One of pandas period strings or corresponding objects + year : int, default None + month : int, default 1 + quarter : int, default None + day : int, default 1 + hour : int, default 0 + minute : int, default 0 + second : int, default 0 + """ + + def __new__(cls, value=None, freq=None, ordinal=None, + year=None, month=None, quarter=None, day=None, + hour=None, minute=None, second=None): + # freq points to a tuple (base, mult); base is one of the defined + # periods such as A, Q, etc. Every five minutes would be, e.g., + # ('T', 5) but may be passed in as a string like '5T' + + # ordinal is the period offset from the gregorian proleptic epoch + + cdef _Period self + + if ordinal is not None and value is not None: + raise ValueError(("Only value or ordinal but not both should be " + "given but not both")) + elif ordinal is not None: + if not lib.is_integer(ordinal): + raise ValueError("Ordinal must be an integer") + if freq is None: + raise ValueError('Must supply freq for ordinal value') + + elif value is None: + if (year is None and month is None and quarter is None and + day is None and hour is None and minute is None and second is None): + ordinal = tslib.iNaT + else: + if freq is None: + raise ValueError("If value is None, freq cannot be None") + + # set defaults + month = 1 if month is None else month + day = 1 if day is None else day + hour = 0 if hour is None else hour + minute = 0 if minute is None else minute + second = 0 if second is None else second + + ordinal = _ordinal_from_fields(year, month, quarter, day, + hour, minute, second, freq) + + elif isinstance(value, Period): + other = value + if freq is None or frequencies.get_freq_code(freq) == frequencies.get_freq_code(other.freq): + ordinal = other.ordinal + freq = other.freq + else: + converted = other.asfreq(freq) + ordinal = converted.ordinal + + elif is_null_datetimelike(value) or value in tslib._nat_strings: + ordinal = tslib.iNaT + + elif isinstance(value, compat.string_types) or lib.is_integer(value): + if lib.is_integer(value): + value = str(value) + value = value.upper() + dt, _, reso = parse_time_string(value, freq) + + if freq is None: + try: + freq = frequencies.Resolution.get_freq(reso) + except KeyError: + raise ValueError("Invalid frequency or could not infer: %s" % reso) + + elif isinstance(value, datetime): + dt = value + if freq is None: + raise ValueError('Must supply freq for datetime value') + elif isinstance(value, np.datetime64): + dt = Timestamp(value) + if freq is None: + raise ValueError('Must supply freq for datetime value') + elif isinstance(value, date): + dt = datetime(year=value.year, month=value.month, day=value.day) + if freq is None: + raise ValueError('Must supply freq for datetime value') + else: + msg = "Value must be Period, string, integer, or datetime" + raise ValueError(msg) + + if ordinal is None: + base, mult = frequencies.get_freq_code(freq) + ordinal = get_period_ordinal(dt.year, dt.month, dt.day, + dt.hour, dt.minute, dt.second, + dt.microsecond, 0, base) + + return cls._from_ordinal(ordinal, freq) + + +def _ordinal_from_fields(year, month, quarter, day, + hour, minute, second, freq): base, mult = frequencies.get_freq_code(freq) if quarter is not None: year, month = _quarter_to_myear(year, quarter, freq) diff --git a/pandas/src/testing.pyx b/pandas/src/testing.pyx index 6780cf311c244..e9563d9168206 100644 --- a/pandas/src/testing.pyx +++ b/pandas/src/testing.pyx @@ -1,7 +1,8 @@ import numpy as np from pandas import compat -from pandas.core.common import isnull, array_equivalent, is_dtype_equal +from pandas.types.missing import isnull, array_equivalent +from pandas.types.common import is_dtype_equal cdef NUMERIC_TYPES = ( bool, @@ -145,8 +146,15 @@ cpdef assert_almost_equal(a, b, if na != nb: from pandas.util.testing import raise_assert_detail + + # if we have a small diff set, print it + if abs(na-nb) < 10: + r = list(set(a) ^ set(b)) + else: + r = None + raise_assert_detail(obj, '{0} length are different'.format(obj), - na, nb) + na, nb, r) for i in xrange(len(a)): try: diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 925c18cd23d8f..75de63acbd7d6 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -450,7 +450,7 @@ static void *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, void *outValue, si static void *PandasDateTimeStructToJSON(pandas_datetimestruct *dts, JSONTypeContext *tc, void *outValue, size_t *_outLen) { - int base = ((PyObjectEncoder*) tc->encoder)->datetimeUnit; + PANDAS_DATETIMEUNIT base = ((PyObjectEncoder*) tc->encoder)->datetimeUnit; if (((PyObjectEncoder*) tc->encoder)->datetimeIso) { @@ -493,7 +493,7 @@ static void *NpyDateTimeScalarToJSON(JSOBJ _obj, JSONTypeContext *tc, void *outV PyDatetimeScalarObject *obj = (PyDatetimeScalarObject *) _obj; PRINTMARK(); - pandas_datetime_to_datetimestruct(obj->obval, obj->obmeta.base, &dts); + pandas_datetime_to_datetimestruct(obj->obval, (PANDAS_DATETIMEUNIT)obj->obmeta.base, &dts); return PandasDateTimeStructToJSON(&dts, tc, outValue, _outLen); } diff --git a/pandas/src/util.pxd b/pandas/src/util.pxd index 96a23a91cc7c2..fcb5583a0a6e7 100644 --- a/pandas/src/util.pxd +++ b/pandas/src/util.pxd @@ -98,4 +98,4 @@ cdef inline bint _checknan(object val): return not cnp.PyArray_Check(val) and val != val cdef inline bint is_period_object(object val): - return getattr(val,'_typ','_typ') == 'period' + return getattr(val, '_typ', '_typ') == 'period' diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index 46d30ab7fe313..bb475e47206c2 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -6,7 +6,7 @@ import warnings import numpy as np -from pandas import lib +from pandas.types.common import is_scalar from pandas.core.api import DataFrame, Series from pandas.util.decorators import Substitution, Appender @@ -226,7 +226,7 @@ def ensure_compat(dispatch, name, arg, func_kw=None, *args, **kwargs): aargs += ',' def f(a, b): - if lib.isscalar(b): + if is_scalar(b): return "{a}={b}".format(a=a, b=b) return "{a}=<{b}>".format(a=a, b=type(b).__name__) aargs = ','.join([f(a, b) for a, b in kwds.items() if b is not None]) diff --git a/pandas/stats/ols.py b/pandas/stats/ols.py index 678689f2d2b30..b533d255bd196 100644 --- a/pandas/stats/ols.py +++ b/pandas/stats/ols.py @@ -13,7 +13,7 @@ from pandas.core.api import DataFrame, Series, isnull from pandas.core.base import StringMixin -from pandas.core.common import _ensure_float64 +from pandas.types.common import _ensure_float64 from pandas.core.index import MultiIndex from pandas.core.panel import Panel from pandas.util.decorators import cache_readonly diff --git a/pandas/tests/formats/test_format.py b/pandas/tests/formats/test_format.py index e67fe2cddde77..7a282e7eb14ad 100644 --- a/pandas/tests/formats/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -456,6 +456,28 @@ def test_to_string_with_formatters(self): '2 0x3 [ 3.0] -False-')) self.assertEqual(result, result2) + def test_to_string_with_datetime64_monthformatter(self): + months = [datetime(2016, 1, 1), datetime(2016, 2, 2)] + x = DataFrame({'months': months}) + + def format_func(x): + return x.strftime('%Y-%m') + result = x.to_string(formatters={'months': format_func}) + expected = 'months\n0 2016-01\n1 2016-02' + self.assertEqual(result.strip(), expected) + + def test_to_string_with_datetime64_hourformatter(self): + + x = DataFrame({'hod': pd.to_datetime(['10:10:10.100', '12:12:12.120'], + format='%H:%M:%S.%f')}) + + def format_func(x): + return x.strftime('%H:%M') + + result = x.to_string(formatters={'hod': format_func}) + expected = 'hod\n0 10:10\n1 12:12' + self.assertEqual(result.strip(), expected) + def test_to_string_with_formatters_unicode(self): df = DataFrame({u('c/\u03c3'): [1, 2, 3]}) result = df.to_string(formatters={u('c/\u03c3'): lambda x: '%s' % x}) @@ -1233,6 +1255,63 @@ def test_to_html_index_formatter(self): self.assertEqual(result, expected) + def test_to_html_datetime64_monthformatter(self): + months = [datetime(2016, 1, 1), datetime(2016, 2, 2)] + x = DataFrame({'months': months}) + + def format_func(x): + return x.strftime('%Y-%m') + result = x.to_html(formatters={'months': format_func}) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>months</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>2016-01</td> + </tr> + <tr> + <th>1</th> + <td>2016-02</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + + def test_to_html_datetime64_hourformatter(self): + + x = DataFrame({'hod': pd.to_datetime(['10:10:10.100', '12:12:12.120'], + format='%H:%M:%S.%f')}) + + def format_func(x): + return x.strftime('%H:%M') + result = x.to_html(formatters={'hod': format_func}) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>hod</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>10:10</td> + </tr> + <tr> + <th>1</th> + <td>12:12</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + def test_to_html_regression_GH6098(self): df = DataFrame({u('clé1'): [u('a'), u('a'), u('b'), u('b'), u('a')], u('clé2'): [u('1er'), u('2ème'), u('1er'), u('2ème'), @@ -2775,6 +2854,33 @@ def test_to_latex_format(self): self.assertEqual(withindex_result, withindex_expected) + def test_to_latex_with_formatters(self): + df = DataFrame({'int': [1, 2, 3], + 'float': [1.0, 2.0, 3.0], + 'object': [(1, 2), True, False], + 'datetime64': [datetime(2016, 1, 1), + datetime(2016, 2, 5), + datetime(2016, 3, 3)]}) + + formatters = {'int': lambda x: '0x%x' % x, + 'float': lambda x: '[% 4.1f]' % x, + 'object': lambda x: '-%s-' % str(x), + 'datetime64': lambda x: x.strftime('%Y-%m'), + '__index__': lambda x: 'index: %s' % x} + result = df.to_latex(formatters=dict(formatters)) + + expected = r"""\begin{tabular}{llrrl} +\toprule +{} & datetime64 & float & int & object \\ +\midrule +index: 0 & 2016-01 & [ 1.0] & 0x1 & -(1, 2)- \\ +index: 1 & 2016-02 & [ 2.0] & 0x2 & -True- \\ +index: 2 & 2016-03 & [ 3.0] & 0x3 & -False- \\ +\bottomrule +\end{tabular} +""" + self.assertEqual(result, expected) + def test_to_latex_multiindex(self): df = DataFrame({('x', 'y'): ['a']}) result = df.to_latex() @@ -3223,12 +3329,6 @@ def test_to_csv_date_format(self): self.assertEqual(df_sec_grouped.mean().to_csv(date_format='%Y-%m-%d'), expected_ymd_sec) - # deprecation GH11274 - def test_to_csv_engine_kw_deprecation(self): - with tm.assert_produces_warning(FutureWarning): - df = DataFrame({'col1': [1], 'col2': ['a'], 'col3': [10.1]}) - df.to_csv(engine='python') - def test_period(self): # GH 12615 df = pd.DataFrame({'A': pd.period_range('2013-01', @@ -4161,6 +4261,28 @@ def test_dates_display(self): self.assertEqual(result[1].strip(), "NaT") self.assertEqual(result[4].strip(), "2013-01-01 09:00:00.000000004") + def test_datetime64formatter_yearmonth(self): + x = Series([datetime(2016, 1, 1), datetime(2016, 2, 2)]) + + def format_func(x): + return x.strftime('%Y-%m') + + formatter = fmt.Datetime64Formatter(x, formatter=format_func) + result = formatter.get_result() + self.assertEqual(result, ['2016-01', '2016-02']) + + def test_datetime64formatter_hoursecond(self): + + x = Series(pd.to_datetime(['10:10:10.100', '12:12:12.120'], + format='%H:%M:%S.%f')) + + def format_func(x): + return x.strftime('%H:%M') + + formatter = fmt.Datetime64Formatter(x, formatter=format_func) + result = formatter.get_result() + self.assertEqual(result, ['10:10', '12:12']) + class TestNaTFormatting(tm.TestCase): diff --git a/pandas/tests/frame/test_apply.py b/pandas/tests/frame/test_apply.py index 2b619b84a5994..020b7f1f1ab9d 100644 --- a/pandas/tests/frame/test_apply.py +++ b/pandas/tests/frame/test_apply.py @@ -10,7 +10,7 @@ from pandas import (notnull, DataFrame, Series, MultiIndex, date_range, Timestamp, compat) import pandas as pd -import pandas.core.common as com +from pandas.types.dtypes import CategoricalDtype from pandas.util.testing import (assert_series_equal, assert_frame_equal) import pandas.util.testing as tm @@ -45,8 +45,8 @@ def test_apply(self): 'c1': ['C', 'C', 'D', 'D']}) df = df.apply(lambda ts: ts.astype('category')) self.assertEqual(df.shape, (4, 2)) - self.assertTrue(isinstance(df['c0'].dtype, com.CategoricalDtype)) - self.assertTrue(isinstance(df['c1'].dtype, com.CategoricalDtype)) + self.assertTrue(isinstance(df['c0'].dtype, CategoricalDtype)) + self.assertTrue(isinstance(df['c1'].dtype, CategoricalDtype)) def test_apply_mixed_datetimelike(self): # mixed datetimelike diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index b42aef9447373..d21db5ba52a45 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -14,6 +14,7 @@ import numpy.ma as ma import numpy.ma.mrecords as mrecords +from pandas.types.common import is_integer_dtype from pandas.compat import (lmap, long, zip, range, lrange, lzip, OrderedDict, is_platform_little_endian) from pandas import compat @@ -809,7 +810,7 @@ def test_constructor_list_of_lists(self): # GH #484 l = [[1, 'a'], [2, 'b']] df = DataFrame(data=l, columns=["num", "str"]) - self.assertTrue(com.is_integer_dtype(df['num'])) + self.assertTrue(is_integer_dtype(df['num'])) self.assertEqual(df['str'].dtype, np.object_) # GH 4851 diff --git a/pandas/tests/frame/test_dtypes.py b/pandas/tests/frame/test_dtypes.py index 5f95ff6b6b601..c650436eefaf3 100644 --- a/pandas/tests/frame/test_dtypes.py +++ b/pandas/tests/frame/test_dtypes.py @@ -1,15 +1,13 @@ # -*- coding: utf-8 -*- from __future__ import print_function - from datetime import timedelta import numpy as np - from pandas import (DataFrame, Series, date_range, Timedelta, Timestamp, compat, option_context) from pandas.compat import u -from pandas.core import common as com +from pandas.types.dtypes import DatetimeTZDtype from pandas.tests.frame.common import TestData from pandas.util.testing import (assert_series_equal, assert_frame_equal, @@ -84,8 +82,8 @@ def test_datetime_with_tz_dtypes(self): tzframe.iloc[1, 2] = pd.NaT result = tzframe.dtypes.sort_index() expected = Series([np.dtype('datetime64[ns]'), - com.DatetimeTZDtype('datetime64[ns, US/Eastern]'), - com.DatetimeTZDtype('datetime64[ns, CET]')], + DatetimeTZDtype('datetime64[ns, US/Eastern]'), + DatetimeTZDtype('datetime64[ns, CET]')], ['A', 'B', 'C']) assert_series_equal(result, expected) diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index d7fed8131a4f4..578df5ba9101e 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -17,6 +17,9 @@ date_range) import pandas as pd +from pandas.types.common import (is_float_dtype, + is_integer, + is_scalar) from pandas.util.testing import (assert_almost_equal, assert_numpy_array_equal, assert_series_equal, @@ -26,7 +29,6 @@ from pandas.core.indexing import IndexingError import pandas.util.testing as tm -import pandas.lib as lib from pandas.tests.frame.common import TestData @@ -1419,15 +1421,15 @@ def test_setitem_single_column_mixed_datetime(self): # set an allowable datetime64 type from pandas import tslib df.ix['b', 'timestamp'] = tslib.iNaT - self.assertTrue(com.isnull(df.ix['b', 'timestamp'])) + self.assertTrue(isnull(df.ix['b', 'timestamp'])) # allow this syntax df.ix['c', 'timestamp'] = nan - self.assertTrue(com.isnull(df.ix['c', 'timestamp'])) + self.assertTrue(isnull(df.ix['c', 'timestamp'])) # allow this syntax df.ix['d', :] = nan - self.assertTrue(com.isnull(df.ix['c', :]).all() == False) # noqa + self.assertTrue(isnull(df.ix['c', :]).all() == False) # noqa # as of GH 3216 this will now work! # try to set with a list like item @@ -1619,7 +1621,7 @@ def test_set_value_resize(self): res = self.frame.copy() res3 = res.set_value('foobar', 'baz', 5) - self.assertTrue(com.is_float_dtype(res3['baz'])) + self.assertTrue(is_float_dtype(res3['baz'])) self.assertTrue(isnull(res3['baz'].drop(['foobar'])).all()) self.assertRaises(ValueError, res3.set_value, 'foobar', 'baz', 'sam') @@ -1662,7 +1664,7 @@ def test_single_element_ix_dont_upcast(self): (int, np.integer))) result = self.frame.ix[self.frame.index[5], 'E'] - self.assertTrue(com.is_integer(result)) + self.assertTrue(is_integer(result)) def test_irow(self): df = DataFrame(np.random.randn(10, 4), index=lrange(0, 20, 2)) @@ -2268,7 +2270,7 @@ def _check_align(df, cond, other, check_dtypes=True): d = df[k].values c = cond[k].reindex(df[k].index).fillna(False).values - if lib.isscalar(other): + if is_scalar(other): o = other else: if isinstance(other, np.ndarray): diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py index ee7c296f563f0..c91585a28d867 100644 --- a/pandas/tests/frame/test_operators.py +++ b/pandas/tests/frame/test_operators.py @@ -417,10 +417,11 @@ def test_arith_flex_frame(self): # ndim >= 3 ndim_5 = np.ones(self.frame.shape + (3, 4, 5)) - with assertRaisesRegexp(ValueError, 'shape'): + msg = "Unable to coerce to Series/DataFrame" + with assertRaisesRegexp(ValueError, msg): f(self.frame, ndim_5) - with assertRaisesRegexp(ValueError, 'shape'): + with assertRaisesRegexp(ValueError, msg): getattr(self.frame, op)(ndim_5) # res_add = self.frame.add(self.frame) @@ -581,8 +582,9 @@ def _check_unaligned_frame(meth, op, df, other): # scalar assert_frame_equal(f(0), o(df, 0)) # NAs + msg = "Unable to coerce to Series/DataFrame" assert_frame_equal(f(np.nan), o(df, np.nan)) - with assertRaisesRegexp(ValueError, 'shape'): + with assertRaisesRegexp(ValueError, msg): f(ndim_5) # Series @@ -662,6 +664,17 @@ def _test_seq(df, idx_ser, col_ser): exp = DataFrame({'col': [False, True, False]}) assert_frame_equal(result, exp) + def test_dti_tz_convert_to_utc(self): + base = pd.DatetimeIndex(['2011-01-01', '2011-01-02', + '2011-01-03'], tz='UTC') + idx1 = base.tz_convert('Asia/Tokyo')[:2] + idx2 = base.tz_convert('US/Eastern')[1:] + + df1 = DataFrame({'A': [1, 2]}, index=idx1) + df2 = DataFrame({'A': [1, 1]}, index=idx2) + exp = DataFrame({'A': [np.nan, 3, np.nan]}, index=base) + assert_frame_equal(df1 + df2, exp) + def test_arith_flex_series(self): df = self.simple @@ -1176,6 +1189,53 @@ def test_inplace_ops_identity(self): assert_frame_equal(df2, expected) self.assertIs(df._data, df2._data) + def test_alignment_non_pandas(self): + index = ['A', 'B', 'C'] + columns = ['X', 'Y', 'Z'] + df = pd.DataFrame(np.random.randn(3, 3), index=index, columns=columns) + + align = pd.core.ops._align_method_FRAME + + for val in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3], dtype=np.intp)]: + + tm.assert_series_equal(align(df, val, 'index'), + Series([1, 2, 3], index=df.index)) + tm.assert_series_equal(align(df, val, 'columns'), + Series([1, 2, 3], index=df.columns)) + + # length mismatch + msg = 'Unable to coerce to Series, length must be 3: given 2' + for val in [[1, 2], (1, 2), np.array([1, 2])]: + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'index') + + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'columns') + + val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + tm.assert_frame_equal(align(df, val, 'index'), + DataFrame(val, index=df.index, + columns=df.columns)) + tm.assert_frame_equal(align(df, val, 'columns'), + DataFrame(val, index=df.index, + columns=df.columns)) + + # shape mismatch + msg = 'Unable to coerce to DataFrame, shape must be' + val = np.array([[1, 2, 3], [4, 5, 6]]) + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'index') + + with tm.assertRaisesRegexp(ValueError, msg): + align(df, val, 'columns') + + val = np.zeros((3, 3, 3)) + with tm.assertRaises(ValueError): + align(df, val, 'index') + with tm.assertRaises(ValueError): + align(df, val, 'columns') + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/frame/test_sorting.py b/pandas/tests/frame/test_sorting.py index ff2159f8b6f40..4d57216c8f870 100644 --- a/pandas/tests/frame/test_sorting.py +++ b/pandas/tests/frame/test_sorting.py @@ -21,75 +21,68 @@ class TestDataFrameSorting(tm.TestCase, TestData): _multiprocess_can_split_ = True - def test_sort_values(self): - # API for 9816 + def test_sort_index(self): + # GH13496 - # sort_index frame = DataFrame(np.arange(16).reshape(4, 4), index=[1, 2, 3, 4], columns=['A', 'B', 'C', 'D']) - # 9816 deprecated - with tm.assert_produces_warning(FutureWarning): - frame.sort(columns='A') - with tm.assert_produces_warning(FutureWarning): - frame.sort() - + # axis=0 : sort rows by index labels unordered = frame.ix[[3, 2, 4, 1]] - expected = unordered.sort_index() - result = unordered.sort_index(axis=0) + expected = frame assert_frame_equal(result, expected) - unordered = frame.ix[:, [2, 1, 3, 0]] - expected = unordered.sort_index(axis=1) + result = unordered.sort_index(ascending=False) + expected = frame[::-1] + assert_frame_equal(result, expected) + # axis=1 : sort columns by column names + unordered = frame.ix[:, [2, 1, 3, 0]] result = unordered.sort_index(axis=1) - assert_frame_equal(result, expected) + assert_frame_equal(result, frame) + + result = unordered.sort_index(axis=1, ascending=False) + expected = frame.ix[:, ::-1] assert_frame_equal(result, expected) - # sortlevel - mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC')) + def test_sort_index_multiindex(self): + # GH13496 + + # sort rows by specified level of multi-index + mi = MultiIndex.from_tuples([[2, 1, 3], [1, 1, 1]], names=list('ABC')) df = DataFrame([[1, 2], [3, 4]], mi) result = df.sort_index(level='A', sort_remaining=False) expected = df.sortlevel('A', sort_remaining=False) assert_frame_equal(result, expected) + # sort columns by specified level of multi-index df = df.T result = df.sort_index(level='A', axis=1, sort_remaining=False) expected = df.sortlevel('A', axis=1, sort_remaining=False) assert_frame_equal(result, expected) - # MI sort, but no by + # MI sort, but no level: sort_level has no effect mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC')) df = DataFrame([[1, 2], [3, 4]], mi) result = df.sort_index(sort_remaining=False) expected = df.sort_index() assert_frame_equal(result, expected) - def test_sort_index(self): + def test_sort(self): frame = DataFrame(np.arange(16).reshape(4, 4), index=[1, 2, 3, 4], columns=['A', 'B', 'C', 'D']) - # axis=0 - unordered = frame.ix[[3, 2, 4, 1]] - sorted_df = unordered.sort_index(axis=0) - expected = frame - assert_frame_equal(sorted_df, expected) - - sorted_df = unordered.sort_index(ascending=False) - expected = frame[::-1] - assert_frame_equal(sorted_df, expected) - - # axis=1 - unordered = frame.ix[:, ['D', 'B', 'C', 'A']] - sorted_df = unordered.sort_index(axis=1) - expected = frame - assert_frame_equal(sorted_df, expected) + # 9816 deprecated + with tm.assert_produces_warning(FutureWarning): + frame.sort(columns='A') + with tm.assert_produces_warning(FutureWarning): + frame.sort() - sorted_df = unordered.sort_index(axis=1, ascending=False) - expected = frame.ix[:, ::-1] - assert_frame_equal(sorted_df, expected) + def test_sort_values(self): + frame = DataFrame([[1, 1, 2], [3, 1, 0], [4, 5, 6]], + index=[1, 2, 3], columns=list('ABC')) # by column sorted_df = frame.sort_values(by='A') @@ -109,16 +102,17 @@ def test_sort_index(self): sorted_df = frame.sort_values(by=['A'], ascending=[False]) assert_frame_equal(sorted_df, expected) - # check for now - sorted_df = frame.sort_values(by='A') - assert_frame_equal(sorted_df, expected[::-1]) - expected = frame.sort_values(by='A') + # multiple bys + sorted_df = frame.sort_values(by=['B', 'C']) + expected = frame.loc[[2, 1, 3]] assert_frame_equal(sorted_df, expected) - expected = frame.sort_values(by=['A', 'B'], ascending=False) - sorted_df = frame.sort_values(by=['A', 'B']) + sorted_df = frame.sort_values(by=['B', 'C'], ascending=False) assert_frame_equal(sorted_df, expected[::-1]) + sorted_df = frame.sort_values(by=['B', 'A'], ascending=[True, False]) + assert_frame_equal(sorted_df, expected) + self.assertRaises(ValueError, lambda: frame.sort_values( by=['A', 'B'], axis=2, inplace=True)) @@ -130,6 +124,25 @@ def test_sort_index(self): with assertRaisesRegexp(ValueError, msg): frame.sort_values(by=['A', 'B'], axis=0, ascending=[True] * 5) + def test_sort_values_inplace(self): + frame = DataFrame(np.random.randn(4, 4), index=[1, 2, 3, 4], + columns=['A', 'B', 'C', 'D']) + + sorted_df = frame.copy() + sorted_df.sort_values(by='A', inplace=True) + expected = frame.sort_values(by='A') + assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + sorted_df.sort_values(by='A', ascending=False, inplace=True) + expected = frame.sort_values(by='A', ascending=False) + assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + sorted_df.sort_values(by=['A', 'B'], ascending=False, inplace=True) + expected = frame.sort_values(by=['A', 'B'], ascending=False) + assert_frame_equal(sorted_df, expected) + def test_sort_index_categorical_index(self): df = (DataFrame({'A': np.arange(6, dtype='int64'), @@ -361,25 +374,6 @@ def test_sort_index_different_sortorder(self): result = idf['C'].sort_index(ascending=[1, 0]) assert_series_equal(result, expected['C']) - def test_sort_inplace(self): - frame = DataFrame(np.random.randn(4, 4), index=[1, 2, 3, 4], - columns=['A', 'B', 'C', 'D']) - - sorted_df = frame.copy() - sorted_df.sort_values(by='A', inplace=True) - expected = frame.sort_values(by='A') - assert_frame_equal(sorted_df, expected) - - sorted_df = frame.copy() - sorted_df.sort_values(by='A', ascending=False, inplace=True) - expected = frame.sort_values(by='A', ascending=False) - assert_frame_equal(sorted_df, expected) - - sorted_df = frame.copy() - sorted_df.sort_values(by=['A', 'B'], ascending=False, inplace=True) - expected = frame.sort_values(by=['A', 'B'], ascending=False) - assert_frame_equal(sorted_df, expected) - def test_sort_index_duplicates(self): # with 9816, these are all translated to .sort_values diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py index c23702ef46ad2..55c7ebb183ce5 100644 --- a/pandas/tests/frame/test_to_csv.py +++ b/pandas/tests/frame/test_to_csv.py @@ -10,7 +10,7 @@ from pandas.compat import (lmap, range, lrange, StringIO, u) from pandas.parser import CParserError from pandas import (DataFrame, Index, Series, MultiIndex, Timestamp, - date_range, read_csv, compat) + date_range, read_csv, compat, to_datetime) import pandas as pd from pandas.util.testing import (assert_almost_equal, @@ -139,7 +139,7 @@ def test_to_csv_from_csv5(self): self.tzframe.to_csv(path) result = pd.read_csv(path, index_col=0, parse_dates=['A']) - converter = lambda c: pd.to_datetime(result[c]).dt.tz_localize( + converter = lambda c: to_datetime(result[c]).dt.tz_localize( 'UTC').dt.tz_convert(self.tzframe[c].dt.tz) result['B'] = converter('B') result['C'] = converter('C') @@ -162,15 +162,6 @@ def test_to_csv_cols_reordering(self): assert_frame_equal(df[cols], rs_c, check_names=False) - def test_to_csv_legacy_raises_on_dupe_cols(self): - df = mkdf(10, 3) - df.columns = ['a', 'a', 'b'] - with ensure_clean() as path: - with tm.assert_produces_warning(FutureWarning, - check_stacklevel=False): - self.assertRaises(NotImplementedError, - df.to_csv, path, engine='python') - def test_to_csv_new_dupe_cols(self): import pandas as pd @@ -712,7 +703,6 @@ def test_to_csv_dups_cols(self): cols.extend([0, 1, 2]) df.columns = cols - from pandas import to_datetime with ensure_clean() as filename: df.to_csv(filename) result = read_csv(filename, index_col=0) @@ -993,72 +983,57 @@ def test_to_csv_compression_value_error(self): filename, compression="zip") def test_to_csv_date_format(self): - from pandas import to_datetime with ensure_clean('__tmp_to_csv_date_format__') as path: - for engine in [None, 'python']: - w = FutureWarning if engine == 'python' else None - - dt_index = self.tsframe.index - datetime_frame = DataFrame( - {'A': dt_index, 'B': dt_index.shift(1)}, index=dt_index) - - with tm.assert_produces_warning(w, check_stacklevel=False): - datetime_frame.to_csv( - path, date_format='%Y%m%d', engine=engine) - - # Check that the data was put in the specified format - test = read_csv(path, index_col=0) - - datetime_frame_int = datetime_frame.applymap( - lambda x: int(x.strftime('%Y%m%d'))) - datetime_frame_int.index = datetime_frame_int.index.map( - lambda x: int(x.strftime('%Y%m%d'))) + dt_index = self.tsframe.index + datetime_frame = DataFrame( + {'A': dt_index, 'B': dt_index.shift(1)}, index=dt_index) + datetime_frame.to_csv(path, date_format='%Y%m%d') - assert_frame_equal(test, datetime_frame_int) + # Check that the data was put in the specified format + test = read_csv(path, index_col=0) - with tm.assert_produces_warning(w, check_stacklevel=False): - datetime_frame.to_csv( - path, date_format='%Y-%m-%d', engine=engine) + datetime_frame_int = datetime_frame.applymap( + lambda x: int(x.strftime('%Y%m%d'))) + datetime_frame_int.index = datetime_frame_int.index.map( + lambda x: int(x.strftime('%Y%m%d'))) - # Check that the data was put in the specified format - test = read_csv(path, index_col=0) - datetime_frame_str = datetime_frame.applymap( - lambda x: x.strftime('%Y-%m-%d')) - datetime_frame_str.index = datetime_frame_str.index.map( - lambda x: x.strftime('%Y-%m-%d')) + assert_frame_equal(test, datetime_frame_int) - assert_frame_equal(test, datetime_frame_str) + datetime_frame.to_csv(path, date_format='%Y-%m-%d') - # Check that columns get converted - datetime_frame_columns = datetime_frame.T + # Check that the data was put in the specified format + test = read_csv(path, index_col=0) + datetime_frame_str = datetime_frame.applymap( + lambda x: x.strftime('%Y-%m-%d')) + datetime_frame_str.index = datetime_frame_str.index.map( + lambda x: x.strftime('%Y-%m-%d')) - with tm.assert_produces_warning(w, check_stacklevel=False): - datetime_frame_columns.to_csv( - path, date_format='%Y%m%d', engine=engine) + assert_frame_equal(test, datetime_frame_str) - test = read_csv(path, index_col=0) + # Check that columns get converted + datetime_frame_columns = datetime_frame.T + datetime_frame_columns.to_csv(path, date_format='%Y%m%d') - datetime_frame_columns = datetime_frame_columns.applymap( - lambda x: int(x.strftime('%Y%m%d'))) - # Columns don't get converted to ints by read_csv - datetime_frame_columns.columns = ( - datetime_frame_columns.columns - .map(lambda x: x.strftime('%Y%m%d'))) + test = read_csv(path, index_col=0) - assert_frame_equal(test, datetime_frame_columns) + datetime_frame_columns = datetime_frame_columns.applymap( + lambda x: int(x.strftime('%Y%m%d'))) + # Columns don't get converted to ints by read_csv + datetime_frame_columns.columns = ( + datetime_frame_columns.columns + .map(lambda x: x.strftime('%Y%m%d'))) - # test NaTs - nat_index = to_datetime( - ['NaT'] * 10 + ['2000-01-01', '1/1/2000', '1-1-2000']) - nat_frame = DataFrame({'A': nat_index}, index=nat_index) + assert_frame_equal(test, datetime_frame_columns) - with tm.assert_produces_warning(w, check_stacklevel=False): - nat_frame.to_csv( - path, date_format='%Y-%m-%d', engine=engine) + # test NaTs + nat_index = to_datetime( + ['NaT'] * 10 + ['2000-01-01', '1/1/2000', '1-1-2000']) + nat_frame = DataFrame({'A': nat_index}, index=nat_index) + nat_frame.to_csv(path, date_format='%Y-%m-%d') - test = read_csv(path, parse_dates=[0, 1], index_col=0) + test = read_csv(path, parse_dates=[0, 1], index_col=0) - assert_frame_equal(test, nat_frame) + assert_frame_equal(test, nat_frame) def test_to_csv_with_dst_transitions(self): @@ -1077,7 +1052,7 @@ def test_to_csv_with_dst_transitions(self): # we have to reconvert the index as we # don't parse the tz's result = read_csv(path, index_col=0) - result.index = pd.to_datetime(result.index).tz_localize( + result.index = to_datetime(result.index).tz_localize( 'UTC').tz_convert('Europe/London') assert_frame_equal(result, df) @@ -1089,9 +1064,9 @@ def test_to_csv_with_dst_transitions(self): with ensure_clean('csv_date_format_with_dst') as path: df.to_csv(path, index=True) result = read_csv(path, index_col=0) - result.index = pd.to_datetime(result.index).tz_localize( + result.index = to_datetime(result.index).tz_localize( 'UTC').tz_convert('Europe/Paris') - result['idx'] = pd.to_datetime(result['idx']).astype( + result['idx'] = to_datetime(result['idx']).astype( 'datetime64[ns, Europe/Paris]') assert_frame_equal(result, df) diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index d535eaa238567..06662e52e3a6f 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -203,6 +203,49 @@ def __array__(self, dtype=None): result = pd.Index(ArrayLike(array)) self.assert_index_equal(result, expected) + def test_index_ctor_infer_nan_nat(self): + # GH 13467 + exp = pd.Float64Index([np.nan, np.nan]) + self.assertEqual(exp.dtype, np.float64) + tm.assert_index_equal(Index([np.nan, np.nan]), exp) + tm.assert_index_equal(Index(np.array([np.nan, np.nan])), exp) + + exp = pd.DatetimeIndex([pd.NaT, pd.NaT]) + self.assertEqual(exp.dtype, 'datetime64[ns]') + tm.assert_index_equal(Index([pd.NaT, pd.NaT]), exp) + tm.assert_index_equal(Index(np.array([pd.NaT, pd.NaT])), exp) + + exp = pd.DatetimeIndex([pd.NaT, pd.NaT]) + self.assertEqual(exp.dtype, 'datetime64[ns]') + + for data in [[pd.NaT, np.nan], [np.nan, pd.NaT], + [np.nan, np.datetime64('nat')], + [np.datetime64('nat'), np.nan]]: + tm.assert_index_equal(Index(data), exp) + tm.assert_index_equal(Index(np.array(data, dtype=object)), exp) + + exp = pd.TimedeltaIndex([pd.NaT, pd.NaT]) + self.assertEqual(exp.dtype, 'timedelta64[ns]') + + for data in [[np.nan, np.timedelta64('nat')], + [np.timedelta64('nat'), np.nan], + [pd.NaT, np.timedelta64('nat')], + [np.timedelta64('nat'), pd.NaT]]: + + tm.assert_index_equal(Index(data), exp) + tm.assert_index_equal(Index(np.array(data, dtype=object)), exp) + + # mixed np.datetime64/timedelta64 nat results in object + data = [np.datetime64('nat'), np.timedelta64('nat')] + exp = pd.Index(data, dtype=object) + tm.assert_index_equal(Index(data), exp) + tm.assert_index_equal(Index(np.array(data, dtype=object)), exp) + + data = [np.timedelta64('nat'), np.datetime64('nat')] + exp = pd.Index(data, dtype=object) + tm.assert_index_equal(Index(data), exp) + tm.assert_index_equal(Index(np.array(data, dtype=object)), exp) + def test_index_ctor_infer_periodindex(self): xp = period_range('2012-1-1', freq='M', periods=3) rs = Index(xp) @@ -1370,6 +1413,12 @@ def test_take_fill_value(self): with tm.assertRaises(IndexError): idx.take(np.array([1, -5])) + def test_reshape_raise(self): + msg = "reshaping is not supported" + idx = pd.Index([0, 1, 2]) + tm.assertRaisesRegexp(NotImplementedError, msg, + idx.reshape, idx.shape) + def test_reindex_preserves_name_if_target_is_list_or_ndarray(self): # GH6552 idx = pd.Index([0, 1, 2]) diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py index 4a664ed3542d7..52cb2964c4ea2 100644 --- a/pandas/tests/indexes/test_datetimelike.py +++ b/pandas/tests/indexes/test_datetimelike.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -from datetime import timedelta, time +from datetime import timedelta, time, date, datetime import numpy as np @@ -19,6 +19,9 @@ class DatetimeLike(Base): + def create_nonmonotonic_index(self): + return self.create_index()[[2, 0, 3, 4, 1]] + def test_shift_identity(self): idx = self.create_index() @@ -64,6 +67,9 @@ def setUp(self): def create_index(self): return date_range('20130101', periods=5) + def create_elem_outside_index(self): + return pd.Timestamp('20130106') + def test_shift(self): # test shift for datetimeIndex and non datetimeIndex @@ -534,9 +540,9 @@ def test_get_loc(self): # time indexing idx = pd.date_range('2000-01-01', periods=24, freq='H') tm.assert_numpy_array_equal(idx.get_loc(time(12)), - np.array([12], dtype=np.int64)) + np.array([12]), check_dtype=False) tm.assert_numpy_array_equal(idx.get_loc(time(12, 30)), - np.array([], dtype=np.int64)) + np.array([]), check_dtype=False) with tm.assertRaises(NotImplementedError): idx.get_loc(time(12, 30), method='pad') @@ -587,7 +593,8 @@ def test_time_loc(self): # GH8667 ts = pd.Series(np.random.randn(n), index=idx) i = np.arange(start, n, step) - tm.assert_numpy_array_equal(ts.index.get_loc(key), i) + tm.assert_numpy_array_equal(ts.index.get_loc(key), i, + check_dtype=False) tm.assert_series_equal(ts[key], ts.iloc[i]) left, right = ts.copy(), ts.copy() @@ -721,6 +728,37 @@ def test_fillna_datetime64(self): dtype=object) self.assert_index_equal(idx.fillna('x'), exp) + def test_contains(self): + # GH13572 + monotonic = self.create_index() + ascending_nat_first = monotonic.insert( + 0, pd.NaT) # Not monotonic after inserting NaT + ascending_nat_last = monotonic.insert(5, pd.NaT) + non_monotonic = self.create_nonmonotonic_index() + non_monotonic_nat_first = non_monotonic.insert(0, pd.NaT) + non_monotonic_nat_last = non_monotonic.insert(5, pd.NaT) + idx_with_nat = [ascending_nat_first, ascending_nat_last, + non_monotonic_nat_first, non_monotonic_nat_last] + idx_no_nat = [monotonic, non_monotonic] + for idx in idx_no_nat + idx_with_nat: + elem = self.create_elem_outside_index() + elem_str = str(elem) + elem_date_str = str(elem.date()) + for e in [elem, elem_str, elem_date_str, elem.date(), elem.to_datetime()]: + self.assertNotIn(e, idx) + for elem in monotonic: + elem_str = str(elem) + elem_date_str = str(elem.date()) + for e in [elem, elem_str, elem_date_str, elem.date(), elem.to_datetime()]: + self.assertIn(e, idx) + nat_elems = [pd.NaT, None, float('nan'), np.nan] + for idx in idx_no_nat: + for nn in nat_elems: + self.assertNotIn(nn, idx) + for idx in idx_with_nat: + for nn in nat_elems: + self.assertIn(nn, idx) + class TestPeriodIndex(DatetimeLike, tm.TestCase): _holder = PeriodIndex @@ -733,6 +771,9 @@ def setUp(self): def create_index(self): return period_range('20130101', periods=5, freq='D') + def create_elem_outside_index(self): + return pd.Period('20130106') + def test_astype(self): # GH 13149, GH 13209 idx = PeriodIndex(['2016-05-16', 'NaT', NaT, np.NaN], freq='D') @@ -740,14 +781,7 @@ def test_astype(self): result = idx.astype(object) expected = Index([Period('2016-05-16', freq='D')] + [Period(NaT, freq='D')] * 3, dtype='object') - # Hack because of lack of support for Period null checking (GH12759) - tm.assert_index_equal(result[:1], expected[:1]) - result_arr = np.asarray([p.ordinal for p in result], dtype=np.int64) - expected_arr = np.asarray([p.ordinal for p in expected], - dtype=np.int64) - tm.assert_numpy_array_equal(result_arr, expected_arr) - # TODO: When GH12759 is resolved, change the above hack to: - # tm.assert_index_equal(result, expected) # now, it raises. + tm.assert_index_equal(result, expected) result = idx.astype(int) expected = Int64Index([16937] + [-9223372036854775808] * 3, @@ -917,6 +951,35 @@ def test_no_millisecond_field(self): with self.assertRaises(AttributeError): DatetimeIndex([]).millisecond + def test_contains(self): + # GH13572 + monotonic = self.create_index() + ascending_nat_first = monotonic.insert( + 0, pd.NaT) # Not monotonic after inserting NaT + ascending_nat_last = monotonic.insert(5, pd.NaT) + non_monotonic = self.create_nonmonotonic_index() + non_monotonic_nat_first = non_monotonic.insert(0, pd.NaT) + non_monotonic_nat_last = non_monotonic.insert(5, pd.NaT) + idx_with_nat = [ascending_nat_first, ascending_nat_last, + non_monotonic_nat_first, non_monotonic_nat_last] + idx_no_nat = [monotonic, non_monotonic] + for idx in idx_no_nat + idx_with_nat: + elem = self.create_elem_outside_index() + elem_str = str(elem) + for e in [elem, elem_str]: + self.assertNotIn(e, idx) + for elem in monotonic: + elem_str = str(elem) + for e in [elem, elem_str]: + self.assertIn(e, idx) + nat_elems = [pd.Period('NaT', freq='D')] + for idx in idx_no_nat: + for nn in nat_elems: + self.assertNotIn(nn, idx) + for idx in idx_with_nat: + for nn in nat_elems: + self.assertIn(nn, idx) + class TestTimedeltaIndex(DatetimeLike, tm.TestCase): _holder = TimedeltaIndex @@ -929,6 +992,9 @@ def setUp(self): def create_index(self): return pd.to_timedelta(range(5), unit='d') + pd.offsets.Hour(1) + def create_elem_outside_index(self): + return pd.Timedelta(days=5, hours=1) + def test_shift(self): # test shift for TimedeltaIndex # err8083 @@ -1117,3 +1183,25 @@ def test_fillna_timedelta(self): exp = pd.Index( [pd.Timedelta('1 day'), 'x', pd.Timedelta('3 day')], dtype=object) self.assert_index_equal(idx.fillna('x'), exp) + + def test_contains(self): + # GH13572 + monotonic = self.create_index() + ascending_nat_first = monotonic.insert( + 0, pd.NaT) # Not monotonic after inserting NaT + ascending_nat_last = monotonic.insert(5, pd.NaT) + non_monotonic = self.create_nonmonotonic_index() + non_monotonic_nat_first = non_monotonic.insert(0, pd.NaT) + non_monotonic_nat_last = non_monotonic.insert(5, pd.NaT) + idx_with_nat = [ascending_nat_first, ascending_nat_last, + non_monotonic_nat_first, non_monotonic_nat_last] + idx_no_nat = [monotonic, non_monotonic] + for idx in idx_no_nat + idx_with_nat: + elem = self.create_elem_outside_index() + elem_str = str(elem) + for e in [elem, elem_str]: + self.assertNotIn(e, idx) + for elem in monotonic: + elem_str = str(elem) + for e in [elem, elem_str]: + self.assertIn(e, idx) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index fb5576bed90b4..e6a8aafc32be4 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -1750,12 +1750,12 @@ def test_reindex_level(self): exp_index2 = self.index.join(idx, level='second', how='left') self.assertTrue(target.equals(exp_index)) - exp_indexer = np.array([0, 2, 4], dtype=np.int64) - tm.assert_numpy_array_equal(indexer, exp_indexer) + exp_indexer = np.array([0, 2, 4]) + tm.assert_numpy_array_equal(indexer, exp_indexer, check_dtype=False) self.assertTrue(target2.equals(exp_index2)) - exp_indexer2 = np.array([0, -1, 0, -1, 0, -1], dtype=np.int64) - tm.assert_numpy_array_equal(indexer2, exp_indexer2) + exp_indexer2 = np.array([0, -1, 0, -1, 0, -1]) + tm.assert_numpy_array_equal(indexer2, exp_indexer2, check_dtype=False) assertRaisesRegexp(TypeError, "Fill method not supported", self.index.reindex, self.index, method='pad', diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index b86b248ead290..44c7f2277293d 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -6,6 +6,9 @@ import warnings from datetime import datetime +from pandas.types.common import (is_integer_dtype, + is_float_dtype, + is_scalar) from pandas.compat import range, lrange, lzip, StringIO, lmap, map from pandas.tslib import NaT from numpy import nan @@ -22,7 +25,7 @@ assert_frame_equal, assert_panel_equal, assert_attr_equal, slow) from pandas.formats.printing import pprint_thing -from pandas import concat, lib +from pandas import concat from pandas.core.common import PerformanceWarning import pandas.util.testing as tm @@ -200,7 +203,7 @@ def _print(result, error=None): return try: - if lib.isscalar(rs) and lib.isscalar(xp): + if is_scalar(rs) and is_scalar(xp): self.assertEqual(rs, xp) elif xp.ndim == 1: assert_series_equal(rs, xp) @@ -775,7 +778,7 @@ def test_ix_loc_consistency(self): # this is not an exhaustive case def compare(result, expected): - if lib.isscalar(expected): + if is_scalar(expected): self.assertEqual(result, expected) else: self.assertTrue(expected.equals(result)) @@ -965,7 +968,7 @@ def test_indexing_with_datetime_tz(self): # indexing - fast_xs df = DataFrame({'a': date_range('2014-01-01', periods=10, tz='UTC')}) result = df.iloc[5] - expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', offset='D') + expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', freq='D') self.assertEqual(result, expected) result = df.loc[5] @@ -2888,8 +2891,8 @@ def test_setitem_dtype_upcast(self): columns=['foo', 'bar', 'baz']) assert_frame_equal(left, right) - self.assertTrue(com.is_integer_dtype(left['foo'])) - self.assertTrue(com.is_integer_dtype(left['baz'])) + self.assertTrue(is_integer_dtype(left['foo'])) + self.assertTrue(is_integer_dtype(left['baz'])) left = DataFrame(np.arange(6, dtype='int64').reshape(2, 3) / 10.0, index=list('ab'), @@ -2900,8 +2903,8 @@ def test_setitem_dtype_upcast(self): columns=['foo', 'bar', 'baz']) assert_frame_equal(left, right) - self.assertTrue(com.is_float_dtype(left['foo'])) - self.assertTrue(com.is_float_dtype(left['baz'])) + self.assertTrue(is_float_dtype(left['foo'])) + self.assertTrue(is_float_dtype(left['baz'])) def test_setitem_iloc(self): diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 433f0f4bc67f5..34cfb2f0c1529 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -5,7 +5,6 @@ from distutils.version import LooseVersion import nose -import random from numpy import nan import numpy as np @@ -262,7 +261,7 @@ def test_kurt(self): self.assertTrue((df.kurt() == 0).all()) def test_argsort(self): - self._check_accum_op('argsort') + self._check_accum_op('argsort', check_dtype=False) argsorted = self.ts.argsort() self.assertTrue(issubclass(argsorted.dtype.type, np.integer)) @@ -289,8 +288,10 @@ def test_argsort_stable(self): mexpected = np.argsort(s.values, kind='mergesort') qexpected = np.argsort(s.values, kind='quicksort') - self.assert_series_equal(mindexer, Series(mexpected)) - self.assert_series_equal(qindexer, Series(qexpected)) + self.assert_series_equal(mindexer, Series(mexpected), + check_dtype=False) + self.assert_series_equal(qindexer, Series(qexpected), + check_dtype=False) self.assertFalse(np.array_equal(qindexer, mindexer)) def test_cumsum(self): @@ -487,10 +488,11 @@ def testit(): except ImportError: pass - def _check_accum_op(self, name): + def _check_accum_op(self, name, check_dtype=True): func = getattr(np, name) self.assert_numpy_array_equal(func(self.ts).values, - func(np.array(self.ts))) + func(np.array(self.ts)), + check_dtype=check_dtype) # with missing values ts = self.ts.copy() @@ -499,7 +501,8 @@ def _check_accum_op(self, name): result = func(ts)[1::2] expected = func(np.array(ts.valid())) - self.assert_numpy_array_equal(result.values, expected) + self.assert_numpy_array_equal(result.values, expected, + check_dtype=False) def test_compress(self): cond = [True, False, True, False, False] @@ -1360,13 +1363,13 @@ def test_searchsorted_numeric_dtypes_scalar(self): self.assertEqual(r, e) r = s.searchsorted([30]) - e = np.array([2], dtype=np.int64) + e = np.array([2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_searchsorted_numeric_dtypes_vector(self): s = Series([1, 2, 90, 1000, 3e9]) r = s.searchsorted([91, 2e6]) - e = np.array([3, 4], dtype=np.int64) + e = np.array([3, 4], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_search_sorted_datetime64_scalar(self): @@ -1380,14 +1383,14 @@ def test_search_sorted_datetime64_list(self): s = Series(pd.date_range('20120101', periods=10, freq='2D')) v = [pd.Timestamp('20120102'), pd.Timestamp('20120104')] r = s.searchsorted(v) - e = np.array([1, 2], dtype=np.int64) + e = np.array([1, 2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_searchsorted_sorter(self): # GH8490 s = Series([3, 1, 2]) r = s.searchsorted([0, 3], sorter=np.argsort(s)) - e = np.array([0, 2], dtype=np.int64) + e = np.array([0, 2], dtype=np.intp) tm.assert_numpy_array_equal(r, e) def test_is_unique(self): @@ -1414,141 +1417,6 @@ def test_is_monotonic(self): self.assertFalse(s.is_monotonic) self.assertTrue(s.is_monotonic_decreasing) - def test_sort_values(self): - - ts = self.ts.copy() - - # 9816 deprecated - with tm.assert_produces_warning(FutureWarning): - ts.sort() - - self.assert_series_equal(ts, self.ts.sort_values()) - self.assert_index_equal(ts.index, self.ts.sort_values().index) - - ts.sort_values(ascending=False, inplace=True) - self.assert_series_equal(ts, self.ts.sort_values(ascending=False)) - self.assert_index_equal(ts.index, - self.ts.sort_values(ascending=False).index) - - # GH 5856/5853 - # Series.sort_values operating on a view - df = DataFrame(np.random.randn(10, 4)) - s = df.iloc[:, 0] - - def f(): - s.sort_values(inplace=True) - - self.assertRaises(ValueError, f) - - # test order/sort inplace - # GH6859 - ts1 = self.ts.copy() - ts1.sort_values(ascending=False, inplace=True) - ts2 = self.ts.copy() - ts2.sort_values(ascending=False, inplace=True) - assert_series_equal(ts1, ts2) - - ts1 = self.ts.copy() - ts1 = ts1.sort_values(ascending=False, inplace=False) - ts2 = self.ts.copy() - ts2 = ts.sort_values(ascending=False) - assert_series_equal(ts1, ts2) - - def test_sort_index(self): - rindex = list(self.ts.index) - random.shuffle(rindex) - - random_order = self.ts.reindex(rindex) - sorted_series = random_order.sort_index() - assert_series_equal(sorted_series, self.ts) - - # descending - sorted_series = random_order.sort_index(ascending=False) - assert_series_equal(sorted_series, - self.ts.reindex(self.ts.index[::-1])) - - def test_sort_index_inplace(self): - - # For #11402 - rindex = list(self.ts.index) - random.shuffle(rindex) - - # descending - random_order = self.ts.reindex(rindex) - result = random_order.sort_index(ascending=False, inplace=True) - self.assertIs(result, None, - msg='sort_index() inplace should return None') - assert_series_equal(random_order, self.ts.reindex(self.ts.index[::-1])) - - # ascending - random_order = self.ts.reindex(rindex) - result = random_order.sort_index(ascending=True, inplace=True) - self.assertIs(result, None, - msg='sort_index() inplace should return None') - assert_series_equal(random_order, self.ts) - - def test_sort_API(self): - - # API for 9816 - - # sortlevel - mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC')) - s = Series([1, 2], mi) - backwards = s.iloc[[1, 0]] - - res = s.sort_index(level='A') - assert_series_equal(backwards, res) - - # sort_index - rindex = list(self.ts.index) - random.shuffle(rindex) - - random_order = self.ts.reindex(rindex) - sorted_series = random_order.sort_index(level=0) - assert_series_equal(sorted_series, self.ts) - - # compat on axis - sorted_series = random_order.sort_index(axis=0) - assert_series_equal(sorted_series, self.ts) - - self.assertRaises(ValueError, lambda: random_order.sort_values(axis=1)) - - sorted_series = random_order.sort_index(level=0, axis=0) - assert_series_equal(sorted_series, self.ts) - - self.assertRaises(ValueError, - lambda: random_order.sort_index(level=0, axis=1)) - - def test_order(self): - - # 9816 deprecated - with tm.assert_produces_warning(FutureWarning): - self.ts.order() - - ts = self.ts.copy() - ts[:5] = np.NaN - vals = ts.values - - result = ts.sort_values() - self.assertTrue(np.isnan(result[-5:]).all()) - self.assert_numpy_array_equal(result[:-5].values, np.sort(vals[5:])) - - result = ts.sort_values(na_position='first') - self.assertTrue(np.isnan(result[:5]).all()) - self.assert_numpy_array_equal(result[5:].values, np.sort(vals[5:])) - - # something object-type - ser = Series(['A', 'B'], [1, 2]) - # no failure - ser.sort_values() - - # ascending=False - ordered = ts.sort_values(ascending=False) - expected = np.sort(ts.valid().values)[::-1] - assert_almost_equal(expected, ordered.valid().values) - ordered = ts.sort_values(ascending=False, na_position='first') - assert_almost_equal(expected, ordered.valid().values) - def test_nsmallest_nlargest(self): # float, int, datetime64 (use i8), timedelts64 (same), # object that are numbers, object that are strings @@ -1686,49 +1554,63 @@ def test_shift_categorical(self): assert_index_equal(s.values.categories, sp1.values.categories) assert_index_equal(s.values.categories, sn2.values.categories) + def test_reshape_deprecate(self): + x = Series(np.random.random(10), name='x') + tm.assert_produces_warning(FutureWarning, x.reshape, x.shape) + def test_reshape_non_2d(self): - # GH 4554 - x = Series(np.random.random(201), name='x') - self.assertTrue(x.reshape(x.shape, ) is x) + # see gh-4554 + with tm.assert_produces_warning(FutureWarning): + x = Series(np.random.random(201), name='x') + self.assertTrue(x.reshape(x.shape, ) is x) - # GH 2719 - a = Series([1, 2, 3, 4]) - result = a.reshape(2, 2) - expected = a.values.reshape(2, 2) - tm.assert_numpy_array_equal(result, expected) - self.assertIsInstance(result, type(expected)) + # see gh-2719 + with tm.assert_produces_warning(FutureWarning): + a = Series([1, 2, 3, 4]) + result = a.reshape(2, 2) + expected = a.values.reshape(2, 2) + tm.assert_numpy_array_equal(result, expected) + self.assertIsInstance(result, type(expected)) def test_reshape_2d_return_array(self): x = Series(np.random.random(201), name='x') - result = x.reshape((-1, 1)) - self.assertNotIsInstance(result, Series) - result2 = np.reshape(x, (-1, 1)) - self.assertNotIsInstance(result2, Series) + with tm.assert_produces_warning(FutureWarning): + result = x.reshape((-1, 1)) + self.assertNotIsInstance(result, Series) - result = x[:, None] - expected = x.reshape((-1, 1)) - assert_almost_equal(result, expected) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result2 = np.reshape(x, (-1, 1)) + self.assertNotIsInstance(result2, Series) + + with tm.assert_produces_warning(FutureWarning): + result = x[:, None] + expected = x.reshape((-1, 1)) + assert_almost_equal(result, expected) def test_reshape_bad_kwarg(self): a = Series([1, 2, 3, 4]) - msg = "'foo' is an invalid keyword argument for this function" - tm.assertRaisesRegexp(TypeError, msg, a.reshape, (2, 2), foo=2) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "'foo' is an invalid keyword argument for this function" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, (2, 2), foo=2) - msg = "reshape\(\) got an unexpected keyword argument 'foo'" - tm.assertRaisesRegexp(TypeError, msg, a.reshape, a.shape, foo=2) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "reshape\(\) got an unexpected keyword argument 'foo'" + tm.assertRaisesRegexp(TypeError, msg, a.reshape, a.shape, foo=2) def test_numpy_reshape(self): a = Series([1, 2, 3, 4]) - result = np.reshape(a, (2, 2)) - expected = a.values.reshape(2, 2) - tm.assert_numpy_array_equal(result, expected) - self.assertIsInstance(result, type(expected)) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result = np.reshape(a, (2, 2)) + expected = a.values.reshape(2, 2) + tm.assert_numpy_array_equal(result, expected) + self.assertIsInstance(result, type(expected)) - result = np.reshape(a, a.shape) - tm.assert_series_equal(result, a) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + result = np.reshape(a, a.shape) + tm.assert_series_equal(result, a) def test_unstack(self): from numpy import nan diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index a80a3af56b18f..c8e04f1ffd75f 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -8,10 +8,11 @@ import numpy.ma as ma import pandas as pd +from pandas.types.common import is_categorical_dtype, is_datetime64tz_dtype from pandas import Index, Series, isnull, date_range, period_range from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp, DatetimeIndex -import pandas.core.common as com + import pandas.lib as lib from pandas.compat import lrange, range, zip, OrderedDict, long @@ -108,6 +109,17 @@ def test_constructor_iterator(self): result = Series(range(10), dtype='int64') assert_series_equal(result, expected) + def test_constructor_list_like(self): + + # make sure that we are coercing different + # list-likes to standard dtypes and not + # platform specific + expected = Series([1, 2, 3], dtype='int64') + for obj in [[1, 2, 3], (1, 2, 3), + np.array([1, 2, 3], dtype='int64')]: + result = Series(obj, index=[0, 1, 2]) + assert_series_equal(result, expected) + def test_constructor_generator(self): gen = (i for i in range(10)) @@ -144,11 +156,11 @@ def test_constructor_categorical(self): ValueError, lambda: Series(pd.Categorical([1, 2, 3]), dtype='int64')) cat = Series(pd.Categorical([1, 2, 3]), dtype='category') - self.assertTrue(com.is_categorical_dtype(cat)) - self.assertTrue(com.is_categorical_dtype(cat.dtype)) + self.assertTrue(is_categorical_dtype(cat)) + self.assertTrue(is_categorical_dtype(cat.dtype)) s = Series([1, 2, 3], dtype='category') - self.assertTrue(com.is_categorical_dtype(s)) - self.assertTrue(com.is_categorical_dtype(s.dtype)) + self.assertTrue(is_categorical_dtype(s)) + self.assertTrue(is_categorical_dtype(s.dtype)) def test_constructor_maskedarray(self): data = ma.masked_all((3, ), dtype=float) @@ -252,6 +264,24 @@ def test_constructor_pass_none(self): expected = Series(index=Index([None])) assert_series_equal(s, expected) + def test_constructor_pass_nan_nat(self): + # GH 13467 + exp = Series([np.nan, np.nan], dtype=np.float64) + self.assertEqual(exp.dtype, np.float64) + tm.assert_series_equal(Series([np.nan, np.nan]), exp) + tm.assert_series_equal(Series(np.array([np.nan, np.nan])), exp) + + exp = Series([pd.NaT, pd.NaT]) + self.assertEqual(exp.dtype, 'datetime64[ns]') + tm.assert_series_equal(Series([pd.NaT, pd.NaT]), exp) + tm.assert_series_equal(Series(np.array([pd.NaT, pd.NaT])), exp) + + tm.assert_series_equal(Series([pd.NaT, np.nan]), exp) + tm.assert_series_equal(Series(np.array([pd.NaT, np.nan])), exp) + + tm.assert_series_equal(Series([np.nan, pd.NaT]), exp) + tm.assert_series_equal(Series(np.array([np.nan, pd.NaT])), exp) + def test_constructor_cast(self): self.assertRaises(ValueError, Series, ['a', 'b', 'c'], dtype=float) @@ -411,7 +441,7 @@ def test_constructor_with_datetime_tz(self): s = Series(dr) self.assertTrue(s.dtype.name == 'datetime64[ns, US/Eastern]') self.assertTrue(s.dtype == 'datetime64[ns, US/Eastern]') - self.assertTrue(com.is_datetime64tz_dtype(s.dtype)) + self.assertTrue(is_datetime64tz_dtype(s.dtype)) self.assertTrue('datetime64[ns, US/Eastern]' in str(s)) # export @@ -426,10 +456,10 @@ def test_constructor_with_datetime_tz(self): # indexing result = s.iloc[0] self.assertEqual(result, Timestamp('2013-01-01 00:00:00-0500', - tz='US/Eastern', offset='D')) + tz='US/Eastern', freq='D')) result = s[0] self.assertEqual(result, Timestamp('2013-01-01 00:00:00-0500', - tz='US/Eastern', offset='D')) + tz='US/Eastern', freq='D')) result = s[Series([True, True, False], index=s.index)] assert_series_equal(result, s[0:2]) @@ -688,8 +718,9 @@ def test_constructor_dtype_timedelta64(self): td = Series([np.timedelta64(300000000), pd.NaT]) self.assertEqual(td.dtype, 'timedelta64[ns]') + # because iNaT is int, not coerced to timedelta td = Series([np.timedelta64(300000000), tslib.iNaT]) - self.assertEqual(td.dtype, 'timedelta64[ns]') + self.assertEqual(td.dtype, 'object') td = Series([np.timedelta64(300000000), np.nan]) self.assertEqual(td.dtype, 'timedelta64[ns]') diff --git a/pandas/tests/series/test_datetime_values.py b/pandas/tests/series/test_datetime_values.py index 6e82f81f901a9..c25895548dcb9 100644 --- a/pandas/tests/series/test_datetime_values.py +++ b/pandas/tests/series/test_datetime_values.py @@ -6,6 +6,7 @@ import numpy as np import pandas as pd +from pandas.types.common import is_integer_dtype, is_list_like from pandas import (Index, Series, DataFrame, bdate_range, date_range, period_range, timedelta_range) from pandas.tseries.period import PeriodIndex @@ -49,16 +50,16 @@ def test_dt_namespace_accessor(self): def get_expected(s, name): result = getattr(Index(s._values), prop) if isinstance(result, np.ndarray): - if com.is_integer_dtype(result): + if is_integer_dtype(result): result = result.astype('int64') - elif not com.is_list_like(result): + elif not is_list_like(result): return result return Series(result, index=s.index, name=s.name) def compare(s, name): a = getattr(s.dt, prop) b = get_expected(s, prop) - if not (com.is_list_like(a) and com.is_list_like(b)): + if not (is_list_like(a) and is_list_like(b)): self.assertEqual(a, b) else: tm.assert_series_equal(a, b) diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index 15ca238ee32a0..64ebaa63cc10f 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -7,16 +7,14 @@ import numpy as np import pandas as pd +from pandas.types.common import is_integer, is_scalar from pandas import Index, Series, DataFrame, isnull, date_range from pandas.core.index import MultiIndex from pandas.core.indexing import IndexingError from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta -import pandas.core.common as com import pandas.core.datetools as datetools -import pandas.lib as lib - from pandas.compat import lrange, range from pandas import compat from pandas.util.testing import assert_series_equal, assert_almost_equal @@ -375,7 +373,7 @@ def test_getitem_ambiguous_keyerror(self): def test_getitem_unordered_dup(self): obj = Series(lrange(5), index=['c', 'a', 'a', 'b', 'b']) - self.assertTrue(lib.isscalar(obj['c'])) + self.assertTrue(is_scalar(obj['c'])) self.assertEqual(obj['c'], 0) def test_getitem_dups_with_missing(self): @@ -1174,23 +1172,23 @@ def test_where_numeric_with_string(self): s = pd.Series([1, 2, 3]) w = s.where(s > 1, 'X') - self.assertFalse(com.is_integer(w[0])) - self.assertTrue(com.is_integer(w[1])) - self.assertTrue(com.is_integer(w[2])) + self.assertFalse(is_integer(w[0])) + self.assertTrue(is_integer(w[1])) + self.assertTrue(is_integer(w[2])) self.assertTrue(isinstance(w[0], str)) self.assertTrue(w.dtype == 'object') w = s.where(s > 1, ['X', 'Y', 'Z']) - self.assertFalse(com.is_integer(w[0])) - self.assertTrue(com.is_integer(w[1])) - self.assertTrue(com.is_integer(w[2])) + self.assertFalse(is_integer(w[0])) + self.assertTrue(is_integer(w[1])) + self.assertTrue(is_integer(w[2])) self.assertTrue(isinstance(w[0], str)) self.assertTrue(w.dtype == 'object') w = s.where(s > 1, np.array(['X', 'Y', 'Z'])) - self.assertFalse(com.is_integer(w[0])) - self.assertTrue(com.is_integer(w[1])) - self.assertTrue(com.is_integer(w[2])) + self.assertFalse(is_integer(w[0])) + self.assertTrue(is_integer(w[1])) + self.assertTrue(is_integer(w[2])) self.assertTrue(isinstance(w[0], str)) self.assertTrue(w.dtype == 'object') diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index 1e23c87fdb4ca..5ebe528ff8cab 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -571,11 +571,11 @@ def run_ops(ops, get_ser, test_ser): td2 / td1 # ## datetime64 ### - dt1 = Series([Timestamp('20111230'), Timestamp('20120101'), Timestamp( - '20120103')]) + dt1 = Series([Timestamp('20111230'), Timestamp('20120101'), + Timestamp('20120103')]) dt1.iloc[2] = np.nan - dt2 = Series([Timestamp('20111231'), Timestamp('20120102'), Timestamp( - '20120104')]) + dt2 = Series([Timestamp('20111231'), Timestamp('20120102'), + Timestamp('20120104')]) ops = ['__add__', '__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__', '__radd__', '__rmul__', '__rfloordiv__', '__rtruediv__', '__rdiv__', '__rpow__'] @@ -607,9 +607,10 @@ def run_ops(ops, get_ser, test_ser): ops = ['__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__', '__rmul__', '__rfloordiv__', '__rtruediv__', '__rdiv__', '__rpow__'] - dt1 = Series( - date_range('2000-01-01 09:00:00', periods=5, - tz='US/Eastern'), name='foo') + + tz = 'US/Eastern' + dt1 = Series(date_range('2000-01-01 09:00:00', periods=5, + tz=tz), name='foo') dt2 = dt1.copy() dt2.iloc[2] = np.nan td1 = Series(timedelta_range('1 days 1 min', periods=5, freq='H')) @@ -618,58 +619,48 @@ def run_ops(ops, get_ser, test_ser): run_ops(ops, dt1, td1) result = dt1 + td1[0] - expected = ( - dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt2 + td2[0] - expected = ( - dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) # odd numpy behavior with scalar timedeltas if not _np_version_under1p8: result = td1[0] + dt1 - expected = ( - dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) result = td2[0] + dt2 - expected = ( - dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt1 - td1[0] - expected = ( - dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) self.assertRaises(TypeError, lambda: td1[0] - dt1) result = dt2 - td2[0] - expected = ( - dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize(tz) + assert_series_equal(result, exp) self.assertRaises(TypeError, lambda: td2[0] - dt2) result = dt1 + td1 - expected = ( - dt1.dt.tz_localize(None) + td1).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) + td1).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt2 + td2 - expected = ( - dt2.dt.tz_localize(None) + td2).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) + td2).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt1 - td1 - expected = ( - dt1.dt.tz_localize(None) - td1).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt1.dt.tz_localize(None) - td1).dt.tz_localize(tz) + assert_series_equal(result, exp) result = dt2 - td2 - expected = ( - dt2.dt.tz_localize(None) - td2).dt.tz_localize('US/Eastern') - assert_series_equal(result, expected) + exp = (dt2.dt.tz_localize(None) - td2).dt.tz_localize(tz) + assert_series_equal(result, exp) self.assertRaises(TypeError, lambda: td1 - dt1) self.assertRaises(TypeError, lambda: td2 - dt2) @@ -980,24 +971,97 @@ def test_comparison_invalid(self): self.assertRaises(TypeError, lambda: x <= y) def test_more_na_comparisons(self): - left = Series(['a', np.nan, 'c']) - right = Series(['a', np.nan, 'd']) + for dtype in [None, object]: + left = Series(['a', np.nan, 'c'], dtype=dtype) + right = Series(['a', np.nan, 'd'], dtype=dtype) - result = left == right - expected = Series([True, False, False]) - assert_series_equal(result, expected) + result = left == right + expected = Series([True, False, False]) + assert_series_equal(result, expected) - result = left != right - expected = Series([False, True, True]) - assert_series_equal(result, expected) + result = left != right + expected = Series([False, True, True]) + assert_series_equal(result, expected) - result = left == np.nan - expected = Series([False, False, False]) - assert_series_equal(result, expected) + result = left == np.nan + expected = Series([False, False, False]) + assert_series_equal(result, expected) - result = left != np.nan - expected = Series([True, True, True]) - assert_series_equal(result, expected) + result = left != np.nan + expected = Series([True, True, True]) + assert_series_equal(result, expected) + + def test_nat_comparisons(self): + data = [([pd.Timestamp('2011-01-01'), pd.NaT, + pd.Timestamp('2011-01-03')], + [pd.NaT, pd.NaT, pd.Timestamp('2011-01-03')]), + + ([pd.Timedelta('1 days'), pd.NaT, + pd.Timedelta('3 days')], + [pd.NaT, pd.NaT, pd.Timedelta('3 days')]), + + ([pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='M')], + [pd.NaT, pd.NaT, pd.Period('2011-03', freq='M')])] + + # add lhs / rhs switched data + data = data + [(r, l) for l, r in data] + + for l, r in data: + for dtype in [None, object]: + left = Series(l, dtype=dtype) + + # Series, Index + for right in [Series(r, dtype=dtype), Index(r, dtype=dtype)]: + expected = Series([False, False, True]) + assert_series_equal(left == right, expected) + + expected = Series([True, True, False]) + assert_series_equal(left != right, expected) + + expected = Series([False, False, False]) + assert_series_equal(left < right, expected) + + expected = Series([False, False, False]) + assert_series_equal(left > right, expected) + + expected = Series([False, False, True]) + assert_series_equal(left >= right, expected) + + expected = Series([False, False, True]) + assert_series_equal(left <= right, expected) + + def test_nat_comparisons_scalar(self): + data = [[pd.Timestamp('2011-01-01'), pd.NaT, + pd.Timestamp('2011-01-03')], + + [pd.Timedelta('1 days'), pd.NaT, pd.Timedelta('3 days')], + + [pd.Period('2011-01', freq='M'), pd.NaT, + pd.Period('2011-03', freq='M')]] + + for l in data: + for dtype in [None, object]: + left = Series(l, dtype=dtype) + + expected = Series([False, False, False]) + assert_series_equal(left == pd.NaT, expected) + assert_series_equal(pd.NaT == left, expected) + + expected = Series([True, True, True]) + assert_series_equal(left != pd.NaT, expected) + assert_series_equal(pd.NaT != left, expected) + + expected = Series([False, False, False]) + assert_series_equal(left < pd.NaT, expected) + assert_series_equal(pd.NaT > left, expected) + assert_series_equal(left <= pd.NaT, expected) + assert_series_equal(pd.NaT >= left, expected) + + assert_series_equal(left > pd.NaT, expected) + assert_series_equal(pd.NaT < left, expected) + assert_series_equal(left >= pd.NaT, expected) + assert_series_equal(pd.NaT <= left, expected) def test_comparison_different_length(self): a = Series(['a', 'b', 'c']) @@ -1259,8 +1323,6 @@ def _check_op(arr, op): _check_op(arr, operator.floordiv) def test_series_frame_radd_bug(self): - import operator - # GH 353 vals = Series(tm.rands_array(5, 10)) result = 'foo_' + vals @@ -1273,7 +1335,78 @@ def test_series_frame_radd_bug(self): tm.assert_frame_equal(result, expected) # really raise this time - self.assertRaises(TypeError, operator.add, datetime.now(), self.ts) + with tm.assertRaises(TypeError): + datetime.now() + self.ts + + with tm.assertRaises(TypeError): + self.ts + datetime.now() + + def test_series_radd_more(self): + data = [[1, 2, 3], + [1.1, 2.2, 3.3], + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.NaT], + ['x', 'y', 1]] + + for d in data: + for dtype in [None, object]: + s = Series(d, dtype=dtype) + with tm.assertRaises(TypeError): + 'foo_' + s + + for dtype in [None, object]: + res = 1 + pd.Series([1, 2, 3], dtype=dtype) + exp = pd.Series([2, 3, 4], dtype=dtype) + tm.assert_series_equal(res, exp) + res = pd.Series([1, 2, 3], dtype=dtype) + 1 + tm.assert_series_equal(res, exp) + + res = np.nan + pd.Series([1, 2, 3], dtype=dtype) + exp = pd.Series([np.nan, np.nan, np.nan], dtype=dtype) + tm.assert_series_equal(res, exp) + res = pd.Series([1, 2, 3], dtype=dtype) + np.nan + tm.assert_series_equal(res, exp) + + s = pd.Series([pd.Timedelta('1 days'), pd.Timedelta('2 days'), + pd.Timedelta('3 days')], dtype=dtype) + exp = pd.Series([pd.Timedelta('4 days'), pd.Timedelta('5 days'), + pd.Timedelta('6 days')]) + tm.assert_series_equal(pd.Timedelta('3 days') + s, exp) + tm.assert_series_equal(s + pd.Timedelta('3 days'), exp) + + s = pd.Series(['x', np.nan, 'x']) + tm.assert_series_equal('a' + s, pd.Series(['ax', np.nan, 'ax'])) + tm.assert_series_equal(s + 'a', pd.Series(['xa', np.nan, 'xa'])) + + def test_frame_radd_more(self): + data = [[1, 2, 3], + [1.1, 2.2, 3.3], + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'), + pd.NaT], + ['x', 'y', 1]] + + for d in data: + for dtype in [None, object]: + s = DataFrame(d, dtype=dtype) + with tm.assertRaises(TypeError): + 'foo_' + s + + for dtype in [None, object]: + res = 1 + pd.DataFrame([1, 2, 3], dtype=dtype) + exp = pd.DataFrame([2, 3, 4], dtype=dtype) + tm.assert_frame_equal(res, exp) + res = pd.DataFrame([1, 2, 3], dtype=dtype) + 1 + tm.assert_frame_equal(res, exp) + + res = np.nan + pd.DataFrame([1, 2, 3], dtype=dtype) + exp = pd.DataFrame([np.nan, np.nan, np.nan], dtype=dtype) + tm.assert_frame_equal(res, exp) + res = pd.DataFrame([1, 2, 3], dtype=dtype) + np.nan + tm.assert_frame_equal(res, exp) + + df = pd.DataFrame(['x', np.nan, 'x']) + tm.assert_frame_equal('a' + df, pd.DataFrame(['ax', np.nan, 'ax'])) + tm.assert_frame_equal(df + 'a', pd.DataFrame(['xa', np.nan, 'xa'])) def test_operators_frame(self): # rpow does not work with DataFrame @@ -1413,3 +1546,12 @@ def test_datetime64_with_index(self): df['expected'] = df['date'] - df.index.to_series() df['result'] = df['date'] - df.index assert_series_equal(df['result'], df['expected'], check_names=False) + + def test_dti_tz_convert_to_utc(self): + base = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], + tz='UTC') + idx1 = base.tz_convert('Asia/Tokyo')[:2] + idx2 = base.tz_convert('US/Eastern')[1:] + + res = Series([1, 2], index=idx1) + Series([1, 1], index=idx2) + assert_series_equal(res, Series([np.nan, 3, np.nan], index=base)) diff --git a/pandas/tests/series/test_quantile.py b/pandas/tests/series/test_quantile.py index e0bff7fbd39e4..7d2517987e526 100644 --- a/pandas/tests/series/test_quantile.py +++ b/pandas/tests/series/test_quantile.py @@ -7,7 +7,7 @@ from pandas import (Index, Series, _np_version_under1p9) from pandas.tseries.index import Timestamp -import pandas.core.common as com +from pandas.types.common import is_integer import pandas.util.testing as tm from .common import TestData @@ -96,11 +96,11 @@ def test_quantile_interpolation_dtype(self): # interpolation = linear (default case) q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='lower') self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) - self.assertTrue(com.is_integer(q)) + self.assertTrue(is_integer(q)) q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='higher') self.assertEqual(q, percentile(np.array([1, 3, 4]), 50)) - self.assertTrue(com.is_integer(q)) + self.assertTrue(is_integer(q)) def test_quantile_interpolation_np_lt_1p9(self): # GH #10174 diff --git a/pandas/tests/series/test_sorting.py b/pandas/tests/series/test_sorting.py new file mode 100644 index 0000000000000..826201adbdb50 --- /dev/null +++ b/pandas/tests/series/test_sorting.py @@ -0,0 +1,146 @@ +# coding=utf-8 + +import numpy as np +import random + +from pandas import (DataFrame, Series, MultiIndex) + +from pandas.util.testing import (assert_series_equal, assert_almost_equal) +import pandas.util.testing as tm + +from .common import TestData + + +class TestSeriesSorting(TestData, tm.TestCase): + + _multiprocess_can_split_ = True + + def test_sort(self): + + ts = self.ts.copy() + + # 9816 deprecated + with tm.assert_produces_warning(FutureWarning): + ts.sort() # sorts inplace + self.assert_series_equal(ts, self.ts.sort_values()) + + def test_order(self): + + # 9816 deprecated + with tm.assert_produces_warning(FutureWarning): + result = self.ts.order() + self.assert_series_equal(result, self.ts.sort_values()) + + def test_sort_values(self): + + # check indexes are reordered corresponding with the values + ser = Series([3, 2, 4, 1], ['A', 'B', 'C', 'D']) + expected = Series([1, 2, 3, 4], ['D', 'B', 'A', 'C']) + result = ser.sort_values() + self.assert_series_equal(expected, result) + + ts = self.ts.copy() + ts[:5] = np.NaN + vals = ts.values + + result = ts.sort_values() + self.assertTrue(np.isnan(result[-5:]).all()) + self.assert_numpy_array_equal(result[:-5].values, np.sort(vals[5:])) + + # na_position + result = ts.sort_values(na_position='first') + self.assertTrue(np.isnan(result[:5]).all()) + self.assert_numpy_array_equal(result[5:].values, np.sort(vals[5:])) + + # something object-type + ser = Series(['A', 'B'], [1, 2]) + # no failure + ser.sort_values() + + # ascending=False + ordered = ts.sort_values(ascending=False) + expected = np.sort(ts.valid().values)[::-1] + assert_almost_equal(expected, ordered.valid().values) + ordered = ts.sort_values(ascending=False, na_position='first') + assert_almost_equal(expected, ordered.valid().values) + + # inplace=True + ts = self.ts.copy() + ts.sort_values(ascending=False, inplace=True) + self.assert_series_equal(ts, self.ts.sort_values(ascending=False)) + self.assert_index_equal(ts.index, + self.ts.sort_values(ascending=False).index) + + # GH 5856/5853 + # Series.sort_values operating on a view + df = DataFrame(np.random.randn(10, 4)) + s = df.iloc[:, 0] + + def f(): + s.sort_values(inplace=True) + + self.assertRaises(ValueError, f) + + def test_sort_index(self): + rindex = list(self.ts.index) + random.shuffle(rindex) + + random_order = self.ts.reindex(rindex) + sorted_series = random_order.sort_index() + assert_series_equal(sorted_series, self.ts) + + # descending + sorted_series = random_order.sort_index(ascending=False) + assert_series_equal(sorted_series, + self.ts.reindex(self.ts.index[::-1])) + + # compat on level + sorted_series = random_order.sort_index(level=0) + assert_series_equal(sorted_series, self.ts) + + # compat on axis + sorted_series = random_order.sort_index(axis=0) + assert_series_equal(sorted_series, self.ts) + + self.assertRaises(ValueError, lambda: random_order.sort_values(axis=1)) + + sorted_series = random_order.sort_index(level=0, axis=0) + assert_series_equal(sorted_series, self.ts) + + self.assertRaises(ValueError, + lambda: random_order.sort_index(level=0, axis=1)) + + def test_sort_index_inplace(self): + + # For #11402 + rindex = list(self.ts.index) + random.shuffle(rindex) + + # descending + random_order = self.ts.reindex(rindex) + result = random_order.sort_index(ascending=False, inplace=True) + self.assertIs(result, None, + msg='sort_index() inplace should return None') + assert_series_equal(random_order, self.ts.reindex(self.ts.index[::-1])) + + # ascending + random_order = self.ts.reindex(rindex) + result = random_order.sort_index(ascending=True, inplace=True) + self.assertIs(result, None, + msg='sort_index() inplace should return None') + assert_series_equal(random_order, self.ts) + + def test_sort_index_multiindex(self): + + mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC')) + s = Series([1, 2], mi) + backwards = s.iloc[[1, 0]] + + # implicit sort_remaining=True + res = s.sort_index(level='A') + assert_series_equal(backwards, res) + + # GH13496 + # rows share same level='A': sort has no effect without remaining lvls + res = s.sort_index(level='A', sort_remaining=False) + assert_series_equal(s, res) diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 8af93ad0ecb2e..cb90110c953c1 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -702,12 +702,14 @@ def test_unique_label_indices(): left = unique_label_indices(a) right = np.unique(a, return_index=True)[1] - tm.assert_numpy_array_equal(left, right) + tm.assert_numpy_array_equal(left, right, + check_dtype=False) a[np.random.choice(len(a), 10)] = -1 left = unique_label_indices(a) right = np.unique(a, return_index=True)[1][1:] - tm.assert_numpy_array_equal(left, right) + tm.assert_numpy_array_equal(left, right, + check_dtype=False) def test_rank(): diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py index 77ae3ca20d123..2721d8d0e5e69 100644 --- a/pandas/tests/test_base.py +++ b/pandas/tests/test_base.py @@ -9,7 +9,7 @@ import pandas as pd import pandas.compat as compat -import pandas.core.common as com +from pandas.types.common import is_object_dtype, is_datetimetz import pandas.util.testing as tm from pandas import (Series, Index, DatetimeIndex, TimedeltaIndex, PeriodIndex, Timedelta) @@ -517,7 +517,7 @@ def test_value_counts_unique_nunique(self): continue # special assign to the numpy array - if com.is_datetimetz(o): + if is_datetimetz(o): if isinstance(o, DatetimeIndex): v = o.asi8 v[0:2] = pd.tslib.iNaT @@ -982,8 +982,8 @@ def test_memory_usage(self): res = o.memory_usage() res_deep = o.memory_usage(deep=True) - if (com.is_object_dtype(o) or (isinstance(o, Series) and - com.is_object_dtype(o.index))): + if (is_object_dtype(o) or (isinstance(o, Series) and + is_object_dtype(o.index))): # if there are objects, only deep will pick them up self.assertTrue(res_deep > res) else: diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index cff5bbe14f1eb..1edd9443fe356 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -8,12 +8,17 @@ import numpy as np +from pandas.types.dtypes import CategoricalDtype +from pandas.types.common import (is_categorical_dtype, + is_object_dtype, + is_float_dtype, + is_integer_dtype) + import pandas as pd import pandas.compat as compat -import pandas.core.common as com import pandas.util.testing as tm from pandas import (Categorical, Index, Series, DataFrame, PeriodIndex, - Timestamp, CategoricalIndex) + Timestamp, CategoricalIndex, isnull) from pandas.compat import range, lrange, u, PY3 from pandas.core.config import option_context @@ -195,18 +200,18 @@ def f(): # This should result in integer categories, not float! cat = pd.Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) - self.assertTrue(com.is_integer_dtype(cat.categories)) + self.assertTrue(is_integer_dtype(cat.categories)) # https://github.com/pydata/pandas/issues/3678 cat = pd.Categorical([np.nan, 1, 2, 3]) - self.assertTrue(com.is_integer_dtype(cat.categories)) + self.assertTrue(is_integer_dtype(cat.categories)) # this should result in floats cat = pd.Categorical([np.nan, 1, 2., 3]) - self.assertTrue(com.is_float_dtype(cat.categories)) + self.assertTrue(is_float_dtype(cat.categories)) cat = pd.Categorical([np.nan, 1., 2., 3.]) - self.assertTrue(com.is_float_dtype(cat.categories)) + self.assertTrue(is_float_dtype(cat.categories)) # Deprecating NaNs in categoires (GH #10748) # preserve int as far as possible by converting to object if NaN is in @@ -214,23 +219,23 @@ def f(): with tm.assert_produces_warning(FutureWarning): cat = pd.Categorical([np.nan, 1, 2, 3], categories=[np.nan, 1, 2, 3]) - self.assertTrue(com.is_object_dtype(cat.categories)) + self.assertTrue(is_object_dtype(cat.categories)) # This doesn't work -> this would probably need some kind of "remember # the original type" feature to try to cast the array interface result # to... # vals = np.asarray(cat[cat.notnull()]) - # self.assertTrue(com.is_integer_dtype(vals)) + # self.assertTrue(is_integer_dtype(vals)) with tm.assert_produces_warning(FutureWarning): cat = pd.Categorical([np.nan, "a", "b", "c"], categories=[np.nan, "a", "b", "c"]) - self.assertTrue(com.is_object_dtype(cat.categories)) + self.assertTrue(is_object_dtype(cat.categories)) # but don't do it for floats with tm.assert_produces_warning(FutureWarning): cat = pd.Categorical([np.nan, 1., 2., 3.], categories=[np.nan, 1., 2., 3.]) - self.assertTrue(com.is_float_dtype(cat.categories)) + self.assertTrue(is_float_dtype(cat.categories)) # corner cases cat = pd.Categorical([1]) @@ -515,17 +520,20 @@ def f(): def test_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) - expected = np.array([2, 4, 1, 3, 0], dtype=np.int64) - tm.assert_numpy_array_equal(c.argsort(ascending=True), expected) + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(c.argsort(ascending=True), expected, + check_dtype=False) expected = expected[::-1] - tm.assert_numpy_array_equal(c.argsort(ascending=False), expected) + tm.assert_numpy_array_equal(c.argsort(ascending=False), expected, + check_dtype=False) def test_numpy_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) - expected = np.array([2, 4, 1, 3, 0], dtype=np.int64) - tm.assert_numpy_array_equal(np.argsort(c), expected) + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(np.argsort(c), expected, + check_dtype=False) msg = "the 'kind' parameter is not supported" tm.assertRaisesRegexp(ValueError, msg, np.argsort, @@ -549,7 +557,7 @@ def test_na_flags_int_categories(self): cat = Categorical(labels, categories, fastpath=True) repr(cat) - self.assert_numpy_array_equal(com.isnull(cat), labels == -1) + self.assert_numpy_array_equal(isnull(cat), labels == -1) def test_categories_none(self): factor = Categorical(['a', 'b', 'b', 'a', @@ -1505,7 +1513,7 @@ def test_searchsorted(self): # Single item array res = c1.searchsorted(['bread']) chk = s1.searchsorted(['bread']) - exp = np.array([1], dtype=np.int64) + exp = np.array([1], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) @@ -1514,21 +1522,21 @@ def test_searchsorted(self): # np.array.searchsorted() res = c1.searchsorted('bread') chk = s1.searchsorted('bread') - exp = np.array([1], dtype=np.int64) + exp = np.array([1], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) # Searching for a value that is not present in the Categorical res = c1.searchsorted(['bread', 'eggs']) chk = s1.searchsorted(['bread', 'eggs']) - exp = np.array([1, 4], dtype=np.int64) + exp = np.array([1, 4], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) # Searching for a value that is not present, to the right res = c1.searchsorted(['bread', 'eggs'], side='right') chk = s1.searchsorted(['bread', 'eggs'], side='right') - exp = np.array([3, 4], dtype=np.int64) # eggs before milk + exp = np.array([3, 4], dtype=np.intp) # eggs before milk self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) @@ -1538,7 +1546,7 @@ def test_searchsorted(self): chk = s2.searchsorted(['bread', 'eggs'], side='right', sorter=[0, 1, 2, 3, 5, 4]) # eggs after donuts, after switching milk and donuts - exp = np.array([3, 5], dtype=np.int64) + exp = np.array([3, 5], dtype=np.intp) self.assert_numpy_array_equal(res, exp) self.assert_numpy_array_equal(res, chk) @@ -1551,18 +1559,6 @@ def test_deprecated_labels(self): res = cat.labels self.assert_numpy_array_equal(res, exp) - def test_deprecated_levels(self): - # TODO: levels is deprecated and should be removed in 0.18 or 2017, - # whatever is earlier - cat = pd.Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) - exp = cat.categories - with tm.assert_produces_warning(FutureWarning): - res = cat.levels - self.assert_index_equal(res, exp) - with tm.assert_produces_warning(FutureWarning): - res = pd.Categorical([1, 2, 3, np.nan], levels=[1, 2, 3]) - self.assert_index_equal(res.categories, exp) - def test_removed_names_produces_warning(self): # 10482 @@ -2073,15 +2069,15 @@ def test_assignment_to_dataframe(self): result = df.dtypes expected = Series( - [np.dtype('int32'), com.CategoricalDtype()], index=['value', 'D']) + [np.dtype('int32'), CategoricalDtype()], index=['value', 'D']) tm.assert_series_equal(result, expected) df['E'] = s str(df) result = df.dtypes - expected = Series([np.dtype('int32'), com.CategoricalDtype(), - com.CategoricalDtype()], + expected = Series([np.dtype('int32'), CategoricalDtype(), + CategoricalDtype()], index=['value', 'D', 'E']) tm.assert_series_equal(result, expected) @@ -3231,7 +3227,7 @@ def test_slicing_and_getting_ops(self): # frame res_df = df.iloc[2:4, :] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) # row res_row = df.iloc[2, :] @@ -3241,7 +3237,7 @@ def test_slicing_and_getting_ops(self): # col res_col = df.iloc[:, 0] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) # single value res_val = df.iloc[2, 0] @@ -3251,7 +3247,7 @@ def test_slicing_and_getting_ops(self): # frame res_df = df.loc["j":"k", :] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) # row res_row = df.loc["j", :] @@ -3261,7 +3257,7 @@ def test_slicing_and_getting_ops(self): # col res_col = df.loc[:, "cats"] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) # single value res_val = df.loc["j", "cats"] @@ -3272,7 +3268,7 @@ def test_slicing_and_getting_ops(self): # res_df = df.ix["j":"k",[0,1]] # doesn't work? res_df = df.ix["j":"k", :] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) # row res_row = df.ix["j", :] @@ -3282,7 +3278,7 @@ def test_slicing_and_getting_ops(self): # col res_col = df.ix[:, "cats"] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) # single value res_val = df.ix["j", 0] @@ -3315,23 +3311,23 @@ def test_slicing_and_getting_ops(self): res_df = df.iloc[slice(2, 4)] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) res_df = df.iloc[[2, 3]] tm.assert_frame_equal(res_df, exp_df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) res_col = df.iloc[:, 0] tm.assert_series_equal(res_col, exp_col) - self.assertTrue(com.is_categorical_dtype(res_col)) + self.assertTrue(is_categorical_dtype(res_col)) res_df = df.iloc[:, slice(0, 2)] tm.assert_frame_equal(res_df, df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) res_df = df.iloc[:, [0, 1]] tm.assert_frame_equal(res_df, df) - self.assertTrue(com.is_categorical_dtype(res_df["cats"])) + self.assertTrue(is_categorical_dtype(res_df["cats"])) def test_slicing_doc_examples(self): @@ -4050,13 +4046,40 @@ def test_numpy_repeat(self): msg = "the 'axis' parameter is not supported" tm.assertRaisesRegexp(ValueError, msg, np.repeat, cat, 2, axis=1) + def test_reshape(self): + cat = pd.Categorical([], categories=["a", "b"]) + tm.assert_produces_warning(FutureWarning, cat.reshape, 0) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical([], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape(0), cat) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical([], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape((5, -1)), cat) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape(cat.shape), cat) + + with tm.assert_produces_warning(FutureWarning): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(cat.reshape(cat.size), cat) + + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "can only specify one unknown dimension" + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + tm.assertRaisesRegexp(ValueError, msg, cat.reshape, (-2, -1)) + def test_numpy_reshape(self): - cat = pd.Categorical(["a", "b"], categories=["a", "b"]) - self.assert_categorical_equal(np.reshape(cat, cat.shape), cat) + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + cat = pd.Categorical(["a", "b"], categories=["a", "b"]) + self.assert_categorical_equal(np.reshape(cat, cat.shape), cat) - msg = "the 'order' parameter is not supported" - tm.assertRaisesRegexp(ValueError, msg, np.reshape, - cat, cat.shape, order='F') + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + msg = "the 'order' parameter is not supported" + tm.assertRaisesRegexp(ValueError, msg, np.reshape, + cat, cat.shape, order='F') def test_na_actions(self): @@ -4111,7 +4134,7 @@ def test_astype_to_other(self): s = self.cat['value_group'] expected = s tm.assert_series_equal(s.astype('category'), expected) - tm.assert_series_equal(s.astype(com.CategoricalDtype()), expected) + tm.assert_series_equal(s.astype(CategoricalDtype()), expected) self.assertRaises(ValueError, lambda: s.astype('float64')) cat = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c'])) @@ -4136,10 +4159,10 @@ def cmp(a, b): # valid conversion for valid in [lambda x: x.astype('category'), - lambda x: x.astype(com.CategoricalDtype()), + lambda x: x.astype(CategoricalDtype()), lambda x: x.astype('object').astype('category'), lambda x: x.astype('object').astype( - com.CategoricalDtype()) + CategoricalDtype()) ]: result = valid(s) @@ -4396,44 +4419,6 @@ def test_dt_accessor_api_for_categorical(self): invalid.dt self.assertFalse(hasattr(invalid, 'str')) - def test_pickle_v0_14_1(self): - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_14_1.pickle') - # This code was executed once on v0.14.1 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - self.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - - def test_pickle_v0_15_2(self): - # ordered -> _ordered - # GH 9347 - - # we have the name warning - # 10482 - with tm.assert_produces_warning(UserWarning): - cat = pd.Categorical(values=['a', 'b', 'c'], - categories=['a', 'b', 'c', 'd'], - name='foobar', ordered=False) - pickle_path = os.path.join(tm.get_data_path(), - 'categorical_0_15_2.pickle') - # This code was executed once on v0.15.2 to generate the pickle: - # - # cat = Categorical(labels=np.arange(3), levels=['a', 'b', 'c', 'd'], - # name='foobar') - # with open(pickle_path, 'wb') as f: pickle.dump(cat, f) - # - self.assert_categorical_equal(cat, pd.read_pickle(pickle_path)) - def test_concat_categorical(self): # See GH 10177 df1 = pd.DataFrame( diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index 56b1b542d547e..09dd3f7ab517c 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -1,21 +1,12 @@ # -*- coding: utf-8 -*- -import collections -from datetime import datetime, timedelta -import re import nose import numpy as np -import pandas as pd -from pandas.tslib import iNaT, NaT -from pandas import (Series, DataFrame, date_range, DatetimeIndex, - TimedeltaIndex, Timestamp, Float64Index) -from pandas import compat -from pandas.compat import range, lrange, lmap, u -from pandas.core.common import notnull, isnull, array_equivalent + +from pandas import Series, Timestamp +from pandas.compat import range, lmap import pandas.core.common as com -import pandas.core.convert as convert import pandas.util.testing as tm -import pandas.core.config as cf _multiprocess_can_split_ = True @@ -28,22 +19,6 @@ def test_mut_exclusive(): assert com._mut_exclusive(major=None, major_axis=None) is None -def test_is_sequence(): - is_seq = com.is_sequence - assert (is_seq((1, 2))) - assert (is_seq([1, 2])) - assert (not is_seq("abcd")) - assert (not is_seq(u("abcd"))) - assert (not is_seq(np.int64)) - - class A(object): - - def __getitem__(self): - return 1 - - assert (not is_seq(A())) - - def test_get_callable_name(): from functools import partial getname = com._get_callable_name @@ -68,407 +43,6 @@ def __call__(self): assert getname(1) is None -class TestInferDtype(tm.TestCase): - - def test_infer_dtype_from_scalar(self): - # Test that _infer_dtype_from_scalar is returning correct dtype for int - # and float. - - for dtypec in [np.uint8, np.int8, np.uint16, np.int16, np.uint32, - np.int32, np.uint64, np.int64]: - data = dtypec(12) - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, type(data)) - - data = 12 - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.int64) - - for dtypec in [np.float16, np.float32, np.float64]: - data = dtypec(12) - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, dtypec) - - data = np.float(12) - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.float64) - - for data in [True, False]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.bool_) - - for data in [np.complex64(1), np.complex128(1)]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.complex_) - - import datetime - for data in [np.datetime64(1, 'ns'), pd.Timestamp(1), - datetime.datetime(2000, 1, 1, 0, 0)]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, 'M8[ns]') - - for data in [np.timedelta64(1, 'ns'), pd.Timedelta(1), - datetime.timedelta(1)]: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, 'm8[ns]') - - for data in [datetime.date(2000, 1, 1), - pd.Timestamp(1, tz='US/Eastern'), 'foo']: - dtype, val = com._infer_dtype_from_scalar(data) - self.assertEqual(dtype, np.object_) - - -def test_notnull(): - assert notnull(1.) - assert not notnull(None) - assert not notnull(np.NaN) - - with cf.option_context("mode.use_inf_as_null", False): - assert notnull(np.inf) - assert notnull(-np.inf) - - arr = np.array([1.5, np.inf, 3.5, -np.inf]) - result = notnull(arr) - assert result.all() - - with cf.option_context("mode.use_inf_as_null", True): - assert not notnull(np.inf) - assert not notnull(-np.inf) - - arr = np.array([1.5, np.inf, 3.5, -np.inf]) - result = notnull(arr) - assert result.sum() == 2 - - with cf.option_context("mode.use_inf_as_null", False): - for s in [tm.makeFloatSeries(), tm.makeStringSeries(), - tm.makeObjectSeries(), tm.makeTimeSeries(), - tm.makePeriodSeries()]: - assert (isinstance(isnull(s), Series)) - - -def test_isnull(): - assert not isnull(1.) - assert isnull(None) - assert isnull(np.NaN) - assert not isnull(np.inf) - assert not isnull(-np.inf) - - # series - for s in [tm.makeFloatSeries(), tm.makeStringSeries(), - tm.makeObjectSeries(), tm.makeTimeSeries(), - tm.makePeriodSeries()]: - assert (isinstance(isnull(s), Series)) - - # frame - for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(), - tm.makeMixedDataFrame()]: - result = isnull(df) - expected = df.apply(isnull) - tm.assert_frame_equal(result, expected) - - # panel - for p in [tm.makePanel(), tm.makePeriodPanel(), tm.add_nans(tm.makePanel()) - ]: - result = isnull(p) - expected = p.apply(isnull) - tm.assert_panel_equal(result, expected) - - # panel 4d - for p in [tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D())]: - result = isnull(p) - expected = p.apply(isnull) - tm.assert_panel4d_equal(result, expected) - - -def test_isnull_lists(): - result = isnull([[False]]) - exp = np.array([[False]]) - assert (np.array_equal(result, exp)) - - result = isnull([[1], [2]]) - exp = np.array([[False], [False]]) - assert (np.array_equal(result, exp)) - - # list of strings / unicode - result = isnull(['foo', 'bar']) - assert (not result.any()) - - result = isnull([u('foo'), u('bar')]) - assert (not result.any()) - - -def test_isnull_nat(): - result = isnull([NaT]) - exp = np.array([True]) - assert (np.array_equal(result, exp)) - - result = isnull(np.array([NaT], dtype=object)) - exp = np.array([True]) - assert (np.array_equal(result, exp)) - - -def test_isnull_numpy_nat(): - arr = np.array([NaT, np.datetime64('NaT'), np.timedelta64('NaT'), - np.datetime64('NaT', 's')]) - result = isnull(arr) - expected = np.array([True] * 4) - tm.assert_numpy_array_equal(result, expected) - - -def test_isnull_datetime(): - assert (not isnull(datetime.now())) - assert notnull(datetime.now()) - - idx = date_range('1/1/1990', periods=20) - assert (notnull(idx).all()) - - idx = np.asarray(idx) - idx[0] = iNaT - idx = DatetimeIndex(idx) - mask = isnull(idx) - assert (mask[0]) - assert (not mask[1:].any()) - - # GH 9129 - pidx = idx.to_period(freq='M') - mask = isnull(pidx) - assert (mask[0]) - assert (not mask[1:].any()) - - mask = isnull(pidx[1:]) - assert (not mask.any()) - - -class TestIsNull(tm.TestCase): - - def test_0d_array(self): - self.assertTrue(isnull(np.array(np.nan))) - self.assertFalse(isnull(np.array(0.0))) - self.assertFalse(isnull(np.array(0))) - # test object dtype - self.assertTrue(isnull(np.array(np.nan, dtype=object))) - self.assertFalse(isnull(np.array(0.0, dtype=object))) - self.assertFalse(isnull(np.array(0, dtype=object))) - - -class TestNumberScalar(tm.TestCase): - - def test_is_number(self): - - self.assertTrue(com.is_number(True)) - self.assertTrue(com.is_number(1)) - self.assertTrue(com.is_number(1.1)) - self.assertTrue(com.is_number(1 + 3j)) - self.assertTrue(com.is_number(np.bool(False))) - self.assertTrue(com.is_number(np.int64(1))) - self.assertTrue(com.is_number(np.float64(1.1))) - self.assertTrue(com.is_number(np.complex128(1 + 3j))) - self.assertTrue(com.is_number(np.nan)) - - self.assertFalse(com.is_number(None)) - self.assertFalse(com.is_number('x')) - self.assertFalse(com.is_number(datetime(2011, 1, 1))) - self.assertFalse(com.is_number(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_number(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_number(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_number(timedelta(1000))) - self.assertFalse(com.is_number(pd.Timedelta('1 days'))) - - # questionable - self.assertFalse(com.is_number(np.bool_(False))) - self.assertTrue(com.is_number(np.timedelta64(1, 'D'))) - - def test_is_bool(self): - self.assertTrue(com.is_bool(True)) - self.assertTrue(com.is_bool(np.bool(False))) - self.assertTrue(com.is_bool(np.bool_(False))) - - self.assertFalse(com.is_bool(1)) - self.assertFalse(com.is_bool(1.1)) - self.assertFalse(com.is_bool(1 + 3j)) - self.assertFalse(com.is_bool(np.int64(1))) - self.assertFalse(com.is_bool(np.float64(1.1))) - self.assertFalse(com.is_bool(np.complex128(1 + 3j))) - self.assertFalse(com.is_bool(np.nan)) - self.assertFalse(com.is_bool(None)) - self.assertFalse(com.is_bool('x')) - self.assertFalse(com.is_bool(datetime(2011, 1, 1))) - self.assertFalse(com.is_bool(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_bool(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_bool(timedelta(1000))) - self.assertFalse(com.is_bool(np.timedelta64(1, 'D'))) - self.assertFalse(com.is_bool(pd.Timedelta('1 days'))) - - def test_is_integer(self): - self.assertTrue(com.is_integer(1)) - self.assertTrue(com.is_integer(np.int64(1))) - - self.assertFalse(com.is_integer(True)) - self.assertFalse(com.is_integer(1.1)) - self.assertFalse(com.is_integer(1 + 3j)) - self.assertFalse(com.is_integer(np.bool(False))) - self.assertFalse(com.is_integer(np.bool_(False))) - self.assertFalse(com.is_integer(np.float64(1.1))) - self.assertFalse(com.is_integer(np.complex128(1 + 3j))) - self.assertFalse(com.is_integer(np.nan)) - self.assertFalse(com.is_integer(None)) - self.assertFalse(com.is_integer('x')) - self.assertFalse(com.is_integer(datetime(2011, 1, 1))) - self.assertFalse(com.is_integer(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_integer(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_integer(timedelta(1000))) - self.assertFalse(com.is_integer(pd.Timedelta('1 days'))) - - # questionable - self.assertTrue(com.is_integer(np.timedelta64(1, 'D'))) - - def test_is_float(self): - self.assertTrue(com.is_float(1.1)) - self.assertTrue(com.is_float(np.float64(1.1))) - self.assertTrue(com.is_float(np.nan)) - - self.assertFalse(com.is_float(True)) - self.assertFalse(com.is_float(1)) - self.assertFalse(com.is_float(1 + 3j)) - self.assertFalse(com.is_float(np.bool(False))) - self.assertFalse(com.is_float(np.bool_(False))) - self.assertFalse(com.is_float(np.int64(1))) - self.assertFalse(com.is_float(np.complex128(1 + 3j))) - self.assertFalse(com.is_float(None)) - self.assertFalse(com.is_float('x')) - self.assertFalse(com.is_float(datetime(2011, 1, 1))) - self.assertFalse(com.is_float(np.datetime64('2011-01-01'))) - self.assertFalse(com.is_float(pd.Timestamp('2011-01-01'))) - self.assertFalse(com.is_float(pd.Timestamp('2011-01-01', - tz='US/Eastern'))) - self.assertFalse(com.is_float(timedelta(1000))) - self.assertFalse(com.is_float(np.timedelta64(1, 'D'))) - self.assertFalse(com.is_float(pd.Timedelta('1 days'))) - - -def test_downcast_conv(): - # test downcasting - - arr = np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]) - result = com._possibly_downcast_to_dtype(arr, 'infer') - assert (np.array_equal(result, arr)) - - arr = np.array([8., 8., 8., 8., 8.9999999999995]) - result = com._possibly_downcast_to_dtype(arr, 'infer') - expected = np.array([8, 8, 8, 8, 9]) - assert (np.array_equal(result, expected)) - - arr = np.array([8., 8., 8., 8., 9.0000000000005]) - result = com._possibly_downcast_to_dtype(arr, 'infer') - expected = np.array([8, 8, 8, 8, 9]) - assert (np.array_equal(result, expected)) - - # conversions - - expected = np.array([1, 2]) - for dtype in [np.float64, object, np.int64]: - arr = np.array([1.0, 2.0], dtype=dtype) - result = com._possibly_downcast_to_dtype(arr, 'infer') - tm.assert_almost_equal(result, expected, check_dtype=False) - - for dtype in [np.float64, object]: - expected = np.array([1.0, 2.0, np.nan], dtype=dtype) - arr = np.array([1.0, 2.0, np.nan], dtype=dtype) - result = com._possibly_downcast_to_dtype(arr, 'infer') - tm.assert_almost_equal(result, expected) - - # empties - for dtype in [np.int32, np.float64, np.float32, np.bool_, - np.int64, object]: - arr = np.array([], dtype=dtype) - result = com._possibly_downcast_to_dtype(arr, 'int64') - tm.assert_almost_equal(result, np.array([], dtype=np.int64)) - assert result.dtype == np.int64 - - -def test_array_equivalent(): - assert array_equivalent(np.array([np.nan, np.nan]), - np.array([np.nan, np.nan])) - assert array_equivalent(np.array([np.nan, 1, np.nan]), - np.array([np.nan, 1, np.nan])) - assert array_equivalent(np.array([np.nan, None], dtype='object'), - np.array([np.nan, None], dtype='object')) - assert array_equivalent(np.array([np.nan, 1 + 1j], dtype='complex'), - np.array([np.nan, 1 + 1j], dtype='complex')) - assert not array_equivalent( - np.array([np.nan, 1 + 1j], dtype='complex'), np.array( - [np.nan, 1 + 2j], dtype='complex')) - assert not array_equivalent( - np.array([np.nan, 1, np.nan]), np.array([np.nan, 2, np.nan])) - assert not array_equivalent( - np.array(['a', 'b', 'c', 'd']), np.array(['e', 'e'])) - assert array_equivalent(Float64Index([0, np.nan]), - Float64Index([0, np.nan])) - assert not array_equivalent( - Float64Index([0, np.nan]), Float64Index([1, np.nan])) - assert array_equivalent(DatetimeIndex([0, np.nan]), - DatetimeIndex([0, np.nan])) - assert not array_equivalent( - DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan])) - assert array_equivalent(TimedeltaIndex([0, np.nan]), - TimedeltaIndex([0, np.nan])) - assert not array_equivalent( - TimedeltaIndex([0, np.nan]), TimedeltaIndex([1, np.nan])) - assert array_equivalent(DatetimeIndex([0, np.nan], tz='US/Eastern'), - DatetimeIndex([0, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan], tz='US/Eastern'), DatetimeIndex( - [1, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan]), DatetimeIndex( - [0, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan], tz='CET'), DatetimeIndex( - [0, np.nan], tz='US/Eastern')) - assert not array_equivalent( - DatetimeIndex([0, np.nan]), TimedeltaIndex([0, np.nan])) - - -def test_array_equivalent_str(): - for dtype in ['O', 'S', 'U']: - assert array_equivalent(np.array(['A', 'B'], dtype=dtype), - np.array(['A', 'B'], dtype=dtype)) - assert not array_equivalent(np.array(['A', 'B'], dtype=dtype), - np.array(['A', 'X'], dtype=dtype)) - - -def test_datetimeindex_from_empty_datetime64_array(): - for unit in ['ms', 'us', 'ns']: - idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit)) - assert (len(idx) == 0) - - -def test_nan_to_nat_conversions(): - - df = DataFrame(dict({ - 'A': np.asarray( - lrange(10), dtype='float64'), - 'B': Timestamp('20010101') - })) - df.iloc[3:6, :] = np.nan - result = df.loc[4, 'B'].value - assert (result == iNaT) - - s = df['B'].copy() - s._data = s._data.setitem(indexer=tuple([slice(8, 9)]), value=np.nan) - assert (isnull(s[8])) - - # numpy < 1.7.0 is wrong - from distutils.version import LooseVersion - if LooseVersion(np.__version__) >= '1.7.0': - assert (s[8].value == np.datetime64('NaT').astype(np.int64)) - - def test_any_none(): assert (com._any_none(1, 2, 3, None)) assert (not com._any_none(1, 2, 3, 4)) @@ -567,122 +141,6 @@ def test_groupby(): assert v == expected[k] -def test_is_list_like(): - passes = ([], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]), - Series([]), Series(['a']).str) - fails = (1, '2', object()) - - for p in passes: - assert com.is_list_like(p) - - for f in fails: - assert not com.is_list_like(f) - - -def test_is_dict_like(): - passes = [{}, {'A': 1}, pd.Series([1])] - fails = ['1', 1, [1, 2], (1, 2), range(2), pd.Index([1])] - - for p in passes: - assert com.is_dict_like(p) - - for f in fails: - assert not com.is_dict_like(f) - - -def test_is_named_tuple(): - passes = (collections.namedtuple('Test', list('abc'))(1, 2, 3), ) - fails = ((1, 2, 3), 'a', Series({'pi': 3.14})) - - for p in passes: - assert com.is_named_tuple(p) - - for f in fails: - assert not com.is_named_tuple(f) - - -def test_is_hashable(): - - # all new-style classes are hashable by default - class HashableClass(object): - pass - - class UnhashableClass1(object): - __hash__ = None - - class UnhashableClass2(object): - - def __hash__(self): - raise TypeError("Not hashable") - - hashable = (1, - 3.14, - np.float64(3.14), - 'a', - tuple(), - (1, ), - HashableClass(), ) - not_hashable = ([], UnhashableClass1(), ) - abc_hashable_not_really_hashable = (([], ), UnhashableClass2(), ) - - for i in hashable: - assert com.is_hashable(i) - for i in not_hashable: - assert not com.is_hashable(i) - for i in abc_hashable_not_really_hashable: - assert not com.is_hashable(i) - - # numpy.array is no longer collections.Hashable as of - # https://github.com/numpy/numpy/pull/5326, just test - # pandas.common.is_hashable() - assert not com.is_hashable(np.array([])) - - # old-style classes in Python 2 don't appear hashable to - # collections.Hashable but also seem to support hash() by default - if compat.PY2: - - class OldStyleClass(): - pass - - c = OldStyleClass() - assert not isinstance(c, collections.Hashable) - assert com.is_hashable(c) - hash(c) # this will not raise - - -def test_ensure_int32(): - values = np.arange(10, dtype=np.int32) - result = com._ensure_int32(values) - assert (result.dtype == np.int32) - - values = np.arange(10, dtype=np.int64) - result = com._ensure_int32(values) - assert (result.dtype == np.int32) - - -def test_is_re(): - passes = re.compile('ad'), - fails = 'x', 2, 3, object() - - for p in passes: - assert com.is_re(p) - - for f in fails: - assert not com.is_re(f) - - -def test_is_recompilable(): - passes = (r'a', u('x'), r'asdf', re.compile('adsf'), u(r'\u2233\s*'), - re.compile(r'')) - fails = 1, [], object() - - for p in passes: - assert com.is_re_compilable(p) - - for f in fails: - assert not com.is_re_compilable(f) - - def test_random_state(): import numpy.random as npr # Check with seed @@ -730,83 +188,6 @@ def test_maybe_match_name(): assert (matched == 'y') -class TestMaybe(tm.TestCase): - - def test_maybe_convert_string_to_array(self): - result = com._maybe_convert_string_to_object('x') - tm.assert_numpy_array_equal(result, np.array(['x'], dtype=object)) - self.assertTrue(result.dtype == object) - - result = com._maybe_convert_string_to_object(1) - self.assertEqual(result, 1) - - arr = np.array(['x', 'y'], dtype=str) - result = com._maybe_convert_string_to_object(arr) - tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) - self.assertTrue(result.dtype == object) - - # unicode - arr = np.array(['x', 'y']).astype('U') - result = com._maybe_convert_string_to_object(arr) - tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) - self.assertTrue(result.dtype == object) - - # object - arr = np.array(['x', 2], dtype=object) - result = com._maybe_convert_string_to_object(arr) - tm.assert_numpy_array_equal(result, np.array(['x', 2], dtype=object)) - self.assertTrue(result.dtype == object) - - def test_maybe_convert_scalar(self): - - # pass thru - result = com._maybe_convert_scalar('x') - self.assertEqual(result, 'x') - result = com._maybe_convert_scalar(np.array([1])) - self.assertEqual(result, np.array([1])) - - # leave scalar dtype - result = com._maybe_convert_scalar(np.int64(1)) - self.assertEqual(result, np.int64(1)) - result = com._maybe_convert_scalar(np.int32(1)) - self.assertEqual(result, np.int32(1)) - result = com._maybe_convert_scalar(np.float32(1)) - self.assertEqual(result, np.float32(1)) - result = com._maybe_convert_scalar(np.int64(1)) - self.assertEqual(result, np.float64(1)) - - # coerce - result = com._maybe_convert_scalar(1) - self.assertEqual(result, np.int64(1)) - result = com._maybe_convert_scalar(1.0) - self.assertEqual(result, np.float64(1)) - result = com._maybe_convert_scalar(pd.Timestamp('20130101')) - self.assertEqual(result, pd.Timestamp('20130101').value) - result = com._maybe_convert_scalar(datetime(2013, 1, 1)) - self.assertEqual(result, pd.Timestamp('20130101').value) - result = com._maybe_convert_scalar(pd.Timedelta('1 day 1 min')) - self.assertEqual(result, pd.Timedelta('1 day 1 min').value) - - -class TestConvert(tm.TestCase): - - def test_possibly_convert_objects_copy(self): - values = np.array([1, 2]) - - out = convert._possibly_convert_objects(values, copy=False) - self.assertTrue(values is out) - - out = convert._possibly_convert_objects(values, copy=True) - self.assertTrue(values is not out) - - values = np.array(['apply', 'banana']) - out = convert._possibly_convert_objects(values, copy=False) - self.assertTrue(values is out) - - out = convert._possibly_convert_objects(values, copy=True) - self.assertTrue(values is not out) - - def test_dict_compat(): data_datetime64 = {np.datetime64('1990-03-15'): 1, np.datetime64('2015-03-15'): 2} @@ -817,39 +198,6 @@ def test_dict_compat(): assert (com._dict_compat(data_unchanged) == data_unchanged) -def test_is_timedelta(): - assert (com.is_timedelta64_dtype('timedelta64')) - assert (com.is_timedelta64_dtype('timedelta64[ns]')) - assert (not com.is_timedelta64_ns_dtype('timedelta64')) - assert (com.is_timedelta64_ns_dtype('timedelta64[ns]')) - - tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64') - assert (com.is_timedelta64_dtype(tdi)) - assert (com.is_timedelta64_ns_dtype(tdi)) - assert (com.is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]'))) - # Conversion to Int64Index: - assert (not com.is_timedelta64_ns_dtype(tdi.astype('timedelta64'))) - assert (not com.is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]'))) - - -def test_array_equivalent_compat(): - # see gh-13388 - m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - n = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - assert (com.array_equivalent(m, n, strict_nan=True)) - assert (com.array_equivalent(m, n, strict_nan=False)) - - m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - n = np.array([(1, 2), (4, 3)], dtype=[('a', int), ('b', float)]) - assert (not com.array_equivalent(m, n, strict_nan=True)) - assert (not com.array_equivalent(m, n, strict_nan=False)) - - m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) - n = np.array([(1, 2), (3, 4)], dtype=[('b', int), ('a', float)]) - assert (not com.array_equivalent(m, n, strict_nan=True)) - assert (not com.array_equivalent(m, n, strict_nan=False)) - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 2f4c2b414cc30..a53e79439b017 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -7,12 +7,12 @@ from numpy import nan import pandas as pd +from pandas.types.common import is_scalar from pandas import (Index, Series, DataFrame, Panel, isnull, date_range, period_range, Panel4D) from pandas.core.index import MultiIndex import pandas.formats.printing as printing -import pandas.lib as lib from pandas.compat import range, zip, PY3 from pandas import compat @@ -53,7 +53,7 @@ def _construct(self, shape, value=None, dtype=None, **kwargs): if isinstance(shape, int): shape = tuple([shape] * self._ndim) if value is not None: - if lib.isscalar(value): + if is_scalar(value): if value == 'empty': arr = None diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index bd19a83ce2b64..5493eb37c358b 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -9,6 +9,7 @@ from datetime import datetime, date +from pandas.types.common import is_list_like import pandas as pd from pandas import (Series, DataFrame, MultiIndex, PeriodIndex, date_range, bdate_range) @@ -16,7 +17,6 @@ iteritems, OrderedDict, PY3) from pandas.util.decorators import cache_readonly from pandas.formats.printing import pprint_thing -import pandas.core.common as com import pandas.util.testing as tm from pandas.util.testing import (ensure_clean, assert_is_valid_plot_return_object, slow) @@ -157,7 +157,7 @@ def _check_visible(self, collections, visible=True): """ from matplotlib.collections import Collection if not isinstance(collections, - Collection) and not com.is_list_like(collections): + Collection) and not is_list_like(collections): collections = [collections] for patch in collections: @@ -242,7 +242,7 @@ def _check_text_labels(self, texts, expected): expected : str or list-like which has the same length as texts expected text label, or its list """ - if not com.is_list_like(texts): + if not is_list_like(texts): self.assertEqual(texts.get_text(), expected) else: labels = [t.get_text() for t in texts] @@ -1330,7 +1330,8 @@ def test_plot(self): self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) df = DataFrame({'x': [1, 2], 'y': [3, 4]}) - with tm.assertRaises(TypeError): + # mpl >= 1.5.2 (or slightly below) throw AttributError + with tm.assertRaises((TypeError, AttributeError)): df.plot.line(blarg=True) df = DataFrame(np.random.rand(10, 3), diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 10362cbb24888..57d43f22757ea 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -5,7 +5,8 @@ from datetime import datetime from numpy import nan -from pandas import date_range, bdate_range, Timestamp +from pandas.types.common import _ensure_platform_int +from pandas import date_range, bdate_range, Timestamp, isnull from pandas.core.index import Index, MultiIndex, CategoricalIndex from pandas.core.api import Categorical, DataFrame from pandas.core.common import UnsupportedFunctionCall @@ -163,9 +164,9 @@ def test_first_last_nth(self): grouped['B'].nth(0) self.df.loc[self.df['A'] == 'foo', 'B'] = np.nan - self.assertTrue(com.isnull(grouped['B'].first()['foo'])) - self.assertTrue(com.isnull(grouped['B'].last()['foo'])) - self.assertTrue(com.isnull(grouped['B'].nth(0)['foo'])) + self.assertTrue(isnull(grouped['B'].first()['foo'])) + self.assertTrue(isnull(grouped['B'].last()['foo'])) + self.assertTrue(isnull(grouped['B'].nth(0)['foo'])) # v0.14.0 whatsnew df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B']) @@ -354,6 +355,35 @@ def test_nth_multi_index_as_expected(self): names=['A', 'B'])) assert_frame_equal(result, expected) + def test_group_selection_cache(self): + # GH 12839 nth, head, and tail should return same result consistently + df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) + expected = df.iloc[[0, 2]].set_index('A') + + g = df.groupby('A') + result1 = g.head(n=2) + result2 = g.nth(0) + assert_frame_equal(result1, df) + assert_frame_equal(result2, expected) + + g = df.groupby('A') + result1 = g.tail(n=2) + result2 = g.nth(0) + assert_frame_equal(result1, df) + assert_frame_equal(result2, expected) + + g = df.groupby('A') + result1 = g.nth(0) + result2 = g.head(n=2) + assert_frame_equal(result1, expected) + assert_frame_equal(result2, df) + + g = df.groupby('A') + result1 = g.nth(0) + result2 = g.tail(n=2) + assert_frame_equal(result1, expected) + assert_frame_equal(result2, df) + def test_grouper_index_types(self): # related GH5375 # groupby misbehaving when using a Floatlike index @@ -1050,8 +1080,9 @@ def test_transform_fast(self): grp = df.groupby('id')['val'] values = np.repeat(grp.mean().values, - com._ensure_platform_int(grp.count().values)) + _ensure_platform_int(grp.count().values)) expected = pd.Series(values, index=df.index, name='val') + result = grp.transform(np.mean) assert_series_equal(result, expected) @@ -2555,6 +2586,16 @@ def test_apply_series_yield_constant(self): result = self.df.groupby(['A', 'B'])['C'].apply(len) self.assertEqual(result.index.names[:2], ('A', 'B')) + def test_apply_frame_yield_constant(self): + # GH13568 + result = self.df.groupby(['A', 'B']).apply(len) + self.assertTrue(isinstance(result, Series)) + self.assertIsNone(result.name) + + result = self.df.groupby(['A', 'B'])[['C', 'D']].apply(len) + self.assertTrue(isinstance(result, Series)) + self.assertIsNone(result.name) + def test_apply_frame_to_series(self): grouped = self.df.groupby(['A', 'B']) result = grouped.apply(len) @@ -5905,49 +5946,49 @@ def test_nargsort(self): result = _nargsort(items, kind='mergesort', ascending=True, na_position='last') exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=True, na_position='first' result = _nargsort(items, kind='mergesort', ascending=True, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='last' result = _nargsort(items, kind='mergesort', ascending=False, na_position='last') exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='first' result = _nargsort(items, kind='mergesort', ascending=False, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=True, na_position='last' result = _nargsort(items2, kind='mergesort', ascending=True, na_position='last') exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=True, na_position='first' result = _nargsort(items2, kind='mergesort', ascending=True, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='last' result = _nargsort(items2, kind='mergesort', ascending=False, na_position='last') exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) # mergesort, ascending=False, na_position='first' result = _nargsort(items2, kind='mergesort', ascending=False, na_position='first') exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)) - tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64)) + tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False) def test_datetime_count(self): df = DataFrame({'a': [1, 2, 3] * 2, @@ -6116,7 +6157,7 @@ def test_cython_transform(self): # bit a of hack to make sure the cythonized shift # is equivalent to pre 0.17.1 behavior if op == 'shift': - gb._set_selection_from_grouper() + gb._set_group_selection() for (op, args), targop in ops: if op != 'shift' and 'int' not in gb_target: diff --git a/pandas/tests/test_infer_and_convert.py b/pandas/tests/test_infer_and_convert.py deleted file mode 100644 index a6941369b35be..0000000000000 --- a/pandas/tests/test_infer_and_convert.py +++ /dev/null @@ -1,444 +0,0 @@ -# -*- coding: utf-8 -*- - -from datetime import datetime, timedelta, date, time - -import numpy as np -import pandas as pd -import pandas.lib as lib -import pandas.util.testing as tm -from pandas import Index - -from pandas.compat import long, u, PY2 - - -class TestInference(tm.TestCase): - - def test_infer_dtype_bytes(self): - compare = 'string' if PY2 else 'bytes' - - # string array of bytes - arr = np.array(list('abc'), dtype='S1') - self.assertEqual(pd.lib.infer_dtype(arr), compare) - - # object array of bytes - arr = arr.astype(object) - self.assertEqual(pd.lib.infer_dtype(arr), compare) - - def test_isinf_scalar(self): - # GH 11352 - self.assertTrue(lib.isposinf_scalar(float('inf'))) - self.assertTrue(lib.isposinf_scalar(np.inf)) - self.assertFalse(lib.isposinf_scalar(-np.inf)) - self.assertFalse(lib.isposinf_scalar(1)) - self.assertFalse(lib.isposinf_scalar('a')) - - self.assertTrue(lib.isneginf_scalar(float('-inf'))) - self.assertTrue(lib.isneginf_scalar(-np.inf)) - self.assertFalse(lib.isneginf_scalar(np.inf)) - self.assertFalse(lib.isneginf_scalar(1)) - self.assertFalse(lib.isneginf_scalar('a')) - - def test_maybe_convert_numeric_infinities(self): - # see gh-13274 - infinities = ['inf', 'inF', 'iNf', 'Inf', - 'iNF', 'InF', 'INf', 'INF'] - na_values = set(['', 'NULL', 'nan']) - - pos = np.array(['inf'], dtype=np.float64) - neg = np.array(['-inf'], dtype=np.float64) - - msg = "Unable to parse string" - - for infinity in infinities: - for maybe_int in (True, False): - out = lib.maybe_convert_numeric( - np.array([infinity], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, pos) - - out = lib.maybe_convert_numeric( - np.array(['-' + infinity], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, neg) - - out = lib.maybe_convert_numeric( - np.array([u(infinity)], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, pos) - - out = lib.maybe_convert_numeric( - np.array(['+' + infinity], dtype=object), - na_values, maybe_int) - tm.assert_numpy_array_equal(out, pos) - - # too many characters - with tm.assertRaisesRegexp(ValueError, msg): - lib.maybe_convert_numeric( - np.array(['foo_' + infinity], dtype=object), - na_values, maybe_int) - - def test_maybe_convert_numeric_post_floatify_nan(self): - # see gh-13314 - data = np.array(['1.200', '-999.000', '4.500'], dtype=object) - expected = np.array([1.2, np.nan, 4.5], dtype=np.float64) - nan_values = set([-999, -999.0]) - - for coerce_type in (True, False): - out = lib.maybe_convert_numeric(data, nan_values, coerce_type) - tm.assert_numpy_array_equal(out, expected) - - def test_convert_infs(self): - arr = np.array(['inf', 'inf', 'inf'], dtype='O') - result = lib.maybe_convert_numeric(arr, set(), False) - self.assertTrue(result.dtype == np.float64) - - arr = np.array(['-inf', '-inf', '-inf'], dtype='O') - result = lib.maybe_convert_numeric(arr, set(), False) - self.assertTrue(result.dtype == np.float64) - - def test_scientific_no_exponent(self): - # See PR 12215 - arr = np.array(['42E', '2E', '99e', '6e'], dtype='O') - result = lib.maybe_convert_numeric(arr, set(), False, True) - self.assertTrue(np.all(np.isnan(result))) - - def test_convert_non_hashable(self): - # GH13324 - # make sure that we are handing non-hashables - arr = np.array([[10.0, 2], 1.0, 'apple']) - result = lib.maybe_convert_numeric(arr, set(), False, True) - tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan])) - - -class TestTypeInference(tm.TestCase): - _multiprocess_can_split_ = True - - def test_length_zero(self): - result = lib.infer_dtype(np.array([], dtype='i4')) - self.assertEqual(result, 'integer') - - result = lib.infer_dtype([]) - self.assertEqual(result, 'empty') - - def test_integers(self): - arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'integer') - - arr = np.array([1, 2, 3, np.int64(4), np.int32(5), 'foo'], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed-integer') - - arr = np.array([1, 2, 3, 4, 5], dtype='i4') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'integer') - - def test_bools(self): - arr = np.array([True, False, True, True, True], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'boolean') - - arr = np.array([np.bool_(True), np.bool_(False)], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'boolean') - - arr = np.array([True, False, True, 'foo'], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed') - - arr = np.array([True, False, True], dtype=bool) - result = lib.infer_dtype(arr) - self.assertEqual(result, 'boolean') - - def test_floats(self): - arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'floating') - - arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'], - dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed-integer') - - arr = np.array([1, 2, 3, 4, 5], dtype='f4') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'floating') - - arr = np.array([1, 2, 3, 4, 5], dtype='f8') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'floating') - - def test_string(self): - pass - - def test_unicode(self): - pass - - def test_datetime(self): - - dates = [datetime(2012, 1, x) for x in range(1, 20)] - index = Index(dates) - self.assertEqual(index.inferred_type, 'datetime64') - - def test_date(self): - - dates = [date(2012, 1, x) for x in range(1, 20)] - index = Index(dates) - self.assertEqual(index.inferred_type, 'date') - - def test_to_object_array_tuples(self): - r = (5, 6) - values = [r] - result = lib.to_object_array_tuples(values) - - try: - # make sure record array works - from collections import namedtuple - record = namedtuple('record', 'x y') - r = record(5, 6) - values = [r] - result = lib.to_object_array_tuples(values) # noqa - except ImportError: - pass - - def test_to_object_array_width(self): - # see gh-13320 - rows = [[1, 2, 3], [4, 5, 6]] - - expected = np.array(rows, dtype=object) - out = lib.to_object_array(rows) - tm.assert_numpy_array_equal(out, expected) - - expected = np.array(rows, dtype=object) - out = lib.to_object_array(rows, min_width=1) - tm.assert_numpy_array_equal(out, expected) - - expected = np.array([[1, 2, 3, None, None], - [4, 5, 6, None, None]], dtype=object) - out = lib.to_object_array(rows, min_width=5) - tm.assert_numpy_array_equal(out, expected) - - def test_object(self): - - # GH 7431 - # cannot infer more than this as only a single element - arr = np.array([None], dtype='O') - result = lib.infer_dtype(arr) - self.assertEqual(result, 'mixed') - - def test_categorical(self): - - # GH 8974 - from pandas import Categorical, Series - arr = Categorical(list('abc')) - result = lib.infer_dtype(arr) - self.assertEqual(result, 'categorical') - - result = lib.infer_dtype(Series(arr)) - self.assertEqual(result, 'categorical') - - arr = Categorical(list('abc'), categories=['cegfab'], ordered=True) - result = lib.infer_dtype(arr) - self.assertEqual(result, 'categorical') - - result = lib.infer_dtype(Series(arr)) - self.assertEqual(result, 'categorical') - - -class TestConvert(tm.TestCase): - - def test_convert_objects(self): - arr = np.array(['a', 'b', np.nan, np.nan, 'd', 'e', 'f'], dtype='O') - result = lib.maybe_convert_objects(arr) - self.assertTrue(result.dtype == np.object_) - - def test_convert_objects_ints(self): - # test that we can detect many kinds of integers - dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'] - - for dtype_str in dtypes: - arr = np.array(list(np.arange(20, dtype=dtype_str)), dtype='O') - self.assertTrue(arr[0].dtype == np.dtype(dtype_str)) - result = lib.maybe_convert_objects(arr) - self.assertTrue(issubclass(result.dtype.type, np.integer)) - - def test_convert_objects_complex_number(self): - for dtype in np.sctypes['complex']: - arr = np.array(list(1j * np.arange(20, dtype=dtype)), dtype='O') - self.assertTrue(arr[0].dtype == np.dtype(dtype)) - result = lib.maybe_convert_objects(arr) - self.assertTrue(issubclass(result.dtype.type, np.complexfloating)) - - -class Testisscalar(tm.TestCase): - - def test_isscalar_builtin_scalars(self): - self.assertTrue(lib.isscalar(None)) - self.assertTrue(lib.isscalar(True)) - self.assertTrue(lib.isscalar(False)) - self.assertTrue(lib.isscalar(0.)) - self.assertTrue(lib.isscalar(np.nan)) - self.assertTrue(lib.isscalar('foobar')) - self.assertTrue(lib.isscalar(b'foobar')) - self.assertTrue(lib.isscalar(u('efoobar'))) - self.assertTrue(lib.isscalar(datetime(2014, 1, 1))) - self.assertTrue(lib.isscalar(date(2014, 1, 1))) - self.assertTrue(lib.isscalar(time(12, 0))) - self.assertTrue(lib.isscalar(timedelta(hours=1))) - self.assertTrue(lib.isscalar(pd.NaT)) - - def test_isscalar_builtin_nonscalars(self): - self.assertFalse(lib.isscalar({})) - self.assertFalse(lib.isscalar([])) - self.assertFalse(lib.isscalar([1])) - self.assertFalse(lib.isscalar(())) - self.assertFalse(lib.isscalar((1, ))) - self.assertFalse(lib.isscalar(slice(None))) - self.assertFalse(lib.isscalar(Ellipsis)) - - def test_isscalar_numpy_array_scalars(self): - self.assertTrue(lib.isscalar(np.int64(1))) - self.assertTrue(lib.isscalar(np.float64(1.))) - self.assertTrue(lib.isscalar(np.int32(1))) - self.assertTrue(lib.isscalar(np.object_('foobar'))) - self.assertTrue(lib.isscalar(np.str_('foobar'))) - self.assertTrue(lib.isscalar(np.unicode_(u('foobar')))) - self.assertTrue(lib.isscalar(np.bytes_(b'foobar'))) - self.assertTrue(lib.isscalar(np.datetime64('2014-01-01'))) - self.assertTrue(lib.isscalar(np.timedelta64(1, 'h'))) - - def test_isscalar_numpy_zerodim_arrays(self): - for zerodim in [np.array(1), np.array('foobar'), - np.array(np.datetime64('2014-01-01')), - np.array(np.timedelta64(1, 'h')), - np.array(np.datetime64('NaT'))]: - self.assertFalse(lib.isscalar(zerodim)) - self.assertTrue(lib.isscalar(lib.item_from_zerodim(zerodim))) - - def test_isscalar_numpy_arrays(self): - self.assertFalse(lib.isscalar(np.array([]))) - self.assertFalse(lib.isscalar(np.array([[]]))) - self.assertFalse(lib.isscalar(np.matrix('1; 2'))) - - def test_isscalar_pandas_scalars(self): - self.assertTrue(lib.isscalar(pd.Timestamp('2014-01-01'))) - self.assertTrue(lib.isscalar(pd.Timedelta(hours=1))) - self.assertTrue(lib.isscalar(pd.Period('2014-01-01'))) - - def test_lisscalar_pandas_containers(self): - self.assertFalse(lib.isscalar(pd.Series())) - self.assertFalse(lib.isscalar(pd.Series([1]))) - self.assertFalse(lib.isscalar(pd.DataFrame())) - self.assertFalse(lib.isscalar(pd.DataFrame([[1]]))) - self.assertFalse(lib.isscalar(pd.Panel())) - self.assertFalse(lib.isscalar(pd.Panel([[[1]]]))) - self.assertFalse(lib.isscalar(pd.Index([]))) - self.assertFalse(lib.isscalar(pd.Index([1]))) - - -class TestParseSQL(tm.TestCase): - - def test_convert_sql_column_floats(self): - arr = np.array([1.5, None, 3, 4.2], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_strings(self): - arr = np.array(['1.5', None, '3', '4.2'], dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_unicode(self): - arr = np.array([u('1.5'), None, u('3'), u('4.2')], - dtype=object) - result = lib.convert_sql_column(arr) - expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], - dtype=object) - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_ints(self): - arr = np.array([1, 2, 3, 4], dtype='O') - arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') - result = lib.convert_sql_column(arr) - result2 = lib.convert_sql_column(arr2) - expected = np.array([1, 2, 3, 4], dtype='i8') - self.assert_numpy_array_equal(result, expected) - self.assert_numpy_array_equal(result2, expected) - - arr = np.array([1, 2, 3, None, 4], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_longs(self): - arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, 4], dtype='i8') - self.assert_numpy_array_equal(result, expected) - - arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_bools(self): - arr = np.array([True, False, True, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, True, False], dtype=bool) - self.assert_numpy_array_equal(result, expected) - - arr = np.array([True, False, None, False], dtype='O') - result = lib.convert_sql_column(arr) - expected = np.array([True, False, np.nan, False], dtype=object) - self.assert_numpy_array_equal(result, expected) - - def test_convert_sql_column_decimals(self): - from decimal import Decimal - arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) - result = lib.convert_sql_column(arr) - expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') - self.assert_numpy_array_equal(result, expected) - - def test_convert_downcast_int64(self): - from pandas.parser import na_values - - arr = np.array([1, 2, 7, 8, 10], dtype=np.int64) - expected = np.array([1, 2, 7, 8, 10], dtype=np.int8) - - # default argument - result = lib.downcast_int64(arr, na_values) - self.assert_numpy_array_equal(result, expected) - - result = lib.downcast_int64(arr, na_values, use_unsigned=False) - self.assert_numpy_array_equal(result, expected) - - expected = np.array([1, 2, 7, 8, 10], dtype=np.uint8) - result = lib.downcast_int64(arr, na_values, use_unsigned=True) - self.assert_numpy_array_equal(result, expected) - - # still cast to int8 despite use_unsigned=True - # because of the negative number as an element - arr = np.array([1, 2, -7, 8, 10], dtype=np.int64) - expected = np.array([1, 2, -7, 8, 10], dtype=np.int8) - result = lib.downcast_int64(arr, na_values, use_unsigned=True) - self.assert_numpy_array_equal(result, expected) - - arr = np.array([1, 2, 7, 8, 300], dtype=np.int64) - expected = np.array([1, 2, 7, 8, 300], dtype=np.int16) - result = lib.downcast_int64(arr, na_values) - self.assert_numpy_array_equal(result, expected) - - int8_na = na_values[np.int8] - int64_na = na_values[np.int64] - arr = np.array([int64_na, 2, 3, 10, 15], dtype=np.int64) - expected = np.array([int8_na, 2, 3, 10, 15], dtype=np.int8) - result = lib.downcast_int64(arr, na_values) - self.assert_numpy_array_equal(result, expected) - -if __name__ == '__main__': - import nose - - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py index 10a6bb5c75b01..84d7226f1b2f5 100644 --- a/pandas/tests/test_lib.py +++ b/pandas/tests/test_lib.py @@ -222,6 +222,7 @@ def test_duplicated_with_nas(): expected = trues + trues assert (np.array_equal(result, expected)) + if __name__ == '__main__': import nose diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index c4ccef13f2844..f3b0becccf596 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -10,6 +10,7 @@ from pandas.core.index import Index, MultiIndex from pandas import Panel, DataFrame, Series, notnull, isnull, Timestamp +from pandas.types.common import is_float_dtype, is_integer_dtype from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assertRaisesRegexp) import pandas.core.common as com @@ -787,8 +788,8 @@ def test_delevel_infer_dtype(self): df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'], index=index) deleveled = df.reset_index() - self.assertTrue(com.is_integer_dtype(deleveled['prm1'])) - self.assertTrue(com.is_float_dtype(deleveled['prm2'])) + self.assertTrue(is_integer_dtype(deleveled['prm1'])) + self.assertTrue(is_float_dtype(deleveled['prm2'])) def test_reset_index_with_drop(self): deleveled = self.ymd.reset_index(drop=True) @@ -2365,7 +2366,7 @@ def test_reset_index_datetime(self): 'a': np.arange(6, dtype='int64')}, columns=['level_0', 'level_1', 'a']) expected['level_1'] = expected['level_1'].apply( - lambda d: pd.Timestamp(d, offset='D', tz=tz)) + lambda d: pd.Timestamp(d, freq='D', tz=tz)) assert_frame_equal(df.reset_index(), expected) def test_reset_index_period(self): diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py index 904bedde03312..eeeddc278c714 100644 --- a/pandas/tests/test_nanops.py +++ b/pandas/tests/test_nanops.py @@ -5,8 +5,8 @@ import warnings import numpy as np -from pandas import Series -from pandas.core.common import isnull, is_integer_dtype +from pandas import Series, isnull +from pandas.types.common import is_integer_dtype import pandas.core.nanops as nanops import pandas.util.testing as tm diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index b1f09ad2685e3..f2e13867d3bf0 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -10,12 +10,13 @@ import numpy as np import pandas as pd +from pandas.types.common import is_float_dtype from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex from pandas.core.datetools import bday from pandas.core.nanops import nanall, nanany from pandas.core.panel import Panel from pandas.core.series import remove_na -import pandas.core.common as com + from pandas.formats.printing import pprint_thing from pandas import compat from pandas.compat import range, lrange, StringIO, OrderedDict, signature @@ -903,7 +904,7 @@ def test_set_value(self): self.assertEqual(res.get_value('ItemE', 'foo', 'bar'), 1.5) res3 = self.panel.set_value('ItemE', 'foobar', 'baz', 5) - self.assertTrue(com.is_float_dtype(res3['ItemE'].values)) + self.assertTrue(is_float_dtype(res3['ItemE'].values)) with tm.assertRaisesRegexp(TypeError, "There must be an argument for each axis" " plus the value provided"): diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py index 607048df29faa..16a55c7ec4aeb 100644 --- a/pandas/tests/test_panel4d.py +++ b/pandas/tests/test_panel4d.py @@ -6,12 +6,12 @@ import numpy as np +from pandas.types.common import is_float_dtype from pandas import Series, Index, isnull, notnull from pandas.core.datetools import bday from pandas.core.panel import Panel from pandas.core.panel4d import Panel4D from pandas.core.series import remove_na -import pandas.core.common as com from pandas.util.testing import (assert_panel_equal, assert_panel4d_equal, @@ -595,7 +595,7 @@ def test_set_value(self): self.assertEqual(res.get_value('l4', 'ItemE', 'foo', 'bar'), 1.5) res3 = self.panel4d.set_value('l4', 'ItemE', 'foobar', 'baz', 5) - self.assertTrue(com.is_float_dtype(res3['l4'].values)) + self.assertTrue(is_float_dtype(res3['l4'].values)) class TestPanel4d(tm.TestCase, CheckIndexing, SafeForSparse, diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 67d171bb8efda..4d23bed620265 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -12,8 +12,7 @@ from pandas.compat import range, u import pandas.compat as compat -from pandas import (Index, Series, DataFrame, isnull, MultiIndex) -import pandas.core.common as com +from pandas import (Index, Series, DataFrame, isnull, MultiIndex, notnull) from pandas.util.testing import assert_series_equal import pandas.util.testing as tm @@ -1350,7 +1349,7 @@ def test_len(self): values = Series(['foo', 'fooo', 'fooooo', np.nan, 'fooooooo']) result = values.str.len() - exp = values.map(lambda x: len(x) if com.notnull(x) else NA) + exp = values.map(lambda x: len(x) if notnull(x) else NA) tm.assert_series_equal(result, exp) # mixed @@ -1368,7 +1367,7 @@ def test_len(self): 'fooooooo')]) result = values.str.len() - exp = values.map(lambda x: len(x) if com.notnull(x) else NA) + exp = values.map(lambda x: len(x) if notnull(x) else NA) tm.assert_series_equal(result, exp) def test_findall(self): diff --git a/pandas/tests/types/test_cast.py b/pandas/tests/types/test_cast.py new file mode 100644 index 0000000000000..dd3f07ea8157f --- /dev/null +++ b/pandas/tests/types/test_cast.py @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- + +""" +These test the private routines in types/cast.py + +""" + + +import nose +from datetime import datetime +import numpy as np + +from pandas import Timedelta, Timestamp +from pandas.types.cast import (_possibly_downcast_to_dtype, + _possibly_convert_objects, + _infer_dtype_from_scalar, + _maybe_convert_string_to_object, + _maybe_convert_scalar) +from pandas.util import testing as tm + +_multiprocess_can_split_ = True + + +def test_downcast_conv(): + # test downcasting + + arr = np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]) + result = _possibly_downcast_to_dtype(arr, 'infer') + assert (np.array_equal(result, arr)) + + arr = np.array([8., 8., 8., 8., 8.9999999999995]) + result = _possibly_downcast_to_dtype(arr, 'infer') + expected = np.array([8, 8, 8, 8, 9]) + assert (np.array_equal(result, expected)) + + arr = np.array([8., 8., 8., 8., 9.0000000000005]) + result = _possibly_downcast_to_dtype(arr, 'infer') + expected = np.array([8, 8, 8, 8, 9]) + assert (np.array_equal(result, expected)) + + # conversions + + expected = np.array([1, 2]) + for dtype in [np.float64, object, np.int64]: + arr = np.array([1.0, 2.0], dtype=dtype) + result = _possibly_downcast_to_dtype(arr, 'infer') + tm.assert_almost_equal(result, expected, check_dtype=False) + + for dtype in [np.float64, object]: + expected = np.array([1.0, 2.0, np.nan], dtype=dtype) + arr = np.array([1.0, 2.0, np.nan], dtype=dtype) + result = _possibly_downcast_to_dtype(arr, 'infer') + tm.assert_almost_equal(result, expected) + + # empties + for dtype in [np.int32, np.float64, np.float32, np.bool_, + np.int64, object]: + arr = np.array([], dtype=dtype) + result = _possibly_downcast_to_dtype(arr, 'int64') + tm.assert_almost_equal(result, np.array([], dtype=np.int64)) + assert result.dtype == np.int64 + + +class TestInferDtype(tm.TestCase): + + def test_infer_dtype_from_scalar(self): + # Test that _infer_dtype_from_scalar is returning correct dtype for int + # and float. + + for dtypec in [np.uint8, np.int8, np.uint16, np.int16, np.uint32, + np.int32, np.uint64, np.int64]: + data = dtypec(12) + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, type(data)) + + data = 12 + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.int64) + + for dtypec in [np.float16, np.float32, np.float64]: + data = dtypec(12) + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, dtypec) + + data = np.float(12) + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.float64) + + for data in [True, False]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.bool_) + + for data in [np.complex64(1), np.complex128(1)]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.complex_) + + import datetime + for data in [np.datetime64(1, 'ns'), Timestamp(1), + datetime.datetime(2000, 1, 1, 0, 0)]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, 'M8[ns]') + + for data in [np.timedelta64(1, 'ns'), Timedelta(1), + datetime.timedelta(1)]: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, 'm8[ns]') + + for data in [datetime.date(2000, 1, 1), + Timestamp(1, tz='US/Eastern'), 'foo']: + dtype, val = _infer_dtype_from_scalar(data) + self.assertEqual(dtype, np.object_) + + +class TestMaybe(tm.TestCase): + + def test_maybe_convert_string_to_array(self): + result = _maybe_convert_string_to_object('x') + tm.assert_numpy_array_equal(result, np.array(['x'], dtype=object)) + self.assertTrue(result.dtype == object) + + result = _maybe_convert_string_to_object(1) + self.assertEqual(result, 1) + + arr = np.array(['x', 'y'], dtype=str) + result = _maybe_convert_string_to_object(arr) + tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) + self.assertTrue(result.dtype == object) + + # unicode + arr = np.array(['x', 'y']).astype('U') + result = _maybe_convert_string_to_object(arr) + tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object)) + self.assertTrue(result.dtype == object) + + # object + arr = np.array(['x', 2], dtype=object) + result = _maybe_convert_string_to_object(arr) + tm.assert_numpy_array_equal(result, np.array(['x', 2], dtype=object)) + self.assertTrue(result.dtype == object) + + def test_maybe_convert_scalar(self): + + # pass thru + result = _maybe_convert_scalar('x') + self.assertEqual(result, 'x') + result = _maybe_convert_scalar(np.array([1])) + self.assertEqual(result, np.array([1])) + + # leave scalar dtype + result = _maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.int64(1)) + result = _maybe_convert_scalar(np.int32(1)) + self.assertEqual(result, np.int32(1)) + result = _maybe_convert_scalar(np.float32(1)) + self.assertEqual(result, np.float32(1)) + result = _maybe_convert_scalar(np.int64(1)) + self.assertEqual(result, np.float64(1)) + + # coerce + result = _maybe_convert_scalar(1) + self.assertEqual(result, np.int64(1)) + result = _maybe_convert_scalar(1.0) + self.assertEqual(result, np.float64(1)) + result = _maybe_convert_scalar(Timestamp('20130101')) + self.assertEqual(result, Timestamp('20130101').value) + result = _maybe_convert_scalar(datetime(2013, 1, 1)) + self.assertEqual(result, Timestamp('20130101').value) + result = _maybe_convert_scalar(Timedelta('1 day 1 min')) + self.assertEqual(result, Timedelta('1 day 1 min').value) + + +class TestConvert(tm.TestCase): + + def test_possibly_convert_objects_copy(self): + values = np.array([1, 2]) + + out = _possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) + + out = _possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) + + values = np.array(['apply', 'banana']) + out = _possibly_convert_objects(values, copy=False) + self.assertTrue(values is out) + + out = _possibly_convert_objects(values, copy=True) + self.assertTrue(values is not out) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_common.py b/pandas/tests/types/test_common.py new file mode 100644 index 0000000000000..0a586410ad5a0 --- /dev/null +++ b/pandas/tests/types/test_common.py @@ -0,0 +1,22 @@ +# -*- coding: utf-8 -*- + +import nose +import numpy as np + +from pandas.types.dtypes import DatetimeTZDtype, CategoricalDtype +from pandas.types.common import pandas_dtype + +_multiprocess_can_split_ = True + + +def test_pandas_dtype(): + + assert pandas_dtype('datetime64[ns, US/Eastern]') == DatetimeTZDtype( + 'datetime64[ns, US/Eastern]') + assert pandas_dtype('category') == CategoricalDtype() + for dtype in ['M8[ns]', 'm8[ns]', 'object', 'float64', 'int64']: + assert pandas_dtype(dtype) == np.dtype(dtype) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_dtypes.py b/pandas/tests/types/test_dtypes.py index d48b9baf64777..1743e80ae01a9 100644 --- a/pandas/tests/types/test_dtypes.py +++ b/pandas/tests/types/test_dtypes.py @@ -4,13 +4,14 @@ import nose import numpy as np from pandas import Series, Categorical, date_range -import pandas.core.common as com -from pandas.types.api import CategoricalDtype -from pandas.core.common import (is_categorical_dtype, - is_categorical, DatetimeTZDtype, - is_datetime64tz_dtype, is_datetimetz, - is_dtype_equal, is_datetime64_ns_dtype, - is_datetime64_dtype) + +from pandas.types.dtypes import CategoricalDtype +from pandas.types.common import (is_categorical_dtype, + is_categorical, DatetimeTZDtype, + is_datetime64tz_dtype, is_datetimetz, + is_dtype_equal, is_datetime64_ns_dtype, + is_datetime64_dtype, + _coerce_to_dtype) import pandas.util.testing as tm _multiprocess_can_split_ = True @@ -124,9 +125,9 @@ def test_subclass(self): self.assertTrue(issubclass(type(a), type(b))) def test_coerce_to_dtype(self): - self.assertEqual(com._coerce_to_dtype('datetime64[ns, US/Eastern]'), + self.assertEqual(_coerce_to_dtype('datetime64[ns, US/Eastern]'), DatetimeTZDtype('ns', 'US/Eastern')) - self.assertEqual(com._coerce_to_dtype('datetime64[ns, Asia/Tokyo]'), + self.assertEqual(_coerce_to_dtype('datetime64[ns, Asia/Tokyo]'), DatetimeTZDtype('ns', 'Asia/Tokyo')) def test_compat(self): diff --git a/pandas/tests/types/test_generic.py b/pandas/tests/types/test_generic.py index 5549a3a376992..89913de6f6069 100644 --- a/pandas/tests/types/test_generic.py +++ b/pandas/tests/types/test_generic.py @@ -3,8 +3,8 @@ import nose import numpy as np import pandas as pd -import pandas.core.common as com import pandas.util.testing as tm +from pandas.types import generic as gt _multiprocess_can_split_ = True @@ -22,24 +22,24 @@ class TestABCClasses(tm.TestCase): sparse_array = pd.SparseArray(np.random.randn(10)) def test_abc_types(self): - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index) - self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index) - self.assertIsInstance(self.multi_index, com.ABCMultiIndex) - self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex) - self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex) - self.assertIsInstance(self.period_index, com.ABCPeriodIndex) + self.assertIsInstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index) + self.assertIsInstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index) + self.assertIsInstance(self.multi_index, gt.ABCMultiIndex) + self.assertIsInstance(self.datetime_index, gt.ABCDatetimeIndex) + self.assertIsInstance(self.timedelta_index, gt.ABCTimedeltaIndex) + self.assertIsInstance(self.period_index, gt.ABCPeriodIndex) self.assertIsInstance(self.categorical_df.index, - com.ABCCategoricalIndex) - self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass) - self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass) - self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries) - self.assertIsInstance(self.df, com.ABCDataFrame) - self.assertIsInstance(self.df.to_panel(), com.ABCPanel) - self.assertIsInstance(self.sparse_series, com.ABCSparseSeries) - self.assertIsInstance(self.sparse_array, com.ABCSparseArray) - self.assertIsInstance(self.categorical, com.ABCCategorical) - self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod) + gt.ABCCategoricalIndex) + self.assertIsInstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass) + self.assertIsInstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass) + self.assertIsInstance(pd.Series([1, 2, 3]), gt.ABCSeries) + self.assertIsInstance(self.df, gt.ABCDataFrame) + self.assertIsInstance(self.df.to_panel(), gt.ABCPanel) + self.assertIsInstance(self.sparse_series, gt.ABCSparseSeries) + self.assertIsInstance(self.sparse_array, gt.ABCSparseArray) + self.assertIsInstance(self.categorical, gt.ABCCategorical) + self.assertIsInstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod) if __name__ == '__main__': diff --git a/pandas/tests/types/test_inference.py b/pandas/tests/types/test_inference.py new file mode 100644 index 0000000000000..34d10ee9dfa42 --- /dev/null +++ b/pandas/tests/types/test_inference.py @@ -0,0 +1,820 @@ +# -*- coding: utf-8 -*- + +""" +These the test the public routines exposed in types/common.py +related to inference and not otherwise tested in types/test_common.py + +""" + +import nose +import collections +import re +from datetime import datetime, date, timedelta, time +import numpy as np + +import pandas as pd +from pandas import lib, tslib +from pandas import (Series, Index, DataFrame, Timedelta, + DatetimeIndex, TimedeltaIndex, Timestamp, + Panel, Period) +from pandas.compat import u, PY2, lrange +from pandas.types import inference +from pandas.types.common import (is_timedelta64_dtype, + is_timedelta64_ns_dtype, + is_number, + is_integer, + is_float, + is_bool, + is_scalar, + _ensure_int32) +from pandas.types.missing import isnull +from pandas.util import testing as tm + +_multiprocess_can_split_ = True + + +def test_is_sequence(): + is_seq = inference.is_sequence + assert (is_seq((1, 2))) + assert (is_seq([1, 2])) + assert (not is_seq("abcd")) + assert (not is_seq(u("abcd"))) + assert (not is_seq(np.int64)) + + class A(object): + + def __getitem__(self): + return 1 + + assert (not is_seq(A())) + + +def test_is_list_like(): + passes = ([], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]), + Series([]), Series(['a']).str) + fails = (1, '2', object()) + + for p in passes: + assert inference.is_list_like(p) + + for f in fails: + assert not inference.is_list_like(f) + + +def test_is_dict_like(): + passes = [{}, {'A': 1}, Series([1])] + fails = ['1', 1, [1, 2], (1, 2), range(2), Index([1])] + + for p in passes: + assert inference.is_dict_like(p) + + for f in fails: + assert not inference.is_dict_like(f) + + +def test_is_named_tuple(): + passes = (collections.namedtuple('Test', list('abc'))(1, 2, 3), ) + fails = ((1, 2, 3), 'a', Series({'pi': 3.14})) + + for p in passes: + assert inference.is_named_tuple(p) + + for f in fails: + assert not inference.is_named_tuple(f) + + +def test_is_hashable(): + + # all new-style classes are hashable by default + class HashableClass(object): + pass + + class UnhashableClass1(object): + __hash__ = None + + class UnhashableClass2(object): + + def __hash__(self): + raise TypeError("Not hashable") + + hashable = (1, + 3.14, + np.float64(3.14), + 'a', + tuple(), + (1, ), + HashableClass(), ) + not_hashable = ([], UnhashableClass1(), ) + abc_hashable_not_really_hashable = (([], ), UnhashableClass2(), ) + + for i in hashable: + assert inference.is_hashable(i) + for i in not_hashable: + assert not inference.is_hashable(i) + for i in abc_hashable_not_really_hashable: + assert not inference.is_hashable(i) + + # numpy.array is no longer collections.Hashable as of + # https://github.com/numpy/numpy/pull/5326, just test + # is_hashable() + assert not inference.is_hashable(np.array([])) + + # old-style classes in Python 2 don't appear hashable to + # collections.Hashable but also seem to support hash() by default + if PY2: + + class OldStyleClass(): + pass + + c = OldStyleClass() + assert not isinstance(c, collections.Hashable) + assert inference.is_hashable(c) + hash(c) # this will not raise + + +def test_is_re(): + passes = re.compile('ad'), + fails = 'x', 2, 3, object() + + for p in passes: + assert inference.is_re(p) + + for f in fails: + assert not inference.is_re(f) + + +def test_is_recompilable(): + passes = (r'a', u('x'), r'asdf', re.compile('adsf'), u(r'\u2233\s*'), + re.compile(r'')) + fails = 1, [], object() + + for p in passes: + assert inference.is_re_compilable(p) + + for f in fails: + assert not inference.is_re_compilable(f) + + +class TestInference(tm.TestCase): + + def test_infer_dtype_bytes(self): + compare = 'string' if PY2 else 'bytes' + + # string array of bytes + arr = np.array(list('abc'), dtype='S1') + self.assertEqual(lib.infer_dtype(arr), compare) + + # object array of bytes + arr = arr.astype(object) + self.assertEqual(lib.infer_dtype(arr), compare) + + def test_isinf_scalar(self): + # GH 11352 + self.assertTrue(lib.isposinf_scalar(float('inf'))) + self.assertTrue(lib.isposinf_scalar(np.inf)) + self.assertFalse(lib.isposinf_scalar(-np.inf)) + self.assertFalse(lib.isposinf_scalar(1)) + self.assertFalse(lib.isposinf_scalar('a')) + + self.assertTrue(lib.isneginf_scalar(float('-inf'))) + self.assertTrue(lib.isneginf_scalar(-np.inf)) + self.assertFalse(lib.isneginf_scalar(np.inf)) + self.assertFalse(lib.isneginf_scalar(1)) + self.assertFalse(lib.isneginf_scalar('a')) + + def test_maybe_convert_numeric_infinities(self): + # see gh-13274 + infinities = ['inf', 'inF', 'iNf', 'Inf', + 'iNF', 'InF', 'INf', 'INF'] + na_values = set(['', 'NULL', 'nan']) + + pos = np.array(['inf'], dtype=np.float64) + neg = np.array(['-inf'], dtype=np.float64) + + msg = "Unable to parse string" + + for infinity in infinities: + for maybe_int in (True, False): + out = lib.maybe_convert_numeric( + np.array([infinity], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, pos) + + out = lib.maybe_convert_numeric( + np.array(['-' + infinity], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, neg) + + out = lib.maybe_convert_numeric( + np.array([u(infinity)], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, pos) + + out = lib.maybe_convert_numeric( + np.array(['+' + infinity], dtype=object), + na_values, maybe_int) + tm.assert_numpy_array_equal(out, pos) + + # too many characters + with tm.assertRaisesRegexp(ValueError, msg): + lib.maybe_convert_numeric( + np.array(['foo_' + infinity], dtype=object), + na_values, maybe_int) + + def test_maybe_convert_numeric_post_floatify_nan(self): + # see gh-13314 + data = np.array(['1.200', '-999.000', '4.500'], dtype=object) + expected = np.array([1.2, np.nan, 4.5], dtype=np.float64) + nan_values = set([-999, -999.0]) + + for coerce_type in (True, False): + out = lib.maybe_convert_numeric(data, nan_values, coerce_type) + tm.assert_numpy_array_equal(out, expected) + + def test_convert_infs(self): + arr = np.array(['inf', 'inf', 'inf'], dtype='O') + result = lib.maybe_convert_numeric(arr, set(), False) + self.assertTrue(result.dtype == np.float64) + + arr = np.array(['-inf', '-inf', '-inf'], dtype='O') + result = lib.maybe_convert_numeric(arr, set(), False) + self.assertTrue(result.dtype == np.float64) + + def test_scientific_no_exponent(self): + # See PR 12215 + arr = np.array(['42E', '2E', '99e', '6e'], dtype='O') + result = lib.maybe_convert_numeric(arr, set(), False, True) + self.assertTrue(np.all(np.isnan(result))) + + def test_convert_non_hashable(self): + # GH13324 + # make sure that we are handing non-hashables + arr = np.array([[10.0, 2], 1.0, 'apple']) + result = lib.maybe_convert_numeric(arr, set(), False, True) + tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan])) + + +class TestTypeInference(tm.TestCase): + _multiprocess_can_split_ = True + + def test_length_zero(self): + result = lib.infer_dtype(np.array([], dtype='i4')) + self.assertEqual(result, 'integer') + + result = lib.infer_dtype([]) + self.assertEqual(result, 'empty') + + def test_integers(self): + arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'integer') + + arr = np.array([1, 2, 3, np.int64(4), np.int32(5), 'foo'], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed-integer') + + arr = np.array([1, 2, 3, 4, 5], dtype='i4') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'integer') + + def test_bools(self): + arr = np.array([True, False, True, True, True], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'boolean') + + arr = np.array([np.bool_(True), np.bool_(False)], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'boolean') + + arr = np.array([True, False, True, 'foo'], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed') + + arr = np.array([True, False, True], dtype=bool) + result = lib.infer_dtype(arr) + self.assertEqual(result, 'boolean') + + def test_floats(self): + arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'floating') + + arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'], + dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed-integer') + + arr = np.array([1, 2, 3, 4, 5], dtype='f4') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'floating') + + arr = np.array([1, 2, 3, 4, 5], dtype='f8') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'floating') + + def test_string(self): + pass + + def test_unicode(self): + pass + + def test_datetime(self): + + dates = [datetime(2012, 1, x) for x in range(1, 20)] + index = Index(dates) + self.assertEqual(index.inferred_type, 'datetime64') + + def test_infer_dtype_datetime(self): + + arr = np.array([Timestamp('2011-01-01'), + Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([np.datetime64('2011-01-01'), + np.datetime64('2011-01-01')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # starts with nan + for n in [pd.NaT, np.nan]: + arr = np.array([n, pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([n, np.datetime64('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([n, datetime(2011, 1, 1)]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([n, pd.Timestamp('2011-01-02'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([n, np.datetime64('2011-01-02'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([n, datetime(2011, 1, 1), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # different type of nat + arr = np.array([np.timedelta64('nat'), + np.datetime64('2011-01-02')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.datetime64('2011-01-02'), + np.timedelta64('nat')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + # mixed datetime + arr = np.array([datetime(2011, 1, 1), + pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # should be datetime? + arr = np.array([np.datetime64('2011-01-01'), + pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([pd.Timestamp('2011-01-02'), + np.datetime64('2011-01-01')]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1]) + self.assertEqual(lib.infer_dtype(arr), 'mixed-integer') + + arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1.1]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.nan, '2011-01-01', pd.Timestamp('2011-01-02')]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + def test_infer_dtype_timedelta(self): + + arr = np.array([pd.Timedelta('1 days'), + pd.Timedelta('2 days')]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([np.timedelta64(1, 'D'), + np.timedelta64(2, 'D')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([timedelta(1), timedelta(2)]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + # starts with nan + for n in [pd.NaT, np.nan]: + arr = np.array([n, Timedelta('1 days')]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, np.timedelta64(1, 'D')]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, timedelta(1)]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, pd.Timedelta('1 days'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, np.timedelta64(1, 'D'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([n, timedelta(1), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + # different type of nat + arr = np.array([np.datetime64('nat'), np.timedelta64(1, 'D')], + dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.timedelta64(1, 'D'), np.datetime64('nat')], + dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + def test_infer_dtype_all_nan_nat_like(self): + arr = np.array([np.nan, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'floating') + + # nan and None mix are result in mixed + arr = np.array([np.nan, np.nan, None]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([None, np.nan, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + # pd.NaT + arr = np.array([pd.NaT]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([pd.NaT, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([np.nan, pd.NaT]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([np.nan, pd.NaT, np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + arr = np.array([None, pd.NaT, None]) + self.assertEqual(lib.infer_dtype(arr), 'datetime') + + # np.datetime64(nat) + arr = np.array([np.datetime64('nat')]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + for n in [np.nan, pd.NaT, None]: + arr = np.array([n, np.datetime64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([pd.NaT, n, np.datetime64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'datetime64') + + arr = np.array([np.timedelta64('nat')], dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + for n in [np.nan, pd.NaT, None]: + arr = np.array([n, np.timedelta64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + arr = np.array([pd.NaT, n, np.timedelta64('nat'), n]) + self.assertEqual(lib.infer_dtype(arr), 'timedelta') + + # datetime / timedelta mixed + arr = np.array([pd.NaT, np.datetime64('nat'), + np.timedelta64('nat'), np.nan]) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + arr = np.array([np.timedelta64('nat'), np.datetime64('nat')], + dtype=object) + self.assertEqual(lib.infer_dtype(arr), 'mixed') + + def test_is_datetimelike_array_all_nan_nat_like(self): + arr = np.array([np.nan, pd.NaT, np.datetime64('nat')]) + self.assertTrue(lib.is_datetime_array(arr)) + self.assertTrue(lib.is_datetime64_array(arr)) + self.assertFalse(lib.is_timedelta_array(arr)) + self.assertFalse(lib.is_timedelta64_array(arr)) + self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, pd.NaT, np.timedelta64('nat')]) + self.assertFalse(lib.is_datetime_array(arr)) + self.assertFalse(lib.is_datetime64_array(arr)) + self.assertTrue(lib.is_timedelta_array(arr)) + self.assertTrue(lib.is_timedelta64_array(arr)) + self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, pd.NaT, np.datetime64('nat'), + np.timedelta64('nat')]) + self.assertFalse(lib.is_datetime_array(arr)) + self.assertFalse(lib.is_datetime64_array(arr)) + self.assertFalse(lib.is_timedelta_array(arr)) + self.assertFalse(lib.is_timedelta64_array(arr)) + self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, pd.NaT]) + self.assertTrue(lib.is_datetime_array(arr)) + self.assertTrue(lib.is_datetime64_array(arr)) + self.assertTrue(lib.is_timedelta_array(arr)) + self.assertTrue(lib.is_timedelta64_array(arr)) + self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr)) + + arr = np.array([np.nan, np.nan], dtype=object) + self.assertFalse(lib.is_datetime_array(arr)) + self.assertFalse(lib.is_datetime64_array(arr)) + self.assertFalse(lib.is_timedelta_array(arr)) + self.assertFalse(lib.is_timedelta64_array(arr)) + self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr)) + + def test_date(self): + + dates = [date(2012, 1, x) for x in range(1, 20)] + index = Index(dates) + self.assertEqual(index.inferred_type, 'date') + + def test_to_object_array_tuples(self): + r = (5, 6) + values = [r] + result = lib.to_object_array_tuples(values) + + try: + # make sure record array works + from collections import namedtuple + record = namedtuple('record', 'x y') + r = record(5, 6) + values = [r] + result = lib.to_object_array_tuples(values) # noqa + except ImportError: + pass + + def test_object(self): + + # GH 7431 + # cannot infer more than this as only a single element + arr = np.array([None], dtype='O') + result = lib.infer_dtype(arr) + self.assertEqual(result, 'mixed') + + def test_to_object_array_width(self): + # see gh-13320 + rows = [[1, 2, 3], [4, 5, 6]] + + expected = np.array(rows, dtype=object) + out = lib.to_object_array(rows) + tm.assert_numpy_array_equal(out, expected) + + expected = np.array(rows, dtype=object) + out = lib.to_object_array(rows, min_width=1) + tm.assert_numpy_array_equal(out, expected) + + expected = np.array([[1, 2, 3, None, None], + [4, 5, 6, None, None]], dtype=object) + out = lib.to_object_array(rows, min_width=5) + tm.assert_numpy_array_equal(out, expected) + + def test_is_period(self): + self.assertTrue(lib.is_period(pd.Period('2011-01', freq='M'))) + self.assertFalse(lib.is_period(pd.PeriodIndex(['2011-01'], freq='M'))) + self.assertFalse(lib.is_period(pd.Timestamp('2011-01'))) + self.assertFalse(lib.is_period(1)) + self.assertFalse(lib.is_period(np.nan)) + + def test_categorical(self): + + # GH 8974 + from pandas import Categorical, Series + arr = Categorical(list('abc')) + result = lib.infer_dtype(arr) + self.assertEqual(result, 'categorical') + + result = lib.infer_dtype(Series(arr)) + self.assertEqual(result, 'categorical') + + arr = Categorical(list('abc'), categories=['cegfab'], ordered=True) + result = lib.infer_dtype(arr) + self.assertEqual(result, 'categorical') + + result = lib.infer_dtype(Series(arr)) + self.assertEqual(result, 'categorical') + + +class TestNumberScalar(tm.TestCase): + + def test_is_number(self): + + self.assertTrue(is_number(True)) + self.assertTrue(is_number(1)) + self.assertTrue(is_number(1.1)) + self.assertTrue(is_number(1 + 3j)) + self.assertTrue(is_number(np.bool(False))) + self.assertTrue(is_number(np.int64(1))) + self.assertTrue(is_number(np.float64(1.1))) + self.assertTrue(is_number(np.complex128(1 + 3j))) + self.assertTrue(is_number(np.nan)) + + self.assertFalse(is_number(None)) + self.assertFalse(is_number('x')) + self.assertFalse(is_number(datetime(2011, 1, 1))) + self.assertFalse(is_number(np.datetime64('2011-01-01'))) + self.assertFalse(is_number(Timestamp('2011-01-01'))) + self.assertFalse(is_number(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_number(timedelta(1000))) + self.assertFalse(is_number(Timedelta('1 days'))) + + # questionable + self.assertFalse(is_number(np.bool_(False))) + self.assertTrue(is_number(np.timedelta64(1, 'D'))) + + def test_is_bool(self): + self.assertTrue(is_bool(True)) + self.assertTrue(is_bool(np.bool(False))) + self.assertTrue(is_bool(np.bool_(False))) + + self.assertFalse(is_bool(1)) + self.assertFalse(is_bool(1.1)) + self.assertFalse(is_bool(1 + 3j)) + self.assertFalse(is_bool(np.int64(1))) + self.assertFalse(is_bool(np.float64(1.1))) + self.assertFalse(is_bool(np.complex128(1 + 3j))) + self.assertFalse(is_bool(np.nan)) + self.assertFalse(is_bool(None)) + self.assertFalse(is_bool('x')) + self.assertFalse(is_bool(datetime(2011, 1, 1))) + self.assertFalse(is_bool(np.datetime64('2011-01-01'))) + self.assertFalse(is_bool(Timestamp('2011-01-01'))) + self.assertFalse(is_bool(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_bool(timedelta(1000))) + self.assertFalse(is_bool(np.timedelta64(1, 'D'))) + self.assertFalse(is_bool(Timedelta('1 days'))) + + def test_is_integer(self): + self.assertTrue(is_integer(1)) + self.assertTrue(is_integer(np.int64(1))) + + self.assertFalse(is_integer(True)) + self.assertFalse(is_integer(1.1)) + self.assertFalse(is_integer(1 + 3j)) + self.assertFalse(is_integer(np.bool(False))) + self.assertFalse(is_integer(np.bool_(False))) + self.assertFalse(is_integer(np.float64(1.1))) + self.assertFalse(is_integer(np.complex128(1 + 3j))) + self.assertFalse(is_integer(np.nan)) + self.assertFalse(is_integer(None)) + self.assertFalse(is_integer('x')) + self.assertFalse(is_integer(datetime(2011, 1, 1))) + self.assertFalse(is_integer(np.datetime64('2011-01-01'))) + self.assertFalse(is_integer(Timestamp('2011-01-01'))) + self.assertFalse(is_integer(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_integer(timedelta(1000))) + self.assertFalse(is_integer(Timedelta('1 days'))) + + # questionable + self.assertTrue(is_integer(np.timedelta64(1, 'D'))) + + def test_is_float(self): + self.assertTrue(is_float(1.1)) + self.assertTrue(is_float(np.float64(1.1))) + self.assertTrue(is_float(np.nan)) + + self.assertFalse(is_float(True)) + self.assertFalse(is_float(1)) + self.assertFalse(is_float(1 + 3j)) + self.assertFalse(is_float(np.bool(False))) + self.assertFalse(is_float(np.bool_(False))) + self.assertFalse(is_float(np.int64(1))) + self.assertFalse(is_float(np.complex128(1 + 3j))) + self.assertFalse(is_float(None)) + self.assertFalse(is_float('x')) + self.assertFalse(is_float(datetime(2011, 1, 1))) + self.assertFalse(is_float(np.datetime64('2011-01-01'))) + self.assertFalse(is_float(Timestamp('2011-01-01'))) + self.assertFalse(is_float(Timestamp('2011-01-01', + tz='US/Eastern'))) + self.assertFalse(is_float(timedelta(1000))) + self.assertFalse(is_float(np.timedelta64(1, 'D'))) + self.assertFalse(is_float(Timedelta('1 days'))) + + def test_is_timedelta(self): + self.assertTrue(is_timedelta64_dtype('timedelta64')) + self.assertTrue(is_timedelta64_dtype('timedelta64[ns]')) + self.assertFalse(is_timedelta64_ns_dtype('timedelta64')) + self.assertTrue(is_timedelta64_ns_dtype('timedelta64[ns]')) + + tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64') + self.assertTrue(is_timedelta64_dtype(tdi)) + self.assertTrue(is_timedelta64_ns_dtype(tdi)) + self.assertTrue(is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]'))) + + # Conversion to Int64Index: + self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64'))) + self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]'))) + + +class Testisscalar(tm.TestCase): + + def test_isscalar_builtin_scalars(self): + self.assertTrue(is_scalar(None)) + self.assertTrue(is_scalar(True)) + self.assertTrue(is_scalar(False)) + self.assertTrue(is_scalar(0.)) + self.assertTrue(is_scalar(np.nan)) + self.assertTrue(is_scalar('foobar')) + self.assertTrue(is_scalar(b'foobar')) + self.assertTrue(is_scalar(u('efoobar'))) + self.assertTrue(is_scalar(datetime(2014, 1, 1))) + self.assertTrue(is_scalar(date(2014, 1, 1))) + self.assertTrue(is_scalar(time(12, 0))) + self.assertTrue(is_scalar(timedelta(hours=1))) + self.assertTrue(is_scalar(pd.NaT)) + + def test_isscalar_builtin_nonscalars(self): + self.assertFalse(is_scalar({})) + self.assertFalse(is_scalar([])) + self.assertFalse(is_scalar([1])) + self.assertFalse(is_scalar(())) + self.assertFalse(is_scalar((1, ))) + self.assertFalse(is_scalar(slice(None))) + self.assertFalse(is_scalar(Ellipsis)) + + def test_isscalar_numpy_array_scalars(self): + self.assertTrue(is_scalar(np.int64(1))) + self.assertTrue(is_scalar(np.float64(1.))) + self.assertTrue(is_scalar(np.int32(1))) + self.assertTrue(is_scalar(np.object_('foobar'))) + self.assertTrue(is_scalar(np.str_('foobar'))) + self.assertTrue(is_scalar(np.unicode_(u('foobar')))) + self.assertTrue(is_scalar(np.bytes_(b'foobar'))) + self.assertTrue(is_scalar(np.datetime64('2014-01-01'))) + self.assertTrue(is_scalar(np.timedelta64(1, 'h'))) + + def test_isscalar_numpy_zerodim_arrays(self): + for zerodim in [np.array(1), np.array('foobar'), + np.array(np.datetime64('2014-01-01')), + np.array(np.timedelta64(1, 'h')), + np.array(np.datetime64('NaT'))]: + self.assertFalse(is_scalar(zerodim)) + self.assertTrue(is_scalar(lib.item_from_zerodim(zerodim))) + + def test_isscalar_numpy_arrays(self): + self.assertFalse(is_scalar(np.array([]))) + self.assertFalse(is_scalar(np.array([[]]))) + self.assertFalse(is_scalar(np.matrix('1; 2'))) + + def test_isscalar_pandas_scalars(self): + self.assertTrue(is_scalar(Timestamp('2014-01-01'))) + self.assertTrue(is_scalar(Timedelta(hours=1))) + self.assertTrue(is_scalar(Period('2014-01-01'))) + + def test_lisscalar_pandas_containers(self): + self.assertFalse(is_scalar(Series())) + self.assertFalse(is_scalar(Series([1]))) + self.assertFalse(is_scalar(DataFrame())) + self.assertFalse(is_scalar(DataFrame([[1]]))) + self.assertFalse(is_scalar(Panel())) + self.assertFalse(is_scalar(Panel([[[1]]]))) + self.assertFalse(is_scalar(Index([]))) + self.assertFalse(is_scalar(Index([1]))) + + +def test_datetimeindex_from_empty_datetime64_array(): + for unit in ['ms', 'us', 'ns']: + idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit)) + assert (len(idx) == 0) + + +def test_nan_to_nat_conversions(): + + df = DataFrame(dict({ + 'A': np.asarray( + lrange(10), dtype='float64'), + 'B': Timestamp('20010101') + })) + df.iloc[3:6, :] = np.nan + result = df.loc[4, 'B'].value + assert (result == tslib.iNaT) + + s = df['B'].copy() + s._data = s._data.setitem(indexer=tuple([slice(8, 9)]), value=np.nan) + assert (isnull(s[8])) + + # numpy < 1.7.0 is wrong + from distutils.version import LooseVersion + if LooseVersion(np.__version__) >= '1.7.0': + assert (s[8].value == np.datetime64('NaT').astype(np.int64)) + + +def test_ensure_int32(): + values = np.arange(10, dtype=np.int32) + result = _ensure_int32(values) + assert (result.dtype == np.int32) + + values = np.arange(10, dtype=np.int64) + result = _ensure_int32(values) + assert (result.dtype == np.int32) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_io.py b/pandas/tests/types/test_io.py new file mode 100644 index 0000000000000..545edf8f1386c --- /dev/null +++ b/pandas/tests/types/test_io.py @@ -0,0 +1,116 @@ +# -*- coding: utf-8 -*- + +import numpy as np +import pandas.lib as lib +import pandas.util.testing as tm + +from pandas.compat import long, u + + +class TestParseSQL(tm.TestCase): + + def test_convert_sql_column_floats(self): + arr = np.array([1.5, None, 3, 4.2], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_strings(self): + arr = np.array(['1.5', None, '3', '4.2'], dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array(['1.5', np.nan, '3', '4.2'], dtype=object) + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_unicode(self): + arr = np.array([u('1.5'), None, u('3'), u('4.2')], + dtype=object) + result = lib.convert_sql_column(arr) + expected = np.array([u('1.5'), np.nan, u('3'), u('4.2')], + dtype=object) + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_ints(self): + arr = np.array([1, 2, 3, 4], dtype='O') + arr2 = np.array([1, 2, 3, 4], dtype='i4').astype('O') + result = lib.convert_sql_column(arr) + result2 = lib.convert_sql_column(arr2) + expected = np.array([1, 2, 3, 4], dtype='i8') + self.assert_numpy_array_equal(result, expected) + self.assert_numpy_array_equal(result2, expected) + + arr = np.array([1, 2, 3, None, 4], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_longs(self): + arr = np.array([long(1), long(2), long(3), long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, 4], dtype='i8') + self.assert_numpy_array_equal(result, expected) + + arr = np.array([long(1), long(2), long(3), None, long(4)], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([1, 2, 3, np.nan, 4], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_bools(self): + arr = np.array([True, False, True, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, True, False], dtype=bool) + self.assert_numpy_array_equal(result, expected) + + arr = np.array([True, False, None, False], dtype='O') + result = lib.convert_sql_column(arr) + expected = np.array([True, False, np.nan, False], dtype=object) + self.assert_numpy_array_equal(result, expected) + + def test_convert_sql_column_decimals(self): + from decimal import Decimal + arr = np.array([Decimal('1.5'), None, Decimal('3'), Decimal('4.2')]) + result = lib.convert_sql_column(arr) + expected = np.array([1.5, np.nan, 3, 4.2], dtype='f8') + self.assert_numpy_array_equal(result, expected) + + def test_convert_downcast_int64(self): + from pandas.parser import na_values + + arr = np.array([1, 2, 7, 8, 10], dtype=np.int64) + expected = np.array([1, 2, 7, 8, 10], dtype=np.int8) + + # default argument + result = lib.downcast_int64(arr, na_values) + self.assert_numpy_array_equal(result, expected) + + result = lib.downcast_int64(arr, na_values, use_unsigned=False) + self.assert_numpy_array_equal(result, expected) + + expected = np.array([1, 2, 7, 8, 10], dtype=np.uint8) + result = lib.downcast_int64(arr, na_values, use_unsigned=True) + self.assert_numpy_array_equal(result, expected) + + # still cast to int8 despite use_unsigned=True + # because of the negative number as an element + arr = np.array([1, 2, -7, 8, 10], dtype=np.int64) + expected = np.array([1, 2, -7, 8, 10], dtype=np.int8) + result = lib.downcast_int64(arr, na_values, use_unsigned=True) + self.assert_numpy_array_equal(result, expected) + + arr = np.array([1, 2, 7, 8, 300], dtype=np.int64) + expected = np.array([1, 2, 7, 8, 300], dtype=np.int16) + result = lib.downcast_int64(arr, na_values) + self.assert_numpy_array_equal(result, expected) + + int8_na = na_values[np.int8] + int64_na = na_values[np.int64] + arr = np.array([int64_na, 2, 3, 10, 15], dtype=np.int64) + expected = np.array([int8_na, 2, 3, 10, 15], dtype=np.int8) + result = lib.downcast_int64(arr, na_values) + self.assert_numpy_array_equal(result, expected) + + +if __name__ == '__main__': + import nose + + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_missing.py b/pandas/tests/types/test_missing.py new file mode 100644 index 0000000000000..edcb69de7bfad --- /dev/null +++ b/pandas/tests/types/test_missing.py @@ -0,0 +1,243 @@ +# -*- coding: utf-8 -*- + +import nose +import numpy as np +from datetime import datetime +from pandas.util import testing as tm + +from pandas.core import config as cf +from pandas.compat import u +from pandas.tslib import iNaT +from pandas import (NaT, Float64Index, Series, + DatetimeIndex, TimedeltaIndex, date_range) +from pandas.types.dtypes import DatetimeTZDtype +from pandas.types.missing import (array_equivalent, isnull, notnull, + na_value_for_dtype) + +_multiprocess_can_split_ = True + + +def test_notnull(): + assert notnull(1.) + assert not notnull(None) + assert not notnull(np.NaN) + + with cf.option_context("mode.use_inf_as_null", False): + assert notnull(np.inf) + assert notnull(-np.inf) + + arr = np.array([1.5, np.inf, 3.5, -np.inf]) + result = notnull(arr) + assert result.all() + + with cf.option_context("mode.use_inf_as_null", True): + assert not notnull(np.inf) + assert not notnull(-np.inf) + + arr = np.array([1.5, np.inf, 3.5, -np.inf]) + result = notnull(arr) + assert result.sum() == 2 + + with cf.option_context("mode.use_inf_as_null", False): + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries(), tm.makeTimeSeries(), + tm.makePeriodSeries()]: + assert (isinstance(isnull(s), Series)) + + +def test_isnull(): + assert not isnull(1.) + assert isnull(None) + assert isnull(np.NaN) + assert not isnull(np.inf) + assert not isnull(-np.inf) + + # series + for s in [tm.makeFloatSeries(), tm.makeStringSeries(), + tm.makeObjectSeries(), tm.makeTimeSeries(), + tm.makePeriodSeries()]: + assert (isinstance(isnull(s), Series)) + + # frame + for df in [tm.makeTimeDataFrame(), tm.makePeriodFrame(), + tm.makeMixedDataFrame()]: + result = isnull(df) + expected = df.apply(isnull) + tm.assert_frame_equal(result, expected) + + # panel + for p in [tm.makePanel(), tm.makePeriodPanel(), tm.add_nans(tm.makePanel()) + ]: + result = isnull(p) + expected = p.apply(isnull) + tm.assert_panel_equal(result, expected) + + # panel 4d + for p in [tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D())]: + result = isnull(p) + expected = p.apply(isnull) + tm.assert_panel4d_equal(result, expected) + + +def test_isnull_lists(): + result = isnull([[False]]) + exp = np.array([[False]]) + assert (np.array_equal(result, exp)) + + result = isnull([[1], [2]]) + exp = np.array([[False], [False]]) + assert (np.array_equal(result, exp)) + + # list of strings / unicode + result = isnull(['foo', 'bar']) + assert (not result.any()) + + result = isnull([u('foo'), u('bar')]) + assert (not result.any()) + + +def test_isnull_nat(): + result = isnull([NaT]) + exp = np.array([True]) + assert (np.array_equal(result, exp)) + + result = isnull(np.array([NaT], dtype=object)) + exp = np.array([True]) + assert (np.array_equal(result, exp)) + + +def test_isnull_numpy_nat(): + arr = np.array([NaT, np.datetime64('NaT'), np.timedelta64('NaT'), + np.datetime64('NaT', 's')]) + result = isnull(arr) + expected = np.array([True] * 4) + tm.assert_numpy_array_equal(result, expected) + + +def test_isnull_datetime(): + assert (not isnull(datetime.now())) + assert notnull(datetime.now()) + + idx = date_range('1/1/1990', periods=20) + assert (notnull(idx).all()) + + idx = np.asarray(idx) + idx[0] = iNaT + idx = DatetimeIndex(idx) + mask = isnull(idx) + assert (mask[0]) + assert (not mask[1:].any()) + + # GH 9129 + pidx = idx.to_period(freq='M') + mask = isnull(pidx) + assert (mask[0]) + assert (not mask[1:].any()) + + mask = isnull(pidx[1:]) + assert (not mask.any()) + + +class TestIsNull(tm.TestCase): + + def test_0d_array(self): + self.assertTrue(isnull(np.array(np.nan))) + self.assertFalse(isnull(np.array(0.0))) + self.assertFalse(isnull(np.array(0))) + # test object dtype + self.assertTrue(isnull(np.array(np.nan, dtype=object))) + self.assertFalse(isnull(np.array(0.0, dtype=object))) + self.assertFalse(isnull(np.array(0, dtype=object))) + + +def test_array_equivalent(): + assert array_equivalent(np.array([np.nan, np.nan]), + np.array([np.nan, np.nan])) + assert array_equivalent(np.array([np.nan, 1, np.nan]), + np.array([np.nan, 1, np.nan])) + assert array_equivalent(np.array([np.nan, None], dtype='object'), + np.array([np.nan, None], dtype='object')) + assert array_equivalent(np.array([np.nan, 1 + 1j], dtype='complex'), + np.array([np.nan, 1 + 1j], dtype='complex')) + assert not array_equivalent( + np.array([np.nan, 1 + 1j], dtype='complex'), np.array( + [np.nan, 1 + 2j], dtype='complex')) + assert not array_equivalent( + np.array([np.nan, 1, np.nan]), np.array([np.nan, 2, np.nan])) + assert not array_equivalent( + np.array(['a', 'b', 'c', 'd']), np.array(['e', 'e'])) + assert array_equivalent(Float64Index([0, np.nan]), + Float64Index([0, np.nan])) + assert not array_equivalent( + Float64Index([0, np.nan]), Float64Index([1, np.nan])) + assert array_equivalent(DatetimeIndex([0, np.nan]), + DatetimeIndex([0, np.nan])) + assert not array_equivalent( + DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan])) + assert array_equivalent(TimedeltaIndex([0, np.nan]), + TimedeltaIndex([0, np.nan])) + assert not array_equivalent( + TimedeltaIndex([0, np.nan]), TimedeltaIndex([1, np.nan])) + assert array_equivalent(DatetimeIndex([0, np.nan], tz='US/Eastern'), + DatetimeIndex([0, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan], tz='US/Eastern'), DatetimeIndex( + [1, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan]), DatetimeIndex( + [0, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan], tz='CET'), DatetimeIndex( + [0, np.nan], tz='US/Eastern')) + assert not array_equivalent( + DatetimeIndex([0, np.nan]), TimedeltaIndex([0, np.nan])) + + +def test_array_equivalent_compat(): + # see gh-13388 + m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + n = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + assert (array_equivalent(m, n, strict_nan=True)) + assert (array_equivalent(m, n, strict_nan=False)) + + m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + n = np.array([(1, 2), (4, 3)], dtype=[('a', int), ('b', float)]) + assert (not array_equivalent(m, n, strict_nan=True)) + assert (not array_equivalent(m, n, strict_nan=False)) + + m = np.array([(1, 2), (3, 4)], dtype=[('a', int), ('b', float)]) + n = np.array([(1, 2), (3, 4)], dtype=[('b', int), ('a', float)]) + assert (not array_equivalent(m, n, strict_nan=True)) + assert (not array_equivalent(m, n, strict_nan=False)) + + +def test_array_equivalent_str(): + for dtype in ['O', 'S', 'U']: + assert array_equivalent(np.array(['A', 'B'], dtype=dtype), + np.array(['A', 'B'], dtype=dtype)) + assert not array_equivalent(np.array(['A', 'B'], dtype=dtype), + np.array(['A', 'X'], dtype=dtype)) + + +def test_na_value_for_dtype(): + for dtype in [np.dtype('M8[ns]'), np.dtype('m8[ns]'), + DatetimeTZDtype('datetime64[ns, US/Eastern]')]: + assert na_value_for_dtype(dtype) is NaT + + for dtype in ['u1', 'u2', 'u4', 'u8', + 'i1', 'i2', 'i4', 'i8']: + assert na_value_for_dtype(np.dtype(dtype)) == 0 + + for dtype in ['bool']: + assert na_value_for_dtype(np.dtype(dtype)) is False + + for dtype in ['f2', 'f4', 'f8']: + assert np.isnan(na_value_for_dtype(np.dtype(dtype))) + + for dtype in ['O']: + assert np.isnan(na_value_for_dtype(np.dtype(dtype))) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/types/test_types.py b/pandas/tests/types/test_types.py deleted file mode 100644 index b9f6006cab731..0000000000000 --- a/pandas/tests/types/test_types.py +++ /dev/null @@ -1,40 +0,0 @@ -# -*- coding: utf-8 -*- -import nose -import numpy as np - -from pandas import NaT -from pandas.types.api import (DatetimeTZDtype, CategoricalDtype, - na_value_for_dtype, pandas_dtype) - - -def test_pandas_dtype(): - - assert pandas_dtype('datetime64[ns, US/Eastern]') == DatetimeTZDtype( - 'datetime64[ns, US/Eastern]') - assert pandas_dtype('category') == CategoricalDtype() - for dtype in ['M8[ns]', 'm8[ns]', 'object', 'float64', 'int64']: - assert pandas_dtype(dtype) == np.dtype(dtype) - - -def test_na_value_for_dtype(): - for dtype in [np.dtype('M8[ns]'), np.dtype('m8[ns]'), - DatetimeTZDtype('datetime64[ns, US/Eastern]')]: - assert na_value_for_dtype(dtype) is NaT - - for dtype in ['u1', 'u2', 'u4', 'u8', - 'i1', 'i2', 'i4', 'i8']: - assert na_value_for_dtype(np.dtype(dtype)) == 0 - - for dtype in ['bool']: - assert na_value_for_dtype(np.dtype(dtype)) is False - - for dtype in ['f2', 'f4', 'f8']: - assert np.isnan(na_value_for_dtype(np.dtype(dtype))) - - for dtype in ['O']: - assert np.isnan(na_value_for_dtype(np.dtype(dtype))) - - -if __name__ == '__main__': - nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], - exit=False) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 4b7162398738e..5b66e55eb60b6 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -12,6 +12,21 @@ from pandas import (Categorical, DataFrame, Series, Index, MultiIndex, Timedelta) from pandas.core.frame import _merge_doc +from pandas.types.generic import ABCSeries +from pandas.types.common import (is_datetime64tz_dtype, + is_datetime64_dtype, + needs_i8_conversion, + is_int64_dtype, + is_integer, + is_int_or_datetime_dtype, + is_dtype_equal, + is_bool, + is_list_like, + _ensure_int64, + _ensure_platform_int, + _ensure_object) +from pandas.types.missing import na_value_for_dtype + from pandas.core.generic import NDFrame from pandas.core.index import (_get_combined_index, _ensure_index, _get_consensus_names, @@ -19,18 +34,10 @@ from pandas.core.internals import (items_overlap_with_suffix, concatenate_block_managers) from pandas.util.decorators import Appender, Substitution -from pandas.core.common import (ABCSeries, is_dtype_equal, - is_datetime64_dtype, - is_int64_dtype, - is_integer, - is_bool, - is_list_like, - needs_i8_conversion) import pandas.core.algorithms as algos import pandas.core.common as com import pandas.types.concat as _concat -from pandas.types.api import na_value_for_dtype import pandas.algos as _algos import pandas.hashtable as _hash @@ -182,7 +189,7 @@ def merge_ordered(left, right, on=None, * outer: use union of keys from both frames (SQL: full outer join) * inner: use intersection of keys from both frames (SQL: inner join) - .. versionadded 0.18.2 + .. versionadded:: 0.19.0 Examples -------- @@ -263,7 +270,7 @@ def merge_asof(left, right, on=None, Optionally perform group-wise merge. This searches for the nearest match on the 'on' key within the same group according to 'by'. - .. versionadded 0.18.2 + .. versionadded:: 0.19.0 Parameters ---------- @@ -436,7 +443,8 @@ def _merger(x, y): # if we DO have duplicates, then # we cannot guarantee order - sorter = np.concatenate([groupby.indices[g] for g, _ in groupby]) + sorter = _ensure_platform_int( + np.concatenate([groupby.indices[g] for g, _ in groupby])) if len(result) != len(sorter): if check_duplicates: raise AssertionError("invalid reverse grouping") @@ -1110,8 +1118,8 @@ def _get_single_indexer(join_key, index, sort=False): left_key, right_key, count = _factorize_keys(join_key, index, sort=sort) left_indexer, right_indexer = _algos.left_outer_join( - com._ensure_int64(left_key), - com._ensure_int64(right_key), + _ensure_int64(left_key), + _ensure_int64(right_key), count, sort=sort) return left_indexer, right_indexer @@ -1157,18 +1165,17 @@ def _right_outer_join(x, y, max_groups): def _factorize_keys(lk, rk, sort=True): - if com.is_datetime64tz_dtype(lk) and com.is_datetime64tz_dtype(rk): + if is_datetime64tz_dtype(lk) and is_datetime64tz_dtype(rk): lk = lk.values rk = rk.values - - if com.is_int_or_datetime_dtype(lk) and com.is_int_or_datetime_dtype(rk): + if is_int_or_datetime_dtype(lk) and is_int_or_datetime_dtype(rk): klass = _hash.Int64Factorizer - lk = com._ensure_int64(com._values_from_object(lk)) - rk = com._ensure_int64(com._values_from_object(rk)) + lk = _ensure_int64(com._values_from_object(lk)) + rk = _ensure_int64(com._values_from_object(rk)) else: klass = _hash.Factorizer - lk = com._ensure_object(lk) - rk = com._ensure_object(rk) + lk = _ensure_object(lk) + rk = _ensure_object(rk) rizer = klass(max(len(lk), len(rk))) @@ -1207,10 +1214,10 @@ def _sort_labels(uniques, left, right): reverse_indexer = np.empty(len(sorter), dtype=np.int64) reverse_indexer.put(sorter, np.arange(len(sorter))) - new_left = reverse_indexer.take(com._ensure_platform_int(left)) + new_left = reverse_indexer.take(_ensure_platform_int(left)) np.putmask(new_left, left == -1, -1) - new_right = reverse_indexer.take(com._ensure_platform_int(right)) + new_right = reverse_indexer.take(_ensure_platform_int(right)) np.putmask(new_right, right == -1, -1) return new_left, new_right diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index e1405bc9e6add..3e2b7c3af460e 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -1,6 +1,7 @@ # pylint: disable=E1103 +from pandas.types.common import is_list_like, is_scalar from pandas import Series, DataFrame from pandas.core.index import MultiIndex, Index from pandas.core.groupby import Grouper @@ -9,7 +10,6 @@ from pandas.compat import range, lrange, zip from pandas import compat import pandas.core.common as com -import pandas.lib as lib import numpy as np @@ -95,7 +95,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', values_passed = values is not None if values_passed: - if com.is_list_like(values): + if is_list_like(values): values_multi = True values = list(values) else: @@ -361,7 +361,7 @@ def _all_key(): def _convert_by(by): if by is None: by = [] - elif (lib.isscalar(by) or + elif (is_scalar(by) or isinstance(by, (np.ndarray, Index, Series, Grouper)) or hasattr(by, '__call__')): by = [by] diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index b6c1926c1e7fc..4cf3364a03056 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -11,10 +11,17 @@ import numpy as np +from pandas.types.common import (is_list_like, + is_integer, + is_number, + is_hashable, + is_iterator) +from pandas.types.missing import isnull, notnull + from pandas.util.decorators import cache_readonly, deprecate_kwarg from pandas.core.base import PandasObject -import pandas.core.common as com -from pandas.core.common import AbstractMethodError + +from pandas.core.common import AbstractMethodError, _try_sort from pandas.core.generic import _shared_docs, _shared_doc_kwargs from pandas.core.index import Index, MultiIndex from pandas.core.series import Series, remove_na @@ -161,7 +168,7 @@ def _get_standard_colors(num_colors=None, colormap=None, color_type='default', if colormap is not None: warnings.warn("'color' and 'colormap' cannot be used " "simultaneously. Using 'color'") - colors = list(color) if com.is_list_like(color) else color + colors = list(color) if is_list_like(color) else color else: if color_type == 'default': # need to call list() on the result to copy so we don't @@ -336,7 +343,7 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, # no gaps between subplots fig.subplots_adjust(wspace=0, hspace=0) - mask = com.notnull(df) + mask = notnull(df) marker = _get_marker_compat(marker) @@ -980,7 +987,7 @@ def _validate_color_args(self): "simultaneously. Using 'color'") if 'color' in self.kwds and self.style is not None: - if com.is_list_like(self.style): + if is_list_like(self.style): styles = self.style else: styles = [self.style] @@ -1001,7 +1008,7 @@ def _iter_data(self, data=None, keep_index=False, fillna=None): # TODO: unused? # if self.sort_columns: - # columns = com._try_sort(data.columns) + # columns = _try_sort(data.columns) # else: # columns = data.columns @@ -1099,13 +1106,13 @@ def result(self): Return result axes """ if self.subplots: - if self.layout is not None and not com.is_list_like(self.ax): + if self.layout is not None and not is_list_like(self.ax): return self.axes.reshape(*self.layout) else: return self.axes else: sec_true = isinstance(self.secondary_y, bool) and self.secondary_y - all_sec = (com.is_list_like(self.secondary_y) and + all_sec = (is_list_like(self.secondary_y) and len(self.secondary_y) == self.nseries) if (sec_true or all_sec): # if all data is plotted on secondary, return right axes @@ -1322,7 +1329,7 @@ def _get_xticks(self, convert_period=False): @classmethod def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds): - mask = com.isnull(y) + mask = isnull(y) if mask.any(): y = np.ma.array(y) y = np.ma.masked_where(mask, y) @@ -1463,8 +1470,8 @@ def match_labels(data, e): err = np.atleast_2d(evalues) err = np.tile(err, (self.nseries, 1)) - elif com.is_list_like(err): - if com.is_iterator(err): + elif is_list_like(err): + if is_iterator(err): err = np.atleast_2d(list(err)) else: # raw error values @@ -1486,7 +1493,7 @@ def match_labels(data, e): if len(err) == 1: err = np.tile(err, (self.nseries, 1)) - elif com.is_number(err): + elif is_number(err): err = np.tile([err], (self.nseries, len(self.data))) else: @@ -1543,9 +1550,9 @@ def __init__(self, data, x, y, **kwargs): MPLPlot.__init__(self, data, **kwargs) if x is None or y is None: raise ValueError(self._kind + ' requires and x and y column') - if com.is_integer(x) and not self.data.columns.holds_integer(): + if is_integer(x) and not self.data.columns.holds_integer(): x = self.data.columns[x] - if com.is_integer(y) and not self.data.columns.holds_integer(): + if is_integer(y) and not self.data.columns.holds_integer(): y = self.data.columns[y] self.x = x self.y = y @@ -1569,7 +1576,7 @@ def __init__(self, data, x, y, s=None, c=None, **kwargs): # the handling of this argument later s = 20 super(ScatterPlot, self).__init__(data, x, y, s=s, **kwargs) - if com.is_integer(c) and not self.data.columns.holds_integer(): + if is_integer(c) and not self.data.columns.holds_integer(): c = self.data.columns[c] self.c = c @@ -1577,7 +1584,7 @@ def _make_plot(self): x, y, c, data = self.x, self.y, self.c, self.data ax = self.axes[0] - c_is_column = com.is_hashable(c) and c in self.data.columns + c_is_column = is_hashable(c) and c in self.data.columns # plot a colorbar only if a colormap is provided or necessary cb = self.kwds.pop('colorbar', self.colormap or c_is_column) @@ -1629,7 +1636,7 @@ class HexBinPlot(PlanePlot): def __init__(self, data, x, y, C=None, **kwargs): super(HexBinPlot, self).__init__(data, x, y, **kwargs) - if com.is_integer(C) and not self.data.columns.holds_integer(): + if is_integer(C) and not self.data.columns.holds_integer(): C = self.data.columns[C] self.C = C @@ -1912,9 +1919,9 @@ def __init__(self, data, **kwargs): self.ax_pos = self.tick_pos - self.tickoffset def _args_adjust(self): - if com.is_list_like(self.bottom): + if is_list_like(self.bottom): self.bottom = np.array(self.bottom) - if com.is_list_like(self.left): + if is_list_like(self.left): self.left = np.array(self.left) @classmethod @@ -2027,18 +2034,18 @@ def __init__(self, data, bins=10, bottom=0, **kwargs): MPLPlot.__init__(self, data, **kwargs) def _args_adjust(self): - if com.is_integer(self.bins): + if is_integer(self.bins): # create common bin edge values = (self.data._convert(datetime=True)._get_numeric_data()) values = np.ravel(values) - values = values[~com.isnull(values)] + values = values[~isnull(values)] hist, self.bins = np.histogram( values, bins=self.bins, range=self.kwds.get('range', None), weights=self.kwds.get('weights', None)) - if com.is_list_like(self.bottom): + if is_list_like(self.bottom): self.bottom = np.array(self.bottom) @classmethod @@ -2046,7 +2053,7 @@ def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, stacking_id=None, **kwds): if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(bins) - 1) - y = y[~com.isnull(y)] + y = y[~isnull(y)] base = np.zeros(len(bins) - 1) bottom = bottom + \ @@ -2411,7 +2418,7 @@ def _plot(data, x=None, y=None, subplots=False, msg = "{0} requires either y column or 'subplots=True'" raise ValueError(msg.format(kind)) elif y is not None: - if com.is_integer(y) and not data.columns.holds_integer(): + if is_integer(y) and not data.columns.holds_integer(): y = data.columns[y] # converted to series actually. copy to not modify data = data[y].copy() @@ -2420,12 +2427,12 @@ def _plot(data, x=None, y=None, subplots=False, else: if isinstance(data, DataFrame): if x is not None: - if com.is_integer(x) and not data.columns.holds_integer(): + if is_integer(x) and not data.columns.holds_integer(): x = data.columns[x] data = data.set_index(x) if y is not None: - if com.is_integer(y) and not data.columns.holds_integer(): + if is_integer(y) and not data.columns.holds_integer(): y = data.columns[y] label = kwds['label'] if 'label' in kwds else y series = data[y].copy() # Don't modify @@ -2434,7 +2441,7 @@ def _plot(data, x=None, y=None, subplots=False, for kw in ['xerr', 'yerr']: if (kw in kwds) and \ (isinstance(kwds[kw], string_types) or - com.is_integer(kwds[kw])): + is_integer(kwds[kw])): try: kwds[kw] = data[kwds[kw]] except (IndexError, KeyError, TypeError): @@ -2897,7 +2904,7 @@ def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, layout=layout) _axes = _flatten(axes) - for i, col in enumerate(com._try_sort(data.columns)): + for i, col in enumerate(_try_sort(data.columns)): ax = _axes[i] ax.hist(data[col].dropna().values, bins=bins, **kwds) ax.set_title(col) @@ -3345,7 +3352,7 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True, if ax is None: fig = plt.figure(**fig_kw) else: - if com.is_list_like(ax): + if is_list_like(ax): ax = _flatten(ax) if layout is not None: warnings.warn("When passing multiple axes, layout keyword is " @@ -3487,7 +3494,7 @@ def _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey): def _flatten(axes): - if not com.is_list_like(axes): + if not is_list_like(axes): return np.array([axes]) elif isinstance(axes, (np.ndarray, Index)): return axes.ravel() diff --git a/pandas/tools/tests/test_concat.py b/pandas/tools/tests/test_concat.py index a8c86657a48cc..568cf63c02e30 100644 --- a/pandas/tools/tests/test_concat.py +++ b/pandas/tools/tests/test_concat.py @@ -17,7 +17,7 @@ assert_almost_equal) -class TestConcatenate(tm.TestCase): +class ConcatenateBase(tm.TestCase): _multiprocess_can_split_ = True @@ -26,6 +26,9 @@ def setUp(self): self.mixed_frame = self.frame.copy() self.mixed_frame['foo'] = 'bar' + +class TestAppend(ConcatenateBase): + def test_append(self): begin_index = self.frame.index[:5] end_index = self.frame.index[5:] @@ -142,42 +145,32 @@ def test_append_preserve_index_name(self): result = df1.append(df2) self.assertEqual(result.index.name, 'A') - def test_join_many(self): - df = DataFrame(np.random.randn(10, 6), columns=list('abcdef')) - df_list = [df[['a', 'b']], df[['c', 'd']], df[['e', 'f']]] - - joined = df_list[0].join(df_list[1:]) - tm.assert_frame_equal(joined, df) - - df_list = [df[['a', 'b']][:-2], - df[['c', 'd']][2:], df[['e', 'f']][1:9]] - - def _check_diff_index(df_list, result, exp_index): - reindexed = [x.reindex(exp_index) for x in df_list] - expected = reindexed[0].join(reindexed[1:]) - tm.assert_frame_equal(result, expected) - - # different join types - joined = df_list[0].join(df_list[1:], how='outer') - _check_diff_index(df_list, joined, df.index) - - joined = df_list[0].join(df_list[1:]) - _check_diff_index(df_list, joined, df_list[0].index) - - joined = df_list[0].join(df_list[1:], how='inner') - _check_diff_index(df_list, joined, df.index[2:8]) - - self.assertRaises(ValueError, df_list[0].join, df_list[1:], on='a') - - def test_join_many_mixed(self): - df = DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) - df['key'] = ['foo', 'bar'] * 4 - df1 = df.ix[:, ['A', 'B']] - df2 = df.ix[:, ['C', 'D']] - df3 = df.ix[:, ['key']] - - result = df1.join([df2, df3]) - assert_frame_equal(result, df) + def test_append_dtype_coerce(self): + + # GH 4993 + # appending with datetime will incorrectly convert datetime64 + import datetime as dt + from pandas import NaT + + df1 = DataFrame(index=[1, 2], data=[dt.datetime(2013, 1, 1, 0, 0), + dt.datetime(2013, 1, 2, 0, 0)], + columns=['start_time']) + df2 = DataFrame(index=[4, 5], data=[[dt.datetime(2013, 1, 3, 0, 0), + dt.datetime(2013, 1, 3, 6, 10)], + [dt.datetime(2013, 1, 4, 0, 0), + dt.datetime(2013, 1, 4, 7, 10)]], + columns=['start_time', 'end_time']) + + expected = concat([Series([NaT, NaT, dt.datetime(2013, 1, 3, 6, 10), + dt.datetime(2013, 1, 4, 7, 10)], + name='end_time'), + Series([dt.datetime(2013, 1, 1, 0, 0), + dt.datetime(2013, 1, 2, 0, 0), + dt.datetime(2013, 1, 3, 0, 0), + dt.datetime(2013, 1, 4, 0, 0)], + name='start_time')], axis=1) + result = df1.append(df2, ignore_index=True) + assert_frame_equal(result, expected) def test_append_missing_column_proper_upcast(self): df1 = DataFrame({'A': np.array([1, 2, 3, 4], dtype='i8')}) @@ -188,6 +181,9 @@ def test_append_missing_column_proper_upcast(self): self.assertEqual(appended['A'].dtype, 'f8') self.assertEqual(appended['B'].dtype, 'O') + +class TestConcatenate(ConcatenateBase): + def test_concat_copy(self): df = DataFrame(np.random.randn(4, 3)) @@ -524,35 +520,6 @@ def test_with_mixed_tuples(self): # it works concat([df1, df2]) - def test_join_dups(self): - - # joining dups - df = concat([DataFrame(np.random.randn(10, 4), - columns=['A', 'A', 'B', 'B']), - DataFrame(np.random.randint(0, 10, size=20) - .reshape(10, 2), - columns=['A', 'C'])], - axis=1) - - expected = concat([df, df], axis=1) - result = df.join(df, rsuffix='_2') - result.columns = expected.columns - assert_frame_equal(result, expected) - - # GH 4975, invalid join on dups - w = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - x = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - y = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - z = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) - - dta = x.merge(y, left_index=True, right_index=True).merge( - z, left_index=True, right_index=True, how="outer") - dta = dta.merge(w, left_index=True, right_index=True) - expected = concat([x, y, z, w], axis=1) - expected.columns = ['x_x', 'y_x', 'x_y', - 'y_y', 'x_x', 'y_x', 'x_y', 'y_y'] - assert_frame_equal(dta, expected) - def test_handle_empty_objects(self): df = DataFrame(np.random.randn(10, 4), columns=list('abcd')) @@ -649,86 +616,40 @@ def test_concat_mixed_objs(self): panel = tm.makePanel() self.assertRaises(ValueError, lambda: concat([panel, s1], axis=1)) - def test_panel_join(self): - panel = tm.makePanel() - tm.add_nans(panel) - - p1 = panel.ix[:2, :10, :3] - p2 = panel.ix[2:, 5:, 2:] - - # left join - result = p1.join(p2) - expected = p1.copy() - expected['ItemC'] = p2['ItemC'] - tm.assert_panel_equal(result, expected) - - # right join - result = p1.join(p2, how='right') - expected = p2.copy() - expected['ItemA'] = p1['ItemA'] - expected['ItemB'] = p1['ItemB'] - expected = expected.reindex(items=['ItemA', 'ItemB', 'ItemC']) - tm.assert_panel_equal(result, expected) - - # inner join - result = p1.join(p2, how='inner') - expected = panel.ix[:, 5:10, 2:3] - tm.assert_panel_equal(result, expected) - - # outer join - result = p1.join(p2, how='outer') - expected = p1.reindex(major=panel.major_axis, - minor=panel.minor_axis) - expected = expected.join(p2.reindex(major=panel.major_axis, - minor=panel.minor_axis)) - tm.assert_panel_equal(result, expected) - - def test_panel_join_overlap(self): - panel = tm.makePanel() - tm.add_nans(panel) - - p1 = panel.ix[['ItemA', 'ItemB', 'ItemC']] - p2 = panel.ix[['ItemB', 'ItemC']] - - # Expected index is - # - # ItemA, ItemB_p1, ItemC_p1, ItemB_p2, ItemC_p2 - joined = p1.join(p2, lsuffix='_p1', rsuffix='_p2') - p1_suf = p1.ix[['ItemB', 'ItemC']].add_suffix('_p1') - p2_suf = p2.ix[['ItemB', 'ItemC']].add_suffix('_p2') - no_overlap = panel.ix[['ItemA']] - expected = no_overlap.join(p1_suf.join(p2_suf)) - tm.assert_panel_equal(joined, expected) - - def test_panel_join_many(self): - tm.K = 10 - panel = tm.makePanel() - tm.K = 4 + def test_empty_dtype_coerce(self): - panels = [panel.ix[:2], panel.ix[2:6], panel.ix[6:]] + # xref to #12411 + # xref to #12045 + # xref to #11594 + # see below - joined = panels[0].join(panels[1:]) - tm.assert_panel_equal(joined, panel) + # 10571 + df1 = DataFrame(data=[[1, None], [2, None]], columns=['a', 'b']) + df2 = DataFrame(data=[[3, None], [4, None]], columns=['a', 'b']) + result = concat([df1, df2]) + expected = df1.dtypes + tm.assert_series_equal(result.dtypes, expected) - panels = [panel.ix[:2, :-5], panel.ix[2:6, 2:], panel.ix[6:, 5:-7]] + def test_dtype_coerceion(self): - data_dict = {} - for p in panels: - data_dict.update(p.iteritems()) + # 12411 + df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), + pd.NaT]}) - joined = panels[0].join(panels[1:], how='inner') - expected = Panel.from_dict(data_dict, intersect=True) - tm.assert_panel_equal(joined, expected) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) - joined = panels[0].join(panels[1:], how='outer') - expected = Panel.from_dict(data_dict, intersect=False) - tm.assert_panel_equal(joined, expected) + # 12045 + import datetime + df = DataFrame({'date': [datetime.datetime(2012, 1, 1), + datetime.datetime(1012, 1, 2)]}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) - # edge cases - self.assertRaises(ValueError, panels[0].join, panels[1:], - how='outer', lsuffix='foo', rsuffix='bar') - self.assertRaises(ValueError, panels[0].join, panels[1:], - how='right') + # 11594 + df = DataFrame({'text': ['some words'] + [None] * 9}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + tm.assert_series_equal(result.dtypes, df.dtypes) def test_panel_concat_other_axes(self): panel = tm.makePanel() @@ -1080,6 +1001,239 @@ def test_concat_invalid_first_argument(self): expected = read_csv(StringIO(data)) assert_frame_equal(result, expected) + def test_concat_NaT_series(self): + # GH 11693 + # test for merging NaT series with datetime series. + x = Series(date_range('20151124 08:00', '20151124 09:00', + freq='1h', tz='US/Eastern')) + y = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]') + expected = Series([x[0], x[1], pd.NaT, pd.NaT]) + + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # all NaT with tz + expected = Series(pd.NaT, index=range(4), + dtype='datetime64[ns, US/Eastern]') + result = pd.concat([y, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # without tz + x = pd.Series(pd.date_range('20151124 08:00', + '20151124 09:00', freq='1h')) + y = pd.Series(pd.date_range('20151124 10:00', + '20151124 11:00', freq='1h')) + y[:] = pd.NaT + expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT]) + result = pd.concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # all NaT without tz + x[:] = pd.NaT + expected = pd.Series(pd.NaT, index=range(4), + dtype='datetime64[ns]') + result = pd.concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + def test_concat_tz_frame(self): + df2 = DataFrame(dict(A=pd.Timestamp('20130102', tz='US/Eastern'), + B=pd.Timestamp('20130603', tz='CET')), + index=range(5)) + + # concat + df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1) + assert_frame_equal(df2, df3) + + def test_concat_tz_series(self): + # GH 11755 + # tz and no tz + x = Series(date_range('20151124 08:00', + '20151124 09:00', + freq='1h', tz='UTC')) + y = Series(date_range('2012-01-01', '2012-01-02')) + expected = Series([x[0], x[1], y[0], y[1]], + dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # GH 11887 + # concat tz and object + x = Series(date_range('20151124 08:00', + '20151124 09:00', + freq='1h', tz='UTC')) + y = Series(['a', 'b']) + expected = Series([x[0], x[1], y[0], y[1]], + dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # 12217 + # 12306 fixed I think + + # Concat'ing two UTC times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('UTC') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('UTC') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, UTC]') + + # Concat'ing two London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 2+1 London times + first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + # Concat'ing 1+2 London times + first = pd.DataFrame([[datetime(2016, 1, 1)]]) + first[0] = first[0].dt.tz_localize('Europe/London') + + second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]]) + second[0] = second[0].dt.tz_localize('Europe/London') + + result = pd.concat([first, second]) + self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + + def test_concat_tz_series_with_datetimelike(self): + # GH 12620 + # tz and timedelta + x = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-02-01', tz='US/Eastern')] + y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + + # tz and period + y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + + def test_concat_tz_series_tzlocal(self): + # GH 13583 + tm._skip_if_no_dateutil() + import dateutil + x = [pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2011-02-01', tz=dateutil.tz.tzlocal())] + y = [pd.Timestamp('2012-01-01', tz=dateutil.tz.tzlocal()), + pd.Timestamp('2012-02-01', tz=dateutil.tz.tzlocal())] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y)) + self.assertEqual(result.dtype, 'datetime64[ns, tzlocal()]') + + def test_concat_period_series(self): + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + # different freq + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + # non-period + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(['A', 'B']) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + self.assertEqual(result.dtype, 'object') + + def test_concat_empty_series(self): + # GH 11082 + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) + tm.assert_frame_equal(res, exp) + + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name='y') + res = pd.concat([s1, s2], axis=0) + # name will be reset + exp = pd.Series([1, 2, 3]) + tm.assert_series_equal(res, exp) + + # empty Series with no name + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series(name=None) + res = pd.concat([s1, s2], axis=1) + exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, + columns=['x', 0]) + tm.assert_frame_equal(res, exp) + + def test_default_index(self): + # is_series and ignore_index + s1 = pd.Series([1, 2, 3], name='x') + s2 = pd.Series([4, 5, 6], name='y') + res = pd.concat([s1, s2], axis=1, ignore_index=True) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + # use check_index_type=True to check the result have + # RangeIndex (default index) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_series and all inputs have no names + s1 = pd.Series([1, 2, 3]) + s2 = pd.Series([4, 5, 6]) + res = pd.concat([s1, s2], axis=1, ignore_index=False) + self.assertIsInstance(res.columns, pd.RangeIndex) + exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) + exp.columns = pd.RangeIndex(2) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + # is_dataframe and ignore_index + df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) + df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) + + res = pd.concat([df1, df2], axis=0, ignore_index=True) + exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], + columns=['A', 'B']) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + + res = pd.concat([df1, df2], axis=1, ignore_index=True) + exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) + tm.assert_frame_equal(res, exp, check_index_type=True, + check_column_type=True) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tools/tests/test_join.py b/pandas/tools/tests/test_join.py new file mode 100644 index 0000000000000..86aee0b4a01c9 --- /dev/null +++ b/pandas/tools/tests/test_join.py @@ -0,0 +1,787 @@ +# pylint: disable=E1103 + +import nose + +from numpy.random import randn +import numpy as np + +import pandas as pd +from pandas.compat import lrange +import pandas.compat as compat +from pandas.tools.merge import merge, concat +from pandas.util.testing import assert_frame_equal +from pandas import DataFrame, MultiIndex, Series + +import pandas.algos as algos +import pandas.util.testing as tm +from pandas.tools.tests.test_merge import get_test_data, N, NGROUPS + + +a_ = np.array + + +class TestJoin(tm.TestCase): + + _multiprocess_can_split_ = True + + def setUp(self): + # aggregate multiple columns + self.df = DataFrame({'key1': get_test_data(), + 'key2': get_test_data(), + 'data1': np.random.randn(N), + 'data2': np.random.randn(N)}) + + # exclude a couple keys for fun + self.df = self.df[self.df['key2'] > 1] + + self.df2 = DataFrame({'key1': get_test_data(n=N // 5), + 'key2': get_test_data(ngroups=NGROUPS // 2, + n=N // 5), + 'value': np.random.randn(N // 5)}) + + index, data = tm.getMixedTypeDict() + self.target = DataFrame(data, index=index) + + # Join on string value + self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']}, + index=data['C']) + + def test_cython_left_outer_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) + max_group = 5 + + ls, rs = algos.left_outer_join(left, right, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, + 6, 6, 7, 7, 8, 8, 9, 10]) + exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, + 4, 5, 4, 5, 4, 5, -1, -1]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_cython_right_outer_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) + max_group = 5 + + rs, ls = algos.left_outer_join(right, left, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + # 0 1 1 1 + exp_li = a_([0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5, + # 2 2 4 + 6, 7, 8, 6, 7, 8, -1]) + exp_ri = a_([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, + 4, 4, 4, 5, 5, 5, 6]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_cython_inner_join(self): + left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) + right = a_([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.int64) + max_group = 5 + + ls, rs = algos.inner_join(left, right, max_group) + + exp_ls = left.argsort(kind='mergesort') + exp_rs = right.argsort(kind='mergesort') + + exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, + 6, 6, 7, 7, 8, 8]) + exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, + 4, 5, 4, 5, 4, 5]) + + exp_ls = exp_ls.take(exp_li) + exp_ls[exp_li == -1] = -1 + + exp_rs = exp_rs.take(exp_ri) + exp_rs[exp_ri == -1] = -1 + + self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False) + self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False) + + def test_left_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='left') + + joined_both = merge(self.df, self.df2) + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='left') + + def test_right_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='right') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='right') + + joined_both = merge(self.df, self.df2, how='right') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='right') + + def test_full_outer_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='outer') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='outer') + + joined_both = merge(self.df, self.df2, how='outer') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='outer') + + def test_inner_join(self): + joined_key2 = merge(self.df, self.df2, on='key2', how='inner') + _check_join(self.df, self.df2, joined_key2, ['key2'], how='inner') + + joined_both = merge(self.df, self.df2, how='inner') + _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], + how='inner') + + def test_handle_overlap(self): + joined = merge(self.df, self.df2, on='key2', + suffixes=['.foo', '.bar']) + + self.assertIn('key1.foo', joined) + self.assertIn('key1.bar', joined) + + def test_handle_overlap_arbitrary_key(self): + joined = merge(self.df, self.df2, + left_on='key2', right_on='key1', + suffixes=['.foo', '.bar']) + self.assertIn('key1.foo', joined) + self.assertIn('key2.bar', joined) + + def test_join_on(self): + target = self.target + source = self.source + + merged = target.join(source, on='C') + self.assert_series_equal(merged['MergedA'], target['A'], + check_names=False) + self.assert_series_equal(merged['MergedD'], target['D'], + check_names=False) + + # join with duplicates (fix regression from DataFrame/Matrix merge) + df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) + joined = df.join(df2, on='key') + expected = DataFrame({'key': ['a', 'a', 'b', 'b', 'c'], + 'value': [0, 0, 1, 1, 2]}) + assert_frame_equal(joined, expected) + + # Test when some are missing + df_a = DataFrame([[1], [2], [3]], index=['a', 'b', 'c'], + columns=['one']) + df_b = DataFrame([['foo'], ['bar']], index=[1, 2], + columns=['two']) + df_c = DataFrame([[1], [2]], index=[1, 2], + columns=['three']) + joined = df_a.join(df_b, on='one') + joined = joined.join(df_c, on='one') + self.assertTrue(np.isnan(joined['two']['c'])) + self.assertTrue(np.isnan(joined['three']['c'])) + + # merge column not p resent + self.assertRaises(KeyError, target.join, source, on='E') + + # overlap + source_copy = source.copy() + source_copy['A'] = 0 + self.assertRaises(ValueError, target.join, source_copy, on='A') + + def test_join_on_fails_with_different_right_index(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}, + index=tm.makeCustomIndex(10, 2)) + merge(df, df2, left_on='a', right_index=True) + + def test_join_on_fails_with_different_left_index(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}, + index=tm.makeCustomIndex(10, 2)) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}) + merge(df, df2, right_on='b', left_index=True) + + def test_join_on_fails_with_different_column_counts(self): + with tm.assertRaises(ValueError): + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), + 'b': np.random.randn(3)}) + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), + 'b': np.random.randn(10)}, + index=tm.makeCustomIndex(10, 2)) + merge(df, df2, right_on='a', left_on=['a', 'b']) + + def test_join_on_fails_with_wrong_object_type(self): + # GH12081 + wrongly_typed = [Series([0, 1]), 2, 'str', None, np.array([0, 1])] + df = DataFrame({'a': [1, 1]}) + + for obj in wrongly_typed: + with tm.assertRaisesRegexp(ValueError, str(type(obj))): + merge(obj, df, left_on='a', right_on='a') + with tm.assertRaisesRegexp(ValueError, str(type(obj))): + merge(df, obj, left_on='a', right_on='a') + + def test_join_on_pass_vector(self): + expected = self.target.join(self.source, on='C') + del expected['C'] + + join_col = self.target.pop('C') + result = self.target.join(self.source, on=join_col) + assert_frame_equal(result, expected) + + def test_join_with_len0(self): + # nothing to merge + merged = self.target.join(self.source.reindex([]), on='C') + for col in self.source: + self.assertIn(col, merged) + self.assertTrue(merged[col].isnull().all()) + + merged2 = self.target.join(self.source.reindex([]), on='C', + how='inner') + self.assert_index_equal(merged2.columns, merged.columns) + self.assertEqual(len(merged2), 0) + + def test_join_on_inner(self): + df = DataFrame({'key': ['a', 'a', 'd', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1]}, index=['a', 'b']) + + joined = df.join(df2, on='key', how='inner') + + expected = df.join(df2, on='key') + expected = expected[expected['value'].notnull()] + self.assert_series_equal(joined['key'], expected['key'], + check_dtype=False) + self.assert_series_equal(joined['value'], expected['value'], + check_dtype=False) + self.assert_index_equal(joined.index, expected.index) + + def test_join_on_singlekey_list(self): + df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) + df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) + + # corner cases + joined = df.join(df2, on=['key']) + expected = df.join(df2, on='key') + + assert_frame_equal(joined, expected) + + def test_join_on_series(self): + result = self.target.join(self.source['MergedA'], on='C') + expected = self.target.join(self.source[['MergedA']], on='C') + assert_frame_equal(result, expected) + + def test_join_on_series_buglet(self): + # GH #638 + df = DataFrame({'a': [1, 1]}) + ds = Series([2], index=[1], name='b') + result = df.join(ds, on='a') + expected = DataFrame({'a': [1, 1], + 'b': [2, 2]}, index=df.index) + tm.assert_frame_equal(result, expected) + + def test_join_index_mixed(self): + df1 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, + index=np.arange(10), + columns=['A', 'B', 'C', 'D']) + self.assertEqual(df1['B'].dtype, np.int64) + self.assertEqual(df1['D'].dtype, np.bool_) + + df2 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, + index=np.arange(0, 10, 2), + columns=['A', 'B', 'C', 'D']) + + # overlap + joined = df1.join(df2, lsuffix='_one', rsuffix='_two') + expected_columns = ['A_one', 'B_one', 'C_one', 'D_one', + 'A_two', 'B_two', 'C_two', 'D_two'] + df1.columns = expected_columns[:4] + df2.columns = expected_columns[4:] + expected = _join_by_hand(df1, df2) + assert_frame_equal(joined, expected) + + # no overlapping blocks + df1 = DataFrame(index=np.arange(10)) + df1['bool'] = True + df1['string'] = 'foo' + + df2 = DataFrame(index=np.arange(5, 15)) + df2['int'] = 1 + df2['float'] = 1. + + for kind in ['inner', 'outer', 'left', 'right']: + + joined = df1.join(df2, how=kind) + expected = _join_by_hand(df1, df2, how=kind) + assert_frame_equal(joined, expected) + + joined = df2.join(df1, how=kind) + expected = _join_by_hand(df2, df1, how=kind) + assert_frame_equal(joined, expected) + + def test_join_empty_bug(self): + # generated an exception in 0.4.3 + x = DataFrame() + x.join(DataFrame([3], index=[0], columns=['A']), how='outer') + + def test_join_unconsolidated(self): + # GH #331 + a = DataFrame(randn(30, 2), columns=['a', 'b']) + c = Series(randn(30)) + a['c'] = c + d = DataFrame(randn(30, 1), columns=['q']) + + # it works! + a.join(d) + d.join(a) + + def test_join_multiindex(self): + index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'], + [1, 2, 3, 1, 2, 3]], + names=['first', 'second']) + + index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'], + [1, 2, 3, 1, 2, 3]], + names=['first', 'second']) + + df1 = DataFrame(data=np.random.randn(6), index=index1, + columns=['var X']) + df2 = DataFrame(data=np.random.randn(6), index=index2, + columns=['var Y']) + + df1 = df1.sortlevel(0) + df2 = df2.sortlevel(0) + + joined = df1.join(df2, how='outer') + ex_index = index1._tuple_index.union(index2._tuple_index) + expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) + expected.index.names = index1.names + assert_frame_equal(joined, expected) + self.assertEqual(joined.index.names, index1.names) + + df1 = df1.sortlevel(1) + df2 = df2.sortlevel(1) + + joined = df1.join(df2, how='outer').sortlevel(0) + ex_index = index1._tuple_index.union(index2._tuple_index) + expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) + expected.index.names = index1.names + + assert_frame_equal(joined, expected) + self.assertEqual(joined.index.names, index1.names) + + def test_join_inner_multiindex(self): + key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux', + 'qux', 'snap'] + key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two', + 'three', 'one'] + + data = np.random.randn(len(key1)) + data = DataFrame({'key1': key1, 'key2': key2, + 'data': data}) + + index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], + ['one', 'two', 'three']], + labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], + [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=['first', 'second']) + to_join = DataFrame(np.random.randn(10, 3), index=index, + columns=['j_one', 'j_two', 'j_three']) + + joined = data.join(to_join, on=['key1', 'key2'], how='inner') + expected = merge(data, to_join.reset_index(), + left_on=['key1', 'key2'], + right_on=['first', 'second'], how='inner', + sort=False) + + expected2 = merge(to_join, data, + right_on=['key1', 'key2'], left_index=True, + how='inner', sort=False) + assert_frame_equal(joined, expected2.reindex_like(joined)) + + expected2 = merge(to_join, data, right_on=['key1', 'key2'], + left_index=True, how='inner', sort=False) + + expected = expected.drop(['first', 'second'], axis=1) + expected.index = joined.index + + self.assertTrue(joined.index.is_monotonic) + assert_frame_equal(joined, expected) + + # _assert_same_contents(expected, expected2.ix[:, expected.columns]) + + def test_join_hierarchical_mixed(self): + # GH 2024 + df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'c']) + new_df = df.groupby(['a']).agg({'b': [np.mean, np.sum]}) + other_df = DataFrame( + [(1, 2, 3), (7, 10, 6)], columns=['a', 'b', 'd']) + other_df.set_index('a', inplace=True) + # GH 9455, 12219 + with tm.assert_produces_warning(UserWarning): + result = merge(new_df, other_df, left_index=True, right_index=True) + self.assertTrue(('b', 'mean') in result) + self.assertTrue('b' in result) + + def test_join_float64_float32(self): + + a = DataFrame(randn(10, 2), columns=['a', 'b'], dtype=np.float64) + b = DataFrame(randn(10, 1), columns=['c'], dtype=np.float32) + joined = a.join(b) + self.assertEqual(joined.dtypes['a'], 'float64') + self.assertEqual(joined.dtypes['b'], 'float64') + self.assertEqual(joined.dtypes['c'], 'float32') + + a = np.random.randint(0, 5, 100).astype('int64') + b = np.random.random(100).astype('float64') + c = np.random.random(100).astype('float32') + df = DataFrame({'a': a, 'b': b, 'c': c}) + xpdf = DataFrame({'a': a, 'b': b, 'c': c}) + s = DataFrame(np.random.random(5).astype('float32'), columns=['md']) + rs = df.merge(s, left_on='a', right_index=True) + self.assertEqual(rs.dtypes['a'], 'int64') + self.assertEqual(rs.dtypes['b'], 'float64') + self.assertEqual(rs.dtypes['c'], 'float32') + self.assertEqual(rs.dtypes['md'], 'float32') + + xp = xpdf.merge(s, left_on='a', right_index=True) + assert_frame_equal(rs, xp) + + def test_join_many_non_unique_index(self): + df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]}) + df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]}) + df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]}) + idf1 = df1.set_index(["a", "b"]) + idf2 = df2.set_index(["a", "b"]) + idf3 = df3.set_index(["a", "b"]) + + result = idf1.join([idf2, idf3], how='outer') + + df_partially_merged = merge(df1, df2, on=['a', 'b'], how='outer') + expected = merge(df_partially_merged, df3, on=['a', 'b'], how='outer') + + result = result.reset_index() + expected = expected[result.columns] + expected['a'] = expected.a.astype('int64') + expected['b'] = expected.b.astype('int64') + assert_frame_equal(result, expected) + + df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]}) + df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]}) + df3 = DataFrame( + {"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]}) + idf1 = df1.set_index(["a", "b"]) + idf2 = df2.set_index(["a", "b"]) + idf3 = df3.set_index(["a", "b"]) + result = idf1.join([idf2, idf3], how='inner') + + df_partially_merged = merge(df1, df2, on=['a', 'b'], how='inner') + expected = merge(df_partially_merged, df3, on=['a', 'b'], how='inner') + + result = result.reset_index() + + assert_frame_equal(result, expected.ix[:, result.columns]) + + # GH 11519 + df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': np.random.randn(8), + 'D': np.random.randn(8)}) + s = Series(np.repeat(np.arange(8), 2), + index=np.repeat(np.arange(8), 2), name='TEST') + inner = df.join(s, how='inner') + outer = df.join(s, how='outer') + left = df.join(s, how='left') + right = df.join(s, how='right') + assert_frame_equal(inner, outer) + assert_frame_equal(inner, left) + assert_frame_equal(inner, right) + + def test_join_sort(self): + left = DataFrame({'key': ['foo', 'bar', 'baz', 'foo'], + 'value': [1, 2, 3, 4]}) + right = DataFrame({'value2': ['a', 'b', 'c']}, + index=['bar', 'baz', 'foo']) + + joined = left.join(right, on='key', sort=True) + expected = DataFrame({'key': ['bar', 'baz', 'foo', 'foo'], + 'value': [2, 3, 1, 4], + 'value2': ['a', 'b', 'c', 'c']}, + index=[1, 2, 0, 3]) + assert_frame_equal(joined, expected) + + # smoke test + joined = left.join(right, on='key', sort=False) + self.assert_index_equal(joined.index, pd.Index(lrange(4))) + + def test_mixed_type_join_with_suffix(self): + # GH #916 + df = DataFrame(np.random.randn(20, 6), + columns=['a', 'b', 'c', 'd', 'e', 'f']) + df.insert(0, 'id', 0) + df.insert(5, 'dt', 'foo') + + grouped = df.groupby('id') + mn = grouped.mean() + cn = grouped.count() + + # it works! + mn.join(cn, rsuffix='_right') + + def test_join_many(self): + df = DataFrame(np.random.randn(10, 6), columns=list('abcdef')) + df_list = [df[['a', 'b']], df[['c', 'd']], df[['e', 'f']]] + + joined = df_list[0].join(df_list[1:]) + tm.assert_frame_equal(joined, df) + + df_list = [df[['a', 'b']][:-2], + df[['c', 'd']][2:], df[['e', 'f']][1:9]] + + def _check_diff_index(df_list, result, exp_index): + reindexed = [x.reindex(exp_index) for x in df_list] + expected = reindexed[0].join(reindexed[1:]) + tm.assert_frame_equal(result, expected) + + # different join types + joined = df_list[0].join(df_list[1:], how='outer') + _check_diff_index(df_list, joined, df.index) + + joined = df_list[0].join(df_list[1:]) + _check_diff_index(df_list, joined, df_list[0].index) + + joined = df_list[0].join(df_list[1:], how='inner') + _check_diff_index(df_list, joined, df.index[2:8]) + + self.assertRaises(ValueError, df_list[0].join, df_list[1:], on='a') + + def test_join_many_mixed(self): + df = DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) + df['key'] = ['foo', 'bar'] * 4 + df1 = df.ix[:, ['A', 'B']] + df2 = df.ix[:, ['C', 'D']] + df3 = df.ix[:, ['key']] + + result = df1.join([df2, df3]) + assert_frame_equal(result, df) + + def test_join_dups(self): + + # joining dups + df = concat([DataFrame(np.random.randn(10, 4), + columns=['A', 'A', 'B', 'B']), + DataFrame(np.random.randint(0, 10, size=20) + .reshape(10, 2), + columns=['A', 'C'])], + axis=1) + + expected = concat([df, df], axis=1) + result = df.join(df, rsuffix='_2') + result.columns = expected.columns + assert_frame_equal(result, expected) + + # GH 4975, invalid join on dups + w = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + x = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + y = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + z = DataFrame(np.random.randn(4, 2), columns=["x", "y"]) + + dta = x.merge(y, left_index=True, right_index=True).merge( + z, left_index=True, right_index=True, how="outer") + dta = dta.merge(w, left_index=True, right_index=True) + expected = concat([x, y, z, w], axis=1) + expected.columns = ['x_x', 'y_x', 'x_y', + 'y_y', 'x_x', 'y_x', 'x_y', 'y_y'] + assert_frame_equal(dta, expected) + + def test_panel_join(self): + panel = tm.makePanel() + tm.add_nans(panel) + + p1 = panel.ix[:2, :10, :3] + p2 = panel.ix[2:, 5:, 2:] + + # left join + result = p1.join(p2) + expected = p1.copy() + expected['ItemC'] = p2['ItemC'] + tm.assert_panel_equal(result, expected) + + # right join + result = p1.join(p2, how='right') + expected = p2.copy() + expected['ItemA'] = p1['ItemA'] + expected['ItemB'] = p1['ItemB'] + expected = expected.reindex(items=['ItemA', 'ItemB', 'ItemC']) + tm.assert_panel_equal(result, expected) + + # inner join + result = p1.join(p2, how='inner') + expected = panel.ix[:, 5:10, 2:3] + tm.assert_panel_equal(result, expected) + + # outer join + result = p1.join(p2, how='outer') + expected = p1.reindex(major=panel.major_axis, + minor=panel.minor_axis) + expected = expected.join(p2.reindex(major=panel.major_axis, + minor=panel.minor_axis)) + tm.assert_panel_equal(result, expected) + + def test_panel_join_overlap(self): + panel = tm.makePanel() + tm.add_nans(panel) + + p1 = panel.ix[['ItemA', 'ItemB', 'ItemC']] + p2 = panel.ix[['ItemB', 'ItemC']] + + # Expected index is + # + # ItemA, ItemB_p1, ItemC_p1, ItemB_p2, ItemC_p2 + joined = p1.join(p2, lsuffix='_p1', rsuffix='_p2') + p1_suf = p1.ix[['ItemB', 'ItemC']].add_suffix('_p1') + p2_suf = p2.ix[['ItemB', 'ItemC']].add_suffix('_p2') + no_overlap = panel.ix[['ItemA']] + expected = no_overlap.join(p1_suf.join(p2_suf)) + tm.assert_panel_equal(joined, expected) + + def test_panel_join_many(self): + tm.K = 10 + panel = tm.makePanel() + tm.K = 4 + + panels = [panel.ix[:2], panel.ix[2:6], panel.ix[6:]] + + joined = panels[0].join(panels[1:]) + tm.assert_panel_equal(joined, panel) + + panels = [panel.ix[:2, :-5], panel.ix[2:6, 2:], panel.ix[6:, 5:-7]] + + data_dict = {} + for p in panels: + data_dict.update(p.iteritems()) + + joined = panels[0].join(panels[1:], how='inner') + expected = pd.Panel.from_dict(data_dict, intersect=True) + tm.assert_panel_equal(joined, expected) + + joined = panels[0].join(panels[1:], how='outer') + expected = pd.Panel.from_dict(data_dict, intersect=False) + tm.assert_panel_equal(joined, expected) + + # edge cases + self.assertRaises(ValueError, panels[0].join, panels[1:], + how='outer', lsuffix='foo', rsuffix='bar') + self.assertRaises(ValueError, panels[0].join, panels[1:], + how='right') + + +def _check_join(left, right, result, join_col, how='left', + lsuffix='_x', rsuffix='_y'): + + # some smoke tests + for c in join_col: + assert(result[c].notnull().all()) + + left_grouped = left.groupby(join_col) + right_grouped = right.groupby(join_col) + + for group_key, group in result.groupby(join_col): + l_joined = _restrict_to_columns(group, left.columns, lsuffix) + r_joined = _restrict_to_columns(group, right.columns, rsuffix) + + try: + lgroup = left_grouped.get_group(group_key) + except KeyError: + if how in ('left', 'inner'): + raise AssertionError('key %s should not have been in the join' + % str(group_key)) + + _assert_all_na(l_joined, left.columns, join_col) + else: + _assert_same_contents(l_joined, lgroup) + + try: + rgroup = right_grouped.get_group(group_key) + except KeyError: + if how in ('right', 'inner'): + raise AssertionError('key %s should not have been in the join' + % str(group_key)) + + _assert_all_na(r_joined, right.columns, join_col) + else: + _assert_same_contents(r_joined, rgroup) + + +def _restrict_to_columns(group, columns, suffix): + found = [c for c in group.columns + if c in columns or c.replace(suffix, '') in columns] + + # filter + group = group.ix[:, found] + + # get rid of suffixes, if any + group = group.rename(columns=lambda x: x.replace(suffix, '')) + + # put in the right order... + group = group.ix[:, columns] + + return group + + +def _assert_same_contents(join_chunk, source): + NA_SENTINEL = -1234567 # drop_duplicates not so NA-friendly... + + jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values + svalues = source.fillna(NA_SENTINEL).drop_duplicates().values + + rows = set(tuple(row) for row in jvalues) + assert(len(rows) == len(source)) + assert(all(tuple(row) in rows for row in svalues)) + + +def _assert_all_na(join_chunk, source_columns, join_col): + for c in source_columns: + if c in join_col: + continue + assert(join_chunk[c].isnull().all()) + + +def _join_by_hand(a, b, how='left'): + join_index = a.index.join(b.index, how=how) + + a_re = a.reindex(join_index) + b_re = b.reindex(join_index) + + result_columns = a.columns.append(b.columns) + + for col, s in compat.iteritems(b_re): + a_re[col] = s + return a_re.reindex(columns=result_columns) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 2505309768997..396b095fabbd6 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -9,23 +9,17 @@ import random import pandas as pd -from pandas.compat import range, lrange, lzip +from pandas.compat import lrange, lzip from pandas.tools.merge import merge, concat, MergeError from pandas.util.testing import (assert_frame_equal, assert_series_equal, slow) -from pandas import (DataFrame, Index, MultiIndex, - Series, date_range, Categorical, - compat) -import pandas.algos as algos +from pandas import DataFrame, Index, MultiIndex, Series, Categorical import pandas.util.testing as tm -a_ = np.array - N = 50 NGROUPS = 8 -JOIN_TYPES = ['inner', 'outer', 'left', 'right'] def get_test_data(ngroups=NGROUPS, n=N): @@ -58,496 +52,16 @@ def setUp(self): n=N // 5), 'value': np.random.randn(N // 5)}) - index, data = tm.getMixedTypeDict() - self.target = DataFrame(data, index=index) - - # Join on string value - self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']}, - index=data['C']) - self.left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'], 'v1': np.random.randn(7)}) self.right = DataFrame({'v2': np.random.randn(4)}, index=['d', 'b', 'c', 'a']) - def test_cython_left_outer_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) - max_group = 5 - - ls, rs = algos.left_outer_join(left, right, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, - 6, 6, 7, 7, 8, 8, 9, 10]) - exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, - 4, 5, 4, 5, 4, 5, -1, -1]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls) - self.assert_numpy_array_equal(rs, exp_rs) - - def test_cython_right_outer_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1], dtype=np.int64) - max_group = 5 - - rs, ls = algos.left_outer_join(right, left, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - # 0 1 1 1 - exp_li = a_([0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5, - # 2 2 4 - 6, 7, 8, 6, 7, 8, -1]) - exp_ri = a_([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, - 4, 4, 4, 5, 5, 5, 6]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls) - self.assert_numpy_array_equal(rs, exp_rs) - - def test_cython_inner_join(self): - left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64) - right = a_([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.int64) - max_group = 5 - - ls, rs = algos.inner_join(left, right, max_group) - - exp_ls = left.argsort(kind='mergesort') - exp_rs = right.argsort(kind='mergesort') - - exp_li = a_([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, - 6, 6, 7, 7, 8, 8]) - exp_ri = a_([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, - 4, 5, 4, 5, 4, 5]) - - exp_ls = exp_ls.take(exp_li) - exp_ls[exp_li == -1] = -1 - - exp_rs = exp_rs.take(exp_ri) - exp_rs[exp_ri == -1] = -1 - - self.assert_numpy_array_equal(ls, exp_ls) - self.assert_numpy_array_equal(rs, exp_rs) - - def test_left_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='left') - - joined_both = merge(self.df, self.df2) - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='left') - - def test_right_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='right') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='right') - - joined_both = merge(self.df, self.df2, how='right') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='right') - - def test_full_outer_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='outer') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='outer') - - joined_both = merge(self.df, self.df2, how='outer') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='outer') - - def test_inner_join(self): - joined_key2 = merge(self.df, self.df2, on='key2', how='inner') - _check_join(self.df, self.df2, joined_key2, ['key2'], how='inner') - - joined_both = merge(self.df, self.df2, how='inner') - _check_join(self.df, self.df2, joined_both, ['key1', 'key2'], - how='inner') - - def test_handle_overlap(self): - joined = merge(self.df, self.df2, on='key2', - suffixes=['.foo', '.bar']) - - self.assertIn('key1.foo', joined) - self.assertIn('key1.bar', joined) - - def test_handle_overlap_arbitrary_key(self): - joined = merge(self.df, self.df2, - left_on='key2', right_on='key1', - suffixes=['.foo', '.bar']) - self.assertIn('key1.foo', joined) - self.assertIn('key2.bar', joined) - def test_merge_common(self): joined = merge(self.df, self.df2) exp = merge(self.df, self.df2, on=['key1', 'key2']) tm.assert_frame_equal(joined, exp) - def test_join_on(self): - target = self.target - source = self.source - - merged = target.join(source, on='C') - self.assert_series_equal(merged['MergedA'], target['A'], - check_names=False) - self.assert_series_equal(merged['MergedD'], target['D'], - check_names=False) - - # join with duplicates (fix regression from DataFrame/Matrix merge) - df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) - joined = df.join(df2, on='key') - expected = DataFrame({'key': ['a', 'a', 'b', 'b', 'c'], - 'value': [0, 0, 1, 1, 2]}) - assert_frame_equal(joined, expected) - - # Test when some are missing - df_a = DataFrame([[1], [2], [3]], index=['a', 'b', 'c'], - columns=['one']) - df_b = DataFrame([['foo'], ['bar']], index=[1, 2], - columns=['two']) - df_c = DataFrame([[1], [2]], index=[1, 2], - columns=['three']) - joined = df_a.join(df_b, on='one') - joined = joined.join(df_c, on='one') - self.assertTrue(np.isnan(joined['two']['c'])) - self.assertTrue(np.isnan(joined['three']['c'])) - - # merge column not p resent - self.assertRaises(KeyError, target.join, source, on='E') - - # overlap - source_copy = source.copy() - source_copy['A'] = 0 - self.assertRaises(ValueError, target.join, source_copy, on='A') - - def test_join_on_fails_with_different_right_index(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}, - index=tm.makeCustomIndex(10, 2)) - merge(df, df2, left_on='a', right_index=True) - - def test_join_on_fails_with_different_left_index(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}, - index=tm.makeCustomIndex(10, 2)) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}) - merge(df, df2, right_on='b', left_index=True) - - def test_join_on_fails_with_different_column_counts(self): - with tm.assertRaises(ValueError): - df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), - 'b': np.random.randn(3)}) - df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), - 'b': np.random.randn(10)}, - index=tm.makeCustomIndex(10, 2)) - merge(df, df2, right_on='a', left_on=['a', 'b']) - - def test_join_on_fails_with_wrong_object_type(self): - # GH12081 - wrongly_typed = [Series([0, 1]), 2, 'str', None, np.array([0, 1])] - df = DataFrame({'a': [1, 1]}) - - for obj in wrongly_typed: - with tm.assertRaisesRegexp(ValueError, str(type(obj))): - merge(obj, df, left_on='a', right_on='a') - with tm.assertRaisesRegexp(ValueError, str(type(obj))): - merge(df, obj, left_on='a', right_on='a') - - def test_join_on_pass_vector(self): - expected = self.target.join(self.source, on='C') - del expected['C'] - - join_col = self.target.pop('C') - result = self.target.join(self.source, on=join_col) - assert_frame_equal(result, expected) - - def test_join_with_len0(self): - # nothing to merge - merged = self.target.join(self.source.reindex([]), on='C') - for col in self.source: - self.assertIn(col, merged) - self.assertTrue(merged[col].isnull().all()) - - merged2 = self.target.join(self.source.reindex([]), on='C', - how='inner') - self.assert_index_equal(merged2.columns, merged.columns) - self.assertEqual(len(merged2), 0) - - def test_join_on_inner(self): - df = DataFrame({'key': ['a', 'a', 'd', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1]}, index=['a', 'b']) - - joined = df.join(df2, on='key', how='inner') - - expected = df.join(df2, on='key') - expected = expected[expected['value'].notnull()] - self.assert_series_equal(joined['key'], expected['key'], - check_dtype=False) - self.assert_series_equal(joined['value'], expected['value'], - check_dtype=False) - self.assert_index_equal(joined.index, expected.index) - - def test_join_on_singlekey_list(self): - df = DataFrame({'key': ['a', 'a', 'b', 'b', 'c']}) - df2 = DataFrame({'value': [0, 1, 2]}, index=['a', 'b', 'c']) - - # corner cases - joined = df.join(df2, on=['key']) - expected = df.join(df2, on='key') - - assert_frame_equal(joined, expected) - - def test_join_on_series(self): - result = self.target.join(self.source['MergedA'], on='C') - expected = self.target.join(self.source[['MergedA']], on='C') - assert_frame_equal(result, expected) - - def test_join_on_series_buglet(self): - # GH #638 - df = DataFrame({'a': [1, 1]}) - ds = Series([2], index=[1], name='b') - result = df.join(ds, on='a') - expected = DataFrame({'a': [1, 1], - 'b': [2, 2]}, index=df.index) - tm.assert_frame_equal(result, expected) - - def test_join_index_mixed(self): - df1 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, - index=np.arange(10), - columns=['A', 'B', 'C', 'D']) - self.assertEqual(df1['B'].dtype, np.int64) - self.assertEqual(df1['D'].dtype, np.bool_) - - df2 = DataFrame({'A': 1., 'B': 2, 'C': 'foo', 'D': True}, - index=np.arange(0, 10, 2), - columns=['A', 'B', 'C', 'D']) - - # overlap - joined = df1.join(df2, lsuffix='_one', rsuffix='_two') - expected_columns = ['A_one', 'B_one', 'C_one', 'D_one', - 'A_two', 'B_two', 'C_two', 'D_two'] - df1.columns = expected_columns[:4] - df2.columns = expected_columns[4:] - expected = _join_by_hand(df1, df2) - assert_frame_equal(joined, expected) - - # no overlapping blocks - df1 = DataFrame(index=np.arange(10)) - df1['bool'] = True - df1['string'] = 'foo' - - df2 = DataFrame(index=np.arange(5, 15)) - df2['int'] = 1 - df2['float'] = 1. - - for kind in JOIN_TYPES: - - joined = df1.join(df2, how=kind) - expected = _join_by_hand(df1, df2, how=kind) - assert_frame_equal(joined, expected) - - joined = df2.join(df1, how=kind) - expected = _join_by_hand(df2, df1, how=kind) - assert_frame_equal(joined, expected) - - def test_join_empty_bug(self): - # generated an exception in 0.4.3 - x = DataFrame() - x.join(DataFrame([3], index=[0], columns=['A']), how='outer') - - def test_join_unconsolidated(self): - # GH #331 - a = DataFrame(randn(30, 2), columns=['a', 'b']) - c = Series(randn(30)) - a['c'] = c - d = DataFrame(randn(30, 1), columns=['q']) - - # it works! - a.join(d) - d.join(a) - - def test_join_multiindex(self): - index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'], - [1, 2, 3, 1, 2, 3]], - names=['first', 'second']) - - index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'], - [1, 2, 3, 1, 2, 3]], - names=['first', 'second']) - - df1 = DataFrame(data=np.random.randn(6), index=index1, - columns=['var X']) - df2 = DataFrame(data=np.random.randn(6), index=index2, - columns=['var Y']) - - df1 = df1.sortlevel(0) - df2 = df2.sortlevel(0) - - joined = df1.join(df2, how='outer') - ex_index = index1._tuple_index.union(index2._tuple_index) - expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) - expected.index.names = index1.names - assert_frame_equal(joined, expected) - self.assertEqual(joined.index.names, index1.names) - - df1 = df1.sortlevel(1) - df2 = df2.sortlevel(1) - - joined = df1.join(df2, how='outer').sortlevel(0) - ex_index = index1._tuple_index.union(index2._tuple_index) - expected = df1.reindex(ex_index).join(df2.reindex(ex_index)) - expected.index.names = index1.names - - assert_frame_equal(joined, expected) - self.assertEqual(joined.index.names, index1.names) - - def test_join_inner_multiindex(self): - key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux', - 'qux', 'snap'] - key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two', - 'three', 'one'] - - data = np.random.randn(len(key1)) - data = DataFrame({'key1': key1, 'key2': key2, - 'data': data}) - - index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], - ['one', 'two', 'three']], - labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], - [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], - names=['first', 'second']) - to_join = DataFrame(np.random.randn(10, 3), index=index, - columns=['j_one', 'j_two', 'j_three']) - - joined = data.join(to_join, on=['key1', 'key2'], how='inner') - expected = merge(data, to_join.reset_index(), - left_on=['key1', 'key2'], - right_on=['first', 'second'], how='inner', - sort=False) - - expected2 = merge(to_join, data, - right_on=['key1', 'key2'], left_index=True, - how='inner', sort=False) - assert_frame_equal(joined, expected2.reindex_like(joined)) - - expected2 = merge(to_join, data, right_on=['key1', 'key2'], - left_index=True, how='inner', sort=False) - - expected = expected.drop(['first', 'second'], axis=1) - expected.index = joined.index - - self.assertTrue(joined.index.is_monotonic) - assert_frame_equal(joined, expected) - - # _assert_same_contents(expected, expected2.ix[:, expected.columns]) - - def test_join_hierarchical_mixed(self): - # GH 2024 - df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=['a', 'b', 'c']) - new_df = df.groupby(['a']).agg({'b': [np.mean, np.sum]}) - other_df = DataFrame( - [(1, 2, 3), (7, 10, 6)], columns=['a', 'b', 'd']) - other_df.set_index('a', inplace=True) - # GH 9455, 12219 - with tm.assert_produces_warning(UserWarning): - result = merge(new_df, other_df, left_index=True, right_index=True) - self.assertTrue(('b', 'mean') in result) - self.assertTrue('b' in result) - - def test_join_float64_float32(self): - - a = DataFrame(randn(10, 2), columns=['a', 'b'], dtype=np.float64) - b = DataFrame(randn(10, 1), columns=['c'], dtype=np.float32) - joined = a.join(b) - self.assertEqual(joined.dtypes['a'], 'float64') - self.assertEqual(joined.dtypes['b'], 'float64') - self.assertEqual(joined.dtypes['c'], 'float32') - - a = np.random.randint(0, 5, 100).astype('int64') - b = np.random.random(100).astype('float64') - c = np.random.random(100).astype('float32') - df = DataFrame({'a': a, 'b': b, 'c': c}) - xpdf = DataFrame({'a': a, 'b': b, 'c': c}) - s = DataFrame(np.random.random(5).astype('float32'), columns=['md']) - rs = df.merge(s, left_on='a', right_index=True) - self.assertEqual(rs.dtypes['a'], 'int64') - self.assertEqual(rs.dtypes['b'], 'float64') - self.assertEqual(rs.dtypes['c'], 'float32') - self.assertEqual(rs.dtypes['md'], 'float32') - - xp = xpdf.merge(s, left_on='a', right_index=True) - assert_frame_equal(rs, xp) - - def test_join_many_non_unique_index(self): - df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]}) - df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]}) - df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]}) - idf1 = df1.set_index(["a", "b"]) - idf2 = df2.set_index(["a", "b"]) - idf3 = df3.set_index(["a", "b"]) - - result = idf1.join([idf2, idf3], how='outer') - - df_partially_merged = merge(df1, df2, on=['a', 'b'], how='outer') - expected = merge(df_partially_merged, df3, on=['a', 'b'], how='outer') - - result = result.reset_index() - expected = expected[result.columns] - expected['a'] = expected.a.astype('int64') - expected['b'] = expected.b.astype('int64') - assert_frame_equal(result, expected) - - df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]}) - df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]}) - df3 = DataFrame( - {"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]}) - idf1 = df1.set_index(["a", "b"]) - idf2 = df2.set_index(["a", "b"]) - idf3 = df3.set_index(["a", "b"]) - result = idf1.join([idf2, idf3], how='inner') - - df_partially_merged = merge(df1, df2, on=['a', 'b'], how='inner') - expected = merge(df_partially_merged, df3, on=['a', 'b'], how='inner') - - result = result.reset_index() - - assert_frame_equal(result, expected.ix[:, result.columns]) - - # GH 11519 - df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo'], - 'B': ['one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three'], - 'C': np.random.randn(8), - 'D': np.random.randn(8)}) - s = Series(np.repeat(np.arange(8), 2), - index=np.repeat(np.arange(8), 2), name='TEST') - inner = df.join(s, how='inner') - outer = df.join(s, how='outer') - left = df.join(s, how='left') - right = df.join(s, how='right') - assert_frame_equal(inner, outer) - assert_frame_equal(inner, left) - assert_frame_equal(inner, right) - def test_merge_index_singlekey_right_vs_left(self): left = DataFrame({'key': ['a', 'b', 'c', 'd', 'e', 'e', 'a'], 'v1': np.random.randn(7)}) @@ -651,23 +165,6 @@ def test_merge_nocopy(self): merged['d'] = 'peekaboo' self.assertTrue((right['d'] == 'peekaboo').all()) - def test_join_sort(self): - left = DataFrame({'key': ['foo', 'bar', 'baz', 'foo'], - 'value': [1, 2, 3, 4]}) - right = DataFrame({'value2': ['a', 'b', 'c']}, - index=['bar', 'baz', 'foo']) - - joined = left.join(right, on='key', sort=True) - expected = DataFrame({'key': ['bar', 'baz', 'foo', 'foo'], - 'value': [2, 3, 1, 4], - 'value2': ['a', 'b', 'c', 'c']}, - index=[1, 2, 0, 3]) - assert_frame_equal(joined, expected) - - # smoke test - joined = left.join(right, on='key', sort=False) - self.assert_index_equal(joined.index, pd.Index(lrange(4))) - def test_intelligently_handle_join_key(self): # #733, be a bit more 1337 about not returning unconsolidated DataFrame @@ -737,20 +234,6 @@ def test_handle_join_key_pass_array(self): merged = merge(left, right, left_index=True, right_on=key, how='outer') self.assert_series_equal(merged['key_0'], Series(key, name='key_0')) - def test_mixed_type_join_with_suffix(self): - # GH #916 - df = DataFrame(np.random.randn(20, 6), - columns=['a', 'b', 'c', 'd', 'e', 'f']) - df.insert(0, 'id', 0) - df.insert(5, 'dt', 'foo') - - grouped = df.groupby('id') - mn = grouped.mean() - cn = grouped.count() - - # it works! - mn.join(cn, rsuffix='_right') - def test_no_overlap_more_informative_error(self): dt = datetime.now() df1 = DataFrame({'x': ['a']}, index=[dt]) @@ -963,68 +446,6 @@ def _constructor(self): tm.assertIsInstance(result, NotADataFrame) - def test_empty_dtype_coerce(self): - - # xref to #12411 - # xref to #12045 - # xref to #11594 - # see below - - # 10571 - df1 = DataFrame(data=[[1, None], [2, None]], columns=['a', 'b']) - df2 = DataFrame(data=[[3, None], [4, None]], columns=['a', 'b']) - result = concat([df1, df2]) - expected = df1.dtypes - assert_series_equal(result.dtypes, expected) - - def test_dtype_coerceion(self): - - # 12411 - df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), - pd.NaT]}) - - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - # 12045 - import datetime - df = DataFrame({'date': [datetime.datetime(2012, 1, 1), - datetime.datetime(1012, 1, 2)]}) - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - # 11594 - df = DataFrame({'text': ['some words'] + [None] * 9}) - result = concat([df.iloc[[0]], df.iloc[[1]]]) - assert_series_equal(result.dtypes, df.dtypes) - - def test_append_dtype_coerce(self): - - # GH 4993 - # appending with datetime will incorrectly convert datetime64 - import datetime as dt - from pandas import NaT - - df1 = DataFrame(index=[1, 2], data=[dt.datetime(2013, 1, 1, 0, 0), - dt.datetime(2013, 1, 2, 0, 0)], - columns=['start_time']) - df2 = DataFrame(index=[4, 5], data=[[dt.datetime(2013, 1, 3, 0, 0), - dt.datetime(2013, 1, 3, 6, 10)], - [dt.datetime(2013, 1, 4, 0, 0), - dt.datetime(2013, 1, 4, 7, 10)]], - columns=['start_time', 'end_time']) - - expected = concat([Series([NaT, NaT, dt.datetime(2013, 1, 3, 6, 10), - dt.datetime(2013, 1, 4, 7, 10)], - name='end_time'), - Series([dt.datetime(2013, 1, 1, 0, 0), - dt.datetime(2013, 1, 2, 0, 0), - dt.datetime(2013, 1, 3, 0, 0), - dt.datetime(2013, 1, 4, 0, 0)], - name='start_time')], axis=1) - result = df1.append(df2, ignore_index=True) - assert_frame_equal(result, expected) - def test_join_append_timedeltas(self): import datetime as dt @@ -1140,227 +561,6 @@ def test_merge_on_periods(self): self.assertEqual(result['value_x'].dtype, 'object') self.assertEqual(result['value_y'].dtype, 'object') - def test_concat_NaT_series(self): - # GH 11693 - # test for merging NaT series with datetime series. - x = Series(date_range('20151124 08:00', '20151124 09:00', - freq='1h', tz='US/Eastern')) - y = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]') - expected = Series([x[0], x[1], pd.NaT, pd.NaT]) - - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # all NaT with tz - expected = Series(pd.NaT, index=range(4), - dtype='datetime64[ns, US/Eastern]') - result = pd.concat([y, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # without tz - x = pd.Series(pd.date_range('20151124 08:00', - '20151124 09:00', freq='1h')) - y = pd.Series(pd.date_range('20151124 10:00', - '20151124 11:00', freq='1h')) - y[:] = pd.NaT - expected = pd.Series([x[0], x[1], pd.NaT, pd.NaT]) - result = pd.concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # all NaT without tz - x[:] = pd.NaT - expected = pd.Series(pd.NaT, index=range(4), - dtype='datetime64[ns]') - result = pd.concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - def test_concat_tz_frame(self): - df2 = DataFrame(dict(A=pd.Timestamp('20130102', tz='US/Eastern'), - B=pd.Timestamp('20130603', tz='CET')), - index=range(5)) - - # concat - df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1) - assert_frame_equal(df2, df3) - - def test_concat_tz_series(self): - # GH 11755 - # tz and no tz - x = Series(date_range('20151124 08:00', - '20151124 09:00', - freq='1h', tz='UTC')) - y = Series(date_range('2012-01-01', '2012-01-02')) - expected = Series([x[0], x[1], y[0], y[1]], - dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # GH 11887 - # concat tz and object - x = Series(date_range('20151124 08:00', - '20151124 09:00', - freq='1h', tz='UTC')) - y = Series(['a', 'b']) - expected = Series([x[0], x[1], y[0], y[1]], - dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - - # 12217 - # 12306 fixed I think - - # Concat'ing two UTC times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('UTC') - - second = pd.DataFrame([[datetime(2016, 1, 2)]]) - second[0] = second[0].dt.tz_localize('UTC') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, UTC]') - - # Concat'ing two London times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 2)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - # Concat'ing 2+1 London times - first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 3)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - # Concat'ing 1+2 London times - first = pd.DataFrame([[datetime(2016, 1, 1)]]) - first[0] = first[0].dt.tz_localize('Europe/London') - - second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]]) - second[0] = second[0].dt.tz_localize('Europe/London') - - result = pd.concat([first, second]) - self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') - - def test_concat_tz_series_with_datetimelike(self): - # GH 12620 - # tz and timedelta - x = [pd.Timestamp('2011-01-01', tz='US/Eastern'), - pd.Timestamp('2011-02-01', tz='US/Eastern')] - y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) - - # tz and period - y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')] - result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) - tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) - - def test_concat_period_series(self): - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - # different freq - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - # non-period - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) - y = Series(['A', 'B']) - expected = Series([x[0], x[1], y[0], y[1]], dtype='object') - result = concat([x, y], ignore_index=True) - tm.assert_series_equal(result, expected) - self.assertEqual(result.dtype, 'object') - - def test_concat_empty_series(self): - # GH 11082 - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name='y') - res = pd.concat([s1, s2], axis=1) - exp = pd.DataFrame({'x': [1, 2, 3], 'y': [np.nan, np.nan, np.nan]}) - tm.assert_frame_equal(res, exp) - - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name='y') - res = pd.concat([s1, s2], axis=0) - # name will be reset - exp = pd.Series([1, 2, 3]) - tm.assert_series_equal(res, exp) - - # empty Series with no name - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series(name=None) - res = pd.concat([s1, s2], axis=1) - exp = pd.DataFrame({'x': [1, 2, 3], 0: [np.nan, np.nan, np.nan]}, - columns=['x', 0]) - tm.assert_frame_equal(res, exp) - - def test_default_index(self): - # is_series and ignore_index - s1 = pd.Series([1, 2, 3], name='x') - s2 = pd.Series([4, 5, 6], name='y') - res = pd.concat([s1, s2], axis=1, ignore_index=True) - self.assertIsInstance(res.columns, pd.RangeIndex) - exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) - # use check_index_type=True to check the result have - # RangeIndex (default index) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - # is_series and all inputs have no names - s1 = pd.Series([1, 2, 3]) - s2 = pd.Series([4, 5, 6]) - res = pd.concat([s1, s2], axis=1, ignore_index=False) - self.assertIsInstance(res.columns, pd.RangeIndex) - exp = pd.DataFrame([[1, 4], [2, 5], [3, 6]]) - exp.columns = pd.RangeIndex(2) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - # is_dataframe and ignore_index - df1 = pd.DataFrame({'A': [1, 2], 'B': [5, 6]}) - df2 = pd.DataFrame({'A': [3, 4], 'B': [7, 8]}) - - res = pd.concat([df1, df2], axis=0, ignore_index=True) - exp = pd.DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], - columns=['A', 'B']) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - - res = pd.concat([df1, df2], axis=1, ignore_index=True) - exp = pd.DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) - tm.assert_frame_equal(res, exp, check_index_type=True, - check_column_type=True) - def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [ @@ -2122,90 +1322,6 @@ def f(): self.assertRaises(NotImplementedError, f) -def _check_join(left, right, result, join_col, how='left', - lsuffix='_x', rsuffix='_y'): - - # some smoke tests - for c in join_col: - assert(result[c].notnull().all()) - - left_grouped = left.groupby(join_col) - right_grouped = right.groupby(join_col) - - for group_key, group in result.groupby(join_col): - l_joined = _restrict_to_columns(group, left.columns, lsuffix) - r_joined = _restrict_to_columns(group, right.columns, rsuffix) - - try: - lgroup = left_grouped.get_group(group_key) - except KeyError: - if how in ('left', 'inner'): - raise AssertionError('key %s should not have been in the join' - % str(group_key)) - - _assert_all_na(l_joined, left.columns, join_col) - else: - _assert_same_contents(l_joined, lgroup) - - try: - rgroup = right_grouped.get_group(group_key) - except KeyError: - if how in ('right', 'inner'): - raise AssertionError('key %s should not have been in the join' - % str(group_key)) - - _assert_all_na(r_joined, right.columns, join_col) - else: - _assert_same_contents(r_joined, rgroup) - - -def _restrict_to_columns(group, columns, suffix): - found = [c for c in group.columns - if c in columns or c.replace(suffix, '') in columns] - - # filter - group = group.ix[:, found] - - # get rid of suffixes, if any - group = group.rename(columns=lambda x: x.replace(suffix, '')) - - # put in the right order... - group = group.ix[:, columns] - - return group - - -def _assert_same_contents(join_chunk, source): - NA_SENTINEL = -1234567 # drop_duplicates not so NA-friendly... - - jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values - svalues = source.fillna(NA_SENTINEL).drop_duplicates().values - - rows = set(tuple(row) for row in jvalues) - assert(len(rows) == len(source)) - assert(all(tuple(row) in rows for row in svalues)) - - -def _assert_all_na(join_chunk, source_columns, join_col): - for c in source_columns: - if c in join_col: - continue - assert(join_chunk[c].isnull().all()) - - -def _join_by_hand(a, b, how='left'): - join_index = a.index.join(b.index, how=how) - - a_re = a.reindex(join_index) - b_re = b.reindex(join_index) - - result_columns = a.columns.append(b.columns) - - for col, s in compat.iteritems(b_re): - a_re[col] = s - return a_re.reindex(columns=result_columns) - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py index bb5429b5e8836..16731620a1dcd 100644 --- a/pandas/tools/tests/test_tile.py +++ b/pandas/tools/tests/test_tile.py @@ -19,8 +19,9 @@ class TestCut(tm.TestCase): def test_simple(self): data = np.ones(5) result = cut(data, 4, labels=False) - desired = np.array([1, 1, 1, 1, 1], dtype=np.int64) - tm.assert_numpy_array_equal(result, desired) + desired = np.array([1, 1, 1, 1, 1]) + tm.assert_numpy_array_equal(result, desired, + check_dtype=False) def test_bins(self): data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]) diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index c592b33bdab9a..5b738086a1ad4 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -291,6 +291,83 @@ def test_non_hashable(self): with self.assertRaisesRegexp(TypeError, "Invalid object type"): pd.to_numeric(s) + def test_downcast(self): + # see gh-13352 + mixed_data = ['1', 2, 3] + int_data = [1, 2, 3] + date_data = np.array(['1970-01-02', '1970-01-03', + '1970-01-04'], dtype='datetime64[D]') + + invalid_downcast = 'unsigned-integer' + msg = 'invalid downcasting method provided' + + smallest_int_dtype = np.dtype(np.typecodes['Integer'][0]) + smallest_uint_dtype = np.dtype(np.typecodes['UnsignedInteger'][0]) + + # support below np.float32 is rare and far between + float_32_char = np.dtype(np.float32).char + smallest_float_dtype = float_32_char + + for data in (mixed_data, int_data, date_data): + with self.assertRaisesRegexp(ValueError, msg): + pd.to_numeric(data, downcast=invalid_downcast) + + expected = np.array([1, 2, 3], dtype=np.int64) + + res = pd.to_numeric(data) + tm.assert_numpy_array_equal(res, expected) + + res = pd.to_numeric(data, downcast=None) + tm.assert_numpy_array_equal(res, expected) + + expected = np.array([1, 2, 3], dtype=smallest_int_dtype) + + for signed_downcast in ('integer', 'signed'): + res = pd.to_numeric(data, downcast=signed_downcast) + tm.assert_numpy_array_equal(res, expected) + + expected = np.array([1, 2, 3], dtype=smallest_uint_dtype) + res = pd.to_numeric(data, downcast='unsigned') + tm.assert_numpy_array_equal(res, expected) + + expected = np.array([1, 2, 3], dtype=smallest_float_dtype) + res = pd.to_numeric(data, downcast='float') + tm.assert_numpy_array_equal(res, expected) + + # if we can't successfully cast the given + # data to a numeric dtype, do not bother + # with the downcast parameter + data = ['foo', 2, 3] + expected = np.array(data, dtype=object) + res = pd.to_numeric(data, errors='ignore', + downcast='unsigned') + tm.assert_numpy_array_equal(res, expected) + + # cannot cast to an unsigned integer because + # we have a negative number + data = ['-1', 2, 3] + expected = np.array([-1, 2, 3], dtype=np.int64) + res = pd.to_numeric(data, downcast='unsigned') + tm.assert_numpy_array_equal(res, expected) + + # cannot cast to an integer (signed or unsigned) + # because we have a float number + data = ['1.1', 2, 3] + expected = np.array([1.1, 2, 3], dtype=np.float64) + + for downcast in ('integer', 'signed', 'unsigned'): + res = pd.to_numeric(data, downcast=downcast) + tm.assert_numpy_array_equal(res, expected) + + # the smallest integer dtype need not be np.(u)int8 + data = ['256', 257, 258] + + for downcast, expected_dtype in zip( + ['integer', 'signed', 'unsigned'], + [np.int16, np.int16, np.uint16]): + expected = np.array([256, 257, 258], dtype=expected_dtype) + res = pd.to_numeric(data, downcast=downcast) + tm.assert_numpy_array_equal(res, expected) if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tools/tile.py b/pandas/tools/tile.py index b0bbf8ba70354..62bbfc2f630a5 100644 --- a/pandas/tools/tile.py +++ b/pandas/tools/tile.py @@ -2,12 +2,14 @@ Quantilization functions and related stuff """ +from pandas.types.missing import isnull +from pandas.types.common import (is_float, is_integer, + is_scalar) + from pandas.core.api import Series from pandas.core.categorical import Categorical import pandas.core.algorithms as algos -import pandas.core.common as com import pandas.core.nanops as nanops -import pandas.lib as lib from pandas.compat import zip import numpy as np @@ -80,7 +82,7 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3, """ # NOTE: this binning code is changed a bit from histogram for var(x) == 0 if not np.iterable(bins): - if lib.isscalar(bins) and bins < 1: + if is_scalar(bins) and bins < 1: raise ValueError("`bins` should be a positive integer.") try: # for array-like sz = x.size @@ -164,7 +166,7 @@ def qcut(x, q, labels=None, retbins=False, precision=3): >>> pd.qcut(range(5), 4, labels=False) array([0, 0, 1, 2, 3], dtype=int64) """ - if com.is_integer(q): + if is_integer(q): quantiles = np.linspace(0, 1, q + 1) else: quantiles = q @@ -194,7 +196,7 @@ def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False, if include_lowest: ids[x == bins[0]] = 1 - na_mask = com.isnull(x) | (ids == len(bins)) | (ids == 0) + na_mask = isnull(x) | (ids == len(bins)) | (ids == 0) has_nas = na_mask.any() if labels is not False: @@ -264,7 +266,7 @@ def _format_label(x, precision=3): fmt_str = '%%.%dg' % precision if np.isinf(x): return str(x) - elif com.is_float(x): + elif is_float(x): frac, whole = np.modf(x) sgn = '-' if x < 0 else '' whole = abs(whole) diff --git a/pandas/tools/util.py b/pandas/tools/util.py index 61d2c0adce2fe..b8b28663387cc 100644 --- a/pandas/tools/util.py +++ b/pandas/tools/util.py @@ -1,6 +1,12 @@ import numpy as np import pandas.lib as lib +from pandas.types.common import (is_number, + is_numeric_dtype, + is_datetime_or_timedelta_dtype, + _ensure_object) +from pandas.types.cast import _possibly_downcast_to_dtype + import pandas as pd from pandas.compat import reduce from pandas.core.index import Index @@ -50,7 +56,7 @@ def compose(*funcs): return reduce(_compose2, funcs) -def to_numeric(arg, errors='raise'): +def to_numeric(arg, errors='raise', downcast=None): """ Convert argument to a numeric type. @@ -61,6 +67,27 @@ def to_numeric(arg, errors='raise'): - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaN - If 'ignore', then invalid parsing will return the input + downcast : {'integer', 'signed', 'unsigned', 'float'} , default None + If not None, and if the data has been successfully cast to a + numerical dtype (or if the data was numeric to begin with), + downcast that resulting data to the smallest numerical dtype + possible according to the following rules: + + - 'integer' or 'signed': smallest signed int dtype (min.: np.int8) + - 'unsigned': smallest unsigned int dtype (min.: np.uint8) + - 'float': smallest float dtype (min.: np.float32) + + As this behaviour is separate from the core conversion to + numeric values, any errors raised during the downcasting + will be surfaced regardless of the value of the 'errors' input. + + In addition, downcasting will only occur if the size + of the resulting data's dtype is strictly larger than + the dtype it is to be cast to, so if none of the dtypes + checked satisfy that specification, no downcasting will be + performed on the data. + + .. versionadded:: 0.19.0 Returns ------- @@ -74,10 +101,37 @@ def to_numeric(arg, errors='raise'): >>> import pandas as pd >>> s = pd.Series(['1.0', '2', -3]) >>> pd.to_numeric(s) + 0 1.0 + 1 2.0 + 2 -3.0 + dtype: float64 + >>> pd.to_numeric(s, downcast='float') + 0 1.0 + 1 2.0 + 2 -3.0 + dtype: float32 + >>> pd.to_numeric(s, downcast='signed') + 0 1 + 1 2 + 2 -3 + dtype: int8 >>> s = pd.Series(['apple', '1.0', '2', -3]) >>> pd.to_numeric(s, errors='ignore') + 0 apple + 1 1.0 + 2 2 + 3 -3 + dtype: object >>> pd.to_numeric(s, errors='coerce') + 0 NaN + 1 1.0 + 2 2.0 + 3 -3.0 + dtype: float64 """ + if downcast not in (None, 'integer', 'signed', 'unsigned', 'float'): + raise ValueError('invalid downcasting method provided') + is_series = False is_index = False is_scalar = False @@ -93,7 +147,7 @@ def to_numeric(arg, errors='raise'): elif isinstance(arg, (list, tuple)): values = np.array(arg, dtype='O') elif np.isscalar(arg): - if com.is_number(arg): + if is_number(arg): return arg is_scalar = True values = np.array([arg], dtype='O') @@ -102,20 +156,50 @@ def to_numeric(arg, errors='raise'): else: values = arg - if com.is_numeric_dtype(values): - pass - elif com.is_datetime_or_timedelta_dtype(values): - values = values.astype(np.int64) - else: - values = com._ensure_object(values) - coerce_numeric = False if errors in ('ignore', 'raise') else True - - try: + try: + if is_numeric_dtype(values): + pass + elif is_datetime_or_timedelta_dtype(values): + values = values.astype(np.int64) + else: + values = _ensure_object(values) + coerce_numeric = False if errors in ('ignore', 'raise') else True values = lib.maybe_convert_numeric(values, set(), coerce_numeric=coerce_numeric) - except: - if errors == 'raise': - raise + + except Exception: + if errors == 'raise': + raise + + # attempt downcast only if the data has been successfully converted + # to a numerical dtype and if a downcast method has been specified + if downcast is not None and is_numeric_dtype(values): + typecodes = None + + if downcast in ('integer', 'signed'): + typecodes = np.typecodes['Integer'] + elif downcast == 'unsigned' and np.min(values) > 0: + typecodes = np.typecodes['UnsignedInteger'] + elif downcast == 'float': + typecodes = np.typecodes['Float'] + + # pandas support goes only to np.float32, + # as float dtypes smaller than that are + # extremely rare and not well supported + float_32_char = np.dtype(np.float32).char + float_32_ind = typecodes.index(float_32_char) + typecodes = typecodes[float_32_ind:] + + if typecodes is not None: + # from smallest to largest + for dtype in typecodes: + if np.dtype(dtype).itemsize < values.dtype.itemsize: + values = _possibly_downcast_to_dtype( + values, dtype) + + # successful conversion + if values.dtype == dtype: + break if is_series: return pd.Series(values, index=arg.index, name=arg.name) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 42631d442a990..37f7e1f284ef5 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -202,7 +202,7 @@ def _format_with_header(self, header, **kwargs): def __contains__(self, key): try: res = self.get_loc(key) - return lib.isscalar(res) or type(res) == slice or np.any(res) + return lib.isscalar(res) or type(res) == slice or np.size(res) except (KeyError, TypeError, ValueError): return False diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 8937e83c7009a..46e8bd43e8ff8 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -3,19 +3,21 @@ """ import numpy as np + +from pandas.types.common import (_NS_DTYPE, _TD_DTYPE, + is_period_arraylike, + is_datetime_arraylike, is_integer_dtype, + is_datetime64_dtype, is_datetime64tz_dtype, + is_timedelta64_dtype, is_categorical_dtype, + is_list_like) + from pandas.core.base import PandasDelegate, NoNewAttributesMixin -from pandas.core import common as com from pandas.tseries.index import DatetimeIndex from pandas._period import IncompatibleFrequency # flake8: noqa from pandas.tseries.period import PeriodIndex from pandas.tseries.tdi import TimedeltaIndex from pandas import tslib from pandas.core.algorithms import take_1d -from pandas.core.common import (_NS_DTYPE, _TD_DTYPE, is_period_arraylike, - is_datetime_arraylike, is_integer_dtype, - is_list_like, - is_datetime64_dtype, is_datetime64tz_dtype, - is_timedelta64_dtype, is_categorical_dtype) def is_datetimelike(data): @@ -129,7 +131,7 @@ def _delegate_method(self, name, *args, **kwargs): method = getattr(self.values, name) result = method(*args, **kwargs) - if not com.is_list_like(result): + if not is_list_like(result): return result result = Series(result, index=self.index, name=self.name) diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py index 78b185ae8cf31..fc23f4f99449b 100644 --- a/pandas/tseries/converter.py +++ b/pandas/tseries/converter.py @@ -10,6 +10,14 @@ from matplotlib.ticker import Formatter, AutoLocator, Locator from matplotlib.transforms import nonsingular + +from pandas.types.common import (is_float, is_integer, + is_integer_dtype, + is_float_dtype, + is_datetime64_ns_dtype, + is_period_arraylike, + ) + from pandas.compat import lrange import pandas.compat as compat import pandas.lib as lib @@ -73,8 +81,8 @@ class TimeConverter(units.ConversionInterface): @staticmethod def convert(value, unit, axis): valid_types = (str, pydt.time) - if (isinstance(value, valid_types) or com.is_integer(value) or - com.is_float(value)): + if (isinstance(value, valid_types) or is_integer(value) or + is_float(value)): return time2num(value) if isinstance(value, Index): return value.map(time2num) @@ -129,14 +137,14 @@ def convert(values, units, axis): raise TypeError('Axis must have `freq` set to convert to Periods') valid_types = (compat.string_types, datetime, Period, pydt.date, pydt.time) - if (isinstance(values, valid_types) or com.is_integer(values) or - com.is_float(values)): + if (isinstance(values, valid_types) or is_integer(values) or + is_float(values)): return get_datevalue(values, axis.freq) if isinstance(values, PeriodIndex): return values.asfreq(axis.freq).values if isinstance(values, Index): return values.map(lambda x: get_datevalue(x, axis.freq)) - if com.is_period_arraylike(values): + if is_period_arraylike(values): return PeriodIndex(values, freq=axis.freq).values if isinstance(values, (list, tuple, np.ndarray, Index)): return [get_datevalue(x, axis.freq) for x in values] @@ -149,7 +157,7 @@ def get_datevalue(date, freq): elif isinstance(date, (compat.string_types, datetime, pydt.date, pydt.time)): return Period(date, freq).ordinal - elif (com.is_integer(date) or com.is_float(date) or + elif (is_integer(date) or is_float(date) or (isinstance(date, (np.ndarray, Index)) and (date.size == 1))): return date elif date is None: @@ -163,8 +171,8 @@ def _dt_to_float_ordinal(dt): preserving hours, minutes, seconds and microseconds. Return value is a :func:`float`. """ - if (isinstance(dt, (np.ndarray, Index, Series)) and - com.is_datetime64_ns_dtype(dt)): + if (isinstance(dt, (np.ndarray, Index, Series) + ) and is_datetime64_ns_dtype(dt)): base = dates.epoch2num(dt.asi8 / 1.0E9) else: base = dates.date2num(dt) @@ -188,7 +196,7 @@ def try_parse(values): return _dt_to_float_ordinal(lib.Timestamp(values)) elif isinstance(values, pydt.time): return dates.date2num(values) - elif (com.is_integer(values) or com.is_float(values)): + elif (is_integer(values) or is_float(values)): return values elif isinstance(values, compat.string_types): return try_parse(values) @@ -198,7 +206,7 @@ def try_parse(values): if not isinstance(values, np.ndarray): values = com._asarray_tuplesafe(values) - if com.is_integer_dtype(values) or com.is_float_dtype(values): + if is_integer_dtype(values) or is_float_dtype(values): return values try: diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 3f1d0c6d969a6..e2132deb97d64 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -6,12 +6,17 @@ import numpy as np +from pandas.types.generic import ABCSeries +from pandas.types.common import (is_integer, + is_period_arraylike, + is_timedelta64_dtype, + is_datetime64_dtype) + import pandas.core.algorithms as algos from pandas.core.algorithms import unique from pandas.tseries.offsets import DateOffset from pandas.util.decorators import cache_readonly import pandas.tseries.offsets as offsets -import pandas.core.common as com import pandas.lib as lib import pandas.tslib as tslib from pandas.tslib import Timedelta @@ -255,8 +260,8 @@ def get_freq_code(freqstr): freqstr = (freqstr.rule_code, freqstr.n) if isinstance(freqstr, tuple): - if (com.is_integer(freqstr[0]) and - com.is_integer(freqstr[1])): + if (is_integer(freqstr[0]) and + is_integer(freqstr[1])): # e.g., freqstr = (2000, 1) return freqstr else: @@ -265,13 +270,13 @@ def get_freq_code(freqstr): code = _period_str_to_code(freqstr[0]) stride = freqstr[1] except: - if com.is_integer(freqstr[1]): + if is_integer(freqstr[1]): raise code = _period_str_to_code(freqstr[1]) stride = freqstr[0] return code, stride - if com.is_integer(freqstr): + if is_integer(freqstr): return (freqstr, 1) base, stride = _base_and_stride(freqstr) @@ -843,16 +848,16 @@ def infer_freq(index, warn=True): """ import pandas as pd - if isinstance(index, com.ABCSeries): + if isinstance(index, ABCSeries): values = index._values - if not (com.is_datetime64_dtype(values) or - com.is_timedelta64_dtype(values) or + if not (is_datetime64_dtype(values) or + is_timedelta64_dtype(values) or values.dtype == object): raise TypeError("cannot infer freq from a non-convertible " "dtype on a Series of {0}".format(index.dtype)) index = values - if com.is_period_arraylike(index): + if is_period_arraylike(index): raise TypeError("PeriodIndex given. Check the `freq` attribute " "instead of using infer_freq.") elif isinstance(index, pd.TimedeltaIndex): diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 77500081be62c..47bb69b8d7ad6 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -6,13 +6,25 @@ from datetime import timedelta import numpy as np from pandas.core.base import _shared_docs -from pandas.core.common import (_INT64_DTYPE, _NS_DTYPE, _maybe_box, - _values_from_object, ABCSeries, - DatetimeTZDtype, PerformanceWarning, - is_datetimetz, is_datetime64_dtype, - is_datetime64_ns_dtype, is_dtype_equal, - is_float, is_integer, is_integer_dtype, - is_object_dtype, is_string_dtype) + +from pandas.types.common import (_NS_DTYPE, _INT64_DTYPE, + is_object_dtype, is_datetime64_dtype, + is_datetimetz, is_dtype_equal, + is_integer, is_float, + is_integer_dtype, + is_datetime64_ns_dtype, + is_bool_dtype, + is_string_dtype, + is_list_like, + is_scalar, + _ensure_int64) +from pandas.types.generic import ABCSeries +from pandas.types.dtypes import DatetimeTZDtype +from pandas.types.missing import isnull + +import pandas.types.concat as _concat +from pandas.core.common import (_values_from_object, _maybe_box, + PerformanceWarning) from pandas.core.index import Index, Int64Index, Float64Index from pandas.indexes.base import _index_shared_docs @@ -27,7 +39,6 @@ from pandas.util.decorators import (Appender, cache_readonly, deprecate_kwarg, Substitution) import pandas.core.common as com -import pandas.types.concat as _concat import pandas.tseries.offsets as offsets import pandas.tseries.tools as tools @@ -87,7 +98,7 @@ def wrapper(self, other): isinstance(other, compat.string_types)): other = _to_m8(other, tz=self.tz) result = func(other) - if com.isnull(other): + if isnull(other): result.fill(nat_result) else: if isinstance(other, list): @@ -109,7 +120,7 @@ def wrapper(self, other): result[self._isnan] = nat_result # support of bool dtype indexers - if com.is_bool_dtype(result): + if is_bool_dtype(result): return result return Index(result) @@ -277,7 +288,7 @@ def __new__(cls, data=None, ambiguous=ambiguous) if not isinstance(data, (np.ndarray, Index, ABCSeries)): - if lib.isscalar(data): + if is_scalar(data): raise ValueError('DatetimeIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) @@ -537,7 +548,7 @@ def _generate(cls, start, end, periods, name, offset, index = _generate_regular_range(start, end, periods, offset) if tz is not None and getattr(index, 'tz', None) is None: - index = tslib.tz_localize_to_utc(com._ensure_int64(index), tz, + index = tslib.tz_localize_to_utc(_ensure_int64(index), tz, ambiguous=ambiguous) index = index.view(_NS_DTYPE) @@ -558,7 +569,7 @@ def _generate(cls, start, end, periods, name, offset, @property def _box_func(self): - return lambda x: Timestamp(x, offset=self.offset, tz=self.tz) + return lambda x: Timestamp(x, freq=self.offset, tz=self.tz) def _convert_for_op(self, value): """ Convert value to be insertable to ndarray """ @@ -601,7 +612,7 @@ def _simple_new(cls, values, name=None, freq=None, tz=None, return cls(values, name=name, freq=freq, tz=tz, dtype=dtype, **kwargs).values elif not is_datetime64_dtype(values): - values = com._ensure_int64(values).view(_NS_DTYPE) + values = _ensure_int64(values).view(_NS_DTYPE) result = object.__new__(cls) result._data = values @@ -1199,8 +1210,9 @@ def __iter__(self): for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, l) - converted = tslib.ints_to_pydatetime( - data[start_i:end_i], tz=self.tz, offset=self.offset, box=True) + converted = tslib.ints_to_pydatetime(data[start_i:end_i], + tz=self.tz, freq=self.freq, + box=True) for v in converted: yield v @@ -1682,7 +1694,7 @@ def inferred_type(self): def dtype(self): if self.tz is None: return _NS_DTYPE - return com.DatetimeTZDtype('ns', self.tz) + return DatetimeTZDtype('ns', self.tz) @property def is_all_dates(self): @@ -1786,9 +1798,9 @@ def delete(self, loc): if loc in (0, -len(self), -1, len(self) - 1): freq = self.freq else: - if com.is_list_like(loc): + if is_list_like(loc): loc = lib.maybe_indices_to_slice( - com._ensure_int64(np.array(loc)), len(self)) + _ensure_int64(np.array(loc)), len(self)) if isinstance(loc, slice) and loc.step in (1, None): if (loc.start in (0, None) or loc.stop in (len(self), None)): freq = self.freq @@ -1857,7 +1869,7 @@ def tz_localize(self, tz, ambiguous='raise', errors='raise'): - 'coerce' will return NaT if the timestamp can not be converted into the specified timezone - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 infer_dst : boolean, default False (DEPRECATED) Attempt to infer fall dst-transition hours based on order diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index f4b75ddd72126..f12ba8083f545 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -3,9 +3,9 @@ from pandas import compat import numpy as np +from pandas.types.generic import ABCSeries, ABCDatetimeIndex, ABCPeriod from pandas.tseries.tools import to_datetime, normalize_date -from pandas.core.common import (ABCSeries, ABCDatetimeIndex, ABCPeriod, - AbstractMethodError) +from pandas.core.common import AbstractMethodError # import after tools, dateutil check from dateutil.relativedelta import relativedelta, weekday @@ -1258,7 +1258,7 @@ class SemiMonthEnd(SemiMonthOffset): Two DateOffset's per month repeating on the last day of the month and day_of_month. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Parameters ---------- @@ -1317,7 +1317,7 @@ class SemiMonthBegin(SemiMonthOffset): Two DateOffset's per month repeating on the first day of the month and day_of_month. - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Parameters ---------- diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index 750e7a5553ef6..dffb71cff526a 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -1,6 +1,24 @@ # pylint: disable=E1101,E1103,W0232 from datetime import datetime, timedelta import numpy as np + + +from pandas.core import common as com +from pandas.types.common import (is_integer, + is_float, + is_object_dtype, + is_integer_dtype, + is_float_dtype, + is_scalar, + is_timedelta64_dtype, + is_bool_dtype, + _ensure_int64, + _ensure_object) + +from pandas.types.generic import ABCSeries +from pandas.types.missing import isnull + + import pandas.tseries.frequencies as frequencies from pandas.tseries.frequencies import get_freq_code as _gfc from pandas.tseries.index import DatetimeIndex, Int64Index, Index @@ -17,15 +35,10 @@ from pandas.core.base import _shared_docs from pandas.indexes.base import _index_shared_docs -import pandas.core.common as com -from pandas.core.common import ( - _maybe_box, _values_from_object, ABCSeries, is_float, is_integer, - is_integer_dtype, is_object_dtype, isnull) from pandas import compat from pandas.compat.numpy import function as nv from pandas.util.decorators import Appender, cache_readonly, Substitution from pandas.lib import Timedelta -import pandas.lib as lib import pandas.tslib as tslib import pandas.core.missing as missing from pandas.compat import zip, u @@ -79,13 +92,14 @@ def wrapper(self, other): result[mask] = nat_result return result + elif other is tslib.NaT: + result = np.empty(len(self.values), dtype=bool) + result.fill(nat_result) else: other = Period(other, freq=self.freq) func = getattr(self.values, opname) result = func(other.ordinal) - if other.ordinal == tslib.iNaT: - result.fill(nat_result) mask = self.values == tslib.iNaT if mask.any(): result[mask] = nat_result @@ -209,7 +223,7 @@ def _generate_range(cls, start, end, periods, freq, fields): def _from_arraylike(cls, data, freq, tz): if not isinstance(data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): - if lib.isscalar(data) or isinstance(data, Period): + if is_scalar(data) or isinstance(data, Period): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) @@ -219,13 +233,13 @@ def _from_arraylike(cls, data, freq, tz): data = list(data) try: - data = com._ensure_int64(data) + data = _ensure_int64(data) if freq is None: raise ValueError('freq not specified') - data = np.array([Period(x, freq=freq).ordinal for x in data], + data = np.array([Period(x, freq=freq) for x in data], dtype=np.int64) except (TypeError, ValueError): - data = com._ensure_object(data) + data = _ensure_object(data) if freq is None: freq = period.extract_freq(data) @@ -242,7 +256,7 @@ def _from_arraylike(cls, data, freq, tz): base1, base2, 1) else: - if freq is None and com.is_object_dtype(data): + if freq is None and is_object_dtype(data): # must contain Period instance and thus extract ordinals freq = period.extract_freq(data) data = period.extract_ordinals(data, freq) @@ -256,9 +270,9 @@ def _from_arraylike(cls, data, freq, tz): data = dt64arr_to_periodarr(data, freq, tz) else: try: - data = com._ensure_int64(data) + data = _ensure_int64(data) except (TypeError, ValueError): - data = com._ensure_object(data) + data = _ensure_object(data) data = period.extract_ordinals(data, freq) return data, freq @@ -266,9 +280,9 @@ def _from_arraylike(cls, data, freq, tz): @classmethod def _simple_new(cls, values, name=None, freq=None, **kwargs): - if not com.is_integer_dtype(values): + if not is_integer_dtype(values): values = np.array(values, copy=False) - if (len(values) > 0 and com.is_float_dtype(values)): + if (len(values) > 0 and is_float_dtype(values)): raise TypeError("PeriodIndex can't take floats") else: return PeriodIndex(values, name=name, freq=freq, **kwargs) @@ -309,15 +323,18 @@ def _na_value(self): return self._box_func(tslib.iNaT) def __contains__(self, key): - if not isinstance(key, Period) or key.freq != self.freq: - if isinstance(key, compat.string_types): - try: - self.get_loc(key) - return True - except Exception: - return False + if isinstance(key, Period): + if key.freq != self.freq: + return False + else: + return key.ordinal in self._engine + else: + try: + self.get_loc(key) + return True + except Exception: + return False return False - return key.ordinal in self._engine def __array_wrap__(self, result, context=None): """ @@ -339,7 +356,7 @@ def __array_wrap__(self, result, context=None): # from here because numpy catches. raise ValueError(msg.format(func.__name__)) - if com.is_bool_dtype(result): + if is_bool_dtype(result): return result return PeriodIndex(result, freq=self.freq, name=self.name) @@ -580,9 +597,9 @@ def _maybe_convert_timedelta(self, other): msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) elif isinstance(other, np.ndarray): - if com.is_integer_dtype(other): + if is_integer_dtype(other): return other - elif com.is_timedelta64_dtype(other): + elif is_timedelta64_dtype(other): offset = frequencies.to_offset(self.freq) if isinstance(offset, offsets.Tick): nanos = tslib._delta_to_nanoseconds(other) @@ -609,17 +626,13 @@ def _sub_period(self, other): msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) raise IncompatibleFrequency(msg) - if other.ordinal == tslib.iNaT: - new_data = np.empty(len(self)) - new_data.fill(np.nan) - else: - asi8 = self.asi8 - new_data = asi8 - other.ordinal + asi8 = self.asi8 + new_data = asi8 - other.ordinal - if self.hasnans: - mask = asi8 == tslib.iNaT - new_data = new_data.astype(np.float64) - new_data[mask] = np.nan + if self.hasnans: + mask = asi8 == tslib.iNaT + new_data = new_data.astype(np.float64) + new_data[mask] = np.nan # result must be Int64Index or Float64Index return Index(new_data, name=self.name) @@ -657,10 +670,11 @@ def get_value(self, series, key): Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing """ - s = _values_from_object(series) + s = com._values_from_object(series) try: - return _maybe_box(self, super(PeriodIndex, self).get_value(s, key), - series, key) + return com._maybe_box(self, + super(PeriodIndex, self).get_value(s, key), + series, key) except (KeyError, IndexError): try: asdt, parsed, reso = parse_time_string(key, self.freq) @@ -683,16 +697,16 @@ def get_value(self, series, key): return series[key] elif grp == freqn: key = Period(asdt, freq=self.freq).ordinal - return _maybe_box(self, self._engine.get_value(s, key), - series, key) + return com._maybe_box(self, self._engine.get_value(s, key), + series, key) else: raise KeyError(key) except TypeError: pass key = Period(key, self.freq).ordinal - return _maybe_box(self, self._engine.get_value(s, key), - series, key) + return com._maybe_box(self, self._engine.get_value(s, key), + series, key) def get_indexer(self, target, method=None, limit=None, tolerance=None): if hasattr(target, 'freq') and target.freq != self.freq: @@ -726,8 +740,10 @@ def get_loc(self, key, method=None, tolerance=None): # we cannot construct the Period # as we have an invalid type raise KeyError(key) + try: - return Index.get_loc(self, key.ordinal, method, tolerance) + ordinal = tslib.iNaT if key is tslib.NaT else key.ordinal + return Index.get_loc(self, ordinal, method, tolerance) except KeyError: raise KeyError(key) @@ -849,7 +865,7 @@ def _apply_meta(self, rawarr): def __getitem__(self, key): getitem = self._data.__getitem__ - if lib.isscalar(key): + if is_scalar(key): val = getitem(key) return Period(ordinal=val, freq=self.freq) else: @@ -1030,8 +1046,7 @@ def _get_ordinal_range(start, end, periods, freq, mult=1): if is_start_per and is_end_per and start.freq != end.freq: raise ValueError('Start and end must have same freq') - if ((is_start_per and start.ordinal == tslib.iNaT) or - (is_end_per and end.ordinal == tslib.iNaT)): + if (start is tslib.NaT or end is tslib.NaT): raise ValueError('Start and end must not be NaT') if freq is None: diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py index 84f357481a28e..f9fb51ebf710c 100644 --- a/pandas/tseries/tdi.py +++ b/pandas/tseries/tdi.py @@ -2,11 +2,20 @@ from datetime import timedelta import numpy as np -from pandas.core.common import (ABCSeries, _TD_DTYPE, _maybe_box, - _values_from_object, isnull, - is_integer, is_float, is_integer_dtype, - is_object_dtype, is_timedelta64_dtype, - is_timedelta64_ns_dtype) +from pandas.types.common import (_TD_DTYPE, + is_integer, is_float, + is_bool_dtype, + is_list_like, + is_scalar, + is_integer_dtype, + is_object_dtype, + is_timedelta64_dtype, + is_timedelta64_ns_dtype, + _ensure_int64) +from pandas.types.missing import isnull +from pandas.types.generic import ABCSeries +from pandas.core.common import _maybe_box, _values_from_object + from pandas.core.index import Index, Int64Index import pandas.compat as compat from pandas.compat import u @@ -35,16 +44,20 @@ def _td_index_cmp(opname, nat_result=False): """ def wrapper(self, other): + msg = "cannot compare a TimedeltaIndex with type {0}" func = getattr(super(TimedeltaIndex, self), opname) - if _is_convertible_to_td(other): - other = _to_m8(other) + if _is_convertible_to_td(other) or other is tslib.NaT: + try: + other = _to_m8(other) + except ValueError: + # failed to parse as timedelta + raise TypeError(msg.format(type(other))) result = func(other) - if com.isnull(other): + if isnull(other): result.fill(nat_result) else: - if not com.is_list_like(other): - raise TypeError("cannot compare a TimedeltaIndex with type " - "{0}".format(type(other))) + if not is_list_like(other): + raise TypeError(msg.format(type(other))) other = TimedeltaIndex(other).values result = func(other) @@ -62,7 +75,7 @@ def wrapper(self, other): result[self._isnan] = nat_result # support of bool dtype indexers - if com.is_bool_dtype(result): + if is_bool_dtype(result): return result return Index(result) @@ -171,7 +184,7 @@ def __new__(cls, data=None, unit=None, data = to_timedelta(data, unit=unit, box=False) if not isinstance(data, (np.ndarray, Index, ABCSeries)): - if lib.isscalar(data): + if is_scalar(data): raise ValueError('TimedeltaIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) @@ -257,7 +270,7 @@ def _simple_new(cls, values, name=None, freq=None, **kwargs): if values.dtype == np.object_: values = tslib.array_to_timedelta64(values) if values.dtype != _TD_DTYPE: - values = com._ensure_int64(values).view(_TD_DTYPE) + values = _ensure_int64(values).view(_TD_DTYPE) result = object.__new__(cls) result._data = values @@ -901,9 +914,9 @@ def delete(self, loc): if loc in (0, -len(self), -1, len(self) - 1): freq = self.freq else: - if com.is_list_like(loc): + if is_list_like(loc): loc = lib.maybe_indices_to_slice( - com._ensure_int64(np.array(loc)), len(self)) + _ensure_int64(np.array(loc)), len(self)) if isinstance(loc, slice) and loc.step in (1, None): if (loc.start in (0, None) or loc.stop in (len(self), None)): freq = self.freq diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 7077a23d5abcb..958a10c329a46 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -124,10 +124,11 @@ def test_minmax(self): def test_numpy_minmax(self): dr = pd.date_range(start='2016-01-15', end='2016-01-20') - self.assertEqual(np.min(dr), Timestamp( - '2016-01-15 00:00:00', offset='D')) - self.assertEqual(np.max(dr), Timestamp( - '2016-01-20 00:00:00', offset='D')) + + self.assertEqual(np.min(dr), + Timestamp('2016-01-15 00:00:00', freq='D')) + self.assertEqual(np.max(dr), + Timestamp('2016-01-20 00:00:00', freq='D')) errmsg = "the 'out' parameter is not supported" tm.assertRaisesRegexp(ValueError, errmsg, np.min, dr, out=0) @@ -148,11 +149,11 @@ def test_round(self): elt = rng[1] expected_rng = DatetimeIndex([ - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 01:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 01:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 02:00:00', tz=tz, freq='30T'), ]) expected_elt = expected_rng[1] @@ -175,10 +176,10 @@ def test_repeat(self): freq='30Min', tz=tz) expected_rng = DatetimeIndex([ - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), ]) tm.assert_index_equal(rng.repeat(reps), expected_rng) @@ -192,10 +193,10 @@ def test_numpy_repeat(self): freq='30Min', tz=tz) expected_rng = DatetimeIndex([ - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), - Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), + Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'), ]) tm.assert_index_equal(np.repeat(rng, reps), expected_rng) @@ -443,6 +444,46 @@ def test_sub_isub(self): rng -= 1 tm.assert_index_equal(rng, expected) + def test_sub_period(self): + # GH 13078 + # not supported, check TypeError + p = pd.Period('2011-01-01', freq='D') + + for freq in [None, 'D']: + idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02'], freq=freq) + + with tm.assertRaises(TypeError): + idx - p + + with tm.assertRaises(TypeError): + p - idx + + def test_comp_nat(self): + left = pd.DatetimeIndex([pd.Timestamp('2011-01-01'), pd.NaT, + pd.Timestamp('2011-01-03')]) + right = pd.DatetimeIndex([pd.NaT, pd.NaT, pd.Timestamp('2011-01-03')]) + + for l, r in [(left, right), (left.asobject, right.asobject)]: + result = l == r + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = l != r + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l == pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT == r, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(l != pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT != l, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l < pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT > l, expected) + def test_value_counts_unique(self): # GH 7735 for tz in [None, 'UTC', 'Asia/Tokyo', 'US/Eastern']: @@ -505,7 +546,8 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) self.assertEqual(ordered.freq, idx.freq) ordered, indexer = idx.sort_values(return_indexer=True, @@ -513,7 +555,8 @@ def test_order(self): expected = idx[::-1] self.assert_index_equal(ordered, expected) self.assert_numpy_array_equal(indexer, - np.array([2, 1, 0], dtype=np.int64)) + np.array([2, 1, 0]), + check_dtype=False) self.assertEqual(ordered.freq, expected.freq) self.assertEqual(ordered.freq.n, -1) @@ -550,16 +593,16 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) def test_getitem(self): @@ -1157,6 +1200,20 @@ def test_dti_tdi_numeric_ops(self): expected = DatetimeIndex(['20121231', pd.NaT, '20130101']) tm.assert_index_equal(result, expected) + def test_sub_period(self): + # GH 13078 + # not supported, check TypeError + p = pd.Period('2011-01-01', freq='D') + + for freq in [None, 'H']: + idx = pd.TimedeltaIndex(['1 hours', '2 hours'], freq=freq) + + with tm.assertRaises(TypeError): + idx - p + + with tm.assertRaises(TypeError): + p - idx + def test_addition_ops(self): # with datetimes/timedelta and tdi/dti @@ -1207,6 +1264,32 @@ def test_addition_ops(self): expected = Timestamp('20130102') self.assertEqual(result, expected) + def test_comp_nat(self): + left = pd.TimedeltaIndex([pd.Timedelta('1 days'), pd.NaT, + pd.Timedelta('3 days')]) + right = pd.TimedeltaIndex([pd.NaT, pd.NaT, pd.Timedelta('3 days')]) + + for l, r in [(left, right), (left.asobject, right.asobject)]: + result = l == r + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = l != r + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l == pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT == r, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(l != pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT != l, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l < pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT > l, expected) + def test_value_counts_unique(self): # GH 7735 @@ -1271,7 +1354,8 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) self.assertEqual(ordered.freq, idx.freq) ordered, indexer = idx.sort_values(return_indexer=True, @@ -1309,16 +1393,16 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertIsNone(ordered.freq) def test_getitem(self): @@ -1503,17 +1587,16 @@ def test_asobject_tolist(self): result = idx.asobject self.assertTrue(isinstance(result, Index)) self.assertEqual(result.dtype, object) + tm.assert_index_equal(result, expected) for i in [0, 1, 3]: - self.assertTrue(result[i], expected[i]) - self.assertTrue(result[2].ordinal, pd.tslib.iNaT) - self.assertTrue(result[2].freq, 'D') + self.assertEqual(result[i], expected[i]) + self.assertIs(result[2], pd.NaT) self.assertEqual(result.name, expected.name) result_list = idx.tolist() for i in [0, 1, 3]: - self.assertTrue(result_list[i], expected_list[i]) - self.assertTrue(result_list[2].ordinal, pd.tslib.iNaT) - self.assertTrue(result_list[2].freq, 'D') + self.assertEqual(result_list[i], expected_list[i]) + self.assertIs(result_list[2], pd.NaT) def test_minmax(self): @@ -1539,18 +1622,15 @@ def test_minmax(self): # Return NaT obj = PeriodIndex([], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) obj = PeriodIndex([pd.NaT], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) obj = PeriodIndex([pd.NaT, pd.NaT, pd.NaT], freq='M') result = getattr(obj, op)() - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') + self.assertIs(result, tslib.NaT) def test_numpy_minmax(self): pr = pd.period_range(start='2016-01-15', end='2016-01-20') @@ -1651,9 +1731,9 @@ def test_representation_to_series(self): 2 2013 dtype: object""" - exp6 = """0 2011-01-01 09:00 -1 2012-02-01 10:00 -2 NaT + exp6 = """0 2011-01-01 09:00 +1 2012-02-01 10:00 +2 NaT dtype: object""" exp7 = """0 2013Q1 @@ -2007,6 +2087,32 @@ def test_sub_isub(self): rng -= 1 tm.assert_index_equal(rng, expected) + def test_comp_nat(self): + left = pd.PeriodIndex([pd.Period('2011-01-01'), pd.NaT, + pd.Period('2011-01-03')]) + right = pd.PeriodIndex([pd.NaT, pd.NaT, pd.Period('2011-01-03')]) + + for l, r in [(left, right), (left.asobject, right.asobject)]: + result = l == r + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = l != r + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l == pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT == r, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(l != pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT != l, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(l < pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT > l, expected) + def test_value_counts_unique(self): # GH 7735 idx = pd.period_range('2011-01-01 09:00', freq='H', periods=10) @@ -2074,14 +2180,16 @@ def _check_freq(index, expected_index): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) _check_freq(ordered, idx) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, idx[::-1]) self.assert_numpy_array_equal(indexer, - np.array([2, 1, 0], dtype=np.int64)) + np.array([2, 1, 0]), + check_dtype=False) _check_freq(ordered, idx[::-1]) pidx = PeriodIndex(['2011', '2013', '2015', '2012', @@ -2103,16 +2211,17 @@ def _check_freq(index, expected_index): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) _check_freq(ordered, idx) ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, + check_dtype=False) _check_freq(ordered, idx) pidx = PeriodIndex(['2011', '2013', 'NaT', '2011'], name='pidx', @@ -2148,7 +2257,8 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, idx) self.assert_numpy_array_equal(indexer, - np.array([0, 1, 2], dtype=np.int64)) + np.array([0, 1, 2]), + check_dtype=False) self.assertEqual(ordered.freq, idx.freq) self.assertEqual(ordered.freq, freq) @@ -2157,7 +2267,8 @@ def test_order(self): expected = idx[::-1] self.assert_index_equal(ordered, expected) self.assert_numpy_array_equal(indexer, - np.array([2, 1, 0], dtype=np.int64)) + np.array([2, 1, 0]), + check_dtype=False) self.assertEqual(ordered.freq, expected.freq) self.assertEqual(ordered.freq, freq) @@ -2191,16 +2302,16 @@ def test_order(self): ordered, indexer = idx.sort_values(return_indexer=True) self.assert_index_equal(ordered, expected) - exp = np.array([0, 4, 3, 1, 2], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([0, 4, 3, 1, 2]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertEqual(ordered.freq, 'D') ordered, indexer = idx.sort_values(return_indexer=True, ascending=False) self.assert_index_equal(ordered, expected[::-1]) - exp = np.array([2, 1, 3, 4, 0], dtype=np.int64) - self.assert_numpy_array_equal(indexer, exp) + exp = np.array([2, 1, 3, 4, 0]) + self.assert_numpy_array_equal(indexer, exp, check_dtype=False) self.assertEqual(ordered.freq, 'D') def test_getitem(self): diff --git a/pandas/tseries/tests/test_bin_groupby.py b/pandas/tseries/tests/test_bin_groupby.py index 6b6c468b7c391..08c0833be0cd6 100644 --- a/pandas/tseries/tests/test_bin_groupby.py +++ b/pandas/tseries/tests/test_bin_groupby.py @@ -3,12 +3,12 @@ from numpy import nan import numpy as np +from pandas.types.common import _ensure_int64 from pandas import Index, isnull from pandas.util.testing import assert_almost_equal import pandas.util.testing as tm import pandas.lib as lib import pandas.algos as algos -from pandas.core import common as com def test_series_grouper(): @@ -90,8 +90,8 @@ def _check(dtype): bins = np.array([6, 12, 20]) out = np.zeros((3, 4), dtype) counts = np.zeros(len(out), dtype=np.int64) - labels = com._ensure_int64(np.repeat(np.arange(3), - np.diff(np.r_[0, bins]))) + labels = _ensure_int64(np.repeat(np.arange(3), + np.diff(np.r_[0, bins]))) func = getattr(algos, 'group_ohlc_%s' % dtype) func(out, counts, obj[:, None], labels) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 807fb86b1b4da..8d217ff0753a6 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -36,14 +36,17 @@ def test_quarterly_negative_ordinals(self): p = Period(ordinal=-1, freq='Q-DEC') self.assertEqual(p.year, 1969) self.assertEqual(p.quarter, 4) + self.assertIsInstance(p, Period) p = Period(ordinal=-2, freq='Q-DEC') self.assertEqual(p.year, 1969) self.assertEqual(p.quarter, 3) + self.assertIsInstance(p, Period) p = Period(ordinal=-2, freq='M') self.assertEqual(p.year, 1969) self.assertEqual(p.month, 11) + self.assertIsInstance(p, Period) def test_period_cons_quarterly(self): # bugs in scikits.timeseries @@ -67,6 +70,7 @@ def test_period_cons_annual(self): stamp = exp.to_timestamp('D', how='end') + timedelta(days=30) p = Period(stamp, freq=freq) self.assertEqual(p, exp + 1) + self.assertIsInstance(p, Period) def test_period_cons_weekly(self): for num in range(10, 17): @@ -77,34 +81,46 @@ def test_period_cons_weekly(self): result = Period(daystr, freq=freq) expected = Period(daystr, freq='D').asfreq(freq) self.assertEqual(result, expected) + self.assertIsInstance(result, Period) + + def test_period_from_ordinal(self): + p = pd.Period('2011-01', freq='M') + res = pd.Period._from_ordinal(p.ordinal, freq='M') + self.assertEqual(p, res) + self.assertIsInstance(res, Period) def test_period_cons_nat(self): p = Period('NaT', freq='M') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'M') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period('nat', freq='W-SUN') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'W-SUN') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period(tslib.iNaT, freq='D') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, 'D') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) p = Period(tslib.iNaT, freq='3D') - self.assertEqual(p.ordinal, tslib.iNaT) - self.assertEqual(p.freq, offsets.Day(3)) - self.assertEqual(p.freqstr, '3D') - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) + self.assertIs(p, pd.NaT) + + p = Period('NaT') + self.assertIs(p, pd.NaT) + + p = Period(tslib.iNaT) + self.assertIs(p, pd.NaT) + + def test_cons_null_like(self): + # check Timestamp compat + self.assertIs(Timestamp('NaT'), pd.NaT) + self.assertIs(Period('NaT'), pd.NaT) + + self.assertIs(Timestamp(None), pd.NaT) + self.assertIs(Period(None), pd.NaT) - self.assertRaises(ValueError, Period, 'NaT') + self.assertIs(Timestamp(float('nan')), pd.NaT) + self.assertIs(Period(float('nan')), pd.NaT) + + self.assertIs(Timestamp(np.nan), pd.NaT) + self.assertIs(Period(np.nan), pd.NaT) def test_period_cons_mult(self): p1 = Period('2011-01', freq='3M') @@ -197,13 +213,6 @@ def test_timestamp_tz_arg_dateutil_from_string(self): freq='M').to_timestamp(tz='dateutil/Europe/Brussels') self.assertEqual(p.tz, gettz('Europe/Brussels')) - def test_timestamp_nat_tz(self): - t = Period('NaT', freq='M').to_timestamp() - self.assertTrue(t is tslib.NaT) - - t = Period('NaT', freq='M').to_timestamp(tz='Asia/Tokyo') - self.assertTrue(t is tslib.NaT) - def test_timestamp_mult(self): p = pd.Period('2011-01', freq='M') self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) @@ -213,12 +222,6 @@ def test_timestamp_mult(self): self.assertEqual(p.to_timestamp(how='S'), pd.Timestamp('2011-01-01')) self.assertEqual(p.to_timestamp(how='E'), pd.Timestamp('2011-03-31')) - def test_timestamp_nat_mult(self): - for freq in ['M', '3M']: - p = pd.Period('NaT', freq=freq) - self.assertTrue(p.to_timestamp(how='S') is pd.NaT) - self.assertTrue(p.to_timestamp(how='E') is pd.NaT) - def test_period_constructor(self): i1 = Period('1/1/2005', freq='M') i2 = Period('Jan 2005') @@ -552,9 +555,6 @@ def _ex(p): result = p.to_timestamp('5S', how='start') self.assertEqual(result, expected) - p = Period('NaT', freq='W') - self.assertTrue(p.to_timestamp() is tslib.NaT) - def test_start_time(self): freq_lst = ['A', 'Q', 'M', 'D', 'H', 'T', 'S'] xp = datetime(2012, 1, 1) @@ -566,9 +566,6 @@ def test_start_time(self): self.assertEqual(Period('2012', freq='W').start_time, datetime(2011, 12, 26)) - p = Period('NaT', freq='W') - self.assertTrue(p.start_time is tslib.NaT) - def test_end_time(self): p = Period('2012', freq='A') @@ -607,9 +604,6 @@ def _ex(*args): xp = _ex(2012, 1, 16) self.assertEqual(xp, p.end_time) - p = Period('NaT', freq='W') - self.assertTrue(p.end_time is tslib.NaT) - def test_anchor_week_end_time(self): def _ex(*args): return Timestamp(Timestamp(datetime(*args)).value - 1) @@ -758,15 +752,14 @@ def test_properties_secondly(self): def test_properties_nat(self): p_nat = Period('NaT', freq='M') t_nat = pd.Timestamp('NaT') + self.assertIs(p_nat, t_nat) + # confirm Period('NaT') work identical with Timestamp('NaT') for f in ['year', 'month', 'day', 'hour', 'minute', 'second', 'week', 'dayofyear', 'quarter', 'days_in_month']: self.assertTrue(np.isnan(getattr(p_nat, f))) self.assertTrue(np.isnan(getattr(t_nat, f))) - for f in ['weekofyear', 'dayofweek', 'weekday', 'qyear']: - self.assertTrue(np.isnan(getattr(p_nat, f))) - def test_pnow(self): dt = datetime.now() @@ -789,7 +782,7 @@ def test_constructor_corner(self): self.assertRaises(ValueError, Period, 1.6, freq='D') self.assertRaises(ValueError, Period, ordinal=1.6, freq='D') self.assertRaises(ValueError, Period, ordinal=2, value=1, freq='D') - self.assertRaises(ValueError, Period) + self.assertIs(Period(None), pd.NaT) self.assertRaises(ValueError, Period, month=1) p = Period('2007-01-01', freq='D') @@ -1526,12 +1519,6 @@ def test_conv_secondly(self): self.assertEqual(ival_S.asfreq('S'), ival_S) - def test_asfreq_nat(self): - p = Period('NaT', freq='A') - result = p.asfreq('M') - self.assertEqual(result.ordinal, tslib.iNaT) - self.assertEqual(result.freq, 'M') - def test_asfreq_mult(self): # normal freq to mult freq p = Period(freq='A', year=2007) @@ -1603,21 +1590,6 @@ def test_asfreq_mult(self): self.assertEqual(result.ordinal, expected.ordinal) self.assertEqual(result.freq, expected.freq) - def test_asfreq_mult_nat(self): - # normal freq to mult freq - for p in [Period('NaT', freq='A'), Period('NaT', freq='3A'), - Period('NaT', freq='2M'), Period('NaT', freq='3D')]: - for freq in ['3A', offsets.YearEnd(3)]: - result = p.asfreq(freq) - expected = Period('NaT', freq='3A') - self.assertEqual(result.ordinal, pd.tslib.iNaT) - self.assertEqual(result.freq, expected.freq) - - result = p.asfreq(freq, how='S') - expected = Period('NaT', freq='3A') - self.assertEqual(result.ordinal, pd.tslib.iNaT) - self.assertEqual(result.freq, expected.freq) - class TestPeriodIndex(tm.TestCase): def setUp(self): @@ -1995,6 +1967,19 @@ def test_getitem_datetime(self): rs = ts[dt1:dt4] tm.assert_series_equal(rs, ts) + def test_getitem_nat(self): + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertEqual(idx[0], pd.Period('2011-01', freq='M')) + self.assertIs(idx[1], tslib.NaT) + + s = pd.Series([0, 1, 2], index=idx) + self.assertEqual(s[pd.NaT], 1) + + s = pd.Series(idx, index=idx) + self.assertEqual(s[pd.Period('2011-01', freq='M')], + pd.Period('2011-01', freq='M')) + self.assertIs(s[pd.NaT], tslib.NaT) + def test_slice_with_negative_step(self): ts = Series(np.arange(20), period_range('2014-01', periods=20, freq='M')) @@ -2038,6 +2023,20 @@ def test_contains(self): self.assertFalse(Period('2007-01', freq='D') in rng) self.assertFalse(Period('2007-01', freq='2M') in rng) + def test_contains_nat(self): + # GH13582 + idx = period_range('2007-01', freq='M', periods=10) + self.assertFalse(pd.NaT in idx) + self.assertFalse(None in idx) + self.assertFalse(float('nan') in idx) + self.assertFalse(np.nan in idx) + + idx = pd.PeriodIndex(['2011-01', 'NaT', '2011-02'], freq='M') + self.assertTrue(pd.NaT in idx) + self.assertTrue(None in idx) + self.assertTrue(float('nan') in idx) + self.assertTrue(np.nan in idx) + def test_sub(self): rng = period_range('2007-01', periods=50) @@ -3292,6 +3291,17 @@ def test_get_loc_msg(self): except KeyError as inst: self.assertEqual(inst.args[0], bad_period) + def test_get_loc_nat(self): + didx = DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03']) + pidx = PeriodIndex(['2011-01-01', 'NaT', '2011-01-03'], freq='M') + + # check DatetimeIndex compat + for idx in [didx, pidx]: + self.assertEqual(idx.get_loc(pd.NaT), 1) + self.assertEqual(idx.get_loc(None), 1) + self.assertEqual(idx.get_loc(float('nan')), 1) + self.assertEqual(idx.get_loc(np.nan), 1) + def test_append_concat(self): # #1815 d1 = date_range('12/31/1990', '12/31/1999', freq='A-DEC') @@ -3576,95 +3586,87 @@ def test_add_offset_nat(self): for freq in ['A', '2A', '3A']: p = Period('NaT', freq=freq) for o in [offsets.YearEnd(2)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) + # freq is Tick for freq in ['D', '2D', '3D']: p = Period('NaT', freq=freq) for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), np.timedelta64(3600 * 24, 's'), timedelta(-2), timedelta(hours=48)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23)]: - - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) for freq in ['H', '2H', '3H']: p = Period('NaT', freq=freq) for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), np.timedelta64(3600, 's'), timedelta(minutes=120), timedelta(days=4, minutes=180)]: - self.assertEqual((p + o).ordinal, tslib.iNaT) + self.assertIs(p + o, tslib.NaT) if not isinstance(o, np.timedelta64): - self.assertEqual((o + p).ordinal, tslib.iNaT) + self.assertIs(o + p, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(3200, 's'), timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p + o + self.assertIs(p + o, tslib.NaT) if isinstance(o, np.timedelta64): with tm.assertRaises(TypeError): o + p else: - with tm.assertRaises(period.IncompatibleFrequency): - o + p + self.assertIs(o + p, tslib.NaT) def test_sub_pdnat(self): # GH 13071 @@ -3749,24 +3751,22 @@ def test_sub_offset_nat(self): for freq in ['A', '2A', '3A']: p = Period('NaT', freq=freq) for o in [offsets.YearEnd(2)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) # freq is Tick for freq in ['D', '2D', '3D']: @@ -3774,37 +3774,33 @@ def test_sub_offset_nat(self): for o in [offsets.Day(5), offsets.Hour(24), np.timedelta64(2, 'D'), np.timedelta64(3600 * 24, 's'), timedelta(-2), timedelta(hours=48)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) for freq in ['H', '2H', '3H']: p = Period('NaT', freq=freq) for o in [offsets.Day(2), offsets.Hour(3), np.timedelta64(3, 'h'), np.timedelta64(3600, 's'), timedelta(minutes=120), timedelta(days=4, minutes=180)]: - self.assertEqual((p - o).ordinal, tslib.iNaT) + self.assertIs(p - o, tslib.NaT) for o in [offsets.YearBegin(2), offsets.MonthBegin(1), offsets.Minute(), np.timedelta64(3200, 's'), timedelta(hours=23, minutes=30)]: - with tm.assertRaises(period.IncompatibleFrequency): - p - o + self.assertIs(p - o, tslib.NaT) def test_nat_ops(self): for freq in ['M', '2M', '3M']: p = Period('NaT', freq=freq) - self.assertEqual((p + 1).ordinal, tslib.iNaT) - self.assertEqual((1 + p).ordinal, tslib.iNaT) - self.assertEqual((p - 1).ordinal, tslib.iNaT) - self.assertEqual((p - Period('2011-01', freq=freq)).ordinal, - tslib.iNaT) - self.assertEqual((Period('2011-01', freq=freq) - p).ordinal, - tslib.iNaT) + self.assertIs(p + 1, tslib.NaT) + self.assertIs(1 + p, tslib.NaT) + self.assertIs(p - 1, tslib.NaT) + self.assertIs(p - Period('2011-01', freq=freq), tslib.NaT) + self.assertIs(Period('2011-01', freq=freq) - p, tslib.NaT) def test_period_ops_offset(self): p = Period('2011-04-01', freq='D') @@ -3830,18 +3826,17 @@ class TestPeriodIndexSeriesMethods(tm.TestCase): def _check(self, values, func, expected): idx = pd.PeriodIndex(values) result = func(idx) - tm.assert_index_equal(result, pd.PeriodIndex(expected)) + if isinstance(expected, pd.Index): + tm.assert_index_equal(result, expected) + else: + # comp op results in bool + tm.assert_numpy_array_equal(result, expected) s = pd.Series(values) result = func(s) - exp = pd.Series(expected) - # Period(NaT) != Period(NaT) - - lmask = result.map(lambda x: x.ordinal != tslib.iNaT) - rmask = exp.map(lambda x: x.ordinal != tslib.iNaT) - tm.assert_series_equal(lmask, rmask) - tm.assert_series_equal(result[lmask], exp[rmask]) + exp = pd.Series(expected, name=values.name) + tm.assert_series_equal(result, exp) def test_pi_ops(self): idx = PeriodIndex(['2011-01', '2011-02', '2011-03', @@ -3962,7 +3957,7 @@ def test_pi_sub_period(self): exp = pd.Index([12, 11, 10, 9], name='idx') tm.assert_index_equal(result, exp) - exp = pd.Index([np.nan, np.nan, np.nan, np.nan], name='idx') + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) @@ -3987,10 +3982,82 @@ def test_pi_sub_period_nat(self): exp = pd.Index([12, np.nan, 10, 9], name='idx') tm.assert_index_equal(result, exp) - exp = pd.Index([np.nan, np.nan, np.nan, np.nan], name='idx') + exp = pd.TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name='idx') tm.assert_index_equal(idx - pd.Period('NaT', freq='M'), exp) tm.assert_index_equal(pd.Period('NaT', freq='M') - idx, exp) + def test_pi_comp_period(self): + idx = PeriodIndex(['2011-01', '2011-02', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > pd.Period('2011-03', freq='M') + exp = np.array([False, False, False, True], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, True, True, False], dtype=np.bool) + self._check(idx, f, exp) + + def test_pi_comp_period_nat(self): + idx = PeriodIndex(['2011-01', 'NaT', '2011-03', + '2011-04'], freq='M', name='idx') + + f = lambda x: x == pd.Period('2011-03', freq='M') + exp = np.array([False, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') == x + self._check(idx, f, exp) + + f = lambda x: x == tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT == x + self._check(idx, f, exp) + + f = lambda x: x != pd.Period('2011-03', freq='M') + exp = np.array([True, True, False, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: pd.Period('2011-03', freq='M') != x + self._check(idx, f, exp) + + f = lambda x: x != tslib.NaT + exp = np.array([True, True, True, True], dtype=np.bool) + self._check(idx, f, exp) + f = lambda x: tslib.NaT != x + self._check(idx, f, exp) + + f = lambda x: pd.Period('2011-03', freq='M') >= x + exp = np.array([True, False, True, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x < pd.Period('2011-03', freq='M') + exp = np.array([True, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: x > tslib.NaT + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + + f = lambda x: tslib.NaT >= x + exp = np.array([False, False, False, False], dtype=np.bool) + self._check(idx, f, exp) + class TestPeriodRepresentation(tm.TestCase): """ @@ -4326,10 +4393,10 @@ def test_NaT_scalar(self): series = Series([0, 1000, 2000, iNaT], dtype='period[D]') val = series[3] - self.assertTrue(com.isnull(val)) + self.assertTrue(isnull(val)) series[2] = val - self.assertTrue(com.isnull(series[2])) + self.assertTrue(isnull(series[2])) def test_NaT_cast(self): result = Series([np.nan]).astype('period[D]') diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 2236d20975eee..518f69485004c 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -11,10 +11,11 @@ import pandas.util.testing as tm from pandas import (Series, DataFrame, Panel, Index, isnull, notnull, Timestamp) + +from pandas.types.generic import ABCSeries, ABCDataFrame from pandas.compat import range, lrange, zip, product, OrderedDict from pandas.core.base import SpecificationError -from pandas.core.common import (ABCSeries, ABCDataFrame, - UnsupportedFunctionCall) +from pandas.core.common import UnsupportedFunctionCall from pandas.core.groupby import DataError from pandas.tseries.frequencies import MONTHS, DAYS from pandas.tseries.frequencies import to_offset diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index e515ba624d203..4f985998d5e20 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -137,12 +137,12 @@ def test_construction(self): self.assertRaises(ValueError, lambda: Timedelta('3.1415')) # invalid construction - tm.assertRaisesRegexp(ValueError, "cannot construct a TimeDelta", + tm.assertRaisesRegexp(ValueError, "cannot construct a Timedelta", lambda: Timedelta()) tm.assertRaisesRegexp(ValueError, "unit abbreviation w/o a number", lambda: Timedelta('foo')) tm.assertRaisesRegexp(ValueError, - "cannot construct a TimeDelta from the passed " + "cannot construct a Timedelta from the passed " "arguments, allowed keywords are ", lambda: Timedelta(day=10)) @@ -472,6 +472,21 @@ class Other: self.assertTrue(td.__mul__(other) is NotImplemented) self.assertTrue(td.__floordiv__(td) is NotImplemented) + def test_ops_error_str(self): + # GH 13624 + td = Timedelta('1 day') + + for l, r in [(td, 'a'), ('a', td)]: + + with tm.assertRaises(TypeError): + l + r + + with tm.assertRaises(TypeError): + l > r + + self.assertFalse(l == r) + self.assertTrue(l != r) + def test_fields(self): def check(value): # that we are int/long like @@ -1432,6 +1447,23 @@ def test_comparisons_nat(self): expected = np.array([True, True, True, True, True, False]) self.assert_numpy_array_equal(result, expected) + def test_ops_error_str(self): + # GH 13624 + tdi = TimedeltaIndex(['1 day', '2 days']) + + for l, r in [(tdi, 'a'), ('a', tdi)]: + with tm.assertRaises(TypeError): + l + r + + with tm.assertRaises(TypeError): + l > r + + with tm.assertRaises(TypeError): + l == r + + with tm.assertRaises(TypeError): + l != r + def test_map(self): rng = timedelta_range('1 day', periods=10) @@ -1547,12 +1579,14 @@ def test_sort_values(self): ordered, dexer = idx.sort_values(return_indexer=True) self.assertTrue(ordered.is_monotonic) self.assert_numpy_array_equal(dexer, - np.array([1, 2, 0], dtype=np.int64)) + np.array([1, 2, 0]), + check_dtype=False) ordered, dexer = idx.sort_values(return_indexer=True, ascending=False) self.assertTrue(ordered[::-1].is_monotonic) self.assert_numpy_array_equal(dexer, - np.array([0, 2, 1], dtype=np.int64)) + np.array([0, 2, 1]), + check_dtype=False) def test_insert(self): diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index b0caa1f6a77cb..299ec374567e7 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -12,6 +12,7 @@ import pandas.lib as lib import pandas.tslib as tslib +from pandas.types.common import is_datetime64_ns_dtype import pandas as pd import pandas.compat as compat import pandas.core.common as com @@ -2282,7 +2283,7 @@ def test_to_datetime_tz_psycopg2(self): i = pd.DatetimeIndex([ '2000-01-01 08:00:00+00:00' ], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None)) - self.assertFalse(com.is_datetime64_ns_dtype(i)) + self.assertFalse(is_datetime64_ns_dtype(i)) # tz coerceion result = pd.to_datetime(i, errors='coerce') @@ -3884,36 +3885,36 @@ def test_datetimeindex_accessors(self): self.assertEqual(dti.is_month_start[0], 1) tests = [ - (Timestamp('2013-06-01', offset='M').is_month_start, 1), - (Timestamp('2013-06-01', offset='BM').is_month_start, 0), - (Timestamp('2013-06-03', offset='M').is_month_start, 0), - (Timestamp('2013-06-03', offset='BM').is_month_start, 1), - (Timestamp('2013-02-28', offset='Q-FEB').is_month_end, 1), - (Timestamp('2013-02-28', offset='Q-FEB').is_quarter_end, 1), - (Timestamp('2013-02-28', offset='Q-FEB').is_year_end, 1), - (Timestamp('2013-03-01', offset='Q-FEB').is_month_start, 1), - (Timestamp('2013-03-01', offset='Q-FEB').is_quarter_start, 1), - (Timestamp('2013-03-01', offset='Q-FEB').is_year_start, 1), - (Timestamp('2013-03-31', offset='QS-FEB').is_month_end, 1), - (Timestamp('2013-03-31', offset='QS-FEB').is_quarter_end, 0), - (Timestamp('2013-03-31', offset='QS-FEB').is_year_end, 0), - (Timestamp('2013-02-01', offset='QS-FEB').is_month_start, 1), - (Timestamp('2013-02-01', offset='QS-FEB').is_quarter_start, 1), - (Timestamp('2013-02-01', offset='QS-FEB').is_year_start, 1), - (Timestamp('2013-06-30', offset='BQ').is_month_end, 0), - (Timestamp('2013-06-30', offset='BQ').is_quarter_end, 0), - (Timestamp('2013-06-30', offset='BQ').is_year_end, 0), - (Timestamp('2013-06-28', offset='BQ').is_month_end, 1), - (Timestamp('2013-06-28', offset='BQ').is_quarter_end, 1), - (Timestamp('2013-06-28', offset='BQ').is_year_end, 0), - (Timestamp('2013-06-30', offset='BQS-APR').is_month_end, 0), - (Timestamp('2013-06-30', offset='BQS-APR').is_quarter_end, 0), - (Timestamp('2013-06-30', offset='BQS-APR').is_year_end, 0), - (Timestamp('2013-06-28', offset='BQS-APR').is_month_end, 1), - (Timestamp('2013-06-28', offset='BQS-APR').is_quarter_end, 1), - (Timestamp('2013-03-29', offset='BQS-APR').is_year_end, 1), - (Timestamp('2013-11-01', offset='AS-NOV').is_year_start, 1), - (Timestamp('2013-10-31', offset='AS-NOV').is_year_end, 1), + (Timestamp('2013-06-01', freq='M').is_month_start, 1), + (Timestamp('2013-06-01', freq='BM').is_month_start, 0), + (Timestamp('2013-06-03', freq='M').is_month_start, 0), + (Timestamp('2013-06-03', freq='BM').is_month_start, 1), + (Timestamp('2013-02-28', freq='Q-FEB').is_month_end, 1), + (Timestamp('2013-02-28', freq='Q-FEB').is_quarter_end, 1), + (Timestamp('2013-02-28', freq='Q-FEB').is_year_end, 1), + (Timestamp('2013-03-01', freq='Q-FEB').is_month_start, 1), + (Timestamp('2013-03-01', freq='Q-FEB').is_quarter_start, 1), + (Timestamp('2013-03-01', freq='Q-FEB').is_year_start, 1), + (Timestamp('2013-03-31', freq='QS-FEB').is_month_end, 1), + (Timestamp('2013-03-31', freq='QS-FEB').is_quarter_end, 0), + (Timestamp('2013-03-31', freq='QS-FEB').is_year_end, 0), + (Timestamp('2013-02-01', freq='QS-FEB').is_month_start, 1), + (Timestamp('2013-02-01', freq='QS-FEB').is_quarter_start, 1), + (Timestamp('2013-02-01', freq='QS-FEB').is_year_start, 1), + (Timestamp('2013-06-30', freq='BQ').is_month_end, 0), + (Timestamp('2013-06-30', freq='BQ').is_quarter_end, 0), + (Timestamp('2013-06-30', freq='BQ').is_year_end, 0), + (Timestamp('2013-06-28', freq='BQ').is_month_end, 1), + (Timestamp('2013-06-28', freq='BQ').is_quarter_end, 1), + (Timestamp('2013-06-28', freq='BQ').is_year_end, 0), + (Timestamp('2013-06-30', freq='BQS-APR').is_month_end, 0), + (Timestamp('2013-06-30', freq='BQS-APR').is_quarter_end, 0), + (Timestamp('2013-06-30', freq='BQS-APR').is_year_end, 0), + (Timestamp('2013-06-28', freq='BQS-APR').is_month_end, 1), + (Timestamp('2013-06-28', freq='BQS-APR').is_quarter_end, 1), + (Timestamp('2013-03-29', freq='BQS-APR').is_year_end, 1), + (Timestamp('2013-11-01', freq='AS-NOV').is_year_start, 1), + (Timestamp('2013-10-31', freq='AS-NOV').is_year_end, 1), (Timestamp('2012-02-01').days_in_month, 29), (Timestamp('2013-02-01').days_in_month, 28)] diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py index d68ff793c9b6a..470aafafec547 100644 --- a/pandas/tseries/tests/test_timezones.py +++ b/pandas/tseries/tests/test_timezones.py @@ -5,6 +5,7 @@ import numpy as np import pytz +from pandas.types.dtypes import DatetimeTZDtype from pandas import (Index, Series, DataFrame, isnull, Timestamp) from pandas import DatetimeIndex, to_datetime, NaT @@ -17,7 +18,6 @@ from pytz import NonExistentTimeError import pandas.util.testing as tm -from pandas.types.api import DatetimeTZDtype from pandas.util.testing import assert_frame_equal, set_timezone from pandas.compat import lrange, zip @@ -1061,6 +1061,46 @@ def test_tslib_tz_convert_dst(self): self.assert_numpy_array_equal(idx.hour, np.array([4, 4], dtype=np.int32)) + def test_tzlocal(self): + # GH 13583 + ts = Timestamp('2011-01-01', tz=dateutil.tz.tzlocal()) + self.assertEqual(ts.tz, dateutil.tz.tzlocal()) + self.assertTrue("tz='tzlocal()')" in repr(ts)) + + tz = tslib.maybe_get_tz('tzlocal()') + self.assertEqual(tz, dateutil.tz.tzlocal()) + + # get offset using normal datetime for test + offset = dateutil.tz.tzlocal().utcoffset(datetime(2011, 1, 1)) + offset = offset.total_seconds() * 1000000000 + self.assertEqual(ts.value + offset, Timestamp('2011-01-01').value) + + def test_tz_localize_tzlocal(self): + # GH 13583 + offset = dateutil.tz.tzlocal().utcoffset(datetime(2011, 1, 1)) + offset = int(offset.total_seconds() * 1000000000) + + dti = date_range(start='2001-01-01', end='2001-03-01') + dti2 = dti.tz_localize(dateutil.tz.tzlocal()) + tm.assert_numpy_array_equal(dti2.asi8 + offset, dti.asi8) + + dti = date_range(start='2001-01-01', end='2001-03-01', + tz=dateutil.tz.tzlocal()) + dti2 = dti.tz_localize(None) + tm.assert_numpy_array_equal(dti2.asi8 - offset, dti.asi8) + + def test_tz_convert_tzlocal(self): + # GH 13583 + # tz_convert doesn't affect to internal + dti = date_range(start='2001-01-01', end='2001-03-01', tz='UTC') + dti2 = dti.tz_convert(dateutil.tz.tzlocal()) + tm.assert_numpy_array_equal(dti2.asi8, dti.asi8) + + dti = date_range(start='2001-01-01', end='2001-03-01', + tz=dateutil.tz.tzlocal()) + dti2 = dti.tz_convert(None) + tm.assert_numpy_array_equal(dti2.asi8, dti.asi8) + class TestTimeZoneCacheKey(tm.TestCase): def test_cache_keys_are_distinct_for_pytz_vs_dateutil(self): diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index c6436163b9edb..6696c03a070f7 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -255,6 +255,48 @@ def test_constructor_keyword(self): hour=1, minute=2, second=3, microsecond=999999)), repr(Timestamp('2015-11-12 01:02:03.999999'))) + def test_constructor_fromordinal(self): + base = datetime.datetime(2000, 1, 1) + + ts = Timestamp.fromordinal(base.toordinal(), freq='D') + self.assertEqual(base, ts) + self.assertEqual(ts.freq, 'D') + self.assertEqual(base.toordinal(), ts.toordinal()) + + ts = Timestamp.fromordinal(base.toordinal(), tz='US/Eastern') + self.assertEqual(pd.Timestamp('2000-01-01', tz='US/Eastern'), ts) + self.assertEqual(base.toordinal(), ts.toordinal()) + + def test_constructor_offset_depr(self): + # GH 12160 + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + ts = Timestamp('2011-01-01', offset='D') + self.assertEqual(ts.freq, 'D') + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + self.assertEqual(ts.offset, 'D') + + msg = "Can only specify freq or offset, not both" + with tm.assertRaisesRegexp(TypeError, msg): + Timestamp('2011-01-01', offset='D', freq='D') + + def test_constructor_offset_depr_fromordinal(self): + # GH 12160 + base = datetime.datetime(2000, 1, 1) + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + ts = Timestamp.fromordinal(base.toordinal(), offset='D') + self.assertEqual(pd.Timestamp('2000-01-01'), ts) + self.assertEqual(ts.freq, 'D') + self.assertEqual(base.toordinal(), ts.toordinal()) + + msg = "Can only specify freq or offset, not both" + with tm.assertRaisesRegexp(TypeError, msg): + Timestamp.fromordinal(base.toordinal(), offset='D', freq='D') + def test_conversion(self): # GH 9255 ts = Timestamp('2000-01-01') @@ -312,13 +354,13 @@ def test_repr(self): self.assertNotIn(freq_repr, repr(date_tz)) self.assertEqual(date_tz, eval(repr(date_tz))) - date_freq = Timestamp(date, offset=freq) + date_freq = Timestamp(date, freq=freq) self.assertIn(date, repr(date_freq)) self.assertNotIn(tz_repr, repr(date_freq)) self.assertIn(freq_repr, repr(date_freq)) self.assertEqual(date_freq, eval(repr(date_freq))) - date_tz_freq = Timestamp(date, tz=tz, offset=freq) + date_tz_freq = Timestamp(date, tz=tz, freq=freq) self.assertIn(date, repr(date_tz_freq)) self.assertIn(tz_repr, repr(date_tz_freq)) self.assertIn(freq_repr, repr(date_tz_freq)) @@ -1182,6 +1224,13 @@ def test_nat_arithmetic(self): self.assertIs(left - right, pd.NaT) self.assertIs(right - left, pd.NaT) + # int addition / subtraction + for (left, right) in [(pd.NaT, 2), (pd.NaT, 0), (pd.NaT, -3)]: + self.assertIs(right + left, pd.NaT) + self.assertIs(left + right, pd.NaT) + self.assertIs(left - right, pd.NaT) + self.assertIs(right - left, pd.NaT) + def test_nat_arithmetic_index(self): # GH 11718 diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 7ff5d7adcaa35..7f28ec86ec40d 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -4,9 +4,11 @@ import numpy as np import pandas.tslib as tslib -from pandas.core.common import (ABCSeries, is_integer_dtype, - is_timedelta64_dtype, is_list_like, - _ensure_object, ABCIndexClass) +from pandas.types.common import (_ensure_object, + is_integer_dtype, + is_timedelta64_dtype, + is_list_like) +from pandas.types.generic import ABCSeries, ABCIndexClass from pandas.util.decorators import deprecate_kwarg @@ -74,8 +76,8 @@ def _convert_listlike(arg, box, unit, name=None): value = arg.astype('timedelta64[{0}]'.format( unit)).astype('timedelta64[ns]', copy=False) else: - value = tslib.array_to_timedelta64( - _ensure_object(arg), unit=unit, errors=errors) + value = tslib.array_to_timedelta64(_ensure_object(arg), + unit=unit, errors=errors) value = value.astype('timedelta64[ns]', copy=False) if box: diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index efb8590dfccf4..067e8ec19f644 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -4,8 +4,17 @@ import pandas.lib as lib import pandas.tslib as tslib -import pandas.core.common as com -from pandas.core.common import ABCIndexClass, ABCSeries, ABCDataFrame + +from pandas.types.common import (_ensure_object, + is_datetime64_ns_dtype, + is_datetime64_dtype, + is_datetime64tz_dtype, + is_integer_dtype, + is_list_like) +from pandas.types.generic import (ABCIndexClass, ABCSeries, + ABCDataFrame) +from pandas.types.missing import notnull + import pandas.compat as compat from pandas.util.decorators import deprecate_kwarg @@ -161,7 +170,7 @@ def _guess_datetime_format(dt_str, dayfirst=False, def _guess_datetime_format_for_array(arr, **kwargs): # Try to guess the format based on the first non-NaN element - non_nan_elements = com.notnull(arr).nonzero()[0] + non_nan_elements = notnull(arr).nonzero()[0] if len(non_nan_elements): return _guess_datetime_format(arr[non_nan_elements[0]], **kwargs) @@ -307,7 +316,7 @@ def _convert_listlike(arg, box, format, name=None): arg = np.array(arg, dtype='O') # these are shortcutable - if com.is_datetime64_ns_dtype(arg): + if is_datetime64_ns_dtype(arg): if box and not isinstance(arg, DatetimeIndex): try: return DatetimeIndex(arg, tz='utc' if utc else None, @@ -317,7 +326,7 @@ def _convert_listlike(arg, box, format, name=None): return arg - elif com.is_datetime64tz_dtype(arg): + elif is_datetime64tz_dtype(arg): if not isinstance(arg, DatetimeIndex): return DatetimeIndex(arg, tz='utc' if utc else None) if utc: @@ -342,7 +351,7 @@ def _convert_listlike(arg, box, format, name=None): raise TypeError('arg must be a string, datetime, list, tuple, ' '1-d array, or Series') - arg = com._ensure_object(arg) + arg = _ensure_object(arg) require_iso8601 = False if infer_datetime_format and format is None: @@ -399,7 +408,7 @@ def _convert_listlike(arg, box, format, name=None): require_iso8601=require_iso8601 ) - if com.is_datetime64_dtype(result) and box: + if is_datetime64_dtype(result) and box: result = DatetimeIndex(result, tz='utc' if utc else None, name=name) @@ -424,7 +433,7 @@ def _convert_listlike(arg, box, format, name=None): return _assemble_from_unit_mappings(arg, errors=errors) elif isinstance(arg, ABCIndexClass): return _convert_listlike(arg, box, format, name=arg.name) - elif com.is_list_like(arg): + elif is_list_like(arg): return _convert_listlike(arg, box, format) return _convert_listlike(np.array([arg]), box, format)[0] @@ -511,7 +520,7 @@ def coerce(values): values = to_numeric(values, errors=errors) # prevent overflow in case of int8 or int16 - if com.is_integer_dtype(values): + if is_integer_dtype(values): values = values.astype('int64', copy=False) return values @@ -574,7 +583,7 @@ def calc_with_mask(carg, mask): # a float with actual np.nan try: carg = arg.astype(np.float64) - return calc_with_mask(carg, com.notnull(carg)) + return calc_with_mask(carg, notnull(carg)) except: pass @@ -654,7 +663,7 @@ def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None): def _guess_time_format_for_array(arr): # Try to guess the format based on the first non-NaN element - non_nan_elements = com.notnull(arr).nonzero()[0] + non_nan_elements = notnull(arr).nonzero()[0] if len(non_nan_elements): element = arr[non_nan_elements[0]] for time_format in _time_formats: @@ -705,7 +714,7 @@ def _convert_listlike(arg, format): raise TypeError('arg must be a string, datetime, list, tuple, ' '1-d array, or Series') - arg = com._ensure_object(arg) + arg = _ensure_object(arg) if infer_time_format and format is None: format = _guess_time_format_for_array(arg) @@ -762,7 +771,7 @@ def _convert_listlike(arg, format): return Series(values, index=arg.index, name=arg.name) elif isinstance(arg, ABCIndexClass): return _convert_listlike(arg, format) - elif com.is_list_like(arg): + elif is_list_like(arg): return _convert_listlike(arg, format) return _convert_listlike(np.array([arg]), format)[0] diff --git a/pandas/tseries/util.py b/pandas/tseries/util.py index 7e314657cb25c..98a93d22b09a6 100644 --- a/pandas/tseries/util.py +++ b/pandas/tseries/util.py @@ -1,6 +1,6 @@ from pandas.compat import lrange import numpy as np -import pandas.core.common as com +from pandas.types.common import _ensure_platform_int from pandas.core.frame import DataFrame import pandas.core.nanops as nanops @@ -69,7 +69,7 @@ def pivot_annual(series, freq=None): raise NotImplementedError(freq) flat_index = (year - years.min()) * width + offset - flat_index = com._ensure_platform_int(flat_index) + flat_index = _ensure_platform_int(flat_index) values = np.empty((len(years), width)) values.fill(np.nan) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 8837881af0b6c..c681cebd84836 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -63,6 +63,7 @@ from pandas.compat import parse_date, string_types, iteritems, StringIO, callabl import operator import collections +import warnings # initialize numpy import_array() @@ -86,23 +87,24 @@ try: except NameError: # py3 basestring = str -cdef inline object create_timestamp_from_ts(int64_t value, pandas_datetimestruct dts, object tz, object offset): +cdef inline object create_timestamp_from_ts(int64_t value, pandas_datetimestruct dts, + object tz, object freq): cdef _Timestamp ts_base ts_base = _Timestamp.__new__(Timestamp, dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz) - ts_base.value = value - ts_base.offset = offset + ts_base.freq = freq ts_base.nanosecond = dts.ps / 1000 return ts_base -cdef inline object create_datetime_from_ts(int64_t value, pandas_datetimestruct dts, object tz, object offset): +cdef inline object create_datetime_from_ts(int64_t value, pandas_datetimestruct dts, + object tz, object freq): return datetime(dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz) -def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): +def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, freq=None, box=False): # convert an i8 repr to an ndarray of datetimes or Timestamp (if box == True) cdef: @@ -113,9 +115,9 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): ndarray[object] result = np.empty(n, dtype=object) object (*func_create)(int64_t, pandas_datetimestruct, object, object) - if box and util.is_string_object(offset): + if box and util.is_string_object(freq): from pandas.tseries.frequencies import to_offset - offset = to_offset(offset) + freq = to_offset(freq) if box: func_create = create_timestamp_from_ts @@ -130,7 +132,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): result[i] = NaT else: pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts) - result[i] = func_create(value, dts, tz, offset) + result[i] = func_create(value, dts, tz, freq) elif _is_tzlocal(tz) or _is_fixed_offset(tz): for i in range(n): value = arr[i] @@ -138,7 +140,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): result[i] = NaT else: pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts) - dt = create_datetime_from_ts(value, dts, tz, offset) + dt = create_datetime_from_ts(value, dts, tz, freq) dt = dt + tz.utcoffset(dt) if box: dt = Timestamp(dt) @@ -163,7 +165,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): new_tz = tz pandas_datetime_to_datetimestruct(value + deltas[pos], PANDAS_FR_ns, &dts) - result[i] = func_create(value, dts, new_tz, offset) + result[i] = func_create(value, dts, new_tz, freq) else: for i in range(n): @@ -172,7 +174,7 @@ def ints_to_pydatetime(ndarray[int64_t] arr, tz=None, offset=None, box=False): result[i] = NaT else: pandas_datetime_to_datetimestruct(value, PANDAS_FR_ns, &dts) - result[i] = func_create(value, dts, None, offset) + result[i] = func_create(value, dts, None, freq) return result @@ -233,12 +235,14 @@ class Timestamp(_Timestamp): ---------- ts_input : datetime-like, str, int, float Value to be converted to Timestamp - offset : str, DateOffset + freq : str, DateOffset Offset which Timestamp will have tz : string, pytz.timezone, dateutil.tz.tzfile or None Time zone for time which Timestamp will have. unit : string numpy unit used for conversion, if ts_input is int or float + offset : str, DateOffset + Deprecated, use freq The other two forms mimic the parameters from ``datetime.datetime``. They can be passed by either position or keyword, but not both mixed together. @@ -246,7 +250,7 @@ class Timestamp(_Timestamp): :func:`datetime.datetime` Parameters ------------------------------------ - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 year : int month : int @@ -259,10 +263,23 @@ class Timestamp(_Timestamp): """ @classmethod - def fromordinal(cls, ordinal, offset=None, tz=None): - """ passed an ordinal, translate and convert to a ts - note: by definition there cannot be any tz info on the ordinal itself """ - return cls(datetime.fromordinal(ordinal),offset=offset,tz=tz) + def fromordinal(cls, ordinal, freq=None, tz=None, offset=None): + """ + passed an ordinal, translate and convert to a ts + note: by definition there cannot be any tz info on the ordinal itself + + Parameters + ---------- + ordinal : int + date corresponding to a proleptic Gregorian ordinal + freq : str, DateOffset + Offset which Timestamp will have + tz : string, pytz.timezone, dateutil.tz.tzfile or None + Time zone for time which Timestamp will have. + offset : str, DateOffset + Deprecated, use freq + """ + return cls(datetime.fromordinal(ordinal), freq=freq, tz=tz, offset=offset) @classmethod def now(cls, tz=None): @@ -309,11 +326,12 @@ class Timestamp(_Timestamp): def combine(cls, date, time): return cls(datetime.combine(date, time)) - def __new__(cls, - object ts_input=_no_input, object offset=None, tz=None, unit=None, - year=None, month=None, day=None, - hour=None, minute=None, second=None, microsecond=None, - tzinfo=None): + def __new__(cls, object ts_input=_no_input, + object freq=None, tz=None, unit=None, + year=None, month=None, day=None, + hour=None, minute=None, second=None, microsecond=None, + tzinfo=None, + object offset=None): # The parameter list folds together legacy parameter names (the first # four) and positional and keyword parameter names from pydatetime. # @@ -338,15 +356,24 @@ class Timestamp(_Timestamp): cdef _TSObject ts cdef _Timestamp ts_base + if offset is not None: + # deprecate offset kwd in 0.19.0, GH13593 + if freq is not None: + msg = "Can only specify freq or offset, not both" + raise TypeError(msg) + warnings.warn("offset is deprecated. Use freq instead", + FutureWarning) + freq = offset + if ts_input is _no_input: # User passed keyword arguments. return Timestamp(datetime(year, month, day, hour or 0, minute or 0, second or 0, microsecond or 0, tzinfo), tz=tzinfo) - elif is_integer_object(offset): + elif is_integer_object(freq): # User passed positional arguments: # Timestamp(year, month, day[, hour[, minute[, second[, microsecond[, tzinfo]]]]]) - return Timestamp(datetime(ts_input, offset, tz, unit or 0, + return Timestamp(datetime(ts_input, freq, tz, unit or 0, year or 0, month or 0, day or 0, hour), tz=hour) ts = convert_to_tsobject(ts_input, tz, unit, 0, 0) @@ -354,9 +381,9 @@ class Timestamp(_Timestamp): if ts.value == NPY_NAT: return NaT - if util.is_string_object(offset): + if util.is_string_object(freq): from pandas.tseries.frequencies import to_offset - offset = to_offset(offset) + freq = to_offset(freq) # make datetime happy ts_base = _Timestamp.__new__(cls, ts.dts.year, ts.dts.month, @@ -365,7 +392,7 @@ class Timestamp(_Timestamp): # fill out rest of data ts_base.value = ts.value - ts_base.offset = offset + ts_base.freq = freq ts_base.nanosecond = ts.dts.ps / 1000 return ts_base @@ -433,16 +460,18 @@ class Timestamp(_Timestamp): return self.tzinfo @property - def freq(self): - return self.offset + def offset(self): + warnings.warn(".offset is deprecated. Use .freq instead", + FutureWarning) + return self.freq def __setstate__(self, state): self.value = state[0] - self.offset = state[1] + self.freq = state[1] self.tzinfo = state[2] def __reduce__(self): - object_state = self.value, self.offset, self.tzinfo + object_state = self.value, self.freq, self.tzinfo return (Timestamp, object_state) def to_period(self, freq=None): @@ -491,7 +520,7 @@ class Timestamp(_Timestamp): @property def freqstr(self): - return getattr(self.offset, 'freqstr', self.offset) + return getattr(self.freq, 'freqstr', self.freq) @property def is_month_start(self): @@ -539,7 +568,7 @@ class Timestamp(_Timestamp): - 'coerce' will return NaT if the timestamp can not be converted into the specified timezone - .. versionadded:: 0.18.2 + .. versionadded:: 0.19.0 Returns ------- @@ -602,7 +631,7 @@ class Timestamp(_Timestamp): def replace(self, **kwds): return Timestamp(datetime.replace(self, **kwds), - offset=self.offset) + freq=self.freq) def to_pydatetime(self, warn=True): """ @@ -829,15 +858,6 @@ cdef _tz_format(object obj, object zone): except: return ', tz=%s' % zone -def is_timestamp_array(ndarray[object] values): - cdef int i, n = len(values) - if n == 0: - return False - for i in range(n): - if not is_timestamp(values[i]): - return False - return True - cpdef object get_value_box(ndarray arr, object loc): cdef: @@ -911,16 +931,6 @@ cdef inline bint _is_multiple(int64_t us, int64_t mult): return us % mult == 0 -def apply_offset(ndarray[object] values, object offset): - cdef: - Py_ssize_t i, n = len(values) - ndarray[int64_t] new_values - object boxed - - result = np.empty(n, dtype='M8[ns]') - new_values = result.view('i8') - - cdef inline bint _cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1: if op == Py_EQ: return lhs == rhs @@ -953,9 +963,10 @@ cdef str _NDIM_STRING = "ndim" # (see Timestamp class above). This will serve as a C extension type that # shadows the python class, where we do any heavy lifting. cdef class _Timestamp(datetime): + cdef readonly: int64_t value, nanosecond - object offset # frequency reference + object freq # frequency reference def __hash__(_Timestamp self): if self.nanosecond: @@ -1029,9 +1040,9 @@ cdef class _Timestamp(datetime): pass tz = ", tz='{0}'".format(zone) if zone is not None else "" - offset = ", offset='{0}'".format(self.offset.freqstr) if self.offset is not None else "" + freq = ", freq='{0}'".format(self.freq.freqstr) if self.freq is not None else "" - return "Timestamp('{stamp}'{tz}{offset})".format(stamp=stamp, tz=tz, offset=offset) + return "Timestamp('{stamp}'{tz}{freq})".format(stamp=stamp, tz=tz, freq=freq) cdef bint _compare_outside_nanorange(_Timestamp self, datetime other, int op) except -1: @@ -1083,17 +1094,20 @@ cdef class _Timestamp(datetime): if is_timedelta64_object(other): other_int = other.astype('timedelta64[ns]').view('i8') - return Timestamp(self.value + other_int, tz=self.tzinfo, offset=self.offset) + return Timestamp(self.value + other_int, tz=self.tzinfo, freq=self.freq) elif is_integer_object(other): - if self.offset is None: + if self is NaT: + # to be compat with Period + return NaT + elif self.freq is None: raise ValueError("Cannot add integral value to Timestamp " - "without offset.") - return Timestamp((self.offset * other).apply(self), offset=self.offset) + "without freq.") + return Timestamp((self.freq * other).apply(self), freq=self.freq) elif isinstance(other, timedelta) or hasattr(other, 'delta'): nanos = _delta_to_nanoseconds(other) - result = Timestamp(self.value + nanos, tz=self.tzinfo, offset=self.offset) + result = Timestamp(self.value + nanos, tz=self.tzinfo, freq=self.freq) if getattr(other, 'normalize', False): result = Timestamp(normalize_date(result)) return result @@ -1591,7 +1605,9 @@ cpdef inline object maybe_get_tz(object tz): Otherwise, just return tz. """ if isinstance(tz, string_types): - if tz.startswith('dateutil/'): + if tz == 'tzlocal()': + tz = _dateutil_tzlocal() + elif tz.startswith('dateutil/'): zone = tz[9:] tz = _dateutil_gettz(zone) # On Python 3 on Windows, the filename is not always set correctly. @@ -2615,7 +2631,7 @@ class Timedelta(_Timedelta): if value is None: if not len(kwargs): - raise ValueError("cannot construct a TimeDelta without a value/unit or descriptive keywords (days,seconds....)") + raise ValueError("cannot construct a Timedelta without a value/unit or descriptive keywords (days,seconds....)") def _to_py_int_float(v): if is_integer_object(v): @@ -2630,7 +2646,7 @@ class Timedelta(_Timedelta): nano = kwargs.pop('nanoseconds',0) value = convert_to_timedelta64(timedelta(**kwargs),'ns',False) + nano except TypeError as e: - raise ValueError("cannot construct a TimeDelta from the passed arguments, allowed keywords are " + raise ValueError("cannot construct a Timedelta from the passed arguments, allowed keywords are " "[weeks, days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds]") if isinstance(value, Timedelta): @@ -2914,10 +2930,17 @@ class Timedelta(_Timedelta): if not self._validate_ops_compat(other): return NotImplemented - other = Timedelta(other) if other is NaT: return NaT + + try: + other = Timedelta(other) + except ValueError: + # failed to parse as timedelta + return NotImplemented + return Timedelta(op(self.value, other.value), unit='ns') + f.__name__ = name return f @@ -3754,11 +3777,11 @@ except: def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): cdef: - ndarray[int64_t] utc_dates, tt, result, trans, deltas, posn + ndarray[int64_t] utc_dates, tt, result, trans, deltas Py_ssize_t i, j, pos, n = len(vals) - int64_t v, offset + ndarray[Py_ssize_t] posn + int64_t v, offset, delta pandas_datetimestruct dts - Py_ssize_t trans_len if not have_pytz: import pytz @@ -3767,7 +3790,6 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): return np.array([], dtype=np.int64) # Convert to UTC - if _get_zone(tz1) != 'UTC': utc_dates = np.empty(n, dtype=np.int64) if _is_tzlocal(tz1): @@ -3790,7 +3812,6 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): if not len(tt): return vals - trans_len = len(trans) posn = trans.searchsorted(tt, side='right') j = 0 for i in range(n): @@ -3822,22 +3843,23 @@ def tz_convert(ndarray[int64_t] vals, object tz1, object tz2): dts.min, dts.sec, dts.us, tz2) delta = int(total_seconds(_get_utcoffset(tz2, dt))) * 1000000000 result[i] = v + delta - return result + return result # Convert UTC to other timezone trans, deltas, typ = _get_dst_info(tz2) - trans_len = len(trans) - - # if all NaT, return all NaT - if (utc_dates==NPY_NAT).all(): - return utc_dates # use first non-NaT element # if all-NaT, return all-NaT if (result==NPY_NAT).all(): return result - posn = trans.searchsorted(utc_dates[utc_dates!=NPY_NAT], side='right') + # if all NaT, return all NaT + tt = utc_dates[utc_dates!=NPY_NAT] + if not len(tt): + return utc_dates + + posn = trans.searchsorted(tt, side='right') + j = 0 for i in range(n): v = utc_dates[i] diff --git a/pandas/types/api.py b/pandas/types/api.py index 721d8d29bba8b..2d68e041f632e 100644 --- a/pandas/types/api.py +++ b/pandas/types/api.py @@ -1,75 +1,54 @@ # flake8: noqa import numpy as np -from pandas.compat import string_types -from .dtypes import (CategoricalDtype, CategoricalDtypeType, - DatetimeTZDtype, DatetimeTZDtypeType) -from .generic import (ABCIndex, ABCInt64Index, ABCRangeIndex, - ABCFloat64Index, ABCMultiIndex, - ABCDatetimeIndex, - ABCTimedeltaIndex, ABCPeriodIndex, - ABCCategoricalIndex, - ABCIndexClass, - ABCSeries, ABCDataFrame, ABCPanel, - ABCSparseSeries, ABCSparseArray, - ABCCategorical, ABCPeriod, - ABCGeneric) - -def pandas_dtype(dtype): - """ - Converts input into a pandas only dtype object or a numpy dtype object. - - Parameters - ---------- - dtype : object to be converted - - Returns - ------- - np.dtype or a pandas dtype - """ - if isinstance(dtype, DatetimeTZDtype): - return dtype - elif isinstance(dtype, CategoricalDtype): - return dtype - elif isinstance(dtype, string_types): - try: - return DatetimeTZDtype.construct_from_string(dtype) - except TypeError: - pass - - try: - return CategoricalDtype.construct_from_string(dtype) - except TypeError: - pass - - return np.dtype(dtype) - -def na_value_for_dtype(dtype): - """ - Return a dtype compat na value - - Parameters - ---------- - dtype : string / dtype - - Returns - ------- - dtype compat na value - """ - - from pandas.core import common as com - from pandas import NaT - dtype = pandas_dtype(dtype) - - if (com.is_datetime64_dtype(dtype) or - com.is_datetime64tz_dtype(dtype) or - com.is_timedelta64_dtype(dtype)): - return NaT - elif com.is_float_dtype(dtype): - return np.nan - elif com.is_integer_dtype(dtype): - return 0 - elif com.is_bool_dtype(dtype): - return False - return np.nan +from .common import (pandas_dtype, + is_dtype_equal, + is_extension_type, + + # categorical + is_categorical, + is_categorical_dtype, + + # datetimelike + is_datetimetz, + is_datetime64_dtype, + is_datetime64tz_dtype, + is_datetime64_any_dtype, + is_datetime64_ns_dtype, + is_timedelta64_dtype, + is_timedelta64_ns_dtype, + + # string-like + is_string_dtype, + is_object_dtype, + + # sparse + is_sparse, + + # numeric types + is_scalar, + is_sparse, + is_bool, + is_integer, + is_float, + is_complex, + is_number, + is_any_int_dtype, + is_integer_dtype, + is_int64_dtype, + is_numeric_dtype, + is_float_dtype, + is_floating_dtype, + is_bool_dtype, + is_complex_dtype, + + # like + is_re, + is_re_compilable, + is_dict_like, + is_iterator, + is_list_like, + is_hashable, + is_named_tuple, + is_sequence) diff --git a/pandas/types/cast.py b/pandas/types/cast.py new file mode 100644 index 0000000000000..ca23d8d26a426 --- /dev/null +++ b/pandas/types/cast.py @@ -0,0 +1,860 @@ +""" routings for casting """ + +from datetime import datetime, timedelta +import numpy as np +from pandas import lib, tslib +from pandas.tslib import iNaT +from pandas.compat import string_types, text_type, PY3 +from .common import (_ensure_object, is_bool, is_integer, is_float, + is_complex, is_datetimetz, is_categorical_dtype, + is_extension_type, is_object_dtype, + is_datetime64tz_dtype, is_datetime64_dtype, + is_timedelta64_dtype, is_dtype_equal, + is_float_dtype, is_complex_dtype, + is_integer_dtype, is_datetime_or_timedelta_dtype, + is_scalar, + _string_dtypes, + _coerce_to_dtype, + _ensure_int8, _ensure_int16, + _ensure_int32, _ensure_int64, + _NS_DTYPE, _TD_DTYPE, _INT64_DTYPE, + _DATELIKE_DTYPES, _POSSIBLY_CAST_DTYPES) +from .generic import ABCDatetimeIndex, ABCPeriodIndex, ABCSeries +from .missing import isnull, notnull +from .inference import is_list_like + +_int8_max = np.iinfo(np.int8).max +_int16_max = np.iinfo(np.int16).max +_int32_max = np.iinfo(np.int32).max +_int64_max = np.iinfo(np.int64).max + + +def _possibly_convert_platform(values): + """ try to do platform conversion, allow ndarray or list here """ + + if isinstance(values, (list, tuple)): + values = lib.list_to_object_array(list(values)) + if getattr(values, 'dtype', None) == np.object_: + if hasattr(values, '_values'): + values = values._values + values = lib.maybe_convert_objects(values) + + return values + + +def _possibly_downcast_to_dtype(result, dtype): + """ try to cast to the specified dtype (e.g. convert back to bool/int + or could be an astype of float64->float32 + """ + + if is_scalar(result): + return result + + def trans(x): + return x + + if isinstance(dtype, string_types): + if dtype == 'infer': + inferred_type = lib.infer_dtype(_ensure_object(result.ravel())) + if inferred_type == 'boolean': + dtype = 'bool' + elif inferred_type == 'integer': + dtype = 'int64' + elif inferred_type == 'datetime64': + dtype = 'datetime64[ns]' + elif inferred_type == 'timedelta64': + dtype = 'timedelta64[ns]' + + # try to upcast here + elif inferred_type == 'floating': + dtype = 'int64' + if issubclass(result.dtype.type, np.number): + + def trans(x): # noqa + return x.round() + else: + dtype = 'object' + + if isinstance(dtype, string_types): + dtype = np.dtype(dtype) + + try: + + # don't allow upcasts here (except if empty) + if dtype.kind == result.dtype.kind: + if (result.dtype.itemsize <= dtype.itemsize and + np.prod(result.shape)): + return result + + if issubclass(dtype.type, np.floating): + return result.astype(dtype) + elif dtype == np.bool_ or issubclass(dtype.type, np.integer): + + # if we don't have any elements, just astype it + if not np.prod(result.shape): + return trans(result).astype(dtype) + + # do a test on the first element, if it fails then we are done + r = result.ravel() + arr = np.array([r[0]]) + + # if we have any nulls, then we are done + if isnull(arr).any() or not np.allclose(arr, + trans(arr).astype(dtype)): + return result + + # a comparable, e.g. a Decimal may slip in here + elif not isinstance(r[0], (np.integer, np.floating, np.bool, int, + float, bool)): + return result + + if (issubclass(result.dtype.type, (np.object_, np.number)) and + notnull(result).all()): + new_result = trans(result).astype(dtype) + try: + if np.allclose(new_result, result): + return new_result + except: + + # comparison of an object dtype with a number type could + # hit here + if (new_result == result).all(): + return new_result + + # a datetimelike + elif dtype.kind in ['M', 'm'] and result.dtype.kind in ['i']: + try: + result = result.astype(dtype) + except: + if dtype.tz: + # convert to datetime and change timezone + from pandas import to_datetime + result = to_datetime(result).tz_localize(dtype.tz) + + except: + pass + + return result + + +def _maybe_upcast_putmask(result, mask, other): + """ + A safe version of putmask that potentially upcasts the result + + Parameters + ---------- + result : ndarray + The destination array. This will be mutated in-place if no upcasting is + necessary. + mask : boolean ndarray + other : ndarray or scalar + The source array or value + + Returns + ------- + result : ndarray + changed : boolean + Set to true if the result array was upcasted + """ + + if mask.any(): + # Two conversions for date-like dtypes that can't be done automatically + # in np.place: + # NaN -> NaT + # integer or integer array -> date-like array + if result.dtype in _DATELIKE_DTYPES: + if is_scalar(other): + if isnull(other): + other = result.dtype.type('nat') + elif is_integer(other): + other = np.array(other, dtype=result.dtype) + elif is_integer_dtype(other): + other = np.array(other, dtype=result.dtype) + + def changeit(): + + # try to directly set by expanding our array to full + # length of the boolean + try: + om = other[mask] + om_at = om.astype(result.dtype) + if (om == om_at).all(): + new_result = result.values.copy() + new_result[mask] = om_at + result[:] = new_result + return result, False + except: + pass + + # we are forced to change the dtype of the result as the input + # isn't compatible + r, _ = _maybe_upcast(result, fill_value=other, copy=True) + np.place(r, mask, other) + + return r, True + + # we want to decide whether place will work + # if we have nans in the False portion of our mask then we need to + # upcast (possibly), otherwise we DON't want to upcast (e.g. if we + # have values, say integers, in the success portion then it's ok to not + # upcast) + new_dtype, _ = _maybe_promote(result.dtype, other) + if new_dtype != result.dtype: + + # we have a scalar or len 0 ndarray + # and its nan and we are changing some values + if (is_scalar(other) or + (isinstance(other, np.ndarray) and other.ndim < 1)): + if isnull(other): + return changeit() + + # we have an ndarray and the masking has nans in it + else: + + if isnull(other[mask]).any(): + return changeit() + + try: + np.place(result, mask, other) + except: + return changeit() + + return result, False + + +def _maybe_promote(dtype, fill_value=np.nan): + + # if we passed an array here, determine the fill value by dtype + if isinstance(fill_value, np.ndarray): + if issubclass(fill_value.dtype.type, (np.datetime64, np.timedelta64)): + fill_value = iNaT + else: + + # we need to change to object type as our + # fill_value is of object type + if fill_value.dtype == np.object_: + dtype = np.dtype(np.object_) + fill_value = np.nan + + # returns tuple of (dtype, fill_value) + if issubclass(dtype.type, (np.datetime64, np.timedelta64)): + # for now: refuse to upcast datetime64 + # (this is because datetime64 will not implicitly upconvert + # to object correctly as of numpy 1.6.1) + if isnull(fill_value): + fill_value = iNaT + else: + if issubclass(dtype.type, np.datetime64): + try: + fill_value = lib.Timestamp(fill_value).value + except: + # the proper thing to do here would probably be to upcast + # to object (but numpy 1.6.1 doesn't do this properly) + fill_value = iNaT + elif issubclass(dtype.type, np.timedelta64): + try: + fill_value = lib.Timedelta(fill_value).value + except: + # as for datetimes, cannot upcast to object + fill_value = iNaT + else: + fill_value = iNaT + elif is_datetimetz(dtype): + if isnull(fill_value): + fill_value = iNaT + elif is_float(fill_value): + if issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif issubclass(dtype.type, np.integer): + dtype = np.float64 + elif is_bool(fill_value): + if not issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif is_integer(fill_value): + if issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif issubclass(dtype.type, np.integer): + # upcast to prevent overflow + arr = np.asarray(fill_value) + if arr != arr.astype(dtype): + dtype = arr.dtype + elif is_complex(fill_value): + if issubclass(dtype.type, np.bool_): + dtype = np.object_ + elif issubclass(dtype.type, (np.integer, np.floating)): + dtype = np.complex128 + elif fill_value is None: + if is_float_dtype(dtype) or is_complex_dtype(dtype): + fill_value = np.nan + elif is_integer_dtype(dtype): + dtype = np.float64 + fill_value = np.nan + elif is_datetime_or_timedelta_dtype(dtype): + fill_value = iNaT + else: + dtype = np.object_ + else: + dtype = np.object_ + + # in case we have a string that looked like a number + if is_categorical_dtype(dtype): + pass + elif is_datetimetz(dtype): + pass + elif issubclass(np.dtype(dtype).type, string_types): + dtype = np.object_ + + return dtype, fill_value + + +def _infer_dtype_from_scalar(val): + """ interpret the dtype from a scalar """ + + dtype = np.object_ + + # a 1-element ndarray + if isinstance(val, np.ndarray): + if val.ndim != 0: + raise ValueError( + "invalid ndarray passed to _infer_dtype_from_scalar") + + dtype = val.dtype + val = val.item() + + elif isinstance(val, string_types): + + # If we create an empty array using a string to infer + # the dtype, NumPy will only allocate one character per entry + # so this is kind of bad. Alternately we could use np.repeat + # instead of np.empty (but then you still don't want things + # coming out as np.str_! + + dtype = np.object_ + + elif isinstance(val, (np.datetime64, + datetime)) and getattr(val, 'tzinfo', None) is None: + val = lib.Timestamp(val).value + dtype = np.dtype('M8[ns]') + + elif isinstance(val, (np.timedelta64, timedelta)): + val = lib.Timedelta(val).value + dtype = np.dtype('m8[ns]') + + elif is_bool(val): + dtype = np.bool_ + + elif is_integer(val): + if isinstance(val, np.integer): + dtype = type(val) + else: + dtype = np.int64 + + elif is_float(val): + if isinstance(val, np.floating): + dtype = type(val) + else: + dtype = np.float64 + + elif is_complex(val): + dtype = np.complex_ + + return dtype, val + + +def _maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): + """ provide explict type promotion and coercion + + Parameters + ---------- + values : the ndarray that we want to maybe upcast + fill_value : what we want to fill with + dtype : if None, then use the dtype of the values, else coerce to this type + copy : if True always make a copy even if no upcast is required + """ + + if is_extension_type(values): + if copy: + values = values.copy() + else: + if dtype is None: + dtype = values.dtype + new_dtype, fill_value = _maybe_promote(dtype, fill_value) + if new_dtype != values.dtype: + values = values.astype(new_dtype) + elif copy: + values = values.copy() + + return values, fill_value + + +def _possibly_cast_item(obj, item, dtype): + chunk = obj[item] + + if chunk.values.dtype != dtype: + if dtype in (np.object_, np.bool_): + obj[item] = chunk.astype(np.object_) + elif not issubclass(dtype, (np.integer, np.bool_)): # pragma: no cover + raise ValueError("Unexpected dtype encountered: %s" % dtype) + + +def _invalidate_string_dtypes(dtype_set): + """Change string like dtypes to object for + ``DataFrame.select_dtypes()``. + """ + non_string_dtypes = dtype_set - _string_dtypes + if non_string_dtypes != dtype_set: + raise TypeError("string dtypes are not allowed, use 'object' instead") + + +def _maybe_convert_string_to_object(values): + """ + + Convert string-like and string-like array to convert object dtype. + This is to avoid numpy to handle the array as str dtype. + """ + if isinstance(values, string_types): + values = np.array([values], dtype=object) + elif (isinstance(values, np.ndarray) and + issubclass(values.dtype.type, (np.string_, np.unicode_))): + values = values.astype(object) + return values + + +def _maybe_convert_scalar(values): + """ + Convert a python scalar to the appropriate numpy dtype if possible + This avoids numpy directly converting according to platform preferences + """ + if is_scalar(values): + dtype, values = _infer_dtype_from_scalar(values) + try: + values = dtype(values) + except TypeError: + pass + return values + + +def _coerce_indexer_dtype(indexer, categories): + """ coerce the indexer input array to the smallest dtype possible """ + l = len(categories) + if l < _int8_max: + return _ensure_int8(indexer) + elif l < _int16_max: + return _ensure_int16(indexer) + elif l < _int32_max: + return _ensure_int32(indexer) + return _ensure_int64(indexer) + + +def _coerce_to_dtypes(result, dtypes): + """ + given a dtypes and a result set, coerce the result elements to the + dtypes + """ + if len(result) != len(dtypes): + raise AssertionError("_coerce_to_dtypes requires equal len arrays") + + from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type + + def conv(r, dtype): + try: + if isnull(r): + pass + elif dtype == _NS_DTYPE: + r = lib.Timestamp(r) + elif dtype == _TD_DTYPE: + r = _coerce_scalar_to_timedelta_type(r) + elif dtype == np.bool_: + # messy. non 0/1 integers do not get converted. + if is_integer(r) and r not in [0, 1]: + return int(r) + r = bool(r) + elif dtype.kind == 'f': + r = float(r) + elif dtype.kind == 'i': + r = int(r) + except: + pass + + return r + + return [conv(r, dtype) for r, dtype in zip(result, dtypes)] + + +def _astype_nansafe(arr, dtype, copy=True): + """ return a view if copy is False, but + need to be very careful as the result shape could change! """ + if not isinstance(dtype, np.dtype): + dtype = _coerce_to_dtype(dtype) + + if issubclass(dtype.type, text_type): + # in Py3 that's str, in Py2 that's unicode + return lib.astype_unicode(arr.ravel()).reshape(arr.shape) + elif issubclass(dtype.type, string_types): + return lib.astype_str(arr.ravel()).reshape(arr.shape) + elif is_datetime64_dtype(arr): + if dtype == object: + return tslib.ints_to_pydatetime(arr.view(np.int64)) + elif dtype == np.int64: + return arr.view(dtype) + elif dtype != _NS_DTYPE: + raise TypeError("cannot astype a datetimelike from [%s] to [%s]" % + (arr.dtype, dtype)) + return arr.astype(_NS_DTYPE) + elif is_timedelta64_dtype(arr): + if dtype == np.int64: + return arr.view(dtype) + elif dtype == object: + return tslib.ints_to_pytimedelta(arr.view(np.int64)) + + # in py3, timedelta64[ns] are int64 + elif ((PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE]) or + (not PY3 and dtype != _TD_DTYPE)): + + # allow frequency conversions + if dtype.kind == 'm': + mask = isnull(arr) + result = arr.astype(dtype).astype(np.float64) + result[mask] = np.nan + return result + + raise TypeError("cannot astype a timedelta from [%s] to [%s]" % + (arr.dtype, dtype)) + + return arr.astype(_TD_DTYPE) + elif (np.issubdtype(arr.dtype, np.floating) and + np.issubdtype(dtype, np.integer)): + + if np.isnan(arr).any(): + raise ValueError('Cannot convert NA to integer') + elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer): + # work around NumPy brokenness, #1987 + return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape) + + if copy: + return arr.astype(dtype) + return arr.view(dtype) + + +def _possibly_convert_objects(values, convert_dates=True, convert_numeric=True, + convert_timedeltas=True, copy=True): + """ if we have an object dtype, try to coerce dates and/or numbers """ + + # if we have passed in a list or scalar + if isinstance(values, (list, tuple)): + values = np.array(values, dtype=np.object_) + if not hasattr(values, 'dtype'): + values = np.array([values], dtype=np.object_) + + # convert dates + if convert_dates and values.dtype == np.object_: + + # we take an aggressive stance and convert to datetime64[ns] + if convert_dates == 'coerce': + new_values = _possibly_cast_to_datetime(values, 'M8[ns]', + errors='coerce') + + # if we are all nans then leave me alone + if not isnull(new_values).all(): + values = new_values + + else: + values = lib.maybe_convert_objects(values, + convert_datetime=convert_dates) + + # convert timedeltas + if convert_timedeltas and values.dtype == np.object_: + + if convert_timedeltas == 'coerce': + from pandas.tseries.timedeltas import to_timedelta + new_values = to_timedelta(values, coerce=True) + + # if we are all nans then leave me alone + if not isnull(new_values).all(): + values = new_values + + else: + values = lib.maybe_convert_objects( + values, convert_timedelta=convert_timedeltas) + + # convert to numeric + if values.dtype == np.object_: + if convert_numeric: + try: + new_values = lib.maybe_convert_numeric(values, set(), + coerce_numeric=True) + + # if we are all nans then leave me alone + if not isnull(new_values).all(): + values = new_values + + except: + pass + else: + # soft-conversion + values = lib.maybe_convert_objects(values) + + values = values.copy() if copy else values + + return values + + +def _soft_convert_objects(values, datetime=True, numeric=True, timedelta=True, + coerce=False, copy=True): + """ if we have an object dtype, try to coerce dates and/or numbers """ + + conversion_count = sum((datetime, numeric, timedelta)) + if conversion_count == 0: + raise ValueError('At least one of datetime, numeric or timedelta must ' + 'be True.') + elif conversion_count > 1 and coerce: + raise ValueError("Only one of 'datetime', 'numeric' or " + "'timedelta' can be True when when coerce=True.") + + if isinstance(values, (list, tuple)): + # List or scalar + values = np.array(values, dtype=np.object_) + elif not hasattr(values, 'dtype'): + values = np.array([values], dtype=np.object_) + elif not is_object_dtype(values.dtype): + # If not object, do not attempt conversion + values = values.copy() if copy else values + return values + + # If 1 flag is coerce, ensure 2 others are False + if coerce: + # Immediate return if coerce + if datetime: + from pandas import to_datetime + return to_datetime(values, errors='coerce', box=False) + elif timedelta: + from pandas import to_timedelta + return to_timedelta(values, errors='coerce', box=False) + elif numeric: + from pandas import to_numeric + return to_numeric(values, errors='coerce') + + # Soft conversions + if datetime: + values = lib.maybe_convert_objects(values, convert_datetime=datetime) + + if timedelta and is_object_dtype(values.dtype): + # Object check to ensure only run if previous did not convert + values = lib.maybe_convert_objects(values, convert_timedelta=timedelta) + + if numeric and is_object_dtype(values.dtype): + try: + converted = lib.maybe_convert_numeric(values, set(), + coerce_numeric=True) + # If all NaNs, then do not-alter + values = converted if not isnull(converted).all() else values + values = values.copy() if copy else values + except: + pass + + return values + + +def _possibly_castable(arr): + # return False to force a non-fastpath + + # check datetime64[ns]/timedelta64[ns] are valid + # otherwise try to coerce + kind = arr.dtype.kind + if kind == 'M' or kind == 'm': + return arr.dtype in _DATELIKE_DTYPES + + return arr.dtype.name not in _POSSIBLY_CAST_DTYPES + + +def _possibly_infer_to_datetimelike(value, convert_dates=False): + """ + we might have a array (or single object) that is datetime like, + and no dtype is passed don't change the value unless we find a + datetime/timedelta set + + this is pretty strict in that a datetime/timedelta is REQUIRED + in addition to possible nulls/string likes + + ONLY strings are NOT datetimelike + + Parameters + ---------- + value : np.array / Series / Index / list-like + convert_dates : boolean, default False + if True try really hard to convert dates (such as datetime.date), other + leave inferred dtype 'date' alone + + """ + + if isinstance(value, (ABCDatetimeIndex, ABCPeriodIndex)): + return value + elif isinstance(value, ABCSeries): + if isinstance(value._values, ABCDatetimeIndex): + return value._values + + v = value + + if not is_list_like(v): + v = [v] + v = np.array(v, copy=False) + shape = v.shape + if not v.ndim == 1: + v = v.ravel() + + if len(v): + + def _try_datetime(v): + # safe coerce to datetime64 + try: + v = tslib.array_to_datetime(v, errors='raise') + except ValueError: + + # we might have a sequence of the same-datetimes with tz's + # if so coerce to a DatetimeIndex; if they are not the same, + # then these stay as object dtype + try: + from pandas import to_datetime + return to_datetime(v) + except: + pass + + except: + pass + + return v.reshape(shape) + + def _try_timedelta(v): + # safe coerce to timedelta64 + + # will try first with a string & object conversion + from pandas import to_timedelta + try: + return to_timedelta(v)._values.reshape(shape) + except: + return v + + # do a quick inference for perf + sample = v[:min(3, len(v))] + inferred_type = lib.infer_dtype(sample) + + if (inferred_type in ['datetime', 'datetime64'] or + (convert_dates and inferred_type in ['date'])): + value = _try_datetime(v) + elif inferred_type in ['timedelta', 'timedelta64']: + value = _try_timedelta(v) + + # It's possible to have nulls intermixed within the datetime or + # timedelta. These will in general have an inferred_type of 'mixed', + # so have to try both datetime and timedelta. + + # try timedelta first to avoid spurious datetime conversions + # e.g. '00:00:01' is a timedelta but technically is also a datetime + elif inferred_type in ['mixed']: + + if lib.is_possible_datetimelike_array(_ensure_object(v)): + value = _try_timedelta(v) + if lib.infer_dtype(value) in ['mixed']: + value = _try_datetime(v) + + return value + + +def _possibly_cast_to_datetime(value, dtype, errors='raise'): + """ try to cast the array/value to a datetimelike dtype, converting float + nan to iNaT + """ + from pandas.tseries.timedeltas import to_timedelta + from pandas.tseries.tools import to_datetime + + if dtype is not None: + if isinstance(dtype, string_types): + dtype = np.dtype(dtype) + + is_datetime64 = is_datetime64_dtype(dtype) + is_datetime64tz = is_datetime64tz_dtype(dtype) + is_timedelta64 = is_timedelta64_dtype(dtype) + + if is_datetime64 or is_datetime64tz or is_timedelta64: + + # force the dtype if needed + if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE): + if dtype.name == 'datetime64[ns]': + dtype = _NS_DTYPE + else: + raise TypeError("cannot convert datetimelike to " + "dtype [%s]" % dtype) + elif is_datetime64tz: + + # our NaT doesn't support tz's + # this will coerce to DatetimeIndex with + # a matching dtype below + if is_scalar(value) and isnull(value): + value = [value] + + elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE): + if dtype.name == 'timedelta64[ns]': + dtype = _TD_DTYPE + else: + raise TypeError("cannot convert timedeltalike to " + "dtype [%s]" % dtype) + + if is_scalar(value): + if value == tslib.iNaT or isnull(value): + value = tslib.iNaT + else: + value = np.array(value, copy=False) + + # have a scalar array-like (e.g. NaT) + if value.ndim == 0: + value = tslib.iNaT + + # we have an array of datetime or timedeltas & nulls + elif np.prod(value.shape) or not is_dtype_equal(value.dtype, + dtype): + try: + if is_datetime64: + value = to_datetime(value, errors=errors)._values + elif is_datetime64tz: + # input has to be UTC at this point, so just + # localize + value = to_datetime( + value, + errors=errors).tz_localize(dtype.tz) + elif is_timedelta64: + value = to_timedelta(value, errors=errors)._values + except (AttributeError, ValueError, TypeError): + pass + + # coerce datetimelike to object + elif is_datetime64_dtype(value) and not is_datetime64_dtype(dtype): + if is_object_dtype(dtype): + ints = np.asarray(value).view('i8') + return tslib.ints_to_pydatetime(ints) + + # we have a non-castable dtype that was passed + raise TypeError('Cannot cast datetime64 to %s' % dtype) + + else: + + is_array = isinstance(value, np.ndarray) + + # catch a datetime/timedelta that is not of ns variety + # and no coercion specified + if is_array and value.dtype.kind in ['M', 'm']: + dtype = value.dtype + + if dtype.kind == 'M' and dtype != _NS_DTYPE: + value = value.astype(_NS_DTYPE) + + elif dtype.kind == 'm' and dtype != _TD_DTYPE: + value = to_timedelta(value) + + # only do this if we have an array and the dtype of the array is not + # setup already we are not an integer/object, so don't bother with this + # conversion + elif not (is_array and not (issubclass(value.dtype.type, np.integer) or + value.dtype == np.object_)): + value = _possibly_infer_to_datetimelike(value) + + return value diff --git a/pandas/types/common.py b/pandas/types/common.py new file mode 100644 index 0000000000000..9d0ccaac843ef --- /dev/null +++ b/pandas/types/common.py @@ -0,0 +1,448 @@ +""" common type operations """ + +import numpy as np +from pandas.compat import string_types, text_type, binary_type +from pandas import lib, algos +from .dtypes import (CategoricalDtype, CategoricalDtypeType, + DatetimeTZDtype, DatetimeTZDtypeType, + ExtensionDtype) +from .generic import (ABCCategorical, ABCPeriodIndex, + ABCDatetimeIndex, ABCSeries, + ABCSparseArray, ABCSparseSeries) +from .inference import is_integer, is_string_like +from .inference import * # noqa + + +_POSSIBLY_CAST_DTYPES = set([np.dtype(t).name + for t in ['O', 'int8', 'uint8', 'int16', 'uint16', + 'int32', 'uint32', 'int64', 'uint64']]) + +_NS_DTYPE = np.dtype('M8[ns]') +_TD_DTYPE = np.dtype('m8[ns]') +_INT64_DTYPE = np.dtype(np.int64) +_DATELIKE_DTYPES = set([np.dtype(t) + for t in ['M8[ns]', '<M8[ns]', '>M8[ns]', + 'm8[ns]', '<m8[ns]', '>m8[ns]']]) + +_ensure_float64 = algos.ensure_float64 +_ensure_float32 = algos.ensure_float32 + + +def _ensure_float(arr): + if issubclass(arr.dtype.type, (np.integer, np.bool_)): + arr = arr.astype(float) + return arr + +_ensure_int64 = algos.ensure_int64 +_ensure_int32 = algos.ensure_int32 +_ensure_int16 = algos.ensure_int16 +_ensure_int8 = algos.ensure_int8 +_ensure_platform_int = algos.ensure_platform_int +_ensure_object = algos.ensure_object + + +def is_object_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.object_) + + +def is_sparse(array): + """ return if we are a sparse array """ + return isinstance(array, (ABCSparseArray, ABCSparseSeries)) + + +def is_categorical(array): + """ return if we are a categorical possibility """ + return isinstance(array, ABCCategorical) or is_categorical_dtype(array) + + +def is_datetimetz(array): + """ return if we are a datetime with tz array """ + return ((isinstance(array, ABCDatetimeIndex) and + getattr(array, 'tz', None) is not None) or + is_datetime64tz_dtype(array)) + + +def is_datetime64_dtype(arr_or_dtype): + try: + tipo = _get_dtype_type(arr_or_dtype) + except TypeError: + return False + return issubclass(tipo, np.datetime64) + + +def is_datetime64tz_dtype(arr_or_dtype): + return DatetimeTZDtype.is_dtype(arr_or_dtype) + + +def is_timedelta64_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.timedelta64) + + +def is_categorical_dtype(arr_or_dtype): + return CategoricalDtype.is_dtype(arr_or_dtype) + + +def is_string_dtype(arr_or_dtype): + dtype = _get_dtype(arr_or_dtype) + return dtype.kind in ('O', 'S', 'U') + + +def is_period_arraylike(arr): + """ return if we are period arraylike / PeriodIndex """ + if isinstance(arr, ABCPeriodIndex): + return True + elif isinstance(arr, (np.ndarray, ABCSeries)): + return arr.dtype == object and lib.infer_dtype(arr) == 'period' + return getattr(arr, 'inferred_type', None) == 'period' + + +def is_datetime_arraylike(arr): + """ return if we are datetime arraylike / DatetimeIndex """ + if isinstance(arr, ABCDatetimeIndex): + return True + elif isinstance(arr, (np.ndarray, ABCSeries)): + return arr.dtype == object and lib.infer_dtype(arr) == 'datetime' + return getattr(arr, 'inferred_type', None) == 'datetime' + + +def is_datetimelike(arr): + return (arr.dtype in _DATELIKE_DTYPES or + isinstance(arr, ABCPeriodIndex) or + is_datetimetz(arr)) + + +def is_dtype_equal(source, target): + """ return a boolean if the dtypes are equal """ + try: + source = _get_dtype(source) + target = _get_dtype(target) + return source == target + except (TypeError, AttributeError): + + # invalid comparison + # object == category will hit this + return False + + +def is_any_int_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.integer) + + +def is_integer_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return (issubclass(tipo, np.integer) and + not issubclass(tipo, (np.datetime64, np.timedelta64))) + + +def is_int64_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.int64) + + +def is_int_or_datetime_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return (issubclass(tipo, np.integer) or + issubclass(tipo, (np.datetime64, np.timedelta64))) + + +def is_datetime64_any_dtype(arr_or_dtype): + return (is_datetime64_dtype(arr_or_dtype) or + is_datetime64tz_dtype(arr_or_dtype)) + + +def is_datetime64_ns_dtype(arr_or_dtype): + try: + tipo = _get_dtype(arr_or_dtype) + except TypeError: + return False + return tipo == _NS_DTYPE + + +def is_timedelta64_ns_dtype(arr_or_dtype): + tipo = _get_dtype(arr_or_dtype) + return tipo == _TD_DTYPE + + +def is_datetime_or_timedelta_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, (np.datetime64, np.timedelta64)) + + +def is_numeric_v_string_like(a, b): + """ + numpy doesn't like to compare numeric arrays vs scalar string-likes + + return a boolean result if this is the case for a,b or b,a + + """ + is_a_array = isinstance(a, np.ndarray) + is_b_array = isinstance(b, np.ndarray) + + is_a_numeric_array = is_a_array and is_numeric_dtype(a) + is_b_numeric_array = is_b_array and is_numeric_dtype(b) + is_a_string_array = is_a_array and is_string_like_dtype(a) + is_b_string_array = is_b_array and is_string_like_dtype(b) + + is_a_scalar_string_like = not is_a_array and is_string_like(a) + is_b_scalar_string_like = not is_b_array and is_string_like(b) + + return ((is_a_numeric_array and is_b_scalar_string_like) or + (is_b_numeric_array and is_a_scalar_string_like) or + (is_a_numeric_array and is_b_string_array) or + (is_b_numeric_array and is_a_string_array)) + + +def is_datetimelike_v_numeric(a, b): + # return if we have an i8 convertible and numeric comparison + if not hasattr(a, 'dtype'): + a = np.asarray(a) + if not hasattr(b, 'dtype'): + b = np.asarray(b) + + def is_numeric(x): + return is_integer_dtype(x) or is_float_dtype(x) + + is_datetimelike = needs_i8_conversion + return ((is_datetimelike(a) and is_numeric(b)) or + (is_datetimelike(b) and is_numeric(a))) + + +def is_datetimelike_v_object(a, b): + # return if we have an i8 convertible and object comparsion + if not hasattr(a, 'dtype'): + a = np.asarray(a) + if not hasattr(b, 'dtype'): + b = np.asarray(b) + + def f(x): + return is_object_dtype(x) + + def is_object(x): + return is_integer_dtype(x) or is_float_dtype(x) + + is_datetimelike = needs_i8_conversion + return ((is_datetimelike(a) and is_object(b)) or + (is_datetimelike(b) and is_object(a))) + + +def needs_i8_conversion(arr_or_dtype): + return (is_datetime_or_timedelta_dtype(arr_or_dtype) or + is_datetime64tz_dtype(arr_or_dtype)) + + +def is_numeric_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return (issubclass(tipo, (np.number, np.bool_)) and + not issubclass(tipo, (np.datetime64, np.timedelta64))) + + +def is_string_like_dtype(arr_or_dtype): + # exclude object as its a mixed dtype + dtype = _get_dtype(arr_or_dtype) + return dtype.kind in ('S', 'U') + + +def is_float_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.floating) + + +def is_floating_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return isinstance(tipo, np.floating) + + +def is_bool_dtype(arr_or_dtype): + try: + tipo = _get_dtype_type(arr_or_dtype) + except ValueError: + # this isn't even a dtype + return False + return issubclass(tipo, np.bool_) + + +def is_extension_type(value): + """ + if we are a klass that is preserved by the internals + these are internal klasses that we represent (and don't use a np.array) + """ + if is_categorical(value): + return True + elif is_sparse(value): + return True + elif is_datetimetz(value): + return True + return False + + +def is_complex_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return issubclass(tipo, np.complexfloating) + + +def _coerce_to_dtype(dtype): + """ coerce a string / np.dtype to a dtype """ + if is_categorical_dtype(dtype): + dtype = CategoricalDtype() + elif is_datetime64tz_dtype(dtype): + dtype = DatetimeTZDtype(dtype) + else: + dtype = np.dtype(dtype) + return dtype + + +def _get_dtype(arr_or_dtype): + if isinstance(arr_or_dtype, np.dtype): + return arr_or_dtype + elif isinstance(arr_or_dtype, type): + return np.dtype(arr_or_dtype) + elif isinstance(arr_or_dtype, CategoricalDtype): + return arr_or_dtype + elif isinstance(arr_or_dtype, DatetimeTZDtype): + return arr_or_dtype + elif isinstance(arr_or_dtype, string_types): + if is_categorical_dtype(arr_or_dtype): + return CategoricalDtype.construct_from_string(arr_or_dtype) + elif is_datetime64tz_dtype(arr_or_dtype): + return DatetimeTZDtype.construct_from_string(arr_or_dtype) + + if hasattr(arr_or_dtype, 'dtype'): + arr_or_dtype = arr_or_dtype.dtype + return np.dtype(arr_or_dtype) + + +def _get_dtype_type(arr_or_dtype): + if isinstance(arr_or_dtype, np.dtype): + return arr_or_dtype.type + elif isinstance(arr_or_dtype, type): + return np.dtype(arr_or_dtype).type + elif isinstance(arr_or_dtype, CategoricalDtype): + return CategoricalDtypeType + elif isinstance(arr_or_dtype, DatetimeTZDtype): + return DatetimeTZDtypeType + elif isinstance(arr_or_dtype, string_types): + if is_categorical_dtype(arr_or_dtype): + return CategoricalDtypeType + elif is_datetime64tz_dtype(arr_or_dtype): + return DatetimeTZDtypeType + return _get_dtype_type(np.dtype(arr_or_dtype)) + try: + return arr_or_dtype.dtype.type + except AttributeError: + return type(None) + + +def _get_dtype_from_object(dtype): + """Get a numpy dtype.type-style object. This handles the datetime64[ns] + and datetime64[ns, TZ] compat + + Notes + ----- + If nothing can be found, returns ``object``. + """ + + # type object from a dtype + if isinstance(dtype, type) and issubclass(dtype, np.generic): + return dtype + elif is_categorical(dtype): + return CategoricalDtype().type + elif is_datetimetz(dtype): + return DatetimeTZDtype(dtype).type + elif isinstance(dtype, np.dtype): # dtype object + try: + _validate_date_like_dtype(dtype) + except TypeError: + # should still pass if we don't have a datelike + pass + return dtype.type + elif isinstance(dtype, string_types): + if dtype == 'datetime' or dtype == 'timedelta': + dtype += '64' + + try: + return _get_dtype_from_object(getattr(np, dtype)) + except (AttributeError, TypeError): + # handles cases like _get_dtype(int) + # i.e., python objects that are valid dtypes (unlike user-defined + # types, in general) + # TypeError handles the float16 typecode of 'e' + # further handle internal types + pass + + return _get_dtype_from_object(np.dtype(dtype)) + + +def _validate_date_like_dtype(dtype): + try: + typ = np.datetime_data(dtype)[0] + except ValueError as e: + raise TypeError('%s' % e) + if typ != 'generic' and typ != 'ns': + raise ValueError('%r is too specific of a frequency, try passing %r' % + (dtype.name, dtype.type.__name__)) + + +def _lcd_dtypes(a_dtype, b_dtype): + """ return the lcd dtype to hold these types """ + + if is_datetime64_dtype(a_dtype) or is_datetime64_dtype(b_dtype): + return _NS_DTYPE + elif is_timedelta64_dtype(a_dtype) or is_timedelta64_dtype(b_dtype): + return _TD_DTYPE + elif is_complex_dtype(a_dtype): + if is_complex_dtype(b_dtype): + return a_dtype + return np.float64 + elif is_integer_dtype(a_dtype): + if is_integer_dtype(b_dtype): + if a_dtype.itemsize == b_dtype.itemsize: + return a_dtype + return np.int64 + return np.float64 + elif is_float_dtype(a_dtype): + if is_float_dtype(b_dtype): + if a_dtype.itemsize == b_dtype.itemsize: + return a_dtype + else: + return np.float64 + elif is_integer(b_dtype): + return np.float64 + return np.object + +_string_dtypes = frozenset(map(_get_dtype_from_object, (binary_type, + text_type))) + + +def pandas_dtype(dtype): + """ + Converts input into a pandas only dtype object or a numpy dtype object. + + Parameters + ---------- + dtype : object to be converted + + Returns + ------- + np.dtype or a pandas dtype + """ + if isinstance(dtype, DatetimeTZDtype): + return dtype + elif isinstance(dtype, CategoricalDtype): + return dtype + elif isinstance(dtype, string_types): + try: + return DatetimeTZDtype.construct_from_string(dtype) + except TypeError: + pass + + try: + return CategoricalDtype.construct_from_string(dtype) + except TypeError: + pass + elif isinstance(dtype, ExtensionDtype): + return dtype + + return np.dtype(dtype) diff --git a/pandas/types/concat.py b/pandas/types/concat.py index 53db9ddf79a5c..3b30531fb30ac 100644 --- a/pandas/types/concat.py +++ b/pandas/types/concat.py @@ -3,10 +3,19 @@ """ import numpy as np -import pandas.core.common as com import pandas.tslib as tslib from pandas import compat from pandas.compat import map +from .common import (is_categorical_dtype, + is_sparse, + is_datetimetz, + is_datetime64_dtype, + is_timedelta64_dtype, + is_object_dtype, + is_bool_dtype, + is_dtype_equal, + _NS_DTYPE, + _TD_DTYPE) def get_dtype_kinds(l): @@ -24,19 +33,19 @@ def get_dtype_kinds(l): for arr in l: dtype = arr.dtype - if com.is_categorical_dtype(dtype): + if is_categorical_dtype(dtype): typ = 'category' - elif com.is_sparse(arr): + elif is_sparse(arr): typ = 'sparse' - elif com.is_datetimetz(arr): + elif is_datetimetz(arr): typ = 'datetimetz' - elif com.is_datetime64_dtype(dtype): + elif is_datetime64_dtype(dtype): typ = 'datetime' - elif com.is_timedelta64_dtype(dtype): + elif is_timedelta64_dtype(dtype): typ = 'timedelta' - elif com.is_object_dtype(dtype): + elif is_object_dtype(dtype): typ = 'object' - elif com.is_bool_dtype(dtype): + elif is_bool_dtype(dtype): typ = 'bool' else: typ = dtype.kind @@ -51,14 +60,14 @@ def _get_series_result_type(result): """ if isinstance(result, dict): # concat Series with axis 1 - if all(com.is_sparse(c) for c in compat.itervalues(result)): + if all(is_sparse(c) for c in compat.itervalues(result)): from pandas.sparse.api import SparseDataFrame return SparseDataFrame else: from pandas.core.frame import DataFrame return DataFrame - elif com.is_sparse(result): + elif is_sparse(result): # concat Series with axis 1 from pandas.sparse.api import SparseSeries return SparseSeries @@ -165,7 +174,7 @@ def _concat_categorical(to_concat, axis=0): def convert_categorical(x): # coerce to object dtype - if com.is_categorical_dtype(x.dtype): + if is_categorical_dtype(x.dtype): return x.get_values() return x.ravel() @@ -177,7 +186,7 @@ def convert_categorical(x): # we could have object blocks and categoricals here # if we only have a single categoricals then combine everything # else its a non-compat categorical - categoricals = [x for x in to_concat if com.is_categorical_dtype(x.dtype)] + categoricals = [x for x in to_concat if is_categorical_dtype(x.dtype)] # validate the categories categories = categoricals[0] @@ -206,7 +215,7 @@ def union_categoricals(to_union): Combine list-like of Categoricals, unioning categories. All must have the same dtype, and none can be ordered. - .. versionadded 0.18.2 + .. versionadded:: 0.19.0 Parameters ---------- @@ -235,7 +244,7 @@ def union_categoricals(to_union): if any(c.ordered for c in to_union): raise TypeError("Can only combine unordered Categoricals") - if not all(com.is_dtype_equal(c.categories.dtype, first.categories.dtype) + if not all(is_dtype_equal(c.categories.dtype, first.categories.dtype) for c in to_union): raise TypeError("dtype of categories must be the same") @@ -272,7 +281,7 @@ def convert_to_pydatetime(x, axis): # coerce to an object dtype # if dtype is of datetimetz or timezone - if x.dtype.kind == com._NS_DTYPE.kind: + if x.dtype.kind == _NS_DTYPE.kind: if getattr(x, 'tz', None) is not None: x = x.asobject.values else: @@ -280,7 +289,7 @@ def convert_to_pydatetime(x, axis): x = tslib.ints_to_pydatetime(x.view(np.int64).ravel()) x = x.reshape(shape) - elif x.dtype == com._TD_DTYPE: + elif x.dtype == _TD_DTYPE: shape = x.shape x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel()) x = x.reshape(shape) @@ -310,12 +319,12 @@ def convert_to_pydatetime(x, axis): elif 'datetime' in typs: new_values = np.concatenate([x.view(np.int64) for x in to_concat], axis=axis) - return new_values.view(com._NS_DTYPE) + return new_values.view(_NS_DTYPE) elif 'timedelta' in typs: new_values = np.concatenate([x.view(np.int64) for x in to_concat], axis=axis) - return new_values.view(com._TD_DTYPE) + return new_values.view(_TD_DTYPE) # need to coerce to object to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] @@ -350,7 +359,7 @@ def convert_sparse(x, axis): return x if typs is None: - typs = com.get_dtype_kinds(to_concat) + typs = get_dtype_kinds(to_concat) if len(typs) == 1: # concat input as it is if all inputs are sparse @@ -374,7 +383,7 @@ def convert_sparse(x, axis): # input may be sparse / dense mixed and may have different fill_value # input must contain sparse at least 1 - sparses = [c for c in to_concat if com.is_sparse(c)] + sparses = [c for c in to_concat if is_sparse(c)] fill_values = [c.fill_value for c in sparses] sp_indexes = [c.sp_index for c in sparses] diff --git a/pandas/types/inference.py b/pandas/types/inference.py new file mode 100644 index 0000000000000..35a2dc2fb831b --- /dev/null +++ b/pandas/types/inference.py @@ -0,0 +1,104 @@ +""" basic inference routines """ + +import collections +import re +import numpy as np +from numbers import Number +from pandas.compat import (string_types, text_type, + string_and_binary_types) +from pandas import lib + +is_bool = lib.is_bool + +is_integer = lib.is_integer + +is_float = lib.is_float + +is_complex = lib.is_complex + +is_scalar = lib.isscalar + + +def is_number(obj): + return isinstance(obj, (Number, np.number)) + + +def is_string_like(obj): + return isinstance(obj, (text_type, string_types)) + + +def _iterable_not_string(x): + return (isinstance(x, collections.Iterable) and + not isinstance(x, string_types)) + + +def is_iterator(obj): + # python 3 generators have __next__ instead of next + return hasattr(obj, 'next') or hasattr(obj, '__next__') + + +def is_re(obj): + return isinstance(obj, re._pattern_type) + + +def is_re_compilable(obj): + try: + re.compile(obj) + except TypeError: + return False + else: + return True + + +def is_list_like(arg): + return (hasattr(arg, '__iter__') and + not isinstance(arg, string_and_binary_types)) + + +def is_dict_like(arg): + return hasattr(arg, '__getitem__') and hasattr(arg, 'keys') + + +def is_named_tuple(arg): + return isinstance(arg, tuple) and hasattr(arg, '_fields') + + +def is_hashable(arg): + """Return True if hash(arg) will succeed, False otherwise. + + Some types will pass a test against collections.Hashable but fail when they + are actually hashed with hash(). + + Distinguish between these and other types by trying the call to hash() and + seeing if they raise TypeError. + + Examples + -------- + >>> a = ([],) + >>> isinstance(a, collections.Hashable) + True + >>> is_hashable(a) + False + """ + # unfortunately, we can't use isinstance(arg, collections.Hashable), which + # can be faster than calling hash, because numpy scalars on Python 3 fail + # this test + + # reconsider this decision once this numpy bug is fixed: + # https://github.com/numpy/numpy/issues/5562 + + try: + hash(arg) + except TypeError: + return False + else: + return True + + +def is_sequence(x): + try: + iter(x) + len(x) # it has a length + return not isinstance(x, string_and_binary_types) + except (TypeError, AttributeError): + return False diff --git a/pandas/types/missing.py b/pandas/types/missing.py new file mode 100644 index 0000000000000..8b4193d02beb7 --- /dev/null +++ b/pandas/types/missing.py @@ -0,0 +1,394 @@ +""" +missing types & inference +""" +import numpy as np +from pandas import lib +from pandas.tslib import NaT, iNaT +from .generic import (ABCMultiIndex, ABCSeries, + ABCIndexClass, ABCGeneric) +from .common import (is_string_dtype, is_datetimelike, + is_datetimelike_v_numeric, is_float_dtype, + is_datetime64_dtype, is_datetime64tz_dtype, + is_timedelta64_dtype, + is_complex_dtype, is_categorical_dtype, + is_string_like_dtype, is_bool_dtype, + is_integer_dtype, is_dtype_equal, + needs_i8_conversion, _ensure_object, + pandas_dtype, + is_scalar, + is_object_dtype, + is_integer, + _TD_DTYPE, + _NS_DTYPE, + _DATELIKE_DTYPES) +from .inference import is_list_like + + +def isnull(obj): + """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) + + Parameters + ---------- + arr : ndarray or object value + Object to check for null-ness + + Returns + ------- + isnulled : array-like of bool or bool + Array or bool indicating whether an object is null or if an array is + given which of the element is null. + + See also + -------- + pandas.notnull: boolean inverse of pandas.isnull + """ + return _isnull(obj) + + +def _isnull_new(obj): + if is_scalar(obj): + return lib.checknull(obj) + # hack (for now) because MI registers as ndarray + elif isinstance(obj, ABCMultiIndex): + raise NotImplementedError("isnull is not defined for MultiIndex") + elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)): + return _isnull_ndarraylike(obj) + elif isinstance(obj, ABCGeneric): + return obj._constructor(obj._data.isnull(func=isnull)) + elif isinstance(obj, list) or hasattr(obj, '__array__'): + return _isnull_ndarraylike(np.asarray(obj)) + else: + return obj is None + + +def _isnull_old(obj): + """Detect missing values. Treat None, NaN, INF, -INF as null. + + Parameters + ---------- + arr: ndarray or object value + + Returns + ------- + boolean ndarray or boolean + """ + if is_scalar(obj): + return lib.checknull_old(obj) + # hack (for now) because MI registers as ndarray + elif isinstance(obj, ABCMultiIndex): + raise NotImplementedError("isnull is not defined for MultiIndex") + elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)): + return _isnull_ndarraylike_old(obj) + elif isinstance(obj, ABCGeneric): + return obj._constructor(obj._data.isnull(func=_isnull_old)) + elif isinstance(obj, list) or hasattr(obj, '__array__'): + return _isnull_ndarraylike_old(np.asarray(obj)) + else: + return obj is None + + +_isnull = _isnull_new + + +def _use_inf_as_null(key): + """Option change callback for null/inf behaviour + Choose which replacement for numpy.isnan / -numpy.isfinite is used. + + Parameters + ---------- + flag: bool + True means treat None, NaN, INF, -INF as null (old way), + False means None and NaN are null, but INF, -INF are not null + (new way). + + Notes + ----- + This approach to setting global module values is discussed and + approved here: + + * http://stackoverflow.com/questions/4859217/ + programmatically-creating-variables-in-python/4859312#4859312 + """ + from pandas.core.config import get_option + flag = get_option(key) + if flag: + globals()['_isnull'] = _isnull_old + else: + globals()['_isnull'] = _isnull_new + + +def _isnull_ndarraylike(obj): + + values = getattr(obj, 'values', obj) + dtype = values.dtype + + if is_string_dtype(dtype): + if is_categorical_dtype(values): + from pandas import Categorical + if not isinstance(values, Categorical): + values = values.values + result = values.isnull() + else: + + # Working around NumPy ticket 1542 + shape = values.shape + + if is_string_like_dtype(dtype): + result = np.zeros(values.shape, dtype=bool) + else: + result = np.empty(shape, dtype=bool) + vec = lib.isnullobj(values.ravel()) + result[...] = vec.reshape(shape) + + elif is_datetimelike(obj): + # this is the NaT pattern + result = values.view('i8') == iNaT + else: + result = np.isnan(values) + + # box + if isinstance(obj, ABCSeries): + from pandas import Series + result = Series(result, index=obj.index, name=obj.name, copy=False) + + return result + + +def _isnull_ndarraylike_old(obj): + values = getattr(obj, 'values', obj) + dtype = values.dtype + + if is_string_dtype(dtype): + # Working around NumPy ticket 1542 + shape = values.shape + + if is_string_like_dtype(dtype): + result = np.zeros(values.shape, dtype=bool) + else: + result = np.empty(shape, dtype=bool) + vec = lib.isnullobj_old(values.ravel()) + result[:] = vec.reshape(shape) + + elif dtype in _DATELIKE_DTYPES: + # this is the NaT pattern + result = values.view('i8') == iNaT + else: + result = ~np.isfinite(values) + + # box + if isinstance(obj, ABCSeries): + from pandas import Series + result = Series(result, index=obj.index, name=obj.name, copy=False) + + return result + + +def notnull(obj): + """Replacement for numpy.isfinite / -numpy.isnan which is suitable for use + on object arrays. + + Parameters + ---------- + arr : ndarray or object value + Object to check for *not*-null-ness + + Returns + ------- + isnulled : array-like of bool or bool + Array or bool indicating whether an object is *not* null or if an array + is given which of the element is *not* null. + + See also + -------- + pandas.isnull : boolean inverse of pandas.notnull + """ + res = isnull(obj) + if is_scalar(res): + return not res + return ~res + + +def is_null_datelike_scalar(other): + """ test whether the object is a null datelike, e.g. Nat + but guard against passing a non-scalar """ + if other is NaT or other is None: + return True + elif is_scalar(other): + + # a timedelta + if hasattr(other, 'dtype'): + return other.view('i8') == iNaT + elif is_integer(other) and other == iNaT: + return True + return isnull(other) + return False + + +def _is_na_compat(arr, fill_value=np.nan): + """ + Parameters + ---------- + arr: a numpy array + fill_value: fill value, default to np.nan + + Returns + ------- + True if we can fill using this fill_value + """ + dtype = arr.dtype + if isnull(fill_value): + return not (is_bool_dtype(dtype) or + is_integer_dtype(dtype)) + return True + + +def array_equivalent(left, right, strict_nan=False): + """ + True if two arrays, left and right, have equal non-NaN elements, and NaNs + in corresponding locations. False otherwise. It is assumed that left and + right are NumPy arrays of the same dtype. The behavior of this function + (particularly with respect to NaNs) is not defined if the dtypes are + different. + + Parameters + ---------- + left, right : ndarrays + strict_nan : bool, default False + If True, consider NaN and None to be different. + + Returns + ------- + b : bool + Returns True if the arrays are equivalent. + + Examples + -------- + >>> array_equivalent( + ... np.array([1, 2, np.nan]), + ... np.array([1, 2, np.nan])) + True + >>> array_equivalent( + ... np.array([1, np.nan, 2]), + ... np.array([1, 2, np.nan])) + False + """ + + left, right = np.asarray(left), np.asarray(right) + + # shape compat + if left.shape != right.shape: + return False + + # Object arrays can contain None, NaN and NaT. + # string dtypes must be come to this path for NumPy 1.7.1 compat + if is_string_dtype(left) or is_string_dtype(right): + + if not strict_nan: + # isnull considers NaN and None to be equivalent. + return lib.array_equivalent_object( + _ensure_object(left.ravel()), _ensure_object(right.ravel())) + + for left_value, right_value in zip(left, right): + if left_value is NaT and right_value is not NaT: + return False + + elif isinstance(left_value, float) and np.isnan(left_value): + if (not isinstance(right_value, float) or + not np.isnan(right_value)): + return False + else: + if left_value != right_value: + return False + return True + + # NaNs can occur in float and complex arrays. + if is_float_dtype(left) or is_complex_dtype(left): + return ((left == right) | (np.isnan(left) & np.isnan(right))).all() + + # numpy will will not allow this type of datetimelike vs integer comparison + elif is_datetimelike_v_numeric(left, right): + return False + + # M8/m8 + elif needs_i8_conversion(left) and needs_i8_conversion(right): + if not is_dtype_equal(left.dtype, right.dtype): + return False + + left = left.view('i8') + right = right.view('i8') + + # NaNs cannot occur otherwise. + try: + return np.array_equal(left, right) + except AttributeError: + # see gh-13388 + # + # NumPy v1.7.1 has a bug in its array_equal + # function that prevents it from correctly + # comparing two arrays with complex dtypes. + # This bug is corrected in v1.8.0, so remove + # this try-except block as soon as we stop + # supporting NumPy versions < 1.8.0 + if not is_dtype_equal(left.dtype, right.dtype): + return False + + left = left.tolist() + right = right.tolist() + + return left == right + + +def _infer_fill_value(val): + """ + infer the fill value for the nan/NaT from the provided + scalar/ndarray/list-like if we are a NaT, return the correct dtyped + element to provide proper block construction + """ + + if not is_list_like(val): + val = [val] + val = np.array(val, copy=False) + if is_datetimelike(val): + return np.array('NaT', dtype=val.dtype) + elif is_object_dtype(val.dtype): + dtype = lib.infer_dtype(_ensure_object(val)) + if dtype in ['datetime', 'datetime64']: + return np.array('NaT', dtype=_NS_DTYPE) + elif dtype in ['timedelta', 'timedelta64']: + return np.array('NaT', dtype=_TD_DTYPE) + return np.nan + + +def _maybe_fill(arr, fill_value=np.nan): + """ + if we have a compatiable fill_value and arr dtype, then fill + """ + if _is_na_compat(arr, fill_value): + arr.fill(fill_value) + return arr + + +def na_value_for_dtype(dtype): + """ + Return a dtype compat na value + + Parameters + ---------- + dtype : string / dtype + + Returns + ------- + np.dtype or a pandas dtype + """ + dtype = pandas_dtype(dtype) + + if (is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype) or + is_timedelta64_dtype(dtype)): + return NaT + elif is_float_dtype(dtype): + return np.nan + elif is_integer_dtype(dtype): + return 0 + elif is_bool_dtype(dtype): + return False + return np.nan diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 2961b2fb2241f..4442eed898b60 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -23,11 +23,14 @@ import numpy as np import pandas as pd -from pandas.core.common import (is_sequence, array_equivalent, - is_list_like, is_datetimelike_v_numeric, - is_datetimelike_v_object, - is_number, is_bool, - needs_i8_conversion, is_categorical_dtype) +from pandas.types.missing import array_equivalent +from pandas.types.common import (is_datetimelike_v_numeric, + is_datetimelike_v_object, + is_number, is_bool, + needs_i8_conversion, + is_categorical_dtype, + is_sequence, + is_list_like) from pandas.formats.printing import pprint_thing from pandas.core.algorithms import take_1d @@ -1001,17 +1004,20 @@ def assert_categorical_equal(left, right, check_dtype=True, assert_attr_equal('ordered', left, right, obj=obj) -def raise_assert_detail(obj, message, left, right): +def raise_assert_detail(obj, message, left, right, diff=None): if isinstance(left, np.ndarray): left = pprint_thing(left) if isinstance(right, np.ndarray): right = pprint_thing(right) + if diff is not None: + diff = "\n[diff]: {diff}".format(diff=diff) + msg = """{0} are different {1} [left]: {2} -[right]: {3}""".format(obj, message, left, right) +[right]: {3}{4}""".format(obj, message, left, right, diff) raise AssertionError(msg) diff --git a/pandas/util/validators.py b/pandas/util/validators.py index bbfd24df9c13e..964fa9d9b38d5 100644 --- a/pandas/util/validators.py +++ b/pandas/util/validators.py @@ -3,6 +3,8 @@ for validating data or function arguments """ +from pandas.types.common import is_bool + def _check_arg_length(fname, args, max_fname_arg_count, compat_args): """ @@ -35,8 +37,6 @@ def _check_for_default_values(fname, arg_val_dict, compat_args): checked that arg_val_dict.keys() is a subset of compat_args """ - from pandas.core.common import is_bool - for key in arg_val_dict: # try checking equality directly with '=' operator, # as comparison may have been overriden for the left diff --git a/setup.py b/setup.py index 8f8865ecc3b7a..c77ca4d9e60fe 100755 --- a/setup.py +++ b/setup.py @@ -547,6 +547,9 @@ def pxd(name): maintainer=AUTHOR, version=versioneer.get_version(), packages=['pandas', + 'pandas.api', + 'pandas.api.tests', + 'pandas.api.types', 'pandas.compat', 'pandas.compat.numpy', 'pandas.computation', @@ -586,6 +589,7 @@ def pxd(name): 'tests/data/legacy_msgpack/*/*.msgpack', 'tests/data/*.csv*', 'tests/data/*.dta', + 'tests/data/*.pickle', 'tests/data/*.txt', 'tests/data/*.xls', 'tests/data/*.xlsx', @@ -602,8 +606,7 @@ def pxd(name): 'tests/data/html_encoding/*.html', 'tests/json/data/*.json'], 'pandas.tools': ['tests/data/*.csv'], - 'pandas.tests': ['data/*.pickle', - 'data/*.csv'], + 'pandas.tests': ['data/*.csv'], 'pandas.tests.formats': ['data/*.csv'], 'pandas.tests.indexes': ['data/*.pickle'], 'pandas.tseries.tests': ['data/*.pickle',
- [ ] closes #13572 - [x] tests added / passed pandas.tests.indexes.test_datetimelike.TestDatetimeIndex:test_contains - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry …ime index and the object in question is first in the index.
https://api.github.com/repos/pandas-dev/pandas/pulls/13574
2016-07-06T21:21:25Z
2016-07-16T23:04:06Z
null
2016-07-16T23:04:21Z
Remove test plot line blarg
diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index be1f745537d05..a345629dde6d5 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -528,3 +528,5 @@ Bug Fixes - Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) + +- Bug in ``test_graphics`` to pass the tests with matplotlib 1.9.2 diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index bd19a83ce2b64..61140c384e8c2 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1329,10 +1329,6 @@ def test_plot(self): subplots=True, use_index=False) self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) - df = DataFrame({'x': [1, 2], 'y': [3, 4]}) - with tm.assertRaises(TypeError): - df.plot.line(blarg=True) - df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))
Superceded by #13641 --- - [x] closes #13570 - [x] tests passed - [x] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry The "blarg" argument is propagated to matplotlib and there it raises an AttributeError (and not a TypeError). However, since the test doesn't seem to have a rationale, it is removed here.
https://api.github.com/repos/pandas-dev/pandas/pulls/13573
2016-07-06T13:38:00Z
2016-07-13T15:41:44Z
null
2023-05-11T01:13:46Z
BUG: read_csv throws UnicodeDecodeError with unicode aliases
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index cc3cc631b9575..89a74ab00048d 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -1075,3 +1075,5 @@ Bug Fixes - Bug in ``Index`` raises ``KeyError`` displaying incorrect column when column is not in the df and columns contains duplicate values (:issue:`13822`) - Bug in ``Period`` and ``PeriodIndex`` creating wrong dates when frequency has combined offset aliases (:issue:`13874`) - Bug in ``.to_string()`` when called with an integer ``line_width`` and ``index=False`` raises an UnboundLocalError exception because ``idx`` referenced before assignment. + +- Bug in ``read_csv()``, where aliases for utf-xx (e.g. UTF-xx, UTF_xx, utf_xx) raised UnicodeDecodeError (:issue:`13549`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 5372203318d69..c2117e206564c 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -350,6 +350,9 @@ def _validate_nrows(nrows): def _read(filepath_or_buffer, kwds): "Generic reader of line files." encoding = kwds.get('encoding', None) + if encoding is not None: + encoding = re.sub('_', '-', encoding).lower() + kwds['encoding'] = encoding # If the input could be a filename, check for a recognizable compression # extension. If we're reading from a URL, the `get_filepath_or_buffer` diff --git a/pandas/io/tests/parser/common.py b/pandas/io/tests/parser/common.py index 96eb0ec6fd7a2..5972569cf020b 100644 --- a/pandas/io/tests/parser/common.py +++ b/pandas/io/tests/parser/common.py @@ -8,6 +8,7 @@ import re import sys from datetime import datetime +from io import BytesIO import nose import numpy as np @@ -1583,3 +1584,13 @@ def test_temporary_file(self): new_file.close() expected = DataFrame([[0, 0]]) tm.assert_frame_equal(result, expected) + + def test_read_csv_utf_aliases(self): + # see gh issue 13549 + expected = pd.DataFrame({'mb_num': [4.8], 'multibyte': ['test']}) + for byte in [8, 16]: + for fmt in ['utf-{0}', 'utf_{0}', 'UTF-{0}', 'UTF_{0}']: + encoding = fmt.format(byte) + data = 'mb_num,multibyte\n4.8,test'.encode(encoding) + result = self.read_csv(BytesIO(data), encoding=encoding) + tm.assert_frame_equal(result, expected)
Rebased as PR https://github.com/pydata/pandas/pull/14060 --- - [x] closes #13549 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry -read_csv with engine=c throws error when encoding=UTF_16 (anything other than utf-16) -improved nosetests and moved to in pandas/io/tests/common.py -passes `pep8radius upstream/master --diff` and `git diff upstream/master | flake8 --diff` -put what's new entry in 0.19.0 in accordance with milestone posted on issue
https://api.github.com/repos/pandas-dev/pandas/pulls/13571
2016-07-05T21:32:40Z
2016-08-19T23:07:50Z
null
2016-08-21T20:47:08Z
BUG: Datetime64Formatter not respecting ``formatter``
diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 64644bd9a7a26..90bca9e11826b 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -529,4 +529,5 @@ Bug Fixes - Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) -- Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) +- Bug in ``.to_html``, ``.to_latex`` and ``.to_string`` silently ignore custom datetime formatter passed through the ``formatters`` key word (:issue:`10690`) +- Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) \ No newline at end of file diff --git a/pandas/formats/format.py b/pandas/formats/format.py index a8e184ce94c89..0c6a15db4ccfe 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -2239,9 +2239,13 @@ def _format_strings(self): """ we by definition have DO NOT have a TZ """ values = self.values + if not isinstance(values, DatetimeIndex): values = DatetimeIndex(values) + if self.formatter is not None and callable(self.formatter): + return [self.formatter(x) for x in values] + fmt_values = format_array_from_datetime( values.asi8.ravel(), format=_get_format_datetime64_from_values(values, diff --git a/pandas/tests/formats/test_format.py b/pandas/tests/formats/test_format.py index e67fe2cddde77..c5e9c258b293a 100644 --- a/pandas/tests/formats/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -456,6 +456,28 @@ def test_to_string_with_formatters(self): '2 0x3 [ 3.0] -False-')) self.assertEqual(result, result2) + def test_to_string_with_datetime64_monthformatter(self): + months = [datetime(2016, 1, 1), datetime(2016, 2, 2)] + x = DataFrame({'months': months}) + + def format_func(x): + return x.strftime('%Y-%m') + result = x.to_string(formatters={'months': format_func}) + expected = 'months\n0 2016-01\n1 2016-02' + self.assertEqual(result.strip(), expected) + + def test_to_string_with_datetime64_hourformatter(self): + + x = DataFrame({'hod': pd.to_datetime(['10:10:10.100', '12:12:12.120'], + format='%H:%M:%S.%f')}) + + def format_func(x): + return x.strftime('%H:%M') + + result = x.to_string(formatters={'hod': format_func}) + expected = 'hod\n0 10:10\n1 12:12' + self.assertEqual(result.strip(), expected) + def test_to_string_with_formatters_unicode(self): df = DataFrame({u('c/\u03c3'): [1, 2, 3]}) result = df.to_string(formatters={u('c/\u03c3'): lambda x: '%s' % x}) @@ -1233,6 +1255,63 @@ def test_to_html_index_formatter(self): self.assertEqual(result, expected) + def test_to_html_datetime64_monthformatter(self): + months = [datetime(2016, 1, 1), datetime(2016, 2, 2)] + x = DataFrame({'months': months}) + + def format_func(x): + return x.strftime('%Y-%m') + result = x.to_html(formatters={'months': format_func}) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>months</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>2016-01</td> + </tr> + <tr> + <th>1</th> + <td>2016-02</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + + def test_to_html_datetime64_hourformatter(self): + + x = DataFrame({'hod': pd.to_datetime(['10:10:10.100', '12:12:12.120'], + format='%H:%M:%S.%f')}) + + def format_func(x): + return x.strftime('%H:%M') + result = x.to_html(formatters={'hod': format_func}) + expected = """\ +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>hod</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>10:10</td> + </tr> + <tr> + <th>1</th> + <td>12:12</td> + </tr> + </tbody> +</table>""" + self.assertEqual(result, expected) + def test_to_html_regression_GH6098(self): df = DataFrame({u('clé1'): [u('a'), u('a'), u('b'), u('b'), u('a')], u('clé2'): [u('1er'), u('2ème'), u('1er'), u('2ème'), @@ -2775,6 +2854,33 @@ def test_to_latex_format(self): self.assertEqual(withindex_result, withindex_expected) + def test_to_latex_with_formatters(self): + df = DataFrame({'int': [1, 2, 3], + 'float': [1.0, 2.0, 3.0], + 'object': [(1, 2), True, False], + 'datetime64': [datetime(2016, 1, 1), + datetime(2016, 2, 5), + datetime(2016, 3, 3)]}) + + formatters = {'int': lambda x: '0x%x' % x, + 'float': lambda x: '[% 4.1f]' % x, + 'object': lambda x: '-%s-' % str(x), + 'datetime64': lambda x: x.strftime('%Y-%m'), + '__index__': lambda x: 'index: %s' % x} + result = df.to_latex(formatters=dict(formatters)) + + expected = r"""\begin{tabular}{llrrl} +\toprule +{} & datetime64 & float & int & object \\ +\midrule +index: 0 & 2016-01 & [ 1.0] & 0x1 & -(1, 2)- \\ +index: 1 & 2016-02 & [ 2.0] & 0x2 & -True- \\ +index: 2 & 2016-03 & [ 3.0] & 0x3 & -False- \\ +\bottomrule +\end{tabular} +""" + self.assertEqual(result, expected) + def test_to_latex_multiindex(self): df = DataFrame({('x', 'y'): ['a']}) result = df.to_latex() @@ -4161,6 +4267,28 @@ def test_dates_display(self): self.assertEqual(result[1].strip(), "NaT") self.assertEqual(result[4].strip(), "2013-01-01 09:00:00.000000004") + def test_datetime64formatter_yearmonth(self): + x = Series([datetime(2016, 1, 1), datetime(2016, 2, 2)]) + + def format_func(x): + return x.strftime('%Y-%m') + + formatter = fmt.Datetime64Formatter(x, formatter=format_func) + result = formatter.get_result() + self.assertEqual(result, ['2016-01', '2016-02']) + + def test_datetime64formatter_hoursecond(self): + + x = Series(pd.to_datetime(['10:10:10.100', '12:12:12.120'], + format='%H:%M:%S.%f')) + + def format_func(x): + return x.strftime('%H:%M') + + formatter = fmt.Datetime64Formatter(x, formatter=format_func) + result = formatter.get_result() + self.assertEqual(result, ['10:10', '12:12']) + class TestNaTFormatting(tm.TestCase):
- [x] closes #10690 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry the Datetime64Formatter class did not accept a `formatter` argument, so custom formatters passed in through `df.to_string` or `df.to_html` were silently ignored.
https://api.github.com/repos/pandas-dev/pandas/pulls/13567
2016-07-05T14:32:16Z
2016-07-08T15:16:46Z
null
2016-07-09T00:43:43Z
TST: Add tests for single group
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index bc25525f936ac..10362cbb24888 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2085,8 +2085,8 @@ def test_groupby_head_tail(self): assert_frame_equal(df.loc[[0, 2]], g_not_as.head(1)) assert_frame_equal(df.loc[[1, 2]], g_not_as.tail(1)) - empty_not_as = DataFrame(columns=df.columns, index=pd.Index( - [], dtype=df.index.dtype)) + empty_not_as = DataFrame(columns=df.columns, + index=pd.Index([], dtype=df.index.dtype)) empty_not_as['A'] = empty_not_as['A'].astype(df.A.dtype) empty_not_as['B'] = empty_not_as['B'].astype(df.B.dtype) assert_frame_equal(empty_not_as, g_not_as.head(0)) @@ -4549,6 +4549,15 @@ def test_groupby_with_empty(self): grouped = series.groupby(grouper) assert next(iter(grouped), None) is None + def test_groupby_with_single_column(self): + df = pd.DataFrame({'a': list('abssbab')}) + tm.assert_frame_equal(df.groupby('a').get_group('a'), df.iloc[[0, 5]]) + # GH 13530 + exp = pd.DataFrame([], index=pd.Index(['a', 'b', 's'], name='a')) + tm.assert_frame_equal(df.groupby('a').count(), exp) + tm.assert_frame_equal(df.groupby('a').sum(), exp) + tm.assert_frame_equal(df.groupby('a').nth(1), exp) + def test_groupby_with_small_elem(self): # GH 8542 # length=2 @@ -4989,8 +4998,8 @@ def test_cumcount_empty(self): ge = DataFrame().groupby(level=0) se = Series().groupby(level=0) - e = Series(dtype='int64' - ) # edge case, as this is usually considered float + # edge case, as this is usually considered float + e = Series(dtype='int64') assert_series_equal(e, ge.cumcount()) assert_series_equal(e, se.cumcount())
- [x] closes #13530 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/13561
2016-07-04T23:19:43Z
2016-07-05T08:39:35Z
2016-07-05T08:39:35Z
2016-07-05T08:45:43Z
Update gotchas.rst
diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst index c79b902d559d5..99d7486cde2d0 100644 --- a/doc/source/gotchas.rst +++ b/doc/source/gotchas.rst @@ -173,7 +173,7 @@ dtype in order to store the NAs. These are summarized by this table: ``integer``, cast to ``float64`` ``boolean``, cast to ``object`` -While this may seem like a heavy trade-off, in practice I have found very few +While this may seem like a heavy trade-off, I have found very few cases where this is an issue in practice. Some explanation for the motivation here in the next section.
Removed redundant words
https://api.github.com/repos/pandas-dev/pandas/pulls/13560
2016-07-04T16:31:16Z
2016-07-05T10:40:45Z
null
2016-07-05T10:40:45Z
TST: confirm bug in partial string multi-index slicing is fixed (GH12685)
diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index bec52f5f47b09..fb5576bed90b4 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -6,7 +6,7 @@ import re import warnings -from pandas import (date_range, MultiIndex, Index, CategoricalIndex, +from pandas import (DataFrame, date_range, MultiIndex, Index, CategoricalIndex, compat) from pandas.core.common import PerformanceWarning from pandas.indexes.base import InvalidIndexError @@ -2201,6 +2201,15 @@ def test_partial_string_timestamp_multiindex(self): with assertRaises(KeyError): df_swap.loc['2016-01-01'] + # GH12685 (partial string with daily resolution or below) + dr = date_range('2013-01-01', periods=100, freq='D') + ix = MultiIndex.from_product([dr, ['a', 'b']]) + df = DataFrame(np.random.randn(200, 1), columns=['A'], index=ix) + + result = df.loc[idx['2013-03':'2013-03', :], :] + expected = df.iloc[118:180] + tm.assert_frame_equal(result, expected) + def test_rangeindex_fallback_coercion_bug(self): # GH 12893 foo = pd.DataFrame(np.arange(100).reshape((10, 10)))
Closes #12685
https://api.github.com/repos/pandas-dev/pandas/pulls/13559
2016-07-04T10:04:22Z
2016-07-05T08:37:18Z
2016-07-05T08:37:17Z
2016-07-05T08:37:18Z
BUG: pivot_table always returns a DataFrame
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt index 725dc7fc52ed0..1c3abcb31d26d 100644 --- a/doc/source/whatsnew/v0.20.0.txt +++ b/doc/source/whatsnew/v0.20.0.txt @@ -771,3 +771,4 @@ Bug Fixes - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`) - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`) - Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`) +- Bug in ``pivot_table`` returns ``Series`` in specific circumstance (:issue:`4386`) diff --git a/pandas/tests/tools/test_pivot.py b/pandas/tests/tools/test_pivot.py index 62863372dbd02..882f25da1b87f 100644 --- a/pandas/tests/tools/test_pivot.py +++ b/pandas/tests/tools/test_pivot.py @@ -939,6 +939,44 @@ def test_categorical_pivot_index_ordering(self): columns=expected_columns) tm.assert_frame_equal(result, expected) + def test_pivot_table_not_series(self): + # GH 4386 + # pivot_table always returns a DataFrame + # when values is not list like and columns is None + # and aggfunc is not instance of list + df = DataFrame({'col1': [3, 4, 5], + 'col2': ['C', 'D', 'E'], + 'col3': [1, 3, 9]}) + + result = df.pivot_table('col1', index=['col3', 'col2'], aggfunc=np.sum) + m = MultiIndex.from_arrays([[1, 3, 9], + ['C', 'D', 'E']], + names=['col3', 'col2']) + expected = DataFrame([3, 4, 5], + index=m, columns=['col1']) + + tm.assert_frame_equal(result, expected) + + result = df.pivot_table( + 'col1', index='col3', columns='col2', aggfunc=np.sum + ) + expected = DataFrame([[3, np.NaN, np.NaN], + [np.NaN, 4, np.NaN], + [np.NaN, np.NaN, 5]], + index=Index([1, 3, 9], name='col3'), + columns=Index(['C', 'D', 'E'], name='col2')) + + tm.assert_frame_equal(result, expected) + + result = df.pivot_table('col1', index='col3', aggfunc=[np.sum]) + m = MultiIndex.from_arrays([['sum'], + ['col1']]) + expected = DataFrame([3, 4, 5], + index=Index([1, 3, 9], name='col3'), + columns=m) + + tm.assert_frame_equal(result, expected) + class TestCrosstab(tm.TestCase): diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index e23beb8332fd4..5f2b6b53cbc63 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -169,7 +169,8 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', margins_name=margins_name) # discard the top level - if values_passed and not values_multi and not table.empty: + if values_passed and not values_multi and not table.empty and \ + (table.columns.nlevels > 1): table = table[values[0]] if len(index) == 0 and len(columns) > 0:
- [x] closes #4386 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Before this commit, if - `values` is not list like - `columns` is `None` - `aggfunc` is not instance of `list` `pivot_table` returns a `Series`. This commit adds checking for `columns.nlevels` is greater than 1 to prevent from casting `table` to a `Series`. This will fix #4386.
https://api.github.com/repos/pandas-dev/pandas/pulls/13554
2016-07-03T15:16:08Z
2017-04-18T22:37:28Z
null
2017-04-18T22:37:28Z
TST: Move `test_crosstab_margins` to `TestPivotTable`
diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 7ec4018d301af..cda2343fbb842 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -801,6 +801,26 @@ def test_pivot_table_margins_name_with_aggfunc_list(self): expected = pd.DataFrame(table.values, index=ix, columns=cols) tm.assert_frame_equal(table, expected) + def test_categorical_margins(self): + # GH 10989 + df = pd.DataFrame({'x': np.arange(8), + 'y': np.arange(8) // 4, + 'z': np.arange(8) % 2}) + + expected = pd.DataFrame([[1.0, 2.0, 1.5], [5, 6, 5.5], [3, 4, 3.5]]) + expected.index = Index([0, 1, 'All'], name='y') + expected.columns = Index([0, 1, 'All'], name='z') + + data = df.copy() + table = data.pivot_table('x', 'y', 'z', margins=True) + tm.assert_frame_equal(table, expected) + + data = df.copy() + data.y = data.y.astype('category') + data.z = data.z.astype('category') + table = data.pivot_table('x', 'y', 'z', margins=True) + tm.assert_frame_equal(table, expected) + class TestCrosstab(tm.TestCase): @@ -919,26 +939,6 @@ def test_crosstab_dropna(self): names=['b', 'c']) tm.assert_index_equal(res.columns, m) - def test_categorical_margins(self): - # GH 10989 - df = pd.DataFrame({'x': np.arange(8), - 'y': np.arange(8) // 4, - 'z': np.arange(8) % 2}) - - expected = pd.DataFrame([[1.0, 2.0, 1.5], [5, 6, 5.5], [3, 4, 3.5]]) - expected.index = Index([0, 1, 'All'], name='y') - expected.columns = Index([0, 1, 'All'], name='z') - - data = df.copy() - table = data.pivot_table('x', 'y', 'z', margins=True) - tm.assert_frame_equal(table, expected) - - data = df.copy() - data.y = data.y.astype('category') - data.z = data.z.astype('category') - table = data.pivot_table('x', 'y', 'z', margins=True) - tm.assert_frame_equal(table, expected) - def test_crosstab_no_overlap(self): # GS 10291
This test case assert `pivot_table` method. So it should be defined on `TestPivotTable`.
https://api.github.com/repos/pandas-dev/pandas/pulls/13553
2016-07-03T13:39:28Z
2016-07-04T16:01:16Z
2016-07-04T16:01:16Z
2016-07-04T16:15:49Z
Fix missing not in format._put_lines
diff --git a/pandas/formats/format.py b/pandas/formats/format.py index f89ceaff2ad64..4349835210150 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -2619,7 +2619,7 @@ def set_eng_float_format(accuracy=3, use_eng_prefix=False): def _put_lines(buf, lines): - if any(isinstance(x, compat.text_type) for x in lines): + if any(not isinstance(x, compat.text_type) for x in lines): lines = [compat.text_type(x) for x in lines] buf.write('\n'.join(lines)) diff --git a/pandas/tests/formats/test_format.py b/pandas/tests/formats/test_format.py index 8a4aca2b320aa..44679cc1c3507 100644 --- a/pandas/tests/formats/test_format.py +++ b/pandas/tests/formats/test_format.py @@ -2781,6 +2781,13 @@ def test_float_trim_zeros(self): self.assertTrue(('+10' in line) or skip) skip = False + def test_put_lines(self): + from io import StringIO + buf = StringIO() + lines = ["<class 'pandas.core.frame.DataFrame'>", 'Index: 0 entries', 'Empty DataFrame'] + fmt._put_lines(buf, lines) + assert (buf.getvalue() == "<class 'pandas.core.frame.DataFrame'>\nIndex: 0 entries\nEmpty DataFrame") + def test_dict_entries(self): df = DataFrame({'A': [{'a': 1, 'b': 2}]}) diff --git a/pandas/tests/frame/test_repr_info.py b/pandas/tests/frame/test_repr_info.py index 66e592c013fb1..61ba230e82cee 100644 --- a/pandas/tests/frame/test_repr_info.py +++ b/pandas/tests/frame/test_repr_info.py @@ -381,3 +381,10 @@ def test_info_memory_usage(self): # deep=True, and add on some GC overhead diff = df.memory_usage(deep=True).sum() - sys.getsizeof(df) self.assertTrue(abs(diff) < 100) + + def test_info_empty_frame(self): + io = StringIO() + pd.DataFrame().info(buf=io) + assert (io.getvalue() == "<class 'pandas.core.frame.DataFrame'>\n" + "Index: 0 entries\n" + "Empty DataFrame")
https://api.github.com/repos/pandas-dev/pandas/pulls/13552
2016-07-03T07:22:19Z
2016-08-17T08:10:03Z
null
2016-08-17T08:10:04Z
BUG: Fix .to_excel() for MultiIndex containing a NaN value #13511
diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 317383e866464..1977548883e9e 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -767,3 +767,5 @@ Bug Fixes - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) - Bug in ``Index.union`` returns an incorrect result with a named empty index (:issue:`13432`) - Bugs in ``Index.difference`` and ``DataFrame.join`` raise in Python3 when using mixed-integer indexes (:issue:`13432`, :issue:`12814`) + +- Bug in ``.to_excel()`` when DataFrame contains a MultiIndex which contains a label with a NaN value (:issue:`13511`) diff --git a/pandas/formats/format.py b/pandas/formats/format.py index 436a9d5d5d4c8..50d54ddb95100 100644 --- a/pandas/formats/format.py +++ b/pandas/formats/format.py @@ -1839,7 +1839,11 @@ def _format_hierarchical_rows(self): for spans, levels, labels in zip(level_lengths, self.df.index.levels, self.df.index.labels): - values = levels.take(labels) + + values = levels.take(labels, + allow_fill=levels._can_hold_na, + fill_value=True) + for i in spans: if spans[i] > 1: yield ExcelCell(self.rowcounter + i, gcolidx, diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 55a7f5350719d..34e47ebcfcf5a 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -1328,6 +1328,20 @@ def test_to_excel_multiindex(self): parse_dates=False) tm.assert_frame_equal(frame, df) + # GH13511 + def test_to_excel_multiindex_nan_label(self): + _skip_if_no_xlrd() + + frame = pd.DataFrame({'A': [None, 2, 3], + 'B': [10, 20, 30], + 'C': np.random.sample(3)}) + frame = frame.set_index(['A', 'B']) + + with ensure_clean(self.ext) as path: + frame.to_excel(path, merge_cells=self.merge_cells) + df = read_excel(path, index_col=[0, 1]) + tm.assert_frame_equal(frame, df) + # Test for Issue 11328. If column indices are integers, make # sure they are handled correctly for either setting of # merge_cells
- [X] closes #13511 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13551
2016-07-03T01:46:10Z
2016-07-25T15:07:00Z
2016-07-25T15:07:00Z
2016-07-25T15:07:12Z
Removed unnecessary params in cum_func
diff --git a/pandas/compat/numpy/function.py b/pandas/compat/numpy/function.py index 274761f5d0b9c..15bf6d31b7109 100644 --- a/pandas/compat/numpy/function.py +++ b/pandas/compat/numpy/function.py @@ -21,7 +21,7 @@ from numpy import ndarray from pandas.util.validators import (validate_args, validate_kwargs, validate_args_and_kwargs) -from pandas.core.common import is_integer, UnsupportedFunctionCall +from pandas.core.common import is_bool, is_integer, UnsupportedFunctionCall from pandas.compat import OrderedDict @@ -148,10 +148,26 @@ def validate_clip_with_axis(axis, args, kwargs): CUM_FUNC_DEFAULTS = OrderedDict() CUM_FUNC_DEFAULTS['dtype'] = None CUM_FUNC_DEFAULTS['out'] = None -validate_cum_func = CompatValidator(CUM_FUNC_DEFAULTS, method='kwargs') +validate_cum_func = CompatValidator(CUM_FUNC_DEFAULTS, method='both', + max_fname_arg_count=1) validate_cumsum = CompatValidator(CUM_FUNC_DEFAULTS, fname='cumsum', method='both', max_fname_arg_count=1) + +def validate_cum_func_with_skipna(skipna, args, kwargs, name): + """ + If this function is called via the 'numpy' library, the third + parameter in its signature is 'dtype', which takes either a + 'numpy' dtype or 'None', so check if the 'skipna' parameter is + a boolean or not + """ + if not is_bool(skipna): + args = (skipna,) + args + skipna = True + + validate_cum_func(args, kwargs, fname=name) + return skipna + LOGICAL_FUNC_DEFAULTS = dict(out=None) validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs') diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 6b25cf6ed71a1..cc5c45158bf4f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5484,8 +5484,8 @@ def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func, axis_descr=axis_descr) @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) - def cum_func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): - nv.validate_cum_func(tuple(), kwargs, fname=name) + def cum_func(self, axis=None, skipna=True, *args, **kwargs): + skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name) if axis is None: axis = self._stat_axis_number else:
Picks up from #13167 by properly removing the parameters and ensuring that `numpy` compatibility has been maintained. The current test suite does a good job of checking that already, so no tests were added. Closes #13541.
https://api.github.com/repos/pandas-dev/pandas/pulls/13550
2016-07-02T22:24:08Z
2016-07-03T23:23:09Z
null
2016-07-03T23:24:33Z
DOC: Update old Google Code and SourceForge links
diff --git a/LICENSES/ULTRAJSON_LICENSE b/LICENSES/ULTRAJSON_LICENSE index defca46e7f820..3b2886eb9cfae 100644 --- a/LICENSES/ULTRAJSON_LICENSE +++ b/LICENSES/ULTRAJSON_LICENSE @@ -25,10 +25,10 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library http://www.opensource.apple.com/source/tcl/tcl-14/tcl/license.terms * Copyright (c) 1988-1993 The Regents of the University of California. - * Copyright (c) 1994 Sun Microsystems, Inc. \ No newline at end of file + * Copyright (c) 1994 Sun Microsystems, Inc. diff --git a/README.md b/README.md index 40b8e1e0a1272..e1149ac10795e 100644 --- a/README.md +++ b/README.md @@ -170,8 +170,8 @@ conda install pandas - [SciPy](http://www.scipy.org): miscellaneous statistical functions - [PyTables](http://www.pytables.org): necessary for HDF5-based storage - [SQLAlchemy](http://www.sqlalchemy.org): for SQL database support. Version 0.8.1 or higher recommended. -- [matplotlib](http://matplotlib.sourceforge.net/): for plotting -- [statsmodels](http://statsmodels.sourceforge.net/) +- [matplotlib](http://matplotlib.org/): for plotting +- [statsmodels](http://www.statsmodels.org/) - Needed for parts of `pandas.stats` - For Excel I/O: - [xlrd/xlwt](http://www.python-excel.org/) diff --git a/doc/source/conf.py b/doc/source/conf.py index 99126527759f6..a1b71f0279c7a 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -288,7 +288,7 @@ # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { - 'statsmodels': ('http://statsmodels.sourceforge.net/devel/', None), + 'statsmodels': ('http://www.statsmodels.org/devel/', None), 'matplotlib': ('http://matplotlib.org/', None), 'python': ('http://docs.python.org/3', None), 'numpy': ('http://docs.scipy.org/doc/numpy', None), diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index 51e00d2e01fd0..8fafe8ec9eaa2 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -24,7 +24,7 @@ substantial projects that you feel should be on this list, please let us know. Statistics and Machine Learning ------------------------------- -`Statsmodels <http://statsmodels.sourceforge.net>`__ +`Statsmodels <http://www.statsmodels.org/>`__ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Statsmodels is the prominent python "statistics and econometrics library" and it has @@ -123,7 +123,7 @@ compatible with non-HTML IPython output formats.) qgrid is "an interactive grid for sorting and filtering DataFrames in IPython Notebook" built with SlickGrid. -`Spyder <https://code.google.com/p/spyderlib/>`__ +`Spyder <https://github.com/spyder-ide/spyder/>`__ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Spyder is a cross-platform Qt-based open-source Python IDE with diff --git a/doc/source/index.rst.template b/doc/source/index.rst.template index 6011c22e9cc2e..1996ad75ea92a 100644 --- a/doc/source/index.rst.template +++ b/doc/source/index.rst.template @@ -92,7 +92,7 @@ Some other notes specialized tool. - pandas is a dependency of `statsmodels - <http://statsmodels.sourceforge.net>`__, making it an important part of the + <http://www.statsmodels.org/stable/index.html>`__, making it an important part of the statistical computing ecosystem in Python. - pandas has been used extensively in production in financial applications. diff --git a/doc/source/install.rst b/doc/source/install.rst index 0abaa70586d5a..b43d2b8aac517 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -252,7 +252,7 @@ Optional Dependencies - `pymysql <https://github.com/PyMySQL/PyMySQL>`__: for MySQL. - `SQLite <https://docs.python.org/3.5/library/sqlite3.html>`__: for SQLite, this is included in Python's standard library by default. -* `matplotlib <http://matplotlib.sourceforge.net/>`__: for plotting +* `matplotlib <http://matplotlib.org/>`__: for plotting * `openpyxl <http://packages.python.org/openpyxl/>`__, `xlrd/xlwt <http://www.python-excel.org/>`__: Needed for Excel I/O * `XlsxWriter <https://pypi.python.org/pypi/XlsxWriter>`__: Alternative Excel writer * `Jinja2 <http://jinja.pocoo.org/>`__: Template engine for conditional HTML formatting. @@ -264,9 +264,9 @@ Optional Dependencies <http://qt-project.org/wiki/Category:LanguageBindings::PySide>`__, `pygtk <http://www.pygtk.org/>`__, `xsel <http://www.vergenet.net/~conrad/software/xsel/>`__, or `xclip - <http://sourceforge.net/projects/xclip/>`__: necessary to use + <https://github.com/astrand/xclip/>`__: necessary to use :func:`~pandas.io.clipboard.read_clipboard`. Most package managers on Linux distributions will have ``xclip`` and/or ``xsel`` immediately available for installation. -* Google's `python-gflags <http://code.google.com/p/python-gflags/>`__ , +* Google's `python-gflags <<https://github.com/google/python-gflags/>`__ , `oauth2client <https://github.com/google/oauth2client>`__ , `httplib2 <http://pypi.python.org/pypi/httplib2>`__ and `google-api-python-client <http://github.com/google/google-api-python-client>`__ diff --git a/doc/source/io.rst b/doc/source/io.rst index e9bd029b30537..da0444a8b8df9 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -4277,7 +4277,7 @@ to existing tables. You will need to install some additional dependencies: -- Google's `python-gflags <http://code.google.com/p/python-gflags/>`__ +- Google's `python-gflags <https://github.com/google/python-gflags/>`__ - `httplib2 <http://pypi.python.org/pypi/httplib2>`__ - `google-api-python-client <http://github.com/google/google-api-python-client>`__ diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index 7e72231c21b15..f3df1ebdf25cb 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -73,7 +73,7 @@ The ``convert_to_r_matrix`` function can be replaced by the normal comparison to the ones in pandas, please report this at the `issue tracker <https://github.com/pydata/pandas/issues>`_. -See also the documentation of the `rpy2 <http://rpy.sourceforge.net/>`__ project. +See also the documentation of the `rpy2 <http://rpy2.bitbucket.org/>`__ project. R interface with rpy2 diff --git a/doc/source/release.rst b/doc/source/release.rst index 37778c46a8ec0..df76c90d0f5e6 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -630,7 +630,7 @@ Highlights include: modules are deprecated. We refer users to external packages like `seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`_, `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_ and - `rpy2 <http://rpy.sourceforge.net/>`_ for similar or equivalent + `rpy2 <http://rpy2.bitbucket.org/>`_ for similar or equivalent functionality, see :ref:`here <whatsnew_0160.deprecations>` See the :ref:`v0.16.0 Whatsnew <whatsnew_0160>` overview or the issue tracker on GitHub for an extensive list diff --git a/doc/source/whatsnew/v0.13.0.txt b/doc/source/whatsnew/v0.13.0.txt index e8f2f54b873d6..0944d849cfafd 100644 --- a/doc/source/whatsnew/v0.13.0.txt +++ b/doc/source/whatsnew/v0.13.0.txt @@ -825,7 +825,7 @@ Experimental # Your Google BigQuery Project ID # To find this, see your dashboard: - # https://code.google.com/apis/console/b/0/?noredirect + # https://console.developers.google.com/iam-admin/projects?authuser=0 projectid = xxxxxxxxx; df = gbq.read_gbq(query, project_id = projectid) diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 68a558a2b7fd0..4255f4839bca0 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -19,7 +19,7 @@ Highlights include: modules are deprecated. We refer users to external packages like `seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`_, `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_ and - `rpy2 <http://rpy.sourceforge.net/>`_ for similar or equivalent + `rpy2 <http://rpy2.bitbucket.org/>`_ for similar or equivalent functionality, see :ref:`here <whatsnew_0160.deprecations>` Check the :ref:`API Changes <whatsnew_0160.api>` and :ref:`deprecations <whatsnew_0160.deprecations>` before updating. @@ -508,7 +508,7 @@ Deprecations We refer users to the external package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_. (:issue:`9615`) - The ``pandas.rpy`` interface is deprecated and will be removed in a future version. - Similar functionaility can be accessed thru the `rpy2 <http://rpy.sourceforge.net/>`_ project (:issue:`9602`) + Similar functionaility can be accessed thru the `rpy2 <http://rpy2.bitbucket.org/>`_ project (:issue:`9602`) - Adding ``DatetimeIndex/PeriodIndex`` to another ``DatetimeIndex/PeriodIndex`` is being deprecated as a set-operation. This will be changed to a ``TypeError`` in a future version. ``.union()`` should be used for the union set operation. (:issue:`9094`) - Subtracting ``DatetimeIndex/PeriodIndex`` from another ``DatetimeIndex/PeriodIndex`` is being deprecated as a set-operation. This will be changed to an actual numeric subtraction yielding a ``TimeDeltaIndex`` in a future version. ``.difference()`` should be used for the differencing set operation. (:issue:`9094`) diff --git a/doc/source/whatsnew/v0.8.0.txt b/doc/source/whatsnew/v0.8.0.txt index a76c4e487d5d8..0d2cfeb2d7cfc 100644 --- a/doc/source/whatsnew/v0.8.0.txt +++ b/doc/source/whatsnew/v0.8.0.txt @@ -241,7 +241,7 @@ matplotlib knows how to handle ``datetime.datetime`` but not Timestamp objects. While I recommend that you plot time series using ``TimeSeries.plot``, you can either use ``to_pydatetime`` or register a converter for the Timestamp type. See `matplotlib documentation -<http://matplotlib.sourceforge.net/api/units_api.html>`__ for more on this. +<http://matplotlib.org/api/units_api.html>`__ for more on this. .. warning:: diff --git a/doc/sphinxext/numpydoc/phantom_import.py b/doc/sphinxext/numpydoc/phantom_import.py index 9a60b4a35b18f..4b4fec863a0e3 100755 --- a/doc/sphinxext/numpydoc/phantom_import.py +++ b/doc/sphinxext/numpydoc/phantom_import.py @@ -11,7 +11,7 @@ can be used to get the current docstrings from a Pydocweb instance without needing to rebuild the documented module. -.. [1] http://code.google.com/p/pydocweb +.. [1] https://github.com/pv/pydocweb """ from __future__ import division, absolute_import, print_function diff --git a/pandas/io/auth.py b/pandas/io/auth.py index b20b7c8ff1b04..e42df6a7309b7 100644 --- a/pandas/io/auth.py +++ b/pandas/io/auth.py @@ -30,7 +30,8 @@ class AuthenticationConfigError(ValueError): %s -with information from the APIs Console <https://code.google.com/apis/console>. +with information from the APIs Console +<https://console.developers.google.com/iam-admin/projects>. """ DOC_URL = ('https://developers.google.com/api-client-library/python/guide/' diff --git a/pandas/io/ga.py b/pandas/io/ga.py index 6dd0bb7472c37..45424e78ddbe7 100644 --- a/pandas/io/ga.py +++ b/pandas/io/ga.py @@ -1,5 +1,5 @@ """ -1. Goto https://code.google.com/apis/console +1. Goto https://console.developers.google.com/iam-admin/projects 2. Create new project 3. Goto APIs and register for OAuth2.0 for installed applications 4. Download JSON secret file and move into same directory as this file diff --git a/pandas/io/stata.py b/pandas/io/stata.py index ae7200cf6fb2e..c7390cf240f8a 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -7,7 +7,7 @@ a once again improved version. You can find more information on http://presbrey.mit.edu/PyDTA and -http://statsmodels.sourceforge.net/devel/ +http://www.statsmodels.org/devel/ """ import numpy as np diff --git a/pandas/src/klib/khash.h b/pandas/src/klib/khash.h index 0f1a17c6333f4..dc004a0e1770b 100644 --- a/pandas/src/klib/khash.h +++ b/pandas/src/klib/khash.h @@ -52,7 +52,7 @@ int main() { * The capacity is a power of 2. This seems to dramatically improve the speed for simple keys. Thank Zilong Tan for the suggestion. Reference: - - http://code.google.com/p/ulib/ + - https://github.com/stefanocasazza/ULib - http://nothings.org/computer/judy/ * Allow to optionally use linear probing which usually has better diff --git a/pandas/src/ujson/lib/ultrajson.h b/pandas/src/ujson/lib/ultrajson.h index f83f74a0fe0da..c37fe8c8e6c38 100644 --- a/pandas/src/ujson/lib/ultrajson.h +++ b/pandas/src/ujson/lib/ultrajson.h @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/src/ujson/lib/ultrajsondec.c b/pandas/src/ujson/lib/ultrajsondec.c index 9a4d5972b101b..5496068832f2e 100644 --- a/pandas/src/ujson/lib/ultrajsondec.c +++ b/pandas/src/ujson/lib/ultrajsondec.c @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/src/ujson/lib/ultrajsonenc.c b/pandas/src/ujson/lib/ultrajsonenc.c index 5e2a226ae8d63..2adf3cb707bdb 100644 --- a/pandas/src/ujson/lib/ultrajsonenc.c +++ b/pandas/src/ujson/lib/ultrajsonenc.c @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/src/ujson/python/JSONtoObj.c b/pandas/src/ujson/python/JSONtoObj.c index 9c1b4febd9895..e4d02db4cb60a 100644 --- a/pandas/src/ujson/python/JSONtoObj.c +++ b/pandas/src/ujson/python/JSONtoObj.c @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 46ae623ae88a7..925c18cd23d8f 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/src/ujson/python/py_defines.h b/pandas/src/ujson/python/py_defines.h index 7a5083e131512..723eaed336f6b 100644 --- a/pandas/src/ujson/python/py_defines.h +++ b/pandas/src/ujson/python/py_defines.h @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/src/ujson/python/ujson.c b/pandas/src/ujson/python/ujson.c index 2eb8a80c0325c..48ea92ed3bc8c 100644 --- a/pandas/src/ujson/python/ujson.c +++ b/pandas/src/ujson/python/ujson.c @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/src/ujson/python/version.h b/pandas/src/ujson/python/version.h index 0ccfbfe74521c..2d4fd137edefe 100644 --- a/pandas/src/ujson/python/version.h +++ b/pandas/src/ujson/python/version.h @@ -26,7 +26,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) -http://code.google.com/p/stringencoders/ +https://github.com/client9/stringencoders Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved. Numeric decoder derived from from TCL library diff --git a/pandas/stats/fama_macbeth.py b/pandas/stats/fama_macbeth.py index caad53df2c7fe..f7d50e8e72a5c 100644 --- a/pandas/stats/fama_macbeth.py +++ b/pandas/stats/fama_macbeth.py @@ -37,7 +37,8 @@ def __init__(self, y, x, intercept=True, nw_lags=None, import warnings warnings.warn("The pandas.stats.fama_macbeth module is deprecated and will be " "removed in a future version. We refer to external packages " - "like statsmodels, see here: http://statsmodels.sourceforge.net/stable/index.html", + "like statsmodels, see here: " + "http://www.statsmodels.org/stable/index.html", FutureWarning, stacklevel=4) if dropped_dummies is None: diff --git a/pandas/stats/ols.py b/pandas/stats/ols.py index e2375ea180ed2..678689f2d2b30 100644 --- a/pandas/stats/ols.py +++ b/pandas/stats/ols.py @@ -51,7 +51,8 @@ def __init__(self, y, x, intercept=True, weights=None, nw_lags=None, import warnings warnings.warn("The pandas.stats.ols module is deprecated and will be " "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: http://statsmodels.sourceforge.net/stable/regression.html", + "like statsmodels, see some examples here: " + "http://www.statsmodels.org/stable/regression.html", FutureWarning, stacklevel=4) try: diff --git a/pandas/stats/plm.py b/pandas/stats/plm.py index dca1977fb19bd..baa30cde9344e 100644 --- a/pandas/stats/plm.py +++ b/pandas/stats/plm.py @@ -39,7 +39,8 @@ def __init__(self, y, x, weights=None, intercept=True, nw_lags=None, import warnings warnings.warn("The pandas.stats.plm module is deprecated and will be " "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: http://statsmodels.sourceforge.net/stable/mixed_linear.html", + "like statsmodels, see some examples here: " + "http://www.statsmodels.org/stable/mixed_linear.html", FutureWarning, stacklevel=4) self._x_orig = x self._y_orig = y @@ -743,7 +744,8 @@ def __init__(self, y, x, window_type='full_sample', window=None, import warnings warnings.warn("The pandas.stats.plm module is deprecated and will be " "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: http://statsmodels.sourceforge.net/stable/mixed_linear.html", + "like statsmodels, see some examples here: " + "http://www.statsmodels.org/stable/mixed_linear.html", FutureWarning, stacklevel=4) for attr in self.ATTRIBUTES: diff --git a/pandas/stats/var.py b/pandas/stats/var.py index cc78ca2886fb3..db4028d60f5c8 100644 --- a/pandas/stats/var.py +++ b/pandas/stats/var.py @@ -31,7 +31,8 @@ def __init__(self, data, p=1, intercept=True): import warnings warnings.warn("The pandas.stats.var module is deprecated and will be " "removed in a future version. We refer to external packages " - "like statsmodels, see some examples here: http://statsmodels.sourceforge.net/stable/vector_ar.html#var", + "like statsmodels, see some examples here: " + "http://www.statsmodels.org/stable/vector_ar.html#var", FutureWarning, stacklevel=4) try: diff --git a/pandas/util/decorators.py b/pandas/util/decorators.py index 58cd0c13d8ec7..e1888a3ffd62a 100644 --- a/pandas/util/decorators.py +++ b/pandas/util/decorators.py @@ -94,7 +94,7 @@ def wrapper(*args, **kwargs): # Substitution and Appender are derived from matplotlib.docstring (1.1.0) -# module http://matplotlib.sourceforge.net/users/license.html +# module http://matplotlib.org/users/license.html class Substitution(object):
https://api.github.com/repos/pandas-dev/pandas/pulls/13534
2016-06-30T03:38:02Z
2016-07-05T08:38:26Z
2016-07-05T08:38:26Z
2016-07-05T08:38:32Z
Update documentation for rename
diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 917d2f2bb8b04..8145e9536a82a 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -1159,14 +1159,17 @@ mapping (a dict or Series) or an arbitrary function. s.rename(str.upper) If you pass a function, it must return a value when called with any of the -labels (and must produce a set of unique values). But if you pass a dict or -Series, it need only contain a subset of the labels as keys: +labels (and must produce a set of unique values). A dict or +Series can also be used: .. ipython:: python df.rename(columns={'one' : 'foo', 'two' : 'bar'}, index={'a' : 'apple', 'b' : 'banana', 'd' : 'durian'}) +If the mapping doesn't include a column/index label, it isn't renamed. Also +extra labels in the mapping don't throw an error. + The :meth:`~DataFrame.rename` method also provides an ``inplace`` named parameter that is by default ``False`` and copies the underlying data. Pass ``inplace=True`` to rename the data in place. diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 348281d1a7e30..5ce9161fdffb0 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -555,8 +555,8 @@ def swaplevel(self, i=-2, j=-1, axis=0): _shared_docs['rename'] = """ Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left - as-is. Alternatively, change ``Series.name`` with a scalar - value (Series only). + as-is. Extra labels listed don't throw an error. Alternatively, change + ``Series.name`` with a scalar value (Series only). Parameters ---------- @@ -611,6 +611,11 @@ def swaplevel(self, i=-2, j=-1, axis=0): 0 1 4 1 2 5 2 3 6 + >>> df.rename(index=str, columns={"A": "a", "C": "c"}) + a B + 0 1 4 + 1 2 5 + 2 3 6 """ @Appender(_shared_docs['rename'] % dict(axes='axes keywords for this'
- [x] closes #13473 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13533
2016-06-30T01:26:49Z
2016-06-30T22:59:11Z
2016-06-30T22:59:11Z
2016-06-30T22:59:17Z
DOC: update sphinx requirements for doc building
diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 6e0c747cd06fc..51fa2a9de953b 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -351,31 +351,33 @@ How to build the *pandas* documentation Requirements ~~~~~~~~~~~~ -To build the *pandas* docs there are some extra requirements: you will need to +First, you need to have a development environment to be able to build pandas +(see the docs on :ref:`creating a development environment above <contributing.dev_env>`). +Further, to build the docs, there are some extra requirements: you will need to have ``sphinx`` and ``ipython`` installed. `numpydoc <https://github.com/numpy/numpydoc>`_ is used to parse the docstrings that follow the Numpy Docstring Standard (see above), but you don't need to install this because a local copy of numpydoc is included in the *pandas* source code. +`nbconvert <https://nbconvert.readthedocs.io/en/latest/>`_ and +`nbformat <http://nbformat.readthedocs.io/en/latest/>`_ are required to build +the Jupyter notebooks included in the documentation. -It is easiest to :ref:`create a development environment <contributing.dev_env>`, then install:: +If you have a conda environment named ``pandas_dev``, you can install the extra +requirements with:: conda install -n pandas_dev sphinx ipython nbconvert nbformat -Furthermore, it is recommended to have all `optional dependencies -<http://pandas.pydata.org/pandas-docs/dev/install.html#optional-dependencies>`_ +Furthermore, it is recommended to have all :ref:`optional dependencies <install.optional_dependencies>`. installed. This is not strictly necessary, but be aware that you will see some error messages when building the docs. This happens because all the code in the documentation is executed during the doc build, and so code examples using optional dependencies will generate errors. Run ``pd.show_versions()`` to get an overview of the installed version of all dependencies. -`nbconvert <https://nbconvert.readthedocs.io/en/latest/>`_ and `nbformat <http://nbformat.readthedocs.io/en/latest/>`_ are required to build the Jupyter notebooks -included in the documentation. .. warning:: - You need to have ``sphinx`` version 1.2.2 or newer, but older than version 1.3. - Versions before 1.1.3 should also work. + You need to have ``sphinx`` version >= 1.3.2. Building the documentation ~~~~~~~~~~~~~~~~~~~~~~~~~~
Simplified the version requirement for sphinx (>= 1.3.2). In principle >= 1.2.2 but < 1.3.0 also works, but left it out for simplicity. @cswarth Do you have other things that you find unclear about the requirements for the doc build environment?
https://api.github.com/repos/pandas-dev/pandas/pulls/13532
2016-06-29T19:16:49Z
2016-07-04T10:37:52Z
2016-07-04T10:37:52Z
2016-07-04T10:37:53Z
DOC: update brief documentation on visualization projects
diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index 0d7315d20eac3..51e00d2e01fd0 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -77,8 +77,16 @@ more advanced types of plots then those offered by pandas. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The `Vincent <https://github.com/wrobstory/vincent>`__ project leverages `Vega <https://github.com/trifacta/vega>`__ -(that in turn, leverages `d3 <http://d3js.org/>`__) to create plots . It has great support -for pandas data objects. +(that in turn, leverages `d3 <http://d3js.org/>`__) to create +plots. Although functional, as of Summer 2016 the Vincent project has not been updated +in over two years and is `unlikely to receive further updates <https://github.com/wrobstory/vincent#2015-08-12-update>`__. + +`IPython Vega <https://github.com/vega/ipyvega>`__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Like Vincent, the `IPython Vega <https://github.com/vega/ipyvega>`__ project leverages `Vega +<https://github.com/trifacta/vega>`__ to create plots, but primarily +targets the IPython Notebook environment. `Plotly <https://plot.ly/python>`__ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- [ X] passes `git diff upstream/master | flake8 --diff` Minor Documentaion change to note that Vincent project has intentionally been abandoned and is not longer maintained. Add a pointer to IPython Vega successor.
https://api.github.com/repos/pandas-dev/pandas/pulls/13528
2016-06-29T01:54:32Z
2016-06-29T10:15:56Z
2016-06-29T10:15:56Z
2016-06-29T10:16:18Z
Cython cache diff compare
diff --git a/ci/prep_cython_cache.sh b/ci/prep_cython_cache.sh index 3295bd15d016c..6f16dce2fb431 100755 --- a/ci/prep_cython_cache.sh +++ b/ci/prep_cython_cache.sh @@ -1,43 +1,73 @@ #!/bin/bash ls "$HOME/.cache/" + +PYX_CACHE_DIR="$HOME/.cache/pyxfiles" +pyx_file_list=`find ${TRAVIS_BUILD_DIR} -name "*.pyx"` +pyx_cache_file_list=`find ${PYX_CACHE_DIR} -name "*.pyx"` + CACHE_File="$HOME/.cache/cython_files.tar" +# Clear the cython cache 0 = NO, 1 = YES clear_cache=0 + +pyx_files=`echo "$pyx_file_list" | wc -l` +pyx_cache_files=`echo "$pyx_cache_file_list" | wc -l` + +if [[ pyx_files -ne pyx_cache_files ]] +then + echo "Different number of pyx files" + clear_cache=1 +fi + home_dir=$(pwd) -if [ -f "$CACHE_File" ] && [ "$USE_CACHE" ]; then +if [ -f "$CACHE_File" ] && [ "$USE_CACHE" ] && [ -d "$PYX_CACHE_DIR" ]; then + + echo "Cache available - checking pyx diff" + + for i in ${pyx_file_list} + do + diff=`diff -u $i $PYX_CACHE_DIR${i}` + if [[ $? -eq 2 ]] + then + echo "${i##*/} can't be diffed; probably not in cache" + clear_cache=1 + fi + if [[ ! -z $diff ]] + then + echo "${i##*/} has changed:" + echo $diff + clear_cache=1 + fi + done - echo "Cache available" - clear_cache=1 - # did the last commit change cython files? - # go back 2 commits if [ "$TRAVIS_PULL_REQUEST" == "false" ] then - echo "Not a PR: checking for cython files changes from last 2 commits" - git diff HEAD~2 --numstat | grep -E "pyx|pxd" - retval=$(git diff HEAD~2 --numstat | grep -E "pyx|pxd"| wc -l) + echo "Not a PR" + # Uncomment next 2 lines to turn off cython caching not in a PR + # echo "Non PR cython caching is disabled" + # clear_cache=1 else - echo "PR: checking for any cython file changes from last 5 commits" - git diff PR_HEAD~5 --numstat | grep -E "pyx|pxd" - retval=$(git diff PR_HEAD~5 --numstat | grep -E "pyx|pxd"| wc -l) - echo "Forcing cython rebuild due to possibility of history rewritting in PR" - retval=-1 + echo "In a PR" + # Uncomment next 2 lines to turn off cython caching in a PR + # echo "PR cython caching is disabled" + # clear_cache=1 fi - echo "number of cython files changed: $retval" + fi -if [ $clear_cache -eq 1 ] && [ $retval -eq 0 ] && [ "$USE_CACHE" ] +if [ $clear_cache -eq 0 ] && [ "$USE_CACHE" ] then - # nope, reuse cython files + # No and use_cache is set echo "Will reuse cached cython file" cd / tar xvmf $CACHE_File cd $home_dir else echo "Rebuilding cythonized files" - echo "Use cache = $USE_CACHE" - echo "Clear cache = $clear_cache" + echo "Use cache (Blank if not set) = $USE_CACHE" + echo "Clear cache (1=YES) = $clear_cache" fi diff --git a/ci/submit_cython_cache.sh b/ci/submit_cython_cache.sh index 3d41d652960c9..4f60df0ccb2d8 100755 --- a/ci/submit_cython_cache.sh +++ b/ci/submit_cython_cache.sh @@ -1,17 +1,23 @@ #!/bin/bash CACHE_File="$HOME/.cache/cython_files.tar" +PYX_CACHE_DIR="$HOME/.cache/pyxfiles" +pyx_file_list=`find ${TRAVIS_BUILD_DIR} -name "*.pyx"` + rm -rf $CACHE_File +rm -rf $PYX_CACHE_DIR home_dir=$(pwd) -pyx_files=`find ${TRAVIS_BUILD_DIR} -name "*.pyx"` +mkdir $PYX_CACHE_DIR +rsync -Rv $pyx_file_list $PYX_CACHE_DIR + echo "pyx files:" -echo $pyx_files +echo $pyx_file_list tar cf ${CACHE_File} --files-from /dev/null -for i in ${pyx_files} +for i in ${pyx_file_list} do f=${i%.pyx} ls $f.{c,cpp} | tar rf ${CACHE_File} -T -
As talked about in #13425 with @gfyoung and @jreback this PR does cython caching by comparing the pyx files and not relying on the git history.
https://api.github.com/repos/pandas-dev/pandas/pulls/13526
2016-06-28T19:21:03Z
2016-07-03T23:24:49Z
null
2016-08-15T09:47:51Z
Fixes a missing ".reshape" call
diff --git a/pandas/core/common.py b/pandas/core/common.py index 28bae362a3411..23175475d29e9 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1194,7 +1194,7 @@ def _try_timedelta(v): try: return to_timedelta(v)._values.reshape(shape) except: - return v + return v.reshape(shape) # do a quick inference for perf sample = v[:min(3, len(v))]
closes https://github.com/pydata/pandas/issues/13287 Currently, some 2D arrays can go into [_possibly_infer_to_datetimelike](https://github.com/andyljones/pandas/blob/59b9e835d49036cabad19cf2c53140ac9f2eb465/pandas/core/common.py#L1133-L1222) and come out 1D. The issue is that the input is ravelled [at one point](https://github.com/andyljones/pandas/blob/59b9e835d49036cabad19cf2c53140ac9f2eb465/pandas/core/common.py#L1165), but only unravelled back to it's original form [if a certain path it taken through the function](https://github.com/andyljones/pandas/blob/59b9e835d49036cabad19cf2c53140ac9f2eb465/pandas/core/common.py#L1187). This pull request adds the same `.reshape(shape)` call to the _other_ path through the function.
https://api.github.com/repos/pandas-dev/pandas/pulls/13525
2016-06-28T16:08:09Z
2016-07-06T21:31:17Z
null
2023-05-11T01:13:44Z
ENH: Allow dict as the argument to Index.map (GH13517)
diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 40fec4d071f16..44b6059c8951b 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -207,6 +207,7 @@ Other enhancements - The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`) - The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`) +- ``Index.map`` now accepts a dict-like object as its argument (:issue:`13517`) - ``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`) - ``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 96472698ba9d9..558e5800036ed 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -2295,14 +2295,18 @@ def map(self, mapper): Parameters ---------- - mapper : callable + mapper : callable or dict-like Function to be applied. Returns ------- applied : array """ - return self._arrmap(self.values, mapper) + if com.is_dict_like(mapper): + map_fn = lambda key: mapper[key] if key in mapper.keys() else None + return self._arrmap(self.values, map_fn) + else: + return self._arrmap(self.values, mapper) def isin(self, values, level=None): """ diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index d535eaa238567..6c9ce343fd80e 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -20,7 +20,7 @@ Float64Index, Int64Index, CategoricalIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex) -from pandas.util.testing import assert_almost_equal +from pandas.util.testing import assert_almost_equal, assert_numpy_array_equal from pandas.compat.numpy import np_datetime64_compat import pandas.core.config as cf @@ -1462,6 +1462,27 @@ def test_conversion_preserves_name(self): self.assertEqual(i.name, pd.to_datetime(i).name) self.assertEqual(i.name, pd.to_timedelta(i).name) + def test_map_dict(self): + # GH 13517 + i = pd.Index(['a', 'b', 'c', 'd']) + + # actual dict + d = {'a': 0, 'b': 2, 'c': 1, 'd': 5} + actual = i.map(d) + expected = np.array([0, 2, 1, 5]) + tm.assert_numpy_array_equal(actual, expected) + + # series + s = pd.Series([0, 2, 1, 5], index=['a', 'b', 'c', 'd']) + actual = i.map(s) + tm.assert_numpy_array_equal(actual, expected) + + # missing values + d2 = {'b': 2, 'd': 5} + actual = i.map(d2) + expected = np.array([np.nan, 2, np.nan, 5]) + tm.assert_numpy_array_equal(actual, expected) + def test_string_index_repr(self): # py3/py2 repr can differ because of "u" prefix # which also affects to displayed element size
- [X] closes #13517 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13518
2016-06-26T17:22:49Z
2016-11-16T22:23:39Z
null
2016-11-16T22:23:39Z
BUG: Can't store callables using __setitem__
diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 40fec4d071f16..eae03b2a86661 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -421,6 +421,7 @@ Other API changes - ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`) - ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`) - ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`) +- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`) .. _whatsnew_0182.deprecations: @@ -482,7 +483,6 @@ Bug Fixes - Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`) - Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`) - - Bug in various index types, which did not propagate the name of passed index (:issue:`12309`) - Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`) - Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b4b35953b4282..e804271d8afa9 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2395,7 +2395,7 @@ def _setitem_frame(self, key, value): self._check_inplace_setting(value) self._check_setitem_copy() - self.where(-key, value, inplace=True) + self._where(-key, value, inplace=True) def _ensure_valid_index(self, value): """ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 348281d1a7e30..261d273ca293d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4419,54 +4419,14 @@ def _align_series(self, other, join='outer', axis=None, level=None, right = right.fillna(fill_value, method=method, limit=limit) return left.__finalize__(self), right.__finalize__(other) - _shared_docs['where'] = (""" - Return an object of same shape as self and whose corresponding - entries are from self where cond is %(cond)s and otherwise are from - other. - - Parameters - ---------- - cond : boolean %(klass)s, array or callable - If cond is callable, it is computed on the %(klass)s and - should return boolean %(klass)s or array. - The callable must not change input %(klass)s - (though pandas doesn't check it). - - .. versionadded:: 0.18.1 - - A callable can be used as cond. - - other : scalar, %(klass)s, or callable - If other is callable, it is computed on the %(klass)s and - should return scalar or %(klass)s. - The callable must not change input %(klass)s - (though pandas doesn't check it). - - .. versionadded:: 0.18.1 - - A callable can be used as other. - - inplace : boolean, default False - Whether to perform the operation in place on the data - axis : alignment axis if needed, default None - level : alignment level if needed, default None - try_cast : boolean, default False - try to cast the result back to the input type (if possible), - raise_on_error : boolean, default True - Whether to raise on invalid data types (e.g. trying to where on - strings) - - Returns - ------- - wh : same type as caller - """) - - @Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="True")) - def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, - try_cast=False, raise_on_error=True): + def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, + try_cast=False, raise_on_error=True): + """ + Equivalent to public method `where`, except that `other` is not + applied as a function even if callable. Used in __setitem__. + """ cond = com._apply_if_callable(cond, self) - other = com._apply_if_callable(other, self) if isinstance(cond, NDFrame): cond, _ = cond.align(self, join='right', broadcast_axis=1) @@ -4622,6 +4582,56 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, return self._constructor(new_data).__finalize__(self) + _shared_docs['where'] = (""" + Return an object of same shape as self and whose corresponding + entries are from self where cond is %(cond)s and otherwise are from + other. + + Parameters + ---------- + cond : boolean %(klass)s, array or callable + If cond is callable, it is computed on the %(klass)s and + should return boolean %(klass)s or array. + The callable must not change input %(klass)s + (though pandas doesn't check it). + + .. versionadded:: 0.18.1 + + A callable can be used as cond. + + other : scalar, %(klass)s, or callable + If other is callable, it is computed on the %(klass)s and + should return scalar or %(klass)s. + The callable must not change input %(klass)s + (though pandas doesn't check it). + + .. versionadded:: 0.18.1 + + A callable can be used as other. + + inplace : boolean, default False + Whether to perform the operation in place on the data + axis : alignment axis if needed, default None + level : alignment level if needed, default None + try_cast : boolean, default False + try to cast the result back to the input type (if possible), + raise_on_error : boolean, default True + Whether to raise on invalid data types (e.g. trying to where on + strings) + + Returns + ------- + wh : same type as caller + """) + + @Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="True")) + def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, + try_cast=False, raise_on_error=True): + + other = com._apply_if_callable(other, self) + return self._where(cond, other, inplace, axis, level, try_cast, + raise_on_error) + @Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="False")) def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True): diff --git a/pandas/core/series.py b/pandas/core/series.py index cf1639bacc3be..e2726bef0bd03 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -738,7 +738,7 @@ def setitem(key, value): if is_bool_indexer(key): key = check_bool_indexer(self.index, key) try: - self.where(~key, value, inplace=True) + self._where(~key, value, inplace=True) return except InvalidIndexError: pass diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 78354f32acbda..d7fed8131a4f4 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -207,6 +207,16 @@ def test_setitem_callable(self): exp = pd.DataFrame({'A': [11, 12, 13, 14], 'B': [5, 6, 7, 8]}) tm.assert_frame_equal(df, exp) + def test_setitem_other_callable(self): + # GH 13299 + inc = lambda x: x + 1 + + df = pd.DataFrame([[-1, 1], [1, -1]]) + df[df > 0] = inc + + expected = pd.DataFrame([[-1, inc], [inc, -1]]) + tm.assert_frame_equal(df, expected) + def test_getitem_boolean(self): # boolean indexing d = self.tsframe.index[10] diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index d01ac3e1aef42..15ca238ee32a0 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -441,6 +441,16 @@ def test_setitem_callable(self): s[lambda x: 'A'] = -1 tm.assert_series_equal(s, pd.Series([-1, 2, 3, 4], index=list('ABCD'))) + def test_setitem_other_callable(self): + # GH 13299 + inc = lambda x: x + 1 + + s = pd.Series([1, 2, -1, 4]) + s[s < 0] = inc + + expected = pd.Series([1, 2, inc, 4]) + tm.assert_series_equal(s, expected) + def test_slice(self): numSlice = self.series[10:20] numSliceEnd = self.series[-10:]
- [X] closes #13299 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/13516
2016-06-26T15:40:42Z
2016-07-01T14:18:37Z
2016-07-01T14:18:37Z
2016-07-01T14:18:43Z
Force rebuilding of python files in PR
diff --git a/ci/prep_cython_cache.sh b/ci/prep_cython_cache.sh index 162f7a1034be6..3295bd15d016c 100755 --- a/ci/prep_cython_cache.sh +++ b/ci/prep_cython_cache.sh @@ -21,6 +21,8 @@ if [ -f "$CACHE_File" ] && [ "$USE_CACHE" ]; then echo "PR: checking for any cython file changes from last 5 commits" git diff PR_HEAD~5 --numstat | grep -E "pyx|pxd" retval=$(git diff PR_HEAD~5 --numstat | grep -E "pyx|pxd"| wc -l) + echo "Forcing cython rebuild due to possibility of history rewritting in PR" + retval=-1 fi echo "number of cython files changed: $retval" fi
Force the rebuilding of cython files in PR as can't rely on the git history
https://api.github.com/repos/pandas-dev/pandas/pulls/13515
2016-06-26T12:14:51Z
2016-06-27T13:04:03Z
null
2016-06-27T13:22:29Z
BUG/PERF: Sort mixed-int in Py3, fix Index.difference
diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py index 8c65f09937df4..a0a1b560d36f3 100644 --- a/asv_bench/benchmarks/index_object.py +++ b/asv_bench/benchmarks/index_object.py @@ -63,6 +63,27 @@ def time_index_datetime_union(self): self.rng.union(self.rng2) +class index_datetime_set_difference(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.A = self.N - 20000 + self.B = self.N + 20000 + self.idx1 = DatetimeIndex(range(self.N)) + self.idx2 = DatetimeIndex(range(self.A, self.B)) + self.idx3 = DatetimeIndex(range(self.N, self.B)) + + def time_index_datetime_difference(self): + self.idx1.difference(self.idx2) + + def time_index_datetime_difference_disjoint(self): + self.idx1.difference(self.idx3) + + def time_index_datetime_symmetric_difference(self): + self.idx1.symmetric_difference(self.idx2) + + class index_float64_boolean_indexer(object): goal_time = 0.2 @@ -183,6 +204,40 @@ def time_index_int64_union(self): self.left.union(self.right) +class index_int64_set_difference(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.options = np.arange(self.N) + self.left = Index(self.options.take( + np.random.permutation(self.N)[:(self.N // 2)])) + self.right = Index(self.options.take( + np.random.permutation(self.N)[:(self.N // 2)])) + + def time_index_int64_difference(self): + self.left.difference(self.right) + + def time_index_int64_symmetric_difference(self): + self.left.symmetric_difference(self.right) + + +class index_str_set_difference(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.strs = tm.rands_array(10, self.N) + self.left = Index(self.strs[:self.N * 2 // 3]) + self.right = Index(self.strs[self.N // 3:]) + + def time_str_difference(self): + self.left.difference(self.right) + + def time_str_symmetric_difference(self): + self.left.symmetric_difference(self.right) + + class index_str_boolean_indexer(object): goal_time = 0.2 diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 0b9695125c0a9..5e7130d99c8ec 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -395,7 +395,7 @@ resulting dtype will be upcast, which is unchanged from previous. pd.merge(df1, df2, how='outer', on='key') pd.merge(df1, df2, how='outer', on='key').dtypes -.. _whatsnew_0190.describe: +.. _whatsnew_0190.api.describe: ``.describe()`` changes ^^^^^^^^^^^^^^^^^^^^^^^ @@ -484,6 +484,34 @@ New Behavior: pd.NaT + 1 pd.NaT - 1 +.. _whatsnew_0190.api.difference: + +``Index.difference`` and ``.symmetric_difference`` changes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``Index.difference`` and ``Index.symmetric_difference`` will now, more consistently, treat ``NaN`` values as any other values. (:issue:`13514`) + +.. ipython:: python + + idx1 = pd.Index([1, 2, 3, np.nan]) + idx2 = pd.Index([0, 1, np.nan]) + +Previous Behavior: + +.. code-block:: ipython + + In [3]: idx1.difference(idx2) + Out[3]: Float64Index([nan, 2.0, 3.0], dtype='float64') + + In [4]: idx1.symmetric_difference(idx2) + Out[4]: Float64Index([0.0, nan, 2.0, 3.0], dtype='float64') + +New Behavior: + +.. ipython:: python + + idx1.difference(idx2) + idx1.symmetric_difference(idx2) .. _whatsnew_0190.deprecations: @@ -520,7 +548,7 @@ Performance Improvements - Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`) - Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`) - +- Improved performance of ``Index.difference`` (:issue:`12044`) .. _whatsnew_0190.bug_fixes: @@ -614,3 +642,5 @@ Bug Fixes - Bug in ``groupby`` with ``as_index=False`` returns all NaN's when grouping on multiple columns including a categorical one (:issue:`13204`) - Bug where ``pd.read_gbq()`` could throw ``ImportError: No module named discovery`` as a result of a naming conflict with another python package called apiclient (:issue:`13454`) +- Bug in ``Index.union`` returns an incorrect result with a named empty index (:issue:`13432`) +- Bugs in ``Index.difference`` and ``DataFrame.join`` raise in Python3 when using mixed-integer indexes (:issue:`13432`, :issue:`12814`) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index c3ba734353a8d..5cc54e61f6b2a 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -163,6 +163,104 @@ def isin(comps, values): return f(comps, values) +def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False): + """ + Sort ``values`` and reorder corresponding ``labels``. + ``values`` should be unique if ``labels`` is not None. + Safe for use with mixed types (int, str), orders ints before strs. + + .. versionadded:: 0.19.0 + + Parameters + ---------- + values : list-like + Sequence; must be unique if ``labels`` is not None. + labels : list_like + Indices to ``values``. All out of bound indices are treated as + "not found" and will be masked with ``na_sentinel``. + na_sentinel : int, default -1 + Value in ``labels`` to mark "not found". + Ignored when ``labels`` is None. + assume_unique : bool, default False + When True, ``values`` are assumed to be unique, which can speed up + the calculation. Ignored when ``labels`` is None. + + Returns + ------- + ordered : ndarray + Sorted ``values`` + new_labels : ndarray + Reordered ``labels``; returned when ``labels`` is not None. + + Raises + ------ + TypeError + * If ``values`` is not list-like or if ``labels`` is neither None + nor list-like + * If ``values`` cannot be sorted + ValueError + * If ``labels`` is not None and ``values`` contain duplicates. + """ + if not is_list_like(values): + raise TypeError("Only list-like objects are allowed to be passed to" + "safe_sort as values") + values = np.array(values, copy=False) + + def sort_mixed(values): + # order ints before strings, safe in py3 + str_pos = np.array([isinstance(x, string_types) for x in values], + dtype=bool) + nums = np.sort(values[~str_pos]) + strs = np.sort(values[str_pos]) + return _ensure_object(np.concatenate([nums, strs])) + + sorter = None + if compat.PY3 and lib.infer_dtype(values) == 'mixed-integer': + # unorderable in py3 if mixed str/int + ordered = sort_mixed(values) + else: + try: + sorter = values.argsort() + ordered = values.take(sorter) + except TypeError: + # try this anyway + ordered = sort_mixed(values) + + # labels: + + if labels is None: + return ordered + + if not is_list_like(labels): + raise TypeError("Only list-like objects or None are allowed to be" + "passed to safe_sort as labels") + labels = _ensure_platform_int(np.asarray(labels)) + + from pandas import Index + if not assume_unique and not Index(values).is_unique: + raise ValueError("values should be unique if labels is not None") + + if sorter is None: + # mixed types + (hash_klass, _), values = _get_data_algo(values, _hashtables) + t = hash_klass(len(values)) + t.map_locations(values) + sorter = _ensure_platform_int(t.lookup(ordered)) + + reverse_indexer = np.empty(len(sorter), dtype=np.int_) + reverse_indexer.put(sorter, np.arange(len(sorter))) + + mask = (labels < -len(values)) | (labels >= len(values)) | \ + (labels == na_sentinel) + + # (Out of bound indices will be masked with `na_sentinel` next, so we may + # deal with them here without performance loss using `mode='wrap'`.) + new_labels = reverse_indexer.take(labels, mode='wrap') + np.putmask(new_labels, mask, na_sentinel) + + return ordered, new_labels + + def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): """ Encode input values as an enumerated type or categorical variable @@ -210,33 +308,10 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): uniques = uniques.to_array() if sort and len(uniques) > 0: - try: - sorter = uniques.argsort() - except: - # unorderable in py3 if mixed str/int - t = hash_klass(len(uniques)) - t.map_locations(_ensure_object(uniques)) - - # order ints before strings - ordered = np.concatenate([ - np.sort(np.array([e for i, e in enumerate(uniques) if f(e)], - dtype=object)) for f in - [lambda x: not isinstance(x, string_types), - lambda x: isinstance(x, string_types)]]) - sorter = _ensure_platform_int(t.lookup( - _ensure_object(ordered))) - - reverse_indexer = np.empty(len(sorter), dtype=np.int_) - reverse_indexer.put(sorter, np.arange(len(sorter))) - - mask = labels < 0 - labels = reverse_indexer.take(labels) - np.putmask(labels, mask, -1) - - uniques = uniques.take(sorter) + uniques, labels = safe_sort(uniques, labels, na_sentinel=na_sentinel, + assume_unique=True) if is_datetimetz_type: - # reset tz uniques = DatetimeIndex(uniques.astype('M8[ns]')).tz_localize( values.tz) diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index b013d6ccb0b8e..71d5fdd17ee5c 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1773,7 +1773,7 @@ def _get_consensus_name(self, other): else: name = None if self.name != name: - return other._shallow_copy(name=name) + return self._shallow_copy(name=name) return self def union(self, other): @@ -1920,7 +1920,8 @@ def difference(self, other): Return a new Index with elements from the index that are not in `other`. - This is the sorted set difference of two Index objects. + This is the set difference of two Index objects. + It's sorted if sorting is possible. Parameters ---------- @@ -1946,14 +1947,27 @@ def difference(self, other): other, result_name = self._convert_can_do_setop(other) - theDiff = sorted(set(self) - set(other)) - return Index(theDiff, name=result_name) + this = self._get_unique_index() + + indexer = this.get_indexer(other) + indexer = indexer.take((indexer != -1).nonzero()[0]) + + label_diff = np.setdiff1d(np.arange(this.size), indexer, + assume_unique=True) + the_diff = this.values.take(label_diff) + try: + the_diff = algos.safe_sort(the_diff) + except TypeError: + pass + + return this._shallow_copy(the_diff, name=result_name) diff = deprecate('diff', difference) def symmetric_difference(self, other, result_name=None): """ - Compute the sorted symmetric difference of two Index objects. + Compute the symmetric difference of two Index objects. + It's sorted if sorting is possible. Parameters ---------- @@ -1970,9 +1984,6 @@ def symmetric_difference(self, other, result_name=None): ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by ``(idx1 - idx2) + (idx2 - idx1)`` with duplicates dropped. - The sorting of a result containing ``NaN`` values is not guaranteed - across Python versions. See GitHub issue #6444. - Examples -------- >>> idx1 = Index([1, 2, 3, 4]) @@ -1990,8 +2001,26 @@ def symmetric_difference(self, other, result_name=None): if result_name is None: result_name = result_name_update - the_diff = sorted(set((self.difference(other)). - union(other.difference(self)))) + this = self._get_unique_index() + other = other._get_unique_index() + indexer = this.get_indexer(other) + + # {this} minus {other} + common_indexer = indexer.take((indexer != -1).nonzero()[0]) + left_indexer = np.setdiff1d(np.arange(this.size), common_indexer, + assume_unique=True) + left_diff = this.values.take(left_indexer) + + # {other} minus {this} + right_indexer = (indexer == -1).nonzero()[0] + right_diff = other.values.take(right_indexer) + + the_diff = _concat._concat_compat([left_diff, right_diff]) + try: + the_diff = algos.safe_sort(the_diff) + except TypeError: + pass + attribs = self._get_attributes_dict() attribs['name'] = result_name if 'freq' in attribs: @@ -2000,6 +2029,36 @@ def symmetric_difference(self, other, result_name=None): sym_diff = deprecate('sym_diff', symmetric_difference) + def _get_unique_index(self, dropna=False): + """ + Returns an index containing unique values. + + Parameters + ---------- + dropna : bool + If True, NaN values are dropped. + + Returns + ------- + uniques : index + """ + if self.is_unique and not dropna: + return self + + values = self.values + + if not self.is_unique: + values = self.unique() + + if dropna: + try: + if self.hasnans: + values = values[~isnull(values)] + except NotImplementedError: + pass + + return self._shallow_copy(values) + def get_loc(self, key, method=None, tolerance=None): """ Get integer location for requested label diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index d6f7493bb25f9..92560363be8fe 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -287,6 +287,45 @@ def test_duplicates(self): self.assertEqual(result.name, 'foo') self.assert_index_equal(result, Index([ind[0]], name='foo')) + def test_get_unique_index(self): + for ind in self.indices.values(): + + # MultiIndex tested separately + if not len(ind) or isinstance(ind, MultiIndex): + continue + + idx = ind[[0] * 5] + idx_unique = ind[[0]] + # We test against `idx_unique`, so first we make sure it's unique + # and doesn't contain nans. + self.assertTrue(idx_unique.is_unique) + try: + self.assertFalse(idx_unique.hasnans) + except NotImplementedError: + pass + + for dropna in [False, True]: + result = idx._get_unique_index(dropna=dropna) + self.assert_index_equal(result, idx_unique) + + # nans: + + if not ind._can_hold_na: + continue + + vals = ind.values[[0] * 5] + vals[0] = np.nan + vals_unique = vals[:2] + idx_nan = ind._shallow_copy(vals) + idx_unique_nan = ind._shallow_copy(vals_unique) + self.assertTrue(idx_unique_nan.is_unique) + + for dropna, expected in zip([False, True], + [idx_unique_nan, idx_unique]): + for i in [idx_nan, idx_unique_nan]: + result = i._get_unique_index(dropna=dropna) + self.assert_index_equal(result, expected) + def test_sort(self): for ind in self.indices.values(): self.assertRaises(TypeError, ind.sort) diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index 06662e52e3a6f..cc5dd24292bb8 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -640,47 +640,56 @@ def test_union(self): first = Index(list('ab'), name='A') second = Index(list('ab'), name='B') union = first.union(second) - self.assertIsNone(union.name) + expected = Index(list('ab'), name=None) + tm.assert_index_equal(union, expected) first = Index(list('ab'), name='A') second = Index([], name='B') union = first.union(second) - self.assertIsNone(union.name) + expected = Index(list('ab'), name=None) + tm.assert_index_equal(union, expected) first = Index([], name='A') second = Index(list('ab'), name='B') union = first.union(second) - self.assertIsNone(union.name) + expected = Index(list('ab'), name=None) + tm.assert_index_equal(union, expected) first = Index(list('ab')) second = Index(list('ab'), name='B') union = first.union(second) - self.assertEqual(union.name, 'B') + expected = Index(list('ab'), name='B') + tm.assert_index_equal(union, expected) first = Index([]) second = Index(list('ab'), name='B') union = first.union(second) - self.assertEqual(union.name, 'B') + expected = Index(list('ab'), name='B') + tm.assert_index_equal(union, expected) first = Index(list('ab')) second = Index([], name='B') union = first.union(second) - self.assertEqual(union.name, 'B') + expected = Index(list('ab'), name='B') + tm.assert_index_equal(union, expected) first = Index(list('ab'), name='A') second = Index(list('ab')) union = first.union(second) - self.assertEqual(union.name, 'A') + expected = Index(list('ab'), name='A') + tm.assert_index_equal(union, expected) first = Index(list('ab'), name='A') second = Index([]) union = first.union(second) - self.assertEqual(union.name, 'A') + expected = Index(list('ab'), name='A') + tm.assert_index_equal(union, expected) first = Index([], name='A') second = Index(list('ab')) union = first.union(second) - self.assertEqual(union.name, 'A') + expected = Index(list('ab'), name='A') + tm.assert_index_equal(union, expected) def test_add(self): @@ -803,17 +812,19 @@ def test_symmetric_difference(self): self.assertTrue(tm.equalContents(result, expected)) # nans: - # GH #6444, sorting of nans. Make sure the number of nans is right - # and the correct non-nan values are there. punt on sorting. - idx1 = Index([1, 2, 3, np.nan]) + # GH 13514 change: {nan} - {nan} == {} + # (GH 6444, sorting of nans, is no longer an issue) + idx1 = Index([1, np.nan, 2, 3]) idx2 = Index([0, 1, np.nan]) + idx3 = Index([0, 1]) + result = idx1.symmetric_difference(idx2) - # expected = Index([0.0, np.nan, 2.0, 3.0, np.nan]) + expected = Index([0.0, 2.0, 3.0]) + tm.assert_index_equal(result, expected) - nans = pd.isnull(result) - self.assertEqual(nans.sum(), 1) - self.assertEqual((~nans).sum(), 3) - [self.assertIn(x, result) for x in [0.0, 2.0, 3.0]] + result = idx1.symmetric_difference(idx3) + expected = Index([0.0, 2.0, 3.0, np.nan]) + tm.assert_index_equal(result, expected) # other not an Index: idx1 = Index([1, 2, 3, 4], name='idx1') @@ -1665,6 +1676,149 @@ def test_string_index_repr(self): self.assertEqual(coerce(idx), expected) +class TestMixedIntIndex(Base, tm.TestCase): + # Mostly the tests from common.py for which the results differ + # in py2 and py3 because ints and strings are uncomparable in py3 + # (GH 13514) + + _holder = Index + _multiprocess_can_split_ = True + + def setUp(self): + self.indices = dict(mixedIndex=Index([0, 'a', 1, 'b', 2, 'c'])) + self.setup_indices() + + def create_index(self): + return self.mixedIndex + + def test_order(self): + idx = self.create_index() + # 9816 deprecated + if PY3: + with tm.assertRaisesRegexp(TypeError, "unorderable types"): + with tm.assert_produces_warning(FutureWarning): + idx.order() + else: + with tm.assert_produces_warning(FutureWarning): + idx.order() + + def test_argsort(self): + idx = self.create_index() + if PY3: + with tm.assertRaisesRegexp(TypeError, "unorderable types"): + result = idx.argsort() + else: + result = idx.argsort() + expected = np.array(idx).argsort() + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + def test_numpy_argsort(self): + idx = self.create_index() + if PY3: + with tm.assertRaisesRegexp(TypeError, "unorderable types"): + result = np.argsort(idx) + else: + result = np.argsort(idx) + expected = idx.argsort() + tm.assert_numpy_array_equal(result, expected) + + def test_copy_name(self): + # Check that "name" argument passed at initialization is honoured + # GH12309 + idx = self.create_index() + + first = idx.__class__(idx, copy=True, name='mario') + second = first.__class__(first, copy=False) + + # Even though "copy=False", we want a new object. + self.assertIsNot(first, second) + # Not using tm.assert_index_equal() since names differ: + self.assertTrue(idx.equals(first)) + + self.assertEqual(first.name, 'mario') + self.assertEqual(second.name, 'mario') + + s1 = Series(2, index=first) + s2 = Series(3, index=second[:-1]) + if PY3: + with tm.assert_produces_warning(RuntimeWarning): + # unorderable types + s3 = s1 * s2 + else: + s3 = s1 * s2 + self.assertEqual(s3.index.name, 'mario') + + def test_union_base(self): + idx = self.create_index() + first = idx[3:] + second = idx[:5] + + if PY3: + with tm.assert_produces_warning(RuntimeWarning): + # unorderable types + result = first.union(second) + expected = Index(['b', 2, 'c', 0, 'a', 1]) + self.assert_index_equal(result, expected) + else: + result = first.union(second) + expected = Index(['b', 2, 'c', 0, 'a', 1]) + self.assert_index_equal(result, expected) + + # GH 10149 + cases = [klass(second.values) + for klass in [np.array, Series, list]] + for case in cases: + if PY3: + with tm.assert_produces_warning(RuntimeWarning): + # unorderable types + result = first.union(case) + self.assertTrue(tm.equalContents(result, idx)) + else: + result = first.union(case) + self.assertTrue(tm.equalContents(result, idx)) + + def test_intersection_base(self): + # (same results for py2 and py3 but sortedness not tested elsewhere) + idx = self.create_index() + first = idx[:5] + second = idx[:3] + result = first.intersection(second) + expected = Index([0, 'a', 1]) + self.assert_index_equal(result, expected) + + # GH 10149 + cases = [klass(second.values) + for klass in [np.array, Series, list]] + for case in cases: + result = first.intersection(case) + self.assertTrue(tm.equalContents(result, second)) + + def test_difference_base(self): + # (same results for py2 and py3 but sortedness not tested elsewhere) + idx = self.create_index() + first = idx[:4] + second = idx[3:] + + result = first.difference(second) + expected = Index([0, 1, 'a']) + self.assert_index_equal(result, expected) + + def test_symmetric_difference(self): + # (same results for py2 and py3 but sortedness not tested elsewhere) + idx = self.create_index() + first = idx[:4] + second = idx[3:] + + result = first.symmetric_difference(second) + expected = Index([0, 1, 2, 'a', 'c']) + self.assert_index_equal(result, expected) + + def test_logical_compat(self): + idx = self.create_index() + self.assertEqual(idx.all(), idx.values.all()) + self.assertEqual(idx.any(), idx.values.any()) + + def test_get_combined_index(): from pandas.core.index import _get_combined_index result = _get_combined_index([]) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index e6a8aafc32be4..2734e90a1971b 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -1877,6 +1877,15 @@ def test_duplicate_meta_data(self): self.assertTrue(idx.has_duplicates) self.assertEqual(idx.drop_duplicates().names, idx.names) + def test_get_unique_index(self): + idx = self.index[[0, 1, 0, 1, 1, 0, 0]] + expected = self.index._shallow_copy(idx[[0, 1]]) + + for dropna in [False, True]: + result = idx._get_unique_index(dropna=dropna) + self.assertTrue(result.unique) + self.assert_index_equal(result, expected) + def test_tolist(self): result = self.index.tolist() exp = list(self.index.values) diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index cb90110c953c1..f18d869b3843d 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -56,6 +56,80 @@ def test_strings(self): tm.assert_series_equal(result, expected) +class TestSafeSort(tm.TestCase): + _multiprocess_can_split_ = True + + def test_basic_sort(self): + values = [3, 1, 2, 0, 4] + result = algos.safe_sort(values) + expected = np.array([0, 1, 2, 3, 4]) + tm.assert_numpy_array_equal(result, expected) + + values = list("baaacb") + result = algos.safe_sort(values) + expected = np.array(list("aaabbc")) + tm.assert_numpy_array_equal(result, expected) + + values = [] + result = algos.safe_sort(values) + expected = np.array([]) + tm.assert_numpy_array_equal(result, expected) + + def test_labels(self): + values = [3, 1, 2, 0, 4] + expected = np.array([0, 1, 2, 3, 4]) + + labels = [0, 1, 1, 2, 3, 0, -1, 4] + result, result_labels = algos.safe_sort(values, labels) + expected_labels = np.array([3, 1, 1, 2, 0, 3, -1, 4]) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + # na_sentinel + labels = [0, 1, 1, 2, 3, 0, 99, 4] + result, result_labels = algos.safe_sort(values, labels, + na_sentinel=99) + expected_labels = np.array([3, 1, 1, 2, 0, 3, 99, 4]) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + # out of bound indices + labels = [0, 101, 102, 2, 3, 0, 99, 4] + result, result_labels = algos.safe_sort(values, labels) + expected_labels = np.array([3, -1, -1, 2, 0, 3, -1, 4]) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + labels = [] + result, result_labels = algos.safe_sort(values, labels) + expected_labels = np.array([], dtype=np.int_) + tm.assert_numpy_array_equal(result, expected) + tm.assert_numpy_array_equal(result_labels, expected_labels) + + def test_mixed_integer(self): + values = np.array(['b', 1, 0, 'a', 0, 'b'], dtype=object) + result = algos.safe_sort(values) + expected = np.array([0, 0, 1, 'a', 'b', 'b'], dtype=object) + tm.assert_numpy_array_equal(result, expected) + + values = np.array(['b', 1, 0, 'a'], dtype=object) + labels = [0, 1, 2, 3, 0, -1, 1] + result, result_labels = algos.safe_sort(values, labels) + expected = np.array([0, 1, 'a', 'b'], dtype=object) + + def test_exceptions(self): + with tm.assertRaisesRegexp(TypeError, + "Only list-like objects are allowed"): + algos.safe_sort(values=1) + + with tm.assertRaisesRegexp(TypeError, + "Only list-like objects or None"): + algos.safe_sort(values=[0, 1, 2], labels=1) + + with tm.assertRaisesRegexp(ValueError, "values should be unique"): + algos.safe_sort(values=[0, 1, 2, 1], labels=[0, 1]) + + class TestFactorize(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 57d43f22757ea..258f36cb1b68f 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -3210,6 +3210,18 @@ def test_groupby_nonstring_columns(self): expected = df.groupby(df[0]).mean() assert_frame_equal(result, expected) + def test_groupby_mixed_type_columns(self): + # GH 13432, unorderable types in py3 + df = DataFrame([[0, 1, 2]], columns=['A', 'B', 0]) + expected = DataFrame([[1, 2]], columns=['B', 0], + index=Index([0], name='A')) + + result = df.groupby('A').first() + tm.assert_frame_equal(result, expected) + + result = df.groupby('A').sum() + tm.assert_frame_equal(result, expected) + def test_cython_grouper_series_bug_noncontig(self): arr = np.empty((100, 100)) arr.fill(np.nan) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 5b66e55eb60b6..e7d165354ec6c 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -1209,16 +1209,12 @@ def _sort_labels(uniques, left, right): # tuplesafe uniques = Index(uniques).values - sorter = uniques.argsort() + l = len(left) + labels = np.concatenate([left, right]) - reverse_indexer = np.empty(len(sorter), dtype=np.int64) - reverse_indexer.put(sorter, np.arange(len(sorter))) - - new_left = reverse_indexer.take(_ensure_platform_int(left)) - np.putmask(new_left, left == -1, -1) - - new_right = reverse_indexer.take(_ensure_platform_int(right)) - np.putmask(new_right, right == -1, -1) + _, new_labels = algos.safe_sort(uniques, labels, na_sentinel=-1) + new_labels = _ensure_int64(new_labels) + new_left, new_right = new_labels[:l], new_labels[l:] return new_left, new_right diff --git a/pandas/tools/tests/test_join.py b/pandas/tools/tests/test_join.py index 86aee0b4a01c9..cb84c1f06653b 100644 --- a/pandas/tools/tests/test_join.py +++ b/pandas/tools/tests/test_join.py @@ -536,6 +536,23 @@ def test_join_sort(self): joined = left.join(right, on='key', sort=False) self.assert_index_equal(joined.index, pd.Index(lrange(4))) + def test_join_mixed_non_unique_index(self): + # GH 12814, unorderable types in py3 with a non-unique index + df1 = DataFrame({'a': [1, 2, 3, 4]}, index=[1, 2, 3, 'a']) + df2 = DataFrame({'b': [5, 6, 7, 8]}, index=[1, 3, 3, 4]) + result = df1.join(df2) + expected = DataFrame({'a': [1, 2, 3, 3, 4], + 'b': [5, np.nan, 6, 7, np.nan]}, + index=[1, 2, 3, 3, 'a']) + tm.assert_frame_equal(result, expected) + + df3 = DataFrame({'a': [1, 2, 3, 4]}, index=[1, 2, 2, 'a']) + df4 = DataFrame({'b': [5, 6, 7, 8]}, index=[1, 2, 3, 4]) + result = df3.join(df4) + expected = DataFrame({'a': [1, 2, 3, 4], 'b': [5, 6, 6, np.nan]}, + index=[1, 2, 2, 'a']) + tm.assert_frame_equal(result, expected) + def test_mixed_type_join_with_suffix(self): # GH #916 df = DataFrame(np.random.randn(20, 6),
- [x] fixes some issues from #13432 - [x] closes #12044 - [x] closes #12814 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry --- 1. Added an internal `safe_sort` to safely sort mixed-integer arrays in Python3. 2. Changed Index.difference and Index.symmetric_difference in order to: - sort mixed-int Indexes (#13432) - improve performance (#12044) 3. Fixed DataFrame.join which raised in Python3 with mixed-int non-unique indexes (issue with sorting mixed-ints, #12814) 4. Fixed Index.union returning an empty Index when one of arguments was a named empty Index (#13432) Benchmarks (for `index_object` only): ``` ]$ asv compare bf47 a02f -f 1.5 -s Benchmarks that have improved: before after ratio [bf47 ] [a02f ] - 155.66ms 10.32ms 0.07 index_object.index_datetime_set_difference.time_index_datetime_difference - 154.66ms 2.98ms 0.02 index_object.index_datetime_set_difference.time_index_datetime_difference_disjoint - 391.45ms 10.65ms 0.03 index_object.index_datetime_set_difference.time_index_datetime_symmetric_difference - 195.55ms 88.95ms 0.45 index_object.index_int64_set_difference.time_index_int64_difference - 440.93ms 103.65ms 0.24 index_object.index_int64_set_difference.time_index_int64_symmetric_difference - 8.77ms 4.88ms 0.56 index_object.index_str_set_difference.time_str_symmetric_difference ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13514
2016-06-26T04:33:36Z
2016-07-19T01:52:00Z
null
2016-07-19T14:50:49Z
ENH: add time-window capability to .rolling
diff --git a/ci/lint.sh b/ci/lint.sh index 9f582f72fcdd7..144febcfcece5 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -17,7 +17,19 @@ if [ "$LINT" ]; then fi done - echo "Linting DONE" + echo "Linting *.py DONE" + + echo "Linting *.pyx" + for path in 'window.pyx' + do + echo "linting -> pandas/$path" + flake8 pandas/$path --filename '*.pyx' --select=E501,E302,E203,E226,E111,E114,E221,E303,E128,E231,E126,E128 + if [ $? -ne "0" ]; then + RET=1 + fi + + done + echo "Linting *.pyx DONE" echo "Check for invalid testing" grep -r -E --include '*.py' --exclude nosetester.py --exclude testing.py '(numpy|np)\.testing' pandas diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 59675e33e724b..12e0ecfba97da 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -391,6 +391,91 @@ For some windowing functions, additional parameters must be specified: such that the weights are normalized with respect to each other. Weights of ``[1, 1, 1]`` and ``[2, 2, 2]`` yield the same result. +.. _stats.moments.ts: + +Time-aware Rolling +~~~~~~~~~~~~~~~~~~ + +.. versionadded:: 0.19.0 + +New in version 0.19.0 are the ability to pass an offset (or convertible) to a ``.rolling()`` method and have it produce +variable sized windows based on the passed time window. For each time point, this includes all preceding values occurring +within the indicated time delta. + +This can be particularly useful for a non-regular time frequency index. + +.. ipython:: python + + dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index=pd.date_range('20130101 09:00:00', periods=5, freq='s')) + dft + +This is a regular frequency index. Using an integer window parameter works to roll along the window frequency. + +.. ipython:: python + + dft.rolling(2).sum() + dft.rolling(2, min_periods=1).sum() + +Specifying an offset allows a more intuitive specification of the rolling frequency. + +.. ipython:: python + + dft.rolling('2s').sum() + +Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation. + + +.. ipython:: python + + + dft = DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index = pd.Index([pd.Timestamp('20130101 09:00:00'), + pd.Timestamp('20130101 09:00:02'), + pd.Timestamp('20130101 09:00:03'), + pd.Timestamp('20130101 09:00:05'), + pd.Timestamp('20130101 09:00:06')], + name='foo')) + + dft + dft.rolling(2).sum() + + +Using the time-specification generates variable windows for this sparse data. + +.. ipython:: python + + dft.rolling('2s').sum() + +Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the +default of the index) in a DataFrame. + +.. ipython:: python + + dft = dft.reset_index() + dft + dft.rolling('2s', on='foo').sum() + +.. _stats.moments.ts-versus-resampling: + +Time-aware Rolling vs. Resampling +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Using ``.rolling()`` with a time-based index is quite similar to :ref:`resampling <timeseries.resampling>`. They +both operate and perform reductive operations on time-indexed pandas objects. + +When using ``.rolling()`` with an offset. The offset is a time-delta. Take a backwards-in-time looking window, and +aggregate all of the values in that window (including the end-point, but not the start-point). This is the new value +at that point in the result. These are variable sized windows in time-space for each point of the input. You will get +a same sized result as the input. + +When using ``.resample()`` with an offset. Construct a new index that is the frequency of the offset. For each frequency +bin, aggregate points from the input within a backwards-in-time looking window that fall in that bin. The result of this +aggregation is the output for that frequency point. The windows are fixed size size in the frequency space. Your result +will have the shape of a regular frequency between the min and the max of the original input object. + +To summarize, ``.rolling()`` is a time-based window operation, while ``.resample()`` is a frequency-based window operation. + Centering Windows ~~~~~~~~~~~~~~~~~ diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 7e832af14c051..0bce0227f4518 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -1324,7 +1324,11 @@ performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. -``resample`` is a time-based groupby, followed by a reduction method on each of its groups. +``.resample()`` is a time-based groupby, followed by a reduction method on each of its groups. + +.. note:: + + ``.resample()`` is similar to using a ``.rolling()`` operation with a time-based offset, see a discussion `here <stats.moments.ts-versus-resampling>` See some :ref:`cookbook examples <cookbook.resample>` for some advanced strategies diff --git a/doc/source/whatsnew/v0.19.0.txt b/doc/source/whatsnew/v0.19.0.txt index 688f3b7ff6ada..9b196ec49d685 100644 --- a/doc/source/whatsnew/v0.19.0.txt +++ b/doc/source/whatsnew/v0.19.0.txt @@ -3,16 +3,17 @@ v0.19.0 (August ??, 2016) ------------------------- -This is a major release from 0.18.2 and includes a small number of API changes, several new features, +This is a major release from 0.18.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. Highlights include: - :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0190.enhancements.asof_merge>` +- ``.rolling()`` are now time-series aware, see :ref:`here <whatsnew_0190.enhancements.rolling_ts>` - pandas development api, see :ref:`here <whatsnew_0190.dev_api>` -.. contents:: What's new in v0.18.2 +.. contents:: What's new in v0.19.0 :local: :backlinks: none @@ -131,6 +132,64 @@ that forward filling happens automatically taking the most recent non-NaN value. This returns a merged DataFrame with the entries in the same order as the original left passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged. +.. _whatsnew_0190.enhancements.rolling_ts: + +``.rolling()`` are now time-series aware +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``.rolling()`` objects are now time-series aware and can accept a time-series offset (or convertible) for the ``window`` argument (:issue:`13327`, :issue:`12995`) +See the full documentation :ref:`here <stats.moments.ts>`. + +.. ipython:: python + + dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index=pd.date_range('20130101 09:00:00', periods=5, freq='s')) + dft + +This is a regular frequency index. Using an integer window parameter works to roll along the window frequency. + +.. ipython:: python + + dft.rolling(2).sum() + dft.rolling(2, min_periods=1).sum() + +Specifying an offset allows a more intuitive specification of the rolling frequency. + +.. ipython:: python + + dft.rolling('2s').sum() + +Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation. + +.. ipython:: python + + + dft = DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index = pd.Index([pd.Timestamp('20130101 09:00:00'), + pd.Timestamp('20130101 09:00:02'), + pd.Timestamp('20130101 09:00:03'), + pd.Timestamp('20130101 09:00:05'), + pd.Timestamp('20130101 09:00:06')], + name='foo')) + + dft + dft.rolling(2).sum() + +Using the time-specification generates variable windows for this sparse data. + +.. ipython:: python + + dft.rolling('2s').sum() + +Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the +default of the index) in a DataFrame. + +.. ipython:: python + + dft = dft.reset_index() + dft + dft.rolling('2s', on='foo').sum() + .. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support: :func:`read_csv` has improved support for duplicate column names diff --git a/pandas/core/generic.py b/pandas/core/generic.py index d6e6f571be53a..2fe53d6c27f8f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5343,11 +5343,12 @@ def _add_series_or_dataframe_operations(cls): @Appender(rwindow.rolling.__doc__) def rolling(self, window, min_periods=None, freq=None, center=False, - win_type=None, axis=0): + win_type=None, on=None, axis=0): axis = self._get_axis_number(axis) return rwindow.rolling(self, window=window, min_periods=min_periods, freq=freq, - center=center, win_type=win_type, axis=axis) + center=center, win_type=win_type, + on=on, axis=axis) cls.rolling = rolling diff --git a/pandas/core/window.py b/pandas/core/window.py index bc4d34529287b..9e2a27adc25a7 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -11,7 +11,11 @@ import numpy as np from collections import defaultdict -from pandas.types.generic import ABCSeries, ABCDataFrame +from pandas.types.generic import (ABCSeries, + ABCDataFrame, + ABCDatetimeIndex, + ABCTimedeltaIndex, + ABCPeriodIndex) from pandas.types.common import (is_integer, is_bool, is_float_dtype, @@ -26,11 +30,14 @@ GroupByMixin) import pandas.core.common as com import pandas._window as _window +from pandas.tseries.offsets import DateOffset from pandas import compat from pandas.compat.numpy import function as nv -from pandas.util.decorators import Substitution, Appender +from pandas.util.decorators import (Substitution, Appender, + cache_readonly) from textwrap import dedent + _shared_docs = dict() _doc_template = """ @@ -47,19 +54,21 @@ class _Window(PandasObject, SelectionMixin): _attributes = ['window', 'min_periods', 'freq', 'center', 'win_type', - 'axis'] + 'axis', 'on'] exclusions = set() def __init__(self, obj, window=None, min_periods=None, freq=None, - center=False, win_type=None, axis=0, **kwargs): + center=False, win_type=None, axis=0, on=None, **kwargs): if freq is not None: warnings.warn("The freq kw is deprecated and will be removed in a " "future version. You can resample prior to passing " "to a window function", FutureWarning, stacklevel=3) + self.__dict__.update(kwargs) self.blocks = [] self.obj = obj + self.on = on self.window = window self.min_periods = min_periods self.freq = freq @@ -72,6 +81,18 @@ def __init__(self, obj, window=None, min_periods=None, freq=None, def _constructor(self): return Window + @property + def is_datetimelike(self): + return None + + @property + def _on(self): + return None + + @property + def is_freq_type(self): + return self.win_type == 'freq' + def validate(self): if self.center is not None and not is_bool(self.center): raise ValueError("center must be a boolean") @@ -83,6 +104,7 @@ def _convert_freq(self, how=None): """ resample according to the how, return a new object """ obj = self._selected_obj + index = None if (self.freq is not None and isinstance(obj, (ABCSeries, ABCDataFrame))): if how is not None: @@ -92,13 +114,24 @@ def _convert_freq(self, how=None): stacklevel=6) obj = obj.resample(self.freq).aggregate(how or 'asfreq') - return obj + + return obj, index def _create_blocks(self, how): """ split data into blocks & return conformed data """ - obj = self._convert_freq(how) - return obj.as_blocks(copy=False).values(), obj + obj, index = self._convert_freq(how) + if index is not None: + index = self._on + + # filter out the on from the object + if self.on is not None: + if obj.ndim == 2: + obj = obj.reindex(columns=obj.columns.difference([self.on]), + copy=False) + blocks = obj.as_blocks(copy=False).values() + + return blocks, obj, index def _gotitem(self, key, ndim, subset=None): """ @@ -152,6 +185,21 @@ def __unicode__(self): return "{klass} [{attrs}]".format(klass=self._window_type, attrs=','.join(attrs)) + def _get_index(self, index=None): + """ + Return index as ndarrays + + Returns + ------- + tuple of (index, index_as_ndarray) + """ + + if self.is_freq_type: + if index is None: + index = self._on + return index, index.asi8 + return index, index + def _prep_values(self, values=None, kill_inf=True, how=None): if values is None: @@ -187,8 +235,8 @@ def _wrap_result(self, result, block=None, obj=None): if obj is None: obj = self._selected_obj - index = obj.index + if isinstance(result, np.ndarray): # coerce if necessary @@ -215,6 +263,9 @@ def _wrap_results(self, results, blocks, obj): obj : conformed data (may be resampled) """ + from pandas import Series + from pandas.core.index import _ensure_index + final = [] for result, block in zip(results, blocks): @@ -223,9 +274,31 @@ def _wrap_results(self, results, blocks, obj): return result final.append(result) + # if we have an 'on' column + # we want to put it back into the results + # in the same location + columns = self._selected_obj.columns + if self.on is not None \ + and not self._on.equals(obj.index): + + name = self._on.name + final.append(Series(self._on, index=obj.index, name=name)) + + if self._selection is not None: + + selection = _ensure_index(self._selection) + + # need to reorder to include original location of + # the on column (if its not already there) + if name not in selection: + columns = self.obj.columns + indexer = columns.get_indexer(selection.tolist() + [name]) + columns = columns.take(sorted(indexer)) + if not len(final): return obj.astype('float64') - return pd.concat(final, axis=1).reindex(columns=obj.columns) + return pd.concat(final, axis=1).reindex(columns=columns, + copy=False) def _center_window(self, result, window): """ center the result in the window """ @@ -271,18 +344,24 @@ def aggregate(self, arg, *args, **kwargs): class Window(_Window): """ - Provides rolling transformations. + Provides rolling window calculcations. .. versionadded:: 0.18.0 Parameters ---------- - window : int - Size of the moving window. This is the number of observations used for - calculating the statistic. + window : int, or offset + Size of the moving window. This is the number of observations used for + calculating the statistic. Each window will be a fixed size. + + If its an offset then this will be the time period of each window. Each + window will be a variable sized based on the observations included in + the time-period. This is only valid for datetimelike indexes. This is + new in 0.19.0 min_periods : int, default None Minimum number of observations in window required to have a value - (otherwise result is NA). + (otherwise result is NA). For a window that is specified by an offset, + this will default to 1. freq : string or DateOffset object, optional (default None) (DEPRECATED) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. @@ -290,11 +369,91 @@ class Window(_Window): Set the labels at the center of the window. win_type : string, default None Provide a window type. See the notes below. - axis : int, default 0 + on : string, optional + For a DataFrame, column on which to calculate + the rolling window, rather than the index + + .. versionadded:: 0.19.0 + + axis : int or string, default 0 Returns ------- - a Window sub-classed for the particular operation + a Window or Rolling sub-classed for the particular operation + + Examples + -------- + + >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) + >>> df + B + 0 0.0 + 1 1.0 + 2 2.0 + 3 NaN + 4 4.0 + + Rolling sum with a window length of 2, using the 'triang' + window type. + + >>> df.rolling(2, win_type='triang').sum() + B + 0 NaN + 1 1.0 + 2 2.5 + 3 NaN + 4 NaN + + Rolling sum with a window length of 2, min_periods defaults + to the window length. + + >>> df.rolling(2).sum() + B + 0 NaN + 1 1.0 + 2 3.0 + 3 NaN + 4 NaN + + Same as above, but explicity set the min_periods + + >>> df.rolling(2, min_periods=1).sum() + B + 0 0.0 + 1 1.0 + 2 3.0 + 3 2.0 + 4 4.0 + + A ragged (meaning not-a-regular frequency), time-indexed DataFrame + + >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, + ....: index = [pd.Timestamp('20130101 09:00:00'), + ....: pd.Timestamp('20130101 09:00:02'), + ....: pd.Timestamp('20130101 09:00:03'), + ....: pd.Timestamp('20130101 09:00:05'), + ....: pd.Timestamp('20130101 09:00:06')]) + + >>> df + B + 2013-01-01 09:00:00 0.0 + 2013-01-01 09:00:02 1.0 + 2013-01-01 09:00:03 2.0 + 2013-01-01 09:00:05 NaN + 2013-01-01 09:00:06 4.0 + + + Contrasting to an integer rolling window, this will roll a variable + length window corresponding to the time period. + The default for min_periods is 1. + + >>> df.rolling('2s').sum() + B + 2013-01-01 09:00:00 0.0 + 2013-01-01 09:00:02 1.0 + 2013-01-01 09:00:03 3.0 + 2013-01-01 09:00:05 NaN + 2013-01-01 09:00:06 4.0 Notes ----- @@ -305,7 +464,10 @@ class Window(_Window): frequency by resampling the data. This is done with the default parameters of :meth:`~pandas.Series.resample` (i.e. using the `mean`). - The recognized window types are: + To learn more about the offsets & frequency strings, please see `this link + <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. + + The recognized win_types are: * ``boxcar`` * ``triang`` @@ -321,7 +483,8 @@ class Window(_Window): * ``gaussian`` (needs std) * ``general_gaussian`` (needs power, width) * ``slepian`` (needs width). -""" + + """ def validate(self): super(Window, self).validate() @@ -329,7 +492,7 @@ def validate(self): window = self.window if isinstance(window, (list, tuple, np.ndarray)): pass - elif com.is_integer(window): + elif is_integer(window): if window < 0: raise ValueError("window must be non-negative") try: @@ -400,7 +563,7 @@ def _apply_window(self, mean=True, how=None, **kwargs): window = self._prep_window(**kwargs) center = self.center - blocks, obj = self._create_blocks(how=how) + blocks, obj, index = self._create_blocks(how=how) results = [] for b in blocks: try: @@ -529,7 +692,8 @@ def _apply(self, func, name=None, window=None, center=None, if check_minp is None: check_minp = _use_window - blocks, obj = self._create_blocks(how=how) + blocks, obj, index = self._create_blocks(how=how) + index, indexi = self._get_index(index=index) results = [] for b in blocks: try: @@ -551,9 +715,10 @@ def _apply(self, func, name=None, window=None, center=None, def func(arg, window, min_periods=None): minp = check_minp(min_periods, window) - # GH #12373: rolling functions error on float32 data - return cfunc(_ensure_float64(arg), - window, minp, **kwargs) + # ensure we are only rolling on floats + arg = _ensure_float64(arg) + return cfunc(arg, + window, minp, indexi, **kwargs) # calculation function if center: @@ -587,11 +752,13 @@ class _Rolling_and_Expanding(_Rolling): observations inside provided window.""" def count(self): - obj = self._convert_freq() + + blocks, obj, index = self._create_blocks(how=None) + index, indexi = self._get_index(index=index) + window = self._get_window() window = min(window, len(obj)) if not self.center else window - blocks, obj = self._create_blocks(how=None) results = [] for b in blocks: @@ -625,10 +792,12 @@ def apply(self, func, args=(), kwargs={}): _level = kwargs.pop('_level', None) # noqa window = self._get_window() offset = _offset(window, self.center) + index, indexi = self._get_index() def f(arg, window, min_periods): minp = _use_window(min_periods, window) - return _window.roll_generic(arg, window, minp, offset, func, args, + return _window.roll_generic(arg, window, minp, indexi, + offset, func, args, kwargs) return self._apply(f, func, args=args, kwargs=kwargs, @@ -695,10 +864,12 @@ def median(self, how=None, **kwargs): def std(self, ddof=1, *args, **kwargs): nv.validate_window_func('std', args, kwargs) window = self._get_window() + index, indexi = self._get_index() def f(arg, *args, **kwargs): minp = _require_min_periods(1)(self.min_periods, window) - return _zsqrt(_window.roll_var(arg, window, minp, ddof)) + return _zsqrt(_window.roll_var(arg, window, minp, indexi, + ddof)) return self._apply(f, 'std', check_minp=_require_min_periods(1), ddof=ddof, **kwargs) @@ -740,10 +911,12 @@ def kurt(self, **kwargs): def quantile(self, quantile, **kwargs): window = self._get_window() + index, indexi = self._get_index() def f(arg, *args, **kwargs): minp = _use_window(self.min_periods, window) - return _window.roll_quantile(arg, window, minp, quantile) + return _window.roll_quantile(arg, window, minp, indexi, + quantile) return self._apply(f, 'quantile', quantile=quantile, **kwargs) @@ -823,43 +996,63 @@ def _get_corr(a, b): class Rolling(_Rolling_and_Expanding): - """ - Provides rolling window calculcations. - - .. versionadded:: 0.18.0 - Parameters - ---------- - window : int - Size of the moving window. This is the number of observations used for - calculating the statistic. - min_periods : int, default None - Minimum number of observations in window required to have a value - (otherwise result is NA). - freq : string or DateOffset object, optional (default None) (DEPRECATED) - Frequency to conform the data to before computing the statistic. - Specified as a frequency string or DateOffset object. - center : boolean, default False - Set the labels at the center of the window. - axis : int, default 0 + @cache_readonly + def is_datetimelike(self): + return isinstance(self._on, + (ABCDatetimeIndex, + ABCTimedeltaIndex, + ABCPeriodIndex)) + + @cache_readonly + def _on(self): + + if self.on is None: + return self.obj.index + elif (isinstance(self.obj, ABCDataFrame) and + self.on in self.obj.columns): + return pd.Index(self.obj[self.on]) + else: + raise ValueError("invalid on specified as {0}, " + "must be a column (if DataFrame) " + "or None".format(self.on)) - Returns - ------- - a Window sub-classed for the particular operation + def validate(self): + super(Rolling, self).validate() - Notes - ----- - By default, the result is set to the right edge of the window. This can be - changed to the center of the window by setting ``center=True``. + # we allow rolling on a datetimelike index + if (self.is_datetimelike and + isinstance(self.window, (compat.string_types, DateOffset))): - The `freq` keyword is used to conform time series data to a specified - frequency by resampling the data. This is done with the default parameters - of :meth:`~pandas.Series.resample` (i.e. using the `mean`). - """ + # must be monotonic for on + if not self._on.is_monotonic: + formatted = self.on or 'index' + raise ValueError("{0} must be " + "monotonic".format(formatted)) - def validate(self): - super(Rolling, self).validate() - if not is_integer(self.window): + from pandas.tseries.frequencies import to_offset + try: + freq = to_offset(self.window) + except (TypeError, ValueError): + raise ValueError("passed window {0} in not " + "compat with a datetimelike " + "index".format(self.window)) + + # we don't allow center + if self.center: + raise NotImplementedError("center is not implemented " + "for datetimelike and offset " + "based windows") + + # this will raise ValueError on non-fixed freqs + self.window = freq.nanos + self.win_type = 'freq' + + # min_periods must be an integer + if self.min_periods is None: + self.min_periods = 1 + + elif not is_integer(self.window): raise ValueError("window must be an integer") elif self.window < 0: raise ValueError("window must be non-negative") @@ -876,6 +1069,11 @@ def aggregate(self, arg, *args, **kwargs): @Appender(_doc_template) @Appender(_shared_docs['count']) def count(self): + + # different impl for freq counting + if self.is_freq_type: + return self._apply('roll_count', 'count') + return super(Rolling, self).count() @Substitution(name='rolling') @@ -993,12 +1191,31 @@ class Expanding(_Rolling_and_Expanding): Specified as a frequency string or DateOffset object. center : boolean, default False Set the labels at the center of the window. - axis : int, default 0 + axis : int or string, default 0 Returns ------- a Window sub-classed for the particular operation + Examples + -------- + + >>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + B + 0 0.0 + 1 1.0 + 2 2.0 + 3 NaN + 4 4.0 + + >>> df.expanding(2).sum() + B + 0 NaN + 1 1.0 + 2 3.0 + 3 3.0 + 4 7.0 + Notes ----- By default, the result is set to the right edge of the window. This can be @@ -1205,6 +1422,25 @@ class EWM(_Rolling): ------- a Window sub-classed for the particular operation + Examples + -------- + + >>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + B + 0 0.0 + 1 1.0 + 2 2.0 + 3 NaN + 4 4.0 + + >>> df.ewm(com=0.5).mean() + B + 0 0.000000 + 1 0.750000 + 2 1.615385 + 3 1.615385 + 4 3.670213 + Notes ----- Exactly one of center of mass, span, half-life, and alpha must be provided. @@ -1248,6 +1484,7 @@ def __init__(self, obj, com=None, span=None, halflife=None, alpha=None, self.adjust = adjust self.ignore_na = ignore_na self.axis = axis + self.on = None @property def _constructor(self): @@ -1276,7 +1513,7 @@ def _apply(self, func, how=None, **kwargs): y : type of input argument """ - blocks, obj = self._create_blocks(how=how) + blocks, obj, index = self._create_blocks(how=how) results = [] for b in blocks: try: diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 3693ebdb12e2f..7a35682eee3b0 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -11,7 +11,7 @@ import pandas as pd from pandas import (Series, DataFrame, Panel, bdate_range, isnull, - notnull, concat) + notnull, concat, Timestamp) import pandas.core.datetools as datetools import pandas.stats.moments as mom import pandas.core.window as rwindow @@ -101,7 +101,7 @@ def tests_skip_nuisance(self): expected = pd.concat([r[['A', 'B']].sum(), df[['C']]], axis=1) result = r.sum() - tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result, expected, check_like=True) def test_agg(self): df = DataFrame({'A': range(5), 'B': range(0, 10, 2)}) @@ -319,6 +319,13 @@ class TestRolling(Base): def setUp(self): self._create_data() + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + df + df.rolling(2).sum() + df.rolling(2, min_periods=1).sum() + def test_constructor(self): # GH 12669 @@ -372,6 +379,12 @@ class TestExpanding(Base): def setUp(self): self._create_data() + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + df + df.expanding(2).sum() + def test_constructor(self): # GH 12669 @@ -408,6 +421,12 @@ class TestEWM(Base): def setUp(self): self._create_data() + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}) + df + df.ewm(com=0.5).mean() + def test_constructor(self): for o in [self.series, self.frame]: c = o.ewm @@ -565,6 +584,7 @@ def _create_data(self): def test_dtypes(self): self._create_data() for f_name, d_name in product(self.funcs.keys(), self.data.keys()): + f = self.funcs[f_name] d = self.data[d_name] exp = self.expects[d_name][f_name] @@ -958,6 +978,7 @@ def test_rolling_median(self): name='median') def test_rolling_min(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): self._check_moment_func(mom.rolling_min, np.min, name='min') @@ -970,6 +991,7 @@ def test_rolling_min(self): window=3, min_periods=5) def test_rolling_max(self): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): self._check_moment_func(mom.rolling_max, np.max, name='max') @@ -2890,6 +2912,7 @@ def test_rolling_median_memory_error(self): Series(np.random.randn(n)).rolling(window=2, center=False).median() def test_rolling_min_max_numeric_types(self): + # GH12373 types_test = [np.dtype("f{}".format(width)) for width in [4, 8]] types_test.extend([np.dtype("{}{}".format(sign, width)) @@ -2961,6 +2984,7 @@ def test_rolling(self): r = g.rolling(window=4) for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() expected = g.apply(lambda x: getattr(x.rolling(4), f)()) tm.assert_frame_equal(result, expected) @@ -3007,6 +3031,7 @@ def test_expanding(self): r = g.expanding() for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']: + result = getattr(r, f)() expected = g.apply(lambda x: getattr(x.expanding(), f)()) tm.assert_frame_equal(result, expected) @@ -3047,3 +3072,547 @@ def test_expanding_apply(self): result = r.apply(lambda x: x.sum()) expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum())) tm.assert_frame_equal(result, expected) + + +class TestRollingTS(tm.TestCase): + + # rolling time-series friendly + # xref GH13327 + + def setUp(self): + + self.regular = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': range(5)}).set_index('A') + + self.ragged = DataFrame({'B': range(5)}) + self.ragged.index = [Timestamp('20130101 09:00:00'), + Timestamp('20130101 09:00:02'), + Timestamp('20130101 09:00:03'), + Timestamp('20130101 09:00:05'), + Timestamp('20130101 09:00:06')] + + def test_doc_string(self): + + df = DataFrame({'B': [0, 1, 2, np.nan, 4]}, + index=[Timestamp('20130101 09:00:00'), + Timestamp('20130101 09:00:02'), + Timestamp('20130101 09:00:03'), + Timestamp('20130101 09:00:05'), + Timestamp('20130101 09:00:06')]) + df + df.rolling('2s').sum() + + def test_valid(self): + + df = self.regular + + # not a valid freq + with self.assertRaises(ValueError): + df.rolling(window='foobar') + + # not a datetimelike index + with self.assertRaises(ValueError): + df.reset_index().rolling(window='foobar') + + # non-fixed freqs + for freq in ['2MS', pd.offsets.MonthBegin(2)]: + with self.assertRaises(ValueError): + df.rolling(window=freq) + + for freq in ['1D', pd.offsets.Day(2), '2ms']: + df.rolling(window=freq) + + # non-integer min_periods + for minp in [1.0, 'foo', np.array([1, 2, 3])]: + with self.assertRaises(ValueError): + df.rolling(window='1D', min_periods=minp) + + # center is not implemented + with self.assertRaises(NotImplementedError): + df.rolling(window='1D', center=True) + + def test_on(self): + + df = self.regular + + # not a valid column + with self.assertRaises(ValueError): + df.rolling(window='2s', on='foobar') + + # column is valid + df = df.copy() + df['C'] = pd.date_range('20130101', periods=len(df)) + df.rolling(window='2d', on='C').sum() + + # invalid columns + with self.assertRaises(ValueError): + df.rolling(window='2d', on='B') + + # ok even though on non-selected + df.rolling(window='2d', on='C').B.sum() + + def test_monotonic_on(self): + + # on/index must be monotonic + df = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': range(5)}) + + self.assertTrue(df.A.is_monotonic) + df.rolling('2s', on='A').sum() + + df = df.set_index('A') + self.assertTrue(df.index.is_monotonic) + df.rolling('2s').sum() + + # non-monotonic + df.index = reversed(df.index.tolist()) + self.assertFalse(df.index.is_monotonic) + + with self.assertRaises(ValueError): + df.rolling('2s').sum() + + df = df.reset_index() + with self.assertRaises(ValueError): + df.rolling('2s', on='A').sum() + + def test_frame_on(self): + + df = DataFrame({'B': range(5), + 'C': pd.date_range('20130101 09:00:00', + periods=5, + freq='3s')}) + + df['A'] = [Timestamp('20130101 09:00:00'), + Timestamp('20130101 09:00:02'), + Timestamp('20130101 09:00:03'), + Timestamp('20130101 09:00:05'), + Timestamp('20130101 09:00:06')] + + # we are doing simulating using 'on' + expected = (df.set_index('A') + .rolling('2s') + .B + .sum() + .reset_index(drop=True) + ) + + result = (df.rolling('2s', on='A') + .B + .sum() + ) + tm.assert_series_equal(result, expected) + + # test as a frame + # we should be ignoring the 'on' as an aggregation column + # note that the expected is setting, computing, and reseting + # so the columns need to be switched compared + # to the actual result where they are ordered as in the + # original + expected = (df.set_index('A') + .rolling('2s')[['B']] + .sum() + .reset_index()[['B', 'A']] + ) + + result = (df.rolling('2s', on='A')[['B']] + .sum() + ) + tm.assert_frame_equal(result, expected) + + def test_frame_on2(self): + + # using multiple aggregation columns + df = DataFrame({'A': [0, 1, 2, 3, 4], + 'B': [0, 1, 2, np.nan, 4], + 'C': pd.Index([pd.Timestamp('20130101 09:00:00'), + pd.Timestamp('20130101 09:00:02'), + pd.Timestamp('20130101 09:00:03'), + pd.Timestamp('20130101 09:00:05'), + pd.Timestamp('20130101 09:00:06')])}, + columns=['A', 'C', 'B']) + + expected1 = DataFrame({'A': [0., 1, 3, 3, 7], + 'B': [0, 1, 3, np.nan, 4], + 'C': df['C']}, + columns=['A', 'C', 'B']) + + result = df.rolling('2s', on='C').sum() + expected = expected1 + tm.assert_frame_equal(result, expected) + + expected = Series([0, 1, 3, np.nan, 4], name='B') + result = df.rolling('2s', on='C').B.sum() + tm.assert_series_equal(result, expected) + + expected = expected1[['A', 'B', 'C']] + result = df.rolling('2s', on='C')[['A', 'B', 'C']].sum() + tm.assert_frame_equal(result, expected) + + def test_basic_regular(self): + + df = self.regular.copy() + + df.index = pd.date_range('20130101', periods=5, freq='D') + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window='1D').sum() + tm.assert_frame_equal(result, expected) + + df.index = pd.date_range('20130101', periods=5, freq='2D') + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window='2D', min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window='2D', min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(window=1).sum() + result = df.rolling(window='2D').sum() + tm.assert_frame_equal(result, expected) + + def test_min_periods(self): + + # compare for min_periods + df = self.regular + + # these slightly different + expected = df.rolling(2, min_periods=1).sum() + result = df.rolling('2s').sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(2, min_periods=1).sum() + result = df.rolling('2s', min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + def test_ragged_sum(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 3, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=2).sum() + expected = df.copy() + expected['B'] = [np.nan, np.nan, 3, np.nan, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 5, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s').sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 5, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='4s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 6, 9] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='4s', min_periods=3).sum() + expected = df.copy() + expected['B'] = [np.nan, np.nan, 3, 6, 9] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).sum() + expected = df.copy() + expected['B'] = [0.0, 1, 3, 6, 10] + tm.assert_frame_equal(result, expected) + + def test_ragged_mean(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).mean() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).mean() + expected = df.copy() + expected['B'] = [0.0, 1, 1.5, 3.0, 3.5] + tm.assert_frame_equal(result, expected) + + def test_ragged_median(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).median() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).median() + expected = df.copy() + expected['B'] = [0.0, 1, 1.5, 3.0, 3.5] + tm.assert_frame_equal(result, expected) + + def test_ragged_quantile(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).quantile(0.5) + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).quantile(0.5) + expected = df.copy() + expected['B'] = [0.0, 1, 1.0, 3.0, 3.0] + tm.assert_frame_equal(result, expected) + + def test_ragged_std(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).std(ddof=0) + expected = df.copy() + expected['B'] = [0.0] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='1s', min_periods=1).std(ddof=1) + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s', min_periods=1).std(ddof=0) + expected = df.copy() + expected['B'] = [0.0] + [0.5] * 4 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).std(ddof=1) + expected = df.copy() + expected['B'] = [np.nan, 0.707107, 1.0, 1.0, 1.290994] + tm.assert_frame_equal(result, expected) + + def test_ragged_var(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).var(ddof=0) + expected = df.copy() + expected['B'] = [0.0] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='1s', min_periods=1).var(ddof=1) + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='3s', min_periods=1).var(ddof=0) + expected = df.copy() + expected['B'] = [0.0] + [0.25] * 4 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).var(ddof=1) + expected = df.copy() + expected['B'] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.] + tm.assert_frame_equal(result, expected) + + def test_ragged_skew(self): + + df = self.ragged + result = df.rolling(window='3s', min_periods=1).skew() + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).skew() + expected = df.copy() + expected['B'] = [np.nan] * 2 + [0.0, 0.0, 0.0] + tm.assert_frame_equal(result, expected) + + def test_ragged_kurt(self): + + df = self.ragged + result = df.rolling(window='3s', min_periods=1).kurt() + expected = df.copy() + expected['B'] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).kurt() + expected = df.copy() + expected['B'] = [np.nan] * 4 + [-1.2] + tm.assert_frame_equal(result, expected) + + def test_ragged_count(self): + + df = self.ragged + result = df.rolling(window='1s', min_periods=1).count() + expected = df.copy() + expected['B'] = [1.0, 1, 1, 1, 1] + tm.assert_frame_equal(result, expected) + + df = self.ragged + result = df.rolling(window='1s').count() + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).count() + expected = df.copy() + expected['B'] = [1.0, 1, 2, 1, 2] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=2).count() + expected = df.copy() + expected['B'] = [np.nan, np.nan, 2, np.nan, 2] + tm.assert_frame_equal(result, expected) + + def test_regular_min(self): + + df = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': [0.0, 1, 2, 3, 4]}).set_index('A') + result = df.rolling('1s').min() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + df = DataFrame({'A': pd.date_range('20130101', + periods=5, + freq='s'), + 'B': [5, 4, 3, 4, 5]}).set_index('A') + + tm.assert_frame_equal(result, expected) + result = df.rolling('2s').min() + expected = df.copy() + expected['B'] = [5.0, 4, 3, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling('5s').min() + expected = df.copy() + expected['B'] = [5.0, 4, 3, 3, 3] + tm.assert_frame_equal(result, expected) + + def test_ragged_min(self): + + df = self.ragged + + result = df.rolling(window='1s', min_periods=1).min() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).min() + expected = df.copy() + expected['B'] = [0.0, 1, 1, 3, 3] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).min() + expected = df.copy() + expected['B'] = [0.0, 0, 0, 1, 1] + tm.assert_frame_equal(result, expected) + + def test_perf_min(self): + + N = 10000 + + dfp = DataFrame({'B': np.random.randn(N)}, + index=pd.date_range('20130101', + periods=N, + freq='s')) + expected = dfp.rolling(2, min_periods=1).min() + result = dfp.rolling('2s').min() + self.assertTrue(((result - expected) < 0.01).all().bool()) + + expected = dfp.rolling(200, min_periods=1).min() + result = dfp.rolling('200s').min() + self.assertTrue(((result - expected) < 0.01).all().bool()) + + def test_ragged_max(self): + + df = self.ragged + + result = df.rolling(window='1s', min_periods=1).max() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).max() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).max() + expected = df.copy() + expected['B'] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + def test_ragged_apply(self): + + df = self.ragged + + f = lambda x: 1 + result = df.rolling(window='1s', min_periods=1).apply(f) + expected = df.copy() + expected['B'] = 1. + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='2s', min_periods=1).apply(f) + expected = df.copy() + expected['B'] = 1. + tm.assert_frame_equal(result, expected) + + result = df.rolling(window='5s', min_periods=1).apply(f) + expected = df.copy() + expected['B'] = 1. + tm.assert_frame_equal(result, expected) + + def test_all(self): + + # simple comparision of integer vs time-based windowing + df = self.regular * 2 + er = df.rolling(window=1) + r = df.rolling(window='1s') + + for f in ['sum', 'mean', 'count', 'median', 'std', + 'var', 'kurt', 'skew', 'min', 'max']: + + result = getattr(r, f)() + expected = getattr(er, f)() + tm.assert_frame_equal(result, expected) + + result = r.quantile(0.5) + expected = er.quantile(0.5) + tm.assert_frame_equal(result, expected) + + result = r.apply(lambda x: 1) + expected = er.apply(lambda x: 1) + tm.assert_frame_equal(result, expected) + + def test_all2(self): + + # more sophisticated comparision of integer vs. + # time-based windowing + df = DataFrame({'B': np.arange(50)}, + index=pd.date_range('20130101', + periods=50, freq='H') + ) + # in-range data + dft = df.between_time("09:00", "16:00") + + r = dft.rolling(window='5H') + + for f in ['sum', 'mean', 'count', 'median', 'std', + 'var', 'kurt', 'skew', 'min', 'max']: + + result = getattr(r, f)() + + # we need to roll the days separately + # to compare with a time-based roll + # finally groupby-apply will return a multi-index + # so we need to drop the day + def agg_by_day(x): + x = x.between_time("09:00", "16:00") + return getattr(x.rolling(5, min_periods=1), f)() + expected = df.groupby(df.index.day).apply( + agg_by_day).reset_index(level=0, drop=True) + + tm.assert_frame_equal(result, expected) diff --git a/pandas/window.pyx b/pandas/window.pyx index bfe9152477a40..8235d68e2a88b 100644 --- a/pandas/window.pyx +++ b/pandas/window.pyx @@ -1,3 +1,6 @@ +# cython: profile=False +# cython: boundscheck=False, wraparound=False, cdivision=True + from numpy cimport * cimport numpy as np import numpy as np @@ -51,9 +54,10 @@ cdef double nan = NaN cdef inline int int_max(int a, int b): return a if a >= b else b cdef inline int int_min(int a, int b): return a if a <= b else b -# this is our util.pxd from util cimport numeric +from skiplist cimport * + cdef extern from "src/headers/math.h": double sqrt(double x) nogil int signbit(double) nogil @@ -69,16 +73,37 @@ include "skiplist.pyx" # - In Cython x * x is faster than x ** 2 for C types, this should be # periodically revisited to see if it's still true. # -# - -def _check_minp(win, minp, N, floor=1): + +def _check_minp(win, minp, N, floor=None): + """ + Parameters + ---------- + win: int + minp: int or None + N: len of window + floor: int, optional + default 1 + + Returns + ------- + minimum period + """ + + if minp is None: + minp = 1 + if not util.is_integer_object(minp): + raise ValueError("min_periods must be an integer") if minp > win: - raise ValueError('min_periods (%d) must be <= window (%d)' - % (minp, win)) + raise ValueError("min_periods (%d) must be <= " + "window (%d)" % (minp, win)) elif minp > N: minp = N + 1 elif minp < 0: raise ValueError('min_periods must be >= 0') + if floor is None: + floor = 1 + return max(minp, floor) # original C implementation by N. Devillard. @@ -96,757 +121,1227 @@ def _check_minp(win, minp, N, floor=1): # Physical description: 366 p. # Series: Prentice-Hall Series in Automatic Computation -#------------------------------------------------------------------------------- -# Rolling sum -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_sum(ndarray[double_t] input, int win, int minp): - cdef double val, prev, sum_x = 0 - cdef int nobs = 0, i - cdef int N = len(input) - - cdef ndarray[double_t] output = np.empty(N, dtype=float) +# ---------------------------------------------------------------------- +# The indexer objects for rolling +# These define start/end indexers to compute offsets - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] - # Not NaN - if val == val: - nobs += 1 - sum_x += val +cdef class WindowIndexer: - output[i] = NaN + cdef: + ndarray start, end + int64_t N, minp, win + bint is_variable - for i from minp - 1 <= i < N: - val = input[i] + def get_data(self): + return (self.start, self.end, <int64_t>self.N, + <int64_t>self.win, <int64_t>self.minp, + self.is_variable) - if val == val: - nobs += 1 - sum_x += val - if i > win - 1: - prev = input[i - win] - if prev == prev: - sum_x -= prev - nobs -= 1 +cdef class MockFixedWindowIndexer(WindowIndexer): + """ - if nobs >= minp: - output[i] = sum_x - else: - output[i] = NaN + We are just checking parameters of the indexer, + and returning a consistent API with fixed/variable + indexers. - return output + Parameters + ---------- + input: ndarray + input data array + win: int64_t + window size + minp: int64_t + min number of obs in a window to consider non-NaN + index: object + index of the input + floor: optional + unit for flooring -#------------------------------------------------------------------------------- -# Rolling mean -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_mean(ndarray[double_t] input, - int win, int minp): - cdef: - double val, prev, result, sum_x = 0 - Py_ssize_t nobs = 0, i, neg_ct = 0 - Py_ssize_t N = len(input) + """ + def __init__(self, ndarray input, int64_t win, int64_t minp, + object index=None, object floor=None): - cdef ndarray[double_t] output = np.empty(N, dtype=float) - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] + assert index is None + self.is_variable = 0 + self.N = len(input) + self.minp = _check_minp(win, minp, self.N, floor=floor) + self.start = np.empty(0, dtype='int64') + self.end = np.empty(0, dtype='int64') + self.win = win - # Not NaN - if val == val: - nobs += 1 - sum_x += val - if signbit(val): - neg_ct += 1 - output[i] = NaN +cdef class FixedWindowIndexer(WindowIndexer): + """ + create a fixed length window indexer object + that has start & end, that point to offsets in + the index object; these are defined based on the win + arguments - for i from minp - 1 <= i < N: - val = input[i] + Parameters + ---------- + input: ndarray + input data array + win: int64_t + window size + minp: int64_t + min number of obs in a window to consider non-NaN + index: object + index of the input + floor: optional + unit for flooring the unit - if val == val: - nobs += 1 - sum_x += val - if signbit(val): - neg_ct += 1 + """ + def __init__(self, ndarray input, int64_t win, int64_t minp, + object index=None, object floor=None): + cdef ndarray start_s, start_e, end_s, end_e - if i > win - 1: - prev = input[i - win] - if prev == prev: - sum_x -= prev - nobs -= 1 - if signbit(prev): - neg_ct -= 1 + assert index is None + self.is_variable = 0 + self.N = len(input) + self.minp = _check_minp(win, minp, self.N, floor=floor) - if nobs >= minp: - result = sum_x / nobs - if neg_ct == 0 and result < 0: - # all positive - output[i] = 0 - elif neg_ct == nobs and result > 0: - # all negative - output[i] = 0 - else: - output[i] = result - else: - output[i] = NaN + start_s = np.zeros(win, dtype='int64') + start_e = np.arange(win, self.N, dtype='int64') - win + 1 + self.start = np.concatenate([start_s, start_e]) - return output + end_s = np.arange(win, dtype='int64') + 1 + end_e = start_e + win + self.end = np.concatenate([end_s, end_e]) + self.win = win -#------------------------------------------------------------------------------- -# Exponentially weighted moving average -def ewma(ndarray[double_t] input, double_t com, int adjust, int ignore_na, int minp): +cdef class VariableWindowIndexer(WindowIndexer): """ - Compute exponentially-weighted moving average using center-of-mass. + create a variable length window indexer object + that has start & end, that point to offsets in + the index object; these are defined based on the win + arguments Parameters ---------- - input : ndarray (float64 type) - com : float64 - adjust: int - ignore_na: int - minp: int + input: ndarray + input data array + win: int64_t + window size + minp: int64_t + min number of obs in a window to consider non-NaN + index: ndarray + index of the input - Returns - ------- - y : ndarray """ + def __init__(self, ndarray input, int64_t win, int64_t minp, + ndarray index): - cdef Py_ssize_t N = len(input) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - if N == 0: - return output + self.is_variable = 1 + self.N = len(index) + self.minp = _check_minp(win, minp, self.N) - minp = max(minp, 1) + self.start = np.empty(self.N, dtype='int64') + self.start.fill(-1) - cdef double alpha, old_wt_factor, new_wt, weighted_avg, old_wt, cur - cdef Py_ssize_t i, nobs + self.end = np.empty(self.N, dtype='int64') + self.end.fill(-1) - alpha = 1. / (1. + com) - old_wt_factor = 1. - alpha - new_wt = 1. if adjust else alpha + self.build(index, win) - weighted_avg = input[0] - is_observation = (weighted_avg == weighted_avg) - nobs = int(is_observation) - output[0] = weighted_avg if (nobs >= minp) else NaN - old_wt = 1. + # max window size + self.win = (self.end - self.start).max() - for i from 1 <= i < N: - cur = input[i] - is_observation = (cur == cur) - nobs += int(is_observation) - if weighted_avg == weighted_avg: - if is_observation or (not ignore_na): - old_wt *= old_wt_factor - if is_observation: - if weighted_avg != cur: # avoid numerical errors on constant series - weighted_avg = ((old_wt * weighted_avg) + (new_wt * cur)) / (old_wt + new_wt) - if adjust: - old_wt += new_wt - else: - old_wt = 1. - elif is_observation: - weighted_avg = cur + def build(self, ndarray[int64_t] index, int64_t win): - output[i] = weighted_avg if (nobs >= minp) else NaN + cdef: + ndarray[int64_t] start, end + int64_t start_bound, end_bound, N + Py_ssize_t i, j - return output + start = self.start + end = self.end + N = self.N -#------------------------------------------------------------------------------- -# Exponentially weighted moving covariance + start[0] = 0 + end[0] = 1 -def ewmcov(ndarray[double_t] input_x, ndarray[double_t] input_y, - double_t com, int adjust, int ignore_na, int minp, int bias): + with nogil: + + # start is start of slice interval (including) + # end is end of slice interval (not including) + for i in range(1, N): + end_bound = index[i] + start_bound = index[i] - win + + # advance the start bound until we are + # within the constraint + start[i] = i + for j in range(start[i - 1], i): + if index[j] > start_bound: + start[i] = j + break + + # end bound is previous end + # or current index + if index[end[i - 1]] <= end_bound: + end[i] = i + 1 + else: + end[i] = end[i - 1] + + +def get_window_indexer(input, win, minp, index, floor=None, + use_mock=True): """ - Compute exponentially-weighted moving variance using center-of-mass. + return the correct window indexer for the computation Parameters ---------- - input_x : ndarray (float64 type) - input_y : ndarray (float64 type) - com : float64 - adjust: int - ignore_na: int - minp: int - bias: int + input: 1d ndarray + win: integer, window size + minp: integer, minimum periods + index: 1d ndarray, optional + index to the input array + floor: optional + unit for flooring the unit + use_mock: boolean, default True + if we are a fixed indexer, return a mock indexer + instead of the FixedWindow Indexer. This is a type + compat Indexer that allows us to use a standard + code path with all of the indexers. Returns ------- - y : ndarray - """ + tuple of 1d int64 ndarrays of the offsets & data about the window - cdef Py_ssize_t N = len(input_x) - if len(input_y) != N: - raise ValueError('arrays are of different lengths (%d and %d)' % (N, len(input_y))) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - if N == 0: - return output - - minp = max(minp, 1) + """ - cdef double alpha, old_wt_factor, new_wt, mean_x, mean_y, cov - cdef double sum_wt, sum_wt2, old_wt, cur_x, cur_y, old_mean_x, old_mean_y - cdef Py_ssize_t i, nobs + if index is not None: + indexer = VariableWindowIndexer(input, win, minp, index) + elif use_mock: + indexer = MockFixedWindowIndexer(input, win, minp, index, floor) + else: + indexer = FixedWindowIndexer(input, win, minp, index, floor) + return indexer.get_data() - alpha = 1. / (1. + com) - old_wt_factor = 1. - alpha - new_wt = 1. if adjust else alpha +# ---------------------------------------------------------------------- +# Rolling count +# this is only an impl for index not None, IOW, freq aware - mean_x = input_x[0] - mean_y = input_y[0] - is_observation = ((mean_x == mean_x) and (mean_y == mean_y)) - nobs = int(is_observation) - if not is_observation: - mean_x = NaN - mean_y = NaN - output[0] = (0. if bias else NaN) if (nobs >= minp) else NaN - cov = 0. - sum_wt = 1. - sum_wt2 = 1. - old_wt = 1. - for i from 1 <= i < N: - cur_x = input_x[i] - cur_y = input_y[i] - is_observation = ((cur_x == cur_x) and (cur_y == cur_y)) - nobs += int(is_observation) - if mean_x == mean_x: - if is_observation or (not ignore_na): - sum_wt *= old_wt_factor - sum_wt2 *= (old_wt_factor * old_wt_factor) - old_wt *= old_wt_factor - if is_observation: - old_mean_x = mean_x - old_mean_y = mean_y - if mean_x != cur_x: # avoid numerical errors on constant series - mean_x = ((old_wt * old_mean_x) + (new_wt * cur_x)) / (old_wt + new_wt) - if mean_y != cur_y: # avoid numerical errors on constant series - mean_y = ((old_wt * old_mean_y) + (new_wt * cur_y)) / (old_wt + new_wt) - cov = ((old_wt * (cov + ((old_mean_x - mean_x) * (old_mean_y - mean_y)))) + - (new_wt * ((cur_x - mean_x) * (cur_y - mean_y)))) / (old_wt + new_wt) - sum_wt += new_wt - sum_wt2 += (new_wt * new_wt) - old_wt += new_wt - if not adjust: - sum_wt /= old_wt - sum_wt2 /= (old_wt * old_wt) - old_wt = 1. - elif is_observation: - mean_x = cur_x - mean_y = cur_y +def roll_count(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, count_x = 0.0 + int64_t s, e, nobs, N + Py_ssize_t i, j + ndarray[int64_t] start, end + ndarray[double_t] output - if nobs >= minp: - if not bias: - numerator = sum_wt * sum_wt - denominator = numerator - sum_wt2 - output[i] = ((numerator / denominator) * cov) if (denominator > 0.) else NaN - else: - output[i] = cov - else: - output[i] = NaN + start, end, N, win, minp, _ = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) - return output + with nogil: -#---------------------------------------------------------------------- -# Rolling variance + for i in range(0, N): + s = start[i] + e = end[i] -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_var(ndarray[double_t] input, int win, int minp, int ddof=1): - """ - Numerically stable implementation using Welford's method. - """ - cdef double val, prev, mean_x = 0, ssqdm_x = 0, nobs = 0, delta - cdef Py_ssize_t i - cdef Py_ssize_t N = len(input) + if i == 0: - cdef ndarray[double_t] output = np.empty(N, dtype=float) + # setup + count_x = 0.0 + for j in range(s, e): + val = input[j] + if val == val: + count_x += 1.0 - minp = _check_minp(win, minp, N) + else: - # Check for windows larger than array, addresses #7297 - win = min(win, N) + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + if val == val: + count_x -= 1.0 - with nogil: - # Over the first window, observations can only be added, never removed - for i from 0 <= i < win: - val = input[i] + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + if val == val: + count_x += 1.0 - # Not NaN - if val == val: - nobs += 1 - delta = (val - mean_x) - mean_x += delta / nobs - ssqdm_x += delta * (val - mean_x) - - if (nobs >= minp) and (nobs > ddof): - #pathological case - if nobs == 1: - val = 0 - else: - val = ssqdm_x / (nobs - ddof) - if val < 0: - val = 0 + if count_x >= minp: + output[i] = count_x else: - val = NaN + output[i] = NaN - output[i] = val + return output - # After the first window, observations can both be added and removed - for i from win <= i < N: - val = input[i] - prev = input[i - win] +# ---------------------------------------------------------------------- +# Rolling sum - if val == val: - if prev == prev: - # Adding one observation and removing another one - delta = val - prev - prev -= mean_x - mean_x += delta / nobs - val -= mean_x - ssqdm_x += (val + prev) * delta - else: - # Adding one observation and not removing any - nobs += 1 - delta = (val - mean_x) - mean_x += delta / nobs - ssqdm_x += delta * (val - mean_x) - elif prev == prev: - # Adding no new observation, but removing one - nobs -= 1 - if nobs: - delta = (prev - mean_x) - mean_x -= delta / nobs - ssqdm_x -= delta * (prev - mean_x) - else: - mean_x = 0 - ssqdm_x = 0 - # Variance is unchanged if no observation is added or removed - - if (nobs >= minp) and (nobs > ddof): - #pathological case - if nobs == 1: - val = 0 - else: - val = ssqdm_x / (nobs - ddof) - if val < 0: - val = 0 - else: - val = NaN - output[i] = val +cdef inline double calc_sum(int64_t minp, int64_t nobs, double sum_x) nogil: + cdef double result - return output + if nobs >= minp: + result = sum_x + else: + result = NaN + return result -#------------------------------------------------------------------------------- -# Rolling skewness -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_skew(ndarray[double_t] input, int win, int minp): - cdef double val, prev - cdef double x = 0, xx = 0, xxx = 0 - cdef Py_ssize_t nobs = 0, i - cdef Py_ssize_t N = len(input) - cdef ndarray[double_t] output = np.empty(N, dtype=float) +cdef inline void add_sum(double val, int64_t *nobs, double *sum_x) nogil: + """ add a value from the sum calc """ - # 3 components of the skewness equation - cdef double A, B, C, R + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + sum_x[0] = sum_x[0] + val - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] - # Not NaN - if val == val: - nobs += 1 - x += val - xx += val * val - xxx += val * val * val +cdef inline void remove_sum(double val, int64_t *nobs, double *sum_x) nogil: + """ remove a value from the sum calc """ - output[i] = NaN + if val == val: + nobs[0] = nobs[0] - 1 + sum_x[0] = sum_x[0] - val - for i from minp - 1 <= i < N: - val = input[i] - if val == val: - nobs += 1 - x += val - xx += val * val - xxx += val * val * val +def roll_sum(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev_x, sum_x = 0 + int64_t s, e + int64_t nobs = 0, i, j, N + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output - if i > win - 1: - prev = input[i - win] - if prev == prev: - x -= prev - xx -= prev * prev - xxx -= prev * prev * prev + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) - nobs -= 1 - if nobs >= minp: - A = x / nobs - B = xx / nobs - A * A - C = xxx / nobs - A * A * A - 3 * A * B - if B <= 0 or nobs < 3: - output[i] = NaN - else: - R = sqrt(B) - output[i] = ((sqrt(nobs * (nobs - 1.)) * C) / - ((nobs-2) * R * R * R)) - else: - output[i] = NaN + # for performance we are going to iterate + # fixed windows separately, makes the code more complex as we have 2 paths + # but is faster - return output + if is_variable: -#------------------------------------------------------------------------------- -# Rolling kurtosis -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_kurt(ndarray[double_t] input, - int win, int minp): - cdef double val, prev - cdef double x = 0, xx = 0, xxx = 0, xxxx = 0 - cdef Py_ssize_t nobs = 0, i - cdef Py_ssize_t N = len(input) + # variable window + with nogil: - cdef ndarray[double_t] output = np.empty(N, dtype=float) + for i in range(0, N): + s = start[i] + e = end[i] - # 5 components of the kurtosis equation - cdef double A, B, C, D, R, K + if i == 0: - minp = _check_minp(win, minp, N) - with nogil: - for i from 0 <= i < minp - 1: - val = input[i] + # setup + sum_x = 0.0 + nobs = 0 + for j in range(s, e): + add_sum(input[j], &nobs, &sum_x) - # Not NaN - if val == val: - nobs += 1 + else: - # seriously don't ask me why this is faster - x += val - xx += val * val - xxx += val * val * val - xxxx += val * val * val * val + # calculate deletes + for j in range(start[i - 1], s): + remove_sum(input[j], &nobs, &sum_x) - output[i] = NaN + # calculate adds + for j in range(end[i - 1], e): + add_sum(input[j], &nobs, &sum_x) - for i from minp - 1 <= i < N: - val = input[i] + output[i] = calc_sum(minp, nobs, sum_x) - if val == val: - nobs += 1 - x += val - xx += val * val - xxx += val * val * val - xxxx += val * val * val * val + else: - if i > win - 1: - prev = input[i - win] - if prev == prev: - x -= prev - xx -= prev * prev - xxx -= prev * prev * prev - xxxx -= prev * prev * prev * prev + # fixed window - nobs -= 1 + with nogil: - if nobs >= minp: - A = x / nobs - R = A * A - B = xx / nobs - R - R = R * A - C = xxx / nobs - R - 3 * A * B - R = R * A - D = xxxx / nobs - R - 6*B*A*A - 4*C*A - - if B == 0 or nobs < 4: - output[i] = NaN + for i in range(0, minp - 1): + add_sum(input[i], &nobs, &sum_x) + output[i] = NaN - else: - K = (nobs * nobs - 1.)*D/(B*B) - 3*((nobs-1.)**2) - K = K / ((nobs - 2.)*(nobs-3.)) + for i in range(minp - 1, N): + val = input[i] + add_sum(val, &nobs, &sum_x) - output[i] = K + if i > win - 1: + prev_x = input[i - win] + remove_sum(prev_x, &nobs, &sum_x) - else: - output[i] = NaN + output[i] = calc_sum(minp, nobs, sum_x) return output -#------------------------------------------------------------------------------- -# Rolling median, min, max +# ---------------------------------------------------------------------- +# Rolling mean -from skiplist cimport * -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_median_c(ndarray[float64_t] arg, int win, int minp): - cdef: - double val, res, prev - bint err=0 - int ret=0 - skiplist_t *sl - Py_ssize_t midpoint, nobs = 0, i +cdef inline double calc_mean(int64_t minp, Py_ssize_t nobs, + Py_ssize_t neg_ct, double sum_x) nogil: + cdef double result + if nobs >= minp: + result = sum_x / <double>nobs + if neg_ct == 0 and result < 0: + # all positive + result = 0 + elif neg_ct == nobs and result > 0: + # all negative + result = 0 + else: + pass + else: + result = NaN + return result - cdef Py_ssize_t N = len(arg) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - sl = skiplist_init(win) - if sl == NULL: - raise MemoryError("skiplist_init failed") +cdef inline void add_mean(double val, Py_ssize_t *nobs, double *sum_x, + Py_ssize_t *neg_ct) nogil: + """ add a value from the mean calc """ - minp = _check_minp(win, minp, N) + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + sum_x[0] = sum_x[0] + val + if signbit(val): + neg_ct[0] = neg_ct[0] + 1 - with nogil: - for i from 0 <= i < minp - 1: - val = arg[i] - # Not NaN - if val == val: - nobs += 1 - err = skiplist_insert(sl, val) != 1 - if err: - break - output[i] = NaN +cdef inline void remove_mean(double val, Py_ssize_t *nobs, double *sum_x, + Py_ssize_t *neg_ct) nogil: + """ remove a value from the mean calc """ + + if val == val: + nobs[0] = nobs[0] - 1 + sum_x[0] = sum_x[0] - val + if signbit(val): + neg_ct[0] = neg_ct[0] - 1 - with nogil: - if not err: - for i from minp - 1 <= i < N: - val = arg[i] +def roll_mean(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev_x, result, sum_x = 0 + int64_t s, e + bint is_variable + Py_ssize_t nobs = 0, i, j, neg_ct = 0, N + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + # for performance we are going to iterate + # fixed windows separately, makes the code more complex as we have 2 paths + # but is faster + + if is_variable: + + with nogil: + + for i in range(0, N): + s = start[i] + e = end[i] + + if i == 0: + + # setup + sum_x = 0.0 + nobs = 0 + for j in range(s, e): + val = input[j] + add_mean(val, &nobs, &sum_x, &neg_ct) + + else: + + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + remove_mean(val, &nobs, &sum_x, &neg_ct) + + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + add_mean(val, &nobs, &sum_x, &neg_ct) + + output[i] = calc_mean(minp, nobs, neg_ct, sum_x) + + else: + + with nogil: + for i from 0 <= i < minp - 1: + val = input[i] + add_mean(val, &nobs, &sum_x, &neg_ct) + output[i] = NaN + + for i from minp - 1 <= i < N: + val = input[i] + add_mean(val, &nobs, &sum_x, &neg_ct) if i > win - 1: - prev = arg[i - win] + prev_x = input[i - win] + remove_mean(prev_x, &nobs, &sum_x, &neg_ct) + + output[i] = calc_mean(minp, nobs, neg_ct, sum_x) + + return output + +# ---------------------------------------------------------------------- +# Rolling variance + + +cdef inline double calc_var(int64_t minp, int ddof, double nobs, + double ssqdm_x) nogil: + cdef double result + + # Variance is unchanged if no observation is added or removed + if (nobs >= minp) and (nobs > ddof): + + # pathological case + if nobs == 1: + result = 0 + else: + result = ssqdm_x / (nobs - <double>ddof) + if result < 0: + result = 0 + else: + result = NaN + + return result + + +cdef inline void add_var(double val, double *nobs, double *mean_x, + double *ssqdm_x) nogil: + """ add a value from the var calc """ + cdef double delta + + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + + delta = (val - mean_x[0]) + mean_x[0] = mean_x[0] + delta / nobs[0] + ssqdm_x[0] = ssqdm_x[0] + delta * (val - mean_x[0]) + +cdef inline void remove_var(double val, double *nobs, double *mean_x, + double *ssqdm_x) nogil: + """ remove a value from the var calc """ + cdef double delta + + # Not NaN + if val == val: + nobs[0] = nobs[0] - 1 + if nobs[0]: + delta = (val - mean_x[0]) + mean_x[0] = mean_x[0] - delta / nobs[0] + ssqdm_x[0] = ssqdm_x[0] - delta * (val - mean_x[0]) + else: + mean_x[0] = 0 + ssqdm_x[0] = 0 + + +def roll_var(ndarray[double_t] input, int64_t win, int64_t minp, + object index, int ddof=1): + """ + Numerically stable implementation using Welford's method. + """ + cdef: + double val, prev, mean_x = 0, ssqdm_x = 0, nobs = 0, delta + int64_t s, e + bint is_variable + Py_ssize_t i, j, N + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + # Check for windows larger than array, addresses #7297 + win = min(win, N) + + # for performance we are going to iterate + # fixed windows separately, makes the code more complex as we + # have 2 paths but is faster + + if is_variable: + + with nogil: + + for i in range(0, N): + + s = start[i] + e = end[i] + + # Over the first window, observations can only be added + # never removed + if i == 0: + + for j in range(s, e): + add_var(input[j], &nobs, &mean_x, &ssqdm_x) + + else: + + # After the first window, observations can both be added + # and removed + + # calculate adds + for j in range(end[i - 1], e): + add_var(input[j], &nobs, &mean_x, &ssqdm_x) + + # calculate deletes + for j in range(start[i - 1], s): + remove_var(input[j], &nobs, &mean_x, &ssqdm_x) + + output[i] = calc_var(minp, ddof, nobs, ssqdm_x) + + else: + + with nogil: + + # Over the first window, observations can only be added, never + # removed + for i from 0 <= i < win: + add_var(input[i], &nobs, &mean_x, &ssqdm_x) + output[i] = calc_var(minp, ddof, nobs, ssqdm_x) + + # After the first window, observations can both be added and + # removed + for i from win <= i < N: + val = input[i] + prev = input[i - win] + + if val == val: if prev == prev: - skiplist_remove(sl, prev) - nobs -= 1 + # Adding one observation and removing another one + delta = val - prev + prev -= mean_x + mean_x += delta / nobs + val -= mean_x + ssqdm_x += (val + prev) * delta + + else: + add_var(val, &nobs, &mean_x, &ssqdm_x) + elif prev == prev: + remove_var(prev, &nobs, &mean_x, &ssqdm_x) + + output[i] = calc_var(minp, ddof, nobs, ssqdm_x) + + return output + + +# ---------------------------------------------------------------------- +# Rolling skewness + +cdef inline double calc_skew(int64_t minp, int64_t nobs, double x, double xx, + double xxx) nogil: + cdef double result, dnobs + cdef double A, B, C, R + + if nobs >= minp: + dnobs = <double>nobs + A = x / dnobs + B = xx / dnobs - A * A + C = xxx / dnobs - A * A * A - 3 * A * B + if B <= 0 or nobs < 3: + result = NaN + else: + R = sqrt(B) + result = ((sqrt(dnobs * (dnobs - 1.)) * C) / + ((dnobs - 2) * R * R * R)) + else: + result = NaN + + return result + +cdef inline void add_skew(double val, int64_t *nobs, double *x, double *xx, + double *xxx) nogil: + """ add a value from the skew calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + + # seriously don't ask me why this is faster + x[0] = x[0] + val + xx[0] = xx[0] + val * val + xxx[0] = xxx[0] + val * val * val + +cdef inline void remove_skew(double val, int64_t *nobs, double *x, double *xx, + double *xxx) nogil: + """ remove a value from the skew calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] - 1 + + # seriously don't ask me why this is faster + x[0] = x[0] - val + xx[0] = xx[0] - val * val + xxx[0] = xxx[0] - val * val * val + + +def roll_skew(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev + double x = 0, xx = 0, xxx = 0 + int64_t nobs = 0, i, j, N + int64_t s, e + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + if is_variable: + + with nogil: + + for i in range(0, N): + + s = start[i] + e = end[i] + + # Over the first window, observations can only be added + # never removed + if i == 0: + + for j in range(s, e): + val = input[j] + add_skew(val, &nobs, &x, &xx, &xxx) + + else: + + # After the first window, observations can both be added + # and removed + + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + add_skew(val, &nobs, &x, &xx, &xxx) + + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + remove_skew(val, &nobs, &x, &xx, &xxx) + + output[i] = calc_skew(minp, nobs, x, xx, xxx) + + else: + + with nogil: + for i from 0 <= i < minp - 1: + val = input[i] + add_skew(val, &nobs, &x, &xx, &xxx) + output[i] = NaN + + for i from minp - 1 <= i < N: + val = input[i] + add_skew(val, &nobs, &x, &xx, &xxx) + + if i > win - 1: + prev = input[i - win] + remove_skew(prev, &nobs, &x, &xx, &xxx) + + output[i] = calc_skew(minp, nobs, x, xx, xxx) + + return output + +# ---------------------------------------------------------------------- +# Rolling kurtosis + + +cdef inline double calc_kurt(int64_t minp, int64_t nobs, double x, double xx, + double xxx, double xxxx) nogil: + cdef double result, dnobs + cdef double A, B, C, D, R, K + + if nobs >= minp: + dnobs = <double>nobs + A = x / dnobs + R = A * A + B = xx / dnobs - R + R = R * A + C = xxx / dnobs - R - 3 * A * B + R = R * A + D = xxxx / dnobs - R - 6 * B * A * A - 4 * C * A + + if B == 0 or nobs < 4: + result = NaN + else: + K = (dnobs * dnobs - 1.) * D / (B * B) - 3 * ((dnobs - 1.) ** 2) + result = K / ((dnobs - 2.) * (dnobs - 3.)) + else: + result = NaN + + return result + +cdef inline void add_kurt(double val, int64_t *nobs, double *x, double *xx, + double *xxx, double *xxxx) nogil: + """ add a value from the kurotic calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] + 1 + + # seriously don't ask me why this is faster + x[0] = x[0] + val + xx[0] = xx[0] + val * val + xxx[0] = xxx[0] + val * val * val + xxxx[0] = xxxx[0] + val * val * val * val + +cdef inline void remove_kurt(double val, int64_t *nobs, double *x, double *xx, + double *xxx, double *xxxx) nogil: + """ remove a value from the kurotic calc """ + + # Not NaN + if val == val: + nobs[0] = nobs[0] - 1 + + # seriously don't ask me why this is faster + x[0] = x[0] - val + xx[0] = xx[0] - val * val + xxx[0] = xxx[0] - val * val * val + xxxx[0] = xxxx[0] - val * val * val * val + + +def roll_kurt(ndarray[double_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, prev + double x = 0, xx = 0, xxx = 0, xxxx = 0 + int64_t nobs = 0, i, j, N + int64_t s, e + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output + + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index) + output = np.empty(N, dtype=float) + + if is_variable: + + with nogil: + + for i in range(0, N): + + s = start[i] + e = end[i] + + # Over the first window, observations can only be added + # never removed + if i == 0: + + for j in range(s, e): + add_kurt(input[j], &nobs, &x, &xx, &xxx, &xxxx) + + else: + + # After the first window, observations can both be added + # and removed + + # calculate adds + for j in range(end[i - 1], e): + add_kurt(input[j], &nobs, &x, &xx, &xxx, &xxxx) + + # calculate deletes + for j in range(start[i - 1], s): + remove_kurt(input[j], &nobs, &x, &xx, &xxx, &xxxx) + + output[i] = calc_kurt(minp, nobs, x, xx, xxx, xxxx) + + else: + + with nogil: + + for i from 0 <= i < minp - 1: + add_kurt(input[i], &nobs, &x, &xx, &xxx, &xxxx) + output[i] = NaN + + for i from minp - 1 <= i < N: + add_kurt(input[i], &nobs, &x, &xx, &xxx, &xxxx) + + if i > win - 1: + prev = input[i - win] + remove_kurt(prev, &nobs, &x, &xx, &xxx, &xxxx) + + output[i] = calc_kurt(minp, nobs, x, xx, xxx, xxxx) + + return output + +# ---------------------------------------------------------------------- +# Rolling median, min, max + + +def roll_median_c(ndarray[float64_t] input, int64_t win, int64_t minp, + object index): + cdef: + double val, res, prev + bint err=0, is_variable + int ret=0 + skiplist_t *sl + Py_ssize_t i, j + int64_t nobs = 0, N, s, e + int midpoint + ndarray[int64_t] start, end + ndarray[double_t] output + + # we use the Fixed/Variable Indexer here as the + # actual skiplist ops outweigh any window computation costs + start, end, N, win, minp, is_variable = get_window_indexer( + input, win, + minp, index, + use_mock=False) + output = np.empty(N, dtype=float) + + sl = skiplist_init(<int>win) + if sl == NULL: + raise MemoryError("skiplist_init failed") + + with nogil: + + for i in range(0, N): + s = start[i] + e = end[i] + + if i == 0: + + # setup + val = input[i] if val == val: nobs += 1 err = skiplist_insert(sl, val) != 1 if err: break - if nobs >= minp: - midpoint = nobs / 2 - if nobs % 2: - res = skiplist_get(sl, midpoint, &ret) - else: - res = (skiplist_get(sl, midpoint, &ret) + - skiplist_get(sl, (midpoint - 1), &ret)) / 2 + else: + + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + if val == val: + skiplist_remove(sl, val) + nobs -= 1 + + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + if val == val: + nobs += 1 + err = skiplist_insert(sl, val) != 1 + if err: + break + + if nobs >= minp: + midpoint = <int>(nobs / 2) + if nobs % 2: + res = skiplist_get(sl, midpoint, &ret) else: - res = NaN + res = (skiplist_get(sl, midpoint, &ret) + + skiplist_get(sl, (midpoint - 1), &ret)) / 2 + else: + res = NaN - output[i] = res + output[i] = res - skiplist_destroy(sl) + skiplist_destroy(sl) if err: raise MemoryError("skiplist_insert failed") return output -#---------------------------------------------------------------------- +# ---------------------------------------------------------------------- # Moving maximum / minimum code taken from Bottleneck under the terms # of its Simplified BSD license # https://github.com/kwgoodman/bottleneck -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_max(ndarray[numeric] a, int window, int minp): + +cdef inline numeric init_mm(numeric ai, Py_ssize_t *nobs, bint is_max) nogil: + + if numeric in cython.floating: + if ai == ai: + nobs[0] = nobs[0] + 1 + elif is_max: + if numeric == cython.float: + ai = MINfloat32 + else: + ai = MINfloat64 + else: + if numeric == cython.float: + ai = MAXfloat32 + else: + ai = MAXfloat64 + + else: + nobs[0] = nobs[0] + 1 + + return ai + + +cdef inline void remove_mm(numeric aold, Py_ssize_t *nobs) nogil: + """ remove a value from the mm calc """ + if numeric in cython.floating and aold == aold: + nobs[0] = nobs[0] - 1 + + +cdef inline numeric calc_mm(int64_t minp, Py_ssize_t nobs, + numeric value) nogil: + cdef numeric result + + if numeric in cython.floating: + if nobs >= minp: + result = value + else: + result = NaN + else: + result = value + + return result + + +def roll_max(ndarray[numeric] input, int64_t win, int64_t minp, + object index): """ Moving max of 1d array of any numeric type along axis=0 ignoring NaNs. Parameters ---------- - a: numpy array + input: numpy array window: int, size of rolling window minp: if number of observations in window is below this, output a NaN + index: ndarray, optional + index for window computation """ - return _roll_min_max(a, window, minp, 1) + return _roll_min_max(input, win, minp, index, is_max=1) + -@cython.boundscheck(False) -@cython.wraparound(False) -def roll_min(ndarray[numeric] a, int window, int minp): +def roll_min(ndarray[numeric] input, int64_t win, int64_t minp, + object index): """ Moving max of 1d array of any numeric type along axis=0 ignoring NaNs. Parameters ---------- - a: numpy array + input: numpy array window: int, size of rolling window minp: if number of observations in window is below this, output a NaN + index: ndarray, optional + index for window computation """ - return _roll_min_max(a, window, minp, 0) - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef _roll_min_max(ndarray[numeric] a, int window, int minp, bint is_max): - "Moving min/max of 1d array of any numeric type along axis=0 ignoring NaNs." - cdef numeric ai, aold - cdef Py_ssize_t count - cdef Py_ssize_t* death - cdef numeric* ring - cdef numeric* minvalue - cdef numeric* end - cdef numeric* last - cdef Py_ssize_t i0 - cdef np.npy_intp *dim - dim = PyArray_DIMS(a) - cdef Py_ssize_t n0 = dim[0] - cdef np.npy_intp *dims = [n0] - cdef bint should_replace - cdef np.ndarray[numeric, ndim=1] y = PyArray_EMPTY(1, dims, PyArray_TYPE(a), 0) - - if window < 1: - raise ValueError('Invalid window size %d' - % (window)) - - if minp > window: - raise ValueError('Invalid min_periods size %d greater than window %d' - % (minp, window)) - - minp = _check_minp(window, minp, n0) - with nogil: - ring = <numeric*>malloc(window * sizeof(numeric)) - death = <Py_ssize_t*>malloc(window * sizeof(Py_ssize_t)) - end = ring + window - last = ring + return _roll_min_max(input, win, minp, index, is_max=0) + +cdef _roll_min_max(ndarray[numeric] input, int64_t win, int64_t minp, + object index, bint is_max): + """ + Moving min/max of 1d array of any numeric type along axis=0 + ignoring NaNs. + """ + + cdef: + numeric ai + bint is_variable, should_replace + int64_t s, e, N, i, j, removed + Py_ssize_t nobs = 0 + ndarray[int64_t] starti, endi + ndarray[numeric, ndim=1] output + cdef: + int64_t* death + numeric* ring + numeric* minvalue + numeric* end + numeric* last + + cdef: + cdef numeric r + + starti, endi, N, win, minp, is_variable = get_window_indexer( + input, win, + minp, index) + + output = np.empty(N, dtype=input.dtype) + + if is_variable: + + with nogil: + + for i in range(N): + s = starti[i] + e = endi[i] + + r = input[s] + nobs = 0 + for j in range(s, e): + + # adds, death at the i offset + ai = init_mm(input[j], &nobs, is_max) + + if is_max: + if ai > r: + r = ai + else: + if ai < r: + r = ai + + output[i] = calc_mm(minp, nobs, r) + + else: + + # setup the rings of death! + ring = <numeric *>malloc(win * sizeof(numeric)) + death = <int64_t *>malloc(win * sizeof(int64_t)) + + end = ring + win + last = ring minvalue = ring - ai = a[0] - if numeric in cython.floating: - if ai == ai: - minvalue[0] = ai - elif is_max: - minvalue[0] = MINfloat64 - else: - minvalue[0] = MAXfloat64 - else: - minvalue[0] = ai - death[0] = window - - count = 0 - for i0 in range(n0): - ai = a[i0] - if numeric in cython.floating: - if ai == ai: - count += 1 - elif is_max: - ai = MINfloat64 + ai = input[0] + minvalue[0] = init_mm(input[0], &nobs, is_max) + death[0] = win + nobs = 0 + + with nogil: + + for i in range(N): + ai = init_mm(input[i], &nobs, is_max) + + if i >= win: + remove_mm(input[i - win], &nobs) + + if death[minvalue - ring] == i: + minvalue = minvalue + 1 + if minvalue >= end: + minvalue = ring + + if is_max: + should_replace = ai >= minvalue[0] else: - ai = MAXfloat64 - else: - count += 1 - if i0 >= window: - aold = a[i0 - window] - if aold == aold: - count -= 1 - if death[minvalue-ring] == i0: - minvalue += 1 - if minvalue >= end: - minvalue = ring - should_replace = ai >= minvalue[0] if is_max else ai <= minvalue[0] - if should_replace: - minvalue[0] = ai - death[minvalue-ring] = i0 + window - last = minvalue - else: - should_replace = last[0] <= ai if is_max else last[0] >= ai - while should_replace: - if last == ring: - last = end - last -= 1 - should_replace = last[0] <= ai if is_max else last[0] >= ai - last += 1 - if last == end: - last = ring - last[0] = ai - death[last - ring] = i0 + window - if numeric in cython.floating: - if count >= minp: - y[i0] = minvalue[0] + should_replace = ai <= minvalue[0] + if should_replace: + + minvalue[0] = ai + death[minvalue - ring] = i + win + last = minvalue + else: - y[i0] = NaN - else: - y[i0] = minvalue[0] - for i0 in range(minp - 1): - if numeric in cython.floating: - y[i0] = NaN - else: - y[i0] = 0 + if is_max: + should_replace = last[0] <= ai + else: + should_replace = last[0] >= ai + while should_replace: + if last == ring: + last = end + last -= 1 + if is_max: + should_replace = last[0] <= ai + else: + should_replace = last[0] >= ai + + last += 1 + if last == end: + last = ring + last[0] = ai + death[last - ring] = i + win + + output[i] = calc_mm(minp, nobs, minvalue[0]) + + for i in range(minp - 1): + if numeric in cython.floating: + output[i] = NaN + else: + output[i] = 0 + + free(ring) + free(death) + + # print("output: {0}".format(output)) + return output - free(ring) - free(death) - return y -def roll_quantile(ndarray[float64_t, cast=True] input, int win, - int minp, double quantile): +def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win, + int64_t minp, object index, double quantile): """ O(N log(window)) implementation using skip list """ - cdef double val, prev, midpoint - cdef IndexableSkiplist skiplist - cdef Py_ssize_t nobs = 0, i - cdef Py_ssize_t N = len(input) - cdef ndarray[double_t] output = np.empty(N, dtype=float) - + cdef: + double val, prev, midpoint + IndexableSkiplist skiplist + int64_t nobs = 0, i, j, s, e, N + Py_ssize_t idx + bint is_variable + ndarray[int64_t] start, end + ndarray[double_t] output + + # we use the Fixed/Variable Indexer here as the + # actual skiplist ops outweigh any window computation costs + start, end, N, win, minp, is_variable = get_window_indexer( + input, win, + minp, index, + use_mock=False) + output = np.empty(N, dtype=float) skiplist = IndexableSkiplist(win) - minp = _check_minp(win, minp, N) - - for i from 0 <= i < minp - 1: - val = input[i] + for i in range(0, N): + s = start[i] + e = end[i] - # Not NaN - if val == val: - nobs += 1 - skiplist.insert(val) + if i == 0: - output[i] = NaN - - for i from minp - 1 <= i < N: - val = input[i] + # setup + val = input[i] + if val == val: + nobs += 1 + skiplist.insert(val) - if i > win - 1: - prev = input[i - win] + else: - if prev == prev: - skiplist.remove(prev) - nobs -= 1 + # calculate deletes + for j in range(start[i - 1], s): + val = input[j] + if val == val: + skiplist.remove(val) + nobs -= 1 - if val == val: - nobs += 1 - skiplist.insert(val) + # calculate adds + for j in range(end[i - 1], e): + val = input[j] + if val == val: + nobs += 1 + skiplist.insert(val) if nobs >= minp: - idx = int((quantile / 1.) * (nobs - 1)) + idx = int(quantile * <double>(nobs - 1)) output[i] = skiplist.get(idx) else: output[i] = NaN return output + def roll_generic(ndarray[float64_t, cast=True] input, - int win, int minp, int offset, - object func, object args, object kwargs): - cdef ndarray[double_t] output, counts, bufarr - cdef Py_ssize_t i, n - cdef float64_t *buf - cdef float64_t *oldbuf + int64_t win, int64_t minp, object index, + int offset, object func, + object args, object kwargs): + cdef: + ndarray[double_t] output, counts, bufarr + float64_t *buf + float64_t *oldbuf + int64_t nobs = 0, i, j, s, e, N + bint is_variable + ndarray[int64_t] start, end if not input.flags.c_contiguous: input = input.copy('C') @@ -855,36 +1350,60 @@ def roll_generic(ndarray[float64_t, cast=True] input, if n == 0: return input - minp = _check_minp(win, minp, n, floor=0) - output = np.empty(n, dtype=float) - counts = roll_sum(np.concatenate((np.isfinite(input).astype(float), np.array([0.] * offset))), win, minp)[offset:] + start, end, N, win, minp, is_variable = get_window_indexer(input, win, + minp, index, + floor=0) + output = np.empty(N, dtype=float) - # truncated windows at the beginning, through first full-length window - for i from 0 <= i < (int_min(win, n) - offset): - if counts[i] >= minp: - output[i] = func(input[0 : (i + offset + 1)], *args, **kwargs) - else: - output[i] = NaN + counts = roll_sum(np.concatenate([np.isfinite(input).astype(float), + np.array([0.] * offset)]), + win, minp, index)[offset:] - # remaining full-length windows - buf = <float64_t*> input.data - bufarr = np.empty(win, dtype=float) - oldbuf = <float64_t*> bufarr.data - for i from (win - offset) <= i < (n - offset): - buf = buf + 1 - bufarr.data = <char*> buf - if counts[i] >= minp: - output[i] = func(bufarr, *args, **kwargs) - else: - output[i] = NaN - bufarr.data = <char*> oldbuf + if is_variable: - # truncated windows at the end - for i from int_max(n - offset, 0) <= i < n: - if counts[i] >= minp: - output[i] = func(input[int_max(i + offset - win + 1, 0) : n], *args, **kwargs) - else: - output[i] = NaN + # variable window + if offset != 0: + raise ValueError("unable to roll_generic with a non-zero offset") + + for i in range(0, N): + s = start[i] + e = end[i] + + if counts[i] >= minp: + output[i] = func(input[s:e], *args, **kwargs) + else: + output[i] = NaN + + else: + + # truncated windows at the beginning, through first full-length window + for i from 0 <= i < (int_min(win, N) - offset): + if counts[i] >= minp: + output[i] = func(input[0: (i + offset + 1)], *args, **kwargs) + else: + output[i] = NaN + + # remaining full-length windows + buf = <float64_t *> input.data + bufarr = np.empty(win, dtype=float) + oldbuf = <float64_t *> bufarr.data + for i from (win - offset) <= i < (N - offset): + buf = buf + 1 + bufarr.data = <char *> buf + if counts[i] >= minp: + output[i] = func(bufarr, *args, **kwargs) + else: + output[i] = NaN + bufarr.data = <char *> oldbuf + + # truncated windows at the end + for i from int_max(N - offset, 0) <= i < N: + if counts[i] >= minp: + output[i] = func(input[int_max(i + offset - win + 1, 0): N], + *args, + **kwargs) + else: + output[i] = NaN return output @@ -952,3 +1471,179 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] input, output[in_i] = NaN return output + +# ---------------------------------------------------------------------- +# Exponentially weighted moving average + + +def ewma(ndarray[double_t] input, double_t com, int adjust, int ignore_na, + int minp): + """ + Compute exponentially-weighted moving average using center-of-mass. + + Parameters + ---------- + input : ndarray (float64 type) + com : float64 + adjust: int + ignore_na: int + minp: int + + Returns + ------- + y : ndarray + """ + + cdef Py_ssize_t N = len(input) + cdef ndarray[double_t] output = np.empty(N, dtype=float) + if N == 0: + return output + + minp = max(minp, 1) + + cdef double alpha, old_wt_factor, new_wt, weighted_avg, old_wt, cur + cdef Py_ssize_t i, nobs + + alpha = 1. / (1. + com) + old_wt_factor = 1. - alpha + new_wt = 1. if adjust else alpha + + weighted_avg = input[0] + is_observation = (weighted_avg == weighted_avg) + nobs = int(is_observation) + output[0] = weighted_avg if (nobs >= minp) else NaN + old_wt = 1. + + for i from 1 <= i < N: + cur = input[i] + is_observation = (cur == cur) + nobs += int(is_observation) + if weighted_avg == weighted_avg: + + if is_observation or (not ignore_na): + + old_wt *= old_wt_factor + if is_observation: + + # avoid numerical errors on constant series + if weighted_avg != cur: + weighted_avg = ((old_wt * weighted_avg) + + (new_wt * cur)) / (old_wt + new_wt) + if adjust: + old_wt += new_wt + else: + old_wt = 1. + elif is_observation: + weighted_avg = cur + + output[i] = weighted_avg if (nobs >= minp) else NaN + + return output + +# ---------------------------------------------------------------------- +# Exponentially weighted moving covariance + + +def ewmcov(ndarray[double_t] input_x, ndarray[double_t] input_y, + double_t com, int adjust, int ignore_na, int minp, int bias): + """ + Compute exponentially-weighted moving variance using center-of-mass. + + Parameters + ---------- + input_x : ndarray (float64 type) + input_y : ndarray (float64 type) + com : float64 + adjust: int + ignore_na: int + minp: int + bias: int + + Returns + ------- + y : ndarray + """ + + cdef Py_ssize_t N = len(input_x) + if len(input_y) != N: + raise ValueError("arrays are of different lengths " + "(%d and %d)" % (N, len(input_y))) + cdef ndarray[double_t] output = np.empty(N, dtype=float) + if N == 0: + return output + + minp = max(minp, 1) + + cdef double alpha, old_wt_factor, new_wt, mean_x, mean_y, cov + cdef double sum_wt, sum_wt2, old_wt, cur_x, cur_y, old_mean_x, old_mean_y + cdef Py_ssize_t i, nobs + + alpha = 1. / (1. + com) + old_wt_factor = 1. - alpha + new_wt = 1. if adjust else alpha + + mean_x = input_x[0] + mean_y = input_y[0] + is_observation = ((mean_x == mean_x) and (mean_y == mean_y)) + nobs = int(is_observation) + if not is_observation: + mean_x = NaN + mean_y = NaN + output[0] = (0. if bias else NaN) if (nobs >= minp) else NaN + cov = 0. + sum_wt = 1. + sum_wt2 = 1. + old_wt = 1. + + for i from 1 <= i < N: + cur_x = input_x[i] + cur_y = input_y[i] + is_observation = ((cur_x == cur_x) and (cur_y == cur_y)) + nobs += int(is_observation) + if mean_x == mean_x: + if is_observation or (not ignore_na): + sum_wt *= old_wt_factor + sum_wt2 *= (old_wt_factor * old_wt_factor) + old_wt *= old_wt_factor + if is_observation: + old_mean_x = mean_x + old_mean_y = mean_y + + # avoid numerical errors on constant series + if mean_x != cur_x: + mean_x = ((old_wt * old_mean_x) + + (new_wt * cur_x)) / (old_wt + new_wt) + + # avoid numerical errors on constant series + if mean_y != cur_y: + mean_y = ((old_wt * old_mean_y) + + (new_wt * cur_y)) / (old_wt + new_wt) + cov = ((old_wt * (cov + ((old_mean_x - mean_x) * + (old_mean_y - mean_y)))) + + (new_wt * ((cur_x - mean_x) * + (cur_y - mean_y)))) / (old_wt + new_wt) + sum_wt += new_wt + sum_wt2 += (new_wt * new_wt) + old_wt += new_wt + if not adjust: + sum_wt /= old_wt + sum_wt2 /= (old_wt * old_wt) + old_wt = 1. + elif is_observation: + mean_x = cur_x + mean_y = cur_y + + if nobs >= minp: + if not bias: + numerator = sum_wt * sum_wt + denominator = numerator - sum_wt2 + if (denominator > 0.): + output[i] = ((numerator / denominator) * cov) + else: + output[i] = NaN + else: + output[i] = cov + else: + output[i] = NaN + + return output diff --git a/setup.py b/setup.py index 650357588570a..bdc54a147bd1a 100755 --- a/setup.py +++ b/setup.py @@ -430,9 +430,9 @@ def pxd(name): 'depends': [srcpath('generated', suffix='.pyx'), srcpath('join', suffix='.pyx')]}, _window={'pyxfile': 'window', - 'pxdfiles': ['src/skiplist','src/util'], - 'depends': ['pandas/src/skiplist.pyx', - 'pandas/src/skiplist.h']}, + 'pxdfiles': ['src/skiplist', 'src/util'], + 'depends': ['pandas/src/skiplist.pyx', + 'pandas/src/skiplist.h']}, parser={'pyxfile': 'parser', 'depends': ['pandas/src/parser/tokenizer.h', 'pandas/src/parser/io.h',
xref #13327 closes #936 This [notebook shows the usecase](http://nbviewer.jupyter.org/gist/jreback/186d09a99902a17a095d99ac6a5e4cd3) - implement lint checking for cython (currently only for windows.pyx), xref #12995 This implements time-ware windows, IOW, to a `.rolling()` you can now pass a ragged / sparse timeseries and have it work with an offset (e.g. like `2s`). Previously you _could_ achieve these results by resampling first, then using an integer period (though you would have to jump thru hoops when crossing day boundaries and such). This now provides a nice easy / performant implementation (as indicated on the linked notebook, min/max impl is giving scaling issues). ``` In [1]: df = DataFrame({'B': range(5)}) In [2]: df.index = [Timestamp('20130101 09:00:00'), ...: Timestamp('20130101 09:00:02'), ...: Timestamp('20130101 09:00:03'), ...: Timestamp('20130101 09:00:05'), ...: Timestamp('20130101 09:00:06')] In [3]: df Out[3]: B 2013-01-01 09:00:00 0 2013-01-01 09:00:02 1 2013-01-01 09:00:03 2 2013-01-01 09:00:05 3 2013-01-01 09:00:06 4 In [4]: df.rolling(2, min_periods=1).sum() Out[4]: B 2013-01-01 09:00:00 0.0 2013-01-01 09:00:02 1.0 2013-01-01 09:00:03 3.0 2013-01-01 09:00:05 5.0 2013-01-01 09:00:06 7.0 In [5]: df.rolling('2s', min_periods=1).sum() Out[5]: B 2013-01-01 09:00:00 0.0 2013-01-01 09:00:02 1.0 2013-01-01 09:00:03 3.0 2013-01-01 09:00:05 3.0 2013-01-01 09:00:06 7.0 ```
https://api.github.com/repos/pandas-dev/pandas/pulls/13513
2016-06-26T02:15:05Z
2016-07-20T17:23:59Z
null
2020-09-18T11:02:50Z
To numeric enhance
diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py index 3fceed087facb..ca25e55ac8b95 100644 --- a/asv_bench/benchmarks/inference.py +++ b/asv_bench/benchmarks/inference.py @@ -135,4 +135,25 @@ def setup(self): self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) def time_dtype_infer_uint32(self): - (self.df_uint32['A'] + self.df_uint32['B']) \ No newline at end of file + (self.df_uint32['A'] + self.df_uint32['B']) + + +class to_numeric(object): + N = 500000 + + param_names = ['data', 'dtype'] + params = [ + [(['1'] * N / 2) + ([2] * N / 2), + (['-1'] * N / 2) + ([2] * N / 2), + np.repeat(np.array('1970-01-01', '1970-01-02', + dtype='datetime64[D]'), N), + (['1.1'] * N / 2) + ([2] * N / 2), + ([1] * N / 2) + ([2] * N / 2), + np.repeat(np.int32(1), N)], + ['int64', 'uint64', 'int32', 'uint32', + 'int16', 'uint16', 'int8', 'uint8', + 'float64', 'float32', float, int], + ] + + def time_to_numeric(self, data, dtype): + pd.to_numeric(data, downcast=dtype) diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 40fec4d071f16..d1a8d6c51bee8 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -186,6 +186,12 @@ Other enhancements ^^^^^^^^^^^^^^^^^^ - The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behaviour remains to raising a ``NonExistentTimeError`` (:issue:`13057`) +- ``pd.to_numeric()`` now accepts a ``downcast`` parameter (:issue:`13352`) + + .. ipython:: python + + s = ['1', 2, 3] + pd.to_numeric(s, downcast=np.int8) - ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see :ref:`documentation here <text.extractall>` (:issue:`10008`, :issue:`13156`) - ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`) diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index c592b33bdab9a..22d13ab77ee82 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -291,6 +291,59 @@ def test_non_hashable(self): with self.assertRaisesRegexp(TypeError, "Invalid object type"): pd.to_numeric(s) + def test_downcast(self): + mixed_data = ['1', 2, 3] + int_data = [1, 2, 3] + date_data = np.array(['1970-01-02', '1970-01-03', + '1970-01-04'], dtype='datetime64[D]') + + smaller_dtypes = [np.int8, np.uint8, np.int16, np.uint16, + np.int32, np.uint32, np.float32] + + larger_dtypes = [np.int64, np.uint64, np.float64] + + msg = "'downcast' must be a numerical dtype" + bad_dtype = 'datetime64[D]' + + for data in [mixed_data, int_data, date_data]: + tm.assertRaisesRegexp(ValueError, msg, pd.to_numeric, + data, downcast=bad_dtype) + + for smaller_dtype in smaller_dtypes: + res = pd.to_numeric(data, downcast=smaller_dtype) + expected = np.array([1, 2, 3], dtype=smaller_dtype) + tm.assert_numpy_array_equal(res, expected) + + expected = np.array([1, 2, 3], dtype=np.int64) + + # default + res = pd.to_numeric(data) + tm.assert_numpy_array_equal(res, expected) + + # explicit + res = pd.to_numeric(data, downcast=None) + tm.assert_numpy_array_equal(res, expected) + + for larger_dtype in larger_dtypes: + res = pd.to_numeric(data, downcast=larger_dtype) + tm.assert_numpy_array_equal(res, expected) + + # cannot downcast because it is the wrong type + data = ['-1', 2, 3] + res = pd.to_numeric(data, downcast=np.uint8) + expected = np.array([-1, 2, 3], dtype=np.int64) + tm.assert_numpy_array_equal(res, expected) + + # ensure behaviour is respected when values are of + # different integer dtypes (i.e. not 'np.int') + data = np.array([1, 2, 3], dtype=np.int16) + + res = pd.to_numeric(data, downcast=np.int16) + tm.assert_numpy_array_equal(res, data) + + expected = np.array([1, 2, 3], dtype=np.uint8) + res = pd.to_numeric(data, downcast=np.uint8) + tm.assert_numpy_array_equal(res, expected) if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tools/util.py b/pandas/tools/util.py index 61d2c0adce2fe..5de56a272d80f 100644 --- a/pandas/tools/util.py +++ b/pandas/tools/util.py @@ -50,7 +50,7 @@ def compose(*funcs): return reduce(_compose2, funcs) -def to_numeric(arg, errors='raise'): +def to_numeric(arg, errors='raise', downcast=None): """ Convert argument to a numeric type. @@ -61,6 +61,13 @@ def to_numeric(arg, errors='raise'): - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaN - If 'ignore', then invalid parsing will return the input + downcast : numpy.dtype or Python dtype, default None + If possible, downcast the resulting numerical data to the + specified **numerical** dtype in 'downcast'. If the size of + the data's dtype is smaller or equal to that of 'downcast', + then this parameter will be ignored. + + .. versionadded:: 0.18.2 Returns ------- @@ -74,6 +81,7 @@ def to_numeric(arg, errors='raise'): >>> import pandas as pd >>> s = pd.Series(['1.0', '2', -3]) >>> pd.to_numeric(s) + >>> pd.to_numeric(s, downcast=np.int8) >>> s = pd.Series(['apple', '1.0', '2', -3]) >>> pd.to_numeric(s, errors='ignore') >>> pd.to_numeric(s, errors='coerce') @@ -102,20 +110,29 @@ def to_numeric(arg, errors='raise'): else: values = arg - if com.is_numeric_dtype(values): - pass - elif com.is_datetime_or_timedelta_dtype(values): - values = values.astype(np.int64) - else: - values = com._ensure_object(values) - coerce_numeric = False if errors in ('ignore', 'raise') else True + try: + if com.is_numeric_dtype(values): + pass + elif com.is_datetime_or_timedelta_dtype(values): + values = values.astype(np.int64) + else: + values = com._ensure_object(values) + coerce_numeric = False if errors in ('ignore', 'raise') else True - try: values = lib.maybe_convert_numeric(values, set(), coerce_numeric=coerce_numeric) - except: - if errors == 'raise': - raise + + if downcast is not None: + dtype = np.dtype(downcast) + if not com.is_numeric_dtype(dtype): + raise ValueError("'downcast' must be a numerical dtype") + + if values.dtype.itemsize > dtype.itemsize: + values = com._possibly_downcast_to_dtype(values, dtype) + + except Exception: + if errors == 'raise': + raise if is_series: return pd.Series(values, index=arg.index, name=arg.name)
This is a test PR copy of #13425 to test travis - do not merge
https://api.github.com/repos/pandas-dev/pandas/pulls/13512
2016-06-25T16:27:22Z
2016-06-27T06:42:30Z
null
2016-06-27T06:42:38Z
BUG: date_range closed keyword with timezone aware start/end (GH12684)
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index ba14ac51012c7..7f74d8a769e4b 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -658,7 +658,7 @@ Bug Fixes - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) - Bug in ``PeriodIndex.resample`` where name not propagated (:issue:`12769`) - +- Bug in ``date_range`` ``closed`` keyword and timezones (:issue:`12684`). - Bug in ``pd.concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) - Bug in ``pd.concat`` did not handle empty ``Series`` properly (:issue:`11082`) diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index af60a2d028c93..77500081be62c 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -541,6 +541,13 @@ def _generate(cls, start, end, periods, name, offset, ambiguous=ambiguous) index = index.view(_NS_DTYPE) + # index is localized datetime64 array -> have to convert + # start/end as well to compare + if start is not None: + start = start.tz_localize(tz).asm8 + if end is not None: + end = end.tz_localize(tz).asm8 + if not left_closed and len(index) and index[0] == start: index = index[1:] if not right_closed and len(index) and index[-1] == end: diff --git a/pandas/tseries/tests/test_daterange.py b/pandas/tseries/tests/test_daterange.py index 6ad33b6b973de..854b60c17853b 100644 --- a/pandas/tseries/tests/test_daterange.py +++ b/pandas/tseries/tests/test_daterange.py @@ -485,7 +485,7 @@ def test_range_closed(self): begin = datetime(2011, 1, 1) end = datetime(2014, 1, 1) - for freq in ["3D", "2M", "7W", "3H", "A"]: + for freq in ["1D", "3D", "2M", "7W", "3H", "A"]: closed = date_range(begin, end, closed=None, freq=freq) left = date_range(begin, end, closed="left", freq=freq) right = date_range(begin, end, closed="right", freq=freq) @@ -501,11 +501,11 @@ def test_range_closed(self): self.assert_index_equal(expected_right, right) def test_range_closed_with_tz_aware_start_end(self): - # GH12409 + # GH12409, GH12684 begin = Timestamp('2011/1/1', tz='US/Eastern') end = Timestamp('2014/1/1', tz='US/Eastern') - for freq in ["3D", "2M", "7W", "3H", "A"]: + for freq in ["1D", "3D", "2M", "7W", "3H", "A"]: closed = date_range(begin, end, closed=None, freq=freq) left = date_range(begin, end, closed="left", freq=freq) right = date_range(begin, end, closed="right", freq=freq) @@ -520,15 +520,28 @@ def test_range_closed_with_tz_aware_start_end(self): self.assert_index_equal(expected_left, left) self.assert_index_equal(expected_right, right) - # test with default frequency, UTC - begin = Timestamp('2011/1/1', tz='UTC') - end = Timestamp('2014/1/1', tz='UTC') + begin = Timestamp('2011/1/1') + end = Timestamp('2014/1/1') + begintz = Timestamp('2011/1/1', tz='US/Eastern') + endtz = Timestamp('2014/1/1', tz='US/Eastern') + + for freq in ["1D", "3D", "2M", "7W", "3H", "A"]: + closed = date_range(begin, end, closed=None, freq=freq, + tz='US/Eastern') + left = date_range(begin, end, closed="left", freq=freq, + tz='US/Eastern') + right = date_range(begin, end, closed="right", freq=freq, + tz='US/Eastern') + expected_left = left + expected_right = right - intervals = ['left', 'right', None] - for i in intervals: - result = date_range(start=begin, end=end, closed=i) - self.assertEqual(result[0], begin) - self.assertEqual(result[-1], end) + if endtz == closed[-1]: + expected_left = closed[:-1] + if begintz == closed[0]: + expected_right = closed[1:] + + self.assert_index_equal(expected_left, left) + self.assert_index_equal(expected_right, right) def test_range_closed_boundary(self): # GH 11804
closes #12684
https://api.github.com/repos/pandas-dev/pandas/pulls/13510
2016-06-24T22:08:11Z
2016-06-29T12:16:22Z
2016-06-29T12:16:22Z
2016-06-29T12:16:23Z
mmap error is not always returned in English
diff --git a/pandas/io/tests/test_common.py b/pandas/io/tests/test_common.py index cf5ec7d911051..5740944558a5d 100644 --- a/pandas/io/tests/test_common.py +++ b/pandas/io/tests/test_common.py @@ -105,7 +105,7 @@ def test_constructor_bad_file(self): msg = "The parameter is incorrect" err = OSError else: - msg = "Invalid argument" + msg = "[Errno 22]" err = mmap.error tm.assertRaisesRegexp(err, msg, common.MMapWrapper, non_file)
Fixes a build error from https://github.com/pydata/pandas/pull/12946 caused by mmap error being returned in Italian when `LOCALE_OVERRIDE="it_IT.UTF-8"`. The test fails with: `AssertionError: "Invalid argument" does not match "[Errno 22] Argomento non valido"` ``` python msg = "Invalid argument" tm.assertRaisesRegexp(mmap.error, msg, common.MMapWrapper, non_file) ``` i.e. message is not being matched. Change to match the errno instead as that's the same across languages.
https://api.github.com/repos/pandas-dev/pandas/pulls/13507
2016-06-23T23:17:34Z
2016-06-23T23:59:45Z
null
2016-06-27T06:43:37Z
BUG: Fix bug with symmetric difference of two equal MultiIndexes GH12490
diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index 40fec4d071f16..b581e71ec5c50 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -482,7 +482,7 @@ Bug Fixes - Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`) - Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`) - +- Bug in ``MultiIndex.symmetric_difference`` with two equal MultiIndexes (:issue:`13490`) - Bug in various index types, which did not propagate the name of passed index (:issue:`12309`) - Bug in ``DatetimeIndex``, which did not honour the ``copy=True`` (:issue:`13205`) - Bug in ``DatetimeIndex.is_normalized`` returns incorrectly for normalized date_range in case of local timezones (:issue:`13459`) @@ -526,4 +526,4 @@ Bug Fixes - Bug in ``pd.to_numeric`` when ``errors='coerce'`` and input contains non-hashable objects (:issue:`13324`) -- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) +- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`) \ No newline at end of file diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 05b2045a4850f..9b7b280121164 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -2042,9 +2042,7 @@ def intersection(self, other): other_tuples = other._values uniq_tuples = sorted(set(self_tuples) & set(other_tuples)) if len(uniq_tuples) == 0: - return MultiIndex(levels=[[]] * self.nlevels, - labels=[[]] * self.nlevels, - names=result_names, verify_integrity=False) + return self._create_as_empty(names=result_names) else: return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0, names=result_names) @@ -2053,6 +2051,10 @@ def difference(self, other): """ Compute sorted set difference of two MultiIndex objects + Parameters + ---------- + other : MultiIndex or array / Index of tuples + Returns ------- diff : MultiIndex @@ -2064,20 +2066,69 @@ def difference(self, other): return self if self.equals(other): - return MultiIndex(levels=[[]] * self.nlevels, - labels=[[]] * self.nlevels, - names=result_names, verify_integrity=False) + return self._create_as_empty(names=result_names) difference = sorted(set(self._values) - set(other._values)) if len(difference) == 0: - return MultiIndex(levels=[[]] * self.nlevels, - labels=[[]] * self.nlevels, - names=result_names, verify_integrity=False) + return self._create_as_empty(names=result_names) else: return MultiIndex.from_tuples(difference, sortorder=0, names=result_names) + def _create_as_empty(self, nlevels=None, names=None, + verify_integrity=False): + """ + Creates an empty MultiIndex + + Parameters + ------- + nlevels : optional int, default None + The number of levels in the empty MultiIndex. If None defaults + to the current number of levels + names : optional sequence of objects, default None + Names for each of the index levels. If None defaults to the + current names. + verify_integrity : boolean, default False + Check that the levels/labels are consistent and valid + + Returns + ------- + empty : MultiIndex + """ + + if nlevels is None: + nlevels = len(self.levels) + if names is None: + names = self.names + + return MultiIndex(levels=[[]] * nlevels, + labels=[[]] * nlevels, + names=names, verify_integrity=verify_integrity) + + def symmetric_difference(self, other, result_name=None): + """ + Compute sorted set symmetric difference of two MultiIndex objects + + Returns + ------- + diff : MultiIndex + """ + self._assert_can_do_setop(other) + other, result_name_update = self._convert_can_do_setop(other) + + if result_name is None: + result_name = result_name_update + + if self.equals(other): + return self._create_as_empty(names=result_name) + + difference = sorted(set((self.difference(other)). + union(other.difference(self)))) + + return MultiIndex.from_tuples(difference, sortorder=0, + names=result_name) + @Appender(_index_shared_docs['astype']) def astype(self, dtype, copy=True): if not is_object_dtype(np.dtype(dtype)): @@ -2092,9 +2143,7 @@ def _convert_can_do_setop(self, other): if not hasattr(other, 'names'): if len(other) == 0: - other = MultiIndex(levels=[[]] * self.nlevels, - labels=[[]] * self.nlevels, - verify_integrity=False) + other = self._create_as_empty() else: msg = 'other must be a MultiIndex or a list of tuples' try: diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index d535eaa238567..6146618f29fbb 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -752,13 +752,6 @@ def test_symmetric_difference(self): self.assertTrue(tm.equalContents(result, expected)) self.assertIsNone(result.name) - # multiIndex - idx1 = MultiIndex.from_tuples(self.tuples) - idx2 = MultiIndex.from_tuples([('foo', 1), ('bar', 3)]) - result = idx1.symmetric_difference(idx2) - expected = MultiIndex.from_tuples([('bar', 2), ('baz', 3), ('bar', 3)]) - self.assertTrue(tm.equalContents(result, expected)) - # nans: # GH #6444, sorting of nans. Make sure the number of nans is right # and the correct non-nan values are there. punt on sorting. diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index bec52f5f47b09..7216734a686cb 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -1353,6 +1353,20 @@ def test_difference(self): assertRaisesRegexp(TypeError, "other must be a MultiIndex or a list" " of tuples", first.difference, [1, 2, 3, 4, 5]) + def test_symmetric_difference(self): + idx1 = MultiIndex.from_tuples(self.index, names=('A', 'B')) + idx2 = MultiIndex.from_tuples([('foo', 'one'), ('bar', 'one'), + ('baz', 'two'), ('qux', 'two'), + ('qux', 'one')], names=('A', 'B')) + result = idx1.symmetric_difference(idx2) + expected = MultiIndex.from_tuples([('foo', 'two')], names=('A', 'B')) + tm.assert_index_equal(result, expected) + + # Test for equal multiIndexes + result = self.index.symmetric_difference(self.index) + expected = result._create_as_empty() + tm.assert_index_equal(result, expected) + def test_from_tuples(self): assertRaisesRegexp(TypeError, 'Cannot infer number of levels from' ' empty list', MultiIndex.from_tuples, [])
- [x] closes #13490 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Fixes a bug where the symmetric difference of two equal MultiIndexes would raise a TypeError. MultiIndex used to use the `Index.symmetric_difference`. With this PR it it's own implementation that is more like `MultiIndex.difference`.
https://api.github.com/repos/pandas-dev/pandas/pulls/13504
2016-06-23T18:08:16Z
2016-11-16T22:23:17Z
null
2016-11-16T22:23:17Z