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BUG: Index(categorical, dtype=object) not returning object dtype
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 7449c62a5ad31..a4c991dcc166c 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -103,7 +103,7 @@ Bug fixes Categorical ^^^^^^^^^^^ - +- Bug when passing categorical data to :class:`Index` constructor along with ``dtype=object`` incorrectly returning a :class:`CategoricalIndex` instead of object-dtype :class:`Index` (:issue:`32167`) - - diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index c896e68f7a188..53c4dfde2775b 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -304,7 +304,7 @@ def __new__( # Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423 from pandas.core.indexes.category import CategoricalIndex - return CategoricalIndex(data, dtype=dtype, copy=copy, name=name, **kwargs) + return _maybe_asobject(dtype, CategoricalIndex, data, copy, name, **kwargs) # interval elif is_interval_dtype(data) or is_interval_dtype(dtype): diff --git a/pandas/tests/indexes/test_index_new.py b/pandas/tests/indexes/test_index_new.py index e150df971da2d..33f61de6a4ebf 100644 --- a/pandas/tests/indexes/test_index_new.py +++ b/pandas/tests/indexes/test_index_new.py @@ -6,7 +6,7 @@ from pandas.core.dtypes.common import is_unsigned_integer_dtype -from pandas import Index, Int64Index, MultiIndex, UInt64Index +from pandas import CategoricalIndex, Index, Int64Index, MultiIndex, UInt64Index import pandas._testing as tm @@ -47,3 +47,9 @@ def test_constructor_dtypes_to_object(self, cast_index, vals): assert type(index) is Index assert index.dtype == object + + def test_constructor_categorical_to_object(self): + # GH#32167 Categorical data and dtype=object should return object-dtype + ci = CategoricalIndex(range(5)) + result = Index(ci, dtype=object) + assert not isinstance(result, CategoricalIndex)
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry In master we get a `CategoricalIndex` back. Fixing this turns out to be pre-requisite to fixing a bug in `CategoricalDtype._from_values_or_dtype`, which in turn is needed to simplify the CategoricalIndex constructors (xref #32141)
https://api.github.com/repos/pandas-dev/pandas/pulls/32167
2020-02-22T00:21:01Z
2020-02-23T15:50:38Z
2020-02-23T15:50:38Z
2020-02-23T17:12:53Z
REGR: preserve freq in DTI/TDI outer join
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index f491774991090..e91bab0925bf7 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -23,6 +23,7 @@ Fixed regressions - Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`). - Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`) - Fixed regression in :meth:`GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`) +- Joining on :class:`DatetimeIndex` or :class:`TimedeltaIndex` will preserve ``freq`` in simple cases (:issue:`32166`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py index 349b582de4358..72a2aba2d8a88 100644 --- a/pandas/core/indexes/datetimelike.py +++ b/pandas/core/indexes/datetimelike.py @@ -910,17 +910,14 @@ def _is_convertible_to_index_for_join(cls, other: Index) -> bool: return True return False - def _wrap_joined_index(self, joined, other): + def _wrap_joined_index(self, joined: np.ndarray, other): + assert other.dtype == self.dtype, (other.dtype, self.dtype) name = get_op_result_name(self, other) - if self._can_fast_union(other): - joined = self._shallow_copy(joined) - joined.name = name - return joined - else: - kwargs = {} - if hasattr(self, "tz"): - kwargs["tz"] = getattr(other, "tz", None) - return type(self)._simple_new(joined, name, **kwargs) + + freq = self.freq if self._can_fast_union(other) else None + new_data = type(self._data)._simple_new(joined, dtype=self.dtype, freq=freq) + + return type(self)._simple_new(new_data, name=name) # -------------------------------------------------------------------- # List-Like Methods diff --git a/pandas/tests/indexes/datetimes/test_join.py b/pandas/tests/indexes/datetimes/test_join.py index e4d6958dbd3d8..f2f88fd7dc90c 100644 --- a/pandas/tests/indexes/datetimes/test_join.py +++ b/pandas/tests/indexes/datetimes/test_join.py @@ -129,3 +129,16 @@ def test_naive_aware_conflicts(self): with pytest.raises(TypeError, match=msg): aware.join(naive) + + @pytest.mark.parametrize("tz", [None, "US/Pacific"]) + def test_join_preserves_freq(self, tz): + # GH#32157 + dti = date_range("2016-01-01", periods=10, tz=tz) + result = dti[:5].join(dti[5:], how="outer") + assert result.freq == dti.freq + tm.assert_index_equal(result, dti) + + result = dti[:5].join(dti[6:], how="outer") + assert result.freq is None + expected = dti.delete(5) + tm.assert_index_equal(result, expected) diff --git a/pandas/tests/indexes/timedeltas/test_join.py b/pandas/tests/indexes/timedeltas/test_join.py index 3e73ed35dae96..aaf4ef29e162b 100644 --- a/pandas/tests/indexes/timedeltas/test_join.py +++ b/pandas/tests/indexes/timedeltas/test_join.py @@ -35,3 +35,15 @@ def test_does_not_convert_mixed_integer(self): assert cols.dtype == np.dtype("O") assert cols.dtype == joined.dtype tm.assert_index_equal(cols, joined) + + def test_join_preserves_freq(self): + # GH#32157 + tdi = timedelta_range("1 day", periods=10) + result = tdi[:5].join(tdi[5:], how="outer") + assert result.freq == tdi.freq + tm.assert_index_equal(result, tdi) + + result = tdi[:5].join(tdi[6:], how="outer") + assert result.freq is None + expected = tdi.delete(5) + tm.assert_index_equal(result, expected)
- [x] closes #32157 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry also just a nice cleanup, hoping to get this into ExtensionIndex before long
https://api.github.com/repos/pandas-dev/pandas/pulls/32166
2020-02-22T00:09:16Z
2020-02-26T12:36:36Z
2020-02-26T12:36:36Z
2020-02-27T15:34:46Z
CLN: Fix exception causes in datetimelike.py
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py index e39d1dc03adf5..ce1d01e139540 100644 --- a/pandas/core/arrays/datetimelike.py +++ b/pandas/core/arrays/datetimelike.py @@ -775,8 +775,10 @@ def searchsorted(self, value, side="left", sorter=None): if isinstance(value, str): try: value = self._scalar_from_string(value) - except ValueError: - raise TypeError("searchsorted requires compatible dtype or scalar") + except ValueError as e: + raise TypeError( + "searchsorted requires compatible dtype or scalar" + ) from e elif is_valid_nat_for_dtype(value, self.dtype): value = NaT @@ -1039,7 +1041,7 @@ def _validate_frequency(cls, index, freq, **kwargs): raise ValueError( f"Inferred frequency {inferred} from passed values " f"does not conform to passed frequency {freq.freqstr}" - ) + ) from e # monotonicity/uniqueness properties are called via frequencies.infer_freq, # see GH#23789
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32164
2020-02-21T21:21:34Z
2020-02-25T01:52:33Z
2020-02-25T01:52:33Z
2020-02-25T01:52:38Z
Error on C Warnings
diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in index 811025a4b5764..11eecc46b335d 100644 --- a/pandas/_libs/hashtable_class_helper.pxi.in +++ b/pandas/_libs/hashtable_class_helper.pxi.in @@ -193,7 +193,7 @@ cdef class StringVector: append_data_string(self.data, x) - cdef extend(self, ndarray[:] x): + cdef extend(self, ndarray[object] x): for i in range(len(x)): self.append(x[i]) @@ -238,7 +238,7 @@ cdef class ObjectVector: self.external_view_exists = True return self.ao - cdef extend(self, ndarray[:] x): + cdef extend(self, ndarray[object] x): for i in range(len(x)): self.append(x[i]) diff --git a/pandas/_libs/internals.pyx b/pandas/_libs/internals.pyx index 63f076b7ee993..c65205e406607 100644 --- a/pandas/_libs/internals.pyx +++ b/pandas/_libs/internals.pyx @@ -378,25 +378,23 @@ def get_blkno_indexers(int64_t[:] blknos, bint group=True): object blkno object group_dict = defaultdict(list) - int64_t[:] res_view n = blknos.shape[0] - - if n == 0: - return - + result = list() start = 0 cur_blkno = blknos[start] - if group is False: + if n == 0: + pass + elif group is False: for i in range(1, n): if blknos[i] != cur_blkno: - yield cur_blkno, slice(start, i) + result.append((cur_blkno, slice(start, i))) start = i cur_blkno = blknos[i] - yield cur_blkno, slice(start, n) + result.append((cur_blkno, slice(start, n))) else: for i in range(1, n): if blknos[i] != cur_blkno: @@ -409,19 +407,20 @@ def get_blkno_indexers(int64_t[:] blknos, bint group=True): for blkno, slices in group_dict.items(): if len(slices) == 1: - yield blkno, slice(slices[0][0], slices[0][1]) + result.append((blkno, slice(slices[0][0], slices[0][1]))) else: tot_len = sum(stop - start for start, stop in slices) - result = np.empty(tot_len, dtype=np.int64) - res_view = result + arr = np.empty(tot_len, dtype=np.int64) i = 0 for start, stop in slices: for diff in range(start, stop): - res_view[i] = diff + arr[i] = diff i += 1 - yield blkno, result + result.append((blkno, arr)) + + return result def get_blkno_placements(blknos, group: bool = True): diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx index 4f7d75e0aaad6..e532d991acf1e 100644 --- a/pandas/_libs/parsers.pyx +++ b/pandas/_libs/parsers.pyx @@ -806,7 +806,6 @@ cdef class TextReader: self._tokenize_rows(1) header = [ self.names ] - data_line = 0 if self.parser.lines < 1: field_count = len(header[0]) diff --git a/pandas/_libs/src/ujson/python/date_conversions.c b/pandas/_libs/src/ujson/python/date_conversions.c index bcb1334d978ef..4c25ab572bebe 100644 --- a/pandas/_libs/src/ujson/python/date_conversions.c +++ b/pandas/_libs/src/ujson/python/date_conversions.c @@ -67,7 +67,7 @@ npy_datetime NpyDateTimeToEpoch(npy_datetime dt, NPY_DATETIMEUNIT base) { } /* Convert PyDatetime To ISO C-string. mutates len */ -char *PyDateTimeToIso(PyDateTime_Date *obj, NPY_DATETIMEUNIT base, +char *PyDateTimeToIso(PyObject *obj, NPY_DATETIMEUNIT base, size_t *len) { npy_datetimestruct dts; int ret; @@ -98,7 +98,7 @@ char *PyDateTimeToIso(PyDateTime_Date *obj, NPY_DATETIMEUNIT base, return result; } -npy_datetime PyDateTimeToEpoch(PyDateTime_Date *dt, NPY_DATETIMEUNIT base) { +npy_datetime PyDateTimeToEpoch(PyObject *dt, NPY_DATETIMEUNIT base) { npy_datetimestruct dts; int ret; diff --git a/pandas/_libs/src/ujson/python/date_conversions.h b/pandas/_libs/src/ujson/python/date_conversions.h index 1b5cbf2a7e307..23e36999be43f 100644 --- a/pandas/_libs/src/ujson/python/date_conversions.h +++ b/pandas/_libs/src/ujson/python/date_conversions.h @@ -4,7 +4,6 @@ #define PY_SSIZE_T_CLEAN #include <Python.h> #include <numpy/ndarraytypes.h> -#include "datetime.h" // Scales value inplace from nanosecond resolution to unit resolution int scaleNanosecToUnit(npy_int64 *value, NPY_DATETIMEUNIT unit); @@ -23,10 +22,10 @@ npy_datetime NpyDateTimeToEpoch(npy_datetime dt, NPY_DATETIMEUNIT base); // up to precision `base` e.g. base="s" yields 2020-01-03T00:00:00Z // while base="ns" yields "2020-01-01T00:00:00.000000000Z" // len is mutated to save the length of the returned string -char *PyDateTimeToIso(PyDateTime_Date *obj, NPY_DATETIMEUNIT base, size_t *len); +char *PyDateTimeToIso(PyObject *obj, NPY_DATETIMEUNIT base, size_t *len); // Convert a Python Date/Datetime to Unix epoch with resolution base -npy_datetime PyDateTimeToEpoch(PyDateTime_Date *dt, NPY_DATETIMEUNIT base); +npy_datetime PyDateTimeToEpoch(PyObject *dt, NPY_DATETIMEUNIT base); char *int64ToIsoDuration(int64_t value, size_t *len); diff --git a/pandas/_libs/src/ujson/python/objToJSON.c b/pandas/_libs/src/ujson/python/objToJSON.c index 95e98779c2368..965d6aec2c1cf 100644 --- a/pandas/_libs/src/ujson/python/objToJSON.c +++ b/pandas/_libs/src/ujson/python/objToJSON.c @@ -1451,7 +1451,7 @@ char **NpyArr_encodeLabels(PyArrayObject *labels, PyObjectEncoder *enc, } else { // datetime.* objects don't follow above rules nanosecVal = - PyDateTimeToEpoch((PyDateTime_Date *)item, NPY_FR_ns); + PyDateTimeToEpoch(item, NPY_FR_ns); } } } @@ -1469,8 +1469,7 @@ char **NpyArr_encodeLabels(PyArrayObject *labels, PyObjectEncoder *enc, if (type_num == NPY_DATETIME) { cLabel = int64ToIso(nanosecVal, base, &len); } else { - cLabel = PyDateTimeToIso((PyDateTime_Date *)item, - base, &len); + cLabel = PyDateTimeToIso(item, base, &len); } } if (cLabel == NULL) { @@ -1683,7 +1682,7 @@ void Object_beginTypeContext(JSOBJ _obj, JSONTypeContext *tc) { NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit; GET_TC(tc)->longValue = - PyDateTimeToEpoch((PyDateTime_Date *)obj, base); + PyDateTimeToEpoch(obj, base); tc->type = JT_LONG; } return; @@ -1710,7 +1709,7 @@ void Object_beginTypeContext(JSOBJ _obj, JSONTypeContext *tc) { NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit; GET_TC(tc)->longValue = - PyDateTimeToEpoch((PyDateTime_Date *)obj, base); + PyDateTimeToEpoch(obj, base); tc->type = JT_LONG; } return; diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.c b/pandas/_libs/tslibs/src/datetime/np_datetime.c index a8a47e2e90f93..f647098140528 100644 --- a/pandas/_libs/tslibs/src/datetime/np_datetime.c +++ b/pandas/_libs/tslibs/src/datetime/np_datetime.c @@ -21,7 +21,6 @@ This file is derived from NumPy 1.7. See NUMPY_LICENSE.txt #endif // NPY_NO_DEPRECATED_API #include <Python.h> -#include <datetime.h> #include <numpy/arrayobject.h> #include <numpy/arrayscalars.h> @@ -313,15 +312,14 @@ int cmp_npy_datetimestruct(const npy_datetimestruct *a, * object into a NumPy npy_datetimestruct. Uses tzinfo (if present) * to convert to UTC time. * - * While the C API has PyDate_* and PyDateTime_* functions, the following - * implementation just asks for attributes, and thus supports - * datetime duck typing. The tzinfo time zone conversion would require - * this style of access anyway. + * The following implementation just asks for attributes, and thus + * supports datetime duck typing. The tzinfo time zone conversion + * requires this style of access as well. * * Returns -1 on error, 0 on success, and 1 (with no error set) * if obj doesn't have the needed date or datetime attributes. */ -int convert_pydatetime_to_datetimestruct(PyDateTime_Date *dtobj, +int convert_pydatetime_to_datetimestruct(PyObject *dtobj, npy_datetimestruct *out) { // Assumes that obj is a valid datetime object PyObject *tmp; diff --git a/pandas/_libs/tslibs/src/datetime/np_datetime.h b/pandas/_libs/tslibs/src/datetime/np_datetime.h index 549d38409ca83..0bbc24ed822c5 100644 --- a/pandas/_libs/tslibs/src/datetime/np_datetime.h +++ b/pandas/_libs/tslibs/src/datetime/np_datetime.h @@ -22,7 +22,6 @@ This file is derived from NumPy 1.7. See NUMPY_LICENSE.txt #endif // NPY_NO_DEPRECATED_API #include <numpy/ndarraytypes.h> -#include <datetime.h> typedef struct { npy_int64 days; @@ -35,7 +34,7 @@ extern const npy_datetimestruct _NS_MAX_DTS; // stuff pandas needs // ---------------------------------------------------------------------------- -int convert_pydatetime_to_datetimestruct(PyDateTime_Date *dtobj, +int convert_pydatetime_to_datetimestruct(PyObject *dtobj, npy_datetimestruct *out); npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT base, diff --git a/setup.py b/setup.py index 2d49d7e1e85f2..461ef005c3df3 100755 --- a/setup.py +++ b/setup.py @@ -433,8 +433,7 @@ def run(self): extra_compile_args.append("/Z7") extra_link_args.append("/DEBUG") else: - # args to ignore warnings - extra_compile_args = [] + extra_compile_args = ["-Werror"] extra_link_args = [] if debugging_symbols_requested: extra_compile_args.append("-g") @@ -477,6 +476,14 @@ def run(self): # we can't do anything about these warnings because they stem from # cython+numpy version mismatches. macros.append(("NPY_NO_DEPRECATED_API", "0")) +if "-Werror" in extra_compile_args: + try: + import numpy as np + except ImportError: + pass + else: + if np.__version__ < LooseVersion("1.16.0"): + extra_compile_args.remove("-Werror") # ----------------------------------------------------------------------
Using Clang and latest numpy locally looks like we can use `-Werror`. WIP as I think there may still be things with gcc and older numpy versions to sort out Note that there is also a warning emitted by cython for unreachable code in groupby, but -Werror wouldn't affect the "cythonization" step.
https://api.github.com/repos/pandas-dev/pandas/pulls/32163
2020-02-21T20:39:06Z
2020-03-19T21:29:19Z
2020-03-19T21:29:19Z
2020-03-19T21:31:19Z
Backport PR #32155 on branch 1.0.x (TST: add test for DataFrame.reindex on nearest tz-aware DatetimeIndex)
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py index bb81a1cd64541..4418dee66c092 100644 --- a/pandas/tests/frame/indexing/test_indexing.py +++ b/pandas/tests/frame/indexing/test_indexing.py @@ -1618,6 +1618,14 @@ def test_reindex_nearest_tz(self, tz_aware_fixture): actual = df.reindex(idx[:3], method="nearest") tm.assert_frame_equal(expected, actual) + def test_reindex_nearest_tz_empty_frame(self): + # https://github.com/pandas-dev/pandas/issues/31964 + dti = pd.DatetimeIndex(["2016-06-26 14:27:26+00:00"]) + df = pd.DataFrame(index=pd.DatetimeIndex(["2016-07-04 14:00:59+00:00"])) + expected = pd.DataFrame(index=dti) + result = df.reindex(dti, method="nearest") + tm.assert_frame_equal(result, expected) + def test_reindex_frame_add_nat(self): rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s") df = DataFrame({"A": np.random.randn(len(rng)), "B": rng})
xref #32155
https://api.github.com/repos/pandas-dev/pandas/pulls/32161
2020-02-21T19:45:00Z
2020-02-21T21:13:22Z
2020-02-21T21:13:22Z
2020-02-29T13:37:02Z
Backport PR #32152 on branch 1.0.x (TST: add test for get_loc on tz-aware DatetimeIndex)
diff --git a/pandas/tests/indexes/datetimes/test_indexing.py b/pandas/tests/indexes/datetimes/test_indexing.py index 4c600e510790a..7a396f8148f9f 100644 --- a/pandas/tests/indexes/datetimes/test_indexing.py +++ b/pandas/tests/indexes/datetimes/test_indexing.py @@ -343,6 +343,19 @@ def test_take_fill_value_with_timezone(self): idx.take(np.array([1, -5])) +class TestGetLoc: + def test_get_loc_tz_aware(self): + # https://github.com/pandas-dev/pandas/issues/32140 + dti = pd.date_range( + pd.Timestamp("2019-12-12 00:00:00", tz="US/Eastern"), + pd.Timestamp("2019-12-13 00:00:00", tz="US/Eastern"), + freq="5s", + ) + key = pd.Timestamp("2019-12-12 10:19:25", tz="US/Eastern") + result = dti.get_loc(key, method="nearest") + assert result == 7433 + + class TestDatetimeIndex: @pytest.mark.parametrize( "null", [None, np.nan, np.datetime64("NaT"), pd.NaT, pd.NA]
xref #32152 @jbrockmendel with tests moving around, is this OK for the backport. i.e. in the same place as on master.
https://api.github.com/repos/pandas-dev/pandas/pulls/32160
2020-02-21T19:37:32Z
2020-02-21T21:12:58Z
2020-02-21T21:12:58Z
2020-02-29T13:34:06Z
Backport PR #32121 on branch 1.0.x (REG: dont call func on empty input)
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index da79f651b63a9..910389f648b60 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -21,6 +21,7 @@ Fixed regressions - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`) - Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`). - Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`) +- Fixed regression in :meth:`GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py index 6864260809bfb..57d865a051c31 100644 --- a/pandas/core/groupby/groupby.py +++ b/pandas/core/groupby/groupby.py @@ -923,17 +923,10 @@ def _python_agg_general(self, func, *args, **kwargs): try: # if this function is invalid for this dtype, we will ignore it. - func(obj[:0]) + result, counts = self.grouper.agg_series(obj, f) except TypeError: continue - except AssertionError: - raise - except Exception: - # Our function depends on having a non-empty argument - # See test_groupby_agg_err_catching - pass - - result, counts = self.grouper.agg_series(obj, f) + assert result is not None key = base.OutputKey(label=name, position=idx) output[key] = self._try_cast(result, obj, numeric_only=True) diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py index d521a3a500f97..cd1ea2415b0d2 100644 --- a/pandas/tests/groupby/aggregate/test_aggregate.py +++ b/pandas/tests/groupby/aggregate/test_aggregate.py @@ -14,6 +14,18 @@ from pandas.core.groupby.grouper import Grouping +def test_groupby_agg_no_extra_calls(): + # GH#31760 + df = pd.DataFrame({"key": ["a", "b", "c", "c"], "value": [1, 2, 3, 4]}) + gb = df.groupby("key")["value"] + + def dummy_func(x): + assert len(x) != 0 + return x.sum() + + gb.agg(dummy_func) + + def test_agg_regression1(tsframe): grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month]) result = grouped.agg(np.mean)
Backport PR #32121: REG: dont call func on empty input
https://api.github.com/repos/pandas-dev/pandas/pulls/32159
2020-02-21T18:38:44Z
2020-02-21T19:25:50Z
2020-02-21T19:25:50Z
2020-02-21T19:25:50Z
TST: Fixed bare pytest.raises in test_window.py
diff --git a/pandas/tests/window/test_window.py b/pandas/tests/window/test_window.py index cc29ab4f2cd62..41b9d9e84f27e 100644 --- a/pandas/tests/window/test_window.py +++ b/pandas/tests/window/test_window.py @@ -29,14 +29,15 @@ def test_constructor(self, which): c(win_type="boxcar", window=2, min_periods=1, center=False) # not valid + msg = "|".join(["min_periods must be an integer", "center must be a boolean"]) for w in [2.0, "foo", np.array([2])]: - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=msg): c(win_type="boxcar", window=2, min_periods=w) - with pytest.raises(ValueError): + with pytest.raises(ValueError, match=msg): c(win_type="boxcar", window=2, min_periods=1, center=w) for wt in ["foobar", 1]: - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="Invalid win_type"): c(win_type=wt, window=2) @td.skip_if_no_scipy
- [x] ref #30999 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/32158
2020-02-21T16:52:19Z
2020-02-23T15:16:22Z
2020-02-23T15:16:22Z
2020-02-23T19:29:02Z
TST: add test for DataFrame.reindex on nearest tz-aware DatetimeIndex
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py index fcf0a41e0f74e..636cca0df9d4e 100644 --- a/pandas/tests/frame/indexing/test_indexing.py +++ b/pandas/tests/frame/indexing/test_indexing.py @@ -1623,6 +1623,14 @@ def test_reindex_nearest_tz(self, tz_aware_fixture): actual = df.reindex(idx[:3], method="nearest") tm.assert_frame_equal(expected, actual) + def test_reindex_nearest_tz_empty_frame(self): + # https://github.com/pandas-dev/pandas/issues/31964 + dti = pd.DatetimeIndex(["2016-06-26 14:27:26+00:00"]) + df = pd.DataFrame(index=pd.DatetimeIndex(["2016-07-04 14:00:59+00:00"])) + expected = pd.DataFrame(index=dti) + result = df.reindex(dti, method="nearest") + tm.assert_frame_equal(result, expected) + def test_reindex_frame_add_nat(self): rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s") df = DataFrame({"A": np.random.randn(len(rng)), "B": rng})
- [ ] closes #31964 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32155
2020-02-21T14:56:29Z
2020-02-21T18:36:10Z
2020-02-21T18:36:09Z
2020-02-21T19:40:51Z
Backport PR #32055 on branch 1.0.x (REGR: read_pickle fallback to encoding=latin_1 upon a UnicodeDecodeError)
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index c9031ac1ae9fe..57ed6adf667c8 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -19,6 +19,7 @@ Fixed regressions - Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`) - Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`) - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`) +- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`). - Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`) - diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py index e51f24b551f31..4e731b8ecca11 100644 --- a/pandas/io/pickle.py +++ b/pandas/io/pickle.py @@ -171,21 +171,22 @@ def read_pickle( # 1) try standard library Pickle # 2) try pickle_compat (older pandas version) to handle subclass changes - - excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError) + # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError try: - with warnings.catch_warnings(record=True): - # We want to silence any warnings about, e.g. moved modules. - warnings.simplefilter("ignore", Warning) - return pickle.load(f) - except excs_to_catch: - # e.g. - # "No module named 'pandas.core.sparse.series'" - # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib" - return pc.load(f, encoding=None) + excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError) + try: + with warnings.catch_warnings(record=True): + # We want to silence any warnings about, e.g. moved modules. + warnings.simplefilter("ignore", Warning) + return pickle.load(f) + except excs_to_catch: + # e.g. + # "No module named 'pandas.core.sparse.series'" + # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib" + return pc.load(f, encoding=None) except UnicodeDecodeError: - # e.g. can occur for files written in py27; see GH#28645 + # e.g. can occur for files written in py27; see GH#28645 and GH#31988 return pc.load(f, encoding="latin-1") finally: f.close() diff --git a/pandas/tests/io/data/pickle/test_mi_py27.pkl b/pandas/tests/io/data/pickle/test_mi_py27.pkl new file mode 100644 index 0000000000000..89021dd828108 Binary files /dev/null and b/pandas/tests/io/data/pickle/test_mi_py27.pkl differ diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py index 3d427dde573af..7605fae945962 100644 --- a/pandas/tests/io/test_pickle.py +++ b/pandas/tests/io/test_pickle.py @@ -382,14 +382,23 @@ def test_read(self, protocol, get_random_path): tm.assert_frame_equal(df, df2) -def test_unicode_decode_error(datapath): +@pytest.mark.parametrize( + ["pickle_file", "excols"], + [ + ("test_py27.pkl", pd.Index(["a", "b", "c"])), + ( + "test_mi_py27.pkl", + pd.MultiIndex.from_arrays([["a", "b", "c"], ["A", "B", "C"]]), + ), + ], +) +def test_unicode_decode_error(datapath, pickle_file, excols): # pickle file written with py27, should be readable without raising - # UnicodeDecodeError, see GH#28645 - path = datapath("io", "data", "pickle", "test_py27.pkl") + # UnicodeDecodeError, see GH#28645 and GH#31988 + path = datapath("io", "data", "pickle", pickle_file) df = pd.read_pickle(path) # just test the columns are correct since the values are random - excols = pd.Index(["a", "b", "c"]) tm.assert_index_equal(df.columns, excols)
Backport PR #32055: REGR: read_pickle fallback to encoding=latin_1 upon a UnicodeDecodeError
https://api.github.com/repos/pandas-dev/pandas/pulls/32154
2020-02-21T14:52:34Z
2020-02-21T16:07:24Z
2020-02-21T16:07:24Z
2020-02-21T16:07:24Z
Backport PR #32126 on branch 1.0.x (BUG: Fix for convert_dtypes with mix of int and string)
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index c9031ac1ae9fe..e0d2de634bf3d 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -45,6 +45,7 @@ Bug fixes - Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`). - Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`) +- Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`) .. --------------------------------------------------------------------------- diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py index fa80e5c7b3700..4d99a13ccd0b5 100644 --- a/pandas/core/dtypes/cast.py +++ b/pandas/core/dtypes/cast.py @@ -1059,11 +1059,6 @@ def convert_dtypes( if convert_integer: target_int_dtype = "Int64" - if isinstance(inferred_dtype, str) and ( - inferred_dtype == "mixed-integer" - or inferred_dtype == "mixed-integer-float" - ): - inferred_dtype = target_int_dtype if is_integer_dtype(input_array.dtype) and not is_extension_array_dtype( input_array.dtype ): diff --git a/pandas/tests/series/test_convert_dtypes.py b/pandas/tests/series/test_convert_dtypes.py index a6b5fed40a9d7..17527a09f07a1 100644 --- a/pandas/tests/series/test_convert_dtypes.py +++ b/pandas/tests/series/test_convert_dtypes.py @@ -81,6 +81,18 @@ class TestSeriesConvertDtypes: ), }, ), + ( # GH32117 + ["h", "i", 1], + np.dtype("O"), + { + ( + (True, False), + (True, False), + (True, False), + (True, False), + ): np.dtype("O"), + }, + ), ( [10, np.nan, 20], np.dtype("float"), @@ -144,11 +156,23 @@ class TestSeriesConvertDtypes: [1, 2.0], object, { - ((True, False), (True, False), (True,), (True, False)): "Int64", + ((True,), (True, False), (True,), (True, False)): "Int64", ((True,), (True, False), (False,), (True, False)): np.dtype( "float" ), - ((False,), (True, False), (False,), (True, False)): np.dtype( + ((False,), (True, False), (True, False), (True, False)): np.dtype( + "object" + ), + }, + ), + ( + [1, 2.5], + object, + { + ((True,), (True, False), (True, False), (True, False)): np.dtype( + "float" + ), + ((False,), (True, False), (True, False), (True, False)): np.dtype( "object" ), },
Backport PR #32126: BUG: Fix for convert_dtypes with mix of int and string
https://api.github.com/repos/pandas-dev/pandas/pulls/32153
2020-02-21T14:49:02Z
2020-02-21T16:07:01Z
2020-02-21T16:07:01Z
2020-02-21T16:07:01Z
TST: add test for get_loc on tz-aware DatetimeIndex
diff --git a/pandas/tests/indexes/datetimes/test_indexing.py b/pandas/tests/indexes/datetimes/test_indexing.py index c358e72538788..ceab670fb5041 100644 --- a/pandas/tests/indexes/datetimes/test_indexing.py +++ b/pandas/tests/indexes/datetimes/test_indexing.py @@ -423,6 +423,17 @@ def test_get_loc(self): with pytest.raises(NotImplementedError): idx.get_loc(time(12, 30), method="pad") + def test_get_loc_tz_aware(self): + # https://github.com/pandas-dev/pandas/issues/32140 + dti = pd.date_range( + pd.Timestamp("2019-12-12 00:00:00", tz="US/Eastern"), + pd.Timestamp("2019-12-13 00:00:00", tz="US/Eastern"), + freq="5s", + ) + key = pd.Timestamp("2019-12-12 10:19:25", tz="US/Eastern") + result = dti.get_loc(key, method="nearest") + assert result == 7433 + def test_get_loc_nat(self): # GH#20464 index = DatetimeIndex(["1/3/2000", "NaT"])
- [ ] closes #32140 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32152
2020-02-21T14:32:40Z
2020-02-21T18:35:15Z
2020-02-21T18:35:15Z
2020-02-21T19:39:31Z
DOC: move whatsnew to sync master with Backport PR #31511
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index c9031ac1ae9fe..c9717fa5998c7 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -29,6 +29,10 @@ Fixed regressions Bug fixes ~~~~~~~~~ +**Datetimelike** + +- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`) + **Categorical** - Fixed bug where :meth:`Categorical.from_codes` improperly raised a ``ValueError`` when passed nullable integer codes. (:issue:`31779`) diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 13827e8fc4c33..0e2b15974dbbd 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -111,7 +111,6 @@ Datetimelike - Bug in :class:`Timestamp` where constructing :class:`Timestamp` from ambiguous epoch time and calling constructor again changed :meth:`Timestamp.value` property (:issue:`24329`) - :meth:`DatetimeArray.searchsorted`, :meth:`TimedeltaArray.searchsorted`, :meth:`PeriodArray.searchsorted` not recognizing non-pandas scalars and incorrectly raising ``ValueError`` instead of ``TypeError`` (:issue:`30950`) - Bug in :class:`Timestamp` where constructing :class:`Timestamp` with dateutil timezone less than 128 nanoseconds before daylight saving time switch from winter to summer would result in nonexistent time (:issue:`31043`) -- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`) - Bug in :meth:`Period.to_timestamp`, :meth:`Period.start_time` with microsecond frequency returning a timestamp one nanosecond earlier than the correct time (:issue:`31475`) Timedelta
xref #32150
https://api.github.com/repos/pandas-dev/pandas/pulls/32151
2020-02-21T14:10:39Z
2020-02-21T19:06:46Z
2020-02-21T19:06:45Z
2020-02-21T19:27:42Z
Backport PR #31511 on branch 1.0.x (BUG: fix reindexing with a tz-awre index and method='nearest')
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index c9031ac1ae9fe..c9717fa5998c7 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -29,6 +29,10 @@ Fixed regressions Bug fixes ~~~~~~~~~ +**Datetimelike** + +- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`) + **Categorical** - Fixed bug where :meth:`Categorical.from_codes` improperly raised a ``ValueError`` when passed nullable integer codes. (:issue:`31779`) diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index 4fcddb5cade4a..38f8521bfa2d4 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -2817,9 +2817,8 @@ def _get_nearest_indexer(self, target, limit, tolerance): left_indexer = self.get_indexer(target, "pad", limit=limit) right_indexer = self.get_indexer(target, "backfill", limit=limit) - target = np.asarray(target) - left_distances = abs(self.values[left_indexer] - target) - right_distances = abs(self.values[right_indexer] - target) + left_distances = np.abs(self[left_indexer] - target) + right_distances = np.abs(self[right_indexer] - target) op = operator.lt if self.is_monotonic_increasing else operator.le indexer = np.where( diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py index 928b4dc7fecb2..bb81a1cd64541 100644 --- a/pandas/tests/frame/indexing/test_indexing.py +++ b/pandas/tests/frame/indexing/test_indexing.py @@ -1608,6 +1608,16 @@ def test_reindex_methods_nearest_special(self): actual = df.reindex(target, method="nearest", tolerance=[0.5, 0.01, 0.4, 0.1]) tm.assert_frame_equal(expected, actual) + def test_reindex_nearest_tz(self, tz_aware_fixture): + # GH26683 + tz = tz_aware_fixture + idx = pd.date_range("2019-01-01", periods=5, tz=tz) + df = pd.DataFrame({"x": list(range(5))}, index=idx) + + expected = df.head(3) + actual = df.reindex(idx[:3], method="nearest") + tm.assert_frame_equal(expected, actual) + def test_reindex_frame_add_nat(self): rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s") df = DataFrame({"A": np.random.randn(len(rng)), "B": rng})
with whatsnew moved to doc/source/whatsnew/v1.0.2.rst
https://api.github.com/repos/pandas-dev/pandas/pulls/32150
2020-02-21T14:07:13Z
2020-02-21T19:06:21Z
2020-02-21T19:06:21Z
2020-02-21T19:28:49Z
Backport PR #32148 on branch 1.0.x (CI: skip geopandas downstream test (Anaconda installation issue))
diff --git a/pandas/tests/test_downstream.py b/pandas/tests/test_downstream.py index ee006233c4c1b..d4d7803a724e2 100644 --- a/pandas/tests/test_downstream.py +++ b/pandas/tests/test_downstream.py @@ -107,6 +107,7 @@ def test_pandas_datareader(): # importing from pandas, Cython import warning @pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning") +@pytest.mark.skip(reason="Anaconda installation issue - GH32144") def test_geopandas(): geopandas = import_module("geopandas") # noqa
Backport PR #32148: CI: skip geopandas downstream test (Anaconda installation issue)
https://api.github.com/repos/pandas-dev/pandas/pulls/32149
2020-02-21T13:12:05Z
2020-02-21T13:47:28Z
2020-02-21T13:47:28Z
2020-02-21T13:47:28Z
CI: skip geopandas downstream test (Anaconda installation issue)
diff --git a/pandas/tests/test_downstream.py b/pandas/tests/test_downstream.py index 122ef1f47968e..b2a85b539fd86 100644 --- a/pandas/tests/test_downstream.py +++ b/pandas/tests/test_downstream.py @@ -107,6 +107,7 @@ def test_pandas_datareader(): # importing from pandas, Cython import warning @pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning") +@pytest.mark.skip(reason="Anaconda installation issue - GH32144") def test_geopandas(): geopandas = import_module("geopandas") # noqa
xref #32144
https://api.github.com/repos/pandas-dev/pandas/pulls/32148
2020-02-21T12:20:08Z
2020-02-21T13:11:35Z
2020-02-21T13:11:35Z
2020-02-21T13:12:11Z
Add ecosystem link to pandas-tfrecords project
diff --git a/doc/source/ecosystem.rst b/doc/source/ecosystem.rst index fb06ee122ae88..b7e53b84f0e02 100644 --- a/doc/source/ecosystem.rst +++ b/doc/source/ecosystem.rst @@ -56,6 +56,11 @@ joining paths, replacing file extensions, and checking if files exist are also a Statistics and machine learning ------------------------------- +`pandas-tfrecords <https://pypi.org/project/pandas-tfrecords/>`__ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Easy saving pandas dataframe to tensorflow tfrecords format and reading tfrecords to pandas. + `Statsmodels <https://www.statsmodels.org/>`__ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
to understand tfrecords format requires quite much time and finally its details are not so needed in daily work with data. [pandas-tfrecords](https://pypi.org/project/pandas-tfrecords/) hides low-level details and provides easy human-usable api to convert pandas to tfrecords and restore back.
https://api.github.com/repos/pandas-dev/pandas/pulls/32143
2020-02-21T08:16:09Z
2020-02-26T05:50:36Z
2020-02-26T05:50:36Z
2020-02-26T05:50:43Z
Import OptionError in pandas.errors
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 13827e8fc4c33..9e40c28db004b 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -43,7 +43,7 @@ Other enhancements - :class:`Styler` may now render CSS more efficiently where multiple cells have the same styling (:issue:`30876`) - When writing directly to a sqlite connection :func:`to_sql` now supports the ``multi`` method (:issue:`29921`) -- +- `OptionError` is now exposed in `pandas.errors` (:issue:`27553`) - .. --------------------------------------------------------------------------- diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py index ebe9a3d5bf472..29e69cc5fe509 100644 --- a/pandas/errors/__init__.py +++ b/pandas/errors/__init__.py @@ -4,6 +4,8 @@ Expose public exceptions & warnings """ +from pandas._config.config import OptionError + from pandas._libs.tslibs import NullFrequencyError, OutOfBoundsDatetime diff --git a/pandas/tests/test_errors.py b/pandas/tests/test_errors.py index d72c00ceb0045..515d798fe4322 100644 --- a/pandas/tests/test_errors.py +++ b/pandas/tests/test_errors.py @@ -17,6 +17,7 @@ "EmptyDataError", "ParserWarning", "MergeError", + "OptionError", ], ) def test_exception_importable(exc):
- [x] closes #27553 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32142
2020-02-21T07:10:00Z
2020-02-24T16:32:47Z
2020-02-24T16:32:46Z
2020-02-24T16:32:50Z
REF: standardize CategoricalIndex._shallow_copy usage
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py index adb2ed9211bfe..caa6a9a93141f 100644 --- a/pandas/core/indexes/category.py +++ b/pandas/core/indexes/category.py @@ -264,10 +264,14 @@ def _simple_new(cls, values, name=None, dtype=None): # -------------------------------------------------------------------- @Appender(Index._shallow_copy.__doc__) - def _shallow_copy(self, values=None, dtype=None, **kwargs): - if dtype is None: - dtype = self.dtype - return super()._shallow_copy(values=values, dtype=dtype, **kwargs) + def _shallow_copy(self, values=None, **kwargs): + if values is None: + values = self.values + + cat = Categorical(values, dtype=self.dtype) + + name = kwargs.get("name", self.name) + return type(self)._simple_new(cat, name=name) def _is_dtype_compat(self, other) -> bool: """ @@ -422,9 +426,9 @@ def unique(self, level=None): if level is not None: self._validate_index_level(level) result = self.values.unique() - # CategoricalIndex._shallow_copy keeps original dtype - # if not otherwise specified - return self._shallow_copy(result, dtype=result.dtype) + # Use _simple_new instead of _shallow_copy to ensure we keep dtype + # of result, not self. + return type(self)._simple_new(result, name=self.name) @Appender(Index.duplicated.__doc__) def duplicated(self, keep="first"): @@ -450,7 +454,7 @@ def where(self, cond, other=None): other = self._na_value values = np.where(cond, self.values, other) cat = Categorical(values, dtype=self.dtype) - return self._shallow_copy(cat, **self._get_attributes_dict()) + return self._shallow_copy(cat) def reindex(self, target, method=None, level=None, limit=None, tolerance=None): """
The dtype keyword is akward, so this removes the one usage. In turn this will allow us to simplify `_simple_new` and tighten up what it expects. Saved for separate PR.
https://api.github.com/repos/pandas-dev/pandas/pulls/32141
2020-02-21T02:42:26Z
2020-02-22T16:12:43Z
2020-02-22T16:12:43Z
2020-02-22T16:34:04Z
TST: show_versions test with unmerged commits
diff --git a/pandas/tests/util/test_show_versions.py b/pandas/tests/util/test_show_versions.py index 0d2c81c4ea6c7..e36ea662fac8b 100644 --- a/pandas/tests/util/test_show_versions.py +++ b/pandas/tests/util/test_show_versions.py @@ -1,8 +1,26 @@ import re +import pytest + import pandas as pd +@pytest.mark.filterwarnings( + # openpyxl + "ignore:defusedxml.lxml is no longer supported:DeprecationWarning" +) +@pytest.mark.filterwarnings( + # html5lib + "ignore:Using or importing the ABCs from:DeprecationWarning" +) +@pytest.mark.filterwarnings( + # fastparquet + "ignore:pandas.core.index is deprecated:FutureWarning" +) +@pytest.mark.filterwarnings( + # pandas_datareader + "ignore:pandas.util.testing is deprecated:FutureWarning" +) def test_show_versions(capsys): # gh-32041 pd.show_versions() diff --git a/pandas/util/_print_versions.py b/pandas/util/_print_versions.py index 99b2b9e9f5f6e..f9502cc22b0c6 100644 --- a/pandas/util/_print_versions.py +++ b/pandas/util/_print_versions.py @@ -4,13 +4,23 @@ import os import platform import struct -import subprocess import sys from typing import List, Optional, Tuple, Union from pandas.compat._optional import VERSIONS, _get_version, import_optional_dependency +def _get_commit_hash() -> Optional[str]: + """ + Use vendored versioneer code to get git hash, which handles + git worktree correctly. + """ + from pandas._version import get_versions + + versions = get_versions() + return versions["full-revisionid"] + + def get_sys_info() -> List[Tuple[str, Optional[Union[str, int]]]]: """ Returns system information as a list @@ -18,20 +28,7 @@ def get_sys_info() -> List[Tuple[str, Optional[Union[str, int]]]]: blob: List[Tuple[str, Optional[Union[str, int]]]] = [] # get full commit hash - commit = None - if os.path.isdir(".git") and os.path.isdir("pandas"): - try: - pipe = subprocess.Popen( - 'git log --format="%H" -n 1'.split(" "), - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - so, serr = pipe.communicate() - except (OSError, ValueError): - pass - else: - if pipe.returncode == 0: - commit = so.decode("utf-8").strip().strip('"') + commit = _get_commit_hash() blob.append(("commit", commit))
- [x] closes #32120 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32139
2020-02-20T22:19:56Z
2020-02-26T01:37:31Z
2020-02-26T01:37:30Z
2020-02-26T01:41:32Z
CLN: make tm.N, tm.K private
diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst index bbec9a770477d..58733b852e3a1 100644 --- a/doc/source/user_guide/reshaping.rst +++ b/doc/source/user_guide/reshaping.rst @@ -17,7 +17,6 @@ Reshaping by pivoting DataFrame objects :suppress: import pandas._testing as tm - tm.N = 3 def unpivot(frame): N, K = frame.shape @@ -27,7 +26,7 @@ Reshaping by pivoting DataFrame objects columns = ['date', 'variable', 'value'] return pd.DataFrame(data, columns=columns) - df = unpivot(tm.makeTimeDataFrame()) + df = unpivot(tm.makeTimeDataFrame(3)) Data is often stored in so-called "stacked" or "record" format: @@ -42,9 +41,6 @@ For the curious here is how the above ``DataFrame`` was created: import pandas._testing as tm - tm.N = 3 - - def unpivot(frame): N, K = frame.shape data = {'value': frame.to_numpy().ravel('F'), @@ -53,7 +49,7 @@ For the curious here is how the above ``DataFrame`` was created: return pd.DataFrame(data, columns=['date', 'variable', 'value']) - df = unpivot(tm.makeTimeDataFrame()) + df = unpivot(tm.makeTimeDataFrame(3)) To select out everything for variable ``A`` we could do: diff --git a/pandas/_testing.py b/pandas/_testing.py index 7ebf2c282f8c9..3a12eba5bf881 100644 --- a/pandas/_testing.py +++ b/pandas/_testing.py @@ -69,8 +69,8 @@ lzma = _import_lzma() -N = 30 -K = 4 +_N = 30 +_K = 4 _RAISE_NETWORK_ERROR_DEFAULT = False # set testing_mode @@ -1794,45 +1794,45 @@ def all_timeseries_index_generator(k=10): # make series def makeFloatSeries(name=None): - index = makeStringIndex(N) - return Series(randn(N), index=index, name=name) + index = makeStringIndex(_N) + return Series(randn(_N), index=index, name=name) def makeStringSeries(name=None): - index = makeStringIndex(N) - return Series(randn(N), index=index, name=name) + index = makeStringIndex(_N) + return Series(randn(_N), index=index, name=name) def makeObjectSeries(name=None): - data = makeStringIndex(N) + data = makeStringIndex(_N) data = Index(data, dtype=object) - index = makeStringIndex(N) + index = makeStringIndex(_N) return Series(data, index=index, name=name) def getSeriesData(): - index = makeStringIndex(N) - return {c: Series(randn(N), index=index) for c in getCols(K)} + index = makeStringIndex(_N) + return {c: Series(randn(_N), index=index) for c in getCols(_K)} def makeTimeSeries(nper=None, freq="B", name=None): if nper is None: - nper = N + nper = _N return Series(randn(nper), index=makeDateIndex(nper, freq=freq), name=name) def makePeriodSeries(nper=None, name=None): if nper is None: - nper = N + nper = _N return Series(randn(nper), index=makePeriodIndex(nper), name=name) def getTimeSeriesData(nper=None, freq="B"): - return {c: makeTimeSeries(nper, freq) for c in getCols(K)} + return {c: makeTimeSeries(nper, freq) for c in getCols(_K)} def getPeriodData(nper=None): - return {c: makePeriodSeries(nper) for c in getCols(K)} + return {c: makePeriodSeries(nper) for c in getCols(_K)} # make frame
AFAICT this gets rid of the last two places where we set `tm.N` externally. With those gone, this privatizes these variables in the hopes of not falling back into that pattern.
https://api.github.com/repos/pandas-dev/pandas/pulls/32138
2020-02-20T21:47:02Z
2020-02-21T18:34:11Z
2020-02-21T18:34:11Z
2020-02-21T18:34:34Z
CLN: remove unused tm.isiterable
diff --git a/pandas/_testing.py b/pandas/_testing.py index 7ebf2c282f8c9..c7c2e315812ac 100644 --- a/pandas/_testing.py +++ b/pandas/_testing.py @@ -806,10 +806,6 @@ def assert_is_valid_plot_return_object(objs): assert isinstance(objs, (plt.Artist, tuple, dict)), msg -def isiterable(obj): - return hasattr(obj, "__iter__") - - def assert_is_sorted(seq): """Assert that the sequence is sorted.""" if isinstance(seq, (Index, Series)):
https://api.github.com/repos/pandas-dev/pandas/pulls/32137
2020-02-20T21:16:20Z
2020-02-21T02:15:04Z
2020-02-21T02:15:04Z
2020-02-21T02:15:08Z
used f-strings in docs
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst index bd19b35e8d9e8..c34247a49335d 100644 --- a/doc/source/user_guide/io.rst +++ b/doc/source/user_guide/io.rst @@ -3815,7 +3815,7 @@ The right-hand side of the sub-expression (after a comparison operator) can be: .. code-block:: ipython string = "HolyMoly'" - store.select('df', 'index == %s' % string) + store.select('df', f'index == {string}') The latter will **not** work and will raise a ``SyntaxError``.Note that there's a single quote followed by a double quote in the ``string``
- [x] xref #32039 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32133
2020-02-20T17:39:09Z
2020-02-22T16:33:47Z
2020-02-22T16:33:47Z
2020-02-22T16:33:51Z
CLN: NDFrame.__init__ unnecessary code
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 9fe1ec7b792c8..fdb8aad680115 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -514,7 +514,7 @@ def __init__( else: raise ValueError("DataFrame constructor not properly called!") - NDFrame.__init__(self, mgr, fastpath=True) + NDFrame.__init__(self, mgr) # ---------------------------------------------------------------------- diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 579daae2b15c6..500969d2476cc 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -33,7 +33,6 @@ from pandas._libs import Timestamp, iNaT, lib from pandas._typing import ( Axis, - Dtype, FilePathOrBuffer, FrameOrSeries, JSONSerializable, @@ -200,22 +199,10 @@ class NDFrame(PandasObject, SelectionMixin, indexing.IndexingMixin): def __init__( self, data: BlockManager, - axes: Optional[List[Index]] = None, copy: bool = False, - dtype: Optional[Dtype] = None, attrs: Optional[Mapping[Optional[Hashable], Any]] = None, - fastpath: bool = False, ): - - if not fastpath: - if dtype is not None: - data = data.astype(dtype) - elif copy: - data = data.copy() - - if axes is not None: - for i, ax in enumerate(axes): - data = data.reindex_axis(ax, axis=i) + # copy kwarg is retained for mypy compat, is not used object.__setattr__(self, "_is_copy", None) object.__setattr__(self, "_data", data) @@ -226,12 +213,13 @@ def __init__( attrs = dict(attrs) object.__setattr__(self, "_attrs", attrs) - def _init_mgr(self, mgr, axes=None, dtype=None, copy=False): + @classmethod + def _init_mgr(cls, mgr, axes=None, dtype=None, copy=False): """ passed a manager and a axes dict """ for a, axe in axes.items(): if axe is not None: mgr = mgr.reindex_axis( - axe, axis=self._get_block_manager_axis(a), copy=False + axe, axis=cls._get_block_manager_axis(a), copy=False ) # make a copy if explicitly requested @@ -262,7 +250,8 @@ def attrs(self) -> Dict[Optional[Hashable], Any]: def attrs(self, value: Mapping[Optional[Hashable], Any]) -> None: self._attrs = dict(value) - def _validate_dtype(self, dtype): + @classmethod + def _validate_dtype(cls, dtype): """ validate the passed dtype """ if dtype is not None: dtype = pandas_dtype(dtype) @@ -271,7 +260,7 @@ def _validate_dtype(self, dtype): if dtype.kind == "V": raise NotImplementedError( "compound dtypes are not implemented " - f"in the {type(self).__name__} constructor" + f"in the {cls.__name__} constructor" ) return dtype @@ -324,8 +313,9 @@ def _construct_axes_dict(self, axes=None, **kwargs): d.update(kwargs) return d + @classmethod def _construct_axes_from_arguments( - self, args, kwargs, require_all: bool = False, sentinel=None + cls, args, kwargs, require_all: bool = False, sentinel=None ): """ Construct and returns axes if supplied in args/kwargs. @@ -339,7 +329,7 @@ def _construct_axes_from_arguments( """ # construct the args args = list(args) - for a in self._AXIS_ORDERS: + for a in cls._AXIS_ORDERS: # look for a argument by position if a not in kwargs: @@ -349,7 +339,7 @@ def _construct_axes_from_arguments( if require_all: raise TypeError("not enough/duplicate arguments specified!") - axes = {a: kwargs.pop(a, sentinel) for a in self._AXIS_ORDERS} + axes = {a: kwargs.pop(a, sentinel) for a in cls._AXIS_ORDERS} return axes, kwargs @classmethod @@ -495,7 +485,7 @@ def ndim(self) -> int: return self._data.ndim @property - def size(self): + def size(self) -> int: """ Return an int representing the number of elements in this object. @@ -3660,7 +3650,7 @@ def get(self, key, default=None): return default @property - def _is_view(self): + def _is_view(self) -> bool_t: """Return boolean indicating if self is view of another array """ return self._data.is_view @@ -5176,12 +5166,12 @@ def _consolidate(self, inplace: bool_t = False): return self._constructor(cons_data).__finalize__(self) @property - def _is_mixed_type(self): + def _is_mixed_type(self) -> bool_t: f = lambda: self._data.is_mixed_type return self._protect_consolidate(f) @property - def _is_numeric_mixed_type(self): + def _is_numeric_mixed_type(self) -> bool_t: f = lambda: self._data.is_numeric_mixed_type return self._protect_consolidate(f) diff --git a/pandas/core/series.py b/pandas/core/series.py index 9c0ff9780da3e..2182374337c84 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -324,7 +324,7 @@ def __init__( data = SingleBlockManager(data, index, fastpath=True) - generic.NDFrame.__init__(self, data, fastpath=True) + generic.NDFrame.__init__(self, data) self.name = name self._set_axis(0, index, fastpath=True) diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index 1b3e301b0fef0..8af0fe548e48a 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -7,14 +7,11 @@ import numpy as np import pytest -from pandas.errors import AbstractMethodError - from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype import pandas as pd from pandas import DataFrame, Index, NaT, Series import pandas._testing as tm -from pandas.core.generic import NDFrame from pandas.core.indexers import validate_indices from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice from pandas.tests.indexing.common import _mklbl @@ -1094,29 +1091,6 @@ def test_extension_array_cross_section_converts(): tm.assert_series_equal(result, expected) -@pytest.mark.parametrize( - "idxr, error, error_message", - [ - (lambda x: x, AbstractMethodError, None), - ( - lambda x: x.loc, - AttributeError, - "type object 'NDFrame' has no attribute '_AXIS_NAMES'", - ), - ( - lambda x: x.iloc, - AttributeError, - "type object 'NDFrame' has no attribute '_AXIS_NAMES'", - ), - ], -) -def test_ndframe_indexing_raises(idxr, error, error_message): - # GH 25567 - frame = NDFrame(np.random.randint(5, size=(2, 2, 2))) - with pytest.raises(error, match=error_message): - idxr(frame)[0] - - def test_readonly_indices(): # GH#17192 iloc with read-only array raising TypeError df = pd.DataFrame({"data": np.ones(100, dtype="float64")})
Make methods into classmethods where feasible, annotate in a couple of places. Removes nonsense test (constructs an invalid NDFrame) Preparatory for making a potential fastpath _from_mgr constructor. `attrs` is never passed. Is there a plan to do so @TomAugspurger ?
https://api.github.com/repos/pandas-dev/pandas/pulls/32131
2020-02-20T17:12:27Z
2020-02-22T16:10:24Z
2020-02-22T16:10:24Z
2020-02-22T16:39:02Z
PERF: IntegerIndex._shallow_copy (and by extension, indexing)
diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py index 877b3d1d2ba30..367870f0ee467 100644 --- a/pandas/core/indexes/numeric.py +++ b/pandas/core/indexes/numeric.py @@ -103,11 +103,16 @@ def _maybe_cast_slice_bound(self, label, side, kind): return self._maybe_cast_indexer(label) @Appender(Index._shallow_copy.__doc__) - def _shallow_copy(self, values=None, **kwargs): - if values is not None and not self._can_hold_na: + def _shallow_copy(self, values=None, name=lib.no_default): + name = name if name is not lib.no_default else self.name + + if values is not None and not self._can_hold_na and values.dtype.kind == "f": # Ensure we are not returning an Int64Index with float data: - return self._shallow_copy_with_infer(values=values, **kwargs) - return super()._shallow_copy(values=values, **kwargs) + return Float64Index._simple_new(values, name=name) + + if values is None: + values = self.values + return type(self)._simple_new(values, name=name) def _convert_for_op(self, value): """
``` In [2]: idx = pd.Index(range(1000)).insert(-1, 1) In [3]: %timeit idx[:5] 2.84 µs ± 97.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR 24.5 µs ± 292 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master In [4]: idx2 = idx.astype('uint64') In [5]: %timeit idx2[5:] 2.99 µs ± 331 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR 25.8 µs ± 957 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master ``` Motivated by profiling in #32086 and seeing that _shallow_copy_with_infer was taking over a third of the runtime in the relevant benchmark.
https://api.github.com/repos/pandas-dev/pandas/pulls/32130
2020-02-20T16:32:29Z
2020-02-22T16:09:15Z
2020-02-22T16:09:14Z
2020-02-22T16:40:45Z
CLN: auto-inherit specified Index method docstrings
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index ae2387f0fd7b4..2d5a642b83018 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -5439,6 +5439,32 @@ def shape(self): # overridden in MultiIndex.shape to avoid materializing the values return self._values.shape + @classmethod + def __init_subclass__(cls): + """ + Automatically inherit some docstrings, so we dont have to decorate + every method with @Appender(Index.foo.__doc__) + """ + names = [ + "_shallow_copy", + "_convert_arr_indexer", + "_convert_index_indexer", + "astype", + "dropna", + "duplicated", + ] + + for name in names: + method = getattr(cls, name) + if method.__doc__ is None: + for parent in cls.__mro__[1:]: + parent_method = getattr(parent, name, None) + if parent_method is not None: + if parent_method.__doc__ is not None: + # Implicitly assuming here that it is a method + method.__doc__ = parent_method.__doc__ + break + Index._add_numeric_methods_disabled() Index._add_logical_methods() diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py index 8c2d7f4aa6c0e..f6360fe5851bb 100644 --- a/pandas/core/indexes/category.py +++ b/pandas/core/indexes/category.py @@ -240,7 +240,6 @@ def _simple_new(cls, values: Categorical, name: Label = None): # -------------------------------------------------------------------- - @Appender(Index._shallow_copy.__doc__) def _shallow_copy(self, values=None, name: Label = no_default): name = self.name if name is no_default else name @@ -368,7 +367,6 @@ def __array__(self, dtype=None) -> np.ndarray: """ the array interface, return my values """ return np.array(self._data, dtype=dtype) - @Appender(Index.astype.__doc__) def astype(self, dtype, copy=True): if is_interval_dtype(dtype): from pandas import IntervalIndex @@ -409,7 +407,6 @@ def unique(self, level=None): # of result, not self. return type(self)._simple_new(result, name=self.name) - @Appender(Index.duplicated.__doc__) def duplicated(self, keep="first"): codes = self.codes.astype("i8") return duplicated_int64(codes, keep) @@ -601,7 +598,6 @@ def _convert_list_indexer(self, keyarr): return self.get_indexer(keyarr) - @Appender(Index._convert_arr_indexer.__doc__) def _convert_arr_indexer(self, keyarr): keyarr = com.asarray_tuplesafe(keyarr) @@ -610,7 +606,6 @@ def _convert_arr_indexer(self, keyarr): return self._shallow_copy(keyarr) - @Appender(Index._convert_index_indexer.__doc__) def _convert_index_indexer(self, keyarr): return self._shallow_copy(keyarr) diff --git a/pandas/core/indexes/extension.py b/pandas/core/indexes/extension.py index daccb35864e98..8d433148a8bb7 100644 --- a/pandas/core/indexes/extension.py +++ b/pandas/core/indexes/extension.py @@ -228,7 +228,6 @@ def __iter__(self): def _ndarray_values(self) -> np.ndarray: return self._data._ndarray_values - @Appender(Index.dropna.__doc__) def dropna(self, how="any"): if how not in ("any", "all"): raise ValueError(f"invalid how option: {how}") @@ -293,7 +292,6 @@ def map(self, mapper, na_action=None): except Exception: return self.astype(object).map(mapper) - @Appender(Index.astype.__doc__) def astype(self, dtype, copy=True): if is_dtype_equal(self.dtype, dtype) and copy is False: # Ensure that self.astype(self.dtype) is self diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py index d396d1c76f357..44602764562ee 100644 --- a/pandas/core/indexes/interval.py +++ b/pandas/core/indexes/interval.py @@ -330,10 +330,10 @@ def from_tuples( # -------------------------------------------------------------------- - @Appender(Index._shallow_copy.__doc__) def _shallow_copy(self, values=None, **kwargs): if values is None: values = self._data + attributes = self._get_attributes_dict() attributes.update(kwargs) return self._simple_new(values, **attributes) @@ -401,7 +401,6 @@ def __reduce__(self): d.update(self._get_attributes_dict()) return _new_IntervalIndex, (type(self), d), None - @Appender(Index.astype.__doc__) def astype(self, dtype, copy=True): with rewrite_exception("IntervalArray", type(self).__name__): new_values = self.values.astype(dtype, copy=copy) diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py index f70975e19b9a4..69aebf4c7f8fd 100644 --- a/pandas/core/indexes/multi.py +++ b/pandas/core/indexes/multi.py @@ -983,7 +983,6 @@ def _engine(self): def _constructor(self): return MultiIndex.from_tuples - @Appender(Index._shallow_copy.__doc__) def _shallow_copy(self, values=None, **kwargs): if values is not None: names = kwargs.pop("names", kwargs.pop("name", self.names)) @@ -1435,7 +1434,6 @@ def _inferred_type_levels(self): """ return a list of the inferred types, one for each level """ return [i.inferred_type for i in self.levels] - @Appender(Index.duplicated.__doc__) def duplicated(self, keep="first"): shape = map(len, self.levels) ids = get_group_index(self.codes, shape, sort=False, xnull=False) @@ -1448,7 +1446,6 @@ def fillna(self, value=None, downcast=None): """ raise NotImplementedError("isna is not defined for MultiIndex") - @Appender(Index.dropna.__doc__) def dropna(self, how="any"): nans = [level_codes == -1 for level_codes in self.codes] if how == "any": @@ -3378,7 +3375,6 @@ def _convert_can_do_setop(self, other): # -------------------------------------------------------------------- - @Appender(Index.astype.__doc__) def astype(self, dtype, copy=True): dtype = pandas_dtype(dtype) if is_categorical_dtype(dtype): diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py index 06a26cc90555e..c9b8650192324 100644 --- a/pandas/core/indexes/numeric.py +++ b/pandas/core/indexes/numeric.py @@ -102,7 +102,6 @@ def _maybe_cast_slice_bound(self, label, side, kind): # we will try to coerce to integers return self._maybe_cast_indexer(label) - @Appender(Index._shallow_copy.__doc__) def _shallow_copy(self, values=None, name: Label = lib.no_default): name = name if name is not lib.no_default else self.name @@ -307,7 +306,6 @@ class UInt64Index(IntegerIndex): _engine_type = libindex.UInt64Engine _default_dtype = np.dtype(np.uint64) - @Appender(Index._convert_arr_indexer.__doc__) def _convert_arr_indexer(self, keyarr): # Cast the indexer to uint64 if possible so that the values returned # from indexing are also uint64. @@ -319,7 +317,6 @@ def _convert_arr_indexer(self, keyarr): return com.asarray_tuplesafe(keyarr, dtype=dtype) - @Appender(Index._convert_index_indexer.__doc__) def _convert_index_indexer(self, keyarr): # Cast the indexer to uint64 if possible so # that the values returned from indexing are @@ -369,7 +366,6 @@ def inferred_type(self) -> str: """ return "floating" - @Appender(Index.astype.__doc__) def astype(self, dtype, copy=True): dtype = pandas_dtype(dtype) if needs_i8_conversion(dtype): diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py index 9eeb41f735015..e5a77a00879cd 100644 --- a/pandas/core/indexes/period.py +++ b/pandas/core/indexes/period.py @@ -412,7 +412,6 @@ def asof_locs(self, where, mask): return result - @Appender(Index.astype.__doc__) def astype(self, dtype, copy=True, how="start"): dtype = pandas_dtype(dtype) diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py index f621a3c153adf..45f827a6c3b05 100644 --- a/pandas/core/indexes/range.py +++ b/pandas/core/indexes/range.py @@ -386,7 +386,6 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): def tolist(self): return list(self._range) - @Appender(Int64Index._shallow_copy.__doc__) def _shallow_copy(self, values=None, name: Label = no_default): name = self.name if name is no_default else name diff --git a/pandas/core/indexes/timedeltas.py b/pandas/core/indexes/timedeltas.py index 5e4a8e83bd95b..eebaac4ddd5bd 100644 --- a/pandas/core/indexes/timedeltas.py +++ b/pandas/core/indexes/timedeltas.py @@ -1,7 +1,6 @@ """ implement the TimedeltaIndex """ from pandas._libs import NaT, Timedelta, index as libindex -from pandas.util._decorators import Appender from pandas.core.dtypes.common import ( _TD_DTYPE, @@ -197,7 +196,6 @@ def _formatter_func(self): # ------------------------------------------------------------------- - @Appender(Index.astype.__doc__) def astype(self, dtype, copy=True): dtype = pandas_dtype(dtype) if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
When working on indexing methods, I find myself grepping for the names, and having the extra Appender lines is a small-but-repeated nuisance. In py36+ we can use `__init_subclass_` to automatically inherit docstrings without needing to repeat these Appenders. This PR only does this for a few methods as a proof of concept, but we could expand this list quite a bit. Based on a handful of measurements, no obvious impact on import time. cc @WillAyd you were -1 on the proof of concept when it was on PandasObject and automatic for all methods; are you more amenable to this more finely-tuned approach?
https://api.github.com/repos/pandas-dev/pandas/pulls/32128
2020-02-20T15:23:09Z
2020-03-13T22:37:15Z
null
2021-03-07T16:55:45Z
BUG: Fix for convert_dtypes with mix of int and string
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index c9031ac1ae9fe..e0d2de634bf3d 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -45,6 +45,7 @@ Bug fixes - Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`). - Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`) +- Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`) .. --------------------------------------------------------------------------- diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py index 1c969d40c2c7f..c2b600b5d8c5b 100644 --- a/pandas/core/dtypes/cast.py +++ b/pandas/core/dtypes/cast.py @@ -1062,11 +1062,6 @@ def convert_dtypes( if convert_integer: target_int_dtype = "Int64" - if isinstance(inferred_dtype, str) and ( - inferred_dtype == "mixed-integer" - or inferred_dtype == "mixed-integer-float" - ): - inferred_dtype = target_int_dtype if is_integer_dtype(input_array.dtype) and not is_extension_array_dtype( input_array.dtype ): diff --git a/pandas/tests/series/methods/test_convert_dtypes.py b/pandas/tests/series/methods/test_convert_dtypes.py index a6b5fed40a9d7..17527a09f07a1 100644 --- a/pandas/tests/series/methods/test_convert_dtypes.py +++ b/pandas/tests/series/methods/test_convert_dtypes.py @@ -81,6 +81,18 @@ class TestSeriesConvertDtypes: ), }, ), + ( # GH32117 + ["h", "i", 1], + np.dtype("O"), + { + ( + (True, False), + (True, False), + (True, False), + (True, False), + ): np.dtype("O"), + }, + ), ( [10, np.nan, 20], np.dtype("float"), @@ -144,11 +156,23 @@ class TestSeriesConvertDtypes: [1, 2.0], object, { - ((True, False), (True, False), (True,), (True, False)): "Int64", + ((True,), (True, False), (True,), (True, False)): "Int64", ((True,), (True, False), (False,), (True, False)): np.dtype( "float" ), - ((False,), (True, False), (False,), (True, False)): np.dtype( + ((False,), (True, False), (True, False), (True, False)): np.dtype( + "object" + ), + }, + ), + ( + [1, 2.5], + object, + { + ((True,), (True, False), (True, False), (True, False)): np.dtype( + "float" + ), + ((False,), (True, False), (True, False), (True, False)): np.dtype( "object" ), },
- [x] closes #32117 - [x] tests added / passed - added new cases for `test_convert_dtypes` - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry - placed in v1.0.2 whatsnew
https://api.github.com/repos/pandas-dev/pandas/pulls/32126
2020-02-20T13:52:45Z
2020-02-21T14:48:51Z
2020-02-21T14:48:51Z
2023-02-13T20:50:48Z
BUG: Avoid ambiguous condition in GroupBy.first / last
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index affe019d0ac86..82d603cfb8c15 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -76,6 +76,7 @@ Bug fixes - Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`). - Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`) +- Fixed bug where :meth:`GroupBy.first` and :meth:`GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`) - Fix bug in :meth:`Series.convert_dtypes` for series with mix of integers and strings (:issue:`32117`) .. --------------------------------------------------------------------------- diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx index edc44f1c94589..27b3095d8cb4f 100644 --- a/pandas/_libs/groupby.pyx +++ b/pandas/_libs/groupby.pyx @@ -22,6 +22,8 @@ from pandas._libs.algos cimport (swap, TiebreakEnumType, TIEBREAK_AVERAGE, from pandas._libs.algos import (take_2d_axis1_float64_float64, groupsort_indexer, tiebreakers) +from pandas._libs.missing cimport checknull + cdef int64_t NPY_NAT = get_nat() _int64_max = np.iinfo(np.int64).max @@ -887,7 +889,7 @@ def group_last(rank_t[:, :] out, for j in range(K): val = values[i, j] - if val == val: + if not checknull(val): # NB: use _treat_as_na here once # conditional-nogil is available. nobs[lab, j] += 1 @@ -976,7 +978,7 @@ def group_nth(rank_t[:, :] out, for j in range(K): val = values[i, j] - if val == val: + if not checknull(val): # NB: use _treat_as_na here once # conditional-nogil is available. nobs[lab, j] += 1 diff --git a/pandas/tests/groupby/test_nth.py b/pandas/tests/groupby/test_nth.py index 0f850f2e94581..b1476f1059d84 100644 --- a/pandas/tests/groupby/test_nth.py +++ b/pandas/tests/groupby/test_nth.py @@ -54,6 +54,46 @@ def test_first_last_nth(df): tm.assert_frame_equal(result, expected) +@pytest.mark.parametrize("method", ["first", "last"]) +def test_first_last_with_na_object(method, nulls_fixture): + # https://github.com/pandas-dev/pandas/issues/32123 + groups = pd.DataFrame({"a": [1, 1, 2, 2], "b": [1, 2, 3, nulls_fixture]}).groupby( + "a" + ) + result = getattr(groups, method)() + + if method == "first": + values = [1, 3] + else: + values = [2, 3] + + values = np.array(values, dtype=result["b"].dtype) + idx = pd.Index([1, 2], name="a") + expected = pd.DataFrame({"b": values}, index=idx) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("index", [0, -1]) +def test_nth_with_na_object(index, nulls_fixture): + # https://github.com/pandas-dev/pandas/issues/32123 + groups = pd.DataFrame({"a": [1, 1, 2, 2], "b": [1, 2, 3, nulls_fixture]}).groupby( + "a" + ) + result = groups.nth(index) + + if index == 0: + values = [1, 3] + else: + values = [2, nulls_fixture] + + values = np.array(values, dtype=result["b"].dtype) + idx = pd.Index([1, 2], name="a") + expected = pd.DataFrame({"b": values}, index=idx) + + tm.assert_frame_equal(result, expected) + + def test_first_last_nth_dtypes(df_mixed_floats): df = df_mixed_floats.copy()
- [x] closes #32123 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry This comparison `val == val` happens in a lot of these groupby operations but only seems to raise here in the presence of `NA`. Are we just always / mostly converting to numpy beforehand in the other cases?
https://api.github.com/repos/pandas-dev/pandas/pulls/32124
2020-02-20T01:37:52Z
2020-02-23T14:59:00Z
2020-02-23T14:59:00Z
2020-02-23T15:07:44Z
CLN: Replace old string formatting syntax with f-strings for scripts/validate_docstrings.py
diff --git a/scripts/validate_docstrings.py b/scripts/validate_docstrings.py index d43086756769a..051bd5b9761ae 100755 --- a/scripts/validate_docstrings.py +++ b/scripts/validate_docstrings.py @@ -243,14 +243,12 @@ def pandas_validate(func_name: str): "EX03", error_code=err.error_code, error_message=err.message, - times_happening=" ({} times)".format(err.count) - if err.count > 1 - else "", + times_happening=f" ({err.count} times)" if err.count > 1 else "", ) ) examples_source_code = "".join(doc.examples_source_code) for wrong_import in ("numpy", "pandas"): - if "import {}".format(wrong_import) in examples_source_code: + if f"import {wrong_import}" in examples_source_code: result["errors"].append( pandas_error("EX04", imported_library=wrong_import) ) @@ -345,9 +343,7 @@ def header(title, width=80, char="#"): full_line = char * width side_len = (width - len(title) - 2) // 2 adj = "" if len(title) % 2 == 0 else " " - title_line = "{side} {title}{adj} {side}".format( - side=char * side_len, title=title, adj=adj - ) + title_line = f"{char * side_len} {title}{adj} {char * side_len}" return f"\n{full_line}\n{title_line}\n{full_line}\n\n"
- [ ] xref #29547 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32122
2020-02-19T22:08:21Z
2020-02-20T01:06:00Z
2020-02-20T01:06:00Z
2020-02-20T01:06:05Z
REG: dont call func on empty input
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index c9031ac1ae9fe..88a343664f304 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -20,6 +20,7 @@ Fixed regressions - Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`) - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`) - Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`) +- Fixed regression in :meth:`GroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py index cc46485b4a2e8..f946f0e63a583 100644 --- a/pandas/core/groupby/groupby.py +++ b/pandas/core/groupby/groupby.py @@ -923,17 +923,10 @@ def _python_agg_general(self, func, *args, **kwargs): try: # if this function is invalid for this dtype, we will ignore it. - func(obj[:0]) + result, counts = self.grouper.agg_series(obj, f) except TypeError: continue - except AssertionError: - raise - except Exception: - # Our function depends on having a non-empty argument - # See test_groupby_agg_err_catching - pass - - result, counts = self.grouper.agg_series(obj, f) + assert result is not None key = base.OutputKey(label=name, position=idx) output[key] = self._try_cast(result, obj, numeric_only=True) diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py index ff99081521ffb..48f8de7e51ae4 100644 --- a/pandas/tests/groupby/aggregate/test_aggregate.py +++ b/pandas/tests/groupby/aggregate/test_aggregate.py @@ -13,6 +13,18 @@ from pandas.core.groupby.grouper import Grouping +def test_groupby_agg_no_extra_calls(): + # GH#31760 + df = pd.DataFrame({"key": ["a", "b", "c", "c"], "value": [1, 2, 3, 4]}) + gb = df.groupby("key")["value"] + + def dummy_func(x): + assert len(x) != 0 + return x.sum() + + gb.agg(dummy_func) + + def test_agg_regression1(tsframe): grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month]) result = grouped.agg(np.mean)
- [x] closes #31760 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Could use confirmation from OP that this does in fact close #31760.
https://api.github.com/repos/pandas-dev/pandas/pulls/32121
2020-02-19T21:35:24Z
2020-02-21T18:38:13Z
2020-02-21T18:38:13Z
2020-02-21T18:40:03Z
REF: make pd._testing directory, split out asserters
diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst index bbec9a770477d..58733b852e3a1 100644 --- a/doc/source/user_guide/reshaping.rst +++ b/doc/source/user_guide/reshaping.rst @@ -17,7 +17,6 @@ Reshaping by pivoting DataFrame objects :suppress: import pandas._testing as tm - tm.N = 3 def unpivot(frame): N, K = frame.shape @@ -27,7 +26,7 @@ Reshaping by pivoting DataFrame objects columns = ['date', 'variable', 'value'] return pd.DataFrame(data, columns=columns) - df = unpivot(tm.makeTimeDataFrame()) + df = unpivot(tm.makeTimeDataFrame(3)) Data is often stored in so-called "stacked" or "record" format: @@ -42,9 +41,6 @@ For the curious here is how the above ``DataFrame`` was created: import pandas._testing as tm - tm.N = 3 - - def unpivot(frame): N, K = frame.shape data = {'value': frame.to_numpy().ravel('F'), @@ -53,7 +49,7 @@ For the curious here is how the above ``DataFrame`` was created: return pd.DataFrame(data, columns=['date', 'variable', 'value']) - df = unpivot(tm.makeTimeDataFrame()) + df = unpivot(tm.makeTimeDataFrame(3)) To select out everything for variable ``A`` we could do: diff --git a/pandas/_testing.py b/pandas/_testing.py deleted file mode 100644 index 7ebf2c282f8c9..0000000000000 --- a/pandas/_testing.py +++ /dev/null @@ -1,2799 +0,0 @@ -import bz2 -from collections import Counter -from contextlib import contextmanager -from datetime import datetime -from functools import wraps -import gzip -import os -from shutil import rmtree -import string -import tempfile -from typing import Any, Callable, List, Optional, Type, Union, cast -import warnings -import zipfile - -import numpy as np -from numpy.random import rand, randn - -from pandas._config.localization import ( # noqa:F401 - can_set_locale, - get_locales, - set_locale, -) - -import pandas._libs.testing as _testing -from pandas._typing import FilePathOrBuffer, FrameOrSeries -from pandas.compat import _get_lzma_file, _import_lzma - -from pandas.core.dtypes.common import ( - is_bool, - is_categorical_dtype, - is_datetime64_dtype, - is_datetime64tz_dtype, - is_extension_array_dtype, - is_interval_dtype, - is_list_like, - is_number, - is_period_dtype, - is_sequence, - is_timedelta64_dtype, - needs_i8_conversion, -) -from pandas.core.dtypes.missing import array_equivalent - -import pandas as pd -from pandas import ( - Categorical, - CategoricalIndex, - DataFrame, - DatetimeIndex, - Index, - IntervalIndex, - MultiIndex, - RangeIndex, - Series, - bdate_range, -) -from pandas.core.algorithms import take_1d -from pandas.core.arrays import ( - DatetimeArray, - ExtensionArray, - IntervalArray, - PeriodArray, - TimedeltaArray, - period_array, -) - -from pandas.io.common import urlopen -from pandas.io.formats.printing import pprint_thing - -lzma = _import_lzma() - -N = 30 -K = 4 -_RAISE_NETWORK_ERROR_DEFAULT = False - -# set testing_mode -_testing_mode_warnings = (DeprecationWarning, ResourceWarning) - - -def set_testing_mode(): - # set the testing mode filters - testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None") - if "deprecate" in testing_mode: - warnings.simplefilter("always", _testing_mode_warnings) - - -def reset_testing_mode(): - # reset the testing mode filters - testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None") - if "deprecate" in testing_mode: - warnings.simplefilter("ignore", _testing_mode_warnings) - - -set_testing_mode() - - -def reset_display_options(): - """ - Reset the display options for printing and representing objects. - """ - pd.reset_option("^display.", silent=True) - - -def round_trip_pickle( - obj: Any, path: Optional[FilePathOrBuffer] = None -) -> FrameOrSeries: - """ - Pickle an object and then read it again. - - Parameters - ---------- - obj : any object - The object to pickle and then re-read. - path : str, path object or file-like object, default None - The path where the pickled object is written and then read. - - Returns - ------- - pandas object - The original object that was pickled and then re-read. - """ - _path = path - if _path is None: - _path = f"__{rands(10)}__.pickle" - with ensure_clean(_path) as temp_path: - pd.to_pickle(obj, temp_path) - return pd.read_pickle(temp_path) - - -def round_trip_pathlib(writer, reader, path: Optional[str] = None): - """ - Write an object to file specified by a pathlib.Path and read it back - - Parameters - ---------- - writer : callable bound to pandas object - IO writing function (e.g. DataFrame.to_csv ) - reader : callable - IO reading function (e.g. pd.read_csv ) - path : str, default None - The path where the object is written and then read. - - Returns - ------- - pandas object - The original object that was serialized and then re-read. - """ - import pytest - - Path = pytest.importorskip("pathlib").Path - if path is None: - path = "___pathlib___" - with ensure_clean(path) as path: - writer(Path(path)) - obj = reader(Path(path)) - return obj - - -def round_trip_localpath(writer, reader, path: Optional[str] = None): - """ - Write an object to file specified by a py.path LocalPath and read it back. - - Parameters - ---------- - writer : callable bound to pandas object - IO writing function (e.g. DataFrame.to_csv ) - reader : callable - IO reading function (e.g. pd.read_csv ) - path : str, default None - The path where the object is written and then read. - - Returns - ------- - pandas object - The original object that was serialized and then re-read. - """ - import pytest - - LocalPath = pytest.importorskip("py.path").local - if path is None: - path = "___localpath___" - with ensure_clean(path) as path: - writer(LocalPath(path)) - obj = reader(LocalPath(path)) - return obj - - -@contextmanager -def decompress_file(path, compression): - """ - Open a compressed file and return a file object. - - Parameters - ---------- - path : str - The path where the file is read from. - - compression : {'gzip', 'bz2', 'zip', 'xz', None} - Name of the decompression to use - - Returns - ------- - file object - """ - if compression is None: - f = open(path, "rb") - elif compression == "gzip": - f = gzip.open(path, "rb") - elif compression == "bz2": - f = bz2.BZ2File(path, "rb") - elif compression == "xz": - f = _get_lzma_file(lzma)(path, "rb") - elif compression == "zip": - zip_file = zipfile.ZipFile(path) - zip_names = zip_file.namelist() - if len(zip_names) == 1: - f = zip_file.open(zip_names.pop()) - else: - raise ValueError(f"ZIP file {path} error. Only one file per ZIP.") - else: - raise ValueError(f"Unrecognized compression type: {compression}") - - try: - yield f - finally: - f.close() - if compression == "zip": - zip_file.close() - - -def write_to_compressed(compression, path, data, dest="test"): - """ - Write data to a compressed file. - - Parameters - ---------- - compression : {'gzip', 'bz2', 'zip', 'xz'} - The compression type to use. - path : str - The file path to write the data. - data : str - The data to write. - dest : str, default "test" - The destination file (for ZIP only) - - Raises - ------ - ValueError : An invalid compression value was passed in. - """ - if compression == "zip": - import zipfile - - compress_method = zipfile.ZipFile - elif compression == "gzip": - import gzip - - compress_method = gzip.GzipFile - elif compression == "bz2": - import bz2 - - compress_method = bz2.BZ2File - elif compression == "xz": - compress_method = _get_lzma_file(lzma) - else: - raise ValueError(f"Unrecognized compression type: {compression}") - - if compression == "zip": - mode = "w" - args = (dest, data) - method = "writestr" - else: - mode = "wb" - args = (data,) - method = "write" - - with compress_method(path, mode=mode) as f: - getattr(f, method)(*args) - - -def assert_almost_equal( - left, - right, - check_dtype: Union[bool, str] = "equiv", - check_less_precise: Union[bool, int] = False, - **kwargs, -): - """ - Check that the left and right objects are approximately equal. - - By approximately equal, we refer to objects that are numbers or that - contain numbers which may be equivalent to specific levels of precision. - - Parameters - ---------- - left : object - right : object - check_dtype : bool or {'equiv'}, default 'equiv' - Check dtype if both a and b are the same type. If 'equiv' is passed in, - then `RangeIndex` and `Int64Index` are also considered equivalent - when doing type checking. - check_less_precise : bool or int, default False - Specify comparison precision. 5 digits (False) or 3 digits (True) - after decimal points are compared. If int, then specify the number - of digits to compare. - - When comparing two numbers, if the first number has magnitude less - than 1e-5, we compare the two numbers directly and check whether - they are equivalent within the specified precision. Otherwise, we - compare the **ratio** of the second number to the first number and - check whether it is equivalent to 1 within the specified precision. - """ - if isinstance(left, pd.Index): - assert_index_equal( - left, - right, - check_exact=False, - exact=check_dtype, - check_less_precise=check_less_precise, - **kwargs, - ) - - elif isinstance(left, pd.Series): - assert_series_equal( - left, - right, - check_exact=False, - check_dtype=check_dtype, - check_less_precise=check_less_precise, - **kwargs, - ) - - elif isinstance(left, pd.DataFrame): - assert_frame_equal( - left, - right, - check_exact=False, - check_dtype=check_dtype, - check_less_precise=check_less_precise, - **kwargs, - ) - - else: - # Other sequences. - if check_dtype: - if is_number(left) and is_number(right): - # Do not compare numeric classes, like np.float64 and float. - pass - elif is_bool(left) and is_bool(right): - # Do not compare bool classes, like np.bool_ and bool. - pass - else: - if isinstance(left, np.ndarray) or isinstance(right, np.ndarray): - obj = "numpy array" - else: - obj = "Input" - assert_class_equal(left, right, obj=obj) - _testing.assert_almost_equal( - left, - right, - check_dtype=check_dtype, - check_less_precise=check_less_precise, - **kwargs, - ) - - -def _check_isinstance(left, right, cls): - """ - Helper method for our assert_* methods that ensures that - the two objects being compared have the right type before - proceeding with the comparison. - - Parameters - ---------- - left : The first object being compared. - right : The second object being compared. - cls : The class type to check against. - - Raises - ------ - AssertionError : Either `left` or `right` is not an instance of `cls`. - """ - cls_name = cls.__name__ - - if not isinstance(left, cls): - raise AssertionError( - f"{cls_name} Expected type {cls}, found {type(left)} instead" - ) - if not isinstance(right, cls): - raise AssertionError( - f"{cls_name} Expected type {cls}, found {type(right)} instead" - ) - - -def assert_dict_equal(left, right, compare_keys: bool = True): - - _check_isinstance(left, right, dict) - _testing.assert_dict_equal(left, right, compare_keys=compare_keys) - - -def randbool(size=(), p: float = 0.5): - return rand(*size) <= p - - -RANDS_CHARS = np.array(list(string.ascii_letters + string.digits), dtype=(np.str_, 1)) -RANDU_CHARS = np.array( - list("".join(map(chr, range(1488, 1488 + 26))) + string.digits), - dtype=(np.unicode_, 1), -) - - -def rands_array(nchars, size, dtype="O"): - """ - Generate an array of byte strings. - """ - retval = ( - np.random.choice(RANDS_CHARS, size=nchars * np.prod(size)) - .view((np.str_, nchars)) - .reshape(size) - ) - if dtype is None: - return retval - else: - return retval.astype(dtype) - - -def randu_array(nchars, size, dtype="O"): - """ - Generate an array of unicode strings. - """ - retval = ( - np.random.choice(RANDU_CHARS, size=nchars * np.prod(size)) - .view((np.unicode_, nchars)) - .reshape(size) - ) - if dtype is None: - return retval - else: - return retval.astype(dtype) - - -def rands(nchars): - """ - Generate one random byte string. - - See `rands_array` if you want to create an array of random strings. - - """ - return "".join(np.random.choice(RANDS_CHARS, nchars)) - - -def randu(nchars): - """ - Generate one random unicode string. - - See `randu_array` if you want to create an array of random unicode strings. - - """ - return "".join(np.random.choice(RANDU_CHARS, nchars)) - - -def close(fignum=None): - from matplotlib.pyplot import get_fignums, close as _close - - if fignum is None: - for fignum in get_fignums(): - _close(fignum) - else: - _close(fignum) - - -# ----------------------------------------------------------------------------- -# contextmanager to ensure the file cleanup - - -@contextmanager -def ensure_clean(filename=None, return_filelike=False, **kwargs): - """ - Gets a temporary path and agrees to remove on close. - - Parameters - ---------- - filename : str (optional) - if None, creates a temporary file which is then removed when out of - scope. if passed, creates temporary file with filename as ending. - return_filelike : bool (default False) - if True, returns a file-like which is *always* cleaned. Necessary for - savefig and other functions which want to append extensions. - **kwargs - Additional keywords passed in for creating a temporary file. - :meth:`tempFile.TemporaryFile` is used when `return_filelike` is ``True``. - :meth:`tempfile.mkstemp` is used when `return_filelike` is ``False``. - Note that the `filename` parameter will be passed in as the `suffix` - argument to either function. - - See Also - -------- - tempfile.TemporaryFile - tempfile.mkstemp - """ - filename = filename or "" - fd = None - - kwargs["suffix"] = filename - - if return_filelike: - f = tempfile.TemporaryFile(**kwargs) - - try: - yield f - finally: - f.close() - else: - # Don't generate tempfile if using a path with directory specified. - if len(os.path.dirname(filename)): - raise ValueError("Can't pass a qualified name to ensure_clean()") - - try: - fd, filename = tempfile.mkstemp(**kwargs) - except UnicodeEncodeError: - import pytest - - pytest.skip("no unicode file names on this system") - - try: - yield filename - finally: - try: - os.close(fd) - except OSError: - print(f"Couldn't close file descriptor: {fd} (file: {filename})") - try: - if os.path.exists(filename): - os.remove(filename) - except OSError as e: - print(f"Exception on removing file: {e}") - - -@contextmanager -def ensure_clean_dir(): - """ - Get a temporary directory path and agrees to remove on close. - - Yields - ------ - Temporary directory path - """ - directory_name = tempfile.mkdtemp(suffix="") - try: - yield directory_name - finally: - try: - rmtree(directory_name) - except OSError: - pass - - -@contextmanager -def ensure_safe_environment_variables(): - """ - Get a context manager to safely set environment variables - - All changes will be undone on close, hence environment variables set - within this contextmanager will neither persist nor change global state. - """ - saved_environ = dict(os.environ) - try: - yield - finally: - os.environ.clear() - os.environ.update(saved_environ) - - -# ----------------------------------------------------------------------------- -# Comparators - - -def equalContents(arr1, arr2) -> bool: - """ - Checks if the set of unique elements of arr1 and arr2 are equivalent. - """ - return frozenset(arr1) == frozenset(arr2) - - -def assert_index_equal( - left: Index, - right: Index, - exact: Union[bool, str] = "equiv", - check_names: bool = True, - check_less_precise: Union[bool, int] = False, - check_exact: bool = True, - check_categorical: bool = True, - obj: str = "Index", -) -> None: - """ - Check that left and right Index are equal. - - Parameters - ---------- - left : Index - right : Index - exact : bool or {'equiv'}, default 'equiv' - Whether to check the Index class, dtype and inferred_type - are identical. If 'equiv', then RangeIndex can be substituted for - Int64Index as well. - check_names : bool, default True - Whether to check the names attribute. - check_less_precise : bool or int, default False - Specify comparison precision. Only used when check_exact is False. - 5 digits (False) or 3 digits (True) after decimal points are compared. - If int, then specify the digits to compare. - check_exact : bool, default True - Whether to compare number exactly. - check_categorical : bool, default True - Whether to compare internal Categorical exactly. - obj : str, default 'Index' - Specify object name being compared, internally used to show appropriate - assertion message. - """ - __tracebackhide__ = True - - def _check_types(l, r, obj="Index"): - if exact: - assert_class_equal(l, r, exact=exact, obj=obj) - - # Skip exact dtype checking when `check_categorical` is False - if check_categorical: - assert_attr_equal("dtype", l, r, obj=obj) - - # allow string-like to have different inferred_types - if l.inferred_type in ("string"): - assert r.inferred_type in ("string") - else: - assert_attr_equal("inferred_type", l, r, obj=obj) - - def _get_ilevel_values(index, level): - # accept level number only - unique = index.levels[level] - level_codes = index.codes[level] - filled = take_1d(unique._values, level_codes, fill_value=unique._na_value) - values = unique._shallow_copy(filled, name=index.names[level]) - return values - - # instance validation - _check_isinstance(left, right, Index) - - # class / dtype comparison - _check_types(left, right, obj=obj) - - # level comparison - if left.nlevels != right.nlevels: - msg1 = f"{obj} levels are different" - msg2 = f"{left.nlevels}, {left}" - msg3 = f"{right.nlevels}, {right}" - raise_assert_detail(obj, msg1, msg2, msg3) - - # length comparison - if len(left) != len(right): - msg1 = f"{obj} length are different" - msg2 = f"{len(left)}, {left}" - msg3 = f"{len(right)}, {right}" - raise_assert_detail(obj, msg1, msg2, msg3) - - # MultiIndex special comparison for little-friendly error messages - if left.nlevels > 1: - left = cast(MultiIndex, left) - right = cast(MultiIndex, right) - - for level in range(left.nlevels): - # cannot use get_level_values here because it can change dtype - llevel = _get_ilevel_values(left, level) - rlevel = _get_ilevel_values(right, level) - - lobj = f"MultiIndex level [{level}]" - assert_index_equal( - llevel, - rlevel, - exact=exact, - check_names=check_names, - check_less_precise=check_less_precise, - check_exact=check_exact, - obj=lobj, - ) - # get_level_values may change dtype - _check_types(left.levels[level], right.levels[level], obj=obj) - - # skip exact index checking when `check_categorical` is False - if check_exact and check_categorical: - if not left.equals(right): - diff = np.sum((left.values != right.values).astype(int)) * 100.0 / len(left) - msg = f"{obj} values are different ({np.round(diff, 5)} %)" - raise_assert_detail(obj, msg, left, right) - else: - _testing.assert_almost_equal( - left.values, - right.values, - check_less_precise=check_less_precise, - check_dtype=exact, - obj=obj, - lobj=left, - robj=right, - ) - - # metadata comparison - if check_names: - assert_attr_equal("names", left, right, obj=obj) - if isinstance(left, pd.PeriodIndex) or isinstance(right, pd.PeriodIndex): - assert_attr_equal("freq", left, right, obj=obj) - if isinstance(left, pd.IntervalIndex) or isinstance(right, pd.IntervalIndex): - assert_interval_array_equal(left.values, right.values) - - if check_categorical: - if is_categorical_dtype(left) or is_categorical_dtype(right): - assert_categorical_equal(left.values, right.values, obj=f"{obj} category") - - -def assert_class_equal(left, right, exact: Union[bool, str] = True, obj="Input"): - """ - Checks classes are equal. - """ - __tracebackhide__ = True - - def repr_class(x): - if isinstance(x, Index): - # return Index as it is to include values in the error message - return x - - try: - return type(x).__name__ - except AttributeError: - return repr(type(x)) - - if exact == "equiv": - if type(left) != type(right): - # allow equivalence of Int64Index/RangeIndex - types = {type(left).__name__, type(right).__name__} - if len(types - {"Int64Index", "RangeIndex"}): - msg = f"{obj} classes are not equivalent" - raise_assert_detail(obj, msg, repr_class(left), repr_class(right)) - elif exact: - if type(left) != type(right): - msg = f"{obj} classes are different" - raise_assert_detail(obj, msg, repr_class(left), repr_class(right)) - - -def assert_attr_equal(attr, left, right, obj="Attributes"): - """ - checks attributes are equal. Both objects must have attribute. - - Parameters - ---------- - attr : str - Attribute name being compared. - left : object - right : object - obj : str, default 'Attributes' - Specify object name being compared, internally used to show appropriate - assertion message - """ - __tracebackhide__ = True - - left_attr = getattr(left, attr) - right_attr = getattr(right, attr) - - if left_attr is right_attr: - return True - elif ( - is_number(left_attr) - and np.isnan(left_attr) - and is_number(right_attr) - and np.isnan(right_attr) - ): - # np.nan - return True - - try: - result = left_attr == right_attr - except TypeError: - # datetimetz on rhs may raise TypeError - result = False - if not isinstance(result, bool): - result = result.all() - - if result: - return True - else: - msg = f'Attribute "{attr}" are different' - raise_assert_detail(obj, msg, left_attr, right_attr) - - -def assert_is_valid_plot_return_object(objs): - import matplotlib.pyplot as plt - - if isinstance(objs, (pd.Series, np.ndarray)): - for el in objs.ravel(): - msg = ( - "one of 'objs' is not a matplotlib Axes instance, " - f"type encountered {repr(type(el).__name__)}" - ) - assert isinstance(el, (plt.Axes, dict)), msg - else: - msg = ( - "objs is neither an ndarray of Artist instances nor a single " - "ArtistArtist instance, tuple, or dict, 'objs' is a " - f"{repr(type(objs).__name__)}" - ) - assert isinstance(objs, (plt.Artist, tuple, dict)), msg - - -def isiterable(obj): - return hasattr(obj, "__iter__") - - -def assert_is_sorted(seq): - """Assert that the sequence is sorted.""" - if isinstance(seq, (Index, Series)): - seq = seq.values - # sorting does not change precisions - assert_numpy_array_equal(seq, np.sort(np.array(seq))) - - -def assert_categorical_equal( - left, right, check_dtype=True, check_category_order=True, obj="Categorical" -): - """ - Test that Categoricals are equivalent. - - Parameters - ---------- - left : Categorical - right : Categorical - check_dtype : bool, default True - Check that integer dtype of the codes are the same - check_category_order : bool, default True - Whether the order of the categories should be compared, which - implies identical integer codes. If False, only the resulting - values are compared. The ordered attribute is - checked regardless. - obj : str, default 'Categorical' - Specify object name being compared, internally used to show appropriate - assertion message - """ - _check_isinstance(left, right, Categorical) - - if check_category_order: - assert_index_equal(left.categories, right.categories, obj=f"{obj}.categories") - assert_numpy_array_equal( - left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes", - ) - else: - assert_index_equal( - left.categories.sort_values(), - right.categories.sort_values(), - obj=f"{obj}.categories", - ) - assert_index_equal( - left.categories.take(left.codes), - right.categories.take(right.codes), - obj=f"{obj}.values", - ) - - assert_attr_equal("ordered", left, right, obj=obj) - - -def assert_interval_array_equal(left, right, exact="equiv", obj="IntervalArray"): - """ - Test that two IntervalArrays are equivalent. - - Parameters - ---------- - left, right : IntervalArray - The IntervalArrays to compare. - exact : bool or {'equiv'}, default 'equiv' - Whether to check the Index class, dtype and inferred_type - are identical. If 'equiv', then RangeIndex can be substituted for - Int64Index as well. - obj : str, default 'IntervalArray' - Specify object name being compared, internally used to show appropriate - assertion message - """ - _check_isinstance(left, right, IntervalArray) - - assert_index_equal(left.left, right.left, exact=exact, obj=f"{obj}.left") - assert_index_equal(left.right, right.right, exact=exact, obj=f"{obj}.left") - assert_attr_equal("closed", left, right, obj=obj) - - -def assert_period_array_equal(left, right, obj="PeriodArray"): - _check_isinstance(left, right, PeriodArray) - - assert_numpy_array_equal(left._data, right._data, obj=f"{obj}.values") - assert_attr_equal("freq", left, right, obj=obj) - - -def assert_datetime_array_equal(left, right, obj="DatetimeArray"): - __tracebackhide__ = True - _check_isinstance(left, right, DatetimeArray) - - assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data") - assert_attr_equal("freq", left, right, obj=obj) - assert_attr_equal("tz", left, right, obj=obj) - - -def assert_timedelta_array_equal(left, right, obj="TimedeltaArray"): - __tracebackhide__ = True - _check_isinstance(left, right, TimedeltaArray) - assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data") - assert_attr_equal("freq", left, right, obj=obj) - - -def raise_assert_detail(obj, message, left, right, diff=None): - __tracebackhide__ = True - - if isinstance(left, np.ndarray): - left = pprint_thing(left) - elif is_categorical_dtype(left): - left = repr(left) - - if isinstance(right, np.ndarray): - right = pprint_thing(right) - elif is_categorical_dtype(right): - right = repr(right) - - msg = f"""{obj} are different - -{message} -[left]: {left} -[right]: {right}""" - - if diff is not None: - msg += f"\n[diff]: {diff}" - - raise AssertionError(msg) - - -def assert_numpy_array_equal( - left, - right, - strict_nan=False, - check_dtype=True, - err_msg=None, - check_same=None, - obj="numpy array", -): - """ - Check that 'np.ndarray' is equivalent. - - Parameters - ---------- - left, right : numpy.ndarray or iterable - The two arrays to be compared. - strict_nan : bool, default False - If True, consider NaN and None to be different. - check_dtype : bool, default True - Check dtype if both a and b are np.ndarray. - err_msg : str, default None - If provided, used as assertion message. - check_same : None|'copy'|'same', default None - Ensure left and right refer/do not refer to the same memory area. - obj : str, default 'numpy array' - Specify object name being compared, internally used to show appropriate - assertion message. - """ - __tracebackhide__ = True - - # instance validation - # Show a detailed error message when classes are different - assert_class_equal(left, right, obj=obj) - # both classes must be an np.ndarray - _check_isinstance(left, right, np.ndarray) - - def _get_base(obj): - return obj.base if getattr(obj, "base", None) is not None else obj - - left_base = _get_base(left) - right_base = _get_base(right) - - if check_same == "same": - if left_base is not right_base: - raise AssertionError(f"{repr(left_base)} is not {repr(right_base)}") - elif check_same == "copy": - if left_base is right_base: - raise AssertionError(f"{repr(left_base)} is {repr(right_base)}") - - def _raise(left, right, err_msg): - if err_msg is None: - if left.shape != right.shape: - raise_assert_detail( - obj, f"{obj} shapes are different", left.shape, right.shape, - ) - - diff = 0 - for l, r in zip(left, right): - # count up differences - if not array_equivalent(l, r, strict_nan=strict_nan): - diff += 1 - - diff = diff * 100.0 / left.size - msg = f"{obj} values are different ({np.round(diff, 5)} %)" - raise_assert_detail(obj, msg, left, right) - - raise AssertionError(err_msg) - - # compare shape and values - if not array_equivalent(left, right, strict_nan=strict_nan): - _raise(left, right, err_msg) - - if check_dtype: - if isinstance(left, np.ndarray) and isinstance(right, np.ndarray): - assert_attr_equal("dtype", left, right, obj=obj) - - -def assert_extension_array_equal( - left, right, check_dtype=True, check_less_precise=False, check_exact=False -): - """ - Check that left and right ExtensionArrays are equal. - - Parameters - ---------- - left, right : ExtensionArray - The two arrays to compare. - check_dtype : bool, default True - Whether to check if the ExtensionArray dtypes are identical. - check_less_precise : bool or int, default False - Specify comparison precision. Only used when check_exact is False. - 5 digits (False) or 3 digits (True) after decimal points are compared. - If int, then specify the digits to compare. - check_exact : bool, default False - Whether to compare number exactly. - - Notes - ----- - Missing values are checked separately from valid values. - A mask of missing values is computed for each and checked to match. - The remaining all-valid values are cast to object dtype and checked. - """ - assert isinstance(left, ExtensionArray), "left is not an ExtensionArray" - assert isinstance(right, ExtensionArray), "right is not an ExtensionArray" - if check_dtype: - assert_attr_equal("dtype", left, right, obj="ExtensionArray") - - if hasattr(left, "asi8") and type(right) == type(left): - # Avoid slow object-dtype comparisons - assert_numpy_array_equal(left.asi8, right.asi8) - return - - left_na = np.asarray(left.isna()) - right_na = np.asarray(right.isna()) - assert_numpy_array_equal(left_na, right_na, obj="ExtensionArray NA mask") - - left_valid = np.asarray(left[~left_na].astype(object)) - right_valid = np.asarray(right[~right_na].astype(object)) - if check_exact: - assert_numpy_array_equal(left_valid, right_valid, obj="ExtensionArray") - else: - _testing.assert_almost_equal( - left_valid, - right_valid, - check_dtype=check_dtype, - check_less_precise=check_less_precise, - obj="ExtensionArray", - ) - - -# This could be refactored to use the NDFrame.equals method -def assert_series_equal( - left, - right, - check_dtype=True, - check_index_type="equiv", - check_series_type=True, - check_less_precise=False, - check_names=True, - check_exact=False, - check_datetimelike_compat=False, - check_categorical=True, - check_category_order=True, - obj="Series", -): - """ - Check that left and right Series are equal. - - Parameters - ---------- - left : Series - right : Series - check_dtype : bool, default True - Whether to check the Series dtype is identical. - check_index_type : bool or {'equiv'}, default 'equiv' - Whether to check the Index class, dtype and inferred_type - are identical. - check_series_type : bool, default True - Whether to check the Series class is identical. - check_less_precise : bool or int, default False - Specify comparison precision. Only used when check_exact is False. - 5 digits (False) or 3 digits (True) after decimal points are compared. - If int, then specify the digits to compare. - - When comparing two numbers, if the first number has magnitude less - than 1e-5, we compare the two numbers directly and check whether - they are equivalent within the specified precision. Otherwise, we - compare the **ratio** of the second number to the first number and - check whether it is equivalent to 1 within the specified precision. - check_names : bool, default True - Whether to check the Series and Index names attribute. - check_exact : bool, default False - Whether to compare number exactly. - check_datetimelike_compat : bool, default False - Compare datetime-like which is comparable ignoring dtype. - check_categorical : bool, default True - Whether to compare internal Categorical exactly. - check_category_order : bool, default True - Whether to compare category order of internal Categoricals - - .. versionadded:: 1.0.2 - obj : str, default 'Series' - Specify object name being compared, internally used to show appropriate - assertion message. - """ - __tracebackhide__ = True - - # instance validation - _check_isinstance(left, right, Series) - - if check_series_type: - # ToDo: There are some tests using rhs is sparse - # lhs is dense. Should use assert_class_equal in future - assert isinstance(left, type(right)) - # assert_class_equal(left, right, obj=obj) - - # length comparison - if len(left) != len(right): - msg1 = f"{len(left)}, {left.index}" - msg2 = f"{len(right)}, {right.index}" - raise_assert_detail(obj, "Series length are different", msg1, msg2) - - # index comparison - assert_index_equal( - left.index, - right.index, - exact=check_index_type, - check_names=check_names, - check_less_precise=check_less_precise, - check_exact=check_exact, - check_categorical=check_categorical, - obj=f"{obj}.index", - ) - - if check_dtype: - # We want to skip exact dtype checking when `check_categorical` - # is False. We'll still raise if only one is a `Categorical`, - # regardless of `check_categorical` - if ( - is_categorical_dtype(left) - and is_categorical_dtype(right) - and not check_categorical - ): - pass - else: - assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}") - - if check_exact: - assert_numpy_array_equal( - left._internal_get_values(), - right._internal_get_values(), - check_dtype=check_dtype, - obj=str(obj), - ) - elif check_datetimelike_compat: - # we want to check only if we have compat dtypes - # e.g. integer and M|m are NOT compat, but we can simply check - # the values in that case - if needs_i8_conversion(left) or needs_i8_conversion(right): - - # datetimelike may have different objects (e.g. datetime.datetime - # vs Timestamp) but will compare equal - if not Index(left.values).equals(Index(right.values)): - msg = ( - f"[datetimelike_compat=True] {left.values} " - f"is not equal to {right.values}." - ) - raise AssertionError(msg) - else: - assert_numpy_array_equal( - left._internal_get_values(), - right._internal_get_values(), - check_dtype=check_dtype, - ) - elif is_interval_dtype(left) or is_interval_dtype(right): - assert_interval_array_equal(left.array, right.array) - elif is_extension_array_dtype(left.dtype) and is_datetime64tz_dtype(left.dtype): - # .values is an ndarray, but ._values is the ExtensionArray. - # TODO: Use .array - assert is_extension_array_dtype(right.dtype) - assert_extension_array_equal(left._values, right._values) - elif ( - is_extension_array_dtype(left) - and not is_categorical_dtype(left) - and is_extension_array_dtype(right) - and not is_categorical_dtype(right) - ): - assert_extension_array_equal(left.array, right.array) - else: - _testing.assert_almost_equal( - left._internal_get_values(), - right._internal_get_values(), - check_less_precise=check_less_precise, - check_dtype=check_dtype, - obj=str(obj), - ) - - # metadata comparison - if check_names: - assert_attr_equal("name", left, right, obj=obj) - - if check_categorical: - if is_categorical_dtype(left) or is_categorical_dtype(right): - assert_categorical_equal( - left.values, - right.values, - obj=f"{obj} category", - check_category_order=check_category_order, - ) - - -# This could be refactored to use the NDFrame.equals method -def assert_frame_equal( - left, - right, - check_dtype=True, - check_index_type="equiv", - check_column_type="equiv", - check_frame_type=True, - check_less_precise=False, - check_names=True, - by_blocks=False, - check_exact=False, - check_datetimelike_compat=False, - check_categorical=True, - check_like=False, - obj="DataFrame", -): - """ - Check that left and right DataFrame are equal. - - This function is intended to compare two DataFrames and output any - differences. Is is mostly intended for use in unit tests. - Additional parameters allow varying the strictness of the - equality checks performed. - - Parameters - ---------- - left : DataFrame - First DataFrame to compare. - right : DataFrame - Second DataFrame to compare. - check_dtype : bool, default True - Whether to check the DataFrame dtype is identical. - check_index_type : bool or {'equiv'}, default 'equiv' - Whether to check the Index class, dtype and inferred_type - are identical. - check_column_type : bool or {'equiv'}, default 'equiv' - Whether to check the columns class, dtype and inferred_type - are identical. Is passed as the ``exact`` argument of - :func:`assert_index_equal`. - check_frame_type : bool, default True - Whether to check the DataFrame class is identical. - check_less_precise : bool or int, default False - Specify comparison precision. Only used when check_exact is False. - 5 digits (False) or 3 digits (True) after decimal points are compared. - If int, then specify the digits to compare. - - When comparing two numbers, if the first number has magnitude less - than 1e-5, we compare the two numbers directly and check whether - they are equivalent within the specified precision. Otherwise, we - compare the **ratio** of the second number to the first number and - check whether it is equivalent to 1 within the specified precision. - check_names : bool, default True - Whether to check that the `names` attribute for both the `index` - and `column` attributes of the DataFrame is identical. - by_blocks : bool, default False - Specify how to compare internal data. If False, compare by columns. - If True, compare by blocks. - check_exact : bool, default False - Whether to compare number exactly. - check_datetimelike_compat : bool, default False - Compare datetime-like which is comparable ignoring dtype. - check_categorical : bool, default True - Whether to compare internal Categorical exactly. - check_like : bool, default False - If True, ignore the order of index & columns. - Note: index labels must match their respective rows - (same as in columns) - same labels must be with the same data. - obj : str, default 'DataFrame' - Specify object name being compared, internally used to show appropriate - assertion message. - - See Also - -------- - assert_series_equal : Equivalent method for asserting Series equality. - DataFrame.equals : Check DataFrame equality. - - Examples - -------- - This example shows comparing two DataFrames that are equal - but with columns of differing dtypes. - - >>> from pandas._testing import assert_frame_equal - >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]}) - >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]}) - - df1 equals itself. - - >>> assert_frame_equal(df1, df1) - - df1 differs from df2 as column 'b' is of a different type. - - >>> assert_frame_equal(df1, df2) - Traceback (most recent call last): - ... - AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different - - Attribute "dtype" are different - [left]: int64 - [right]: float64 - - Ignore differing dtypes in columns with check_dtype. - - >>> assert_frame_equal(df1, df2, check_dtype=False) - """ - __tracebackhide__ = True - - # instance validation - _check_isinstance(left, right, DataFrame) - - if check_frame_type: - assert isinstance(left, type(right)) - # assert_class_equal(left, right, obj=obj) - - # shape comparison - if left.shape != right.shape: - raise_assert_detail( - obj, f"{obj} shape mismatch", f"{repr(left.shape)}", f"{repr(right.shape)}", - ) - - if check_like: - left, right = left.reindex_like(right), right - - # index comparison - assert_index_equal( - left.index, - right.index, - exact=check_index_type, - check_names=check_names, - check_less_precise=check_less_precise, - check_exact=check_exact, - check_categorical=check_categorical, - obj=f"{obj}.index", - ) - - # column comparison - assert_index_equal( - left.columns, - right.columns, - exact=check_column_type, - check_names=check_names, - check_less_precise=check_less_precise, - check_exact=check_exact, - check_categorical=check_categorical, - obj=f"{obj}.columns", - ) - - # compare by blocks - if by_blocks: - rblocks = right._to_dict_of_blocks() - lblocks = left._to_dict_of_blocks() - for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))): - assert dtype in lblocks - assert dtype in rblocks - assert_frame_equal( - lblocks[dtype], rblocks[dtype], check_dtype=check_dtype, obj=obj - ) - - # compare by columns - else: - for i, col in enumerate(left.columns): - assert col in right - lcol = left.iloc[:, i] - rcol = right.iloc[:, i] - assert_series_equal( - lcol, - rcol, - check_dtype=check_dtype, - check_index_type=check_index_type, - check_less_precise=check_less_precise, - check_exact=check_exact, - check_names=check_names, - check_datetimelike_compat=check_datetimelike_compat, - check_categorical=check_categorical, - obj=f'{obj}.iloc[:, {i}] (column name="{col}")', - ) - - -def assert_equal(left, right, **kwargs): - """ - Wrapper for tm.assert_*_equal to dispatch to the appropriate test function. - - Parameters - ---------- - left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray - The two items to be compared. - **kwargs - All keyword arguments are passed through to the underlying assert method. - """ - __tracebackhide__ = True - - if isinstance(left, pd.Index): - assert_index_equal(left, right, **kwargs) - elif isinstance(left, pd.Series): - assert_series_equal(left, right, **kwargs) - elif isinstance(left, pd.DataFrame): - assert_frame_equal(left, right, **kwargs) - elif isinstance(left, IntervalArray): - assert_interval_array_equal(left, right, **kwargs) - elif isinstance(left, PeriodArray): - assert_period_array_equal(left, right, **kwargs) - elif isinstance(left, DatetimeArray): - assert_datetime_array_equal(left, right, **kwargs) - elif isinstance(left, TimedeltaArray): - assert_timedelta_array_equal(left, right, **kwargs) - elif isinstance(left, ExtensionArray): - assert_extension_array_equal(left, right, **kwargs) - elif isinstance(left, np.ndarray): - assert_numpy_array_equal(left, right, **kwargs) - elif isinstance(left, str): - assert kwargs == {} - assert left == right - else: - raise NotImplementedError(type(left)) - - -def box_expected(expected, box_cls, transpose=True): - """ - Helper function to wrap the expected output of a test in a given box_class. - - Parameters - ---------- - expected : np.ndarray, Index, Series - box_cls : {Index, Series, DataFrame} - - Returns - ------- - subclass of box_cls - """ - if box_cls is pd.Index: - expected = pd.Index(expected) - elif box_cls is pd.Series: - expected = pd.Series(expected) - elif box_cls is pd.DataFrame: - expected = pd.Series(expected).to_frame() - if transpose: - # for vector operations, we we need a DataFrame to be a single-row, - # not a single-column, in order to operate against non-DataFrame - # vectors of the same length. - expected = expected.T - elif box_cls is PeriodArray: - # the PeriodArray constructor is not as flexible as period_array - expected = period_array(expected) - elif box_cls is DatetimeArray: - expected = DatetimeArray(expected) - elif box_cls is TimedeltaArray: - expected = TimedeltaArray(expected) - elif box_cls is np.ndarray: - expected = np.array(expected) - elif box_cls is to_array: - expected = to_array(expected) - else: - raise NotImplementedError(box_cls) - return expected - - -def to_array(obj): - # temporary implementation until we get pd.array in place - if is_period_dtype(obj): - return period_array(obj) - elif is_datetime64_dtype(obj) or is_datetime64tz_dtype(obj): - return DatetimeArray._from_sequence(obj) - elif is_timedelta64_dtype(obj): - return TimedeltaArray._from_sequence(obj) - else: - return np.array(obj) - - -# ----------------------------------------------------------------------------- -# Sparse - - -def assert_sp_array_equal( - left, - right, - check_dtype=True, - check_kind=True, - check_fill_value=True, - consolidate_block_indices=False, -): - """ - Check that the left and right SparseArray are equal. - - Parameters - ---------- - left : SparseArray - right : SparseArray - check_dtype : bool, default True - Whether to check the data dtype is identical. - check_kind : bool, default True - Whether to just the kind of the sparse index for each column. - check_fill_value : bool, default True - Whether to check that left.fill_value matches right.fill_value - consolidate_block_indices : bool, default False - Whether to consolidate contiguous blocks for sparse arrays with - a BlockIndex. Some operations, e.g. concat, will end up with - block indices that could be consolidated. Setting this to true will - create a new BlockIndex for that array, with consolidated - block indices. - """ - _check_isinstance(left, right, pd.arrays.SparseArray) - - assert_numpy_array_equal(left.sp_values, right.sp_values, check_dtype=check_dtype) - - # SparseIndex comparison - assert isinstance(left.sp_index, pd._libs.sparse.SparseIndex) - assert isinstance(right.sp_index, pd._libs.sparse.SparseIndex) - - if not check_kind: - left_index = left.sp_index.to_block_index() - right_index = right.sp_index.to_block_index() - else: - left_index = left.sp_index - right_index = right.sp_index - - if consolidate_block_indices and left.kind == "block": - # we'll probably remove this hack... - left_index = left_index.to_int_index().to_block_index() - right_index = right_index.to_int_index().to_block_index() - - if not left_index.equals(right_index): - raise_assert_detail( - "SparseArray.index", "index are not equal", left_index, right_index - ) - else: - # Just ensure a - pass - - if check_fill_value: - assert_attr_equal("fill_value", left, right) - if check_dtype: - assert_attr_equal("dtype", left, right) - assert_numpy_array_equal(left.to_dense(), right.to_dense(), check_dtype=check_dtype) - - -# ----------------------------------------------------------------------------- -# Others - - -def assert_contains_all(iterable, dic): - for k in iterable: - assert k in dic, f"Did not contain item: {repr(k)}" - - -def assert_copy(iter1, iter2, **eql_kwargs): - """ - iter1, iter2: iterables that produce elements - comparable with assert_almost_equal - - Checks that the elements are equal, but not - the same object. (Does not check that items - in sequences are also not the same object) - """ - for elem1, elem2 in zip(iter1, iter2): - assert_almost_equal(elem1, elem2, **eql_kwargs) - msg = ( - f"Expected object {repr(type(elem1))} and object {repr(type(elem2))} to be " - "different objects, but they were the same object." - ) - assert elem1 is not elem2, msg - - -def getCols(k): - return string.ascii_uppercase[:k] - - -# make index -def makeStringIndex(k=10, name=None): - return Index(rands_array(nchars=10, size=k), name=name) - - -def makeUnicodeIndex(k=10, name=None): - return Index(randu_array(nchars=10, size=k), name=name) - - -def makeCategoricalIndex(k=10, n=3, name=None, **kwargs): - """ make a length k index or n categories """ - x = rands_array(nchars=4, size=n) - return CategoricalIndex( - Categorical.from_codes(np.arange(k) % n, categories=x), name=name, **kwargs - ) - - -def makeIntervalIndex(k=10, name=None, **kwargs): - """ make a length k IntervalIndex """ - x = np.linspace(0, 100, num=(k + 1)) - return IntervalIndex.from_breaks(x, name=name, **kwargs) - - -def makeBoolIndex(k=10, name=None): - if k == 1: - return Index([True], name=name) - elif k == 2: - return Index([False, True], name=name) - return Index([False, True] + [False] * (k - 2), name=name) - - -def makeIntIndex(k=10, name=None): - return Index(list(range(k)), name=name) - - -def makeUIntIndex(k=10, name=None): - return Index([2 ** 63 + i for i in range(k)], name=name) - - -def makeRangeIndex(k=10, name=None, **kwargs): - return RangeIndex(0, k, 1, name=name, **kwargs) - - -def makeFloatIndex(k=10, name=None): - values = sorted(np.random.random_sample(k)) - np.random.random_sample(1) - return Index(values * (10 ** np.random.randint(0, 9)), name=name) - - -def makeDateIndex(k=10, freq="B", name=None, **kwargs): - dt = datetime(2000, 1, 1) - dr = bdate_range(dt, periods=k, freq=freq, name=name) - return DatetimeIndex(dr, name=name, **kwargs) - - -def makeTimedeltaIndex(k=10, freq="D", name=None, **kwargs): - return pd.timedelta_range(start="1 day", periods=k, freq=freq, name=name, **kwargs) - - -def makePeriodIndex(k=10, name=None, **kwargs): - dt = datetime(2000, 1, 1) - dr = pd.period_range(start=dt, periods=k, freq="B", name=name, **kwargs) - return dr - - -def makeMultiIndex(k=10, names=None, **kwargs): - return MultiIndex.from_product((("foo", "bar"), (1, 2)), names=names, **kwargs) - - -_names = [ - "Alice", - "Bob", - "Charlie", - "Dan", - "Edith", - "Frank", - "George", - "Hannah", - "Ingrid", - "Jerry", - "Kevin", - "Laura", - "Michael", - "Norbert", - "Oliver", - "Patricia", - "Quinn", - "Ray", - "Sarah", - "Tim", - "Ursula", - "Victor", - "Wendy", - "Xavier", - "Yvonne", - "Zelda", -] - - -def _make_timeseries(start="2000-01-01", end="2000-12-31", freq="1D", seed=None): - """ - Make a DataFrame with a DatetimeIndex - - Parameters - ---------- - start : str or Timestamp, default "2000-01-01" - The start of the index. Passed to date_range with `freq`. - end : str or Timestamp, default "2000-12-31" - The end of the index. Passed to date_range with `freq`. - freq : str or Freq - The frequency to use for the DatetimeIndex - seed : int, optional - The random state seed. - - * name : object dtype with string names - * id : int dtype with - * x, y : float dtype - - Examples - -------- - >>> _make_timeseries() - id name x y - timestamp - 2000-01-01 982 Frank 0.031261 0.986727 - 2000-01-02 1025 Edith -0.086358 -0.032920 - 2000-01-03 982 Edith 0.473177 0.298654 - 2000-01-04 1009 Sarah 0.534344 -0.750377 - 2000-01-05 963 Zelda -0.271573 0.054424 - ... ... ... ... ... - 2000-12-27 980 Ingrid -0.132333 -0.422195 - 2000-12-28 972 Frank -0.376007 -0.298687 - 2000-12-29 1009 Ursula -0.865047 -0.503133 - 2000-12-30 1000 Hannah -0.063757 -0.507336 - 2000-12-31 972 Tim -0.869120 0.531685 - """ - index = pd.date_range(start=start, end=end, freq=freq, name="timestamp") - n = len(index) - state = np.random.RandomState(seed) - columns = { - "name": state.choice(_names, size=n), - "id": state.poisson(1000, size=n), - "x": state.rand(n) * 2 - 1, - "y": state.rand(n) * 2 - 1, - } - df = pd.DataFrame(columns, index=index, columns=sorted(columns)) - if df.index[-1] == end: - df = df.iloc[:-1] - return df - - -def all_index_generator(k=10): - """ - Generator which can be iterated over to get instances of all the various - index classes. - - Parameters - ---------- - k: length of each of the index instances - """ - all_make_index_funcs = [ - makeIntIndex, - makeFloatIndex, - makeStringIndex, - makeUnicodeIndex, - makeDateIndex, - makePeriodIndex, - makeTimedeltaIndex, - makeBoolIndex, - makeRangeIndex, - makeIntervalIndex, - makeCategoricalIndex, - ] - for make_index_func in all_make_index_funcs: - yield make_index_func(k=k) - - -def index_subclass_makers_generator(): - make_index_funcs = [ - makeDateIndex, - makePeriodIndex, - makeTimedeltaIndex, - makeRangeIndex, - makeIntervalIndex, - makeCategoricalIndex, - makeMultiIndex, - ] - for make_index_func in make_index_funcs: - yield make_index_func - - -def all_timeseries_index_generator(k=10): - """ - Generator which can be iterated over to get instances of all the classes - which represent time-series. - - Parameters - ---------- - k: length of each of the index instances - """ - make_index_funcs = [makeDateIndex, makePeriodIndex, makeTimedeltaIndex] - for make_index_func in make_index_funcs: - yield make_index_func(k=k) - - -# make series -def makeFloatSeries(name=None): - index = makeStringIndex(N) - return Series(randn(N), index=index, name=name) - - -def makeStringSeries(name=None): - index = makeStringIndex(N) - return Series(randn(N), index=index, name=name) - - -def makeObjectSeries(name=None): - data = makeStringIndex(N) - data = Index(data, dtype=object) - index = makeStringIndex(N) - return Series(data, index=index, name=name) - - -def getSeriesData(): - index = makeStringIndex(N) - return {c: Series(randn(N), index=index) for c in getCols(K)} - - -def makeTimeSeries(nper=None, freq="B", name=None): - if nper is None: - nper = N - return Series(randn(nper), index=makeDateIndex(nper, freq=freq), name=name) - - -def makePeriodSeries(nper=None, name=None): - if nper is None: - nper = N - return Series(randn(nper), index=makePeriodIndex(nper), name=name) - - -def getTimeSeriesData(nper=None, freq="B"): - return {c: makeTimeSeries(nper, freq) for c in getCols(K)} - - -def getPeriodData(nper=None): - return {c: makePeriodSeries(nper) for c in getCols(K)} - - -# make frame -def makeTimeDataFrame(nper=None, freq="B"): - data = getTimeSeriesData(nper, freq) - return DataFrame(data) - - -def makeDataFrame(): - data = getSeriesData() - return DataFrame(data) - - -def getMixedTypeDict(): - index = Index(["a", "b", "c", "d", "e"]) - - data = { - "A": [0.0, 1.0, 2.0, 3.0, 4.0], - "B": [0.0, 1.0, 0.0, 1.0, 0.0], - "C": ["foo1", "foo2", "foo3", "foo4", "foo5"], - "D": bdate_range("1/1/2009", periods=5), - } - - return index, data - - -def makeMixedDataFrame(): - return DataFrame(getMixedTypeDict()[1]) - - -def makePeriodFrame(nper=None): - data = getPeriodData(nper) - return DataFrame(data) - - -def makeCustomIndex( - nentries, nlevels, prefix="#", names=False, ndupe_l=None, idx_type=None -): - """ - Create an index/multindex with given dimensions, levels, names, etc' - - nentries - number of entries in index - nlevels - number of levels (> 1 produces multindex) - prefix - a string prefix for labels - names - (Optional), bool or list of strings. if True will use default - names, if false will use no names, if a list is given, the name of - each level in the index will be taken from the list. - ndupe_l - (Optional), list of ints, the number of rows for which the - label will repeated at the corresponding level, you can specify just - the first few, the rest will use the default ndupe_l of 1. - len(ndupe_l) <= nlevels. - idx_type - "i"/"f"/"s"/"u"/"dt"/"p"/"td". - If idx_type is not None, `idx_nlevels` must be 1. - "i"/"f" creates an integer/float index, - "s"/"u" creates a string/unicode index - "dt" create a datetime index. - "td" create a datetime index. - - if unspecified, string labels will be generated. - """ - if ndupe_l is None: - ndupe_l = [1] * nlevels - assert is_sequence(ndupe_l) and len(ndupe_l) <= nlevels - assert names is None or names is False or names is True or len(names) is nlevels - assert idx_type is None or ( - idx_type in ("i", "f", "s", "u", "dt", "p", "td") and nlevels == 1 - ) - - if names is True: - # build default names - names = [prefix + str(i) for i in range(nlevels)] - if names is False: - # pass None to index constructor for no name - names = None - - # make singleton case uniform - if isinstance(names, str) and nlevels == 1: - names = [names] - - # specific 1D index type requested? - idx_func = dict( - i=makeIntIndex, - f=makeFloatIndex, - s=makeStringIndex, - u=makeUnicodeIndex, - dt=makeDateIndex, - td=makeTimedeltaIndex, - p=makePeriodIndex, - ).get(idx_type) - if idx_func: - idx = idx_func(nentries) - # but we need to fill in the name - if names: - idx.name = names[0] - return idx - elif idx_type is not None: - raise ValueError( - f"{repr(idx_type)} is not a legal value for `idx_type`, " - "use 'i'/'f'/'s'/'u'/'dt'/'p'/'td'." - ) - - if len(ndupe_l) < nlevels: - ndupe_l.extend([1] * (nlevels - len(ndupe_l))) - assert len(ndupe_l) == nlevels - - assert all(x > 0 for x in ndupe_l) - - tuples = [] - for i in range(nlevels): - - def keyfunc(x): - import re - - numeric_tuple = re.sub(r"[^\d_]_?", "", x).split("_") - return [int(num) for num in numeric_tuple] - - # build a list of lists to create the index from - div_factor = nentries // ndupe_l[i] + 1 - cnt = Counter() - for j in range(div_factor): - label = f"{prefix}_l{i}_g{j}" - cnt[label] = ndupe_l[i] - # cute Counter trick - result = sorted(cnt.elements(), key=keyfunc)[:nentries] - tuples.append(result) - - tuples = list(zip(*tuples)) - - # convert tuples to index - if nentries == 1: - # we have a single level of tuples, i.e. a regular Index - index = Index(tuples[0], name=names[0]) - elif nlevels == 1: - name = None if names is None else names[0] - index = Index((x[0] for x in tuples), name=name) - else: - index = MultiIndex.from_tuples(tuples, names=names) - return index - - -def makeCustomDataframe( - nrows, - ncols, - c_idx_names=True, - r_idx_names=True, - c_idx_nlevels=1, - r_idx_nlevels=1, - data_gen_f=None, - c_ndupe_l=None, - r_ndupe_l=None, - dtype=None, - c_idx_type=None, - r_idx_type=None, -): - """ - Create a DataFrame using supplied parameters. - - Parameters - ---------- - nrows, ncols - number of data rows/cols - c_idx_names, idx_names - False/True/list of strings, yields No names , - default names or uses the provided names for the levels of the - corresponding index. You can provide a single string when - c_idx_nlevels ==1. - c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex - r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex - data_gen_f - a function f(row,col) which return the data value - at that position, the default generator used yields values of the form - "RxCy" based on position. - c_ndupe_l, r_ndupe_l - list of integers, determines the number - of duplicates for each label at a given level of the corresponding - index. The default `None` value produces a multiplicity of 1 across - all levels, i.e. a unique index. Will accept a partial list of length - N < idx_nlevels, for just the first N levels. If ndupe doesn't divide - nrows/ncol, the last label might have lower multiplicity. - dtype - passed to the DataFrame constructor as is, in case you wish to - have more control in conjunction with a custom `data_gen_f` - r_idx_type, c_idx_type - "i"/"f"/"s"/"u"/"dt"/"td". - If idx_type is not None, `idx_nlevels` must be 1. - "i"/"f" creates an integer/float index, - "s"/"u" creates a string/unicode index - "dt" create a datetime index. - "td" create a timedelta index. - - if unspecified, string labels will be generated. - - Examples - -------- - # 5 row, 3 columns, default names on both, single index on both axis - >> makeCustomDataframe(5,3) - - # make the data a random int between 1 and 100 - >> mkdf(5,3,data_gen_f=lambda r,c:randint(1,100)) - - # 2-level multiindex on rows with each label duplicated - # twice on first level, default names on both axis, single - # index on both axis - >> a=makeCustomDataframe(5,3,r_idx_nlevels=2,r_ndupe_l=[2]) - - # DatetimeIndex on row, index with unicode labels on columns - # no names on either axis - >> a=makeCustomDataframe(5,3,c_idx_names=False,r_idx_names=False, - r_idx_type="dt",c_idx_type="u") - - # 4-level multindex on rows with names provided, 2-level multindex - # on columns with default labels and default names. - >> a=makeCustomDataframe(5,3,r_idx_nlevels=4, - r_idx_names=["FEE","FI","FO","FAM"], - c_idx_nlevels=2) - - >> a=mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4) - """ - assert c_idx_nlevels > 0 - assert r_idx_nlevels > 0 - assert r_idx_type is None or ( - r_idx_type in ("i", "f", "s", "u", "dt", "p", "td") and r_idx_nlevels == 1 - ) - assert c_idx_type is None or ( - c_idx_type in ("i", "f", "s", "u", "dt", "p", "td") and c_idx_nlevels == 1 - ) - - columns = makeCustomIndex( - ncols, - nlevels=c_idx_nlevels, - prefix="C", - names=c_idx_names, - ndupe_l=c_ndupe_l, - idx_type=c_idx_type, - ) - index = makeCustomIndex( - nrows, - nlevels=r_idx_nlevels, - prefix="R", - names=r_idx_names, - ndupe_l=r_ndupe_l, - idx_type=r_idx_type, - ) - - # by default, generate data based on location - if data_gen_f is None: - data_gen_f = lambda r, c: f"R{r}C{c}" - - data = [[data_gen_f(r, c) for c in range(ncols)] for r in range(nrows)] - - return DataFrame(data, index, columns, dtype=dtype) - - -def _create_missing_idx(nrows, ncols, density, random_state=None): - if random_state is None: - random_state = np.random - else: - random_state = np.random.RandomState(random_state) - - # below is cribbed from scipy.sparse - size = int(np.round((1 - density) * nrows * ncols)) - # generate a few more to ensure unique values - min_rows = 5 - fac = 1.02 - extra_size = min(size + min_rows, fac * size) - - def _gen_unique_rand(rng, _extra_size): - ind = rng.rand(int(_extra_size)) - return np.unique(np.floor(ind * nrows * ncols))[:size] - - ind = _gen_unique_rand(random_state, extra_size) - while ind.size < size: - extra_size *= 1.05 - ind = _gen_unique_rand(random_state, extra_size) - - j = np.floor(ind * 1.0 / nrows).astype(int) - i = (ind - j * nrows).astype(int) - return i.tolist(), j.tolist() - - -def makeMissingCustomDataframe( - nrows, - ncols, - density=0.9, - random_state=None, - c_idx_names=True, - r_idx_names=True, - c_idx_nlevels=1, - r_idx_nlevels=1, - data_gen_f=None, - c_ndupe_l=None, - r_ndupe_l=None, - dtype=None, - c_idx_type=None, - r_idx_type=None, -): - """ - Parameters - ---------- - Density : float, optional - Float in (0, 1) that gives the percentage of non-missing numbers in - the DataFrame. - random_state : {np.random.RandomState, int}, optional - Random number generator or random seed. - - See makeCustomDataframe for descriptions of the rest of the parameters. - """ - df = makeCustomDataframe( - nrows, - ncols, - c_idx_names=c_idx_names, - r_idx_names=r_idx_names, - c_idx_nlevels=c_idx_nlevels, - r_idx_nlevels=r_idx_nlevels, - data_gen_f=data_gen_f, - c_ndupe_l=c_ndupe_l, - r_ndupe_l=r_ndupe_l, - dtype=dtype, - c_idx_type=c_idx_type, - r_idx_type=r_idx_type, - ) - - i, j = _create_missing_idx(nrows, ncols, density, random_state) - df.values[i, j] = np.nan - return df - - -def makeMissingDataframe(density=0.9, random_state=None): - df = makeDataFrame() - i, j = _create_missing_idx(*df.shape, density=density, random_state=random_state) - df.values[i, j] = np.nan - return df - - -def optional_args(decorator): - """ - allows a decorator to take optional positional and keyword arguments. - Assumes that taking a single, callable, positional argument means that - it is decorating a function, i.e. something like this:: - - @my_decorator - def function(): pass - - Calls decorator with decorator(f, *args, **kwargs) - """ - - @wraps(decorator) - def wrapper(*args, **kwargs): - def dec(f): - return decorator(f, *args, **kwargs) - - is_decorating = not kwargs and len(args) == 1 and callable(args[0]) - if is_decorating: - f = args[0] - args = [] - return dec(f) - else: - return dec - - return wrapper - - -# skip tests on exceptions with this message -_network_error_messages = ( - # 'urlopen error timed out', - # 'timeout: timed out', - # 'socket.timeout: timed out', - "timed out", - "Server Hangup", - "HTTP Error 503: Service Unavailable", - "502: Proxy Error", - "HTTP Error 502: internal error", - "HTTP Error 502", - "HTTP Error 503", - "HTTP Error 403", - "HTTP Error 400", - "Temporary failure in name resolution", - "Name or service not known", - "Connection refused", - "certificate verify", -) - -# or this e.errno/e.reason.errno -_network_errno_vals = ( - 101, # Network is unreachable - 111, # Connection refused - 110, # Connection timed out - 104, # Connection reset Error - 54, # Connection reset by peer - 60, # urllib.error.URLError: [Errno 60] Connection timed out -) - -# Both of the above shouldn't mask real issues such as 404's -# or refused connections (changed DNS). -# But some tests (test_data yahoo) contact incredibly flakey -# servers. - -# and conditionally raise on exception types in _get_default_network_errors - - -def _get_default_network_errors(): - # Lazy import for http.client because it imports many things from the stdlib - import http.client - - return (IOError, http.client.HTTPException, TimeoutError) - - -def can_connect(url, error_classes=None): - """ - Try to connect to the given url. True if succeeds, False if IOError - raised - - Parameters - ---------- - url : basestring - The URL to try to connect to - - Returns - ------- - connectable : bool - Return True if no IOError (unable to connect) or URLError (bad url) was - raised - """ - if error_classes is None: - error_classes = _get_default_network_errors() - - try: - with urlopen(url): - pass - except error_classes: - return False - else: - return True - - -@optional_args -def network( - t, - url="http://www.google.com", - raise_on_error=_RAISE_NETWORK_ERROR_DEFAULT, - check_before_test=False, - error_classes=None, - skip_errnos=_network_errno_vals, - _skip_on_messages=_network_error_messages, -): - """ - Label a test as requiring network connection and, if an error is - encountered, only raise if it does not find a network connection. - - In comparison to ``network``, this assumes an added contract to your test: - you must assert that, under normal conditions, your test will ONLY fail if - it does not have network connectivity. - - You can call this in 3 ways: as a standard decorator, with keyword - arguments, or with a positional argument that is the url to check. - - Parameters - ---------- - t : callable - The test requiring network connectivity. - url : path - The url to test via ``pandas.io.common.urlopen`` to check - for connectivity. Defaults to 'http://www.google.com'. - raise_on_error : bool - If True, never catches errors. - check_before_test : bool - If True, checks connectivity before running the test case. - error_classes : tuple or Exception - error classes to ignore. If not in ``error_classes``, raises the error. - defaults to IOError. Be careful about changing the error classes here. - skip_errnos : iterable of int - Any exception that has .errno or .reason.erno set to one - of these values will be skipped with an appropriate - message. - _skip_on_messages: iterable of string - any exception e for which one of the strings is - a substring of str(e) will be skipped with an appropriate - message. Intended to suppress errors where an errno isn't available. - - Notes - ----- - * ``raise_on_error`` supercedes ``check_before_test`` - - Returns - ------- - t : callable - The decorated test ``t``, with checks for connectivity errors. - - Example - ------- - - Tests decorated with @network will fail if it's possible to make a network - connection to another URL (defaults to google.com):: - - >>> from pandas._testing import network - >>> from pandas.io.common import urlopen - >>> @network - ... def test_network(): - ... with urlopen("rabbit://bonanza.com"): - ... pass - Traceback - ... - URLError: <urlopen error unknown url type: rabit> - - You can specify alternative URLs:: - - >>> @network("http://www.yahoo.com") - ... def test_something_with_yahoo(): - ... raise IOError("Failure Message") - >>> test_something_with_yahoo() - Traceback (most recent call last): - ... - IOError: Failure Message - - If you set check_before_test, it will check the url first and not run the - test on failure:: - - >>> @network("failing://url.blaher", check_before_test=True) - ... def test_something(): - ... print("I ran!") - ... raise ValueError("Failure") - >>> test_something() - Traceback (most recent call last): - ... - - Errors not related to networking will always be raised. - """ - from pytest import skip - - if error_classes is None: - error_classes = _get_default_network_errors() - - t.network = True - - @wraps(t) - def wrapper(*args, **kwargs): - if check_before_test and not raise_on_error: - if not can_connect(url, error_classes): - skip() - try: - return t(*args, **kwargs) - except Exception as err: - errno = getattr(err, "errno", None) - if not errno and hasattr(errno, "reason"): - errno = getattr(err.reason, "errno", None) - - if errno in skip_errnos: - skip(f"Skipping test due to known errno and error {err}") - - e_str = str(err) - - if any(m.lower() in e_str.lower() for m in _skip_on_messages): - skip( - f"Skipping test because exception message is known and error {err}" - ) - - if not isinstance(err, error_classes): - raise - - if raise_on_error or can_connect(url, error_classes): - raise - else: - skip(f"Skipping test due to lack of connectivity and error {err}") - - return wrapper - - -with_connectivity_check = network - - -@contextmanager -def assert_produces_warning( - expected_warning=Warning, - filter_level="always", - clear=None, - check_stacklevel=True, - raise_on_extra_warnings=True, -): - """ - Context manager for running code expected to either raise a specific - warning, or not raise any warnings. Verifies that the code raises the - expected warning, and that it does not raise any other unexpected - warnings. It is basically a wrapper around ``warnings.catch_warnings``. - - Parameters - ---------- - expected_warning : {Warning, False, None}, default Warning - The type of Exception raised. ``exception.Warning`` is the base - class for all warnings. To check that no warning is returned, - specify ``False`` or ``None``. - filter_level : str or None, default "always" - Specifies whether warnings are ignored, displayed, or turned - into errors. - Valid values are: - - * "error" - turns matching warnings into exceptions - * "ignore" - discard the warning - * "always" - always emit a warning - * "default" - print the warning the first time it is generated - from each location - * "module" - print the warning the first time it is generated - from each module - * "once" - print the warning the first time it is generated - - clear : str, default None - If not ``None`` then remove any previously raised warnings from - the ``__warningsregistry__`` to ensure that no warning messages are - suppressed by this context manager. If ``None`` is specified, - the ``__warningsregistry__`` keeps track of which warnings have been - shown, and does not show them again. - check_stacklevel : bool, default True - If True, displays the line that called the function containing - the warning to show were the function is called. Otherwise, the - line that implements the function is displayed. - raise_on_extra_warnings : bool, default True - Whether extra warnings not of the type `expected_warning` should - cause the test to fail. - - Examples - -------- - >>> import warnings - >>> with assert_produces_warning(): - ... warnings.warn(UserWarning()) - ... - >>> with assert_produces_warning(False): - ... warnings.warn(RuntimeWarning()) - ... - Traceback (most recent call last): - ... - AssertionError: Caused unexpected warning(s): ['RuntimeWarning']. - >>> with assert_produces_warning(UserWarning): - ... warnings.warn(RuntimeWarning()) - Traceback (most recent call last): - ... - AssertionError: Did not see expected warning of class 'UserWarning'. - - ..warn:: This is *not* thread-safe. - """ - __tracebackhide__ = True - - with warnings.catch_warnings(record=True) as w: - - if clear is not None: - # make sure that we are clearing these warnings - # if they have happened before - # to guarantee that we will catch them - if not is_list_like(clear): - clear = [clear] - for m in clear: - try: - m.__warningregistry__.clear() - except AttributeError: - # module may not have __warningregistry__ - pass - - saw_warning = False - warnings.simplefilter(filter_level) - yield w - extra_warnings = [] - - for actual_warning in w: - if expected_warning and issubclass( - actual_warning.category, expected_warning - ): - saw_warning = True - - if check_stacklevel and issubclass( - actual_warning.category, (FutureWarning, DeprecationWarning) - ): - from inspect import getframeinfo, stack - - caller = getframeinfo(stack()[2][0]) - msg = ( - "Warning not set with correct stacklevel. " - f"File where warning is raised: {actual_warning.filename} != " - f"{caller.filename}. Warning message: {actual_warning.message}" - ) - assert actual_warning.filename == caller.filename, msg - else: - extra_warnings.append( - ( - actual_warning.category.__name__, - actual_warning.message, - actual_warning.filename, - actual_warning.lineno, - ) - ) - if expected_warning: - msg = ( - f"Did not see expected warning of class " - f"{repr(expected_warning.__name__)}" - ) - assert saw_warning, msg - if raise_on_extra_warnings and extra_warnings: - raise AssertionError( - f"Caused unexpected warning(s): {repr(extra_warnings)}" - ) - - -class RNGContext: - """ - Context manager to set the numpy random number generator speed. Returns - to the original value upon exiting the context manager. - - Parameters - ---------- - seed : int - Seed for numpy.random.seed - - Examples - -------- - with RNGContext(42): - np.random.randn() - """ - - def __init__(self, seed): - self.seed = seed - - def __enter__(self): - - self.start_state = np.random.get_state() - np.random.seed(self.seed) - - def __exit__(self, exc_type, exc_value, traceback): - - np.random.set_state(self.start_state) - - -@contextmanager -def with_csv_dialect(name, **kwargs): - """ - Context manager to temporarily register a CSV dialect for parsing CSV. - - Parameters - ---------- - name : str - The name of the dialect. - kwargs : mapping - The parameters for the dialect. - - Raises - ------ - ValueError : the name of the dialect conflicts with a builtin one. - - See Also - -------- - csv : Python's CSV library. - """ - import csv - - _BUILTIN_DIALECTS = {"excel", "excel-tab", "unix"} - - if name in _BUILTIN_DIALECTS: - raise ValueError("Cannot override builtin dialect.") - - csv.register_dialect(name, **kwargs) - yield - csv.unregister_dialect(name) - - -@contextmanager -def use_numexpr(use, min_elements=None): - from pandas.core.computation import expressions as expr - - if min_elements is None: - min_elements = expr._MIN_ELEMENTS - - olduse = expr._USE_NUMEXPR - oldmin = expr._MIN_ELEMENTS - expr.set_use_numexpr(use) - expr._MIN_ELEMENTS = min_elements - yield - expr._MIN_ELEMENTS = oldmin - expr.set_use_numexpr(olduse) - - -def test_parallel(num_threads=2, kwargs_list=None): - """ - Decorator to run the same function multiple times in parallel. - - Parameters - ---------- - num_threads : int, optional - The number of times the function is run in parallel. - kwargs_list : list of dicts, optional - The list of kwargs to update original - function kwargs on different threads. - - Notes - ----- - This decorator does not pass the return value of the decorated function. - - Original from scikit-image: - - https://github.com/scikit-image/scikit-image/pull/1519 - - """ - assert num_threads > 0 - has_kwargs_list = kwargs_list is not None - if has_kwargs_list: - assert len(kwargs_list) == num_threads - import threading - - def wrapper(func): - @wraps(func) - def inner(*args, **kwargs): - if has_kwargs_list: - update_kwargs = lambda i: dict(kwargs, **kwargs_list[i]) - else: - update_kwargs = lambda i: kwargs - threads = [] - for i in range(num_threads): - updated_kwargs = update_kwargs(i) - thread = threading.Thread(target=func, args=args, kwargs=updated_kwargs) - threads.append(thread) - for thread in threads: - thread.start() - for thread in threads: - thread.join() - - return inner - - return wrapper - - -class SubclassedSeries(Series): - _metadata = ["testattr", "name"] - - @property - def _constructor(self): - return SubclassedSeries - - @property - def _constructor_expanddim(self): - return SubclassedDataFrame - - -class SubclassedDataFrame(DataFrame): - _metadata = ["testattr"] - - @property - def _constructor(self): - return SubclassedDataFrame - - @property - def _constructor_sliced(self): - return SubclassedSeries - - -class SubclassedCategorical(Categorical): - @property - def _constructor(self): - return SubclassedCategorical - - -@contextmanager -def set_timezone(tz: str): - """ - Context manager for temporarily setting a timezone. - - Parameters - ---------- - tz : str - A string representing a valid timezone. - - Examples - -------- - >>> from datetime import datetime - >>> from dateutil.tz import tzlocal - >>> tzlocal().tzname(datetime.now()) - 'IST' - - >>> with set_timezone('US/Eastern'): - ... tzlocal().tzname(datetime.now()) - ... - 'EDT' - """ - import os - import time - - def setTZ(tz): - if tz is None: - try: - del os.environ["TZ"] - except KeyError: - pass - else: - os.environ["TZ"] = tz - time.tzset() - - orig_tz = os.environ.get("TZ") - setTZ(tz) - try: - yield - finally: - setTZ(orig_tz) - - -def _make_skipna_wrapper(alternative, skipna_alternative=None): - """ - Create a function for calling on an array. - - Parameters - ---------- - alternative : function - The function to be called on the array with no NaNs. - Only used when 'skipna_alternative' is None. - skipna_alternative : function - The function to be called on the original array - - Returns - ------- - function - """ - if skipna_alternative: - - def skipna_wrapper(x): - return skipna_alternative(x.values) - - else: - - def skipna_wrapper(x): - nona = x.dropna() - if len(nona) == 0: - return np.nan - return alternative(nona) - - return skipna_wrapper - - -def convert_rows_list_to_csv_str(rows_list: List[str]): - """ - Convert list of CSV rows to single CSV-formatted string for current OS. - - This method is used for creating expected value of to_csv() method. - - Parameters - ---------- - rows_list : List[str] - Each element represents the row of csv. - - Returns - ------- - str - Expected output of to_csv() in current OS. - """ - sep = os.linesep - expected = sep.join(rows_list) + sep - return expected - - -def external_error_raised( - expected_exception: Type[Exception], -) -> Callable[[Type[Exception], None], None]: - """ - Helper function to mark pytest.raises that have an external error message. - - Parameters - ---------- - expected_exception : Exception - Expected error to raise. - - Returns - ------- - Callable - Regular `pytest.raises` function with `match` equal to `None`. - """ - import pytest - - return pytest.raises(expected_exception, match=None) diff --git a/pandas/_testing/__init__.py b/pandas/_testing/__init__.py new file mode 100644 index 0000000000000..5aab31dfaafe3 --- /dev/null +++ b/pandas/_testing/__init__.py @@ -0,0 +1,471 @@ +from functools import wraps +import os +from typing import Callable, List, Type +import warnings + +import numpy as np +from numpy.random import rand + +from pandas._config.localization import ( # noqa:F401 + can_set_locale, + get_locales, + set_locale, +) + +from pandas.compat import _get_lzma_file, _import_lzma + +from pandas.core.dtypes.common import ( + is_datetime64_dtype, + is_datetime64tz_dtype, + is_period_dtype, + is_timedelta64_dtype, +) + +import pandas as pd +from pandas import Categorical, DataFrame, Series +from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray, period_array + +from .asserters import ( # noqa:F401 + assert_almost_equal, + assert_attr_equal, + assert_categorical_equal, + assert_class_equal, + assert_contains_all, + assert_copy, + assert_datetime_array_equal, + assert_dict_equal, + assert_equal, + assert_extension_array_equal, + assert_frame_equal, + assert_index_equal, + assert_interval_array_equal, + assert_is_sorted, + assert_numpy_array_equal, + assert_period_array_equal, + assert_series_equal, + assert_sp_array_equal, + assert_timedelta_array_equal, + raise_assert_detail, +) +from .contexts import ( # noqa:F401 + RNGContext, + assert_produces_warning, + decompress_file, + ensure_clean, + ensure_clean_dir, + ensure_safe_environment_variables, + set_timezone, + use_numexpr, + with_csv_dialect, +) +from .graphics import assert_is_valid_plot_return_object, close # noqa:F401 +from .makers import ( # noqa:F401 + _create_missing_idx, + _make_timeseries, + getMixedTypeDict, + getPeriodData, + getSeriesData, + getTimeSeriesData, + makeBoolIndex, + makeCategoricalIndex, + makeCustomDataframe, + makeCustomIndex, + makeDataFrame, + makeDateIndex, + makeFloatIndex, + makeFloatSeries, + makeIntervalIndex, + makeIntIndex, + makeMissingCustomDataframe, + makeMissingDataframe, + makeMixedDataFrame, + makeMultiIndex, + makeObjectSeries, + makePeriodFrame, + makePeriodIndex, + makePeriodSeries, + makeRangeIndex, + makeStringIndex, + makeStringSeries, + makeTimeDataFrame, + makeTimedeltaIndex, + makeTimeSeries, + makeUIntIndex, + makeUnicodeIndex, + rands, + rands_array, + randu, + randu_array, +) +from .network import ( # noqa:F401 + can_connect, + network, + optional_args, + with_connectivity_check, +) +from .roundtrips import ( # noqa:F401 + round_trip_localpath, + round_trip_pathlib, + round_trip_pickle, +) + +lzma = _import_lzma() + + +# set testing_mode +_testing_mode_warnings = (DeprecationWarning, ResourceWarning) + + +def set_testing_mode(): + # set the testing mode filters + testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None") + if "deprecate" in testing_mode: + warnings.simplefilter("always", _testing_mode_warnings) + + +def reset_testing_mode(): + # reset the testing mode filters + testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None") + if "deprecate" in testing_mode: + warnings.simplefilter("ignore", _testing_mode_warnings) + + +set_testing_mode() + + +def reset_display_options(): + """ + Reset the display options for printing and representing objects. + """ + pd.reset_option("^display.", silent=True) + + +def write_to_compressed(compression, path, data, dest="test"): + """ + Write data to a compressed file. + + Parameters + ---------- + compression : {'gzip', 'bz2', 'zip', 'xz'} + The compression type to use. + path : str + The file path to write the data. + data : str + The data to write. + dest : str, default "test" + The destination file (for ZIP only) + + Raises + ------ + ValueError : An invalid compression value was passed in. + """ + if compression == "zip": + import zipfile + + compress_method = zipfile.ZipFile + elif compression == "gzip": + import gzip + + compress_method = gzip.GzipFile + elif compression == "bz2": + import bz2 + + compress_method = bz2.BZ2File + elif compression == "xz": + compress_method = _get_lzma_file(lzma) + else: + raise ValueError(f"Unrecognized compression type: {compression}") + + if compression == "zip": + mode = "w" + args = (dest, data) + method = "writestr" + else: + mode = "wb" + args = (data,) + method = "write" + + with compress_method(path, mode=mode) as f: + getattr(f, method)(*args) + + +def randbool(size=(), p: float = 0.5): + return rand(*size) <= p + + +# ----------------------------------------------------------------------------- +# Comparators + + +def equalContents(arr1, arr2) -> bool: + """ + Checks if the set of unique elements of arr1 and arr2 are equivalent. + """ + return frozenset(arr1) == frozenset(arr2) + + +def isiterable(obj): + return hasattr(obj, "__iter__") + + +def box_expected(expected, box_cls, transpose=True): + """ + Helper function to wrap the expected output of a test in a given box_class. + + Parameters + ---------- + expected : np.ndarray, Index, Series + box_cls : {Index, Series, DataFrame} + + Returns + ------- + subclass of box_cls + """ + if box_cls is pd.Index: + expected = pd.Index(expected) + elif box_cls is pd.Series: + expected = pd.Series(expected) + elif box_cls is pd.DataFrame: + expected = pd.Series(expected).to_frame() + if transpose: + # for vector operations, we we need a DataFrame to be a single-row, + # not a single-column, in order to operate against non-DataFrame + # vectors of the same length. + expected = expected.T + elif box_cls is PeriodArray: + # the PeriodArray constructor is not as flexible as period_array + expected = period_array(expected) + elif box_cls is DatetimeArray: + expected = DatetimeArray(expected) + elif box_cls is TimedeltaArray: + expected = TimedeltaArray(expected) + elif box_cls is np.ndarray: + expected = np.array(expected) + elif box_cls is to_array: + expected = to_array(expected) + else: + raise NotImplementedError(box_cls) + return expected + + +def to_array(obj): + # temporary implementation until we get pd.array in place + if is_period_dtype(obj): + return period_array(obj) + elif is_datetime64_dtype(obj) or is_datetime64tz_dtype(obj): + return DatetimeArray._from_sequence(obj) + elif is_timedelta64_dtype(obj): + return TimedeltaArray._from_sequence(obj) + else: + return np.array(obj) + + +# ----------------------------------------------------------------------------- +# Others + + +def all_index_generator(k=10): + """ + Generator which can be iterated over to get instances of all the various + index classes. + + Parameters + ---------- + k: length of each of the index instances + """ + all_make_index_funcs = [ + makeIntIndex, + makeFloatIndex, + makeStringIndex, + makeUnicodeIndex, + makeDateIndex, + makePeriodIndex, + makeTimedeltaIndex, + makeBoolIndex, + makeRangeIndex, + makeIntervalIndex, + makeCategoricalIndex, + ] + for make_index_func in all_make_index_funcs: + yield make_index_func(k=k) + + +def index_subclass_makers_generator(): + make_index_funcs = [ + makeDateIndex, + makePeriodIndex, + makeTimedeltaIndex, + makeRangeIndex, + makeIntervalIndex, + makeCategoricalIndex, + makeMultiIndex, + ] + for make_index_func in make_index_funcs: + yield make_index_func + + +def all_timeseries_index_generator(k=10): + """ + Generator which can be iterated over to get instances of all the classes + which represent time-series. + + Parameters + ---------- + k: length of each of the index instances + """ + make_index_funcs = [makeDateIndex, makePeriodIndex, makeTimedeltaIndex] + for make_index_func in make_index_funcs: + yield make_index_func(k=k) + + +def test_parallel(num_threads=2, kwargs_list=None): + """ + Decorator to run the same function multiple times in parallel. + + Parameters + ---------- + num_threads : int, optional + The number of times the function is run in parallel. + kwargs_list : list of dicts, optional + The list of kwargs to update original + function kwargs on different threads. + + Notes + ----- + This decorator does not pass the return value of the decorated function. + + Original from scikit-image: + + https://github.com/scikit-image/scikit-image/pull/1519 + + """ + assert num_threads > 0 + has_kwargs_list = kwargs_list is not None + if has_kwargs_list: + assert len(kwargs_list) == num_threads + import threading + + def wrapper(func): + @wraps(func) + def inner(*args, **kwargs): + if has_kwargs_list: + update_kwargs = lambda i: dict(kwargs, **kwargs_list[i]) + else: + update_kwargs = lambda i: kwargs + threads = [] + for i in range(num_threads): + updated_kwargs = update_kwargs(i) + thread = threading.Thread(target=func, args=args, kwargs=updated_kwargs) + threads.append(thread) + for thread in threads: + thread.start() + for thread in threads: + thread.join() + + return inner + + return wrapper + + +class SubclassedSeries(Series): + _metadata = ["testattr", "name"] + + @property + def _constructor(self): + return SubclassedSeries + + @property + def _constructor_expanddim(self): + return SubclassedDataFrame + + +class SubclassedDataFrame(DataFrame): + _metadata = ["testattr"] + + @property + def _constructor(self): + return SubclassedDataFrame + + @property + def _constructor_sliced(self): + return SubclassedSeries + + +class SubclassedCategorical(Categorical): + @property + def _constructor(self): + return SubclassedCategorical + + +def _make_skipna_wrapper(alternative, skipna_alternative=None): + """ + Create a function for calling on an array. + + Parameters + ---------- + alternative : function + The function to be called on the array with no NaNs. + Only used when 'skipna_alternative' is None. + skipna_alternative : function + The function to be called on the original array + + Returns + ------- + function + """ + if skipna_alternative: + + def skipna_wrapper(x): + return skipna_alternative(x.values) + + else: + + def skipna_wrapper(x): + nona = x.dropna() + if len(nona) == 0: + return np.nan + return alternative(nona) + + return skipna_wrapper + + +def convert_rows_list_to_csv_str(rows_list: List[str]): + """ + Convert list of CSV rows to single CSV-formatted string for current OS. + + This method is used for creating expected value of to_csv() method. + + Parameters + ---------- + rows_list : List[str] + Each element represents the row of csv. + + Returns + ------- + str + Expected output of to_csv() in current OS. + """ + sep = os.linesep + expected = sep.join(rows_list) + sep + return expected + + +def external_error_raised( + expected_exception: Type[Exception], +) -> Callable[[Type[Exception], None], None]: + """ + Helper function to mark pytest.raises that have an external error message. + + Parameters + ---------- + expected_exception : Exception + Expected error to raise. + + Returns + ------- + Callable + Regular `pytest.raises` function with `match` equal to `None`. + """ + import pytest + + return pytest.raises(expected_exception, match=None) diff --git a/pandas/_testing/asserters.py b/pandas/_testing/asserters.py new file mode 100644 index 0000000000000..726b7bff9527a --- /dev/null +++ b/pandas/_testing/asserters.py @@ -0,0 +1,1073 @@ +""" +Helpers for making specific test assertions. +""" +from typing import Union, cast + +import numpy as np + +import pandas._libs.testing as _testing + +from pandas.core.dtypes.common import ( + is_bool, + is_categorical_dtype, + is_datetime64tz_dtype, + is_extension_array_dtype, + is_interval_dtype, + is_number, + needs_i8_conversion, +) +from pandas.core.dtypes.missing import array_equivalent + +import pandas as pd +from pandas import Categorical, DataFrame, Index, MultiIndex, Series +from pandas.core.algorithms import take_1d +from pandas.core.arrays import ( + DatetimeArray, + ExtensionArray, + IntervalArray, + PeriodArray, + TimedeltaArray, +) + +from pandas.io.formats.printing import pprint_thing + + +def _check_isinstance(left, right, cls): + """ + Helper method for our assert_* methods that ensures that + the two objects being compared have the right type before + proceeding with the comparison. + + Parameters + ---------- + left : The first object being compared. + right : The second object being compared. + cls : The class type to check against. + + Raises + ------ + AssertionError : Either `left` or `right` is not an instance of `cls`. + """ + cls_name = cls.__name__ + + if not isinstance(left, cls): + raise AssertionError( + f"{cls_name} Expected type {cls}, found {type(left)} instead" + ) + if not isinstance(right, cls): + raise AssertionError( + f"{cls_name} Expected type {cls}, found {type(right)} instead" + ) + + +def assert_almost_equal( + left, + right, + check_dtype: Union[bool, str] = "equiv", + check_less_precise: Union[bool, int] = False, + **kwargs, +): + """ + Check that the left and right objects are approximately equal. + + By approximately equal, we refer to objects that are numbers or that + contain numbers which may be equivalent to specific levels of precision. + + Parameters + ---------- + left : object + right : object + check_dtype : bool or {'equiv'}, default 'equiv' + Check dtype if both a and b are the same type. If 'equiv' is passed in, + then `RangeIndex` and `Int64Index` are also considered equivalent + when doing type checking. + check_less_precise : bool or int, default False + Specify comparison precision. 5 digits (False) or 3 digits (True) + after decimal points are compared. If int, then specify the number + of digits to compare. + + When comparing two numbers, if the first number has magnitude less + than 1e-5, we compare the two numbers directly and check whether + they are equivalent within the specified precision. Otherwise, we + compare the **ratio** of the second number to the first number and + check whether it is equivalent to 1 within the specified precision. + """ + if isinstance(left, pd.Index): + assert_index_equal( + left, + right, + check_exact=False, + exact=check_dtype, + check_less_precise=check_less_precise, + **kwargs, + ) + + elif isinstance(left, pd.Series): + assert_series_equal( + left, + right, + check_exact=False, + check_dtype=check_dtype, + check_less_precise=check_less_precise, + **kwargs, + ) + + elif isinstance(left, pd.DataFrame): + assert_frame_equal( + left, + right, + check_exact=False, + check_dtype=check_dtype, + check_less_precise=check_less_precise, + **kwargs, + ) + + else: + # Other sequences. + if check_dtype: + if is_number(left) and is_number(right): + # Do not compare numeric classes, like np.float64 and float. + pass + elif is_bool(left) and is_bool(right): + # Do not compare bool classes, like np.bool_ and bool. + pass + else: + if isinstance(left, np.ndarray) or isinstance(right, np.ndarray): + obj = "numpy array" + else: + obj = "Input" + assert_class_equal(left, right, obj=obj) + _testing.assert_almost_equal( + left, + right, + check_dtype=check_dtype, + check_less_precise=check_less_precise, + **kwargs, + ) + + +def assert_dict_equal(left, right, compare_keys: bool = True): + + _check_isinstance(left, right, dict) + _testing.assert_dict_equal(left, right, compare_keys=compare_keys) + + +def assert_index_equal( + left: Index, + right: Index, + exact: Union[bool, str] = "equiv", + check_names: bool = True, + check_less_precise: Union[bool, int] = False, + check_exact: bool = True, + check_categorical: bool = True, + obj: str = "Index", +) -> None: + """ + Check that left and right Index are equal. + + Parameters + ---------- + left : Index + right : Index + exact : bool or {'equiv'}, default 'equiv' + Whether to check the Index class, dtype and inferred_type + are identical. If 'equiv', then RangeIndex can be substituted for + Int64Index as well. + check_names : bool, default True + Whether to check the names attribute. + check_less_precise : bool or int, default False + Specify comparison precision. Only used when check_exact is False. + 5 digits (False) or 3 digits (True) after decimal points are compared. + If int, then specify the digits to compare. + check_exact : bool, default True + Whether to compare number exactly. + check_categorical : bool, default True + Whether to compare internal Categorical exactly. + obj : str, default 'Index' + Specify object name being compared, internally used to show appropriate + assertion message. + """ + __tracebackhide__ = True + + def _check_types(l, r, obj="Index"): + if exact: + assert_class_equal(l, r, exact=exact, obj=obj) + + # Skip exact dtype checking when `check_categorical` is False + if check_categorical: + assert_attr_equal("dtype", l, r, obj=obj) + + # allow string-like to have different inferred_types + if l.inferred_type in ("string"): + assert r.inferred_type in ("string") + else: + assert_attr_equal("inferred_type", l, r, obj=obj) + + def _get_ilevel_values(index, level): + # accept level number only + unique = index.levels[level] + level_codes = index.codes[level] + filled = take_1d(unique._values, level_codes, fill_value=unique._na_value) + values = unique._shallow_copy(filled, name=index.names[level]) + return values + + # instance validation + _check_isinstance(left, right, Index) + + # class / dtype comparison + _check_types(left, right, obj=obj) + + # level comparison + if left.nlevels != right.nlevels: + msg1 = f"{obj} levels are different" + msg2 = f"{left.nlevels}, {left}" + msg3 = f"{right.nlevels}, {right}" + raise_assert_detail(obj, msg1, msg2, msg3) + + # length comparison + if len(left) != len(right): + msg1 = f"{obj} length are different" + msg2 = f"{len(left)}, {left}" + msg3 = f"{len(right)}, {right}" + raise_assert_detail(obj, msg1, msg2, msg3) + + # MultiIndex special comparison for little-friendly error messages + if left.nlevels > 1: + left = cast(MultiIndex, left) + right = cast(MultiIndex, right) + + for level in range(left.nlevels): + # cannot use get_level_values here because it can change dtype + llevel = _get_ilevel_values(left, level) + rlevel = _get_ilevel_values(right, level) + + lobj = f"MultiIndex level [{level}]" + assert_index_equal( + llevel, + rlevel, + exact=exact, + check_names=check_names, + check_less_precise=check_less_precise, + check_exact=check_exact, + obj=lobj, + ) + # get_level_values may change dtype + _check_types(left.levels[level], right.levels[level], obj=obj) + + # skip exact index checking when `check_categorical` is False + if check_exact and check_categorical: + if not left.equals(right): + diff = np.sum((left.values != right.values).astype(int)) * 100.0 / len(left) + msg = f"{obj} values are different ({np.round(diff, 5)} %)" + raise_assert_detail(obj, msg, left, right) + else: + _testing.assert_almost_equal( + left.values, + right.values, + check_less_precise=check_less_precise, + check_dtype=exact, + obj=obj, + lobj=left, + robj=right, + ) + + # metadata comparison + if check_names: + assert_attr_equal("names", left, right, obj=obj) + if isinstance(left, pd.PeriodIndex) or isinstance(right, pd.PeriodIndex): + assert_attr_equal("freq", left, right, obj=obj) + if isinstance(left, pd.IntervalIndex) or isinstance(right, pd.IntervalIndex): + assert_interval_array_equal(left.values, right.values) + + if check_categorical: + if is_categorical_dtype(left) or is_categorical_dtype(right): + assert_categorical_equal(left.values, right.values, obj=f"{obj} category") + + +def assert_class_equal(left, right, exact: Union[bool, str] = True, obj="Input"): + """ + Checks classes are equal. + """ + __tracebackhide__ = True + + def repr_class(x): + if isinstance(x, Index): + # return Index as it is to include values in the error message + return x + + try: + return type(x).__name__ + except AttributeError: + return repr(type(x)) + + if exact == "equiv": + if type(left) != type(right): + # allow equivalence of Int64Index/RangeIndex + types = {type(left).__name__, type(right).__name__} + if len(types - {"Int64Index", "RangeIndex"}): + msg = f"{obj} classes are not equivalent" + raise_assert_detail(obj, msg, repr_class(left), repr_class(right)) + elif exact: + if type(left) != type(right): + msg = f"{obj} classes are different" + raise_assert_detail(obj, msg, repr_class(left), repr_class(right)) + + +def assert_attr_equal(attr, left, right, obj="Attributes"): + """ + checks attributes are equal. Both objects must have attribute. + + Parameters + ---------- + attr : str + Attribute name being compared. + left : object + right : object + obj : str, default 'Attributes' + Specify object name being compared, internally used to show appropriate + assertion message + """ + __tracebackhide__ = True + + left_attr = getattr(left, attr) + right_attr = getattr(right, attr) + + if left_attr is right_attr: + return True + elif ( + is_number(left_attr) + and np.isnan(left_attr) + and is_number(right_attr) + and np.isnan(right_attr) + ): + # np.nan + return True + + try: + result = left_attr == right_attr + except TypeError: + # datetimetz on rhs may raise TypeError + result = False + if not isinstance(result, bool): + result = result.all() + + if result: + return True + else: + msg = f'Attribute "{attr}" are different' + raise_assert_detail(obj, msg, left_attr, right_attr) + + +def assert_categorical_equal( + left, right, check_dtype=True, check_category_order=True, obj="Categorical" +): + """ + Test that Categoricals are equivalent. + + Parameters + ---------- + left : Categorical + right : Categorical + check_dtype : bool, default True + Check that integer dtype of the codes are the same + check_category_order : bool, default True + Whether the order of the categories should be compared, which + implies identical integer codes. If False, only the resulting + values are compared. The ordered attribute is + checked regardless. + obj : str, default 'Categorical' + Specify object name being compared, internally used to show appropriate + assertion message + """ + _check_isinstance(left, right, Categorical) + + if check_category_order: + assert_index_equal(left.categories, right.categories, obj=f"{obj}.categories") + assert_numpy_array_equal( + left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes", + ) + else: + assert_index_equal( + left.categories.sort_values(), + right.categories.sort_values(), + obj=f"{obj}.categories", + ) + assert_index_equal( + left.categories.take(left.codes), + right.categories.take(right.codes), + obj=f"{obj}.values", + ) + + assert_attr_equal("ordered", left, right, obj=obj) + + +def assert_interval_array_equal(left, right, exact="equiv", obj="IntervalArray"): + """ + Test that two IntervalArrays are equivalent. + + Parameters + ---------- + left, right : IntervalArray + The IntervalArrays to compare. + exact : bool or {'equiv'}, default 'equiv' + Whether to check the Index class, dtype and inferred_type + are identical. If 'equiv', then RangeIndex can be substituted for + Int64Index as well. + obj : str, default 'IntervalArray' + Specify object name being compared, internally used to show appropriate + assertion message + """ + _check_isinstance(left, right, IntervalArray) + + assert_index_equal(left.left, right.left, exact=exact, obj=f"{obj}.left") + assert_index_equal(left.right, right.right, exact=exact, obj=f"{obj}.left") + assert_attr_equal("closed", left, right, obj=obj) + + +def assert_period_array_equal(left, right, obj="PeriodArray"): + _check_isinstance(left, right, PeriodArray) + + assert_numpy_array_equal(left._data, right._data, obj=f"{obj}.values") + assert_attr_equal("freq", left, right, obj=obj) + + +def assert_datetime_array_equal(left, right, obj="DatetimeArray"): + __tracebackhide__ = True + _check_isinstance(left, right, DatetimeArray) + + assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data") + assert_attr_equal("freq", left, right, obj=obj) + assert_attr_equal("tz", left, right, obj=obj) + + +def assert_timedelta_array_equal(left, right, obj="TimedeltaArray"): + __tracebackhide__ = True + _check_isinstance(left, right, TimedeltaArray) + assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data") + assert_attr_equal("freq", left, right, obj=obj) + + +def assert_sp_array_equal( + left, + right, + check_dtype=True, + check_kind=True, + check_fill_value=True, + consolidate_block_indices=False, +): + """ + Check that the left and right SparseArray are equal. + + Parameters + ---------- + left : SparseArray + right : SparseArray + check_dtype : bool, default True + Whether to check the data dtype is identical. + check_kind : bool, default True + Whether to just the kind of the sparse index for each column. + check_fill_value : bool, default True + Whether to check that left.fill_value matches right.fill_value + consolidate_block_indices : bool, default False + Whether to consolidate contiguous blocks for sparse arrays with + a BlockIndex. Some operations, e.g. concat, will end up with + block indices that could be consolidated. Setting this to true will + create a new BlockIndex for that array, with consolidated + block indices. + """ + _check_isinstance(left, right, pd.arrays.SparseArray) + + assert_numpy_array_equal(left.sp_values, right.sp_values, check_dtype=check_dtype) + + # SparseIndex comparison + assert isinstance(left.sp_index, pd._libs.sparse.SparseIndex) + assert isinstance(right.sp_index, pd._libs.sparse.SparseIndex) + + if not check_kind: + left_index = left.sp_index.to_block_index() + right_index = right.sp_index.to_block_index() + else: + left_index = left.sp_index + right_index = right.sp_index + + if consolidate_block_indices and left.kind == "block": + # we'll probably remove this hack... + left_index = left_index.to_int_index().to_block_index() + right_index = right_index.to_int_index().to_block_index() + + if not left_index.equals(right_index): + raise_assert_detail( + "SparseArray.index", "index are not equal", left_index, right_index + ) + else: + # Just ensure a + pass + + if check_fill_value: + assert_attr_equal("fill_value", left, right) + if check_dtype: + assert_attr_equal("dtype", left, right) + assert_numpy_array_equal(left.to_dense(), right.to_dense(), check_dtype=check_dtype) + + +def raise_assert_detail(obj, message, left, right, diff=None): + __tracebackhide__ = True + + if isinstance(left, np.ndarray): + left = pprint_thing(left) + elif is_categorical_dtype(left): + left = repr(left) + + if isinstance(right, np.ndarray): + right = pprint_thing(right) + elif is_categorical_dtype(right): + right = repr(right) + + msg = f"""{obj} are different + +{message} +[left]: {left} +[right]: {right}""" + + if diff is not None: + msg += f"\n[diff]: {diff}" + + raise AssertionError(msg) + + +def assert_numpy_array_equal( + left, + right, + strict_nan=False, + check_dtype=True, + err_msg=None, + check_same=None, + obj="numpy array", +): + """ + Check that 'np.ndarray' is equivalent. + + Parameters + ---------- + left, right : numpy.ndarray or iterable + The two arrays to be compared. + strict_nan : bool, default False + If True, consider NaN and None to be different. + check_dtype : bool, default True + Check dtype if both a and b are np.ndarray. + err_msg : str, default None + If provided, used as assertion message. + check_same : None|'copy'|'same', default None + Ensure left and right refer/do not refer to the same memory area. + obj : str, default 'numpy array' + Specify object name being compared, internally used to show appropriate + assertion message. + """ + __tracebackhide__ = True + + # instance validation + # Show a detailed error message when classes are different + assert_class_equal(left, right, obj=obj) + # both classes must be an np.ndarray + _check_isinstance(left, right, np.ndarray) + + def _get_base(obj): + return obj.base if getattr(obj, "base", None) is not None else obj + + left_base = _get_base(left) + right_base = _get_base(right) + + if check_same == "same": + if left_base is not right_base: + raise AssertionError(f"{repr(left_base)} is not {repr(right_base)}") + elif check_same == "copy": + if left_base is right_base: + raise AssertionError(f"{repr(left_base)} is {repr(right_base)}") + + def _raise(left, right, err_msg): + if err_msg is None: + if left.shape != right.shape: + raise_assert_detail( + obj, f"{obj} shapes are different", left.shape, right.shape, + ) + + diff = 0 + for l, r in zip(left, right): + # count up differences + if not array_equivalent(l, r, strict_nan=strict_nan): + diff += 1 + + diff = diff * 100.0 / left.size + msg = f"{obj} values are different ({np.round(diff, 5)} %)" + raise_assert_detail(obj, msg, left, right) + + raise AssertionError(err_msg) + + # compare shape and values + if not array_equivalent(left, right, strict_nan=strict_nan): + _raise(left, right, err_msg) + + if check_dtype: + if isinstance(left, np.ndarray) and isinstance(right, np.ndarray): + assert_attr_equal("dtype", left, right, obj=obj) + + +def assert_extension_array_equal( + left, right, check_dtype=True, check_less_precise=False, check_exact=False +): + """ + Check that left and right ExtensionArrays are equal. + + Parameters + ---------- + left, right : ExtensionArray + The two arrays to compare. + check_dtype : bool, default True + Whether to check if the ExtensionArray dtypes are identical. + check_less_precise : bool or int, default False + Specify comparison precision. Only used when check_exact is False. + 5 digits (False) or 3 digits (True) after decimal points are compared. + If int, then specify the digits to compare. + check_exact : bool, default False + Whether to compare number exactly. + + Notes + ----- + Missing values are checked separately from valid values. + A mask of missing values is computed for each and checked to match. + The remaining all-valid values are cast to object dtype and checked. + """ + assert isinstance(left, ExtensionArray), "left is not an ExtensionArray" + assert isinstance(right, ExtensionArray), "right is not an ExtensionArray" + if check_dtype: + assert_attr_equal("dtype", left, right, obj="ExtensionArray") + + if hasattr(left, "asi8") and type(right) == type(left): + # Avoid slow object-dtype comparisons + assert_numpy_array_equal(left.asi8, right.asi8) # type: ignore + return + + left_na = np.asarray(left.isna()) + right_na = np.asarray(right.isna()) + assert_numpy_array_equal(left_na, right_na, obj="ExtensionArray NA mask") + + left_valid = np.asarray(left[~left_na].astype(object)) + right_valid = np.asarray(right[~right_na].astype(object)) + if check_exact: + assert_numpy_array_equal(left_valid, right_valid, obj="ExtensionArray") + else: + _testing.assert_almost_equal( + left_valid, + right_valid, + check_dtype=check_dtype, + check_less_precise=check_less_precise, + obj="ExtensionArray", + ) + + +# This could be refactored to use the NDFrame.equals method +def assert_series_equal( + left, + right, + check_dtype=True, + check_index_type="equiv", + check_series_type=True, + check_less_precise=False, + check_names=True, + check_exact=False, + check_datetimelike_compat=False, + check_categorical=True, + check_category_order=True, + obj="Series", +): + """ + Check that left and right Series are equal. + + Parameters + ---------- + left : Series + right : Series + check_dtype : bool, default True + Whether to check the Series dtype is identical. + check_index_type : bool or {'equiv'}, default 'equiv' + Whether to check the Index class, dtype and inferred_type + are identical. + check_series_type : bool, default True + Whether to check the Series class is identical. + check_less_precise : bool or int, default False + Specify comparison precision. Only used when check_exact is False. + 5 digits (False) or 3 digits (True) after decimal points are compared. + If int, then specify the digits to compare. + + When comparing two numbers, if the first number has magnitude less + than 1e-5, we compare the two numbers directly and check whether + they are equivalent within the specified precision. Otherwise, we + compare the **ratio** of the second number to the first number and + check whether it is equivalent to 1 within the specified precision. + check_names : bool, default True + Whether to check the Series and Index names attribute. + check_exact : bool, default False + Whether to compare number exactly. + check_datetimelike_compat : bool, default False + Compare datetime-like which is comparable ignoring dtype. + check_categorical : bool, default True + Whether to compare internal Categorical exactly. + check_category_order : bool, default True + Whether to compare category order of internal Categoricals + + .. versionadded:: 1.0.2 + obj : str, default 'Series' + Specify object name being compared, internally used to show appropriate + assertion message. + """ + __tracebackhide__ = True + + # instance validation + _check_isinstance(left, right, Series) + + if check_series_type: + # ToDo: There are some tests using rhs is sparse + # lhs is dense. Should use assert_class_equal in future + assert isinstance(left, type(right)) + # assert_class_equal(left, right, obj=obj) + + # length comparison + if len(left) != len(right): + msg1 = f"{len(left)}, {left.index}" + msg2 = f"{len(right)}, {right.index}" + raise_assert_detail(obj, "Series length are different", msg1, msg2) + + # index comparison + assert_index_equal( + left.index, + right.index, + exact=check_index_type, + check_names=check_names, + check_less_precise=check_less_precise, + check_exact=check_exact, + check_categorical=check_categorical, + obj=f"{obj}.index", + ) + + if check_dtype: + # We want to skip exact dtype checking when `check_categorical` + # is False. We'll still raise if only one is a `Categorical`, + # regardless of `check_categorical` + if ( + is_categorical_dtype(left) + and is_categorical_dtype(right) + and not check_categorical + ): + pass + else: + assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}") + + if check_exact: + assert_numpy_array_equal( + left._internal_get_values(), + right._internal_get_values(), + check_dtype=check_dtype, + obj=str(obj), + ) + elif check_datetimelike_compat: + # we want to check only if we have compat dtypes + # e.g. integer and M|m are NOT compat, but we can simply check + # the values in that case + if needs_i8_conversion(left) or needs_i8_conversion(right): + + # datetimelike may have different objects (e.g. datetime.datetime + # vs Timestamp) but will compare equal + if not Index(left.values).equals(Index(right.values)): + msg = ( + f"[datetimelike_compat=True] {left.values} " + f"is not equal to {right.values}." + ) + raise AssertionError(msg) + else: + assert_numpy_array_equal( + left._internal_get_values(), + right._internal_get_values(), + check_dtype=check_dtype, + ) + elif is_interval_dtype(left) or is_interval_dtype(right): + assert_interval_array_equal(left.array, right.array) + elif is_extension_array_dtype(left.dtype) and is_datetime64tz_dtype(left.dtype): + # .values is an ndarray, but ._values is the ExtensionArray. + # TODO: Use .array + assert is_extension_array_dtype(right.dtype) + assert_extension_array_equal(left._values, right._values) + elif ( + is_extension_array_dtype(left) + and not is_categorical_dtype(left) + and is_extension_array_dtype(right) + and not is_categorical_dtype(right) + ): + assert_extension_array_equal(left.array, right.array) + else: + _testing.assert_almost_equal( + left._internal_get_values(), + right._internal_get_values(), + check_less_precise=check_less_precise, + check_dtype=check_dtype, + obj=str(obj), + ) + + # metadata comparison + if check_names: + assert_attr_equal("name", left, right, obj=obj) + + if check_categorical: + if is_categorical_dtype(left) or is_categorical_dtype(right): + assert_categorical_equal( + left.values, + right.values, + obj=f"{obj} category", + check_category_order=check_category_order, + ) + + +# This could be refactored to use the NDFrame.equals method +def assert_frame_equal( + left, + right, + check_dtype=True, + check_index_type="equiv", + check_column_type="equiv", + check_frame_type=True, + check_less_precise=False, + check_names=True, + by_blocks=False, + check_exact=False, + check_datetimelike_compat=False, + check_categorical=True, + check_like=False, + obj="DataFrame", +): + """ + Check that left and right DataFrame are equal. + + This function is intended to compare two DataFrames and output any + differences. Is is mostly intended for use in unit tests. + Additional parameters allow varying the strictness of the + equality checks performed. + + Parameters + ---------- + left : DataFrame + First DataFrame to compare. + right : DataFrame + Second DataFrame to compare. + check_dtype : bool, default True + Whether to check the DataFrame dtype is identical. + check_index_type : bool or {'equiv'}, default 'equiv' + Whether to check the Index class, dtype and inferred_type + are identical. + check_column_type : bool or {'equiv'}, default 'equiv' + Whether to check the columns class, dtype and inferred_type + are identical. Is passed as the ``exact`` argument of + :func:`assert_index_equal`. + check_frame_type : bool, default True + Whether to check the DataFrame class is identical. + check_less_precise : bool or int, default False + Specify comparison precision. Only used when check_exact is False. + 5 digits (False) or 3 digits (True) after decimal points are compared. + If int, then specify the digits to compare. + + When comparing two numbers, if the first number has magnitude less + than 1e-5, we compare the two numbers directly and check whether + they are equivalent within the specified precision. Otherwise, we + compare the **ratio** of the second number to the first number and + check whether it is equivalent to 1 within the specified precision. + check_names : bool, default True + Whether to check that the `names` attribute for both the `index` + and `column` attributes of the DataFrame is identical. + by_blocks : bool, default False + Specify how to compare internal data. If False, compare by columns. + If True, compare by blocks. + check_exact : bool, default False + Whether to compare number exactly. + check_datetimelike_compat : bool, default False + Compare datetime-like which is comparable ignoring dtype. + check_categorical : bool, default True + Whether to compare internal Categorical exactly. + check_like : bool, default False + If True, ignore the order of index & columns. + Note: index labels must match their respective rows + (same as in columns) - same labels must be with the same data. + obj : str, default 'DataFrame' + Specify object name being compared, internally used to show appropriate + assertion message. + + See Also + -------- + assert_series_equal : Equivalent method for asserting Series equality. + DataFrame.equals : Check DataFrame equality. + + Examples + -------- + This example shows comparing two DataFrames that are equal + but with columns of differing dtypes. + + >>> from pandas._testing import assert_frame_equal + >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]}) + >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]}) + + df1 equals itself. + + >>> assert_frame_equal(df1, df1) + + df1 differs from df2 as column 'b' is of a different type. + + >>> assert_frame_equal(df1, df2) + Traceback (most recent call last): + ... + AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different + + Attribute "dtype" are different + [left]: int64 + [right]: float64 + + Ignore differing dtypes in columns with check_dtype. + + >>> assert_frame_equal(df1, df2, check_dtype=False) + """ + __tracebackhide__ = True + + # instance validation + _check_isinstance(left, right, DataFrame) + + if check_frame_type: + assert isinstance(left, type(right)) + # assert_class_equal(left, right, obj=obj) + + # shape comparison + if left.shape != right.shape: + raise_assert_detail( + obj, f"{obj} shape mismatch", f"{repr(left.shape)}", f"{repr(right.shape)}", + ) + + if check_like: + left, right = left.reindex_like(right), right + + # index comparison + assert_index_equal( + left.index, + right.index, + exact=check_index_type, + check_names=check_names, + check_less_precise=check_less_precise, + check_exact=check_exact, + check_categorical=check_categorical, + obj=f"{obj}.index", + ) + + # column comparison + assert_index_equal( + left.columns, + right.columns, + exact=check_column_type, + check_names=check_names, + check_less_precise=check_less_precise, + check_exact=check_exact, + check_categorical=check_categorical, + obj=f"{obj}.columns", + ) + + # compare by blocks + if by_blocks: + rblocks = right._to_dict_of_blocks() + lblocks = left._to_dict_of_blocks() + for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))): + assert dtype in lblocks + assert dtype in rblocks + assert_frame_equal( + lblocks[dtype], rblocks[dtype], check_dtype=check_dtype, obj=obj + ) + + # compare by columns + else: + for i, col in enumerate(left.columns): + assert col in right + lcol = left.iloc[:, i] + rcol = right.iloc[:, i] + assert_series_equal( + lcol, + rcol, + check_dtype=check_dtype, + check_index_type=check_index_type, + check_less_precise=check_less_precise, + check_exact=check_exact, + check_names=check_names, + check_datetimelike_compat=check_datetimelike_compat, + check_categorical=check_categorical, + obj=f'{obj}.iloc[:, {i}] (column name="{col}")', + ) + + +def assert_equal(left, right, **kwargs): + """ + Wrapper for tm.assert_*_equal to dispatch to the appropriate test function. + + Parameters + ---------- + left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray + The two items to be compared. + **kwargs + All keyword arguments are passed through to the underlying assert method. + """ + __tracebackhide__ = True + + if isinstance(left, pd.Index): + assert_index_equal(left, right, **kwargs) + elif isinstance(left, pd.Series): + assert_series_equal(left, right, **kwargs) + elif isinstance(left, pd.DataFrame): + assert_frame_equal(left, right, **kwargs) + elif isinstance(left, IntervalArray): + assert_interval_array_equal(left, right, **kwargs) + elif isinstance(left, PeriodArray): + assert_period_array_equal(left, right, **kwargs) + elif isinstance(left, DatetimeArray): + assert_datetime_array_equal(left, right, **kwargs) + elif isinstance(left, TimedeltaArray): + assert_timedelta_array_equal(left, right, **kwargs) + elif isinstance(left, ExtensionArray): + assert_extension_array_equal(left, right, **kwargs) + elif isinstance(left, np.ndarray): + assert_numpy_array_equal(left, right, **kwargs) + elif isinstance(left, str): + assert kwargs == {} + assert left == right + else: + raise NotImplementedError(type(left)) + + +def assert_contains_all(iterable, dic): + for k in iterable: + assert k in dic, f"Did not contain item: {repr(k)}" + + +def assert_copy(iter1, iter2, **eql_kwargs): + """ + iter1, iter2: iterables that produce elements + comparable with assert_almost_equal + + Checks that the elements are equal, but not + the same object. (Does not check that items + in sequences are also not the same object) + """ + for elem1, elem2 in zip(iter1, iter2): + assert_almost_equal(elem1, elem2, **eql_kwargs) + msg = ( + f"Expected object {repr(type(elem1))} and object {repr(type(elem2))} to be " + "different objects, but they were the same object." + ) + assert elem1 is not elem2, msg + + +def assert_is_sorted(seq): + """Assert that the sequence is sorted.""" + if isinstance(seq, (Index, Series)): + seq = seq.values + # sorting does not change precisions + assert_numpy_array_equal(seq, np.sort(np.array(seq))) diff --git a/pandas/_testing/contexts.py b/pandas/_testing/contexts.py new file mode 100644 index 0000000000000..ba516e4287dae --- /dev/null +++ b/pandas/_testing/contexts.py @@ -0,0 +1,410 @@ +import bz2 +from contextlib import contextmanager +import gzip +import os +from shutil import rmtree +import tempfile +import warnings +import zipfile + +import numpy as np + +from pandas.compat import _get_lzma_file, _import_lzma + +from pandas.core.dtypes.common import is_list_like + +lzma = _import_lzma() + + +@contextmanager +def assert_produces_warning( + expected_warning=Warning, + filter_level="always", + clear=None, + check_stacklevel=True, + raise_on_extra_warnings=True, +): + """ + Context manager for running code expected to either raise a specific + warning, or not raise any warnings. Verifies that the code raises the + expected warning, and that it does not raise any other unexpected + warnings. It is basically a wrapper around ``warnings.catch_warnings``. + + Parameters + ---------- + expected_warning : {Warning, False, None}, default Warning + The type of Exception raised. ``exception.Warning`` is the base + class for all warnings. To check that no warning is returned, + specify ``False`` or ``None``. + filter_level : str or None, default "always" + Specifies whether warnings are ignored, displayed, or turned + into errors. + Valid values are: + + * "error" - turns matching warnings into exceptions + * "ignore" - discard the warning + * "always" - always emit a warning + * "default" - print the warning the first time it is generated + from each location + * "module" - print the warning the first time it is generated + from each module + * "once" - print the warning the first time it is generated + + clear : str, default None + If not ``None`` then remove any previously raised warnings from + the ``__warningsregistry__`` to ensure that no warning messages are + suppressed by this context manager. If ``None`` is specified, + the ``__warningsregistry__`` keeps track of which warnings have been + shown, and does not show them again. + check_stacklevel : bool, default True + If True, displays the line that called the function containing + the warning to show were the function is called. Otherwise, the + line that implements the function is displayed. + raise_on_extra_warnings : bool, default True + Whether extra warnings not of the type `expected_warning` should + cause the test to fail. + + Examples + -------- + >>> import warnings + >>> with assert_produces_warning(): + ... warnings.warn(UserWarning()) + ... + >>> with assert_produces_warning(False): + ... warnings.warn(RuntimeWarning()) + ... + Traceback (most recent call last): + ... + AssertionError: Caused unexpected warning(s): ['RuntimeWarning']. + >>> with assert_produces_warning(UserWarning): + ... warnings.warn(RuntimeWarning()) + Traceback (most recent call last): + ... + AssertionError: Did not see expected warning of class 'UserWarning'. + + ..warn:: This is *not* thread-safe. + """ + __tracebackhide__ = True + + with warnings.catch_warnings(record=True) as w: + + if clear is not None: + # make sure that we are clearing these warnings + # if they have happened before + # to guarantee that we will catch them + if not is_list_like(clear): + clear = [clear] + for m in clear: + try: + m.__warningregistry__.clear() + except AttributeError: + # module may not have __warningregistry__ + pass + + saw_warning = False + warnings.simplefilter(filter_level) + yield w + extra_warnings = [] + + for actual_warning in w: + if expected_warning and issubclass( + actual_warning.category, expected_warning + ): + saw_warning = True + + if check_stacklevel and issubclass( + actual_warning.category, (FutureWarning, DeprecationWarning) + ): + from inspect import getframeinfo, stack + + caller = getframeinfo(stack()[2][0]) + msg = ( + "Warning not set with correct stacklevel. " + f"File where warning is raised: {actual_warning.filename} != " + f"{caller.filename}. Warning message: {actual_warning.message}" + ) + assert actual_warning.filename == caller.filename, msg + else: + extra_warnings.append( + ( + actual_warning.category.__name__, + actual_warning.message, + actual_warning.filename, + actual_warning.lineno, + ) + ) + if expected_warning: + msg = ( + f"Did not see expected warning of class " + f"{repr(expected_warning.__name__)}" + ) + assert saw_warning, msg + if raise_on_extra_warnings and extra_warnings: + raise AssertionError( + f"Caused unexpected warning(s): {repr(extra_warnings)}" + ) + + +class RNGContext: + """ + Context manager to set the numpy random number generator speed. Returns + to the original value upon exiting the context manager. + + Parameters + ---------- + seed : int + Seed for numpy.random.seed + + Examples + -------- + with RNGContext(42): + np.random.randn() + """ + + def __init__(self, seed): + self.seed = seed + + def __enter__(self): + + self.start_state = np.random.get_state() + np.random.seed(self.seed) + + def __exit__(self, exc_type, exc_value, traceback): + + np.random.set_state(self.start_state) + + +@contextmanager +def use_numexpr(use, min_elements=None): + from pandas.core.computation import expressions as expr + + if min_elements is None: + min_elements = expr._MIN_ELEMENTS + + olduse = expr._USE_NUMEXPR + oldmin = expr._MIN_ELEMENTS + expr.set_use_numexpr(use) + expr._MIN_ELEMENTS = min_elements + yield + expr._MIN_ELEMENTS = oldmin + expr.set_use_numexpr(olduse) + + +@contextmanager +def decompress_file(path, compression): + """ + Open a compressed file and return a file object. + + Parameters + ---------- + path : str + The path where the file is read from. + + compression : {'gzip', 'bz2', 'zip', 'xz', None} + Name of the decompression to use + + Returns + ------- + file object + """ + if compression is None: + f = open(path, "rb") + elif compression == "gzip": + f = gzip.open(path, "rb") + elif compression == "bz2": + f = bz2.BZ2File(path, "rb") + elif compression == "xz": + f = _get_lzma_file(lzma)(path, "rb") + elif compression == "zip": + zip_file = zipfile.ZipFile(path) + zip_names = zip_file.namelist() + if len(zip_names) == 1: + f = zip_file.open(zip_names.pop()) + else: + raise ValueError(f"ZIP file {path} error. Only one file per ZIP.") + else: + raise ValueError(f"Unrecognized compression type: {compression}") + + try: + yield f + finally: + f.close() + if compression == "zip": + zip_file.close() + + +@contextmanager +def with_csv_dialect(name, **kwargs): + """ + Context manager to temporarily register a CSV dialect for parsing CSV. + + Parameters + ---------- + name : str + The name of the dialect. + kwargs : mapping + The parameters for the dialect. + + Raises + ------ + ValueError : the name of the dialect conflicts with a builtin one. + + See Also + -------- + csv : Python's CSV library. + """ + import csv + + _BUILTIN_DIALECTS = {"excel", "excel-tab", "unix"} + + if name in _BUILTIN_DIALECTS: + raise ValueError("Cannot override builtin dialect.") + + csv.register_dialect(name, **kwargs) + yield + csv.unregister_dialect(name) + + +@contextmanager +def set_timezone(tz: str): + """ + Context manager for temporarily setting a timezone. + + Parameters + ---------- + tz : str + A string representing a valid timezone. + + Examples + -------- + >>> from datetime import datetime + >>> from dateutil.tz import tzlocal + >>> tzlocal().tzname(datetime.now()) + 'IST' + + >>> with set_timezone('US/Eastern'): + ... tzlocal().tzname(datetime.now()) + ... + 'EDT' + """ + import os + import time + + def setTZ(tz): + if tz is None: + try: + del os.environ["TZ"] + except KeyError: + pass + else: + os.environ["TZ"] = tz + time.tzset() + + orig_tz = os.environ.get("TZ") + setTZ(tz) + try: + yield + finally: + setTZ(orig_tz) + + +# ----------------------------------------------------------------------------- +# contextmanager to ensure the file cleanup + + +@contextmanager +def ensure_clean(filename=None, return_filelike=False, **kwargs): + """ + Gets a temporary path and agrees to remove on close. + + Parameters + ---------- + filename : str (optional) + if None, creates a temporary file which is then removed when out of + scope. if passed, creates temporary file with filename as ending. + return_filelike : bool (default False) + if True, returns a file-like which is *always* cleaned. Necessary for + savefig and other functions which want to append extensions. + **kwargs + Additional keywords passed in for creating a temporary file. + :meth:`tempFile.TemporaryFile` is used when `return_filelike` is ``True``. + :meth:`tempfile.mkstemp` is used when `return_filelike` is ``False``. + Note that the `filename` parameter will be passed in as the `suffix` + argument to either function. + + See Also + -------- + tempfile.TemporaryFile + tempfile.mkstemp + """ + filename = filename or "" + fd = None + + kwargs["suffix"] = filename + + if return_filelike: + f = tempfile.TemporaryFile(**kwargs) + + try: + yield f + finally: + f.close() + else: + # Don't generate tempfile if using a path with directory specified. + if len(os.path.dirname(filename)): + raise ValueError("Can't pass a qualified name to ensure_clean()") + + try: + fd, filename = tempfile.mkstemp(**kwargs) + except UnicodeEncodeError: + import pytest + + pytest.skip("no unicode file names on this system") + + try: + yield filename + finally: + try: + os.close(fd) + except OSError: + print(f"Couldn't close file descriptor: {fd} (file: {filename})") + try: + if os.path.exists(filename): + os.remove(filename) + except OSError as e: + print(f"Exception on removing file: {e}") + + +@contextmanager +def ensure_clean_dir(): + """ + Get a temporary directory path and agrees to remove on close. + + Yields + ------ + Temporary directory path + """ + directory_name = tempfile.mkdtemp(suffix="") + try: + yield directory_name + finally: + try: + rmtree(directory_name) + except OSError: + pass + + +@contextmanager +def ensure_safe_environment_variables(): + """ + Get a context manager to safely set environment variables + + All changes will be undone on close, hence environment variables set + within this contextmanager will neither persist nor change global state. + """ + saved_environ = dict(os.environ) + try: + yield + finally: + os.environ.clear() + os.environ.update(saved_environ) diff --git a/pandas/_testing/graphics.py b/pandas/_testing/graphics.py new file mode 100644 index 0000000000000..899de02e80881 --- /dev/null +++ b/pandas/_testing/graphics.py @@ -0,0 +1,32 @@ +import numpy as np + +import pandas as pd + + +def close(fignum=None): + from matplotlib.pyplot import get_fignums, close as _close + + if fignum is None: + for fignum in get_fignums(): + _close(fignum) + else: + _close(fignum) + + +def assert_is_valid_plot_return_object(objs): + import matplotlib.pyplot as plt + + if isinstance(objs, (pd.Series, np.ndarray)): + for el in objs.ravel(): + msg = ( + "one of 'objs' is not a matplotlib Axes instance, " + f"type encountered {repr(type(el).__name__)}" + ) + assert isinstance(el, (plt.Axes, dict)), msg + else: + msg = ( + "objs is neither an ndarray of Artist instances nor a single " + "ArtistArtist instance, tuple, or dict, 'objs' is a " + f"{repr(type(objs).__name__)}" + ) + assert isinstance(objs, (plt.Artist, tuple, dict)), msg diff --git a/pandas/_testing/makers.py b/pandas/_testing/makers.py new file mode 100644 index 0000000000000..1e63cf5a8351d --- /dev/null +++ b/pandas/_testing/makers.py @@ -0,0 +1,615 @@ +from collections import Counter +from datetime import datetime +import string + +import numpy as np + +from pandas.core.dtypes.common import is_sequence + +import pandas as pd +from pandas import ( + Categorical, + CategoricalIndex, + DataFrame, + DatetimeIndex, + Index, + IntervalIndex, + MultiIndex, + RangeIndex, + Series, + bdate_range, +) + +RANDS_CHARS = np.array(list(string.ascii_letters + string.digits), dtype=(np.str_, 1)) +RANDU_CHARS = np.array( + list("".join(map(chr, range(1488, 1488 + 26))) + string.digits), + dtype=(np.unicode_, 1), +) + +N = 30 +K = 4 + + +def getCols(k): + return string.ascii_uppercase[:k] + + +def rands(nchars): + """ + Generate one random byte string. + + See `rands_array` if you want to create an array of random strings. + + """ + return "".join(np.random.choice(RANDS_CHARS, nchars)) + + +def randu(nchars): + """ + Generate one random unicode string. + + See `randu_array` if you want to create an array of random unicode strings. + + """ + return "".join(np.random.choice(RANDU_CHARS, nchars)) + + +def rands_array(nchars, size, dtype="O"): + """ + Generate an array of byte strings. + """ + retval = ( + np.random.choice(RANDS_CHARS, size=nchars * np.prod(size)) + .view((np.str_, nchars)) + .reshape(size) + ) + if dtype is None: + return retval + else: + return retval.astype(dtype) + + +def randu_array(nchars, size, dtype="O"): + """ + Generate an array of unicode strings. + """ + retval = ( + np.random.choice(RANDU_CHARS, size=nchars * np.prod(size)) + .view((np.unicode_, nchars)) + .reshape(size) + ) + if dtype is None: + return retval + else: + return retval.astype(dtype) + + +# ---------------------------------------------------------------------- + +_names = [ + "Alice", + "Bob", + "Charlie", + "Dan", + "Edith", + "Frank", + "George", + "Hannah", + "Ingrid", + "Jerry", + "Kevin", + "Laura", + "Michael", + "Norbert", + "Oliver", + "Patricia", + "Quinn", + "Ray", + "Sarah", + "Tim", + "Ursula", + "Victor", + "Wendy", + "Xavier", + "Yvonne", + "Zelda", +] + + +def _make_timeseries(start="2000-01-01", end="2000-12-31", freq="1D", seed=None): + """ + Make a DataFrame with a DatetimeIndex + + Parameters + ---------- + start : str or Timestamp, default "2000-01-01" + The start of the index. Passed to date_range with `freq`. + end : str or Timestamp, default "2000-12-31" + The end of the index. Passed to date_range with `freq`. + freq : str or Freq + The frequency to use for the DatetimeIndex + seed : int, optional + The random state seed. + + * name : object dtype with string names + * id : int dtype with + * x, y : float dtype + + Examples + -------- + >>> _make_timeseries() + id name x y + timestamp + 2000-01-01 982 Frank 0.031261 0.986727 + 2000-01-02 1025 Edith -0.086358 -0.032920 + 2000-01-03 982 Edith 0.473177 0.298654 + 2000-01-04 1009 Sarah 0.534344 -0.750377 + 2000-01-05 963 Zelda -0.271573 0.054424 + ... ... ... ... ... + 2000-12-27 980 Ingrid -0.132333 -0.422195 + 2000-12-28 972 Frank -0.376007 -0.298687 + 2000-12-29 1009 Ursula -0.865047 -0.503133 + 2000-12-30 1000 Hannah -0.063757 -0.507336 + 2000-12-31 972 Tim -0.869120 0.531685 + """ + index = pd.date_range(start=start, end=end, freq=freq, name="timestamp") + n = len(index) + state = np.random.RandomState(seed) + columns = { + "name": state.choice(_names, size=n), + "id": state.poisson(1000, size=n), + "x": state.rand(n) * 2 - 1, + "y": state.rand(n) * 2 - 1, + } + df = pd.DataFrame(columns, index=index, columns=sorted(columns)) + if df.index[-1] == end: + df = df.iloc[:-1] + return df + + +# ---------------------------------------------------------------------- + + +def _create_missing_idx(nrows, ncols, density, random_state=None): + if random_state is None: + random_state = np.random + else: + random_state = np.random.RandomState(random_state) + + # below is cribbed from scipy.sparse + size = int(np.round((1 - density) * nrows * ncols)) + # generate a few more to ensure unique values + min_rows = 5 + fac = 1.02 + extra_size = min(size + min_rows, fac * size) + + def _gen_unique_rand(rng, _extra_size): + ind = rng.rand(int(_extra_size)) + return np.unique(np.floor(ind * nrows * ncols))[:size] + + ind = _gen_unique_rand(random_state, extra_size) + while ind.size < size: + extra_size *= 1.05 + ind = _gen_unique_rand(random_state, extra_size) + + j = np.floor(ind * 1.0 / nrows).astype(int) + i = (ind - j * nrows).astype(int) + return i.tolist(), j.tolist() + + +# ---------------------------------------------------------------------- +# Make Indexes + + +def makeStringIndex(k=10, name=None): + return Index(rands_array(nchars=10, size=k), name=name) + + +def makeUnicodeIndex(k=10, name=None): + return Index(randu_array(nchars=10, size=k), name=name) + + +def makeCategoricalIndex(k=10, n=3, name=None, **kwargs): + """ make a length k index or n categories """ + x = rands_array(nchars=4, size=n) + return CategoricalIndex( + Categorical.from_codes(np.arange(k) % n, categories=x), name=name, **kwargs + ) + + +def makeIntervalIndex(k=10, name=None, **kwargs): + """ make a length k IntervalIndex """ + x = np.linspace(0, 100, num=(k + 1)) + return IntervalIndex.from_breaks(x, name=name, **kwargs) + + +def makeBoolIndex(k=10, name=None): + if k == 1: + return Index([True], name=name) + elif k == 2: + return Index([False, True], name=name) + return Index([False, True] + [False] * (k - 2), name=name) + + +def makeIntIndex(k=10, name=None): + return Index(list(range(k)), name=name) + + +def makeUIntIndex(k=10, name=None): + return Index([2 ** 63 + i for i in range(k)], name=name) + + +def makeRangeIndex(k=10, name=None, **kwargs): + return RangeIndex(0, k, 1, name=name, **kwargs) + + +def makeFloatIndex(k=10, name=None): + values = sorted(np.random.random_sample(k)) - np.random.random_sample(1) + return Index(values * (10 ** np.random.randint(0, 9)), name=name) + + +def makeDateIndex(k=10, freq="B", name=None, **kwargs): + dt = datetime(2000, 1, 1) + dr = bdate_range(dt, periods=k, freq=freq, name=name) + return DatetimeIndex(dr, name=name, **kwargs) + + +def makeTimedeltaIndex(k=10, freq="D", name=None, **kwargs): + return pd.timedelta_range(start="1 day", periods=k, freq=freq, name=name, **kwargs) + + +def makePeriodIndex(k=10, name=None, **kwargs): + dt = datetime(2000, 1, 1) + dr = pd.period_range(start=dt, periods=k, freq="B", name=name, **kwargs) + return dr + + +def makeMultiIndex(k=10, names=None, **kwargs): + return MultiIndex.from_product((("foo", "bar"), (1, 2)), names=names, **kwargs) + + +# ---------------------------------------------------------------------- +# Make Series + + +def makeFloatSeries(name=None): + index = makeStringIndex(N) + return Series(np.random.randn(N), index=index, name=name) + + +def makeStringSeries(name=None): + index = makeStringIndex(N) + return Series(np.random.randn(N), index=index, name=name) + + +def makeObjectSeries(name=None): + data = makeStringIndex(N) + data = Index(data, dtype=object) + index = makeStringIndex(N) + return Series(data, index=index, name=name) + + +def getSeriesData(): + index = makeStringIndex(N) + return {c: Series(np.random.randn(N), index=index) for c in getCols(K)} + + +def makeTimeSeries(nper=None, freq="B", name=None): + if nper is None: + nper = N + return Series( + np.random.randn(nper), index=makeDateIndex(nper, freq=freq), name=name + ) + + +def makePeriodSeries(nper=None, name=None): + if nper is None: + nper = N + return Series(np.random.randn(nper), index=makePeriodIndex(nper), name=name) + + +def getTimeSeriesData(nper=None, freq="B"): + return {c: makeTimeSeries(nper, freq) for c in getCols(K)} + + +def getPeriodData(nper=None): + return {c: makePeriodSeries(nper) for c in getCols(K)} + + +# make frame +def makeTimeDataFrame(nper=None, freq="B"): + data = getTimeSeriesData(nper, freq) + return DataFrame(data) + + +def makeDataFrame(): + data = getSeriesData() + return DataFrame(data) + + +def getMixedTypeDict(): + index = Index(["a", "b", "c", "d", "e"]) + + data = { + "A": [0.0, 1.0, 2.0, 3.0, 4.0], + "B": [0.0, 1.0, 0.0, 1.0, 0.0], + "C": ["foo1", "foo2", "foo3", "foo4", "foo5"], + "D": bdate_range("1/1/2009", periods=5), + } + + return index, data + + +def makeMixedDataFrame(): + return DataFrame(getMixedTypeDict()[1]) + + +def makePeriodFrame(nper=None): + data = getPeriodData(nper) + return DataFrame(data) + + +def makeCustomIndex( + nentries, nlevels, prefix="#", names=False, ndupe_l=None, idx_type=None +): + """ + Create an index/multindex with given dimensions, levels, names, etc' + + nentries - number of entries in index + nlevels - number of levels (> 1 produces multindex) + prefix - a string prefix for labels + names - (Optional), bool or list of strings. if True will use default + names, if false will use no names, if a list is given, the name of + each level in the index will be taken from the list. + ndupe_l - (Optional), list of ints, the number of rows for which the + label will repeated at the corresponding level, you can specify just + the first few, the rest will use the default ndupe_l of 1. + len(ndupe_l) <= nlevels. + idx_type - "i"/"f"/"s"/"u"/"dt"/"p"/"td". + If idx_type is not None, `idx_nlevels` must be 1. + "i"/"f" creates an integer/float index, + "s"/"u" creates a string/unicode index + "dt" create a datetime index. + "td" create a datetime index. + + if unspecified, string labels will be generated. + """ + if ndupe_l is None: + ndupe_l = [1] * nlevels + assert is_sequence(ndupe_l) and len(ndupe_l) <= nlevels + assert names is None or names is False or names is True or len(names) is nlevels + assert idx_type is None or ( + idx_type in ("i", "f", "s", "u", "dt", "p", "td") and nlevels == 1 + ) + + if names is True: + # build default names + names = [prefix + str(i) for i in range(nlevels)] + if names is False: + # pass None to index constructor for no name + names = None + + # make singleton case uniform + if isinstance(names, str) and nlevels == 1: + names = [names] + + # specific 1D index type requested? + idx_func = dict( + i=makeIntIndex, + f=makeFloatIndex, + s=makeStringIndex, + u=makeUnicodeIndex, + dt=makeDateIndex, + td=makeTimedeltaIndex, + p=makePeriodIndex, + ).get(idx_type) + if idx_func: + idx = idx_func(nentries) + # but we need to fill in the name + if names: + idx.name = names[0] + return idx + elif idx_type is not None: + raise ValueError( + f"{repr(idx_type)} is not a legal value for `idx_type`, " + "use 'i'/'f'/'s'/'u'/'dt'/'p'/'td'." + ) + + if len(ndupe_l) < nlevels: + ndupe_l.extend([1] * (nlevels - len(ndupe_l))) + assert len(ndupe_l) == nlevels + + assert all(x > 0 for x in ndupe_l) + + tuples = [] + for i in range(nlevels): + + def keyfunc(x): + import re + + numeric_tuple = re.sub(r"[^\d_]_?", "", x).split("_") + return [int(num) for num in numeric_tuple] + + # build a list of lists to create the index from + div_factor = nentries // ndupe_l[i] + 1 + cnt = Counter() + for j in range(div_factor): + label = f"{prefix}_l{i}_g{j}" + cnt[label] = ndupe_l[i] + # cute Counter trick + result = sorted(cnt.elements(), key=keyfunc)[:nentries] + tuples.append(result) + + tuples = list(zip(*tuples)) + + # convert tuples to index + if nentries == 1: + # we have a single level of tuples, i.e. a regular Index + index = Index(tuples[0], name=names[0]) + elif nlevels == 1: + name = None if names is None else names[0] + index = Index((x[0] for x in tuples), name=name) + else: + index = MultiIndex.from_tuples(tuples, names=names) + return index + + +def makeCustomDataframe( + nrows, + ncols, + c_idx_names=True, + r_idx_names=True, + c_idx_nlevels=1, + r_idx_nlevels=1, + data_gen_f=None, + c_ndupe_l=None, + r_ndupe_l=None, + dtype=None, + c_idx_type=None, + r_idx_type=None, +): + """ + Create a DataFrame using supplied parameters. + + Parameters + ---------- + nrows, ncols - number of data rows/cols + c_idx_names, idx_names - False/True/list of strings, yields No names , + default names or uses the provided names for the levels of the + corresponding index. You can provide a single string when + c_idx_nlevels ==1. + c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex + r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex + data_gen_f - a function f(row,col) which return the data value + at that position, the default generator used yields values of the form + "RxCy" based on position. + c_ndupe_l, r_ndupe_l - list of integers, determines the number + of duplicates for each label at a given level of the corresponding + index. The default `None` value produces a multiplicity of 1 across + all levels, i.e. a unique index. Will accept a partial list of length + N < idx_nlevels, for just the first N levels. If ndupe doesn't divide + nrows/ncol, the last label might have lower multiplicity. + dtype - passed to the DataFrame constructor as is, in case you wish to + have more control in conjunction with a custom `data_gen_f` + r_idx_type, c_idx_type - "i"/"f"/"s"/"u"/"dt"/"td". + If idx_type is not None, `idx_nlevels` must be 1. + "i"/"f" creates an integer/float index, + "s"/"u" creates a string/unicode index + "dt" create a datetime index. + "td" create a timedelta index. + + if unspecified, string labels will be generated. + + Examples + -------- + # 5 row, 3 columns, default names on both, single index on both axis + >> makeCustomDataframe(5,3) + + # make the data a random int between 1 and 100 + >> mkdf(5,3,data_gen_f=lambda r,c:randint(1,100)) + + # 2-level multiindex on rows with each label duplicated + # twice on first level, default names on both axis, single + # index on both axis + >> a=makeCustomDataframe(5,3,r_idx_nlevels=2,r_ndupe_l=[2]) + + # DatetimeIndex on row, index with unicode labels on columns + # no names on either axis + >> a=makeCustomDataframe(5,3,c_idx_names=False,r_idx_names=False, + r_idx_type="dt",c_idx_type="u") + + # 4-level multindex on rows with names provided, 2-level multindex + # on columns with default labels and default names. + >> a=makeCustomDataframe(5,3,r_idx_nlevels=4, + r_idx_names=["FEE","FI","FO","FAM"], + c_idx_nlevels=2) + + >> a=mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4) + """ + assert c_idx_nlevels > 0 + assert r_idx_nlevels > 0 + assert r_idx_type is None or ( + r_idx_type in ("i", "f", "s", "u", "dt", "p", "td") and r_idx_nlevels == 1 + ) + assert c_idx_type is None or ( + c_idx_type in ("i", "f", "s", "u", "dt", "p", "td") and c_idx_nlevels == 1 + ) + + columns = makeCustomIndex( + ncols, + nlevels=c_idx_nlevels, + prefix="C", + names=c_idx_names, + ndupe_l=c_ndupe_l, + idx_type=c_idx_type, + ) + index = makeCustomIndex( + nrows, + nlevels=r_idx_nlevels, + prefix="R", + names=r_idx_names, + ndupe_l=r_ndupe_l, + idx_type=r_idx_type, + ) + + # by default, generate data based on location + if data_gen_f is None: + data_gen_f = lambda r, c: f"R{r}C{c}" + + data = [[data_gen_f(r, c) for c in range(ncols)] for r in range(nrows)] + + return DataFrame(data, index, columns, dtype=dtype) + + +def makeMissingCustomDataframe( + nrows, + ncols, + density=0.9, + random_state=None, + c_idx_names=True, + r_idx_names=True, + c_idx_nlevels=1, + r_idx_nlevels=1, + data_gen_f=None, + c_ndupe_l=None, + r_ndupe_l=None, + dtype=None, + c_idx_type=None, + r_idx_type=None, +): + """ + Parameters + ---------- + Density : float, optional + Float in (0, 1) that gives the percentage of non-missing numbers in + the DataFrame. + random_state : {np.random.RandomState, int}, optional + Random number generator or random seed. + + See makeCustomDataframe for descriptions of the rest of the parameters. + """ + df = makeCustomDataframe( + nrows, + ncols, + c_idx_names=c_idx_names, + r_idx_names=r_idx_names, + c_idx_nlevels=c_idx_nlevels, + r_idx_nlevels=r_idx_nlevels, + data_gen_f=data_gen_f, + c_ndupe_l=c_ndupe_l, + r_ndupe_l=r_ndupe_l, + dtype=dtype, + c_idx_type=c_idx_type, + r_idx_type=r_idx_type, + ) + + i, j = _create_missing_idx(nrows, ncols, density, random_state) + df.values[i, j] = np.nan + return df + + +def makeMissingDataframe(density=0.9, random_state=None): + df = makeDataFrame() + i, j = _create_missing_idx(*df.shape, density=density, random_state=random_state) + df.values[i, j] = np.nan + return df diff --git a/pandas/_testing/network.py b/pandas/_testing/network.py new file mode 100644 index 0000000000000..3ef07032d06cb --- /dev/null +++ b/pandas/_testing/network.py @@ -0,0 +1,240 @@ +from functools import wraps + +from pandas.io.common import urlopen + +_RAISE_NETWORK_ERROR_DEFAULT = False + +# skip tests on exceptions with this message +_network_error_messages = ( + # 'urlopen error timed out', + # 'timeout: timed out', + # 'socket.timeout: timed out', + "timed out", + "Server Hangup", + "HTTP Error 503: Service Unavailable", + "502: Proxy Error", + "HTTP Error 502: internal error", + "HTTP Error 502", + "HTTP Error 503", + "HTTP Error 403", + "HTTP Error 400", + "Temporary failure in name resolution", + "Name or service not known", + "Connection refused", + "certificate verify", +) + +# or this e.errno/e.reason.errno +_network_errno_vals = ( + 101, # Network is unreachable + 111, # Connection refused + 110, # Connection timed out + 104, # Connection reset Error + 54, # Connection reset by peer + 60, # urllib.error.URLError: [Errno 60] Connection timed out +) + +# Both of the above shouldn't mask real issues such as 404's +# or refused connections (changed DNS). +# But some tests (test_data yahoo) contact incredibly flakey +# servers. + +# and conditionally raise on exception types in _get_default_network_errors + + +def _get_default_network_errors(): + # Lazy import for http.client because it imports many things from the stdlib + import http.client + + return (IOError, http.client.HTTPException, TimeoutError) + + +def can_connect(url, error_classes=None): + """ + Try to connect to the given url. True if succeeds, False if IOError + raised + + Parameters + ---------- + url : basestring + The URL to try to connect to + + Returns + ------- + connectable : bool + Return True if no IOError (unable to connect) or URLError (bad url) was + raised + """ + if error_classes is None: + error_classes = _get_default_network_errors() + + try: + with urlopen(url): + pass + except error_classes: + return False + else: + return True + + +def optional_args(decorator): + """ + allows a decorator to take optional positional and keyword arguments. + Assumes that taking a single, callable, positional argument means that + it is decorating a function, i.e. something like this:: + + @my_decorator + def function(): pass + + Calls decorator with decorator(f, *args, **kwargs) + """ + + @wraps(decorator) + def wrapper(*args, **kwargs): + def dec(f): + return decorator(f, *args, **kwargs) + + is_decorating = not kwargs and len(args) == 1 and callable(args[0]) + if is_decorating: + f = args[0] + args = [] + return dec(f) + else: + return dec + + return wrapper + + +@optional_args +def network( + t, + url="http://www.google.com", + raise_on_error=_RAISE_NETWORK_ERROR_DEFAULT, + check_before_test=False, + error_classes=None, + skip_errnos=_network_errno_vals, + _skip_on_messages=_network_error_messages, +): + """ + Label a test as requiring network connection and, if an error is + encountered, only raise if it does not find a network connection. + + In comparison to ``network``, this assumes an added contract to your test: + you must assert that, under normal conditions, your test will ONLY fail if + it does not have network connectivity. + + You can call this in 3 ways: as a standard decorator, with keyword + arguments, or with a positional argument that is the url to check. + + Parameters + ---------- + t : callable + The test requiring network connectivity. + url : path + The url to test via ``pandas.io.common.urlopen`` to check + for connectivity. Defaults to 'http://www.google.com'. + raise_on_error : bool + If True, never catches errors. + check_before_test : bool + If True, checks connectivity before running the test case. + error_classes : tuple or Exception + error classes to ignore. If not in ``error_classes``, raises the error. + defaults to IOError. Be careful about changing the error classes here. + skip_errnos : iterable of int + Any exception that has .errno or .reason.erno set to one + of these values will be skipped with an appropriate + message. + _skip_on_messages: iterable of string + any exception e for which one of the strings is + a substring of str(e) will be skipped with an appropriate + message. Intended to suppress errors where an errno isn't available. + + Notes + ----- + * ``raise_on_error`` supercedes ``check_before_test`` + + Returns + ------- + t : callable + The decorated test ``t``, with checks for connectivity errors. + + Example + ------- + + Tests decorated with @network will fail if it's possible to make a network + connection to another URL (defaults to google.com):: + + >>> from pandas._testing import network + >>> from pandas.io.common import urlopen + >>> @network + ... def test_network(): + ... with urlopen("rabbit://bonanza.com"): + ... pass + Traceback + ... + URLError: <urlopen error unknown url type: rabit> + + You can specify alternative URLs:: + + >>> @network("http://www.yahoo.com") + ... def test_something_with_yahoo(): + ... raise IOError("Failure Message") + >>> test_something_with_yahoo() + Traceback (most recent call last): + ... + IOError: Failure Message + + If you set check_before_test, it will check the url first and not run the + test on failure:: + + >>> @network("failing://url.blaher", check_before_test=True) + ... def test_something(): + ... print("I ran!") + ... raise ValueError("Failure") + >>> test_something() + Traceback (most recent call last): + ... + + Errors not related to networking will always be raised. + """ + from pytest import skip + + if error_classes is None: + error_classes = _get_default_network_errors() + + t.network = True + + @wraps(t) + def wrapper(*args, **kwargs): + if check_before_test and not raise_on_error: + if not can_connect(url, error_classes): + skip() + try: + return t(*args, **kwargs) + except Exception as err: + errno = getattr(err, "errno", None) + if not errno and hasattr(errno, "reason"): + errno = getattr(err.reason, "errno", None) + + if errno in skip_errnos: + skip(f"Skipping test due to known errno and error {err}") + + e_str = str(err) + + if any(m.lower() in e_str.lower() for m in _skip_on_messages): + skip( + f"Skipping test because exception message is known and error {err}" + ) + + if not isinstance(err, error_classes): + raise + + if raise_on_error or can_connect(url, error_classes): + raise + else: + skip(f"Skipping test due to lack of connectivity and error {err}") + + return wrapper + + +with_connectivity_check = network diff --git a/pandas/_testing/roundtrips.py b/pandas/_testing/roundtrips.py new file mode 100644 index 0000000000000..1c4e30e98d5a5 --- /dev/null +++ b/pandas/_testing/roundtrips.py @@ -0,0 +1,92 @@ +from typing import Any, Optional + +from pandas._typing import FilePathOrBuffer, FrameOrSeries + +import pandas as pd + +from .contexts import ensure_clean +from .makers import rands + + +def round_trip_pickle( + obj: Any, path: Optional[FilePathOrBuffer] = None +) -> FrameOrSeries: + """ + Pickle an object and then read it again. + + Parameters + ---------- + obj : any object + The object to pickle and then re-read. + path : str, path object or file-like object, default None + The path where the pickled object is written and then read. + + Returns + ------- + pandas object + The original object that was pickled and then re-read. + """ + _path = path + if _path is None: + _path = f"__{rands(10)}__.pickle" + with ensure_clean(_path) as temp_path: + pd.to_pickle(obj, temp_path) + return pd.read_pickle(temp_path) + + +def round_trip_pathlib(writer, reader, path: Optional[str] = None): + """ + Write an object to file specified by a pathlib.Path and read it back + + Parameters + ---------- + writer : callable bound to pandas object + IO writing function (e.g. DataFrame.to_csv ) + reader : callable + IO reading function (e.g. pd.read_csv ) + path : str, default None + The path where the object is written and then read. + + Returns + ------- + pandas object + The original object that was serialized and then re-read. + """ + import pytest + + Path = pytest.importorskip("pathlib").Path + if path is None: + path = "___pathlib___" + with ensure_clean(path) as path: + writer(Path(path)) + obj = reader(Path(path)) + return obj + + +def round_trip_localpath(writer, reader, path: Optional[str] = None): + """ + Write an object to file specified by a py.path LocalPath and read it back. + + Parameters + ---------- + writer : callable bound to pandas object + IO writing function (e.g. DataFrame.to_csv ) + reader : callable + IO reading function (e.g. pd.read_csv ) + path : str, default None + The path where the object is written and then read. + + Returns + ------- + pandas object + The original object that was serialized and then re-read. + """ + import pytest + + LocalPath = pytest.importorskip("py.path").local + if path is None: + path = "___localpath___" + with ensure_clean(path) as path: + writer(LocalPath(path)) + obj = reader(LocalPath(path)) + return obj diff --git a/pandas/tests/internals/test_internals.py b/pandas/tests/internals/test_internals.py index 0c9ddbf5473b3..c39eb86ccc17d 100644 --- a/pandas/tests/internals/test_internals.py +++ b/pandas/tests/internals/test_internals.py @@ -240,8 +240,8 @@ def test_attrs(self): assert len(self.fblock) == len(self.fblock.values) def test_merge(self): - avals = tm.randn(2, 10) - bvals = tm.randn(2, 10) + avals = np.random.randn(2, 10) + bvals = np.random.randn(2, 10) ref_cols = Index(["e", "a", "b", "d", "f"]) @@ -435,10 +435,10 @@ def test_set_change_dtype(self, mgr): mgr2.set("baz", np.repeat("foo", N)) assert mgr2.get("baz").dtype == np.object_ - mgr2.set("quux", tm.randn(N).astype(int)) + mgr2.set("quux", np.random.randn(N).astype(int)) assert mgr2.get("quux").dtype == np.int_ - mgr2.set("quux", tm.randn(N)) + mgr2.set("quux", np.random.randn(N)) assert mgr2.get("quux").dtype == np.float_ def test_set_change_dtype_slice(self): # GH8850 @@ -667,11 +667,11 @@ def test_consolidate(self): pass def test_consolidate_ordering_issues(self, mgr): - mgr.set("f", tm.randn(N)) - mgr.set("d", tm.randn(N)) - mgr.set("b", tm.randn(N)) - mgr.set("g", tm.randn(N)) - mgr.set("h", tm.randn(N)) + mgr.set("f", np.random.randn(N)) + mgr.set("d", np.random.randn(N)) + mgr.set("b", np.random.randn(N)) + mgr.set("g", np.random.randn(N)) + mgr.set("h", np.random.randn(N)) # we have datetime/tz blocks in mgr cons = mgr.consolidate() diff --git a/pandas/tests/series/methods/test_combine_first.py b/pandas/tests/series/methods/test_combine_first.py index 1ee55fbe39513..eb22bf364c15a 100644 --- a/pandas/tests/series/methods/test_combine_first.py +++ b/pandas/tests/series/methods/test_combine_first.py @@ -46,7 +46,7 @@ def test_combine_first(self): # mixed types index = tm.makeStringIndex(20) - floats = Series(tm.randn(20), index=index) + floats = Series(np.random.randn(20), index=index) strings = Series(tm.makeStringIndex(10), index=index[::2]) combined = strings.combine_first(floats) diff --git a/pandas/tests/series/test_apply.py b/pandas/tests/series/test_apply.py index a4c55a80a9f0f..0445b3a0a47d7 100644 --- a/pandas/tests/series/test_apply.py +++ b/pandas/tests/series/test_apply.py @@ -4,6 +4,8 @@ import numpy as np import pytest +from pandas.core.dtypes.common import is_number + import pandas as pd from pandas import DataFrame, Index, Series, isna import pandas._testing as tm @@ -401,7 +403,7 @@ def test_agg_cython_table(self, series, func, expected): # test reducing functions in # pandas.core.base.SelectionMixin._cython_table result = series.agg(func) - if tm.is_number(expected): + if is_number(expected): assert np.isclose(result, expected, equal_nan=True) else: assert result == expected diff --git a/pandas/tests/series/test_repr.py b/pandas/tests/series/test_repr.py index 64a8c4569406e..302fb845ca072 100644 --- a/pandas/tests/series/test_repr.py +++ b/pandas/tests/series/test_repr.py @@ -71,8 +71,8 @@ def test_repr(self, datetime_series, string_series, object_series): str(string_series.astype(int)) str(object_series) - str(Series(tm.randn(1000), index=np.arange(1000))) - str(Series(tm.randn(1000), index=np.arange(1000, 0, step=-1))) + str(Series(np.random.randn(1000), index=np.arange(1000))) + str(Series(np.random.randn(1000), index=np.arange(1000, 0, step=-1))) # empty str(Series(dtype=object)) @@ -104,7 +104,7 @@ def test_repr(self, datetime_series, string_series, object_series): repr(string_series) biggie = Series( - tm.randn(1000), index=np.arange(1000), name=("foo", "bar", "baz") + np.random.randn(1000), index=np.arange(1000), name=("foo", "bar", "baz") ) repr(biggie)
Scattered throughout the tests we have other helpers like assert_block_equal that probably belong in `tm`. Before trying to collect all of these, it makes sense to split this already-cumbersome module.
https://api.github.com/repos/pandas-dev/pandas/pulls/32118
2020-02-19T20:37:12Z
2020-02-21T02:15:45Z
null
2021-01-04T06:20:41Z
REF: collect arithmetic benchmarks
diff --git a/asv_bench/benchmarks/binary_ops.py b/asv_bench/benchmarks/arithmetic.py similarity index 51% rename from asv_bench/benchmarks/binary_ops.py rename to asv_bench/benchmarks/arithmetic.py index 64e067d25a454..d1e94f62967f4 100644 --- a/asv_bench/benchmarks/binary_ops.py +++ b/asv_bench/benchmarks/arithmetic.py @@ -1,14 +1,23 @@ import operator +import warnings import numpy as np -from pandas import DataFrame, Series, date_range +import pandas as pd +from pandas import DataFrame, Series, Timestamp, date_range, to_timedelta +import pandas._testing as tm from pandas.core.algorithms import checked_add_with_arr +from .pandas_vb_common import numeric_dtypes + try: import pandas.core.computation.expressions as expr except ImportError: import pandas.computation.expressions as expr +try: + import pandas.tseries.holiday +except ImportError: + pass class IntFrameWithScalar: @@ -151,6 +160,110 @@ def time_timestamp_ops_diff_with_shift(self, tz): self.s - self.s.shift() +class IrregularOps: + def setup(self): + N = 10 ** 5 + idx = date_range(start="1/1/2000", periods=N, freq="s") + s = Series(np.random.randn(N), index=idx) + self.left = s.sample(frac=1) + self.right = s.sample(frac=1) + + def time_add(self): + self.left + self.right + + +class TimedeltaOps: + def setup(self): + self.td = to_timedelta(np.arange(1000000)) + self.ts = Timestamp("2000") + + def time_add_td_ts(self): + self.td + self.ts + + +class CategoricalComparisons: + params = ["__lt__", "__le__", "__eq__", "__ne__", "__ge__", "__gt__"] + param_names = ["op"] + + def setup(self, op): + N = 10 ** 5 + self.cat = pd.Categorical(list("aabbcd") * N, ordered=True) + + def time_categorical_op(self, op): + getattr(self.cat, op)("b") + + +class IndexArithmetic: + + params = ["float", "int"] + param_names = ["dtype"] + + def setup(self, dtype): + N = 10 ** 6 + indexes = {"int": "makeIntIndex", "float": "makeFloatIndex"} + self.index = getattr(tm, indexes[dtype])(N) + + def time_add(self, dtype): + self.index + 2 + + def time_subtract(self, dtype): + self.index - 2 + + def time_multiply(self, dtype): + self.index * 2 + + def time_divide(self, dtype): + self.index / 2 + + def time_modulo(self, dtype): + self.index % 2 + + +class NumericInferOps: + # from GH 7332 + params = numeric_dtypes + param_names = ["dtype"] + + def setup(self, dtype): + N = 5 * 10 ** 5 + self.df = DataFrame( + {"A": np.arange(N).astype(dtype), "B": np.arange(N).astype(dtype)} + ) + + def time_add(self, dtype): + self.df["A"] + self.df["B"] + + def time_subtract(self, dtype): + self.df["A"] - self.df["B"] + + def time_multiply(self, dtype): + self.df["A"] * self.df["B"] + + def time_divide(self, dtype): + self.df["A"] / self.df["B"] + + def time_modulo(self, dtype): + self.df["A"] % self.df["B"] + + +class DateInferOps: + # from GH 7332 + def setup_cache(self): + N = 5 * 10 ** 5 + df = DataFrame({"datetime64": np.arange(N).astype("datetime64[ms]")}) + df["timedelta"] = df["datetime64"] - df["datetime64"] + return df + + def time_subtract_datetimes(self, df): + df["datetime64"] - df["datetime64"] + + def time_timedelta_plus_datetime(self, df): + df["timedelta"] + df["datetime64"] + + def time_add_timedeltas(self, df): + df["timedelta"] + df["timedelta"] + + class AddOverflowScalar: params = [1, -1, 0] @@ -188,4 +301,68 @@ def time_add_overflow_both_arg_nan(self): ) +hcal = pd.tseries.holiday.USFederalHolidayCalendar() +# These offsets currently raise a NotImplimentedError with .apply_index() +non_apply = [ + pd.offsets.Day(), + pd.offsets.BYearEnd(), + pd.offsets.BYearBegin(), + pd.offsets.BQuarterEnd(), + pd.offsets.BQuarterBegin(), + pd.offsets.BMonthEnd(), + pd.offsets.BMonthBegin(), + pd.offsets.CustomBusinessDay(), + pd.offsets.CustomBusinessDay(calendar=hcal), + pd.offsets.CustomBusinessMonthBegin(calendar=hcal), + pd.offsets.CustomBusinessMonthEnd(calendar=hcal), + pd.offsets.CustomBusinessMonthEnd(calendar=hcal), +] +other_offsets = [ + pd.offsets.YearEnd(), + pd.offsets.YearBegin(), + pd.offsets.QuarterEnd(), + pd.offsets.QuarterBegin(), + pd.offsets.MonthEnd(), + pd.offsets.MonthBegin(), + pd.offsets.DateOffset(months=2, days=2), + pd.offsets.BusinessDay(), + pd.offsets.SemiMonthEnd(), + pd.offsets.SemiMonthBegin(), +] +offsets = non_apply + other_offsets + + +class OffsetArrayArithmetic: + + params = offsets + param_names = ["offset"] + + def setup(self, offset): + N = 10000 + rng = pd.date_range(start="1/1/2000", periods=N, freq="T") + self.rng = rng + self.ser = pd.Series(rng) + + def time_add_series_offset(self, offset): + with warnings.catch_warnings(record=True): + self.ser + offset + + def time_add_dti_offset(self, offset): + with warnings.catch_warnings(record=True): + self.rng + offset + + +class ApplyIndex: + params = other_offsets + param_names = ["offset"] + + def setup(self, offset): + N = 10000 + rng = pd.date_range(start="1/1/2000", periods=N, freq="T") + self.rng = rng + + def time_apply_index(self, offset): + offset.apply_index(self.rng) + + from .pandas_vb_common import setup # noqa: F401 isort:skip diff --git a/asv_bench/benchmarks/categoricals.py b/asv_bench/benchmarks/categoricals.py index 1dcd52ac074a6..6f43a6fd3fc9b 100644 --- a/asv_bench/benchmarks/categoricals.py +++ b/asv_bench/benchmarks/categoricals.py @@ -63,18 +63,6 @@ def time_existing_series(self): pd.Categorical(self.series) -class CategoricalOps: - params = ["__lt__", "__le__", "__eq__", "__ne__", "__ge__", "__gt__"] - param_names = ["op"] - - def setup(self, op): - N = 10 ** 5 - self.cat = pd.Categorical(list("aabbcd") * N, ordered=True) - - def time_categorical_op(self, op): - getattr(self.cat, op)("b") - - class Concat: def setup(self): N = 10 ** 5 diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py index 103141545504b..cf51a4d35f805 100644 --- a/asv_bench/benchmarks/index_object.py +++ b/asv_bench/benchmarks/index_object.py @@ -63,32 +63,6 @@ def time_is_dates_only(self): self.dr._is_dates_only -class Ops: - - params = ["float", "int"] - param_names = ["dtype"] - - def setup(self, dtype): - N = 10 ** 6 - indexes = {"int": "makeIntIndex", "float": "makeFloatIndex"} - self.index = getattr(tm, indexes[dtype])(N) - - def time_add(self, dtype): - self.index + 2 - - def time_subtract(self, dtype): - self.index - 2 - - def time_multiply(self, dtype): - self.index * 2 - - def time_divide(self, dtype): - self.index / 2 - - def time_modulo(self, dtype): - self.index % 2 - - class Range: def setup(self): self.idx_inc = RangeIndex(start=0, stop=10 ** 7, step=3) diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py index 1a8d5ede52512..40b064229ae49 100644 --- a/asv_bench/benchmarks/inference.py +++ b/asv_bench/benchmarks/inference.py @@ -1,53 +1,8 @@ import numpy as np -from pandas import DataFrame, Series, to_numeric +from pandas import Series, to_numeric -from .pandas_vb_common import lib, numeric_dtypes, tm - - -class NumericInferOps: - # from GH 7332 - params = numeric_dtypes - param_names = ["dtype"] - - def setup(self, dtype): - N = 5 * 10 ** 5 - self.df = DataFrame( - {"A": np.arange(N).astype(dtype), "B": np.arange(N).astype(dtype)} - ) - - def time_add(self, dtype): - self.df["A"] + self.df["B"] - - def time_subtract(self, dtype): - self.df["A"] - self.df["B"] - - def time_multiply(self, dtype): - self.df["A"] * self.df["B"] - - def time_divide(self, dtype): - self.df["A"] / self.df["B"] - - def time_modulo(self, dtype): - self.df["A"] % self.df["B"] - - -class DateInferOps: - # from GH 7332 - def setup_cache(self): - N = 5 * 10 ** 5 - df = DataFrame({"datetime64": np.arange(N).astype("datetime64[ms]")}) - df["timedelta"] = df["datetime64"] - df["datetime64"] - return df - - def time_subtract_datetimes(self, df): - df["datetime64"] - df["datetime64"] - - def time_timedelta_plus_datetime(self, df): - df["timedelta"] + df["datetime64"] - - def time_add_timedeltas(self, df): - df["timedelta"] + df["timedelta"] +from .pandas_vb_common import lib, tm class ToNumeric: diff --git a/asv_bench/benchmarks/offset.py b/asv_bench/benchmarks/offset.py deleted file mode 100644 index 77ce1b2763bce..0000000000000 --- a/asv_bench/benchmarks/offset.py +++ /dev/null @@ -1,80 +0,0 @@ -import warnings - -import pandas as pd - -try: - import pandas.tseries.holiday -except ImportError: - pass - -hcal = pd.tseries.holiday.USFederalHolidayCalendar() -# These offsets currently raise a NotImplimentedError with .apply_index() -non_apply = [ - pd.offsets.Day(), - pd.offsets.BYearEnd(), - pd.offsets.BYearBegin(), - pd.offsets.BQuarterEnd(), - pd.offsets.BQuarterBegin(), - pd.offsets.BMonthEnd(), - pd.offsets.BMonthBegin(), - pd.offsets.CustomBusinessDay(), - pd.offsets.CustomBusinessDay(calendar=hcal), - pd.offsets.CustomBusinessMonthBegin(calendar=hcal), - pd.offsets.CustomBusinessMonthEnd(calendar=hcal), - pd.offsets.CustomBusinessMonthEnd(calendar=hcal), -] -other_offsets = [ - pd.offsets.YearEnd(), - pd.offsets.YearBegin(), - pd.offsets.QuarterEnd(), - pd.offsets.QuarterBegin(), - pd.offsets.MonthEnd(), - pd.offsets.MonthBegin(), - pd.offsets.DateOffset(months=2, days=2), - pd.offsets.BusinessDay(), - pd.offsets.SemiMonthEnd(), - pd.offsets.SemiMonthBegin(), -] -offsets = non_apply + other_offsets - - -class ApplyIndex: - - params = other_offsets - param_names = ["offset"] - - def setup(self, offset): - N = 10000 - self.rng = pd.date_range(start="1/1/2000", periods=N, freq="T") - - def time_apply_index(self, offset): - offset.apply_index(self.rng) - - -class OffsetSeriesArithmetic: - - params = offsets - param_names = ["offset"] - - def setup(self, offset): - N = 1000 - rng = pd.date_range(start="1/1/2000", periods=N, freq="T") - self.data = pd.Series(rng) - - def time_add_offset(self, offset): - with warnings.catch_warnings(record=True): - self.data + offset - - -class OffsetDatetimeIndexArithmetic: - - params = offsets - param_names = ["offset"] - - def setup(self, offset): - N = 1000 - self.data = pd.date_range(start="1/1/2000", periods=N, freq="T") - - def time_add_offset(self, offset): - with warnings.catch_warnings(record=True): - self.data + offset diff --git a/asv_bench/benchmarks/timedelta.py b/asv_bench/benchmarks/timedelta.py index 37418d752f833..208c8f9d14a5e 100644 --- a/asv_bench/benchmarks/timedelta.py +++ b/asv_bench/benchmarks/timedelta.py @@ -5,7 +5,7 @@ import numpy as np -from pandas import DataFrame, Series, Timestamp, timedelta_range, to_timedelta +from pandas import DataFrame, Series, timedelta_range, to_timedelta class ToTimedelta: @@ -41,15 +41,6 @@ def time_convert(self, errors): to_timedelta(self.arr, errors=errors) -class TimedeltaOps: - def setup(self): - self.td = to_timedelta(np.arange(1000000)) - self.ts = Timestamp("2000") - - def time_add_td_ts(self): - self.td + self.ts - - class DatetimeAccessor: def setup_cache(self): N = 100000 diff --git a/asv_bench/benchmarks/timeseries.py b/asv_bench/benchmarks/timeseries.py index ba0b51922fd31..2f7ea8b9c0873 100644 --- a/asv_bench/benchmarks/timeseries.py +++ b/asv_bench/benchmarks/timeseries.py @@ -262,18 +262,6 @@ def time_get_slice(self, monotonic): self.s[:10000] -class IrregularOps: - def setup(self): - N = 10 ** 5 - idx = date_range(start="1/1/2000", periods=N, freq="s") - s = Series(np.random.randn(N), index=idx) - self.left = s.sample(frac=1) - self.right = s.sample(frac=1) - - def time_add(self): - self.left + self.right - - class Lookup: def setup(self): N = 1500000
ATM these are scattered, making it tough to a) run just arithmetic-relevant benchmarks and b) determine what we have good benchmark coverage for. This collects the scattered benchmarks in one place, cleaning up and fleshing these out is for a separate pass. I also intend to do something analogous for indexing benchmarks.
https://api.github.com/repos/pandas-dev/pandas/pulls/32116
2020-02-19T18:53:09Z
2020-02-22T16:06:41Z
2020-02-22T16:06:41Z
2020-02-22T16:08:29Z
add test for "Allow definition of `pd.CategoricalDtype` with a specific `categories.dtype`"
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py index 8aaebe89871b6..70e677411b4a9 100644 --- a/pandas/core/dtypes/dtypes.py +++ b/pandas/core/dtypes/dtypes.py @@ -175,6 +175,8 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype): ---------- categories : sequence, optional Must be unique, and must not contain any nulls. + The categories are stored in an Index, + and if an index is provided the dtype of that index will be used. ordered : bool or None, default False Whether or not this categorical is treated as a ordered categorical. None can be used to maintain the ordered value of existing categoricals when @@ -210,6 +212,12 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype): 3 NaN dtype: category Categories (2, object): [b < a] + + An empty CategoricalDtype with a specific dtype can be created + by providing an empty index. As follows, + + >>> pd.CategoricalDtype(pd.DatetimeIndex([])).categories.dtype + dtype('<M8[ns]') """ # TODO: Document public vs. private API diff --git a/pandas/tests/dtypes/test_dtypes.py b/pandas/tests/dtypes/test_dtypes.py index dd99b81fb6764..645912b2a2c21 100644 --- a/pandas/tests/dtypes/test_dtypes.py +++ b/pandas/tests/dtypes/test_dtypes.py @@ -26,7 +26,14 @@ ) import pandas as pd -from pandas import Categorical, CategoricalIndex, IntervalIndex, Series, date_range +from pandas import ( + Categorical, + CategoricalIndex, + DatetimeIndex, + IntervalIndex, + Series, + date_range, +) import pandas._testing as tm from pandas.core.arrays.sparse import SparseArray, SparseDtype @@ -172,6 +179,11 @@ def test_is_boolean(self, categories, expected): assert is_bool_dtype(cat) is expected assert is_bool_dtype(cat.dtype) is expected + def test_dtype_specific_categorical_dtype(self): + expected = "datetime64[ns]" + result = str(Categorical(DatetimeIndex([])).categories.dtype) + assert result == expected + class TestDatetimeTZDtype(Base): @pytest.fixture
- [x] closes #32096 - [x] tests added / passed
https://api.github.com/repos/pandas-dev/pandas/pulls/32115
2020-02-19T17:17:23Z
2020-03-02T15:30:18Z
2020-03-02T15:30:18Z
2020-03-02T15:30:34Z
REF/TST: implement test_interpolate for Series
diff --git a/pandas/tests/series/methods/test_interpolate.py b/pandas/tests/series/methods/test_interpolate.py new file mode 100644 index 0000000000000..6844225a81a8f --- /dev/null +++ b/pandas/tests/series/methods/test_interpolate.py @@ -0,0 +1,673 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import Index, MultiIndex, Series, date_range, isna +import pandas._testing as tm + + +@pytest.fixture( + params=[ + "linear", + "index", + "values", + "nearest", + "slinear", + "zero", + "quadratic", + "cubic", + "barycentric", + "krogh", + "polynomial", + "spline", + "piecewise_polynomial", + "from_derivatives", + "pchip", + "akima", + ] +) +def nontemporal_method(request): + """ Fixture that returns an (method name, required kwargs) pair. + + This fixture does not include method 'time' as a parameterization; that + method requires a Series with a DatetimeIndex, and is generally tested + separately from these non-temporal methods. + """ + method = request.param + kwargs = dict(order=1) if method in ("spline", "polynomial") else dict() + return method, kwargs + + +@pytest.fixture( + params=[ + "linear", + "slinear", + "zero", + "quadratic", + "cubic", + "barycentric", + "krogh", + "polynomial", + "spline", + "piecewise_polynomial", + "from_derivatives", + "pchip", + "akima", + ] +) +def interp_methods_ind(request): + """ Fixture that returns a (method name, required kwargs) pair to + be tested for various Index types. + + This fixture does not include methods - 'time', 'index', 'nearest', + 'values' as a parameterization + """ + method = request.param + kwargs = dict(order=1) if method in ("spline", "polynomial") else dict() + return method, kwargs + + +class TestSeriesInterpolateData: + def test_interpolate(self, datetime_series, string_series): + ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index) + + ts_copy = ts.copy() + ts_copy[5:10] = np.NaN + + linear_interp = ts_copy.interpolate(method="linear") + tm.assert_series_equal(linear_interp, ts) + + ord_ts = Series( + [d.toordinal() for d in datetime_series.index], index=datetime_series.index + ).astype(float) + + ord_ts_copy = ord_ts.copy() + ord_ts_copy[5:10] = np.NaN + + time_interp = ord_ts_copy.interpolate(method="time") + tm.assert_series_equal(time_interp, ord_ts) + + def test_interpolate_time_raises_for_non_timeseries(self): + # When method='time' is used on a non-TimeSeries that contains a null + # value, a ValueError should be raised. + non_ts = Series([0, 1, 2, np.NaN]) + msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex" + with pytest.raises(ValueError, match=msg): + non_ts.interpolate(method="time") + + @td.skip_if_no_scipy + def test_interpolate_pchip(self): + + ser = Series(np.sort(np.random.uniform(size=100))) + + # interpolate at new_index + new_index = ser.index.union( + Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75]) + ).astype(float) + interp_s = ser.reindex(new_index).interpolate(method="pchip") + # does not blow up, GH5977 + interp_s[49:51] + + @td.skip_if_no_scipy + def test_interpolate_akima(self): + + ser = Series([10, 11, 12, 13]) + + expected = Series( + [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + interp_s = ser.reindex(new_index).interpolate(method="akima") + tm.assert_series_equal(interp_s[1:3], expected) + + @td.skip_if_no_scipy + def test_interpolate_piecewise_polynomial(self): + ser = Series([10, 11, 12, 13]) + + expected = Series( + [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial") + tm.assert_series_equal(interp_s[1:3], expected) + + @td.skip_if_no_scipy + def test_interpolate_from_derivatives(self): + ser = Series([10, 11, 12, 13]) + + expected = Series( + [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + interp_s = ser.reindex(new_index).interpolate(method="from_derivatives") + tm.assert_series_equal(interp_s[1:3], expected) + + @pytest.mark.parametrize( + "kwargs", + [ + {}, + pytest.param( + {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy + ), + ], + ) + def test_interpolate_corners(self, kwargs): + s = Series([np.nan, np.nan]) + tm.assert_series_equal(s.interpolate(**kwargs), s) + + s = Series([], dtype=object).interpolate() + tm.assert_series_equal(s.interpolate(**kwargs), s) + + def test_interpolate_index_values(self): + s = Series(np.nan, index=np.sort(np.random.rand(30))) + s[::3] = np.random.randn(10) + + vals = s.index.values.astype(float) + + result = s.interpolate(method="index") + + expected = s.copy() + bad = isna(expected.values) + good = ~bad + expected = Series( + np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad] + ) + + tm.assert_series_equal(result[bad], expected) + + # 'values' is synonymous with 'index' for the method kwarg + other_result = s.interpolate(method="values") + + tm.assert_series_equal(other_result, result) + tm.assert_series_equal(other_result[bad], expected) + + def test_interpolate_non_ts(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + msg = ( + "time-weighted interpolation only works on Series or DataFrames " + "with a DatetimeIndex" + ) + with pytest.raises(ValueError, match=msg): + s.interpolate(method="time") + + @pytest.mark.parametrize( + "kwargs", + [ + {}, + pytest.param( + {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy + ), + ], + ) + def test_nan_interpolate(self, kwargs): + s = Series([0, 1, np.nan, 3]) + result = s.interpolate(**kwargs) + expected = Series([0.0, 1.0, 2.0, 3.0]) + tm.assert_series_equal(result, expected) + + def test_nan_irregular_index(self): + s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9]) + result = s.interpolate() + expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9]) + tm.assert_series_equal(result, expected) + + def test_nan_str_index(self): + s = Series([0, 1, 2, np.nan], index=list("abcd")) + result = s.interpolate() + expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd")) + tm.assert_series_equal(result, expected) + + @td.skip_if_no_scipy + def test_interp_quad(self): + sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4]) + result = sq.interpolate(method="quadratic") + expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4]) + tm.assert_series_equal(result, expected) + + @td.skip_if_no_scipy + def test_interp_scipy_basic(self): + s = Series([1, 3, np.nan, 12, np.nan, 25]) + # slinear + expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0]) + result = s.interpolate(method="slinear") + tm.assert_series_equal(result, expected) + + result = s.interpolate(method="slinear", downcast="infer") + tm.assert_series_equal(result, expected) + # nearest + expected = Series([1, 3, 3, 12, 12, 25]) + result = s.interpolate(method="nearest") + tm.assert_series_equal(result, expected.astype("float")) + + result = s.interpolate(method="nearest", downcast="infer") + tm.assert_series_equal(result, expected) + # zero + expected = Series([1, 3, 3, 12, 12, 25]) + result = s.interpolate(method="zero") + tm.assert_series_equal(result, expected.astype("float")) + + result = s.interpolate(method="zero", downcast="infer") + tm.assert_series_equal(result, expected) + # quadratic + # GH #15662. + expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0]) + result = s.interpolate(method="quadratic") + tm.assert_series_equal(result, expected) + + result = s.interpolate(method="quadratic", downcast="infer") + tm.assert_series_equal(result, expected) + # cubic + expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0]) + result = s.interpolate(method="cubic") + tm.assert_series_equal(result, expected) + + def test_interp_limit(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0]) + result = s.interpolate(method="linear", limit=2) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("limit", [-1, 0]) + def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit): + # GH 9217: make sure limit is greater than zero. + s = pd.Series([1, 2, np.nan, 4]) + method, kwargs = nontemporal_method + with pytest.raises(ValueError, match="Limit must be greater than 0"): + s.interpolate(limit=limit, method=method, **kwargs) + + def test_interpolate_invalid_float_limit(self, nontemporal_method): + # GH 9217: make sure limit is an integer. + s = pd.Series([1, 2, np.nan, 4]) + method, kwargs = nontemporal_method + limit = 2.0 + with pytest.raises(ValueError, match="Limit must be an integer"): + s.interpolate(limit=limit, method=method, **kwargs) + + @pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"]) + def test_interp_invalid_method(self, invalid_method): + s = Series([1, 3, np.nan, 12, np.nan, 25]) + + msg = f"method must be one of.* Got '{invalid_method}' instead" + with pytest.raises(ValueError, match=msg): + s.interpolate(method=invalid_method) + + # When an invalid method and invalid limit (such as -1) are + # provided, the error message reflects the invalid method. + with pytest.raises(ValueError, match=msg): + s.interpolate(method=invalid_method, limit=-1) + + def test_interp_limit_forward(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + # Provide 'forward' (the default) explicitly here. + expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0]) + + result = s.interpolate(method="linear", limit=2, limit_direction="forward") + tm.assert_series_equal(result, expected) + + result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD") + tm.assert_series_equal(result, expected) + + def test_interp_unlimited(self): + # these test are for issue #16282 default Limit=None is unlimited + s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan]) + expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0]) + result = s.interpolate(method="linear", limit_direction="both") + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0]) + result = s.interpolate(method="linear", limit_direction="forward") + tm.assert_series_equal(result, expected) + + expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan]) + result = s.interpolate(method="linear", limit_direction="backward") + tm.assert_series_equal(result, expected) + + def test_interp_limit_bad_direction(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + msg = ( + r"Invalid limit_direction: expecting one of \['forward', " + r"'backward', 'both'\], got 'abc'" + ) + with pytest.raises(ValueError, match=msg): + s.interpolate(method="linear", limit=2, limit_direction="abc") + + # raises an error even if no limit is specified. + with pytest.raises(ValueError, match=msg): + s.interpolate(method="linear", limit_direction="abc") + + # limit_area introduced GH #16284 + def test_interp_limit_area(self): + # These tests are for issue #9218 -- fill NaNs in both directions. + s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan]) + + expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan]) + result = s.interpolate(method="linear", limit_area="inside") + tm.assert_series_equal(result, expected) + + expected = Series( + [np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan] + ) + result = s.interpolate(method="linear", limit_area="inside", limit=1) + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan]) + result = s.interpolate( + method="linear", limit_area="inside", limit_direction="both", limit=1 + ) + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0]) + result = s.interpolate(method="linear", limit_area="outside") + tm.assert_series_equal(result, expected) + + expected = Series( + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan] + ) + result = s.interpolate(method="linear", limit_area="outside", limit=1) + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]) + result = s.interpolate( + method="linear", limit_area="outside", limit_direction="both", limit=1 + ) + tm.assert_series_equal(result, expected) + + expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan]) + result = s.interpolate( + method="linear", limit_area="outside", limit_direction="backward" + ) + tm.assert_series_equal(result, expected) + + # raises an error even if limit type is wrong. + msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc" + with pytest.raises(ValueError, match=msg): + s.interpolate(method="linear", limit_area="abc") + + def test_interp_limit_direction(self): + # These tests are for issue #9218 -- fill NaNs in both directions. + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0]) + result = s.interpolate(method="linear", limit=2, limit_direction="backward") + tm.assert_series_equal(result, expected) + + expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0]) + result = s.interpolate(method="linear", limit=1, limit_direction="both") + tm.assert_series_equal(result, expected) + + # Check that this works on a longer series of nans. + s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan]) + + expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]) + result = s.interpolate(method="linear", limit=2, limit_direction="both") + tm.assert_series_equal(result, expected) + + expected = Series( + [1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0] + ) + result = s.interpolate(method="linear", limit=1, limit_direction="both") + tm.assert_series_equal(result, expected) + + def test_interp_limit_to_ends(self): + # These test are for issue #10420 -- flow back to beginning. + s = Series([np.nan, np.nan, 5, 7, 9, np.nan]) + + expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan]) + result = s.interpolate(method="linear", limit=2, limit_direction="backward") + tm.assert_series_equal(result, expected) + + expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0]) + result = s.interpolate(method="linear", limit=2, limit_direction="both") + tm.assert_series_equal(result, expected) + + def test_interp_limit_before_ends(self): + # These test are for issue #11115 -- limit ends properly. + s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan]) + + expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan]) + result = s.interpolate(method="linear", limit=1, limit_direction="forward") + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan]) + result = s.interpolate(method="linear", limit=1, limit_direction="backward") + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan]) + result = s.interpolate(method="linear", limit=1, limit_direction="both") + tm.assert_series_equal(result, expected) + + @td.skip_if_no_scipy + def test_interp_all_good(self): + s = Series([1, 2, 3]) + result = s.interpolate(method="polynomial", order=1) + tm.assert_series_equal(result, s) + + # non-scipy + result = s.interpolate() + tm.assert_series_equal(result, s) + + @pytest.mark.parametrize( + "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)] + ) + def test_interp_multiIndex(self, check_scipy): + idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")]) + s = Series([1, 2, np.nan], index=idx) + + expected = s.copy() + expected.loc[2] = 2 + result = s.interpolate() + tm.assert_series_equal(result, expected) + + msg = "Only `method=linear` interpolation is supported on MultiIndexes" + if check_scipy: + with pytest.raises(ValueError, match=msg): + s.interpolate(method="polynomial", order=1) + + @td.skip_if_no_scipy + def test_interp_nonmono_raise(self): + s = Series([1, np.nan, 3], index=[0, 2, 1]) + msg = "krogh interpolation requires that the index be monotonic" + with pytest.raises(ValueError, match=msg): + s.interpolate(method="krogh") + + @td.skip_if_no_scipy + @pytest.mark.parametrize("method", ["nearest", "pad"]) + def test_interp_datetime64(self, method, tz_naive_fixture): + df = Series( + [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture) + ) + result = df.interpolate(method=method) + expected = Series( + [1.0, 1.0, 3.0], + index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture), + ) + tm.assert_series_equal(result, expected) + + def test_interp_pad_datetime64tz_values(self): + # GH#27628 missing.interpolate_2d should handle datetimetz values + dti = pd.date_range("2015-04-05", periods=3, tz="US/Central") + ser = pd.Series(dti) + ser[1] = pd.NaT + result = ser.interpolate(method="pad") + + expected = pd.Series(dti) + expected[1] = expected[0] + tm.assert_series_equal(result, expected) + + def test_interp_limit_no_nans(self): + # GH 7173 + s = pd.Series([1.0, 2.0, 3.0]) + result = s.interpolate(limit=1) + expected = s + tm.assert_series_equal(result, expected) + + @td.skip_if_no_scipy + @pytest.mark.parametrize("method", ["polynomial", "spline"]) + def test_no_order(self, method): + # see GH-10633, GH-24014 + s = Series([0, 1, np.nan, 3]) + msg = "You must specify the order of the spline or polynomial" + with pytest.raises(ValueError, match=msg): + s.interpolate(method=method) + + @td.skip_if_no_scipy + @pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan]) + def test_interpolate_spline_invalid_order(self, order): + s = Series([0, 1, np.nan, 3]) + msg = "order needs to be specified and greater than 0" + with pytest.raises(ValueError, match=msg): + s.interpolate(method="spline", order=order) + + @td.skip_if_no_scipy + def test_spline(self): + s = Series([1, 2, np.nan, 4, 5, np.nan, 7]) + result = s.interpolate(method="spline", order=1) + expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) + tm.assert_series_equal(result, expected) + + @td.skip_if_no_scipy + def test_spline_extrapolate(self): + s = Series([1, 2, 3, 4, np.nan, 6, np.nan]) + result3 = s.interpolate(method="spline", order=1, ext=3) + expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0]) + tm.assert_series_equal(result3, expected3) + + result1 = s.interpolate(method="spline", order=1, ext=0) + expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) + tm.assert_series_equal(result1, expected1) + + @td.skip_if_no_scipy + def test_spline_smooth(self): + s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7]) + assert ( + s.interpolate(method="spline", order=3, s=0)[5] + != s.interpolate(method="spline", order=3)[5] + ) + + @td.skip_if_no_scipy + def test_spline_interpolation(self): + s = Series(np.arange(10) ** 2) + s[np.random.randint(0, 9, 3)] = np.nan + result1 = s.interpolate(method="spline", order=1) + expected1 = s.interpolate(method="spline", order=1) + tm.assert_series_equal(result1, expected1) + + def test_interp_timedelta64(self): + # GH 6424 + df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3])) + result = df.interpolate(method="time") + expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3])) + tm.assert_series_equal(result, expected) + + # test for non uniform spacing + df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4])) + result = df.interpolate(method="time") + expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4])) + tm.assert_series_equal(result, expected) + + def test_series_interpolate_method_values(self): + # GH#1646 + rng = date_range("1/1/2000", "1/20/2000", freq="D") + ts = Series(np.random.randn(len(rng)), index=rng) + + ts[::2] = np.nan + + result = ts.interpolate(method="values") + exp = ts.interpolate() + tm.assert_series_equal(result, exp) + + def test_series_interpolate_intraday(self): + # #1698 + index = pd.date_range("1/1/2012", periods=4, freq="12D") + ts = pd.Series([0, 12, 24, 36], index) + new_index = index.append(index + pd.DateOffset(days=1)).sort_values() + + exp = ts.reindex(new_index).interpolate(method="time") + + index = pd.date_range("1/1/2012", periods=4, freq="12H") + ts = pd.Series([0, 12, 24, 36], index) + new_index = index.append(index + pd.DateOffset(hours=1)).sort_values() + result = ts.reindex(new_index).interpolate(method="time") + + tm.assert_numpy_array_equal(result.values, exp.values) + + @pytest.mark.parametrize( + "ind", + [ + ["a", "b", "c", "d"], + pd.period_range(start="2019-01-01", periods=4), + pd.interval_range(start=0, end=4), + ], + ) + def test_interp_non_timedelta_index(self, interp_methods_ind, ind): + # gh 21662 + df = pd.DataFrame([0, 1, np.nan, 3], index=ind) + + method, kwargs = interp_methods_ind + if method == "pchip": + pytest.importorskip("scipy") + + if method == "linear": + result = df[0].interpolate(**kwargs) + expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind) + tm.assert_series_equal(result, expected) + else: + expected_error = ( + "Index column must be numeric or datetime type when " + f"using {method} method other than linear. " + "Try setting a numeric or datetime index column before " + "interpolating." + ) + with pytest.raises(ValueError, match=expected_error): + df[0].interpolate(method=method, **kwargs) + + def test_interpolate_timedelta_index(self, interp_methods_ind): + """ + Tests for non numerical index types - object, period, timedelta + Note that all methods except time, index, nearest and values + are tested here. + """ + # gh 21662 + ind = pd.timedelta_range(start=1, periods=4) + df = pd.DataFrame([0, 1, np.nan, 3], index=ind) + + method, kwargs = interp_methods_ind + if method == "pchip": + pytest.importorskip("scipy") + + if method in {"linear", "pchip"}: + result = df[0].interpolate(method=method, **kwargs) + expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind) + tm.assert_series_equal(result, expected) + else: + pytest.skip( + "This interpolation method is not supported for Timedelta Index yet." + ) + + @pytest.mark.parametrize( + "ascending, expected_values", + [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])], + ) + def test_interpolate_unsorted_index(self, ascending, expected_values): + # GH 21037 + ts = pd.Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1]) + result = ts.sort_index(ascending=ascending).interpolate(method="index") + expected = pd.Series(data=expected_values, index=expected_values, dtype=float) + tm.assert_series_equal(result, expected) diff --git a/pandas/tests/series/test_internals.py b/pandas/tests/series/test_internals.py index 4c817ed2e2d59..1566d8f36373b 100644 --- a/pandas/tests/series/test_internals.py +++ b/pandas/tests/series/test_internals.py @@ -169,6 +169,7 @@ def test_convert(self): result = s._convert(datetime=True, coerce=True) tm.assert_series_equal(result, s) + # FIXME: dont leave commented-out # r = s.copy() # r[0] = np.nan # result = r._convert(convert_dates=True,convert_numeric=False) diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index 6b7d9e00a5228..bac005465034f 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -5,7 +5,6 @@ import pytz from pandas._libs.tslib import iNaT -import pandas.util._test_decorators as td import pandas as pd from pandas import ( @@ -13,7 +12,6 @@ DataFrame, Index, IntervalIndex, - MultiIndex, NaT, Series, Timedelta, @@ -24,11 +22,6 @@ import pandas._testing as tm -def _simple_ts(start, end, freq="D"): - rng = date_range(start, end, freq=freq) - return Series(np.random.randn(len(rng)), index=rng) - - class TestSeriesMissingData: def test_timedelta_fillna(self): # GH 3371 @@ -988,666 +981,3 @@ def test_series_pad_backfill_limit(self): expected = s[-2:].reindex(index).fillna(method="backfill") expected[:3] = np.nan tm.assert_series_equal(result, expected) - - -@pytest.fixture( - params=[ - "linear", - "index", - "values", - "nearest", - "slinear", - "zero", - "quadratic", - "cubic", - "barycentric", - "krogh", - "polynomial", - "spline", - "piecewise_polynomial", - "from_derivatives", - "pchip", - "akima", - ] -) -def nontemporal_method(request): - """ Fixture that returns an (method name, required kwargs) pair. - - This fixture does not include method 'time' as a parameterization; that - method requires a Series with a DatetimeIndex, and is generally tested - separately from these non-temporal methods. - """ - method = request.param - kwargs = dict(order=1) if method in ("spline", "polynomial") else dict() - return method, kwargs - - -@pytest.fixture( - params=[ - "linear", - "slinear", - "zero", - "quadratic", - "cubic", - "barycentric", - "krogh", - "polynomial", - "spline", - "piecewise_polynomial", - "from_derivatives", - "pchip", - "akima", - ] -) -def interp_methods_ind(request): - """ Fixture that returns a (method name, required kwargs) pair to - be tested for various Index types. - - This fixture does not include methods - 'time', 'index', 'nearest', - 'values' as a parameterization - """ - method = request.param - kwargs = dict(order=1) if method in ("spline", "polynomial") else dict() - return method, kwargs - - -class TestSeriesInterpolateData: - def test_interpolate(self, datetime_series, string_series): - ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index) - - ts_copy = ts.copy() - ts_copy[5:10] = np.NaN - - linear_interp = ts_copy.interpolate(method="linear") - tm.assert_series_equal(linear_interp, ts) - - ord_ts = Series( - [d.toordinal() for d in datetime_series.index], index=datetime_series.index - ).astype(float) - - ord_ts_copy = ord_ts.copy() - ord_ts_copy[5:10] = np.NaN - - time_interp = ord_ts_copy.interpolate(method="time") - tm.assert_series_equal(time_interp, ord_ts) - - def test_interpolate_time_raises_for_non_timeseries(self): - # When method='time' is used on a non-TimeSeries that contains a null - # value, a ValueError should be raised. - non_ts = Series([0, 1, 2, np.NaN]) - msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex" - with pytest.raises(ValueError, match=msg): - non_ts.interpolate(method="time") - - @td.skip_if_no_scipy - def test_interpolate_pchip(self): - - ser = Series(np.sort(np.random.uniform(size=100))) - - # interpolate at new_index - new_index = ser.index.union( - Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75]) - ).astype(float) - interp_s = ser.reindex(new_index).interpolate(method="pchip") - # does not blow up, GH5977 - interp_s[49:51] - - @td.skip_if_no_scipy - def test_interpolate_akima(self): - - ser = Series([10, 11, 12, 13]) - - expected = Series( - [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], - index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), - ) - # interpolate at new_index - new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( - float - ) - interp_s = ser.reindex(new_index).interpolate(method="akima") - tm.assert_series_equal(interp_s[1:3], expected) - - @td.skip_if_no_scipy - def test_interpolate_piecewise_polynomial(self): - ser = Series([10, 11, 12, 13]) - - expected = Series( - [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], - index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), - ) - # interpolate at new_index - new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( - float - ) - interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial") - tm.assert_series_equal(interp_s[1:3], expected) - - @td.skip_if_no_scipy - def test_interpolate_from_derivatives(self): - ser = Series([10, 11, 12, 13]) - - expected = Series( - [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], - index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), - ) - # interpolate at new_index - new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( - float - ) - interp_s = ser.reindex(new_index).interpolate(method="from_derivatives") - tm.assert_series_equal(interp_s[1:3], expected) - - @pytest.mark.parametrize( - "kwargs", - [ - {}, - pytest.param( - {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy - ), - ], - ) - def test_interpolate_corners(self, kwargs): - s = Series([np.nan, np.nan]) - tm.assert_series_equal(s.interpolate(**kwargs), s) - - s = Series([], dtype=object).interpolate() - tm.assert_series_equal(s.interpolate(**kwargs), s) - - def test_interpolate_index_values(self): - s = Series(np.nan, index=np.sort(np.random.rand(30))) - s[::3] = np.random.randn(10) - - vals = s.index.values.astype(float) - - result = s.interpolate(method="index") - - expected = s.copy() - bad = isna(expected.values) - good = ~bad - expected = Series( - np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad] - ) - - tm.assert_series_equal(result[bad], expected) - - # 'values' is synonymous with 'index' for the method kwarg - other_result = s.interpolate(method="values") - - tm.assert_series_equal(other_result, result) - tm.assert_series_equal(other_result[bad], expected) - - def test_interpolate_non_ts(self): - s = Series([1, 3, np.nan, np.nan, np.nan, 11]) - msg = ( - "time-weighted interpolation only works on Series or DataFrames " - "with a DatetimeIndex" - ) - with pytest.raises(ValueError, match=msg): - s.interpolate(method="time") - - @pytest.mark.parametrize( - "kwargs", - [ - {}, - pytest.param( - {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy - ), - ], - ) - def test_nan_interpolate(self, kwargs): - s = Series([0, 1, np.nan, 3]) - result = s.interpolate(**kwargs) - expected = Series([0.0, 1.0, 2.0, 3.0]) - tm.assert_series_equal(result, expected) - - def test_nan_irregular_index(self): - s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9]) - result = s.interpolate() - expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9]) - tm.assert_series_equal(result, expected) - - def test_nan_str_index(self): - s = Series([0, 1, 2, np.nan], index=list("abcd")) - result = s.interpolate() - expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd")) - tm.assert_series_equal(result, expected) - - @td.skip_if_no_scipy - def test_interp_quad(self): - sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4]) - result = sq.interpolate(method="quadratic") - expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4]) - tm.assert_series_equal(result, expected) - - @td.skip_if_no_scipy - def test_interp_scipy_basic(self): - s = Series([1, 3, np.nan, 12, np.nan, 25]) - # slinear - expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0]) - result = s.interpolate(method="slinear") - tm.assert_series_equal(result, expected) - - result = s.interpolate(method="slinear", downcast="infer") - tm.assert_series_equal(result, expected) - # nearest - expected = Series([1, 3, 3, 12, 12, 25]) - result = s.interpolate(method="nearest") - tm.assert_series_equal(result, expected.astype("float")) - - result = s.interpolate(method="nearest", downcast="infer") - tm.assert_series_equal(result, expected) - # zero - expected = Series([1, 3, 3, 12, 12, 25]) - result = s.interpolate(method="zero") - tm.assert_series_equal(result, expected.astype("float")) - - result = s.interpolate(method="zero", downcast="infer") - tm.assert_series_equal(result, expected) - # quadratic - # GH #15662. - expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0]) - result = s.interpolate(method="quadratic") - tm.assert_series_equal(result, expected) - - result = s.interpolate(method="quadratic", downcast="infer") - tm.assert_series_equal(result, expected) - # cubic - expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0]) - result = s.interpolate(method="cubic") - tm.assert_series_equal(result, expected) - - def test_interp_limit(self): - s = Series([1, 3, np.nan, np.nan, np.nan, 11]) - - expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0]) - result = s.interpolate(method="linear", limit=2) - tm.assert_series_equal(result, expected) - - @pytest.mark.parametrize("limit", [-1, 0]) - def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit): - # GH 9217: make sure limit is greater than zero. - s = pd.Series([1, 2, np.nan, 4]) - method, kwargs = nontemporal_method - with pytest.raises(ValueError, match="Limit must be greater than 0"): - s.interpolate(limit=limit, method=method, **kwargs) - - def test_interpolate_invalid_float_limit(self, nontemporal_method): - # GH 9217: make sure limit is an integer. - s = pd.Series([1, 2, np.nan, 4]) - method, kwargs = nontemporal_method - limit = 2.0 - with pytest.raises(ValueError, match="Limit must be an integer"): - s.interpolate(limit=limit, method=method, **kwargs) - - @pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"]) - def test_interp_invalid_method(self, invalid_method): - s = Series([1, 3, np.nan, 12, np.nan, 25]) - - msg = f"method must be one of.* Got '{invalid_method}' instead" - with pytest.raises(ValueError, match=msg): - s.interpolate(method=invalid_method) - - # When an invalid method and invalid limit (such as -1) are - # provided, the error message reflects the invalid method. - with pytest.raises(ValueError, match=msg): - s.interpolate(method=invalid_method, limit=-1) - - def test_interp_limit_forward(self): - s = Series([1, 3, np.nan, np.nan, np.nan, 11]) - - # Provide 'forward' (the default) explicitly here. - expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0]) - - result = s.interpolate(method="linear", limit=2, limit_direction="forward") - tm.assert_series_equal(result, expected) - - result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD") - tm.assert_series_equal(result, expected) - - def test_interp_unlimited(self): - # these test are for issue #16282 default Limit=None is unlimited - s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan]) - expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0]) - result = s.interpolate(method="linear", limit_direction="both") - tm.assert_series_equal(result, expected) - - expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0]) - result = s.interpolate(method="linear", limit_direction="forward") - tm.assert_series_equal(result, expected) - - expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan]) - result = s.interpolate(method="linear", limit_direction="backward") - tm.assert_series_equal(result, expected) - - def test_interp_limit_bad_direction(self): - s = Series([1, 3, np.nan, np.nan, np.nan, 11]) - - msg = ( - r"Invalid limit_direction: expecting one of \['forward', " - r"'backward', 'both'\], got 'abc'" - ) - with pytest.raises(ValueError, match=msg): - s.interpolate(method="linear", limit=2, limit_direction="abc") - - # raises an error even if no limit is specified. - with pytest.raises(ValueError, match=msg): - s.interpolate(method="linear", limit_direction="abc") - - # limit_area introduced GH #16284 - def test_interp_limit_area(self): - # These tests are for issue #9218 -- fill NaNs in both directions. - s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan]) - - expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan]) - result = s.interpolate(method="linear", limit_area="inside") - tm.assert_series_equal(result, expected) - - expected = Series( - [np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan] - ) - result = s.interpolate(method="linear", limit_area="inside", limit=1) - tm.assert_series_equal(result, expected) - - expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan]) - result = s.interpolate( - method="linear", limit_area="inside", limit_direction="both", limit=1 - ) - tm.assert_series_equal(result, expected) - - expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0]) - result = s.interpolate(method="linear", limit_area="outside") - tm.assert_series_equal(result, expected) - - expected = Series( - [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan] - ) - result = s.interpolate(method="linear", limit_area="outside", limit=1) - tm.assert_series_equal(result, expected) - - expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]) - result = s.interpolate( - method="linear", limit_area="outside", limit_direction="both", limit=1 - ) - tm.assert_series_equal(result, expected) - - expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan]) - result = s.interpolate( - method="linear", limit_area="outside", limit_direction="backward" - ) - tm.assert_series_equal(result, expected) - - # raises an error even if limit type is wrong. - msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc" - with pytest.raises(ValueError, match=msg): - s.interpolate(method="linear", limit_area="abc") - - def test_interp_limit_direction(self): - # These tests are for issue #9218 -- fill NaNs in both directions. - s = Series([1, 3, np.nan, np.nan, np.nan, 11]) - - expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0]) - result = s.interpolate(method="linear", limit=2, limit_direction="backward") - tm.assert_series_equal(result, expected) - - expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0]) - result = s.interpolate(method="linear", limit=1, limit_direction="both") - tm.assert_series_equal(result, expected) - - # Check that this works on a longer series of nans. - s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan]) - - expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]) - result = s.interpolate(method="linear", limit=2, limit_direction="both") - tm.assert_series_equal(result, expected) - - expected = Series( - [1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0] - ) - result = s.interpolate(method="linear", limit=1, limit_direction="both") - tm.assert_series_equal(result, expected) - - def test_interp_limit_to_ends(self): - # These test are for issue #10420 -- flow back to beginning. - s = Series([np.nan, np.nan, 5, 7, 9, np.nan]) - - expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan]) - result = s.interpolate(method="linear", limit=2, limit_direction="backward") - tm.assert_series_equal(result, expected) - - expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0]) - result = s.interpolate(method="linear", limit=2, limit_direction="both") - tm.assert_series_equal(result, expected) - - def test_interp_limit_before_ends(self): - # These test are for issue #11115 -- limit ends properly. - s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan]) - - expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan]) - result = s.interpolate(method="linear", limit=1, limit_direction="forward") - tm.assert_series_equal(result, expected) - - expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan]) - result = s.interpolate(method="linear", limit=1, limit_direction="backward") - tm.assert_series_equal(result, expected) - - expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan]) - result = s.interpolate(method="linear", limit=1, limit_direction="both") - tm.assert_series_equal(result, expected) - - @td.skip_if_no_scipy - def test_interp_all_good(self): - s = Series([1, 2, 3]) - result = s.interpolate(method="polynomial", order=1) - tm.assert_series_equal(result, s) - - # non-scipy - result = s.interpolate() - tm.assert_series_equal(result, s) - - @pytest.mark.parametrize( - "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)] - ) - def test_interp_multiIndex(self, check_scipy): - idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")]) - s = Series([1, 2, np.nan], index=idx) - - expected = s.copy() - expected.loc[2] = 2 - result = s.interpolate() - tm.assert_series_equal(result, expected) - - msg = "Only `method=linear` interpolation is supported on MultiIndexes" - if check_scipy: - with pytest.raises(ValueError, match=msg): - s.interpolate(method="polynomial", order=1) - - @td.skip_if_no_scipy - def test_interp_nonmono_raise(self): - s = Series([1, np.nan, 3], index=[0, 2, 1]) - msg = "krogh interpolation requires that the index be monotonic" - with pytest.raises(ValueError, match=msg): - s.interpolate(method="krogh") - - @td.skip_if_no_scipy - @pytest.mark.parametrize("method", ["nearest", "pad"]) - def test_interp_datetime64(self, method, tz_naive_fixture): - df = Series( - [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture) - ) - result = df.interpolate(method=method) - expected = Series( - [1.0, 1.0, 3.0], - index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture), - ) - tm.assert_series_equal(result, expected) - - def test_interp_pad_datetime64tz_values(self): - # GH#27628 missing.interpolate_2d should handle datetimetz values - dti = pd.date_range("2015-04-05", periods=3, tz="US/Central") - ser = pd.Series(dti) - ser[1] = pd.NaT - result = ser.interpolate(method="pad") - - expected = pd.Series(dti) - expected[1] = expected[0] - tm.assert_series_equal(result, expected) - - def test_interp_limit_no_nans(self): - # GH 7173 - s = pd.Series([1.0, 2.0, 3.0]) - result = s.interpolate(limit=1) - expected = s - tm.assert_series_equal(result, expected) - - @td.skip_if_no_scipy - @pytest.mark.parametrize("method", ["polynomial", "spline"]) - def test_no_order(self, method): - # see GH-10633, GH-24014 - s = Series([0, 1, np.nan, 3]) - msg = "You must specify the order of the spline or polynomial" - with pytest.raises(ValueError, match=msg): - s.interpolate(method=method) - - @td.skip_if_no_scipy - @pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan]) - def test_interpolate_spline_invalid_order(self, order): - s = Series([0, 1, np.nan, 3]) - msg = "order needs to be specified and greater than 0" - with pytest.raises(ValueError, match=msg): - s.interpolate(method="spline", order=order) - - @td.skip_if_no_scipy - def test_spline(self): - s = Series([1, 2, np.nan, 4, 5, np.nan, 7]) - result = s.interpolate(method="spline", order=1) - expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) - tm.assert_series_equal(result, expected) - - @td.skip_if_no_scipy - def test_spline_extrapolate(self): - s = Series([1, 2, 3, 4, np.nan, 6, np.nan]) - result3 = s.interpolate(method="spline", order=1, ext=3) - expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0]) - tm.assert_series_equal(result3, expected3) - - result1 = s.interpolate(method="spline", order=1, ext=0) - expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) - tm.assert_series_equal(result1, expected1) - - @td.skip_if_no_scipy - def test_spline_smooth(self): - s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7]) - assert ( - s.interpolate(method="spline", order=3, s=0)[5] - != s.interpolate(method="spline", order=3)[5] - ) - - @td.skip_if_no_scipy - def test_spline_interpolation(self): - s = Series(np.arange(10) ** 2) - s[np.random.randint(0, 9, 3)] = np.nan - result1 = s.interpolate(method="spline", order=1) - expected1 = s.interpolate(method="spline", order=1) - tm.assert_series_equal(result1, expected1) - - def test_interp_timedelta64(self): - # GH 6424 - df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3])) - result = df.interpolate(method="time") - expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3])) - tm.assert_series_equal(result, expected) - - # test for non uniform spacing - df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4])) - result = df.interpolate(method="time") - expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4])) - tm.assert_series_equal(result, expected) - - def test_series_interpolate_method_values(self): - # #1646 - ts = _simple_ts("1/1/2000", "1/20/2000") - ts[::2] = np.nan - - result = ts.interpolate(method="values") - exp = ts.interpolate() - tm.assert_series_equal(result, exp) - - def test_series_interpolate_intraday(self): - # #1698 - index = pd.date_range("1/1/2012", periods=4, freq="12D") - ts = pd.Series([0, 12, 24, 36], index) - new_index = index.append(index + pd.DateOffset(days=1)).sort_values() - - exp = ts.reindex(new_index).interpolate(method="time") - - index = pd.date_range("1/1/2012", periods=4, freq="12H") - ts = pd.Series([0, 12, 24, 36], index) - new_index = index.append(index + pd.DateOffset(hours=1)).sort_values() - result = ts.reindex(new_index).interpolate(method="time") - - tm.assert_numpy_array_equal(result.values, exp.values) - - @pytest.mark.parametrize( - "ind", - [ - ["a", "b", "c", "d"], - pd.period_range(start="2019-01-01", periods=4), - pd.interval_range(start=0, end=4), - ], - ) - def test_interp_non_timedelta_index(self, interp_methods_ind, ind): - # gh 21662 - df = pd.DataFrame([0, 1, np.nan, 3], index=ind) - - method, kwargs = interp_methods_ind - if method == "pchip": - pytest.importorskip("scipy") - - if method == "linear": - result = df[0].interpolate(**kwargs) - expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind) - tm.assert_series_equal(result, expected) - else: - expected_error = ( - "Index column must be numeric or datetime type when " - f"using {method} method other than linear. " - "Try setting a numeric or datetime index column before " - "interpolating." - ) - with pytest.raises(ValueError, match=expected_error): - df[0].interpolate(method=method, **kwargs) - - def test_interpolate_timedelta_index(self, interp_methods_ind): - """ - Tests for non numerical index types - object, period, timedelta - Note that all methods except time, index, nearest and values - are tested here. - """ - # gh 21662 - ind = pd.timedelta_range(start=1, periods=4) - df = pd.DataFrame([0, 1, np.nan, 3], index=ind) - - method, kwargs = interp_methods_ind - if method == "pchip": - pytest.importorskip("scipy") - - if method in {"linear", "pchip"}: - result = df[0].interpolate(method=method, **kwargs) - expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind) - tm.assert_series_equal(result, expected) - else: - pytest.skip( - "This interpolation method is not supported for Timedelta Index yet." - ) - - @pytest.mark.parametrize( - "ascending, expected_values", - [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])], - ) - def test_interpolate_unsorted_index(self, ascending, expected_values): - # GH 21037 - ts = pd.Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1]) - result = ts.sort_index(ascending=ascending).interpolate(method="index") - expected = pd.Series(data=expected_values, index=expected_values, dtype=float) - tm.assert_series_equal(result, expected)
https://api.github.com/repos/pandas-dev/pandas/pulls/32112
2020-02-19T16:34:58Z
2020-02-22T16:01:54Z
2020-02-22T16:01:54Z
2020-02-22T16:49:18Z
REF: misplaced Series.combine_first tests
diff --git a/pandas/tests/series/methods/test_combine_first.py b/pandas/tests/series/methods/test_combine_first.py index aed6425e50117..1ee55fbe39513 100644 --- a/pandas/tests/series/methods/test_combine_first.py +++ b/pandas/tests/series/methods/test_combine_first.py @@ -1,6 +1,9 @@ +from datetime import datetime + import numpy as np -from pandas import Period, Series, date_range, period_range +import pandas as pd +from pandas import Period, Series, date_range, period_range, to_datetime import pandas._testing as tm @@ -17,3 +20,75 @@ def test_combine_first_period_datetime(self): result = a.combine_first(b) expected = Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64) tm.assert_series_equal(result, expected) + + def test_combine_first_name(self, datetime_series): + result = datetime_series.combine_first(datetime_series[:5]) + assert result.name == datetime_series.name + + def test_combine_first(self): + values = tm.makeIntIndex(20).values.astype(float) + series = Series(values, index=tm.makeIntIndex(20)) + + series_copy = series * 2 + series_copy[::2] = np.NaN + + # nothing used from the input + combined = series.combine_first(series_copy) + + tm.assert_series_equal(combined, series) + + # Holes filled from input + combined = series_copy.combine_first(series) + assert np.isfinite(combined).all() + + tm.assert_series_equal(combined[::2], series[::2]) + tm.assert_series_equal(combined[1::2], series_copy[1::2]) + + # mixed types + index = tm.makeStringIndex(20) + floats = Series(tm.randn(20), index=index) + strings = Series(tm.makeStringIndex(10), index=index[::2]) + + combined = strings.combine_first(floats) + + tm.assert_series_equal(strings, combined.loc[index[::2]]) + tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]]) + + # corner case + ser = Series([1.0, 2, 3], index=[0, 1, 2]) + empty = Series([], index=[], dtype=object) + result = ser.combine_first(empty) + ser.index = ser.index.astype("O") + tm.assert_series_equal(ser, result) + + def test_combine_first_dt64(self): + + s0 = to_datetime(Series(["2010", np.NaN])) + s1 = to_datetime(Series([np.NaN, "2011"])) + rs = s0.combine_first(s1) + xp = to_datetime(Series(["2010", "2011"])) + tm.assert_series_equal(rs, xp) + + s0 = to_datetime(Series(["2010", np.NaN])) + s1 = Series([np.NaN, "2011"]) + rs = s0.combine_first(s1) + xp = Series([datetime(2010, 1, 1), "2011"]) + tm.assert_series_equal(rs, xp) + + def test_combine_first_dt_tz_values(self, tz_naive_fixture): + ser1 = pd.Series( + pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture), + name="ser1", + ) + ser2 = pd.Series( + pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture), + index=[2, 3, 4], + name="ser2", + ) + result = ser1.combine_first(ser2) + exp_vals = pd.DatetimeIndex( + ["20150101", "20150102", "20150103", "20160515", "20160516"], + tz=tz_naive_fixture, + ) + exp = pd.Series(exp_vals, name="ser1") + tm.assert_series_equal(exp, result) diff --git a/pandas/tests/series/test_api.py b/pandas/tests/series/test_api.py index 33706c00c53f4..3e877cf2fc787 100644 --- a/pandas/tests/series/test_api.py +++ b/pandas/tests/series/test_api.py @@ -85,10 +85,6 @@ def test_binop_maybe_preserve_name(self, datetime_series): result = getattr(s, op)(cp) assert result.name is None - def test_combine_first_name(self, datetime_series): - result = datetime_series.combine_first(datetime_series[:5]) - assert result.name == datetime_series.name - def test_getitem_preserve_name(self, datetime_series): result = datetime_series[datetime_series > 0] assert result.name == datetime_series.name diff --git a/pandas/tests/series/test_combine_concat.py b/pandas/tests/series/test_combine_concat.py index 4cb471597b67a..4afa083e97c7c 100644 --- a/pandas/tests/series/test_combine_concat.py +++ b/pandas/tests/series/test_combine_concat.py @@ -1,10 +1,8 @@ -from datetime import datetime - import numpy as np import pytest import pandas as pd -from pandas import DataFrame, Series, to_datetime +from pandas import DataFrame, Series import pandas._testing as tm @@ -22,42 +20,6 @@ def test_combine_scalar(self): expected = pd.Series([min(i * 10, 22) for i in range(5)]) tm.assert_series_equal(result, expected) - def test_combine_first(self): - values = tm.makeIntIndex(20).values.astype(float) - series = Series(values, index=tm.makeIntIndex(20)) - - series_copy = series * 2 - series_copy[::2] = np.NaN - - # nothing used from the input - combined = series.combine_first(series_copy) - - tm.assert_series_equal(combined, series) - - # Holes filled from input - combined = series_copy.combine_first(series) - assert np.isfinite(combined).all() - - tm.assert_series_equal(combined[::2], series[::2]) - tm.assert_series_equal(combined[1::2], series_copy[1::2]) - - # mixed types - index = tm.makeStringIndex(20) - floats = Series(tm.randn(20), index=index) - strings = Series(tm.makeStringIndex(10), index=index[::2]) - - combined = strings.combine_first(floats) - - tm.assert_series_equal(strings, combined.loc[index[::2]]) - tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]]) - - # corner case - s = Series([1.0, 2, 3], index=[0, 1, 2]) - empty = Series([], index=[], dtype=object) - result = s.combine_first(empty) - s.index = s.index.astype("O") - tm.assert_series_equal(s, result) - def test_update(self): s = Series([1.5, np.nan, 3.0, 4.0, np.nan]) s2 = Series([np.nan, 3.5, np.nan, 5.0]) @@ -156,24 +118,6 @@ def get_result_type(dtype, dtype2): result = pd.concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype assert result.kind == expected - def test_combine_first_dt_tz_values(self, tz_naive_fixture): - ser1 = pd.Series( - pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture), - name="ser1", - ) - ser2 = pd.Series( - pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture), - index=[2, 3, 4], - name="ser2", - ) - result = ser1.combine_first(ser2) - exp_vals = pd.DatetimeIndex( - ["20150101", "20150102", "20150103", "20160515", "20160516"], - tz=tz_naive_fixture, - ) - exp = pd.Series(exp_vals, name="ser1") - tm.assert_series_equal(exp, result) - def test_concat_empty_series_dtypes(self): # booleans @@ -250,17 +194,3 @@ def test_concat_empty_series_dtypes(self): # TODO: release-note: concat sparse dtype expected = pd.SparseDtype("object") assert result.dtype == expected - - def test_combine_first_dt64(self): - - s0 = to_datetime(Series(["2010", np.NaN])) - s1 = to_datetime(Series([np.NaN, "2011"])) - rs = s0.combine_first(s1) - xp = to_datetime(Series(["2010", "2011"])) - tm.assert_series_equal(rs, xp) - - s0 = to_datetime(Series(["2010", np.NaN])) - s1 = Series([np.NaN, "2011"]) - rs = s0.combine_first(s1) - xp = Series([datetime(2010, 1, 1), "2011"]) - tm.assert_series_equal(rs, xp)
https://api.github.com/repos/pandas-dev/pandas/pulls/32111
2020-02-19T16:18:36Z
2020-02-20T12:44:52Z
2020-02-20T12:44:52Z
2020-02-20T15:00:13Z
TST: method-specific files for DataFrame assign, interpolate
diff --git a/pandas/tests/frame/methods/test_assign.py b/pandas/tests/frame/methods/test_assign.py new file mode 100644 index 0000000000000..63b9f031de188 --- /dev/null +++ b/pandas/tests/frame/methods/test_assign.py @@ -0,0 +1,82 @@ +import pytest + +from pandas import DataFrame +import pandas._testing as tm + + +class TestAssign: + def test_assign(self): + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + original = df.copy() + result = df.assign(C=df.B / df.A) + expected = df.copy() + expected["C"] = [4, 2.5, 2] + tm.assert_frame_equal(result, expected) + + # lambda syntax + result = df.assign(C=lambda x: x.B / x.A) + tm.assert_frame_equal(result, expected) + + # original is unmodified + tm.assert_frame_equal(df, original) + + # Non-Series array-like + result = df.assign(C=[4, 2.5, 2]) + tm.assert_frame_equal(result, expected) + # original is unmodified + tm.assert_frame_equal(df, original) + + result = df.assign(B=df.B / df.A) + expected = expected.drop("B", axis=1).rename(columns={"C": "B"}) + tm.assert_frame_equal(result, expected) + + # overwrite + result = df.assign(A=df.A + df.B) + expected = df.copy() + expected["A"] = [5, 7, 9] + tm.assert_frame_equal(result, expected) + + # lambda + result = df.assign(A=lambda x: x.A + x.B) + tm.assert_frame_equal(result, expected) + + def test_assign_multiple(self): + df = DataFrame([[1, 4], [2, 5], [3, 6]], columns=["A", "B"]) + result = df.assign(C=[7, 8, 9], D=df.A, E=lambda x: x.B) + expected = DataFrame( + [[1, 4, 7, 1, 4], [2, 5, 8, 2, 5], [3, 6, 9, 3, 6]], columns=list("ABCDE") + ) + tm.assert_frame_equal(result, expected) + + def test_assign_order(self): + # GH 9818 + df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) + result = df.assign(D=df.A + df.B, C=df.A - df.B) + + expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC")) + tm.assert_frame_equal(result, expected) + result = df.assign(C=df.A - df.B, D=df.A + df.B) + + expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], columns=list("ABCD")) + + tm.assert_frame_equal(result, expected) + + def test_assign_bad(self): + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + + # non-keyword argument + with pytest.raises(TypeError): + df.assign(lambda x: x.A) + with pytest.raises(AttributeError): + df.assign(C=df.A, D=df.A + df.C) + + def test_assign_dependent(self): + df = DataFrame({"A": [1, 2], "B": [3, 4]}) + + result = df.assign(C=df.A, D=lambda x: x["A"] + x["C"]) + expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) + tm.assert_frame_equal(result, expected) + + result = df.assign(C=lambda df: df.A, D=lambda df: df["A"] + df["C"]) + expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) + tm.assert_frame_equal(result, expected) diff --git a/pandas/tests/frame/methods/test_interpolate.py b/pandas/tests/frame/methods/test_interpolate.py new file mode 100644 index 0000000000000..3b8fa0dfbb603 --- /dev/null +++ b/pandas/tests/frame/methods/test_interpolate.py @@ -0,0 +1,286 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import DataFrame, Series, date_range +import pandas._testing as tm + + +class TestDataFrameInterpolate: + def test_interp_basic(self): + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + expected = DataFrame( + { + "A": [1.0, 2.0, 3.0, 4.0], + "B": [1.0, 4.0, 9.0, 9.0], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + result = df.interpolate() + tm.assert_frame_equal(result, expected) + + result = df.set_index("C").interpolate() + expected = df.set_index("C") + expected.loc[3, "A"] = 3 + expected.loc[5, "B"] = 9 + tm.assert_frame_equal(result, expected) + + def test_interp_bad_method(self): + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + with pytest.raises(ValueError): + df.interpolate(method="not_a_method") + + def test_interp_combo(self): + df = DataFrame( + { + "A": [1.0, 2.0, np.nan, 4.0], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + + result = df["A"].interpolate() + expected = Series([1.0, 2.0, 3.0, 4.0], name="A") + tm.assert_series_equal(result, expected) + + result = df["A"].interpolate(downcast="infer") + expected = Series([1, 2, 3, 4], name="A") + tm.assert_series_equal(result, expected) + + def test_interp_nan_idx(self): + df = DataFrame({"A": [1, 2, np.nan, 4], "B": [np.nan, 2, 3, 4]}) + df = df.set_index("A") + with pytest.raises(NotImplementedError): + df.interpolate(method="values") + + @td.skip_if_no_scipy + def test_interp_various(self): + df = DataFrame( + {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} + ) + df = df.set_index("C") + expected = df.copy() + result = df.interpolate(method="polynomial", order=1) + + expected.A.loc[3] = 2.66666667 + expected.A.loc[13] = 5.76923076 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="cubic") + # GH #15662. + expected.A.loc[3] = 2.81547781 + expected.A.loc[13] = 5.52964175 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="nearest") + expected.A.loc[3] = 2 + expected.A.loc[13] = 5 + tm.assert_frame_equal(result, expected, check_dtype=False) + + result = df.interpolate(method="quadratic") + expected.A.loc[3] = 2.82150771 + expected.A.loc[13] = 6.12648668 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="slinear") + expected.A.loc[3] = 2.66666667 + expected.A.loc[13] = 5.76923077 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="zero") + expected.A.loc[3] = 2.0 + expected.A.loc[13] = 5 + tm.assert_frame_equal(result, expected, check_dtype=False) + + @td.skip_if_no_scipy + def test_interp_alt_scipy(self): + df = DataFrame( + {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} + ) + result = df.interpolate(method="barycentric") + expected = df.copy() + expected.loc[2, "A"] = 3 + expected.loc[5, "A"] = 6 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="barycentric", downcast="infer") + tm.assert_frame_equal(result, expected.astype(np.int64)) + + result = df.interpolate(method="krogh") + expectedk = df.copy() + expectedk["A"] = expected["A"] + tm.assert_frame_equal(result, expectedk) + + result = df.interpolate(method="pchip") + expected.loc[2, "A"] = 3 + expected.loc[5, "A"] = 6.0 + + tm.assert_frame_equal(result, expected) + + def test_interp_rowwise(self): + df = DataFrame( + { + 0: [1, 2, np.nan, 4], + 1: [2, 3, 4, np.nan], + 2: [np.nan, 4, 5, 6], + 3: [4, np.nan, 6, 7], + 4: [1, 2, 3, 4], + } + ) + result = df.interpolate(axis=1) + expected = df.copy() + expected.loc[3, 1] = 5 + expected.loc[0, 2] = 3 + expected.loc[1, 3] = 3 + expected[4] = expected[4].astype(np.float64) + tm.assert_frame_equal(result, expected) + + result = df.interpolate(axis=1, method="values") + tm.assert_frame_equal(result, expected) + + result = df.interpolate(axis=0) + expected = df.interpolate() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "axis_name, axis_number", + [ + pytest.param("rows", 0, id="rows_0"), + pytest.param("index", 0, id="index_0"), + pytest.param("columns", 1, id="columns_1"), + ], + ) + def test_interp_axis_names(self, axis_name, axis_number): + # GH 29132: test axis names + data = {0: [0, np.nan, 6], 1: [1, np.nan, 7], 2: [2, 5, 8]} + + df = DataFrame(data, dtype=np.float64) + result = df.interpolate(axis=axis_name, method="linear") + expected = df.interpolate(axis=axis_number, method="linear") + tm.assert_frame_equal(result, expected) + + def test_rowwise_alt(self): + df = DataFrame( + { + 0: [0, 0.5, 1.0, np.nan, 4, 8, np.nan, np.nan, 64], + 1: [1, 2, 3, 4, 3, 2, 1, 0, -1], + } + ) + df.interpolate(axis=0) + # TODO: assert something? + + @pytest.mark.parametrize( + "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)] + ) + def test_interp_leading_nans(self, check_scipy): + df = DataFrame( + {"A": [np.nan, np.nan, 0.5, 0.25, 0], "B": [np.nan, -3, -3.5, np.nan, -4]} + ) + result = df.interpolate() + expected = df.copy() + expected["B"].loc[3] = -3.75 + tm.assert_frame_equal(result, expected) + + if check_scipy: + result = df.interpolate(method="polynomial", order=1) + tm.assert_frame_equal(result, expected) + + def test_interp_raise_on_only_mixed(self): + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": ["a", "b", "c", "d"], + "C": [np.nan, 2, 5, 7], + "D": [np.nan, np.nan, 9, 9], + "E": [1, 2, 3, 4], + } + ) + with pytest.raises(TypeError): + df.interpolate(axis=1) + + def test_interp_raise_on_all_object_dtype(self): + # GH 22985 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, dtype="object") + msg = ( + "Cannot interpolate with all object-dtype columns " + "in the DataFrame. Try setting at least one " + "column to a numeric dtype." + ) + with pytest.raises(TypeError, match=msg): + df.interpolate() + + def test_interp_inplace(self): + df = DataFrame({"a": [1.0, 2.0, np.nan, 4.0]}) + expected = DataFrame({"a": [1.0, 2.0, 3.0, 4.0]}) + result = df.copy() + result["a"].interpolate(inplace=True) + tm.assert_frame_equal(result, expected) + + result = df.copy() + result["a"].interpolate(inplace=True, downcast="infer") + tm.assert_frame_equal(result, expected.astype("int64")) + + def test_interp_inplace_row(self): + # GH 10395 + result = DataFrame( + {"a": [1.0, 2.0, 3.0, 4.0], "b": [np.nan, 2.0, 3.0, 4.0], "c": [3, 2, 2, 2]} + ) + expected = result.interpolate(method="linear", axis=1, inplace=False) + result.interpolate(method="linear", axis=1, inplace=True) + tm.assert_frame_equal(result, expected) + + def test_interp_ignore_all_good(self): + # GH + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [1, 2, 3, 4], + "C": [1.0, 2.0, np.nan, 4.0], + "D": [1.0, 2.0, 3.0, 4.0], + } + ) + expected = DataFrame( + { + "A": np.array([1, 2, 3, 4], dtype="float64"), + "B": np.array([1, 2, 3, 4], dtype="int64"), + "C": np.array([1.0, 2.0, 3, 4.0], dtype="float64"), + "D": np.array([1.0, 2.0, 3.0, 4.0], dtype="float64"), + } + ) + + result = df.interpolate(downcast=None) + tm.assert_frame_equal(result, expected) + + # all good + result = df[["B", "D"]].interpolate(downcast=None) + tm.assert_frame_equal(result, df[["B", "D"]]) + + @pytest.mark.parametrize("axis", [0, 1]) + def test_interp_time_inplace_axis(self, axis): + # GH 9687 + periods = 5 + idx = date_range(start="2014-01-01", periods=periods) + data = np.random.rand(periods, periods) + data[data < 0.5] = np.nan + expected = DataFrame(index=idx, columns=idx, data=data) + + result = expected.interpolate(axis=0, method="time") + expected.interpolate(axis=0, method="time", inplace=True) + tm.assert_frame_equal(result, expected) diff --git a/pandas/tests/frame/test_missing.py b/pandas/tests/frame/test_missing.py index ae0516dd29a1f..196df8ba00476 100644 --- a/pandas/tests/frame/test_missing.py +++ b/pandas/tests/frame/test_missing.py @@ -4,8 +4,6 @@ import numpy as np import pytest -import pandas.util._test_decorators as td - import pandas as pd from pandas import Categorical, DataFrame, Series, Timestamp, date_range import pandas._testing as tm @@ -705,281 +703,3 @@ def test_fill_value_when_combine_const(self): exp = df.fillna(0).add(2) res = df.add(2, fill_value=0) tm.assert_frame_equal(res, exp) - - -class TestDataFrameInterpolate: - def test_interp_basic(self): - df = DataFrame( - { - "A": [1, 2, np.nan, 4], - "B": [1, 4, 9, np.nan], - "C": [1, 2, 3, 5], - "D": list("abcd"), - } - ) - expected = DataFrame( - { - "A": [1.0, 2.0, 3.0, 4.0], - "B": [1.0, 4.0, 9.0, 9.0], - "C": [1, 2, 3, 5], - "D": list("abcd"), - } - ) - result = df.interpolate() - tm.assert_frame_equal(result, expected) - - result = df.set_index("C").interpolate() - expected = df.set_index("C") - expected.loc[3, "A"] = 3 - expected.loc[5, "B"] = 9 - tm.assert_frame_equal(result, expected) - - def test_interp_bad_method(self): - df = DataFrame( - { - "A": [1, 2, np.nan, 4], - "B": [1, 4, 9, np.nan], - "C": [1, 2, 3, 5], - "D": list("abcd"), - } - ) - with pytest.raises(ValueError): - df.interpolate(method="not_a_method") - - def test_interp_combo(self): - df = DataFrame( - { - "A": [1.0, 2.0, np.nan, 4.0], - "B": [1, 4, 9, np.nan], - "C": [1, 2, 3, 5], - "D": list("abcd"), - } - ) - - result = df["A"].interpolate() - expected = Series([1.0, 2.0, 3.0, 4.0], name="A") - tm.assert_series_equal(result, expected) - - result = df["A"].interpolate(downcast="infer") - expected = Series([1, 2, 3, 4], name="A") - tm.assert_series_equal(result, expected) - - def test_interp_nan_idx(self): - df = DataFrame({"A": [1, 2, np.nan, 4], "B": [np.nan, 2, 3, 4]}) - df = df.set_index("A") - with pytest.raises(NotImplementedError): - df.interpolate(method="values") - - @td.skip_if_no_scipy - def test_interp_various(self): - df = DataFrame( - {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} - ) - df = df.set_index("C") - expected = df.copy() - result = df.interpolate(method="polynomial", order=1) - - expected.A.loc[3] = 2.66666667 - expected.A.loc[13] = 5.76923076 - tm.assert_frame_equal(result, expected) - - result = df.interpolate(method="cubic") - # GH #15662. - expected.A.loc[3] = 2.81547781 - expected.A.loc[13] = 5.52964175 - tm.assert_frame_equal(result, expected) - - result = df.interpolate(method="nearest") - expected.A.loc[3] = 2 - expected.A.loc[13] = 5 - tm.assert_frame_equal(result, expected, check_dtype=False) - - result = df.interpolate(method="quadratic") - expected.A.loc[3] = 2.82150771 - expected.A.loc[13] = 6.12648668 - tm.assert_frame_equal(result, expected) - - result = df.interpolate(method="slinear") - expected.A.loc[3] = 2.66666667 - expected.A.loc[13] = 5.76923077 - tm.assert_frame_equal(result, expected) - - result = df.interpolate(method="zero") - expected.A.loc[3] = 2.0 - expected.A.loc[13] = 5 - tm.assert_frame_equal(result, expected, check_dtype=False) - - @td.skip_if_no_scipy - def test_interp_alt_scipy(self): - df = DataFrame( - {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} - ) - result = df.interpolate(method="barycentric") - expected = df.copy() - expected.loc[2, "A"] = 3 - expected.loc[5, "A"] = 6 - tm.assert_frame_equal(result, expected) - - result = df.interpolate(method="barycentric", downcast="infer") - tm.assert_frame_equal(result, expected.astype(np.int64)) - - result = df.interpolate(method="krogh") - expectedk = df.copy() - expectedk["A"] = expected["A"] - tm.assert_frame_equal(result, expectedk) - - result = df.interpolate(method="pchip") - expected.loc[2, "A"] = 3 - expected.loc[5, "A"] = 6.0 - - tm.assert_frame_equal(result, expected) - - def test_interp_rowwise(self): - df = DataFrame( - { - 0: [1, 2, np.nan, 4], - 1: [2, 3, 4, np.nan], - 2: [np.nan, 4, 5, 6], - 3: [4, np.nan, 6, 7], - 4: [1, 2, 3, 4], - } - ) - result = df.interpolate(axis=1) - expected = df.copy() - expected.loc[3, 1] = 5 - expected.loc[0, 2] = 3 - expected.loc[1, 3] = 3 - expected[4] = expected[4].astype(np.float64) - tm.assert_frame_equal(result, expected) - - result = df.interpolate(axis=1, method="values") - tm.assert_frame_equal(result, expected) - - result = df.interpolate(axis=0) - expected = df.interpolate() - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize( - "axis_name, axis_number", - [ - pytest.param("rows", 0, id="rows_0"), - pytest.param("index", 0, id="index_0"), - pytest.param("columns", 1, id="columns_1"), - ], - ) - def test_interp_axis_names(self, axis_name, axis_number): - # GH 29132: test axis names - data = {0: [0, np.nan, 6], 1: [1, np.nan, 7], 2: [2, 5, 8]} - - df = DataFrame(data, dtype=np.float64) - result = df.interpolate(axis=axis_name, method="linear") - expected = df.interpolate(axis=axis_number, method="linear") - tm.assert_frame_equal(result, expected) - - def test_rowwise_alt(self): - df = DataFrame( - { - 0: [0, 0.5, 1.0, np.nan, 4, 8, np.nan, np.nan, 64], - 1: [1, 2, 3, 4, 3, 2, 1, 0, -1], - } - ) - df.interpolate(axis=0) - - @pytest.mark.parametrize( - "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)] - ) - def test_interp_leading_nans(self, check_scipy): - df = DataFrame( - {"A": [np.nan, np.nan, 0.5, 0.25, 0], "B": [np.nan, -3, -3.5, np.nan, -4]} - ) - result = df.interpolate() - expected = df.copy() - expected["B"].loc[3] = -3.75 - tm.assert_frame_equal(result, expected) - - if check_scipy: - result = df.interpolate(method="polynomial", order=1) - tm.assert_frame_equal(result, expected) - - def test_interp_raise_on_only_mixed(self): - df = DataFrame( - { - "A": [1, 2, np.nan, 4], - "B": ["a", "b", "c", "d"], - "C": [np.nan, 2, 5, 7], - "D": [np.nan, np.nan, 9, 9], - "E": [1, 2, 3, 4], - } - ) - with pytest.raises(TypeError): - df.interpolate(axis=1) - - def test_interp_raise_on_all_object_dtype(self): - # GH 22985 - df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, dtype="object") - msg = ( - "Cannot interpolate with all object-dtype columns " - "in the DataFrame. Try setting at least one " - "column to a numeric dtype." - ) - with pytest.raises(TypeError, match=msg): - df.interpolate() - - def test_interp_inplace(self): - df = DataFrame({"a": [1.0, 2.0, np.nan, 4.0]}) - expected = DataFrame({"a": [1.0, 2.0, 3.0, 4.0]}) - result = df.copy() - result["a"].interpolate(inplace=True) - tm.assert_frame_equal(result, expected) - - result = df.copy() - result["a"].interpolate(inplace=True, downcast="infer") - tm.assert_frame_equal(result, expected.astype("int64")) - - def test_interp_inplace_row(self): - # GH 10395 - result = DataFrame( - {"a": [1.0, 2.0, 3.0, 4.0], "b": [np.nan, 2.0, 3.0, 4.0], "c": [3, 2, 2, 2]} - ) - expected = result.interpolate(method="linear", axis=1, inplace=False) - result.interpolate(method="linear", axis=1, inplace=True) - tm.assert_frame_equal(result, expected) - - def test_interp_ignore_all_good(self): - # GH - df = DataFrame( - { - "A": [1, 2, np.nan, 4], - "B": [1, 2, 3, 4], - "C": [1.0, 2.0, np.nan, 4.0], - "D": [1.0, 2.0, 3.0, 4.0], - } - ) - expected = DataFrame( - { - "A": np.array([1, 2, 3, 4], dtype="float64"), - "B": np.array([1, 2, 3, 4], dtype="int64"), - "C": np.array([1.0, 2.0, 3, 4.0], dtype="float64"), - "D": np.array([1.0, 2.0, 3.0, 4.0], dtype="float64"), - } - ) - - result = df.interpolate(downcast=None) - tm.assert_frame_equal(result, expected) - - # all good - result = df[["B", "D"]].interpolate(downcast=None) - tm.assert_frame_equal(result, df[["B", "D"]]) - - @pytest.mark.parametrize("axis", [0, 1]) - def test_interp_time_inplace_axis(self, axis): - # GH 9687 - periods = 5 - idx = pd.date_range(start="2014-01-01", periods=periods) - data = np.random.rand(periods, periods) - data[data < 0.5] = np.nan - expected = pd.DataFrame(index=idx, columns=idx, data=data) - - result = expected.interpolate(axis=0, method="time") - expected.interpolate(axis=0, method="time", inplace=True) - tm.assert_frame_equal(result, expected) diff --git a/pandas/tests/frame/test_mutate_columns.py b/pandas/tests/frame/test_mutate_columns.py index 8bc2aa214e035..33f71602f4713 100644 --- a/pandas/tests/frame/test_mutate_columns.py +++ b/pandas/tests/frame/test_mutate_columns.py @@ -10,82 +10,6 @@ class TestDataFrameMutateColumns: - def test_assign(self): - df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) - original = df.copy() - result = df.assign(C=df.B / df.A) - expected = df.copy() - expected["C"] = [4, 2.5, 2] - tm.assert_frame_equal(result, expected) - - # lambda syntax - result = df.assign(C=lambda x: x.B / x.A) - tm.assert_frame_equal(result, expected) - - # original is unmodified - tm.assert_frame_equal(df, original) - - # Non-Series array-like - result = df.assign(C=[4, 2.5, 2]) - tm.assert_frame_equal(result, expected) - # original is unmodified - tm.assert_frame_equal(df, original) - - result = df.assign(B=df.B / df.A) - expected = expected.drop("B", axis=1).rename(columns={"C": "B"}) - tm.assert_frame_equal(result, expected) - - # overwrite - result = df.assign(A=df.A + df.B) - expected = df.copy() - expected["A"] = [5, 7, 9] - tm.assert_frame_equal(result, expected) - - # lambda - result = df.assign(A=lambda x: x.A + x.B) - tm.assert_frame_equal(result, expected) - - def test_assign_multiple(self): - df = DataFrame([[1, 4], [2, 5], [3, 6]], columns=["A", "B"]) - result = df.assign(C=[7, 8, 9], D=df.A, E=lambda x: x.B) - expected = DataFrame( - [[1, 4, 7, 1, 4], [2, 5, 8, 2, 5], [3, 6, 9, 3, 6]], columns=list("ABCDE") - ) - tm.assert_frame_equal(result, expected) - - def test_assign_order(self): - # GH 9818 - df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) - result = df.assign(D=df.A + df.B, C=df.A - df.B) - - expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC")) - tm.assert_frame_equal(result, expected) - result = df.assign(C=df.A - df.B, D=df.A + df.B) - - expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], columns=list("ABCD")) - - tm.assert_frame_equal(result, expected) - - def test_assign_bad(self): - df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) - - # non-keyword argument - with pytest.raises(TypeError): - df.assign(lambda x: x.A) - with pytest.raises(AttributeError): - df.assign(C=df.A, D=df.A + df.C) - - def test_assign_dependent(self): - df = DataFrame({"A": [1, 2], "B": [3, 4]}) - - result = df.assign(C=df.A, D=lambda x: x["A"] + x["C"]) - expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) - tm.assert_frame_equal(result, expected) - - result = df.assign(C=lambda df: df.A, D=lambda df: df["A"] + df["C"]) - expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) - tm.assert_frame_equal(result, expected) - def test_insert_error_msmgs(self): # GH 7432
https://api.github.com/repos/pandas-dev/pandas/pulls/32110
2020-02-19T16:05:16Z
2020-02-22T16:03:21Z
2020-02-22T16:03:21Z
2020-02-22T16:48:55Z
DataFrame: Ctor from non abc.Iterable 2-d array like
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 9fe1ec7b792c8..5afd527113fa6 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -511,6 +511,10 @@ def __init__( mgr = init_ndarray( values, index, columns, dtype=values.dtype, copy=False ) + elif arr.ndim == 2 and columns is not None: + if index is None: + index = ibase.default_index(len(arr)) + mgr = init_ndarray(arr, index, columns, dtype=dtype, copy=copy) else: raise ValueError("DataFrame constructor not properly called!") diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index 8c9b7cd060059..e1672290e88ba 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -2590,3 +2590,24 @@ def test_from_2d_ndarray_with_dtype(self): expected = pd.DataFrame(array_dim2).astype("datetime64[ns, UTC]") tm.assert_frame_equal(df, expected) + + def test_from_noniterable_2d_array_like(self): + class Sample: + def __init__(self, length, width): + self.length = length + self.width = width + self.columns = ["X" + str(i) for i in range(self.width)] + + def __getitem__(self, index): + if isinstance(index, int): + i = index + if i >= self.length: + raise IndexError("end") + return [3 * i + j for j in range(self.width)] + + def __len__(self): + return self.length + + sample = Sample(4, 5) + df = pd.DataFrame(sample, columns=sample.columns) + assert df.shape == (4, 5)
In case we pass a 2d array like that do not use __iter__ but the __getitem__ interface, it falls outside the collection.abc.Iterable case and throw. - [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32109
2020-02-19T15:56:14Z
2020-02-26T07:34:02Z
null
2020-02-26T17:43:30Z
BUG: Allow addition of Timedelta to Timestamp interval
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index cd1cb0b64f74a..8a05d0d36cd2f 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -525,6 +525,7 @@ Indexing - Bug in :class:`Index` constructor where an unhelpful error message was raised for ``numpy`` scalars (:issue:`33017`) - Bug in :meth:`DataFrame.lookup` incorrectly raising an ``AttributeError`` when ``frame.index`` or ``frame.columns`` is not unique; this will now raise a ``ValueError`` with a helpful error message (:issue:`33041`) - Bug in :meth:`DataFrame.iloc.__setitem__` creating a new array instead of overwriting ``Categorical`` values in-place (:issue:`32831`) +- Bug in :class:`Interval` where a :class:`Timedelta` could not be added or subtracted from a :class:`Timestamp` interval (:issue:`32023`) - Bug in :meth:`DataFrame.copy` _item_cache not invalidated after copy causes post-copy value updates to not be reflected (:issue:`31784`) - Bug in `Series.__getitem__` with an integer key and a :class:`MultiIndex` with leading integer level failing to raise ``KeyError`` if the key is not present in the first level (:issue:`33355`) - Bug in :meth:`DataFrame.iloc` when slicing a single column-:class:`DataFrame`` with ``ExtensionDtype`` (e.g. ``df.iloc[:, :1]``) returning an invalid result (:issue:`32957`) diff --git a/pandas/_libs/interval.pyx b/pandas/_libs/interval.pyx index a47303ddc93cf..e52474e233510 100644 --- a/pandas/_libs/interval.pyx +++ b/pandas/_libs/interval.pyx @@ -1,6 +1,9 @@ import numbers from operator import le, lt +from cpython.datetime cimport PyDelta_Check, PyDateTime_IMPORT +PyDateTime_IMPORT + from cpython.object cimport ( Py_EQ, Py_GE, @@ -11,7 +14,6 @@ from cpython.object cimport ( PyObject_RichCompare, ) - import cython from cython import Py_ssize_t @@ -34,7 +36,11 @@ cnp.import_array() cimport pandas._libs.util as util from pandas._libs.hashtable cimport Int64Vector -from pandas._libs.tslibs.util cimport is_integer_object, is_float_object +from pandas._libs.tslibs.util cimport ( + is_integer_object, + is_float_object, + is_timedelta64_object, +) from pandas._libs.tslibs import Timestamp from pandas._libs.tslibs.timedeltas import Timedelta @@ -294,6 +300,7 @@ cdef class Interval(IntervalMixin): True """ _typ = "interval" + __array_priority__ = 1000 cdef readonly object left """ @@ -398,14 +405,29 @@ cdef class Interval(IntervalMixin): return f'{start_symbol}{left}, {right}{end_symbol}' def __add__(self, y): - if isinstance(y, numbers.Number): + if ( + isinstance(y, numbers.Number) + or PyDelta_Check(y) + or is_timedelta64_object(y) + ): return Interval(self.left + y, self.right + y, closed=self.closed) - elif isinstance(y, Interval) and isinstance(self, numbers.Number): + elif ( + isinstance(y, Interval) + and ( + isinstance(self, numbers.Number) + or PyDelta_Check(self) + or is_timedelta64_object(self) + ) + ): return Interval(y.left + self, y.right + self, closed=y.closed) return NotImplemented def __sub__(self, y): - if isinstance(y, numbers.Number): + if ( + isinstance(y, numbers.Number) + or PyDelta_Check(y) + or is_timedelta64_object(y) + ): return Interval(self.left - y, self.right - y, closed=self.closed) return NotImplemented diff --git a/pandas/tests/scalar/interval/test_arithmetic.py b/pandas/tests/scalar/interval/test_arithmetic.py new file mode 100644 index 0000000000000..5252f1a4d5a24 --- /dev/null +++ b/pandas/tests/scalar/interval/test_arithmetic.py @@ -0,0 +1,47 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas import Interval, Timedelta, Timestamp + + +@pytest.mark.parametrize("method", ["__add__", "__sub__"]) +@pytest.mark.parametrize( + "interval", + [ + Interval(Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")), + Interval(Timedelta(days=7), Timedelta(days=14)), + ], +) +@pytest.mark.parametrize( + "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")] +) +def test_time_interval_add_subtract_timedelta(interval, delta, method): + # https://github.com/pandas-dev/pandas/issues/32023 + result = getattr(interval, method)(delta) + left = getattr(interval.left, method)(delta) + right = getattr(interval.right, method)(delta) + expected = Interval(left, right) + + assert result == expected + + +@pytest.mark.parametrize("interval", [Interval(1, 2), Interval(1.0, 2.0)]) +@pytest.mark.parametrize( + "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")] +) +def test_numeric_interval_add_timedelta_raises(interval, delta): + # https://github.com/pandas-dev/pandas/issues/32023 + msg = "|".join( + [ + "unsupported operand", + "cannot use operands", + "Only numeric, Timestamp and Timedelta endpoints are allowed", + ] + ) + with pytest.raises((TypeError, ValueError), match=msg): + interval + delta + + with pytest.raises((TypeError, ValueError), match=msg): + delta + interval
- [x] closes #32023 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32107
2020-02-19T14:53:31Z
2020-04-25T23:58:52Z
2020-04-25T23:58:52Z
2020-04-26T00:06:54Z
Added in a error message
diff --git a/pandas/tests/arrays/test_array.py b/pandas/tests/arrays/test_array.py index b1b5a9482e34f..f42b16cf18f20 100644 --- a/pandas/tests/arrays/test_array.py +++ b/pandas/tests/arrays/test_array.py @@ -291,7 +291,7 @@ class DecimalArray2(DecimalArray): @classmethod def _from_sequence(cls, scalars, dtype=None, copy=False): if isinstance(scalars, (pd.Series, pd.Index)): - raise TypeError + raise TypeError("scalars should not be of type pd.Series or pd.Index") return super()._from_sequence(scalars, dtype=dtype, copy=copy) @@ -301,7 +301,9 @@ def test_array_unboxes(index_or_series): data = box([decimal.Decimal("1"), decimal.Decimal("2")]) # make sure it works - with pytest.raises(TypeError): + with pytest.raises( + TypeError, match="scalars should not be of type pd.Series or pd.Index" + ): DecimalArray2._from_sequence(data) result = pd.array(data, dtype="decimal2")
- [x] ref #30999 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] Added in an error message
https://api.github.com/repos/pandas-dev/pandas/pulls/32105
2020-02-19T14:01:05Z
2020-02-27T19:31:58Z
2020-02-27T19:31:57Z
2020-02-27T21:00:45Z
BUG: Pickle NA objects
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index 1b6098e6b6ac1..808e6ae709ce9 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -74,6 +74,7 @@ Bug fixes **I/O** - Using ``pd.NA`` with :meth:`DataFrame.to_json` now correctly outputs a null value instead of an empty object (:issue:`31615`) +- Fixed pickling of ``pandas.NA``. Previously a new object was returned, which broke computations relying on ``NA`` being a singleton (:issue:`31847`) - Fixed bug in parquet roundtrip with nullable unsigned integer dtypes (:issue:`31896`). **Experimental dtypes** diff --git a/pandas/_libs/missing.pyx b/pandas/_libs/missing.pyx index 4d17a6f883c1c..c54cb652d7b21 100644 --- a/pandas/_libs/missing.pyx +++ b/pandas/_libs/missing.pyx @@ -364,6 +364,9 @@ class NAType(C_NAType): exponent = 31 if is_32bit else 61 return 2 ** exponent - 1 + def __reduce__(self): + return "NA" + # Binary arithmetic and comparison ops -> propagate __add__ = _create_binary_propagating_op("__add__") diff --git a/pandas/tests/scalar/test_na_scalar.py b/pandas/tests/scalar/test_na_scalar.py index dcb9d66708724..07656de2e9062 100644 --- a/pandas/tests/scalar/test_na_scalar.py +++ b/pandas/tests/scalar/test_na_scalar.py @@ -1,3 +1,5 @@ +import pickle + import numpy as np import pytest @@ -267,3 +269,26 @@ def test_integer_hash_collision_set(): assert len(result) == 2 assert NA in result assert hash(NA) in result + + +def test_pickle_roundtrip(): + # https://github.com/pandas-dev/pandas/issues/31847 + result = pickle.loads(pickle.dumps(pd.NA)) + assert result is pd.NA + + +def test_pickle_roundtrip_pandas(): + result = tm.round_trip_pickle(pd.NA) + assert result is pd.NA + + +@pytest.mark.parametrize( + "values, dtype", [([1, 2, pd.NA], "Int64"), (["A", "B", pd.NA], "string")] +) +@pytest.mark.parametrize("as_frame", [True, False]) +def test_pickle_roundtrip_containers(as_frame, values, dtype): + s = pd.Series(pd.array(values, dtype=dtype)) + if as_frame: + s = s.to_frame(name="A") + result = tm.round_trip_pickle(s) + tm.assert_equal(result, s)
According to https://docs.python.org/3/library/pickle.html#object.__reduce__, > If a string is returned, the string should be interpreted as the name > of a global variable. It should be the object’s local name relative to > its module; the pickle module searches the module namespace to determine > the object’s module. This behaviour is typically useful for singletons. Closes https://github.com/pandas-dev/pandas/issues/31847
https://api.github.com/repos/pandas-dev/pandas/pulls/32104
2020-02-19T13:46:22Z
2020-03-02T23:13:33Z
2020-03-02T23:13:33Z
2020-03-03T12:44:06Z
added msg to TypeError test_to_boolean_array_error
diff --git a/pandas/tests/arrays/test_boolean.py b/pandas/tests/arrays/test_boolean.py index cb9b07db4a0df..d14d6f3ff0c41 100644 --- a/pandas/tests/arrays/test_boolean.py +++ b/pandas/tests/arrays/test_boolean.py @@ -131,7 +131,8 @@ def test_to_boolean_array_missing_indicators(a, b): ) def test_to_boolean_array_error(values): # error in converting existing arrays to BooleanArray - with pytest.raises(TypeError): + msg = "Need to pass bool-like value" + with pytest.raises(TypeError, match=msg): pd.array(values, dtype="boolean")
- [x] ref #30999 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` Fixed bare pytest.raises (#30999) for test_to_boolean_array_error in test_boolean.py
https://api.github.com/repos/pandas-dev/pandas/pulls/32103
2020-02-19T12:52:58Z
2020-02-20T15:01:13Z
2020-02-20T15:01:13Z
2020-02-25T10:48:57Z
TST: Fix bare pytest.raises in test_parsing.py
diff --git a/pandas/tests/tslibs/test_parsing.py b/pandas/tests/tslibs/test_parsing.py index 1ba7832c47e6c..dc7421ea63464 100644 --- a/pandas/tests/tslibs/test_parsing.py +++ b/pandas/tests/tslibs/test_parsing.py @@ -2,6 +2,7 @@ Tests for Timestamp parsing, aimed at pandas/_libs/tslibs/parsing.pyx """ from datetime import datetime +import re from dateutil.parser import parse import numpy as np @@ -24,7 +25,8 @@ def test_parse_time_string(): def test_parse_time_string_invalid_type(): # Raise on invalid input, don't just return it - with pytest.raises(TypeError): + msg = "Argument 'arg' has incorrect type (expected str, got tuple)" + with pytest.raises(TypeError, match=re.escape(msg)): parse_time_string((4, 5)) @@ -217,7 +219,8 @@ def test_try_parse_dates(): def test_parse_time_string_check_instance_type_raise_exception(): # issue 20684 - with pytest.raises(TypeError): + msg = "Argument 'arg' has incorrect type (expected str, got tuple)" + with pytest.raises(TypeError, match=re.escape(msg)): parse_time_string((1, 2, 3)) result = parse_time_string("2019")
- [x] ref #30999 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` Fixes bare pytest.raises issue for `test_parsing.py`.
https://api.github.com/repos/pandas-dev/pandas/pulls/32102
2020-02-19T12:52:56Z
2020-02-20T16:39:44Z
2020-02-20T16:39:44Z
2020-02-20T16:39:53Z
TYP: check_untyped_defs core.tools.datetimes
diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py index 6d45ddd29d783..b10b736b9134e 100644 --- a/pandas/core/tools/datetimes.py +++ b/pandas/core/tools/datetimes.py @@ -2,7 +2,7 @@ from datetime import datetime, time from functools import partial from itertools import islice -from typing import Optional, TypeVar, Union +from typing import List, Optional, TypeVar, Union import numpy as np @@ -296,7 +296,9 @@ def _convert_listlike_datetimes( if not isinstance(arg, (DatetimeArray, DatetimeIndex)): return DatetimeIndex(arg, tz=tz, name=name) if tz == "utc": - arg = arg.tz_convert(None).tz_localize(tz) + # error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has + # no attribute "tz_convert" + arg = arg.tz_convert(None).tz_localize(tz) # type: ignore return arg elif is_datetime64_ns_dtype(arg): @@ -307,7 +309,9 @@ def _convert_listlike_datetimes( pass elif tz: # DatetimeArray, DatetimeIndex - return arg.tz_localize(tz) + # error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has + # no attribute "tz_localize" + return arg.tz_localize(tz) # type: ignore return arg @@ -826,18 +830,18 @@ def f(value): required = ["year", "month", "day"] req = sorted(set(required) - set(unit_rev.keys())) if len(req): - required = ",".join(req) + _required = ",".join(req) raise ValueError( "to assemble mappings requires at least that " - f"[year, month, day] be specified: [{required}] is missing" + f"[year, month, day] be specified: [{_required}] is missing" ) # keys we don't recognize excess = sorted(set(unit_rev.keys()) - set(_unit_map.values())) if len(excess): - excess = ",".join(excess) + _excess = ",".join(excess) raise ValueError( - f"extra keys have been passed to the datetime assemblage: [{excess}]" + f"extra keys have been passed to the datetime assemblage: [{_excess}]" ) def coerce(values): @@ -992,7 +996,7 @@ def _convert_listlike(arg, format): if infer_time_format and format is None: format = _guess_time_format_for_array(arg) - times = [] + times: List[Optional[time]] = [] if format is not None: for element in arg: try: diff --git a/setup.cfg b/setup.cfg index 4a900e581c353..32efec7d2cf0d 100644 --- a/setup.cfg +++ b/setup.cfg @@ -234,9 +234,6 @@ check_untyped_defs=False [mypy-pandas.core.strings] check_untyped_defs=False -[mypy-pandas.core.tools.datetimes] -check_untyped_defs=False - [mypy-pandas.core.window.common] check_untyped_defs=False
pandas\core\tools\datetimes.py:299: error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has no attribute "tz_convert" pandas\core\tools\datetimes.py:310: error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has no attribute "tz_localize" pandas\core\tools\datetimes.py:829: error: Incompatible types in assignment (expression has type "str", variable has type "List[str]") pandas\core\tools\datetimes.py:838: error: Incompatible types in assignment (expression has type "str", variable has type "List[Any]") pandas\core\tools\datetimes.py:1010: error: Argument 1 to "append" of "list" has incompatible type "None"; expected "time" pandas\core\tools\datetimes.py:1035: error: Argument 1 to "append" of "list" has incompatible type "None"; expected "time"
https://api.github.com/repos/pandas-dev/pandas/pulls/32101
2020-02-19T12:33:10Z
2020-02-20T01:24:38Z
2020-02-20T01:24:38Z
2020-02-20T09:40:16Z
TYP: check_untyped_defs arrays.sparse.array
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py index b17a4647ffc9f..542cfd334b810 100644 --- a/pandas/core/arrays/sparse/array.py +++ b/pandas/core/arrays/sparse/array.py @@ -4,7 +4,7 @@ from collections import abc import numbers import operator -from typing import Any, Callable +from typing import Any, Callable, Union import warnings import numpy as np @@ -479,7 +479,7 @@ def sp_index(self): return self._sparse_index @property - def sp_values(self): + def sp_values(self) -> np.ndarray: """ An ndarray containing the non- ``fill_value`` values. @@ -798,13 +798,13 @@ def _get_val_at(self, loc): val = com.maybe_box_datetimelike(val, self.sp_values.dtype) return val - def take(self, indices, allow_fill=False, fill_value=None): + def take(self, indices, allow_fill=False, fill_value=None) -> "SparseArray": if is_scalar(indices): raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.") indices = np.asarray(indices, dtype=np.int32) if indices.size == 0: - result = [] + result = np.array([], dtype="object") kwargs = {"dtype": self.dtype} elif allow_fill: result = self._take_with_fill(indices, fill_value=fill_value) @@ -815,7 +815,7 @@ def take(self, indices, allow_fill=False, fill_value=None): return type(self)(result, fill_value=self.fill_value, kind=self.kind, **kwargs) - def _take_with_fill(self, indices, fill_value=None): + def _take_with_fill(self, indices, fill_value=None) -> np.ndarray: if fill_value is None: fill_value = self.dtype.na_value @@ -878,7 +878,7 @@ def _take_with_fill(self, indices, fill_value=None): return taken - def _take_without_fill(self, indices): + def _take_without_fill(self, indices) -> Union[np.ndarray, "SparseArray"]: to_shift = indices < 0 indices = indices.copy() diff --git a/setup.cfg b/setup.cfg index 87054f864813f..61d5b1030a500 100644 --- a/setup.cfg +++ b/setup.cfg @@ -150,9 +150,6 @@ check_untyped_defs=False [mypy-pandas.core.arrays.interval] check_untyped_defs=False -[mypy-pandas.core.arrays.sparse.array] -check_untyped_defs=False - [mypy-pandas.core.base] check_untyped_defs=False
pandas\core\arrays\sparse\array.py:807: error: Need type annotation for 'result' (hint: "result: List[<type>] = ...")
https://api.github.com/repos/pandas-dev/pandas/pulls/32099
2020-02-19T11:51:01Z
2020-02-20T22:37:25Z
2020-02-20T22:37:25Z
2020-02-21T08:16:55Z
TYP: check_untyped_defs core.arrays.interval
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py index ab3ee5bbcdc3a..bb8817c3a7fa2 100644 --- a/pandas/core/arrays/interval.py +++ b/pandas/core/arrays/interval.py @@ -1,5 +1,6 @@ from operator import le, lt import textwrap +from typing import Any, Sequence, Tuple import numpy as np @@ -433,30 +434,33 @@ def from_arrays(cls, left, right, closed="right", copy=False, dtype=None): ), ) ) - def from_tuples(cls, data, closed="right", copy=False, dtype=None): + def from_tuples( + cls, data: Sequence[Tuple[Any, Any]], closed="right", copy=False, dtype=None + ): + left: Sequence + right: Sequence if len(data): left, right = [], [] + for d in data: + if isna(d): + lhs = rhs = np.nan + else: + name = cls.__name__ + try: + # need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...] + lhs, rhs = d + except ValueError: + msg = f"{name}.from_tuples requires tuples of length 2, got {d}" + raise ValueError(msg) + except TypeError: + msg = f"{name}.from_tuples received an invalid item, {d}" + raise TypeError(msg) + left.append(lhs) + right.append(rhs) else: # ensure that empty data keeps input dtype left = right = data - for d in data: - if isna(d): - lhs = rhs = np.nan - else: - name = cls.__name__ - try: - # need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...] - lhs, rhs = d - except ValueError: - msg = f"{name}.from_tuples requires tuples of length 2, got {d}" - raise ValueError(msg) - except TypeError: - msg = f"{name}.from_tuples received an invalid item, {d}" - raise TypeError(msg) - left.append(lhs) - right.append(rhs) - return cls.from_arrays(left, right, closed, copy=False, dtype=dtype) def _validate(self): diff --git a/setup.cfg b/setup.cfg index 4a900e581c353..d8801f02b5a27 100644 --- a/setup.cfg +++ b/setup.cfg @@ -150,9 +150,6 @@ check_untyped_defs=False [mypy-pandas.core.arrays.categorical] check_untyped_defs=False -[mypy-pandas.core.arrays.interval] -check_untyped_defs=False - [mypy-pandas.core.arrays.sparse.array] check_untyped_defs=False
pandas\core\arrays\interval.py:438: error: Need type annotation for 'left' (hint: "left: List[<type>] = ...") pandas\core\arrays\interval.py:438: error: Need type annotation for 'right' (hint: "right: List[<type>] = ...")
https://api.github.com/repos/pandas-dev/pandas/pulls/32098
2020-02-19T11:32:43Z
2020-02-20T11:32:37Z
null
2020-02-20T11:32:37Z
TYP: check_untyped_defs core.arrays.categorical
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py index d469b574820f9..a5048e3aae899 100644 --- a/pandas/core/arrays/categorical.py +++ b/pandas/core/arrays/categorical.py @@ -341,10 +341,7 @@ def __init__( values = _convert_to_list_like(values) # By convention, empty lists result in object dtype: - if len(values) == 0: - sanitize_dtype = "object" - else: - sanitize_dtype = None + sanitize_dtype = "object" if len(values) == 0 else None null_mask = isna(values) if null_mask.any(): values = [values[idx] for idx in np.where(~null_mask)[0]] @@ -1496,7 +1493,7 @@ def check_for_ordered(self, op): def _values_for_argsort(self): return self._codes.copy() - def argsort(self, ascending=True, kind="quicksort", *args, **kwargs): + def argsort(self, ascending=True, kind="quicksort", **kwargs): """ Return the indices that would sort the Categorical. @@ -1511,7 +1508,7 @@ def argsort(self, ascending=True, kind="quicksort", *args, **kwargs): or descending sort. kind : {'quicksort', 'mergesort', 'heapsort'}, optional Sorting algorithm. - *args, **kwargs: + **kwargs: passed through to :func:`numpy.argsort`. Returns @@ -1547,7 +1544,7 @@ def argsort(self, ascending=True, kind="quicksort", *args, **kwargs): >>> cat.argsort() array([2, 0, 1]) """ - return super().argsort(ascending=ascending, kind=kind, *args, **kwargs) + return super().argsort(ascending=ascending, kind=kind, **kwargs) def sort_values(self, inplace=False, ascending=True, na_position="last"): """ diff --git a/setup.cfg b/setup.cfg index 4a900e581c353..4b0b32b09ca8d 100644 --- a/setup.cfg +++ b/setup.cfg @@ -147,9 +147,6 @@ check_untyped_defs=False [mypy-pandas._version] check_untyped_defs=False -[mypy-pandas.core.arrays.categorical] -check_untyped_defs=False - [mypy-pandas.core.arrays.interval] check_untyped_defs=False
pandas\core\arrays\categorical.py:347: error: Incompatible types in assignment (expression has type "None", variable has type "str") pandas\core\arrays\categorical.py:1550: error: "argsort" of "ExtensionArray" gets multiple values for keyword argument "ascending" pandas\core\arrays\categorical.py:1550: error: "argsort" of "ExtensionArray" gets multiple values for keyword argument "kind"
https://api.github.com/repos/pandas-dev/pandas/pulls/32097
2020-02-19T11:22:04Z
2020-02-19T22:29:57Z
2020-02-19T22:29:57Z
2020-02-20T09:38:34Z
Backport PR #31841: BUG: Fix rolling.corr with time frequency
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index 1622a6dc8ce56..19358689a2186 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -18,6 +18,7 @@ Fixed regressions - Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`) - Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`) - Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`) +- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py index 3d5cee3bd5a31..b7e1779a08562 100644 --- a/pandas/core/window/rolling.py +++ b/pandas/core/window/rolling.py @@ -1782,7 +1782,7 @@ def corr(self, other=None, pairwise=None, **kwargs): # only default unset pairwise = True if pairwise is None else pairwise other = self._shallow_copy(other) - window = self._get_window(other) + window = self._get_window(other) if not self.is_freq_type else self.win_freq def _get_corr(a, b): a = a.rolling( diff --git a/pandas/tests/window/test_pairwise.py b/pandas/tests/window/test_pairwise.py index 717273cff64ea..bb305e93a3cf1 100644 --- a/pandas/tests/window/test_pairwise.py +++ b/pandas/tests/window/test_pairwise.py @@ -1,8 +1,9 @@ import warnings +import numpy as np import pytest -from pandas import DataFrame, Series +from pandas import DataFrame, Series, date_range import pandas._testing as tm from pandas.core.algorithms import safe_sort @@ -181,3 +182,10 @@ def test_pairwise_with_series(self, f): for i, result in enumerate(results): if i > 0: self.compare(result, results[0]) + + def test_corr_freq_memory_error(self): + # GH 31789 + s = Series(range(5), index=date_range("2020", periods=5)) + result = s.rolling("12H").corr(s) + expected = Series([np.nan] * 5, index=date_range("2020", periods=5)) + tm.assert_series_equal(result, expected)
https://github.com/pandas-dev/pandas/pull/31841
https://api.github.com/repos/pandas-dev/pandas/pulls/32094
2020-02-19T07:40:21Z
2020-02-19T09:58:57Z
2020-02-19T09:58:57Z
2020-02-19T10:01:25Z
Backport PR #31842 on branch 1.0.x (BUG: Fix raw parameter not being respected in groupby.rolling.apply)
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index 805f87b63eef8..1622a6dc8ce56 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -17,6 +17,7 @@ Fixed regressions - Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`) - Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`) +- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py index c1e34757b45d4..3d5cee3bd5a31 100644 --- a/pandas/core/window/rolling.py +++ b/pandas/core/window/rolling.py @@ -1296,13 +1296,14 @@ def apply( raise ValueError("engine must be either 'numba' or 'cython'") # TODO: Why do we always pass center=False? - # name=func for WindowGroupByMixin._apply + # name=func & raw=raw for WindowGroupByMixin._apply return self._apply( apply_func, center=False, floor=0, name=func, use_numba_cache=engine == "numba", + raw=raw, ) def _generate_cython_apply_func(self, args, kwargs, raw, offset, func): diff --git a/pandas/tests/window/test_grouper.py b/pandas/tests/window/test_grouper.py index 355ef3a90d424..5b2687271f9d6 100644 --- a/pandas/tests/window/test_grouper.py +++ b/pandas/tests/window/test_grouper.py @@ -190,3 +190,21 @@ def test_expanding_apply(self, raw): result = r.apply(lambda x: x.sum(), raw=raw) expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw)) tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("expected_value,raw_value", [[1.0, True], [0.0, False]]) + def test_groupby_rolling(self, expected_value, raw_value): + # GH 31754 + + def foo(x): + return int(isinstance(x, np.ndarray)) + + df = pd.DataFrame({"id": [1, 1, 1], "value": [1, 2, 3]}) + result = df.groupby("id").value.rolling(1).apply(foo, raw=raw_value) + expected = Series( + [expected_value] * 3, + index=pd.MultiIndex.from_tuples( + ((1, 0), (1, 1), (1, 2)), names=["id", None] + ), + name="value", + ) + tm.assert_series_equal(result, expected)
Backport PR #31842: BUG: Fix raw parameter not being respected in groupby.rolling.apply
https://api.github.com/repos/pandas-dev/pandas/pulls/32093
2020-02-19T07:35:30Z
2020-02-19T08:15:47Z
2020-02-19T08:15:47Z
2020-02-19T08:15:47Z
TST: parametrize and de-duplicate timedelta64 arithmetic tests
diff --git a/pandas/tests/arithmetic/test_timedelta64.py b/pandas/tests/arithmetic/test_timedelta64.py index abdeb1b30b626..300e468c34e65 100644 --- a/pandas/tests/arithmetic/test_timedelta64.py +++ b/pandas/tests/arithmetic/test_timedelta64.py @@ -25,6 +25,19 @@ get_upcast_box, ) + +def assert_dtype(obj, expected_dtype): + """ + Helper to check the dtype for a Series, Index, or single-column DataFrame. + """ + if isinstance(obj, DataFrame): + dtype = obj.dtypes.iat[0] + else: + dtype = obj.dtype + + assert dtype == expected_dtype + + # ------------------------------------------------------------------ # Timedelta64[ns] dtype Comparisons @@ -522,19 +535,35 @@ def test_tda_add_sub_index(self): # ------------------------------------------------------------- # Binary operations TimedeltaIndex and timedelta-like - def test_tdi_iadd_timedeltalike(self, two_hours): + def test_tdi_iadd_timedeltalike(self, two_hours, box_with_array): # only test adding/sub offsets as + is now numeric rng = timedelta_range("1 days", "10 days") expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D") + + rng = tm.box_expected(rng, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + orig_rng = rng rng += two_hours - tm.assert_index_equal(rng, expected) + tm.assert_equal(rng, expected) + if box_with_array is not pd.Index: + # Check that operation is actually inplace + tm.assert_equal(orig_rng, expected) - def test_tdi_isub_timedeltalike(self, two_hours): + def test_tdi_isub_timedeltalike(self, two_hours, box_with_array): # only test adding/sub offsets as - is now numeric rng = timedelta_range("1 days", "10 days") expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00") + + rng = tm.box_expected(rng, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + orig_rng = rng rng -= two_hours - tm.assert_index_equal(rng, expected) + tm.assert_equal(rng, expected) + if box_with_array is not pd.Index: + # Check that operation is actually inplace + tm.assert_equal(orig_rng, expected) # ------------------------------------------------------------- @@ -1013,15 +1042,6 @@ def test_td64arr_add_datetime64_nat(self, box_with_array): # ------------------------------------------------------------------ # Invalid __add__/__sub__ operations - # TODO: moved from frame tests; needs parametrization/de-duplication - def test_td64_df_add_int_frame(self): - # GH#22696 Check that we don't dispatch to numpy implementation, - # which treats int64 as m8[ns] - tdi = pd.timedelta_range("1", periods=3) - df = tdi.to_frame() - other = pd.DataFrame([1, 2, 3], index=tdi) # indexed like `df` - assert_invalid_addsub_type(df, other) - @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "H"]) @pytest.mark.parametrize("tdi_freq", [None, "H"]) def test_td64arr_sub_periodlike(self, box_with_array, tdi_freq, pi_freq): @@ -1100,6 +1120,9 @@ def test_td64arr_add_sub_int(self, box_with_array, one): def test_td64arr_add_sub_integer_array(self, box_with_array): # GH#19959, deprecated GH#22535 + # GH#22696 for DataFrame case, check that we don't dispatch to numpy + # implementation, which treats int64 as m8[ns] + rng = timedelta_range("1 days 09:00:00", freq="H", periods=3) tdarr = tm.box_expected(rng, box_with_array) other = tm.box_expected([4, 3, 2], box_with_array) @@ -1119,60 +1142,6 @@ def test_td64arr_addsub_integer_array_no_freq(self, box_with_array): # ------------------------------------------------------------------ # Operations with timedelta-like others - # TODO: this was taken from tests.series.test_ops; de-duplicate - def test_operators_timedelta64_with_timedelta(self, scalar_td): - # smoke tests - td1 = Series([timedelta(minutes=5, seconds=3)] * 3) - td1.iloc[2] = np.nan - - td1 + scalar_td - scalar_td + td1 - td1 - scalar_td - scalar_td - td1 - td1 / scalar_td - scalar_td / td1 - - # TODO: this was taken from tests.series.test_ops; de-duplicate - def test_timedelta64_operations_with_timedeltas(self): - # td operate with td - td1 = Series([timedelta(minutes=5, seconds=3)] * 3) - td2 = timedelta(minutes=5, seconds=4) - result = td1 - td2 - expected = Series([timedelta(seconds=0)] * 3) - Series( - [timedelta(seconds=1)] * 3 - ) - assert result.dtype == "m8[ns]" - tm.assert_series_equal(result, expected) - - result2 = td2 - td1 - expected = Series([timedelta(seconds=1)] * 3) - Series( - [timedelta(seconds=0)] * 3 - ) - tm.assert_series_equal(result2, expected) - - # roundtrip - tm.assert_series_equal(result + td2, td1) - - # Now again, using pd.to_timedelta, which should build - # a Series or a scalar, depending on input. - td1 = Series(pd.to_timedelta(["00:05:03"] * 3)) - td2 = pd.to_timedelta("00:05:04") - result = td1 - td2 - expected = Series([timedelta(seconds=0)] * 3) - Series( - [timedelta(seconds=1)] * 3 - ) - assert result.dtype == "m8[ns]" - tm.assert_series_equal(result, expected) - - result2 = td2 - td1 - expected = Series([timedelta(seconds=1)] * 3) - Series( - [timedelta(seconds=0)] * 3 - ) - tm.assert_series_equal(result2, expected) - - # roundtrip - tm.assert_series_equal(result + td2, td1) - def test_td64arr_add_td64_array(self, box_with_array): box = box_with_array dti = pd.date_range("2016-01-01", periods=3) @@ -1203,7 +1172,6 @@ def test_td64arr_sub_td64_array(self, box_with_array): result = tdarr - tdi tm.assert_equal(result, expected) - # TODO: parametrize over [add, sub, radd, rsub]? @pytest.mark.parametrize( "names", [ @@ -1232,17 +1200,11 @@ def test_td64arr_add_sub_tdi(self, box, names): result = tdi + ser tm.assert_equal(result, expected) - if box is not pd.DataFrame: - assert result.dtype == "timedelta64[ns]" - else: - assert result.dtypes[0] == "timedelta64[ns]" + assert_dtype(result, "timedelta64[ns]") result = ser + tdi tm.assert_equal(result, expected) - if box is not pd.DataFrame: - assert result.dtype == "timedelta64[ns]" - else: - assert result.dtypes[0] == "timedelta64[ns]" + assert_dtype(result, "timedelta64[ns]") expected = Series( [Timedelta(hours=-3), Timedelta(days=1, hours=-4)], name=names[2] @@ -1251,17 +1213,11 @@ def test_td64arr_add_sub_tdi(self, box, names): result = tdi - ser tm.assert_equal(result, expected) - if box is not pd.DataFrame: - assert result.dtype == "timedelta64[ns]" - else: - assert result.dtypes[0] == "timedelta64[ns]" + assert_dtype(result, "timedelta64[ns]") result = ser - tdi tm.assert_equal(result, -expected) - if box is not pd.DataFrame: - assert result.dtype == "timedelta64[ns]" - else: - assert result.dtypes[0] == "timedelta64[ns]" + assert_dtype(result, "timedelta64[ns]") def test_td64arr_add_sub_td64_nat(self, box_with_array): # GH#23320 special handling for timedelta64("NaT") @@ -1296,6 +1252,7 @@ def test_td64arr_sub_NaT(self, box_with_array): def test_td64arr_add_timedeltalike(self, two_hours, box_with_array): # only test adding/sub offsets as + is now numeric + # GH#10699 for Tick cases box = box_with_array rng = timedelta_range("1 days", "10 days") expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D") @@ -1305,8 +1262,12 @@ def test_td64arr_add_timedeltalike(self, two_hours, box_with_array): result = rng + two_hours tm.assert_equal(result, expected) + result = two_hours + rng + tm.assert_equal(result, expected) + def test_td64arr_sub_timedeltalike(self, two_hours, box_with_array): # only test adding/sub offsets as - is now numeric + # GH#10699 for Tick cases box = box_with_array rng = timedelta_range("1 days", "10 days") expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00") @@ -1317,46 +1278,12 @@ def test_td64arr_sub_timedeltalike(self, two_hours, box_with_array): result = rng - two_hours tm.assert_equal(result, expected) + result = two_hours - rng + tm.assert_equal(result, -expected) + # ------------------------------------------------------------------ # __add__/__sub__ with DateOffsets and arrays of DateOffsets - # TODO: this was taken from tests.series.test_operators; de-duplicate - def test_timedelta64_operations_with_DateOffset(self): - # GH#10699 - td = Series([timedelta(minutes=5, seconds=3)] * 3) - result = td + pd.offsets.Minute(1) - expected = Series([timedelta(minutes=6, seconds=3)] * 3) - tm.assert_series_equal(result, expected) - - result = td - pd.offsets.Minute(1) - expected = Series([timedelta(minutes=4, seconds=3)] * 3) - tm.assert_series_equal(result, expected) - - with tm.assert_produces_warning(PerformanceWarning): - result = td + Series( - [pd.offsets.Minute(1), pd.offsets.Second(3), pd.offsets.Hour(2)] - ) - expected = Series( - [ - timedelta(minutes=6, seconds=3), - timedelta(minutes=5, seconds=6), - timedelta(hours=2, minutes=5, seconds=3), - ] - ) - tm.assert_series_equal(result, expected) - - result = td + pd.offsets.Minute(1) + pd.offsets.Second(12) - expected = Series([timedelta(minutes=6, seconds=15)] * 3) - tm.assert_series_equal(result, expected) - - # valid DateOffsets - for do in ["Hour", "Minute", "Second", "Day", "Micro", "Milli", "Nano"]: - op = getattr(pd.offsets, do) - td + op(5) - op(5) + td - td - op(5) - op(5) - td - @pytest.mark.parametrize( "names", [(None, None, None), ("foo", "bar", None), ("foo", "foo", "foo")] ) @@ -1561,26 +1488,6 @@ class TestTimedeltaArraylikeMulDivOps: # Tests for timedelta64[ns] # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__ - # TODO: Moved from tests.series.test_operators; needs cleanup - @pytest.mark.parametrize("m", [1, 3, 10]) - @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"]) - def test_timedelta64_conversions(self, m, unit): - startdate = Series(pd.date_range("2013-01-01", "2013-01-03")) - enddate = Series(pd.date_range("2013-03-01", "2013-03-03")) - - ser = enddate - startdate - ser[2] = np.nan - - # op - expected = Series([x / np.timedelta64(m, unit) for x in ser]) - result = ser / np.timedelta64(m, unit) - tm.assert_series_equal(result, expected) - - # reverse op - expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in ser]) - result = np.timedelta64(m, unit) / ser - tm.assert_series_equal(result, expected) - # ------------------------------------------------------------------ # Multiplication # organized with scalar others first, then array-like @@ -1734,6 +1641,29 @@ def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array): expected = 1 / expected tm.assert_equal(result, expected) + @pytest.mark.parametrize("m", [1, 3, 10]) + @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"]) + def test_td64arr_div_td64_scalar(self, m, unit, box_with_array): + startdate = Series(pd.date_range("2013-01-01", "2013-01-03")) + enddate = Series(pd.date_range("2013-03-01", "2013-03-03")) + + ser = enddate - startdate + ser[2] = np.nan + flat = ser + ser = tm.box_expected(ser, box_with_array) + + # op + expected = Series([x / np.timedelta64(m, unit) for x in flat]) + expected = tm.box_expected(expected, box_with_array) + result = ser / np.timedelta64(m, unit) + tm.assert_equal(result, expected) + + # reverse op + expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in flat]) + expected = tm.box_expected(expected, box_with_array) + result = np.timedelta64(m, unit) / ser + tm.assert_equal(result, expected) + def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array): rng = TimedeltaIndex(["1 days", pd.NaT, "2 days"], name="foo") expected = pd.Float64Index([12, np.nan, 24], name="foo")
https://api.github.com/repos/pandas-dev/pandas/pulls/32091
2020-02-19T03:45:07Z
2020-02-23T15:56:30Z
2020-02-23T15:56:30Z
2020-02-23T17:05:17Z
Series append raises TypeError
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 57c53f73962dc..7c68005144cbc 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -327,6 +327,7 @@ Reshaping - Bug in :func:`crosstab` when inputs are two Series and have tuple names, the output will keep dummy MultiIndex as columns. (:issue:`18321`) - :meth:`DataFrame.pivot` can now take lists for ``index`` and ``columns`` arguments (:issue:`21425`) - Bug in :func:`concat` where the resulting indices are not copied when ``copy=True`` (:issue:`29879`) +- :meth:`Series.append` will now raise a ``TypeError`` when passed a DataFrame or a sequence containing Dataframe (:issue:`31413`) - :meth:`DataFrame.replace` and :meth:`Series.replace` will raise a ``TypeError`` if ``to_replace`` is not an expected type. Previously the ``replace`` would fail silently (:issue:`18634`) @@ -349,7 +350,6 @@ Other instead of ``TypeError: Can only append a Series if ignore_index=True or if the Series has a name`` (:issue:`30871`) - Set operations on an object-dtype :class:`Index` now always return object-dtype results (:issue:`31401`) - Bug in :meth:`AbstractHolidayCalendar.holidays` when no rules were defined (:issue:`31415`) -- .. --------------------------------------------------------------------------- diff --git a/pandas/core/series.py b/pandas/core/series.py index d565cbbdd5344..9e67393df3370 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2535,6 +2535,12 @@ def append(self, to_append, ignore_index=False, verify_integrity=False): to_concat.extend(to_append) else: to_concat = [self, to_append] + if any(isinstance(x, (ABCDataFrame,)) for x in to_concat[1:]): + msg = ( + f"to_append should be a Series or list/tuple of Series, " + f"got DataFrame" + ) + raise TypeError(msg) return concat( to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity ) diff --git a/pandas/tests/series/methods/test_append.py b/pandas/tests/series/methods/test_append.py index 4742d6ae3544f..158c759fdaef3 100644 --- a/pandas/tests/series/methods/test_append.py +++ b/pandas/tests/series/methods/test_append.py @@ -61,15 +61,15 @@ def test_append_tuples(self): tm.assert_series_equal(expected, result) - def test_append_dataframe_regression(self): - # GH 30975 - df = pd.DataFrame({"A": [1, 2]}) - result = df.A.append([df]) - expected = pd.DataFrame( - {0: [1.0, 2.0, None, None], "A": [None, None, 1.0, 2.0]}, index=[0, 1, 0, 1] - ) - - tm.assert_frame_equal(expected, result) + def test_append_dataframe_raises(self): + # GH 31413 + df = pd.DataFrame({"A": [1, 2], "B": [3, 4]}) + + msg = "to_append should be a Series or list/tuple of Series, got DataFrame" + with pytest.raises(TypeError, match=msg): + df.A.append(df) + with pytest.raises(TypeError, match=msg): + df.A.append([df]) class TestSeriesAppendWithDatetimeIndex:
- [ ] closes #31413 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Series.append now raises TypeError when a DataFrame or a sequence containing DataFrame is passed.
https://api.github.com/repos/pandas-dev/pandas/pulls/32090
2020-02-19T02:18:53Z
2020-03-04T14:17:16Z
2020-03-04T14:17:16Z
2020-03-04T21:07:57Z
BUG: DataFrame.iat incorrectly wrapping datetime objects
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 13827e8fc4c33..add046674e9dc 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -160,6 +160,7 @@ Indexing - Bug in :meth:`PeriodIndex.is_monotonic` incorrectly returning ``True`` when containing leading ``NaT`` entries (:issue:`31437`) - Bug in :meth:`DatetimeIndex.get_loc` raising ``KeyError`` with converted-integer key instead of the user-passed key (:issue:`31425`) - Bug in :meth:`Series.xs` incorrectly returning ``Timestamp`` instead of ``datetime64`` in some object-dtype cases (:issue:`31630`) +- Bug in :meth:`DataFrame.iat` incorrectly returning ``Timestamp`` instead of ``datetime`` in some object-dtype cases (:issue:`32809`) Missing ^^^^^^^ diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 4ad80273f77ba..3651fdf081846 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2894,8 +2894,8 @@ def _get_value(self, index, col, takeable: bool = False): scalar """ if takeable: - series = self._iget_item_cache(col) - return com.maybe_box_datetimelike(series._values[index]) + series = self._ixs(col, axis=1) + return series._values[index] series = self._get_item_cache(col) engine = self.index._engine diff --git a/pandas/tests/indexing/test_scalar.py b/pandas/tests/indexing/test_scalar.py index 899c58eb5edea..c4750778e2eb8 100644 --- a/pandas/tests/indexing/test_scalar.py +++ b/pandas/tests/indexing/test_scalar.py @@ -1,4 +1,5 @@ """ test scalar indexing, including at and iat """ +from datetime import datetime, timedelta import numpy as np import pytest @@ -288,3 +289,24 @@ def test_getitem_zerodim_np_array(self): s = Series([1, 2]) result = s[np.array(0)] assert result == 1 + + +def test_iat_dont_wrap_object_datetimelike(): + # GH#32809 .iat calls go through DataFrame._get_value, should not + # call maybe_box_datetimelike + dti = date_range("2016-01-01", periods=3) + tdi = dti - dti + ser = Series(dti.to_pydatetime(), dtype=object) + ser2 = Series(tdi.to_pytimedelta(), dtype=object) + df = DataFrame({"A": ser, "B": ser2}) + assert (df.dtypes == object).all() + + for result in [df.at[0, "A"], df.iat[0, 0], df.loc[0, "A"], df.iloc[0, 0]]: + assert result is ser[0] + assert isinstance(result, datetime) + assert not isinstance(result, Timestamp) + + for result in [df.at[1, "B"], df.iat[1, 1], df.loc[1, "B"], df.iloc[1, 1]]: + assert result is ser2[1] + assert isinstance(result, timedelta) + assert not isinstance(result, Timedelta)
https://api.github.com/repos/pandas-dev/pandas/pulls/32089
2020-02-19T02:08:53Z
2020-02-22T15:56:04Z
2020-02-22T15:56:04Z
2020-02-22T15:58:16Z
"Backport PR #31679 on branch 1.0.x"
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index 19358689a2186..c9031ac1ae9fe 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -19,6 +19,7 @@ Fixed regressions - Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`) - Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`) - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`) +- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`) - .. --------------------------------------------------------------------------- diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py index f51d71d5507a0..9db0306e53a50 100644 --- a/pandas/core/ops/__init__.py +++ b/pandas/core/ops/__init__.py @@ -5,7 +5,7 @@ """ import datetime import operator -from typing import Set, Tuple, Union +from typing import TYPE_CHECKING, Set, Tuple, Union import numpy as np @@ -60,6 +60,9 @@ rxor, ) +if TYPE_CHECKING: + from pandas import DataFrame # noqa:F401 + # ----------------------------------------------------------------------------- # constants ARITHMETIC_BINOPS: Set[str] = { @@ -675,6 +678,58 @@ def to_series(right): return right +def _should_reindex_frame_op( + left: "DataFrame", right, axis, default_axis: int, fill_value, level +) -> bool: + """ + Check if this is an operation between DataFrames that will need to reindex. + """ + assert isinstance(left, ABCDataFrame) + + if not isinstance(right, ABCDataFrame): + return False + + if fill_value is None and level is None and axis is default_axis: + # TODO: any other cases we should handle here? + cols = left.columns.intersection(right.columns) + if not (cols.equals(left.columns) and cols.equals(right.columns)): + return True + + return False + + +def _frame_arith_method_with_reindex( + left: "DataFrame", right: "DataFrame", op +) -> "DataFrame": + """ + For DataFrame-with-DataFrame operations that require reindexing, + operate only on shared columns, then reindex. + + Parameters + ---------- + left : DataFrame + right : DataFrame + op : binary operator + + Returns + ------- + DataFrame + """ + # GH#31623, only operate on shared columns + cols = left.columns.intersection(right.columns) + + new_left = left[cols] + new_right = right[cols] + result = op(new_left, new_right) + + # Do the join on the columns instead of using _align_method_FRAME + # to avoid constructing two potentially large/sparse DataFrames + join_columns, _, _ = left.columns.join( + right.columns, how="outer", level=None, return_indexers=True + ) + return result.reindex(join_columns, axis=1) + + def _arith_method_FRAME(cls, op, special): str_rep = _get_opstr(op) op_name = _get_op_name(op, special) @@ -692,6 +747,9 @@ def _arith_method_FRAME(cls, op, special): @Appender(doc) def f(self, other, axis=default_axis, level=None, fill_value=None): + if _should_reindex_frame_op(self, other, axis, default_axis, fill_value, level): + return _frame_arith_method_with_reindex(self, other, op) + other = _align_method_FRAME(self, other, axis) if isinstance(other, ABCDataFrame): diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py index 659b55756c4b6..fadba0a8673d0 100644 --- a/pandas/tests/frame/test_arithmetic.py +++ b/pandas/tests/frame/test_arithmetic.py @@ -696,6 +696,25 @@ def test_operations_with_interval_categories_index(self, all_arithmetic_operator expected = pd.DataFrame([[getattr(n, op)(num) for n in data]], columns=ind) tm.assert_frame_equal(result, expected) + def test_frame_with_frame_reindex(self): + # GH#31623 + df = pd.DataFrame( + { + "foo": [pd.Timestamp("2019"), pd.Timestamp("2020")], + "bar": [pd.Timestamp("2018"), pd.Timestamp("2021")], + }, + columns=["foo", "bar"], + ) + df2 = df[["foo"]] + + result = df - df2 + + expected = pd.DataFrame( + {"foo": [pd.Timedelta(0), pd.Timedelta(0)], "bar": [np.nan, np.nan]}, + columns=["bar", "foo"], + ) + tm.assert_frame_equal(result, expected) + def test_frame_with_zero_len_series_corner_cases(): # GH#28600
#31679
https://api.github.com/repos/pandas-dev/pandas/pulls/32088
2020-02-19T01:33:22Z
2020-02-19T11:05:40Z
2020-02-19T11:05:39Z
2020-02-19T15:55:41Z
REF: remove compute_reduction
diff --git a/pandas/_libs/reduction.pyx b/pandas/_libs/reduction.pyx index b27072aa66708..fdcce6ad1df9e 100644 --- a/pandas/_libs/reduction.pyx +++ b/pandas/_libs/reduction.pyx @@ -27,126 +27,6 @@ cdef _check_result_array(object obj, Py_ssize_t cnt): raise ValueError('Function does not reduce') -cdef class Reducer: - """ - Performs generic reduction operation on a C or Fortran-contiguous ndarray - while avoiding ndarray construction overhead - """ - cdef: - Py_ssize_t increment, chunksize, nresults - object dummy, f, labels, typ, ityp, index - ndarray arr - - def __init__(self, ndarray arr, object f, axis=1, dummy=None, labels=None): - n, k = (<object>arr).shape - - if axis == 0: - if not arr.flags.f_contiguous: - arr = arr.copy('F') - - self.nresults = k - self.chunksize = n - self.increment = n * arr.dtype.itemsize - else: - if not arr.flags.c_contiguous: - arr = arr.copy('C') - - self.nresults = n - self.chunksize = k - self.increment = k * arr.dtype.itemsize - - self.f = f - self.arr = arr - self.labels = labels - self.dummy, self.typ, self.index, self.ityp = self._check_dummy( - dummy=dummy) - - cdef _check_dummy(self, dummy=None): - cdef: - object index = None, typ = None, ityp = None - - if dummy is None: - dummy = np.empty(self.chunksize, dtype=self.arr.dtype) - - # our ref is stolen later since we are creating this array - # in cython, so increment first - Py_INCREF(dummy) - - else: - - # we passed a Series - typ = type(dummy) - index = dummy.index - dummy = dummy.values - - if dummy.dtype != self.arr.dtype: - raise ValueError('Dummy array must be same dtype') - if len(dummy) != self.chunksize: - raise ValueError(f'Dummy array must be length {self.chunksize}') - - return dummy, typ, index, ityp - - def get_result(self): - cdef: - char* dummy_buf - ndarray arr, result, chunk - Py_ssize_t i - flatiter it - object res, name, labels - object cached_typ = None - - arr = self.arr - chunk = self.dummy - dummy_buf = chunk.data - chunk.data = arr.data - labels = self.labels - - result = np.empty(self.nresults, dtype='O') - it = <flatiter>PyArray_IterNew(result) - - try: - for i in range(self.nresults): - - # create the cached type - # each time just reassign the data - if i == 0: - - if self.typ is not None: - # In this case, we also have self.index - name = labels[i] - cached_typ = self.typ( - chunk, index=self.index, name=name, dtype=arr.dtype) - - # use the cached_typ if possible - if cached_typ is not None: - # In this case, we also have non-None labels - name = labels[i] - - object.__setattr__( - cached_typ._data._block, 'values', chunk) - object.__setattr__(cached_typ, 'name', name) - res = self.f(cached_typ) - else: - res = self.f(chunk) - - # TODO: reason for not squeezing here? - res = _extract_result(res, squeeze=False) - if i == 0: - # On the first pass, we check the output shape to see - # if this looks like a reduction. - _check_result_array(res, len(self.dummy)) - - PyArray_SETITEM(result, PyArray_ITER_DATA(it), res) - chunk.data = chunk.data + self.increment - PyArray_ITER_NEXT(it) - finally: - # so we don't free the wrong memory - chunk.data = dummy_buf - - result = maybe_convert_objects(result) - return result - - cdef class _BaseGrouper: cdef _check_dummy(self, dummy): # both values and index must be an ndarray! @@ -588,30 +468,3 @@ cdef class BlockSlider: # axis=1 is the frame's axis=0 arr.data = self.base_ptrs[i] arr.shape[1] = 0 - - -def compute_reduction(arr: np.ndarray, f, axis: int = 0, dummy=None, labels=None): - """ - - Parameters - ----------- - arr : np.ndarray - f : function - axis : integer axis - dummy : type of reduced output (series) - labels : Index or None - """ - - # We either have both dummy and labels, or neither of them - if (labels is None) ^ (dummy is None): - raise ValueError("Must pass either dummy and labels, or neither") - - if labels is not None: - # Caller is responsible for ensuring we don't have MultiIndex - assert labels.nlevels == 1 - - # pass as an ndarray/ExtensionArray - labels = labels._values - - reducer = Reducer(arr, f, axis=axis, dummy=dummy, labels=labels) - return reducer.get_result() diff --git a/pandas/core/apply.py b/pandas/core/apply.py index 70e0a129c055f..23b0056261fbb 100644 --- a/pandas/core/apply.py +++ b/pandas/core/apply.py @@ -4,16 +4,10 @@ import numpy as np -from pandas._libs import reduction as libreduction from pandas._typing import Axis from pandas.util._decorators import cache_readonly -from pandas.core.dtypes.common import ( - is_dict_like, - is_extension_array_dtype, - is_list_like, - is_sequence, -) +from pandas.core.dtypes.common import is_dict_like, is_list_like, is_sequence from pandas.core.dtypes.generic import ABCSeries from pandas.core.construction import create_series_with_explicit_dtype @@ -221,15 +215,7 @@ def apply_empty_result(self): def apply_raw(self): """ apply to the values as a numpy array """ - try: - result = libreduction.compute_reduction(self.values, self.f, axis=self.axis) - except ValueError as err: - if "Function does not reduce" not in str(err): - # catch only ValueError raised intentionally in libreduction - raise - # We expect np.apply_along_axis to give a two-dimensional result, or - # also raise. - result = np.apply_along_axis(self.f, self.axis, self.values) + result = np.apply_along_axis(self.f, self.axis, self.values) # TODO: mixed type case if result.ndim == 2: @@ -265,51 +251,6 @@ def apply_broadcast(self, target: "DataFrame") -> "DataFrame": return result def apply_standard(self): - - # try to reduce first (by default) - # this only matters if the reduction in values is of different dtype - # e.g. if we want to apply to a SparseFrame, then can't directly reduce - - # we cannot reduce using non-numpy dtypes, - # as demonstrated in gh-12244 - if ( - self.result_type in ["reduce", None] - and not self.dtypes.apply(is_extension_array_dtype).any() - # Disallow dtypes where setting _index_data will break - # ExtensionArray values, see GH#31182 - and not self.dtypes.apply(lambda x: x.kind in ["m", "M"]).any() - # Disallow complex_internals since libreduction shortcut raises a TypeError - and not self.agg_axis._has_complex_internals - ): - - values = self.values - index = self.obj._get_axis(self.axis) - labels = self.agg_axis - empty_arr = np.empty(len(index), dtype=values.dtype) - - # Preserve subclass for e.g. test_subclassed_apply - dummy = self.obj._constructor_sliced( - empty_arr, index=index, dtype=values.dtype - ) - - try: - result = libreduction.compute_reduction( - values, self.f, axis=self.axis, dummy=dummy, labels=labels - ) - except ValueError as err: - if "Function does not reduce" not in str(err): - # catch only ValueError raised intentionally in libreduction - raise - except TypeError: - # e.g. test_apply_ignore_failures we just ignore - if not self.ignore_failures: - raise - except ZeroDivisionError: - # reached via numexpr; fall back to python implementation - pass - else: - return self.obj._constructor_sliced(result, index=labels) - # compute the result using the series generator results, res_index = self.apply_series_generator() @@ -350,9 +291,23 @@ def wrap_results( from pandas import Series # see if we can infer the results - if len(results) > 0 and 0 in results and is_sequence(results[0]): + if ( + len(results) > 0 + and 0 in results + and is_sequence(results[0]) + and (not isinstance(results[0], dict) or self.result_type == "expand") + ): - return self.wrap_results_for_axis(results, res_index) + try: + return self.wrap_results_for_axis(results, res_index) + except ValueError as err: + # See e.g. test_agg_listlike_result + # FIXME: kludge for ragged array + if "arrays must all be same length" in str(err): + # Fall back to constructing Series + pass + else: + raise # dict of scalars @@ -440,9 +395,8 @@ def wrap_results_for_axis( # we have a non-series and don't want inference elif not isinstance(results[0], ABCSeries): - from pandas import Series - result = Series(results) + result = self.obj._constructor_sliced(results) result.index = res_index # we may want to infer results diff --git a/pandas/tests/groupby/test_bin_groupby.py b/pandas/tests/groupby/test_bin_groupby.py index ff74d374e5e3f..b0e354050b9a4 100644 --- a/pandas/tests/groupby/test_bin_groupby.py +++ b/pandas/tests/groupby/test_bin_groupby.py @@ -5,7 +5,7 @@ from pandas.core.dtypes.common import ensure_int64 -from pandas import Index, Series, isna +from pandas import Series, isna import pandas._testing as tm @@ -111,37 +111,3 @@ def _ohlc(group): class TestMoments: pass - - -class TestReducer: - def test_int_index(self): - arr = np.random.randn(100, 4) - - msg = "Must pass either dummy and labels, or neither" - # we must pass either both labels and dummy, or neither - with pytest.raises(ValueError, match=msg): - libreduction.compute_reduction(arr, np.sum, labels=Index(np.arange(4))) - - with pytest.raises(ValueError, match=msg): - libreduction.compute_reduction( - arr, np.sum, axis=1, labels=Index(np.arange(100)) - ) - - dummy = Series(0.0, index=np.arange(100)) - result = libreduction.compute_reduction( - arr, np.sum, dummy=dummy, labels=Index(np.arange(4)) - ) - expected = arr.sum(0) - tm.assert_almost_equal(result, expected) - - dummy = Series(0.0, index=np.arange(4)) - result = libreduction.compute_reduction( - arr, np.sum, axis=1, dummy=dummy, labels=Index(np.arange(100)) - ) - expected = arr.sum(1) - tm.assert_almost_equal(result, expected) - - result = libreduction.compute_reduction( - arr, np.sum, axis=1, dummy=dummy, labels=Index(np.arange(100)) - ) - tm.assert_almost_equal(result, expected)
Along with #32083, #32086 this gets rid of _libs.reduction. Now, time to run some asvs
https://api.github.com/repos/pandas-dev/pandas/pulls/32087
2020-02-19T01:20:55Z
2020-02-20T02:46:09Z
null
2020-08-27T20:18:21Z
REF: remove fast_apply
diff --git a/pandas/_libs/reduction.pyx b/pandas/_libs/reduction.pyx index b27072aa66708..db897014a1061 100644 --- a/pandas/_libs/reduction.pyx +++ b/pandas/_libs/reduction.pyx @@ -454,142 +454,6 @@ cdef class Slider: self.buf.strides[0] = self.orig_stride -class InvalidApply(Exception): - pass - - -def apply_frame_axis0(object frame, object f, object names, - const int64_t[:] starts, const int64_t[:] ends): - cdef: - BlockSlider slider - Py_ssize_t i, n = len(starts) - list results - object piece - dict item_cache - - # We have already checked that we don't have a MultiIndex before calling - assert frame.index.nlevels == 1 - - results = [] - - slider = BlockSlider(frame) - - mutated = False - item_cache = slider.dummy._item_cache - try: - for i in range(n): - slider.move(starts[i], ends[i]) - - item_cache.clear() # ugh - chunk = slider.dummy - object.__setattr__(chunk, 'name', names[i]) - - try: - piece = f(chunk) - except Exception: - # We can't be more specific without knowing something about `f` - raise InvalidApply('Let this error raise above us') - - # Need to infer if low level index slider will cause segfaults - require_slow_apply = i == 0 and piece is chunk - try: - if piece.index is not chunk.index: - mutated = True - except AttributeError: - # `piece` might not have an index, could be e.g. an int - pass - - if not is_scalar(piece): - # Need to copy data to avoid appending references - try: - piece = piece.copy(deep="all") - except (TypeError, AttributeError): - piece = copy(piece) - - results.append(piece) - - # If the data was modified inplace we need to - # take the slow path to not risk segfaults - # we have already computed the first piece - if require_slow_apply: - break - finally: - slider.reset() - - return results, mutated - - -cdef class BlockSlider: - """ - Only capable of sliding on axis=0 - """ - - cdef public: - object frame, dummy, index - int nblocks - Slider idx_slider - list blocks - - cdef: - char **base_ptrs - - def __init__(self, frame): - self.frame = frame - self.dummy = frame[:0] - self.index = self.dummy.index - - self.blocks = [b.values for b in self.dummy._data.blocks] - - for x in self.blocks: - util.set_array_not_contiguous(x) - - self.nblocks = len(self.blocks) - # See the comment in indexes/base.py about _index_data. - # We need this for EA-backed indexes that have a reference to a 1-d - # ndarray like datetime / timedelta / period. - self.idx_slider = Slider( - self.frame.index._index_data, self.dummy.index._index_data) - - self.base_ptrs = <char**>malloc(sizeof(char*) * len(self.blocks)) - for i, block in enumerate(self.blocks): - self.base_ptrs[i] = (<ndarray>block).data - - def __dealloc__(self): - free(self.base_ptrs) - - cdef move(self, int start, int end): - cdef: - ndarray arr - Py_ssize_t i - - # move blocks - for i in range(self.nblocks): - arr = self.blocks[i] - - # axis=1 is the frame's axis=0 - arr.data = self.base_ptrs[i] + arr.strides[1] * start - arr.shape[1] = end - start - - # move and set the index - self.idx_slider.move(start, end) - - object.__setattr__(self.index, '_index_data', self.idx_slider.buf) - self.index._engine.clear_mapping() - - cdef reset(self): - cdef: - ndarray arr - Py_ssize_t i - - # reset blocks - for i in range(self.nblocks): - arr = self.blocks[i] - - # axis=1 is the frame's axis=0 - arr.data = self.base_ptrs[i] - arr.shape[1] = 0 - - def compute_reduction(arr: np.ndarray, f, axis: int = 0, dummy=None, labels=None): """ diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py index 7259268ac3f2b..c33748deef1c2 100644 --- a/pandas/core/groupby/ops.py +++ b/pandas/core/groupby/ops.py @@ -43,7 +43,7 @@ import pandas.core.common as com from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame -from pandas.core.groupby import base, grouper +from pandas.core.groupby import grouper from pandas.core.indexes.api import Index, MultiIndex, ensure_index from pandas.core.series import Series from pandas.core.sorting import ( @@ -154,37 +154,6 @@ def apply(self, f, data: FrameOrSeries, axis: int = 0): group_keys = self._get_group_keys() result_values = None - sdata: FrameOrSeries = splitter._get_sorted_data() - if sdata.ndim == 2 and np.any(sdata.dtypes.apply(is_extension_array_dtype)): - # calling splitter.fast_apply will raise TypeError via apply_frame_axis0 - # if we pass EA instead of ndarray - # TODO: can we have a workaround for EAs backed by ndarray? - pass - - elif ( - com.get_callable_name(f) not in base.plotting_methods - and isinstance(splitter, FrameSplitter) - and axis == 0 - # fast_apply/libreduction doesn't allow non-numpy backed indexes - and not sdata.index._has_complex_internals - ): - try: - result_values, mutated = splitter.fast_apply(f, sdata, group_keys) - - except libreduction.InvalidApply as err: - # This Exception is raised if `f` triggers an exception - # but it is preferable to raise the exception in Python. - if "Let this error raise above us" not in str(err): - # TODO: can we infer anything about whether this is - # worth-retrying in pure-python? - raise - - else: - # If the fast apply path could be used we can return here. - # Otherwise we need to fall back to the slow implementation. - if len(result_values) == len(group_keys): - return group_keys, result_values, mutated - for key, (i, group) in zip(group_keys, splitter): object.__setattr__(group, "name", key) @@ -925,11 +894,6 @@ def _chop(self, sdata: Series, slice_obj: slice) -> Series: class FrameSplitter(DataSplitter): - def fast_apply(self, f, sdata: FrameOrSeries, names): - # must return keys::list, values::list, mutated::bool - starts, ends = lib.generate_slices(self.slabels, self.ngroups) - return libreduction.apply_frame_axis0(sdata, f, names, starts, ends) - def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame: if self.axis == 0: return sdata.iloc[slice_obj] diff --git a/pandas/tests/groupby/test_apply.py b/pandas/tests/groupby/test_apply.py index 18ad5d90b3f60..7afb449dbe37e 100644 --- a/pandas/tests/groupby/test_apply.py +++ b/pandas/tests/groupby/test_apply.py @@ -82,39 +82,6 @@ def test_apply_trivial_fail(): tm.assert_frame_equal(result, expected) -def test_fast_apply(): - # make sure that fast apply is correctly called - # rather than raising any kind of error - # otherwise the python path will be callsed - # which slows things down - N = 1000 - labels = np.random.randint(0, 2000, size=N) - labels2 = np.random.randint(0, 3, size=N) - df = DataFrame( - { - "key": labels, - "key2": labels2, - "value1": np.random.randn(N), - "value2": ["foo", "bar", "baz", "qux"] * (N // 4), - } - ) - - def f(g): - return 1 - - g = df.groupby(["key", "key2"]) - - grouper = g.grouper - - splitter = grouper._get_splitter(g._selected_obj, axis=g.axis) - group_keys = grouper._get_group_keys() - sdata = splitter._get_sorted_data() - - values, mutated = splitter.fast_apply(f, sdata, group_keys) - - assert not mutated - - @pytest.mark.parametrize( "df, group_names", [ diff --git a/pandas/tests/groupby/test_whitelist.py b/pandas/tests/groupby/test_whitelist.py index 8e387e9202ef6..eaab2757097bf 100644 --- a/pandas/tests/groupby/test_whitelist.py +++ b/pandas/tests/groupby/test_whitelist.py @@ -367,7 +367,6 @@ def test_groupby_selection_with_methods(df): "ffill", "bfill", "pct_change", - "tshift", ] for m in methods: @@ -377,6 +376,11 @@ def test_groupby_selection_with_methods(df): # should always be frames! tm.assert_frame_equal(res, exp) + with pytest.raises(ValueError, match="Freq was not given"): + g.tshift() + with pytest.raises(ValueError, match="Freq was not given"): + g_exp.tshift() + # methods which aren't just .foo() tm.assert_frame_equal(g.fillna(0), g_exp.fillna(0)) tm.assert_frame_equal(g.dtypes, g_exp.dtypes)
xref #32083, one more coming that i think finishes off libreduction
https://api.github.com/repos/pandas-dev/pandas/pulls/32086
2020-02-19T01:19:21Z
2020-02-20T02:46:29Z
null
2020-08-27T20:18:08Z
BUG: Fix incorrect _is_scalar_access check in iloc
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 579daae2b15c6..ac03843a0e27d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3467,13 +3467,13 @@ def _get_item_cache(self, item): res._is_copy = self._is_copy return res - def _iget_item_cache(self, item): + def _iget_item_cache(self, item: int): """Return the cached item, item represents a positional indexer.""" ax = self._info_axis if ax.is_unique: lower = self._get_item_cache(ax[item]) else: - lower = self._take_with_is_copy(item, axis=self._info_axis_number) + return self._ixs(item, axis=1) return lower def _box_item_values(self, key, values): diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index d3e75d43c6bd7..c366b0621fe93 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1445,10 +1445,6 @@ def _is_scalar_access(self, key: Tuple) -> bool: if not is_integer(k): return False - ax = self.obj.axes[i] - if not ax.is_unique: - return False - return True def _validate_integer(self, key: int, axis: int) -> None: diff --git a/pandas/tests/indexing/test_iloc.py b/pandas/tests/indexing/test_iloc.py index 500bd1853e9a4..683d4f2605712 100644 --- a/pandas/tests/indexing/test_iloc.py +++ b/pandas/tests/indexing/test_iloc.py @@ -47,6 +47,17 @@ def test_iloc_getitem_list_int(self): class TestiLoc2: # TODO: better name, just separating out things that dont rely on base class + + def test_is_scalar_access(self): + # GH#32085 index with duplicates doesnt matter for _is_scalar_access + index = pd.Index([1, 2, 1]) + ser = pd.Series(range(3), index=index) + + assert ser.iloc._is_scalar_access((1,)) + + df = ser.to_frame() + assert df.iloc._is_scalar_access((1, 0,)) + def test_iloc_exceeds_bounds(self): # GH6296
We only have two test cases that make it to the _take_with_is_copy line in _iget_item_cache
https://api.github.com/repos/pandas-dev/pandas/pulls/32085
2020-02-19T00:08:20Z
2020-02-22T16:08:23Z
2020-02-22T16:08:23Z
2020-02-22T16:10:24Z
POC/REF: remove SeriesBinGrouper, SeriesGrouper
diff --git a/pandas/_libs/reduction.pyx b/pandas/_libs/reduction.pyx index b27072aa66708..382a5c6692b55 100644 --- a/pandas/_libs/reduction.pyx +++ b/pandas/_libs/reduction.pyx @@ -147,253 +147,6 @@ cdef class Reducer: return result -cdef class _BaseGrouper: - cdef _check_dummy(self, dummy): - # both values and index must be an ndarray! - - values = dummy.values - # GH 23683: datetimetz types are equivalent to datetime types here - if (dummy.dtype != self.arr.dtype - and values.dtype != self.arr.dtype): - raise ValueError('Dummy array must be same dtype') - if util.is_array(values) and not values.flags.contiguous: - # e.g. Categorical has no `flags` attribute - values = values.copy() - index = dummy.index.values - if not index.flags.contiguous: - index = index.copy() - - return values, index - - cdef inline _update_cached_objs(self, object cached_typ, object cached_ityp, - Slider islider, Slider vslider): - if cached_typ is None: - cached_ityp = self.ityp(islider.buf) - cached_typ = self.typ(vslider.buf, index=cached_ityp, name=self.name) - else: - # See the comment in indexes/base.py about _index_data. - # We need this for EA-backed indexes that have a reference - # to a 1-d ndarray like datetime / timedelta / period. - object.__setattr__(cached_ityp, '_index_data', islider.buf) - cached_ityp._engine.clear_mapping() - object.__setattr__(cached_typ._data._block, 'values', vslider.buf) - object.__setattr__(cached_typ, '_index', cached_ityp) - object.__setattr__(cached_typ, 'name', self.name) - - return cached_typ, cached_ityp - - cdef inline object _apply_to_group(self, - object cached_typ, object cached_ityp, - Slider islider, Slider vslider, - Py_ssize_t group_size, bint initialized): - """ - Call self.f on our new group, then update to the next group. - """ - cached_ityp._engine.clear_mapping() - res = self.f(cached_typ) - res = _extract_result(res) - if not initialized: - # On the first pass, we check the output shape to see - # if this looks like a reduction. - initialized = 1 - _check_result_array(res, len(self.dummy_arr)) - - islider.advance(group_size) - vslider.advance(group_size) - - return res, initialized - - -cdef class SeriesBinGrouper(_BaseGrouper): - """ - Performs grouping operation according to bin edges, rather than labels - """ - cdef: - Py_ssize_t nresults, ngroups - - cdef public: - ndarray arr, index, dummy_arr, dummy_index - object values, f, bins, typ, ityp, name - - def __init__(self, object series, object f, object bins, object dummy): - - assert dummy is not None # always obj[:0] - assert len(bins) > 0 # otherwise we get IndexError in get_result - - self.bins = bins - self.f = f - - values = series.values - if util.is_array(values) and not values.flags.c_contiguous: - # e.g. Categorical has no `flags` attribute - values = values.copy('C') - self.arr = values - self.typ = series._constructor - self.ityp = series.index._constructor - self.index = series.index.values - self.name = series.name - - self.dummy_arr, self.dummy_index = self._check_dummy(dummy) - - # kludge for #1688 - if len(bins) > 0 and bins[-1] == len(series): - self.ngroups = len(bins) - else: - self.ngroups = len(bins) + 1 - - def get_result(self): - cdef: - ndarray arr, result - ndarray[int64_t] counts - Py_ssize_t i, n, group_size - object res - bint initialized = 0 - Slider vslider, islider - object cached_typ = None, cached_ityp = None - - counts = np.zeros(self.ngroups, dtype=np.int64) - - if self.ngroups > 0: - counts[0] = self.bins[0] - for i in range(1, self.ngroups): - if i == self.ngroups - 1: - counts[i] = len(self.arr) - self.bins[i - 1] - else: - counts[i] = self.bins[i] - self.bins[i - 1] - - group_size = 0 - n = len(self.arr) - - vslider = Slider(self.arr, self.dummy_arr) - islider = Slider(self.index, self.dummy_index) - - result = np.empty(self.ngroups, dtype='O') - - try: - for i in range(self.ngroups): - group_size = counts[i] - - islider.set_length(group_size) - vslider.set_length(group_size) - - cached_typ, cached_ityp = self._update_cached_objs( - cached_typ, cached_ityp, islider, vslider) - - res, initialized = self._apply_to_group(cached_typ, cached_ityp, - islider, vslider, - group_size, initialized) - - result[i] = res - - finally: - # so we don't free the wrong memory - islider.reset() - vslider.reset() - - result = maybe_convert_objects(result) - return result, counts - - -cdef class SeriesGrouper(_BaseGrouper): - """ - Performs generic grouping operation while avoiding ndarray construction - overhead - """ - cdef: - Py_ssize_t nresults, ngroups - - cdef public: - ndarray arr, index, dummy_arr, dummy_index - object f, labels, values, typ, ityp, name - - def __init__(self, object series, object f, object labels, - Py_ssize_t ngroups, object dummy): - - # in practice we always pass obj.iloc[:0] or equivalent - assert dummy is not None - - if len(series) == 0: - # get_result would never assign `result` - raise ValueError("SeriesGrouper requires non-empty `series`") - - self.labels = labels - self.f = f - - values = series.values - if util.is_array(values) and not values.flags.c_contiguous: - # e.g. Categorical has no `flags` attribute - values = values.copy('C') - self.arr = values - self.typ = series._constructor - self.ityp = series.index._constructor - self.index = series.index.values - self.name = series.name - - self.dummy_arr, self.dummy_index = self._check_dummy(dummy) - self.ngroups = ngroups - - def get_result(self): - cdef: - # Define result to avoid UnboundLocalError - ndarray arr, result = None - ndarray[int64_t] labels, counts - Py_ssize_t i, n, group_size, lab - object res - bint initialized = 0 - Slider vslider, islider - object cached_typ = None, cached_ityp = None - - labels = self.labels - counts = np.zeros(self.ngroups, dtype=np.int64) - group_size = 0 - n = len(self.arr) - - vslider = Slider(self.arr, self.dummy_arr) - islider = Slider(self.index, self.dummy_index) - - result = np.empty(self.ngroups, dtype='O') - - try: - for i in range(n): - group_size += 1 - - lab = labels[i] - - if i == n - 1 or lab != labels[i + 1]: - if lab == -1: - islider.advance(group_size) - vslider.advance(group_size) - group_size = 0 - continue - - islider.set_length(group_size) - vslider.set_length(group_size) - - cached_typ, cached_ityp = self._update_cached_objs( - cached_typ, cached_ityp, islider, vslider) - - res, initialized = self._apply_to_group(cached_typ, cached_ityp, - islider, vslider, - group_size, initialized) - - result[lab] = res - counts[lab] = group_size - group_size = 0 - - finally: - # so we don't free the wrong memory - islider.reset() - vslider.reset() - - # We check for empty series in the constructor, so should always - # have result initialized by this point. - assert initialized, "`result` has not been initialized." - - result = maybe_convert_objects(result) - - return result, counts - - cdef inline _extract_result(object res, bint squeeze=True): """ extract the result object, it might be a 0-dim ndarray or a len-1 0-dim, or a scalar """ diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py index cc46485b4a2e8..f946f0e63a583 100644 --- a/pandas/core/groupby/groupby.py +++ b/pandas/core/groupby/groupby.py @@ -923,17 +923,10 @@ def _python_agg_general(self, func, *args, **kwargs): try: # if this function is invalid for this dtype, we will ignore it. - func(obj[:0]) + result, counts = self.grouper.agg_series(obj, f) except TypeError: continue - except AssertionError: - raise - except Exception: - # Our function depends on having a non-empty argument - # See test_groupby_agg_err_catching - pass - - result, counts = self.grouper.agg_series(obj, f) + assert result is not None key = base.OutputKey(label=name, position=idx) output[key] = self._try_cast(result, obj, numeric_only=True) diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py index 7259268ac3f2b..2b89a51eb635c 100644 --- a/pandas/core/groupby/ops.py +++ b/pandas/core/groupby/ops.py @@ -621,50 +621,8 @@ def agg_series(self, obj: Series, func): # Caller is responsible for checking ngroups != 0 assert self.ngroups != 0 - if len(obj) == 0: - # SeriesGrouper would raise if we were to call _aggregate_series_fast - return self._aggregate_series_pure_python(obj, func) - - elif is_extension_array_dtype(obj.dtype): - # _aggregate_series_fast would raise TypeError when - # calling libreduction.Slider - # In the datetime64tz case it would incorrectly cast to tz-naive - # TODO: can we get a performant workaround for EAs backed by ndarray? - return self._aggregate_series_pure_python(obj, func) - - elif obj.index._has_complex_internals: - # Pre-empt TypeError in _aggregate_series_fast - return self._aggregate_series_pure_python(obj, func) - - try: - return self._aggregate_series_fast(obj, func) - except ValueError as err: - if "Function does not reduce" in str(err): - # raised in libreduction - pass - else: - raise return self._aggregate_series_pure_python(obj, func) - def _aggregate_series_fast(self, obj: Series, func): - # At this point we have already checked that - # - obj.index is not a MultiIndex - # - obj is backed by an ndarray, not ExtensionArray - # - len(obj) > 0 - # - ngroups != 0 - func = self._is_builtin_func(func) - - group_index, _, ngroups = self.group_info - - # avoids object / Series creation overhead - dummy = obj.iloc[:0] - indexer = get_group_index_sorter(group_index, ngroups) - obj = obj.take(indexer) - group_index = algorithms.take_nd(group_index, indexer, allow_fill=False) - grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy) - result, counts = grouper.get_result() - return result, counts - def _aggregate_series_pure_python(self, obj: Series, func): group_index, _, ngroups = self.group_info @@ -856,13 +814,7 @@ def agg_series(self, obj: Series, func): assert self.ngroups != 0 assert len(self.bins) > 0 # otherwise we'd get IndexError in get_result - if is_extension_array_dtype(obj.dtype): - # pre-empt SeriesBinGrouper from raising TypeError - return self._aggregate_series_pure_python(obj, func) - - dummy = obj[:0] - grouper = libreduction.SeriesBinGrouper(obj, func, self.bins, dummy) - return grouper.get_result() + return self._aggregate_series_pure_python(obj, func) def _is_indexed_like(obj, axes) -> bool: diff --git a/pandas/tests/groupby/test_bin_groupby.py b/pandas/tests/groupby/test_bin_groupby.py index ff74d374e5e3f..5f20155fc46e4 100644 --- a/pandas/tests/groupby/test_bin_groupby.py +++ b/pandas/tests/groupby/test_bin_groupby.py @@ -9,48 +9,6 @@ import pandas._testing as tm -def test_series_grouper(): - obj = Series(np.random.randn(10)) - dummy = obj.iloc[:0] - - labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64) - - grouper = libreduction.SeriesGrouper(obj, np.mean, labels, 2, dummy) - result, counts = grouper.get_result() - - expected = np.array([obj[3:6].mean(), obj[6:].mean()]) - tm.assert_almost_equal(result, expected) - - exp_counts = np.array([3, 4], dtype=np.int64) - tm.assert_almost_equal(counts, exp_counts) - - -def test_series_grouper_requires_nonempty_raises(): - # GH#29500 - obj = Series(np.random.randn(10)) - dummy = obj.iloc[:0] - labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64) - - with pytest.raises(ValueError, match="SeriesGrouper requires non-empty `series`"): - libreduction.SeriesGrouper(dummy, np.mean, labels, 2, dummy) - - -def test_series_bin_grouper(): - obj = Series(np.random.randn(10)) - dummy = obj[:0] - - bins = np.array([3, 6]) - - grouper = libreduction.SeriesBinGrouper(obj, np.mean, bins, dummy) - result, counts = grouper.get_result() - - expected = np.array([obj[:3].mean(), obj[3:6].mean(), obj[6:].mean()]) - tm.assert_almost_equal(result, expected) - - exp_counts = np.array([3, 3, 4], dtype=np.int64) - tm.assert_almost_equal(counts, exp_counts) - - @pytest.mark.parametrize( "binner,closed,expected", [
Maintenance of libreduction is a PITA, so I want to see how big a performance hit we take if we rip it out. I get a test failure locally because apparently something in resample behaves differently if we go through the python path, which itself should be considered a bug. cc @mroeschke any ideas how to fix the failing test/
https://api.github.com/repos/pandas-dev/pandas/pulls/32083
2020-02-18T21:00:12Z
2020-02-20T02:45:47Z
null
2020-08-27T20:18:36Z
CLN: indexing comments and cleanups
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index c53e690b5f46c..a614c731df879 100755 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -732,14 +732,15 @@ def _getitem_lowerdim(self, tup: Tuple): raise IndexingError("Too many indexers. handle elsewhere") for i, key in enumerate(tup): - if is_label_like(key) or isinstance(key, tuple): + if is_label_like(key): + # We don't need to check for tuples here because those are + # caught by the _is_nested_tuple_indexer check above. section = self._getitem_axis(key, axis=i) - # we have yielded a scalar ? - if not is_list_like_indexer(section): - return section - - elif section.ndim == self.ndim: + # We should never have a scalar section here, because + # _getitem_lowerdim is only called after a check for + # is_scalar_access, which that would be. + if section.ndim == self.ndim: # we're in the middle of slicing through a MultiIndex # revise the key wrt to `section` by inserting an _NS new_key = tup[:i] + (_NS,) + tup[i + 1 :] @@ -757,7 +758,7 @@ def _getitem_lowerdim(self, tup: Tuple): # slice returns a new object. if com.is_null_slice(new_key): return section - # This is an elided recursive call to iloc/loc/etc' + # This is an elided recursive call to iloc/loc return getattr(section, self.name)[new_key] raise IndexingError("not applicable") @@ -1013,15 +1014,7 @@ def _getitem_tuple(self, tup: Tuple): return self._getitem_tuple_same_dim(tup) def _get_label(self, label, axis: int): - if self.ndim == 1: - # for perf reasons we want to try _xs first - # as its basically direct indexing - # but will fail when the index is not present - # see GH5667 - return self.obj._xs(label, axis=axis) - elif isinstance(label, tuple) and isinstance(label[axis], slice): - raise IndexingError("no slices here, handle elsewhere") - + # GH#5667 this will fail if the label is not present in the axis. return self.obj._xs(label, axis=axis) def _handle_lowerdim_multi_index_axis0(self, tup: Tuple): @@ -1298,7 +1291,7 @@ def _validate_read_indexer( # We (temporarily) allow for some missing keys with .loc, except in # some cases (e.g. setting) in which "raise_missing" will be False - if not (self.name == "loc" and not raise_missing): + if raise_missing: not_found = list(set(key) - set(ax)) raise KeyError(f"{not_found} not in index") @@ -1363,10 +1356,7 @@ def _validate_key(self, key, axis: int): else: raise ValueError(f"Can only index by location with a [{self._valid_types}]") - def _has_valid_setitem_indexer(self, indexer): - self._has_valid_positional_setitem_indexer(indexer) - - def _has_valid_positional_setitem_indexer(self, indexer) -> bool: + def _has_valid_setitem_indexer(self, indexer) -> bool: """ Validate that a positional indexer cannot enlarge its target will raise if needed, does not modify the indexer externally. @@ -1376,7 +1366,7 @@ def _has_valid_positional_setitem_indexer(self, indexer) -> bool: bool """ if isinstance(indexer, dict): - raise IndexError(f"{self.name} cannot enlarge its target object") + raise IndexError("iloc cannot enlarge its target object") else: if not isinstance(indexer, tuple): indexer = _tuplify(self.ndim, indexer) @@ -1389,11 +1379,9 @@ def _has_valid_positional_setitem_indexer(self, indexer) -> bool: pass elif is_integer(i): if i >= len(ax): - raise IndexError( - f"{self.name} cannot enlarge its target object" - ) + raise IndexError("iloc cannot enlarge its target object") elif isinstance(i, dict): - raise IndexError(f"{self.name} cannot enlarge its target object") + raise IndexError("iloc cannot enlarge its target object") return True @@ -1520,8 +1508,8 @@ def _convert_to_indexer(self, key, axis: int, is_setter: bool = False): return key elif is_float(key): + # _validate_indexer call will always raise labels._validate_indexer("positional", key, "iloc") - return key self._validate_key(key, axis) return key @@ -1582,7 +1570,7 @@ def _setitem_with_indexer(self, indexer, value): # this correctly sets the dtype and avoids cache issues # essentially this separates out the block that is needed # to possibly be modified - if self.ndim > 1 and i == self.obj._info_axis_number: + if self.ndim > 1 and i == info_axis: # add the new item, and set the value # must have all defined axes if we have a scalar
https://api.github.com/repos/pandas-dev/pandas/pulls/32082
2020-02-18T19:57:15Z
2020-02-23T15:52:04Z
2020-02-23T15:52:04Z
2020-02-23T17:10:08Z
REGR: Series.str.encode("base64")
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index 19358689a2186..025924b1c0a5a 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -19,7 +19,7 @@ Fixed regressions - Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`) - Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`) - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`) -- +- Fixed regression in :meth:`Series.str.encode` where ``base64`` was no longer valid for bytes objects (:issue:`32048`) .. --------------------------------------------------------------------------- diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 4b0fc3e47356c..017504d9f002a 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -2898,7 +2898,6 @@ def decode(self, encoding, errors="strict"): return self._wrap_result(result, returns_string=False) @copy(str_encode) - @forbid_nonstring_types(["bytes"]) def encode(self, encoding, errors="strict"): result = str_encode(self._parent, encoding, errors) return self._wrap_result(result, returns_string=False) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 1338d801e39f4..f7d945f1f8879 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -3592,3 +3592,11 @@ def test_string_array_extract(): result = result.astype(object) tm.assert_equal(result, expected) + + +def test_bytes_encode(): + # gh-32049 + ser = pd.Series(list("abc")) + result = ser.str.encode(encoding="utf-8").str.encode(encoding="base64") + expected = pd.Series([b"YQ==\n", b"Yg==\n", b"Yw==\n"]) + tm.assert_series_equal(result, expected)
- [ ] closes #32048 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32080
2020-02-18T18:15:58Z
2020-02-18T19:32:06Z
null
2020-02-18T19:32:06Z
BUG: fix in categorical merges
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 705c335acfb48..822b7dc2e5caa 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -103,9 +103,10 @@ Bug fixes Categorical ^^^^^^^^^^^ + +- Bug where :func:`merge` was unable to join on non-unique categorical indices (:issue:`28189`) - Bug when passing categorical data to :class:`Index` constructor along with ``dtype=object`` incorrectly returning a :class:`CategoricalIndex` instead of object-dtype :class:`Index` (:issue:`32167`) - -- Datetimelike ^^^^^^^^^^^^ diff --git a/pandas/_libs/join.pyx b/pandas/_libs/join.pyx index dfa7aa708d681..f696591cf3bd1 100644 --- a/pandas/_libs/join.pyx +++ b/pandas/_libs/join.pyx @@ -254,6 +254,8 @@ ctypedef fused join_t: float64_t float32_t object + int8_t + int16_t int32_t int64_t uint64_t diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py index caa6a9a93141f..11b603f3478ae 100644 --- a/pandas/core/indexes/category.py +++ b/pandas/core/indexes/category.py @@ -28,6 +28,7 @@ from pandas.core.indexes.base import Index, _index_shared_docs, maybe_extract_name from pandas.core.indexes.extension import ExtensionIndex, inherit_names import pandas.core.missing as missing +from pandas.core.ops import get_op_result_name _index_doc_kwargs = dict(ibase._index_doc_kwargs) _index_doc_kwargs.update(dict(target_klass="CategoricalIndex")) @@ -785,6 +786,12 @@ def _delegate_method(self, name: str, *args, **kwargs): return res return CategoricalIndex(res, name=self.name) + def _wrap_joined_index( + self, joined: np.ndarray, other: "CategoricalIndex" + ) -> "CategoricalIndex": + name = get_op_result_name(self, other) + return self._create_from_codes(joined, name=name) + CategoricalIndex._add_numeric_methods_add_sub_disabled() CategoricalIndex._add_numeric_methods_disabled() diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py index 4f2cd878df613..d80e2e7afceef 100644 --- a/pandas/tests/reshape/merge/test_merge.py +++ b/pandas/tests/reshape/merge/test_merge.py @@ -2163,3 +2163,25 @@ def test_merge_datetime_upcast_dtype(): } ) tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("n_categories", [5, 128]) +def test_categorical_non_unique_monotonic(n_categories): + # GH 28189 + # With n_categories as 5, we test the int8 case is hit in libjoin, + # with n_categories as 128 we test the int16 case. + left_index = CategoricalIndex([0] + list(range(n_categories))) + df1 = DataFrame(range(n_categories + 1), columns=["value"], index=left_index) + df2 = DataFrame( + [[6]], + columns=["value"], + index=CategoricalIndex([0], categories=np.arange(n_categories)), + ) + + result = merge(df1, df2, how="left", left_index=True, right_index=True) + expected = DataFrame( + [[i, 6.0] if i < 2 else [i, np.nan] for i in range(n_categories + 1)], + columns=["value_x", "value_y"], + index=left_index, + ) + tm.assert_frame_equal(expected, result)
test adapted (at this point kind of loosely) from #28296 - [x] closes #28189 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32079
2020-02-18T18:12:14Z
2020-02-27T23:07:26Z
2020-02-27T23:07:26Z
2020-10-10T14:14:58Z
REF: de-nest Series.__setitem__
diff --git a/pandas/core/series.py b/pandas/core/series.py index 12164a4b8ff6b..e3f4261eb0b73 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -978,14 +978,15 @@ def __setitem__(self, key, value): key = com.apply_if_callable(key, self) cacher_needs_updating = self._check_is_chained_assignment_possible() + if key is Ellipsis: + key = slice(None) + try: self._set_with_engine(key, value) except (KeyError, ValueError): values = self._values if is_integer(key) and not self.index.inferred_type == "integer": values[key] = value - elif key is Ellipsis: - self[:] = value else: self.loc[key] = value @@ -1003,9 +1004,10 @@ def __setitem__(self, key, value): self._where(~key, value, inplace=True) return except InvalidIndexError: - pass + self._set_values(key.astype(np.bool_), value) - self._set_with(key, value) + else: + self._set_with(key, value) if cacher_needs_updating: self._maybe_update_cacher() @@ -1046,13 +1048,13 @@ def _set_with(self, key, value): else: key_type = lib.infer_dtype(key, skipna=False) + # Note: key_type == "boolean" should not occur because that + # should be caught by the is_bool_indexer check in __setitem__ if key_type == "integer": if self.index.inferred_type == "integer": self._set_labels(key, value) else: return self._set_values(key, value) - elif key_type == "boolean": - self._set_values(key.astype(np.bool_), value) else: self._set_labels(key, value)
Splitting this into a couple of steps for readability. The two main things I have in mind here are a) de-nesting, b) try to parallel the structure of `__getitem__`
https://api.github.com/repos/pandas-dev/pandas/pulls/32078
2020-02-18T17:32:32Z
2020-03-07T11:18:57Z
2020-03-07T11:18:57Z
2020-03-07T15:16:18Z
CI: Remove docs build from pipelines
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d87fa5203bd52..7493be34d10c7 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -154,6 +154,39 @@ jobs: echo "region = BHS" >> $RCLONE_CONFIG_PATH if: github.event_name == 'push' - - name: Sync web + - name: Sync web with OVH run: rclone sync pandas_web ovh_cloud_pandas_web:dev if: github.event_name == 'push' + + - name: Create git repo to upload the built docs to GitHub pages + run: | + cd pandas_web + git init + touch .nojekyll + echo "dev.pandas.io" > CNAME + printf "User-agent: *\nDisallow: /" > robots.txt + git add --all . + git config user.email "pandas-dev@python.org" + git config user.name "pandas-bot" + git commit -m "pandas web and documentation in master" + if: github.event_name == 'push' + + # For this task to work, next steps are required: + # 1. Generate a pair of private/public keys (i.e. `ssh-keygen -t rsa -b 4096 -C "your_email@example.com"`) + # 2. Go to https://github.com/pandas-dev/pandas/settings/secrets + # 3. Click on "Add a new secret" + # 4. Name: "github_pagas_ssh_key", Value: <Content of the private ssh key> + # 5. The public key needs to be upladed to https://github.com/pandas-dev/pandas-dev.github.io/settings/keys + - name: Install GitHub pages ssh deployment key + uses: shimataro/ssh-key-action@v2 + with: + key: ${{ secrets.github_pages_ssh_key }} + known_hosts: 'github.com,192.30.252.128 ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ==' + if: github.event_name == 'push' + + - name: Publish web and docs to GitHub pages + run: | + cd pandas_web + git remote add origin git@github.com:pandas-dev/pandas-dev.github.io.git + git push -f origin master + if: github.event_name == 'push' diff --git a/azure-pipelines.yml b/azure-pipelines.yml index d992c64073476..42a039af46e94 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -15,78 +15,3 @@ jobs: parameters: name: Windows vmImage: vs2017-win2016 - -- job: 'Web_and_Docs' - pool: - vmImage: ubuntu-16.04 - timeoutInMinutes: 90 - steps: - - script: | - echo '##vso[task.setvariable variable=ENV_FILE]environment.yml' - echo '##vso[task.prependpath]$(HOME)/miniconda3/bin' - displayName: 'Setting environment variables' - - - script: | - sudo apt-get install -y libc6-dev-i386 - ci/setup_env.sh - displayName: 'Setup environment and build pandas' - - - script: | - source activate pandas-dev - python web/pandas_web.py web/pandas --target-path=web/build - displayName: 'Build website' - - - script: | - source activate pandas-dev - # Next we should simply have `doc/make.py --warnings-are-errors`, everything else is required because the ipython directive doesn't fail the build on errors (https://github.com/ipython/ipython/issues/11547) - doc/make.py --warnings-are-errors | tee sphinx.log ; SPHINX_RET=${PIPESTATUS[0]} - grep -B1 "^<<<-------------------------------------------------------------------------$" sphinx.log ; IPY_RET=$(( $? != 1 )) - exit $(( $SPHINX_RET + $IPY_RET )) - displayName: 'Build documentation' - - - script: | - mkdir -p to_deploy/docs - cp -r web/build/* to_deploy/ - cp -r doc/build/html/* to_deploy/docs/ - displayName: 'Merge website and docs' - - - script: | - cd to_deploy - git init - touch .nojekyll - echo "dev.pandas.io" > CNAME - printf "User-agent: *\nDisallow: /" > robots.txt - git add --all . - git config user.email "pandas-dev@python.org" - git config user.name "pandas-bot" - git commit -m "pandas web and documentation in master" - displayName: 'Create git repo for docs build' - condition : | - and(not(eq(variables['Build.Reason'], 'PullRequest')), - eq(variables['Build.SourceBranch'], 'refs/heads/master')) - - # For `InstallSSHKey@0` to work, next steps are required: - # 1. Generate a pair of private/public keys (i.e. `ssh-keygen -t rsa -b 4096 -C "your_email@example.com"`) - # 2. Go to "Library > Secure files" in the Azure Pipelines dashboard: https://dev.azure.com/pandas-dev/pandas/_library?itemType=SecureFiles - # 3. Click on "+ Secure file" - # 4. Upload the private key (the name of the file must match with the specified in "sshKeySecureFile" input below, "pandas_docs_key") - # 5. Click on file name after it is created, tick the box "Authorize for use in all pipelines" and save - # 6. The public key specified in "sshPublicKey" is the pair of the uploaded private key, and needs to be set as a deploy key of the repo where the docs will be pushed (with write access): https://github.com/pandas-dev/pandas-dev.github.io/settings/keys - - task: InstallSSHKey@0 - inputs: - hostName: 'github.com,192.30.252.128 ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ==' - sshPublicKey: 'ssh-rsa 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 pandas-dev@python.org' - sshKeySecureFile: 'pandas_docs_key' - displayName: 'Install GitHub ssh deployment key' - condition : | - and(not(eq(variables['Build.Reason'], 'PullRequest')), - eq(variables['Build.SourceBranch'], 'refs/heads/master')) - - - script: | - cd to_deploy - git remote add origin git@github.com:pandas-dev/pandas-dev.github.io.git - git push -f origin master - displayName: 'Publish web and docs to GitHub pages' - condition : | - and(not(eq(variables['Build.Reason'], 'PullRequest')), - eq(variables['Build.SourceBranch'], 'refs/heads/master'))
- [X] closes #29472 At the moment we're building the web and the docs from both Actions and Pipelines. While is still under discussion where things should be hosted, it's probably worth to just build on Actions for now, and publish from there to both the OVH cloud, and to GitHub pages. **This needs the GitHub pages ssh key to be added to the secrets of this repo with name "github_pages_ssh_key".*** Can someone with permissions please add it? Or if we already got it with a different name, please let me know the name.
https://api.github.com/repos/pandas-dev/pandas/pulls/32074
2020-02-18T10:18:24Z
2020-02-26T21:08:06Z
2020-02-26T21:08:05Z
2020-02-26T21:08:06Z
CLN: Remove unused script find_commits_touching_func.py
diff --git a/scripts/find_commits_touching_func.py b/scripts/find_commits_touching_func.py deleted file mode 100755 index 85675cb6df42b..0000000000000 --- a/scripts/find_commits_touching_func.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -# copyright 2013, y-p @ github -""" -Search the git history for all commits touching a named method - -You need the sh module to run this -WARNING: this script uses git clean -f, running it on a repo with untracked -files will probably erase them. - -Usage:: - $ ./find_commits_touching_func.py (see arguments below) -""" -import argparse -from collections import namedtuple -import logging -import os -import re - -from dateutil.parser import parse - -try: - import sh -except ImportError: - raise ImportError("The 'sh' package is required to run this script.") - - -desc = """ -Find all commits touching a specified function across the codebase. -""".strip() -argparser = argparse.ArgumentParser(description=desc) -argparser.add_argument( - "funcname", - metavar="FUNCNAME", - help="Name of function/method to search for changes on", -) -argparser.add_argument( - "-f", - "--file-masks", - metavar="f_re(,f_re)*", - default=[r"\.py.?$"], - help="comma separated list of regexes to match " - "filenames against\ndefaults all .py? files", -) -argparser.add_argument( - "-d", - "--dir-masks", - metavar="d_re(,d_re)*", - default=[], - help="comma separated list of regexes to match base path against", -) -argparser.add_argument( - "-p", - "--path-masks", - metavar="p_re(,p_re)*", - default=[], - help="comma separated list of regexes to match full file path against", -) -argparser.add_argument( - "-y", - "--saw-the-warning", - action="store_true", - default=False, - help="must specify this to run, acknowledge you " - "realize this will erase untracked files", -) -argparser.add_argument( - "--debug-level", - default="CRITICAL", - help="debug level of messages (DEBUG, INFO, etc...)", -) -args = argparser.parse_args() - - -lfmt = logging.Formatter(fmt="%(levelname)-8s %(message)s", datefmt="%m-%d %H:%M:%S") -shh = logging.StreamHandler() -shh.setFormatter(lfmt) -logger = logging.getLogger("findit") -logger.addHandler(shh) - -Hit = namedtuple("Hit", "commit path") -HASH_LEN = 8 - - -def clean_checkout(comm): - h, s, d = get_commit_vitals(comm) - if len(s) > 60: - s = s[:60] + "..." - s = s.split("\n")[0] - logger.info("CO: %s %s" % (comm, s)) - - sh.git("checkout", comm, _tty_out=False) - sh.git("clean", "-f") - - -def get_hits(defname, files=()): - cs = set() - for f in files: - try: - r = sh.git( - "blame", - "-L", - r"/def\s*{start}/,/def/".format(start=defname), - f, - _tty_out=False, - ) - except sh.ErrorReturnCode_128: - logger.debug("no matches in %s" % f) - continue - - lines = r.strip().splitlines()[:-1] - # remove comment lines - lines = [x for x in lines if not re.search(r"^\w+\s*\(.+\)\s*#", x)] - hits = set(map(lambda x: x.split(" ")[0], lines)) - cs.update({Hit(commit=c, path=f) for c in hits}) - - return cs - - -def get_commit_info(c, fmt, sep="\t"): - r = sh.git( - "log", - "--format={}".format(fmt), - "{}^..{}".format(c, c), - "-n", - "1", - _tty_out=False, - ) - return str(r).split(sep) - - -def get_commit_vitals(c, hlen=HASH_LEN): - h, s, d = get_commit_info(c, "%H\t%s\t%ci", "\t") - return h[:hlen], s, parse(d) - - -def file_filter(state, dirname, fnames): - if args.dir_masks and not any(re.search(x, dirname) for x in args.dir_masks): - return - for f in fnames: - p = os.path.abspath(os.path.join(os.path.realpath(dirname), f)) - if any(re.search(x, f) for x in args.file_masks) or any( - re.search(x, p) for x in args.path_masks - ): - if os.path.isfile(p): - state["files"].append(p) - - -def search(defname, head_commit="HEAD"): - HEAD, s = get_commit_vitals("HEAD")[:2] - logger.info("HEAD at %s: %s" % (HEAD, s)) - done_commits = set() - # allhits = set() - files = [] - state = dict(files=files) - os.walk(".", file_filter, state) - # files now holds a list of paths to files - - # seed with hits from q - allhits = set(get_hits(defname, files=files)) - q = {HEAD} - try: - while q: - h = q.pop() - clean_checkout(h) - hits = get_hits(defname, files=files) - for x in hits: - prevc = get_commit_vitals(x.commit + "^")[0] - if prevc not in done_commits: - q.add(prevc) - allhits.update(hits) - done_commits.add(h) - - logger.debug("Remaining: %s" % q) - finally: - logger.info("Restoring HEAD to %s" % HEAD) - clean_checkout(HEAD) - return allhits - - -def pprint_hits(hits): - SUBJ_LEN = 50 - PATH_LEN = 20 - hits = list(hits) - max_p = 0 - for hit in hits: - p = hit.path.split(os.path.realpath(os.curdir) + os.path.sep)[-1] - max_p = max(max_p, len(p)) - - if max_p < PATH_LEN: - SUBJ_LEN += PATH_LEN - max_p - PATH_LEN = max_p - - def sorter(i): - h, s, d = get_commit_vitals(hits[i].commit) - return hits[i].path, d - - print( - ("\nThese commits touched the %s method in these files on these dates:\n") - % args.funcname - ) - for i in sorted(range(len(hits)), key=sorter): - hit = hits[i] - h, s, d = get_commit_vitals(hit.commit) - p = hit.path.split(os.path.realpath(os.curdir) + os.path.sep)[-1] - - fmt = "{:%d} {:10} {:<%d} {:<%d}" % (HASH_LEN, SUBJ_LEN, PATH_LEN) - if len(s) > SUBJ_LEN: - s = s[: SUBJ_LEN - 5] + " ..." - print(fmt.format(h[:HASH_LEN], d.isoformat()[:10], s, p[-20:])) - - print("\n") - - -def main(): - if not args.saw_the_warning: - argparser.print_help() - print( - """ -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -WARNING: -this script uses git clean -f, running it on a repo with untracked files. -It's recommended that you make a fresh clone and run from its root directory. -You must specify the -y argument to ignore this warning. -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -""" - ) - return - if isinstance(args.file_masks, str): - args.file_masks = args.file_masks.split(",") - if isinstance(args.path_masks, str): - args.path_masks = args.path_masks.split(",") - if isinstance(args.dir_masks, str): - args.dir_masks = args.dir_masks.split(",") - - logger.setLevel(getattr(logging, args.debug_level)) - - hits = search(args.funcname) - pprint_hits(hits) - - -if __name__ == "__main__": - import sys - - sys.exit(main())
- [X] xref #31039 I've been trying the script `find_commits_touching_func.py` and doesn't seem to be working. Tried for many existing functions, and never get any result. I guess it doesn't work with newer git versions, or with the latest dependencies. And if nobody realized of this before, I assume nobody is using this script (and in any case, a separate project would be more appropriate for it, since I don't think it's pandas specific). The script was being refactored in #32044, that's why I was having a look.
https://api.github.com/repos/pandas-dev/pandas/pulls/32071
2020-02-18T09:17:07Z
2020-02-20T01:04:11Z
2020-02-20T01:04:11Z
2020-02-20T01:04:20Z
TST: corrwith and tshift in groupby/groupby.transform
diff --git a/pandas/tests/groupby/test_transform.py b/pandas/tests/groupby/test_transform.py index 740103eec185a..73a99f2dd4402 100644 --- a/pandas/tests/groupby/test_transform.py +++ b/pandas/tests/groupby/test_transform.py @@ -1,4 +1,5 @@ """ test with the .transform """ +from datetime import timedelta from io import StringIO import numpy as np @@ -23,6 +24,17 @@ from pandas.core.groupby.groupby import DataError +@pytest.fixture +def df_for_transformation_func(): + return DataFrame( + { + "A": [121, 121, 121, 121, 231, 231, 676], + "B": [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0], + "C": pd.date_range("2013-11-03", periods=7, freq="D"), + } + ) + + def assert_fp_equal(a, b): assert (np.abs(a - b) < 1e-12).all() @@ -318,7 +330,7 @@ def test_dispatch_transform(tsframe): tm.assert_frame_equal(filled, expected) -def test_transform_transformation_func(transformation_func): +def test_transform_transformation_func(transformation_func, df_for_transformation_func): # GH 30918 df = DataFrame( { @@ -346,6 +358,47 @@ def test_transform_transformation_func(transformation_func): tm.assert_frame_equal(result, expected) +def test_groupby_transform_corrwith(df_for_transformation_func): + + # GH 27905 + df = df_for_transformation_func + g = df.groupby("A") + + result = g.corrwith(df) + expected = pd.DataFrame(dict(B=[1, np.nan, np.nan], A=[np.nan] * 3)) + expected.index = pd.Index([121, 231, 676], name="A") + tm.assert_frame_equal(result, expected) + + msg = "'Series' object has no attribute 'corrwith'" + + with pytest.raises(AttributeError, match=msg): + g.transform("corrwith", df) + + +def test_groupby_transform_tshift(df_for_transformation_func): + + # GH 27905 + df = df_for_transformation_func + g = df.set_index("C").groupby("A") + result = g.tshift(2, "D") + df["C"] = df["C"] + timedelta(days=2) + expected = df + tm.assert_frame_equal( + result.reset_index().reindex(columns=["A", "B", "C"]), expected + ) + + op1 = g.transform(lambda x: x.tshift(2, "D")) + op2 = g.transform("tshift", *[2, "D"]) + + for result in [op1, op2]: + pytest.xfail( + "The output of groupby.transform with tshift is wrong, see GH 32344" + ) + tm.assert_frame_equal( + result.reset_index().reindex(columns=["A", "B", "C"]), expected + ) + + def test_transform_select_columns(df): f = lambda x: x.mean() result = df.groupby("A")[["C", "D"]].transform(f)
This PR tests `corrwith` and `tshift`, so not sure if it fully closes the original issue or not, but the other functions`backfill`, `pad` and `cumcount` don't seem to be supported in `groupby.transform` as reported in issues #27472 and #31269. - [x] closes #27905 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/32069
2020-02-18T01:54:52Z
2020-03-03T15:45:24Z
null
2020-03-03T15:45:25Z
DOC: Add example for multiindex series and dataframe merge
diff --git a/doc/source/user_guide/merging.rst b/doc/source/user_guide/merging.rst index 8fdcd8d281a41..8302b5c5dea60 100644 --- a/doc/source/user_guide/merging.rst +++ b/doc/source/user_guide/merging.rst @@ -724,6 +724,27 @@ either the left or right tables, the values in the joined table will be labels=['left', 'right'], vertical=False); plt.close('all'); +You can merge a mult-indexed Series and a DataFrame, if the names of +the MultiIndex correspond to the columns from the DataFrame. Transform +the Series to a DataFrame using :meth:`Series.reset_index` before merging, +as shown in the following example. + +.. ipython:: python + + df = pd.DataFrame({"Let": ["A", "B", "C"], "Num": [1, 2, 3]}) + df + + ser = pd.Series( + ["a", "b", "c", "d", "e", "f"], + index=pd.MultiIndex.from_arrays( + [["A", "B", "C"] * 2, [1, 2, 3, 4, 5, 6]], names=["Let", "Num"] + ), + ) + ser + + result = pd.merge(df, ser.reset_index(), on=['Let', 'Num']) + + Here is another example with duplicate join keys in DataFrames: .. ipython:: python
- [x] closes #12550
https://api.github.com/repos/pandas-dev/pandas/pulls/32068
2020-02-18T00:05:32Z
2020-02-27T21:42:40Z
2020-02-27T21:42:39Z
2020-03-02T11:41:50Z
Backport PR #32064 on branch 1.0.x (DOC: pin gitdb2)
diff --git a/environment.yml b/environment.yml index e244350a0bea0..0d624c3c716a0 100644 --- a/environment.yml +++ b/environment.yml @@ -26,6 +26,7 @@ dependencies: # documentation - gitpython # obtain contributors from git for whatsnew + - gitdb2=2.0.6 # GH-32060 - sphinx - numpydoc>=0.9.0 diff --git a/requirements-dev.txt b/requirements-dev.txt index 017e6258d9941..b10ea0c54b96c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -15,6 +15,7 @@ isort mypy==0.730 pycodestyle gitpython +gitdb2==2.0.6 sphinx numpydoc>=0.9.0 nbconvert>=5.4.1
Backport PR #32064: DOC: pin gitdb2
https://api.github.com/repos/pandas-dev/pandas/pulls/32066
2020-02-17T21:35:32Z
2020-02-17T22:57:28Z
2020-02-17T22:57:27Z
2020-02-17T22:57:28Z
DOC: Use container directives instead of raw html for the doc "cards"
diff --git a/doc/source/_static/css/getting_started.css b/doc/source/_static/css/getting_started.css index bb24761cdb159..58d633245937d 100644 --- a/doc/source/_static/css/getting_started.css +++ b/doc/source/_static/css/getting_started.css @@ -66,6 +66,7 @@ } /* reference to user guide */ + .gs-torefguide { align-items: center; font-size: 0.9rem; @@ -129,69 +130,6 @@ ul.task-bullet > li > p:first-child { padding-left: 0.75rem; } -/* Getting started index page */ - -.intro-card { - background:#FFF; - border-radius:0; - padding: 30px 10px 10px 10px; - margin: 10px 0px; -} - -.intro-card .card-text { - margin:20px 0px; - /*min-height: 150px; */ -} - -.intro-card .card-img-top { - margin: 10px; -} - -.install-block { - padding-bottom: 30px; -} - -.install-card .card-header { - border: none; - background-color:white; - color: #150458; - font-size: 1.1rem; - font-weight: bold; - padding: 1rem 1rem 0rem 1rem; -} - -.install-card .card-footer { - border: none; - background-color:white; -} - -.install-card pre { - margin: 0 1em 1em 1em; -} - -.custom-button { - background-color:#DCDCDC; - border: none; - color: #484848; - text-align: center; - text-decoration: none; - display: inline-block; - font-size: 0.9rem; - border-radius: 0.5rem; - max-width: 120px; - padding: 0.5rem 0rem; -} - -.custom-button a { - color: #484848; -} - -.custom-button p { - margin-top: 0; - margin-bottom: 0rem; - color: #484848; -} - /* intro to tutorial collapsed cards */ .tutorial-accordion { diff --git a/doc/source/_static/css/pandas.css b/doc/source/_static/css/pandas.css index 43cd631890330..6ecae296a237e 100644 --- a/doc/source/_static/css/pandas.css +++ b/doc/source/_static/css/pandas.css @@ -1,15 +1,30 @@ /* Getting started index page */ .intro-card { - background: #fff; - border-radius: 0; - padding: 30px 10px 10px 10px; + background:#FFF; + border-radius:0; + padding: 10px 10px 10px 10px; margin: 10px 0px; } +.intro-card .card-title { + color: #150458; + font-size: 1.1rem; + font-weight: bold; +} + .intro-card .card-text { margin: 20px 0px; - /*min-height: 150px; */ +} + +.intro-card .card-img-top { + margin: 20px 10px 10px 10px; +} + +.intro-card .card-footer { + border: none; + background-color:white; + padding: 0rem 1.25rem; } .custom-button { @@ -23,6 +38,7 @@ border-radius: 0.5rem; max-width: 220px; padding: 0.5rem 0rem; + width: 100% } .custom-button a { @@ -33,4 +49,4 @@ margin-top: 0; margin-bottom: 0rem; color: #484848; -} +} \ No newline at end of file diff --git a/doc/source/conf.py b/doc/source/conf.py index a95cd4ab696f7..4936238e129c6 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -255,6 +255,9 @@ # Additional templates that should be rendered to pages, maps page names to # template names. +# Do not make scaled images a link +html_scaled_image_link = False + # Add redirect for previously existing API pages # each item is like `(from_old, to_new)` # To redirect a class and all its methods, see below diff --git a/doc/source/getting_started/index.rst b/doc/source/getting_started/index.rst index a2f8f79f22ae4..1551b62aa1b8a 100644 --- a/doc/source/getting_started/index.rst +++ b/doc/source/getting_started/index.rst @@ -11,82 +11,62 @@ Installation Before you can use pandas, you’ll need to get it installed. -.. raw:: html +.. container:: container - <div class="container"> - <div class="row"> - <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block"> - <div class="card install-card shadow w-100"> - <div class="card-header"> - Working with conda? - </div> - <div class="card-body"> - <p class="card-text"> + .. container:: row -Pandas is part of the `Anaconda <http://docs.continuum.io/anaconda/>`__ distribution and can be -installed with Anaconda or Miniconda: + .. container:: col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block -.. raw:: html + .. container:: card intro-card shadow w-100 - </p> - </div> - <div class="card-footer text-muted"> + .. container:: card-body -.. code-block:: bash + .. container:: card-title - conda install pandas + Working with conda? -.. raw:: html + Pandas is part of the `Anaconda <http://docs.continuum.io/anaconda/>`_ distribution and can be + installed with Anaconda or Miniconda: - </div> - </div> - </div> - <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block"> - <div class="card install-card shadow w-100"> - <div class="card-header"> - Prefer pip? - </div> - <div class="card-body"> - <p class="card-text"> + .. container:: card-footer text-muted -Pandas can be installed via pip from `PyPI <https://pypi.org/project/pandas>`__. + .. code-block:: bash -.. raw:: html + conda install pandas - </p> - </div> - <div class="card-footer text-muted"> + .. container:: col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block -.. code-block:: bash + .. container:: card intro-card shadow w-100 - pip install pandas + .. container:: card-body -.. raw:: html + .. container:: card-title - </div> - </div> - </div> - <div class="col-12 d-flex install-block"> - <div class="card install-card shadow w-100"> - <div class="card-header"> - In-depth instructions? - </div> - <div class="card-body"> - <p class="card-text">Installing a specific version? - Installing from source? - Check the advanced installation page.</p> + Prefer pip? -.. container:: custom-button + Pandas can be installed via pip from `PyPI <https://pypi.org/project/pandas>`__. - :ref:`Learn more <install>` + .. container:: card-footer text-muted -.. raw:: html + .. code-block:: bash - </div> - </div> - </div> - </div> - </div> + pip install pandas + + .. container:: col-12 d-flex install-block + + .. container:: card intro-card shadow w-100 + + .. container:: card-body + + .. container:: card-title + + In-depth instructions? + + Installing a specific version? Installing from source? Check the advanced installation page. + + .. container:: custom-button + + :ref:`Learn more <install>` .. _gentle_intro: @@ -572,81 +552,84 @@ Currently working with other software for data manipulation in a tabular format? data operations and know *what* to do with your tabular data, but lacking the syntax to execute these operations. Get to know the pandas syntax by looking for equivalents from the software you already know: -.. raw:: html +.. container:: container - <div class="container"> - <div class="row"> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="../_static/logo_r.svg" class="card-img-top" alt="R project logo" height="72"> - <div class="card-body flex-fill"> - <p class="card-text">The <a href="https://www.r-project.org/">R programming language</a> provides the <code>data.frame</code> data structure and multiple packages, - such as <a href="https://www.tidyverse.org/">tidyverse</a> use and extend <code>data.frame</code>s for convenient data handling - functionalities similar to pandas.</p> + .. container:: row -.. container:: custom-button + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex - :ref:`Learn more <compare_with_r>` + .. container:: card text-center intro-card shadow -.. raw:: html + .. image:: ../_static/logo_r.svg + :class: card-img-top + :height: 72px + :alt: R project logo - </div> - </div> - </div> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="../_static/logo_sql.svg" class="card-img-top" alt="SQL logo" height="72"> - <div class="card-body flex-fill"> - <p class="card-text">Already familiar to <code>SELECT</code>, <code>GROUP BY</code>, <code>JOIN</code>,...? - Most of these SQL manipulations do have equivalents in pandas.</p> + .. container:: card-body flex-fill -.. container:: custom-button + The `R programming language <https://www.r-project.org/>`_ provides the ``data.frame`` data + structure and multiple packages, such as `tidyverse <https://www.tidyverse.org/>`_ use and + extend ``data.frame`` s for convenient data handling functionalities similar to pandas. - :ref:`Learn more <compare_with_sql>` + .. container:: custom-button -.. raw:: html + :ref:`Learn more <compare_with_r>` - </div> - </div> - </div> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="../_static/logo_stata.svg" class="card-img-top" alt="STATA logo" height="52"> - <div class="card-body flex-fill"> - <p class="card-text">The <code>data set</code> included in the - <a href="https://en.wikipedia.org/wiki/Stata">STATA</a> statistical software suite corresponds - to the pandas <code>data.frame</code>. Many of the operations known from STATA have an equivalent - in pandas.</p> + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex -.. container:: custom-button + .. container:: card text-center intro-card shadow - :ref:`Learn more <compare_with_stata>` + .. image:: ../_static/logo_sql.svg + :class: card-img-top + :height: 72px + :alt: SQL logo -.. raw:: html + .. container:: card-body flex-fill - </div> - </div> - </div> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="../_static/logo_sas.svg" class="card-img-top" alt="SAS logo" height="52"> - <div class="card-body flex-fill"> - <p class="card-text">The <a href="https://en.wikipedia.org/wiki/SAS_(software)">SAS</a> statistical software suite - also provides the <code>data set</code> corresponding to the pandas <code>data.frame</code>. - Also vectorized operations, filtering, string processing operations,... from SAS have similar - functions in pandas.</p> + Already familiar to ``SELECT``, ``GROUP BY``, ``JOIN``,...? + Most of these SQL manipulations do have equivalents in pandas. -.. container:: custom-button + .. container:: custom-button - :ref:`Learn more <compare_with_sas>` + :ref:`Learn more <compare_with_sql>` -.. raw:: html + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex - </div> - </div> - </div> - </div> - </div> + .. container:: card text-center intro-card shadow + + .. image:: ../_static/logo_stata.svg + :class: card-img-top + :height: 52px + :alt: Stata logo + + .. container:: card-body flex-fill + + The ``data set`` included in the `STATA <https://en.wikipedia.org/wiki/Stata>`_ statistical software + suite corresponds to the pandas ``data.frame``. Many of the operations known from STATA have an equivalent + in pandas. + + .. container:: custom-button + + :ref:`Learn more <compare_with_stata>` + + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex + + .. container:: card text-center intro-card shadow + + .. image:: ../_static/logo_sas.svg + :class: card-img-top + :height: 52px + :alt: SAS logo + + .. container:: card-body flex-fill + + The `SAS statistical software suite <https://en.wikipedia.org/wiki/SAS_(software)>`_ + provides the ``data set`` corresponding to the pandas ``data.frame``. Also vectorized operations, + filtering, string processing operations,... from SAS have similar functions in pandas. + + .. container:: custom-button + + :ref:`Learn more <compare_with_sas>` Community tutorials ------------------- diff --git a/doc/source/index.rst.template b/doc/source/index.rst.template index 7eb25790f6a7a..11968af4f7539 100644 --- a/doc/source/index.rst.template +++ b/doc/source/index.rst.template @@ -23,82 +23,100 @@ pandas documentation easy-to-use data structures and data analysis tools for the `Python <https://www.python.org/>`__ programming language. -.. raw:: html - - <div class="container"> - <div class="row"> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="_static/index_getting_started.svg" class="card-img-top" alt="getting started with pandas action icon" height="52"> - <div class="card-body flex-fill"> - <h5 class="card-title">Getting started</h5> - <p class="card-text">New to <em>pandas</em>? Check out the getting started guides. They - contain an introduction to <em>pandas'</em> main concepts and links to additional tutorials.</p> - -.. container:: custom-button - - :ref:`To the getting started guides<getting_started>` - -.. raw:: html - - </div> - </div> - </div> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="_static/index_user_guide.svg" class="card-img-top" alt="pandas user guide action icon" height="52"> - <div class="card-body flex-fill"> - <h5 class="card-title">User guide</h5> - <p class="card-text">The user guide provides in-depth information on the - key concepts of pandas with useful background information and explanation.</p> - -.. container:: custom-button - - :ref:`To the user guide<user_guide>` - -.. raw:: html - - </div> - </div> - </div> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="_static/index_api.svg" class="card-img-top" alt="api of pandas action icon" height="52"> - <div class="card-body flex-fill"> - <h5 class="card-title">API reference</h5> - <p class="card-text">The reference guide contains a detailed description of - the pandas API. The reference describes how the methods work and which parameters can - be used. It assumes that you have an understanding of the key concepts.</p> - -.. container:: custom-button - - :ref:`To the reference guide<api>` - -.. raw:: html - - </div> - </div> - </div> - <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> - <div class="card text-center intro-card shadow"> - <img src="_static/index_contribute.svg" class="card-img-top" alt="contribute to pandas action icon" height="52"> - <div class="card-body flex-fill"> - <h5 class="card-title">Developer guide</h5> - <p class="card-text">Saw a typo in the documentation? Want to improve - existing functionalities? The contributing guidelines will guide - you through the process of improving pandas.</p> +.. container:: container + + .. container:: row + + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex + + .. container:: card text-center intro-card shadow + + .. image:: _static/index_getting_started.svg + :class: card-img-top + :height: 52px + :alt: getting started with pandas action icon + + .. container:: card-body flex-fill + + .. container:: card-title + + Getting started + + New to *pandas*? Check out the getting started guides. They + contain an introduction to *pandas'* main concepts and links to + additional tutorials. + + .. container:: custom-button + + :ref:`To the getting started guides<getting_started>` + + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex + + .. container:: card text-center intro-card shadow + + .. image:: _static/index_user_guide.svg + :class: card-img-top + :height: 52px + :alt: pandas user guide action icon + + .. container:: card-body flex-fill + + .. container:: card-title + + User guide + + The user guide provides in-depth information on the key concepts + of pandas with useful background information and explanation. -.. container:: custom-button + .. container:: custom-button - :ref:`To the development guide<development>` + :ref:`To the user guide<user_guide>` + + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex + + .. container:: card text-center intro-card shadow + + .. image:: _static/index_api.svg + :class: card-img-top + :height: 52px + :alt: api of pandas action icon + + .. container:: card-body flex-fill + + .. container:: card-title + + API reference + + The reference guide contains a detailed description of the pandas API. The reference + describes how the methods work and which parameters can be used. It assumes that + you have an understanding of the key concepts. + + .. container:: custom-button + + :ref:`To the reference guide<api>` + + .. container:: col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex + + .. container:: card text-center intro-card shadow + + .. image:: _static/index_contribute.svg + :class: card-img-top + :height: 52px + :alt: contribute to pandas action icon + + .. container:: card-body flex-fill + + .. container:: card-title + + Developer guide + + Saw a typo in the documentation? Want to improve + existing functionalities? The contributing guidelines will guide + you through the process of improving pandas. -.. raw:: html + .. container:: custom-button - </div> - </div> - </div> - </div> - </div> + :ref:`To the development guide<development>` {% if single_doc and single_doc.endswith('.rst') -%}
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry The PR is a follow up of #31156 and #31148 translating the raw html to docutils/sphinx directives, making it more _readable_. Furthermore, it fixes the redundant css in `pandas.css` versus `getting_started.css` by removing the `install-card` class and merging it with the existing `intro-card` class. __Note:__ The setup required an [adjustment to the theme](https://github.com/pandas-dev/pydata-bootstrap-sphinx-theme/pull/92) to overcome the usage of the `container` class by both docutils and bootstrap.
https://api.github.com/repos/pandas-dev/pandas/pulls/32065
2020-02-17T19:12:24Z
2020-05-25T08:54:50Z
null
2020-05-25T08:54:51Z
DOC: pin gitdb2
diff --git a/environment.yml b/environment.yml index 5f1184e921119..cbdaf8e6c4217 100644 --- a/environment.yml +++ b/environment.yml @@ -26,6 +26,7 @@ dependencies: # documentation - gitpython # obtain contributors from git for whatsnew + - gitdb2=2.0.6 # GH-32060 - sphinx # documentation (jupyter notebooks) diff --git a/requirements-dev.txt b/requirements-dev.txt index 08cbef2c7fc6b..a469cbdd93ceb 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -15,6 +15,7 @@ isort mypy==0.730 pycodestyle gitpython +gitdb2==2.0.6 sphinx nbconvert>=5.4.1 nbsphinx
ref #32060
https://api.github.com/repos/pandas-dev/pandas/pulls/32064
2020-02-17T18:44:41Z
2020-02-17T21:35:22Z
2020-02-17T21:35:22Z
2020-02-17T21:35:25Z
CLN: GH29547 replace old string formatting
diff --git a/pandas/tests/tslibs/test_parsing.py b/pandas/tests/tslibs/test_parsing.py index c452d5b12ce01..1ba7832c47e6c 100644 --- a/pandas/tests/tslibs/test_parsing.py +++ b/pandas/tests/tslibs/test_parsing.py @@ -42,9 +42,9 @@ def test_parse_time_quarter_with_dash(dashed, normal): @pytest.mark.parametrize("dashed", ["-2Q1992", "2-Q1992", "4-4Q1992"]) def test_parse_time_quarter_with_dash_error(dashed): - msg = "Unknown datetime string format, unable to parse: {dashed}" + msg = f"Unknown datetime string format, unable to parse: {dashed}" - with pytest.raises(parsing.DateParseError, match=msg.format(dashed=dashed)): + with pytest.raises(parsing.DateParseError, match=msg): parse_time_string(dashed) @@ -115,12 +115,12 @@ def test_parsers_quarter_invalid(date_str): if date_str == "6Q-20": msg = ( "Incorrect quarterly string is given, quarter " - "must be between 1 and 4: {date_str}" + f"must be between 1 and 4: {date_str}" ) else: - msg = "Unknown datetime string format, unable to parse: {date_str}" + msg = f"Unknown datetime string format, unable to parse: {date_str}" - with pytest.raises(ValueError, match=msg.format(date_str=date_str)): + with pytest.raises(ValueError, match=msg): parsing.parse_time_string(date_str)
Hi there! I've added some missed f-strings to: * tests/tslibs * tests/tseries - [x] tests passed - [x] passes `black pandas` Thank you.
https://api.github.com/repos/pandas-dev/pandas/pulls/32063
2020-02-17T17:06:14Z
2020-02-18T11:56:22Z
2020-02-18T11:56:21Z
2020-02-18T15:18:19Z
Backport PR #31734 on branch 1.0.x (BUG: list-like to_replace on Categorical.replace is ignored or crash)
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index 0c012e5c1417b..805f87b63eef8 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -29,6 +29,7 @@ Bug fixes **Categorical** - Fixed bug where :meth:`Categorical.from_codes` improperly raised a ``ValueError`` when passed nullable integer codes. (:issue:`31779`) +- Bug in :class:`Categorical` that would ignore or crash when calling :meth:`Series.replace` with a list-like ``to_replace`` (:issue:`31720`) **I/O** diff --git a/pandas/_testing.py b/pandas/_testing.py index 1fdc5d478aaf6..ca378e5ce8f77 100644 --- a/pandas/_testing.py +++ b/pandas/_testing.py @@ -1056,6 +1056,7 @@ def assert_series_equal( check_exact=False, check_datetimelike_compat=False, check_categorical=True, + check_category_order=True, obj="Series", ): """ @@ -1090,6 +1091,10 @@ def assert_series_equal( Compare datetime-like which is comparable ignoring dtype. check_categorical : bool, default True Whether to compare internal Categorical exactly. + check_category_order : bool, default True + Whether to compare category order of internal Categoricals + + .. versionadded:: 1.0.2 obj : str, default 'Series' Specify object name being compared, internally used to show appropriate assertion message. @@ -1192,7 +1197,12 @@ def assert_series_equal( if check_categorical: if is_categorical_dtype(left) or is_categorical_dtype(right): - assert_categorical_equal(left.values, right.values, obj=f"{obj} category") + assert_categorical_equal( + left.values, + right.values, + obj=f"{obj} category", + check_category_order=check_category_order, + ) # This could be refactored to use the NDFrame.equals method diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py index 52d9df0c2d508..d8a2fbdd58382 100644 --- a/pandas/core/arrays/categorical.py +++ b/pandas/core/arrays/categorical.py @@ -2447,18 +2447,30 @@ def replace(self, to_replace, value, inplace: bool = False): """ inplace = validate_bool_kwarg(inplace, "inplace") cat = self if inplace else self.copy() - if to_replace in cat.categories: - if isna(value): - cat.remove_categories(to_replace, inplace=True) - else: + + # build a dict of (to replace -> value) pairs + if is_list_like(to_replace): + # if to_replace is list-like and value is scalar + replace_dict = {replace_value: value for replace_value in to_replace} + else: + # if both to_replace and value are scalar + replace_dict = {to_replace: value} + + # other cases, like if both to_replace and value are list-like or if + # to_replace is a dict, are handled separately in NDFrame + for replace_value, new_value in replace_dict.items(): + if replace_value in cat.categories: + if isna(new_value): + cat.remove_categories(replace_value, inplace=True) + continue categories = cat.categories.tolist() - index = categories.index(to_replace) - if value in cat.categories: - value_index = categories.index(value) + index = categories.index(replace_value) + if new_value in cat.categories: + value_index = categories.index(new_value) cat._codes[cat._codes == index] = value_index - cat.remove_categories(to_replace, inplace=True) + cat.remove_categories(replace_value, inplace=True) else: - categories[index] = value + categories[index] = new_value cat.rename_categories(categories, inplace=True) if not inplace: return cat diff --git a/pandas/tests/arrays/categorical/test_replace.py b/pandas/tests/arrays/categorical/test_replace.py new file mode 100644 index 0000000000000..52530123bd52f --- /dev/null +++ b/pandas/tests/arrays/categorical/test_replace.py @@ -0,0 +1,48 @@ +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize( + "to_replace,value,expected,check_types,check_categorical", + [ + # one-to-one + (1, 2, [2, 2, 3], True, True), + (1, 4, [4, 2, 3], True, True), + (4, 1, [1, 2, 3], True, True), + (5, 6, [1, 2, 3], True, True), + # many-to-one + ([1], 2, [2, 2, 3], True, True), + ([1, 2], 3, [3, 3, 3], True, True), + ([1, 2], 4, [4, 4, 3], True, True), + ((1, 2, 4), 5, [5, 5, 3], True, True), + ((5, 6), 2, [1, 2, 3], True, True), + # many-to-many, handled outside of Categorical and results in separate dtype + ([1], [2], [2, 2, 3], False, False), + ([1, 4], [5, 2], [5, 2, 3], False, False), + # check_categorical sorts categories, which crashes on mixed dtypes + (3, "4", [1, 2, "4"], True, False), + ([1, 2, "3"], "5", ["5", "5", 3], True, False), + ], +) +def test_replace(to_replace, value, expected, check_types, check_categorical): + # GH 31720 + s = pd.Series([1, 2, 3], dtype="category") + result = s.replace(to_replace, value) + expected = pd.Series(expected, dtype="category") + s.replace(to_replace, value, inplace=True) + tm.assert_series_equal( + expected, + result, + check_dtype=check_types, + check_categorical=check_categorical, + check_category_order=False, + ) + tm.assert_series_equal( + expected, + s, + check_dtype=check_types, + check_categorical=check_categorical, + check_category_order=False, + )
Backport PR #31734: BUG: list-like to_replace on Categorical.replace is ignored or crash
https://api.github.com/repos/pandas-dev/pandas/pulls/32062
2020-02-17T17:00:05Z
2020-02-19T07:32:30Z
2020-02-19T07:32:30Z
2020-02-19T07:32:30Z
Backport PR #32025 and #32031 silence numpy-dev failures
diff --git a/pandas/__init__.py b/pandas/__init__.py index 491bcb21f245d..6eda468eed23a 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -25,6 +25,7 @@ _np_version_under1p16, _np_version_under1p17, _np_version_under1p18, + _is_numpy_dev, ) try: diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py index 406d5f055797d..5aab5b814bae7 100644 --- a/pandas/tests/api/test_api.py +++ b/pandas/tests/api/test_api.py @@ -198,6 +198,7 @@ class TestPDApi(Base): "_np_version_under1p16", "_np_version_under1p17", "_np_version_under1p18", + "_is_numpy_dev", "_testing", "_tslib", "_typing", diff --git a/pandas/tests/frame/test_cumulative.py b/pandas/tests/frame/test_cumulative.py index b545d6aa8afd3..486cbfb2761e0 100644 --- a/pandas/tests/frame/test_cumulative.py +++ b/pandas/tests/frame/test_cumulative.py @@ -7,8 +7,9 @@ """ import numpy as np +import pytest -from pandas import DataFrame, Series +from pandas import DataFrame, Series, _is_numpy_dev import pandas._testing as tm @@ -73,6 +74,11 @@ def test_cumprod(self, datetime_frame): df.cumprod(0) df.cumprod(1) + @pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, + ) def test_cummin(self, datetime_frame): datetime_frame.loc[5:10, 0] = np.nan datetime_frame.loc[10:15, 1] = np.nan @@ -96,6 +102,11 @@ def test_cummin(self, datetime_frame): cummin_xs = datetime_frame.cummin(axis=1) assert np.shape(cummin_xs) == np.shape(datetime_frame) + @pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, + ) def test_cummax(self, datetime_frame): datetime_frame.loc[5:10, 0] = np.nan datetime_frame.loc[10:15, 1] = np.nan diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py index 97cf1af1d2e9e..087b1286151d7 100644 --- a/pandas/tests/groupby/test_function.py +++ b/pandas/tests/groupby/test_function.py @@ -17,6 +17,7 @@ NaT, Series, Timestamp, + _is_numpy_dev, date_range, isna, ) @@ -685,6 +686,11 @@ def test_numpy_compat(func): getattr(g, func)(foo=1) +@pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, +) def test_cummin_cummax(): # GH 15048 num_types = [np.int32, np.int64, np.float32, np.float64] diff --git a/pandas/tests/scalar/timedelta/test_arithmetic.py b/pandas/tests/scalar/timedelta/test_arithmetic.py index 6a9ef86c11292..555b47c8dc0fc 100644 --- a/pandas/tests/scalar/timedelta/test_arithmetic.py +++ b/pandas/tests/scalar/timedelta/test_arithmetic.py @@ -8,7 +8,7 @@ import pytest import pandas as pd -from pandas import NaT, Timedelta, Timestamp, offsets +from pandas import NaT, Timedelta, Timestamp, _is_numpy_dev, offsets import pandas._testing as tm from pandas.core import ops @@ -377,7 +377,21 @@ def test_td_div_numeric_scalar(self): assert isinstance(result, Timedelta) assert result == Timedelta(days=2) - @pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")]) + @pytest.mark.parametrize( + "nan", + [ + np.nan, + pytest.param( + np.float64("NaN"), + marks=pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, + ), + ), + float("nan"), + ], + ) def test_td_div_nan(self, nan): # np.float64('NaN') has a 'dtype' attr, avoid treating as array td = Timedelta(10, unit="d") diff --git a/pandas/tests/series/test_cumulative.py b/pandas/tests/series/test_cumulative.py index 885b5bf0476f2..0cb1c038478f5 100644 --- a/pandas/tests/series/test_cumulative.py +++ b/pandas/tests/series/test_cumulative.py @@ -11,6 +11,7 @@ import pytest import pandas as pd +from pandas import _is_numpy_dev import pandas._testing as tm @@ -37,6 +38,11 @@ def test_cumsum(self, datetime_series): def test_cumprod(self, datetime_series): _check_accum_op("cumprod", datetime_series) + @pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, + ) def test_cummin(self, datetime_series): tm.assert_numpy_array_equal( datetime_series.cummin().values, @@ -49,6 +55,11 @@ def test_cummin(self, datetime_series): tm.assert_series_equal(result, expected) + @pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, + ) def test_cummax(self, datetime_series): tm.assert_numpy_array_equal( datetime_series.cummax().values,
https://api.github.com/repos/pandas-dev/pandas/pulls/32057
2020-02-17T13:20:18Z
2020-02-17T22:57:40Z
2020-02-17T22:57:40Z
2020-02-18T10:54:15Z
REGR: read_pickle fallback to encoding=latin_1 upon a UnicodeDecodeError
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index c9031ac1ae9fe..57ed6adf667c8 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -19,6 +19,7 @@ Fixed regressions - Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`) - Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`) - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`) +- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`). - Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`) - diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py index e51f24b551f31..4e731b8ecca11 100644 --- a/pandas/io/pickle.py +++ b/pandas/io/pickle.py @@ -171,21 +171,22 @@ def read_pickle( # 1) try standard library Pickle # 2) try pickle_compat (older pandas version) to handle subclass changes - - excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError) + # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError try: - with warnings.catch_warnings(record=True): - # We want to silence any warnings about, e.g. moved modules. - warnings.simplefilter("ignore", Warning) - return pickle.load(f) - except excs_to_catch: - # e.g. - # "No module named 'pandas.core.sparse.series'" - # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib" - return pc.load(f, encoding=None) + excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError) + try: + with warnings.catch_warnings(record=True): + # We want to silence any warnings about, e.g. moved modules. + warnings.simplefilter("ignore", Warning) + return pickle.load(f) + except excs_to_catch: + # e.g. + # "No module named 'pandas.core.sparse.series'" + # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib" + return pc.load(f, encoding=None) except UnicodeDecodeError: - # e.g. can occur for files written in py27; see GH#28645 + # e.g. can occur for files written in py27; see GH#28645 and GH#31988 return pc.load(f, encoding="latin-1") finally: f.close() diff --git a/pandas/tests/io/data/pickle/test_mi_py27.pkl b/pandas/tests/io/data/pickle/test_mi_py27.pkl new file mode 100644 index 0000000000000..89021dd828108 Binary files /dev/null and b/pandas/tests/io/data/pickle/test_mi_py27.pkl differ diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py index 78b630bb5ada1..584a545769c4c 100644 --- a/pandas/tests/io/test_pickle.py +++ b/pandas/tests/io/test_pickle.py @@ -382,14 +382,23 @@ def test_read(self, protocol, get_random_path): tm.assert_frame_equal(df, df2) -def test_unicode_decode_error(datapath): +@pytest.mark.parametrize( + ["pickle_file", "excols"], + [ + ("test_py27.pkl", pd.Index(["a", "b", "c"])), + ( + "test_mi_py27.pkl", + pd.MultiIndex.from_arrays([["a", "b", "c"], ["A", "B", "C"]]), + ), + ], +) +def test_unicode_decode_error(datapath, pickle_file, excols): # pickle file written with py27, should be readable without raising - # UnicodeDecodeError, see GH#28645 - path = datapath("io", "data", "pickle", "test_py27.pkl") + # UnicodeDecodeError, see GH#28645 and GH#31988 + path = datapath("io", "data", "pickle", pickle_file) df = pd.read_pickle(path) # just test the columns are correct since the values are random - excols = pd.Index(["a", "b", "c"]) tm.assert_index_equal(df.columns, excols)
When a reading a pickle with MultiIndex columns generated in py27 `pickle_compat.load()` with `enconding=None` would throw an UnicodeDecodeError when reading a pickle created in py27. Now, `read_pickle` catches that exception and fallback to use `latin-1` explicitly. - [x] closes #31988 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32055
2020-02-17T12:56:42Z
2020-02-21T14:52:24Z
2020-02-21T14:52:24Z
2020-02-21T14:52:39Z
[#16737] Index type for Series with empty data
diff --git a/doc/source/user_guide/missing_data.rst b/doc/source/user_guide/missing_data.rst index 2e68a0598bb71..45e35b78eb390 100644 --- a/doc/source/user_guide/missing_data.rst +++ b/doc/source/user_guide/missing_data.rst @@ -190,7 +190,7 @@ The sum of an empty or all-NA Series or column of a DataFrame is 0. pd.Series([np.nan]).sum() - pd.Series([], dtype="float64").sum() + pd.Series([], dtype="float64", index=[]).sum() The product of an empty or all-NA Series or column of a DataFrame is 1. @@ -198,7 +198,7 @@ The product of an empty or all-NA Series or column of a DataFrame is 1. pd.Series([np.nan]).prod() - pd.Series([], dtype="float64").prod() + pd.Series([], dtype="float64", index=[]).prod() NA values in GroupBy diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 07849702c646d..3d733bb3352f2 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -406,6 +406,7 @@ Deprecations arguments (:issue:`27573`). - :func:`pandas.api.types.is_categorical` is deprecated and will be removed in a future version; use `:func:pandas.api.types.is_categorical_dtype` instead (:issue:`33385`) +- ``Series([])`` will raise a `DeprecationWarning` regarding its index. The default index type will change from :class:`RangeIndex` to :class:`Index` in a future version, matching the behaviour of ``Series()`` (:issue:`16737`) .. --------------------------------------------------------------------------- diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index e6967630b97ac..f551953fff862 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -53,7 +53,11 @@ ) from pandas.core.dtypes.missing import isna, na_value_for_dtype -from pandas.core.construction import array, extract_array +from pandas.core.construction import ( + array, + create_series_with_explicit_index, + extract_array, +) from pandas.core.indexers import validate_indices if TYPE_CHECKING: @@ -835,7 +839,7 @@ def mode(values, dropna: bool = True) -> "Series": warn(f"Unable to sort modes: {err}") result = _reconstruct_data(result, original.dtype, original) - return Series(result) + return create_series_with_explicit_index(result) def rank( diff --git a/pandas/core/apply.py b/pandas/core/apply.py index a013434491589..ba27e11eea4f0 100644 --- a/pandas/core/apply.py +++ b/pandas/core/apply.py @@ -16,7 +16,10 @@ ) from pandas.core.dtypes.generic import ABCSeries -from pandas.core.construction import create_series_with_explicit_dtype +from pandas.core.construction import ( + create_series_with_explicit_dtype, + create_series_with_explicit_index, +) if TYPE_CHECKING: from pandas import DataFrame, Series, Index @@ -202,7 +205,7 @@ def apply_empty_result(self): if not should_reduce: try: - r = self.f(Series([], dtype=np.float64)) + r = self.f(create_series_with_explicit_index([], dtype=np.float64)) except Exception: pass else: @@ -210,7 +213,7 @@ def apply_empty_result(self): if should_reduce: if len(self.agg_axis): - r = self.f(Series([], dtype=np.float64)) + r = self.f(create_series_with_explicit_index([], dtype=np.float64)) else: r = np.nan diff --git a/pandas/core/construction.py b/pandas/core/construction.py index 2d60ad9ba50bf..fbc02f9e76bc9 100644 --- a/pandas/core/construction.py +++ b/pandas/core/construction.py @@ -621,10 +621,63 @@ def create_series_with_explicit_dtype( ------- Series """ - from pandas.core.series import Series + from pandas import RangeIndex if is_empty_data(data) and dtype is None: dtype = dtype_if_empty + + return create_series_with_explicit_index( + data=data, + index=index, + dtype=dtype, + name=name, + copy=copy, + fastpath=fastpath, + index_if_empty=RangeIndex(0), # non-breaking yet + ) + + +def create_series_with_explicit_index( + data: Any = None, + index: Optional[Union[ArrayLike, "Index"]] = None, + dtype: Optional[Dtype] = None, + name: Optional[str] = None, + copy: bool = False, + fastpath: bool = False, + index_if_empty: Optional["Index"] = None, +) -> "Series": + """ + Helper to pass an explicit index type when instantiating an Series where + data is list-like and empty. + + This silences a DeprecationWarning described in GitHub-16737. + + Parameters + ---------- + data : Mirrored from Series.__init__ + index : Mirrored from Series.__init__ + dtype : Mirrored from Series.__init__ + name : Mirrored from Series.__init__ + copy : Mirrored from Series.__init__ + fastpath : Mirrored from Series.__init__ + index_if_empty : instance of (Index, RangeIndex) + This index type will be passed explicitly when Series is initialised + with `data` being list-like and empty. + + Returns + ------- + Series + """ + from pandas import Index, Series # noqa: F811 + + # to avoid circular imports + if index_if_empty is None: + index_if_empty = Index([]) + + # dict's are handled separately in Series.__init__ + is_relevant_type = is_list_like(data) and not isinstance(data, dict) + if index is None and is_relevant_type and len(data) == 0: + index = index_if_empty return Series( data=data, index=index, dtype=dtype, name=name, copy=copy, fastpath=fastpath ) diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index 5cad76fd18c58..7ef479fd7ccbc 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -71,6 +71,7 @@ from pandas.core.arrays.datetimes import tz_to_dtype, validate_tz_from_dtype from pandas.core.base import IndexOpsMixin, PandasObject import pandas.core.common as com +from pandas.core.construction import create_series_with_explicit_index from pandas.core.indexers import deprecate_ndim_indexing from pandas.core.indexes.frozen import FrozenList import pandas.core.missing as missing @@ -142,9 +143,7 @@ def index_arithmetic_method(self, other): if isinstance(other, (ABCSeries, ABCDataFrame, ABCTimedeltaIndex)): return NotImplemented - from pandas import Series - - result = op(Series(self), other) + result = op(create_series_with_explicit_index(self), other) if isinstance(result, tuple): return (Index(result[0]), Index(result[1])) return Index(result) diff --git a/pandas/core/series.py b/pandas/core/series.py index 9ef865a964123..1dbd2eba8179e 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -308,7 +308,18 @@ def __init__( if index is None: if not is_list_like(data): data = [data] - index = ibase.default_index(len(data)) + + n = len(data) + if n == 0: + # gh-16737 + warnings.warn( + "The default index type for empty data will be 'Index' " + "instead of 'RangeIndex' in a future version. " + "Specify an index explicitly to silence this warning.", + DeprecationWarning, + stacklevel=2, + ) + index = ibase.default_index(n) elif is_list_like(data): # a scalar numpy array is list-like but doesn't diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 76b851d8ac923..1f0681e0f1190 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -36,7 +36,7 @@ from pandas.core.algorithms import take_1d from pandas.core.base import NoNewAttributesMixin -from pandas.core.construction import extract_array +from pandas.core.construction import create_series_with_explicit_index, extract_array if TYPE_CHECKING: from pandas.arrays import StringArray @@ -2180,7 +2180,7 @@ def _wrap_result( returns_string=True, ): - from pandas import Index, Series, MultiIndex + from pandas import Index, MultiIndex # for category, we do the stuff on the categories, so blow it up # to the full series again @@ -2190,7 +2190,9 @@ def _wrap_result( if use_codes and self._is_categorical: # if self._orig is a CategoricalIndex, there is no .cat-accessor result = take_1d( - result, Series(self._orig, copy=False).cat.codes, fill_value=fill_value + result, + create_series_with_explicit_index(self._orig, copy=False).cat.codes, + fill_value=fill_value, ) if not hasattr(result, "ndim") or not hasattr(result, "dtype"): diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py index 207c5cc98449a..25e5de2d1b1c6 100644 --- a/pandas/core/tools/datetimes.py +++ b/pandas/core/tools/datetimes.py @@ -41,6 +41,7 @@ from pandas.core import algorithms from pandas.core.algorithms import unique from pandas.core.arrays.datetimes import tz_to_dtype +from pandas.core.construction import create_series_with_explicit_index # --------------------------------------------------------------------- # types used in annotations @@ -764,9 +765,10 @@ def to_datetime( if errors == "raise": raise # ... otherwise, continue without the cache. - from pandas import Series - cache_array = Series([], dtype=object) # just an empty array + cache_array = create_series_with_explicit_index( + [], dtype=object + ) # just an empty array if not cache_array.empty: result = _convert_and_box_cache(arg, cache_array) else: diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 2df81ba0aa51a..f685b01179282 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -53,6 +53,7 @@ from pandas.core import algorithms from pandas.core.arrays import Categorical +from pandas.core.construction import create_series_with_explicit_index from pandas.core.frame import DataFrame from pandas.core.indexes.api import ( Index, @@ -60,7 +61,6 @@ RangeIndex, ensure_index_from_sequences, ) -from pandas.core.series import Series from pandas.core.tools import datetimes as tools from pandas.io.common import ( @@ -3494,14 +3494,20 @@ def _get_empty_meta(columns, index_col, index_names, dtype=None): if (index_col is None or index_col is False) or index_names is None: index = Index([]) else: - data = [Series([], dtype=dtype[name]) for name in index_names] + data = [ + create_series_with_explicit_index([], dtype=dtype[name]) + for name in index_names + ] index = ensure_index_from_sequences(data, names=index_names) index_col.sort() for i, n in enumerate(index_col): columns.pop(n - i) - col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns} + col_dict = { + col_name: create_series_with_explicit_index([], dtype=dtype[col_name]) + for col_name in columns + } return index, columns, col_dict diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 311d8d0d55341..7a16cc8d32e3e 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -51,6 +51,7 @@ from pandas.core.arrays import Categorical, DatetimeArray, PeriodArray import pandas.core.common as com from pandas.core.computation.pytables import PyTablesExpr, maybe_expression +from pandas.core.construction import create_series_with_explicit_index from pandas.core.indexes.api import ensure_index from pandas.io.common import stringify_path @@ -3313,7 +3314,7 @@ def write_metadata(self, key: str, values: np.ndarray): key : str values : ndarray """ - values = Series(values) + values = create_series_with_explicit_index(values) self.parent.put( self._get_metadata_path(key), values, @@ -4051,7 +4052,9 @@ def read_column( encoding=self.encoding, errors=self.errors, ) - return Series(_set_tz(col_values[1], a.tz), name=column) + return create_series_with_explicit_index( + _set_tz(col_values[1], a.tz), name=column + ) raise KeyError(f"column [{column}] not found in the table") diff --git a/pandas/tests/arrays/boolean/test_reduction.py b/pandas/tests/arrays/boolean/test_reduction.py index a5c18a25f8e16..2c4cbaa0d9536 100644 --- a/pandas/tests/arrays/boolean/test_reduction.py +++ b/pandas/tests/arrays/boolean/test_reduction.py @@ -2,6 +2,7 @@ import pytest import pandas as pd +from pandas.core.construction import create_series_with_explicit_index @pytest.fixture @@ -31,7 +32,7 @@ def test_any_all(values, exp_any, exp_all, exp_any_noskip, exp_all_noskip): exp_any_noskip = pd.NA if exp_any_noskip is pd.NA else np.bool_(exp_any_noskip) exp_all_noskip = pd.NA if exp_all_noskip is pd.NA else np.bool_(exp_all_noskip) - for con in [pd.array, pd.Series]: + for con in [pd.array, create_series_with_explicit_index]: a = con(values, dtype="boolean") assert a.any() is exp_any assert a.all() is exp_all diff --git a/pandas/tests/arrays/integer/test_function.py b/pandas/tests/arrays/integer/test_function.py index bdf902d1aca62..937684465e47c 100644 --- a/pandas/tests/arrays/integer/test_function.py +++ b/pandas/tests/arrays/integer/test_function.py @@ -4,6 +4,7 @@ import pandas as pd import pandas._testing as tm from pandas.core.arrays import integer_array +from pandas.core.construction import create_series_with_explicit_index @pytest.mark.parametrize("ufunc", [np.abs, np.sign]) @@ -105,7 +106,7 @@ def test_value_counts_na(): def test_value_counts_empty(): # https://github.com/pandas-dev/pandas/issues/33317 - s = pd.Series([], dtype="Int64") + s = create_series_with_explicit_index([], dtype="Int64") result = s.value_counts() # TODO: The dtype of the index seems wrong (it's int64 for non-empty) idx = pd.Index([], dtype="object") diff --git a/pandas/tests/base/test_unique.py b/pandas/tests/base/test_unique.py index c6225c9b5ca64..7df2f8e8df7fe 100644 --- a/pandas/tests/base/test_unique.py +++ b/pandas/tests/base/test_unique.py @@ -7,6 +7,7 @@ import pandas as pd import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.tests.base.common import allow_na_ops @@ -94,7 +95,9 @@ def test_nunique_null(null_obj, index_or_series_obj): else: values[0:2] = null_obj - klass = type(obj) + klass = ( + create_series_with_explicit_index if isinstance(obj, pd.Series) else type(obj) + ) repeated_values = np.repeat(values, range(1, len(values) + 1)) obj = klass(repeated_values, dtype=obj.dtype) diff --git a/pandas/tests/base/test_value_counts.py b/pandas/tests/base/test_value_counts.py index d45feaff68dde..5ee30d367dfa8 100644 --- a/pandas/tests/base/test_value_counts.py +++ b/pandas/tests/base/test_value_counts.py @@ -21,6 +21,7 @@ TimedeltaIndex, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.tests.base.common import allow_na_ops @@ -180,7 +181,7 @@ def test_value_counts_bins(index_or_series): assert s.nunique() == 3 s = klass({}) if klass is dict else klass({}, dtype=object) - expected = Series([], dtype=np.int64) + expected = create_series_with_explicit_index([], dtype=np.int64) tm.assert_series_equal(s.value_counts(), expected, check_index_type=False) # returned dtype differs depending on original if isinstance(s, Index): diff --git a/pandas/tests/dtypes/test_common.py b/pandas/tests/dtypes/test_common.py index b9c8f3a8dd494..5d8215afb7f7e 100644 --- a/pandas/tests/dtypes/test_common.py +++ b/pandas/tests/dtypes/test_common.py @@ -20,6 +20,7 @@ import pandas as pd import pandas._testing as tm from pandas.arrays import SparseArray +from pandas.core.construction import create_series_with_explicit_index # EA & Actual Dtypes @@ -236,7 +237,9 @@ def test_is_timedelta64_dtype(): assert not com.is_timedelta64_dtype("NO DATE") assert com.is_timedelta64_dtype(np.timedelta64) - assert com.is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]")) + assert com.is_timedelta64_dtype( + create_series_with_explicit_index([], dtype="timedelta64[ns]") + ) assert com.is_timedelta64_dtype(pd.to_timedelta(["0 days", "1 days"])) @@ -499,7 +502,9 @@ def test_needs_i8_conversion(): assert not com.needs_i8_conversion(np.array(["a", "b"])) assert com.needs_i8_conversion(np.datetime64) - assert com.needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]")) + assert com.needs_i8_conversion( + create_series_with_explicit_index([], dtype="timedelta64[ns]") + ) assert com.needs_i8_conversion(pd.DatetimeIndex(["2000"], tz="US/Eastern")) @@ -567,7 +572,7 @@ def test_is_extension_type(check_scipy): assert com.is_extension_type(pd.DatetimeIndex(["2000"], tz="US/Eastern")) dtype = DatetimeTZDtype("ns", tz="US/Eastern") - s = pd.Series([], dtype=dtype) + s = create_series_with_explicit_index([], dtype=dtype) assert com.is_extension_type(s) if check_scipy: @@ -596,7 +601,7 @@ def test_is_extension_array_dtype(check_scipy): assert com.is_extension_array_dtype(pd.DatetimeIndex(["2000"], tz="US/Eastern")) dtype = DatetimeTZDtype("ns", tz="US/Eastern") - s = pd.Series([], dtype=dtype) + s = create_series_with_explicit_index([], dtype=dtype) assert com.is_extension_array_dtype(s) if check_scipy: diff --git a/pandas/tests/dtypes/test_concat.py b/pandas/tests/dtypes/test_concat.py index 1fbbd3356ae13..584d1b3bb6d59 100644 --- a/pandas/tests/dtypes/test_concat.py +++ b/pandas/tests/dtypes/test_concat.py @@ -5,6 +5,7 @@ import pandas as pd from pandas import DatetimeIndex, Period, PeriodIndex, Series, TimedeltaIndex import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index @pytest.mark.parametrize( @@ -83,7 +84,7 @@ def test_get_dtype_kinds_period(to_concat, expected): def test_concat_mismatched_categoricals_with_empty(): # concat_compat behavior on series._values should match pd.concat on series ser1 = Series(["a", "b", "c"], dtype="category") - ser2 = Series([], dtype="category") + ser2 = create_series_with_explicit_index([], dtype="category") result = _concat.concat_compat([ser1._values, ser2._values]) expected = pd.concat([ser1, ser2])._values diff --git a/pandas/tests/dtypes/test_inference.py b/pandas/tests/dtypes/test_inference.py index 8c0580b7cf047..505aeda4acbcf 100644 --- a/pandas/tests/dtypes/test_inference.py +++ b/pandas/tests/dtypes/test_inference.py @@ -52,6 +52,10 @@ ) import pandas._testing as tm from pandas.core.arrays import IntegerArray +from pandas.core.construction import ( + create_series_with_explicit_dtype, + create_series_with_explicit_index, +) @pytest.fixture(params=[True, False], ids=str) @@ -77,9 +81,9 @@ def coerce(request): ((x for x in [1, 2]), True, "generator"), ((_ for _ in []), True, "generator-empty"), (Series([1]), True, "Series"), - (Series([], dtype=object), True, "Series-empty"), + (create_series_with_explicit_dtype([], dtype=object), True, "Series-empty"), (Series(["a"]).str, True, "StringMethods"), - (Series([], dtype="O").str, True, "StringMethods-empty"), + (create_series_with_explicit_dtype([], dtype="O").str, True, "StringMethods-empty"), (Index([1]), True, "Index"), (Index([]), True, "Index-empty"), (DataFrame([[1]]), True, "DataFrame"), @@ -138,7 +142,7 @@ def __getitem__(self): def test_is_array_like(): - assert inference.is_array_like(Series([], dtype=object)) + assert inference.is_array_like(create_series_with_explicit_index([], dtype=object)) assert inference.is_array_like(Series([1, 2])) assert inference.is_array_like(np.array(["a", "b"])) assert inference.is_array_like(Index(["2016-01-01"])) @@ -164,7 +168,7 @@ class DtypeList(list): {"a": 1}, {1, "a"}, Series([1]), - Series([], dtype=object), + create_series_with_explicit_index([], dtype=object), Series(["a"]).str, (x for x in range(5)), ], diff --git a/pandas/tests/extension/base/missing.py b/pandas/tests/extension/base/missing.py index 2393d2edcd2c6..1049edac328e7 100644 --- a/pandas/tests/extension/base/missing.py +++ b/pandas/tests/extension/base/missing.py @@ -19,7 +19,7 @@ def test_isna(self, data_missing): # GH 21189 result = pd.Series(data_missing).drop([0, 1]).isna() - expected = pd.Series([], dtype=bool) + expected = pd.Series([], dtype=bool, index=pd.RangeIndex(0)) self.assert_series_equal(result, expected) def test_dropna_array(self, data_missing): diff --git a/pandas/tests/extension/test_common.py b/pandas/tests/extension/test_common.py index e43650c291200..bdb49d2f86a0c 100644 --- a/pandas/tests/extension/test_common.py +++ b/pandas/tests/extension/test_common.py @@ -7,6 +7,7 @@ import pandas as pd import pandas._testing as tm from pandas.core.arrays import ExtensionArray +from pandas.core.construction import create_series_with_explicit_index class DummyDtype(dtypes.ExtensionDtype): @@ -40,7 +41,7 @@ class TestExtensionArrayDtype: [ pd.Categorical([]), pd.Categorical([]).dtype, - pd.Series(pd.Categorical([])), + create_series_with_explicit_index(pd.Categorical([])), DummyDtype(), DummyArray(np.array([1, 2])), ], @@ -48,7 +49,9 @@ class TestExtensionArrayDtype: def test_is_extension_array_dtype(self, values): assert is_extension_array_dtype(values) - @pytest.mark.parametrize("values", [np.array([]), pd.Series(np.array([]))]) + @pytest.mark.parametrize( + "values", [np.array([]), create_series_with_explicit_index(np.array([]))] + ) def test_is_not_extension_array_dtype(self, values): assert not is_extension_array_dtype(values) diff --git a/pandas/tests/extension/test_sparse.py b/pandas/tests/extension/test_sparse.py index 694bbee59606f..a71dec28b1e29 100644 --- a/pandas/tests/extension/test_sparse.py +++ b/pandas/tests/extension/test_sparse.py @@ -187,7 +187,7 @@ def test_isna(self, data_missing): # GH 21189 result = pd.Series(data_missing).drop([0, 1]).isna() - expected = pd.Series([], dtype=expected_dtype) + expected = pd.Series([], dtype=expected_dtype, index=pd.RangeIndex(0)) self.assert_series_equal(result, expected) def test_fillna_limit_pad(self, data_missing): diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py index a9fb686d5bc50..140b7aafb64e4 100644 --- a/pandas/tests/frame/methods/test_replace.py +++ b/pandas/tests/frame/methods/test_replace.py @@ -9,6 +9,7 @@ import pandas as pd from pandas import DataFrame, Index, Series, Timestamp, date_range import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index @pytest.fixture @@ -1251,7 +1252,9 @@ def test_replace_with_empty_dictlike(self, mix_abc): # GH 15289 df = DataFrame(mix_abc) tm.assert_frame_equal(df, df.replace({})) - tm.assert_frame_equal(df, df.replace(Series([], dtype=object))) + tm.assert_frame_equal( + df, df.replace(create_series_with_explicit_index([], dtype=object)) + ) tm.assert_frame_equal(df, df.replace({"b": {}})) tm.assert_frame_equal(df, df.replace(Series({"b": {}}))) diff --git a/pandas/tests/frame/methods/test_value_counts.py b/pandas/tests/frame/methods/test_value_counts.py index c409b0bbe6fa9..4b2e8148c2ddb 100644 --- a/pandas/tests/frame/methods/test_value_counts.py +++ b/pandas/tests/frame/methods/test_value_counts.py @@ -88,7 +88,7 @@ def test_data_frame_value_counts_empty(): df_no_cols = pd.DataFrame() result = df_no_cols.value_counts() - expected = pd.Series([], dtype=np.int64) + expected = pd.Series([], dtype=np.int64, index=pd.RangeIndex(0)) tm.assert_series_equal(result, expected) @@ -97,6 +97,6 @@ def test_data_frame_value_counts_empty_normalize(): df_no_cols = pd.DataFrame() result = df_no_cols.value_counts(normalize=True) - expected = pd.Series([], dtype=np.float64) + expected = pd.Series([], dtype=np.float64, index=pd.RangeIndex(0)) tm.assert_series_equal(result, expected) diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py index d929d3e030508..a93796d9d974b 100644 --- a/pandas/tests/frame/test_arithmetic.py +++ b/pandas/tests/frame/test_arithmetic.py @@ -11,6 +11,7 @@ from pandas import DataFrame, MultiIndex, Series import pandas._testing as tm import pandas.core.common as com +from pandas.core.construction import create_series_with_explicit_index from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int # ------------------------------------------------------------------- @@ -522,7 +523,7 @@ def test_arith_flex_series(self, simple_frame): def test_arith_flex_zero_len_raises(self): # GH 19522 passing fill_value to frame flex arith methods should # raise even in the zero-length special cases - ser_len0 = pd.Series([], dtype=object) + ser_len0 = create_series_with_explicit_index([], dtype=object) df_len0 = pd.DataFrame(columns=["A", "B"]) df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index baac87755c6d2..fcfd32500d875 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/pandas/tests/frame/test_constructors.py @@ -30,7 +30,10 @@ ) import pandas._testing as tm from pandas.arrays import IntervalArray, PeriodArray, SparseArray -from pandas.core.construction import create_series_with_explicit_dtype +from pandas.core.construction import ( + create_series_with_explicit_dtype, + create_series_with_explicit_index, +) MIXED_FLOAT_DTYPES = ["float16", "float32", "float64"] MIXED_INT_DTYPES = [ @@ -1542,7 +1545,7 @@ def test_constructor_Series_named(self): DataFrame(s, columns=[1, 2]) # #2234 - a = Series([], name="x", dtype=object) + a = create_series_with_explicit_index([], name="x", dtype=object) df = DataFrame(a) assert df.columns[0] == "x" diff --git a/pandas/tests/generic/test_generic.py b/pandas/tests/generic/test_generic.py index 05588ead54be4..1b7cd347f7b31 100644 --- a/pandas/tests/generic/test_generic.py +++ b/pandas/tests/generic/test_generic.py @@ -11,6 +11,7 @@ from pandas import DataFrame, MultiIndex, Series, date_range import pandas._testing as tm import pandas.core.common as com +from pandas.core.construction import create_series_with_explicit_index # ---------------------------------------------------------------------- # Generic types test cases @@ -54,7 +55,9 @@ def _construct(self, shape, value=None, dtype=None, **kwargs): arr = np.repeat(arr, new_shape).reshape(shape) else: arr = np.random.randn(*shape) - return self._typ(arr, dtype=dtype, **kwargs) + + typ = create_series_with_explicit_index if self._typ is Series else self._typ + return typ(arr, dtype=dtype, **kwargs) def _compare(self, result, expected): self._comparator(result, expected) @@ -680,7 +683,9 @@ def test_squeeze(self): tm.assert_series_equal(df.squeeze(), df["A"]) # don't fail with 0 length dimensions GH11229 & GH8999 - empty_series = Series([], name="five", dtype=np.float64) + empty_series = create_series_with_explicit_index( + [], name="five", dtype=np.float64 + ) empty_frame = DataFrame([empty_series]) tm.assert_series_equal(empty_series, empty_series.squeeze()) tm.assert_series_equal(empty_series, empty_frame.squeeze()) diff --git a/pandas/tests/generic/test_to_xarray.py b/pandas/tests/generic/test_to_xarray.py index 2fde96a1c8f89..19b2ef6343811 100644 --- a/pandas/tests/generic/test_to_xarray.py +++ b/pandas/tests/generic/test_to_xarray.py @@ -6,6 +6,7 @@ import pandas as pd from pandas import DataFrame, Series import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index class TestDataFrameToXArray: @@ -115,7 +116,7 @@ def test_to_xarray_index_types(self, indices): def test_to_xarray(self): from xarray import DataArray - s = Series([], dtype=object) + s = create_series_with_explicit_index([], dtype=object) s.index.name = "foo" result = s.to_xarray() assert len(result) == 0 diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py index 8e4a7141875bb..de96056b5bd93 100644 --- a/pandas/tests/groupby/test_categorical.py +++ b/pandas/tests/groupby/test_categorical.py @@ -16,6 +16,7 @@ qcut, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index def cartesian_product_for_groupers(result, args, names): @@ -792,7 +793,7 @@ def test_groupby_empty_with_category(): result = df.groupby("A").first()["B"] expected = pd.Series( pd.Categorical([], categories=["test", "train"]), - index=pd.Series([], dtype="object", name="A"), + index=create_series_with_explicit_index([], dtype="object", name="A"), name="B", ) tm.assert_series_equal(result, expected) diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py index efcd22f9c0c82..895a21ccff18e 100644 --- a/pandas/tests/groupby/test_grouping.py +++ b/pandas/tests/groupby/test_grouping.py @@ -14,6 +14,7 @@ date_range, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.groupby.grouper import Grouping # selection @@ -637,11 +638,12 @@ def test_evaluate_with_empty_groups(self, func, expected): def test_groupby_empty(self): # https://github.com/pandas-dev/pandas/issues/27190 - s = pd.Series([], name="name", dtype="float64") + s = create_series_with_explicit_index([], name="name", dtype="float64") gr = s.groupby([]) result = gr.mean() - tm.assert_series_equal(result, s) + expected = pd.Series([], name="name", dtype="float64", index=pd.RangeIndex(0)) + tm.assert_series_equal(result, expected) # check group properties assert len(gr.grouper.groupings) == 1 diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index fd23e95106ab0..126339c260ea5 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -25,6 +25,7 @@ isna, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.indexes.base import InvalidIndexError from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin @@ -429,7 +430,10 @@ def test_intersection_base(self, indices): return # GH 10149 - cases = [klass(second.values) for klass in [np.array, Series, list]] + cases = [ + klass(second.values) + for klass in [np.array, create_series_with_explicit_index, list] + ] for case in cases: result = first.intersection(case) assert tm.equalContents(result, second) @@ -452,7 +456,10 @@ def test_union_base(self, indices): return # GH 10149 - cases = [klass(second.values) for klass in [np.array, Series, list]] + cases = [ + klass(second.values) + for klass in [np.array, create_series_with_explicit_index, list] + ] for case in cases: if not isinstance(indices, CategoricalIndex): result = first.union(case) @@ -474,7 +481,10 @@ def test_difference_base(self, sort, indices): assert tm.equalContents(result, answer) # GH 10149 - cases = [klass(second.values) for klass in [np.array, Series, list]] + cases = [ + klass(second.values) + for klass in [np.array, create_series_with_explicit_index, list] + ] for case in cases: if isinstance(indices, (DatetimeIndex, TimedeltaIndex)): assert type(result) == type(answer) @@ -564,7 +574,7 @@ def test_equals(self, indices): if indices.nlevels == 1: # do not test MultiIndex - assert not indices.equals(Series(indices)) + assert not indices.equals(create_series_with_explicit_index(indices)) def test_equals_op(self): # GH9947, GH10637 diff --git a/pandas/tests/indexes/datetimes/test_formats.py b/pandas/tests/indexes/datetimes/test_formats.py index f34019e06fd5f..424c80a409ffd 100644 --- a/pandas/tests/indexes/datetimes/test_formats.py +++ b/pandas/tests/indexes/datetimes/test_formats.py @@ -6,8 +6,9 @@ import pytz import pandas as pd -from pandas import DatetimeIndex, Series +from pandas import DatetimeIndex import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index def test_to_native_types(): @@ -168,7 +169,7 @@ def test_dti_representation_to_series(self): [idx1, idx2, idx3, idx4, idx5, idx6, idx7], [exp1, exp2, exp3, exp4, exp5, exp6, exp7], ): - result = repr(Series(idx)) + result = repr(create_series_with_explicit_index(idx)) assert result == expected def test_dti_summary(self): diff --git a/pandas/tests/indexes/period/test_formats.py b/pandas/tests/indexes/period/test_formats.py index 5db373a9f07ae..bdfdb6928449e 100644 --- a/pandas/tests/indexes/period/test_formats.py +++ b/pandas/tests/indexes/period/test_formats.py @@ -4,6 +4,7 @@ import pandas as pd from pandas import PeriodIndex import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index def test_to_native_types(): @@ -160,7 +161,7 @@ def test_representation_to_series(self): [idx1, idx2, idx3, idx4, idx5, idx6, idx7, idx8, idx9], [exp1, exp2, exp3, exp4, exp5, exp6, exp7, exp8, exp9], ): - result = repr(pd.Series(idx)) + result = repr(create_series_with_explicit_index(idx)) assert result == expected def test_summary(self): diff --git a/pandas/tests/indexes/period/test_period.py b/pandas/tests/indexes/period/test_period.py index 0ce10fb8779a1..0d9c0b1d899f3 100644 --- a/pandas/tests/indexes/period/test_period.py +++ b/pandas/tests/indexes/period/test_period.py @@ -18,6 +18,7 @@ period_range, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from ..datetimelike import DatetimeLike @@ -262,7 +263,7 @@ def _check_all_fields(self, periodindex): ] periods = list(periodindex) - s = pd.Series(periodindex) + s = create_series_with_explicit_index(periodindex) for field in fields: field_idx = getattr(periodindex, field) diff --git a/pandas/tests/indexes/timedeltas/test_formats.py b/pandas/tests/indexes/timedeltas/test_formats.py index 1dfc5b5305008..6076fa67361ed 100644 --- a/pandas/tests/indexes/timedeltas/test_formats.py +++ b/pandas/tests/indexes/timedeltas/test_formats.py @@ -2,6 +2,7 @@ import pandas as pd from pandas import TimedeltaIndex +from pandas.core.construction import create_series_with_explicit_index class TestTimedeltaIndexRendering: @@ -62,7 +63,7 @@ def test_representation_to_series(self): for idx, expected in zip( [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5] ): - result = repr(pd.Series(idx)) + result = repr(create_series_with_explicit_index(idx)) assert result == expected def test_summary(self): diff --git a/pandas/tests/indexing/multiindex/test_loc.py b/pandas/tests/indexing/multiindex/test_loc.py index b7802d9b8fe0c..cd59b3b087299 100644 --- a/pandas/tests/indexing/multiindex/test_loc.py +++ b/pandas/tests/indexing/multiindex/test_loc.py @@ -4,6 +4,7 @@ import pandas as pd from pandas import DataFrame, Index, MultiIndex, Series import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.indexing import IndexingError @@ -46,7 +47,7 @@ def test_loc_getitem_series(self): result = x.loc[y1] tm.assert_series_equal(result, expected) - empty = Series(data=[], dtype=np.float64) + empty = create_series_with_explicit_index(data=[], dtype=np.float64) expected = Series( [], index=MultiIndex(levels=index.levels, codes=[[], []], dtype=np.float64), diff --git a/pandas/tests/indexing/test_partial.py b/pandas/tests/indexing/test_partial.py index 2e691c6fd76d8..9d6ab4314c867 100644 --- a/pandas/tests/indexing/test_partial.py +++ b/pandas/tests/indexing/test_partial.py @@ -10,6 +10,7 @@ import pandas as pd from pandas import DataFrame, Index, Series, date_range import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index class TestPartialSetting: @@ -401,14 +402,14 @@ def test_partial_set_empty_frame(self): def f(): df = DataFrame(index=Index([], dtype="object")) - df["foo"] = Series([], dtype="object") + df["foo"] = create_series_with_explicit_index([], dtype="object") return df tm.assert_frame_equal(f(), expected) def f(): df = DataFrame() - df["foo"] = Series(df.index) + df["foo"] = create_series_with_explicit_index(df.index) return df tm.assert_frame_equal(f(), expected) @@ -432,7 +433,9 @@ def f(): def f(): df = DataFrame(index=Index([], dtype="int64")) - df["foo"] = Series(np.arange(len(df)), dtype="float64") + df["foo"] = create_series_with_explicit_index( + np.arange(len(df)), dtype="float64" + ) return df tm.assert_frame_equal(f(), expected) diff --git a/pandas/tests/io/parser/test_dtypes.py b/pandas/tests/io/parser/test_dtypes.py index e68dcb3aa577e..12f03c3704c6c 100644 --- a/pandas/tests/io/parser/test_dtypes.py +++ b/pandas/tests/io/parser/test_dtypes.py @@ -13,8 +13,9 @@ from pandas.core.dtypes.dtypes import CategoricalDtype import pandas as pd -from pandas import Categorical, DataFrame, Index, MultiIndex, Series, Timestamp, concat +from pandas import Categorical, DataFrame, Index, MultiIndex, Timestamp, concat import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index @pytest.mark.parametrize("dtype", [str, object]) @@ -432,7 +433,10 @@ def test_empty_with_dup_column_pass_dtype_by_indexes(all_parsers): # see gh-9424 parser = all_parsers expected = concat( - [Series([], name="one", dtype="u1"), Series([], name="one.1", dtype="f")], + [ + create_series_with_explicit_index([], name="one", dtype="u1"), + create_series_with_explicit_index([], name="one.1", dtype="f"), + ], axis=1, ) expected.index = expected.index.astype(object) @@ -446,7 +450,10 @@ def test_empty_with_dup_column_pass_dtype_by_indexes_raises(all_parsers): # see gh-9424 parser = all_parsers expected = concat( - [Series([], name="one", dtype="u1"), Series([], name="one.1", dtype="f")], + [ + create_series_with_explicit_index([], name="one", dtype="u1"), + create_series_with_explicit_index([], name="one.1", dtype="f"), + ], axis=1, ) expected.index = expected.index.astype(object) @@ -502,8 +509,8 @@ def test_dtype_with_converters(all_parsers): "timedelta64[ns]", DataFrame( { - "a": Series([], dtype="timedelta64[ns]"), - "b": Series([], dtype="timedelta64[ns]"), + "a": create_series_with_explicit_index([], dtype="timedelta64[ns]"), + "b": create_series_with_explicit_index([], dtype="timedelta64[ns]"), }, index=[], ), @@ -511,21 +518,30 @@ def test_dtype_with_converters(all_parsers): ( dict(a=np.int64, b=np.int32), DataFrame( - {"a": Series([], dtype=np.int64), "b": Series([], dtype=np.int32)}, + { + "a": create_series_with_explicit_index([], dtype=np.int64), + "b": create_series_with_explicit_index([], dtype=np.int32), + }, index=[], ), ), ( {0: np.int64, 1: np.int32}, DataFrame( - {"a": Series([], dtype=np.int64), "b": Series([], dtype=np.int32)}, + { + "a": create_series_with_explicit_index([], dtype=np.int64), + "b": create_series_with_explicit_index([], dtype=np.int32), + }, index=[], ), ), ( {"a": np.int64, 1: np.int32}, DataFrame( - {"a": Series([], dtype=np.int64), "b": Series([], dtype=np.int32)}, + { + "a": create_series_with_explicit_index([], dtype=np.int64), + "b": create_series_with_explicit_index([], dtype=np.int32), + }, index=[], ), ), diff --git a/pandas/tests/io/parser/test_unsupported.py b/pandas/tests/io/parser/test_unsupported.py index 267fae760398a..ba0b318bfe524 100644 --- a/pandas/tests/io/parser/test_unsupported.py +++ b/pandas/tests/io/parser/test_unsupported.py @@ -55,7 +55,10 @@ def test_c_engine(self): read_csv(StringIO(data), sep=r"\s") with tm.assert_produces_warning(parsers.ParserWarning): read_csv(StringIO(data), sep="\t", quotechar=chr(128)) - with tm.assert_produces_warning(parsers.ParserWarning): + with tm.assert_produces_warning( + parsers.ParserWarning, raise_on_extra_warnings=False + ): + # Additionally raises DeprecationWarning: gh-16737 read_csv(StringIO(data), skipfooter=1) text = """ A B C D E diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py index 235aa8e4aa922..ca26035bb87a8 100644 --- a/pandas/tests/reductions/test_reductions.py +++ b/pandas/tests/reductions/test_reductions.py @@ -23,6 +23,7 @@ ) import pandas._testing as tm from pandas.core import nanops +from pandas.core.construction import create_series_with_explicit_index def get_objs(): @@ -78,6 +79,7 @@ def test_ops(self, opname, obj): def test_nanminmax(self, opname, dtype, val, index_or_series): # GH#7261 klass = index_or_series + klass = create_series_with_explicit_index if klass is Series else klass if dtype in ["Int64", "boolean"] and klass == pd.Index: pytest.skip("EAs can't yet be stored in an index") @@ -137,6 +139,7 @@ def test_nanargminmax(self, opname, index_or_series): @pytest.mark.parametrize("dtype", ["M8[ns]", "datetime64[ns, UTC]"]) def test_nanops_empty_object(self, opname, index_or_series, dtype): klass = index_or_series + klass = create_series_with_explicit_index if klass is Series else klass arg_op = "arg" + opname if klass is Index else "idx" + opname obj = klass([], dtype=dtype) @@ -566,7 +569,7 @@ def test_empty(self, method, unit, use_bottleneck, dtype): with pd.option_context("use_bottleneck", use_bottleneck): # GH#9422 / GH#18921 # Entirely empty - s = Series([], dtype=dtype) + s = create_series_with_explicit_index([], dtype=dtype) # NA by default result = getattr(s, method)() assert result == unit @@ -690,7 +693,7 @@ def test_ops_consistency_on_empty(self, method): assert pd.isna(result) # timedelta64[ns] - tdser = Series([], dtype="m8[ns]") + tdser = create_series_with_explicit_index([], dtype="m8[ns]") if method == "var": msg = "|".join( [ @@ -740,10 +743,16 @@ def test_sum_overflow(self, use_bottleneck): def test_empty_timeseries_reductions_return_nat(self): # covers GH#11245 for dtype in ("m8[ns]", "m8[ns]", "M8[ns]", "M8[ns, UTC]"): - assert Series([], dtype=dtype).min() is pd.NaT - assert Series([], dtype=dtype).max() is pd.NaT - assert Series([], dtype=dtype).min(skipna=False) is pd.NaT - assert Series([], dtype=dtype).max(skipna=False) is pd.NaT + assert create_series_with_explicit_index([], dtype=dtype).min() is pd.NaT + assert create_series_with_explicit_index([], dtype=dtype).max() is pd.NaT + assert ( + create_series_with_explicit_index([], dtype=dtype).min(skipna=False) + is pd.NaT + ) + assert ( + create_series_with_explicit_index([], dtype=dtype).max(skipna=False) + is pd.NaT + ) def test_numpy_argmin(self): # See GH#16830 @@ -962,7 +971,7 @@ def test_timedelta64_analytics(self): @pytest.mark.parametrize( "test_input,error_type", [ - (pd.Series([], dtype="float64"), ValueError), + (create_series_with_explicit_index([], dtype="float64"), ValueError), # For strings, or any Series with dtype 'O' (pd.Series(["foo", "bar", "baz"]), TypeError), (pd.Series([(1,), (2,)]), TypeError), @@ -1128,10 +1137,13 @@ class TestSeriesMode: @pytest.mark.parametrize( "dropna, expected", - [(True, Series([], dtype=np.float64)), (False, Series([], dtype=np.float64))], + [ + (True, create_series_with_explicit_index([], dtype=np.float64)), + (False, create_series_with_explicit_index([], dtype=np.float64)), + ], ) def test_mode_empty(self, dropna, expected): - s = Series([], dtype=np.float64) + s = create_series_with_explicit_index([], dtype=np.float64) result = s.mode(dropna) tm.assert_series_equal(result, expected) diff --git a/pandas/tests/resample/test_base.py b/pandas/tests/resample/test_base.py index 6384c5f19c898..94025cf16043c 100644 --- a/pandas/tests/resample/test_base.py +++ b/pandas/tests/resample/test_base.py @@ -6,6 +6,7 @@ import pandas as pd from pandas import DataFrame, Series import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.groupby.groupby import DataError from pandas.core.groupby.grouper import Grouper from pandas.core.indexes.datetimes import date_range @@ -136,7 +137,7 @@ def test_resample_empty_dataframe(empty_frame_dti, freq, resample_method): expected = df.copy() else: # GH14962 - expected = Series([], dtype=object) + expected = create_series_with_explicit_index([], dtype=object) expected.index = _asfreq_compat(df.index, freq) diff --git a/pandas/tests/reshape/test_concat.py b/pandas/tests/reshape/test_concat.py index bccae2c4c2772..cb41a1403444c 100644 --- a/pandas/tests/reshape/test_concat.py +++ b/pandas/tests/reshape/test_concat.py @@ -29,7 +29,10 @@ ) import pandas._testing as tm from pandas.core.arrays import SparseArray -from pandas.core.construction import create_series_with_explicit_dtype +from pandas.core.construction import ( + create_series_with_explicit_dtype, + create_series_with_explicit_index, +) from pandas.tests.extension.decimal import to_decimal @@ -724,7 +727,7 @@ def test_concat_categorical_coercion_nan(self): def test_concat_categorical_empty(self): # GH 13524 - s1 = pd.Series([], dtype="category") + s1 = create_series_with_explicit_index([], dtype="category") s2 = pd.Series([1, 2], dtype="category") tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2) @@ -733,22 +736,24 @@ def test_concat_categorical_empty(self): tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2) tm.assert_series_equal(s2.append(s1, ignore_index=True), s2) - s1 = pd.Series([], dtype="category") - s2 = pd.Series([], dtype="category") + s1 = create_series_with_explicit_index([], dtype="category") + s2 = create_series_with_explicit_index([], dtype="category") + expected = pd.Series([], dtype="category", index=pd.RangeIndex(0)) - tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2) - tm.assert_series_equal(s1.append(s2, ignore_index=True), s2) + tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), expected) + tm.assert_series_equal(s1.append(s2, ignore_index=True), expected) - s1 = pd.Series([], dtype="category") - s2 = pd.Series([], dtype="object") + s1 = create_series_with_explicit_index([], dtype="category") + s2 = create_series_with_explicit_index([], dtype="object") + expected = pd.Series([], dtype="object", index=pd.RangeIndex(0)) # different dtype => not-category - tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2) - tm.assert_series_equal(s1.append(s2, ignore_index=True), s2) - tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2) - tm.assert_series_equal(s2.append(s1, ignore_index=True), s2) + tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), expected) + tm.assert_series_equal(s1.append(s2, ignore_index=True), expected) + tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), expected) + tm.assert_series_equal(s2.append(s1, ignore_index=True), expected) - s1 = pd.Series([], dtype="category") + s1 = create_series_with_explicit_index([], dtype="category") s2 = pd.Series([np.nan, np.nan]) # empty Series is ignored @@ -2195,17 +2200,20 @@ def test_concat_empty_series(self): def test_concat_empty_series_timelike(self, tz, values): # GH 18447 - first = Series([], dtype="M8[ns]").dt.tz_localize(tz) + first = create_series_with_explicit_index([], dtype="M8[ns]").dt.tz_localize(tz) dtype = None if values else np.float64 - second = Series(values, dtype=dtype) + second = create_series_with_explicit_index(values, dtype=dtype) + result = concat([first, second], axis=1) expected = DataFrame( { - 0: pd.Series([pd.NaT] * len(values), dtype="M8[ns]").dt.tz_localize(tz), + 0: create_series_with_explicit_index( + [pd.NaT] * len(values), dtype="M8[ns]" + ).dt.tz_localize(tz), 1: values, - } + }, ) - result = concat([first, second], axis=1) + expected.index = expected.index.astype(object) tm.assert_frame_equal(result, expected) def test_default_index(self): @@ -2595,7 +2603,7 @@ def test_concat_empty_and_non_empty_frame_regression(): def test_concat_empty_and_non_empty_series_regression(): # GH 18187 regression test s1 = pd.Series([1]) - s2 = pd.Series([], dtype=object) + s2 = create_series_with_explicit_index([], dtype=object) expected = s1 result = pd.concat([s1, s2]) diff --git a/pandas/tests/series/indexing/test_boolean.py b/pandas/tests/series/indexing/test_boolean.py index e2b71b1f2f412..6d7196535d230 100644 --- a/pandas/tests/series/indexing/test_boolean.py +++ b/pandas/tests/series/indexing/test_boolean.py @@ -3,6 +3,7 @@ from pandas import Index, Series import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.indexing import IndexingError from pandas.tseries.offsets import BDay @@ -20,7 +21,7 @@ def test_getitem_boolean(string_series): def test_getitem_boolean_empty(): - s = Series([], dtype=np.int64) + s = create_series_with_explicit_index([], dtype=np.int64) s.index.name = "index_name" s = s[s.isna()] assert s.index.name == "index_name" @@ -30,7 +31,7 @@ def test_getitem_boolean_empty(): # indexing with empty series s = Series(["A", "B"]) expected = Series(dtype=object, index=Index([], dtype="int64")) - result = s[Series([], dtype=object)] + result = s[create_series_with_explicit_index([], dtype=object)] tm.assert_series_equal(result, expected) # invalid because of the boolean indexer @@ -40,7 +41,7 @@ def test_getitem_boolean_empty(): r"the boolean Series and of the indexed object do not match" ) with pytest.raises(IndexingError, match=msg): - s[Series([], dtype=bool)] + s[create_series_with_explicit_index([], dtype=bool)] with pytest.raises(IndexingError, match=msg): s[Series([True], dtype=bool)] diff --git a/pandas/tests/series/indexing/test_indexing.py b/pandas/tests/series/indexing/test_indexing.py index c2b5117d395f9..bc88e783cc232 100644 --- a/pandas/tests/series/indexing/test_indexing.py +++ b/pandas/tests/series/indexing/test_indexing.py @@ -10,6 +10,7 @@ import pandas as pd from pandas import Categorical, DataFrame, MultiIndex, Series, Timedelta, Timestamp import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.tseries.offsets import BDay @@ -160,7 +161,7 @@ def test_getitem_out_of_bounds(datetime_series): # GH #917 # With a RangeIndex, an int key gives a KeyError - s = Series([], dtype=object) + s = Series([], index=pd.RangeIndex(0), dtype=object) with pytest.raises(KeyError, match="-1"): s[-1] @@ -617,7 +618,7 @@ def test_setitem_na(): def test_timedelta_assignment(): # GH 8209 - s = Series([], dtype=object) + s = create_series_with_explicit_index([], dtype=object) s.loc["B"] = timedelta(1) tm.assert_series_equal(s, Series(Timedelta("1 days"), index=["B"])) diff --git a/pandas/tests/series/methods/test_drop_duplicates.py b/pandas/tests/series/methods/test_drop_duplicates.py index a4532ebb3d8c5..255f92d8cf449 100644 --- a/pandas/tests/series/methods/test_drop_duplicates.py +++ b/pandas/tests/series/methods/test_drop_duplicates.py @@ -3,6 +3,7 @@ from pandas import Categorical, Series import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index @pytest.mark.parametrize( @@ -46,8 +47,8 @@ def test_drop_duplicates_bool(keep, expected): @pytest.mark.parametrize("values", [[], list(range(5))]) def test_drop_duplicates_no_duplicates(any_numpy_dtype, keep, values): - tc = Series(values, dtype=np.dtype(any_numpy_dtype)) - expected = Series([False] * len(tc), dtype="bool") + tc = create_series_with_explicit_index(values, dtype=np.dtype(any_numpy_dtype)) + expected = create_series_with_explicit_index([False] * len(tc), dtype="bool") if tc.dtype == "bool": # 0 -> False and 1-> True diff --git a/pandas/tests/series/methods/test_interpolate.py b/pandas/tests/series/methods/test_interpolate.py index 6844225a81a8f..b924d15c6bf16 100644 --- a/pandas/tests/series/methods/test_interpolate.py +++ b/pandas/tests/series/methods/test_interpolate.py @@ -169,7 +169,7 @@ def test_interpolate_corners(self, kwargs): s = Series([np.nan, np.nan]) tm.assert_series_equal(s.interpolate(**kwargs), s) - s = Series([], dtype=object).interpolate() + s = Series([], dtype=object, index=pd.RangeIndex(0)).interpolate() tm.assert_series_equal(s.interpolate(**kwargs), s) def test_interpolate_index_values(self): diff --git a/pandas/tests/series/methods/test_quantile.py b/pandas/tests/series/methods/test_quantile.py index 79f50afca658f..3c4372b6fb58e 100644 --- a/pandas/tests/series/methods/test_quantile.py +++ b/pandas/tests/series/methods/test_quantile.py @@ -6,6 +6,7 @@ import pandas as pd from pandas import Index, Series import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.indexes.datetimes import Timestamp @@ -104,7 +105,7 @@ def test_quantile_nan(self): assert result == expected # all nan/empty - s1 = Series([], dtype=object) + s1 = create_series_with_explicit_index([], dtype=object) cases = [s1, Series([np.nan, np.nan])] for s in cases: @@ -163,8 +164,12 @@ def test_quantile_box(self, case): def test_datetime_timedelta_quantiles(self): # covers #9694 - assert pd.isna(Series([], dtype="M8[ns]").quantile(0.5)) - assert pd.isna(Series([], dtype="m8[ns]").quantile(0.5)) + assert pd.isna( + create_series_with_explicit_index([], dtype="M8[ns]").quantile(0.5) + ) + assert pd.isna( + create_series_with_explicit_index([], dtype="m8[ns]").quantile(0.5) + ) def test_quantile_nat(self): res = Series([pd.NaT, pd.NaT]).quantile(0.5) @@ -186,7 +191,7 @@ def test_quantile_sparse(self, values, dtype): def test_quantile_empty(self): # floats - s = Series([], dtype="float64") + s = create_series_with_explicit_index([], dtype="float64") res = s.quantile(0.5) assert np.isnan(res) @@ -196,7 +201,7 @@ def test_quantile_empty(self): tm.assert_series_equal(res, exp) # int - s = Series([], dtype="int64") + s = create_series_with_explicit_index([], dtype="int64") res = s.quantile(0.5) assert np.isnan(res) @@ -206,7 +211,7 @@ def test_quantile_empty(self): tm.assert_series_equal(res, exp) # datetime - s = Series([], dtype="datetime64[ns]") + s = create_series_with_explicit_index([], dtype="datetime64[ns]") res = s.quantile(0.5) assert res is pd.NaT diff --git a/pandas/tests/series/test_apply.py b/pandas/tests/series/test_apply.py index 0661828814888..5c16de0a512c4 100644 --- a/pandas/tests/series/test_apply.py +++ b/pandas/tests/series/test_apply.py @@ -10,6 +10,7 @@ from pandas import DataFrame, Index, Series, isna import pandas._testing as tm from pandas.core.base import SpecificationError +from pandas.core.construction import create_series_with_explicit_index class TestSeriesApply: @@ -519,7 +520,7 @@ def test_map_empty(self, indices): if isinstance(indices, ABCMultiIndex): pytest.skip("Initializing a Series from a MultiIndex is not supported") - s = Series(indices) + s = create_series_with_explicit_index(indices) result = s.map({}) expected = pd.Series(np.nan, index=s.index) diff --git a/pandas/tests/series/test_combine_concat.py b/pandas/tests/series/test_combine_concat.py index 0766bfc37d7ca..c409b274b6964 100644 --- a/pandas/tests/series/test_combine_concat.py +++ b/pandas/tests/series/test_combine_concat.py @@ -3,6 +3,7 @@ import pandas as pd from pandas import Series +from pandas.core.construction import create_series_with_explicit_index class TestSeriesConcat: @@ -94,7 +95,10 @@ def test_concat_empty_series_dtype_category_with_array(self): # GH 18515 assert ( pd.concat( - [Series(np.array([]), dtype="category"), Series(dtype="float64")] + [ + create_series_with_explicit_index(np.array([]), dtype="category"), + Series(dtype="float64"), + ] ).dtype == "float64" ) diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index effb324298c95..6d0b7691b664f 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -28,6 +28,7 @@ ) import pandas._testing as tm from pandas.core.arrays import IntervalArray, period_array +from pandas.core.construction import create_series_with_explicit_index class TestSeriesConstructors: @@ -134,12 +135,21 @@ def test_constructor_empty(self, input_class): # With explicit dtype: empty = Series(dtype="float64") - empty2 = Series(input_class(), dtype="float64") + + if input_class is list: + with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): + empty2 = Series(input_class(), dtype="float64") + else: + empty2 = Series(input_class(), dtype="float64") tm.assert_series_equal(empty, empty2, check_index_type=False) # GH 18515 : with dtype=category: empty = Series(dtype="category") - empty2 = Series(input_class(), dtype="category") + if input_class is list: + with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): + empty2 = Series(input_class(), dtype="category") + else: + empty2 = Series(input_class(), dtype="category") tm.assert_series_equal(empty, empty2, check_index_type=False) if input_class is not list: @@ -1391,7 +1401,7 @@ def test_constructor_generic_timestamp_no_frequency(self, dtype): msg = "dtype has no unit. Please pass in" with pytest.raises(ValueError, match=msg): - Series([], dtype=dtype) + create_series_with_explicit_index([], dtype=dtype) @pytest.mark.parametrize( "dtype,msg", @@ -1404,7 +1414,7 @@ def test_constructor_generic_timestamp_bad_frequency(self, dtype, msg): # see gh-15524, gh-15987 with pytest.raises(TypeError, match=msg): - Series([], dtype=dtype) + create_series_with_explicit_index([], dtype=dtype) @pytest.mark.parametrize("dtype", [None, "uint8", "category"]) def test_constructor_range_dtype(self, dtype): diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py index 05e708e575a64..1d02f24ea6455 100644 --- a/pandas/tests/series/test_dtypes.py +++ b/pandas/tests/series/test_dtypes.py @@ -21,6 +21,7 @@ date_range, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index class TestSeriesDtypes: @@ -404,9 +405,9 @@ def test_astype_empty_constructor_equality(self, dtype): "M", "m", # Generic timestamps raise a ValueError. Already tested. ): - init_empty = Series([], dtype=dtype) + init_empty = create_series_with_explicit_index([], dtype=dtype) with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): - as_type_empty = Series([]).astype(dtype) + as_type_empty = create_series_with_explicit_index([]).astype(dtype) tm.assert_series_equal(init_empty, as_type_empty) def test_arg_for_errors_in_astype(self): diff --git a/pandas/tests/series/test_duplicates.py b/pandas/tests/series/test_duplicates.py index 89181a08819b1..50ad0e329851e 100644 --- a/pandas/tests/series/test_duplicates.py +++ b/pandas/tests/series/test_duplicates.py @@ -3,7 +3,10 @@ from pandas import Categorical, Series import pandas._testing as tm -from pandas.core.construction import create_series_with_explicit_dtype +from pandas.core.construction import ( + create_series_with_explicit_dtype, + create_series_with_explicit_index, +) def test_nunique(): @@ -15,7 +18,7 @@ def test_nunique(): assert result == 11 # GH 18051 - s = Series(Categorical([])) + s = create_series_with_explicit_index(Categorical([])) assert s.nunique() == 0 s = Series(Categorical([np.nan])) assert s.nunique() == 0 @@ -46,7 +49,7 @@ def test_unique(): tm.assert_numpy_array_equal(result, expected) # GH 18051 - s = Series(Categorical([])) + s = create_series_with_explicit_index(Categorical([])) tm.assert_categorical_equal(s.unique(), Categorical([])) s = Series(Categorical([np.nan])) tm.assert_categorical_equal(s.unique(), Categorical([np.nan])) diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py index 9e9b93a499487..76272f5aaa988 100644 --- a/pandas/tests/series/test_missing.py +++ b/pandas/tests/series/test_missing.py @@ -20,6 +20,7 @@ isna, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index class TestSeriesMissingData: @@ -562,7 +563,7 @@ def test_fillna(self, datetime_series): tm.assert_series_equal(result, expected) result = s1.fillna({}) tm.assert_series_equal(result, s1) - result = s1.fillna(Series((), dtype=object)) + result = s1.fillna(create_series_with_explicit_index((), dtype=object)) tm.assert_series_equal(result, s1) result = s2.fillna(s1) tm.assert_series_equal(result, s2) @@ -677,7 +678,7 @@ def test_timedelta64_nan(self): # tm.assert_series_equal(selector, expected) def test_dropna_empty(self): - s = Series([], dtype=object) + s = create_series_with_explicit_index([], dtype=object) assert len(s.dropna()) == 0 s.dropna(inplace=True) diff --git a/pandas/tests/series/test_operators.py b/pandas/tests/series/test_operators.py index 1340f514e31ce..d5277efc3971c 100644 --- a/pandas/tests/series/test_operators.py +++ b/pandas/tests/series/test_operators.py @@ -8,6 +8,7 @@ from pandas import DataFrame, Index, Series, bdate_range import pandas._testing as tm from pandas.core import ops +from pandas.core.construction import create_series_with_explicit_index class TestSeriesLogicalOps: @@ -32,7 +33,7 @@ def test_logical_operators_bool_dtype_with_empty(self): s_tft = Series([True, False, True], index=index) s_fff = Series([False, False, False], index=index) - s_empty = Series([], dtype=object) + s_empty = create_series_with_explicit_index([], dtype=object) res = s_tft & s_empty expected = s_fff @@ -407,7 +408,7 @@ def test_logical_ops_label_based(self): # filling # vs empty - empty = Series([], dtype=object) + empty = create_series_with_explicit_index([], dtype=object) result = a & empty.copy() expected = Series([False, False, False], list("bca")) diff --git a/pandas/tests/series/test_repr.py b/pandas/tests/series/test_repr.py index 77f942a9e32ec..a21a4eaa4fa6b 100644 --- a/pandas/tests/series/test_repr.py +++ b/pandas/tests/series/test_repr.py @@ -16,6 +16,7 @@ timedelta_range, ) import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index class TestSeriesRepr: @@ -124,10 +125,10 @@ def test_repr(self, datetime_series, string_series, object_series): assert "a\n" not in repr(ser) # with empty series (#4651) - s = Series([], dtype=np.int64, name="foo") + s = create_series_with_explicit_index([], dtype=np.int64, name="foo") assert repr(s) == "Series([], Name: foo, dtype: int64)" - s = Series([], dtype=np.int64, name=None) + s = create_series_with_explicit_index([], dtype=np.int64, name=None) assert repr(s) == "Series([], dtype: int64)" def test_tidy_repr(self): diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index 5f904241da485..579869762f4d6 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -34,6 +34,7 @@ import pandas.core.algorithms as algos from pandas.core.arrays import DatetimeArray import pandas.core.common as com +from pandas.core.construction import create_series_with_explicit_index class TestFactorize: @@ -2171,7 +2172,7 @@ def test_int64_add_overflow(): class TestMode: def test_no_mode(self): - exp = Series([], dtype=np.float64) + exp = create_series_with_explicit_index([], dtype=np.float64) tm.assert_series_equal(algos.mode([]), exp) def test_mode_single(self): diff --git a/pandas/tests/test_register_accessor.py b/pandas/tests/test_register_accessor.py index d839936f731a3..d8062a6fdcfeb 100644 --- a/pandas/tests/test_register_accessor.py +++ b/pandas/tests/test_register_accessor.py @@ -4,6 +4,10 @@ import pandas as pd import pandas._testing as tm +from pandas.core.construction import ( + create_series_with_explicit_dtype, + create_series_with_explicit_index, +) @contextlib.contextmanager @@ -46,7 +50,8 @@ def test_register(obj, registrar): with ensure_removed(obj, "mine"): before = set(dir(obj)) registrar("mine")(MyAccessor) - o = obj([]) if obj is not pd.Series else obj([], dtype=object) + klass = create_series_with_explicit_dtype if obj is pd.Series else obj + o = klass([]) assert o.mine.prop == "item" after = set(dir(obj)) assert (before ^ after) == {"mine"} @@ -90,4 +95,4 @@ def __init__(self, data): raise AttributeError("whoops") with pytest.raises(AttributeError, match="whoops"): - pd.Series([], dtype=object).bad + create_series_with_explicit_index([], dtype=object).bad diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 6260d13524da3..63a57dd559ff2 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -10,6 +10,7 @@ import pandas as pd from pandas import DataFrame, Index, MultiIndex, Series, concat, isna, notna import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index import pandas.core.strings as strings @@ -207,6 +208,7 @@ def test_api_mi_raises(self): def test_api_per_dtype(self, index_or_series, dtype, any_skipna_inferred_dtype): # one instance of parametrized fixture box = index_or_series + box = create_series_with_explicit_index if box is Series else box inferred_dtype, values = any_skipna_inferred_dtype if dtype == "category" and len(values) and values[1] is pd.NA: @@ -252,6 +254,7 @@ def test_api_per_method( # just that the methods work on the specified (inferred) dtypes, # and raise on all others box = index_or_series + box = create_series_with_explicit_index if box is Series else box # one instance of each parametrized fixture inferred_dtype, values = any_allowed_skipna_inferred_dtype @@ -351,7 +354,7 @@ def test_iter(self): assert s.dropna().values.item() == "l" def test_iter_empty(self): - ds = Series([], dtype=object) + ds = create_series_with_explicit_index([], dtype=object) i, s = 100, 1 diff --git a/pandas/tests/tools/test_to_numeric.py b/pandas/tests/tools/test_to_numeric.py index 263887a8ea36e..ba905b8afafa5 100644 --- a/pandas/tests/tools/test_to_numeric.py +++ b/pandas/tests/tools/test_to_numeric.py @@ -7,6 +7,7 @@ import pandas as pd from pandas import DataFrame, Index, Series, to_numeric import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index @pytest.fixture(params=[None, "ignore", "raise", "coerce"]) @@ -54,10 +55,10 @@ def transform_assert_equal(request): ) def test_empty(input_kwargs, result_kwargs): # see gh-16302 - ser = Series([], dtype=object) + ser = create_series_with_explicit_index([], dtype=object) result = to_numeric(ser, **input_kwargs) - expected = Series([], **result_kwargs) + expected = create_series_with_explicit_index([], **result_kwargs) tm.assert_series_equal(result, expected) diff --git a/pandas/tests/util/test_hashing.py b/pandas/tests/util/test_hashing.py index ff29df39e1871..6d3fb66eb29e4 100644 --- a/pandas/tests/util/test_hashing.py +++ b/pandas/tests/util/test_hashing.py @@ -4,6 +4,7 @@ import pandas as pd from pandas import DataFrame, Index, MultiIndex, Series import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.util.hashing import hash_tuples from pandas.util import hash_array, hash_pandas_object @@ -178,7 +179,12 @@ def test_hash_pandas_object2(series, index): @pytest.mark.parametrize( - "obj", [Series([], dtype="float64"), Series([], dtype="object"), Index([])] + "obj", + [ + create_series_with_explicit_index([], dtype="float64"), + create_series_with_explicit_index([], dtype="object"), + Index([]), + ], ) def test_hash_pandas_empty_object(obj, index): # These are by-definition the same with diff --git a/pandas/tests/window/common.py b/pandas/tests/window/common.py index 6aeada3152dbb..69c5e10b46280 100644 --- a/pandas/tests/window/common.py +++ b/pandas/tests/window/common.py @@ -5,6 +5,7 @@ from pandas import DataFrame, Series, bdate_range, notna import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index N, K = 100, 10 @@ -375,7 +376,7 @@ def check_binary_ew_min_periods(name, min_periods, A, B): assert not np.isnan(result.values[11:]).any() # check series of length 0 - empty = Series([], dtype=np.float64) + empty = create_series_with_explicit_index([], dtype=np.float64) result = ew_func(empty, empty, 50, name=name, min_periods=min_periods) tm.assert_series_equal(result, empty) diff --git a/pandas/tests/window/moments/test_moments_ewm.py b/pandas/tests/window/moments/test_moments_ewm.py index 599761259e041..c068ddbe5d82f 100644 --- a/pandas/tests/window/moments/test_moments_ewm.py +++ b/pandas/tests/window/moments/test_moments_ewm.py @@ -5,6 +5,7 @@ import pandas as pd from pandas import DataFrame, Series, concat import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.tests.window.common import ( Base, ConsistencyBase, @@ -205,7 +206,7 @@ def test_ewm_domain_checks(self): @pytest.mark.parametrize("method", ["mean", "vol", "var"]) def test_ew_empty_series(self, method): - vals = pd.Series([], dtype=np.float64) + vals = create_series_with_explicit_index([], dtype=np.float64) ewm = vals.ewm(3) result = getattr(ewm, method)() diff --git a/pandas/tests/window/moments/test_moments_expanding.py b/pandas/tests/window/moments/test_moments_expanding.py index 9dfaecee9caeb..444e2b7d00443 100644 --- a/pandas/tests/window/moments/test_moments_expanding.py +++ b/pandas/tests/window/moments/test_moments_expanding.py @@ -6,6 +6,7 @@ from pandas import DataFrame, Index, MultiIndex, Series, isna, notna import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.tests.window.common import ConsistencyBase @@ -209,7 +210,7 @@ def expanding_mean(x, min_periods=1): def test_expanding_apply_empty_series(self, engine_and_raw): engine, raw = engine_and_raw - ser = Series([], dtype=np.float64) + ser = create_series_with_explicit_index([], dtype=np.float64) tm.assert_series_equal( ser, ser.expanding().apply(lambda x: x.mean(), raw=raw, engine=engine) ) diff --git a/pandas/tests/window/moments/test_moments_rolling.py b/pandas/tests/window/moments/test_moments_rolling.py index 3c5352fcd997d..c57bb4b9dd49e 100644 --- a/pandas/tests/window/moments/test_moments_rolling.py +++ b/pandas/tests/window/moments/test_moments_rolling.py @@ -11,6 +11,7 @@ import pandas as pd from pandas import DataFrame, DatetimeIndex, Index, Series, isna, notna import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index from pandas.core.window.common import _flex_binary_moment from pandas.tests.window.common import Base, ConsistencyBase @@ -108,7 +109,7 @@ def test_cmov_window_corner(self): assert np.isnan(result).all() # empty - vals = pd.Series([], dtype=object) + vals = create_series_with_explicit_index([], dtype=object) result = vals.rolling(5, center=True, win_type="boxcar").mean() assert len(result) == 0 diff --git a/pandas/tests/window/test_apply.py b/pandas/tests/window/test_apply.py index 7132e64c1191c..66823488c9d90 100644 --- a/pandas/tests/window/test_apply.py +++ b/pandas/tests/window/test_apply.py @@ -5,6 +5,7 @@ from pandas import DataFrame, Series, Timestamp, date_range import pandas._testing as tm +from pandas.core.construction import create_series_with_explicit_index @pytest.mark.parametrize("bad_raw", [None, 1, 0]) @@ -53,7 +54,7 @@ def f(x): def test_rolling_apply(engine_and_raw): engine, raw = engine_and_raw - expected = Series([], dtype="float64") + expected = create_series_with_explicit_index([], dtype="float64") result = expected.rolling(10).apply(lambda x: x.mean(), engine=engine, raw=raw) tm.assert_series_equal(result, expected)
- [x] closes #16737 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry --------------------------------- I picked up all the notes from #16737 where it was suggested to use `Index` over `RangeIndex` for empty data.
https://api.github.com/repos/pandas-dev/pandas/pulls/32053
2020-02-17T12:08:01Z
2020-09-10T18:57:11Z
null
2020-09-10T18:57:12Z
Backport PR #31156 on branch 1.0.x (DOC: Update of the 'getting started' pages in the sphinx section of the documentation)
diff --git a/doc/data/air_quality_long.csv b/doc/data/air_quality_long.csv new file mode 100644 index 0000000000000..6225d65d8e276 --- /dev/null +++ b/doc/data/air_quality_long.csv @@ -0,0 +1,5273 @@ +city,country,date.utc,location,parameter,value,unit +Antwerpen,BE,2019-06-18 06:00:00+00:00,BETR801,pm25,18.0,µg/m³ +Antwerpen,BE,2019-06-17 08:00:00+00:00,BETR801,pm25,6.5,µg/m³ +Antwerpen,BE,2019-06-17 07:00:00+00:00,BETR801,pm25,18.5,µg/m³ +Antwerpen,BE,2019-06-17 06:00:00+00:00,BETR801,pm25,16.0,µg/m³ +Antwerpen,BE,2019-06-17 05:00:00+00:00,BETR801,pm25,7.5,µg/m³ +Antwerpen,BE,2019-06-17 04:00:00+00:00,BETR801,pm25,7.5,µg/m³ +Antwerpen,BE,2019-06-17 03:00:00+00:00,BETR801,pm25,7.0,µg/m³ +Antwerpen,BE,2019-06-17 02:00:00+00:00,BETR801,pm25,7.0,µg/m³ +Antwerpen,BE,2019-06-17 01:00:00+00:00,BETR801,pm25,8.0,µg/m³ +Antwerpen,BE,2019-06-16 01:00:00+00:00,BETR801,pm25,15.0,µg/m³ +Antwerpen,BE,2019-06-15 01:00:00+00:00,BETR801,pm25,11.0,µg/m³ +Antwerpen,BE,2019-06-14 09:00:00+00:00,BETR801,pm25,12.0,µg/m³ +Antwerpen,BE,2019-06-13 01:00:00+00:00,BETR801,pm25,3.0,µg/m³ +Antwerpen,BE,2019-06-12 01:00:00+00:00,BETR801,pm25,16.0,µg/m³ +Antwerpen,BE,2019-06-11 01:00:00+00:00,BETR801,pm25,3.5,µg/m³ +Antwerpen,BE,2019-06-10 01:00:00+00:00,BETR801,pm25,8.5,µg/m³ +Antwerpen,BE,2019-06-09 01:00:00+00:00,BETR801,pm25,6.0,µg/m³ +Antwerpen,BE,2019-06-08 01:00:00+00:00,BETR801,pm25,6.5,µg/m³ +Antwerpen,BE,2019-06-06 01:00:00+00:00,BETR801,pm25,6.5,µg/m³ +Antwerpen,BE,2019-06-05 01:00:00+00:00,BETR801,pm25,11.0,µg/m³ +Antwerpen,BE,2019-06-04 01:00:00+00:00,BETR801,pm25,10.5,µg/m³ +Antwerpen,BE,2019-06-03 01:00:00+00:00,BETR801,pm25,12.5,µg/m³ +Antwerpen,BE,2019-06-02 01:00:00+00:00,BETR801,pm25,19.0,µg/m³ +Antwerpen,BE,2019-06-01 01:00:00+00:00,BETR801,pm25,9.0,µg/m³ +Antwerpen,BE,2019-05-31 01:00:00+00:00,BETR801,pm25,6.0,µg/m³ +Antwerpen,BE,2019-05-30 01:00:00+00:00,BETR801,pm25,5.0,µg/m³ +Antwerpen,BE,2019-05-29 01:00:00+00:00,BETR801,pm25,5.5,µg/m³ 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00:00:00+00:00,BETR801,pm25,49.5,µg/m³ +Antwerpen,BE,2019-05-18 23:00:00+00:00,BETR801,pm25,45.5,µg/m³ +Antwerpen,BE,2019-05-18 22:00:00+00:00,BETR801,pm25,42.0,µg/m³ +Antwerpen,BE,2019-05-18 21:00:00+00:00,BETR801,pm25,40.5,µg/m³ +Antwerpen,BE,2019-05-18 20:00:00+00:00,BETR801,pm25,41.0,µg/m³ +Antwerpen,BE,2019-05-18 19:00:00+00:00,BETR801,pm25,36.5,µg/m³ +Antwerpen,BE,2019-05-18 18:00:00+00:00,BETR801,pm25,37.0,µg/m³ +Antwerpen,BE,2019-05-18 01:00:00+00:00,BETR801,pm25,24.0,µg/m³ +Antwerpen,BE,2019-05-17 01:00:00+00:00,BETR801,pm25,13.5,µg/m³ +Antwerpen,BE,2019-05-16 01:00:00+00:00,BETR801,pm25,11.0,µg/m³ +Antwerpen,BE,2019-05-15 02:00:00+00:00,BETR801,pm25,12.5,µg/m³ +Antwerpen,BE,2019-05-15 01:00:00+00:00,BETR801,pm25,13.0,µg/m³ +Antwerpen,BE,2019-05-14 02:00:00+00:00,BETR801,pm25,4.0,µg/m³ +Antwerpen,BE,2019-05-14 01:00:00+00:00,BETR801,pm25,4.0,µg/m³ +Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,pm25,5.5,µg/m³ +Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,pm25,5.0,µg/m³ 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01:00:00+00:00,BETR801,pm25,5.0,µg/m³ +Antwerpen,BE,2019-05-04 02:00:00+00:00,BETR801,pm25,4.5,µg/m³ +Antwerpen,BE,2019-05-04 01:00:00+00:00,BETR801,pm25,4.0,µg/m³ +Antwerpen,BE,2019-05-03 02:00:00+00:00,BETR801,pm25,9.5,µg/m³ +Antwerpen,BE,2019-05-03 01:00:00+00:00,BETR801,pm25,8.5,µg/m³ +Antwerpen,BE,2019-05-02 02:00:00+00:00,BETR801,pm25,45.5,µg/m³ +Antwerpen,BE,2019-05-02 01:00:00+00:00,BETR801,pm25,46.0,µg/m³ +Antwerpen,BE,2019-05-01 02:00:00+00:00,BETR801,pm25,28.5,µg/m³ +Antwerpen,BE,2019-05-01 01:00:00+00:00,BETR801,pm25,34.5,µg/m³ +Antwerpen,BE,2019-04-30 02:00:00+00:00,BETR801,pm25,13.5,µg/m³ +Antwerpen,BE,2019-04-30 01:00:00+00:00,BETR801,pm25,18.5,µg/m³ +Antwerpen,BE,2019-04-29 02:00:00+00:00,BETR801,pm25,14.5,µg/m³ +Antwerpen,BE,2019-04-29 01:00:00+00:00,BETR801,pm25,14.0,µg/m³ +Antwerpen,BE,2019-04-28 02:00:00+00:00,BETR801,pm25,4.5,µg/m³ +Antwerpen,BE,2019-04-28 01:00:00+00:00,BETR801,pm25,6.5,µg/m³ +Antwerpen,BE,2019-04-27 02:00:00+00:00,BETR801,pm25,7.0,µg/m³ 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01:00:00+00:00,BETR801,pm25,20.0,µg/m³ +Antwerpen,BE,2019-04-18 02:00:00+00:00,BETR801,pm25,26.5,µg/m³ +Antwerpen,BE,2019-04-18 01:00:00+00:00,BETR801,pm25,25.0,µg/m³ +Antwerpen,BE,2019-04-17 03:00:00+00:00,BETR801,pm25,9.0,µg/m³ +Antwerpen,BE,2019-04-17 02:00:00+00:00,BETR801,pm25,8.5,µg/m³ +Antwerpen,BE,2019-04-17 01:00:00+00:00,BETR801,pm25,8.0,µg/m³ +Antwerpen,BE,2019-04-16 02:00:00+00:00,BETR801,pm25,23.0,µg/m³ +Antwerpen,BE,2019-04-16 01:00:00+00:00,BETR801,pm25,24.0,µg/m³ +Antwerpen,BE,2019-04-15 15:00:00+00:00,BETR801,pm25,26.5,µg/m³ +Antwerpen,BE,2019-04-15 14:00:00+00:00,BETR801,pm25,25.5,µg/m³ +Antwerpen,BE,2019-04-15 13:00:00+00:00,BETR801,pm25,26.5,µg/m³ +Antwerpen,BE,2019-04-15 12:00:00+00:00,BETR801,pm25,26.5,µg/m³ +Antwerpen,BE,2019-04-15 11:00:00+00:00,BETR801,pm25,26.0,µg/m³ +Antwerpen,BE,2019-04-15 10:00:00+00:00,BETR801,pm25,26.0,µg/m³ +Antwerpen,BE,2019-04-15 09:00:00+00:00,BETR801,pm25,21.5,µg/m³ +Antwerpen,BE,2019-04-15 08:00:00+00:00,BETR801,pm25,24.0,µg/m³ 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11:00:00+00:00,BETR801,pm25,45.0,µg/m³ +Antwerpen,BE,2019-04-09 10:00:00+00:00,BETR801,pm25,44.5,µg/m³ +Antwerpen,BE,2019-04-09 09:00:00+00:00,BETR801,pm25,43.0,µg/m³ +Antwerpen,BE,2019-04-09 08:00:00+00:00,BETR801,pm25,44.0,µg/m³ +Antwerpen,BE,2019-04-09 07:00:00+00:00,BETR801,pm25,46.5,µg/m³ +Antwerpen,BE,2019-04-09 06:00:00+00:00,BETR801,pm25,52.5,µg/m³ +Antwerpen,BE,2019-04-09 05:00:00+00:00,BETR801,pm25,68.0,µg/m³ +Antwerpen,BE,2019-04-09 04:00:00+00:00,BETR801,pm25,83.5,µg/m³ +Antwerpen,BE,2019-04-09 03:00:00+00:00,BETR801,pm25,99.0,µg/m³ +Antwerpen,BE,2019-04-09 02:00:00+00:00,BETR801,pm25,91.5,µg/m³ +Antwerpen,BE,2019-04-09 01:00:00+00:00,BETR801,pm25,76.0,µg/m³ +London,GB,2019-06-21 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-20 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-20 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-20 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-20 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-20 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-20 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-20 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-20 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-20 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-19 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-06-19 12:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-06-19 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-06-19 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-06-18 23:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-06-18 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-06-18 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-06-18 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-18 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-17 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-17 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-16 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-16 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-15 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-15 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-14 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-14 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-14 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-06-13 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-06-13 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-06-13 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-06-13 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-13 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-12 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-12 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-11 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-11 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-10 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-10 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-06-09 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-09 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-08 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-08 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-08 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-08 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-08 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-08 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-06-08 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-08 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-06-08 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Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 20:00:00+00:00,London 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Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-12 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-12 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-12 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 09:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 08:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 07:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-12 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-12 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-12 03:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-12 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Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-05 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-05 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-05 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-05 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-05 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-05 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-05 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-05 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-05 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-04 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-04 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-04 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-04 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-04 19:00:00+00:00,London 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13:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-29 12:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-29 11:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-29 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-29 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-29 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-29 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-29 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-29 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-29 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-29 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-04-29 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-04-29 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-04-29 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-04-28 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10:00:00+00:00,London Westminster,pm25,35.0,µg/m³ +London,GB,2019-04-23 09:00:00+00:00,London Westminster,pm25,36.0,µg/m³ +London,GB,2019-04-23 08:00:00+00:00,London Westminster,pm25,37.0,µg/m³ +London,GB,2019-04-23 07:00:00+00:00,London Westminster,pm25,38.0,µg/m³ +London,GB,2019-04-23 06:00:00+00:00,London Westminster,pm25,40.0,µg/m³ +London,GB,2019-04-23 05:00:00+00:00,London Westminster,pm25,41.0,µg/m³ +London,GB,2019-04-23 04:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-23 03:00:00+00:00,London Westminster,pm25,44.0,µg/m³ +London,GB,2019-04-23 02:00:00+00:00,London Westminster,pm25,45.0,µg/m³ +London,GB,2019-04-23 01:00:00+00:00,London Westminster,pm25,45.0,µg/m³ +London,GB,2019-04-23 00:00:00+00:00,London Westminster,pm25,45.0,µg/m³ +London,GB,2019-04-22 23:00:00+00:00,London Westminster,pm25,44.0,µg/m³ +London,GB,2019-04-22 22:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-22 21:00:00+00:00,London Westminster,pm25,43.0,µg/m³ 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Westminster,pm25,42.0,µg/m³ +London,GB,2019-04-17 02:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-17 00:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 23:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 22:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 21:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 20:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 19:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 18:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 17:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-16 15:00:00+00:00,London Westminster,pm25,41.0,µg/m³ +London,GB,2019-04-16 14:00:00+00:00,London Westminster,pm25,41.0,µg/m³ +London,GB,2019-04-16 13:00:00+00:00,London Westminster,pm25,41.0,µg/m³ +London,GB,2019-04-16 12:00:00+00:00,London Westminster,pm25,40.0,µg/m³ +London,GB,2019-04-16 11:00:00+00:00,London Westminster,pm25,40.0,µg/m³ +London,GB,2019-04-16 10:00:00+00:00,London Westminster,pm25,38.0,µg/m³ +London,GB,2019-04-16 09:00:00+00:00,London Westminster,pm25,37.0,µg/m³ +London,GB,2019-04-16 08:00:00+00:00,London Westminster,pm25,36.0,µg/m³ +London,GB,2019-04-16 07:00:00+00:00,London Westminster,pm25,36.0,µg/m³ +London,GB,2019-04-16 06:00:00+00:00,London Westminster,pm25,35.0,µg/m³ +London,GB,2019-04-16 05:00:00+00:00,London Westminster,pm25,34.0,µg/m³ +London,GB,2019-04-16 04:00:00+00:00,London Westminster,pm25,32.0,µg/m³ +London,GB,2019-04-16 03:00:00+00:00,London Westminster,pm25,32.0,µg/m³ +London,GB,2019-04-16 02:00:00+00:00,London Westminster,pm25,31.0,µg/m³ +London,GB,2019-04-16 00:00:00+00:00,London Westminster,pm25,30.0,µg/m³ +London,GB,2019-04-15 23:00:00+00:00,London Westminster,pm25,30.0,µg/m³ +London,GB,2019-04-15 22:00:00+00:00,London Westminster,pm25,29.0,µg/m³ +London,GB,2019-04-15 21:00:00+00:00,London Westminster,pm25,29.0,µg/m³ 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Westminster,pm25,24.0,µg/m³ +London,GB,2019-04-15 06:00:00+00:00,London Westminster,pm25,23.0,µg/m³ +London,GB,2019-04-15 05:00:00+00:00,London Westminster,pm25,22.0,µg/m³ +London,GB,2019-04-15 04:00:00+00:00,London Westminster,pm25,22.0,µg/m³ +London,GB,2019-04-15 03:00:00+00:00,London Westminster,pm25,21.0,µg/m³ +London,GB,2019-04-15 02:00:00+00:00,London Westminster,pm25,20.0,µg/m³ +London,GB,2019-04-15 01:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-04-15 00:00:00+00:00,London Westminster,pm25,18.0,µg/m³ +London,GB,2019-04-14 23:00:00+00:00,London Westminster,pm25,17.0,µg/m³ +London,GB,2019-04-14 22:00:00+00:00,London Westminster,pm25,16.0,µg/m³ +London,GB,2019-04-14 21:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-04-14 20:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-14 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-14 18:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 17:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 16:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 15:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 14:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 13:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 12:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 11:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 09:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-14 08:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 07:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-14 01:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-14 00:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-13 23:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 16:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 14:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 13:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 12:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 11:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 10:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 09:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-13 08:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 07:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 06:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 05:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 04:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 03:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-13 00:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 23:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 16:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 15:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 14:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-12 12:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-12 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³ 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Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-11 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-11 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-11 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-11 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-04-11 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-04-11 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-11 02:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-11 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-04-10 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-10 22:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-04-10 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-10 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-10 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-10 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-10 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-04-10 16:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-04-10 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³ 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Westminster,pm25,29.0,µg/m³ +London,GB,2019-04-10 00:00:00+00:00,London Westminster,pm25,30.0,µg/m³ +London,GB,2019-04-09 23:00:00+00:00,London Westminster,pm25,32.0,µg/m³ +London,GB,2019-04-09 22:00:00+00:00,London Westminster,pm25,34.0,µg/m³ +London,GB,2019-04-09 21:00:00+00:00,London Westminster,pm25,35.0,µg/m³ +London,GB,2019-04-09 20:00:00+00:00,London Westminster,pm25,36.0,µg/m³ +London,GB,2019-04-09 19:00:00+00:00,London Westminster,pm25,37.0,µg/m³ +London,GB,2019-04-09 18:00:00+00:00,London Westminster,pm25,38.0,µg/m³ +London,GB,2019-04-09 17:00:00+00:00,London Westminster,pm25,39.0,µg/m³ +London,GB,2019-04-09 16:00:00+00:00,London Westminster,pm25,39.0,µg/m³ +London,GB,2019-04-09 15:00:00+00:00,London Westminster,pm25,40.0,µg/m³ +London,GB,2019-04-09 14:00:00+00:00,London Westminster,pm25,41.0,µg/m³ +London,GB,2019-04-09 13:00:00+00:00,London Westminster,pm25,41.0,µg/m³ +London,GB,2019-04-09 12:00:00+00:00,London Westminster,pm25,42.0,µg/m³ +London,GB,2019-04-09 11:00:00+00:00,London Westminster,pm25,42.0,µg/m³ +London,GB,2019-04-09 10:00:00+00:00,London Westminster,pm25,42.0,µg/m³ +London,GB,2019-04-09 09:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-09 08:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-09 07:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-09 06:00:00+00:00,London Westminster,pm25,44.0,µg/m³ +London,GB,2019-04-09 05:00:00+00:00,London Westminster,pm25,44.0,µg/m³ +London,GB,2019-04-09 04:00:00+00:00,London Westminster,pm25,43.0,µg/m³ +London,GB,2019-04-09 03:00:00+00:00,London Westminster,pm25,42.0,µg/m³ +London,GB,2019-04-09 02:00:00+00:00,London Westminster,pm25,42.0,µg/m³ +Paris,FR,2019-06-21 00:00:00+00:00,FR04014,no2,20.0,µg/m³ +Paris,FR,2019-06-20 23:00:00+00:00,FR04014,no2,21.8,µg/m³ +Paris,FR,2019-06-20 22:00:00+00:00,FR04014,no2,26.5,µg/m³ +Paris,FR,2019-06-20 21:00:00+00:00,FR04014,no2,24.9,µg/m³ +Paris,FR,2019-06-20 20:00:00+00:00,FR04014,no2,21.4,µg/m³ 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01:00:00+00:00,BETR801,no2,14.5,µg/m³ +Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,no2,14.5,µg/m³ +Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,no2,14.5,µg/m³ +Antwerpen,BE,2019-05-12 02:00:00+00:00,BETR801,no2,20.0,µg/m³ +Antwerpen,BE,2019-05-12 01:00:00+00:00,BETR801,no2,17.5,µg/m³ +Antwerpen,BE,2019-05-11 02:00:00+00:00,BETR801,no2,21.0,µg/m³ +Antwerpen,BE,2019-05-11 01:00:00+00:00,BETR801,no2,26.5,µg/m³ +Antwerpen,BE,2019-05-10 02:00:00+00:00,BETR801,no2,11.5,µg/m³ +Antwerpen,BE,2019-05-10 01:00:00+00:00,BETR801,no2,10.5,µg/m³ +Antwerpen,BE,2019-05-09 02:00:00+00:00,BETR801,no2,20.5,µg/m³ +Antwerpen,BE,2019-05-09 01:00:00+00:00,BETR801,no2,20.0,µg/m³ +Antwerpen,BE,2019-05-08 02:00:00+00:00,BETR801,no2,20.5,µg/m³ +Antwerpen,BE,2019-05-08 01:00:00+00:00,BETR801,no2,23.0,µg/m³ +Antwerpen,BE,2019-05-07 02:00:00+00:00,BETR801,no2,45.0,µg/m³ +Antwerpen,BE,2019-05-07 01:00:00+00:00,BETR801,no2,50.5,µg/m³ +Antwerpen,BE,2019-05-06 02:00:00+00:00,BETR801,no2,27.0,µg/m³ 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02:00:00+00:00,BETR801,no2,10.5,µg/m³ +Antwerpen,BE,2019-04-28 01:00:00+00:00,BETR801,no2,8.5,µg/m³ +Antwerpen,BE,2019-04-27 02:00:00+00:00,BETR801,no2,14.0,µg/m³ +Antwerpen,BE,2019-04-27 01:00:00+00:00,BETR801,no2,22.0,µg/m³ +Antwerpen,BE,2019-04-26 02:00:00+00:00,BETR801,no2,15.0,µg/m³ +Antwerpen,BE,2019-04-26 01:00:00+00:00,BETR801,no2,25.5,µg/m³ +Antwerpen,BE,2019-04-25 02:00:00+00:00,BETR801,no2,12.0,µg/m³ +Antwerpen,BE,2019-04-25 01:00:00+00:00,BETR801,no2,13.0,µg/m³ +Antwerpen,BE,2019-04-22 01:00:00+00:00,BETR801,no2,24.5,µg/m³ +Antwerpen,BE,2019-04-21 02:00:00+00:00,BETR801,no2,15.0,µg/m³ +Antwerpen,BE,2019-04-21 01:00:00+00:00,BETR801,no2,18.0,µg/m³ +Antwerpen,BE,2019-04-19 01:00:00+00:00,BETR801,no2,25.0,µg/m³ +Antwerpen,BE,2019-04-18 02:00:00+00:00,BETR801,no2,35.0,µg/m³ +Antwerpen,BE,2019-04-17 03:00:00+00:00,BETR801,no2,38.5,µg/m³ +Antwerpen,BE,2019-04-17 02:00:00+00:00,BETR801,no2,33.0,µg/m³ +Antwerpen,BE,2019-04-17 01:00:00+00:00,BETR801,no2,33.0,µg/m³ 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02:00:00+00:00,BETR801,no2,32.0,µg/m³ +Antwerpen,BE,2019-04-15 01:00:00+00:00,BETR801,no2,30.5,µg/m³ +Antwerpen,BE,2019-04-12 02:00:00+00:00,BETR801,no2,22.5,µg/m³ +Antwerpen,BE,2019-04-12 01:00:00+00:00,BETR801,no2,25.0,µg/m³ +Antwerpen,BE,2019-04-11 02:00:00+00:00,BETR801,no2,14.0,µg/m³ +Antwerpen,BE,2019-04-11 01:00:00+00:00,BETR801,no2,13.5,µg/m³ +Antwerpen,BE,2019-04-10 02:00:00+00:00,BETR801,no2,11.5,µg/m³ +Antwerpen,BE,2019-04-10 01:00:00+00:00,BETR801,no2,13.5,µg/m³ +Antwerpen,BE,2019-04-09 13:00:00+00:00,BETR801,no2,27.5,µg/m³ +Antwerpen,BE,2019-04-09 12:00:00+00:00,BETR801,no2,30.0,µg/m³ +Antwerpen,BE,2019-04-09 11:00:00+00:00,BETR801,no2,28.5,µg/m³ +Antwerpen,BE,2019-04-09 10:00:00+00:00,BETR801,no2,33.5,µg/m³ +Antwerpen,BE,2019-04-09 09:00:00+00:00,BETR801,no2,35.0,µg/m³ +Antwerpen,BE,2019-04-09 08:00:00+00:00,BETR801,no2,39.0,µg/m³ +Antwerpen,BE,2019-04-09 07:00:00+00:00,BETR801,no2,38.5,µg/m³ +Antwerpen,BE,2019-04-09 06:00:00+00:00,BETR801,no2,50.0,µg/m³ 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Westminster,no2,14.0,µg/m³ +London,GB,2019-06-17 01:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-17 00:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-16 23:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-16 20:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-16 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-16 18:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-16 17:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-16 14:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-16 13:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-16 12:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-16 11:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-16 10:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-16 09:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-16 08:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-16 07:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-16 06:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-16 05:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-16 04:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-16 03:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-16 02:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-16 01:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-16 00:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-15 23:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-15 22:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-15 21:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-15 20:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-15 19:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-15 18:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-15 17:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-15 16:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-15 15:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-15 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-15 13:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-15 12:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-15 11:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-15 10:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-15 09:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-15 08:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-15 07:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-15 06:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-15 05:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-15 04:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-15 00:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-14 20:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 19:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 18:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-14 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 16:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-14 15:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-14 14:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-14 13:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-14 12:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-14 11:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-14 10:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-14 09:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-14 08:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-14 07:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-14 06:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-14 05:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-14 04:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-14 03:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-14 02:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-14 00:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-13 23:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 22:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 21:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 20:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-13 19:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-13 18:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-13 17:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 16:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-13 15:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-13 14:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 13:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 12:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 11:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 10:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-13 09:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 08:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 07:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-13 06:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 05:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 04:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-13 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-13 00:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-12 23:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-12 21:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-12 20:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-06-12 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-12 18:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-12 17:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-12 16:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-12 15:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-06-12 14:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-12 13:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-12 12:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-12 11:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-12 10:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-12 09:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-12 08:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-12 07:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-12 06:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-12 05:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-12 04:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-12 03:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-12 00:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-11 23:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-11 22:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-11 21:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 20:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 19:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-06-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-11 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-06-11 16:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-11 15:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-11 14:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-11 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-11 12:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-11 11:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 10:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 09:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-11 08:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-11 07:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-11 06:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-11 05:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-11 04:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-11 03:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-11 02:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-11 01:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-11 00:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-10 23:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-10 22:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-10 21:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 20:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-10 19:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-10 17:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-10 16:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-10 15:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-06-10 14:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-06-10 13:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-06-10 12:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-10 11:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-06-10 10:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-10 08:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-10 07:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-10 06:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 05:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 04:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 03:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-10 02:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-10 01:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-10 00:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-09 23:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-09 21:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-09 20:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-09 19:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-09 18:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-09 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-09 16:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-09 15:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-09 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-09 13:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-09 12:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-09 11:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-09 10:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-09 09:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-09 08:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-09 07:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-09 06:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-09 05:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-09 04:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-09 03:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-09 02:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-09 01:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-09 00:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-08 23:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-08 21:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-08 20:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-08 19:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-08 18:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-08 17:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-08 16:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-08 15:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-08 14:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-08 13:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-08 12:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-08 11:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-08 10:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 09:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-08 08:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-08 07:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 06:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-08 05:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 04:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 03:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-08 02:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-08 00:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-07 23:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-07 21:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-07 20:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-07 19:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-07 18:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-07 16:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-07 15:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-07 14:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-07 12:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 11:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-07 10:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 09:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-07 08:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-07 07:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 06:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-07 05:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 04:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 03:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 02:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-06 23:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-06 22:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-06 21:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 20:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-06 19:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 18:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 16:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-06 15:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-06 14:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-06 13:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-06 12:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-06 11:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-06 10:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-06 09:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-06 08:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-06 07:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-06 06:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-06 05:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-06 04:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-06 03:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-06 02:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-06 00:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-05 23:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-05 22:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-05 21:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 20:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 19:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 18:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 17:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 16:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 15:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-05 14:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-05 13:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-05 12:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-05 11:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-05 10:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-05 09:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-05 08:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-05 07:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-05 06:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-05 05:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-05 04:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-05 03:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-05 02:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-05 01:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-05 00:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-04 23:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-04 22:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-04 21:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-04 20:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-04 19:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-04 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-04 17:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-06-04 16:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-06-04 15:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-06-04 14:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-04 13:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-06-04 12:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-04 11:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-04 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-04 09:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-04 08:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-04 07:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-04 06:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-04 05:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-04 04:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-04 03:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-04 02:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-04 01:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-04 00:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-03 23:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-03 22:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-03 21:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-03 20:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-03 19:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-03 18:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-03 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-03 16:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-03 15:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-03 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-03 13:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-03 12:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-03 11:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-03 10:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-03 09:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-03 08:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-03 07:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-03 06:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-03 05:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-03 04:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-03 03:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-03 02:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-03 01:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-03 00:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-02 23:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-02 22:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-02 21:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-02 20:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 19:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-02 18:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-02 17:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 15:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-02 14:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-02 13:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-02 12:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-02 11:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-02 10:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-02 09:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 08:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-06-02 07:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 06:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 05:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-06-02 04:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-06-02 03:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-02 02:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-02 01:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-06-02 00:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-06-01 23:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-06-01 22:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-06-01 21:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-06-01 20:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-06-01 19:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-06-01 18:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-01 17:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-01 16:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-01 15:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-01 14:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-01 13:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-01 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-01 11:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-01 10:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-01 09:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-01 08:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-01 07:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-01 06:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-01 05:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-01 04:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-01 03:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-01 02:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-01 01:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-01 00:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-31 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-31 20:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-31 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-31 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-31 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 16:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 15:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-31 14:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-31 13:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-31 12:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-31 11:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-31 10:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-31 09:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-31 08:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-05-31 07:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-31 06:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-05-31 05:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-31 04:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-31 03:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-05-31 02:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-05-31 01:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-31 00:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-30 23:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-30 22:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-30 21:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-30 20:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-30 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-30 18:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-30 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-30 16:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-30 15:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-30 14:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-30 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-30 12:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-30 11:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-05-30 10:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-30 09:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-30 08:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-05-30 07:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-05-30 06:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 05:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 04:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 03:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 02:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 01:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-05-30 00:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-05-29 23:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 22:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 21:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-05-29 20:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-05-29 19:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 18:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 17:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 16:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-05-29 15:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 14:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-29 13:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-05-29 12:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-29 11:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-29 10:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-29 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-29 08:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-29 07:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-29 06:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-29 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-29 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-29 03:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-29 02:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-29 01:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-29 00:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-28 23:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-28 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-28 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 19:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 17:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-28 16:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-28 15:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-28 14:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-28 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-28 11:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-28 10:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-28 09:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-28 08:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-28 07:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-28 06:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-28 05:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-28 04:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-28 03:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-28 02:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-28 01:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-28 00:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-27 23:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 22:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 21:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-27 20:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-27 19:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 18:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 17:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 16:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 15:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 14:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-27 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-27 12:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-27 11:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-27 10:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-27 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 08:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 07:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 06:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 03:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-27 02:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-27 01:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-27 00:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 21:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-26 20:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-26 19:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 18:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-26 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 16:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-26 15:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 14:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-26 13:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-26 12:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-26 11:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-26 10:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-26 09:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-26 08:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-26 07:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-26 06:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-26 05:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-26 04:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-26 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 01:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-26 00:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-25 23:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-25 22:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-25 21:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-25 20:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-25 19:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-25 18:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-25 17:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-25 16:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-25 15:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-25 14:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-25 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-25 12:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-25 11:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-25 10:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-25 09:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-25 08:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-25 07:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-25 06:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-25 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-25 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-25 03:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-25 02:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-25 01:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-25 00:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-24 23:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-24 22:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-24 21:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-24 20:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-24 19:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-24 18:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-24 17:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-24 16:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-05-24 15:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-24 14:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-24 13:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-24 12:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-24 11:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-24 10:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-24 09:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-24 08:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-24 07:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-24 06:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-24 05:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-24 04:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-24 03:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-24 02:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-24 00:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-23 23:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-23 22:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-23 21:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-23 20:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-05-23 19:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-05-23 18:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-05-23 17:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-05-23 16:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-05-23 15:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-23 14:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-23 13:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-23 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-23 11:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-23 10:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-23 09:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-23 08:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-23 07:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-23 06:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-23 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-23 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-23 03:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-23 02:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-23 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-23 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-22 23:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-22 22:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-22 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 20:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-22 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-22 18:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-22 17:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-22 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-22 15:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-22 14:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-22 13:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-22 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-22 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-22 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 09:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 08:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 07:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-22 06:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-22 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-22 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-22 03:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-22 02:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-22 01:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-22 00:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-21 23:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 22:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 21:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 20:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-21 19:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-21 18:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-21 17:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-21 16:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-21 15:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-21 14:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 13:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-21 12:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 11:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 10:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-21 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-21 08:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-21 07:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-21 06:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-21 05:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-21 04:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-21 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 01:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-21 00:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-20 23:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-20 22:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-20 21:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-20 20:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 19:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 18:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-20 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-20 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 15:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 14:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 13:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 10:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 08:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 07:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-20 06:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-20 05:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-20 04:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-20 03:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 02:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 01:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 00:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-19 23:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 22:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 21:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 20:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 19:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-19 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-19 17:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-19 16:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-19 15:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-19 14:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-19 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-19 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-19 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-19 10:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-19 09:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-19 08:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-19 07:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-19 06:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-19 05:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 04:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 03:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 02:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 01:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 00:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-18 23:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-18 22:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-18 21:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-18 20:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-18 19:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-18 18:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-18 17:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-18 16:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-18 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-18 14:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-18 13:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 12:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-18 11:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-18 10:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-18 09:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-18 08:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-18 07:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 06:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-18 05:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 04:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 03:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-18 02:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-18 01:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-18 00:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-17 23:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-17 22:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-17 21:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-17 20:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-17 19:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-17 18:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-17 17:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-17 16:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 15:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-17 14:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-17 13:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-17 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 11:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 10:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-17 08:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-17 07:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-17 06:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-17 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 03:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 02:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-17 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-16 23:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 22:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 20:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 19:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-16 18:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 14:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-16 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-16 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-16 11:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-16 09:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-16 08:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 07:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-16 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 05:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 04:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 03:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-16 02:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-16 01:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 00:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 23:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 22:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 21:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-15 20:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-15 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 18:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-15 17:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 16:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-15 15:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-15 14:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-15 13:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-15 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-15 11:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-15 10:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-15 09:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-15 08:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-15 07:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 05:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-15 04:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-15 03:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-15 02:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-15 00:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 20:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-14 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-14 18:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 17:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-14 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-14 15:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-14 14:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 13:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 11:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-14 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-14 09:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 08:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-14 07:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-14 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-14 05:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 04:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-14 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-13 23:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 22:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 20:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 19:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 18:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-13 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-13 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-13 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-13 14:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-13 13:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-13 12:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-13 11:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-13 10:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-13 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-13 08:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 07:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-13 06:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-13 05:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-13 04:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-13 03:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-13 02:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-13 01:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-13 00:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 23:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 22:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 21:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-12 20:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 19:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 18:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-12 16:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-12 15:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 14:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-12 13:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-12 12:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-12 11:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-12 10:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-12 09:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-12 08:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-12 07:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-12 06:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-12 05:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-12 04:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-12 03:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-12 02:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-12 01:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-12 00:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-11 23:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-11 22:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-11 21:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-11 20:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-11 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-11 17:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-11 16:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-11 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-11 09:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-11 08:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-11 07:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-11 06:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-11 05:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-11 04:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-11 03:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-11 02:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-11 01:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-11 00:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-10 23:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-10 22:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-10 21:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 19:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-10 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-10 16:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-10 15:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-10 14:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-10 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-10 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-10 11:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-10 08:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-10 07:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-10 06:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-10 05:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-10 04:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-10 03:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-10 02:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-10 01:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-05-10 00:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-05-09 23:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-05-09 22:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-05-09 21:00:00+00:00,London Westminster,no2,65.0,µg/m³ +London,GB,2019-05-09 20:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-05-09 19:00:00+00:00,London Westminster,no2,62.0,µg/m³ +London,GB,2019-05-09 18:00:00+00:00,London Westminster,no2,58.0,µg/m³ +London,GB,2019-05-09 17:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-05-09 16:00:00+00:00,London Westminster,no2,67.0,µg/m³ +London,GB,2019-05-09 15:00:00+00:00,London Westminster,no2,97.0,µg/m³ +London,GB,2019-05-09 14:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-09 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-09 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-09 11:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-09 10:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-09 09:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-09 08:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-09 07:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 05:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 04:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 03:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-09 02:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-09 00:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-08 23:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-08 21:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-08 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-08 19:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-08 18:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-08 17:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-08 16:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-08 15:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-08 14:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-08 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-08 12:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-08 11:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-08 10:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-08 09:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-08 08:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-08 07:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-08 06:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-08 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-08 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-08 03:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-08 02:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-08 01:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-08 00:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-07 23:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-07 21:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-07 20:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-07 19:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 18:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 17:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-07 16:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 15:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 14:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 12:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-07 11:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-07 10:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-07 09:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-07 08:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-07 07:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-07 06:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-07 04:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-07 03:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-07 02:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-06 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-06 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-06 21:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-06 20:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-06 19:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-06 18:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-06 17:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-06 16:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-06 15:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-06 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-06 13:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-06 12:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-06 11:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-06 10:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-06 09:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-06 08:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-06 07:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-06 06:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-06 05:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-06 04:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-06 03:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-06 02:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-06 01:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-06 00:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-05 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-05 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-05 21:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-05 20:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-05 19:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-05 18:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-05 17:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-05 16:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-05 15:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-05 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-05 13:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-05 12:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-05 11:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-05 10:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-05 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-05 08:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-05 07:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-05 06:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-05 05:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-05 04:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-05 03:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-05 02:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-05 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-05 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-04 23:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-04 22:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-04 21:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-04 20:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-04 19:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-04 18:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-04 17:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-04 16:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-04 15:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-04 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-04 13:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-04 12:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-04 11:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-04 10:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-04 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-04 08:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-04 07:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-04 06:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-04 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-04 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-04 03:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-04 02:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-04 01:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-04 00:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-03 23:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-03 22:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-03 21:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-03 20:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-03 19:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-03 18:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-03 17:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-03 16:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-03 15:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-03 14:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-03 13:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-03 12:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-03 11:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-05-03 10:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-03 09:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-03 08:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-03 07:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-03 06:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-03 05:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-03 04:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-03 03:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-03 02:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-03 01:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-03 00:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-02 23:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-02 22:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-02 21:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-05-02 20:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-05-02 19:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-02 18:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-02 17:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-02 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-02 15:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-02 14:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-02 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-02 12:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-02 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-02 10:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-02 09:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-02 08:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-02 07:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-02 06:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-02 05:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-02 04:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-02 03:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-02 02:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-02 01:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-02 00:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-01 23:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-01 22:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-01 21:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-01 20:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-01 19:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-01 18:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-01 17:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-01 16:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-01 15:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-01 14:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-01 13:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-01 12:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-01 11:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-01 10:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-01 09:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-01 08:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-01 07:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-01 06:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-01 05:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-01 04:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-01 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-01 00:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-04-30 23:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-30 22:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-30 21:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-30 20:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-04-30 19:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-30 18:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-30 17:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-30 16:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-30 15:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-30 14:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-30 13:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-30 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-30 11:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-30 10:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-30 09:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-30 08:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-30 07:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-30 06:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-30 05:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-30 04:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-30 03:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-30 02:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-30 01:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-30 00:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-29 23:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-29 22:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-29 21:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-29 20:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-29 19:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-29 18:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-29 17:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-29 16:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-29 15:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-29 14:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-29 13:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-29 12:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-29 11:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-04-29 10:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-29 09:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-29 08:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-29 07:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-29 06:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-29 05:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-29 04:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-29 03:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-29 02:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-29 01:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-29 00:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-28 23:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-28 22:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-28 21:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-28 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-28 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-28 18:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-28 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-28 16:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-28 15:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-28 14:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-28 13:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-28 12:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-28 11:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-28 10:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-28 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-04-27 13:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-04-27 12:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-27 11:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-04-27 10:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-04-27 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-04-27 08:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-04-27 07:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-04-27 06:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-04-27 05:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-04-27 04:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-04-27 03:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-04-27 02:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-04-27 00:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-04-26 23:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-04-26 22:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-04-26 21:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-04-26 20:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-26 19:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-26 18:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-04-26 17:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-04-26 16:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-04-26 15:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-26 14:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-26 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-04-26 12:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-26 11:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-04-26 10:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-26 09:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-26 08:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-26 07:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-26 06:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-26 05:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-26 04:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-26 03:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-26 02:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-26 01:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-26 00:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-25 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-25 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-25 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-04-25 20:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-25 19:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-04-25 18:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-25 17:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-25 16:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-25 15:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-25 14:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-25 13:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-04-25 12:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-25 11:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-25 10:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-25 09:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-25 08:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-25 07:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-25 06:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-25 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-25 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-25 03:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-25 02:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-25 00:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-24 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-24 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-24 21:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-24 20:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-24 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-24 18:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-24 17:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-24 16:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-24 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-24 14:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-24 13:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-24 12:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-24 11:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-24 10:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-24 09:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-04-24 08:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-04-24 07:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-24 06:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-24 05:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-24 04:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-24 03:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-04-24 02:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-04-24 00:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-23 23:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-23 22:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-23 21:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-23 20:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-23 19:00:00+00:00,London Westminster,no2,48.0,µg/m³ +London,GB,2019-04-23 18:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-23 17:00:00+00:00,London Westminster,no2,62.0,µg/m³ +London,GB,2019-04-23 16:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-23 15:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-23 14:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-23 13:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-04-23 12:00:00+00:00,London Westminster,no2,67.0,µg/m³ +London,GB,2019-04-23 11:00:00+00:00,London Westminster,no2,67.0,µg/m³ +London,GB,2019-04-23 10:00:00+00:00,London Westminster,no2,63.0,µg/m³ +London,GB,2019-04-23 09:00:00+00:00,London Westminster,no2,61.0,µg/m³ +London,GB,2019-04-23 08:00:00+00:00,London Westminster,no2,63.0,µg/m³ +London,GB,2019-04-23 07:00:00+00:00,London Westminster,no2,62.0,µg/m³ +London,GB,2019-04-23 06:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-23 05:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-23 04:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-23 03:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-23 02:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-23 01:00:00+00:00,London Westminster,no2,75.0,µg/m³ +London,GB,2019-04-23 00:00:00+00:00,London Westminster,no2,75.0,µg/m³ +London,GB,2019-04-22 23:00:00+00:00,London Westminster,no2,84.0,µg/m³ +London,GB,2019-04-22 22:00:00+00:00,London Westminster,no2,84.0,µg/m³ +London,GB,2019-04-22 21:00:00+00:00,London Westminster,no2,73.0,µg/m³ +London,GB,2019-04-22 20:00:00+00:00,London Westminster,no2,66.0,µg/m³ +London,GB,2019-04-22 19:00:00+00:00,London Westminster,no2,66.0,µg/m³ +London,GB,2019-04-22 18:00:00+00:00,London Westminster,no2,64.0,µg/m³ +London,GB,2019-04-22 17:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-22 16:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-22 15:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-22 14:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-22 13:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-22 12:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-22 11:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-22 10:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-22 09:00:00+00:00,London Westminster,no2,48.0,µg/m³ +London,GB,2019-04-22 08:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-22 07:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-22 06:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-04-22 05:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-22 04:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-22 03:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-22 02:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-22 01:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-22 00:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-21 23:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-21 22:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-21 21:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-21 20:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-21 19:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-21 18:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-21 17:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-21 16:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-21 15:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-21 14:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-21 13:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-21 12:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-21 11:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-21 10:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-21 09:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-04-21 08:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-21 07:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-21 06:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-21 05:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-21 04:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-21 03:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-21 02:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-21 01:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-04-21 00:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-04-20 23:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-20 22:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-20 21:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-20 20:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-20 19:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-20 18:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-20 17:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-20 16:00:00+00:00,London Westminster,no2,48.0,µg/m³ +London,GB,2019-04-20 15:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-20 14:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-20 13:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-20 12:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-20 11:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-20 10:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-20 09:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-20 08:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-20 07:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-20 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-20 05:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-20 04:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-20 03:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-04-20 02:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-04-20 01:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-04-20 00:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-04-19 23:00:00+00:00,London Westminster,no2,77.0,µg/m³ +London,GB,2019-04-19 22:00:00+00:00,London Westminster,no2,77.0,µg/m³ +London,GB,2019-04-19 21:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-19 20:00:00+00:00,London Westminster,no2,58.0,µg/m³ +London,GB,2019-04-19 19:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-19 18:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-19 17:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-04-19 16:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-19 15:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-19 14:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-19 13:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-19 12:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-19 11:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-19 10:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-04-19 09:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-19 08:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-19 07:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-19 06:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-04-19 05:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-04-19 04:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-04-19 03:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-19 02:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-19 00:00:00+00:00,London Westminster,no2,58.0,µg/m³ +London,GB,2019-04-18 23:00:00+00:00,London Westminster,no2,61.0,µg/m³ +London,GB,2019-04-18 22:00:00+00:00,London Westminster,no2,61.0,µg/m³ +London,GB,2019-04-18 21:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-04-18 20:00:00+00:00,London Westminster,no2,69.0,µg/m³ +London,GB,2019-04-18 19:00:00+00:00,London Westminster,no2,63.0,µg/m³ +London,GB,2019-04-18 18:00:00+00:00,London Westminster,no2,63.0,µg/m³ +London,GB,2019-04-18 17:00:00+00:00,London Westminster,no2,56.0,µg/m³ +London,GB,2019-04-18 16:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-18 15:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-18 14:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-18 13:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-18 12:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-18 11:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-18 10:00:00+00:00,London Westminster,no2,56.0,µg/m³ +London,GB,2019-04-18 09:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-18 08:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-18 07:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-18 06:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-18 05:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-18 04:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-18 03:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-18 02:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-18 01:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-18 00:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-17 23:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-17 22:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-17 21:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-04-17 20:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-04-17 19:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-17 18:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-17 17:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-04-17 16:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-17 15:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-17 14:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-04-17 13:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-17 12:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-04-17 11:00:00+00:00,London Westminster,no2,67.0,µg/m³ +London,GB,2019-04-17 10:00:00+00:00,London Westminster,no2,56.0,µg/m³ +London,GB,2019-04-17 09:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-17 08:00:00+00:00,London Westminster,no2,48.0,µg/m³ +London,GB,2019-04-17 07:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-17 06:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-17 05:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-17 04:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-17 03:00:00+00:00,London Westminster,no2,72.0,µg/m³ +London,GB,2019-04-17 02:00:00+00:00,London Westminster,no2,72.0,µg/m³ +London,GB,2019-04-17 00:00:00+00:00,London Westminster,no2,71.0,µg/m³ +London,GB,2019-04-16 23:00:00+00:00,London Westminster,no2,81.0,µg/m³ +London,GB,2019-04-16 22:00:00+00:00,London Westminster,no2,81.0,µg/m³ +London,GB,2019-04-16 21:00:00+00:00,London Westminster,no2,84.0,µg/m³ +London,GB,2019-04-16 20:00:00+00:00,London Westminster,no2,83.0,µg/m³ +London,GB,2019-04-16 19:00:00+00:00,London Westminster,no2,76.0,µg/m³ +London,GB,2019-04-16 18:00:00+00:00,London Westminster,no2,70.0,µg/m³ +London,GB,2019-04-16 17:00:00+00:00,London Westminster,no2,65.0,µg/m³ +London,GB,2019-04-16 15:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-16 14:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-16 13:00:00+00:00,London Westminster,no2,63.0,µg/m³ +London,GB,2019-04-16 12:00:00+00:00,London Westminster,no2,75.0,µg/m³ +London,GB,2019-04-16 11:00:00+00:00,London Westminster,no2,79.0,µg/m³ +London,GB,2019-04-16 10:00:00+00:00,London Westminster,no2,70.0,µg/m³ +London,GB,2019-04-16 09:00:00+00:00,London Westminster,no2,66.0,µg/m³ +London,GB,2019-04-16 08:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-04-16 07:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-16 06:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-04-16 05:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-16 04:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-16 03:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-16 02:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-16 00:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-15 23:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-04-15 22:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-04-15 21:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-15 20:00:00+00:00,London Westminster,no2,48.0,µg/m³ +London,GB,2019-04-15 19:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-15 18:00:00+00:00,London Westminster,no2,48.0,µg/m³ +London,GB,2019-04-15 17:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-04-15 16:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-15 15:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-15 14:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-15 13:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-15 12:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-15 11:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-15 10:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-15 09:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-15 08:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-15 07:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-15 06:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-15 05:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-15 04:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-15 03:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-15 02:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-15 01:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-15 00:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-14 23:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-14 22:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-14 21:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-14 20:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-14 19:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-14 18:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-14 17:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-14 16:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-04-14 15:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-04-14 14:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-04-14 13:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-14 12:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-14 11:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-14 10:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-04-14 09:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-14 08:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-14 07:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-14 06:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-04-14 05:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-04-14 04:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-04-14 03:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-14 02:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-14 01:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-14 00:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-13 23:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-13 22:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-13 21:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-13 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-13 19:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-13 18:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-04-13 17:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-13 16:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-13 15:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-13 14:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-13 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-13 12:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-13 11:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-13 10:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-13 09:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-13 08:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-13 07:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-04-13 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-13 05:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-13 04:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-13 03:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-13 02:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-13 01:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-13 00:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-12 23:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-12 22:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-12 21:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-12 20:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-04-12 19:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-12 18:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-12 17:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-12 16:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-04-12 15:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-04-12 14:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-12 13:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-12 12:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-12 11:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-12 10:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-12 09:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-12 08:00:00+00:00,London Westminster,no2,57.0,µg/m³ +London,GB,2019-04-12 07:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-12 06:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-12 05:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-12 04:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-12 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-12 00:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-11 23:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-11 22:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-11 21:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-11 20:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-11 19:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-04-11 18:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-04-11 17:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-11 16:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-11 15:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-11 14:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-11 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-04-11 12:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-11 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-11 10:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-11 09:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-11 08:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-11 07:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-04-11 06:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-04-11 05:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-11 04:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-11 03:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-11 02:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-04-11 00:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-10 23:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-10 22:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-10 21:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-10 20:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-10 19:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-04-10 18:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-10 17:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-10 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-04-10 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-10 14:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-04-10 13:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-04-10 12:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-10 11:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-04-10 10:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-04-10 09:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-04-10 08:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-10 07:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-10 06:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-10 05:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-10 04:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-04-10 03:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-10 02:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-04-10 01:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-04-10 00:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-04-09 23:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-09 22:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-04-09 21:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-09 20:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-04-09 19:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-09 18:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-04-09 17:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-04-09 16:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-04-09 15:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-04-09 14:00:00+00:00,London Westminster,no2,58.0,µg/m³ +London,GB,2019-04-09 13:00:00+00:00,London Westminster,no2,56.0,µg/m³ +London,GB,2019-04-09 12:00:00+00:00,London Westminster,no2,55.0,µg/m³ +London,GB,2019-04-09 11:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-09 10:00:00+00:00,London Westminster,no2,50.0,µg/m³ +London,GB,2019-04-09 09:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-04-09 08:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-04-09 07:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-04-09 06:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-09 05:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-04-09 04:00:00+00:00,London 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00:00:00+00:00,FR04014,no2,32.8,µg/m³ +Paris,FR,2019-05-23 23:00:00+00:00,FR04014,no2,23.2,µg/m³ +Paris,FR,2019-05-23 22:00:00+00:00,FR04014,no2,61.9,µg/m³ +Paris,FR,2019-05-23 21:00:00+00:00,FR04014,no2,47.0,µg/m³ +Paris,FR,2019-05-23 20:00:00+00:00,FR04014,no2,33.8,µg/m³ +Paris,FR,2019-05-23 19:00:00+00:00,FR04014,no2,28.0,µg/m³ +Paris,FR,2019-05-23 18:00:00+00:00,FR04014,no2,23.5,µg/m³ +Paris,FR,2019-05-23 17:00:00+00:00,FR04014,no2,22.7,µg/m³ +Paris,FR,2019-05-23 16:00:00+00:00,FR04014,no2,17.8,µg/m³ +Paris,FR,2019-05-23 15:00:00+00:00,FR04014,no2,17.5,µg/m³ +Paris,FR,2019-05-23 14:00:00+00:00,FR04014,no2,17.2,µg/m³ +Paris,FR,2019-05-23 13:00:00+00:00,FR04014,no2,21.2,µg/m³ +Paris,FR,2019-05-23 12:00:00+00:00,FR04014,no2,16.4,µg/m³ +Paris,FR,2019-05-23 11:00:00+00:00,FR04014,no2,17.0,µg/m³ +Paris,FR,2019-05-23 10:00:00+00:00,FR04014,no2,28.3,µg/m³ +Paris,FR,2019-05-23 09:00:00+00:00,FR04014,no2,79.4,µg/m³ +Paris,FR,2019-05-23 08:00:00+00:00,FR04014,no2,97.0,µg/m³ 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06:00:00+00:00,FR04014,no2,77.7,µg/m³ +Paris,FR,2019-05-07 05:00:00+00:00,FR04014,no2,72.4,µg/m³ +Paris,FR,2019-05-07 04:00:00+00:00,FR04014,no2,61.9,µg/m³ +Paris,FR,2019-05-07 03:00:00+00:00,FR04014,no2,50.4,µg/m³ +Paris,FR,2019-05-07 02:00:00+00:00,FR04014,no2,27.7,µg/m³ +Paris,FR,2019-05-07 01:00:00+00:00,FR04014,no2,25.0,µg/m³ +Antwerpen,BE,2019-06-17 08:00:00+00:00,BETR801,no2,41.0,µg/m³ +Antwerpen,BE,2019-06-17 07:00:00+00:00,BETR801,no2,45.0,µg/m³ +Antwerpen,BE,2019-06-17 06:00:00+00:00,BETR801,no2,43.5,µg/m³ +Antwerpen,BE,2019-06-17 05:00:00+00:00,BETR801,no2,42.5,µg/m³ +Antwerpen,BE,2019-06-17 04:00:00+00:00,BETR801,no2,39.5,µg/m³ +Antwerpen,BE,2019-06-17 03:00:00+00:00,BETR801,no2,36.0,µg/m³ +Antwerpen,BE,2019-06-17 02:00:00+00:00,BETR801,no2,35.5,µg/m³ +Antwerpen,BE,2019-06-17 01:00:00+00:00,BETR801,no2,42.0,µg/m³ +Antwerpen,BE,2019-06-16 01:00:00+00:00,BETR801,no2,42.5,µg/m³ +Antwerpen,BE,2019-06-15 01:00:00+00:00,BETR801,no2,17.5,µg/m³ +Antwerpen,BE,2019-06-14 09:00:00+00:00,BETR801,no2,36.5,µg/m³ +Antwerpen,BE,2019-06-13 01:00:00+00:00,BETR801,no2,28.5,µg/m³ +Antwerpen,BE,2019-06-12 01:00:00+00:00,BETR801,no2,21.0,µg/m³ +Antwerpen,BE,2019-06-11 01:00:00+00:00,BETR801,no2,7.5,µg/m³ +Antwerpen,BE,2019-06-10 01:00:00+00:00,BETR801,no2,18.5,µg/m³ +Antwerpen,BE,2019-06-09 01:00:00+00:00,BETR801,no2,10.0,µg/m³ +Antwerpen,BE,2019-06-05 01:00:00+00:00,BETR801,no2,15.0,µg/m³ +Antwerpen,BE,2019-06-01 01:00:00+00:00,BETR801,no2,52.5,µg/m³ +Antwerpen,BE,2019-05-31 01:00:00+00:00,BETR801,no2,9.0,µg/m³ +Antwerpen,BE,2019-05-30 01:00:00+00:00,BETR801,no2,7.5,µg/m³ +Antwerpen,BE,2019-05-29 01:00:00+00:00,BETR801,no2,21.0,µg/m³ +Antwerpen,BE,2019-05-28 01:00:00+00:00,BETR801,no2,11.0,µg/m³ +Antwerpen,BE,2019-05-27 01:00:00+00:00,BETR801,no2,10.5,µg/m³ +Antwerpen,BE,2019-05-26 01:00:00+00:00,BETR801,no2,53.0,µg/m³ +Antwerpen,BE,2019-05-25 01:00:00+00:00,BETR801,no2,29.0,µg/m³ +Antwerpen,BE,2019-05-24 01:00:00+00:00,BETR801,no2,74.5,µg/m³ 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03:00:00+00:00,BETR801,no2,9.0,µg/m³ +Antwerpen,BE,2019-05-20 02:00:00+00:00,BETR801,no2,10.5,µg/m³ +Antwerpen,BE,2019-05-20 01:00:00+00:00,BETR801,no2,17.0,µg/m³ +Antwerpen,BE,2019-05-20 00:00:00+00:00,BETR801,no2,26.0,µg/m³ +Antwerpen,BE,2019-05-19 23:00:00+00:00,BETR801,no2,16.5,µg/m³ +Antwerpen,BE,2019-05-19 22:00:00+00:00,BETR801,no2,18.5,µg/m³ +Antwerpen,BE,2019-05-19 21:00:00+00:00,BETR801,no2,12.5,µg/m³ +Antwerpen,BE,2019-05-19 20:00:00+00:00,BETR801,no2,15.0,µg/m³ +Antwerpen,BE,2019-05-19 19:00:00+00:00,BETR801,no2,26.0,µg/m³ +Antwerpen,BE,2019-05-19 18:00:00+00:00,BETR801,no2,15.5,µg/m³ +Antwerpen,BE,2019-05-19 17:00:00+00:00,BETR801,no2,18.5,µg/m³ +Antwerpen,BE,2019-05-19 16:00:00+00:00,BETR801,no2,17.5,µg/m³ +Antwerpen,BE,2019-05-19 15:00:00+00:00,BETR801,no2,33.0,µg/m³ +Antwerpen,BE,2019-05-19 14:00:00+00:00,BETR801,no2,23.0,µg/m³ +Antwerpen,BE,2019-05-19 13:00:00+00:00,BETR801,no2,14.5,µg/m³ +Antwerpen,BE,2019-05-19 12:00:00+00:00,BETR801,no2,16.0,µg/m³ 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20:00:00+00:00,BETR801,no2,40.0,µg/m³ +Antwerpen,BE,2019-05-18 19:00:00+00:00,BETR801,no2,35.5,µg/m³ +Antwerpen,BE,2019-05-18 18:00:00+00:00,BETR801,no2,35.5,µg/m³ +Antwerpen,BE,2019-05-18 01:00:00+00:00,BETR801,no2,41.5,µg/m³ +Antwerpen,BE,2019-05-16 01:00:00+00:00,BETR801,no2,28.0,µg/m³ +Antwerpen,BE,2019-05-15 02:00:00+00:00,BETR801,no2,22.5,µg/m³ +Antwerpen,BE,2019-05-15 01:00:00+00:00,BETR801,no2,25.5,µg/m³ +Antwerpen,BE,2019-05-14 02:00:00+00:00,BETR801,no2,11.5,µg/m³ +Antwerpen,BE,2019-05-14 01:00:00+00:00,BETR801,no2,14.5,µg/m³ +Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,no2,14.5,µg/m³ +Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,no2,14.5,µg/m³ +Antwerpen,BE,2019-05-12 02:00:00+00:00,BETR801,no2,20.0,µg/m³ +Antwerpen,BE,2019-05-12 01:00:00+00:00,BETR801,no2,17.5,µg/m³ +Antwerpen,BE,2019-05-11 02:00:00+00:00,BETR801,no2,21.0,µg/m³ +Antwerpen,BE,2019-05-11 01:00:00+00:00,BETR801,no2,26.5,µg/m³ +Antwerpen,BE,2019-05-10 02:00:00+00:00,BETR801,no2,11.5,µg/m³ 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Westminster,no2,11.0,µg/m³ +London,GB,2019-06-17 03:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-17 02:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-17 01:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-17 00:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-16 23:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-16 20:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-16 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-16 18:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-16 17:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-16 14:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-16 13:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-16 12:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-16 11:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-16 10:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-16 09:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-16 08:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-16 07:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-16 06:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-16 05:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-16 04:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-16 03:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-16 02:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-16 01:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-16 00:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-15 23:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-15 22:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-15 21:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-15 20:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-15 19:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-15 18:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-15 17:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-15 16:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-15 15:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-15 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-15 13:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-15 12:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-15 11:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-15 10:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-15 09:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-15 08:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-15 07:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-15 06:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-15 05:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-15 04:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-15 00:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-14 20:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 19:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 18:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-14 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-14 16:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-14 15:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-14 14:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-14 13:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-14 12:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-14 11:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-14 10:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-14 09:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-14 08:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-14 07:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-14 06:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-14 05:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-14 04:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-14 03:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-14 02:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-14 00:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-13 23:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 22:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 21:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 20:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-13 19:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-13 18:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-13 17:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 16:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-13 15:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-13 14:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-13 13:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 12:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 11:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 10:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-13 09:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 08:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 07:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-13 06:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-13 05:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 04:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-13 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-13 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-13 00:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-12 23:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-12 21:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-12 20:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-06-12 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-12 18:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-12 17:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-12 16:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-12 15:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-06-12 14:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-12 13:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-12 12:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-12 11:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-12 10:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-12 09:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-12 08:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-12 07:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-12 06:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-12 05:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-12 04:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-12 03:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-12 00:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-11 23:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-11 22:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-11 21:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 20:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 19:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-06-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-11 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-06-11 16:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-11 15:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-11 14:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-11 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-11 12:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-11 11:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 10:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-11 09:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-11 08:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-11 07:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-11 06:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-11 05:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-11 04:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-11 03:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-11 02:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-11 01:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-11 00:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-10 23:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-10 22:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-10 21:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 20:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-10 19:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-10 17:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-10 16:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-10 15:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-06-10 14:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-06-10 13:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-06-10 12:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-10 11:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-06-10 10:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-06-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-10 08:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-10 07:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-10 06:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 05:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 04:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-10 03:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-10 02:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-10 01:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-10 00:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-09 23:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-09 21:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-09 20:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-09 19:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-09 18:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-09 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-06-09 16:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-09 15:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-09 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-09 13:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-09 12:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-09 11:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-09 10:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-09 09:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-09 08:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-09 07:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-09 06:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-09 05:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-09 04:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-09 03:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-09 02:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-09 01:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-09 00:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-08 23:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-08 21:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-08 20:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-08 19:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-08 18:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-08 17:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-08 16:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-08 15:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-08 14:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-08 13:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-08 12:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-08 11:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-08 10:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 09:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-08 08:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-08 07:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 06:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-08 05:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 04:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-08 03:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-08 02:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-08 00:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-07 23:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-07 21:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-07 20:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-07 19:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-07 18:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-07 16:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-07 15:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-07 14:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-07 12:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 11:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-07 10:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 09:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-07 08:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-07 07:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 06:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-06-07 05:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 04:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 03:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 02:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-07 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-06 23:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-06 22:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-06 21:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 20:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-06 19:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 18:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-06 16:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-06 15:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-06 14:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-06 13:00:00+00:00,London Westminster,no2,10.0,µg/m³ +London,GB,2019-06-06 12:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-06 11:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-06 10:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-06 09:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-06 08:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-06 07:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-06 06:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-06 05:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-06 04:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-06 03:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-06 02:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-06 00:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-05 23:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-05 22:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-05 21:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 20:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 19:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 18:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 17:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 16:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-05 15:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-05 14:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-05 13:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-05 12:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-05 11:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-05 10:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-05 09:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-05 08:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-06-05 07:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-06-05 06:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-05 05:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-05 04:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-05 03:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-05 02:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-05 01:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-05 00:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-06-04 23:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-04 22:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-04 21:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-04 20:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-04 19:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-04 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-06-04 17:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-06-04 16:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-06-04 15:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-06-04 14:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-04 13:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-06-04 12:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-04 11:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-04 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-04 09:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-04 08:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-04 07:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-06-04 06:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-04 05:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-04 04:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-04 03:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-04 02:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-04 01:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-04 00:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-03 23:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-03 22:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-03 21:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-03 20:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-06-03 19:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-06-03 18:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-03 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-06-03 16:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-03 15:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-03 14:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-06-03 13:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-06-03 12:00:00+00:00,London Westminster,no2,14.0,µg/m³ +London,GB,2019-06-03 11:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-03 10:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-03 09:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-03 08:00:00+00:00,London Westminster,no2,7.0,µg/m³ +London,GB,2019-06-03 07:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-06-03 06:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-06-03 05:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-03 04:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-06-03 03:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-03 02:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-03 01:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-03 00:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-06-02 23:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-02 22:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-06-02 21:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-02 20:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 19:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-02 18:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-02 17:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 15:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-02 14:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-02 13:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-06-02 12:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-02 11:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-02 10:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-06-02 09:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 08:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-06-02 07:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 06:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-02 05:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-06-02 04:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-06-02 03:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-02 02:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-06-02 01:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-06-02 00:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-06-01 23:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-06-01 22:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-06-01 21:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-06-01 20:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-06-01 19:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-06-01 18:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-01 17:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-06-01 16:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-06-01 15:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-01 14:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-06-01 13:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-06-01 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-06-01 11:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-06-01 10:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-06-01 09:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-06-01 08:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-06-01 07:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-06-01 06:00:00+00:00,London Westminster,no2,4.0,µg/m³ +London,GB,2019-06-01 05:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-01 04:00:00+00:00,London Westminster,no2,11.0,µg/m³ +London,GB,2019-06-01 03:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-01 02:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-06-01 01:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-06-01 00:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-31 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-31 20:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-31 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-31 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-31 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 16:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-31 15:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-31 14:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-31 13:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-31 12:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-31 11:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-31 10:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-31 09:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-31 08:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-05-31 07:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-31 06:00:00+00:00,London Westminster,no2,8.0,µg/m³ +London,GB,2019-05-31 05:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-31 04:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-31 03:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-05-31 02:00:00+00:00,London Westminster,no2,12.0,µg/m³ +London,GB,2019-05-31 01:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-31 00:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-30 23:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-30 22:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-30 21:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-30 20:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-30 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-30 18:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-30 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-30 16:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-30 15:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-30 14:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-30 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-30 12:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-30 11:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-05-30 10:00:00+00:00,London Westminster,no2,9.0,µg/m³ +London,GB,2019-05-30 09:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-30 08:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-05-30 07:00:00+00:00,London Westminster,no2,2.0,µg/m³ +London,GB,2019-05-30 06:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 05:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 04:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 03:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 02:00:00+00:00,London Westminster,no2,0.0,µg/m³ +London,GB,2019-05-30 01:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-05-30 00:00:00+00:00,London Westminster,no2,1.0,µg/m³ +London,GB,2019-05-29 23:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 22:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 21:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-05-29 20:00:00+00:00,London Westminster,no2,6.0,µg/m³ +London,GB,2019-05-29 19:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 18:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 17:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 16:00:00+00:00,London Westminster,no2,3.0,µg/m³ +London,GB,2019-05-29 15:00:00+00:00,London Westminster,no2,5.0,µg/m³ +London,GB,2019-05-29 14:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-29 13:00:00+00:00,London Westminster,no2,13.0,µg/m³ +London,GB,2019-05-29 12:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-29 11:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-29 10:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-29 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-29 08:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-29 07:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-29 06:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-29 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-29 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-29 03:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-29 02:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-29 01:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-29 00:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-28 23:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-28 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-28 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 19:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 17:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-28 16:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-28 15:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-28 14:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-28 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-28 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-28 11:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-28 10:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-28 09:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-28 08:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-28 07:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-28 06:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-28 05:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-28 04:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-28 03:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-28 02:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-28 01:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-28 00:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-27 23:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 22:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 21:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-27 20:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-27 19:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 18:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 17:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 16:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 15:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-27 14:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-27 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-27 12:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-27 11:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-27 10:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-27 09:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 08:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 07:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 06:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-27 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-27 03:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-27 02:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-27 01:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-27 00:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 21:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-26 20:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-26 19:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 18:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-26 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 16:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-26 15:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 14:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-26 13:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-26 12:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-26 11:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-26 10:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-26 09:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-26 08:00:00+00:00,London Westminster,no2,15.0,µg/m³ +London,GB,2019-05-26 07:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-26 06:00:00+00:00,London Westminster,no2,17.0,µg/m³ +London,GB,2019-05-26 05:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-26 04:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-26 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-26 01:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-26 00:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-25 23:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-25 22:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-25 21:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-25 20:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-25 19:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-25 18:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-25 17:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-25 16:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-25 15:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-25 14:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-25 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-25 12:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-25 11:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-25 10:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-25 09:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-25 08:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-25 07:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-25 06:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-25 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-25 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-25 03:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-25 02:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-25 01:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-25 00:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-24 23:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-24 22:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-24 21:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-24 20:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-24 19:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-24 18:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-24 17:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-24 16:00:00+00:00,London Westminster,no2,43.0,µg/m³ +London,GB,2019-05-24 15:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-24 14:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-24 13:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-24 12:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-24 11:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-24 10:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-24 09:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-24 08:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-24 07:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-24 06:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-24 05:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-24 04:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-24 03:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-24 02:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-24 00:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-23 23:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-23 22:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-23 21:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-23 20:00:00+00:00,London Westminster,no2,45.0,µg/m³ +London,GB,2019-05-23 19:00:00+00:00,London Westminster,no2,51.0,µg/m³ +London,GB,2019-05-23 18:00:00+00:00,London Westminster,no2,54.0,µg/m³ +London,GB,2019-05-23 17:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-05-23 16:00:00+00:00,London Westminster,no2,53.0,µg/m³ +London,GB,2019-05-23 15:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-23 14:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-23 13:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-23 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-23 11:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-23 10:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-23 09:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-23 08:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-23 07:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-23 06:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-23 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-23 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-23 03:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-23 02:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-23 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-23 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-22 23:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-22 22:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-22 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 20:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-22 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-22 18:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-22 17:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-22 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-22 15:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-22 14:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-22 13:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-22 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-22 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-22 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 09:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 08:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-22 07:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-22 06:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-22 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-22 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-22 03:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-22 02:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-22 01:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-22 00:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-21 23:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 22:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 21:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 20:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-21 19:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-21 18:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-21 17:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-21 16:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-21 15:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-21 14:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-21 13:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-21 12:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 11:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 10:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-21 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-21 08:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-21 07:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-21 06:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-21 05:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-21 04:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-21 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-21 01:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-21 00:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-20 23:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-20 22:00:00+00:00,London Westminster,no2,47.0,µg/m³ +London,GB,2019-05-20 21:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-20 20:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 19:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 18:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-20 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-20 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 15:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 14:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 13:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 10:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 08:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-20 07:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-20 06:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-20 05:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-20 04:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-20 03:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 02:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-20 01:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-20 00:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-19 23:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 22:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 21:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 20:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-19 19:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-19 18:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-19 17:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-19 16:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-19 15:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-19 14:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-19 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-19 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-19 11:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-19 10:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-19 09:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-19 08:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-19 07:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-19 06:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-19 05:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 04:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 03:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 02:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 01:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-19 00:00:00+00:00,London Westminster,no2,49.0,µg/m³ +London,GB,2019-05-18 23:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-18 22:00:00+00:00,London Westminster,no2,46.0,µg/m³ +London,GB,2019-05-18 21:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-18 20:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-18 19:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-18 18:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-18 17:00:00+00:00,London Westminster,no2,42.0,µg/m³ +London,GB,2019-05-18 16:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-18 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-18 14:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-18 13:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 12:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-18 11:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-18 10:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-18 09:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-18 08:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-18 07:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 06:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-18 05:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 04:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-18 03:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-18 02:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-18 01:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-18 00:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-17 23:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-17 22:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-17 21:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-17 20:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-17 19:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-17 18:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-17 17:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-17 16:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 15:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-17 14:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-17 13:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-17 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 11:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 10:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-17 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-17 08:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-17 07:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-17 06:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-17 05:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 04:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 03:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 02:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-17 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-17 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-16 23:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 22:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 20:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 19:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-16 18:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-16 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 14:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-16 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-16 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-16 11:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-16 09:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-16 08:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 07:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-16 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 05:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 04:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-16 03:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-16 02:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-16 01:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-16 00:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 23:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 22:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 21:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-15 20:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-15 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 18:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-15 17:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 16:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-15 15:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-15 14:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-15 13:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-15 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-15 11:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-15 10:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-15 09:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-15 08:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-15 07:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-15 05:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-15 04:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-15 03:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-15 02:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-15 00:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 20:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-14 19:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-14 18:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 17:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-14 16:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-14 15:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-14 14:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 13:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 12:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-14 11:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-14 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-14 09:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 08:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-14 07:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-14 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-14 05:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 04:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-14 03:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 02:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-14 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-14 00:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-13 23:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 22:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 21:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 20:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 19:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 18:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-13 17:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-13 16:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-13 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-13 14:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-13 13:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-13 12:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-13 11:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-13 10:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-13 09:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-13 08:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-13 07:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-13 06:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-13 05:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-13 04:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-13 03:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-13 02:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-13 01:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-13 00:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 23:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 22:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 21:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-12 20:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 19:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 18:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 17:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-12 16:00:00+00:00,London Westminster,no2,23.0,µg/m³ +London,GB,2019-05-12 15:00:00+00:00,London Westminster,no2,22.0,µg/m³ +London,GB,2019-05-12 14:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-12 13:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-12 12:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-12 11:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-12 10:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-12 09:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-12 08:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-12 07:00:00+00:00,London Westminster,no2,44.0,µg/m³ +London,GB,2019-05-12 06:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-12 05:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-12 04:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-12 03:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-12 02:00:00+00:00,London Westminster,no2,38.0,µg/m³ +London,GB,2019-05-12 01:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-12 00:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-11 23:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-11 22:00:00+00:00,London Westminster,no2,37.0,µg/m³ +London,GB,2019-05-11 21:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-11 20:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-11 19:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-11 17:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-11 16:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-11 15:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-11 09:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-11 08:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-11 07:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-11 06:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-11 05:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-11 04:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-11 03:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-11 02:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-11 01:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-11 00:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-10 23:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-10 22:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-10 21:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 19:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-10 17:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-10 16:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-10 15:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-10 14:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-10 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-10 12:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-10 11:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-10 10:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-10 08:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-10 07:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-10 06:00:00+00:00,London Westminster,no2,39.0,µg/m³ +London,GB,2019-05-10 05:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-10 04:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-10 03:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-10 02:00:00+00:00,London Westminster,no2,41.0,µg/m³ +London,GB,2019-05-10 01:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-05-10 00:00:00+00:00,London Westminster,no2,52.0,µg/m³ +London,GB,2019-05-09 23:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-05-09 22:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-05-09 21:00:00+00:00,London Westminster,no2,65.0,µg/m³ +London,GB,2019-05-09 20:00:00+00:00,London Westminster,no2,59.0,µg/m³ +London,GB,2019-05-09 19:00:00+00:00,London Westminster,no2,62.0,µg/m³ +London,GB,2019-05-09 18:00:00+00:00,London Westminster,no2,58.0,µg/m³ +London,GB,2019-05-09 17:00:00+00:00,London Westminster,no2,60.0,µg/m³ +London,GB,2019-05-09 16:00:00+00:00,London Westminster,no2,67.0,µg/m³ +London,GB,2019-05-09 15:00:00+00:00,London Westminster,no2,97.0,µg/m³ +London,GB,2019-05-09 14:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-09 13:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-09 12:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-09 11:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-09 10:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-09 09:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-09 08:00:00+00:00,London Westminster,no2,35.0,µg/m³ +London,GB,2019-05-09 07:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 06:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 05:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 04:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-09 03:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-09 02:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-09 00:00:00+00:00,London Westminster,no2,30.0,µg/m³ +London,GB,2019-05-08 23:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-08 21:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-08 20:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-08 19:00:00+00:00,London Westminster,no2,25.0,µg/m³ +London,GB,2019-05-08 18:00:00+00:00,London Westminster,no2,40.0,µg/m³ +London,GB,2019-05-08 17:00:00+00:00,London Westminster,no2,31.0,µg/m³ +London,GB,2019-05-08 16:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-08 15:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-08 14:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-08 13:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-08 12:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-08 11:00:00+00:00,London Westminster,no2,27.0,µg/m³ +London,GB,2019-05-08 10:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-08 09:00:00+00:00,London Westminster,no2,33.0,µg/m³ +London,GB,2019-05-08 08:00:00+00:00,London Westminster,no2,36.0,µg/m³ +London,GB,2019-05-08 07:00:00+00:00,London Westminster,no2,34.0,µg/m³ +London,GB,2019-05-08 06:00:00+00:00,London Westminster,no2,29.0,µg/m³ +London,GB,2019-05-08 05:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-08 04:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-08 03:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-08 02:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-08 01:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-08 00:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-07 23:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-07 21:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-07 20:00:00+00:00,London Westminster,no2,24.0,µg/m³ +London,GB,2019-05-07 19:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 18:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 17:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-07 16:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 15:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 14:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³ +London,GB,2019-05-07 12:00:00+00:00,London Westminster,no2,18.0,µg/m³ +London,GB,2019-05-07 11:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-07 10:00:00+00:00,London Westminster,no2,21.0,µg/m³ +London,GB,2019-05-07 09:00:00+00:00,London Westminster,no2,28.0,µg/m³ +London,GB,2019-05-07 08:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-07 07:00:00+00:00,London Westminster,no2,32.0,µg/m³ +London,GB,2019-05-07 06:00:00+00:00,London Westminster,no2,26.0,µg/m³ +London,GB,2019-05-07 04:00:00+00:00,London Westminster,no2,16.0,µg/m³ +London,GB,2019-05-07 03:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-07 02:00:00+00:00,London Westminster,no2,19.0,µg/m³ +London,GB,2019-05-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³ diff --git a/doc/data/air_quality_parameters.csv b/doc/data/air_quality_parameters.csv new file mode 100644 index 0000000000000..915f6300e43b8 --- /dev/null +++ b/doc/data/air_quality_parameters.csv @@ -0,0 +1,8 @@ +id,description,name +bc,Black Carbon,BC +co,Carbon Monoxide,CO +no2,Nitrogen Dioxide,NO2 +o3,Ozone,O3 +pm10,Particulate matter less than 10 micrometers in diameter,PM10 +pm25,Particulate matter less than 2.5 micrometers in diameter,PM2.5 +so2,Sulfur Dioxide,SO2 diff --git a/doc/data/air_quality_pm25_long.csv b/doc/data/air_quality_pm25_long.csv new file mode 100644 index 0000000000000..f74053c249339 --- /dev/null +++ b/doc/data/air_quality_pm25_long.csv @@ -0,0 +1,1111 @@ +city,country,date.utc,location,parameter,value,unit +Antwerpen,BE,2019-06-18 06:00:00+00:00,BETR801,pm25,18.0,µg/m³ +Antwerpen,BE,2019-06-17 08:00:00+00:00,BETR801,pm25,6.5,µg/m³ +Antwerpen,BE,2019-06-17 07:00:00+00:00,BETR801,pm25,18.5,µg/m³ +Antwerpen,BE,2019-06-17 06:00:00+00:00,BETR801,pm25,16.0,µg/m³ +Antwerpen,BE,2019-06-17 05:00:00+00:00,BETR801,pm25,7.5,µg/m³ +Antwerpen,BE,2019-06-17 04:00:00+00:00,BETR801,pm25,7.5,µg/m³ +Antwerpen,BE,2019-06-17 03:00:00+00:00,BETR801,pm25,7.0,µg/m³ +Antwerpen,BE,2019-06-17 02:00:00+00:00,BETR801,pm25,7.0,µg/m³ +Antwerpen,BE,2019-06-17 01:00:00+00:00,BETR801,pm25,8.0,µg/m³ +Antwerpen,BE,2019-06-16 01:00:00+00:00,BETR801,pm25,15.0,µg/m³ +Antwerpen,BE,2019-06-15 01:00:00+00:00,BETR801,pm25,11.0,µg/m³ +Antwerpen,BE,2019-06-14 09:00:00+00:00,BETR801,pm25,12.0,µg/m³ +Antwerpen,BE,2019-06-13 01:00:00+00:00,BETR801,pm25,3.0,µg/m³ +Antwerpen,BE,2019-06-12 01:00:00+00:00,BETR801,pm25,16.0,µg/m³ +Antwerpen,BE,2019-06-11 01:00:00+00:00,BETR801,pm25,3.5,µg/m³ +Antwerpen,BE,2019-06-10 01:00:00+00:00,BETR801,pm25,8.5,µg/m³ +Antwerpen,BE,2019-06-09 01:00:00+00:00,BETR801,pm25,6.0,µg/m³ +Antwerpen,BE,2019-06-08 01:00:00+00:00,BETR801,pm25,6.5,µg/m³ +Antwerpen,BE,2019-06-06 01:00:00+00:00,BETR801,pm25,6.5,µg/m³ +Antwerpen,BE,2019-06-05 01:00:00+00:00,BETR801,pm25,11.0,µg/m³ +Antwerpen,BE,2019-06-04 01:00:00+00:00,BETR801,pm25,10.5,µg/m³ +Antwerpen,BE,2019-06-03 01:00:00+00:00,BETR801,pm25,12.5,µg/m³ +Antwerpen,BE,2019-06-02 01:00:00+00:00,BETR801,pm25,19.0,µg/m³ +Antwerpen,BE,2019-06-01 01:00:00+00:00,BETR801,pm25,9.0,µg/m³ +Antwerpen,BE,2019-05-31 01:00:00+00:00,BETR801,pm25,6.0,µg/m³ +Antwerpen,BE,2019-05-30 01:00:00+00:00,BETR801,pm25,5.0,µg/m³ +Antwerpen,BE,2019-05-29 01:00:00+00:00,BETR801,pm25,5.5,µg/m³ +Antwerpen,BE,2019-05-28 01:00:00+00:00,BETR801,pm25,7.0,µg/m³ +Antwerpen,BE,2019-05-27 01:00:00+00:00,BETR801,pm25,7.5,µg/m³ +Antwerpen,BE,2019-05-26 01:00:00+00:00,BETR801,pm25,26.5,µg/m³ +Antwerpen,BE,2019-05-25 01:00:00+00:00,BETR801,pm25,10.0,µg/m³ +Antwerpen,BE,2019-05-24 01:00:00+00:00,BETR801,pm25,13.0,µg/m³ +Antwerpen,BE,2019-05-23 01:00:00+00:00,BETR801,pm25,7.5,µg/m³ +Antwerpen,BE,2019-05-22 01:00:00+00:00,BETR801,pm25,15.5,µg/m³ +Antwerpen,BE,2019-05-21 01:00:00+00:00,BETR801,pm25,20.5,µg/m³ +Antwerpen,BE,2019-05-20 17:00:00+00:00,BETR801,pm25,18.5,µg/m³ +Antwerpen,BE,2019-05-20 16:00:00+00:00,BETR801,pm25,17.0,µg/m³ +Antwerpen,BE,2019-05-20 15:00:00+00:00,BETR801,pm25,18.5,µg/m³ +Antwerpen,BE,2019-05-20 14:00:00+00:00,BETR801,pm25,14.5,µg/m³ +Antwerpen,BE,2019-05-20 13:00:00+00:00,BETR801,pm25,17.0,µg/m³ +Antwerpen,BE,2019-05-20 12:00:00+00:00,BETR801,pm25,17.5,µg/m³ +Antwerpen,BE,2019-05-20 11:00:00+00:00,BETR801,pm25,13.5,µg/m³ +Antwerpen,BE,2019-05-20 10:00:00+00:00,BETR801,pm25,10.5,µg/m³ +Antwerpen,BE,2019-05-20 09:00:00+00:00,BETR801,pm25,13.5,µg/m³ +Antwerpen,BE,2019-05-20 08:00:00+00:00,BETR801,pm25,19.5,µg/m³ +Antwerpen,BE,2019-05-20 07:00:00+00:00,BETR801,pm25,23.5,µg/m³ +Antwerpen,BE,2019-05-20 06:00:00+00:00,BETR801,pm25,22.0,µg/m³ +Antwerpen,BE,2019-05-20 05:00:00+00:00,BETR801,pm25,25.0,µg/m³ +Antwerpen,BE,2019-05-20 04:00:00+00:00,BETR801,pm25,24.5,µg/m³ +Antwerpen,BE,2019-05-20 03:00:00+00:00,BETR801,pm25,15.0,µg/m³ +Antwerpen,BE,2019-05-20 02:00:00+00:00,BETR801,pm25,18.5,µg/m³ +Antwerpen,BE,2019-05-20 01:00:00+00:00,BETR801,pm25,28.0,µg/m³ +Antwerpen,BE,2019-05-19 21:00:00+00:00,BETR801,pm25,35.5,µg/m³ +Antwerpen,BE,2019-05-19 20:00:00+00:00,BETR801,pm25,40.0,µg/m³ +Antwerpen,BE,2019-05-19 19:00:00+00:00,BETR801,pm25,43.5,µg/m³ +Antwerpen,BE,2019-05-19 18:00:00+00:00,BETR801,pm25,35.0,µg/m³ +Antwerpen,BE,2019-05-19 17:00:00+00:00,BETR801,pm25,34.0,µg/m³ +Antwerpen,BE,2019-05-19 16:00:00+00:00,BETR801,pm25,36.5,µg/m³ +Antwerpen,BE,2019-05-19 15:00:00+00:00,BETR801,pm25,44.0,µg/m³ +Antwerpen,BE,2019-05-19 14:00:00+00:00,BETR801,pm25,43.5,µg/m³ +Antwerpen,BE,2019-05-19 13:00:00+00:00,BETR801,pm25,46.0,µg/m³ +Antwerpen,BE,2019-05-19 12:00:00+00:00,BETR801,pm25,43.0,µg/m³ +Antwerpen,BE,2019-05-19 11:00:00+00:00,BETR801,pm25,41.0,µg/m³ +Antwerpen,BE,2019-05-19 10:00:00+00:00,BETR801,pm25,41.5,µg/m³ +Antwerpen,BE,2019-05-19 09:00:00+00:00,BETR801,pm25,42.5,µg/m³ +Antwerpen,BE,2019-05-19 08:00:00+00:00,BETR801,pm25,51.5,µg/m³ +Antwerpen,BE,2019-05-19 07:00:00+00:00,BETR801,pm25,56.0,µg/m³ +Antwerpen,BE,2019-05-19 06:00:00+00:00,BETR801,pm25,58.5,µg/m³ +Antwerpen,BE,2019-05-19 05:00:00+00:00,BETR801,pm25,60.0,µg/m³ +Antwerpen,BE,2019-05-19 04:00:00+00:00,BETR801,pm25,56.5,µg/m³ +Antwerpen,BE,2019-05-19 03:00:00+00:00,BETR801,pm25,52.5,µg/m³ +Antwerpen,BE,2019-05-19 02:00:00+00:00,BETR801,pm25,51.5,µg/m³ +Antwerpen,BE,2019-05-19 01:00:00+00:00,BETR801,pm25,52.0,µg/m³ +Antwerpen,BE,2019-05-19 00:00:00+00:00,BETR801,pm25,49.5,µg/m³ +Antwerpen,BE,2019-05-18 23:00:00+00:00,BETR801,pm25,45.5,µg/m³ +Antwerpen,BE,2019-05-18 22:00:00+00:00,BETR801,pm25,42.0,µg/m³ +Antwerpen,BE,2019-05-18 21:00:00+00:00,BETR801,pm25,40.5,µg/m³ +Antwerpen,BE,2019-05-18 20:00:00+00:00,BETR801,pm25,41.0,µg/m³ +Antwerpen,BE,2019-05-18 19:00:00+00:00,BETR801,pm25,36.5,µg/m³ +Antwerpen,BE,2019-05-18 18:00:00+00:00,BETR801,pm25,37.0,µg/m³ +Antwerpen,BE,2019-05-18 01:00:00+00:00,BETR801,pm25,24.0,µg/m³ +Antwerpen,BE,2019-05-17 01:00:00+00:00,BETR801,pm25,13.5,µg/m³ +Antwerpen,BE,2019-05-16 01:00:00+00:00,BETR801,pm25,11.0,µg/m³ +Antwerpen,BE,2019-05-15 02:00:00+00:00,BETR801,pm25,12.5,µg/m³ +Antwerpen,BE,2019-05-15 01:00:00+00:00,BETR801,pm25,13.0,µg/m³ +Antwerpen,BE,2019-05-14 02:00:00+00:00,BETR801,pm25,4.0,µg/m³ +Antwerpen,BE,2019-05-14 01:00:00+00:00,BETR801,pm25,4.0,µg/m³ +Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,pm25,5.5,µg/m³ +Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,pm25,5.0,µg/m³ +Antwerpen,BE,2019-05-12 02:00:00+00:00,BETR801,pm25,6.0,µg/m³ +Antwerpen,BE,2019-05-12 01:00:00+00:00,BETR801,pm25,6.0,µg/m³ +Antwerpen,BE,2019-05-11 02:00:00+00:00,BETR801,pm25,19.5,µg/m³ +Antwerpen,BE,2019-05-11 01:00:00+00:00,BETR801,pm25,17.0,µg/m³ +Antwerpen,BE,2019-05-10 02:00:00+00:00,BETR801,pm25,13.5,µg/m³ +Antwerpen,BE,2019-05-10 01:00:00+00:00,BETR801,pm25,11.5,µg/m³ +Antwerpen,BE,2019-05-09 02:00:00+00:00,BETR801,pm25,3.5,µg/m³ +Antwerpen,BE,2019-05-09 01:00:00+00:00,BETR801,pm25,4.5,µg/m³ +Antwerpen,BE,2019-05-08 02:00:00+00:00,BETR801,pm25,14.0,µg/m³ +Antwerpen,BE,2019-05-08 01:00:00+00:00,BETR801,pm25,14.5,µg/m³ +Antwerpen,BE,2019-05-07 02:00:00+00:00,BETR801,pm25,14.0,µg/m³ +Antwerpen,BE,2019-05-07 01:00:00+00:00,BETR801,pm25,12.5,µg/m³ +London,GB,2019-06-21 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ 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21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-30 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-30 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-29 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-29 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-29 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-29 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-29 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-29 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-28 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-28 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-28 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-28 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-28 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-28 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-28 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-28 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-27 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-27 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-27 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-27 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-27 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-27 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-27 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-26 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-25 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-25 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-25 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-25 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-25 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-25 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-25 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-25 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-25 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-25 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-25 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-25 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-25 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-25 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-25 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-25 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-25 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-25 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-25 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-25 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-25 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-25 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-25 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-25 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-24 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-24 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-24 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-24 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-24 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-23 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-23 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-22 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-22 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-22 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-22 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-22 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-22 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-22 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-22 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-21 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-21 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-21 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-21 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-21 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-21 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-21 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-21 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-21 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-21 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-21 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-21 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-21 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-21 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-21 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-21 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-21 07:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-21 06:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-21 05:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-21 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-21 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-21 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-21 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-21 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-20 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-20 22:00:00+00:00,London Westminster,pm25,14.0,µg/m³ 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Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-20 07:00:00+00:00,London Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-20 06:00:00+00:00,London Westminster,pm25,18.0,µg/m³ +London,GB,2019-05-20 05:00:00+00:00,London Westminster,pm25,18.0,µg/m³ +London,GB,2019-05-20 04:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-20 03:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-20 02:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-20 01:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-20 00:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 23:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 22:00:00+00:00,London Westminster,pm25,20.0,µg/m³ +London,GB,2019-05-19 21:00:00+00:00,London Westminster,pm25,20.0,µg/m³ +London,GB,2019-05-19 20:00:00+00:00,London Westminster,pm25,20.0,µg/m³ +London,GB,2019-05-19 19:00:00+00:00,London Westminster,pm25,20.0,µg/m³ +London,GB,2019-05-19 18:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 17:00:00+00:00,London Westminster,pm25,20.0,µg/m³ +London,GB,2019-05-19 16:00:00+00:00,London Westminster,pm25,20.0,µg/m³ +London,GB,2019-05-19 15:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 14:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 13:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 12:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 11:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 10:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 09:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 08:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 07:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 06:00:00+00:00,London Westminster,pm25,19.0,µg/m³ +London,GB,2019-05-19 05:00:00+00:00,London Westminster,pm25,18.0,µg/m³ 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Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-18 14:00:00+00:00,London Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-18 13:00:00+00:00,London Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-18 12:00:00+00:00,London Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-18 11:00:00+00:00,London Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-18 10:00:00+00:00,London Westminster,pm25,17.0,µg/m³ +London,GB,2019-05-18 09:00:00+00:00,London Westminster,pm25,16.0,µg/m³ +London,GB,2019-05-18 08:00:00+00:00,London Westminster,pm25,16.0,µg/m³ +London,GB,2019-05-18 07:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-18 06:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-18 05:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-18 04:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-18 03:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-18 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-18 01:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-18 00:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-17 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-17 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-17 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-17 20:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-17 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-17 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-17 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-17 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-17 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-17 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-17 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-17 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³ 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08:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-16 07:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-16 06:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-16 05:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-16 04:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-16 03:00:00+00:00,London Westminster,pm25,15.0,µg/m³ +London,GB,2019-05-16 02:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-16 01:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-16 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-15 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-15 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-15 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-15 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-15 19:00:00+00:00,London Westminster,pm25,12.0,µg/m³ 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Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-15 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-15 03:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-15 02:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-15 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-14 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-14 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-14 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-14 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-13 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-13 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³ +London,GB,2019-05-13 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³ +London,GB,2019-05-13 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-13 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-13 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-13 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-13 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-12 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-12 22:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-12 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-12 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-12 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-12 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 09:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 08:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 07:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-12 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-12 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-12 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-12 03:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-12 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-12 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-12 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-11 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-11 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 16:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³ +London,GB,2019-05-11 09:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 08:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 07:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-11 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-11 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-11 04:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-11 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-11 02:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-11 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-11 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-10 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-10 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-10 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-10 20:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-10 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-10 18:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 17:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 16:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 15:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 14:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 12:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 10:00:00+00:00,London Westminster,pm25,13.0,µg/m³ +London,GB,2019-05-10 09:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 08:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 07:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 02:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-10 01:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-10 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-09 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³ +London,GB,2019-05-09 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-09 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-09 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-09 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 03:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 02:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-09 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-08 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 04:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-08 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-08 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-08 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-07 18:00:00+00:00,London Westminster,pm25,11.0,µg/m³ +London,GB,2019-05-07 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³ +London,GB,2019-05-07 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-07 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³ +London,GB,2019-05-07 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-07 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-07 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³ +London,GB,2019-05-07 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³ diff --git a/doc/data/air_quality_stations.csv b/doc/data/air_quality_stations.csv new file mode 100644 index 0000000000000..9ab1a377dcdae --- /dev/null +++ b/doc/data/air_quality_stations.csv @@ -0,0 +1,67 @@ +location,coordinates.latitude,coordinates.longitude +BELAL01,51.23619,4.38522 +BELHB23,51.1703,4.341 +BELLD01,51.10998,5.00486 +BELLD02,51.12038,5.02155 +BELR833,51.32766,4.36226 +BELSA04,51.31393,4.40387 +BELWZ02,51.1928,5.22153 +BETM802,51.26099,4.4244 +BETN016,51.23365,5.16398 +BETR801,51.20966,4.43182 +BETR802,51.20952,4.43179 +BETR803,51.22863,4.42845 +BETR805,51.20823,4.42156 +BETR811,51.2521,4.49136 +BETR815,51.2147,4.33221 +BETR817,51.17713,4.41795 +BETR820,51.32042,4.44481 +BETR822,51.26429,4.34128 +BETR831,51.3488,4.33971 +BETR834,51.092,4.3801 +BETR891,51.25581,4.38536 +BETR893,51.28138,4.38577 +BETR894,51.2835,4.3495 +BETR897,51.25011,4.3421 +FR04004,48.89167,2.34667 +FR04012,48.82778,2.3275 +FR04014,48.83724,2.3939 +FR04014,48.83722,2.3939 +FR04031,48.86887,2.31194 +FR04031,48.86889,2.31194 +FR04037,48.82861,2.36028 +FR04060,48.8572,2.2933 +FR04071,48.8564,2.33528 +FR04071,48.85639,2.33528 +FR04118,48.87027,2.3325 +FR04118,48.87029,2.3325 +FR04131,48.87333,2.33028 +FR04135,48.83795,2.40806 +FR04135,48.83796,2.40806 +FR04141,48.85278,2.36056 +FR04141,48.85279,2.36056 +FR04143,48.859,2.351 +FR04143,48.85944,2.35111 +FR04179,48.83038,2.26989 +FR04329,48.8386,2.41279 +FR04329,48.83862,2.41278 +Camden Kerbside,51.54421,-0.17527 +Ealing Horn Lane,51.51895,-0.26562 +Haringey Roadside,51.5993,-0.06822 +London Bexley,51.46603,0.18481 +London Bloomsbury,51.52229,-0.12589 +London Eltham,51.45258,0.07077 +London Haringey Priory Park South,51.58413,-0.12525 +London Harlington,51.48879,-0.44161 +London Harrow Stanmore,51.61733,-0.29878 +London Hillingdon,51.49633,-0.46086 +London Marylebone Road,51.52253,-0.15461 +London N. Kensington,51.52105,-0.21349 +London Teddington,51.42099,-0.33965 +London Teddington Bushy Park,51.42529,-0.34561 +London Westminster,51.49467,-0.13193 +Southend-on-Sea,51.5442,0.67841 +Southwark A2 Old Kent Road,51.4805,-0.05955 +Thurrock,51.47707,0.31797 +Tower Hamlets Roadside,51.52253,-0.04216 +Groton Fort Griswold,41.3536,-72.0789 diff --git a/doc/data/titanic.csv b/doc/data/titanic.csv new file mode 100644 index 0000000000000..5cc466e97cf12 --- /dev/null +++ b/doc/data/titanic.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. 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a/doc/source/getting_started/dsintro.rst +++ b/doc/source/getting_started/dsintro.rst @@ -444,6 +444,7 @@ dtype. For example: data pd.DataFrame.from_records(data, index='C') +.. _basics.dataframe.sel_add_del: Column selection, addition, deletion ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/getting_started/index.rst b/doc/source/getting_started/index.rst index 34bb4f930f175..a2f8f79f22ae4 100644 --- a/doc/source/getting_started/index.rst +++ b/doc/source/getting_started/index.rst @@ -6,15 +6,666 @@ Getting started =============== +Installation +------------ + +Before you can use pandas, you’ll need to get it installed. + +.. raw:: html + + <div class="container"> + <div class="row"> + <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block"> + <div class="card install-card shadow w-100"> + <div class="card-header"> + Working with conda? + </div> + <div class="card-body"> + <p class="card-text"> + +Pandas is part of the `Anaconda <http://docs.continuum.io/anaconda/>`__ distribution and can be +installed with Anaconda or Miniconda: + +.. raw:: html + + </p> + </div> + <div class="card-footer text-muted"> + +.. code-block:: bash + + conda install pandas + +.. raw:: html + + </div> + </div> + </div> + <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block"> + <div class="card install-card shadow w-100"> + <div class="card-header"> + Prefer pip? + </div> + <div class="card-body"> + <p class="card-text"> + +Pandas can be installed via pip from `PyPI <https://pypi.org/project/pandas>`__. + +.. raw:: html + + </p> + </div> + <div class="card-footer text-muted"> + +.. code-block:: bash + + pip install pandas + +.. raw:: html + + </div> + </div> + </div> + <div class="col-12 d-flex install-block"> + <div class="card install-card shadow w-100"> + <div class="card-header"> + In-depth instructions? + </div> + <div class="card-body"> + <p class="card-text">Installing a specific version? + Installing from source? + Check the advanced installation page.</p> + +.. container:: custom-button + + :ref:`Learn more <install>` + +.. raw:: html + + </div> + </div> + </div> + </div> + </div> + +.. _gentle_intro: + +Intro to pandas +--------------- + +.. raw:: html + + <div class="container"> + <div id="accordion" class="shadow tutorial-accordion"> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseOne"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + What kind of data does Pandas handle? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_01_tableoriented>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseOne" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +When working with tabular data, such as data stored in spreadsheets or databases, Pandas is the right tool for you. Pandas will help you +to explore, clean and process your data. In Pandas, a data table is called a :class:`DataFrame`. + +.. image:: ../_static/schemas/01_table_dataframe.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_01_tableoriented>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <dsintro>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseTwo"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How do I read and write tabular data? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_02_read_write>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseTwo" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these +data sources is provided by function with the prefix ``read_*``. Similarly, the ``to_*`` methods are used to store data. + +.. image:: ../_static/schemas/02_io_readwrite.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_02_read_write>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <io>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseThree"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How do I select a subset of a table? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_03_subset>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseThree" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the +data you need are available in Pandas. + +.. image:: ../_static/schemas/03_subset_columns_rows.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_03_subset>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <indexing>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseFour"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How to create plots in pandas? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_04_plotting>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseFour" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Pandas provides plotting your data out of the box, using the power of Matplotlib. You can pick the plot type (scatter, bar, boxplot,...) +corresponding to your data. + +.. image:: ../_static/schemas/04_plot_overview.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_04_plotting>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <visualization>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseFive"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How to create new columns derived from existing columns? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_05_columns>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseFive" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. +Adding a column to a :class:`DataFrame` based on existing data in other columns is straightforward. + +.. image:: ../_static/schemas/05_newcolumn_2.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_05_columns>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <basics.dataframe.sel_add_del>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseSix"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How to calculate summary statistics? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_06_stats>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseSix" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Basic statistics (mean, median, min, max, counts...) are easily calculable. These or custom aggregations can be applied on the entire +data set, a sliding window of the data or grouped by categories. The latter is also known as the split-apply-combine approach. + +.. image:: ../_static/schemas/06_groupby.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_06_stats>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <groupby>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseSeven"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How to reshape the layout of tables? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_07_reshape>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseSeven" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Change the structure of your data table in multiple ways. You can :func:`~pandas.melt` your data table from wide to long/tidy form or :func:`~pandas.pivot` +from long to wide format. With aggregations built-in, a pivot table is created with a sinlge command. + +.. image:: ../_static/schemas/07_melt.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_07_reshape>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <reshaping>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseEight"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How to combine data from multiple tables? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_08_combine>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseEight" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are provided to combine multiple tables of data. + +.. image:: ../_static/schemas/08_concat_row.svg + :align: center + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_08_combine>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <merging>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseNine"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How to handle time series data? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_09_timeseries>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseNine" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_09_timeseries>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <timeseries>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + <div class="card tutorial-card"> + <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseTen"> + <div class="d-flex flex-row tutorial-card-header-1"> + <div class="d-flex flex-row tutorial-card-header-2"> + <button class="btn btn-dark btn-sm"></button> + How to manipulate textual data? + </div> + <span class="badge gs-badge-link"> + +:ref:`Straight to tutorial...<10min_tut_10_text>` + +.. raw:: html + + </span> + </div> + </div> + <div id="collapseTen" class="collapse" data-parent="#accordion"> + <div class="card-body"> + +Data sets do not only contain numerical data. Pandas provides a wide range of functions to cleaning textual data and extract useful information from it. + +.. raw:: html + + <div class="d-flex flex-row"> + <span class="badge gs-badge-link"> + +:ref:`To introduction tutorial <10min_tut_10_text>` + +.. raw:: html + + </span> + <span class="badge gs-badge-link"> + +:ref:`To user guide <timeseries>` + +.. raw:: html + + </span> + </div> + </div> + </div> + </div> + + </div> + </div> + + +.. _comingfrom: + +Coming from... +-------------- + +Currently working with other software for data manipulation in a tabular format? You're probably familiar to typical +data operations and know *what* to do with your tabular data, but lacking the syntax to execute these operations. Get to know +the pandas syntax by looking for equivalents from the software you already know: + +.. raw:: html + + <div class="container"> + <div class="row"> + <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> + <div class="card text-center intro-card shadow"> + <img src="../_static/logo_r.svg" class="card-img-top" alt="R project logo" height="72"> + <div class="card-body flex-fill"> + <p class="card-text">The <a href="https://www.r-project.org/">R programming language</a> provides the <code>data.frame</code> data structure and multiple packages, + such as <a href="https://www.tidyverse.org/">tidyverse</a> use and extend <code>data.frame</code>s for convenient data handling + functionalities similar to pandas.</p> + +.. container:: custom-button + + :ref:`Learn more <compare_with_r>` + +.. raw:: html + + </div> + </div> + </div> + <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> + <div class="card text-center intro-card shadow"> + <img src="../_static/logo_sql.svg" class="card-img-top" alt="SQL logo" height="72"> + <div class="card-body flex-fill"> + <p class="card-text">Already familiar to <code>SELECT</code>, <code>GROUP BY</code>, <code>JOIN</code>,...? + Most of these SQL manipulations do have equivalents in pandas.</p> + +.. container:: custom-button + + :ref:`Learn more <compare_with_sql>` + +.. raw:: html + + </div> + </div> + </div> + <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> + <div class="card text-center intro-card shadow"> + <img src="../_static/logo_stata.svg" class="card-img-top" alt="STATA logo" height="52"> + <div class="card-body flex-fill"> + <p class="card-text">The <code>data set</code> included in the + <a href="https://en.wikipedia.org/wiki/Stata">STATA</a> statistical software suite corresponds + to the pandas <code>data.frame</code>. Many of the operations known from STATA have an equivalent + in pandas.</p> + +.. container:: custom-button + + :ref:`Learn more <compare_with_stata>` + +.. raw:: html + + </div> + </div> + </div> + <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex"> + <div class="card text-center intro-card shadow"> + <img src="../_static/logo_sas.svg" class="card-img-top" alt="SAS logo" height="52"> + <div class="card-body flex-fill"> + <p class="card-text">The <a href="https://en.wikipedia.org/wiki/SAS_(software)">SAS</a> statistical software suite + also provides the <code>data set</code> corresponding to the pandas <code>data.frame</code>. + Also vectorized operations, filtering, string processing operations,... from SAS have similar + functions in pandas.</p> + +.. container:: custom-button + + :ref:`Learn more <compare_with_sas>` + +.. raw:: html + + </div> + </div> + </div> + </div> + </div> + +Community tutorials +------------------- + +The community produces a wide variety of tutorials available online. Some of the +material is enlisted in the community contributed :ref:`tutorials`. + + .. If you update this toctree, also update the manual toctree in the main index.rst.template .. toctree:: :maxdepth: 2 + :hidden: install overview 10min + intro_tutorials/index basics dsintro comparison/index diff --git a/doc/source/getting_started/intro_tutorials/01_table_oriented.rst b/doc/source/getting_started/intro_tutorials/01_table_oriented.rst new file mode 100644 index 0000000000000..02e59b3c81755 --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/01_table_oriented.rst @@ -0,0 +1,218 @@ +.. _10min_tut_01_tableoriented: + +{{ header }} + +What kind of data does pandas handle? +===================================== + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to start using pandas + +.. ipython:: python + + import pandas as pd + +To load the pandas package and start working with it, import the +package. The community agreed alias for pandas is ``pd``, so loading +pandas as ``pd`` is assumed standard practice for all of the pandas +documentation. + +.. raw:: html + + </li> + </ul> + +Pandas data table representation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/01_table_dataframe.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to store passenger data of the Titanic. For a number of passengers, I know the name (characters), age (integers) and sex (male/female) data. + +.. ipython:: python + + df = pd.DataFrame({ + "Name": ["Braund, Mr. Owen Harris", + "Allen, Mr. William Henry", + "Bonnell, Miss. Elizabeth"], + "Age": [22, 35, 58], + "Sex": ["male", "male", "female"]} + ) + df + +To manually store data in a table, create a ``DataFrame``. When using a Python dictionary of lists, the dictionary keys will be used as column headers and +the values in each list as rows of the ``DataFrame``. + +.. raw:: html + + </li> + </ul> + +A :class:`DataFrame` is a 2-dimensional data structure that can store data of +different types (including characters, integers, floating point values, +categorical data and more) in columns. It is similar to a spreadsheet, a +SQL table or the ``data.frame`` in R. + +- The table has 3 columns, each of them with a column label. The column + labels are respectively ``Name``, ``Age`` and ``Sex``. +- The column ``Name`` consists of textual data with each value a + string, the column ``Age`` are numbers and the column ``Sex`` is + textual data. + +In spreadsheet software, the table representation of our data would look +very similar: + +.. image:: ../../_static/schemas/01_table_spreadsheet.png + :align: center + +Each column in a ``DataFrame`` is a ``Series`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/01_table_series.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m just interested in working with the data in the column ``Age`` + +.. ipython:: python + + df["Age"] + +When selecting a single column of a pandas :class:`DataFrame`, the result is +a pandas :class:`Series`. To select the column, use the column label in +between square brackets ``[]``. + +.. raw:: html + + </li> + </ul> + +.. note:: + If you are familiar to Python + :ref:`dictionaries <python:tut-dictionaries>`, the selection of a + single column is very similar to selection of dictionary values based on + the key. + +You can create a ``Series`` from scratch as well: + +.. ipython:: python + + ages = pd.Series([22, 35, 58], name="Age") + ages + +A pandas ``Series`` has no column labels, as it is just a single column +of a ``DataFrame``. A Series does have row labels. + +Do something with a DataFrame or Series +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to know the maximum Age of the passengers + +We can do this on the ``DataFrame`` by selecting the ``Age`` column and +applying ``max()``: + +.. ipython:: python + + df["Age"].max() + +Or to the ``Series``: + +.. ipython:: python + + ages.max() + +.. raw:: html + + </li> + </ul> + +As illustrated by the ``max()`` method, you can *do* things with a +``DataFrame`` or ``Series``. pandas provides a lot of functionalities, +each of them a *method* you can apply to a ``DataFrame`` or ``Series``. +As methods are functions, do not forget to use parentheses ``()``. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in some basic statistics of the numerical data of my data table + +.. ipython:: python + + df.describe() + +The :func:`~DataFrame.describe` method provides a quick overview of the numerical data in +a ``DataFrame``. As the ``Name`` and ``Sex`` columns are textual data, +these are by default not taken into account by the :func:`~DataFrame.describe` method. + +.. raw:: html + + </li> + </ul> + +Many pandas operations return a ``DataFrame`` or a ``Series``. The +:func:`~DataFrame.describe` method is an example of a pandas operation returning a +pandas ``Series``. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +Check more options on ``describe`` in the user guide section about :ref:`aggregations with describe <basics.describe>` + +.. raw:: html + + </div> + +.. note:: + This is just a starting point. Similar to spreadsheet + software, pandas represents data as a table with columns and rows. Apart + from the representation, also the data manipulations and calculations + you would do in spreadsheet software are supported by pandas. Continue + reading the next tutorials to get started! + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- Import the package, aka ``import pandas as pd`` +- A table of data is stored as a pandas ``DataFrame`` +- Each column in a ``DataFrame`` is a ``Series`` +- You can do things by applying a method to a ``DataFrame`` or ``Series`` + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A more extended explanation to ``DataFrame`` and ``Series`` is provided in the :ref:`introduction to data structures <dsintro>`. + +.. raw:: html + + </div> \ No newline at end of file diff --git a/doc/source/getting_started/intro_tutorials/02_read_write.rst b/doc/source/getting_started/intro_tutorials/02_read_write.rst new file mode 100644 index 0000000000000..797bdbcf25d17 --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/02_read_write.rst @@ -0,0 +1,232 @@ +.. _10min_tut_02_read_write: + +{{ header }} + +.. ipython:: python + + import pandas as pd + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Titanic data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +This tutorial uses the titanic data set, stored as CSV. The data +consists of the following data columns: + +- PassengerId: Id of every passenger. +- Survived: This feature have value 0 and 1. 0 for not survived and 1 + for survived. +- Pclass: There are 3 classes: Class 1, Class 2 and Class 3. +- Name: Name of passenger. +- Sex: Gender of passenger. +- Age: Age of passenger. +- SibSp: Indication that passenger have siblings and spouse. +- Parch: Whether a passenger is alone or have family. +- Ticket: Ticket number of passenger. +- Fare: Indicating the fare. +- Cabin: The cabin of passenger. +- Embarked: The embarked category. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + </li> + </ul> + </div> + +How do I read and write tabular data? +===================================== + +.. image:: ../../_static/schemas/02_io_readwrite.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to analyse the titanic passenger data, available as a CSV file. + +.. ipython:: python + + titanic = pd.read_csv("data/titanic.csv") + +pandas provides the :func:`read_csv` function to read data stored as a csv +file into a pandas ``DataFrame``. pandas supports many different file +formats or data sources out of the box (csv, excel, sql, json, parquet, +…), each of them with the prefix ``read_*``. + +.. raw:: html + + </li> + </ul> + +Make sure to always have a check on the data after reading in the +data. When displaying a ``DataFrame``, the first and last 5 rows will be +shown by default: + +.. ipython:: python + + titanic + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to see the first 8 rows of a pandas DataFrame. + +.. ipython:: python + + titanic.head(8) + +To see the first N rows of a ``DataFrame``, use the :meth:`~DataFrame.head` method with +the required number of rows (in this case 8) as argument. + +.. raw:: html + + </li> + </ul> + +.. note:: + + Interested in the last N rows instead? pandas also provides a + :meth:`~DataFrame.tail` method. For example, ``titanic.tail(10)`` will return the last + 10 rows of the DataFrame. + +A check on how pandas interpreted each of the column data types can be +done by requesting the pandas ``dtypes`` attribute: + +.. ipython:: python + + titanic.dtypes + +For each of the columns, the used data type is enlisted. The data types +in this ``DataFrame`` are integers (``int64``), floats (``float63``) and +strings (``object``). + +.. note:: + When asking for the ``dtypes``, no brackets are used! + ``dtypes`` is an attribute of a ``DataFrame`` and ``Series``. Attributes + of ``DataFrame`` or ``Series`` do not need brackets. Attributes + represent a characteristic of a ``DataFrame``/``Series``, whereas a + method (which requires brackets) *do* something with the + ``DataFrame``/``Series`` as introduced in the :ref:`first tutorial <10min_tut_01_tableoriented>`. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +My colleague requested the titanic data as a spreadsheet. + +.. ipython:: python + + titanic.to_excel('titanic.xlsx', sheet_name='passengers', index=False) + +Whereas ``read_*`` functions are used to read data to pandas, the +``to_*`` methods are used to store data. The :meth:`~DataFrame.to_excel` method stores +the data as an excel file. In the example here, the ``sheet_name`` is +named *passengers* instead of the default *Sheet1*. By setting +``index=False`` the row index labels are not saved in the spreadsheet. + +.. raw:: html + + </li> + </ul> + +The equivalent read function :meth:`~DataFrame.to_excel` will reload the data to a +``DataFrame``: + +.. ipython:: python + + titanic = pd.read_excel('titanic.xlsx', sheet_name='passengers') + +.. ipython:: python + + titanic.head() + +.. ipython:: python + :suppress: + + import os + os.remove('titanic.xlsx') + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in a technical summary of a ``DataFrame`` + +.. ipython:: python + + titanic.info() + + +The method :meth:`~DataFrame.info` provides technical information about a +``DataFrame``, so let’s explain the output in more detail: + +- It is indeed a :class:`DataFrame`. +- There are 891 entries, i.e. 891 rows. +- Each row has a row label (aka the ``index``) with values ranging from + 0 to 890. +- The table has 12 columns. Most columns have a value for each of the + rows (all 891 values are ``non-null``). Some columns do have missing + values and less than 891 ``non-null`` values. +- The columns ``Name``, ``Sex``, ``Cabin`` and ``Embarked`` consists of + textual data (strings, aka ``object``). The other columns are + numerical data with some of them whole numbers (aka ``integer``) and + others are real numbers (aka ``float``). +- The kind of data (characters, integers,…) in the different columns + are summarized by listing the ``dtypes``. +- The approximate amount of RAM used to hold the DataFrame is provided + as well. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- Getting data in to pandas from many different file formats or data + sources is supported by ``read_*`` functions. +- Exporting data out of pandas is provided by different + ``to_*``\ methods. +- The ``head``/``tail``/``info`` methods and the ``dtypes`` attribute + are convenient for a first check. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row bg-light gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +For a complete overview of the input and output possibilites from and to pandas, see the user guide section about :ref:`reader and writer functions <io>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/03_subset_data.rst b/doc/source/getting_started/intro_tutorials/03_subset_data.rst new file mode 100644 index 0000000000000..7a4347905ad8d --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/03_subset_data.rst @@ -0,0 +1,405 @@ +.. _10min_tut_03_subset: + +{{ header }} + +.. ipython:: python + + import pandas as pd + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Titanic data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +This tutorial uses the titanic data set, stored as CSV. The data +consists of the following data columns: + +- PassengerId: Id of every passenger. +- Survived: This feature have value 0 and 1. 0 for not survived and 1 + for survived. +- Pclass: There are 3 classes: Class 1, Class 2 and Class 3. +- Name: Name of passenger. +- Sex: Gender of passenger. +- Age: Age of passenger. +- SibSp: Indication that passenger have siblings and spouse. +- Parch: Whether a passenger is alone or have family. +- Ticket: Ticket number of passenger. +- Fare: Indicating the fare. +- Cabin: The cabin of passenger. +- Embarked: The embarked category. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + titanic = pd.read_csv("data/titanic.csv") + titanic.head() + +.. raw:: html + + </li> + </ul> + </div> + +How do I select a subset of a ``DataFrame``? +============================================ + +How do I select specific columns from a ``DataFrame``? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/03_subset_columns.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in the age of the titanic passengers. + +.. ipython:: python + + ages = titanic["Age"] + ages.head() + +To select a single column, use square brackets ``[]`` with the column +name of the column of interest. + +.. raw:: html + + </li> + </ul> + +Each column in a :class:`DataFrame` is a :class:`Series`. As a single column is +selected, the returned object is a pandas :class:`DataFrame`. We can verify this +by checking the type of the output: + +.. ipython:: python + + type(titanic["Age"]) + +And have a look at the ``shape`` of the output: + +.. ipython:: python + + titanic["Age"].shape + +:attr:`DataFrame.shape` is an attribute (remember :ref:`tutorial on reading and writing <10min_tut_02_read_write>`, do not use parantheses for attributes) of a +pandas ``Series`` and ``DataFrame`` containing the number of rows and +columns: *(nrows, ncolumns)*. A pandas Series is 1-dimensional and only +the number of rows is returned. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in the age and sex of the titanic passengers. + +.. ipython:: python + + age_sex = titanic[["Age", "Sex"]] + age_sex.head() + +To select multiple columns, use a list of column names within the +selection brackets ``[]``. + +.. raw:: html + + </li> + </ul> + +.. note:: + The inner square brackets define a + :ref:`Python list <python:tut-morelists>` with column names, whereas + the outer brackets are used to select the data from a pandas + ``DataFrame`` as seen in the previous example. + +The returned data type is a pandas DataFrame: + +.. ipython:: python + + type(titanic[["Age", "Sex"]]) + +.. ipython:: python + + titanic[["Age", "Sex"]].shape + +The selection returned a ``DataFrame`` with 891 rows and 2 columns. Remember, a +``DataFrame`` is 2-dimensional with both a row and column dimension. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +For basic information on indexing, see the user guide section on :ref:`indexing and selecting data <indexing.basics>`. + +.. raw:: html + + </div> + +How do I filter specific rows from a ``DataFrame``? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/03_subset_rows.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in the passengers older than 35 years. + +.. ipython:: python + + above_35 = titanic[titanic["Age"] > 35] + above_35.head() + +To select rows based on a conditional expression, use a condition inside +the selection brackets ``[]``. + +.. raw:: html + + </li> + </ul> + +The condition inside the selection +brackets ``titanic["Age"] > 35`` checks for which rows the ``Age`` +column has a value larger than 35: + +.. ipython:: python + + titanic["Age"] > 35 + +The output of the conditional expression (``>``, but also ``==``, +``!=``, ``<``, ``<=``,… would work) is actually a pandas ``Series`` of +boolean values (either ``True`` or ``False``) with the same number of +rows as the original ``DataFrame``. Such a ``Series`` of boolean values +can be used to filter the ``DataFrame`` by putting it in between the +selection brackets ``[]``. Only rows for which the value is ``True`` +will be selected. + +We now from before that the original titanic ``DataFrame`` consists of +891 rows. Let’s have a look at the amount of rows which satisfy the +condition by checking the ``shape`` attribute of the resulting +``DataFrame`` ``above_35``: + +.. ipython:: python + + above_35.shape + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in the titanic passengers from cabin class 2 and 3. + +.. ipython:: python + + class_23 = titanic[titanic["Pclass"].isin([2, 3])] + class_23.head() + +Similar to the conditional expression, the :func:`~Series.isin` conditional function +returns a ``True`` for each row the values are in the provided list. To +filter the rows based on such a function, use the conditional function +inside the selection brackets ``[]``. In this case, the condition inside +the selection brackets ``titanic["Pclass"].isin([2, 3])`` checks for +which rows the ``Pclass`` column is either 2 or 3. + +.. raw:: html + + </li> + </ul> + +The above is equivalent to filtering by rows for which the class is +either 2 or 3 and combining the two statements with an ``|`` (or) +operator: + +.. ipython:: python + + class_23 = titanic[(titanic["Pclass"] == 2) | (titanic["Pclass"] == 3)] + class_23.head() + +.. note:: + When combining multiple conditional statements, each condition + must be surrounded by parentheses ``()``. Moreover, you can not use + ``or``/``and`` but need to use the ``or`` operator ``|`` and the ``and`` + operator ``&``. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +See the dedicated section in the user guide about :ref:`boolean indexing <indexing.boolean>` or about the :ref:`isin function <indexing.basics.indexing_isin>`. + +.. raw:: html + + </div> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to work with passenger data for which the age is known. + +.. ipython:: python + + age_no_na = titanic[titanic["Age"].notna()] + age_no_na.head() + +The :meth:`~Series.notna` conditional function returns a ``True`` for each row the +values are not an ``Null`` value. As such, this can be combined with the +selection brackets ``[]`` to filter the data table. + +.. raw:: html + + </li> + </ul> + +You might wonder what actually changed, as the first 5 lines are still +the same values. One way to verify is to check if the shape has changed: + +.. ipython:: python + + age_no_na.shape + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +For more dedicated functions on missing values, see the user guide section about :ref:`handling missing data <missing_data>`. + +.. raw:: html + + </div> + +How do I select specific rows and columns from a ``DataFrame``? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/03_subset_columns_rows.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in the names of the passengers older than 35 years. + +.. ipython:: python + + adult_names = titanic.loc[titanic["Age"] > 35, "Name"] + adult_names.head() + +In this case, a subset of both rows and columns is made in one go and +just using selection brackets ``[]`` is not sufficient anymore. The +``loc``/``iloc`` operators are required in front of the selection +brackets ``[]``. When using ``loc``/``iloc``, the part before the comma +is the rows you want, and the part after the comma is the columns you +want to select. + +.. raw:: html + + </li> + </ul> + +When using the column names, row labels or a condition expression, use +the ``loc`` operator in front of the selection brackets ``[]``. For both +the part before and after the comma, you can use a single label, a list +of labels, a slice of labels, a conditional expression or a colon. Using +a colon specificies you want to select all rows or columns. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I’m interested in rows 10 till 25 and columns 3 to 5. + +.. ipython:: python + + titanic.iloc[9:25, 2:5] + +Again, a subset of both rows and columns is made in one go and just +using selection brackets ``[]`` is not sufficient anymore. When +specifically interested in certain rows and/or columns based on their +position in the table, use the ``iloc`` operator in front of the +selection brackets ``[]``. + +.. raw:: html + + </li> + </ul> + +When selecting specific rows and/or columns with ``loc`` or ``iloc``, +new values can be assigned to the selected data. For example, to assign +the name ``anonymous`` to the first 3 elements of the third column: + +.. ipython:: python + + titanic.iloc[0:3, 3] = "anonymous" + titanic.head() + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +See the user guide section on :ref:`different choices for indexing <indexing.choice>` to get more insight in the usage of ``loc`` and ``iloc``. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- When selecting subsets of data, square brackets ``[]`` are used. +- Inside these brackets, you can use a single column/row label, a list + of column/row labels, a slice of labels, a conditional expression or + a colon. +- Select specific rows and/or columns using ``loc`` when using the row + and column names +- Select specific rows and/or columns using ``iloc`` when using the + positions in the table +- You can assign new values to a selection based on ``loc``/``iloc``. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A full overview about indexing is provided in the user guide pages on :ref:`indexing and selecting data <indexing>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/04_plotting.rst b/doc/source/getting_started/intro_tutorials/04_plotting.rst new file mode 100644 index 0000000000000..f3d99ee56359a --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/04_plotting.rst @@ -0,0 +1,252 @@ +.. _10min_tut_04_plotting: + +{{ header }} + +.. ipython:: python + + import pandas as pd + import matplotlib.pyplot as plt + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Air quality data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +For this tutorial, air quality data about :math:`NO_2` is used, made +available by `openaq <https://openaq.org>`__ and using the +`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. +The ``air_quality_no2.csv`` data set provides :math:`NO_2` values for +the measurement stations *FR04014*, *BETR801* and *London Westminster* +in respectively Paris, Antwerp and London. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + air_quality = pd.read_csv("data/air_quality_no2.csv", + index_col=0, parse_dates=True) + air_quality.head() + +.. note:: + The usage of the ``index_col`` and ``parse_dates`` parameters of the ``read_csv`` function to define the first (0th) column as + index of the resulting ``DataFrame`` and convert the dates in the column to :class:`Timestamp` objects, respectively. + +.. raw:: html + + </li> + </ul> + </div> + +How to create plots in pandas? +------------------------------ + +.. image:: ../../_static/schemas/04_plot_overview.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want a quick visual check of the data. + +.. ipython:: python + + @savefig 04_airqual_quick.png + air_quality.plot() + +With a ``DataFrame``, pandas creates by default one line plot for each of +the columns with numeric data. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to plot only the columns of the data table with the data from Paris. + +.. ipython:: python + + @savefig 04_airqual_paris.png + air_quality["station_paris"].plot() + +To plot a specific column, use the selection method of the +:ref:`subset data tutorial <10min_tut_03_subset>` in combination with the :meth:`~DataFrame.plot` +method. Hence, the :meth:`~DataFrame.plot` method works on both ``Series`` and +``DataFrame``. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to visually compare the :math:`N0_2` values measured in London versus Paris. + +.. ipython:: python + + @savefig 04_airqual_scatter.png + air_quality.plot.scatter(x="station_london", + y="station_paris", + alpha=0.5) + +.. raw:: html + + </li> + </ul> + +Apart from the default ``line`` plot when using the ``plot`` function, a +number of alternatives are available to plot data. Let’s use some +standard Python to get an overview of the available plot methods: + +.. ipython:: python + + [method_name for method_name in dir(air_quality.plot) + if not method_name.startswith("_")] + +.. note:: + In many development environments as well as ipython and + jupyter notebook, use the TAB button to get an overview of the available + methods, for example ``air_quality.plot.`` + TAB. + +One of the options is :meth:`DataFrame.plot.box`, which refers to a +`boxplot <https://en.wikipedia.org/wiki/Box_plot>`__. The ``box`` +method is applicable on the air quality example data: + +.. ipython:: python + + @savefig 04_airqual_boxplot.png + air_quality.plot.box() + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +For an introduction to plots other than the default line plot, see the user guide section about :ref:`supported plot styles <visualization.other>`. + +.. raw:: html + + </div> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want each of the columns in a separate subplot. + +.. ipython:: python + + @savefig 04_airqual_area_subplot.png + axs = air_quality.plot.area(figsize=(12, 4), subplots=True) + +Separate subplots for each of the data columns is supported by the ``subplots`` argument +of the ``plot`` functions. The builtin options available in each of the pandas plot +functions that are worthwhile to have a look. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +Some more formatting options are explained in the user guide section on :ref:`plot formatting <visualization.formatting>`. + +.. raw:: html + + </div> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to further customize, extend or save the resulting plot. + +.. ipython:: python + + fig, axs = plt.subplots(figsize=(12, 4)); + air_quality.plot.area(ax=axs); + @savefig 04_airqual_customized.png + axs.set_ylabel("NO$_2$ concentration"); + fig.savefig("no2_concentrations.png") + +.. ipython:: python + :suppress: + + import os + os.remove('no2_concentrations.png') + +.. raw:: html + + </li> + </ul> + +Each of the plot objects created by pandas are a +`matplotlib <https://matplotlib.org/>`__ object. As Matplotlib provides +plenty of options to customize plots, making the link between pandas and +Matplotlib explicit enables all the power of matplotlib to the plot. +This strategy is applied in the previous example: + +:: + + fig, axs = plt.subplots(figsize=(12, 4)) # Create an empty matplotlib Figure and Axes + air_quality.plot.area(ax=axs) # Use pandas to put the area plot on the prepared Figure/Axes + axs.set_ylabel("NO$_2$ concentration") # Do any matplotlib customization you like + fig.savefig("no2_concentrations.png") # Save the Figure/Axes using the existing matplotlib method. + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- The ``.plot.*`` methods are applicable on both Series and DataFrames +- By default, each of the columns is plotted as a different element + (line, boxplot,…) +- Any plot created by pandas is a Matplotlib object. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A full overview of plotting in pandas is provided in the :ref:`visualization pages <visualization>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/05_add_columns.rst b/doc/source/getting_started/intro_tutorials/05_add_columns.rst new file mode 100644 index 0000000000000..d4f6a8d6bb4a2 --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/05_add_columns.rst @@ -0,0 +1,186 @@ +.. _10min_tut_05_columns: + +{{ header }} + +.. ipython:: python + + import pandas as pd + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Air quality data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +For this tutorial, air quality data about :math:`NO_2` is used, made +available by `openaq <https://openaq.org>`__ and using the +`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. +The ``air_quality_no2.csv`` data set provides :math:`NO_2` values for +the measurement stations *FR04014*, *BETR801* and *London Westminster* +in respectively Paris, Antwerp and London. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + air_quality = pd.read_csv("data/air_quality_no2.csv", + index_col=0, parse_dates=True) + air_quality.head() + +.. raw:: html + + </li> + </ul> + </div> + +How to create new columns derived from existing columns? +-------------------------------------------------------- + +.. image:: ../../_static/schemas/05_newcolumn_1.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to express the :math:`NO_2` concentration of the station in London in mg/m\ :math:`^3` + +(*If we assume temperature of 25 degrees Celsius and pressure of 1013 +hPa, the conversion factor is 1.882*) + +.. ipython:: python + + air_quality["london_mg_per_cubic"] = air_quality["station_london"] * 1.882 + air_quality.head() + +To create a new column, use the ``[]`` brackets with the new column name +at the left side of the assignment. + +.. raw:: html + + </li> + </ul> + +.. note:: + The calculation of the values is done **element_wise**. This + means all values in the given column are multiplied by the value 1.882 + at once. You do not need to use a loop to iterate each of the rows! + +.. image:: ../../_static/schemas/05_newcolumn_2.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column + +.. ipython:: python + + air_quality["ratio_paris_antwerp"] = \ + air_quality["station_paris"] / air_quality["station_antwerp"] + air_quality.head() + +The calculation is again element-wise, so the ``/`` is applied *for the +values in each row*. + +.. raw:: html + + </li> + </ul> + +Also other mathematical operators (+, -, \*, /) or +logical operators (<, >, =,…) work element wise. The latter was already +used in the :ref:`subset data tutorial <10min_tut_03_subset>` to filter +rows of a table using a conditional expression. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to rename the data columns to the corresponding station identifiers used by openAQ + +.. ipython:: python + + air_quality_renamed = air_quality.rename( + columns={"station_antwerp": "BETR801", + "station_paris": "FR04014", + "station_london": "London Westminster"}) + +.. ipython:: python + + air_quality_renamed.head() + +The :meth:`~DataFrame.rename` function can be used for both row labels and column +labels. Provide a dictionary with the keys the current names and the +values the new names to update the corresponding names. + +.. raw:: html + + </li> + </ul> + +The mapping should not be restricted to fixed names only, but can be a +mapping function as well. For example, converting the column names to +lowercase letters can be done using a function as well: + +.. ipython:: python + + air_quality_renamed = air_quality_renamed.rename(columns=str.lower) + air_quality_renamed.head() + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +Details about column or row label renaming is provided in the user guide section on :ref:`renaming labels <basics.rename>`. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- Create a new column by assigning the output to the DataFrame with a + new column name in between the ``[]``. +- Operations are element-wise, no need to loop over rows. +- Use ``rename`` with a dictionary or function to rename row labels or + column names. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +The user guide contains a separate section on :ref:`column addition and deletion <basics.dataframe.sel_add_del>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst b/doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst new file mode 100644 index 0000000000000..7a94c90525027 --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst @@ -0,0 +1,310 @@ +.. _10min_tut_06_stats: + +{{ header }} + +.. ipython:: python + + import pandas as pd + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Titanic data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +This tutorial uses the titanic data set, stored as CSV. The data +consists of the following data columns: + +- PassengerId: Id of every passenger. +- Survived: This feature have value 0 and 1. 0 for not survived and 1 + for survived. +- Pclass: There are 3 classes: Class 1, Class 2 and Class 3. +- Name: Name of passenger. +- Sex: Gender of passenger. +- Age: Age of passenger. +- SibSp: Indication that passenger have siblings and spouse. +- Parch: Whether a passenger is alone or have family. +- Ticket: Ticket number of passenger. +- Fare: Indicating the fare. +- Cabin: The cabin of passenger. +- Embarked: The embarked category. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + titanic = pd.read_csv("data/titanic.csv") + titanic.head() + +.. raw:: html + + </li> + </ul> + </div> + +How to calculate summary statistics? +------------------------------------ + +Aggregating statistics +~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/06_aggregate.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +What is the average age of the titanic passengers? + +.. ipython:: python + + titanic["Age"].mean() + +.. raw:: html + + </li> + </ul> + +Different statistics are available and can be applied to columns with +numerical data. Operations in general exclude missing data and operate +across rows by default. + +.. image:: ../../_static/schemas/06_reduction.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +What is the median age and ticket fare price of the titanic passengers? + +.. ipython:: python + + titanic[["Age", "Fare"]].median() + +The statistic applied to multiple columns of a ``DataFrame`` (the selection of two columns +return a ``DataFrame``, see the :ref:`subset data tutorial <10min_tut_03_subset>`) is calculated for each numeric column. + +.. raw:: html + + </li> + </ul> + +The aggregating statistic can be calculated for multiple columns at the +same time. Remember the ``describe`` function from :ref:`first tutorial <10min_tut_01_tableoriented>` tutorial? + +.. ipython:: python + + titanic[["Age", "Fare"]].describe() + +Instead of the predefined statistics, specific combinations of +aggregating statistics for given columns can be defined using the +:func:`DataFrame.agg` method: + +.. ipython:: python + + titanic.agg({'Age': ['min', 'max', 'median', 'skew'], + 'Fare': ['min', 'max', 'median', 'mean']}) + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +Details about descriptive statistics are provided in the user guide section on :ref:`descriptive statistics <basics.stats>`. + +.. raw:: html + + </div> + + +Aggregating statistics grouped by category +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/06_groupby.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +What is the average age for male versus female titanic passengers? + +.. ipython:: python + + titanic[["Sex", "Age"]].groupby("Sex").mean() + +As our interest is the average age for each gender, a subselection on +these two columns is made first: ``titanic[["Sex", "Age"]]``. Next, the +:meth:`~DataFrame.groupby` method is applied on the ``Sex`` column to make a group per +category. The average age *for each gender* is calculated and +returned. + +.. raw:: html + + </li> + </ul> + +Calculating a given statistic (e.g. ``mean`` age) *for each category in +a column* (e.g. male/female in the ``Sex`` column) is a common pattern. +The ``groupby`` method is used to support this type of operations. More +general, this fits in the more general ``split-apply-combine`` pattern: + +- **Split** the data into groups +- **Apply** a function to each group independently +- **Combine** the results into a data structure + +The apply and combine steps are typically done together in pandas. + +In the previous example, we explicitly selected the 2 columns first. If +not, the ``mean`` method is applied to each column containing numerical +columns: + +.. ipython:: python + + titanic.groupby("Sex").mean() + +It does not make much sense to get the average value of the ``Pclass``. +if we are only interested in the average age for each gender, the +selection of columns (rectangular brackets ``[]`` as usual) is supported +on the grouped data as well: + +.. ipython:: python + + titanic.groupby("Sex")["Age"].mean() + +.. image:: ../../_static/schemas/06_groupby_select_detail.svg + :align: center + +.. note:: + The `Pclass` column contains numerical data but actually + represents 3 categories (or factors) with respectively the labels ‘1’, + ‘2’ and ‘3’. Calculating statistics on these does not make much sense. + Therefore, pandas provides a ``Categorical`` data type to handle this + type of data. More information is provided in the user guide + :ref:`categorical` section. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +What is the mean ticket fare price for each of the sex and cabin class combinations? + +.. ipython:: python + + titanic.groupby(["Sex", "Pclass"])["Fare"].mean() + +Grouping can be done by multiple columns at the same time. Provide the +column names as a list to the :meth:`~DataFrame.groupby` method. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A full description on the split-apply-combine approach is provided in the user guide section on :ref:`groupby operations <groupby>`. + +.. raw:: html + + </div> + +Count number of records by category +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/06_valuecounts.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +What is the number of passengers in each of the cabin classes? + +.. ipython:: python + + titanic["Pclass"].value_counts() + +The :meth:`~Series.value_counts` method counts the number of records for each +category in a column. + +.. raw:: html + + </li> + </ul> + +The function is a shortcut, as it is actually a groupby operation in combination with counting of the number of records +within each group: + +.. ipython:: python + + titanic.groupby("Pclass")["Pclass"].count() + +.. note:: + Both ``size`` and ``count`` can be used in combination with + ``groupby``. Whereas ``size`` includes ``NaN`` values and just provides + the number of rows (size of the table), ``count`` excludes the missing + values. In the ``value_counts`` method, use the ``dropna`` argument to + include or exclude the ``NaN`` values. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +The user guide has a dedicated section on ``value_counts`` , see page on :ref:`discretization <basics.discretization>`. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- Aggregation statistics can be calculated on entire columns or rows +- ``groupby`` provides the power of the *split-apply-combine* pattern +- ``value_counts`` is a convenient shortcut to count the number of + entries in each category of a variable + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A full description on the split-apply-combine approach is provided in the user guide pages about :ref:`groupby operations <groupby>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst b/doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst new file mode 100644 index 0000000000000..b28a9012a4ad9 --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst @@ -0,0 +1,404 @@ +.. _10min_tut_07_reshape: + +{{ header }} + +.. ipython:: python + + import pandas as pd + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Titanic data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +This tutorial uses the titanic data set, stored as CSV. The data +consists of the following data columns: + +- PassengerId: Id of every passenger. +- Survived: This feature have value 0 and 1. 0 for not survived and 1 + for survived. +- Pclass: There are 3 classes: Class 1, Class 2 and Class 3. +- Name: Name of passenger. +- Sex: Gender of passenger. +- Age: Age of passenger. +- SibSp: Indication that passenger have siblings and spouse. +- Parch: Whether a passenger is alone or have family. +- Ticket: Ticket number of passenger. +- Fare: Indicating the fare. +- Cabin: The cabin of passenger. +- Embarked: The embarked category. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + titanic = pd.read_csv("data/titanic.csv") + titanic.head() + +.. raw:: html + + </li> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata2" role="button" aria-expanded="false" aria-controls="collapsedata2"> + <span class="badge badge-dark">Air quality data</span> + </div> + <div class="collapse" id="collapsedata2"> + <div class="card-body"> + <p class="card-text"> + +This tutorial uses air quality data about :math:`NO_2` and Particulate matter less than 2.5 +micrometers, made available by +`openaq <https://openaq.org>`__ and using the +`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. +The ``air_quality_long.csv`` data set provides :math:`NO_2` and +:math:`PM_{25}` values for the measurement stations *FR04014*, *BETR801* +and *London Westminster* in respectively Paris, Antwerp and London. + +The air-quality data set has the following columns: + +- city: city where the sensor is used, either Paris, Antwerp or London +- country: country where the sensor is used, either FR, BE or GB +- location: the id of the sensor, either *FR04014*, *BETR801* or + *London Westminster* +- parameter: the parameter measured by the sensor, either :math:`NO_2` + or Particulate matter +- value: the measured value +- unit: the unit of the measured parameter, in this case ‘µg/m³’ + +and the index of the ``DataFrame`` is ``datetime``, the datetime of the +measurement. + +.. note:: + The air-quality data is provided in a so-called *long format* + data representation with each observation on a separate row and each + variable a separate column of the data table. The long/narrow format is + also known as the `tidy data + format <https://www.jstatsoft.org/article/view/v059i10>`__. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_long.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + air_quality = pd.read_csv("data/air_quality_long.csv", + index_col="date.utc", parse_dates=True) + air_quality.head() + +.. raw:: html + + </li> + </ul> + </div> + +How to reshape the layout of tables? +------------------------------------ + +Sort table rows +~~~~~~~~~~~~~~~ + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to sort the titanic data according to the age of the passengers. + +.. ipython:: python + + titanic.sort_values(by="Age").head() + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to sort the titanic data according to the cabin class and age in descending order. + +.. ipython:: python + + titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head() + +With :meth:`Series.sort_values`, the rows in the table are sorted according to the +defined column(s). The index will follow the row order. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +More details about sorting of tables is provided in the using guide section on :ref:`sorting data <basics.sorting>`. + +.. raw:: html + + </div> + +Long to wide table format +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Let’s use a small subset of the air quality data set. We focus on +:math:`NO_2` data and only use the first two measurements of each +location (i.e. the head of each group). The subset of data will be +called ``no2_subset`` + +.. ipython:: python + + # filter for no2 data only + no2 = air_quality[air_quality["parameter"] == "no2"] + +.. ipython:: python + + # use 2 measurements (head) for each location (groupby) + no2_subset = no2.sort_index().groupby(["location"]).head(2) + no2_subset + +.. image:: ../../_static/schemas/07_pivot.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want the values for the three stations as separate columns next to each other + +.. ipython:: python + + no2_subset.pivot(columns="location", values="value") + +The :meth:`~pandas.pivot_table` function is purely reshaping of the data: a single value +for each index/column combination is required. + +.. raw:: html + + </li> + </ul> + +As pandas support plotting of multiple columns (see :ref:`plotting tutorial <10min_tut_04_plotting>`) out of the box, the conversion from +*long* to *wide* table format enables the plotting of the different time +series at the same time: + +.. ipython:: python + + no2.head() + +.. ipython:: python + + @savefig 7_reshape_columns.png + no2.pivot(columns="location", values="value").plot() + +.. note:: + When the ``index`` parameter is not defined, the existing + index (row labels) is used. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +For more information about :meth:`~DataFrame.pivot`, see the user guide section on :ref:`pivoting DataFrame objects <reshaping.reshaping>`. + +.. raw:: html + + </div> + +Pivot table +~~~~~~~~~~~ + +.. image:: ../../_static/schemas/07_pivot_table.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want the mean concentrations for :math:`NO_2` and :math:`PM_{2.5}` in each of the stations in table form + +.. ipython:: python + + air_quality.pivot_table(values="value", index="location", + columns="parameter", aggfunc="mean") + +In the case of :meth:`~DataFrame.pivot`, the data is only rearranged. When multiple +values need to be aggregated (in this specific case, the values on +different time steps) :meth:`~DataFrame.pivot_table` can be used, providing an +aggregation function (e.g. mean) on how to combine these values. + +.. raw:: html + + </li> + </ul> + +Pivot table is a well known concept in spreadsheet software. When +interested in summary columns for each variable separately as well, put +the ``margin`` parameter to ``True``: + +.. ipython:: python + + air_quality.pivot_table(values="value", index="location", + columns="parameter", aggfunc="mean", + margins=True) + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +For more information about :meth:`~DataFrame.pivot_table`, see the user guide section on :ref:`pivot tables <reshaping.pivot>`. + +.. raw:: html + + </div> + +.. note:: + If case you are wondering, :meth:`~DataFrame.pivot_table` is indeed directly linked + to :meth:`~DataFrame.groupby`. The same result can be derived by grouping on both + ``parameter`` and ``location``: + + :: + + air_quality.groupby(["parameter", "location"]).mean() + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +Have a look at :meth:`~DataFrame.groupby` in combination with :meth:`~DataFrame.unstack` at the user guide section on :ref:`combining stats and groupby <reshaping.combine_with_groupby>`. + +.. raw:: html + + </div> + +Wide to long format +~~~~~~~~~~~~~~~~~~~ + +Starting again from the wide format table created in the previous +section: + +.. ipython:: python + + no2_pivoted = no2.pivot(columns="location", values="value").reset_index() + no2_pivoted.head() + +.. image:: ../../_static/schemas/07_melt.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to collect all air quality :math:`NO_2` measurements in a single column (long format) + +.. ipython:: python + + no_2 = no2_pivoted.melt(id_vars="date.utc") + no_2.head() + +The :func:`pandas.melt` method on a ``DataFrame`` converts the data table from wide +format to long format. The column headers become the variable names in a +newly created column. + +.. raw:: html + + </li> + </ul> + +The solution is the short version on how to apply :func:`pandas.melt`. The method +will *melt* all columns NOT mentioned in ``id_vars`` together into two +columns: A columns with the column header names and a column with the +values itself. The latter column gets by default the name ``value``. + +The :func:`pandas.melt` method can be defined in more detail: + +.. ipython:: python + + no_2 = no2_pivoted.melt(id_vars="date.utc", + value_vars=["BETR801", + "FR04014", + "London Westminster"], + value_name="NO_2", + var_name="id_location") + no_2.head() + +The result in the same, but in more detail defined: + +- ``value_vars`` defines explicitly which columns to *melt* together +- ``value_name`` provides a custom column name for the values column + instead of the default columns name ``value`` +- ``var_name`` provides a custom column name for the columns collecting + the column header names. Otherwise it takes the index name or a + default ``variable`` + +Hence, the arguments ``value_name`` and ``var_name`` are just +user-defined names for the two generated columns. The columns to melt +are defined by ``id_vars`` and ``value_vars``. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +Conversion from wide to long format with :func:`pandas.melt` is explained in the user guide section on :ref:`reshaping by melt <reshaping.melt>`. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- Sorting by one or more columns is supported by ``sort_values`` +- The ``pivot`` function is purely restructering of the data, + ``pivot_table`` supports aggregations +- The reverse of ``pivot`` (long to wide format) is ``melt`` (wide to + long format) + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A full overview is available in the user guide on the pages about :ref:`reshaping and pivoting <reshaping>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst b/doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst new file mode 100644 index 0000000000000..f317e7a1f91b4 --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst @@ -0,0 +1,326 @@ +.. _10min_tut_08_combine: + +{{ header }} + +.. ipython:: python + + import pandas as pd + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Air quality Nitrate data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +For this tutorial, air quality data about :math:`NO_2` is used, made available by +`openaq <https://openaq.org>`__ and downloaded using the +`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. + +The ``air_quality_no2_long.csv`` data set provides :math:`NO_2` +values for the measurement stations *FR04014*, *BETR801* and *London +Westminster* in respectively Paris, Antwerp and London. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2_long.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + air_quality_no2 = pd.read_csv("data/air_quality_no2_long.csv", + parse_dates=True) + air_quality_no2 = air_quality_no2[["date.utc", "location", + "parameter", "value"]] + air_quality_no2.head() + +.. raw:: html + + </li> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata2" role="button" aria-expanded="false" aria-controls="collapsedata2"> + <span class="badge badge-dark">Air quality Particulate matter data</span> + </div> + <div class="collapse" id="collapsedata2"> + <div class="card-body"> + <p class="card-text"> + +For this tutorial, air quality data about Particulate +matter less than 2.5 micrometers is used, made available by +`openaq <https://openaq.org>`__ and downloaded using the +`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. + +The ``air_quality_pm25_long.csv`` data set provides :math:`PM_{25}` +values for the measurement stations *FR04014*, *BETR801* and *London +Westminster* in respectively Paris, Antwerp and London. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_pm25_long.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + air_quality_pm25 = pd.read_csv("data/air_quality_pm25_long.csv", + parse_dates=True) + air_quality_pm25 = air_quality_pm25[["date.utc", "location", + "parameter", "value"]] + air_quality_pm25.head() + +.. raw:: html + + </li> + </ul> + </div> + + +How to combine data from multiple tables? +----------------------------------------- + +Concatenating objects +~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/08_concat_row.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to combine the measurements of :math:`NO_2` and :math:`PM_{25}`, two tables with a similar structure, in a single table + +.. ipython:: python + + air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis=0) + air_quality.head() + +The :func:`~pandas.concat` function performs concatenation operations of multiple +tables along one of the axis (row-wise or column-wise). + +.. raw:: html + + </li> + </ul> + +By default concatenation is along axis 0, so the resulting table combines the rows +of the input tables. Let’s check the shape of the original and the +concatenated tables to verify the operation: + +.. ipython:: python + + print('Shape of the `air_quality_pm25` table: ', air_quality_pm25.shape) + print('Shape of the `air_quality_no2` table: ', air_quality_no2.shape) + print('Shape of the resulting `air_quality` table: ', air_quality.shape) + +Hence, the resulting table has 3178 = 1110 + 2068 rows. + +.. note:: + The **axis** argument will return in a number of pandas + methods that can be applied **along an axis**. A ``DataFrame`` has two + corresponding axes: the first running vertically downwards across rows + (axis 0), and the second running horizontally across columns (axis 1). + Most operations like concatenation or summary statistics are by default + across rows (axis 0), but can be applied across columns as well. + +Sorting the table on the datetime information illustrates also the +combination of both tables, with the ``parameter`` column defining the +origin of the table (either ``no2`` from table ``air_quality_no2`` or +``pm25`` from table ``air_quality_pm25``): + +.. ipython:: python + + air_quality = air_quality.sort_values("date.utc") + air_quality.head() + +In this specific example, the ``parameter`` column provided by the data +ensures that each of the original tables can be identified. This is not +always the case. the ``concat`` function provides a convenient solution +with the ``keys`` argument, adding an additional (hierarchical) row +index. For example: + +.. ipython:: python + + air_quality_ = pd.concat([air_quality_pm25, air_quality_no2], + keys=["PM25", "NO2"]) + +.. ipython:: python + + air_quality_.head() + +.. note:: + The existence of multiple row/column indices at the same time + has not been mentioned within these tutorials. *Hierarchical indexing* + or *MultiIndex* is an advanced and powerfull pandas feature to analyze + higher dimensional data. + + Multi-indexing is out of scope for this pandas introduction. For the + moment, remember that the function ``reset_index`` can be used to + convert any level of an index to a column, e.g. + ``air_quality.reset_index(level=0)`` + + .. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + + Feel free to dive into the world of multi-indexing at the user guide section on :ref:`advanced indexing <advanced>`. + + .. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +More options on table concatenation (row and column +wise) and how ``concat`` can be used to define the logic (union or +intersection) of the indexes on the other axes is provided at the section on +:ref:`object concatenation <merging.concat>`. + +.. raw:: html + + </div> + +Join tables using a common identifier +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: ../../_static/schemas/08_merge_left.svg + :align: center + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Add the station coordinates, provided by the stations metadata table, to the corresponding rows in the measurements table. + +.. warning:: + The air quality measurement station coordinates are stored in a data + file ``air_quality_stations.csv``, downloaded using the + `py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. + +.. ipython:: python + + stations_coord = pd.read_csv("data/air_quality_stations.csv") + stations_coord.head() + +.. note:: + The stations used in this example (FR04014, BETR801 and London + Westminster) are just three entries enlisted in the metadata table. We + only want to add the coordinates of these three to the measurements + table, each on the corresponding rows of the ``air_quality`` table. + +.. ipython:: python + + air_quality.head() + +.. ipython:: python + + air_quality = pd.merge(air_quality, stations_coord, + how='left', on='location') + air_quality.head() + +Using the :meth:`~pandas.merge` function, for each of the rows in the +``air_quality`` table, the corresponding coordinates are added from the +``air_quality_stations_coord`` table. Both tables have the column +``location`` in common which is used as a key to combine the +information. By choosing the ``left`` join, only the locations available +in the ``air_quality`` (left) table, i.e. FR04014, BETR801 and London +Westminster, end up in the resulting table. The ``merge`` function +supports multiple join options similar to database-style operations. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Add the parameter full description and name, provided by the parameters metadata table, to the measurements table + +.. warning:: + The air quality parameters metadata are stored in a data file + ``air_quality_parameters.csv``, downloaded using the + `py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. + +.. ipython:: python + + air_quality_parameters = pd.read_csv("data/air_quality_parameters.csv") + air_quality_parameters.head() + +.. ipython:: python + + air_quality = pd.merge(air_quality, air_quality_parameters, + how='left', left_on='parameter', right_on='id') + air_quality.head() + +Compared to the previous example, there is no common column name. +However, the ``parameter`` column in the ``air_quality`` table and the +``id`` column in the ``air_quality_parameters_name`` both provide the +measured variable in a common format. The ``left_on`` and ``right_on`` +arguments are used here (instead of just ``on``) to make the link +between the two tables. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +pandas supports also inner, outer, and right joins. +More information on join/merge of tables is provided in the user guide section on +:ref:`database style merging of tables <merging.join>`. Or have a look at the +:ref:`comparison with SQL<compare_with_sql.join>` page. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- Multiple tables can be concatenated both column as row wise using + the ``concat`` function. +- For database-like merging/joining of tables, use the ``merge`` + function. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +See the user guide for a full description of the various :ref:`facilities to combine data tables <merging>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/09_timeseries.rst b/doc/source/getting_started/intro_tutorials/09_timeseries.rst new file mode 100644 index 0000000000000..d5b4b316130bb --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/09_timeseries.rst @@ -0,0 +1,389 @@ +.. _10min_tut_09_timeseries: + +{{ header }} + +.. ipython:: python + + import pandas as pd + import matplotlib.pyplot as plt + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Air quality data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +For this tutorial, air quality data about :math:`NO_2` and Particulate +matter less than 2.5 micrometers is used, made available by +`openaq <https://openaq.org>`__ and downloaded using the +`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package. +The ``air_quality_no2_long.csv"`` data set provides :math:`NO_2` values +for the measurement stations *FR04014*, *BETR801* and *London +Westminster* in respectively Paris, Antwerp and London. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2_long.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + air_quality = pd.read_csv("data/air_quality_no2_long.csv") + air_quality = air_quality.rename(columns={"date.utc": "datetime"}) + air_quality.head() + +.. ipython:: python + + air_quality.city.unique() + +.. raw:: html + + </li> + </ul> + </div> + +How to handle time series data with ease? +----------------------------------------- + +Using pandas datetime properties +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to work with the dates in the column ``datetime`` as datetime objects instead of plain text + +.. ipython:: python + + air_quality["datetime"] = pd.to_datetime(air_quality["datetime"]) + air_quality["datetime"] + +Initially, the values in ``datetime`` are character strings and do not +provide any datetime operations (e.g. extract the year, day of the +week,…). By applying the ``to_datetime`` function, pandas interprets the +strings and convert these to datetime (i.e. ``datetime64[ns, UTC]``) +objects. In pandas we call these datetime objects similar to +``datetime.datetime`` from the standard library a :class:`pandas.Timestamp`. + +.. raw:: html + + </li> + </ul> + +.. note:: + As many data sets do contain datetime information in one of + the columns, pandas input function like :func:`pandas.read_csv` and :func:`pandas.read_json` + can do the transformation to dates when reading the data using the + ``parse_dates`` parameter with a list of the columns to read as + Timestamp: + + :: + + pd.read_csv("../data/air_quality_no2_long.csv", parse_dates=["datetime"]) + +Why are these :class:`pandas.Timestamp` objects useful. Let’s illustrate the added +value with some example cases. + + What is the start and end date of the time series data set working + with? + +.. ipython:: python + + air_quality["datetime"].min(), air_quality["datetime"].max() + +Using :class:`pandas.Timestamp` for datetimes enable us to calculate with date +information and make them comparable. Hence, we can use this to get the +length of our time series: + +.. ipython:: python + + air_quality["datetime"].max() - air_quality["datetime"].min() + +The result is a :class:`pandas.Timedelta` object, similar to ``datetime.timedelta`` +from the standard Python library and defining a time duration. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +The different time concepts supported by pandas are explained in the user guide section on :ref:`time related concepts <timeseries.overview>`. + +.. raw:: html + + </div> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +I want to add a new column to the ``DataFrame`` containing only the month of the measurement + +.. ipython:: python + + air_quality["month"] = air_quality["datetime"].dt.month + air_quality.head() + +By using ``Timestamp`` objects for dates, a lot of time-related +properties are provided by pandas. For example the ``month``, but also +``year``, ``weekofyear``, ``quarter``,… All of these properties are +accessible by the ``dt`` accessor. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +An overview of the existing date properties is given in the +:ref:`time and date components overview table <timeseries.components>`. More details about the ``dt`` accessor +to return datetime like properties is explained in a dedicated section on the :ref:`dt accessor <basics.dt_accessors>`. + +.. raw:: html + + </div> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +What is the average :math:`NO_2` concentration for each day of the week for each of the measurement locations? + +.. ipython:: python + + air_quality.groupby( + [air_quality["datetime"].dt.weekday, "location"])["value"].mean() + +Remember the split-apply-combine pattern provided by ``groupby`` from the +:ref:`tutorial on statistics calculation <10min_tut_06_stats>`? +Here, we want to calculate a given statistic (e.g. mean :math:`NO_2`) +**for each weekday** and **for each measurement location**. To group on +weekdays, we use the datetime property ``weekday`` (with Monday=0 and +Sunday=6) of pandas ``Timestamp``, which is also accessible by the +``dt`` accessor. The grouping on both locations and weekdays can be done +to split the calculation of the mean on each of these combinations. + +.. danger:: + As we are working with a very short time series in these + examples, the analysis does not provide a long-term representative + result! + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Plot the typical :math:`NO_2` pattern during the day of our time series of all stations together. In other words, what is the average value for each hour of the day? + +.. ipython:: python + + fig, axs = plt.subplots(figsize=(12, 4)) + air_quality.groupby( + air_quality["datetime"].dt.hour)["value"].mean().plot(kind='bar', + rot=0, + ax=axs) + plt.xlabel("Hour of the day"); # custom x label using matplotlib + @savefig 09_bar_chart.png + plt.ylabel("$NO_2 (µg/m^3)$"); + +Similar to the previous case, we want to calculate a given statistic +(e.g. mean :math:`NO_2`) **for each hour of the day** and we can use the +split-apply-combine approach again. For this case, the datetime property ``hour`` +of pandas ``Timestamp``, which is also accessible by the ``dt`` accessor. + +.. raw:: html + + </li> + </ul> + +Datetime as index +~~~~~~~~~~~~~~~~~ + +In the :ref:`tutorial on reshaping <10min_tut_07_reshape>`, +:meth:`~pandas.pivot` was introduced to reshape the data table with each of the +measurements locations as a separate column: + +.. ipython:: python + + no_2 = air_quality.pivot(index="datetime", columns="location", values="value") + no_2.head() + +.. note:: + By pivoting the data, the datetime information became the + index of the table. In general, setting a column as an index can be + achieved by the ``set_index`` function. + +Working with a datetime index (i.e. ``DatetimeIndex``) provides powerful +functionalities. For example, we do not need the ``dt`` accessor to get +the time series properties, but have these properties available on the +index directly: + +.. ipython:: python + + no_2.index.year, no_2.index.weekday + +Some other advantages are the convenient subsetting of time period or +the adapted time scale on plots. Let’s apply this on our data. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Create a plot of the :math:`NO_2` values in the different stations from the 20th of May till the end of 21st of May + +.. ipython:: python + :okwarning: + + @savefig 09_time_section.png + no_2["2019-05-20":"2019-05-21"].plot(); + +By providing a **string that parses to a datetime**, a specific subset of the data can be selected on a ``DatetimeIndex``. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +More information on the ``DatetimeIndex`` and the slicing by using strings is provided in the section on :ref:`time series indexing <timeseries.datetimeindex>`. + +.. raw:: html + + </div> + +Resample a time series to another frequency +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Aggregate the current hourly time series values to the monthly maximum value in each of the stations. + +.. ipython:: python + + monthly_max = no_2.resample("M").max() + monthly_max + +A very powerful method on time series data with a datetime index, is the +ability to :meth:`~Series.resample` time series to another frequency (e.g., +converting secondly data into 5-minutely data). + +.. raw:: html + + </li> + </ul> + +The :meth:`~Series.resample` method is similar to a groupby operation: + +- it provides a time-based grouping, by using a string (e.g. ``M``, + ``5H``,…) that defines the target frequency +- it requires an aggregation function such as ``mean``, ``max``,… + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +An overview of the aliases used to define time series frequencies is given in the :ref:`offset aliases overview table <timeseries.offset_aliases>`. + +.. raw:: html + + </div> + +When defined, the frequency of the time series is provided by the +``freq`` attribute: + +.. ipython:: python + + monthly_max.index.freq + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Make a plot of the daily median :math:`NO_2` value in each of the stations. + +.. ipython:: python + :okwarning: + + @savefig 09_resample_mean.png + no_2.resample("D").mean().plot(style="-o", figsize=(10, 5)); + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +More details on the power of time series ``resampling`` is provided in the user gudie section on :ref:`resampling <timeseries.resampling>`. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- Valid date strings can be converted to datetime objects using + ``to_datetime`` function or as part of read functions. +- Datetime objects in pandas supports calculations, logical operations + and convenient date-related properties using the ``dt`` accessor. +- A ``DatetimeIndex`` contains these date-related properties and + supports convenient slicing. +- ``Resample`` is a powerful method to change the frequency of a time + series. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A full overview on time series is given in the pages on :ref:`time series and date functionality <timeseries>`. + +.. raw:: html + + </div> \ No newline at end of file diff --git a/doc/source/getting_started/intro_tutorials/10_text_data.rst b/doc/source/getting_started/intro_tutorials/10_text_data.rst new file mode 100644 index 0000000000000..3ff64875d807b --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/10_text_data.rst @@ -0,0 +1,278 @@ +.. _10min_tut_10_text: + +{{ header }} + +.. ipython:: python + + import pandas as pd + +.. raw:: html + + <div class="card gs-data"> + <div class="card-header"> + <div class="gs-data-title"> + Data used for this tutorial: + </div> + </div> + <ul class="list-group list-group-flush"> + <li class="list-group-item"> + <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata"> + <span class="badge badge-dark">Titanic data</span> + </div> + <div class="collapse" id="collapsedata"> + <div class="card-body"> + <p class="card-text"> + +This tutorial uses the titanic data set, stored as CSV. The data +consists of the following data columns: + +- PassengerId: Id of every passenger. +- Survived: This feature have value 0 and 1. 0 for not survived and 1 + for survived. +- Pclass: There are 3 classes: Class 1, Class 2 and Class 3. +- Name: Name of passenger. +- Sex: Gender of passenger. +- Age: Age of passenger. +- SibSp: Indication that passenger have siblings and spouse. +- Parch: Whether a passenger is alone or have family. +- Ticket: Ticket number of passenger. +- Fare: Indicating the fare. +- Cabin: The cabin of passenger. +- Embarked: The embarked category. + +.. raw:: html + + </p> + <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a> + </div> + </div> + +.. ipython:: python + + titanic = pd.read_csv("data/titanic.csv") + titanic.head() + +.. raw:: html + + </li> + </ul> + </div> + +How to manipulate textual data? +------------------------------- + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Make all name characters lowercase + +.. ipython:: python + + titanic["Name"].str.lower() + +To make each of the strings in the ``Name`` column lowercase, select the ``Name`` column +(see :ref:`tutorial on selection of data <10min_tut_03_subset>`), add the ``str`` accessor and +apply the ``lower`` method. As such, each of the strings is converted element wise. + +.. raw:: html + + </li> + </ul> + +Similar to datetime objects in the :ref:`time series tutorial <10min_tut_09_timeseries>` +having a ``dt`` accessor, a number of +specialized string methods are available when using the ``str`` +accessor. These methods have in general matching names with the +equivalent built-in string methods for single elements, but are applied +element-wise (remember :ref:`element wise calculations <10min_tut_05_columns>`?) +on each of the values of the columns. + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Create a new column ``Surname`` that contains the surname of the Passengers by extracting the part before the comma. + +.. ipython:: python + + titanic["Name"].str.split(",") + +Using the :meth:`Series.str.split` method, each of the values is returned as a list of +2 elements. The first element is the part before the comma and the +second element the part after the comma. + +.. ipython:: python + + titanic["Surname"] = titanic["Name"].str.split(",").str.get(0) + titanic["Surname"] + +As we are only interested in the first part representing the surname +(element 0), we can again use the ``str`` accessor and apply :meth:`Series.str.get` to +extract the relevant part. Indeed, these string functions can be +concatenated to combine multiple functions at once! + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +More information on extracting parts of strings is available in the user guide section on :ref:`splitting and replacing strings <text.split>`. + +.. raw:: html + + </div> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Extract the passenger data about the Countess on board of the Titanic. + +.. ipython:: python + + titanic["Name"].str.contains("Countess") + +.. ipython:: python + + titanic[titanic["Name"].str.contains("Countess")] + +(*Interested in her story? See*\ `Wikipedia <https://en.wikipedia.org/wiki/No%C3%ABl_Leslie,_Countess_of_Rothes>`__\ *!*) + +The string method :meth:`Series.str.contains` checks for each of the values in the +column ``Name`` if the string contains the word ``Countess`` and returns +for each of the values ``True`` (``Countess`` is part of the name) of +``False`` (``Countess`` is notpart of the name). This output can be used +to subselect the data using conditional (boolean) indexing introduced in +the :ref:`subsetting of data tutorial <10min_tut_03_subset>`. As there was +only 1 Countess on the Titanic, we get one row as a result. + +.. raw:: html + + </li> + </ul> + +.. note:: + More powerful extractions on strings is supported, as the + :meth:`Series.str.contains` and :meth:`Series.str.extract` methods accepts `regular + expressions <https://docs.python.org/3/library/re.html>`__, but out of + scope of this tutorial. + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +More information on extracting parts of strings is available in the user guide section on :ref:`string matching and extracting <text.extract>`. + +.. raw:: html + + </div> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +Which passenger of the titanic has the longest name? + +.. ipython:: python + + titanic["Name"].str.len() + +To get the longest name we first have to get the lenghts of each of the +names in the ``Name`` column. By using pandas string methods, the +:meth:`Series.str.len` function is applied to each of the names individually +(element-wise). + +.. ipython:: python + + titanic["Name"].str.len().idxmax() + +Next, we need to get the corresponding location, preferably the index +label, in the table for which the name length is the largest. The +:meth:`~Series.idxmax`` method does exactly that. It is not a string method and is +applied to integers, so no ``str`` is used. + +.. ipython:: python + + titanic.loc[titanic["Name"].str.len().idxmax(), "Name"] + +Based on the index name of the row (``307``) and the column (``Name``), +we can do a selection using the ``loc`` operator, introduced in the +`tutorial on subsetting <3_subset_data.ipynb>`__. + +.. raw:: html + + </li> + </ul> + +.. raw:: html + + <ul class="task-bullet"> + <li> + +In the ‘Sex’ columns, replace values of ‘male’ by ‘M’ and all ‘female’ values by ‘F’ + +.. ipython:: python + + titanic["Sex_short"] = titanic["Sex"].replace({"male": "M", + "female": "F"}) + titanic["Sex_short"] + +Whereas :meth:`~Series.replace` is not a string method, it provides a convenient way +to use mappings or vocabularies to translate certain values. It requires +a ``dictionary`` to define the mapping ``{from : to}``. + +.. raw:: html + + </li> + </ul> + +.. warning:: + There is also a :meth:`~Series.str.replace` methods available to replace a + specific set of characters. However, when having a mapping of multiple + values, this would become: + + :: + + titanic["Sex_short"] = titanic["Sex"].str.replace("female", "F") + titanic["Sex_short"] = titanic["Sex_short"].str.replace("male", "M") + + This would become cumbersome and easily lead to mistakes. Just think (or + try out yourself) what would happen if those two statements are applied + in the opposite order… + +.. raw:: html + + <div class="shadow gs-callout gs-callout-remember"> + <h4>REMEMBER</h4> + +- String methods are available using the ``str`` accessor. +- String methods work element wise and can be used for conditional + indexing. +- The ``replace`` method is a convenient method to convert values + according to a given dictionary. + +.. raw:: html + + </div> + +.. raw:: html + + <div class="d-flex flex-row gs-torefguide"> + <span class="badge badge-info">To user guide</span> + +A full overview is provided in the user guide pages on :ref:`working with text data <text>`. + +.. raw:: html + + </div> diff --git a/doc/source/getting_started/intro_tutorials/index.rst b/doc/source/getting_started/intro_tutorials/index.rst new file mode 100644 index 0000000000000..28e7610866461 --- /dev/null +++ b/doc/source/getting_started/intro_tutorials/index.rst @@ -0,0 +1,22 @@ +{{ header }} + +.. _10times1minute: + +========================= +Getting started tutorials +========================= + +.. toctree:: + :maxdepth: 1 + + 01_table_oriented + 02_read_write + 03_subset_data + 04_plotting + 05_add_columns + 06_calculate_statistics + 07_reshape_table_layout + 08_combine_dataframes + 09_timeseries + 10_text_data + diff --git a/doc/source/index.rst.template b/doc/source/index.rst.template index 820a9d6285e0e..5428239ed2e27 100644 --- a/doc/source/index.rst.template +++ b/doc/source/index.rst.template @@ -104,6 +104,7 @@ programming language. {% if single_doc and single_doc.endswith('.rst') -%} .. toctree:: :maxdepth: 3 + :titlesonly: {{ single_doc[:-4] }} {% elif single_doc %} @@ -115,6 +116,7 @@ programming language. .. toctree:: :maxdepth: 3 :hidden: + :titlesonly: {% endif %} {% if not single_doc %} What's New in 1.0.1 <whatsnew/v1.0.1> diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst index b28354cd8b5f2..bbec9a770477d 100644 --- a/doc/source/user_guide/reshaping.rst +++ b/doc/source/user_guide/reshaping.rst @@ -6,6 +6,8 @@ Reshaping and pivot tables ************************** +.. _reshaping.reshaping: + Reshaping by pivoting DataFrame objects --------------------------------------- @@ -314,6 +316,8 @@ user-friendly. dft pd.wide_to_long(dft, ["A", "B"], i="id", j="year") +.. _reshaping.combine_with_groupby: + Combining with stats and GroupBy -------------------------------- diff --git a/doc/source/user_guide/text.rst b/doc/source/user_guide/text.rst index 88c86ac212f11..2e4d0fecaf5cf 100644 --- a/doc/source/user_guide/text.rst +++ b/doc/source/user_guide/text.rst @@ -189,12 +189,11 @@ and replacing any remaining whitespaces with underscores: Generally speaking, the ``.str`` accessor is intended to work only on strings. With very few exceptions, other uses are not supported, and may be disabled at a later point. +.. _text.split: Splitting and replacing strings ------------------------------- -.. _text.split: - Methods like ``split`` return a Series of lists: .. ipython:: python
Backport PR #31156: DOC: Update of the 'getting started' pages in the sphinx section of the documentation
https://api.github.com/repos/pandas-dev/pandas/pulls/32052
2020-02-17T11:51:00Z
2020-02-18T15:42:01Z
2020-02-18T15:42:01Z
2020-02-18T15:42:02Z
WEB: update blog link to only include my pandas blog posts
diff --git a/web/pandas/config.yml b/web/pandas/config.yml index 83eb152c9d944..a52c580f23530 100644 --- a/web/pandas/config.yml +++ b/web/pandas/config.yml @@ -54,7 +54,7 @@ blog: - https://dev.pandas.io/pandas-blog/feeds/all.atom.xml - https://wesmckinney.com/feeds/pandas.atom.xml - https://tomaugspurger.github.io/feed - - https://jorisvandenbossche.github.io/feeds/all.atom.xml + - https://jorisvandenbossche.github.io/feeds/pandas.atom.xml - https://datapythonista.github.io/blog/feeds/pandas.atom.xml - https://numfocus.org/tag/pandas/feed/ maintainers:
https://api.github.com/repos/pandas-dev/pandas/pulls/32051
2020-02-17T10:42:48Z
2020-02-18T08:16:33Z
2020-02-18T08:16:33Z
2020-02-18T08:16:38Z
use numexpr for Series comparisons
diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py index 1ee4c8e85be6b..d0adf2da04db3 100644 --- a/pandas/core/ops/__init__.py +++ b/pandas/core/ops/__init__.py @@ -510,6 +510,7 @@ def _comp_method_SERIES(cls, op, special): Wrapper function for Series arithmetic operations, to avoid code duplication. """ + str_rep = _get_opstr(op) op_name = _get_op_name(op, special) @unpack_zerodim_and_defer(op_name) @@ -523,7 +524,7 @@ def wrapper(self, other): lvalues = extract_array(self, extract_numpy=True) rvalues = extract_array(other, extract_numpy=True) - res_values = comparison_op(lvalues, rvalues, op) + res_values = comparison_op(lvalues, rvalues, op, str_rep) return _construct_result(self, res_values, index=self.index, name=res_name) diff --git a/pandas/core/ops/array_ops.py b/pandas/core/ops/array_ops.py index 10e3f32de3958..b216a927f65b3 100644 --- a/pandas/core/ops/array_ops.py +++ b/pandas/core/ops/array_ops.py @@ -126,7 +126,7 @@ def na_op(x, y): return na_op -def na_arithmetic_op(left, right, op, str_rep: str): +def na_arithmetic_op(left, right, op, str_rep: Optional[str], is_cmp: bool = False): """ Return the result of evaluating op on the passed in values. @@ -137,6 +137,8 @@ def na_arithmetic_op(left, right, op, str_rep: str): left : np.ndarray right : np.ndarray or scalar str_rep : str or None + is_cmp : bool, default False + If this a comparison operation. Returns ------- @@ -151,8 +153,18 @@ def na_arithmetic_op(left, right, op, str_rep: str): try: result = expressions.evaluate(op, str_rep, left, right) except TypeError: + if is_cmp: + # numexpr failed on comparison op, e.g. ndarray[float] > datetime + # In this case we do not fall back to the masked op, as that + # will handle complex numbers incorrectly, see GH#32047 + raise result = masked_arith_op(left, right, op) + if is_cmp and (is_scalar(result) or result is NotImplemented): + # numpy returned a scalar instead of operating element-wise + # e.g. numeric array vs str + return invalid_comparison(left, right, op) + return missing.dispatch_fill_zeros(op, left, right, result) @@ -199,7 +211,9 @@ def arithmetic_op(left: ArrayLike, right: Any, op, str_rep: str): return res_values -def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike: +def comparison_op( + left: ArrayLike, right: Any, op, str_rep: Optional[str] = None, +) -> ArrayLike: """ Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`. @@ -244,16 +258,8 @@ def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike: res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues) else: - op_name = f"__{op.__name__}__" - method = getattr(lvalues, op_name) with np.errstate(all="ignore"): - res_values = method(rvalues) - - if res_values is NotImplemented: - res_values = invalid_comparison(lvalues, rvalues, op) - if is_scalar(res_values): - typ = type(rvalues) - raise TypeError(f"Could not compare {typ} type with Series") + res_values = na_arithmetic_op(lvalues, rvalues, op, str_rep, is_cmp=True) return res_values @@ -380,7 +386,7 @@ def get_array_op(op, str_rep: Optional[str] = None): """ op_name = op.__name__.strip("_") if op_name in {"eq", "ne", "lt", "le", "gt", "ge"}: - return partial(comparison_op, op=op) + return partial(comparison_op, op=op, str_rep=str_rep) elif op_name in {"and", "or", "xor", "rand", "ror", "rxor"}: return partial(logical_op, op=op) else: diff --git a/pandas/tests/arithmetic/test_numeric.py b/pandas/tests/arithmetic/test_numeric.py index 51d09a92773b1..d4baf2f374cdf 100644 --- a/pandas/tests/arithmetic/test_numeric.py +++ b/pandas/tests/arithmetic/test_numeric.py @@ -66,8 +66,7 @@ def test_df_numeric_cmp_dt64_raises(self): ts = pd.Timestamp.now() df = pd.DataFrame({"x": range(5)}) - msg = "Invalid comparison between dtype=int64 and Timestamp" - + msg = "'[<>]' not supported between instances of 'Timestamp' and 'int'" with pytest.raises(TypeError, match=msg): df > ts with pytest.raises(TypeError, match=msg):
subsumes #31984.
https://api.github.com/repos/pandas-dev/pandas/pulls/32047
2020-02-17T03:31:33Z
2020-02-26T12:53:46Z
2020-02-26T12:53:46Z
2020-02-26T15:36:19Z
Use fixtures in pandas/tests/base
diff --git a/pandas/conftest.py b/pandas/conftest.py index 7851cba9cd91a..0d3f8b034beb7 100644 --- a/pandas/conftest.py +++ b/pandas/conftest.py @@ -967,7 +967,7 @@ def __len__(self): "uint": tm.makeUIntIndex(100), "range": tm.makeRangeIndex(100), "float": tm.makeFloatIndex(100), - "bool": tm.makeBoolIndex(2), + "bool": tm.makeBoolIndex(10), "categorical": tm.makeCategoricalIndex(100), "interval": tm.makeIntervalIndex(100), "empty": Index([]), @@ -978,6 +978,15 @@ def __len__(self): @pytest.fixture(params=indices_dict.keys()) def indices(request): + """ + Fixture for many "simple" kinds of indices. + + These indices are unlikely to cover corner cases, e.g. + - no names + - no NaTs/NaNs + - no values near implementation bounds + - ... + """ # copy to avoid mutation, e.g. setting .name return indices_dict[request.param].copy() @@ -995,6 +1004,14 @@ def _create_series(index): } +@pytest.fixture +def series_with_simple_index(indices): + """ + Fixture for tests on series with changing types of indices. + """ + return _create_series(indices) + + _narrow_dtypes = [ np.float16, np.float32, diff --git a/pandas/tests/base/test_ops.py b/pandas/tests/base/test_ops.py index 9deb56f070d56..625d559001e72 100644 --- a/pandas/tests/base/test_ops.py +++ b/pandas/tests/base/test_ops.py @@ -137,227 +137,238 @@ def setup_method(self, method): self.is_valid_objs = self.objs self.not_valid_objs = [] - def test_none_comparison(self): + def test_none_comparison(self, series_with_simple_index): + series = series_with_simple_index + if isinstance(series.index, IntervalIndex): + # IntervalIndex breaks on "series[0] = np.nan" below + pytest.skip("IntervalIndex doesn't support assignment") + if len(series) < 1: + pytest.skip("Test doesn't make sense on empty data") # bug brought up by #1079 # changed from TypeError in 0.17.0 - for o in self.is_valid_objs: - if isinstance(o, Series): + series[0] = np.nan + + # noinspection PyComparisonWithNone + result = series == None # noqa + assert not result.iat[0] + assert not result.iat[1] + + # noinspection PyComparisonWithNone + result = series != None # noqa + assert result.iat[0] + assert result.iat[1] + + result = None == series # noqa + assert not result.iat[0] + assert not result.iat[1] + + result = None != series # noqa + assert result.iat[0] + assert result.iat[1] + + if is_datetime64_dtype(series) or is_datetime64tz_dtype(series): + # Following DatetimeIndex (and Timestamp) convention, + # inequality comparisons with Series[datetime64] raise + msg = "Invalid comparison" + with pytest.raises(TypeError, match=msg): + None > series + with pytest.raises(TypeError, match=msg): + series > None + else: + result = None > series + assert not result.iat[0] + assert not result.iat[1] - o[0] = np.nan - - # noinspection PyComparisonWithNone - result = o == None # noqa - assert not result.iat[0] - assert not result.iat[1] - - # noinspection PyComparisonWithNone - result = o != None # noqa - assert result.iat[0] - assert result.iat[1] - - result = None == o # noqa - assert not result.iat[0] - assert not result.iat[1] - - result = None != o # noqa - assert result.iat[0] - assert result.iat[1] - - if is_datetime64_dtype(o) or is_datetime64tz_dtype(o): - # Following DatetimeIndex (and Timestamp) convention, - # inequality comparisons with Series[datetime64] raise - msg = "Invalid comparison" - with pytest.raises(TypeError, match=msg): - None > o - with pytest.raises(TypeError, match=msg): - o > None - else: - result = None > o - assert not result.iat[0] - assert not result.iat[1] + result = series < None + assert not result.iat[0] + assert not result.iat[1] - result = o < None - assert not result.iat[0] - assert not result.iat[1] + def test_ndarray_compat_properties(self, index_or_series_obj): + obj = index_or_series_obj - def test_ndarray_compat_properties(self): + # Check that we work. + for p in ["shape", "dtype", "T", "nbytes"]: + assert getattr(obj, p, None) is not None - for o in self.objs: - # Check that we work. - for p in ["shape", "dtype", "T", "nbytes"]: - assert getattr(o, p, None) is not None + # deprecated properties + for p in ["flags", "strides", "itemsize", "base", "data"]: + assert not hasattr(obj, p) - # deprecated properties - for p in ["flags", "strides", "itemsize", "base", "data"]: - assert not hasattr(o, p) + msg = "can only convert an array of size 1 to a Python scalar" + with pytest.raises(ValueError, match=msg): + obj.item() # len > 1 - msg = "can only convert an array of size 1 to a Python scalar" - with pytest.raises(ValueError, match=msg): - o.item() # len > 1 - - assert o.ndim == 1 - assert o.size == len(o) + assert obj.ndim == 1 + assert obj.size == len(obj) assert Index([1]).item() == 1 assert Series([1]).item() == 1 - def test_value_counts_unique_nunique(self): - for orig in self.objs: - o = orig.copy() - klass = type(o) - values = o._values - - if isinstance(values, Index): - # reset name not to affect latter process - values.name = None - - # create repeated values, 'n'th element is repeated by n+1 times - # skip boolean, because it only has 2 values at most - if isinstance(o, Index) and o.is_boolean(): - continue - elif isinstance(o, Index): - expected_index = Index(o[::-1]) - expected_index.name = None - o = o.repeat(range(1, len(o) + 1)) - o.name = "a" - else: - expected_index = Index(values[::-1]) - idx = o.index.repeat(range(1, len(o) + 1)) - # take-based repeat - indices = np.repeat(np.arange(len(o)), range(1, len(o) + 1)) - rep = values.take(indices) - o = klass(rep, index=idx, name="a") - - # check values has the same dtype as the original - assert o.dtype == orig.dtype - - expected_s = Series( - range(10, 0, -1), index=expected_index, dtype="int64", name="a" + def test_value_counts_unique_nunique(self, index_or_series_obj): + orig = index_or_series_obj + obj = orig.copy() + klass = type(obj) + values = obj._values + + if orig.duplicated().any(): + pytest.xfail( + "The test implementation isn't flexible enough to deal" + " with duplicated values. This isn't a bug in the" + " application code, but in the test code." ) - result = o.value_counts() - tm.assert_series_equal(result, expected_s) - assert result.index.name is None - assert result.name == "a" + # create repeated values, 'n'th element is repeated by n+1 times + if isinstance(obj, Index): + expected_index = Index(obj[::-1]) + expected_index.name = None + obj = obj.repeat(range(1, len(obj) + 1)) + else: + expected_index = Index(values[::-1]) + idx = obj.index.repeat(range(1, len(obj) + 1)) + # take-based repeat + indices = np.repeat(np.arange(len(obj)), range(1, len(obj) + 1)) + rep = values.take(indices) + obj = klass(rep, index=idx) + + # check values has the same dtype as the original + assert obj.dtype == orig.dtype + + expected_s = Series( + range(len(orig), 0, -1), index=expected_index, dtype="int64" + ) - result = o.unique() - if isinstance(o, Index): - assert isinstance(result, type(o)) - tm.assert_index_equal(result, orig) - assert result.dtype == orig.dtype - elif is_datetime64tz_dtype(o): - # datetimetz Series returns array of Timestamp - assert result[0] == orig[0] - for r in result: - assert isinstance(r, Timestamp) - - tm.assert_numpy_array_equal( - result.astype(object), orig._values.astype(object) - ) - else: - tm.assert_numpy_array_equal(result, orig.values) - assert result.dtype == orig.dtype + result = obj.value_counts() + tm.assert_series_equal(result, expected_s) + assert result.index.name is None + + result = obj.unique() + if isinstance(obj, Index): + assert isinstance(result, type(obj)) + tm.assert_index_equal(result, orig) + assert result.dtype == orig.dtype + elif is_datetime64tz_dtype(obj): + # datetimetz Series returns array of Timestamp + assert result[0] == orig[0] + for r in result: + assert isinstance(r, Timestamp) + + tm.assert_numpy_array_equal( + result.astype(object), orig._values.astype(object) + ) + else: + tm.assert_numpy_array_equal(result, orig.values) + assert result.dtype == orig.dtype - assert o.nunique() == len(np.unique(o.values)) + # dropna=True would break for MultiIndex + assert obj.nunique(dropna=False) == len(np.unique(obj.values)) @pytest.mark.parametrize("null_obj", [np.nan, None]) - def test_value_counts_unique_nunique_null(self, null_obj): - - for orig in self.objs: - o = orig.copy() - klass = type(o) - values = o._ndarray_values - - if not allow_na_ops(o): - continue - - # special assign to the numpy array - if is_datetime64tz_dtype(o): - if isinstance(o, DatetimeIndex): - v = o.asi8 - v[0:2] = iNaT - values = o._shallow_copy(v) - else: - o = o.copy() - o[0:2] = pd.NaT - values = o._values - - elif needs_i8_conversion(o): - values[0:2] = iNaT - values = o._shallow_copy(values) + def test_value_counts_unique_nunique_null(self, null_obj, index_or_series_obj): + orig = index_or_series_obj + obj = orig.copy() + klass = type(obj) + values = obj._ndarray_values + num_values = len(orig) + + if not allow_na_ops(obj): + pytest.skip("type doesn't allow for NA operations") + elif isinstance(orig, (pd.CategoricalIndex, pd.IntervalIndex)): + pytest.skip(f"values of {klass} cannot be changed") + elif isinstance(orig, pd.MultiIndex): + pytest.skip("MultiIndex doesn't support isna") + + # special assign to the numpy array + if is_datetime64tz_dtype(obj): + if isinstance(obj, DatetimeIndex): + v = obj.asi8 + v[0:2] = iNaT + values = obj._shallow_copy(v) else: - values[0:2] = null_obj - # check values has the same dtype as the original - - assert values.dtype == o.dtype + obj = obj.copy() + obj[0:2] = pd.NaT + values = obj._values - # create repeated values, 'n'th element is repeated by n+1 - # times - if isinstance(o, (DatetimeIndex, PeriodIndex)): - expected_index = o.copy() - expected_index.name = None + elif needs_i8_conversion(obj): + values[0:2] = iNaT + values = obj._shallow_copy(values) + else: + values[0:2] = null_obj - # attach name to klass - o = klass(values.repeat(range(1, len(o) + 1))) - o.name = "a" - else: - if isinstance(o, DatetimeIndex): - expected_index = orig._values._shallow_copy(values) - else: - expected_index = Index(values) - expected_index.name = None - o = o.repeat(range(1, len(o) + 1)) - o.name = "a" - - # check values has the same dtype as the original - assert o.dtype == orig.dtype - # check values correctly have NaN - nanloc = np.zeros(len(o), dtype=np.bool) - nanloc[:3] = True - if isinstance(o, Index): - tm.assert_numpy_array_equal(pd.isna(o), nanloc) - else: - exp = Series(nanloc, o.index, name="a") - tm.assert_series_equal(pd.isna(o), exp) - - expected_s_na = Series( - list(range(10, 2, -1)) + [3], - index=expected_index[9:0:-1], - dtype="int64", - name="a", - ) - expected_s = Series( - list(range(10, 2, -1)), - index=expected_index[9:1:-1], - dtype="int64", - name="a", - ) + # check values has the same dtype as the original + assert values.dtype == obj.dtype - result_s_na = o.value_counts(dropna=False) - tm.assert_series_equal(result_s_na, expected_s_na) - assert result_s_na.index.name is None - assert result_s_na.name == "a" - result_s = o.value_counts() - tm.assert_series_equal(o.value_counts(), expected_s) - assert result_s.index.name is None - assert result_s.name == "a" + # create repeated values, 'n'th element is repeated by n+1 + # times + if isinstance(obj, (DatetimeIndex, PeriodIndex)): + expected_index = obj.copy() + expected_index.name = None - result = o.unique() - if isinstance(o, Index): - tm.assert_index_equal(result, Index(values[1:], name="a")) - elif is_datetime64tz_dtype(o): - # unable to compare NaT / nan - tm.assert_extension_array_equal(result[1:], values[2:]) - assert result[0] is pd.NaT + # attach name to klass + obj = klass(values.repeat(range(1, len(obj) + 1))) + obj.name = "a" + else: + if isinstance(obj, DatetimeIndex): + expected_index = orig._values._shallow_copy(values) else: - tm.assert_numpy_array_equal(result[1:], values[2:]) - - assert pd.isna(result[0]) - assert result.dtype == orig.dtype + expected_index = Index(values) + expected_index.name = None + obj = obj.repeat(range(1, len(obj) + 1)) + obj.name = "a" + + # check values has the same dtype as the original + assert obj.dtype == orig.dtype + + # check values correctly have NaN + nanloc = np.zeros(len(obj), dtype=np.bool) + nanloc[:3] = True + if isinstance(obj, Index): + tm.assert_numpy_array_equal(pd.isna(obj), nanloc) + else: + exp = Series(nanloc, obj.index, name="a") + tm.assert_series_equal(pd.isna(obj), exp) + + expected_data = list(range(num_values, 2, -1)) + expected_data_na = expected_data.copy() + if expected_data_na: + expected_data_na.append(3) + expected_s_na = Series( + expected_data_na, + index=expected_index[num_values - 1 : 0 : -1], + dtype="int64", + name="a", + ) + expected_s = Series( + expected_data, + index=expected_index[num_values - 1 : 1 : -1], + dtype="int64", + name="a", + ) - assert o.nunique() == 8 - assert o.nunique(dropna=False) == 9 + result_s_na = obj.value_counts(dropna=False) + tm.assert_series_equal(result_s_na, expected_s_na) + assert result_s_na.index.name is None + assert result_s_na.name == "a" + result_s = obj.value_counts() + tm.assert_series_equal(obj.value_counts(), expected_s) + assert result_s.index.name is None + assert result_s.name == "a" + + result = obj.unique() + if isinstance(obj, Index): + tm.assert_index_equal(result, Index(values[1:], name="a")) + elif is_datetime64tz_dtype(obj): + # unable to compare NaT / nan + tm.assert_extension_array_equal(result[1:], values[2:]) + assert result[0] is pd.NaT + elif len(obj) > 0: + tm.assert_numpy_array_equal(result[1:], values[2:]) + + assert pd.isna(result[0]) + assert result.dtype == orig.dtype + + assert obj.nunique() == max(0, num_values - 2) + assert obj.nunique(dropna=False) == max(0, num_values - 1) def test_value_counts_inferred(self, index_or_series): klass = index_or_series diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index 2073aa0727809..a7437b39872be 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -514,12 +514,12 @@ def test_union_base(self, indices): @pytest.mark.parametrize("sort", [None, False]) def test_difference_base(self, sort, indices): - if isinstance(indices, CategoricalIndex): - return - first = indices[2:] second = indices[:4] - answer = indices[4:] + if isinstance(indices, CategoricalIndex) or indices.is_boolean(): + answer = [] + else: + answer = indices[4:] result = first.difference(second, sort) assert tm.equalContents(result, answer)
part of #23877 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` **Note:** The diff is a bit inflated. Most changes are just indentation because for loops are replaced by a parametrized fixture.
https://api.github.com/repos/pandas-dev/pandas/pulls/32046
2020-02-17T00:50:11Z
2020-02-23T17:06:03Z
2020-02-23T17:06:03Z
2020-02-23T17:46:40Z
CLN: 29547 replace .format() with f-strings
diff --git a/scripts/find_commits_touching_func.py b/scripts/find_commits_touching_func.py index 85675cb6df42b..ba2f54dd9bbe3 100755 --- a/scripts/find_commits_touching_func.py +++ b/scripts/find_commits_touching_func.py @@ -96,13 +96,7 @@ def get_hits(defname, files=()): cs = set() for f in files: try: - r = sh.git( - "blame", - "-L", - r"/def\s*{start}/,/def/".format(start=defname), - f, - _tty_out=False, - ) + r = sh.git("blame", "-L", rf"/def\s*{defname}/,/def/", f, _tty_out=False,) except sh.ErrorReturnCode_128: logger.debug("no matches in %s" % f) continue @@ -117,14 +111,7 @@ def get_hits(defname, files=()): def get_commit_info(c, fmt, sep="\t"): - r = sh.git( - "log", - "--format={}".format(fmt), - "{}^..{}".format(c, c), - "-n", - "1", - _tty_out=False, - ) + r = sh.git("log", f"--format={fmt}", f"{c}^..{c}", "-n", "1", _tty_out=False,) return str(r).split(sep) @@ -203,10 +190,14 @@ def sorter(i): h, s, d = get_commit_vitals(hit.commit) p = hit.path.split(os.path.realpath(os.curdir) + os.path.sep)[-1] - fmt = "{:%d} {:10} {:<%d} {:<%d}" % (HASH_LEN, SUBJ_LEN, PATH_LEN) if len(s) > SUBJ_LEN: s = s[: SUBJ_LEN - 5] + " ..." - print(fmt.format(h[:HASH_LEN], d.isoformat()[:10], s, p[-20:])) + print( + f"{h[:HASH_LEN]:{HASH_LEN}} " + f"{d.isoformat()[:10]:10} " + f"{s:<{SUBJ_LEN}} " + f"{p[-20:]:<{PATH_LEN}}" + ) print("\n")
This PR replaces .format() with f-strings as requested in https://github.com/pandas-dev/pandas/issues/29547. It did this replacement in scripts/find_commits_touching_func.py - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/32044
2020-02-16T20:58:54Z
2020-02-18T09:19:41Z
null
2020-02-18T09:19:42Z
DOC: Mention black and PEP8 in pandas style guide
diff --git a/doc/source/development/code_style.rst b/doc/source/development/code_style.rst index bcddc033a61f5..17f8783f71bfb 100644 --- a/doc/source/development/code_style.rst +++ b/doc/source/development/code_style.rst @@ -9,6 +9,12 @@ pandas code style guide .. contents:: Table of contents: :local: +*pandas* follows the `PEP8 <https://www.python.org/dev/peps/pep-0008/>`_ +standard and uses `Black <https://black.readthedocs.io/en/stable/>`_ +and `Flake8 <https://flake8.pycqa.org/en/latest/>`_ to ensure a +consistent code format throughout the project. For details see the +:ref:`contributing guide to pandas<contributing.code-formatting>`. + Patterns ========
- [ ] closes #31828 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32043
2020-02-16T18:51:03Z
2020-02-19T01:46:26Z
2020-02-19T01:46:26Z
2020-02-19T01:46:33Z
CLN: clean-up show_versions and consistently use null for json output
diff --git a/pandas/_typing.py b/pandas/_typing.py index e2858441605f7..3b7392f781525 100644 --- a/pandas/_typing.py +++ b/pandas/_typing.py @@ -64,7 +64,7 @@ Label = Optional[Hashable] Level = Union[Label, int] Ordered = Optional[bool] -JSONSerializable = Union[PythonScalar, List, Dict] +JSONSerializable = Optional[Union[PythonScalar, List, Dict]] Axes = Collection # For functions like rename that convert one label to another diff --git a/pandas/util/_print_versions.py b/pandas/util/_print_versions.py index f9502cc22b0c6..7fc85a04e7d84 100644 --- a/pandas/util/_print_versions.py +++ b/pandas/util/_print_versions.py @@ -5,8 +5,9 @@ import platform import struct import sys -from typing import List, Optional, Tuple, Union +from typing import Dict, Optional, Union +from pandas._typing import JSONSerializable from pandas.compat._optional import VERSIONS, _get_version, import_optional_dependency @@ -21,43 +22,32 @@ def _get_commit_hash() -> Optional[str]: return versions["full-revisionid"] -def get_sys_info() -> List[Tuple[str, Optional[Union[str, int]]]]: +def _get_sys_info() -> Dict[str, JSONSerializable]: """ - Returns system information as a list + Returns system information as a JSON serializable dictionary. + """ + uname_result = platform.uname() + language_code, encoding = locale.getlocale() + return { + "commit": _get_commit_hash(), + "python": ".".join(str(i) for i in sys.version_info), + "python-bits": struct.calcsize("P") * 8, + "OS": uname_result.system, + "OS-release": uname_result.release, + "Version": uname_result.version, + "machine": uname_result.machine, + "processor": uname_result.processor, + "byteorder": sys.byteorder, + "LC_ALL": os.environ.get("LC_ALL"), + "LANG": os.environ.get("LANG"), + "LOCALE": {"language-code": language_code, "encoding": encoding}, + } + + +def _get_dependency_info() -> Dict[str, JSONSerializable]: + """ + Returns dependency information as a JSON serializable dictionary. """ - blob: List[Tuple[str, Optional[Union[str, int]]]] = [] - - # get full commit hash - commit = _get_commit_hash() - - blob.append(("commit", commit)) - - try: - (sysname, nodename, release, version, machine, processor) = platform.uname() - blob.extend( - [ - ("python", ".".join(map(str, sys.version_info))), - ("python-bits", struct.calcsize("P") * 8), - ("OS", f"{sysname}"), - ("OS-release", f"{release}"), - # FIXME: dont leave commented-out - # ("Version", f"{version}"), - ("machine", f"{machine}"), - ("processor", f"{processor}"), - ("byteorder", f"{sys.byteorder}"), - ("LC_ALL", f"{os.environ.get('LC_ALL', 'None')}"), - ("LANG", f"{os.environ.get('LANG', 'None')}"), - ("LOCALE", ".".join(map(str, locale.getlocale()))), - ] - ) - except (KeyError, ValueError): - pass - - return blob - - -def show_versions(as_json=False): - sys_info = get_sys_info() deps = [ "pandas", # required @@ -86,39 +76,45 @@ def show_versions(as_json=False): "IPython", "pandas_datareader", ] - deps.extend(list(VERSIONS)) - deps_blob = [] + result: Dict[str, JSONSerializable] = {} for modname in deps: mod = import_optional_dependency( modname, raise_on_missing=False, on_version="ignore" ) - ver: Optional[str] - if mod: - ver = _get_version(mod) - else: - ver = None - deps_blob.append((modname, ver)) + result[modname] = _get_version(mod) if mod else None + return result + + +def show_versions(as_json: Union[str, bool] = False) -> None: + sys_info = _get_sys_info() + deps = _get_dependency_info() if as_json: - j = dict(system=dict(sys_info), dependencies=dict(deps_blob)) + j = dict(system=sys_info, dependencies=deps) if as_json is True: print(j) else: + assert isinstance(as_json, str) # needed for mypy with codecs.open(as_json, "wb", encoding="utf8") as f: json.dump(j, f, indent=2) else: + assert isinstance(sys_info["LOCALE"], dict) # needed for mypy + language_code = sys_info["LOCALE"]["language-code"] + encoding = sys_info["LOCALE"]["encoding"] + sys_info["LOCALE"] = f"{language_code}.{encoding}" + maxlen = max(len(x) for x in deps) print("\nINSTALLED VERSIONS") print("------------------") - for k, stat in sys_info: - print(f"{k:<{maxlen}}: {stat}") + for k, v in sys_info.items(): + print(f"{k:<{maxlen}}: {v}") print("") - for k, stat in deps_blob: - print(f"{k:<{maxlen}}: {stat}") + for k, v in deps.items(): + print(f"{k:<{maxlen}}: {v}") def main() -> int:
note changes to LC_ALL and LOCALE, null is already used for dependencies on master, see blosc and feather. master ``` { "system": { "commit": "a7ecced88a42c426bf61016c0131cab023c0cdff", "python": "3.7.5.final.0", "python-bits": 64, "OS": "Windows", "OS-release": "10", "machine": "AMD64", "processor": "Intel64 Family 6 Model 58 Stepping 9, GenuineIntel", "byteorder": "little", "LC_ALL": "None", "LANG": "en_GB.UTF-8", "LOCALE": "None.None" }, "dependencies": { "pandas": "1.1.0.dev0+497.ga7ecced88", "numpy": "1.17.2", "pytz": "2019.3", "dateutil": "2.8.0", "pip": "19.3.1", "setuptools": "41.6.0.post20191030", "Cython": "0.29.13", "pytest": "5.2.2", "hypothesis": "4.36.2", "sphinx": "2.2.1", "blosc": null, "feather": null, ... ``` this pr ``` { "system": { "commit": "2ac9d30f302e59de035cbea89188d5421212b000", "python": "3.7.5.final.0", "python-bits": 64, "OS": "Windows", "OS-release": "10", "Version": "10.0.18362", "machine": "AMD64", "processor": "Intel64 Family 6 Model 58 Stepping 9, GenuineIntel", "byteorder": "little", "LC_ALL": null, "LANG": "en_GB.UTF-8", "LOCALE": { "language-code": null, "encoding": null } }, "dependencies": { "pandas": "1.1.0.dev0+500.g2ac9d30f3", "numpy": "1.17.2", "pytz": "2019.3", "dateutil": "2.8.0", "pip": "19.3.1", "setuptools": "41.6.0.post20191030", "Cython": "0.29.13", "pytest": "5.2.2", "hypothesis": "4.36.2", "sphinx": "2.2.1", "blosc": null, ... ``` console output is unchanged ``` INSTALLED VERSIONS ------------------ commit : 2ac9d30f302e59de035cbea89188d5421212b000 python : 3.7.5.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : None.None pandas : 1.1.0.dev0+500.g2ac9d30f3 numpy : 1.17.2 pytz : 2019.3 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.6.0.post20191030 Cython : 0.29.13 pytest : 5.2.2 hypothesis : 4.36.2 sphinx : 2.2.1 blosc : None feather : None ... ```
https://api.github.com/repos/pandas-dev/pandas/pulls/32042
2020-02-16T16:56:07Z
2020-03-03T03:36:27Z
2020-03-03T03:36:27Z
2020-03-03T11:43:14Z
REGR: show_versions
diff --git a/pandas/tests/test_optional_dependency.py b/pandas/tests/test_optional_dependency.py index ce527214e55e7..e5ed69b7703b1 100644 --- a/pandas/tests/test_optional_dependency.py +++ b/pandas/tests/test_optional_dependency.py @@ -22,12 +22,12 @@ def test_xlrd_version_fallback(): import_optional_dependency("xlrd") -def test_bad_version(): +def test_bad_version(monkeypatch): name = "fakemodule" module = types.ModuleType(name) module.__version__ = "0.9.0" sys.modules[name] = module - VERSIONS[name] = "1.0.0" + monkeypatch.setitem(VERSIONS, name, "1.0.0") match = "Pandas requires .*1.0.0.* of .fakemodule.*'0.9.0'" with pytest.raises(ImportError, match=match): @@ -42,11 +42,11 @@ def test_bad_version(): assert result is module -def test_no_version_raises(): +def test_no_version_raises(monkeypatch): name = "fakemodule" module = types.ModuleType(name) sys.modules[name] = module - VERSIONS[name] = "1.0.0" + monkeypatch.setitem(VERSIONS, name, "1.0.0") with pytest.raises(ImportError, match="Can't determine .* fakemodule"): import_optional_dependency(name) diff --git a/pandas/tests/util/test_show_versions.py b/pandas/tests/util/test_show_versions.py new file mode 100644 index 0000000000000..0d2c81c4ea6c7 --- /dev/null +++ b/pandas/tests/util/test_show_versions.py @@ -0,0 +1,22 @@ +import re + +import pandas as pd + + +def test_show_versions(capsys): + # gh-32041 + pd.show_versions() + captured = capsys.readouterr() + result = captured.out + + # check header + assert "INSTALLED VERSIONS" in result + + # check full commit hash + assert re.search(r"commit\s*:\s[0-9a-f]{40}\n", result) + + # check required dependency + assert re.search(r"numpy\s*:\s([0-9\.\+a-f]|dev)+\n", result) + + # check optional dependency + assert re.search(r"pyarrow\s*:\s([0-9\.]+|None)\n", result) diff --git a/pandas/util/_print_versions.py b/pandas/util/_print_versions.py index fdfa436ce6536..99b2b9e9f5f6e 100644 --- a/pandas/util/_print_versions.py +++ b/pandas/util/_print_versions.py @@ -118,10 +118,10 @@ def show_versions(as_json=False): print("\nINSTALLED VERSIONS") print("------------------") for k, stat in sys_info: - print(f"{{k:<{maxlen}}}: {{stat}}") + print(f"{k:<{maxlen}}: {stat}") print("") for k, stat in deps_blob: - print(f"{{k:<{maxlen}}}: {{stat}}") + print(f"{k:<{maxlen}}: {stat}") def main() -> int:
regression in #31660 (no need to backport) master ``` INSTALLED VERSIONS ------------------ {k:<17}: {stat} {k:<17}: {stat} {k:<17}: {stat} ... ``` this PR ``` INSTALLED VERSIONS ------------------ commit : b11e0647d42a27c279f3c46d5ce26d79bb5f5dec python : 3.7.4.final.0 python-bits : 64 ... ```
https://api.github.com/repos/pandas-dev/pandas/pulls/32041
2020-02-16T11:49:56Z
2020-02-19T01:47:39Z
2020-02-19T01:47:39Z
2020-02-19T09:39:44Z
BUG: GroupBy aggregation of DataFrame with MultiIndex columns breaks with custom function
diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst index 123dfa07f4331..18a431cc2be5e 100644 --- a/doc/source/whatsnew/v1.0.2.rst +++ b/doc/source/whatsnew/v1.0.2.rst @@ -17,6 +17,7 @@ Fixed regressions - Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`) - Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`) +- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` which was failing on frames with MultiIndex columns and a custom function (:issue:`31777`) - Fixed regression in ``groupby(..).rolling(..).apply()`` (``RollingGroupby``) where the ``raw`` parameter was ignored (:issue:`31754`) - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.rolling.Rolling.corr>` when using a time offset (:issue:`31789`) - Fixed regression in :meth:`groupby(..).nunique() <pandas.core.groupby.DataFrameGroupBy.nunique>` which was modifying the original values if ``NaN`` values were present (:issue:`31950`) diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py index b7ac3048631c5..fda66f68f7adc 100644 --- a/pandas/core/groupby/generic.py +++ b/pandas/core/groupby/generic.py @@ -955,9 +955,11 @@ def aggregate(self, func=None, *args, **kwargs): raise result = self._aggregate_frame(func) else: - result.columns = Index( - result.columns.levels[0], name=self._selected_obj.columns.name - ) + # select everything except for the last level, which is the one + # containing the name of the function(s), see GH 32040 + result.columns = result.columns.rename( + [self._selected_obj.columns.name] * result.columns.nlevels + ).droplevel(-1) if not self.as_index: self._insert_inaxis_grouper_inplace(result) diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py index 48f8de7e51ae4..1265547653d7b 100644 --- a/pandas/tests/groupby/aggregate/test_aggregate.py +++ b/pandas/tests/groupby/aggregate/test_aggregate.py @@ -691,6 +691,19 @@ def test_agg_relabel_multiindex_duplicates(): tm.assert_frame_equal(result, expected) +@pytest.mark.parametrize( + "func", [lambda s: s.mean(), lambda s: np.mean(s), lambda s: np.nanmean(s)] +) +def test_multiindex_custom_func(func): + # GH 31777 + data = [[1, 4, 2], [5, 7, 1]] + df = pd.DataFrame(data, columns=pd.MultiIndex.from_arrays([[1, 1, 2], [3, 4, 3]])) + result = df.groupby(np.array([0, 1])).agg(func) + expected_dict = {(1, 3): {0: 1, 1: 5}, (1, 4): {0: 4, 1: 7}, (2, 3): {0: 2, 1: 1}} + expected = pd.DataFrame(expected_dict) + tm.assert_frame_equal(result, expected) + + def myfunc(s): return np.percentile(s, q=0.90)
- [x] closes #31777 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32040
2020-02-16T11:32:31Z
2020-03-12T02:32:56Z
2020-03-12T02:32:55Z
2020-07-12T09:24:28Z
BUG: Preserve name in Index.astype
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst index 0ebe57bfbb3a1..c8c078a4e685d 100644 --- a/doc/source/whatsnew/v1.1.0.rst +++ b/doc/source/whatsnew/v1.1.0.rst @@ -410,6 +410,7 @@ Reshaping - Bug in :func:`crosstab` when inputs are two Series and have tuple names, the output will keep dummy MultiIndex as columns. (:issue:`18321`) - :meth:`DataFrame.pivot` can now take lists for ``index`` and ``columns`` arguments (:issue:`21425`) - Bug in :func:`concat` where the resulting indices are not copied when ``copy=True`` (:issue:`29879`) +- Bug where :meth:`Index.astype` would lose the name attribute when converting from ``Float64Index`` to ``Int64Index``, or when casting to an ``ExtensionArray`` dtype (:issue:`32013`) - :meth:`Series.append` will now raise a ``TypeError`` when passed a DataFrame or a sequence containing Dataframe (:issue:`31413`) - :meth:`DataFrame.replace` and :meth:`Series.replace` will raise a ``TypeError`` if ``to_replace`` is not an expected type. Previously the ``replace`` would fail silently (:issue:`18634`) - Bug on inplace operation of a Series that was adding a column to the DataFrame from where it was originally dropped from (using inplace=True) (:issue:`30484`) diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index 5b439a851a709..507adac789fa0 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -670,7 +670,7 @@ def astype(self, dtype, copy=True): return CategoricalIndex(self.values, name=self.name, dtype=dtype, copy=copy) elif is_extension_array_dtype(dtype): - return Index(np.asarray(self), dtype=dtype, copy=copy) + return Index(np.asarray(self), name=self.name, dtype=dtype, copy=copy) try: casted = self.values.astype(dtype, copy=copy) diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py index 3a6f3630c19e7..4dbe5ffde7e52 100644 --- a/pandas/core/indexes/numeric.py +++ b/pandas/core/indexes/numeric.py @@ -369,7 +369,7 @@ def astype(self, dtype, copy=True): # TODO(jreback); this can change once we have an EA Index type # GH 13149 arr = astype_nansafe(self._values, dtype=dtype) - return Int64Index(arr) + return Int64Index(arr, name=self.name) return super().astype(dtype, copy=copy) # ---------------------------------------------------------------- diff --git a/pandas/tests/indexes/datetimes/test_astype.py b/pandas/tests/indexes/datetimes/test_astype.py index 916f722247a14..34169a670c169 100644 --- a/pandas/tests/indexes/datetimes/test_astype.py +++ b/pandas/tests/indexes/datetimes/test_astype.py @@ -22,27 +22,32 @@ class TestDatetimeIndex: def test_astype(self): # GH 13149, GH 13209 - idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN]) + idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], name="idx") result = idx.astype(object) - expected = Index([Timestamp("2016-05-16")] + [NaT] * 3, dtype=object) + expected = Index( + [Timestamp("2016-05-16")] + [NaT] * 3, dtype=object, name="idx" + ) tm.assert_index_equal(result, expected) result = idx.astype(int) expected = Int64Index( - [1463356800000000000] + [-9223372036854775808] * 3, dtype=np.int64 + [1463356800000000000] + [-9223372036854775808] * 3, + dtype=np.int64, + name="idx", ) tm.assert_index_equal(result, expected) - rng = date_range("1/1/2000", periods=10) + rng = date_range("1/1/2000", periods=10, name="idx") result = rng.astype("i8") - tm.assert_index_equal(result, Index(rng.asi8)) + tm.assert_index_equal(result, Index(rng.asi8, name="idx")) tm.assert_numpy_array_equal(result.values, rng.asi8) def test_astype_uint(self): - arr = date_range("2000", periods=2) + arr = date_range("2000", periods=2, name="idx") expected = pd.UInt64Index( - np.array([946684800000000000, 946771200000000000], dtype="uint64") + np.array([946684800000000000, 946771200000000000], dtype="uint64"), + name="idx", ) tm.assert_index_equal(arr.astype("uint64"), expected) @@ -148,7 +153,7 @@ def test_astype_str(self): def test_astype_datetime64(self): # GH 13149, GH 13209 - idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN]) + idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], name="idx") result = idx.astype("datetime64[ns]") tm.assert_index_equal(result, idx) @@ -158,10 +163,12 @@ def test_astype_datetime64(self): tm.assert_index_equal(result, idx) assert result is idx - idx_tz = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], tz="EST") + idx_tz = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], tz="EST", name="idx") result = idx_tz.astype("datetime64[ns]") expected = DatetimeIndex( - ["2016-05-16 05:00:00", "NaT", "NaT", "NaT"], dtype="datetime64[ns]" + ["2016-05-16 05:00:00", "NaT", "NaT", "NaT"], + dtype="datetime64[ns]", + name="idx", ) tm.assert_index_equal(result, expected) @@ -273,8 +280,8 @@ def _check_rng(rng): def test_integer_index_astype_datetime(self, tz, dtype): # GH 20997, 20964, 24559 val = [pd.Timestamp("2018-01-01", tz=tz).value] - result = pd.Index(val).astype(dtype) - expected = pd.DatetimeIndex(["2018-01-01"], tz=tz) + result = pd.Index(val, name="idx").astype(dtype) + expected = pd.DatetimeIndex(["2018-01-01"], tz=tz, name="idx") tm.assert_index_equal(result, expected) def test_dti_astype_period(self): @@ -292,10 +299,11 @@ def test_dti_astype_period(self): class TestAstype: @pytest.mark.parametrize("tz", [None, "US/Central"]) def test_astype_category(self, tz): - obj = pd.date_range("2000", periods=2, tz=tz) + obj = pd.date_range("2000", periods=2, tz=tz, name="idx") result = obj.astype("category") expected = pd.CategoricalIndex( - [pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)] + [pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)], + name="idx", ) tm.assert_index_equal(result, expected) @@ -305,9 +313,9 @@ def test_astype_category(self, tz): @pytest.mark.parametrize("tz", [None, "US/Central"]) def test_astype_array_fallback(self, tz): - obj = pd.date_range("2000", periods=2, tz=tz) + obj = pd.date_range("2000", periods=2, tz=tz, name="idx") result = obj.astype(bool) - expected = pd.Index(np.array([True, True])) + expected = pd.Index(np.array([True, True]), name="idx") tm.assert_index_equal(result, expected) result = obj._data.astype(bool) diff --git a/pandas/tests/indexes/period/test_astype.py b/pandas/tests/indexes/period/test_astype.py index 2f10e45193d5d..b286191623ebb 100644 --- a/pandas/tests/indexes/period/test_astype.py +++ b/pandas/tests/indexes/period/test_astype.py @@ -27,31 +27,34 @@ def test_astype_raises(self, dtype): def test_astype_conversion(self): # GH#13149, GH#13209 - idx = PeriodIndex(["2016-05-16", "NaT", NaT, np.NaN], freq="D") + idx = PeriodIndex(["2016-05-16", "NaT", NaT, np.NaN], freq="D", name="idx") result = idx.astype(object) expected = Index( [Period("2016-05-16", freq="D")] + [Period(NaT, freq="D")] * 3, dtype="object", + name="idx", ) tm.assert_index_equal(result, expected) result = idx.astype(np.int64) - expected = Int64Index([16937] + [-9223372036854775808] * 3, dtype=np.int64) + expected = Int64Index( + [16937] + [-9223372036854775808] * 3, dtype=np.int64, name="idx" + ) tm.assert_index_equal(result, expected) result = idx.astype(str) - expected = Index(str(x) for x in idx) + expected = Index([str(x) for x in idx], name="idx") tm.assert_index_equal(result, expected) - idx = period_range("1990", "2009", freq="A") + idx = period_range("1990", "2009", freq="A", name="idx") result = idx.astype("i8") - tm.assert_index_equal(result, Index(idx.asi8)) + tm.assert_index_equal(result, Index(idx.asi8, name="idx")) tm.assert_numpy_array_equal(result.values, idx.asi8) def test_astype_uint(self): - arr = period_range("2000", periods=2) - expected = UInt64Index(np.array([10957, 10958], dtype="uint64")) + arr = period_range("2000", periods=2, name="idx") + expected = UInt64Index(np.array([10957, 10958], dtype="uint64"), name="idx") tm.assert_index_equal(arr.astype("uint64"), expected) tm.assert_index_equal(arr.astype("uint32"), expected) @@ -116,10 +119,10 @@ def test_astype_object2(self): assert result_list[2] is NaT def test_astype_category(self): - obj = period_range("2000", periods=2) + obj = period_range("2000", periods=2, name="idx") result = obj.astype("category") expected = CategoricalIndex( - [Period("2000-01-01", freq="D"), Period("2000-01-02", freq="D")] + [Period("2000-01-01", freq="D"), Period("2000-01-02", freq="D")], name="idx" ) tm.assert_index_equal(result, expected) @@ -128,9 +131,9 @@ def test_astype_category(self): tm.assert_categorical_equal(result, expected) def test_astype_array_fallback(self): - obj = period_range("2000", periods=2) + obj = period_range("2000", periods=2, name="idx") result = obj.astype(bool) - expected = Index(np.array([True, True])) + expected = Index(np.array([True, True]), name="idx") tm.assert_index_equal(result, expected) result = obj._data.astype(bool) diff --git a/pandas/tests/indexes/test_common.py b/pandas/tests/indexes/test_common.py index 80c577253f536..01d72670f37aa 100644 --- a/pandas/tests/indexes/test_common.py +++ b/pandas/tests/indexes/test_common.py @@ -369,3 +369,29 @@ def test_has_duplicates(self, indices): idx = holder([indices[0]] * 5) assert idx.is_unique is False assert idx.has_duplicates is True + + @pytest.mark.parametrize( + "dtype", + ["int64", "uint64", "float64", "category", "datetime64[ns]", "timedelta64[ns]"], + ) + @pytest.mark.parametrize("copy", [True, False]) + def test_astype_preserves_name(self, indices, dtype, copy): + # https://github.com/pandas-dev/pandas/issues/32013 + if isinstance(indices, MultiIndex): + indices.names = ["idx" + str(i) for i in range(indices.nlevels)] + else: + indices.name = "idx" + + try: + # Some of these conversions cannot succeed so we use a try / except + if copy: + result = indices.copy(dtype=dtype) + else: + result = indices.astype(dtype) + except (ValueError, TypeError, NotImplementedError, SystemError): + return + + if isinstance(indices, MultiIndex): + assert result.names == indices.names + else: + assert result.name == indices.name diff --git a/pandas/tests/indexes/timedeltas/test_astype.py b/pandas/tests/indexes/timedeltas/test_astype.py index 82c9d995c9c7c..d9f24b4a35520 100644 --- a/pandas/tests/indexes/timedeltas/test_astype.py +++ b/pandas/tests/indexes/timedeltas/test_astype.py @@ -47,20 +47,22 @@ def test_astype_object_with_nat(self): def test_astype(self): # GH 13149, GH 13209 - idx = TimedeltaIndex([1e14, "NaT", NaT, np.NaN]) + idx = TimedeltaIndex([1e14, "NaT", NaT, np.NaN], name="idx") result = idx.astype(object) - expected = Index([Timedelta("1 days 03:46:40")] + [NaT] * 3, dtype=object) + expected = Index( + [Timedelta("1 days 03:46:40")] + [NaT] * 3, dtype=object, name="idx" + ) tm.assert_index_equal(result, expected) result = idx.astype(int) expected = Int64Index( - [100000000000000] + [-9223372036854775808] * 3, dtype=np.int64 + [100000000000000] + [-9223372036854775808] * 3, dtype=np.int64, name="idx" ) tm.assert_index_equal(result, expected) result = idx.astype(str) - expected = Index(str(x) for x in idx) + expected = Index([str(x) for x in idx], name="idx") tm.assert_index_equal(result, expected) rng = timedelta_range("1 days", periods=10)
- [x] closes #32013 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32036
2020-02-16T03:15:52Z
2020-03-27T00:00:35Z
2020-03-27T00:00:34Z
2020-03-27T01:06:31Z
CLN: Clean reductions/test_reductions.py
diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py index 0b312fe2f8990..211d0d52d8357 100644 --- a/pandas/tests/reductions/test_reductions.py +++ b/pandas/tests/reductions/test_reductions.py @@ -66,60 +66,64 @@ def test_ops(self, opname, obj): expected = expected.astype("M8[ns]").astype("int64") assert result.value == expected - def test_nanops(self): + @pytest.mark.parametrize("opname", ["max", "min"]) + def test_nanops(self, opname, index_or_series): # GH#7261 - for opname in ["max", "min"]: - for klass in [Index, Series]: - arg_op = "arg" + opname if klass is Index else "idx" + opname - - obj = klass([np.nan, 2.0]) - assert getattr(obj, opname)() == 2.0 - - obj = klass([np.nan]) - assert pd.isna(getattr(obj, opname)()) - assert pd.isna(getattr(obj, opname)(skipna=False)) - - obj = klass([], dtype=object) - assert pd.isna(getattr(obj, opname)()) - assert pd.isna(getattr(obj, opname)(skipna=False)) - - obj = klass([pd.NaT, datetime(2011, 11, 1)]) - # check DatetimeIndex monotonic path - assert getattr(obj, opname)() == datetime(2011, 11, 1) - assert getattr(obj, opname)(skipna=False) is pd.NaT - - assert getattr(obj, arg_op)() == 1 - result = getattr(obj, arg_op)(skipna=False) - if klass is Series: - assert np.isnan(result) - else: - assert result == -1 - - obj = klass([pd.NaT, datetime(2011, 11, 1), pd.NaT]) - # check DatetimeIndex non-monotonic path - assert getattr(obj, opname)(), datetime(2011, 11, 1) - assert getattr(obj, opname)(skipna=False) is pd.NaT - - assert getattr(obj, arg_op)() == 1 - result = getattr(obj, arg_op)(skipna=False) - if klass is Series: - assert np.isnan(result) - else: - assert result == -1 - - for dtype in ["M8[ns]", "datetime64[ns, UTC]"]: - # cases with empty Series/DatetimeIndex - obj = klass([], dtype=dtype) - - assert getattr(obj, opname)() is pd.NaT - assert getattr(obj, opname)(skipna=False) is pd.NaT - - with pytest.raises(ValueError, match="empty sequence"): - getattr(obj, arg_op)() - with pytest.raises(ValueError, match="empty sequence"): - getattr(obj, arg_op)(skipna=False) - - # argmin/max + klass = index_or_series + arg_op = "arg" + opname if klass is Index else "idx" + opname + + obj = klass([np.nan, 2.0]) + assert getattr(obj, opname)() == 2.0 + + obj = klass([np.nan]) + assert pd.isna(getattr(obj, opname)()) + assert pd.isna(getattr(obj, opname)(skipna=False)) + + obj = klass([], dtype=object) + assert pd.isna(getattr(obj, opname)()) + assert pd.isna(getattr(obj, opname)(skipna=False)) + + obj = klass([pd.NaT, datetime(2011, 11, 1)]) + # check DatetimeIndex monotonic path + assert getattr(obj, opname)() == datetime(2011, 11, 1) + assert getattr(obj, opname)(skipna=False) is pd.NaT + + assert getattr(obj, arg_op)() == 1 + result = getattr(obj, arg_op)(skipna=False) + if klass is Series: + assert np.isnan(result) + else: + assert result == -1 + + obj = klass([pd.NaT, datetime(2011, 11, 1), pd.NaT]) + # check DatetimeIndex non-monotonic path + assert getattr(obj, opname)(), datetime(2011, 11, 1) + assert getattr(obj, opname)(skipna=False) is pd.NaT + + assert getattr(obj, arg_op)() == 1 + result = getattr(obj, arg_op)(skipna=False) + if klass is Series: + assert np.isnan(result) + else: + assert result == -1 + + @pytest.mark.parametrize("opname", ["max", "min"]) + @pytest.mark.parametrize("dtype", ["M8[ns]", "datetime64[ns, UTC]"]) + def test_nanops_empty_object(self, opname, index_or_series, dtype): + klass = index_or_series + arg_op = "arg" + opname if klass is Index else "idx" + opname + + obj = klass([], dtype=dtype) + + assert getattr(obj, opname)() is pd.NaT + assert getattr(obj, opname)(skipna=False) is pd.NaT + + with pytest.raises(ValueError, match="empty sequence"): + getattr(obj, arg_op)() + with pytest.raises(ValueError, match="empty sequence"): + getattr(obj, arg_op)(skipna=False) + + def test_argminmax(self): obj = Index(np.arange(5, dtype="int64")) assert obj.argmin() == 0 assert obj.argmax() == 4 @@ -224,16 +228,17 @@ def test_minmax_timedelta64(self): assert idx.argmin() == 0 assert idx.argmax() == 2 - for op in ["min", "max"]: - # Return NaT - obj = TimedeltaIndex([]) - assert pd.isna(getattr(obj, op)()) + @pytest.mark.parametrize("op", ["min", "max"]) + def test_minmax_timedelta_empty_or_na(self, op): + # Return NaT + obj = TimedeltaIndex([]) + assert getattr(obj, op)() is pd.NaT - obj = TimedeltaIndex([pd.NaT]) - assert pd.isna(getattr(obj, op)()) + obj = TimedeltaIndex([pd.NaT]) + assert getattr(obj, op)() is pd.NaT - obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT]) - assert pd.isna(getattr(obj, op)()) + obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT]) + assert getattr(obj, op)() is pd.NaT def test_numpy_minmax_timedelta64(self): td = timedelta_range("16815 days", "16820 days", freq="D")
Some more parameterizing / splitting up of tests
https://api.github.com/repos/pandas-dev/pandas/pulls/32035
2020-02-16T01:27:07Z
2020-02-17T20:03:06Z
2020-02-17T20:03:06Z
2020-02-17T20:05:00Z
CLN: 29547 replace old string formatting
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py index ab3ee5bbcdc3a..b11736248c12a 100644 --- a/pandas/core/arrays/interval.py +++ b/pandas/core/arrays/interval.py @@ -1127,8 +1127,8 @@ def __arrow_array__(self, type=None): subtype = pyarrow.from_numpy_dtype(self.dtype.subtype) except TypeError: raise TypeError( - "Conversion to arrow with subtype '{}' " - "is not supported".format(self.dtype.subtype) + f"Conversion to arrow with subtype '{self.dtype.subtype}' " + "is not supported" ) interval_type = ArrowIntervalType(subtype, self.closed) storage_array = pyarrow.StructArray.from_arrays( @@ -1157,14 +1157,12 @@ def __arrow_array__(self, type=None): if not type.equals(interval_type): raise TypeError( "Not supported to convert IntervalArray to type with " - "different 'subtype' ({0} vs {1}) and 'closed' ({2} vs {3}) " - "attributes".format( - self.dtype.subtype, type.subtype, self.closed, type.closed - ) + f"different 'subtype' ({self.dtype.subtype} vs {type.subtype}) " + f"and 'closed' ({self.closed} vs {type.closed}) attributes" ) else: raise TypeError( - "Not supported to convert IntervalArray to '{0}' type".format(type) + f"Not supported to convert IntervalArray to '{type}' type" ) return pyarrow.ExtensionArray.from_storage(interval_type, storage_array) diff --git a/pandas/core/util/hashing.py b/pandas/core/util/hashing.py index 160d328ec16ec..d9c8611c94cdb 100644 --- a/pandas/core/util/hashing.py +++ b/pandas/core/util/hashing.py @@ -294,7 +294,7 @@ def hash_array( elif issubclass(dtype.type, (np.datetime64, np.timedelta64)): vals = vals.view("i8").astype("u8", copy=False) elif issubclass(dtype.type, np.number) and dtype.itemsize <= 8: - vals = vals.view("u{}".format(vals.dtype.itemsize)).astype("u8") + vals = vals.view(f"u{vals.dtype.itemsize}").astype("u8") else: # With repeated values, its MUCH faster to categorize object dtypes, # then hash and rename categories. We allow skipping the categorization diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py index 0693c083c9ddc..b5ddd15c1312a 100644 --- a/pandas/io/formats/format.py +++ b/pandas/io/formats/format.py @@ -187,7 +187,7 @@ def _get_footer(self) -> str: if self.length: if footer: footer += ", " - footer += "Length: {length}".format(length=len(self.categorical)) + footer += f"Length: {len(self.categorical)}" level_info = self.categorical._repr_categories_info() @@ -217,7 +217,6 @@ def to_string(self) -> str: fmt_values = self._get_formatted_values() - fmt_values = ["{i}".format(i=i) for i in fmt_values] fmt_values = [i.strip() for i in fmt_values] values = ", ".join(fmt_values) result = ["[" + values + "]"] @@ -301,28 +300,26 @@ def _get_footer(self) -> str: assert isinstance( self.series.index, (ABCDatetimeIndex, ABCPeriodIndex, ABCTimedeltaIndex) ) - footer += "Freq: {freq}".format(freq=self.series.index.freqstr) + footer += f"Freq: {self.series.index.freqstr}" if self.name is not False and name is not None: if footer: footer += ", " series_name = pprint_thing(name, escape_chars=("\t", "\r", "\n")) - footer += ( - ("Name: {sname}".format(sname=series_name)) if name is not None else "" - ) + footer += f"Name: {series_name}" if self.length is True or (self.length == "truncate" and self.truncate_v): if footer: footer += ", " - footer += "Length: {length}".format(length=len(self.series)) + footer += f"Length: {len(self.series)}" if self.dtype is not False and self.dtype is not None: - name = getattr(self.tr_series.dtype, "name", None) - if name: + dtype_name = getattr(self.tr_series.dtype, "name", None) + if dtype_name: if footer: footer += ", " - footer += "dtype: {typ}".format(typ=pprint_thing(name)) + footer += f"dtype: {pprint_thing(dtype_name)}" # level infos are added to the end and in a new line, like it is done # for Categoricals @@ -359,9 +356,7 @@ def to_string(self) -> str: footer = self._get_footer() if len(series) == 0: - return "{name}([], {footer})".format( - name=type(self.series).__name__, footer=footer - ) + return f"{type(self.series).__name__}([], {footer})" fmt_index, have_header = self._get_formatted_index() fmt_values = self._get_formatted_values() @@ -584,10 +579,8 @@ def __init__( self.formatters = formatters else: raise ValueError( - ( - "Formatters length({flen}) should match " - "DataFrame number of columns({dlen})" - ).format(flen=len(formatters), dlen=len(frame.columns)) + f"Formatters length({len(formatters)}) should match " + f"DataFrame number of columns({len(frame.columns)})" ) self.na_rep = na_rep self.decimal = decimal @@ -816,10 +809,10 @@ def write_result(self, buf: IO[str]) -> None: frame = self.frame if len(frame.columns) == 0 or len(frame.index) == 0: - info_line = "Empty {name}\nColumns: {col}\nIndex: {idx}".format( - name=type(self.frame).__name__, - col=pprint_thing(frame.columns), - idx=pprint_thing(frame.index), + info_line = ( + f"Empty {type(self.frame).__name__}\n" + f"Columns: {pprint_thing(frame.columns)}\n" + f"Index: {pprint_thing(frame.index)}" ) text = info_line else: @@ -865,11 +858,7 @@ def write_result(self, buf: IO[str]) -> None: buf.writelines(text) if self.should_show_dimensions: - buf.write( - "\n\n[{nrows} rows x {ncols} columns]".format( - nrows=len(frame), ncols=len(frame.columns) - ) - ) + buf.write(f"\n\n[{len(frame)} rows x {len(frame.columns)} columns]") def _join_multiline(self, *args) -> str: lwidth = self.line_width @@ -1074,7 +1063,7 @@ def _get_formatted_index(self, frame: "DataFrame") -> List[str]: # empty space for columns if self.show_col_idx_names: - col_header = ["{x}".format(x=x) for x in self._get_column_name_list()] + col_header = [str(x) for x in self._get_column_name_list()] else: col_header = [""] * columns.nlevels @@ -1209,10 +1198,8 @@ def _format_strings(self) -> List[str]: if self.float_format is None: float_format = get_option("display.float_format") if float_format is None: - fmt_str = "{{x: .{prec:d}g}}".format( - prec=get_option("display.precision") - ) - float_format = lambda x: fmt_str.format(x=x) + precision = get_option("display.precision") + float_format = lambda x: f"{x: .{precision:d}g}" else: float_format = self.float_format @@ -1238,10 +1225,10 @@ def _format(x): pass return self.na_rep elif isinstance(x, PandasObject): - return "{x}".format(x=x) + return str(x) else: # object dtype - return "{x}".format(x=formatter(x)) + return str(formatter(x)) vals = self.values if isinstance(vals, Index): @@ -1257,7 +1244,7 @@ def _format(x): fmt_values = [] for i, v in enumerate(vals): if not is_float_type[i] and leading_space: - fmt_values.append(" {v}".format(v=_format(v))) + fmt_values.append(f" {_format(v)}") elif is_float_type[i]: fmt_values.append(float_format(v)) else: @@ -1437,7 +1424,7 @@ def _format_strings(self) -> List[str]: class IntArrayFormatter(GenericArrayFormatter): def _format_strings(self) -> List[str]: - formatter = self.formatter or (lambda x: "{x: d}".format(x=x)) + formatter = self.formatter or (lambda x: f"{x: d}") fmt_values = [formatter(x) for x in self.values] return fmt_values @@ -1716,7 +1703,7 @@ def _formatter(x): x = Timedelta(x) result = x._repr_base(format=format) if box: - result = "'{res}'".format(res=result) + result = f"'{result}'" return result return _formatter @@ -1880,16 +1867,16 @@ def __call__(self, num: Union[int, float]) -> str: prefix = self.ENG_PREFIXES[int_pow10] else: if int_pow10 < 0: - prefix = "E-{pow10:02d}".format(pow10=-int_pow10) + prefix = f"E-{-int_pow10:02d}" else: - prefix = "E+{pow10:02d}".format(pow10=int_pow10) + prefix = f"E+{int_pow10:02d}" mant = sign * dnum / (10 ** pow10) if self.accuracy is None: # pragma: no cover format_str = "{mant: g}{prefix}" else: - format_str = "{{mant: .{acc:d}f}}{{prefix}}".format(acc=self.accuracy) + format_str = f"{{mant: .{self.accuracy:d}f}}{{prefix}}" formatted = format_str.format(mant=mant, prefix=prefix) diff --git a/pandas/io/formats/html.py b/pandas/io/formats/html.py index e3161415fe2bc..585e1af3dbc01 100644 --- a/pandas/io/formats/html.py +++ b/pandas/io/formats/html.py @@ -56,7 +56,7 @@ def __init__( self.table_id = self.fmt.table_id self.render_links = self.fmt.render_links if isinstance(self.fmt.col_space, int): - self.fmt.col_space = "{colspace}px".format(colspace=self.fmt.col_space) + self.fmt.col_space = f"{self.fmt.col_space}px" @property def show_row_idx_names(self) -> bool: @@ -124,7 +124,7 @@ def write_th( """ if header and self.fmt.col_space is not None: tags = tags or "" - tags += 'style="min-width: {colspace};"'.format(colspace=self.fmt.col_space) + tags += f'style="min-width: {self.fmt.col_space};"' self._write_cell(s, kind="th", indent=indent, tags=tags) @@ -135,9 +135,9 @@ def _write_cell( self, s: Any, kind: str = "td", indent: int = 0, tags: Optional[str] = None ) -> None: if tags is not None: - start_tag = "<{kind} {tags}>".format(kind=kind, tags=tags) + start_tag = f"<{kind} {tags}>" else: - start_tag = "<{kind}>".format(kind=kind) + start_tag = f"<{kind}>" if self.escape: # escape & first to prevent double escaping of & @@ -149,17 +149,12 @@ def _write_cell( if self.render_links and is_url(rs): rs_unescaped = pprint_thing(s, escape_chars={}).strip() - start_tag += '<a href="{url}" target="_blank">'.format(url=rs_unescaped) + start_tag += f'<a href="{rs_unescaped}" target="_blank">' end_a = "</a>" else: end_a = "" - self.write( - "{start}{rs}{end_a}</{kind}>".format( - start=start_tag, rs=rs, end_a=end_a, kind=kind - ), - indent, - ) + self.write(f"{start_tag}{rs}{end_a}</{kind}>", indent) def write_tr( self, @@ -177,7 +172,7 @@ def write_tr( if align is None: self.write("<tr>", indent) else: - self.write('<tr style="text-align: {align};">'.format(align=align), indent) + self.write(f'<tr style="text-align: {align};">', indent) indent += indent_delta for i, s in enumerate(line): @@ -196,9 +191,7 @@ def render(self) -> List[str]: if self.should_show_dimensions: by = chr(215) # × self.write( - "<p>{rows} rows {by} {cols} columns</p>".format( - rows=len(self.frame), by=by, cols=len(self.frame.columns) - ) + f"<p>{len(self.frame)} rows {by} {len(self.frame.columns)} columns</p>" ) return self.elements @@ -224,12 +217,10 @@ def _write_table(self, indent: int = 0) -> None: if self.table_id is None: id_section = "" else: - id_section = ' id="{table_id}"'.format(table_id=self.table_id) + id_section = f' id="{self.table_id}"' self.write( - '<table border="{border}" class="{cls}"{id_section}>'.format( - border=self.border, cls=" ".join(_classes), id_section=id_section - ), + f'<table border="{self.border}" class="{" ".join(_classes)}"{id_section}>', indent, ) diff --git a/pandas/io/formats/latex.py b/pandas/io/formats/latex.py index 935762598f78a..3a3ca84642d51 100644 --- a/pandas/io/formats/latex.py +++ b/pandas/io/formats/latex.py @@ -58,10 +58,10 @@ def write_result(self, buf: IO[str]) -> None: """ # string representation of the columns if len(self.frame.columns) == 0 or len(self.frame.index) == 0: - info_line = "Empty {name}\nColumns: {col}\nIndex: {idx}".format( - name=type(self.frame).__name__, - col=self.frame.columns, - idx=self.frame.index, + info_line = ( + f"Empty {type(self.frame).__name__}\n" + f"Columns: {self.frame.columns}\n" + f"Index: {self.frame.index}" ) strcols = [[info_line]] else: @@ -140,8 +140,8 @@ def pad_empties(x): buf.write("\\endhead\n") buf.write("\\midrule\n") buf.write( - "\\multicolumn{{{n}}}{{r}}{{{{Continued on next " - "page}}}} \\\\\n".format(n=len(row)) + f"\\multicolumn{{{len(row)}}}{{r}}" + "{{Continued on next page}} \\\\\n" ) buf.write("\\midrule\n") buf.write("\\endfoot\n\n") @@ -171,7 +171,7 @@ def pad_empties(x): if self.bold_rows and self.fmt.index: # bold row labels crow = [ - "\\textbf{{{x}}}".format(x=x) + f"\\textbf{{{x}}}" if j < ilevels and x.strip() not in ["", "{}"] else x for j, x in enumerate(crow) @@ -210,9 +210,8 @@ def append_col(): # write multicolumn if needed if ncol > 1: row2.append( - "\\multicolumn{{{ncol:d}}}{{{fmt:s}}}{{{txt:s}}}".format( - ncol=ncol, fmt=self.multicolumn_format, txt=coltext.strip() - ) + f"\\multicolumn{{{ncol:d}}}{{{self.multicolumn_format}}}" + f"{{{coltext.strip()}}}" ) # don't modify where not needed else: @@ -255,9 +254,7 @@ def _format_multirow( break if nrow > 1: # overwrite non-multirow entry - row[j] = "\\multirow{{{nrow:d}}}{{*}}{{{row:s}}}".format( - nrow=nrow, row=row[j].strip() - ) + row[j] = f"\\multirow{{{nrow:d}}}{{*}}{{{row[j].strip()}}}" # save when to end the current block with \cline self.clinebuf.append([i + nrow - 1, j + 1]) return row @@ -268,7 +265,7 @@ def _print_cline(self, buf: IO[str], i: int, icol: int) -> None: """ for cl in self.clinebuf: if cl[0] == i: - buf.write("\\cline{{{cl:d}-{icol:d}}}\n".format(cl=cl[1], icol=icol)) + buf.write(f"\\cline{{{cl[1]:d}-{icol:d}}}\n") # remove entries that have been written to buffer self.clinebuf = [x for x in self.clinebuf if x[0] != i] @@ -292,19 +289,19 @@ def _write_tabular_begin(self, buf, column_format: str): if self.caption is None: caption_ = "" else: - caption_ = "\n\\caption{{{}}}".format(self.caption) + caption_ = f"\n\\caption{{{self.caption}}}" if self.label is None: label_ = "" else: - label_ = "\n\\label{{{}}}".format(self.label) + label_ = f"\n\\label{{{self.label}}}" - buf.write("\\begin{{table}}\n\\centering{}{}\n".format(caption_, label_)) + buf.write(f"\\begin{{table}}\n\\centering{caption_}{label_}\n") else: # then write output only in a tabular environment pass - buf.write("\\begin{{tabular}}{{{fmt}}}\n".format(fmt=column_format)) + buf.write(f"\\begin{{tabular}}{{{column_format}}}\n") def _write_tabular_end(self, buf): """ @@ -340,18 +337,18 @@ def _write_longtable_begin(self, buf, column_format: str): <https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g 'rcl' for 3 columns """ - buf.write("\\begin{{longtable}}{{{fmt}}}\n".format(fmt=column_format)) + buf.write(f"\\begin{{longtable}}{{{column_format}}}\n") if self.caption is not None or self.label is not None: if self.caption is None: pass else: - buf.write("\\caption{{{}}}".format(self.caption)) + buf.write(f"\\caption{{{self.caption}}}") if self.label is None: pass else: - buf.write("\\label{{{}}}".format(self.label)) + buf.write(f"\\label{{{self.label}}}") # a double-backslash is required at the end of the line # as discussed here: diff --git a/pandas/io/formats/printing.py b/pandas/io/formats/printing.py index 13b18a0b5fb6f..36e774305b577 100644 --- a/pandas/io/formats/printing.py +++ b/pandas/io/formats/printing.py @@ -229,7 +229,7 @@ def as_escaped_string( max_seq_items=max_seq_items, ) elif isinstance(thing, str) and quote_strings: - result = "'{thing}'".format(thing=as_escaped_string(thing)) + result = f"'{as_escaped_string(thing)}'" else: result = as_escaped_string(thing) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index d7eb69d3a6048..8a3ad6cb45b57 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -3605,8 +3605,8 @@ def get_rows(self, infer_nrows, skiprows=None): def detect_colspecs(self, infer_nrows=100, skiprows=None): # Regex escape the delimiters - delimiters = "".join(r"\{}".format(x) for x in self.delimiter) - pattern = re.compile("([^{}]+)".format(delimiters)) + delimiters = "".join(fr"\{x}" for x in self.delimiter) + pattern = re.compile(f"([^{delimiters}]+)") rows = self.get_rows(infer_nrows, skiprows) if not rows: raise EmptyDataError("No rows from which to infer column width") diff --git a/pandas/tests/arrays/categorical/test_dtypes.py b/pandas/tests/arrays/categorical/test_dtypes.py index 19746d7d72162..9922a8863ebc2 100644 --- a/pandas/tests/arrays/categorical/test_dtypes.py +++ b/pandas/tests/arrays/categorical/test_dtypes.py @@ -92,22 +92,20 @@ def test_codes_dtypes(self): result = Categorical(["foo", "bar", "baz"]) assert result.codes.dtype == "int8" - result = Categorical(["foo{i:05d}".format(i=i) for i in range(400)]) + result = Categorical([f"foo{i:05d}" for i in range(400)]) assert result.codes.dtype == "int16" - result = Categorical(["foo{i:05d}".format(i=i) for i in range(40000)]) + result = Categorical([f"foo{i:05d}" for i in range(40000)]) assert result.codes.dtype == "int32" # adding cats result = Categorical(["foo", "bar", "baz"]) assert result.codes.dtype == "int8" - result = result.add_categories(["foo{i:05d}".format(i=i) for i in range(400)]) + result = result.add_categories([f"foo{i:05d}" for i in range(400)]) assert result.codes.dtype == "int16" # removing cats - result = result.remove_categories( - ["foo{i:05d}".format(i=i) for i in range(300)] - ) + result = result.remove_categories([f"foo{i:05d}" for i in range(300)]) assert result.codes.dtype == "int8" @pytest.mark.parametrize("ordered", [True, False]) diff --git a/pandas/tests/arrays/categorical/test_operators.py b/pandas/tests/arrays/categorical/test_operators.py index 0c830c65e0f8b..6ea003c122eea 100644 --- a/pandas/tests/arrays/categorical/test_operators.py +++ b/pandas/tests/arrays/categorical/test_operators.py @@ -338,7 +338,7 @@ def test_compare_unordered_different_order(self): def test_numeric_like_ops(self): df = DataFrame({"value": np.random.randint(0, 10000, 100)}) - labels = ["{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500)] + labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)] cat_labels = Categorical(labels, labels) df = df.sort_values(by=["value"], ascending=True) @@ -353,9 +353,7 @@ def test_numeric_like_ops(self): ("__mul__", r"\*"), ("__truediv__", "/"), ]: - msg = r"Series cannot perform the operation {}|unsupported operand".format( - str_rep - ) + msg = f"Series cannot perform the operation {str_rep}|unsupported operand" with pytest.raises(TypeError, match=msg): getattr(df, op)(df) @@ -363,7 +361,7 @@ def test_numeric_like_ops(self): # min/max) s = df["value_group"] for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]: - msg = "Categorical cannot perform the operation {}".format(op) + msg = f"Categorical cannot perform the operation {op}" with pytest.raises(TypeError, match=msg): getattr(s, op)(numeric_only=False) @@ -383,9 +381,7 @@ def test_numeric_like_ops(self): ("__mul__", r"\*"), ("__truediv__", "/"), ]: - msg = r"Series cannot perform the operation {}|unsupported operand".format( - str_rep - ) + msg = f"Series cannot perform the operation {str_rep}|unsupported operand" with pytest.raises(TypeError, match=msg): getattr(s, op)(2)
I splitted PR #31844 in batches, this is the **last** For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case - [ x ] tests added / passed - [ x ] passes `black pandas` - [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ x ] Ref #29547
https://api.github.com/repos/pandas-dev/pandas/pulls/32034
2020-02-16T00:34:51Z
2020-02-20T23:32:42Z
2020-02-20T23:32:41Z
2020-02-21T01:29:53Z
CI: add pydocstyle to code_checks
diff --git a/ci/code_checks.sh b/ci/code_checks.sh index bb7d8a388e6e2..71aad6347085c 100755 --- a/ci/code_checks.sh +++ b/ci/code_checks.sh @@ -316,6 +316,13 @@ fi ### DOCSTRINGS ### if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then + echo "pydocstyle --version" + pydocstyle --version + + MSG='Validate docstrings using pydocstyle (see setup.cfg for selected error codes)' ; echo $MSG + pydocstyle pandas + RET=$(($RET + $?)) ; echo $MSG "DONE" + MSG='Validate docstrings (GL03, GL04, GL05, GL06, GL07, GL09, GL10, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT01, RT04, RT05, SA02, SA03, SA05)' ; echo $MSG $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=GL03,GL04,GL05,GL06,GL07,GL09,GL10,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT01,RT04,RT05,SA02,SA03,SA05 RET=$(($RET + $?)) ; echo $MSG "DONE" diff --git a/environment.yml b/environment.yml index cbdaf8e6c4217..812cb0b3c1c17 100644 --- a/environment.yml +++ b/environment.yml @@ -23,6 +23,7 @@ dependencies: - isort # check that imports are in the right order - mypy=0.730 - pycodestyle # used by flake8 + - pydocstyle # documentation - gitpython # obtain contributors from git for whatsnew diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py index a75536e46e60d..8599065830b57 100644 --- a/pandas/core/arrays/datetimes.py +++ b/pandas/core/arrays/datetimes.py @@ -559,7 +559,6 @@ def __iter__(self): ------ tstamp : Timestamp """ - # convert in chunks of 10k for efficiency data = self.asi8 length = len(self) @@ -820,7 +819,9 @@ def tz_convert(self, tz): dtype = tz_to_dtype(tz) return self._simple_new(self.asi8, dtype=dtype, freq=self.freq) - def tz_localize(self, tz, ambiguous="raise", nonexistent="raise"): + # TODO: remove # noqa once https://github.com/PyCQA/pydocstyle/pull/441 + # is merged + def tz_localize(self, tz, ambiguous="raise", nonexistent="raise"): # noqa """ Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index. @@ -1640,7 +1641,6 @@ def to_julian_date(self): 0 Julian date is noon January 1, 4713 BC. https://en.wikipedia.org/wiki/Julian_day """ - # http://mysite.verizon.net/aesir_research/date/jdalg2.htm year = np.asarray(self.year) month = np.asarray(self.month) @@ -1705,7 +1705,6 @@ def sequence_to_dt64ns( ------ TypeError : PeriodDType data is passed """ - inferred_freq = None dtype = _validate_dt64_dtype(dtype) diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py index 6d45ddd29d783..27ff1fe3022ef 100644 --- a/pandas/core/tools/datetimes.py +++ b/pandas/core/tools/datetimes.py @@ -538,7 +538,7 @@ def to_datetime( infer_datetime_format=False, origin="unix", cache=True, -): +): # noqa: D207 """ Convert argument to datetime. diff --git a/pandas/io/excel/_openpyxl.py b/pandas/io/excel/_openpyxl.py index a96c0f814e2d8..716f2b0fe2341 100644 --- a/pandas/io/excel/_openpyxl.py +++ b/pandas/io/excel/_openpyxl.py @@ -393,6 +393,7 @@ def _convert_to_protection(cls, protection_dict): Returns ------- + openpyxl.styles.Protection """ from openpyxl.styles import Protection diff --git a/requirements-dev.txt b/requirements-dev.txt index a469cbdd93ceb..34abff0fc25dc 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -14,6 +14,7 @@ flake8-rst>=0.6.0,<=0.7.0 isort mypy==0.730 pycodestyle +pydocstyle gitpython gitdb2==2.0.6 sphinx diff --git a/setup.cfg b/setup.cfg index 4a900e581c353..4c70122c329ea 100644 --- a/setup.cfg +++ b/setup.cfg @@ -49,6 +49,20 @@ ignore = E402, # module level import not at top of file exclude = doc/source/development/contributing_docstring.rst +[pydocstyle] +select = D201, # No blank lines allowed before function docstring + D202, # No blank lines allowed after function docstring + D204, # 1 blank line required after class docstring + D207, # Docstring is under-indented + D208, # Docstring is over-indented + D209, # Multi-line docstring closing quotes should be on a separate line + D213, # Multi-line docstring summary should start at the second line + D300, # Use triple double quotes + D409, # Section underline should match the length of its name + D411, # Missing blank line before section + D412, # No blank lines allowed between a section header and its content + D414, # Section has no content + [tool:pytest] # sync minversion with setup.cfg & install.rst minversion = 4.0.2
http://www.pydocstyle.org/en/5.0.2/error_codes.html
https://api.github.com/repos/pandas-dev/pandas/pulls/32033
2020-02-15T22:21:31Z
2020-03-17T12:13:37Z
null
2020-03-17T12:13:38Z
CLN: 29547 replace old string formatting 8
diff --git a/pandas/tests/test_downstream.py b/pandas/tests/test_downstream.py index 02898988ca8aa..122ef1f47968e 100644 --- a/pandas/tests/test_downstream.py +++ b/pandas/tests/test_downstream.py @@ -19,7 +19,7 @@ def import_module(name): try: return importlib.import_module(name) except ModuleNotFoundError: # noqa - pytest.skip("skipping as {} not available".format(name)) + pytest.skip(f"skipping as {name} not available") @pytest.fixture diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py index b377ca2869bd3..efaedfad1e093 100644 --- a/pandas/tests/test_multilevel.py +++ b/pandas/tests/test_multilevel.py @@ -896,10 +896,10 @@ def test_stack_unstack_unordered_multiindex(self): values = np.arange(5) data = np.vstack( [ - ["b{}".format(x) for x in values], # b0, b1, .. - ["a{}".format(x) for x in values], + [f"b{x}" for x in values], # b0, b1, .. + [f"a{x}" for x in values], # a0, a1, .. ] - ) # a0, a1, .. + ) df = pd.DataFrame(data.T, columns=["b", "a"]) df.columns.name = "first" second_level_dict = {"x": df} diff --git a/pandas/tests/tools/test_numeric.py b/pandas/tests/tools/test_numeric.py index 2fd39d5a7b703..19385e797467c 100644 --- a/pandas/tests/tools/test_numeric.py +++ b/pandas/tests/tools/test_numeric.py @@ -308,7 +308,7 @@ def test_really_large_in_arr_consistent(large_val, signed, multiple_elts, errors if errors in (None, "raise"): index = int(multiple_elts) - msg = "Integer out of range. at position {index}".format(index=index) + msg = f"Integer out of range. at position {index}" with pytest.raises(ValueError, match=msg): to_numeric(arr, **kwargs) diff --git a/pandas/tests/tslibs/test_parse_iso8601.py b/pandas/tests/tslibs/test_parse_iso8601.py index a58f227c20c7f..1c01e826d9794 100644 --- a/pandas/tests/tslibs/test_parse_iso8601.py +++ b/pandas/tests/tslibs/test_parse_iso8601.py @@ -51,7 +51,7 @@ def test_parsers_iso8601(date_str, exp): ], ) def test_parsers_iso8601_invalid(date_str): - msg = 'Error parsing datetime string "{s}"'.format(s=date_str) + msg = f'Error parsing datetime string "{date_str}"' with pytest.raises(ValueError, match=msg): tslib._test_parse_iso8601(date_str) diff --git a/pandas/tests/window/moments/test_moments_rolling.py b/pandas/tests/window/moments/test_moments_rolling.py index fd18d37ab13b6..f3a14971ef2e7 100644 --- a/pandas/tests/window/moments/test_moments_rolling.py +++ b/pandas/tests/window/moments/test_moments_rolling.py @@ -860,7 +860,7 @@ def get_result(obj, window, min_periods=None, center=False): tm.assert_series_equal(result, expected) # shifter index - s = ["x{x:d}".format(x=x) for x in range(12)] + s = [f"x{x:d}" for x in range(12)] if has_min_periods: minp = 10 @@ -1437,13 +1437,9 @@ def test_rolling_median_memory_error(self): def test_rolling_min_max_numeric_types(self): # GH12373 - types_test = [np.dtype("f{}".format(width)) for width in [4, 8]] + types_test = [np.dtype(f"f{width}") for width in [4, 8]] types_test.extend( - [ - np.dtype("{}{}".format(sign, width)) - for width in [1, 2, 4, 8] - for sign in "ui" - ] + [np.dtype(f"{sign}{width}") for width in [1, 2, 4, 8] for sign in "ui"] ) for data_type in types_test: # Just testing that these don't throw exceptions and that
I splitted PR #31844 in batches, this is the eighth For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case - [ x ] tests added / passed - [ x ] passes `black pandas` - [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
https://api.github.com/repos/pandas-dev/pandas/pulls/32032
2020-02-15T19:52:37Z
2020-02-15T23:24:24Z
2020-02-15T23:24:24Z
2020-02-21T01:29:55Z
CI: change np-dev xfails to not strict
diff --git a/pandas/tests/frame/test_cumulative.py b/pandas/tests/frame/test_cumulative.py index 2466547e2948b..486cbfb2761e0 100644 --- a/pandas/tests/frame/test_cumulative.py +++ b/pandas/tests/frame/test_cumulative.py @@ -75,7 +75,9 @@ def test_cumprod(self, datetime_frame): df.cumprod(1) @pytest.mark.xfail( - _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, ) def test_cummin(self, datetime_frame): datetime_frame.loc[5:10, 0] = np.nan @@ -101,7 +103,9 @@ def test_cummin(self, datetime_frame): assert np.shape(cummin_xs) == np.shape(datetime_frame) @pytest.mark.xfail( - _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, ) def test_cummax(self, datetime_frame): datetime_frame.loc[5:10, 0] = np.nan diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py index 8830b84a52421..176c0272ca527 100644 --- a/pandas/tests/groupby/test_function.py +++ b/pandas/tests/groupby/test_function.py @@ -687,7 +687,9 @@ def test_numpy_compat(func): @pytest.mark.xfail( - _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, ) def test_cummin_cummax(): # GH 15048 diff --git a/pandas/tests/scalar/timedelta/test_arithmetic.py b/pandas/tests/scalar/timedelta/test_arithmetic.py index f0ad5fa70471b..230a14aeec60a 100644 --- a/pandas/tests/scalar/timedelta/test_arithmetic.py +++ b/pandas/tests/scalar/timedelta/test_arithmetic.py @@ -386,6 +386,7 @@ def test_td_div_numeric_scalar(self): marks=pytest.mark.xfail( _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, ), ), float("nan"), diff --git a/pandas/tests/series/test_cumulative.py b/pandas/tests/series/test_cumulative.py index b0065992b850a..0cb1c038478f5 100644 --- a/pandas/tests/series/test_cumulative.py +++ b/pandas/tests/series/test_cumulative.py @@ -39,7 +39,9 @@ def test_cumprod(self, datetime_series): _check_accum_op("cumprod", datetime_series) @pytest.mark.xfail( - _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, ) def test_cummin(self, datetime_series): tm.assert_numpy_array_equal( @@ -54,7 +56,9 @@ def test_cummin(self, datetime_series): tm.assert_series_equal(result, expected) @pytest.mark.xfail( - _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, ) def test_cummax(self, datetime_series): tm.assert_numpy_array_equal(
follow-up to #32025
https://api.github.com/repos/pandas-dev/pandas/pulls/32031
2020-02-15T19:52:07Z
2020-02-16T17:43:56Z
2020-02-16T17:43:56Z
2020-02-18T10:56:10Z
CLN: remove unused from MultiIndex
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py index 02e11c0e71cbe..0a79df0cc9744 100644 --- a/pandas/core/indexes/multi.py +++ b/pandas/core/indexes/multi.py @@ -58,7 +58,6 @@ indexer_from_factorized, lexsort_indexer, ) -from pandas.core.util.hashing import hash_tuple, hash_tuples from pandas.io.formats.printing import ( format_object_attrs, @@ -247,6 +246,7 @@ class MultiIndex(Index): rename = Index.set_names _tuples = None + sortorder: Optional[int] # -------------------------------------------------------------------- # Constructors @@ -1430,55 +1430,11 @@ def is_monotonic_decreasing(self) -> bool: # monotonic decreasing if and only if reverse is monotonic increasing return self[::-1].is_monotonic_increasing - @cache_readonly - def _have_mixed_levels(self): - """ return a boolean list indicated if we have mixed levels """ - return ["mixed" in l for l in self._inferred_type_levels] - @cache_readonly def _inferred_type_levels(self): """ return a list of the inferred types, one for each level """ return [i.inferred_type for i in self.levels] - @cache_readonly - def _hashed_values(self): - """ return a uint64 ndarray of my hashed values """ - return hash_tuples(self) - - def _hashed_indexing_key(self, key): - """ - validate and return the hash for the provided key - - *this is internal for use for the cython routines* - - Parameters - ---------- - key : string or tuple - - Returns - ------- - np.uint64 - - Notes - ----- - we need to stringify if we have mixed levels - """ - if not isinstance(key, tuple): - return hash_tuples(key) - - if not len(key) == self.nlevels: - raise KeyError - - def f(k, stringify): - if stringify and not isinstance(k, str): - k = str(k) - return k - - key = tuple( - f(k, stringify) for k, stringify in zip(key, self._have_mixed_levels) - ) - return hash_tuple(key) - @Appender(Index.duplicated.__doc__) def duplicated(self, keep="first"): shape = map(len, self.levels) @@ -1858,27 +1814,6 @@ def __reduce__(self): ) return ibase._new_Index, (type(self), d), None - def __setstate__(self, state): - """Necessary for making this object picklable""" - if isinstance(state, dict): - levels = state.get("levels") - codes = state.get("codes") - sortorder = state.get("sortorder") - names = state.get("names") - - elif isinstance(state, tuple): - - nd_state, own_state = state - levels, codes, sortorder, names = own_state - - self._set_levels([Index(x) for x in levels], validate=False) - self._set_codes(codes) - new_codes = self._verify_integrity() - self._set_codes(new_codes) - self._set_names(names) - self.sortorder = sortorder - self._reset_identity() - # -------------------------------------------------------------------- def __getitem__(self, key): diff --git a/pandas/tests/util/test_hashing.py b/pandas/tests/util/test_hashing.py index c856585f20138..6411b9ab654f1 100644 --- a/pandas/tests/util/test_hashing.py +++ b/pandas/tests/util/test_hashing.py @@ -178,23 +178,6 @@ def test_multiindex_objects(): assert mi.equals(recons) assert Index(mi.values).equals(Index(recons.values)) - # _hashed_values and hash_pandas_object(..., index=False) equivalency. - expected = hash_pandas_object(mi, index=False).values - result = mi._hashed_values - - tm.assert_numpy_array_equal(result, expected) - - expected = hash_pandas_object(recons, index=False).values - result = recons._hashed_values - - tm.assert_numpy_array_equal(result, expected) - - expected = mi._hashed_values - result = recons._hashed_values - - # Values should match, but in different order. - tm.assert_numpy_array_equal(np.sort(result), np.sort(expected)) - @pytest.mark.parametrize( "obj",
https://api.github.com/repos/pandas-dev/pandas/pulls/32030
2020-02-15T19:39:51Z
2020-02-18T12:38:28Z
2020-02-18T12:38:28Z
2020-02-18T15:30:59Z
CLN: GH29547 replace old string formatting
diff --git a/pandas/tests/arrays/categorical/test_analytics.py b/pandas/tests/arrays/categorical/test_analytics.py index 90fcf12093909..0ff7d3e59abb3 100644 --- a/pandas/tests/arrays/categorical/test_analytics.py +++ b/pandas/tests/arrays/categorical/test_analytics.py @@ -15,10 +15,10 @@ class TestCategoricalAnalytics: def test_min_max_not_ordered_raises(self, aggregation): # unordered cats have no min/max cat = Categorical(["a", "b", "c", "d"], ordered=False) - msg = "Categorical is not ordered for operation {}" + msg = f"Categorical is not ordered for operation {aggregation}" agg_func = getattr(cat, aggregation) - with pytest.raises(TypeError, match=msg.format(aggregation)): + with pytest.raises(TypeError, match=msg): agg_func() def test_min_max_ordered(self):
Hi there! This is my first contribution. Please let me know if there are any issues, thank you. - [x] tests passed - [x] passes `black pandas`
https://api.github.com/repos/pandas-dev/pandas/pulls/32029
2020-02-15T18:27:21Z
2020-02-15T19:05:32Z
2020-02-15T19:05:32Z
2020-02-15T20:10:57Z
CLN: Clean groupby/test_function.py
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py index 176c0272ca527..6205dfb87bbd0 100644 --- a/pandas/tests/groupby/test_function.py +++ b/pandas/tests/groupby/test_function.py @@ -26,6 +26,26 @@ from pandas.util import _test_decorators as td +@pytest.fixture( + params=[np.int32, np.int64, np.float32, np.float64], + ids=["np.int32", "np.int64", "np.float32", "np.float64"], +) +def numpy_dtypes_for_minmax(request): + """ + Fixture of numpy dtypes with min and max values used for testing + cummin and cummax + """ + dtype = request.param + min_val = ( + np.iinfo(dtype).min if np.dtype(dtype).kind == "i" else np.finfo(dtype).min + ) + max_val = ( + np.iinfo(dtype).max if np.dtype(dtype).kind == "i" else np.finfo(dtype).max + ) + + return (dtype, min_val, max_val) + + @pytest.mark.parametrize("agg_func", ["any", "all"]) @pytest.mark.parametrize("skipna", [True, False]) @pytest.mark.parametrize( @@ -174,11 +194,10 @@ def test_arg_passthru(): ) for attr in ["mean", "median"]: - f = getattr(df.groupby("group"), attr) - result = f() + result = getattr(df.groupby("group"), attr)() tm.assert_index_equal(result.columns, expected_columns_numeric) - result = f(numeric_only=False) + result = getattr(df.groupby("group"), attr)(numeric_only=False) tm.assert_frame_equal(result.reindex_like(expected), expected) # TODO: min, max *should* handle @@ -195,11 +214,10 @@ def test_arg_passthru(): ] ) for attr in ["min", "max"]: - f = getattr(df.groupby("group"), attr) - result = f() + result = getattr(df.groupby("group"), attr)() tm.assert_index_equal(result.columns, expected_columns) - result = f(numeric_only=False) + result = getattr(df.groupby("group"), attr)(numeric_only=False) tm.assert_index_equal(result.columns, expected_columns) expected_columns = Index( @@ -215,29 +233,26 @@ def test_arg_passthru(): ] ) for attr in ["first", "last"]: - f = getattr(df.groupby("group"), attr) - result = f() + result = getattr(df.groupby("group"), attr)() tm.assert_index_equal(result.columns, expected_columns) - result = f(numeric_only=False) + result = getattr(df.groupby("group"), attr)(numeric_only=False) tm.assert_index_equal(result.columns, expected_columns) expected_columns = Index(["int", "float", "string", "category_int", "timedelta"]) - for attr in ["sum"]: - f = getattr(df.groupby("group"), attr) - result = f() - tm.assert_index_equal(result.columns, expected_columns_numeric) - result = f(numeric_only=False) - tm.assert_index_equal(result.columns, expected_columns) + result = df.groupby("group").sum() + tm.assert_index_equal(result.columns, expected_columns_numeric) + + result = df.groupby("group").sum(numeric_only=False) + tm.assert_index_equal(result.columns, expected_columns) expected_columns = Index(["int", "float", "category_int"]) for attr in ["prod", "cumprod"]: - f = getattr(df.groupby("group"), attr) - result = f() + result = getattr(df.groupby("group"), attr)() tm.assert_index_equal(result.columns, expected_columns_numeric) - result = f(numeric_only=False) + result = getattr(df.groupby("group"), attr)(numeric_only=False) tm.assert_index_equal(result.columns, expected_columns) # like min, max, but don't include strings @@ -245,22 +260,20 @@ def test_arg_passthru(): ["int", "float", "category_int", "datetime", "datetimetz", "timedelta"] ) for attr in ["cummin", "cummax"]: - f = getattr(df.groupby("group"), attr) - result = f() + result = getattr(df.groupby("group"), attr)() # GH 15561: numeric_only=False set by default like min/max tm.assert_index_equal(result.columns, expected_columns) - result = f(numeric_only=False) + result = getattr(df.groupby("group"), attr)(numeric_only=False) tm.assert_index_equal(result.columns, expected_columns) expected_columns = Index(["int", "float", "category_int", "timedelta"]) - for attr in ["cumsum"]: - f = getattr(df.groupby("group"), attr) - result = f() - tm.assert_index_equal(result.columns, expected_columns_numeric) - result = f(numeric_only=False) - tm.assert_index_equal(result.columns, expected_columns) + result = getattr(df.groupby("group"), "cumsum")() + tm.assert_index_equal(result.columns, expected_columns_numeric) + + result = getattr(df.groupby("group"), "cumsum")(numeric_only=False) + tm.assert_index_equal(result.columns, expected_columns) def test_non_cython_api(): @@ -691,59 +704,31 @@ def test_numpy_compat(func): reason="https://github.com/pandas-dev/pandas/issues/31992", strict=False, ) -def test_cummin_cummax(): +def test_cummin(numpy_dtypes_for_minmax): + dtype = numpy_dtypes_for_minmax[0] + min_val = numpy_dtypes_for_minmax[1] + # GH 15048 - num_types = [np.int32, np.int64, np.float32, np.float64] - num_mins = [ - np.iinfo(np.int32).min, - np.iinfo(np.int64).min, - np.finfo(np.float32).min, - np.finfo(np.float64).min, - ] - num_max = [ - np.iinfo(np.int32).max, - np.iinfo(np.int64).max, - np.finfo(np.float32).max, - np.finfo(np.float64).max, - ] base_df = pd.DataFrame( {"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]} ) expected_mins = [3, 3, 3, 2, 2, 2, 2, 1] - expected_maxs = [3, 4, 4, 4, 2, 3, 3, 3] - for dtype, min_val, max_val in zip(num_types, num_mins, num_max): - df = base_df.astype(dtype) + df = base_df.astype(dtype) - # cummin - expected = pd.DataFrame({"B": expected_mins}).astype(dtype) - result = df.groupby("A").cummin() - tm.assert_frame_equal(result, expected) - result = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame() - tm.assert_frame_equal(result, expected) - - # Test cummin w/ min value for dtype - df.loc[[2, 6], "B"] = min_val - expected.loc[[2, 3, 6, 7], "B"] = min_val - result = df.groupby("A").cummin() - tm.assert_frame_equal(result, expected) - expected = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame() - tm.assert_frame_equal(result, expected) - - # cummax - expected = pd.DataFrame({"B": expected_maxs}).astype(dtype) - result = df.groupby("A").cummax() - tm.assert_frame_equal(result, expected) - result = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() - tm.assert_frame_equal(result, expected) + expected = pd.DataFrame({"B": expected_mins}).astype(dtype) + result = df.groupby("A").cummin() + tm.assert_frame_equal(result, expected) + result = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame() + tm.assert_frame_equal(result, expected) - # Test cummax w/ max value for dtype - df.loc[[2, 6], "B"] = max_val - expected.loc[[2, 3, 6, 7], "B"] = max_val - result = df.groupby("A").cummax() - tm.assert_frame_equal(result, expected) - expected = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() - tm.assert_frame_equal(result, expected) + # Test w/ min value for dtype + df.loc[[2, 6], "B"] = min_val + expected.loc[[2, 3, 6, 7], "B"] = min_val + result = df.groupby("A").cummin() + tm.assert_frame_equal(result, expected) + expected = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame() + tm.assert_frame_equal(result, expected) # Test nan in some values base_df.loc[[0, 2, 4, 6], "B"] = np.nan @@ -753,30 +738,80 @@ def test_cummin_cummax(): expected = base_df.groupby("A").B.apply(lambda x: x.cummin()).to_frame() tm.assert_frame_equal(result, expected) - expected = pd.DataFrame({"B": [np.nan, 4, np.nan, 4, np.nan, 3, np.nan, 3]}) - result = base_df.groupby("A").cummax() - tm.assert_frame_equal(result, expected) - expected = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() - tm.assert_frame_equal(result, expected) + # GH 15561 + df = pd.DataFrame(dict(a=[1], b=pd.to_datetime(["2001"]))) + expected = pd.Series(pd.to_datetime("2001"), index=[0], name="b") + + result = df.groupby("a")["b"].cummin() + tm.assert_series_equal(expected, result) + + # GH 15635 + df = pd.DataFrame(dict(a=[1, 2, 1], b=[1, 2, 2])) + result = df.groupby("a").b.cummin() + expected = pd.Series([1, 2, 1], name="b") + tm.assert_series_equal(result, expected) + + +@pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, +) +def test_cummin_all_nan_column(): + base_df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [np.nan] * 8}) - # Test nan in entire column - base_df["B"] = np.nan expected = pd.DataFrame({"B": [np.nan] * 8}) result = base_df.groupby("A").cummin() tm.assert_frame_equal(expected, result) result = base_df.groupby("A").B.apply(lambda x: x.cummin()).to_frame() tm.assert_frame_equal(expected, result) + + +@pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, +) +def test_cummax(numpy_dtypes_for_minmax): + dtype = numpy_dtypes_for_minmax[0] + max_val = numpy_dtypes_for_minmax[2] + + # GH 15048 + base_df = pd.DataFrame( + {"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]} + ) + expected_maxs = [3, 4, 4, 4, 2, 3, 3, 3] + + df = base_df.astype(dtype) + + expected = pd.DataFrame({"B": expected_maxs}).astype(dtype) + result = df.groupby("A").cummax() + tm.assert_frame_equal(result, expected) + result = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() + tm.assert_frame_equal(result, expected) + + # Test w/ max value for dtype + df.loc[[2, 6], "B"] = max_val + expected.loc[[2, 3, 6, 7], "B"] = max_val + result = df.groupby("A").cummax() + tm.assert_frame_equal(result, expected) + expected = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() + tm.assert_frame_equal(result, expected) + + # Test nan in some values + base_df.loc[[0, 2, 4, 6], "B"] = np.nan + expected = pd.DataFrame({"B": [np.nan, 4, np.nan, 4, np.nan, 3, np.nan, 3]}) result = base_df.groupby("A").cummax() - tm.assert_frame_equal(expected, result) - result = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() - tm.assert_frame_equal(expected, result) + tm.assert_frame_equal(result, expected) + expected = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() + tm.assert_frame_equal(result, expected) # GH 15561 df = pd.DataFrame(dict(a=[1], b=pd.to_datetime(["2001"]))) expected = pd.Series(pd.to_datetime("2001"), index=[0], name="b") - for method in ["cummax", "cummin"]: - result = getattr(df.groupby("a")["b"], method)() - tm.assert_series_equal(expected, result) + + result = df.groupby("a")["b"].cummax() + tm.assert_series_equal(expected, result) # GH 15635 df = pd.DataFrame(dict(a=[1, 2, 1], b=[2, 1, 1])) @@ -784,10 +819,20 @@ def test_cummin_cummax(): expected = pd.Series([2, 1, 2], name="b") tm.assert_series_equal(result, expected) - df = pd.DataFrame(dict(a=[1, 2, 1], b=[1, 2, 2])) - result = df.groupby("a").b.cummin() - expected = pd.Series([1, 2, 1], name="b") - tm.assert_series_equal(result, expected) + +@pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + strict=False, +) +def test_cummax_all_nan_column(): + base_df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [np.nan] * 8}) + + expected = pd.DataFrame({"B": [np.nan] * 8}) + result = base_df.groupby("A").cummax() + tm.assert_frame_equal(expected, result) + result = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame() + tm.assert_frame_equal(expected, result) @pytest.mark.parametrize(
Some small cleanups (removing unnecessary for loops / adding parameterization)
https://api.github.com/repos/pandas-dev/pandas/pulls/32027
2020-02-15T15:56:52Z
2020-02-20T04:09:21Z
2020-02-20T04:09:21Z
2020-02-20T14:21:20Z
CI: silence numpy-dev failures
diff --git a/pandas/__init__.py b/pandas/__init__.py index d526531b159b2..2d3d3f7d92a9c 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -25,6 +25,7 @@ _np_version_under1p16, _np_version_under1p17, _np_version_under1p18, + _is_numpy_dev, ) try: diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py index 406d5f055797d..5aab5b814bae7 100644 --- a/pandas/tests/api/test_api.py +++ b/pandas/tests/api/test_api.py @@ -198,6 +198,7 @@ class TestPDApi(Base): "_np_version_under1p16", "_np_version_under1p17", "_np_version_under1p18", + "_is_numpy_dev", "_testing", "_tslib", "_typing", diff --git a/pandas/tests/frame/test_cumulative.py b/pandas/tests/frame/test_cumulative.py index b545d6aa8afd3..2466547e2948b 100644 --- a/pandas/tests/frame/test_cumulative.py +++ b/pandas/tests/frame/test_cumulative.py @@ -7,8 +7,9 @@ """ import numpy as np +import pytest -from pandas import DataFrame, Series +from pandas import DataFrame, Series, _is_numpy_dev import pandas._testing as tm @@ -73,6 +74,9 @@ def test_cumprod(self, datetime_frame): df.cumprod(0) df.cumprod(1) + @pytest.mark.xfail( + _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + ) def test_cummin(self, datetime_frame): datetime_frame.loc[5:10, 0] = np.nan datetime_frame.loc[10:15, 1] = np.nan @@ -96,6 +100,9 @@ def test_cummin(self, datetime_frame): cummin_xs = datetime_frame.cummin(axis=1) assert np.shape(cummin_xs) == np.shape(datetime_frame) + @pytest.mark.xfail( + _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + ) def test_cummax(self, datetime_frame): datetime_frame.loc[5:10, 0] = np.nan datetime_frame.loc[10:15, 1] = np.nan diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py index 73e36cb5e6c84..8830b84a52421 100644 --- a/pandas/tests/groupby/test_function.py +++ b/pandas/tests/groupby/test_function.py @@ -17,6 +17,7 @@ NaT, Series, Timestamp, + _is_numpy_dev, date_range, isna, ) @@ -685,6 +686,9 @@ def test_numpy_compat(func): getattr(g, func)(foo=1) +@pytest.mark.xfail( + _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" +) def test_cummin_cummax(): # GH 15048 num_types = [np.int32, np.int64, np.float32, np.float64] diff --git a/pandas/tests/groupby/test_transform.py b/pandas/tests/groupby/test_transform.py index 0ad829dd4de7a..740103eec185a 100644 --- a/pandas/tests/groupby/test_transform.py +++ b/pandas/tests/groupby/test_transform.py @@ -15,6 +15,7 @@ MultiIndex, Series, Timestamp, + _is_numpy_dev, concat, date_range, ) @@ -329,6 +330,8 @@ def test_transform_transformation_func(transformation_func): if transformation_func in ["pad", "backfill", "tshift", "corrwith", "cumcount"]: # These transformation functions are not yet covered in this test pytest.xfail("See GH 31269 and GH 31270") + elif _is_numpy_dev and transformation_func in ["cummin"]: + pytest.xfail("https://github.com/pandas-dev/pandas/issues/31992") elif transformation_func == "fillna": test_op = lambda x: x.transform("fillna", value=0) mock_op = lambda x: x.fillna(value=0) diff --git a/pandas/tests/scalar/timedelta/test_arithmetic.py b/pandas/tests/scalar/timedelta/test_arithmetic.py index 60e278f47d0f8..f0ad5fa70471b 100644 --- a/pandas/tests/scalar/timedelta/test_arithmetic.py +++ b/pandas/tests/scalar/timedelta/test_arithmetic.py @@ -8,7 +8,7 @@ import pytest import pandas as pd -from pandas import NaT, Timedelta, Timestamp, offsets +from pandas import NaT, Timedelta, Timestamp, _is_numpy_dev, offsets import pandas._testing as tm from pandas.core import ops @@ -377,18 +377,28 @@ def test_td_div_numeric_scalar(self): assert isinstance(result, Timedelta) assert result == Timedelta(days=2) - @pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")]) + @pytest.mark.parametrize( + "nan", + [ + np.nan, + pytest.param( + np.float64("NaN"), + marks=pytest.mark.xfail( + _is_numpy_dev, + reason="https://github.com/pandas-dev/pandas/issues/31992", + ), + ), + float("nan"), + ], + ) def test_td_div_nan(self, nan): # np.float64('NaN') has a 'dtype' attr, avoid treating as array td = Timedelta(10, unit="d") result = td / nan assert result is NaT - # TODO: Don't leave commented, this is just a temporary fix for - # https://github.com/pandas-dev/pandas/issues/31992 - - # result = td // nan - # assert result is NaT + result = td // nan + assert result is NaT # --------------------------------------------------------------- # Timedelta.__rdiv__ diff --git a/pandas/tests/series/test_cumulative.py b/pandas/tests/series/test_cumulative.py index 885b5bf0476f2..b0065992b850a 100644 --- a/pandas/tests/series/test_cumulative.py +++ b/pandas/tests/series/test_cumulative.py @@ -11,6 +11,7 @@ import pytest import pandas as pd +from pandas import _is_numpy_dev import pandas._testing as tm @@ -37,6 +38,9 @@ def test_cumsum(self, datetime_series): def test_cumprod(self, datetime_series): _check_accum_op("cumprod", datetime_series) + @pytest.mark.xfail( + _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + ) def test_cummin(self, datetime_series): tm.assert_numpy_array_equal( datetime_series.cummin().values, @@ -49,6 +53,9 @@ def test_cummin(self, datetime_series): tm.assert_series_equal(result, expected) + @pytest.mark.xfail( + _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992" + ) def test_cummax(self, datetime_series): tm.assert_numpy_array_equal( datetime_series.cummax().values,
xref #31992
https://api.github.com/repos/pandas-dev/pandas/pulls/32025
2020-02-15T14:43:34Z
2020-02-15T18:34:58Z
2020-02-15T18:34:58Z
2020-02-18T10:54:50Z
WEB: Add greeting note to CoC
diff --git a/web/pandas/community/coc.md b/web/pandas/community/coc.md index bf62f4e00f847..d2af9c3fdd25b 100644 --- a/web/pandas/community/coc.md +++ b/web/pandas/community/coc.md @@ -20,6 +20,9 @@ Examples of unacceptable behavior by participants include: addresses, without explicit permission * Other unethical or unprofessional conduct +Furthermore, we encourage inclusive behavior - for example, +please don’t say “hey guys!” but “hey everyone!”. + Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or
I noticed something similar in the [CPython contributing docs](https://cpython-core-tutorial.readthedocs.io/en/latest/diversity.html): > For example, don’t say “hey guys!” but “hey everyone!”. and thought it was really nice. Pushing directly rather than opening as good first issue to avoid invoking a s*******m
https://api.github.com/repos/pandas-dev/pandas/pulls/32024
2020-02-15T13:40:06Z
2020-02-27T23:08:16Z
2020-02-27T23:08:16Z
2020-03-26T16:13:32Z
DOC: Add missing period to parameter description
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py index d0c64d54f30d6..018441dacd9a8 100644 --- a/pandas/io/formats/style.py +++ b/pandas/io/formats/style.py @@ -452,7 +452,7 @@ def format(self, formatter, subset=None, na_rep: Optional[str] = None) -> "Style ``formatter`` is applied to. na_rep : str, optional Representation for missing values. - If ``na_rep`` is None, no special formatting is applied + If ``na_rep`` is None, no special formatting is applied. .. versionadded:: 1.0.0 diff --git a/pandas/io/sql.py b/pandas/io/sql.py index 0eb7deb5574ec..e97872d880dee 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -361,7 +361,7 @@ def read_sql( Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable. See - `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_ + `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_. index_col : str or list of str, optional, default: None Column(s) to set as index(MultiIndex). coerce_float : bool, default True
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32022
2020-02-15T12:05:33Z
2020-02-15T21:50:43Z
2020-02-15T21:50:42Z
2020-02-15T21:50:54Z
DOC SS06 Make the summery in one line on offsets.py
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index df1e750b32138..959dd19a50d90 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -1017,8 +1017,7 @@ def __init__( class CustomBusinessDay(_CustomMixin, BusinessDay): """ - DateOffset subclass representing possibly n custom business days, - excluding holidays. + DateOffset subclass representing custom business days excluding holidays. Parameters ----------
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32021
2020-02-15T12:01:20Z
2020-02-15T19:07:14Z
2020-02-15T19:07:14Z
2020-02-15T19:07:25Z
DOC: Fix errors in pandas.Series.argmax
diff --git a/pandas/core/base.py b/pandas/core/base.py index 85424e35fa0e0..b9aeb32eea5c1 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -927,22 +927,50 @@ def max(self, axis=None, skipna=True, *args, **kwargs): def argmax(self, axis=None, skipna=True, *args, **kwargs): """ - Return an ndarray of the maximum argument indexer. + Return int position of the largest value in the Series. + + If the maximum is achieved in multiple locations, + the first row position is returned. Parameters ---------- axis : {None} Dummy argument for consistency with Series. skipna : bool, default True + Exclude NA/null values when showing the result. + *args, **kwargs + Additional arguments and keywords for compatibility with NumPy. Returns ------- - numpy.ndarray - Indices of the maximum values. + int + Row position of the maximum values. See Also -------- - numpy.ndarray.argmax + numpy.ndarray.argmax : Equivalent method for numpy arrays. + Series.argmin : Similar method, but returning the minimum. + Series.idxmax : Return index label of the maximum values. + Series.idxmin : Return index label of the minimum values. + + Examples + -------- + Consider dataset containing cereal calories + + >>> s = pd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0, + ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}) + >>> s + Corn Flakes 100.0 + Almond Delight 110.0 + Cinnamon Toast Crunch 120.0 + Cocoa Puff 110.0 + dtype: float64 + + >>> s.argmax() + 2 + + The maximum cereal calories is in the third element, + since series is zero-indexed. """ nv.validate_minmax_axis(axis) nv.validate_argmax_with_skipna(skipna, args, kwargs)
- [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/18 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry output of `python scripts/validate_docstrings.py pandas.Series.argmax`: ``` ################################################################################ ################################## Validation ################################## ################################################################################ ```
https://api.github.com/repos/pandas-dev/pandas/pulls/32019
2020-02-15T11:42:12Z
2020-02-26T20:53:54Z
2020-02-26T20:53:54Z
2020-02-27T07:35:55Z
DOC add extended summary, update parameter types desc, update return types desc, and add whitespaces after commas in list declarations to DataFrame.first in core/generic.py
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e6c5ac9dbf733..b039630efc989 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -8014,15 +8014,21 @@ def resample( def first(self: FrameOrSeries, offset) -> FrameOrSeries: """ - Method to subset initial periods of time series data based on a date offset. + Select initial periods of time series data based on a date offset. + + When having a DataFrame with dates as index, this function can + select the first few rows based on a date offset. Parameters ---------- - offset : str, DateOffset, dateutil.relativedelta + offset : str, DateOffset or dateutil.relativedelta + The offset length of the data that will be selected. For instance, + '1M' will display all the rows having their index within the first month. Returns ------- - subset : same type as caller + Series or DataFrame + A subset of the caller. Raises ------ @@ -8038,7 +8044,7 @@ def first(self: FrameOrSeries, offset) -> FrameOrSeries: Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') - >>> ts = pd.DataFrame({'A': [1,2,3,4]}, index=i) + >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32018
2020-02-15T11:32:50Z
2020-03-10T21:30:06Z
2020-03-10T21:30:06Z
2020-03-11T08:48:54Z
Fix docstring validation errors in pandas.Index.slice_indexer
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index 14ee21ea5614c..dc39e2808f62f 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -4856,6 +4856,8 @@ def _get_string_slice(self, key: str_t, use_lhs: bool = True, use_rhs: bool = Tr def slice_indexer(self, start=None, end=None, step=None, kind=None): """ + Return index-based slice indexer from labels-based one. + For an ordered or unique index, compute the slice indexer for input labels and step. @@ -4866,17 +4868,23 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): end : label, default None If None, defaults to the end. step : int, default None - kind : str, default None + If None, defaults to 1. + kind : {'loc', 'getitem'}, optional Returns ------- - indexer : slice + slice : + Slice indexer for input labels and step. Raises ------ KeyError : If key does not exist, or key is not unique and index is not ordered. + See Also + -------- + Index.slice_locs : Compute slice locations for input labels. + Notes ----- This function assumes that the data is sorted, so use at your own peril @@ -4887,11 +4895,11 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None): >>> idx = pd.Index(list('abcd')) >>> idx.slice_indexer(start='b', end='c') - slice(1, 3) + slice(1, 3, None) >>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')]) >>> idx.slice_indexer(start='b', end=('c', 'g')) - slice(1, 3) + slice(1, 3, None) """ start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
- [ ] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/6 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32017
2020-02-15T11:28:33Z
2020-03-20T22:26:23Z
null
2020-03-20T22:26:23Z
DOC: Add reference in keywords arguments to Line2D mathplotlib
diff --git a/pandas/plotting/_misc.py b/pandas/plotting/_misc.py index 47a4fd8ff0e95..3788cc447b087 100644 --- a/pandas/plotting/_misc.py +++ b/pandas/plotting/_misc.py @@ -280,7 +280,8 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds): samples : int, default 500 Number of times the bootstrap procedure is performed. **kwds - Options to pass to matplotlib plotting method. + Optional, two dimensional properties from + :func:`matplotlib.lines.Line2D`. Returns ------- @@ -291,6 +292,7 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds): -------- DataFrame.plot : Basic plotting for DataFrame objects. Series.plot : Basic plotting for Series objects. + matplotlib.lines.Line2D : Matplotlib Line2D objects. Examples --------
Keyword arguments in `pandas.plotting.bootstrap_plot` doesn't clearly specify argument options - [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/32016
2020-02-15T11:18:33Z
2020-02-26T20:52:15Z
null
2020-02-26T20:52:16Z
DOC: Improve docstring of Index.delete
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index 3d549405592d6..14ee21ea5614c 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -5135,9 +5135,30 @@ def delete(self, loc): """ Make new Index with passed location(-s) deleted. + Parameters + ---------- + loc : int or list of int + Location of item(-s) which will be deleted. + Use a list of locations to delete more than one value at the same time. + Returns ------- - new_index : Index + Index + New Index with passed location(-s) deleted. + + See Also + -------- + numpy.delete : Delete any rows and column from NumPy array (ndarray). + + Examples + -------- + >>> idx = pd.Index(['a', 'b', 'c']) + >>> idx.delete(1) + Index(['a', 'c'], dtype='object') + + >>> idx = pd.Index(['a', 'b', 'c']) + >>> idx.delete([0, 2]) + Index(['b'], dtype='object') """ return self._shallow_copy(np.delete(self._data, loc))
- [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/13 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Output from validate_docstrings.py ################################################################################ ####################### Docstring (pandas.Index.delete) ####################### ################################################################################ Make new Index with passed location(-s) deleted. Use array of integer as loc parameter to delete multiple locations. Parameters ---------- loc : int Location of item(-s) which will be deleted. Returns ------- Index New Index with passed location(-s) deleted. See Also -------- numpy.delete : Delete any rows and column from NumPy array (ndarray). Examples -------- >>> idx = pd.Index(['a', 'b', 'c']) >>> idx.delete(1) # Deleting 'b' Index(['a', 'c'], dtype='object') >>> idx = pd.Index(['a', 'b', 'c']) >>> idx.delete([0, 2]) Index(['b'], dtype='object') ################################################################################ ################################## Validation ################################## ################################################################################
https://api.github.com/repos/pandas-dev/pandas/pulls/32015
2020-02-15T11:12:36Z
2020-02-15T19:08:13Z
2020-02-15T19:08:13Z
2020-02-24T15:11:04Z
DOC: Update pandas.Series.between_time docstring params
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 04e8b78fb1b87..e996c47ac7ad3 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -7538,16 +7538,22 @@ def between_time( Parameters ---------- start_time : datetime.time or str + Initial time as a time filter limit. end_time : datetime.time or str + End time as a time filter limit. include_start : bool, default True + Whether the start time needs to be included in the result. include_end : bool, default True + Whether the end time needs to be included in the result. axis : {0 or 'index', 1 or 'columns'}, default 0 + Determine range time on index or columns value. .. versionadded:: 0.24.0 Returns ------- Series or DataFrame + Data from the original object filtered to the specified dates range. Raises ------
- [x] closes [https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/20](https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/20) - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry output from python scripts/validate_docstrings.py pandas.Series.between_time: ################################################################################ #################### Docstring (pandas.Series.between_time) #################### ################################################################################ Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting ``start_time`` to be later than ``end_time``, you can get the times that are *not* between the two times. Parameters ---------- start_time : datetime.time or str The first time value. end_time : datetime.time or str The second time value. include_start : bool, default True Adding start_time value in the result. include_end : bool, default True Adding end_time value in the result. axis : {0 or 'index', 1 or 'columns'}, default 0 Determine range time on index or columns value. .. versionadded:: 0.24.0 Returns ------- Series or DataFrame If axis set on columns, it will return DataFrame format, vice versa. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- at_time : Select values at a particular time of the day. first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 2018-04-12 01:00:00 4 >>> ts.between_time('0:15', '0:45') A 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 You get the times that are *not* between two times by setting ``start_time`` later than ``end_time``: >>> ts.between_time('0:45', '0:15') A 2018-04-09 00:00:00 1 2018-04-12 01:00:00 4 ################################################################################ ################################## Validation ################################## ################################################################################
https://api.github.com/repos/pandas-dev/pandas/pulls/32014
2020-02-15T11:05:41Z
2020-02-15T19:10:24Z
2020-02-15T19:10:24Z
2020-02-15T19:10:34Z