title stringlengths 1 185 | diff stringlengths 0 32.2M | body stringlengths 0 123k ⌀ | url stringlengths 57 58 | created_at stringlengths 20 20 | closed_at stringlengths 20 20 | merged_at stringlengths 20 20 ⌀ | updated_at stringlengths 20 20 |
|---|---|---|---|---|---|---|---|
CI: Update web and docs to OVH with the right structure | diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index a337ccbc98650..025b6f1813df7 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -125,68 +125,32 @@ jobs:
- name: Check ipython directive errors
run: "! grep -B1 \"^<<<-------------------------------------------------------------------------$\" sphinx.log"
- - name: Merge website and docs
- run: |
- mkdir -p pandas_web/docs
- cp -r web/build/* pandas_web/
- cp -r doc/build/html/* pandas_web/docs/
- if: github.event_name == 'push'
-
- name: Install Rclone
run: sudo apt install rclone -y
if: github.event_name == 'push'
- name: Set up Rclone
run: |
- RCLONE_CONFIG_PATH=$HOME/.config/rclone/rclone.conf
- mkdir -p `dirname $RCLONE_CONFIG_PATH`
- echo "[ovh_cloud_pandas_web]" > $RCLONE_CONFIG_PATH
- echo "type = swift" >> $RCLONE_CONFIG_PATH
- echo "env_auth = false" >> $RCLONE_CONFIG_PATH
- echo "auth_version = 3" >> $RCLONE_CONFIG_PATH
- echo "auth = https://auth.cloud.ovh.net/v3/" >> $RCLONE_CONFIG_PATH
- echo "endpoint_type = public" >> $RCLONE_CONFIG_PATH
- echo "tenant_domain = default" >> $RCLONE_CONFIG_PATH
- echo "tenant = 2977553886518025" >> $RCLONE_CONFIG_PATH
- echo "domain = default" >> $RCLONE_CONFIG_PATH
- echo "user = w4KGs3pmDxpd" >> $RCLONE_CONFIG_PATH
- echo "key = ${{ secrets.ovh_object_store_key }}" >> $RCLONE_CONFIG_PATH
- echo "region = BHS" >> $RCLONE_CONFIG_PATH
+ CONF=$HOME/.config/rclone/rclone.conf
+ mkdir -p `dirname $CONF`
+ echo "[ovh_host]" > $CONF
+ echo "type = swift" >> $CONF
+ echo "env_auth = false" >> $CONF
+ echo "auth_version = 3" >> $CONF
+ echo "auth = https://auth.cloud.ovh.net/v3/" >> $CONF
+ echo "endpoint_type = public" >> $CONF
+ echo "tenant_domain = default" >> $CONF
+ echo "tenant = 2977553886518025" >> $CONF
+ echo "domain = default" >> $CONF
+ echo "user = w4KGs3pmDxpd" >> $CONF
+ echo "key = ${{ secrets.ovh_object_store_key }}" >> $CONF
+ echo "region = BHS" >> $CONF
if: github.event_name == 'push'
- 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"
+ run: rclone sync --exclude pandas-docs/** web/build ovh_host:prod
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 || true
+ - name: Sync dev docs with OVH
+ run: rclone sync doc/build/html ovh_host:prod/pandas-docs/dev
if: github.event_name == 'push'
| - [X] closes #32303
I'm uploading all the docs from the NumFOCUS server to the OVH (manually). And I'm changing the CI in this PR to upload the website and the dev docs to the server automatically.
I'll have a look and see if we can upload a new version of the docs automatically to OVH when a new GitHub release is generated. So, we never need to upload the website manually.
After merging this PR, and when we're confident this is working well, we'll have to redirect pandas.pydata.org to the OVH server. The dev docs will be available at pandas.pydata.org/pandas-docs/dev/ (and we can stop using pandas.io and dev.pandas.io). | https://api.github.com/repos/pandas-dev/pandas/pulls/32530 | 2020-03-07T21:53:36Z | 2020-03-11T12:58:03Z | 2020-03-11T12:58:03Z | 2020-03-20T16:36:14Z |
TST: make tests stricter | diff --git a/pandas/tests/arithmetic/test_numeric.py b/pandas/tests/arithmetic/test_numeric.py
index d4baf2f374cdf..202e30287881f 100644
--- a/pandas/tests/arithmetic/test_numeric.py
+++ b/pandas/tests/arithmetic/test_numeric.py
@@ -913,13 +913,13 @@ def test_frame_operators(self, float_frame):
# TODO: taken from tests.series.test_operators; needs cleanup
def test_series_operators(self):
- def _check_op(series, other, op, pos_only=False, check_dtype=True):
+ def _check_op(series, other, op, pos_only=False):
left = np.abs(series) if pos_only else series
right = np.abs(other) if pos_only else other
cython_or_numpy = op(left, right)
python = left.combine(right, op)
- tm.assert_series_equal(cython_or_numpy, python, check_dtype=check_dtype)
+ tm.assert_series_equal(cython_or_numpy, python)
def check(series, other):
simple_ops = ["add", "sub", "mul", "truediv", "floordiv", "mod"]
@@ -942,15 +942,15 @@ def check(series, other):
check(tser, tser[::2])
check(tser, 5)
- def check_comparators(series, other, check_dtype=True):
- _check_op(series, other, operator.gt, check_dtype=check_dtype)
- _check_op(series, other, operator.ge, check_dtype=check_dtype)
- _check_op(series, other, operator.eq, check_dtype=check_dtype)
- _check_op(series, other, operator.lt, check_dtype=check_dtype)
- _check_op(series, other, operator.le, check_dtype=check_dtype)
+ def check_comparators(series, other):
+ _check_op(series, other, operator.gt)
+ _check_op(series, other, operator.ge)
+ _check_op(series, other, operator.eq)
+ _check_op(series, other, operator.lt)
+ _check_op(series, other, operator.le)
check_comparators(tser, 5)
- check_comparators(tser, tser + 1, check_dtype=False)
+ check_comparators(tser, tser + 1)
# TODO: taken from tests.series.test_operators; needs cleanup
def test_divmod(self):
diff --git a/pandas/tests/dtypes/cast/test_construct_from_scalar.py b/pandas/tests/dtypes/cast/test_construct_from_scalar.py
index cc823a3d6e02c..ed272cef3e7ba 100644
--- a/pandas/tests/dtypes/cast/test_construct_from_scalar.py
+++ b/pandas/tests/dtypes/cast/test_construct_from_scalar.py
@@ -15,6 +15,4 @@ def test_cast_1d_array_like_from_scalar_categorical():
expected = Categorical(["a", "a"], categories=cats)
result = construct_1d_arraylike_from_scalar("a", len(expected), cat_type)
- tm.assert_categorical_equal(
- result, expected, check_category_order=True, check_dtype=True
- )
+ tm.assert_categorical_equal(result, expected)
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index 071d2409f1be2..172800e74d181 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -916,10 +916,7 @@ def test_constructor_mrecarray(self):
# from GH3479
assert_fr_equal = functools.partial(
- tm.assert_frame_equal,
- check_index_type=True,
- check_column_type=True,
- check_frame_type=True,
+ tm.assert_frame_equal, check_index_type=True, check_column_type=True,
)
arrays = [
("float", np.array([1.5, 2.0])),
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index 59899673cfc31..7ef4c454c5a5d 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -452,7 +452,7 @@ def test_float_types(self, np_type, path):
reader = ExcelFile(path)
recons = pd.read_excel(reader, "test1", index_col=0).astype(np_type)
- tm.assert_frame_equal(df, recons, check_dtype=False)
+ tm.assert_frame_equal(df, recons)
@pytest.mark.parametrize("np_type", [np.bool8, np.bool_])
def test_bool_types(self, np_type, path):
diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py
index f2d35bfb3b5ae..276dfd666c5d0 100644
--- a/pandas/tests/io/json/test_pandas.py
+++ b/pandas/tests/io/json/test_pandas.py
@@ -572,7 +572,6 @@ def test_blocks_compat_GH9037(self):
df_roundtrip,
check_index_type=True,
check_column_type=True,
- check_frame_type=True,
by_blocks=True,
check_exact=True,
)
diff --git a/pandas/tests/io/json/test_ujson.py b/pandas/tests/io/json/test_ujson.py
index e966db7a1cc71..e86667626deda 100644
--- a/pandas/tests/io/json/test_ujson.py
+++ b/pandas/tests/io/json/test_ujson.py
@@ -48,6 +48,18 @@ def numpy(request):
return request.param
+def get_int32_compat_dtype(numpy, orient):
+ # See GH#32527
+ dtype = np.int64
+ if not ((numpy is None or orient == "index") or (numpy is True and orient is None)):
+ if compat.is_platform_windows():
+ dtype = np.int32
+ else:
+ dtype = np.intp
+
+ return dtype
+
+
class TestUltraJSONTests:
@pytest.mark.skipif(
compat.is_platform_32bit(), reason="not compliant on 32-bit, xref #15865"
@@ -833,13 +845,20 @@ def test_dataframe(self, orient, numpy):
if orient == "records" and numpy:
pytest.skip("Not idiomatic pandas")
+ dtype = get_int32_compat_dtype(numpy, orient)
+
df = DataFrame(
- [[1, 2, 3], [4, 5, 6]], index=["a", "b"], columns=["x", "y", "z"]
+ [[1, 2, 3], [4, 5, 6]],
+ index=["a", "b"],
+ columns=["x", "y", "z"],
+ dtype=dtype,
)
encode_kwargs = {} if orient is None else dict(orient=orient)
decode_kwargs = {} if numpy is None else dict(numpy=numpy)
+ assert (df.dtypes == dtype).all()
output = ujson.decode(ujson.encode(df, **encode_kwargs), **decode_kwargs)
+ assert (df.dtypes == dtype).all()
# Ensure proper DataFrame initialization.
if orient == "split":
@@ -857,7 +876,8 @@ def test_dataframe(self, orient, numpy):
elif orient == "index":
df = df.transpose()
- tm.assert_frame_equal(output, df, check_dtype=False)
+ assert (df.dtypes == dtype).all()
+ tm.assert_frame_equal(output, df)
def test_dataframe_nested(self, orient):
df = DataFrame(
@@ -897,14 +917,20 @@ def test_dataframe_numpy_labelled(self, orient):
tm.assert_frame_equal(output, df)
def test_series(self, orient, numpy):
+ dtype = get_int32_compat_dtype(numpy, orient)
s = Series(
- [10, 20, 30, 40, 50, 60], name="series", index=[6, 7, 8, 9, 10, 15]
+ [10, 20, 30, 40, 50, 60],
+ name="series",
+ index=[6, 7, 8, 9, 10, 15],
+ dtype=dtype,
).sort_values()
+ assert s.dtype == dtype
encode_kwargs = {} if orient is None else dict(orient=orient)
decode_kwargs = {} if numpy is None else dict(numpy=numpy)
output = ujson.decode(ujson.encode(s, **encode_kwargs), **decode_kwargs)
+ assert s.dtype == dtype
if orient == "split":
dec = _clean_dict(output)
@@ -920,7 +946,8 @@ def test_series(self, orient, numpy):
s.name = None
s.index = [0, 1, 2, 3, 4, 5]
- tm.assert_series_equal(output, s, check_dtype=False)
+ assert s.dtype == dtype
+ tm.assert_series_equal(output, s)
def test_series_nested(self, orient):
s = Series(
diff --git a/pandas/tests/io/pytables/test_store.py b/pandas/tests/io/pytables/test_store.py
index 2702d378fd153..61ca2e7f5f19d 100644
--- a/pandas/tests/io/pytables/test_store.py
+++ b/pandas/tests/io/pytables/test_store.py
@@ -2298,9 +2298,7 @@ def test_index_types(self, setup_path):
with catch_warnings(record=True):
values = np.random.randn(2)
- func = lambda l, r: tm.assert_series_equal(
- l, r, check_dtype=True, check_index_type=True
- )
+ func = lambda l, r: tm.assert_series_equal(l, r, check_index_type=True)
with catch_warnings(record=True):
ser = Series(values, [0, "y"])
diff --git a/pandas/tests/io/test_clipboard.py b/pandas/tests/io/test_clipboard.py
index 3458cfb6ad254..b627e0e1cad54 100644
--- a/pandas/tests/io/test_clipboard.py
+++ b/pandas/tests/io/test_clipboard.py
@@ -75,7 +75,11 @@ def df(request):
)
elif data_type == "mixed":
return DataFrame(
- {"a": np.arange(1.0, 6.0) + 0.01, "b": np.arange(1, 6), "c": list("abcde")}
+ {
+ "a": np.arange(1.0, 6.0) + 0.01,
+ "b": np.arange(1, 6).astype(np.int64),
+ "c": list("abcde"),
+ }
)
elif data_type == "float":
return tm.makeCustomDataframe(
@@ -146,7 +150,7 @@ class TestClipboard:
def check_round_trip_frame(self, data, excel=None, sep=None, encoding=None):
data.to_clipboard(excel=excel, sep=sep, encoding=encoding)
result = read_clipboard(sep=sep or "\t", index_col=0, encoding=encoding)
- tm.assert_frame_equal(data, result, check_dtype=False)
+ tm.assert_frame_equal(data, result)
# Test that default arguments copy as tab delimited
def test_round_trip_frame(self, df):
diff --git a/pandas/tests/reshape/merge/test_join.py b/pandas/tests/reshape/merge/test_join.py
index 725157b7c8523..dc1efa46403be 100644
--- a/pandas/tests/reshape/merge/test_join.py
+++ b/pandas/tests/reshape/merge/test_join.py
@@ -298,7 +298,7 @@ def test_join_on_inner(self):
expected = df.join(df2, on="key")
expected = expected[expected["value"].notna()]
- tm.assert_series_equal(joined["key"], expected["key"], check_dtype=False)
+ tm.assert_series_equal(joined["key"], expected["key"])
tm.assert_series_equal(joined["value"], expected["value"], check_dtype=False)
tm.assert_index_equal(joined.index, expected.index)
diff --git a/pandas/tests/reshape/test_union_categoricals.py b/pandas/tests/reshape/test_union_categoricals.py
index a503173bd74b1..8918d19e4ba7b 100644
--- a/pandas/tests/reshape/test_union_categoricals.py
+++ b/pandas/tests/reshape/test_union_categoricals.py
@@ -41,7 +41,7 @@ def test_union_categorical(self):
for box in [Categorical, CategoricalIndex, Series]:
result = union_categoricals([box(Categorical(a)), box(Categorical(b))])
expected = Categorical(combined)
- tm.assert_categorical_equal(result, expected, check_category_order=True)
+ tm.assert_categorical_equal(result, expected)
# new categories ordered by appearance
s = Categorical(["x", "y", "z"])
diff --git a/pandas/tests/series/methods/test_argsort.py b/pandas/tests/series/methods/test_argsort.py
index c7fe6ed19a2eb..4353eb4c8cd64 100644
--- a/pandas/tests/series/methods/test_argsort.py
+++ b/pandas/tests/series/methods/test_argsort.py
@@ -49,8 +49,8 @@ def test_argsort_stable(self):
mexpected = np.argsort(s.values, kind="mergesort")
qexpected = np.argsort(s.values, kind="quicksort")
- tm.assert_series_equal(mindexer, Series(mexpected), check_dtype=False)
- tm.assert_series_equal(qindexer, Series(qexpected), check_dtype=False)
+ tm.assert_series_equal(mindexer.astype(np.intp), Series(mexpected))
+ tm.assert_series_equal(qindexer.astype(np.intp), Series(qexpected))
msg = (
r"ndarray Expected type <class 'numpy\.ndarray'>, "
r"found <class 'pandas\.core\.series\.Series'> instead"
diff --git a/pandas/tests/series/methods/test_replace.py b/pandas/tests/series/methods/test_replace.py
index 26eaf53616282..904a455870ab1 100644
--- a/pandas/tests/series/methods/test_replace.py
+++ b/pandas/tests/series/methods/test_replace.py
@@ -294,7 +294,7 @@ def test_replace_categorical(self, categorical, numeric):
s = pd.Series(categorical)
result = s.replace({"A": 1, "B": 2})
expected = pd.Series(numeric)
- tm.assert_series_equal(expected, result, check_dtype=False)
+ tm.assert_series_equal(expected, result)
def test_replace_categorical_single(self):
# GH 26988
diff --git a/pandas/tests/series/test_duplicates.py b/pandas/tests/series/test_duplicates.py
index 3513db6177951..89181a08819b1 100644
--- a/pandas/tests/series/test_duplicates.py
+++ b/pandas/tests/series/test_duplicates.py
@@ -47,9 +47,9 @@ def test_unique():
# GH 18051
s = Series(Categorical([]))
- tm.assert_categorical_equal(s.unique(), Categorical([]), check_dtype=False)
+ tm.assert_categorical_equal(s.unique(), Categorical([]))
s = Series(Categorical([np.nan]))
- tm.assert_categorical_equal(s.unique(), Categorical([np.nan]), check_dtype=False)
+ tm.assert_categorical_equal(s.unique(), Categorical([np.nan]))
def test_unique_data_ownership():
| I expect to see some platform-specific failures, will be nice to document those where possible. | https://api.github.com/repos/pandas-dev/pandas/pulls/32527 | 2020-03-07T19:52:24Z | 2020-03-14T03:41:07Z | 2020-03-14T03:41:06Z | 2020-03-14T16:09:26Z |
TST: remove unused kwargs in assert_sp_array_equal | diff --git a/pandas/_testing.py b/pandas/_testing.py
index 33ec4e4886aa6..c30810f724245 100644
--- a/pandas/_testing.py
+++ b/pandas/_testing.py
@@ -1491,14 +1491,7 @@ def to_array(obj):
# Sparse
-def assert_sp_array_equal(
- left,
- right,
- check_dtype=True,
- check_kind=True,
- check_fill_value=True,
- consolidate_block_indices=False,
-):
+def assert_sp_array_equal(left, right):
"""
Check that the left and right SparseArray are equal.
@@ -1506,38 +1499,17 @@ def assert_sp_array_equal(
----------
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)
+ assert_numpy_array_equal(left.sp_values, right.sp_values)
# 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()
+ left_index = left.sp_index
+ right_index = right.sp_index
if not left_index.equals(right_index):
raise_assert_detail(
@@ -1547,11 +1519,9 @@ def assert_sp_array_equal(
# 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)
+ assert_attr_equal("fill_value", left, right)
+ assert_attr_equal("dtype", left, right)
+ assert_numpy_array_equal(left.to_dense(), right.to_dense())
# -----------------------------------------------------------------------------
| https://api.github.com/repos/pandas-dev/pandas/pulls/32525 | 2020-03-07T19:02:03Z | 2020-03-10T11:43:37Z | 2020-03-10T11:43:37Z | 2020-03-10T15:01:32Z | |
CLN: avoid Block.get_values in io.sql | diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index 9a53e7cd241e1..560e7e4781cbb 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -705,8 +705,16 @@ def insert_data(self):
else:
# convert to microsecond resolution for datetime.datetime
d = b.values.astype("M8[us]").astype(object)
+ elif b.is_timedelta:
+ # numpy converts this to an object array of integers,
+ # whereas b.astype(object).values would convert to
+ # object array of Timedeltas
+ d = b.values.astype(object)
else:
- d = np.array(b.get_values(), dtype=object)
+ # TODO(2DEA): astype-first can be avoided with 2D EAs
+ # astype on the block instead of values to ensure we
+ # get the right shape
+ d = b.astype(object).values
# replace NaN with None
if b._can_hold_na:
| This particular usage is especially non-transparent, since it is effectively `blk.get_values().astype(object)` which is apparently _not_ equivalent to `blk.get_values(object)` | https://api.github.com/repos/pandas-dev/pandas/pulls/32524 | 2020-03-07T18:27:24Z | 2020-03-11T02:52:55Z | 2020-03-11T02:52:55Z | 2020-03-11T03:07:37Z |
CLN: simplify get_values usage in groupby | diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index ac522fc7863b2..b7ac3048631c5 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -49,7 +49,7 @@
is_scalar,
needs_i8_conversion,
)
-from pandas.core.dtypes.missing import _isna_ndarraylike, isna, notna
+from pandas.core.dtypes.missing import isna, notna
from pandas.core.aggregation import (
is_multi_agg_with_relabel,
@@ -1772,10 +1772,8 @@ def count(self):
ids, _, ngroups = self.grouper.group_info
mask = ids != -1
- vals = (
- (mask & ~_isna_ndarraylike(np.atleast_2d(blk.get_values())))
- for blk in data.blocks
- )
+ # TODO(2DEA): reshape would not be necessary with 2D EAs
+ vals = ((mask & ~isna(blk.values).reshape(blk.shape)) for blk in data.blocks)
locs = (blk.mgr_locs for blk in data.blocks)
counted = (
| This will avoid an object-casting for e.g. IntervalArray-backed ExtensionBlock. | https://api.github.com/repos/pandas-dev/pandas/pulls/32523 | 2020-03-07T18:17:49Z | 2020-03-11T02:52:01Z | 2020-03-11T02:52:00Z | 2020-03-11T03:10:49Z |
CLN: remove SingleBlockManager.get_values | diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 98afc5ac3a0e3..b21b3d1b475d9 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -1596,10 +1596,6 @@ def internal_values(self):
"""The array that Series._values returns"""
return self._block.internal_values()
- def get_values(self) -> np.ndarray:
- """ return a dense type view """
- return np.array(self._block.to_dense(), copy=False)
-
@property
def _can_hold_na(self) -> bool:
return self._block._can_hold_na
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 568e99622dd29..96177da1698a5 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -537,7 +537,8 @@ def _internal_get_values(self):
numpy.ndarray
Data of the Series.
"""
- return self._data.get_values()
+ blk = self._data._block
+ return np.array(blk.to_dense(), copy=False)
# ops
def ravel(self, order="C"):
| It is only used in one place, and its behavior doesn't match `self.blocks[0].get_values()`, which is counter-intuitive. | https://api.github.com/repos/pandas-dev/pandas/pulls/32522 | 2020-03-07T18:14:02Z | 2020-03-11T02:20:33Z | 2020-03-11T02:20:33Z | 2020-03-11T14:42:41Z |
CLN: rename get_block_values, simplify | diff --git a/pandas/_libs/src/ujson/python/objToJSON.c b/pandas/_libs/src/ujson/python/objToJSON.c
index 8cfc20ffd2c1c..1d17cce0bc3a9 100644
--- a/pandas/_libs/src/ujson/python/objToJSON.c
+++ b/pandas/_libs/src/ujson/python/objToJSON.c
@@ -237,9 +237,9 @@ static PyObject *get_values(PyObject *obj) {
}
}
- if ((values == NULL) && PyObject_HasAttrString(obj, "get_block_values")) {
+ if ((values == NULL) && PyObject_HasAttrString(obj, "get_block_values_for_json")) {
PRINTMARK();
- values = PyObject_CallMethod(obj, "get_block_values", NULL);
+ values = PyObject_CallMethod(obj, "get_block_values_for_json", NULL);
if (values == NULL) {
// Clear so we can subsequently try another method
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index c088b7020927b..c14225672154b 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -231,11 +231,12 @@ def get_values(self, dtype=None):
return self.values.astype(object)
return self.values
- def get_block_values(self, dtype=None):
+ def get_block_values_for_json(self) -> np.ndarray:
"""
- This is used in the JSON C code
+ This is used in the JSON C code.
"""
- return self.get_values(dtype=dtype)
+ # TODO(2DEA): reshape will be unnecessary with 2D EAs
+ return np.asarray(self.values).reshape(self.shape)
def to_dense(self):
return self.values.view()
| cc @WillAyd name more explicit, implementation more transparent. | https://api.github.com/repos/pandas-dev/pandas/pulls/32521 | 2020-03-07T17:23:11Z | 2020-03-11T02:22:42Z | 2020-03-11T02:22:42Z | 2020-03-11T02:27:23Z |
BUG: Multiindexed series .at fix | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 3b60085e9fa66..f98bd53b6cdf8 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -731,6 +731,7 @@ Indexing
- Bug in :meth:`Series.__getitem__` allowing missing labels with ``np.ndarray``, :class:`Index`, :class:`Series` indexers but not ``list``, these now all raise ``KeyError`` (:issue:`33646`)
- Bug in :meth:`DataFrame.truncate` and :meth:`Series.truncate` where index was assumed to be monotone increasing (:issue:`33756`)
- Indexing with a list of strings representing datetimes failed on :class:`DatetimeIndex` or :class:`PeriodIndex`(:issue:`11278`)
+- Bug in :meth:`Series.at` when used with a :class:`MultiIndex` would raise an exception on valid inputs (:issue:`26989`)
Missing
^^^^^^^
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index b857a59195695..3a146bb0438c5 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -2016,10 +2016,10 @@ def __setitem__(self, key, value):
if not isinstance(key, tuple):
key = _tuplify(self.ndim, key)
+ key = list(self._convert_key(key, is_setter=True))
if len(key) != self.ndim:
raise ValueError("Not enough indexers for scalar access (setting)!")
- key = list(self._convert_key(key, is_setter=True))
self.obj._set_value(*key, value=value, takeable=self._takeable)
@@ -2032,6 +2032,12 @@ def _convert_key(self, key, is_setter: bool = False):
Require they keys to be the same type as the index. (so we don't
fallback)
"""
+ # GH 26989
+ # For series, unpacking key needs to result in the label.
+ # This is already the case for len(key) == 1; e.g. (1,)
+ if self.ndim == 1 and len(key) > 1:
+ key = (key,)
+
# allow arbitrary setting
if is_setter:
return list(key)
diff --git a/pandas/tests/indexing/test_scalar.py b/pandas/tests/indexing/test_scalar.py
index 216d554e22b49..4337f01ea33e0 100644
--- a/pandas/tests/indexing/test_scalar.py
+++ b/pandas/tests/indexing/test_scalar.py
@@ -351,3 +351,65 @@ def test_iat_series_with_period_index():
expected = ser[index[0]]
result = ser.iat[0]
assert expected == result
+
+
+def test_at_with_tuple_index_get():
+ # GH 26989
+ # DataFrame.at getter works with Index of tuples
+ df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)])
+ assert df.index.nlevels == 1
+ assert df.at[(1, 2), "a"] == 1
+
+ # Series.at getter works with Index of tuples
+ series = df["a"]
+ assert series.index.nlevels == 1
+ assert series.at[(1, 2)] == 1
+
+
+def test_at_with_tuple_index_set():
+ # GH 26989
+ # DataFrame.at setter works with Index of tuples
+ df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)])
+ assert df.index.nlevels == 1
+ df.at[(1, 2), "a"] = 2
+ assert df.at[(1, 2), "a"] == 2
+
+ # Series.at setter works with Index of tuples
+ series = df["a"]
+ assert series.index.nlevels == 1
+ series.at[1, 2] = 3
+ assert series.at[1, 2] == 3
+
+
+def test_multiindex_at_get():
+ # GH 26989
+ # DataFrame.at and DataFrame.loc getter works with MultiIndex
+ df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]])
+ assert df.index.nlevels == 2
+ assert df.at[(1, 3), "a"] == 1
+ assert df.loc[(1, 3), "a"] == 1
+
+ # Series.at and Series.loc getter works with MultiIndex
+ series = df["a"]
+ assert series.index.nlevels == 2
+ assert series.at[1, 3] == 1
+ assert series.loc[1, 3] == 1
+
+
+def test_multiindex_at_set():
+ # GH 26989
+ # DataFrame.at and DataFrame.loc setter works with MultiIndex
+ df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]])
+ assert df.index.nlevels == 2
+ df.at[(1, 3), "a"] = 3
+ assert df.at[(1, 3), "a"] == 3
+ df.loc[(1, 3), "a"] = 4
+ assert df.loc[(1, 3), "a"] == 4
+
+ # Series.at and Series.loc setter works with MultiIndex
+ series = df["a"]
+ assert series.index.nlevels == 2
+ series.at[1, 3] = 5
+ assert series.at[1, 3] == 5
+ series.loc[1, 3] = 6
+ assert series.loc[1, 3] == 6
| - [x] closes #26989
- [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/32520 | 2020-03-07T17:00:57Z | 2020-05-25T22:07:51Z | 2020-05-25T22:07:50Z | 2020-07-11T16:02:07Z |
TST: fix test creating invalid CategoricalBlock | diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index f07fa99fe57d6..83980e9028e9a 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1739,6 +1739,10 @@ def __init__(self, values, placement, ndim=None):
values = self._maybe_coerce_values(values)
super().__init__(values, placement, ndim)
+ if self.ndim == 2 and len(self.mgr_locs) != 1:
+ # TODO(2DEA): check unnecessary with 2D EAs
+ raise AssertionError("block.size != values.size")
+
def _maybe_coerce_values(self, values):
"""
Unbox to an extension array.
diff --git a/pandas/tests/internals/test_internals.py b/pandas/tests/internals/test_internals.py
index 0bbea671bae19..1a7d5839d9a11 100644
--- a/pandas/tests/internals/test_internals.py
+++ b/pandas/tests/internals/test_internals.py
@@ -540,6 +540,13 @@ def _compare(old_mgr, new_mgr):
assert new_mgr.get("g").dtype == np.float64
assert new_mgr.get("h").dtype == np.float16
+ def test_invalid_ea_block(self):
+ with pytest.raises(AssertionError, match="block.size != values.size"):
+ create_mgr("a: category; b: category")
+
+ with pytest.raises(AssertionError, match="block.size != values.size"):
+ create_mgr("a: category2; b: category2")
+
def test_interleave(self):
# self
@@ -552,14 +559,10 @@ def test_interleave(self):
# will be converted according the actual dtype of the underlying
mgr = create_mgr("a: category")
assert mgr.as_array().dtype == "i8"
- mgr = create_mgr("a: category; b: category")
- assert mgr.as_array().dtype == "i8"
mgr = create_mgr("a: category; b: category2")
assert mgr.as_array().dtype == "object"
mgr = create_mgr("a: category2")
assert mgr.as_array().dtype == "object"
- mgr = create_mgr("a: category2; b: category2")
- assert mgr.as_array().dtype == "object"
# combinations
mgr = create_mgr("a: f8")
@@ -702,7 +705,7 @@ def test_equals(self):
"a:i8;b:f8", # basic case
"a:i8;b:f8;c:c8;d:b", # many types
"a:i8;e:dt;f:td;g:string", # more types
- "a:i8;b:category;c:category2;d:category2", # categories
+ "a:i8;b:category;c:category2", # categories
"c:sparse;d:sparse_na;b:f8", # sparse
],
)
| The incorrect tests here turned up when trying to replace Block.get_values calls with simpler alternatives. | https://api.github.com/repos/pandas-dev/pandas/pulls/32519 | 2020-03-07T16:24:17Z | 2020-03-14T15:50:17Z | 2020-03-14T15:50:16Z | 2020-03-14T15:50:59Z |
CLN: removed unused import | diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index b59a1101e0bf7..9a8a8fdae6d2f 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -1,12 +1,15 @@
from cpython.object cimport Py_EQ, Py_NE, Py_GE, Py_GT, Py_LT, Py_LE
-from cpython.datetime cimport (datetime, date,
- PyDateTime_IMPORT,
- PyDateTime_GET_YEAR, PyDateTime_GET_MONTH,
- PyDateTime_GET_DAY, PyDateTime_DATE_GET_HOUR,
- PyDateTime_DATE_GET_MINUTE,
- PyDateTime_DATE_GET_SECOND,
- PyDateTime_DATE_GET_MICROSECOND)
+from cpython.datetime cimport (
+ PyDateTime_DATE_GET_HOUR,
+ PyDateTime_DATE_GET_MICROSECOND,
+ PyDateTime_DATE_GET_MINUTE,
+ PyDateTime_DATE_GET_SECOND,
+ PyDateTime_GET_DAY,
+ PyDateTime_GET_MONTH,
+ PyDateTime_GET_YEAR,
+ PyDateTime_IMPORT,
+)
PyDateTime_IMPORT
from numpy cimport int64_t
| - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Not 100% sure about this, can close anytime if not a good PR | https://api.github.com/repos/pandas-dev/pandas/pulls/32518 | 2020-03-07T13:43:01Z | 2020-03-12T17:24:36Z | 2020-03-12T17:24:35Z | 2020-03-14T14:06:31Z |
CLN: Suppres compile warnings of pandas/io/sas/sas.pyx | diff --git a/pandas/io/sas/sas.pyx b/pandas/io/sas/sas.pyx
index 40fea0aaf0d07..11171af1e0c82 100644
--- a/pandas/io/sas/sas.pyx
+++ b/pandas/io/sas/sas.pyx
@@ -120,7 +120,7 @@ cdef const uint8_t[:] rdc_decompress(int result_length, const uint8_t[:] inbuff)
cdef:
uint8_t cmd
- uint16_t ctrl_bits, ctrl_mask = 0, ofs, cnt
+ uint16_t ctrl_bits = 0, ctrl_mask = 0, ofs, cnt
int rpos = 0, k
uint8_t[:] outbuff = np.zeros(result_length, dtype=np.uint8)
Py_ssize_t ipos = 0, length = len(inbuff)
| - [x] ref #32163
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
This is the warning that get removed:
```
pandas/io/sas/sas.c: In function ‘__pyx_f_6pandas_2io_3sas_4_sas_rdc_decompress’:
pandas/io/sas/sas.c:3732:65: warning: ‘__pyx_v_ctrl_bits’ may be used uninitialized in this function [-Wmaybe-uninitialized]
3732 | __pyx_t_8 = (((__pyx_v_ctrl_bits & __pyx_v_ctrl_mask) == 0) != 0);
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/32517 | 2020-03-07T13:15:03Z | 2020-03-12T04:46:18Z | 2020-03-12T04:46:18Z | 2020-03-13T16:59:08Z |
Deprecate Aliases as orient Argument in DataFrame.to_dict | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 48eff0543ad4d..3a7c7e798ed88 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -175,7 +175,7 @@ Deprecations
- Lookups on a :class:`Series` with a single-item list containing a slice (e.g. ``ser[[slice(0, 4)]]``) are deprecated, will raise in a future version. Either convert the list to tuple, or pass the slice directly instead (:issue:`31333`)
- :meth:`DataFrame.mean` and :meth:`DataFrame.median` with ``numeric_only=None`` will include datetime64 and datetime64tz columns in a future version (:issue:`29941`)
- Setting values with ``.loc`` using a positional slice is deprecated and will raise in a future version. Use ``.loc`` with labels or ``.iloc`` with positions instead (:issue:`31840`)
--
+- :meth:`DataFrame.to_dict` has deprecated accepting short names for ``orient`` in future versions (:issue:`32515`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index b0909e23b44c5..204c916c88fa0 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1401,11 +1401,45 @@ def to_dict(self, orient="dict", into=dict):
)
# GH16122
into_c = com.standardize_mapping(into)
- if orient.lower().startswith("d"):
+
+ orient = orient.lower()
+ # GH32515
+ if orient.startswith(("d", "l", "s", "r", "i")) and orient not in {
+ "dict",
+ "list",
+ "series",
+ "split",
+ "records",
+ "index",
+ }:
+ warnings.warn(
+ "Using short name for 'orient' is deprecated. Only the "
+ "options: ('dict', list, 'series', 'split', 'records', 'index') "
+ "will be used in a future version. Use one of the above "
+ "to silence this warning.",
+ FutureWarning,
+ )
+
+ if orient.startswith("d"):
+ orient = "dict"
+ elif orient.startswith("l"):
+ orient = "list"
+ elif orient.startswith("sp"):
+ orient = "split"
+ elif orient.startswith("s"):
+ orient = "series"
+ elif orient.startswith("r"):
+ orient = "records"
+ elif orient.startswith("i"):
+ orient = "index"
+
+ if orient == "dict":
return into_c((k, v.to_dict(into)) for k, v in self.items())
- elif orient.lower().startswith("l"):
+
+ elif orient == "list":
return into_c((k, v.tolist()) for k, v in self.items())
- elif orient.lower().startswith("sp"):
+
+ elif orient == "split":
return into_c(
(
("index", self.index.tolist()),
@@ -1419,9 +1453,11 @@ def to_dict(self, orient="dict", into=dict):
),
)
)
- elif orient.lower().startswith("s"):
+
+ elif orient == "series":
return into_c((k, com.maybe_box_datetimelike(v)) for k, v in self.items())
- elif orient.lower().startswith("r"):
+
+ elif orient == "records":
columns = self.columns.tolist()
rows = (
dict(zip(columns, row))
@@ -1431,13 +1467,15 @@ def to_dict(self, orient="dict", into=dict):
into_c((k, com.maybe_box_datetimelike(v)) for k, v in row.items())
for row in rows
]
- elif orient.lower().startswith("i"):
+
+ elif orient == "index":
if not self.index.is_unique:
raise ValueError("DataFrame index must be unique for orient='index'.")
return into_c(
(t[0], dict(zip(self.columns, t[1:])))
for t in self.itertuples(name=None)
)
+
else:
raise ValueError(f"orient '{orient}' not understood")
diff --git a/pandas/tests/frame/methods/test_to_dict.py b/pandas/tests/frame/methods/test_to_dict.py
index cd9bd169322fd..f1656b46cf356 100644
--- a/pandas/tests/frame/methods/test_to_dict.py
+++ b/pandas/tests/frame/methods/test_to_dict.py
@@ -70,8 +70,17 @@ def test_to_dict_invalid_orient(self):
with pytest.raises(ValueError, match=msg):
df.to_dict(orient="xinvalid")
+ @pytest.mark.parametrize("orient", ["d", "l", "r", "sp", "s", "i"])
+ def test_to_dict_short_orient_warns(self, orient):
+ # GH#32515
+ df = DataFrame({"A": [0, 1]})
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+ df.to_dict(orient=orient)
+
@pytest.mark.parametrize("mapping", [dict, defaultdict(list), OrderedDict])
def test_to_dict(self, mapping):
+ # orient= should only take the listed options
+ # see GH#32515
test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}}
# GH#16122
@@ -81,19 +90,19 @@ def test_to_dict(self, mapping):
for k2, v2 in v.items():
assert v2 == recons_data[k][k2]
- recons_data = DataFrame(test_data).to_dict("l", mapping)
+ recons_data = DataFrame(test_data).to_dict("list", mapping)
for k, v in test_data.items():
for k2, v2 in v.items():
assert v2 == recons_data[k][int(k2) - 1]
- recons_data = DataFrame(test_data).to_dict("s", mapping)
+ recons_data = DataFrame(test_data).to_dict("series", mapping)
for k, v in test_data.items():
for k2, v2 in v.items():
assert v2 == recons_data[k][k2]
- recons_data = DataFrame(test_data).to_dict("sp", mapping)
+ recons_data = DataFrame(test_data).to_dict("split", mapping)
expected_split = {
"columns": ["A", "B"],
"index": ["1", "2", "3"],
@@ -101,7 +110,7 @@ def test_to_dict(self, mapping):
}
tm.assert_dict_equal(recons_data, expected_split)
- recons_data = DataFrame(test_data).to_dict("r", mapping)
+ recons_data = DataFrame(test_data).to_dict("records", mapping)
expected_records = [
{"A": 1.0, "B": "1"},
{"A": 2.0, "B": "2"},
@@ -113,7 +122,7 @@ def test_to_dict(self, mapping):
tm.assert_dict_equal(l, r)
# GH#10844
- recons_data = DataFrame(test_data).to_dict("i")
+ recons_data = DataFrame(test_data).to_dict("index")
for k, v in test_data.items():
for k2, v2 in v.items():
@@ -121,7 +130,7 @@ def test_to_dict(self, mapping):
df = DataFrame(test_data)
df["duped"] = df[df.columns[0]]
- recons_data = df.to_dict("i")
+ recons_data = df.to_dict("index")
comp_data = test_data.copy()
comp_data["duped"] = comp_data[df.columns[0]]
for k, v in comp_data.items():
| - [ x] closes #32515
- [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/32516 | 2020-03-07T12:53:26Z | 2020-03-14T21:33:06Z | 2020-03-14T21:33:06Z | 2020-06-01T14:52:06Z |
CLN: remove check_series_type | diff --git a/pandas/_testing.py b/pandas/_testing.py
index 33ec4e4886aa6..5e94ac3b3d108 100644
--- a/pandas/_testing.py
+++ b/pandas/_testing.py
@@ -34,6 +34,7 @@
is_interval_dtype,
is_list_like,
is_number,
+ is_numeric_dtype,
is_period_dtype,
is_sequence,
is_timedelta64_dtype,
@@ -1064,7 +1065,6 @@ def assert_series_equal(
right,
check_dtype=True,
check_index_type="equiv",
- check_series_type=True,
check_less_precise=False,
check_names=True,
check_exact=False,
@@ -1085,8 +1085,6 @@ def assert_series_equal(
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.
@@ -1118,11 +1116,10 @@ def assert_series_equal(
# 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)
+ # 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):
@@ -1147,8 +1144,8 @@ def assert_series_equal(
# 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)
+ is_categorical_dtype(left.dtype)
+ and is_categorical_dtype(right.dtype)
and not check_categorical
):
pass
@@ -1156,38 +1153,31 @@ def assert_series_equal(
assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
if check_exact:
+ if not is_numeric_dtype(left.dtype):
+ raise AssertionError("check_exact may only be used with numeric Series")
+
assert_numpy_array_equal(
- left._internal_get_values(),
- right._internal_get_values(),
- check_dtype=check_dtype,
- obj=str(obj),
+ left._values, right._values, check_dtype=check_dtype, obj=str(obj)
)
- elif check_datetimelike_compat:
+ elif check_datetimelike_compat and (
+ needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype)
+ ):
# 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,
+
+ # 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}."
)
- elif is_interval_dtype(left) or is_interval_dtype(right):
+ raise AssertionError(msg)
+ elif is_interval_dtype(left.dtype) or is_interval_dtype(right.dtype):
assert_interval_array_equal(left.array, right.array)
- elif is_extension_array_dtype(left.dtype) and is_datetime64tz_dtype(left.dtype):
+ elif 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)
diff --git a/pandas/tests/io/pytables/test_store.py b/pandas/tests/io/pytables/test_store.py
index fd585a73f6ce6..888222b503b10 100644
--- a/pandas/tests/io/pytables/test_store.py
+++ b/pandas/tests/io/pytables/test_store.py
@@ -2312,7 +2312,7 @@ def test_index_types(self, setup_path):
values = np.random.randn(2)
func = lambda l, r: tm.assert_series_equal(
- l, r, check_dtype=True, check_index_type=True, check_series_type=True
+ l, r, check_dtype=True, check_index_type=True
)
with catch_warnings(record=True):
| Trying to clean up assert_series_equal and avoid usage of _internal_get_values; I'm finding that these functions are _weird_, and in particular categorical dtype handling is opaque | https://api.github.com/repos/pandas-dev/pandas/pulls/32513 | 2020-03-07T04:52:54Z | 2020-03-08T16:13:46Z | 2020-03-08T16:13:46Z | 2020-03-09T21:13:50Z |
[BUG] add consistency to_numeric on empty list | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 21e59805fa143..a48278d1ce81a 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -268,6 +268,7 @@ Numeric
^^^^^^^
- Bug in :meth:`DataFrame.floordiv` with ``axis=0`` not treating division-by-zero like :meth:`Series.floordiv` (:issue:`31271`)
- Bug in :meth:`to_numeric` with string argument ``"uint64"`` and ``errors="coerce"`` silently fails (:issue:`32394`)
+- Bug in :meth:`to_numeric` with ``downcast="unsigned"`` fails for empty data (:issue:`32493`)
-
Conversion
diff --git a/pandas/core/tools/numeric.py b/pandas/core/tools/numeric.py
index 7d1e4bbd8fb05..a6198f8b752ae 100644
--- a/pandas/core/tools/numeric.py
+++ b/pandas/core/tools/numeric.py
@@ -162,7 +162,7 @@ def to_numeric(arg, errors="raise", downcast=None):
if downcast in ("integer", "signed"):
typecodes = np.typecodes["Integer"]
- elif downcast == "unsigned" and np.min(values) >= 0:
+ elif downcast == "unsigned" and (not len(values) or np.min(values) >= 0):
typecodes = np.typecodes["UnsignedInteger"]
elif downcast == "float":
typecodes = np.typecodes["Float"]
diff --git a/pandas/tests/tools/test_to_numeric.py b/pandas/tests/tools/test_to_numeric.py
index e0dfeac4ab475..263887a8ea36e 100644
--- a/pandas/tests/tools/test_to_numeric.py
+++ b/pandas/tests/tools/test_to_numeric.py
@@ -629,6 +629,18 @@ def test_non_coerce_uint64_conflict(errors, exp):
tm.assert_series_equal(result, ser)
+@pytest.mark.parametrize("dc1", ["integer", "float", "unsigned"])
+@pytest.mark.parametrize("dc2", ["integer", "float", "unsigned"])
+def test_downcast_empty(dc1, dc2):
+ # GH32493
+
+ tm.assert_numpy_array_equal(
+ pd.to_numeric([], downcast=dc1),
+ pd.to_numeric([], downcast=dc2),
+ check_dtype=False,
+ )
+
+
def test_failure_to_convert_uint64_string_to_NaN():
# GH 32394
result = to_numeric("uint64", errors="coerce")
| to_numeric should work similarly on empty lists for
downcast=unsigned/float/integer
Addresses: GH32493
- [x] closes #32493
- [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/32512 | 2020-03-07T04:52:51Z | 2020-03-16T02:30:10Z | 2020-03-16T02:30:10Z | 2020-03-20T02:04:17Z |
[BUG] Loosen random_state input restriction | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 21e59805fa143..6bd5723c5efcf 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -69,6 +69,7 @@ Other enhancements
- `OptionError` is now exposed in `pandas.errors` (:issue:`27553`)
- :func:`timedelta_range` will now infer a frequency when passed ``start``, ``stop``, and ``periods`` (:issue:`32377`)
- Positional slicing on a :class:`IntervalIndex` now supports slices with ``step > 1`` (:issue:`31658`)
+- :meth:`DataFrame.sample` will now also allow array-like and BitGenerator objects to be passed to ``random_state`` as seeds (:issue:`32503`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 6230ee34bcd50..d9ce65d44177b 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -15,6 +15,7 @@
from pandas._libs import lib, tslibs
from pandas._typing import T
+from pandas.compat.numpy import _np_version_under1p17
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
from pandas.core.dtypes.common import (
@@ -395,18 +396,30 @@ def random_state(state=None):
Parameters
----------
- state : int, np.random.RandomState, None.
- If receives an int, passes to np.random.RandomState() as seed.
+ state : int, array-like, BitGenerator (NumPy>=1.17), np.random.RandomState, None.
+ If receives an int, array-like, or BitGenerator, passes to
+ np.random.RandomState() as seed.
If receives an np.random.RandomState object, just returns object.
If receives `None`, returns np.random.
If receives anything else, raises an informative ValueError.
+
+ ..versionchanged:: 1.1.0
+
+ array-like and BitGenerator (for NumPy>=1.17) object now passed to
+ np.random.RandomState() as seed
+
Default None.
Returns
-------
np.random.RandomState
+
"""
- if is_integer(state):
+ if (
+ is_integer(state)
+ or is_array_like(state)
+ or (not _np_version_under1p17 and isinstance(state, np.random.BitGenerator))
+ ):
return np.random.RandomState(state)
elif isinstance(state, np.random.RandomState):
return state
@@ -414,7 +427,10 @@ def random_state(state=None):
return np.random
else:
raise ValueError(
- "random_state must be an integer, a numpy RandomState, or None"
+ (
+ "random_state must be an integer, array-like, a BitGenerator, "
+ "a numpy RandomState, or None"
+ )
)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 8d56311331d4d..ba4d6dee7f984 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -4794,9 +4794,16 @@ def sample(
If weights do not sum to 1, they will be normalized to sum to 1.
Missing values in the weights column will be treated as zero.
Infinite values not allowed.
- random_state : int or numpy.random.RandomState, optional
- Seed for the random number generator (if int), or numpy RandomState
- object.
+ random_state : int, array-like, BitGenerator, np.random.RandomState, optional
+ If int, array-like, or BitGenerator (NumPy>=1.17), seed for
+ random number generator
+ If np.random.RandomState, use as numpy RandomState object.
+
+ ..versionchanged:: 1.1.0
+
+ array-like and BitGenerator (for NumPy>=1.17) object now passed to
+ np.random.RandomState() as seed
+
axis : {0 or ‘index’, 1 or ‘columns’, None}, default None
Axis to sample. Accepts axis number or name. Default is stat axis
for given data type (0 for Series and DataFrames).
diff --git a/pandas/tests/generic/test_generic.py b/pandas/tests/generic/test_generic.py
index 9c759ff1414fe..6999dea6adfa3 100644
--- a/pandas/tests/generic/test_generic.py
+++ b/pandas/tests/generic/test_generic.py
@@ -3,11 +3,14 @@
import numpy as np
import pytest
+from pandas.compat.numpy import _np_version_under1p17
+
from pandas.core.dtypes.common import is_scalar
import pandas as pd
from pandas import DataFrame, MultiIndex, Series, date_range
import pandas._testing as tm
+import pandas.core.common as com
# ----------------------------------------------------------------------
# Generic types test cases
@@ -641,6 +644,29 @@ def test_sample(sel):
with pytest.raises(ValueError):
df.sample(1, weights=s4)
+ @pytest.mark.parametrize(
+ "func_str,arg",
+ [
+ ("np.array", [2, 3, 1, 0]),
+ pytest.param(
+ "np.random.MT19937",
+ 3,
+ marks=pytest.mark.skipif(_np_version_under1p17, reason="NumPy<1.17"),
+ ),
+ pytest.param(
+ "np.random.PCG64",
+ 11,
+ marks=pytest.mark.skipif(_np_version_under1p17, reason="NumPy<1.17"),
+ ),
+ ],
+ )
+ def test_sample_random_state(self, func_str, arg):
+ # GH32503
+ df = pd.DataFrame({"col1": range(10, 20), "col2": range(20, 30)})
+ result = df.sample(n=3, random_state=eval(func_str)(arg))
+ expected = df.sample(n=3, random_state=com.random_state(eval(func_str)(arg)))
+ tm.assert_frame_equal(result, expected)
+
def test_squeeze(self):
# noop
for s in [tm.makeFloatSeries(), tm.makeStringSeries(), tm.makeObjectSeries()]:
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index 186c735a0bff9..bcfed2d0d3a10 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -6,6 +6,8 @@
import numpy as np
import pytest
+from pandas.compat.numpy import _np_version_under1p17
+
import pandas as pd
from pandas import Series, Timestamp
from pandas.core import ops
@@ -59,8 +61,31 @@ def test_random_state():
# check with no arg random state
assert com.random_state() is np.random
+ # check array-like
+ # GH32503
+ state_arr_like = npr.randint(0, 2 ** 31, size=624, dtype="uint32")
+ assert (
+ com.random_state(state_arr_like).uniform()
+ == npr.RandomState(state_arr_like).uniform()
+ )
+
+ # Check BitGenerators
+ # GH32503
+ if not _np_version_under1p17:
+ assert (
+ com.random_state(npr.MT19937(3)).uniform()
+ == npr.RandomState(npr.MT19937(3)).uniform()
+ )
+ assert (
+ com.random_state(npr.PCG64(11)).uniform()
+ == npr.RandomState(npr.PCG64(11)).uniform()
+ )
+
# Error for floats or strings
- msg = "random_state must be an integer, a numpy RandomState, or None"
+ msg = (
+ "random_state must be an integer, array-like, a BitGenerator, "
+ "a numpy RandomState, or None"
+ )
with pytest.raises(ValueError, match=msg):
com.random_state("test")
| Alllow for array-like as well as BitGenerator inputs
Addresses: GH32503
- [x] closes #32503
- [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/32510 | 2020-03-07T02:57:25Z | 2020-03-17T02:38:05Z | 2020-03-17T02:38:04Z | 2020-03-17T02:38:05Z |
CLN: unused code in reshape.merge | diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index c301d6e7c7155..08c04cb49f125 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -92,9 +92,7 @@ def merge(
merge.__doc__ = _merge_doc % "\nleft : DataFrame"
-def _groupby_and_merge(
- by, on, left, right: "DataFrame", _merge_pieces, check_duplicates: bool = True
-):
+def _groupby_and_merge(by, on, left: "DataFrame", right: "DataFrame", merge_pieces):
"""
groupby & merge; we are always performing a left-by type operation
@@ -102,11 +100,9 @@ def _groupby_and_merge(
----------
by: field to group
on: duplicates field
- left: left frame
- right: right frame
- _merge_pieces: function for merging
- check_duplicates: bool, default True
- should we check & clean duplicates
+ left: DataFrame
+ right: DataFrame
+ merge_pieces: function for merging
"""
pieces = []
if not isinstance(by, (list, tuple)):
@@ -118,18 +114,6 @@ def _groupby_and_merge(
# if we can groupby the rhs
# then we can get vastly better perf
- # we will check & remove duplicates if indicated
- if check_duplicates:
- if on is None:
- on = []
- elif not isinstance(on, (list, tuple)):
- on = [on]
-
- if right.duplicated(by + on).any():
- _right = right.drop_duplicates(by + on, keep="last")
- # TODO: use overload to refine return type of drop_duplicates
- assert _right is not None # needed for mypy
- right = _right
try:
rby = right.groupby(by, sort=False)
except KeyError:
@@ -151,16 +135,13 @@ def _groupby_and_merge(
pieces.append(merged)
continue
- merged = _merge_pieces(lhs, rhs)
+ merged = merge_pieces(lhs, rhs)
# make sure join keys are in the merged
- # TODO, should _merge_pieces do this?
+ # TODO, should merge_pieces do this?
for k in by:
- try:
- if k in merged:
- merged[k] = key
- except KeyError:
- pass
+ if k in merged:
+ merged[k] = key
pieces.append(merged)
@@ -287,16 +268,11 @@ def _merger(x, y):
raise ValueError("Can only group either left or right frames")
elif left_by is not None:
result, _ = _groupby_and_merge(
- left_by, on, left, right, lambda x, y: _merger(x, y), check_duplicates=False
+ left_by, on, left, right, lambda x, y: _merger(x, y)
)
elif right_by is not None:
result, _ = _groupby_and_merge(
- right_by,
- on,
- right,
- left,
- lambda x, y: _merger(y, x),
- check_duplicates=False,
+ right_by, on, right, left, lambda x, y: _merger(y, x)
)
else:
result = _merger(left, right)
@@ -1605,11 +1581,6 @@ def _validate_specification(self):
if self.direction not in ["backward", "forward", "nearest"]:
raise MergeError(f"direction invalid: {self.direction}")
- @property
- def _asof_key(self):
- """ This is our asof key, the 'on' """
- return self.left_on[-1]
-
def _get_merge_keys(self):
# note this function has side effects
| check_duplicates is always passed as False, so we can trim some code | https://api.github.com/repos/pandas-dev/pandas/pulls/32509 | 2020-03-07T01:56:29Z | 2020-03-10T11:48:33Z | 2020-03-10T11:48:33Z | 2020-03-10T15:01:00Z |
CLN: avoid values_from_object in nanops | diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 4398a1569ac56..f0883528db69f 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -32,6 +32,8 @@
)
from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
+from pandas.core.construction import extract_array
+
bn = import_optional_dependency("bottleneck", raise_on_missing=False, on_version="warn")
_BOTTLENECK_INSTALLED = bn is not None
_USE_BOTTLENECK = False
@@ -284,14 +286,8 @@ def _get_values(
mask = _maybe_get_mask(values, skipna, mask)
- if is_datetime64tz_dtype(values):
- # lib.values_from_object returns M8[ns] dtype instead of tz-aware,
- # so this case must be handled separately from the rest
- dtype = values.dtype
- values = getattr(values, "_values", values)
- else:
- values = lib.values_from_object(values)
- dtype = values.dtype
+ values = extract_array(values, extract_numpy=True)
+ dtype = values.dtype
if is_datetime_or_timedelta_dtype(values) or is_datetime64tz_dtype(values):
# changing timedelta64/datetime64 to int64 needs to happen after
@@ -758,7 +754,7 @@ def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
>>> nanops.nanvar(s)
1.0
"""
- values = lib.values_from_object(values)
+ values = extract_array(values, extract_numpy=True)
dtype = values.dtype
mask = _maybe_get_mask(values, skipna, mask)
if is_any_int_dtype(values):
@@ -985,7 +981,7 @@ def nanskew(
>>> nanops.nanskew(s)
1.7320508075688787
"""
- values = lib.values_from_object(values)
+ values = extract_array(values, extract_numpy=True)
mask = _maybe_get_mask(values, skipna, mask)
if not is_float_dtype(values.dtype):
values = values.astype("f8")
@@ -1069,7 +1065,7 @@ def nankurt(
>>> nanops.nankurt(s)
-1.2892561983471076
"""
- values = lib.values_from_object(values)
+ values = extract_array(values, extract_numpy=True)
mask = _maybe_get_mask(values, skipna, mask)
if not is_float_dtype(values.dtype):
values = values.astype("f8")
| - [ ] 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/32508 | 2020-03-06T23:40:32Z | 2020-03-08T16:09:09Z | 2020-03-08T16:09:09Z | 2020-03-16T15:03:06Z |
CLN: Avoid bare pytest.raises in computation/test_eval.py | diff --git a/pandas/tests/computation/test_eval.py b/pandas/tests/computation/test_eval.py
index a240e6cef5930..08d8d5ca342b7 100644
--- a/pandas/tests/computation/test_eval.py
+++ b/pandas/tests/computation/test_eval.py
@@ -375,7 +375,8 @@ def check_pow(self, lhs, arith1, rhs):
and is_scalar(rhs)
and _is_py3_complex_incompat(result, expected)
):
- with pytest.raises(AssertionError):
+ msg = "(DataFrame.columns|numpy array) are different"
+ with pytest.raises(AssertionError, match=msg):
tm.assert_numpy_array_equal(result, expected)
else:
tm.assert_almost_equal(result, expected)
@@ -449,16 +450,19 @@ def test_frame_invert(self):
# float always raises
lhs = DataFrame(randn(5, 2))
if self.engine == "numexpr":
- with pytest.raises(NotImplementedError):
+ msg = "couldn't find matching opcode for 'invert_dd'"
+ with pytest.raises(NotImplementedError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
- with pytest.raises(TypeError):
+ msg = "ufunc 'invert' not supported for the input types"
+ with pytest.raises(TypeError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
# int raises on numexpr
lhs = DataFrame(randint(5, size=(5, 2)))
if self.engine == "numexpr":
- with pytest.raises(NotImplementedError):
+ msg = "couldn't find matching opcode for 'invert"
+ with pytest.raises(NotImplementedError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = ~lhs
@@ -474,10 +478,11 @@ def test_frame_invert(self):
# object raises
lhs = DataFrame({"b": ["a", 1, 2.0], "c": rand(3) > 0.5})
if self.engine == "numexpr":
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match="unknown type object"):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
- with pytest.raises(TypeError):
+ msg = "bad operand type for unary ~: 'str'"
+ with pytest.raises(TypeError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
def test_series_invert(self):
@@ -488,16 +493,19 @@ def test_series_invert(self):
# float raises
lhs = Series(randn(5))
if self.engine == "numexpr":
- with pytest.raises(NotImplementedError):
+ msg = "couldn't find matching opcode for 'invert_dd'"
+ with pytest.raises(NotImplementedError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
- with pytest.raises(TypeError):
+ msg = "ufunc 'invert' not supported for the input types"
+ with pytest.raises(TypeError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
# int raises on numexpr
lhs = Series(randint(5, size=5))
if self.engine == "numexpr":
- with pytest.raises(NotImplementedError):
+ msg = "couldn't find matching opcode for 'invert"
+ with pytest.raises(NotImplementedError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = ~lhs
@@ -517,10 +525,11 @@ def test_series_invert(self):
# object
lhs = Series(["a", 1, 2.0])
if self.engine == "numexpr":
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match="unknown type object"):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
- with pytest.raises(TypeError):
+ msg = "bad operand type for unary ~: 'str'"
+ with pytest.raises(TypeError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
def test_frame_negate(self):
@@ -541,7 +550,8 @@ def test_frame_negate(self):
# bool doesn't work with numexpr but works elsewhere
lhs = DataFrame(rand(5, 2) > 0.5)
if self.engine == "numexpr":
- with pytest.raises(NotImplementedError):
+ msg = "couldn't find matching opcode for 'neg_bb'"
+ with pytest.raises(NotImplementedError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = -lhs
@@ -566,7 +576,8 @@ def test_series_negate(self):
# bool doesn't work with numexpr but works elsewhere
lhs = Series(rand(5) > 0.5)
if self.engine == "numexpr":
- with pytest.raises(NotImplementedError):
+ msg = "couldn't find matching opcode for 'neg_bb'"
+ with pytest.raises(NotImplementedError, match=msg):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = -lhs
@@ -610,7 +621,8 @@ def test_series_pos(self, lhs):
tm.assert_series_equal(expect, result)
def test_scalar_unary(self):
- with pytest.raises(TypeError):
+ msg = "bad operand type for unary ~: 'float'"
+ with pytest.raises(TypeError, match=msg):
pd.eval("~1.0", engine=self.engine, parser=self.parser)
assert pd.eval("-1.0", parser=self.parser, engine=self.engine) == -1.0
@@ -671,7 +683,8 @@ def test_disallow_scalar_bool_ops(self):
x, a, b, df = np.random.randn(3), 1, 2, DataFrame(randn(3, 2)) # noqa
for ex in exprs:
- with pytest.raises(NotImplementedError):
+ msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(ex, engine=self.engine, parser=self.parser)
def test_identical(self):
@@ -772,7 +785,8 @@ def setup_ops(self):
def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs):
ex1 = f"lhs {cmp1} mid {cmp2} rhs"
- with pytest.raises(NotImplementedError):
+ msg = "'BoolOp' nodes are not implemented"
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(ex1, engine=self.engine, parser=self.parser)
@@ -1183,7 +1197,8 @@ def test_bool_ops_with_constants(self):
def test_4d_ndarray_fails(self):
x = randn(3, 4, 5, 6)
y = Series(randn(10))
- with pytest.raises(NotImplementedError):
+ msg = "N-dimensional objects, where N > 2, are not supported with eval"
+ with pytest.raises(NotImplementedError, match=msg):
self.eval("x + y", local_dict={"x": x, "y": y})
def test_constant(self):
@@ -1232,7 +1247,7 @@ def test_truediv(self):
def test_failing_subscript_with_name_error(self):
df = DataFrame(np.random.randn(5, 3)) # noqa
- with pytest.raises(NameError):
+ with pytest.raises(NameError, match="name 'x' is not defined"):
self.eval("df[x > 2] > 2")
def test_lhs_expression_subscript(self):
@@ -1379,7 +1394,8 @@ def test_multi_line_expression(self):
assert ans is None
# multi-line not valid if not all assignments
- with pytest.raises(ValueError):
+ msg = "Multi-line expressions are only valid if all expressions contain"
+ with pytest.raises(ValueError, match=msg):
df.eval(
"""
a = b + 2
@@ -1474,7 +1490,8 @@ def test_assignment_in_query(self):
# GH 8664
df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df_orig = df.copy()
- with pytest.raises(ValueError):
+ msg = "cannot assign without a target object"
+ with pytest.raises(ValueError, match=msg):
df.query("a = 1")
tm.assert_frame_equal(df, df_orig)
@@ -1593,19 +1610,21 @@ def test_simple_in_ops(self):
)
assert res
else:
- with pytest.raises(NotImplementedError):
+ msg = "'In' nodes are not implemented"
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval("1 in [1, 2]", engine=self.engine, parser=self.parser)
- with pytest.raises(NotImplementedError):
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval("2 in (1, 2)", engine=self.engine, parser=self.parser)
- with pytest.raises(NotImplementedError):
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval("3 in (1, 2)", engine=self.engine, parser=self.parser)
- with pytest.raises(NotImplementedError):
- pd.eval("3 not in (1, 2)", engine=self.engine, parser=self.parser)
- with pytest.raises(NotImplementedError):
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(
"[(3,)] in (1, 2, [(3,)])", engine=self.engine, parser=self.parser
)
- with pytest.raises(NotImplementedError):
+ msg = "'NotIn' nodes are not implemented"
+ with pytest.raises(NotImplementedError, match=msg):
+ pd.eval("3 not in (1, 2)", engine=self.engine, parser=self.parser)
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(
"[3] not in (1, 2, [[3]])", engine=self.engine, parser=self.parser
)
@@ -1664,13 +1683,15 @@ def test_fails_not(self):
def test_fails_ampersand(self):
df = DataFrame(np.random.randn(5, 3)) # noqa
ex = "(df + 2)[df > 1] > 0 & (df > 0)"
- with pytest.raises(NotImplementedError):
+ msg = "cannot evaluate scalar only bool ops"
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(ex, parser=self.parser, engine=self.engine)
def test_fails_pipe(self):
df = DataFrame(np.random.randn(5, 3)) # noqa
ex = "(df + 2)[df > 1] > 0 | (df > 0)"
- with pytest.raises(NotImplementedError):
+ msg = "cannot evaluate scalar only bool ops"
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(ex, parser=self.parser, engine=self.engine)
def test_bool_ops_with_constants(self):
@@ -1679,7 +1700,8 @@ def test_bool_ops_with_constants(self):
):
ex = f"{lhs} {op} {rhs}"
if op in ("and", "or"):
- with pytest.raises(NotImplementedError):
+ msg = "'BoolOp' nodes are not implemented"
+ with pytest.raises(NotImplementedError, match=msg):
self.eval(ex)
else:
res = self.eval(ex)
@@ -1690,7 +1712,8 @@ def test_simple_bool_ops(self):
for op, lhs, rhs in product(expr._bool_ops_syms, (True, False), (True, False)):
ex = f"lhs {op} rhs"
if op in ("and", "or"):
- with pytest.raises(NotImplementedError):
+ msg = "'BoolOp' nodes are not implemented"
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(ex, engine=self.engine, parser=self.parser)
else:
res = pd.eval(ex, engine=self.engine, parser=self.parser)
@@ -1902,19 +1925,21 @@ def test_disallowed_nodes(engine, parser):
inst = VisitorClass("x + 1", engine, parser)
for ops in uns_ops:
- with pytest.raises(NotImplementedError):
+ msg = "nodes are not implemented"
+ with pytest.raises(NotImplementedError, match=msg):
getattr(inst, ops)()
def test_syntax_error_exprs(engine, parser):
e = "s +"
- with pytest.raises(SyntaxError):
+ with pytest.raises(SyntaxError, match="invalid syntax"):
pd.eval(e, engine=engine, parser=parser)
def test_name_error_exprs(engine, parser):
e = "s + t"
- with pytest.raises(NameError):
+ msg = "name 's' is not defined"
+ with pytest.raises(NameError, match=msg):
pd.eval(e, engine=engine, parser=parser)
@@ -1973,7 +1998,8 @@ def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser):
ex2 = f"lhs {cmp} mid and mid {cmp} rhs"
ex3 = f"(lhs {cmp} mid) & (mid {cmp} rhs)"
for ex in (ex1, ex2, ex3):
- with pytest.raises(NotImplementedError):
+ msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+ with pytest.raises(NotImplementedError, match=msg):
pd.eval(ex, engine=engine, parser=parser)
@@ -2029,7 +2055,8 @@ def test_negate_lt_eq_le(engine, parser):
tm.assert_frame_equal(result, expected)
if parser == "python":
- with pytest.raises(NotImplementedError):
+ msg = "'Not' nodes are not implemented"
+ with pytest.raises(NotImplementedError, match=msg):
df.query("not (cat > 0)", engine=engine, parser=parser)
else:
result = df.query("not (cat > 0)", engine=engine, parser=parser)
@@ -2041,5 +2068,6 @@ def test_validate_bool_args(self):
invalid_values = [1, "True", [1, 2, 3], 5.0]
for value in invalid_values:
- with pytest.raises(ValueError):
+ msg = 'For argument "inplace" expected type bool, received type'
+ with pytest.raises(ValueError, match=msg):
pd.eval("2+2", inplace=value)
| * [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff origin/master -u -- "*.py" | flake8 --diff` | https://api.github.com/repos/pandas-dev/pandas/pulls/32507 | 2020-03-06T23:31:35Z | 2020-03-10T23:07:48Z | 2020-03-10T23:07:48Z | 2020-03-11T09:09:06Z |
CLN: avoid values_from_object in Index.equals | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 3eab757311ccb..d912dbbfd1d1d 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -4104,7 +4104,6 @@ def __getitem__(self, key):
if com.is_bool_indexer(key):
key = np.asarray(key, dtype=bool)
- key = com.values_from_object(key)
result = getitem(key)
if not is_scalar(result):
if np.ndim(result) > 1:
@@ -4229,19 +4228,19 @@ def equals(self, other) -> bool:
if not isinstance(other, Index):
return False
- if is_object_dtype(self) and not is_object_dtype(other):
+ if is_object_dtype(self.dtype) and not is_object_dtype(other.dtype):
# if other is not object, use other's logic for coercion
return other.equals(self)
if isinstance(other, ABCMultiIndex):
# d-level MultiIndex can equal d-tuple Index
- if not is_object_dtype(self.dtype):
- if self.nlevels != other.nlevels:
- return False
+ return other.equals(self)
- return array_equivalent(
- com.values_from_object(self), com.values_from_object(other)
- )
+ if is_extension_array_dtype(other.dtype):
+ # All EA-backed Index subclasses override equals
+ return other.equals(self)
+
+ return array_equivalent(self._values, other._values)
def identical(self, other) -> bool:
"""
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index c22b289bb4017..0bb88145646ed 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -3141,11 +3141,10 @@ def equals(self, other) -> bool:
if not isinstance(other, MultiIndex):
# d-level MultiIndex can equal d-tuple Index
if not is_object_dtype(other.dtype):
- if self.nlevels != other.nlevels:
- return False
+ # other cannot contain tuples, so cannot match self
+ return False
- other_vals = com.values_from_object(other)
- return array_equivalent(self._values, other_vals)
+ return array_equivalent(self._values, other._values)
if self.nlevels != other.nlevels:
return False
| https://api.github.com/repos/pandas-dev/pandas/pulls/32505 | 2020-03-06T21:26:37Z | 2020-03-11T02:39:19Z | 2020-03-11T02:39:19Z | 2020-03-11T02:43:30Z | |
CLN: avoid values_from_object in construction | diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py
index 57ed2555761be..ab363e10eb098 100644
--- a/pandas/core/internals/construction.py
+++ b/pandas/core/internals/construction.py
@@ -36,7 +36,7 @@
from pandas.core import algorithms, common as com
from pandas.core.arrays import Categorical
-from pandas.core.construction import sanitize_array
+from pandas.core.construction import extract_array, sanitize_array
from pandas.core.indexes import base as ibase
from pandas.core.indexes.api import (
Index,
@@ -519,7 +519,7 @@ def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None):
else:
indexer = indexer_cache[id(index)] = index.get_indexer(columns)
- values = com.values_from_object(s)
+ values = extract_array(s, extract_numpy=True)
aligned_values.append(algorithms.take_1d(values, indexer))
values = np.vstack(aligned_values)
| https://api.github.com/repos/pandas-dev/pandas/pulls/32504 | 2020-03-06T21:10:41Z | 2020-03-08T16:14:49Z | 2020-03-08T16:14:49Z | 2020-03-08T16:18:52Z | |
Disallow lossy SparseArray conversion | diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index 549606795f528..bcd54698689de 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -27,6 +27,7 @@
is_array_like,
is_bool_dtype,
is_datetime64_any_dtype,
+ is_datetime64tz_dtype,
is_dtype_equal,
is_integer,
is_object_dtype,
@@ -42,7 +43,7 @@
from pandas.core.arrays.sparse.dtype import SparseDtype
from pandas.core.base import PandasObject
import pandas.core.common as com
-from pandas.core.construction import sanitize_array
+from pandas.core.construction import extract_array, sanitize_array
from pandas.core.indexers import check_array_indexer
from pandas.core.missing import interpolate_2d
import pandas.core.ops as ops
@@ -312,7 +313,7 @@ def __init__(
dtype = dtype.subtype
if index is not None and not is_scalar(data):
- raise Exception("must only pass scalars with an index ")
+ raise Exception("must only pass scalars with an index")
if is_scalar(data):
if index is not None:
@@ -367,6 +368,19 @@ def __init__(
sparse_index = data._sparse_index
sparse_values = np.asarray(data.sp_values, dtype=dtype)
elif sparse_index is None:
+ data = extract_array(data, extract_numpy=True)
+ if not isinstance(data, np.ndarray):
+ # EA
+ if is_datetime64tz_dtype(data.dtype):
+ warnings.warn(
+ f"Creating SparseArray from {data.dtype} data "
+ "loses timezone information. Cast to object before "
+ "sparse to retain timezone information.",
+ UserWarning,
+ stacklevel=2,
+ )
+ data = np.asarray(data, dtype="datetime64[ns]")
+ data = np.asarray(data)
sparse_values, sparse_index, fill_value = make_sparse(
data, kind=kind, fill_value=fill_value, dtype=dtype
)
@@ -1497,7 +1511,7 @@ def _formatter(self, boxed=False):
SparseArray._add_unary_ops()
-def make_sparse(arr, kind="block", fill_value=None, dtype=None, copy=False):
+def make_sparse(arr: np.ndarray, kind="block", fill_value=None, dtype=None, copy=False):
"""
Convert ndarray to sparse format
@@ -1513,7 +1527,7 @@ def make_sparse(arr, kind="block", fill_value=None, dtype=None, copy=False):
-------
(sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
"""
- arr = com.values_from_object(arr)
+ assert isinstance(arr, np.ndarray)
if arr.ndim > 1:
raise TypeError("expected dimension <= 1 data")
diff --git a/pandas/tests/arrays/sparse/test_array.py b/pandas/tests/arrays/sparse/test_array.py
index baca18239b929..4dab86166e13c 100644
--- a/pandas/tests/arrays/sparse/test_array.py
+++ b/pandas/tests/arrays/sparse/test_array.py
@@ -96,6 +96,22 @@ def test_constructor_na_dtype(self, dtype):
with pytest.raises(ValueError, match="Cannot convert"):
SparseArray([0, 1, np.nan], dtype=dtype)
+ def test_constructor_warns_when_losing_timezone(self):
+ # GH#32501 warn when losing timezone inforamtion
+ dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
+
+ expected = SparseArray(np.asarray(dti, dtype="datetime64[ns]"))
+
+ with tm.assert_produces_warning(UserWarning):
+ result = SparseArray(dti)
+
+ tm.assert_sp_array_equal(result, expected)
+
+ with tm.assert_produces_warning(UserWarning):
+ result = SparseArray(pd.Series(dti))
+
+ tm.assert_sp_array_equal(result, expected)
+
def test_constructor_spindex_dtype(self):
arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))
# XXX: Behavior change: specifying SparseIndex no longer changes the
| cc @TomAugspurger | https://api.github.com/repos/pandas-dev/pandas/pulls/32501 | 2020-03-06T18:58:19Z | 2020-03-14T16:33:20Z | 2020-03-14T16:33:20Z | 2020-03-14T16:47:12Z |
Better error message for OOB result | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index eec471f989037..8a8b77fbbb426 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -65,6 +65,7 @@ Bug fixes
- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`)
- Bug where :func:`to_datetime` would raise when passed ``pd.NA`` (:issue:`32213`)
+- Improved error message when subtracting two :class:`Timestamp` that result in an out-of-bounds :class:`Timedelta` (:issue:`31774`)
**Categorical**
diff --git a/pandas/_libs/tslibs/c_timestamp.pyx b/pandas/_libs/tslibs/c_timestamp.pyx
index 2c72cec18f096..3c30460a74ece 100644
--- a/pandas/_libs/tslibs/c_timestamp.pyx
+++ b/pandas/_libs/tslibs/c_timestamp.pyx
@@ -286,6 +286,10 @@ cdef class _Timestamp(datetime):
# coerce if necessary if we are a Timestamp-like
if (PyDateTime_Check(self)
and (PyDateTime_Check(other) or is_datetime64_object(other))):
+ # both_timestamps is to determine whether Timedelta(self - other)
+ # should raise the OOB error, or fall back returning a timedelta.
+ both_timestamps = (isinstance(other, _Timestamp) and
+ isinstance(self, _Timestamp))
if isinstance(self, _Timestamp):
other = type(self)(other)
else:
@@ -301,7 +305,14 @@ cdef class _Timestamp(datetime):
from pandas._libs.tslibs.timedeltas import Timedelta
try:
return Timedelta(self.value - other.value)
- except (OverflowError, OutOfBoundsDatetime):
+ except (OverflowError, OutOfBoundsDatetime) as err:
+ if isinstance(other, _Timestamp):
+ if both_timestamps:
+ raise OutOfBoundsDatetime(
+ "Result is too large for pandas.Timedelta. Convert inputs "
+ "to datetime.datetime with 'Timestamp.to_pydatetime()' "
+ "before subtracting."
+ ) from err
pass
elif is_datetime64_object(self):
# GH#28286 cython semantics for __rsub__, `other` is actually
diff --git a/pandas/tests/scalar/timestamp/test_arithmetic.py b/pandas/tests/scalar/timestamp/test_arithmetic.py
index 1cab007c20a0e..ccd7bf721430a 100644
--- a/pandas/tests/scalar/timestamp/test_arithmetic.py
+++ b/pandas/tests/scalar/timestamp/test_arithmetic.py
@@ -3,6 +3,8 @@
import numpy as np
import pytest
+from pandas.errors import OutOfBoundsDatetime
+
from pandas import Timedelta, Timestamp
from pandas.tseries import offsets
@@ -60,6 +62,18 @@ def test_overflow_offset_raises(self):
with pytest.raises(OverflowError, match=msg):
stamp - offset_overflow
+ def test_overflow_timestamp_raises(self):
+ # https://github.com/pandas-dev/pandas/issues/31774
+ msg = "Result is too large"
+ a = Timestamp("2101-01-01 00:00:00")
+ b = Timestamp("1688-01-01 00:00:00")
+
+ with pytest.raises(OutOfBoundsDatetime, match=msg):
+ a - b
+
+ # but we're OK for timestamp and datetime.datetime
+ assert (a - b.to_pydatetime()) == (a.to_pydatetime() - b)
+
def test_delta_preserve_nanos(self):
val = Timestamp(1337299200000000123)
result = val + timedelta(1)
| Closes #31774
| https://api.github.com/repos/pandas-dev/pandas/pulls/32499 | 2020-03-06T17:10:53Z | 2020-03-11T01:45:54Z | 2020-03-11T01:45:54Z | 2020-03-12T12:22:19Z |
BUG: Fix bug, where BooleanDtype columns are converted to Int64 | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 462f243f14494..7a25f7b58c2e7 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -64,6 +64,7 @@ Bug fixes
**Datetimelike**
- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` when reindexing with a tz-aware index (:issue:`26683`)
+- Bug in :meth:`Series.astype` not copying for tz-naive and tz-aware datetime64 dtype (:issue:`32490`)
- Bug where :func:`to_datetime` would raise when passed ``pd.NA`` (:issue:`32213`)
**Categorical**
@@ -84,6 +85,7 @@ Bug fixes
- 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`)
+- Fixed bug in :meth:`DataFrame.convert_dtypes`, where ``BooleanDtype`` columns were converted to ``Int64`` (:issue:`32287`)
**Strings**
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index 9f19c7ba0be6e..7223eda22b3d9 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -587,6 +587,8 @@ def astype(self, dtype, copy=True):
if getattr(self.dtype, "tz", None) is None:
return self.tz_localize(new_tz)
result = self.tz_convert(new_tz)
+ if copy:
+ result = result.copy()
if new_tz is None:
# Do we want .astype('datetime64[ns]') to be an ndarray.
# The astype in Block._astype expects this to return an
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 7dac36b53fce5..97c02428cbdf9 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -1049,7 +1049,8 @@ def convert_dtypes(
dtype
new dtype
"""
- if convert_string or convert_integer or convert_boolean:
+ is_extension = is_extension_array_dtype(input_array.dtype)
+ if (convert_string or convert_integer or convert_boolean) and not is_extension:
try:
inferred_dtype = lib.infer_dtype(input_array)
except ValueError:
@@ -1062,9 +1063,7 @@ def convert_dtypes(
if convert_integer:
target_int_dtype = "Int64"
- if is_integer_dtype(input_array.dtype) and not is_extension_array_dtype(
- input_array.dtype
- ):
+ if is_integer_dtype(input_array.dtype):
from pandas.core.arrays.integer import _dtypes
inferred_dtype = _dtypes.get(input_array.dtype.name, target_int_dtype)
@@ -1078,9 +1077,7 @@ def convert_dtypes(
inferred_dtype = input_array.dtype
if convert_boolean:
- if is_bool_dtype(input_array.dtype) and not is_extension_array_dtype(
- input_array.dtype
- ):
+ if is_bool_dtype(input_array.dtype):
inferred_dtype = "boolean"
else:
if isinstance(inferred_dtype, str) and inferred_dtype == "boolean":
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index c088b7020927b..bce440fe09319 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -2228,6 +2228,9 @@ def astype(self, dtype, copy: bool = False, errors: str = "raise"):
# if we are passed a datetime64[ns, tz]
if is_datetime64tz_dtype(dtype):
values = self.values
+ if copy:
+ # this should be the only copy
+ values = values.copy()
if getattr(values, "tz", None) is None:
values = DatetimeArray(values).tz_localize("UTC")
values = values.tz_convert(dtype.tz)
diff --git a/pandas/tests/arrays/test_datetimes.py b/pandas/tests/arrays/test_datetimes.py
index a59ed429cc404..2c26e72a245f7 100644
--- a/pandas/tests/arrays/test_datetimes.py
+++ b/pandas/tests/arrays/test_datetimes.py
@@ -151,6 +151,18 @@ def test_astype_to_same(self):
result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
assert result is arr
+ @pytest.mark.parametrize("dtype", ["datetime64[ns]", "datetime64[ns, UTC]"])
+ @pytest.mark.parametrize(
+ "other", ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, CET]"]
+ )
+ def test_astype_copies(self, dtype, other):
+ # https://github.com/pandas-dev/pandas/pull/32490
+ s = pd.Series([1, 2], dtype=dtype)
+ orig = s.copy()
+ t = s.astype(other)
+ t[:] = pd.NaT
+ tm.assert_series_equal(s, orig)
+
@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
def test_astype_int(self, dtype):
arr = DatetimeArray._from_sequence([pd.Timestamp("2000"), pd.Timestamp("2001")])
diff --git a/pandas/tests/series/methods/test_convert_dtypes.py b/pandas/tests/series/methods/test_convert_dtypes.py
index 17527a09f07a1..a41f893e3753f 100644
--- a/pandas/tests/series/methods/test_convert_dtypes.py
+++ b/pandas/tests/series/methods/test_convert_dtypes.py
@@ -279,3 +279,8 @@ def test_convert_string_dtype(self):
)
result = df.convert_dtypes()
tm.assert_frame_equal(df, result)
+
+ def test_convert_bool_dtype(self):
+ # GH32287
+ df = pd.DataFrame({"A": pd.array([True])})
+ tm.assert_frame_equal(df, df.convert_dtypes())
| - [x] closes #32287
- [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/32490 | 2020-03-06T12:34:41Z | 2020-03-11T01:48:35Z | 2020-03-11T01:48:34Z | 2020-03-26T14:28:41Z |
Backport PR #27932 on branch 0.25.x (BUG: Ensure that fill_na in Categorical only replaces null values) | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 102ecb9cb0f4e..245aa42b0de14 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -25,7 +25,7 @@ Bug fixes
Categorical
^^^^^^^^^^^
--
+- Bug in :meth:`Categorical.fillna` would replace all values, not just those that are ``NaN`` (:issue:`26215`)
-
-
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index df5cd12a479f0..86a22d03440dd 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1864,8 +1864,8 @@ def fillna(self, value=None, method=None, limit=None):
raise ValueError("fill value must be in categories")
values_codes = _get_codes_for_values(value, self.categories)
- indexer = np.where(values_codes != -1)
- codes[indexer] = values_codes[values_codes != -1]
+ indexer = np.where(codes == -1)
+ codes[indexer] = values_codes[indexer]
# If value is not a dict or Series it should be a scalar
elif is_hashable(value):
diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py
index 10375719be8d2..f847a39541558 100644
--- a/pandas/tests/series/test_missing.py
+++ b/pandas/tests/series/test_missing.py
@@ -578,6 +578,28 @@ def test_fillna_categorical(self, fill_value, expected_output):
exp = Series(Categorical(expected_output, categories=["a", "b"]))
tm.assert_series_equal(s.fillna(fill_value), exp)
+ @pytest.mark.parametrize(
+ "fill_value, expected_output",
+ [
+ (Series(["a", "b", "c", "d", "e"]), ["a", "b", "b", "d", "e"]),
+ (Series(["b", "d", "a", "d", "a"]), ["a", "d", "b", "d", "a"]),
+ (
+ Series(
+ Categorical(
+ ["b", "d", "a", "d", "a"], categories=["b", "c", "d", "e", "a"]
+ )
+ ),
+ ["a", "d", "b", "d", "a"],
+ ),
+ ],
+ )
+ def test_fillna_categorical_with_new_categories(self, fill_value, expected_output):
+ # GH 26215
+ data = ["a", np.nan, "b", np.nan, np.nan]
+ s = Series(Categorical(data, categories=["a", "b", "c", "d", "e"]))
+ exp = Series(Categorical(expected_output, categories=["a", "b", "c", "d", "e"]))
+ tm.assert_series_equal(s.fillna(fill_value), exp)
+
def test_fillna_categorical_raise(self):
data = ["a", np.nan, "b", np.nan, np.nan]
s = Series(Categorical(data, categories=["a", "b"]))
| Backport PR #27932: BUG: Ensure that fill_na in Categorical only replaces null values | https://api.github.com/repos/pandas-dev/pandas/pulls/27947 | 2019-08-16T11:22:41Z | 2019-08-16T12:07:42Z | 2019-08-16T12:07:42Z | 2019-08-16T12:07:42Z |
BUG: Merge with readonly arrays | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index f4308684e286a..ee250d62b7a74 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -127,9 +127,9 @@ Reshaping
^^^^^^^^^
- A ``KeyError`` is now raised if ``.unstack()`` is called on a :class:`Series` or :class:`DataFrame` with a flat :class:`Index` passing a name which is not the correct one (:issue:`18303`)
-- Bug in :meth:`DataFrame.crosstab` when ``margins`` set to ``True`` and ``normalize`` is not ``False``, an error is raised. (:issue:`27500`)
+- Bug in :meth:`DataFrame.crosstab` when ``margins`` set to ``True`` and ``normalize`` is not ``False``, an error is raised. (:issue:`27500`)
- :meth:`DataFrame.join` now suppresses the ``FutureWarning`` when the sort parameter is specified (:issue:`21952`)
--
+- Bug in :meth:`DataFrame.join` raising with readonly arrays (:issue:`27943`)
Sparse
^^^^^^
diff --git a/pandas/_libs/hashtable.pyx b/pandas/_libs/hashtable.pyx
index 3e620f5934d5e..b8df78e600a46 100644
--- a/pandas/_libs/hashtable.pyx
+++ b/pandas/_libs/hashtable.pyx
@@ -108,7 +108,7 @@ cdef class Int64Factorizer:
def get_count(self):
return self.count
- def factorize(self, int64_t[:] values, sort=False,
+ def factorize(self, const int64_t[:] values, sort=False,
na_sentinel=-1, na_value=None):
"""
Factorize values with nans replaced by na_sentinel
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index b6c6f967333a8..a04f093ee7818 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -1340,6 +1340,18 @@ def test_merge_take_missing_values_from_index_of_other_dtype(self):
expected = expected.reindex(columns=["a", "key", "b"])
tm.assert_frame_equal(result, expected)
+ def test_merge_readonly(self):
+ # https://github.com/pandas-dev/pandas/issues/27943
+ data1 = pd.DataFrame(
+ np.arange(20).reshape((4, 5)) + 1, columns=["a", "b", "c", "d", "e"]
+ )
+ data2 = pd.DataFrame(
+ np.arange(20).reshape((5, 4)) + 1, columns=["a", "b", "x", "y"]
+ )
+
+ data1._data.blocks[0].values.flags.writeable = False
+ data1.merge(data2) # no error
+
def _check_merge(x, y):
for how in ["inner", "left", "outer"]:
| Closes #27943 | https://api.github.com/repos/pandas-dev/pandas/pulls/27946 | 2019-08-16T11:17:20Z | 2019-08-16T12:08:17Z | 2019-08-16T12:08:16Z | 2019-08-16T13:24:02Z |
CLN: docstring cleanups | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 20398069847b1..603a615c1f8cb 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1190,7 +1190,7 @@ def to_numpy(self, dtype=None, copy=False):
Parameters
----------
dtype : str or numpy.dtype, optional
- The dtype to pass to :meth:`numpy.asarray`
+ The dtype to pass to :meth:`numpy.asarray`.
copy : bool, default False
Whether to ensure that the returned value is a not a view on
another array. Note that ``copy=False`` does not *ensure* that
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 71d5068e2e0fc..ba1c516b9b444 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -131,7 +131,7 @@ class NDFrame(PandasObject, SelectionMixin):
----------
data : BlockManager
axes : list
- copy : boolean, default False
+ copy : bool, default False
"""
_internal_names = [
@@ -280,7 +280,8 @@ def _setup_axes(
ns=None,
docs=None,
):
- """Provide axes setup for the major PandasObjects.
+ """
+ Provide axes setup for the major PandasObjects.
Parameters
----------
@@ -288,8 +289,8 @@ def _setup_axes(
info_axis_num : the axis of the selector dimension (int)
stat_axis_num : the number of axis for the default stats (int)
aliases : other names for a single axis (dict)
- axes_are_reversed : boolean whether to treat passed axes as
- reversed (DataFrame)
+ axes_are_reversed : bool
+ Whether to treat passed axes as reversed (DataFrame).
build_axes : setup the axis properties (default True)
"""
@@ -676,7 +677,7 @@ def transpose(self, *args, **kwargs):
Parameters
----------
args : %(args_transpose)s
- copy : boolean, default False
+ copy : bool, default False
Make a copy of the underlying data. Mixed-dtype data will
always result in a copy
**kwargs
@@ -2257,10 +2258,10 @@ def to_json(
Parameters
----------
- path_or_buf : string or file handle, optional
+ path_or_buf : str or file handle, optional
File path or object. If not specified, the result is returned as
a string.
- orient : string
+ orient : str
Indication of expected JSON string format.
* Series
@@ -2539,7 +2540,7 @@ def to_msgpack(self, path_or_buf=None, encoding="utf-8", **kwargs):
def to_sql(
self,
- name,
+ name: str,
con,
schema=None,
if_exists="fail",
@@ -2557,12 +2558,12 @@ def to_sql(
Parameters
----------
- name : string
+ name : str
Name of SQL table.
con : sqlalchemy.engine.Engine or sqlite3.Connection
Using SQLAlchemy makes it possible to use any DB supported by that
library. Legacy support is provided for sqlite3.Connection objects.
- schema : string, optional
+ schema : str, optional
Specify the schema (if database flavor supports this). If None, use
default schema.
if_exists : {'fail', 'replace', 'append'}, default 'fail'
@@ -2575,7 +2576,7 @@ def to_sql(
index : bool, default True
Write DataFrame index as a column. Uses `index_label` as the column
name in the table.
- index_label : string or sequence, default None
+ index_label : str or sequence, default None
Column label for index column(s). If None is given (default) and
`index` is True, then the index names are used.
A sequence should be given if the DataFrame uses MultiIndex.
@@ -3251,11 +3252,10 @@ def _maybe_update_cacher(self, clear=False, verify_is_copy=True):
Parameters
----------
- clear : boolean, default False
- clear the item cache
- verify_is_copy : boolean, default True
- provide is_copy checks
-
+ clear : bool, default False
+ Clear the item cache.
+ verify_is_copy : bool, default True
+ Provide is_copy checks.
"""
cacher = getattr(self, "_cacher", None)
@@ -3621,11 +3621,11 @@ def _check_setitem_copy(self, stacklevel=4, t="setting", force=False):
Parameters
----------
- stacklevel : integer, default 4
+ stacklevel : int, default 4
the level to show of the stack when the error is output
- t : string, the type of setting error
- force : boolean, default False
- if True, then force showing an error
+ t : str, the type of setting error
+ force : bool, default False
+ If True, then force showing an error.
validate if we are doing a setitem on a chained copy.
@@ -3954,9 +3954,8 @@ def _update_inplace(self, result, verify_is_copy=True):
Parameters
----------
- verify_is_copy : boolean, default True
- provide is_copy checks
-
+ verify_is_copy : bool, default True
+ Provide is_copy checks.
"""
# NOTE: This does *not* call __finalize__ and that's an explicit
# decision that we may revisit in the future.
@@ -4571,9 +4570,9 @@ def filter(self, items=None, like=None, regex=None, axis=None):
----------
items : list-like
Keep labels from axis which are in items.
- like : string
+ like : str
Keep labels from axis for which "like in label == True".
- regex : string (regular expression)
+ regex : str (regular expression)
Keep labels from axis for which re.search(regex, label) == True.
axis : int or string axis name
The axis to filter on. By default this is the info axis,
@@ -5233,8 +5232,8 @@ def _consolidate(self, inplace=False):
Parameters
----------
- inplace : boolean, default False
- If False return new object, otherwise modify existing object
+ inplace : bool, default False
+ If False return new object, otherwise modify existing object.
Returns
-------
@@ -5680,11 +5679,12 @@ def as_blocks(self, copy=True):
Parameters
----------
- copy : boolean, default True
+ copy : bool, default True
Returns
-------
- values : a dict of dtype -> Constructor Types
+ dict
+ Mapping dtype -> Constructor Types.
"""
warnings.warn(
"as_blocks is deprecated and will be removed in a future version",
@@ -5993,17 +5993,17 @@ def _convert(
Parameters
----------
- datetime : boolean, default False
+ datetime : bool, default False
If True, convert to date where possible.
- numeric : boolean, default False
+ numeric : bool, default False
If True, attempt to convert to numbers (including strings), with
unconvertible values becoming NaN.
- timedelta : boolean, default False
+ timedelta : bool, default False
If True, convert to timedelta where possible.
- coerce : boolean, default False
+ coerce : bool, default False
If True, force conversion with unconvertible values converted to
- nulls (NaN or NaT)
- copy : boolean, default True
+ nulls (NaN or NaT).
+ copy : bool, default True
If True, return a copy even if no copy is necessary (e.g. no
conversion was done). Note: This is meant for internal use, and
should not be confused with inplace.
@@ -7870,7 +7870,7 @@ def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
Parameters
----------
- freq : DateOffset object, or string
+ freq : DateOffset or str
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
@@ -8671,7 +8671,7 @@ def ranker(data):
level : int or level name, default None
Broadcast across a level, matching Index values on the
passed MultiIndex level
- copy : boolean, default True
+ copy : bool, default True
Always returns new objects. If copy=False and no reindexing is
required then original objects are returned.
fill_value : scalar, default np.NaN
@@ -9463,7 +9463,7 @@ def truncate(self, before=None, after=None, axis=None, copy=True):
Truncate all rows after this index value.
axis : {0 or 'index', 1 or 'columns'}, optional
Axis to truncate. Truncates the index (rows) by default.
- copy : boolean, default is True,
+ copy : bool, default is True,
Return a copy of the truncated section.
Returns
@@ -9607,13 +9607,13 @@ def tz_convert(self, tz, axis=0, level=None, copy=True):
Parameters
----------
- tz : string or pytz.timezone object
+ tz : str or tzinfo object
axis : the axis to convert
level : int, str, default None
- If axis ia a MultiIndex, convert a specific level. Otherwise
- must be None
- copy : boolean, default True
- Also make a copy of the underlying data
+ If axis is a MultiIndex, convert a specific level. Otherwise
+ must be None.
+ copy : bool, default True
+ Also make a copy of the underlying data.
Returns
-------
@@ -9667,12 +9667,12 @@ def tz_localize(
Parameters
----------
- tz : string or pytz.timezone object
+ tz : str or tzinfo
axis : the axis to localize
level : int, str, default None
If axis ia a MultiIndex, localize a specific level. Otherwise
must be None
- copy : boolean, default True
+ copy : bool, default True
Also make a copy of the underlying data
ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
When clocks moved backward due to DST, ambiguous times may arise.
@@ -11009,7 +11009,7 @@ def _doc_parms(cls):
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The index or the name of the axis. 0 is equivalent to None or 'index'.
-skipna : boolean, default True
+skipna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
*args, **kwargs :
diff --git a/pandas/core/series.py b/pandas/core/series.py
index c891298d6e499..3f04970ee4e58 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -675,8 +675,8 @@ def nonzero(self):
3 4
dtype: int64
- >>> s = pd.Series([0, 3, 0, 4], index=['a', 'b', 'c', 'd'])
# same return although index of s is different
+ >>> s = pd.Series([0, 3, 0, 4], index=['a', 'b', 'c', 'd'])
>>> s.nonzero()
(array([1, 3]),)
>>> s.iloc[s.nonzero()[0]]
diff --git a/pandas/plotting/_core.py b/pandas/plotting/_core.py
index ec5c609c1b267..2e6a401b49efc 100644
--- a/pandas/plotting/_core.py
+++ b/pandas/plotting/_core.py
@@ -53,7 +53,7 @@ def hist_series(
rotation of y axis labels
figsize : tuple, default None
figure size in inches by default
- bins : integer or sequence, default 10
+ bins : int or sequence, default 10
Number of histogram bins to be used. If an integer is given, bins + 1
bin edges are calculated and returned. If bins is a sequence, gives
bin edges, including left edge of first bin and right edge of last
@@ -116,7 +116,7 @@ def hist_frame(
----------
data : DataFrame
The pandas object holding the data.
- column : string or sequence
+ column : str or sequence
If passed, will be used to limit data to a subset of columns.
by : object, optional
If passed, then used to form histograms for separate groups.
@@ -148,7 +148,7 @@ def hist_frame(
`matplotlib.rcParams` by default.
layout : tuple, optional
Tuple of (rows, columns) for the layout of the histograms.
- bins : integer or sequence, default 10
+ bins : int or sequence, default 10
Number of histogram bins to be used. If an integer is given, bins + 1
bin edges are calculated and returned. If bins is a sequence, gives
bin edges, including left edge of first bin and right edge of last
@@ -177,7 +177,7 @@ def hist_frame(
>>> df = pd.DataFrame({
... 'length': [1.5, 0.5, 1.2, 0.9, 3],
... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]
- ... }, index= ['pig', 'rabbit', 'duck', 'chicken', 'horse'])
+ ... }, index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
>>> hist = df.hist(bins=3)
"""
plot_backend = _get_plot_backend()
@@ -370,8 +370,8 @@ def boxplot(
If ``return_type`` is `None`, a NumPy array of axes with the same shape
as ``layout`` is returned:
- >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
- ... return_type=None)
+ >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
+ ... return_type=None)
>>> type(boxplot)
<class 'numpy.ndarray'>
"""
@@ -446,7 +446,7 @@ def boxplot_frame_groupby(
* ``True`` - create a subplot for each group
column : column name or list of names, or vector
Can be any valid input to groupby
- fontsize : int or string
+ fontsize : int or str
rot : label rotation angle
grid : Setting this to True will show the grid
ax : Matplotlib axis object, default None
@@ -530,7 +530,7 @@ class PlotAccessor(PandasObject):
figsize : a tuple (width, height) in inches
use_index : bool, default True
Use index as ticks for x axis
- title : string or list
+ title : str or list
Title to use for the plot. If a string is passed, print the string
at the top of the figure. If a list is passed and `subplots` is
True, print each item in the list above the corresponding subplot.
@@ -553,16 +553,16 @@ class PlotAccessor(PandasObject):
.. versionchanged:: 0.25.0
xticks : sequence
- Values to use for the xticks
+ Values to use for the xticks.
yticks : sequence
- Values to use for the yticks
+ Values to use for the yticks.
xlim : 2-tuple/list
ylim : 2-tuple/list
rot : int, default None
Rotation for ticks (xticks for vertical, yticks for horizontal
plots)
fontsize : int, default None
- Font size for xticks and yticks
+ Font size for xticks and yticks.
colormap : str or matplotlib colormap object, default None
Colormap to select colors from. If string, load colormap with that
name from matplotlib.
@@ -587,9 +587,9 @@ class PlotAccessor(PandasObject):
When using a secondary_y axis, automatically mark the column
labels with "(right)" in the legend
include_bool : bool, default is False
- If True, boolean values can be plotted
+ If True, boolean values can be plotted.
`**kwds` : keywords
- Options to pass to matplotlib plotting method
+ Options to pass to matplotlib plotting method.
Returns
-------
@@ -985,7 +985,7 @@ def barh(self, x=None, y=None, **kwargs):
.. plot::
:context: close-figs
- >>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
+ >>> df = pd.DataFrame({'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]})
>>> ax = df.plot.barh(x='lab', y='val')
Plot a whole DataFrame to a horizontal bar plot
@@ -1049,7 +1049,7 @@ def box(self, by=None, **kwargs):
Parameters
----------
- by : string or sequence
+ by : str or sequence
Column in the DataFrame to group by.
**kwds : optional
Additional keywords are documented in
| cc @datapythonista | https://api.github.com/repos/pandas-dev/pandas/pulls/27942 | 2019-08-15T23:13:17Z | 2019-08-16T19:18:53Z | 2019-08-16T19:18:53Z | 2019-08-16T20:32:18Z |
CLN: internals.blocks cleanup, typing | diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index e24e6e088b92a..ea895db226ba0 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -7,7 +7,7 @@
import numpy as np
-from pandas._libs import NaT, Timestamp, lib, tslib, tslibs
+from pandas._libs import NaT, Timestamp, lib, tslib
import pandas._libs.internals as libinternals
from pandas._libs.tslibs import Timedelta, conversion
from pandas._libs.tslibs.timezones import tz_compare
@@ -407,7 +407,7 @@ def fillna(self, value, limit=None, inplace=False, downcast=None):
return self.copy()
if self._can_hold_element(value):
- # equivalent: self._try_coerce_args(value) would not raise
+ # equivalent: _try_coerce_args(value) would not raise
blocks = self.putmask(mask, value, inplace=inplace)
return self._maybe_downcast(blocks, downcast)
@@ -669,7 +669,7 @@ def convert(
return self.copy() if copy else self
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
""" require the same dtype as ourselves """
dtype = self.values.dtype.type
tipo = maybe_infer_dtype_type(element)
@@ -837,12 +837,6 @@ def setitem(self, indexer, value):
if self._can_hold_element(value):
value = self._try_coerce_args(value)
- # can keep its own dtype
- if hasattr(value, "dtype") and is_dtype_equal(values.dtype, value.dtype):
- dtype = self.dtype
- else:
- dtype = "infer"
-
else:
# current dtype cannot store value, coerce to common dtype
find_dtype = False
@@ -851,15 +845,9 @@ def setitem(self, indexer, value):
dtype = value.dtype
find_dtype = True
- elif lib.is_scalar(value):
- if isna(value):
- # NaN promotion is handled in latter path
- dtype = False
- else:
- dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=True)
- find_dtype = True
- else:
- dtype = "infer"
+ elif lib.is_scalar(value) and not isna(value):
+ dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=True)
+ find_dtype = True
if find_dtype:
dtype = find_common_type([values.dtype, dtype])
@@ -1068,7 +1056,7 @@ def coerce_to_target_dtype(self, other):
mytz = getattr(self.dtype, "tz", None)
othertz = getattr(dtype, "tz", None)
- if str(mytz) != str(othertz):
+ if not tz_compare(mytz, othertz):
return self.astype(object)
raise AssertionError(
@@ -1288,7 +1276,7 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None):
else:
return self.make_block_same_class(new_values, new_mgr_locs)
- def diff(self, n, axis=1):
+ def diff(self, n: int, axis: int = 1) -> List["Block"]:
""" return block for the diff of the values """
new_values = algos.diff(self.values, n, axis=axis)
return [self.make_block(values=new_values)]
@@ -1377,7 +1365,7 @@ def func(cond, values, other):
if not (
(self.is_integer or self.is_bool)
- and lib.is_scalar(other)
+ and lib.is_float(other)
and np.isnan(other)
):
# np.where will cast integer array to floats in this case
@@ -1430,7 +1418,7 @@ def func(cond, values, other):
return result_blocks
- def equals(self, other):
+ def equals(self, other) -> bool:
if self.dtype != other.dtype or self.shape != other.shape:
return False
return array_equivalent(self.values, other.values)
@@ -1810,7 +1798,7 @@ def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None):
return self.make_block_same_class(new_values, new_mgr_locs)
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
# XXX: We may need to think about pushing this onto the array.
# We're doing the same as CategoricalBlock here.
return True
@@ -1980,7 +1968,7 @@ class NumericBlock(Block):
class FloatOrComplexBlock(NumericBlock):
__slots__ = ()
- def equals(self, other):
+ def equals(self, other) -> bool:
if self.dtype != other.dtype or self.shape != other.shape:
return False
left, right = self.values, other.values
@@ -1991,7 +1979,7 @@ class FloatBlock(FloatOrComplexBlock):
__slots__ = ()
is_float = True
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
tipo = maybe_infer_dtype_type(element)
if tipo is not None:
return issubclass(tipo.type, (np.floating, np.integer)) and not issubclass(
@@ -2055,7 +2043,7 @@ class ComplexBlock(FloatOrComplexBlock):
__slots__ = ()
is_complex = True
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
tipo = maybe_infer_dtype_type(element)
if tipo is not None:
return issubclass(tipo.type, (np.floating, np.integer, np.complexfloating))
@@ -2072,7 +2060,7 @@ class IntBlock(NumericBlock):
is_integer = True
_can_hold_na = False
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
tipo = maybe_infer_dtype_type(element)
if tipo is not None:
return (
@@ -2162,7 +2150,7 @@ def _astype(self, dtype, **kwargs):
# delegate
return super()._astype(dtype=dtype, **kwargs)
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
tipo = maybe_infer_dtype_type(element)
if tipo is not None:
if self.is_datetimetz:
@@ -2352,41 +2340,19 @@ def _slice(self, slicer):
return self.values[slicer]
def _try_coerce_args(self, other):
- """
- localize and return i8 for the values
-
- Parameters
- ----------
- other : ndarray-like or scalar
-
- Returns
- -------
- base-type other
- """
- if is_valid_nat_for_dtype(other, self.dtype):
- other = np.datetime64("NaT", "ns")
- elif isinstance(other, self._holder):
- if not tz_compare(other.tz, self.values.tz):
- raise ValueError("incompatible or non tz-aware value")
-
- elif isinstance(other, (np.datetime64, datetime, date)):
- other = tslibs.Timestamp(other)
-
- # test we can have an equal time zone
- if not tz_compare(other.tz, self.values.tz):
- raise ValueError("incompatible or non tz-aware value")
- else:
- raise TypeError(other)
-
+ # DatetimeArray handles this for us
return other
- def diff(self, n, axis=0):
- """1st discrete difference
+ def diff(self, n: int, axis: int = 0) -> List["Block"]:
+ """
+ 1st discrete difference.
Parameters
----------
- n : int, number of periods to diff
- axis : int, axis to diff upon. default 0
+ n : int
+ Number of periods to diff.
+ axis : int, default 0
+ Axis to diff upon.
Returns
-------
@@ -2448,7 +2414,7 @@ def setitem(self, indexer, value):
)
return newb.setitem(indexer, value)
- def equals(self, other):
+ def equals(self, other) -> bool:
# override for significant performance improvement
if self.dtype != other.dtype or self.shape != other.shape:
return False
@@ -2487,7 +2453,7 @@ def __init__(self, values, placement, ndim=None):
def _holder(self):
return TimedeltaArray
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
tipo = maybe_infer_dtype_type(element)
if tipo is not None:
return issubclass(tipo.type, np.timedelta64)
@@ -2580,7 +2546,7 @@ class BoolBlock(NumericBlock):
is_bool = True
_can_hold_na = False
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
tipo = maybe_infer_dtype_type(element)
if tipo is not None:
return issubclass(tipo.type, np.bool_)
@@ -2674,7 +2640,7 @@ def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]
# split and convert the blocks
return _extend_blocks([b.convert(datetime=True, numeric=False) for b in blocks])
- def _can_hold_element(self, element):
+ def _can_hold_element(self, element: Any) -> bool:
return True
def _try_coerce_args(self, other):
| https://api.github.com/repos/pandas-dev/pandas/pulls/27941 | 2019-08-15T22:55:38Z | 2019-08-26T23:52:45Z | 2019-08-26T23:52:45Z | 2019-08-27T20:48:21Z | |
Fix typos in HDFStore docs | diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 947bf15a49c7a..5d7a268631778 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -3572,7 +3572,7 @@ Closing a Store and using a context manager:
Read/write API
''''''''''''''
-``HDFStore`` supports an top-level API using ``read_hdf`` for reading and ``to_hdf`` for writing,
+``HDFStore`` supports a top-level API using ``read_hdf`` for reading and ``to_hdf`` for writing,
similar to how ``read_csv`` and ``to_csv`` work.
.. ipython:: python
@@ -3687,7 +3687,7 @@ Hierarchical keys
Keys to a store can be specified as a string. These can be in a
hierarchical path-name like format (e.g. ``foo/bar/bah``), which will
generate a hierarchy of sub-stores (or ``Groups`` in PyTables
-parlance). Keys can be specified with out the leading '/' and are **always**
+parlance). Keys can be specified without the leading '/' and are **always**
absolute (e.g. 'foo' refers to '/foo'). Removal operations can remove
everything in the sub-store and **below**, so be *careful*.
@@ -3825,7 +3825,7 @@ data.
A query is specified using the ``Term`` class under the hood, as a boolean expression.
-* ``index`` and ``columns`` are supported indexers of a ``DataFrames``.
+* ``index`` and ``columns`` are supported indexers of ``DataFrames``.
* if ``data_columns`` are specified, these can be used as additional indexers.
Valid comparison operators are:
@@ -3917,7 +3917,7 @@ Use boolean expressions, with in-line function evaluation.
store.select('dfq', "index>pd.Timestamp('20130104') & columns=['A', 'B']")
-Use and inline column reference
+Use inline column reference.
.. ipython:: python
@@ -4593,8 +4593,8 @@ Performance
write chunksize (default is 50000). This will significantly lower
your memory usage on writing.
* You can pass ``expectedrows=<int>`` to the first ``append``,
- to set the TOTAL number of expected rows that ``PyTables`` will
- expected. This will optimize read/write performance.
+ to set the TOTAL number of rows that ``PyTables`` will expect.
+ This will optimize read/write performance.
* Duplicate rows can be written to tables, but are filtered out in
selection (with the last items being selected; thus a table is
unique on major, minor pairs)
| https://api.github.com/repos/pandas-dev/pandas/pulls/27940 | 2019-08-15T21:28:26Z | 2019-08-16T11:32:32Z | 2019-08-16T11:32:32Z | 2019-08-16T15:39:10Z | |
CLN: docstrings, circularity in core.computation | diff --git a/pandas/compat/chainmap.py b/pandas/compat/chainmap.py
index 83f1da597d6a6..84824207de2a9 100644
--- a/pandas/compat/chainmap.py
+++ b/pandas/compat/chainmap.py
@@ -15,9 +15,3 @@ def __delitem__(self, key):
del mapping[key]
return
raise KeyError(key)
-
- # override because the m parameter is introduced in Python 3.4
- def new_child(self, m=None):
- if m is None:
- m = {}
- return self.__class__(m, *self.maps)
diff --git a/pandas/core/computation/align.py b/pandas/core/computation/align.py
index 1046401850963..3e1e5ed89d877 100644
--- a/pandas/core/computation/align.py
+++ b/pandas/core/computation/align.py
@@ -9,6 +9,7 @@
from pandas.errors import PerformanceWarning
import pandas as pd
+from pandas.core.base import PandasObject
import pandas.core.common as com
from pandas.core.computation.common import _result_type_many
@@ -34,7 +35,7 @@ def _zip_axes_from_type(typ, new_axes):
def _any_pandas_objects(terms):
"""Check a sequence of terms for instances of PandasObject."""
- return any(isinstance(term.value, pd.core.generic.PandasObject) for term in terms)
+ return any(isinstance(term.value, PandasObject) for term in terms)
def _filter_special_cases(f):
@@ -132,7 +133,8 @@ def _align(terms):
def _reconstruct_object(typ, obj, axes, dtype):
- """Reconstruct an object given its type, raw value, and possibly empty
+ """
+ Reconstruct an object given its type, raw value, and possibly empty
(None) axes.
Parameters
@@ -157,7 +159,7 @@ def _reconstruct_object(typ, obj, axes, dtype):
res_t = np.result_type(obj.dtype, dtype)
- if not isinstance(typ, partial) and issubclass(typ, pd.core.generic.PandasObject):
+ if not isinstance(typ, partial) and issubclass(typ, PandasObject):
return typ(obj, dtype=res_t, **axes)
# special case for pathological things like ~True/~False
diff --git a/pandas/core/computation/common.py b/pandas/core/computation/common.py
index 2a4277e4e0a20..bd32c8bee1cdf 100644
--- a/pandas/core/computation/common.py
+++ b/pandas/core/computation/common.py
@@ -36,8 +36,3 @@ def _remove_spaces_column_name(name):
class NameResolutionError(NameError):
pass
-
-
-class StringMixin:
- # TODO: delete this class. Removing this ATM caused a failure.
- pass
diff --git a/pandas/core/computation/engines.py b/pandas/core/computation/engines.py
index 2c94b142a45b3..3cc34ea1f4ed7 100644
--- a/pandas/core/computation/engines.py
+++ b/pandas/core/computation/engines.py
@@ -17,7 +17,8 @@ class NumExprClobberingError(NameError):
def _check_ne_builtin_clash(expr):
- """Attempt to prevent foot-shooting in a helpful way.
+ """
+ Attempt to prevent foot-shooting in a helpful way.
Parameters
----------
@@ -53,7 +54,8 @@ def convert(self):
return printing.pprint_thing(self.expr)
def evaluate(self):
- """Run the engine on the expression
+ """
+ Run the engine on the expression.
This method performs alignment which is necessary no matter what engine
is being used, thus its implementation is in the base class.
@@ -78,7 +80,8 @@ def _is_aligned(self):
@abc.abstractmethod
def _evaluate(self):
- """Return an evaluated expression.
+ """
+ Return an evaluated expression.
Parameters
----------
@@ -94,7 +97,6 @@ def _evaluate(self):
class NumExprEngine(AbstractEngine):
-
"""NumExpr engine class"""
has_neg_frac = True
@@ -127,8 +129,8 @@ def _evaluate(self):
class PythonEngine(AbstractEngine):
-
- """Evaluate an expression in Python space.
+ """
+ Evaluate an expression in Python space.
Mostly for testing purposes.
"""
diff --git a/pandas/core/computation/expr.py b/pandas/core/computation/expr.py
index d0d87c23e9346..a58f256cf61d4 100644
--- a/pandas/core/computation/expr.py
+++ b/pandas/core/computation/expr.py
@@ -41,7 +41,8 @@
def tokenize_string(source):
- """Tokenize a Python source code string.
+ """
+ Tokenize a Python source code string.
Parameters
----------
diff --git a/pandas/core/computation/expressions.py b/pandas/core/computation/expressions.py
index 0ecf56cf6fe96..5b6d275001d36 100644
--- a/pandas/core/computation/expressions.py
+++ b/pandas/core/computation/expressions.py
@@ -99,15 +99,13 @@ def _evaluate_numexpr(op, op_str, a, b, truediv=True, reversed=False, **eval_kwa
result = None
if _can_use_numexpr(op, op_str, a, b, "evaluate"):
- try:
-
- # we were originally called by a reversed op
- # method
- if reversed:
- a, b = b, a
+ if reversed:
+ # we were originally called by a reversed op method
+ a, b = b, a
- a_value = getattr(a, "values", a)
- b_value = getattr(b, "values", b)
+ a_value = getattr(a, "values", a)
+ b_value = getattr(b, "values", b)
+ try:
result = ne.evaluate(
"a_value {op} b_value".format(op=op_str),
local_dict={"a_value": a_value, "b_value": b_value},
@@ -138,11 +136,11 @@ def _where_numexpr(cond, a, b):
result = None
if _can_use_numexpr(None, "where", a, b, "where"):
+ cond_value = getattr(cond, "values", cond)
+ a_value = getattr(a, "values", a)
+ b_value = getattr(b, "values", b)
try:
- cond_value = getattr(cond, "values", cond)
- a_value = getattr(a, "values", a)
- b_value = getattr(b, "values", b)
result = ne.evaluate(
"where(cond_value, a_value, b_value)",
local_dict={
@@ -209,7 +207,8 @@ def evaluate(op, op_str, a, b, use_numexpr=True, **eval_kwargs):
Parameters
----------
op : the actual operand
- op_str : the string version of the op
+ op_str : str
+ The string version of the op.
a : left operand
b : right operand
use_numexpr : bool, default True
@@ -224,11 +223,11 @@ def evaluate(op, op_str, a, b, use_numexpr=True, **eval_kwargs):
def where(cond, a, b, use_numexpr=True):
"""
- Evaluate the where condition cond on a and b
+ Evaluate the where condition cond on a and b.
Parameters
----------
- cond : ndarray[bool]
+ cond : np.ndarray[bool]
a : return if cond is True
b : return if cond is False
use_numexpr : bool, default True
diff --git a/pandas/core/computation/ops.py b/pandas/core/computation/ops.py
index b49220ae701bc..28b6aef693bfe 100644
--- a/pandas/core/computation/ops.py
+++ b/pandas/core/computation/ops.py
@@ -476,8 +476,8 @@ def isnumeric(dtype):
class Div(BinOp):
-
- """Div operator to special case casting.
+ """
+ Div operator to special case casting.
Parameters
----------
@@ -508,8 +508,8 @@ def __init__(self, lhs, rhs, truediv, *args, **kwargs):
class UnaryOp(Op):
-
- """Hold a unary operator and its operands
+ """
+ Hold a unary operator and its operands.
Parameters
----------
diff --git a/pandas/core/computation/scope.py b/pandas/core/computation/scope.py
index 8ddd0dd7622e7..b11411eb2dc66 100644
--- a/pandas/core/computation/scope.py
+++ b/pandas/core/computation/scope.py
@@ -15,9 +15,6 @@
from pandas._libs.tslibs import Timestamp
from pandas.compat.chainmap import DeepChainMap
-import pandas.core.computation as compu
-from pandas.core.computation.common import StringMixin
-
def _ensure_scope(
level, global_dict=None, local_dict=None, resolvers=(), target=None, **kwargs
@@ -67,7 +64,8 @@ def _raw_hex_id(obj):
def _get_pretty_string(obj):
- """Return a prettier version of obj
+ """
+ Return a prettier version of obj.
Parameters
----------
@@ -84,9 +82,9 @@ def _get_pretty_string(obj):
return sio.getvalue()
-class Scope(StringMixin):
-
- """Object to hold scope, with a few bells to deal with some custom syntax
+class Scope:
+ """
+ Object to hold scope, with a few bells to deal with some custom syntax
and contexts added by pandas.
Parameters
@@ -105,7 +103,7 @@ class Scope(StringMixin):
temps : dict
"""
- __slots__ = "level", "scope", "target", "temps"
+ __slots__ = ["level", "scope", "target", "resolvers", "temps"]
def __init__(
self, level, global_dict=None, local_dict=None, resolvers=(), target=None
@@ -163,7 +161,8 @@ def has_resolvers(self):
return bool(len(self.resolvers))
def resolve(self, key, is_local):
- """Resolve a variable name in a possibly local context
+ """
+ Resolve a variable name in a possibly local context.
Parameters
----------
@@ -198,10 +197,14 @@ def resolve(self, key, is_local):
# e.g., df[df > 0]
return self.temps[key]
except KeyError:
- raise compu.ops.UndefinedVariableError(key, is_local)
+ # runtime import because ops imports from scope
+ from pandas.core.computation.ops import UndefinedVariableError
+
+ raise UndefinedVariableError(key, is_local)
def swapkey(self, old_key, new_key, new_value=None):
- """Replace a variable name, with a potentially new value.
+ """
+ Replace a variable name, with a potentially new value.
Parameters
----------
@@ -225,7 +228,8 @@ def swapkey(self, old_key, new_key, new_value=None):
return
def _get_vars(self, stack, scopes):
- """Get specifically scoped variables from a list of stack frames.
+ """
+ Get specifically scoped variables from a list of stack frames.
Parameters
----------
@@ -247,7 +251,8 @@ def _get_vars(self, stack, scopes):
del frame
def update(self, level):
- """Update the current scope by going back `level` levels.
+ """
+ Update the current scope by going back `level` levels.
Parameters
----------
@@ -266,7 +271,8 @@ def update(self, level):
del stack[:], stack
def add_tmp(self, value):
- """Add a temporary variable to the scope.
+ """
+ Add a temporary variable to the scope.
Parameters
----------
@@ -297,7 +303,8 @@ def ntemps(self):
@property
def full_scope(self):
- """Return the full scope for use with passing to engines transparently
+ """
+ Return the full scope for use with passing to engines transparently
as a mapping.
Returns
| Things noticed while looking through this directory:
- computation.pytables is only used io.pytables, might not belong in core.computation
- UndefinedVariableError looks like it was defined as part of a `# python 3 compat kludge`, might no longer be necessary | https://api.github.com/repos/pandas-dev/pandas/pulls/27939 | 2019-08-15T21:08:43Z | 2019-08-16T12:11:11Z | 2019-08-16T12:11:11Z | 2019-08-16T13:56:17Z |
Backport PR #27878 on branch 0.25.x (TST: Raise ValueError and suggestion to use header=None if header=-1 is pa…) | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index b53b53c654ce3..102ecb9cb0f4e 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -105,7 +105,7 @@ I/O
^^^
- Avoid calling ``S3File.s3`` when reading parquet, as this was removed in s3fs version 0.3.0 (:issue:`27756`)
--
+- Better error message when a negative header is passed in :func:`pandas.read_csv` (:issue:`27779`)
-
Plotting
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 300f17bd25432..45444ffa0c8bd 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -1401,6 +1401,10 @@ def __init__(self, kwds):
if isinstance(self.header, (list, tuple, np.ndarray)):
if not all(map(is_integer, self.header)):
raise ValueError("header must be integer or list of integers")
+ if any(i < 0 for i in self.header):
+ raise ValueError(
+ "cannot specify multi-index header with negative integers"
+ )
if kwds.get("usecols"):
raise ValueError(
"cannot specify usecols when " "specifying a multi-index header"
@@ -1427,6 +1431,13 @@ def __init__(self, kwds):
elif self.header is not None and not is_integer(self.header):
raise ValueError("header must be integer or list of integers")
+ # GH 27779
+ elif self.header is not None and self.header < 0:
+ raise ValueError(
+ "Passing negative integer to header is invalid. "
+ "For no header, use header=None instead"
+ )
+
self._name_processed = False
self._first_chunk = True
diff --git a/pandas/tests/io/parser/test_header.py b/pandas/tests/io/parser/test_header.py
index 99e0181741998..0ecd8be7ddc78 100644
--- a/pandas/tests/io/parser/test_header.py
+++ b/pandas/tests/io/parser/test_header.py
@@ -24,6 +24,35 @@ def test_read_with_bad_header(all_parsers):
parser.read_csv(s, header=[10])
+def test_negative_header(all_parsers):
+ # see gh-27779
+ parser = all_parsers
+ data = """1,2,3,4,5
+6,7,8,9,10
+11,12,13,14,15
+"""
+ with pytest.raises(
+ ValueError,
+ match="Passing negative integer to header is invalid. "
+ "For no header, use header=None instead",
+ ):
+ parser.read_csv(StringIO(data), header=-1)
+
+
+@pytest.mark.parametrize("header", [([-1, 2, 4]), ([-5, 0])])
+def test_negative_multi_index_header(all_parsers, header):
+ # see gh-27779
+ parser = all_parsers
+ data = """1,2,3,4,5
+ 6,7,8,9,10
+ 11,12,13,14,15
+ """
+ with pytest.raises(
+ ValueError, match="cannot specify multi-index header with negative integers"
+ ):
+ parser.read_csv(StringIO(data), header=header)
+
+
@pytest.mark.parametrize("header", [True, False])
def test_bool_header_arg(all_parsers, header):
# see gh-6114
| Backport PR #27878: TST: Raise ValueError and suggestion to use header=None if header=-1 is pa… | https://api.github.com/repos/pandas-dev/pandas/pulls/27938 | 2019-08-15T20:27:58Z | 2019-08-16T01:27:55Z | 2019-08-16T01:27:55Z | 2019-08-16T01:27:55Z |
REF: implement array_ops | diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index 1aad130d9a3f5..093334a815938 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -195,11 +195,11 @@ def wrapper(self, other):
return invalid_comparison(self, other, op)
if is_object_dtype(other):
- # We have to use _comp_method_OBJECT_ARRAY instead of numpy
+ # We have to use comp_method_OBJECT_ARRAY instead of numpy
# comparison otherwise it would fail to raise when
# comparing tz-aware and tz-naive
with np.errstate(all="ignore"):
- result = ops._comp_method_OBJECT_ARRAY(
+ result = ops.comp_method_OBJECT_ARRAY(
op, self.astype(object), other
)
o_mask = isna(other)
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 5f17dde01d2c4..b983117478c61 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -109,7 +109,7 @@ def cmp_method(self, other):
elif is_object_dtype(self) and not isinstance(self, ABCMultiIndex):
# don't pass MultiIndex
with np.errstate(all="ignore"):
- result = ops._comp_method_OBJECT_ARRAY(op, self.values, other)
+ result = ops.comp_method_OBJECT_ARRAY(op, self.values, other)
else:
with np.errstate(all="ignore"):
diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index 26672593f98fb..dbcf09a401f27 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -13,11 +13,7 @@
from pandas.errors import NullFrequencyError
from pandas.util._decorators import Appender
-from pandas.core.dtypes.cast import (
- construct_1d_object_array_from_listlike,
- find_common_type,
- maybe_upcast_putmask,
-)
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
from pandas.core.dtypes.common import (
ensure_object,
is_bool_dtype,
@@ -29,7 +25,6 @@
is_integer_dtype,
is_list_like,
is_object_dtype,
- is_period_dtype,
is_scalar,
is_timedelta64_dtype,
)
@@ -37,7 +32,6 @@
ABCDataFrame,
ABCDatetimeArray,
ABCDatetimeIndex,
- ABCIndex,
ABCIndexClass,
ABCSeries,
ABCSparseSeries,
@@ -47,7 +41,7 @@
import pandas as pd
from pandas._typing import ArrayLike
from pandas.core.construction import array, extract_array
-from pandas.core.ops import missing
+from pandas.core.ops.array_ops import comp_method_OBJECT_ARRAY, define_na_arithmetic_op
from pandas.core.ops.docstrings import (
_arith_doc_FRAME,
_flex_comp_doc_FRAME,
@@ -398,63 +392,6 @@ def mask_cmp_op(x, y, op):
return result
-def masked_arith_op(x, y, op):
- """
- If the given arithmetic operation fails, attempt it again on
- only the non-null elements of the input array(s).
-
- Parameters
- ----------
- x : np.ndarray
- y : np.ndarray, Series, Index
- op : binary operator
- """
- # For Series `x` is 1D so ravel() is a no-op; calling it anyway makes
- # the logic valid for both Series and DataFrame ops.
- xrav = x.ravel()
- assert isinstance(x, np.ndarray), type(x)
- if isinstance(y, np.ndarray):
- dtype = find_common_type([x.dtype, y.dtype])
- result = np.empty(x.size, dtype=dtype)
-
- # PeriodIndex.ravel() returns int64 dtype, so we have
- # to work around that case. See GH#19956
- yrav = y if is_period_dtype(y) else y.ravel()
- mask = notna(xrav) & notna(yrav)
-
- if yrav.shape != mask.shape:
- # FIXME: GH#5284, GH#5035, GH#19448
- # Without specifically raising here we get mismatched
- # errors in Py3 (TypeError) vs Py2 (ValueError)
- # Note: Only = an issue in DataFrame case
- raise ValueError("Cannot broadcast operands together.")
-
- if mask.any():
- with np.errstate(all="ignore"):
- result[mask] = op(xrav[mask], yrav[mask])
-
- else:
- assert is_scalar(y), type(y)
- assert isinstance(x, np.ndarray), type(x)
- # mask is only meaningful for x
- result = np.empty(x.size, dtype=x.dtype)
- mask = notna(xrav)
-
- # 1 ** np.nan is 1. So we have to unmask those.
- if op == pow:
- mask = np.where(x == 1, False, mask)
- elif op == rpow:
- mask = np.where(y == 1, False, mask)
-
- if mask.any():
- with np.errstate(all="ignore"):
- result[mask] = op(xrav[mask], y)
-
- result, changed = maybe_upcast_putmask(result, ~mask, np.nan)
- result = result.reshape(x.shape) # 2D compat
- return result
-
-
# -----------------------------------------------------------------------------
# Dispatch logic
@@ -673,33 +610,7 @@ def _arith_method_SERIES(cls, op, special):
_construct_divmod_result if op in [divmod, rdivmod] else _construct_result
)
- def na_op(x, y):
- """
- Return the result of evaluating op on the passed in values.
-
- If native types are not compatible, try coersion to object dtype.
-
- Parameters
- ----------
- x : array-like
- y : array-like or scalar
-
- Returns
- -------
- array-like
-
- Raises
- ------
- TypeError : invalid operation
- """
- import pandas.core.computation.expressions as expressions
-
- try:
- result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
- except TypeError:
- result = masked_arith_op(x, y, op)
-
- return missing.dispatch_fill_zeros(op, x, y, result)
+ na_op = define_na_arithmetic_op(op, str_rep, eval_kwargs)
def wrapper(left, right):
if isinstance(right, ABCDataFrame):
@@ -735,22 +646,6 @@ def wrapper(left, right):
return wrapper
-def _comp_method_OBJECT_ARRAY(op, x, y):
- if isinstance(y, list):
- y = construct_1d_object_array_from_listlike(y)
- if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
- if not is_object_dtype(y.dtype):
- y = y.astype(np.object_)
-
- if isinstance(y, (ABCSeries, ABCIndex)):
- y = y.values
-
- result = libops.vec_compare(x, y, op)
- else:
- result = libops.scalar_compare(x, y, op)
- return result
-
-
def _comp_method_SERIES(cls, op, special):
"""
Wrapper function for Series arithmetic operations, to avoid
@@ -764,7 +659,7 @@ def na_op(x, y):
# Extension Dtypes are not called here
if is_object_dtype(x.dtype):
- result = _comp_method_OBJECT_ARRAY(op, x, y)
+ result = comp_method_OBJECT_ARRAY(op, x, y)
elif is_datetimelike_v_numeric(x, y):
return invalid_comparison(x, y, op)
@@ -1091,15 +986,7 @@ def _arith_method_FRAME(cls, op, special):
eval_kwargs = _gen_eval_kwargs(op_name)
default_axis = _get_frame_op_default_axis(op_name)
- def na_op(x, y):
- import pandas.core.computation.expressions as expressions
-
- try:
- result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
- except TypeError:
- result = masked_arith_op(x, y, op)
-
- return missing.dispatch_fill_zeros(op, x, y, result)
+ na_op = define_na_arithmetic_op(op, str_rep, eval_kwargs)
if op_name in _op_descriptions:
# i.e. include "add" but not "__add__"
diff --git a/pandas/core/ops/array_ops.py b/pandas/core/ops/array_ops.py
new file mode 100644
index 0000000000000..a3bfb2e10bb66
--- /dev/null
+++ b/pandas/core/ops/array_ops.py
@@ -0,0 +1,127 @@
+"""
+Functions for arithmetic and comparison operations on NumPy arrays and
+ExtensionArrays.
+"""
+import numpy as np
+
+from pandas._libs import ops as libops
+
+from pandas.core.dtypes.cast import (
+ construct_1d_object_array_from_listlike,
+ find_common_type,
+ maybe_upcast_putmask,
+)
+from pandas.core.dtypes.common import is_object_dtype, is_period_dtype, is_scalar
+from pandas.core.dtypes.generic import ABCIndex, ABCSeries
+from pandas.core.dtypes.missing import notna
+
+from pandas.core.ops import missing
+from pandas.core.ops.roperator import rpow
+
+
+def comp_method_OBJECT_ARRAY(op, x, y):
+ if isinstance(y, list):
+ y = construct_1d_object_array_from_listlike(y)
+
+ # TODO: Should the checks below be ABCIndexClass?
+ if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
+ # TODO: should this be ABCIndexClass??
+ if not is_object_dtype(y.dtype):
+ y = y.astype(np.object_)
+
+ if isinstance(y, (ABCSeries, ABCIndex)):
+ y = y.values
+
+ result = libops.vec_compare(x, y, op)
+ else:
+ result = libops.scalar_compare(x, y, op)
+ return result
+
+
+def masked_arith_op(x, y, op):
+ """
+ If the given arithmetic operation fails, attempt it again on
+ only the non-null elements of the input array(s).
+
+ Parameters
+ ----------
+ x : np.ndarray
+ y : np.ndarray, Series, Index
+ op : binary operator
+ """
+ # For Series `x` is 1D so ravel() is a no-op; calling it anyway makes
+ # the logic valid for both Series and DataFrame ops.
+ xrav = x.ravel()
+ assert isinstance(x, np.ndarray), type(x)
+ if isinstance(y, np.ndarray):
+ dtype = find_common_type([x.dtype, y.dtype])
+ result = np.empty(x.size, dtype=dtype)
+
+ # PeriodIndex.ravel() returns int64 dtype, so we have
+ # to work around that case. See GH#19956
+ yrav = y if is_period_dtype(y) else y.ravel()
+ mask = notna(xrav) & notna(yrav)
+
+ if yrav.shape != mask.shape:
+ # FIXME: GH#5284, GH#5035, GH#19448
+ # Without specifically raising here we get mismatched
+ # errors in Py3 (TypeError) vs Py2 (ValueError)
+ # Note: Only = an issue in DataFrame case
+ raise ValueError("Cannot broadcast operands together.")
+
+ if mask.any():
+ with np.errstate(all="ignore"):
+ result[mask] = op(xrav[mask], yrav[mask])
+
+ else:
+ assert is_scalar(y), type(y)
+ assert isinstance(x, np.ndarray), type(x)
+ # mask is only meaningful for x
+ result = np.empty(x.size, dtype=x.dtype)
+ mask = notna(xrav)
+
+ # 1 ** np.nan is 1. So we have to unmask those.
+ if op == pow:
+ mask = np.where(x == 1, False, mask)
+ elif op == rpow:
+ mask = np.where(y == 1, False, mask)
+
+ if mask.any():
+ with np.errstate(all="ignore"):
+ result[mask] = op(xrav[mask], y)
+
+ result, changed = maybe_upcast_putmask(result, ~mask, np.nan)
+ result = result.reshape(x.shape) # 2D compat
+ return result
+
+
+def define_na_arithmetic_op(op, str_rep, eval_kwargs):
+ def na_op(x, y):
+ """
+ Return the result of evaluating op on the passed in values.
+
+ If native types are not compatible, try coersion to object dtype.
+
+ Parameters
+ ----------
+ x : array-like
+ y : array-like or scalar
+
+ Returns
+ -------
+ array-like
+
+ Raises
+ ------
+ TypeError : invalid operation
+ """
+ import pandas.core.computation.expressions as expressions
+
+ try:
+ result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
+ except TypeError:
+ result = masked_arith_op(x, y, op)
+
+ return missing.dispatch_fill_zeros(op, x, y, result)
+
+ return na_op
| This begins the process of separating the (ndarray|EA)-specific parts of Series ops out from the Series ops themselves. Eventually these will a) become the op implementations for PandasArray, and b) be used block-wise instead of column-wise for DataFrame ops. | https://api.github.com/repos/pandas-dev/pandas/pulls/27936 | 2019-08-15T15:44:38Z | 2019-08-16T12:12:28Z | 2019-08-16T12:12:28Z | 2019-08-16T13:56:59Z |
BUG: set_precision format fixed (#13257) | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 7692651db840e..8fe3320b29000 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -354,6 +354,7 @@ I/O
- Bug in :func:`read_hdf` closing stores that it didn't open when Exceptions are raised (:issue:`28699`)
- Bug in :meth:`DataFrame.read_json` where using ``orient="index"`` would not maintain the order (:issue:`28557`)
- Bug in :meth:`DataFrame.to_html` where the length of the ``formatters`` argument was not verified (:issue:`28469`)
+- Bug in :meth:`pandas.io.formats.style.Styler` formatting for floating values not displaying decimals correctly (:issue:`13257`)
Plotting
^^^^^^^^
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index 6b98eaca9dacc..0200b78e02fd2 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -156,7 +156,8 @@ def __init__(
def default_display_func(x):
if is_float(x):
- return "{:>.{precision}g}".format(x, precision=self.precision)
+ display_format = "{0:.{precision}f}".format(x, precision=self.precision)
+ return display_format
else:
return x
diff --git a/pandas/tests/io/formats/test_style.py b/pandas/tests/io/formats/test_style.py
index 61c163d2cdaac..0f1402d7da389 100644
--- a/pandas/tests/io/formats/test_style.py
+++ b/pandas/tests/io/formats/test_style.py
@@ -1157,20 +1157,43 @@ def test_display_format_raises(self):
with pytest.raises(TypeError):
df.style.format(True)
+ def test_display_set_precision(self):
+ # Issue #13257
+ df = pd.DataFrame(data=[[1.0, 2.0090], [3.2121, 4.566]], columns=["a", "b"])
+ s = Styler(df)
+
+ ctx = s.set_precision(1)._translate()
+
+ assert s.precision == 1
+ assert ctx["body"][0][1]["display_value"] == "1.0"
+ assert ctx["body"][0][2]["display_value"] == "2.0"
+ assert ctx["body"][1][1]["display_value"] == "3.2"
+ assert ctx["body"][1][2]["display_value"] == "4.6"
+
+ ctx = s.set_precision(2)._translate()
+ assert s.precision == 2
+ assert ctx["body"][0][1]["display_value"] == "1.00"
+ assert ctx["body"][0][2]["display_value"] == "2.01"
+ assert ctx["body"][1][1]["display_value"] == "3.21"
+ assert ctx["body"][1][2]["display_value"] == "4.57"
+
+ ctx = s.set_precision(3)._translate()
+ assert s.precision == 3
+ assert ctx["body"][0][1]["display_value"] == "1.000"
+ assert ctx["body"][0][2]["display_value"] == "2.009"
+ assert ctx["body"][1][1]["display_value"] == "3.212"
+ assert ctx["body"][1][2]["display_value"] == "4.566"
+
def test_display_subset(self):
df = pd.DataFrame([[0.1234, 0.1234], [1.1234, 1.1234]], columns=["a", "b"])
ctx = df.style.format(
{"a": "{:0.1f}", "b": "{0:.2%}"}, subset=pd.IndexSlice[0, :]
)._translate()
expected = "0.1"
- assert ctx["body"][0][1]["display_value"] == expected
- assert ctx["body"][1][1]["display_value"] == "1.1234"
- assert ctx["body"][0][2]["display_value"] == "12.34%"
-
- raw_11 = "1.1234"
- ctx = df.style.format("{:0.1f}", subset=pd.IndexSlice[0, :])._translate()
+ raw_11 = "1.123400"
assert ctx["body"][0][1]["display_value"] == expected
assert ctx["body"][1][1]["display_value"] == raw_11
+ assert ctx["body"][0][2]["display_value"] == "12.34%"
ctx = df.style.format("{:0.1f}", subset=pd.IndexSlice[0, :])._translate()
assert ctx["body"][0][1]["display_value"] == expected
@@ -1178,7 +1201,7 @@ def test_display_subset(self):
ctx = df.style.format("{:0.1f}", subset=pd.IndexSlice["a"])._translate()
assert ctx["body"][0][1]["display_value"] == expected
- assert ctx["body"][0][2]["display_value"] == "0.1234"
+ assert ctx["body"][0][2]["display_value"] == "0.123400"
ctx = df.style.format("{:0.1f}", subset=pd.IndexSlice[0, "a"])._translate()
assert ctx["body"][0][1]["display_value"] == expected
@@ -1189,8 +1212,8 @@ def test_display_subset(self):
)._translate()
assert ctx["body"][0][1]["display_value"] == expected
assert ctx["body"][1][1]["display_value"] == "1.1"
- assert ctx["body"][0][2]["display_value"] == "0.1234"
- assert ctx["body"][1][2]["display_value"] == "1.1234"
+ assert ctx["body"][0][2]["display_value"] == "0.123400"
+ assert ctx["body"][1][2]["display_value"] == raw_11
def test_display_dict(self):
df = pd.DataFrame([[0.1234, 0.1234], [1.1234, 1.1234]], columns=["a", "b"])
| - [x] closes #13257
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I've tried to fix the set_precision format, does that fix it?
### INPUT -> df.style.set_precision(2)
| A | B |
| --- | --- |
| 1 | 2.0090 |
| 3.2121 | 4.566 |
### OUTPUT
| A | B |
| --- | --- |
| 1 | 2.01 |
| 3.21 | 4.57 |
@vinigl
@samuelgranato
| https://api.github.com/repos/pandas-dev/pandas/pulls/27934 | 2019-08-15T14:32:22Z | 2019-10-22T01:53:38Z | 2019-10-22T01:53:38Z | 2019-12-26T14:51:04Z |
TST: Asserts all types are caterogical in function fillna with correct error message | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 21f1fa7ddec1f..751829f11570f 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -25,7 +25,6 @@ Bug fixes
Categorical
^^^^^^^^^^^
--
-
-
diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index aeed3668fe774..c4f1317ad0c67 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -87,6 +87,7 @@ Bug fixes
Categorical
^^^^^^^^^^^
+- Added test to assert the :func:`fillna` raises the correct ValueError message when the value isn't a value from categories (:issue:`13628`)
-
-
diff --git a/pandas/tests/arrays/categorical/test_missing.py b/pandas/tests/arrays/categorical/test_missing.py
index 1b62479530d24..3037ac79cd592 100644
--- a/pandas/tests/arrays/categorical/test_missing.py
+++ b/pandas/tests/arrays/categorical/test_missing.py
@@ -5,7 +5,7 @@
from pandas.core.dtypes.dtypes import CategoricalDtype
-from pandas import Categorical, Index, isna
+from pandas import Categorical, Index, Series, isna
import pandas.util.testing as tm
@@ -59,11 +59,13 @@ def test_set_item_nan(self):
),
(dict(), "Must specify a fill 'value' or 'method'."),
(dict(method="bad"), "Invalid fill method. Expecting .* bad"),
+ (dict(value=Series([1, 2, 3, 4, "a"])), "fill value must be in categories"),
],
)
def test_fillna_raises(self, fillna_kwargs, msg):
# https://github.com/pandas-dev/pandas/issues/19682
- cat = Categorical([1, 2, 3])
+ # https://github.com/pandas-dev/pandas/issues/13628
+ cat = Categorical([1, 2, 3, None, None])
with pytest.raises(ValueError, match=msg):
cat.fillna(**fillna_kwargs)
| - [x] closes #13628
- [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/27933 | 2019-08-15T14:12:40Z | 2019-08-16T12:14:43Z | 2019-08-16T12:14:43Z | 2019-10-29T12:12:31Z |
BUG: Ensure that fill_na in Categorical only replaces null values | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 21f1fa7ddec1f..647bc750907ce 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -25,7 +25,7 @@ Bug fixes
Categorical
^^^^^^^^^^^
--
+- Bug in :meth:`Categorical.fillna` would replace all values, not just those that are ``NaN`` (:issue:`26215`)
-
-
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index bbbeb812d1fe9..a895da6184eeb 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1840,8 +1840,8 @@ def fillna(self, value=None, method=None, limit=None):
raise ValueError("fill value must be in categories")
values_codes = _get_codes_for_values(value, self.categories)
- indexer = np.where(values_codes != -1)
- codes[indexer] = values_codes[values_codes != -1]
+ indexer = np.where(codes == -1)
+ codes[indexer] = values_codes[indexer]
# If value is not a dict or Series it should be a scalar
elif is_hashable(value):
diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py
index f1b84acf68755..ddd2c566f4cda 100644
--- a/pandas/tests/series/test_missing.py
+++ b/pandas/tests/series/test_missing.py
@@ -578,6 +578,28 @@ def test_fillna_categorical(self, fill_value, expected_output):
exp = Series(Categorical(expected_output, categories=["a", "b"]))
tm.assert_series_equal(s.fillna(fill_value), exp)
+ @pytest.mark.parametrize(
+ "fill_value, expected_output",
+ [
+ (Series(["a", "b", "c", "d", "e"]), ["a", "b", "b", "d", "e"]),
+ (Series(["b", "d", "a", "d", "a"]), ["a", "d", "b", "d", "a"]),
+ (
+ Series(
+ Categorical(
+ ["b", "d", "a", "d", "a"], categories=["b", "c", "d", "e", "a"]
+ )
+ ),
+ ["a", "d", "b", "d", "a"],
+ ),
+ ],
+ )
+ def test_fillna_categorical_with_new_categories(self, fill_value, expected_output):
+ # GH 26215
+ data = ["a", np.nan, "b", np.nan, np.nan]
+ s = Series(Categorical(data, categories=["a", "b", "c", "d", "e"]))
+ exp = Series(Categorical(expected_output, categories=["a", "b", "c", "d", "e"]))
+ tm.assert_series_equal(s.fillna(fill_value), exp)
+
def test_fillna_categorical_raise(self):
data = ["a", np.nan, "b", np.nan, np.nan]
s = Series(Categorical(data, categories=["a", "b"]))
| - [x] closes #26215
- [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/27932 | 2019-08-15T13:27:46Z | 2019-08-16T11:22:20Z | 2019-08-16T11:22:19Z | 2019-08-22T08:38:45Z |
API/DEPR: Change default skipna behaviour + deprecate numeric_only in Categorical.min and max | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 3f8d9d3916797..ee5098749105f 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -260,6 +260,26 @@ The following methods now also correctly output values for unobserved categories
df.groupby(["cat_1", "cat_2"], observed=False)["value"].count()
+By default :meth:`Categorical.min` now returns the minimum instead of np.nan
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+When :class:`Categorical` contains ``np.nan``,
+:meth:`Categorical.min` no longer return ``np.nan`` by default (skipna=True) (:issue:`25303`)
+
+*pandas 0.25.x*
+
+.. code-block:: ipython
+
+ In [1]: pd.Categorical([1, 2, np.nan], ordered=True).min()
+ Out[1]: nan
+
+
+*pandas 1.0.0*
+
+.. ipython:: python
+
+ pd.Categorical([1, 2, np.nan], ordered=True).min()
+
.. _whatsnew_1000.api_breaking.deps:
Increased minimum versions for dependencies
@@ -366,6 +386,8 @@ Deprecations
- :func:`is_extension_type` is deprecated, :func:`is_extension_array_dtype` should be used instead (:issue:`29457`)
- :func:`eval` keyword argument "truediv" is deprecated and will be removed in a future version (:issue:`29812`)
- :meth:`Categorical.take_nd` is deprecated, use :meth:`Categorical.take` instead (:issue:`27745`)
+- The parameter ``numeric_only`` of :meth:`Categorical.min` and :meth:`Categorical.max` is deprecated and replaced with ``skipna`` (:issue:`25303`)
+-
.. _whatsnew_1000.prior_deprecations:
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index ec1f5d2d6214c..6a7a225845440 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -2157,7 +2157,8 @@ def _reduce(self, name, axis=0, **kwargs):
raise TypeError(f"Categorical cannot perform the operation {name}")
return func(**kwargs)
- def min(self, numeric_only=None, **kwargs):
+ @deprecate_kwarg(old_arg_name="numeric_only", new_arg_name="skipna")
+ def min(self, skipna=True):
"""
The minimum value of the object.
@@ -2173,17 +2174,18 @@ def min(self, numeric_only=None, **kwargs):
min : the minimum of this `Categorical`
"""
self.check_for_ordered("min")
- if numeric_only:
- good = self._codes != -1
- pointer = self._codes[good].min(**kwargs)
- else:
- pointer = self._codes.min(**kwargs)
- if pointer == -1:
- return np.nan
+ good = self._codes != -1
+ if not good.all():
+ if skipna:
+ pointer = self._codes[good].min()
+ else:
+ return np.nan
else:
- return self.categories[pointer]
+ pointer = self._codes.min()
+ return self.categories[pointer]
- def max(self, numeric_only=None, **kwargs):
+ @deprecate_kwarg(old_arg_name="numeric_only", new_arg_name="skipna")
+ def max(self, skipna=True):
"""
The maximum value of the object.
@@ -2199,15 +2201,15 @@ def max(self, numeric_only=None, **kwargs):
max : the maximum of this `Categorical`
"""
self.check_for_ordered("max")
- if numeric_only:
- good = self._codes != -1
- pointer = self._codes[good].max(**kwargs)
- else:
- pointer = self._codes.max(**kwargs)
- if pointer == -1:
- return np.nan
+ good = self._codes != -1
+ if not good.all():
+ if skipna:
+ pointer = self._codes[good].max()
+ else:
+ return np.nan
else:
- return self.categories[pointer]
+ pointer = self._codes.max()
+ return self.categories[pointer]
def mode(self, dropna=True):
"""
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 56039605651ac..01a8231f5fd02 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -3869,9 +3869,7 @@ def _reduce(
self._get_axis_number(axis)
if isinstance(delegate, Categorical):
- # TODO deprecate numeric_only argument for Categorical and use
- # skipna as well, see GH25303
- return delegate._reduce(name, numeric_only=numeric_only, **kwds)
+ return delegate._reduce(name, skipna=skipna, **kwds)
elif isinstance(delegate, ExtensionArray):
# dispatch to ExtensionArray interface
return delegate._reduce(name, skipna=skipna, **kwds)
diff --git a/pandas/tests/arrays/categorical/test_analytics.py b/pandas/tests/arrays/categorical/test_analytics.py
index 279f1492d7dad..637a47eba0597 100644
--- a/pandas/tests/arrays/categorical/test_analytics.py
+++ b/pandas/tests/arrays/categorical/test_analytics.py
@@ -35,31 +35,43 @@ def test_min_max(self):
assert _min == "d"
assert _max == "a"
+ @pytest.mark.parametrize("skipna", [True, False])
+ def test_min_max_with_nan(self, skipna):
+ # GH 25303
cat = Categorical(
[np.nan, "b", "c", np.nan], categories=["d", "c", "b", "a"], ordered=True
)
- _min = cat.min()
- _max = cat.max()
- assert np.isnan(_min)
- assert _max == "b"
+ _min = cat.min(skipna=skipna)
+ _max = cat.max(skipna=skipna)
- _min = cat.min(numeric_only=True)
- assert _min == "c"
- _max = cat.max(numeric_only=True)
- assert _max == "b"
+ if skipna is False:
+ assert np.isnan(_min)
+ assert np.isnan(_max)
+ else:
+ assert _min == "c"
+ assert _max == "b"
cat = Categorical(
[np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
)
- _min = cat.min()
- _max = cat.max()
- assert np.isnan(_min)
- assert _max == 1
-
- _min = cat.min(numeric_only=True)
- assert _min == 2
- _max = cat.max(numeric_only=True)
- assert _max == 1
+ _min = cat.min(skipna=skipna)
+ _max = cat.max(skipna=skipna)
+
+ if skipna is False:
+ assert np.isnan(_min)
+ assert np.isnan(_max)
+ else:
+ assert _min == 2
+ assert _max == 1
+
+ @pytest.mark.parametrize("method", ["min", "max"])
+ def test_deprecate_numeric_only_min_max(self, method):
+ # GH 25303
+ cat = Categorical(
+ [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
+ )
+ with tm.assert_produces_warning(expected_warning=FutureWarning):
+ getattr(cat, method)(numeric_only=True)
@pytest.mark.parametrize(
"values,categories,exp_mode",
diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py
index b0ef0c58ca65a..80d148c919ab2 100644
--- a/pandas/tests/reductions/test_reductions.py
+++ b/pandas/tests/reductions/test_reductions.py
@@ -1043,7 +1043,7 @@ def test_min_max(self):
)
_min = cat.min()
_max = cat.max()
- assert np.isnan(_min)
+ assert _min == "c"
assert _max == "b"
cat = Series(
@@ -1053,30 +1053,24 @@ def test_min_max(self):
)
_min = cat.min()
_max = cat.max()
- assert np.isnan(_min)
+ assert _min == 2
assert _max == 1
- def test_min_max_numeric_only(self):
- # TODO deprecate numeric_only argument for Categorical and use
- # skipna as well, see GH25303
+ @pytest.mark.parametrize("skipna", [True, False])
+ def test_min_max_skipna(self, skipna):
+ # GH 25303
cat = Series(
Categorical(["a", "b", np.nan, "a"], categories=["b", "a"], ordered=True)
)
+ _min = cat.min(skipna=skipna)
+ _max = cat.max(skipna=skipna)
- _min = cat.min()
- _max = cat.max()
- assert np.isnan(_min)
- assert _max == "a"
-
- _min = cat.min(numeric_only=True)
- _max = cat.max(numeric_only=True)
- assert _min == "b"
- assert _max == "a"
-
- _min = cat.min(numeric_only=False)
- _max = cat.max(numeric_only=False)
- assert np.isnan(_min)
- assert _max == "a"
+ if skipna is True:
+ assert _min == "b"
+ assert _max == "a"
+ else:
+ assert np.isnan(_min)
+ assert np.isnan(_max)
class TestSeriesMode:
| - closes #25303 and follow up https://github.com/pandas-dev/pandas/pull/27801#discussion_r313075771
- 1 test added
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/27929 | 2019-08-15T10:33:36Z | 2019-12-02T10:37:12Z | 2019-12-02T10:37:12Z | 2023-12-11T23:49:50Z |
Assignment of column via .loc for numpy non-ns datetimes | diff --git a/doc/source/whatsnew/v0.25.2.rst b/doc/source/whatsnew/v0.25.2.rst
index 69f324211e5b2..de411ef63680a 100644
--- a/doc/source/whatsnew/v0.25.2.rst
+++ b/doc/source/whatsnew/v0.25.2.rst
@@ -52,7 +52,6 @@ Indexing
-
-
-
--
Missing
^^^^^^^
diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 77807a338d21f..2913d88fcd15f 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -170,6 +170,7 @@ Indexing
- Bug in assignment using a reverse slicer (:issue:`26939`)
- Bug in reindexing a :meth:`PeriodIndex` with another type of index that contained a `Period` (:issue:`28323`) (:issue:`28337`)
+- Fix assignment of column via `.loc` with numpy non-ns datetime type (:issue:`27395`)
Missing
^^^^^^^
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 749dd735e159d..ac9b57dc8d342 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -1029,7 +1029,10 @@ def maybe_cast_to_datetime(value, dtype, errors="raise"):
)
if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE):
- if dtype.name in ("datetime64", "datetime64[ns]"):
+
+ # pandas supports dtype whose granularity is less than [ns]
+ # e.g., [ps], [fs], [as]
+ if dtype <= np.dtype("M8[ns]"):
if dtype.name == "datetime64":
raise ValueError(msg.format(dtype=dtype.name))
dtype = _NS_DTYPE
@@ -1047,7 +1050,10 @@ def maybe_cast_to_datetime(value, dtype, errors="raise"):
value = [value]
elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE):
- if dtype.name in ("timedelta64", "timedelta64[ns]"):
+
+ # pandas supports dtype whose granularity is less than [ns]
+ # e.g., [ps], [fs], [as]
+ if dtype <= np.dtype("m8[ns]"):
if dtype.name == "timedelta64":
raise ValueError(msg.format(dtype=dtype.name))
dtype = _TD_DTYPE
diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index 9845b1ac3a4b9..35291efecd1ac 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -690,6 +690,28 @@ def test_loc_setitem_consistency_slice_column_len(self):
)
tm.assert_series_equal(df[("Respondent", "Duration")], expected)
+ @pytest.mark.parametrize("unit", ["Y", "M", "D", "h", "m", "s", "ms", "us"])
+ def test_loc_assign_non_ns_datetime(self, unit):
+ # GH 27395, non-ns dtype assignment via .loc should work
+ # and return the same result when using simple assignment
+ df = DataFrame(
+ {
+ "timestamp": [
+ np.datetime64("2017-02-11 12:41:29"),
+ np.datetime64("1991-11-07 04:22:37"),
+ ]
+ }
+ )
+
+ df.loc[:, unit] = df.loc[:, "timestamp"].values.astype(
+ "datetime64[{unit}]".format(unit=unit)
+ )
+ df["expected"] = df.loc[:, "timestamp"].values.astype(
+ "datetime64[{unit}]".format(unit=unit)
+ )
+ expected = Series(df.loc[:, "expected"], name=unit)
+ tm.assert_series_equal(df.loc[:, unit], expected)
+
def test_loc_setitem_frame(self):
df = self.frame_labels
| - [x] closes #27395
- [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/27928 | 2019-08-15T07:16:16Z | 2019-09-13T13:19:38Z | 2019-09-13T13:19:38Z | 2019-09-13T13:19:42Z |
Fix regression in .ix fallback with IntervalIndex | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index dfa216b1db56e..1cc0da8fcc7b3 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -85,6 +85,7 @@ Indexing
- Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']`` (:issue:`27516`)
- Break reference cycle involving :class:`Index` to allow garbage collection of :class:`Index` objects without running the GC. (:issue:`27585`)
- Fix regression in assigning values to a single column of a DataFrame with a ``MultiIndex`` columns (:issue:`27841`).
+- Fix regression in ``.ix`` fallback with an ``IntervalIndex`` (:issue:`27865`).
-
Missing
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index ea00737f776ee..7bb5e2fa3018d 100755
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -124,7 +124,7 @@ def __getitem__(self, key):
key = tuple(com.apply_if_callable(x, self.obj) for x in key)
try:
values = self.obj._get_value(*key)
- except (KeyError, TypeError, InvalidIndexError):
+ except (KeyError, TypeError, InvalidIndexError, AttributeError):
# TypeError occurs here if the key has non-hashable entries,
# generally slice or list.
# TODO(ix): most/all of the TypeError cases here are for ix,
@@ -132,6 +132,9 @@ def __getitem__(self, key):
# The InvalidIndexError is only catched for compatibility
# with geopandas, see
# https://github.com/pandas-dev/pandas/issues/27258
+ # TODO: The AttributeError is for IntervalIndex which
+ # incorrectly implements get_value, see
+ # https://github.com/pandas-dev/pandas/issues/27865
pass
else:
if is_scalar(values):
diff --git a/pandas/tests/indexing/test_ix.py b/pandas/tests/indexing/test_ix.py
index 45ccd8d1b8fb3..6029db8ed66f6 100644
--- a/pandas/tests/indexing/test_ix.py
+++ b/pandas/tests/indexing/test_ix.py
@@ -343,3 +343,13 @@ def test_ix_duplicate_returns_series(self):
r = df.ix[0.2, "a"]
e = df.loc[0.2, "a"]
tm.assert_series_equal(r, e)
+
+ def test_ix_intervalindex(self):
+ # https://github.com/pandas-dev/pandas/issues/27865
+ df = DataFrame(
+ np.random.randn(5, 2),
+ index=pd.IntervalIndex.from_breaks([-np.inf, 0, 1, 2, 3, np.inf]),
+ )
+ result = df.ix[0:2, 0]
+ expected = df.iloc[0:2, 0]
+ tm.assert_series_equal(result, expected)
| closes #27865
cc @jbrockmendel this is another one from limiting the catched exceptions in https://github.com/pandas-dev/pandas/pull/27223/files#diff-7489e290a23303c4db4f803011ecaf8eR121. But also this time it's again rather a bug in the code that raises this error.
For some reason, `DataFrame._get_value` for a DataFrame with an IntervalIndex raises an AttributeError. That's a bug in itself which should be solved (and probably is the cause of more bugs in other places where `_get_value` is used, eg in `.at`). But will open a separate issue for that. | https://api.github.com/repos/pandas-dev/pandas/pulls/27926 | 2019-08-15T06:54:38Z | 2019-08-19T19:26:21Z | 2019-08-19T19:26:21Z | 2019-08-20T06:47:37Z |
BUG: Help python csv engine read binary buffers | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 0be4ebc627b30..3ce558ca336c3 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -158,7 +158,7 @@ MultiIndex
I/O
^^^
--
+- :meth:`read_csv` now accepts binary mode file buffers when using the Python csv engine (:issue:`23779`)
-
Plotting
diff --git a/pandas/io/common.py b/pandas/io/common.py
index ac57cef372399..26b68dda7b464 100644
--- a/pandas/io/common.py
+++ b/pandas/io/common.py
@@ -5,7 +5,7 @@
import csv
import gzip
from http.client import HTTPException # noqa
-from io import BytesIO
+from io import BufferedIOBase, BytesIO
import mmap
import os
import pathlib
@@ -344,9 +344,9 @@ def _get_handle(
try:
from s3fs import S3File
- need_text_wrapping = (BytesIO, S3File)
+ need_text_wrapping = (BufferedIOBase, S3File)
except ImportError:
- need_text_wrapping = (BytesIO,)
+ need_text_wrapping = BufferedIOBase
handles = list()
f = path_or_buf
@@ -422,8 +422,10 @@ def _get_handle(
if is_text and (compression or isinstance(f, need_text_wrapping)):
from io import TextIOWrapper
- f = TextIOWrapper(f, encoding=encoding, newline="")
- handles.append(f)
+ g = TextIOWrapper(f, encoding=encoding, newline="")
+ if not isinstance(f, BufferedIOBase):
+ handles.append(g)
+ f = g
if memory_map and hasattr(f, "fileno"):
try:
diff --git a/pandas/tests/io/parser/test_common.py b/pandas/tests/io/parser/test_common.py
index b94d5cd497ccf..e5366a8357adb 100644
--- a/pandas/tests/io/parser/test_common.py
+++ b/pandas/tests/io/parser/test_common.py
@@ -2020,9 +2020,34 @@ def test_file_handles_with_open(all_parsers, csv1):
# Don't close user provided file handles.
parser = all_parsers
- with open(csv1, "r") as f:
- parser.read_csv(f)
- assert not f.closed
+ for mode in ["r", "rb"]:
+ with open(csv1, mode) as f:
+ parser.read_csv(f)
+ assert not f.closed
+
+
+@pytest.mark.parametrize(
+ "fname,encoding",
+ [
+ ("test1.csv", "utf-8"),
+ ("unicode_series.csv", "latin-1"),
+ ("sauron.SHIFT_JIS.csv", "shiftjis"),
+ ],
+)
+def test_binary_mode_file_buffers(all_parsers, csv_dir_path, fname, encoding):
+ # gh-23779: Python csv engine shouldn't error on files opened in binary.
+ parser = all_parsers
+
+ fpath = os.path.join(csv_dir_path, fname)
+ expected = parser.read_csv(fpath, encoding=encoding)
+
+ with open(fpath, mode="r", encoding=encoding) as fa:
+ result = parser.read_csv(fa)
+ tm.assert_frame_equal(expected, result)
+
+ with open(fpath, mode="rb") as fb:
+ result = parser.read_csv(fb, encoding=encoding)
+ tm.assert_frame_equal(expected, result)
def test_invalid_file_buffer_class(all_parsers):
| The file buffer given to read_csv could have been opened in
binary mode, but the python csv reader errors on binary buffers.
- [x] closes #23779
- [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/27925 | 2019-08-15T04:15:34Z | 2019-08-19T16:23:41Z | 2019-08-19T16:23:41Z | 2019-08-19T16:23:41Z |
CLN: simplify comparison method, docstring cleanups | diff --git a/pandas/core/computation/common.py b/pandas/core/computation/common.py
index b8e212fd2a32e..2a4277e4e0a20 100644
--- a/pandas/core/computation/common.py
+++ b/pandas/core/computation/common.py
@@ -2,7 +2,7 @@
import numpy as np
-import pandas as pd
+from pandas._config import get_option
# A token value Python's tokenizer probably will never use.
_BACKTICK_QUOTED_STRING = 100
@@ -11,7 +11,7 @@
def _ensure_decoded(s):
""" if we have bytes, decode them to unicode """
if isinstance(s, (np.bytes_, bytes)):
- s = s.decode(pd.get_option("display.encoding"))
+ s = s.decode(get_option("display.encoding"))
return s
diff --git a/pandas/core/computation/expressions.py b/pandas/core/computation/expressions.py
index d9dc194d484ae..0ecf56cf6fe96 100644
--- a/pandas/core/computation/expressions.py
+++ b/pandas/core/computation/expressions.py
@@ -203,17 +203,18 @@ def _bool_arith_check(
def evaluate(op, op_str, a, b, use_numexpr=True, **eval_kwargs):
- """ evaluate and return the expression of the op on a and b
-
- Parameters
- ----------
-
- op : the actual operand
- op_str: the string version of the op
- a : left operand
- b : right operand
- use_numexpr : whether to try to use numexpr (default True)
- """
+ """
+ Evaluate and return the expression of the op on a and b.
+
+ Parameters
+ ----------
+ op : the actual operand
+ op_str : the string version of the op
+ a : left operand
+ b : right operand
+ use_numexpr : bool, default True
+ Whether to try to use numexpr.
+ """
use_numexpr = use_numexpr and _bool_arith_check(op_str, a, b)
if use_numexpr:
@@ -222,16 +223,17 @@ def evaluate(op, op_str, a, b, use_numexpr=True, **eval_kwargs):
def where(cond, a, b, use_numexpr=True):
- """ evaluate the where condition cond on a and b
-
- Parameters
- ----------
-
- cond : a boolean array
- a : return if cond is True
- b : return if cond is False
- use_numexpr : whether to try to use numexpr (default True)
- """
+ """
+ Evaluate the where condition cond on a and b
+
+ Parameters
+ ----------
+ cond : ndarray[bool]
+ a : return if cond is True
+ b : return if cond is False
+ use_numexpr : bool, default True
+ Whether to try to use numexpr.
+ """
if use_numexpr:
return _where(cond, a, b)
diff --git a/pandas/core/computation/ops.py b/pandas/core/computation/ops.py
index 2bf09a553ce18..b49220ae701bc 100644
--- a/pandas/core/computation/ops.py
+++ b/pandas/core/computation/ops.py
@@ -51,8 +51,9 @@
class UndefinedVariableError(NameError):
-
- """NameError subclass for local variables."""
+ """
+ NameError subclass for local variables.
+ """
def __init__(self, name, is_local):
if is_local:
@@ -191,8 +192,8 @@ def __repr__(self):
class Op:
-
- """Hold an operator of arbitrary arity
+ """
+ Hold an operator of arbitrary arity.
"""
def __init__(self, op, operands, *args, **kwargs):
@@ -204,8 +205,9 @@ def __iter__(self):
return iter(self.operands)
def __repr__(self):
- """Print a generic n-ary operator and its operands using infix
- notation"""
+ """
+ Print a generic n-ary operator and its operands using infix notation.
+ """
# recurse over the operands
parened = ("({0})".format(pprint_thing(opr)) for opr in self.operands)
return pprint_thing(" {0} ".format(self.op).join(parened))
@@ -296,7 +298,8 @@ def _not_in(x, y):
def _cast_inplace(terms, acceptable_dtypes, dtype):
- """Cast an expression inplace.
+ """
+ Cast an expression inplace.
Parameters
----------
@@ -304,7 +307,6 @@ def _cast_inplace(terms, acceptable_dtypes, dtype):
The expression that should cast.
acceptable_dtypes : list of acceptable numpy.dtype
Will not cast if term's dtype in this list.
-
dtype : str or numpy.dtype
The dtype to cast to.
"""
@@ -325,8 +327,8 @@ def is_term(obj):
class BinOp(Op):
-
- """Hold a binary operator and its operands
+ """
+ Hold a binary operator and its operands.
Parameters
----------
@@ -355,7 +357,8 @@ def __init__(self, op, lhs, rhs, **kwargs):
)
def __call__(self, env):
- """Recursively evaluate an expression in Python space.
+ """
+ Recursively evaluate an expression in Python space.
Parameters
----------
@@ -377,7 +380,8 @@ def __call__(self, env):
return self.func(left, right)
def evaluate(self, env, engine, parser, term_type, eval_in_python):
- """Evaluate a binary operation *before* being passed to the engine.
+ """
+ Evaluate a binary operation *before* being passed to the engine.
Parameters
----------
diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index 843f12c20b07b..26672593f98fb 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -32,7 +32,6 @@
is_period_dtype,
is_scalar,
is_timedelta64_dtype,
- needs_i8_conversion,
)
from pandas.core.dtypes.generic import (
ABCDataFrame,
@@ -758,17 +757,12 @@ def _comp_method_SERIES(cls, op, special):
code duplication.
"""
op_name = _get_op_name(op, special)
- masker = _gen_eval_kwargs(op_name).get("masker", False)
def na_op(x, y):
# TODO:
- # should have guarantess on what x, y can be type-wise
+ # should have guarantees on what x, y can be type-wise
# Extension Dtypes are not called here
- # Checking that cases that were once handled here are no longer
- # reachable.
- assert not (is_categorical_dtype(y) and not is_scalar(y))
-
if is_object_dtype(x.dtype):
result = _comp_method_OBJECT_ARRAY(op, x, y)
@@ -776,32 +770,11 @@ def na_op(x, y):
return invalid_comparison(x, y, op)
else:
-
- # we want to compare like types
- # we only want to convert to integer like if
- # we are not NotImplemented, otherwise
- # we would allow datetime64 (but viewed as i8) against
- # integer comparisons
-
- # we have a datetime/timedelta and may need to convert
- assert not needs_i8_conversion(x)
- mask = None
- if not is_scalar(y) and needs_i8_conversion(y):
- mask = isna(x) | isna(y)
- y = y.view("i8")
- x = x.view("i8")
-
- method = getattr(x, op_name, None)
- if method is not None:
- with np.errstate(all="ignore"):
- result = method(y)
- if result is NotImplemented:
- return invalid_comparison(x, y, op)
- else:
- result = op(x, y)
-
- if mask is not None and mask.any():
- result[mask] = masker
+ method = getattr(x, op_name)
+ with np.errstate(all="ignore"):
+ result = method(y)
+ if result is NotImplemented:
+ return invalid_comparison(x, y, op)
return result
| https://api.github.com/repos/pandas-dev/pandas/pulls/27923 | 2019-08-15T02:41:41Z | 2019-08-15T12:44:11Z | 2019-08-15T12:44:11Z | 2019-08-15T14:20:33Z | |
CLN: Remove incorrect check, comment, rename | diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index 0de28f0a4a8b3..7e03b9544ee72 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -730,9 +730,12 @@ def wrapper(self, other, axis=None):
)
result = self._constructor(res_values, index=self.index)
- # rename is needed in case res_name is None and result.name
- # is not.
- return finalizer(result).rename(res_name)
+ result = finalizer(result)
+
+ # Set the result's name after finalizer is called because finalizer
+ # would set it back to self.name
+ result.name = res_name
+ return result
wrapper.__name__ = op_name
return wrapper
| cc @jorisvandenbossche | https://api.github.com/repos/pandas-dev/pandas/pulls/27922 | 2019-08-14T21:47:59Z | 2019-08-20T06:35:43Z | 2019-08-20T06:35:43Z | 2019-08-20T14:01:01Z |
BUG: Lambda function returns KeyError in DataFrameGroupBy.agg | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 2bfc09e52c68b..a436dcbf60ac4 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -177,6 +177,7 @@ Groupby/resample/rolling
-
-
- Bug in :meth:`DataFrame.groupby` not offering selection by column name when ``axis=1`` (:issue:`27614`)
+- Bug in :meth:`DataFrameGroupby.agg` not able to use lambda function with named aggregation (:issue:`27519`)
Reshaping
^^^^^^^^^
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index ea2bd22cccc3d..e3bc90f49a08f 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -268,7 +268,9 @@ def aggregate(self, func, *args, **kwargs):
result.index = np.arange(len(result))
if relabeling:
- result = result[order]
+
+ # used reordered index of columns
+ result = result.iloc[:, order]
result.columns = columns
return result._convert(datetime=True)
@@ -1731,8 +1733,8 @@ def _normalize_keyword_aggregation(kwargs):
The transformed kwargs.
columns : List[str]
The user-provided keys.
- order : List[Tuple[str, str]]
- Pairs of the input and output column names.
+ col_idx_order : List[int]
+ List of columns indices.
Examples
--------
@@ -1759,7 +1761,39 @@ def _normalize_keyword_aggregation(kwargs):
else:
aggspec[column] = [aggfunc]
order.append((column, com.get_callable_name(aggfunc) or aggfunc))
- return aggspec, columns, order
+
+ # uniquify aggfunc name if duplicated in order list
+ uniquified_order = _make_unique(order)
+
+ # GH 25719, due to aggspec will change the order of assigned columns in aggregation
+ # uniquified_aggspec will store uniquified order list and will compare it with order
+ # based on index
+ aggspec_order = [
+ (column, com.get_callable_name(aggfunc) or aggfunc)
+ for column, aggfuncs in aggspec.items()
+ for aggfunc in aggfuncs
+ ]
+ uniquified_aggspec = _make_unique(aggspec_order)
+
+ # get the new indice of columns by comparison
+ col_idx_order = Index(uniquified_aggspec).get_indexer(uniquified_order)
+ return aggspec, columns, col_idx_order
+
+
+def _make_unique(seq):
+ """Uniquify aggfunc name of the pairs in the order list
+
+ Examples:
+ --------
+ >>> _make_unique([('a', '<lambda>'), ('a', '<lambda>'), ('b', '<lambda>')])
+ [('a', '<lambda>_0'), ('a', '<lambda>_1'), ('b', '<lambda>')]
+ """
+ return [
+ (pair[0], "_".join([pair[1], str(seq[:i].count(pair))]))
+ if seq.count(pair) > 1
+ else pair
+ for i, pair in enumerate(seq)
+ ]
# TODO: Can't use, because mypy doesn't like us setting __name__
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py
index 52d4fa76bf879..aa80c461a00e7 100644
--- a/pandas/tests/groupby/aggregate/test_aggregate.py
+++ b/pandas/tests/groupby/aggregate/test_aggregate.py
@@ -10,7 +10,7 @@
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, compat, concat
from pandas.core.base import SpecificationError
-from pandas.core.groupby.generic import _maybe_mangle_lambdas
+from pandas.core.groupby.generic import _make_unique, _maybe_mangle_lambdas
from pandas.core.groupby.grouper import Grouping
import pandas.util.testing as tm
@@ -560,3 +560,150 @@ def test_with_kwargs(self):
result = pd.Series([1, 2]).groupby([0, 0]).agg([f1, f2], 0, b=10)
expected = pd.DataFrame({"<lambda_0>": [13], "<lambda_1>": [30]})
tm.assert_frame_equal(result, expected)
+
+ def test_agg_with_one_lambda(self):
+ # GH 25719, write tests for DataFrameGroupby.agg with only one lambda
+ df = pd.DataFrame(
+ {
+ "kind": ["cat", "dog", "cat", "dog"],
+ "height": [9.1, 6.0, 9.5, 34.0],
+ "weight": [7.9, 7.5, 9.9, 198.0],
+ }
+ )
+
+ # sort for 35 and earlier
+ columns = ["height_sqr_min", "height_max", "weight_max"]
+ if compat.PY35:
+ columns = ["height_max", "height_sqr_min", "weight_max"]
+ expected = pd.DataFrame(
+ {
+ "height_sqr_min": [82.81, 36.00],
+ "height_max": [9.5, 34.0],
+ "weight_max": [9.9, 198.0],
+ },
+ index=pd.Index(["cat", "dog"], name="kind"),
+ columns=columns,
+ )
+
+ # check pd.NameAgg case
+ result1 = df.groupby(by="kind").agg(
+ height_sqr_min=pd.NamedAgg(
+ column="height", aggfunc=lambda x: np.min(x ** 2)
+ ),
+ height_max=pd.NamedAgg(column="height", aggfunc="max"),
+ weight_max=pd.NamedAgg(column="weight", aggfunc="max"),
+ )
+ tm.assert_frame_equal(result1, expected)
+
+ # check agg(key=(col, aggfunc)) case
+ result2 = df.groupby(by="kind").agg(
+ height_sqr_min=("height", lambda x: np.min(x ** 2)),
+ height_max=("height", "max"),
+ weight_max=("weight", "max"),
+ )
+ tm.assert_frame_equal(result2, expected)
+
+ def test_agg_multiple_lambda(self):
+ # GH25719, test for DataFrameGroupby.agg with multiple lambdas
+ # with mixed aggfunc
+ df = pd.DataFrame(
+ {
+ "kind": ["cat", "dog", "cat", "dog"],
+ "height": [9.1, 6.0, 9.5, 34.0],
+ "weight": [7.9, 7.5, 9.9, 198.0],
+ }
+ )
+ # sort for 35 and earlier
+ columns = [
+ "height_sqr_min",
+ "height_max",
+ "weight_max",
+ "height_max_2",
+ "weight_min",
+ ]
+ if compat.PY35:
+ columns = [
+ "height_max",
+ "height_max_2",
+ "height_sqr_min",
+ "weight_max",
+ "weight_min",
+ ]
+ expected = pd.DataFrame(
+ {
+ "height_sqr_min": [82.81, 36.00],
+ "height_max": [9.5, 34.0],
+ "weight_max": [9.9, 198.0],
+ "height_max_2": [9.5, 34.0],
+ "weight_min": [7.9, 7.5],
+ },
+ index=pd.Index(["cat", "dog"], name="kind"),
+ columns=columns,
+ )
+
+ # check agg(key=(col, aggfunc)) case
+ result1 = df.groupby(by="kind").agg(
+ height_sqr_min=("height", lambda x: np.min(x ** 2)),
+ height_max=("height", "max"),
+ weight_max=("weight", "max"),
+ height_max_2=("height", lambda x: np.max(x)),
+ weight_min=("weight", lambda x: np.min(x)),
+ )
+ tm.assert_frame_equal(result1, expected)
+
+ # check pd.NamedAgg case
+ result2 = df.groupby(by="kind").agg(
+ height_sqr_min=pd.NamedAgg(
+ column="height", aggfunc=lambda x: np.min(x ** 2)
+ ),
+ height_max=pd.NamedAgg(column="height", aggfunc="max"),
+ weight_max=pd.NamedAgg(column="weight", aggfunc="max"),
+ height_max_2=pd.NamedAgg(column="height", aggfunc=lambda x: np.max(x)),
+ weight_min=pd.NamedAgg(column="weight", aggfunc=lambda x: np.min(x)),
+ )
+ tm.assert_frame_equal(result2, expected)
+
+ @pytest.mark.parametrize(
+ "order, expected_reorder",
+ [
+ (
+ [
+ ("height", "<lambda>"),
+ ("height", "max"),
+ ("weight", "max"),
+ ("height", "<lambda>"),
+ ("weight", "<lambda>"),
+ ],
+ [
+ ("height", "<lambda>_0"),
+ ("height", "max"),
+ ("weight", "max"),
+ ("height", "<lambda>_1"),
+ ("weight", "<lambda>"),
+ ],
+ ),
+ (
+ [
+ ("col2", "min"),
+ ("col1", "<lambda>"),
+ ("col1", "<lambda>"),
+ ("col1", "<lambda>"),
+ ],
+ [
+ ("col2", "min"),
+ ("col1", "<lambda>_0"),
+ ("col1", "<lambda>_1"),
+ ("col1", "<lambda>_2"),
+ ],
+ ),
+ (
+ [("col", "<lambda>"), ("col", "<lambda>"), ("col", "<lambda>")],
+ [("col", "<lambda>_0"), ("col", "<lambda>_1"), ("col", "<lambda>_2")],
+ ),
+ ],
+ )
+ def test_make_unique(self, order, expected_reorder):
+ # GH 27519, test if make_unique function reorders correctly
+ result = _make_unique(order)
+
+ assert result == expected_reorder
| - [x] closes #27519
- [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/27921 | 2019-08-14T21:16:03Z | 2019-08-30T14:47:18Z | 2019-08-30T14:47:17Z | 2019-08-30T14:47:18Z |
Updated table with read_fwf() | diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 947bf15a49c7a..ad250dedb7771 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -28,6 +28,7 @@ The pandas I/O API is a set of top level ``reader`` functions accessed like
:delim: ;
text;`CSV <https://en.wikipedia.org/wiki/Comma-separated_values>`__;:ref:`read_csv<io.read_csv_table>`;:ref:`to_csv<io.store_in_csv>`
+ text;`TXT <https://www.oracle.com/webfolder/technetwork/data-quality/edqhelp/Content/introduction/getting_started/configuring_fixed_width_text_file_formats.htm>`__;:ref:`read_fwf<io.fwf_reader>`
text;`JSON <https://www.json.org/>`__;:ref:`read_json<io.json_reader>`;:ref:`to_json<io.json_writer>`
text;`HTML <https://en.wikipedia.org/wiki/HTML>`__;:ref:`read_html<io.read_html>`;:ref:`to_html<io.html>`
text; Local clipboard;:ref:`read_clipboard<io.clipboard>`;:ref:`to_clipboard<io.clipboard>`
@@ -1372,6 +1373,7 @@ should pass the ``escapechar`` option:
print(data)
pd.read_csv(StringIO(data), escapechar='\\')
+.. _io.fwf_reader:
.. _io.fwf:
Files with fixed width columns
| - [X] closes #27884
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
@TomAugspurger Can you merge? | https://api.github.com/repos/pandas-dev/pandas/pulls/27920 | 2019-08-14T21:15:33Z | 2019-08-19T11:43:35Z | 2019-08-19T11:43:34Z | 2019-08-19T12:04:35Z |
DOC: Fix section reference placement in whatsnew | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index b35f230100f8d..aeed3668fe774 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -21,27 +21,27 @@ including other versions of pandas.
Enhancements
~~~~~~~~~~~~
-.. _whatsnew_1000.enhancements.other:
-
-
-
+.. _whatsnew_1000.enhancements.other:
+
Other enhancements
^^^^^^^^^^^^^^^^^^
-.. _whatsnew_1000.api_breaking:
-
-
-
+.. _whatsnew_1000.api_breaking:
+
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. _whatsnew_1000.api.other:
-
- :class:`pandas.core.groupby.GroupBy.transform` now raises on invalid operation names (:issue:`27489`).
-
+.. _whatsnew_1000.api.other:
+
Other API changes
^^^^^^^^^^^^^^^^^
| A few of the section references are misaligned with the tick marks. Moved the misaligned references as appropriate. | https://api.github.com/repos/pandas-dev/pandas/pulls/27919 | 2019-08-14T20:41:21Z | 2019-08-14T22:50:45Z | 2019-08-14T22:50:45Z | 2019-08-14T22:50:52Z |
Backport PR #27840 on branch 0.25.x (PERF: Break reference cycle for all Index types) | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index a81fc19221ef7..b53b53c654ce3 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -83,7 +83,7 @@ Indexing
^^^^^^^^
- Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']`` (:issue:`27516`)
-- Break reference cycle involving :class:`Index` to allow garbage collection of :class:`Index` objects without running the GC. (:issue:`27585`)
+- Break reference cycle involving :class:`Index` and other index classes to allow garbage collection of index objects without running the GC. (:issue:`27585`, :issue:`27840`)
- Fix regression in assigning values to a single column of a DataFrame with a ``MultiIndex`` columns (:issue:`27841`).
-
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index e49ad751ad150..680976f44ee1e 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -711,7 +711,7 @@ def _cleanup(self):
def _engine(self):
# property, for now, slow to look up
- # to avoid a refernce cycle, bind `_ndarray_values` to a local variable, so
+ # to avoid a reference cycle, bind `_ndarray_values` to a local variable, so
# `self` is not passed into the lambda.
_ndarray_values = self._ndarray_values
return self._engine_type(lambda: _ndarray_values, len(self))
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index e14bf7f86c0be..2b7d4ad7243ba 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -445,9 +445,11 @@ def argsort(self, *args, **kwargs):
@cache_readonly
def _engine(self):
-
- # we are going to look things up with the codes themselves
- return self._engine_type(lambda: self.codes, len(self))
+ # we are going to look things up with the codes themselves.
+ # To avoid a reference cycle, bind `codes` to a local variable, so
+ # `self` is not passed into the lambda.
+ codes = self.codes
+ return self._engine_type(lambda: codes, len(self))
# introspection
@cache_readonly
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index 96031645365c6..def2fdaa72768 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -1,5 +1,6 @@
from datetime import datetime, timedelta
import warnings
+import weakref
import numpy as np
@@ -438,7 +439,9 @@ def _formatter_func(self):
@cache_readonly
def _engine(self):
- return self._engine_type(lambda: self, len(self))
+ # To avoid a reference cycle, pass a weakref of self to _engine_type.
+ period = weakref.ref(self)
+ return self._engine_type(period, len(self))
@Appender(_index_shared_docs["contains"])
def __contains__(self, key):
diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py
index 9459069f0ea2d..0e74c87388682 100644
--- a/pandas/tests/indexes/common.py
+++ b/pandas/tests/indexes/common.py
@@ -1,3 +1,5 @@
+import gc
+
import numpy as np
import pytest
@@ -908,3 +910,10 @@ def test_is_unique(self):
# multiple NA should not be unique
index_na_dup = index_na.insert(0, np.nan)
assert index_na_dup.is_unique is False
+
+ def test_engine_reference_cycle(self):
+ # GH27585
+ index = self.create_index()
+ nrefs_pre = len(gc.get_referrers(index))
+ index._engine
+ assert len(gc.get_referrers(index)) == nrefs_pre
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index fe1eb96df1e97..d1ed79118d2fa 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -1,6 +1,5 @@
from collections import defaultdict
from datetime import datetime, timedelta
-import gc
from io import StringIO
import math
import operator
@@ -2425,13 +2424,6 @@ def test_deprecated_contains(self):
with tm.assert_produces_warning(FutureWarning):
index.contains(1)
- def test_engine_reference_cycle(self):
- # https://github.com/pandas-dev/pandas/issues/27585
- index = pd.Index([1, 2, 3])
- nrefs_pre = len(gc.get_referrers(index))
- index._engine
- assert len(gc.get_referrers(index)) == nrefs_pre
-
class TestMixedIntIndex(Base):
# Mostly the tests from common.py for which the results differ
| Backport PR #27840: PERF: Break reference cycle for all Index types | https://api.github.com/repos/pandas-dev/pandas/pulls/27918 | 2019-08-14T20:11:45Z | 2019-08-14T21:56:07Z | 2019-08-14T21:56:07Z | 2019-08-14T21:56:07Z |
Backport PR #27907 on branch 0.25.x (Revert 37 pins) | diff --git a/ci/deps/azure-37-locale.yaml b/ci/deps/azure-37-locale.yaml
index 6844e929eae78..05adbf0c924dc 100644
--- a/ci/deps/azure-37-locale.yaml
+++ b/ci/deps/azure-37-locale.yaml
@@ -17,7 +17,7 @@ dependencies:
- openpyxl
- pytables
- python-dateutil
- - python=3.7.3
+ - python=3.7.*
- pytz
- s3fs
- scipy
diff --git a/ci/deps/azure-37-numpydev.yaml b/ci/deps/azure-37-numpydev.yaml
index 3b68a573497e6..5cf897c98da10 100644
--- a/ci/deps/azure-37-numpydev.yaml
+++ b/ci/deps/azure-37-numpydev.yaml
@@ -2,7 +2,7 @@ name: pandas-dev
channels:
- defaults
dependencies:
- - python=3.7.3
+ - python=3.7.*
- pytz
- Cython>=0.28.2
# universal
diff --git a/ci/deps/travis-37.yaml b/ci/deps/travis-37.yaml
index 2d7f4b483729f..c9a8c274fb144 100644
--- a/ci/deps/travis-37.yaml
+++ b/ci/deps/travis-37.yaml
@@ -4,7 +4,7 @@ channels:
- conda-forge
- c3i_test
dependencies:
- - python=3.7.3
+ - python=3.7.*
- botocore>=1.11
- cython>=0.28.2
- numpy
diff --git a/environment.yml b/environment.yml
index d54caf9d013c7..93e8302b498a0 100644
--- a/environment.yml
+++ b/environment.yml
@@ -5,7 +5,7 @@ channels:
dependencies:
# required
- numpy>=1.15
- - python=3.7.3
+ - python=3
- python-dateutil>=2.6.1
- pytz
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 9d80a13350579..e49ad10bfc99d 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,5 +1,4 @@
numpy>=1.15
-python==3.7.3
python-dateutil>=2.6.1
pytz
asv
| Backport PR #27907: Revert 37 pins | https://api.github.com/repos/pandas-dev/pandas/pulls/27917 | 2019-08-14T19:34:39Z | 2019-08-14T21:55:43Z | 2019-08-14T21:55:43Z | 2019-08-14T21:55:44Z |
BUG: fix to_timestamp out_of_bounds | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 21f1fa7ddec1f..a6e85803244de 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -32,7 +32,7 @@ Categorical
Datetimelike
^^^^^^^^^^^^
- Bug in :func:`to_datetime` where passing a timezone-naive :class:`DatetimeArray` or :class:`DatetimeIndex` and ``utc=True`` would incorrectly return a timezone-naive result (:issue:`27733`)
--
+- Bug in :meth:`Period.to_timestamp` where a :class:`Period` outside the :class:`Timestamp` implementation bounds (roughly 1677-09-21 to 2262-04-11) would return an incorrect :class:`Timestamp` instead of raising ``OutOfBoundsDatetime`` (:issue:`19643`)
-
-
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index c68d686ff2bf2..98e55f50062a2 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -21,7 +21,8 @@ PyDateTime_IMPORT
from pandas._libs.tslibs.np_datetime cimport (
npy_datetimestruct, dtstruct_to_dt64, dt64_to_dtstruct,
- pandas_datetime_to_datetimestruct, NPY_DATETIMEUNIT, NPY_FR_D)
+ pandas_datetime_to_datetimestruct, check_dts_bounds,
+ NPY_DATETIMEUNIT, NPY_FR_D)
cdef extern from "src/datetime/np_datetime.h":
int64_t npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT fr,
@@ -1011,7 +1012,7 @@ def dt64arr_to_periodarr(int64_t[:] dtarr, int freq, tz=None):
@cython.wraparound(False)
@cython.boundscheck(False)
-def periodarr_to_dt64arr(int64_t[:] periodarr, int freq):
+def periodarr_to_dt64arr(const int64_t[:] periodarr, int freq):
"""
Convert array to datetime64 values from a set of ordinals corresponding to
periods per period convention.
@@ -1024,9 +1025,8 @@ def periodarr_to_dt64arr(int64_t[:] periodarr, int freq):
out = np.empty(l, dtype='i8')
- with nogil:
- for i in range(l):
- out[i] = period_ordinal_to_dt64(periodarr[i], freq)
+ for i in range(l):
+ out[i] = period_ordinal_to_dt64(periodarr[i], freq)
return out.base # .base to access underlying np.ndarray
@@ -1179,7 +1179,7 @@ cpdef int64_t period_ordinal(int y, int m, int d, int h, int min,
return get_period_ordinal(&dts, freq)
-cpdef int64_t period_ordinal_to_dt64(int64_t ordinal, int freq) nogil:
+cdef int64_t period_ordinal_to_dt64(int64_t ordinal, int freq) except? -1:
cdef:
npy_datetimestruct dts
@@ -1187,6 +1187,7 @@ cpdef int64_t period_ordinal_to_dt64(int64_t ordinal, int freq) nogil:
return NPY_NAT
get_date_info(ordinal, freq, &dts)
+ check_dts_bounds(&dts)
return dtstruct_to_dt64(&dts)
diff --git a/pandas/tests/arrays/test_datetimelike.py b/pandas/tests/arrays/test_datetimelike.py
index 0b3ccc0ae0e2d..7c482664bca48 100644
--- a/pandas/tests/arrays/test_datetimelike.py
+++ b/pandas/tests/arrays/test_datetimelike.py
@@ -1,6 +1,8 @@
import numpy as np
import pytest
+from pandas._libs import OutOfBoundsDatetime
+
import pandas as pd
from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
import pandas.util.testing as tm
@@ -615,6 +617,15 @@ def test_to_timestamp(self, how, period_index):
# an EA-specific tm.assert_ function
tm.assert_index_equal(pd.Index(result), pd.Index(expected))
+ def test_to_timestamp_out_of_bounds(self):
+ # GH#19643 previously overflowed silently
+ pi = pd.period_range("1500", freq="Y", periods=3)
+ with pytest.raises(OutOfBoundsDatetime):
+ pi.to_timestamp()
+
+ with pytest.raises(OutOfBoundsDatetime):
+ pi._data.to_timestamp()
+
@pytest.mark.parametrize("propname", PeriodArray._bool_ops)
def test_bool_properties(self, period_index, propname):
# in this case _bool_ops is just `is_leap_year`
diff --git a/pandas/tests/scalar/period/test_asfreq.py b/pandas/tests/scalar/period/test_asfreq.py
index 4cff061cabc40..357274e724c68 100644
--- a/pandas/tests/scalar/period/test_asfreq.py
+++ b/pandas/tests/scalar/period/test_asfreq.py
@@ -30,11 +30,8 @@ def test_asfreq_near_zero_weekly(self):
assert week1.asfreq("D", "E") >= per1
assert week2.asfreq("D", "S") <= per2
- @pytest.mark.xfail(
- reason="GH#19643 period_helper asfreq functions fail to check for overflows"
- )
def test_to_timestamp_out_of_bounds(self):
- # GH#19643, currently gives Timestamp('1754-08-30 22:43:41.128654848')
+ # GH#19643, used to incorrectly give Timestamp in 1754
per = Period("0001-01-01", freq="B")
with pytest.raises(OutOfBoundsDatetime):
per.to_timestamp()
| - [x] closes #19643
- [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/27916 | 2019-08-14T15:28:09Z | 2019-08-20T07:00:45Z | 2019-08-20T07:00:45Z | 2019-08-20T13:54:28Z |
STYLE: run pre-commit filters on the repo | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 32ffb3330564c..5cc22c638c9b1 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,17 +1,21 @@
repos:
- - repo: https://github.com/python/black
- rev: stable
- hooks:
- - id: black
- language_version: python3.7
- - repo: https://gitlab.com/pycqa/flake8
- rev: 3.7.7
- hooks:
- - id: flake8
- language: python_venv
- additional_dependencies: [flake8-comprehensions]
- - repo: https://github.com/pre-commit/mirrors-isort
- rev: v4.3.20
- hooks:
- - id: isort
- language: python_venv
+- repo: https://github.com/python/black
+ rev: stable
+ hooks:
+ - id: black
+ language_version: python3.7
+- repo: https://gitlab.com/pycqa/flake8
+ rev: 3.7.7
+ hooks:
+ - id: flake8
+ language: python_venv
+ additional_dependencies: [flake8-comprehensions]
+- repo: https://github.com/pre-commit/mirrors-isort
+ rev: v4.3.20
+ hooks:
+ - id: isort
+ language: python_venv
+- repo: https://github.com/asottile/seed-isort-config
+ rev: v1.9.2
+ hooks:
+ - id: seed-isort-config
diff --git a/asv_bench/benchmarks/attrs_caching.py b/asv_bench/benchmarks/attrs_caching.py
index c43e5dfd729aa..501e27b9078ec 100644
--- a/asv_bench/benchmarks/attrs_caching.py
+++ b/asv_bench/benchmarks/attrs_caching.py
@@ -1,4 +1,5 @@
import numpy as np
+
from pandas import DataFrame
try:
@@ -32,4 +33,4 @@ def time_cache_readonly(self):
self.obj.prop
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/binary_ops.py b/asv_bench/benchmarks/binary_ops.py
index fd3324b78f1c3..58e0db67d6025 100644
--- a/asv_bench/benchmarks/binary_ops.py
+++ b/asv_bench/benchmarks/binary_ops.py
@@ -1,4 +1,5 @@
import numpy as np
+
from pandas import DataFrame, Series, date_range
from pandas.core.algorithms import checked_add_with_arr
@@ -155,4 +156,4 @@ def time_add_overflow_both_arg_nan(self):
)
-from .pandas_vb_common import setup # noqa: F401
+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 8097118a79d20..559aa7050a640 100644
--- a/asv_bench/benchmarks/categoricals.py
+++ b/asv_bench/benchmarks/categoricals.py
@@ -1,7 +1,9 @@
+import warnings
+
import numpy as np
+
import pandas as pd
import pandas.util.testing as tm
-import warnings
try:
from pandas.api.types import union_categoricals
@@ -280,4 +282,4 @@ def time_sort_values(self):
self.index.sort_values(ascending=False)
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/ctors.py b/asv_bench/benchmarks/ctors.py
index 654075292cdf6..ec3dd7a48a89f 100644
--- a/asv_bench/benchmarks/ctors.py
+++ b/asv_bench/benchmarks/ctors.py
@@ -1,6 +1,7 @@
import numpy as np
+
+from pandas import DatetimeIndex, Index, MultiIndex, Series, Timestamp
import pandas.util.testing as tm
-from pandas import Series, Index, DatetimeIndex, Timestamp, MultiIndex
def no_change(arr):
@@ -113,4 +114,4 @@ def time_multiindex_from_iterables(self):
MultiIndex.from_product(self.iterables)
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/dtypes.py b/asv_bench/benchmarks/dtypes.py
index 60800b1f9cae7..24cc1c6f9fa70 100644
--- a/asv_bench/benchmarks/dtypes.py
+++ b/asv_bench/benchmarks/dtypes.py
@@ -1,14 +1,14 @@
+import numpy as np
+
from pandas.api.types import pandas_dtype
-import numpy as np
from .pandas_vb_common import (
- numeric_dtypes,
datetime_dtypes,
- string_dtypes,
extension_dtypes,
+ numeric_dtypes,
+ string_dtypes,
)
-
_numpy_dtypes = [
np.dtype(dtype) for dtype in (numeric_dtypes + datetime_dtypes + string_dtypes)
]
@@ -40,4 +40,4 @@ def time_pandas_dtype_invalid(self, dtype):
pass
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/eval.py b/asv_bench/benchmarks/eval.py
index 84e94315cc28b..06a181875aaa8 100644
--- a/asv_bench/benchmarks/eval.py
+++ b/asv_bench/benchmarks/eval.py
@@ -1,4 +1,5 @@
import numpy as np
+
import pandas as pd
try:
@@ -62,4 +63,4 @@ def time_query_with_boolean_selection(self):
self.df.query("(a >= @self.min_val) & (a <= @self.max_val)")
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/frame_ctor.py b/asv_bench/benchmarks/frame_ctor.py
index acfb26bcf5d7c..3944e0bc523d8 100644
--- a/asv_bench/benchmarks/frame_ctor.py
+++ b/asv_bench/benchmarks/frame_ctor.py
@@ -1,6 +1,7 @@
import numpy as np
+
+from pandas import DataFrame, MultiIndex, Series, Timestamp, date_range
import pandas.util.testing as tm
-from pandas import DataFrame, Series, MultiIndex, Timestamp, date_range
try:
from pandas.tseries.offsets import Nano, Hour
@@ -104,4 +105,4 @@ def time_frame_from_lists(self):
self.df = DataFrame(self.data)
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/frame_methods.py b/asv_bench/benchmarks/frame_methods.py
index e2f6764c76eef..05f98c66faa2b 100644
--- a/asv_bench/benchmarks/frame_methods.py
+++ b/asv_bench/benchmarks/frame_methods.py
@@ -1,5 +1,5 @@
-import warnings
import string
+import warnings
import numpy as np
@@ -609,4 +609,4 @@ def time_dataframe_describe(self):
self.df.describe()
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/gil.py b/asv_bench/benchmarks/gil.py
index 0d0b75561d057..d57492dd37268 100644
--- a/asv_bench/benchmarks/gil.py
+++ b/asv_bench/benchmarks/gil.py
@@ -1,7 +1,8 @@
import numpy as np
-import pandas.util.testing as tm
-from pandas import DataFrame, Series, read_csv, factorize, date_range
+
+from pandas import DataFrame, Series, date_range, factorize, read_csv
from pandas.core.algorithms import take_1d
+import pandas.util.testing as tm
try:
from pandas import (
@@ -36,7 +37,7 @@ def wrapper(fname):
return wrapper
-from .pandas_vb_common import BaseIO
+from .pandas_vb_common import BaseIO # noqa: E402 isort:skip
class ParallelGroupbyMethods:
@@ -301,4 +302,4 @@ def time_loop(self, threads):
self.loop()
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py
index 39b07d4734399..d51c53e2264f1 100644
--- a/asv_bench/benchmarks/groupby.py
+++ b/asv_bench/benchmarks/groupby.py
@@ -15,7 +15,6 @@
)
import pandas.util.testing as tm
-
method_blacklist = {
"object": {
"median",
@@ -626,4 +625,4 @@ def time_first(self):
self.df_nans.groupby("key").transform("first")
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py
index 49834ae94cc38..a94960d494707 100644
--- a/asv_bench/benchmarks/index_object.py
+++ b/asv_bench/benchmarks/index_object.py
@@ -1,15 +1,17 @@
import gc
+
import numpy as np
-import pandas.util.testing as tm
+
from pandas import (
- Series,
- date_range,
DatetimeIndex,
- Index,
- RangeIndex,
Float64Index,
+ Index,
IntervalIndex,
+ RangeIndex,
+ Series,
+ date_range,
)
+import pandas.util.testing as tm
class SetOperations:
@@ -243,4 +245,4 @@ def peakmem_gc_instances(self, N):
gc.enable()
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py
index 84604b8196536..ac35139c1954a 100644
--- a/asv_bench/benchmarks/indexing.py
+++ b/asv_bench/benchmarks/indexing.py
@@ -1,22 +1,23 @@
import warnings
import numpy as np
-import pandas.util.testing as tm
+
from pandas import (
- Series,
+ CategoricalIndex,
DataFrame,
- MultiIndex,
- Int64Index,
- UInt64Index,
Float64Index,
- IntervalIndex,
- CategoricalIndex,
IndexSlice,
+ Int64Index,
+ IntervalIndex,
+ MultiIndex,
+ Series,
+ UInt64Index,
concat,
date_range,
option_context,
period_range,
)
+import pandas.util.testing as tm
class NumericSeriesIndexing:
@@ -371,4 +372,4 @@ def time_chained_indexing(self, mode):
df2["C"] = 1.0
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py
index 66ef4f2aec380..e85b3bd2c7687 100644
--- a/asv_bench/benchmarks/inference.py
+++ b/asv_bench/benchmarks/inference.py
@@ -1,8 +1,9 @@
import numpy as np
-import pandas.util.testing as tm
+
from pandas import DataFrame, Series, to_numeric
+import pandas.util.testing as tm
-from .pandas_vb_common import numeric_dtypes, lib
+from .pandas_vb_common import lib, numeric_dtypes
class NumericInferOps:
@@ -120,4 +121,4 @@ def time_convert(self, data):
lib.maybe_convert_numeric(data, set(), coerce_numeric=False)
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/csv.py b/asv_bench/benchmarks/io/csv.py
index 4525e504fc4dd..9b8599b0a1b64 100644
--- a/asv_bench/benchmarks/io/csv.py
+++ b/asv_bench/benchmarks/io/csv.py
@@ -1,10 +1,11 @@
+from io import StringIO
import random
import string
import numpy as np
+
+from pandas import Categorical, DataFrame, date_range, read_csv, to_datetime
import pandas.util.testing as tm
-from pandas import DataFrame, Categorical, date_range, read_csv, to_datetime
-from io import StringIO
from ..pandas_vb_common import BaseIO
@@ -406,4 +407,4 @@ def time_to_datetime_format_DD_MM_YYYY(self, cache_dates):
to_datetime(df["date"], cache=cache_dates, format="%d-%m-%Y")
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/excel.py b/asv_bench/benchmarks/io/excel.py
index 12e70f84e5203..9aa5cbd5b6f7c 100644
--- a/asv_bench/benchmarks/io/excel.py
+++ b/asv_bench/benchmarks/io/excel.py
@@ -1,6 +1,8 @@
from io import BytesIO
+
import numpy as np
-from pandas import DataFrame, date_range, ExcelWriter, read_excel
+
+from pandas import DataFrame, ExcelWriter, date_range, read_excel
import pandas.util.testing as tm
@@ -35,4 +37,4 @@ def time_write_excel(self, engine):
writer_write.save()
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/hdf.py b/asv_bench/benchmarks/io/hdf.py
index 2874a7889156b..8ec04a2087f1b 100644
--- a/asv_bench/benchmarks/io/hdf.py
+++ b/asv_bench/benchmarks/io/hdf.py
@@ -1,5 +1,6 @@
import numpy as np
-from pandas import DataFrame, date_range, HDFStore, read_hdf
+
+from pandas import DataFrame, HDFStore, date_range, read_hdf
import pandas.util.testing as tm
from ..pandas_vb_common import BaseIO
@@ -127,4 +128,4 @@ def time_write_hdf(self, format):
self.df.to_hdf(self.fname, "df", format=format)
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/json.py b/asv_bench/benchmarks/io/json.py
index fc07f2a484102..b249c92b53e93 100644
--- a/asv_bench/benchmarks/io/json.py
+++ b/asv_bench/benchmarks/io/json.py
@@ -1,6 +1,7 @@
import numpy as np
+
+from pandas import DataFrame, concat, date_range, read_json, timedelta_range
import pandas.util.testing as tm
-from pandas import DataFrame, date_range, timedelta_range, concat, read_json
from ..pandas_vb_common import BaseIO
@@ -214,4 +215,4 @@ def peakmem_float(self, frames):
df.to_json()
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/msgpack.py b/asv_bench/benchmarks/io/msgpack.py
index d97b4ae13f0bd..f5038602539ab 100644
--- a/asv_bench/benchmarks/io/msgpack.py
+++ b/asv_bench/benchmarks/io/msgpack.py
@@ -1,5 +1,7 @@
import warnings
+
import numpy as np
+
from pandas import DataFrame, date_range, read_msgpack
import pandas.util.testing as tm
@@ -27,4 +29,4 @@ def time_write_msgpack(self):
self.df.to_msgpack(self.fname)
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/pickle.py b/asv_bench/benchmarks/io/pickle.py
index 286ac767c02e7..647e9d27dec9d 100644
--- a/asv_bench/benchmarks/io/pickle.py
+++ b/asv_bench/benchmarks/io/pickle.py
@@ -1,4 +1,5 @@
import numpy as np
+
from pandas import DataFrame, date_range, read_pickle
import pandas.util.testing as tm
@@ -25,4 +26,4 @@ def time_write_pickle(self):
self.df.to_pickle(self.fname)
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/sql.py b/asv_bench/benchmarks/io/sql.py
index b80872b17a9e4..fe84c869717e3 100644
--- a/asv_bench/benchmarks/io/sql.py
+++ b/asv_bench/benchmarks/io/sql.py
@@ -1,10 +1,11 @@
import sqlite3
import numpy as np
-import pandas.util.testing as tm
-from pandas import DataFrame, date_range, read_sql_query, read_sql_table
from sqlalchemy import create_engine
+from pandas import DataFrame, date_range, read_sql_query, read_sql_table
+import pandas.util.testing as tm
+
class SQL:
@@ -141,4 +142,4 @@ def time_read_sql_table_column(self, dtype):
read_sql_table(self.table_name, self.con, columns=[dtype])
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/io/stata.py b/asv_bench/benchmarks/io/stata.py
index b3ed71af47dc8..28829785d72e9 100644
--- a/asv_bench/benchmarks/io/stata.py
+++ b/asv_bench/benchmarks/io/stata.py
@@ -1,4 +1,5 @@
import numpy as np
+
from pandas import DataFrame, date_range, read_stata
import pandas.util.testing as tm
@@ -50,4 +51,4 @@ def setup(self, convert_dates):
self.df.to_stata(self.fname, self.convert_dates)
-from ..pandas_vb_common import setup # noqa: F401
+from ..pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/join_merge.py b/asv_bench/benchmarks/join_merge.py
index 7c899e3dc6ac8..6aa82a43a4d6a 100644
--- a/asv_bench/benchmarks/join_merge.py
+++ b/asv_bench/benchmarks/join_merge.py
@@ -1,8 +1,9 @@
import string
import numpy as np
+
+from pandas import DataFrame, MultiIndex, Series, concat, date_range, merge, merge_asof
import pandas.util.testing as tm
-from pandas import DataFrame, Series, MultiIndex, date_range, concat, merge, merge_asof
try:
from pandas import merge_ordered
@@ -348,4 +349,4 @@ def time_series_align_left_monotonic(self):
self.ts1.align(self.ts2, join="left")
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/multiindex_object.py b/asv_bench/benchmarks/multiindex_object.py
index eda059a68e8a5..3f4fd7ad911c1 100644
--- a/asv_bench/benchmarks/multiindex_object.py
+++ b/asv_bench/benchmarks/multiindex_object.py
@@ -1,8 +1,9 @@
import string
import numpy as np
+
+from pandas import DataFrame, MultiIndex, date_range
import pandas.util.testing as tm
-from pandas import date_range, MultiIndex, DataFrame
class GetLoc:
@@ -146,4 +147,4 @@ def time_categorical_level(self):
self.df.set_index(["a", "b"])
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/offset.py b/asv_bench/benchmarks/offset.py
index 31c3b6fb6cb60..d822646e712ae 100644
--- a/asv_bench/benchmarks/offset.py
+++ b/asv_bench/benchmarks/offset.py
@@ -1,7 +1,8 @@
-import warnings
from datetime import datetime
+import warnings
import numpy as np
+
import pandas as pd
try:
diff --git a/asv_bench/benchmarks/pandas_vb_common.py b/asv_bench/benchmarks/pandas_vb_common.py
index fdc8207021c0f..1faf13329110d 100644
--- a/asv_bench/benchmarks/pandas_vb_common.py
+++ b/asv_bench/benchmarks/pandas_vb_common.py
@@ -1,7 +1,8 @@
-import os
from importlib import import_module
+import os
import numpy as np
+
import pandas as pd
# Compatibility import for lib
diff --git a/asv_bench/benchmarks/period.py b/asv_bench/benchmarks/period.py
index 2f8ae0650ab75..7303240a25f29 100644
--- a/asv_bench/benchmarks/period.py
+++ b/asv_bench/benchmarks/period.py
@@ -1,4 +1,5 @@
from pandas import DataFrame, Period, PeriodIndex, Series, date_range, period_range
+
from pandas.tseries.frequencies import to_offset
diff --git a/asv_bench/benchmarks/plotting.py b/asv_bench/benchmarks/plotting.py
index 4fb0876f05a0a..5c718516360ed 100644
--- a/asv_bench/benchmarks/plotting.py
+++ b/asv_bench/benchmarks/plotting.py
@@ -1,11 +1,12 @@
+import matplotlib
import numpy as np
-from pandas import DataFrame, Series, DatetimeIndex, date_range
+
+from pandas import DataFrame, DatetimeIndex, Series, date_range
try:
from pandas.plotting import andrews_curves
except ImportError:
from pandas.tools.plotting import andrews_curves
-import matplotlib
matplotlib.use("Agg")
@@ -93,4 +94,4 @@ def time_plot_andrews_curves(self):
andrews_curves(self.df, "Name")
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/reindex.py b/asv_bench/benchmarks/reindex.py
index 8d4c9ebaf3e89..cd450f801c805 100644
--- a/asv_bench/benchmarks/reindex.py
+++ b/asv_bench/benchmarks/reindex.py
@@ -1,6 +1,8 @@
import numpy as np
+
+from pandas import DataFrame, Index, MultiIndex, Series, date_range, period_range
import pandas.util.testing as tm
-from pandas import DataFrame, Series, MultiIndex, Index, date_range, period_range
+
from .pandas_vb_common import lib
@@ -159,4 +161,4 @@ def time_lib_fast_zip(self):
lib.fast_zip(self.col_array_list)
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/replace.py b/asv_bench/benchmarks/replace.py
index 6137e944e6b9e..f13fd2460e0a8 100644
--- a/asv_bench/benchmarks/replace.py
+++ b/asv_bench/benchmarks/replace.py
@@ -1,4 +1,5 @@
import numpy as np
+
import pandas as pd
@@ -56,4 +57,4 @@ def time_replace(self, constructor, replace_data):
self.data.replace(self.to_replace)
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/reshape.py b/asv_bench/benchmarks/reshape.py
index cc373f413fb88..441f4b380656e 100644
--- a/asv_bench/benchmarks/reshape.py
+++ b/asv_bench/benchmarks/reshape.py
@@ -1,9 +1,10 @@
-import string
from itertools import product
+import string
import numpy as np
-from pandas import DataFrame, MultiIndex, date_range, melt, wide_to_long
+
import pandas as pd
+from pandas import DataFrame, MultiIndex, date_range, melt, wide_to_long
class Melt:
@@ -262,4 +263,4 @@ def time_explode(self, n_rows, max_list_length):
self.series.explode()
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/rolling.py b/asv_bench/benchmarks/rolling.py
index a70977fcf539f..3640513d31be2 100644
--- a/asv_bench/benchmarks/rolling.py
+++ b/asv_bench/benchmarks/rolling.py
@@ -1,6 +1,7 @@
-import pandas as pd
import numpy as np
+import pandas as pd
+
class Methods:
@@ -121,4 +122,4 @@ def peakmem_fixed(self):
self.roll.max()
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/series_methods.py b/asv_bench/benchmarks/series_methods.py
index 6038a2ab4bd9f..a3f1d92545c3f 100644
--- a/asv_bench/benchmarks/series_methods.py
+++ b/asv_bench/benchmarks/series_methods.py
@@ -1,8 +1,9 @@
from datetime import datetime
import numpy as np
+
+from pandas import NaT, Series, date_range
import pandas.util.testing as tm
-from pandas import Series, date_range, NaT
class SeriesConstructor:
@@ -275,4 +276,4 @@ def time_func(self, func, N, dtype):
self.func()
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/sparse.py b/asv_bench/benchmarks/sparse.py
index 19d08c086a508..ac78ca53679fd 100644
--- a/asv_bench/benchmarks/sparse.py
+++ b/asv_bench/benchmarks/sparse.py
@@ -136,4 +136,4 @@ def time_division(self, fill_value):
self.arr1 / self.arr2
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/stat_ops.py b/asv_bench/benchmarks/stat_ops.py
index 620a6de0f5f34..6032bee41958e 100644
--- a/asv_bench/benchmarks/stat_ops.py
+++ b/asv_bench/benchmarks/stat_ops.py
@@ -1,6 +1,6 @@
import numpy as np
-import pandas as pd
+import pandas as pd
ops = ["mean", "sum", "median", "std", "skew", "kurt", "mad", "prod", "sem", "var"]
@@ -148,4 +148,4 @@ def time_cov_series(self, use_bottleneck):
self.s.cov(self.s2)
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/strings.py b/asv_bench/benchmarks/strings.py
index 6be2fa92d9eac..f30b2482615bd 100644
--- a/asv_bench/benchmarks/strings.py
+++ b/asv_bench/benchmarks/strings.py
@@ -1,7 +1,8 @@
import warnings
import numpy as np
-from pandas import Series, DataFrame
+
+from pandas import DataFrame, Series
import pandas.util.testing as tm
diff --git a/asv_bench/benchmarks/timeseries.py b/asv_bench/benchmarks/timeseries.py
index 1020b773f8acb..498774034d642 100644
--- a/asv_bench/benchmarks/timeseries.py
+++ b/asv_bench/benchmarks/timeseries.py
@@ -2,7 +2,9 @@
import dateutil
import numpy as np
-from pandas import to_datetime, date_range, Series, DataFrame, period_range
+
+from pandas import DataFrame, Series, date_range, period_range, to_datetime
+
from pandas.tseries.frequencies import infer_freq
try:
@@ -426,4 +428,4 @@ def time_dt_accessor_year(self, tz):
self.series.dt.year
-from .pandas_vb_common import setup # noqa: F401
+from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/ci/print_skipped.py b/ci/print_skipped.py
index a44281044e11d..6bc1dcfcd320d 100755
--- a/ci/print_skipped.py
+++ b/ci/print_skipped.py
@@ -1,8 +1,8 @@
#!/usr/bin/env python
+import math
import os
import sys
-import math
import xml.etree.ElementTree as et
diff --git a/doc/logo/pandas_logo.py b/doc/logo/pandas_logo.py
index 5a07b094e6ad3..89410e3847bef 100644
--- a/doc/logo/pandas_logo.py
+++ b/doc/logo/pandas_logo.py
@@ -1,7 +1,6 @@
# script to generate the pandas logo
-from matplotlib import pyplot as plt
-from matplotlib import rcParams
+from matplotlib import pyplot as plt, rcParams
import numpy as np
rcParams["mathtext.fontset"] = "cm"
diff --git a/doc/make.py b/doc/make.py
index 48febef20fbe6..cbb1fa6a5324a 100755
--- a/doc/make.py
+++ b/doc/make.py
@@ -11,18 +11,18 @@
$ python make.py html
$ python make.py latex
"""
+import argparse
+import csv
import importlib
-import sys
import os
import shutil
-import csv
import subprocess
-import argparse
+import sys
import webbrowser
+
import docutils
import docutils.parsers.rst
-
DOC_PATH = os.path.dirname(os.path.abspath(__file__))
SOURCE_PATH = os.path.join(DOC_PATH, "source")
BUILD_PATH = os.path.join(DOC_PATH, "build")
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 3ebc5d8b6333b..8a8cc4b61b7d8 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -10,15 +10,15 @@
# All configuration values have a default; values that are commented out
# serve to show the default.
-import sys
-import os
-import inspect
import importlib
+import inspect
import logging
+import os
+import sys
+
import jinja2
-from sphinx.ext.autosummary import _import_by_name
from numpydoc.docscrape import NumpyDocString
-
+from sphinx.ext.autosummary import _import_by_name
logger = logging.getLogger(__name__)
@@ -141,7 +141,7 @@
# built documents.
#
# The short X.Y version.
-import pandas
+import pandas # noqa: E402 isort:skip
# version = '%s r%s' % (pandas.__version__, svn_version())
version = str(pandas.__version__)
@@ -433,10 +433,14 @@
# Add custom Documenter to handle attributes/methods of an AccessorProperty
# eg pandas.Series.str and pandas.Series.dt (see GH9322)
-import sphinx
-from sphinx.util import rpartition
-from sphinx.ext.autodoc import Documenter, MethodDocumenter, AttributeDocumenter
-from sphinx.ext.autosummary import Autosummary
+import sphinx # noqa: E402 isort:skip
+from sphinx.util import rpartition # noqa: E402 isort:skip
+from sphinx.ext.autodoc import ( # noqa: E402 isort:skip
+ AttributeDocumenter,
+ Documenter,
+ MethodDocumenter,
+)
+from sphinx.ext.autosummary import Autosummary # noqa: E402 isort:skip
class AccessorDocumenter(MethodDocumenter):
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 7ba103c5ff996..3d7667c81c24a 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -3206,7 +3206,7 @@ argument to ``to_excel`` and to ``ExcelWriter``. The built-in engines are:
writer = pd.ExcelWriter('path_to_file.xlsx', engine='xlsxwriter')
# Or via pandas configuration.
- from pandas import options # noqa: E402
+ from pandas import options # noqa: E402
options.io.excel.xlsx.writer = 'xlsxwriter'
df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')
diff --git a/doc/sphinxext/contributors.py b/doc/sphinxext/contributors.py
index 4256e4659715d..1a064f71792e9 100644
--- a/doc/sphinxext/contributors.py
+++ b/doc/sphinxext/contributors.py
@@ -8,12 +8,11 @@
code contributors and commits, and then list each contributor
individually.
"""
+from announce import build_components
from docutils import nodes
from docutils.parsers.rst import Directive
import git
-from announce import build_components
-
class ContributorsDirective(Directive):
required_arguments = 1
diff --git a/pandas/core/api.py b/pandas/core/api.py
index 73323d93b8215..bd2a57a15bdd2 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -2,6 +2,16 @@
import numpy as np
+from pandas.core.dtypes.dtypes import (
+ CategoricalDtype,
+ DatetimeTZDtype,
+ IntervalDtype,
+ PeriodDtype,
+)
+from pandas.core.dtypes.missing import isna, isnull, notna, notnull
+
+from pandas.core.algorithms import factorize, unique, value_counts
+from pandas.core.arrays import Categorical
from pandas.core.arrays.integer import (
Int8Dtype,
Int16Dtype,
@@ -12,45 +22,38 @@
UInt32Dtype,
UInt64Dtype,
)
-from pandas.core.algorithms import factorize, unique, value_counts
-from pandas.core.dtypes.missing import isna, isnull, notna, notnull
-from pandas.core.dtypes.dtypes import (
- CategoricalDtype,
- PeriodDtype,
- IntervalDtype,
- DatetimeTZDtype,
-)
-from pandas.core.arrays import Categorical
from pandas.core.construction import array
+
from pandas.core.groupby import Grouper, NamedAgg
-from pandas.io.formats.format import set_eng_float_format
+
+# DataFrame needs to be imported after NamedAgg to avoid a circular import
+from pandas.core.frame import DataFrame # isort:skip
from pandas.core.index import (
- Index,
CategoricalIndex,
- Int64Index,
- UInt64Index,
- RangeIndex,
+ DatetimeIndex,
Float64Index,
- MultiIndex,
+ Index,
+ Int64Index,
IntervalIndex,
- TimedeltaIndex,
- DatetimeIndex,
- PeriodIndex,
+ MultiIndex,
NaT,
+ PeriodIndex,
+ RangeIndex,
+ TimedeltaIndex,
+ UInt64Index,
)
+from pandas.core.indexes.datetimes import Timestamp, bdate_range, date_range
+from pandas.core.indexes.interval import Interval, interval_range
from pandas.core.indexes.period import Period, period_range
from pandas.core.indexes.timedeltas import Timedelta, timedelta_range
-from pandas.core.indexes.datetimes import Timestamp, date_range, bdate_range
-from pandas.core.indexes.interval import Interval, interval_range
-
-from pandas.core.series import Series
-from pandas.core.frame import DataFrame
-
-# TODO: Remove import when statsmodels updates #18264
-from pandas.core.reshape.reshape import get_dummies
-
from pandas.core.indexing import IndexSlice
-from pandas.core.tools.numeric import to_numeric
-from pandas.tseries.offsets import DateOffset
+from pandas.core.reshape.reshape import (
+ get_dummies,
+) # TODO: Remove get_dummies import when statsmodels updates #18264
+from pandas.core.series import Series
from pandas.core.tools.datetimes import to_datetime
+from pandas.core.tools.numeric import to_numeric
from pandas.core.tools.timedeltas import to_timedelta
+
+from pandas.io.formats.format import set_eng_float_format
+from pandas.tseries.offsets import DateOffset
diff --git a/pandas/io/msgpack/__init__.py b/pandas/io/msgpack/__init__.py
index 9b09cffd83f75..7107263c180cb 100644
--- a/pandas/io/msgpack/__init__.py
+++ b/pandas/io/msgpack/__init__.py
@@ -2,8 +2,8 @@
from collections import namedtuple
-from pandas.io.msgpack.exceptions import * # noqa
-from pandas.io.msgpack._version import version # noqa
+from pandas.io.msgpack.exceptions import * # noqa: F401,F403 isort:skip
+from pandas.io.msgpack._version import version # noqa: F401 isort:skip
class ExtType(namedtuple("ExtType", "code data")):
@@ -19,10 +19,14 @@ def __new__(cls, code, data):
return super().__new__(cls, code, data)
-import os # noqa
+import os # noqa: F401,E402 isort:skip
-from pandas.io.msgpack._packer import Packer # noqa
-from pandas.io.msgpack._unpacker import unpack, unpackb, Unpacker # noqa
+from pandas.io.msgpack._unpacker import ( # noqa: F401,E402 isort:skip
+ Unpacker,
+ unpack,
+ unpackb,
+)
+from pandas.io.msgpack._packer import Packer # noqa: E402 isort:skip
def pack(o, stream, **kwargs):
diff --git a/pandas/tests/io/pytables/test_pytables.py b/pandas/tests/io/pytables/test_pytables.py
index d67f2c3b7bd66..7306393a1339e 100644
--- a/pandas/tests/io/pytables/test_pytables.py
+++ b/pandas/tests/io/pytables/test_pytables.py
@@ -37,7 +37,6 @@
import pandas.util.testing as tm
from pandas.util.testing import assert_frame_equal, assert_series_equal, set_timezone
-from pandas.io import pytables as pytables # noqa:E402
from pandas.io.formats.printing import pprint_thing
from pandas.io.pytables import (
ClosedFileError,
@@ -46,7 +45,9 @@
Term,
read_hdf,
)
-from pandas.io.pytables import TableIterator # noqa:E402
+
+from pandas.io import pytables as pytables # noqa: E402 isort:skip
+from pandas.io.pytables import TableIterator # noqa: E402 isort:skip
tables = pytest.importorskip("tables")
diff --git a/pandas/tests/io/test_feather.py b/pandas/tests/io/test_feather.py
index 87a2405a10dd5..ee668d6890756 100644
--- a/pandas/tests/io/test_feather.py
+++ b/pandas/tests/io/test_feather.py
@@ -8,7 +8,7 @@
import pandas.util.testing as tm
from pandas.util.testing import assert_frame_equal, ensure_clean
-from pandas.io.feather_format import read_feather, to_feather # noqa:E402
+from pandas.io.feather_format import read_feather, to_feather # noqa: E402 isort:skip
pyarrow = pytest.importorskip("pyarrow")
diff --git a/scripts/find_commits_touching_func.py b/scripts/find_commits_touching_func.py
index 1075a257d4270..95a892b822cff 100755
--- a/scripts/find_commits_touching_func.py
+++ b/scripts/find_commits_touching_func.py
@@ -10,11 +10,11 @@
Usage::
$ ./find_commits_touching_func.py (see arguments below)
"""
-import logging
-import re
-import os
import argparse
from collections import namedtuple
+import logging
+import os
+import re
from dateutil.parser import parse
diff --git a/scripts/generate_pip_deps_from_conda.py b/scripts/generate_pip_deps_from_conda.py
index 6ae10c2cb07d2..29fe8bf84c12b 100755
--- a/scripts/generate_pip_deps_from_conda.py
+++ b/scripts/generate_pip_deps_from_conda.py
@@ -16,8 +16,8 @@
import os
import re
import sys
-import yaml
+import yaml
EXCLUDE = {"python=3"}
RENAME = {"pytables": "tables", "pyqt": "pyqt5"}
diff --git a/scripts/merge-pr.py b/scripts/merge-pr.py
index 95352751a23c6..300cb149f387f 100755
--- a/scripts/merge-pr.py
+++ b/scripts/merge-pr.py
@@ -22,14 +22,15 @@
# usage: ./apache-pr-merge.py (see config env vars below)
#
# Lightly modified from version of this script in incubator-parquet-format
-from subprocess import check_output
-from requests.auth import HTTPBasicAuth
-import requests
import os
+from subprocess import check_output
import sys
import textwrap
+import requests
+from requests.auth import HTTPBasicAuth
+
PANDAS_HOME = "."
PROJECT_NAME = "pandas"
print("PANDAS_HOME = " + PANDAS_HOME)
diff --git a/scripts/tests/test_validate_docstrings.py b/scripts/tests/test_validate_docstrings.py
index 35aaf10458f44..85e5bf239cbfa 100644
--- a/scripts/tests/test_validate_docstrings.py
+++ b/scripts/tests/test_validate_docstrings.py
@@ -2,12 +2,13 @@
import random
import string
import textwrap
-import pytest
-import numpy as np
-import pandas as pd
+import numpy as np
+import pytest
import validate_docstrings
+import pandas as pd
+
validate_one = validate_docstrings.validate_one
diff --git a/scripts/validate_docstrings.py b/scripts/validate_docstrings.py
index bf5d861281a36..401eaf8ff5ed5 100755
--- a/scripts/validate_docstrings.py
+++ b/scripts/validate_docstrings.py
@@ -13,20 +13,20 @@
$ ./validate_docstrings.py
$ ./validate_docstrings.py pandas.DataFrame.head
"""
-import os
-import sys
-import json
-import re
-import glob
-import functools
-import collections
import argparse
-import pydoc
-import inspect
-import importlib
+import ast
+import collections
import doctest
+import functools
+import glob
+import importlib
+import inspect
+import json
+import os
+import pydoc
+import re
+import sys
import tempfile
-import ast
import textwrap
import flake8.main.application
@@ -41,20 +41,20 @@
# script. Setting here before matplotlib is loaded.
# We don't warn for the number of open plots, as none is actually being opened
os.environ["MPLBACKEND"] = "Template"
-import matplotlib
+import matplotlib # noqa: E402 isort:skip
matplotlib.rc("figure", max_open_warning=10000)
-import numpy
+import numpy # noqa: E402 isort:skip
BASE_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, os.path.join(BASE_PATH))
-import pandas
+import pandas # noqa: E402 isort:skip
sys.path.insert(1, os.path.join(BASE_PATH, "doc", "sphinxext"))
-from numpydoc.docscrape import NumpyDocString
-from pandas.io.formats.printing import pprint_thing
+from numpydoc.docscrape import NumpyDocString # noqa: E402 isort:skip
+from pandas.io.formats.printing import pprint_thing # noqa: E402 isort:skip
PRIVATE_CLASSES = ["NDFrame", "IndexOpsMixin"]
diff --git a/setup.cfg b/setup.cfg
index 716ff5d9d8853..43dbac15f5cfe 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -110,68 +110,25 @@ directory = coverage_html_report
# To be kept consistent with "Import Formatting" section in contributing.rst
[isort]
-known_pre_libs=pandas._config
-known_pre_core=pandas._libs,pandas.util._*,pandas.compat,pandas.errors
-known_dtypes=pandas.core.dtypes
-known_post_core=pandas.tseries,pandas.io,pandas.plotting
-sections=FUTURE,STDLIB,THIRDPARTY,PRE_LIBS,PRE_CORE,DTYPES,FIRSTPARTY,POST_CORE,LOCALFOLDER
-
-known_first_party=pandas
-known_third_party=Cython,numpy,dateutil,matplotlib,python-dateutil,pytz,pyarrow,pytest
-
-multi_line_output=3
-include_trailing_comma=True
-force_grid_wrap=0
-combine_as_imports=True
-line_length=88
-force_sort_within_sections=True
-skip_glob=env,
-skip=
- pandas/__init__.py
- pandas/core/api.py,
- pandas/io/msgpack/__init__.py
- asv_bench/benchmarks/attrs_caching.py,
- asv_bench/benchmarks/binary_ops.py,
- asv_bench/benchmarks/categoricals.py,
- asv_bench/benchmarks/ctors.py,
- asv_bench/benchmarks/eval.py,
- asv_bench/benchmarks/frame_ctor.py,
- asv_bench/benchmarks/frame_methods.py,
- asv_bench/benchmarks/gil.py,
- asv_bench/benchmarks/groupby.py,
- asv_bench/benchmarks/index_object.py,
- asv_bench/benchmarks/indexing.py,
- asv_bench/benchmarks/inference.py,
- asv_bench/benchmarks/io/csv.py,
- asv_bench/benchmarks/io/excel.py,
- asv_bench/benchmarks/io/hdf.py,
- asv_bench/benchmarks/io/json.py,
- asv_bench/benchmarks/io/msgpack.py,
- asv_bench/benchmarks/io/pickle.py,
- asv_bench/benchmarks/io/sql.py,
- asv_bench/benchmarks/io/stata.py,
- asv_bench/benchmarks/join_merge.py,
- asv_bench/benchmarks/multiindex_object.py,
- asv_bench/benchmarks/panel_ctor.py,
- asv_bench/benchmarks/panel_methods.py,
- asv_bench/benchmarks/plotting.py,
- asv_bench/benchmarks/reindex.py,
- asv_bench/benchmarks/replace.py,
- asv_bench/benchmarks/reshape.py,
- asv_bench/benchmarks/rolling.py,
- asv_bench/benchmarks/series_methods.py,
- asv_bench/benchmarks/sparse.py,
- asv_bench/benchmarks/stat_ops.py,
- asv_bench/benchmarks/timeseries.py
- asv_bench/benchmarks/pandas_vb_common.py
- asv_bench/benchmarks/offset.py
- asv_bench/benchmarks/dtypes.py
- asv_bench/benchmarks/strings.py
- asv_bench/benchmarks/period.py
+known_pre_libs = pandas._config
+known_pre_core = pandas._libs,pandas.util._*,pandas.compat,pandas.errors
+known_dtypes = pandas.core.dtypes
+known_post_core = pandas.tseries,pandas.io,pandas.plotting
+sections = FUTURE,STDLIB,THIRDPARTY,PRE_LIBS,PRE_CORE,DTYPES,FIRSTPARTY,POST_CORE,LOCALFOLDER
+known_first_party = pandas
+known_third_party = _pytest,announce,dateutil,docutils,flake8,git,hypothesis,jinja2,lxml,matplotlib,numpy,numpydoc,pkg_resources,pyarrow,pytest,pytz,requests,scipy,setuptools,sphinx,sqlalchemy,validate_docstrings,yaml
+multi_line_output = 3
+include_trailing_comma = True
+force_grid_wrap = 0
+combine_as_imports = True
+line_length = 88
+force_sort_within_sections = True
+skip_glob = env,
+skip = pandas/__init__.py,pandas/core/api.py
[mypy]
ignore_missing_imports=True
no_implicit_optional=True
[mypy-pandas.conftest,pandas.tests.*]
-ignore_errors=True
\ No newline at end of file
+ignore_errors=True
diff --git a/setup.py b/setup.py
index d2c6b18b892cd..a86527ace092b 100755
--- a/setup.py
+++ b/setup.py
@@ -6,16 +6,16 @@
BSD license. Parts are from lxml (https://github.com/lxml/lxml)
"""
+from distutils.sysconfig import get_config_vars
+from distutils.version import LooseVersion
import os
from os.path import join as pjoin
-
-import pkg_resources
import platform
-from distutils.sysconfig import get_config_vars
-import sys
import shutil
-from distutils.version import LooseVersion
-from setuptools import setup, Command, find_packages
+import sys
+
+import pkg_resources
+from setuptools import Command, find_packages, setup
# versioning
import versioneer
@@ -58,8 +58,8 @@ def is_platform_mac():
# The import of Extension must be after the import of Cython, otherwise
# we do not get the appropriately patched class.
# See https://cython.readthedocs.io/en/latest/src/reference/compilation.html
-from distutils.extension import Extension # noqa:E402
-from distutils.command.build import build # noqa:E402
+from distutils.extension import Extension # noqa: E402 isort:skip
+from distutils.command.build import build # noqa: E402 isort:skip
try:
if not _CYTHON_INSTALLED:
@@ -831,9 +831,7 @@ def srcpath(name=None, suffix=".pyx", subdir="src"):
]
},
entry_points={
- "pandas_plotting_backends": [
- "matplotlib = pandas:plotting._matplotlib",
- ],
+ "pandas_plotting_backends": ["matplotlib = pandas:plotting._matplotlib"]
},
**setuptools_kwargs
)
| I noticed that `pre-commit` is used and ran `pre-commit run --all-files` which results in massive diffs, mostly because of `isort`.
I have added the correct `noqa` and `isort:skip` flags to the lines which needed it to function correctly with the current `.pre-commit-config.yaml`, such that the imports are correctly sorted now.
I also added `seed-isort-config` such that `setup.cfg` is parsed for `pre-commit` and fixed the formatting of `.pre-commit-config.yaml`.
~~Finally, there is `pandas/__init__.py` that is touched by `pre-commit run --all-files`, however, this has less trivial changes and might result in something breaking, so I don't dare to touch it.~~
It was because the `skip` entry in `[isort]` in `setup.cfg` is somehow ignored when running pre-commit and I can't figure out why (I will open an issue in the `isort` repo when it's clear that this will be merged). I have also deleted the other ignored files because I fixed the problems there. `pandas/core/api.py` needs to stay in `skip` because `isort` and `black` make conflicting edits. | https://api.github.com/repos/pandas-dev/pandas/pulls/27915 | 2019-08-14T09:40:45Z | 2019-08-30T17:05:32Z | 2019-08-30T17:05:32Z | 2019-08-30T17:05:45Z |
REF: use should_extension_dispatch for comparison method | diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index dbcf09a401f27..0de28f0a4a8b3 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -17,9 +17,7 @@
from pandas.core.dtypes.common import (
ensure_object,
is_bool_dtype,
- is_categorical_dtype,
is_datetime64_dtype,
- is_datetime64tz_dtype,
is_datetimelike_v_numeric,
is_extension_array_dtype,
is_integer_dtype,
@@ -32,6 +30,7 @@
ABCDataFrame,
ABCDatetimeArray,
ABCDatetimeIndex,
+ ABCExtensionArray,
ABCIndexClass,
ABCSeries,
ABCSparseSeries,
@@ -699,42 +698,17 @@ def wrapper(self, other, axis=None):
if isinstance(other, ABCSeries) and not self._indexed_same(other):
raise ValueError("Can only compare identically-labeled Series objects")
- elif (
- is_list_like(other)
- and len(other) != len(self)
- and not isinstance(other, (set, frozenset))
- ):
- raise ValueError("Lengths must match")
- elif isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
+ elif isinstance(
+ other, (np.ndarray, ABCExtensionArray, ABCIndexClass, ABCSeries)
+ ):
# TODO: make this treatment consistent across ops and classes.
# We are not catching all listlikes here (e.g. frozenset, tuple)
# The ambiguous case is object-dtype. See GH#27803
if len(self) != len(other):
raise ValueError("Lengths must match to compare")
- if is_categorical_dtype(self):
- # Dispatch to Categorical implementation; CategoricalIndex
- # behavior is non-canonical GH#19513
- res_values = dispatch_to_extension_op(op, self, other)
-
- elif is_datetime64_dtype(self) or is_datetime64tz_dtype(self):
- # Dispatch to DatetimeIndex to ensure identical
- # Series/Index behavior
- from pandas.core.arrays import DatetimeArray
-
- res_values = dispatch_to_extension_op(op, DatetimeArray(self), other)
-
- elif is_timedelta64_dtype(self):
- from pandas.core.arrays import TimedeltaArray
-
- res_values = dispatch_to_extension_op(op, TimedeltaArray(self), other)
-
- elif is_extension_array_dtype(self) or (
- is_extension_array_dtype(other) and not is_scalar(other)
- ):
- # Note: the `not is_scalar(other)` condition rules out
- # e.g. other == "category"
+ if should_extension_dispatch(self, other):
res_values = dispatch_to_extension_op(op, self, other)
elif is_scalar(other) and isna(other):
| This sits on top of #27815
| https://api.github.com/repos/pandas-dev/pandas/pulls/27912 | 2019-08-14T00:50:52Z | 2019-08-19T21:48:39Z | 2019-08-19T21:48:38Z | 2019-08-19T21:57:36Z |
CLN: missing boilerplate in Sparse op | diff --git a/pandas/core/arrays/sparse.py b/pandas/core/arrays/sparse.py
index 2234167fe0193..201174b6b1995 100644
--- a/pandas/core/arrays/sparse.py
+++ b/pandas/core/arrays/sparse.py
@@ -39,6 +39,7 @@
)
from pandas.core.dtypes.dtypes import register_extension_dtype
from pandas.core.dtypes.generic import (
+ ABCDataFrame,
ABCIndexClass,
ABCSeries,
ABCSparseArray,
@@ -1735,13 +1736,15 @@ def sparse_unary_method(self):
@classmethod
def _create_arithmetic_method(cls, op):
- def sparse_arithmetic_method(self, other):
- op_name = op.__name__
+ op_name = op.__name__
- if isinstance(other, (ABCSeries, ABCIndexClass)):
+ def sparse_arithmetic_method(self, other):
+ if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
# Rely on pandas to dispatch to us.
return NotImplemented
+ other = lib.item_from_zerodim(other)
+
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
diff --git a/pandas/tests/arrays/sparse/test_arithmetics.py b/pandas/tests/arrays/sparse/test_arithmetics.py
index 57e5a35d99e48..cb5b437c962f9 100644
--- a/pandas/tests/arrays/sparse/test_arithmetics.py
+++ b/pandas/tests/arrays/sparse/test_arithmetics.py
@@ -441,6 +441,23 @@ def test_with_list(op):
tm.assert_sp_array_equal(result, expected)
+def test_with_dataframe():
+ # GH#27910
+ arr = pd.SparseArray([0, 1], fill_value=0)
+ df = pd.DataFrame([[1, 2], [3, 4]])
+ result = arr.__add__(df)
+ assert result is NotImplemented
+
+
+def test_with_zerodim_ndarray():
+ # GH#27910
+ arr = pd.SparseArray([0, 1], fill_value=0)
+
+ result = arr * np.array(2)
+ expected = arr * 2
+ tm.assert_sp_array_equal(result, expected)
+
+
@pytest.mark.parametrize("ufunc", [np.abs, np.exp])
@pytest.mark.parametrize(
"arr", [pd.SparseArray([0, 0, -1, 1]), pd.SparseArray([None, None, -1, 1])]
| After this I think we'll be ready for #23853 | https://api.github.com/repos/pandas-dev/pandas/pulls/27910 | 2019-08-13T23:33:04Z | 2019-08-19T16:45:42Z | 2019-08-19T16:45:42Z | 2019-08-19T18:07:22Z |
CLN: Catch more specific exceptions in groupby | diff --git a/pandas/_libs/index.pyx b/pandas/_libs/index.pyx
index 7424c4ddc3d92..979dad6db0838 100644
--- a/pandas/_libs/index.pyx
+++ b/pandas/_libs/index.pyx
@@ -1,4 +1,5 @@
from datetime import datetime, timedelta, date
+import warnings
import cython
diff --git a/pandas/_libs/index_class_helper.pxi.in b/pandas/_libs/index_class_helper.pxi.in
index 3c9a096e7ecc0..4db048eeb0383 100644
--- a/pandas/_libs/index_class_helper.pxi.in
+++ b/pandas/_libs/index_class_helper.pxi.in
@@ -60,7 +60,16 @@ cdef class {{name}}Engine(IndexEngine):
# A view is needed for some subclasses, such as PeriodEngine:
values = self._get_index_values().view('{{dtype}}')
- indexer = values == val
+ try:
+ with warnings.catch_warnings():
+ # e.g. if values is float64 and `val` is a str, suppress warning
+ warnings.filterwarnings("ignore", category=FutureWarning)
+ indexer = values == val
+ except TypeError:
+ # if the equality above returns a bool, cython will raise TypeError
+ # when trying to cast it to ndarray
+ raise KeyError(val)
+
found = np.where(indexer)[0]
count = len(found)
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 6c95b521110a9..c0436e9389078 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -349,7 +349,7 @@ def _decide_output_index(self, output, labels):
output_keys = sorted(output)
try:
output_keys.sort()
- except Exception: # pragma: no cover
+ except TypeError:
pass
if isinstance(labels, MultiIndex):
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 6deef16bdec13..55def024cb1d4 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -727,8 +727,7 @@ def f(g):
with option_context("mode.chained_assignment", None):
try:
result = self._python_apply_general(f)
- except Exception:
-
+ except TypeError:
# gh-20949
# try again, with .apply acting as a filtering
# operation, by excluding the grouping column
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index 31623171e9e63..d079a1c4ef4f7 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -592,9 +592,11 @@ def is_in_axis(key):
# if the grouper is obj[name]
def is_in_obj(gpr):
+ if not hasattr(gpr, "name"):
+ return False
try:
- return id(gpr) == id(obj[gpr.name])
- except Exception:
+ return gpr is obj[gpr.name]
+ except (KeyError, IndexError):
return False
for i, (gpr, level) in enumerate(zip(keys, levels)):
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 7afb0a28f943e..6263973fb0d2f 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -212,8 +212,8 @@ def apply(self, f, data, axis=0):
# This Exception is also raised if `f` triggers an exception
# but it is preferable to raise the exception in Python.
pass
- except Exception:
- # raise this error to the caller
+ except TypeError:
+ # occurs if we have any EAs
pass
for key, (i, group) in zip(group_keys, splitter):
| Working hypothesis is that this will help turn up the problem underlying #27902. Regardless catching more specific exceptions is worthwhile. | https://api.github.com/repos/pandas-dev/pandas/pulls/27909 | 2019-08-13T21:39:09Z | 2019-09-03T11:39:20Z | 2019-09-03T11:39:20Z | 2019-09-03T14:20:56Z |
CLN: remove never-True checks in algorithms.py | diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 21d12d02c9008..c0ed198e200f1 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -28,13 +28,11 @@
is_complex_dtype,
is_datetime64_any_dtype,
is_datetime64_ns_dtype,
- is_datetime64tz_dtype,
is_datetimelike,
is_extension_array_dtype,
is_float_dtype,
is_integer,
is_integer_dtype,
- is_interval_dtype,
is_list_like,
is_numeric_dtype,
is_object_dtype,
@@ -183,8 +181,6 @@ def _reconstruct_data(values, dtype, original):
if is_extension_array_dtype(dtype):
values = dtype.construct_array_type()._from_sequence(values)
- elif is_datetime64tz_dtype(dtype) or is_period_dtype(dtype):
- values = Index(original)._shallow_copy(values, name=None)
elif is_bool_dtype(dtype):
values = values.astype(dtype)
@@ -1645,19 +1641,13 @@ def take_nd(
May be the same type as the input, or cast to an ndarray.
"""
- # TODO(EA): Remove these if / elifs as datetimeTZ, interval, become EAs
- # dispatch to internal type takes
if is_extension_array_dtype(arr):
return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
- elif is_datetime64tz_dtype(arr):
- return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
- elif is_interval_dtype(arr):
- return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
if is_sparse(arr):
arr = arr.to_dense()
elif isinstance(arr, (ABCIndexClass, ABCSeries)):
- arr = arr.values
+ arr = arr._values
arr = np.asarray(arr)
| https://api.github.com/repos/pandas-dev/pandas/pulls/27908 | 2019-08-13T21:30:25Z | 2019-08-14T20:21:30Z | 2019-08-14T20:21:30Z | 2019-08-14T21:35:58Z | |
Revert 37 pins | diff --git a/ci/deps/azure-37-locale.yaml b/ci/deps/azure-37-locale.yaml
index 437fbc24b9411..26dcd213bbfa0 100644
--- a/ci/deps/azure-37-locale.yaml
+++ b/ci/deps/azure-37-locale.yaml
@@ -17,7 +17,7 @@ dependencies:
- openpyxl
- pytables
- python-dateutil
- - python=3.7.3
+ - python=3.7.*
- pytz
- s3fs
- scipy
diff --git a/ci/deps/azure-37-numpydev.yaml b/ci/deps/azure-37-numpydev.yaml
index e1acda7ecf3c7..65c92ec1dcf0d 100644
--- a/ci/deps/azure-37-numpydev.yaml
+++ b/ci/deps/azure-37-numpydev.yaml
@@ -2,7 +2,7 @@ name: pandas-dev
channels:
- defaults
dependencies:
- - python=3.7.3
+ - python=3.7.*
- pytz
- Cython>=0.28.2
# universal
diff --git a/ci/deps/travis-37.yaml b/ci/deps/travis-37.yaml
index d7bed9e3f03f2..9e08c41a3d9c0 100644
--- a/ci/deps/travis-37.yaml
+++ b/ci/deps/travis-37.yaml
@@ -4,7 +4,7 @@ channels:
- conda-forge
- c3i_test
dependencies:
- - python=3.7.3
+ - python=3.7.*
- botocore>=1.11
- cython>=0.28.2
- numpy
diff --git a/environment.yml b/environment.yml
index 0cbd500209b6c..6d2cd701c3854 100644
--- a/environment.yml
+++ b/environment.yml
@@ -5,7 +5,7 @@ channels:
dependencies:
# required
- numpy>=1.15
- - python=3.7.3
+ - python=3
- python-dateutil>=2.6.1
- pytz
diff --git a/requirements-dev.txt b/requirements-dev.txt
index f866f1436edc6..cf11a3ee28258 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,5 +1,4 @@
numpy>=1.15
-python==3.7.3
python-dateutil>=2.6.1
pytz
asv
| ~second commit is temporary.~
Closes #27907
cc @jjhelmus | https://api.github.com/repos/pandas-dev/pandas/pulls/27907 | 2019-08-13T21:23:55Z | 2019-08-14T19:34:11Z | 2019-08-14T19:34:10Z | 2019-08-14T19:46:11Z |
CLN: remove _maybe_update_attributes | diff --git a/pandas/core/arrays/timedeltas.py b/pandas/core/arrays/timedeltas.py
index 6899e47045c1c..3609c68a26c0f 100644
--- a/pandas/core/arrays/timedeltas.py
+++ b/pandas/core/arrays/timedeltas.py
@@ -776,12 +776,14 @@ def __rdivmod__(self, other):
res2 = other - res1 * self
return res1, res2
- # Note: TimedeltaIndex overrides this in call to cls._add_numeric_methods
def __neg__(self):
if self.freq is not None:
return type(self)(-self._data, freq=-self.freq)
return type(self)(-self._data)
+ def __pos__(self):
+ return type(self)(self._data, freq=self.freq)
+
def __abs__(self):
# Note: freq is not preserved
return type(self)(np.abs(self._data))
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 4e098b2f8be9b..5f17dde01d2c4 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -695,7 +695,6 @@ def __array_wrap__(self, result, context=None):
return result
attrs = self._get_attributes_dict()
- attrs = self._maybe_update_attributes(attrs)
return Index(result, **attrs)
@cache_readonly
@@ -5335,12 +5334,6 @@ def _add_numeric_methods_disabled(cls):
cls.__abs__ = make_invalid_op("__abs__")
cls.__inv__ = make_invalid_op("__inv__")
- def _maybe_update_attributes(self, attrs):
- """
- Update Index attributes (e.g. freq) depending on op.
- """
- return attrs
-
@classmethod
def _add_numeric_methods_binary(cls):
"""
@@ -5374,7 +5367,6 @@ def _make_evaluate_unary(op, opstr):
def _evaluate_numeric_unary(self):
attrs = self._get_attributes_dict()
- attrs = self._maybe_update_attributes(attrs)
return Index(op(self.values), **attrs)
_evaluate_numeric_unary.__name__ = opstr
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index af99c7a2754e5..c7664d9777c71 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -15,6 +15,7 @@
from pandas.core.dtypes.common import (
ensure_int64,
+ is_bool_dtype,
is_dtype_equal,
is_float,
is_integer,
@@ -163,6 +164,20 @@ def values(self):
def asi8(self):
return self._data.asi8
+ def __array_wrap__(self, result, context=None):
+ """
+ Gets called after a ufunc.
+ """
+ result = lib.item_from_zerodim(result)
+ if is_bool_dtype(result) or lib.is_scalar(result):
+ return result
+
+ attrs = self._get_attributes_dict()
+ if not is_period_dtype(self) and attrs["freq"]:
+ # no need to infer if freq is None
+ attrs["freq"] = "infer"
+ return Index(result, **attrs)
+
# ------------------------------------------------------------------------
def equals(self, other):
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 9f2b31f23d2fa..caae9a0b5f751 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -465,14 +465,6 @@ def _convert_for_op(self, value):
return _to_M8(value)
raise ValueError("Passed item and index have different timezone")
- def _maybe_update_attributes(self, attrs):
- """ Update Index attributes (e.g. freq) depending on op """
- freq = attrs.get("freq", None)
- if freq is not None:
- # no need to infer if freq is None
- attrs["freq"] = "infer"
- return attrs
-
# --------------------------------------------------------------------
# Rendering Methods
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index cfdaf65955dab..43ed6e7b122ea 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -75,7 +75,7 @@ class RangeIndex(Int64Index):
_engine_type = libindex.Int64Engine
_range = None # type: range
- # check whether self._data has benn called
+ # check whether self._data has been called
_cached_data = None # type: np.ndarray
# --------------------------------------------------------------------
# Constructors
@@ -785,7 +785,6 @@ def _evaluate_numeric_binop(self, other):
other = extract_array(other, extract_numpy=True)
attrs = self._get_attributes_dict()
- attrs = self._maybe_update_attributes(attrs)
left, right = self, other
diff --git a/pandas/core/indexes/timedeltas.py b/pandas/core/indexes/timedeltas.py
index f2ce562536b95..d06afa3daa792 100644
--- a/pandas/core/indexes/timedeltas.py
+++ b/pandas/core/indexes/timedeltas.py
@@ -44,7 +44,12 @@ class TimedeltaDelegateMixin(DatetimelikeDelegateMixin):
# which we we dont' want to expose in the .dt accessor.
_delegate_class = TimedeltaArray
_delegated_properties = TimedeltaArray._datetimelike_ops + ["components"]
- _delegated_methods = TimedeltaArray._datetimelike_methods + ["_box_values"]
+ _delegated_methods = TimedeltaArray._datetimelike_methods + [
+ "_box_values",
+ "__neg__",
+ "__pos__",
+ "__abs__",
+ ]
_raw_properties = {"components"}
_raw_methods = {"to_pytimedelta"}
@@ -56,7 +61,7 @@ class TimedeltaDelegateMixin(DatetimelikeDelegateMixin):
TimedeltaArray,
TimedeltaDelegateMixin._delegated_methods,
typ="method",
- overwrite=False,
+ overwrite=True,
)
class TimedeltaIndex(
DatetimeIndexOpsMixin, dtl.TimelikeOps, Int64Index, TimedeltaDelegateMixin
@@ -279,14 +284,6 @@ def __setstate__(self, state):
_unpickle_compat = __setstate__
- def _maybe_update_attributes(self, attrs):
- """ Update Index attributes (e.g. freq) depending on op """
- freq = attrs.get("freq", None)
- if freq is not None:
- # no need to infer if freq is None
- attrs["freq"] = "infer"
- return attrs
-
# -------------------------------------------------------------------
# Rendering Methods
@@ -689,7 +686,6 @@ def delete(self, loc):
TimedeltaIndex._add_comparison_ops()
-TimedeltaIndex._add_numeric_methods_unary()
TimedeltaIndex._add_logical_methods_disabled()
TimedeltaIndex._add_datetimelike_methods()
| It is only non-trivial for DTI and TDI, and only actually needed in `__array_wrap__`. It is cleaner to implement the relevant patch in `DatetimeIndexOpsMixin.__array_wrap__` and avoid the need for maybe_update_attributes altogether.
| https://api.github.com/repos/pandas-dev/pandas/pulls/27896 | 2019-08-13T03:37:31Z | 2019-08-15T12:42:38Z | 2019-08-15T12:42:38Z | 2019-10-19T12:11:51Z |
DOC: clarify see also for DataFrame.iterrows() | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 02241eeaae7b2..e3edab117668f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -845,8 +845,8 @@ def iterrows(self):
See Also
--------
- itertuples : Iterate over DataFrame rows as namedtuples of the values.
- items : Iterate over (column name, Series) pairs.
+ DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
+ DataFrame.items : Iterate over (column name, Series) pairs.
Notes
-----
| - [x] closes #27862
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
| https://api.github.com/repos/pandas-dev/pandas/pulls/27893 | 2019-08-13T02:57:17Z | 2019-08-13T07:57:20Z | 2019-08-13T07:57:20Z | 2019-08-13T07:57:21Z |
CLN: remove unnecessary validate_for_numeric_unary | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 7272d4e2752be..a2e0582da6ee3 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -5341,16 +5341,6 @@ def _maybe_update_attributes(self, attrs):
"""
return attrs
- def _validate_for_numeric_unaryop(self, op, opstr):
- """
- Validate if we can perform a numeric unary operation.
- """
- if not self._is_numeric_dtype:
- raise TypeError(
- "cannot evaluate a numeric op "
- "{opstr} for type: {typ}".format(opstr=opstr, typ=type(self).__name__)
- )
-
@classmethod
def _add_numeric_methods_binary(cls):
"""
@@ -5383,7 +5373,6 @@ def _add_numeric_methods_unary(cls):
def _make_evaluate_unary(op, opstr):
def _evaluate_numeric_unary(self):
- self._validate_for_numeric_unaryop(op, opstr)
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
return Index(op(self.values), **attrs)
| https://api.github.com/repos/pandas-dev/pandas/pulls/27891 | 2019-08-13T02:01:04Z | 2019-08-14T20:39:53Z | 2019-08-14T20:39:53Z | 2019-08-14T21:33:55Z | |
BUG: fix Sparse reduction | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index b97f45efbeae9..dfa216b1db56e 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -133,7 +133,7 @@ Reshaping
Sparse
^^^^^^
-
+- Bug in reductions for :class:`Series` with Sparse dtypes (:issue:`27080`)
-
-
-
diff --git a/pandas/core/arrays/sparse.py b/pandas/core/arrays/sparse.py
index 8aa83c3fbc37d..2234167fe0193 100644
--- a/pandas/core/arrays/sparse.py
+++ b/pandas/core/arrays/sparse.py
@@ -1693,6 +1693,9 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
for sp_value, fv in zip(sp_values, fill_value)
)
return arrays
+ elif is_scalar(sp_values):
+ # e.g. reductions
+ return sp_values
return self._simple_new(
sp_values, self.sp_index, SparseDtype(sp_values.dtype, fill_value)
diff --git a/pandas/tests/series/test_ufunc.py b/pandas/tests/series/test_ufunc.py
index c024e9caba156..8144a3931b9b8 100644
--- a/pandas/tests/series/test_ufunc.py
+++ b/pandas/tests/series/test_ufunc.py
@@ -252,10 +252,7 @@ def __add__(self, other):
"values",
[
pd.array([1, 3, 2]),
- pytest.param(
- pd.array([1, 10, 0], dtype="Sparse[int]"),
- marks=pytest.mark.xfail(resason="GH-27080. Bug in SparseArray"),
- ),
+ pd.array([1, 10, 0], dtype="Sparse[int]"),
pd.to_datetime(["2000", "2010", "2001"]),
pd.to_datetime(["2000", "2010", "2001"]).tz_localize("CET"),
pd.to_datetime(["2000", "2010", "2001"]).to_period(freq="D"),
| - [x] closes #27080
- [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/27890 | 2019-08-13T01:52:58Z | 2019-08-13T16:15:37Z | 2019-08-13T16:15:36Z | 2019-08-13T16:23:38Z |
CLN: remove unnecessary dtype checks | diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 13058882084ff..a0fa1b5ff6d92 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -1,3 +1,4 @@
+import operator
from shutil import get_terminal_size
import textwrap
from typing import Type, Union, cast
@@ -77,7 +78,9 @@
)
-def _cat_compare_op(opname):
+def _cat_compare_op(op):
+ opname = "__{op}__".format(op=op.__name__)
+
def f(self, other):
# On python2, you can usually compare any type to any type, and
# Categoricals can be seen as a custom type, but having different
@@ -1240,12 +1243,12 @@ def map(self, mapper):
new_categories = new_categories.insert(len(new_categories), np.nan)
return np.take(new_categories, self._codes)
- __eq__ = _cat_compare_op("__eq__")
- __ne__ = _cat_compare_op("__ne__")
- __lt__ = _cat_compare_op("__lt__")
- __gt__ = _cat_compare_op("__gt__")
- __le__ = _cat_compare_op("__le__")
- __ge__ = _cat_compare_op("__ge__")
+ __eq__ = _cat_compare_op(operator.eq)
+ __ne__ = _cat_compare_op(operator.ne)
+ __lt__ = _cat_compare_op(operator.lt)
+ __gt__ = _cat_compare_op(operator.gt)
+ __le__ = _cat_compare_op(operator.le)
+ __ge__ = _cat_compare_op(operator.ge)
# for Series/ndarray like compat
@property
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 0372b8f0c080a..1988726edc79b 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -22,7 +22,6 @@
is_datetime64tz_dtype,
is_datetime_or_timedelta_dtype,
is_dtype_equal,
- is_extension_array_dtype,
is_float_dtype,
is_integer_dtype,
is_list_like,
@@ -1230,29 +1229,17 @@ def __add__(self, other):
if not is_period_dtype(self):
maybe_integer_op_deprecated(self)
result = self._addsub_int_array(other, operator.add)
- elif is_float_dtype(other):
- # Explicitly catch invalid dtypes
- raise TypeError(
- "cannot add {dtype}-dtype to {cls}".format(
- dtype=other.dtype, cls=type(self).__name__
- )
- )
- elif is_period_dtype(other):
- # if self is a TimedeltaArray and other is a PeriodArray with
- # a timedelta-like (i.e. Tick) freq, this operation is valid.
- # Defer to the PeriodArray implementation.
- # In remaining cases, this will end up raising TypeError.
- return NotImplemented
- elif is_extension_array_dtype(other):
- # Categorical op will raise; defer explicitly
- return NotImplemented
- else: # pragma: no cover
+ else:
+ # Includes Categorical, other ExtensionArrays
+ # For PeriodDtype, if self is a TimedeltaArray and other is a
+ # PeriodArray with a timedelta-like (i.e. Tick) freq, this
+ # operation is valid. Defer to the PeriodArray implementation.
+ # In remaining cases, this will end up raising TypeError.
return NotImplemented
if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
from pandas.core.arrays import TimedeltaArray
- # TODO: infer freq?
return TimedeltaArray(result)
return result
@@ -1302,29 +1289,13 @@ def __sub__(self, other):
if not is_period_dtype(self):
maybe_integer_op_deprecated(self)
result = self._addsub_int_array(other, operator.sub)
- elif isinstance(other, ABCIndexClass):
- raise TypeError(
- "cannot subtract {cls} and {typ}".format(
- cls=type(self).__name__, typ=type(other).__name__
- )
- )
- elif is_float_dtype(other):
- # Explicitly catch invalid dtypes
- raise TypeError(
- "cannot subtract {dtype}-dtype from {cls}".format(
- dtype=other.dtype, cls=type(self).__name__
- )
- )
- elif is_extension_array_dtype(other):
- # Categorical op will raise; defer explicitly
- return NotImplemented
- else: # pragma: no cover
+ else:
+ # Includes ExtensionArrays, float_dtype
return NotImplemented
if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
from pandas.core.arrays import TimedeltaArray
- # TODO: infer freq?
return TimedeltaArray(result)
return result
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index 28537124536e7..1aad130d9a3f5 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -223,8 +223,6 @@ def wrapper(self, other):
result = op(self.view("i8"), other.view("i8"))
o_mask = other._isnan
- result = com.values_from_object(result)
-
if o_mask.any():
result[o_mask] = nat_result
diff --git a/pandas/tests/arithmetic/test_datetime64.py b/pandas/tests/arithmetic/test_datetime64.py
index 3920cfcc002d7..1fb93a182ba0b 100644
--- a/pandas/tests/arithmetic/test_datetime64.py
+++ b/pandas/tests/arithmetic/test_datetime64.py
@@ -1097,7 +1097,13 @@ def test_dt64arr_add_timestamp_raises(self, box_with_array):
def test_dt64arr_add_sub_float(self, other, box_with_array):
dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq="D")
dtarr = tm.box_expected(dti, box_with_array)
- msg = "|".join(["unsupported operand type", "cannot (add|subtract)"])
+ msg = "|".join(
+ [
+ "unsupported operand type",
+ "cannot (add|subtract)",
+ "ufunc '?(add|subtract)'? cannot use operands with types",
+ ]
+ )
with pytest.raises(TypeError, match=msg):
dtarr + other
with pytest.raises(TypeError, match=msg):
| these aren't accomplishing anything and are very non-performant.
Also, remove an unnecessary values_from_object call and use operator.foo instead of `"__foo__"` in categorical ops. | https://api.github.com/repos/pandas-dev/pandas/pulls/27889 | 2019-08-13T01:20:48Z | 2019-08-13T12:22:41Z | 2019-08-13T12:22:41Z | 2019-08-13T13:50:42Z |
DOC: Updated Series.items 'See also' section for clarity | diff --git a/pandas/core/series.py b/pandas/core/series.py
index 4e64a25e430eb..c891298d6e499 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1681,7 +1681,8 @@ def items(self):
See Also
--------
- DataFrame.items : Equivalent to Series.items for DataFrame.
+ DataFrame.items : Iterate over (column name, Series) pairs.
+ DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
Examples
--------
| I changed the See also to:
DataFrame.items : Iterate over (column name, Series) pairs.
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
No assertions about the equivalence of DataFrame.items and Series.items
closes #27859
| https://api.github.com/repos/pandas-dev/pandas/pulls/27888 | 2019-08-13T01:14:57Z | 2019-08-13T07:50:40Z | 2019-08-13T07:50:40Z | 2019-08-13T07:50:48Z |
Backport PR #27855 on branch 0.25.x (BUG: add back check for MultiIndex case and take_split_path) | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 637ac5c9c8bd1..9a435e1d18640 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -84,7 +84,7 @@ Indexing
- Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']`` (:issue:`27516`)
- Break reference cycle involving :class:`Index` to allow garbage collection of :class:`Index` objects without running the GC. (:issue:`27585`)
--
+- Fix regression in assigning values to a single column of a DataFrame with a ``MultiIndex`` columns (:issue:`27841`).
-
Missing
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 8f242f0ae7d7c..71985c0707095 100755
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -326,6 +326,17 @@ def _setitem_with_indexer(self, indexer, value):
val = list(value.values()) if isinstance(value, dict) else value
take_split_path = not blk._can_hold_element(val)
+ # if we have any multi-indexes that have non-trivial slices
+ # (not null slices) then we must take the split path, xref
+ # GH 10360, GH 27841
+ if isinstance(indexer, tuple) and len(indexer) == len(self.obj.axes):
+ for i, ax in zip(indexer, self.obj.axes):
+ if isinstance(ax, MultiIndex) and not (
+ is_integer(i) or com.is_null_slice(i)
+ ):
+ take_split_path = True
+ break
+
if isinstance(indexer, tuple):
nindexer = []
for i, idx in enumerate(indexer):
diff --git a/pandas/tests/indexing/multiindex/test_loc.py b/pandas/tests/indexing/multiindex/test_loc.py
index a08b2b4c66af2..8b48c2bf7169f 100644
--- a/pandas/tests/indexing/multiindex/test_loc.py
+++ b/pandas/tests/indexing/multiindex/test_loc.py
@@ -390,3 +390,26 @@ def test_loc_getitem_lowerdim_corner(multiindex_dataframe_random_data):
expected = 0
result = df.sort_index().loc[("bar", "three"), "B"]
assert result == expected
+
+
+def test_loc_setitem_single_column_slice():
+ # case from https://github.com/pandas-dev/pandas/issues/27841
+ df = DataFrame(
+ "string",
+ index=list("abcd"),
+ columns=MultiIndex.from_product([["Main"], ("another", "one")]),
+ )
+ df["labels"] = "a"
+ df.loc[:, "labels"] = df.index
+ tm.assert_numpy_array_equal(np.asarray(df["labels"]), np.asarray(df.index))
+
+ # test with non-object block
+ df = DataFrame(
+ np.nan,
+ index=range(4),
+ columns=MultiIndex.from_tuples([("A", "1"), ("A", "2"), ("B", "1")]),
+ )
+ expected = df.copy()
+ df.loc[:, "B"] = np.arange(4)
+ expected.iloc[:, 2] = np.arange(4)
+ tm.assert_frame_equal(df, expected)
| Backport PR #27855: BUG: add back check for MultiIndex case and take_split_path | https://api.github.com/repos/pandas-dev/pandas/pulls/27887 | 2019-08-13T01:04:32Z | 2019-08-13T08:40:19Z | 2019-08-13T08:40:19Z | 2019-08-13T08:40:19Z |
CLN: remove unnecessary validate_for_numeric_binop | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 598c4dca9ce88..164cc6cda00a9 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1,4 +1,4 @@
-from datetime import datetime, timedelta
+from datetime import datetime
import operator
from textwrap import dedent
from typing import Union
@@ -49,7 +49,6 @@
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.generic import (
ABCDataFrame,
- ABCDateOffset,
ABCDatetimeArray,
ABCDatetimeIndex,
ABCIndexClass,
@@ -5384,51 +5383,6 @@ def _validate_for_numeric_unaryop(self, op, opstr):
"{opstr} for type: {typ}".format(opstr=opstr, typ=type(self).__name__)
)
- def _validate_for_numeric_binop(self, other, op):
- """
- Return valid other; evaluate or raise TypeError if we are not of
- the appropriate type.
-
- Notes
- -----
- This is an internal method called by ops.
- """
- opstr = "__{opname}__".format(opname=op.__name__)
- # if we are an inheritor of numeric,
- # but not actually numeric (e.g. DatetimeIndex/PeriodIndex)
- if not self._is_numeric_dtype:
- raise TypeError(
- "cannot evaluate a numeric op {opstr} "
- "for type: {typ}".format(opstr=opstr, typ=type(self).__name__)
- )
-
- if isinstance(other, Index):
- if not other._is_numeric_dtype:
- raise TypeError(
- "cannot evaluate a numeric op "
- "{opstr} with type: {typ}".format(opstr=opstr, typ=type(other))
- )
- elif isinstance(other, np.ndarray) and not other.ndim:
- other = other.item()
-
- if isinstance(other, (Index, ABCSeries, np.ndarray)):
- if len(self) != len(other):
- raise ValueError("cannot evaluate a numeric op with unequal lengths")
- other = com.values_from_object(other)
- if other.dtype.kind not in ["f", "i", "u"]:
- raise TypeError("cannot evaluate a numeric op with a non-numeric dtype")
- elif isinstance(other, (ABCDateOffset, np.timedelta64, timedelta)):
- # higher up to handle
- pass
- elif isinstance(other, (datetime, np.datetime64)):
- # higher up to handle
- pass
- else:
- if not (is_float(other) or is_integer(other)):
- raise TypeError("can only perform ops with scalar values")
-
- return other
-
@classmethod
def _add_numeric_methods_binary(cls):
"""
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index d2bea5f68b92d..cfdaf65955dab 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -25,6 +25,7 @@
from pandas.core import ops
import pandas.core.common as com
+from pandas.core.construction import extract_array
import pandas.core.indexes.base as ibase
from pandas.core.indexes.base import Index, _index_shared_docs
from pandas.core.indexes.numeric import Int64Index
@@ -782,7 +783,7 @@ def _evaluate_numeric_binop(self, other):
# Must be an np.ndarray; GH#22390
return op(self._int64index, other)
- other = self._validate_for_numeric_binop(other, op)
+ other = extract_array(other, extract_numpy=True)
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
| Lots of Index ops cleanup, breaking it up into small pieces. | https://api.github.com/repos/pandas-dev/pandas/pulls/27886 | 2019-08-12T23:13:41Z | 2019-08-13T12:21:42Z | 2019-08-13T12:21:41Z | 2019-08-13T13:56:57Z |
CLN: Index.__new__ | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 02241eeaae7b2..d2a6d718a6543 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3447,15 +3447,14 @@ def _get_info_slice(obj, indexer):
if not is_list_like(exclude):
exclude = (exclude,) if exclude is not None else ()
- selection = tuple(map(frozenset, (include, exclude)))
+ selection = (frozenset(include), frozenset(exclude))
if not any(selection):
raise ValueError("at least one of include or exclude must be nonempty")
# convert the myriad valid dtypes object to a single representation
- include, exclude = map(
- lambda x: frozenset(map(infer_dtype_from_object, x)), selection
- )
+ include = frozenset(infer_dtype_from_object(x) for x in include)
+ exclude = frozenset(infer_dtype_from_object(x) for x in exclude)
for dtypes in (include, exclude):
invalidate_string_dtypes(dtypes)
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 598c4dca9ce88..0ef63672325ca 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -309,12 +309,12 @@ def __new__(
elif (
is_datetime64_any_dtype(data)
- or (dtype is not None and is_datetime64_any_dtype(dtype))
+ or is_datetime64_any_dtype(dtype)
or "tz" in kwargs
):
from pandas import DatetimeIndex
- if dtype is not None and is_dtype_equal(_o_dtype, dtype):
+ if is_dtype_equal(_o_dtype, dtype):
# GH#23524 passing `dtype=object` to DatetimeIndex is invalid,
# will raise in the where `data` is already tz-aware. So
# we leave it out of this step and cast to object-dtype after
@@ -329,12 +329,10 @@ def __new__(
)
return result
- elif is_timedelta64_dtype(data) or (
- dtype is not None and is_timedelta64_dtype(dtype)
- ):
+ elif is_timedelta64_dtype(data) or is_timedelta64_dtype(dtype):
from pandas import TimedeltaIndex
- if dtype is not None and is_dtype_equal(_o_dtype, dtype):
+ if is_dtype_equal(_o_dtype, dtype):
# Note we can pass copy=False because the .astype below
# will always make a copy
result = TimedeltaIndex(data, copy=False, name=name, **kwargs)
@@ -355,11 +353,9 @@ def __new__(
elif is_extension_array_dtype(data) or is_extension_array_dtype(dtype):
data = np.asarray(data)
if not (dtype is None or is_object_dtype(dtype)):
-
# coerce to the provided dtype
- data = dtype.construct_array_type()._from_sequence(
- data, dtype=dtype, copy=False
- )
+ ea_cls = dtype.construct_array_type()
+ data = ea_cls._from_sequence(data, dtype=dtype, copy=False)
# coerce to the object dtype
data = data.astype(object)
@@ -368,58 +364,48 @@ def __new__(
# index-like
elif isinstance(data, (np.ndarray, Index, ABCSeries)):
if dtype is not None:
- try:
-
- # we need to avoid having numpy coerce
- # things that look like ints/floats to ints unless
- # they are actually ints, e.g. '0' and 0.0
- # should not be coerced
- # GH 11836
- if is_integer_dtype(dtype):
- inferred = lib.infer_dtype(data, skipna=False)
- if inferred == "integer":
- data = maybe_cast_to_integer_array(data, dtype, copy=copy)
- elif inferred in ["floating", "mixed-integer-float"]:
- if isna(data).any():
- raise ValueError("cannot convert float NaN to integer")
-
- if inferred == "mixed-integer-float":
- data = maybe_cast_to_integer_array(data, dtype)
-
- # If we are actually all equal to integers,
- # then coerce to integer.
- try:
- return cls._try_convert_to_int_index(
- data, copy, name, dtype
- )
- except ValueError:
- pass
-
- # Return an actual float index.
- from .numeric import Float64Index
-
- return Float64Index(data, copy=copy, dtype=dtype, name=name)
-
- elif inferred == "string":
- pass
- else:
- data = data.astype(dtype)
- elif is_float_dtype(dtype):
- inferred = lib.infer_dtype(data, skipna=False)
- if inferred == "string":
+ # we need to avoid having numpy coerce
+ # things that look like ints/floats to ints unless
+ # they are actually ints, e.g. '0' and 0.0
+ # should not be coerced
+ # GH 11836
+ if is_integer_dtype(dtype):
+ inferred = lib.infer_dtype(data, skipna=False)
+ if inferred == "integer":
+ data = maybe_cast_to_integer_array(data, dtype, copy=copy)
+ elif inferred in ["floating", "mixed-integer-float"]:
+ if isna(data).any():
+ raise ValueError("cannot convert float NaN to integer")
+
+ if inferred == "mixed-integer-float":
+ data = maybe_cast_to_integer_array(data, dtype)
+
+ # If we are actually all equal to integers,
+ # then coerce to integer.
+ try:
+ return cls._try_convert_to_int_index(
+ data, copy, name, dtype
+ )
+ except ValueError:
pass
- else:
- data = data.astype(dtype)
+
+ # Return an actual float index.
+ from .numeric import Float64Index
+
+ return Float64Index(data, copy=copy, dtype=dtype, name=name)
+
+ elif inferred == "string":
+ pass
else:
- data = np.array(data, dtype=dtype, copy=copy)
-
- except (TypeError, ValueError) as e:
- msg = str(e)
- if (
- "cannot convert float" in msg
- or "Trying to coerce float values to integer" in msg
- ):
- raise
+ data = data.astype(dtype)
+ elif is_float_dtype(dtype):
+ inferred = lib.infer_dtype(data, skipna=False)
+ if inferred == "string":
+ pass
+ else:
+ data = data.astype(dtype)
+ else:
+ data = np.array(data, dtype=dtype, copy=copy)
# maybe coerce to a sub-class
from pandas.core.indexes.period import PeriodIndex, IncompatibleFrequency
@@ -555,16 +541,6 @@ def _simple_new(cls, values, name=None, dtype=None, **kwargs):
Must be careful not to recurse.
"""
- if not hasattr(values, "dtype"):
- if (values is None or not len(values)) and dtype is not None:
- values = np.empty(0, dtype=dtype)
- else:
- values = np.array(values, copy=False)
- if is_object_dtype(values):
- values = cls(
- values, name=name, dtype=dtype, **kwargs
- )._ndarray_values
-
if isinstance(values, (ABCSeries, ABCIndexClass)):
# Index._data must always be an ndarray.
# This is no-copy for when _values is an ndarray,
@@ -1862,8 +1838,6 @@ def inferred_type(self):
@cache_readonly
def is_all_dates(self):
- if self._data is None:
- return False
return is_datetime_array(ensure_object(self.values))
# --------------------------------------------------------------------
@@ -3134,13 +3108,9 @@ def _convert_scalar_indexer(self, key, kind=None):
"""
@Appender(_index_shared_docs["_convert_slice_indexer"])
- def _convert_slice_indexer(self, key, kind=None):
+ def _convert_slice_indexer(self, key: slice, kind=None):
assert kind in ["ix", "loc", "getitem", "iloc", None]
- # if we are not a slice, then we are done
- if not isinstance(key, slice):
- return key
-
# validate iloc
if kind == "iloc":
return slice(
| underlying goal is to share code between Index.__new__ and pd.array
Unrelated cleanup in DataFrame.select_dtypes because I found it cumbersome. | https://api.github.com/repos/pandas-dev/pandas/pulls/27883 | 2019-08-12T21:16:22Z | 2019-08-13T12:20:40Z | 2019-08-13T12:20:40Z | 2019-08-13T13:51:13Z |
BUG: lzma is a required part of python, make it optional | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 7c2e488b98509..b8475b41a4950 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -103,7 +103,6 @@ MultiIndex
I/O
^^^
-
- Avoid calling ``S3File.s3`` when reading parquet, as this was removed in s3fs version 0.3.0 (:issue:`27756`)
-
-
@@ -160,6 +159,14 @@ Other
-
-
+I/O and LZMA
+~~~~~~~~~~~~
+
+Some users may unknowingly have an incomplete Python installation, which lacks the `lzma` module from the standard library. In this case, `import pandas` failed due to an `ImportError` (:issue: `27575`).
+Pandas will now warn, rather than raising an `ImportError` if the `lzma` module is not present. Any subsequent attempt to use `lzma` methods will raise a `RuntimeError`.
+A possible fix for the lack of the `lzma` module is to ensure you have the necessary libraries and then re-install Python.
+For example, on MacOS installing Python with `pyenv` may lead to an incomplete Python installation due to unmet system dependencies at compilation time (like `xz`). Compilation will succeed, but Python might fail at run time. The issue can be solved by installing the necessary dependencies and then re-installing Python.
+
.. _whatsnew_0.251.contributors:
Contributors
diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx
index cafc31dad3568..6cc9dd22ce7c9 100644
--- a/pandas/_libs/parsers.pyx
+++ b/pandas/_libs/parsers.pyx
@@ -2,7 +2,6 @@
# See LICENSE for the license
import bz2
import gzip
-import lzma
import os
import sys
import time
@@ -59,9 +58,12 @@ from pandas.core.arrays import Categorical
from pandas.core.dtypes.concat import union_categoricals
import pandas.io.common as icom
+from pandas.compat import _import_lzma, _get_lzma_file
from pandas.errors import (ParserError, DtypeWarning,
EmptyDataError, ParserWarning)
+lzma = _import_lzma()
+
# Import CParserError as alias of ParserError for backwards compatibility.
# Ultimately, we want to remove this import. See gh-12665 and gh-14479.
CParserError = ParserError
@@ -645,9 +647,9 @@ cdef class TextReader:
'zip file %s', str(zip_names))
elif self.compression == 'xz':
if isinstance(source, str):
- source = lzma.LZMAFile(source, 'rb')
+ source = _get_lzma_file(lzma)(source, 'rb')
else:
- source = lzma.LZMAFile(filename=source)
+ source = _get_lzma_file(lzma)(filename=source)
else:
raise ValueError('Unrecognized compression type: %s' %
self.compression)
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py
index 5ecd641fc68be..b32da8da3a1fb 100644
--- a/pandas/compat/__init__.py
+++ b/pandas/compat/__init__.py
@@ -10,6 +10,7 @@
import platform
import struct
import sys
+import warnings
PY35 = sys.version_info[:2] == (3, 5)
PY36 = sys.version_info >= (3, 6)
@@ -65,3 +66,32 @@ def is_platform_mac():
def is_platform_32bit():
return struct.calcsize("P") * 8 < 64
+
+
+def _import_lzma():
+ """Attempts to import lzma, warning the user when lzma is not available.
+ """
+ try:
+ import lzma
+
+ return lzma
+ except ImportError:
+ msg = (
+ "Could not import the lzma module. "
+ "Your installed Python is incomplete. "
+ "Attempting to use lzma compression will result in a RuntimeError."
+ )
+ warnings.warn(msg)
+
+
+def _get_lzma_file(lzma):
+ """Returns the lzma method LZMAFile when the module was correctly imported.
+ Otherwise, raises a RuntimeError.
+ """
+ if lzma is None:
+ raise RuntimeError(
+ "lzma module not available. "
+ "A Python re-install with the proper "
+ "dependencies might be required to solve this issue."
+ )
+ return lzma.LZMAFile
diff --git a/pandas/io/common.py b/pandas/io/common.py
index e01e473047b88..ac57cef372399 100644
--- a/pandas/io/common.py
+++ b/pandas/io/common.py
@@ -6,7 +6,6 @@
import gzip
from http.client import HTTPException # noqa
from io import BytesIO
-import lzma
import mmap
import os
import pathlib
@@ -23,6 +22,7 @@
from urllib.request import pathname2url, urlopen
import zipfile
+from pandas.compat import _get_lzma_file, _import_lzma
from pandas.errors import ( # noqa
AbstractMethodError,
DtypeWarning,
@@ -35,6 +35,8 @@
from pandas._typing import FilePathOrBuffer
+lzma = _import_lzma()
+
# gh-12665: Alias for now and remove later.
CParserError = ParserError
@@ -395,7 +397,7 @@ def _get_handle(
# XZ Compression
elif compression == "xz":
- f = lzma.LZMAFile(path_or_buf, mode)
+ f = _get_lzma_file(lzma)(path_or_buf, mode)
# Unrecognized Compression
else:
diff --git a/pandas/tests/io/test_compression.py b/pandas/tests/io/test_compression.py
index ce459ab24afe0..16ca1109f266c 100644
--- a/pandas/tests/io/test_compression.py
+++ b/pandas/tests/io/test_compression.py
@@ -1,5 +1,7 @@
import contextlib
import os
+import subprocess
+import textwrap
import warnings
import pytest
@@ -125,3 +127,33 @@ def test_compression_warning(compression_only):
with tm.assert_produces_warning(RuntimeWarning, check_stacklevel=False):
with f:
df.to_csv(f, compression=compression_only)
+
+
+def test_with_missing_lzma():
+ """Tests if import pandas works when lzma is not present."""
+ # https://github.com/pandas-dev/pandas/issues/27575
+ code = textwrap.dedent(
+ """\
+ import sys
+ sys.modules['lzma'] = None
+ import pandas
+ """
+ )
+ subprocess.check_output(["python", "-c", code])
+
+
+def test_with_missing_lzma_runtime():
+ """Tests if RuntimeError is hit when calling lzma without
+ having the module available."""
+ code = textwrap.dedent(
+ """
+ import sys
+ import pytest
+ sys.modules['lzma'] = None
+ import pandas
+ df = pandas.DataFrame()
+ with pytest.raises(RuntimeError, match='lzma module'):
+ df.to_csv('foo.csv', compression='xz')
+ """
+ )
+ subprocess.check_output(["python", "-c", code])
diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py
index 076d0c9f947c7..30555508f0998 100644
--- a/pandas/tests/io/test_pickle.py
+++ b/pandas/tests/io/test_pickle.py
@@ -13,7 +13,6 @@
import bz2
import glob
import gzip
-import lzma
import os
import pickle
import shutil
@@ -22,7 +21,7 @@
import pytest
-from pandas.compat import is_platform_little_endian
+from pandas.compat import _get_lzma_file, _import_lzma, is_platform_little_endian
import pandas as pd
from pandas import Index
@@ -30,6 +29,8 @@
from pandas.tseries.offsets import Day, MonthEnd
+lzma = _import_lzma()
+
@pytest.fixture(scope="module")
def current_pickle_data():
@@ -270,7 +271,7 @@ def compress_file(self, src_path, dest_path, compression):
with zipfile.ZipFile(dest_path, "w", compression=zipfile.ZIP_DEFLATED) as f:
f.write(src_path, os.path.basename(src_path))
elif compression == "xz":
- f = lzma.LZMAFile(dest_path, "w")
+ f = _get_lzma_file(lzma)(dest_path, "w")
else:
msg = "Unrecognized compression type: {}".format(compression)
raise ValueError(msg)
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index cf8452cdd0c59..a8f0d0da52e1f 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -5,7 +5,6 @@
from functools import wraps
import gzip
import http.client
-import lzma
import os
import re
from shutil import rmtree
@@ -26,7 +25,7 @@
)
import pandas._libs.testing as _testing
-from pandas.compat import raise_with_traceback
+from pandas.compat import _get_lzma_file, _import_lzma, raise_with_traceback
from pandas.core.dtypes.common import (
is_bool,
@@ -70,6 +69,8 @@
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
@@ -211,7 +212,7 @@ def decompress_file(path, compression):
elif compression == "bz2":
f = bz2.BZ2File(path, "rb")
elif compression == "xz":
- f = lzma.LZMAFile(path, "rb")
+ f = _get_lzma_file(lzma)(path, "rb")
elif compression == "zip":
zip_file = zipfile.ZipFile(path)
zip_names = zip_file.namelist()
@@ -264,9 +265,7 @@ def write_to_compressed(compression, path, data, dest="test"):
compress_method = bz2.BZ2File
elif compression == "xz":
- import lzma
-
- compress_method = lzma.LZMAFile
+ compress_method = _get_lzma_file(lzma)
else:
msg = "Unrecognized compression type: {}".format(compression)
raise ValueError(msg)
| Importing `lzma` when Python has been compiled without its support (usually due to a lack of `xz` in the system) will raise a warning. Substituted `import lzma` for helper function.
- [x] closes #27575
- [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/27882 | 2019-08-12T21:01:21Z | 2019-08-19T13:06:15Z | 2019-08-19T13:06:14Z | 2021-09-08T17:02:03Z |
CLN: use invalid_comparison for incorrect case in Index comparison | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 598c4dca9ce88..0c8169b4818a0 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -112,17 +112,11 @@ def cmp_method(self, other):
with np.errstate(all="ignore"):
result = op(self.values, np.asarray(other))
- # technically we could support bool dtyped Index
- # for now just return the indexing array directly
if is_bool_dtype(result):
return result
- try:
- return Index(result)
- except TypeError:
- return result
+ return ops.invalid_comparison(self, other, op)
name = "__{name}__".format(name=op.__name__)
- # TODO: docstring?
return set_function_name(cmp_method, name, cls)
diff --git a/pandas/tests/indexes/test_numpy_compat.py b/pandas/tests/indexes/test_numpy_compat.py
index f9ca1bca04165..645ad19ea4cc9 100644
--- a/pandas/tests/indexes/test_numpy_compat.py
+++ b/pandas/tests/indexes/test_numpy_compat.py
@@ -118,4 +118,7 @@ def test_elementwise_comparison_warning():
# this test.
idx = Index([1, 2])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
- idx == "a"
+ result = idx == "a"
+
+ expected = np.array([False, False])
+ tm.assert_numpy_array_equal(result, expected)
| Only one test reaches this point, and we aren't currently checking for the output, just that a warning is emitted. This fixes the currently-incorrect output and updates the test to check for it. | https://api.github.com/repos/pandas-dev/pandas/pulls/27879 | 2019-08-12T20:07:29Z | 2019-08-13T12:18:36Z | 2019-08-13T12:18:36Z | 2019-08-13T13:55:52Z |
TST: Raise ValueError and suggestion to use header=None if header=-1 is pa… | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index dfa216b1db56e..823064949859f 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -105,7 +105,7 @@ I/O
^^^
- Avoid calling ``S3File.s3`` when reading parquet, as this was removed in s3fs version 0.3.0 (:issue:`27756`)
--
+- Better error message when a negative header is passed in :func:`pandas.read_csv` (:issue:`27779`)
-
Plotting
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index f4b00b0aac5f7..a3ff837bc7f52 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -1393,6 +1393,10 @@ def __init__(self, kwds):
if isinstance(self.header, (list, tuple, np.ndarray)):
if not all(map(is_integer, self.header)):
raise ValueError("header must be integer or list of integers")
+ if any(i < 0 for i in self.header):
+ raise ValueError(
+ "cannot specify multi-index header with negative integers"
+ )
if kwds.get("usecols"):
raise ValueError(
"cannot specify usecols when specifying a multi-index header"
@@ -1419,6 +1423,13 @@ def __init__(self, kwds):
elif self.header is not None and not is_integer(self.header):
raise ValueError("header must be integer or list of integers")
+ # GH 27779
+ elif self.header is not None and self.header < 0:
+ raise ValueError(
+ "Passing negative integer to header is invalid. "
+ "For no header, use header=None instead"
+ )
+
self._name_processed = False
self._first_chunk = True
diff --git a/pandas/tests/io/parser/test_header.py b/pandas/tests/io/parser/test_header.py
index 99e0181741998..0ecd8be7ddc78 100644
--- a/pandas/tests/io/parser/test_header.py
+++ b/pandas/tests/io/parser/test_header.py
@@ -24,6 +24,35 @@ def test_read_with_bad_header(all_parsers):
parser.read_csv(s, header=[10])
+def test_negative_header(all_parsers):
+ # see gh-27779
+ parser = all_parsers
+ data = """1,2,3,4,5
+6,7,8,9,10
+11,12,13,14,15
+"""
+ with pytest.raises(
+ ValueError,
+ match="Passing negative integer to header is invalid. "
+ "For no header, use header=None instead",
+ ):
+ parser.read_csv(StringIO(data), header=-1)
+
+
+@pytest.mark.parametrize("header", [([-1, 2, 4]), ([-5, 0])])
+def test_negative_multi_index_header(all_parsers, header):
+ # see gh-27779
+ parser = all_parsers
+ data = """1,2,3,4,5
+ 6,7,8,9,10
+ 11,12,13,14,15
+ """
+ with pytest.raises(
+ ValueError, match="cannot specify multi-index header with negative integers"
+ ):
+ parser.read_csv(StringIO(data), header=header)
+
+
@pytest.mark.parametrize("header", [True, False])
def test_bool_header_arg(all_parsers, header):
# see gh-6114
| …ssed
- [x] closes #27779
- [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/27878 | 2019-08-12T20:05:22Z | 2019-08-15T20:27:18Z | 2019-08-15T20:27:18Z | 2019-08-15T21:14:43Z |
Backport PR #27777 on branch 0.25.x (Avoid calling S3File.s3) | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 637ac5c9c8bd1..01141122dde0a 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -104,7 +104,7 @@ MultiIndex
I/O
^^^
--
+- Avoid calling ``S3File.s3`` when reading parquet, as this was removed in s3fs version 0.3.0 (:issue:`27756`)
-
-
diff --git a/pandas/io/parquet.py b/pandas/io/parquet.py
index 617f4f44ae8af..c7a8e597ffb41 100644
--- a/pandas/io/parquet.py
+++ b/pandas/io/parquet.py
@@ -184,12 +184,14 @@ def write(
def read(self, path, columns=None, **kwargs):
if is_s3_url(path):
+ from pandas.io.s3 import get_file_and_filesystem
+
# When path is s3:// an S3File is returned.
# We need to retain the original path(str) while also
# pass the S3File().open function to fsatparquet impl.
- s3, _, _, should_close = get_filepath_or_buffer(path)
+ s3, filesystem = get_file_and_filesystem(path)
try:
- parquet_file = self.api.ParquetFile(path, open_with=s3.s3.open)
+ parquet_file = self.api.ParquetFile(path, open_with=filesystem.open)
finally:
s3.close()
else:
diff --git a/pandas/io/s3.py b/pandas/io/s3.py
index 0a7c082fec51c..7e0a37e8cba20 100644
--- a/pandas/io/s3.py
+++ b/pandas/io/s3.py
@@ -1,8 +1,11 @@
""" s3 support for remote file interactivity """
+from typing import IO, Any, Optional, Tuple
from urllib.parse import urlparse as parse_url
from pandas.compat._optional import import_optional_dependency
+from pandas._typing import FilePathOrBuffer
+
s3fs = import_optional_dependency(
"s3fs", extra="The s3fs package is required to handle s3 files."
)
@@ -14,9 +17,9 @@ def _strip_schema(url):
return result.netloc + result.path
-def get_filepath_or_buffer(
- filepath_or_buffer, encoding=None, compression=None, mode=None
-):
+def get_file_and_filesystem(
+ filepath_or_buffer: FilePathOrBuffer, mode: Optional[str] = None
+) -> Tuple[IO, Any]:
from botocore.exceptions import NoCredentialsError
if mode is None:
@@ -24,7 +27,7 @@ def get_filepath_or_buffer(
fs = s3fs.S3FileSystem(anon=False)
try:
- filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
+ file = fs.open(_strip_schema(filepath_or_buffer), mode)
except (FileNotFoundError, NoCredentialsError):
# boto3 has troubles when trying to access a public file
# when credentialed...
@@ -33,5 +36,15 @@ def get_filepath_or_buffer(
# A NoCredentialsError is raised if you don't have creds
# for that bucket.
fs = s3fs.S3FileSystem(anon=True)
- filepath_or_buffer = fs.open(_strip_schema(filepath_or_buffer), mode)
- return filepath_or_buffer, None, compression, True
+ file = fs.open(_strip_schema(filepath_or_buffer), mode)
+ return file, fs
+
+
+def get_filepath_or_buffer(
+ filepath_or_buffer: FilePathOrBuffer,
+ encoding: Optional[str] = None,
+ compression: Optional[str] = None,
+ mode: Optional[str] = None,
+) -> Tuple[IO, Optional[str], Optional[str], bool]:
+ file, _fs = get_file_and_filesystem(filepath_or_buffer, mode=mode)
+ return file, None, compression, True
| Backport PR #27777: Avoid calling S3File.s3 | https://api.github.com/repos/pandas-dev/pandas/pulls/27877 | 2019-08-12T19:11:06Z | 2019-08-13T07:54:42Z | 2019-08-13T07:54:42Z | 2019-08-13T07:54:42Z |
DOC:Use of "Yields" for documentation of DataFrame.iteritems() | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 02241eeaae7b2..aa59dc5de6896 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -775,7 +775,8 @@ def style(self):
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
- %s
+ Yields
+ ------
label : object
The column names for the DataFrame being iterated over.
content : Series
@@ -816,7 +817,7 @@ def style(self):
Name: population, dtype: int64
"""
- @Appender(_shared_docs["items"] % "Yields\n ------")
+ @Appender(_shared_docs["items"])
def items(self):
if self.columns.is_unique and hasattr(self, "_item_cache"):
for k in self.columns:
@@ -825,9 +826,9 @@ def items(self):
for i, k in enumerate(self.columns):
yield k, self._ixs(i, axis=1)
- @Appender(_shared_docs["items"] % "Returns\n -------")
+ @Appender(_shared_docs["items"])
def iteritems(self):
- return self.items()
+ yield from self.items()
def iterrows(self):
"""
| changed `Returns` to `Yields`
closes #27860 | https://api.github.com/repos/pandas-dev/pandas/pulls/27876 | 2019-08-12T18:52:46Z | 2019-08-14T18:54:49Z | 2019-08-14T18:54:49Z | 2019-08-15T02:06:23Z |
COMPAT: unique() should preserve the dtype of the input | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 53041441ba040..4816e6a519884 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -151,6 +151,7 @@ Other API changes
- :meth:`pandas.api.types.infer_dtype` will now return "integer-na" for integer and ``np.nan`` mix (:issue:`27283`)
- :meth:`MultiIndex.from_arrays` will no longer infer names from arrays if ``names=None`` is explicitly provided (:issue:`27292`)
+- The returned dtype of ::func:`pd.unique` now matches the input dtype. (:issue:`27874`)
-
.. _whatsnew_1000.api.documentation:
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 4073ede84c6f6..2e5ab0d182aff 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -180,13 +180,13 @@ def _reconstruct_data(values, dtype, original):
if is_extension_array_dtype(dtype):
values = dtype.construct_array_type()._from_sequence(values)
elif is_bool_dtype(dtype):
- values = values.astype(dtype)
+ values = values.astype(dtype, copy=False)
# we only support object dtypes bool Index
if isinstance(original, ABCIndexClass):
- values = values.astype(object)
+ values = values.astype(object, copy=False)
elif dtype is not None:
- values = values.astype(dtype)
+ values = values.astype(dtype, copy=False)
return values
@@ -396,7 +396,7 @@ def unique(values):
table = htable(len(values))
uniques = table.unique(values)
- uniques = _reconstruct_data(uniques, dtype, original)
+ uniques = _reconstruct_data(uniques, original.dtype, original)
return uniques
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index c760c75e44f6b..483122a0eeaba 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -159,8 +159,8 @@ def test_memory_usage(self):
class Ops:
def _allow_na_ops(self, obj):
"""Whether to skip test cases including NaN"""
- if isinstance(obj, Index) and (obj.is_boolean() or not obj._can_hold_na):
- # don't test boolean / int64 index
+ if (isinstance(obj, Index) and obj.is_boolean()) or not obj._can_hold_na:
+ # don't test boolean / integer dtypes
return False
return True
@@ -187,7 +187,24 @@ def setup_method(self, method):
types = ["bool", "int", "float", "dt", "dt_tz", "period", "string", "unicode"]
self.indexes = [getattr(self, "{}_index".format(t)) for t in types]
self.series = [getattr(self, "{}_series".format(t)) for t in types]
- self.objs = self.indexes + self.series
+
+ # To test narrow dtypes, we use narrower *data* elements, not *index* elements
+ index = self.int_index
+ self.float32_series = Series(arr.astype(np.float32), index=index, name="a")
+
+ arr_int = np.random.choice(10, size=10, replace=False)
+ self.int8_series = Series(arr_int.astype(np.int8), index=index, name="a")
+ self.int16_series = Series(arr_int.astype(np.int16), index=index, name="a")
+ self.int32_series = Series(arr_int.astype(np.int32), index=index, name="a")
+
+ self.uint8_series = Series(arr_int.astype(np.uint8), index=index, name="a")
+ self.uint16_series = Series(arr_int.astype(np.uint16), index=index, name="a")
+ self.uint32_series = Series(arr_int.astype(np.uint32), index=index, name="a")
+
+ nrw_types = ["float32", "int8", "int16", "int32", "uint8", "uint16", "uint32"]
+ self.narrow_series = [getattr(self, "{}_series".format(t)) for t in nrw_types]
+
+ self.objs = self.indexes + self.series + self.narrow_series
def check_ops_properties(self, props, filter=None, ignore_failures=False):
for op in props:
@@ -385,6 +402,7 @@ def test_value_counts_unique_nunique(self):
if isinstance(o, Index):
assert isinstance(result, o.__class__)
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]
@@ -396,6 +414,7 @@ def test_value_counts_unique_nunique(self):
)
else:
tm.assert_numpy_array_equal(result, orig.values)
+ assert result.dtype == orig.dtype
assert o.nunique() == len(np.unique(o.values))
@@ -904,7 +923,7 @@ def test_fillna(self):
expected = [fill_value] * 2 + list(values[2:])
- expected = klass(expected)
+ expected = klass(expected, dtype=orig.dtype)
o = klass(values)
# check values has the same dtype as the original
| The behavior of `pd.unique()` is surprising, because -- unlike `np.unique()` -- the result does not have the same `dtype` as the input:
```python
In [1]: pd.Series([1,2,3], dtype=np.uint8).unique()
Out[1]: array([1, 2, 3], dtype=uint64)
```
This PR just casts the output array to match the input dtype. Supercedes #27869.
**Update:** Augmented the tests to cover narrow dtypes.
~I added a new assertion in `test_value_counts_unique_nunique()`, but it may not be sufficient. From what I can see, there isn't good coverage of `Series` whose *data* is not `int`/`float`/ etc. There is only good coverage of various *index* types. Any advice concerning test coverage?~
- [x] closes #27869
- [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/27874 | 2019-08-12T18:38:24Z | 2019-10-07T01:08:25Z | 2019-10-07T01:08:25Z | 2019-10-07T01:08:28Z |
requested edit to comparison method | diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index a56521b9c9fec..c243b28aedd3c 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -835,13 +835,12 @@ def wrapper(self, other, axis=None):
elif isinstance(other, ABCSeries) and not self._indexed_same(other):
raise ValueError("Can only compare identically-labeled Series objects")
- elif (
- is_list_like(other)
- and len(other) != len(self)
- and not isinstance(other, frozenset)
- ):
- # TODO: why are we treating len-1 frozenset differently?
- raise ValueError("Lengths must match to compare")
+ elif isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
+ # TODO: make this treatment consistent across ops and classes.
+ # We are not catching all listlikes here (e.g. frozenset, tuple)
+ # The ambiguous case is object-dtype. See GH#27803
+ if len(self) != len(other):
+ raise ValueError("Lengths must match to compare")
if is_categorical_dtype(self):
# Dispatch to Categorical implementation; CategoricalIndex
| cc @jorisvandenbossche discussed in #27803 | https://api.github.com/repos/pandas-dev/pandas/pulls/27873 | 2019-08-12T17:20:51Z | 2019-08-13T12:17:38Z | 2019-08-13T12:17:37Z | 2019-08-14T20:09:42Z |
Backport PR #27868 on branch 0.25.x (CI: Pin Python to 3.7.3) | diff --git a/ci/deps/azure-37-locale.yaml b/ci/deps/azure-37-locale.yaml
index 05adbf0c924dc..6844e929eae78 100644
--- a/ci/deps/azure-37-locale.yaml
+++ b/ci/deps/azure-37-locale.yaml
@@ -17,7 +17,7 @@ dependencies:
- openpyxl
- pytables
- python-dateutil
- - python=3.7.*
+ - python=3.7.3
- pytz
- s3fs
- scipy
diff --git a/ci/deps/azure-37-numpydev.yaml b/ci/deps/azure-37-numpydev.yaml
index 5cf897c98da10..3b68a573497e6 100644
--- a/ci/deps/azure-37-numpydev.yaml
+++ b/ci/deps/azure-37-numpydev.yaml
@@ -2,7 +2,7 @@ name: pandas-dev
channels:
- defaults
dependencies:
- - python=3.7.*
+ - python=3.7.3
- pytz
- Cython>=0.28.2
# universal
diff --git a/ci/deps/travis-37.yaml b/ci/deps/travis-37.yaml
index c9a8c274fb144..2d7f4b483729f 100644
--- a/ci/deps/travis-37.yaml
+++ b/ci/deps/travis-37.yaml
@@ -4,7 +4,7 @@ channels:
- conda-forge
- c3i_test
dependencies:
- - python=3.7.*
+ - python=3.7.3
- botocore>=1.11
- cython>=0.28.2
- numpy
diff --git a/environment.yml b/environment.yml
index 93e8302b498a0..d54caf9d013c7 100644
--- a/environment.yml
+++ b/environment.yml
@@ -5,7 +5,7 @@ channels:
dependencies:
# required
- numpy>=1.15
- - python=3
+ - python=3.7.3
- python-dateutil>=2.6.1
- pytz
diff --git a/requirements-dev.txt b/requirements-dev.txt
index e49ad10bfc99d..9d80a13350579 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,4 +1,5 @@
numpy>=1.15
+python==3.7.3
python-dateutil>=2.6.1
pytz
asv
| Backport PR #27868: CI: Pin Python to 3.7.3 | https://api.github.com/repos/pandas-dev/pandas/pulls/27872 | 2019-08-12T16:38:19Z | 2019-08-12T19:10:35Z | 2019-08-12T19:10:35Z | 2019-08-12T19:10:35Z |
DOC:Standardize use of "Iterate" and "Iterator" | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 02241eeaae7b2..9752af6eb352f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -770,7 +770,7 @@ def style(self):
_shared_docs[
"items"
] = r"""
- Iterator over (column name, Series) pairs.
+ Iterate over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
| changed `Iterator over` --> `Iterate over`
closes #27861
| https://api.github.com/repos/pandas-dev/pandas/pulls/27871 | 2019-08-12T16:20:20Z | 2019-08-13T11:57:29Z | 2019-08-13T11:57:29Z | 2019-08-13T11:57:31Z |
CI: Pin Python to 3.7.3 | diff --git a/ci/deps/azure-37-locale.yaml b/ci/deps/azure-37-locale.yaml
index 26dcd213bbfa0..437fbc24b9411 100644
--- a/ci/deps/azure-37-locale.yaml
+++ b/ci/deps/azure-37-locale.yaml
@@ -17,7 +17,7 @@ dependencies:
- openpyxl
- pytables
- python-dateutil
- - python=3.7.*
+ - python=3.7.3
- pytz
- s3fs
- scipy
diff --git a/ci/deps/azure-37-numpydev.yaml b/ci/deps/azure-37-numpydev.yaml
index 65c92ec1dcf0d..e1acda7ecf3c7 100644
--- a/ci/deps/azure-37-numpydev.yaml
+++ b/ci/deps/azure-37-numpydev.yaml
@@ -2,7 +2,7 @@ name: pandas-dev
channels:
- defaults
dependencies:
- - python=3.7.*
+ - python=3.7.3
- pytz
- Cython>=0.28.2
# universal
diff --git a/ci/deps/travis-37.yaml b/ci/deps/travis-37.yaml
index 9e08c41a3d9c0..d7bed9e3f03f2 100644
--- a/ci/deps/travis-37.yaml
+++ b/ci/deps/travis-37.yaml
@@ -4,7 +4,7 @@ channels:
- conda-forge
- c3i_test
dependencies:
- - python=3.7.*
+ - python=3.7.3
- botocore>=1.11
- cython>=0.28.2
- numpy
diff --git a/environment.yml b/environment.yml
index 6d2cd701c3854..0cbd500209b6c 100644
--- a/environment.yml
+++ b/environment.yml
@@ -5,7 +5,7 @@ channels:
dependencies:
# required
- numpy>=1.15
- - python=3
+ - python=3.7.3
- python-dateutil>=2.6.1
- pytz
diff --git a/requirements-dev.txt b/requirements-dev.txt
index cf11a3ee28258..f866f1436edc6 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,4 +1,5 @@
numpy>=1.15
+python==3.7.3
python-dateutil>=2.6.1
pytz
asv
| https://api.github.com/repos/pandas-dev/pandas/pulls/27868 | 2019-08-12T13:56:29Z | 2019-08-12T16:37:50Z | 2019-08-12T16:37:50Z | 2019-08-21T14:40:00Z | |
Added missing space to error description | diff --git a/pandas/io/feather_format.py b/pandas/io/feather_format.py
index 6fe22f14c2c5b..25a6db675265d 100644
--- a/pandas/io/feather_format.py
+++ b/pandas/io/feather_format.py
@@ -39,7 +39,7 @@ def to_feather(df, path):
if not isinstance(df.index, Int64Index):
raise ValueError(
"feather does not support serializing {} "
- "for the index; you can .reset_index()"
+ "for the index; you can .reset_index() "
"to make the index into column(s)".format(type(df.index))
)
| https://api.github.com/repos/pandas-dev/pandas/pulls/27866 | 2019-08-12T12:28:32Z | 2019-08-13T01:08:38Z | 2019-08-13T01:08:38Z | 2019-08-13T01:08:46Z | |
BUG: add back check for MultiIndex case and take_split_path | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 7c2e488b98509..b97f45efbeae9 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -84,7 +84,7 @@ Indexing
- Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']`` (:issue:`27516`)
- Break reference cycle involving :class:`Index` to allow garbage collection of :class:`Index` objects without running the GC. (:issue:`27585`)
--
+- Fix regression in assigning values to a single column of a DataFrame with a ``MultiIndex`` columns (:issue:`27841`).
-
Missing
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index e308ae03730b3..ea00737f776ee 100755
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -321,6 +321,17 @@ def _setitem_with_indexer(self, indexer, value):
val = list(value.values()) if isinstance(value, dict) else value
take_split_path = not blk._can_hold_element(val)
+ # if we have any multi-indexes that have non-trivial slices
+ # (not null slices) then we must take the split path, xref
+ # GH 10360, GH 27841
+ if isinstance(indexer, tuple) and len(indexer) == len(self.obj.axes):
+ for i, ax in zip(indexer, self.obj.axes):
+ if isinstance(ax, MultiIndex) and not (
+ is_integer(i) or com.is_null_slice(i)
+ ):
+ take_split_path = True
+ break
+
if isinstance(indexer, tuple):
nindexer = []
for i, idx in enumerate(indexer):
diff --git a/pandas/tests/indexing/multiindex/test_loc.py b/pandas/tests/indexing/multiindex/test_loc.py
index a08b2b4c66af2..8b48c2bf7169f 100644
--- a/pandas/tests/indexing/multiindex/test_loc.py
+++ b/pandas/tests/indexing/multiindex/test_loc.py
@@ -390,3 +390,26 @@ def test_loc_getitem_lowerdim_corner(multiindex_dataframe_random_data):
expected = 0
result = df.sort_index().loc[("bar", "three"), "B"]
assert result == expected
+
+
+def test_loc_setitem_single_column_slice():
+ # case from https://github.com/pandas-dev/pandas/issues/27841
+ df = DataFrame(
+ "string",
+ index=list("abcd"),
+ columns=MultiIndex.from_product([["Main"], ("another", "one")]),
+ )
+ df["labels"] = "a"
+ df.loc[:, "labels"] = df.index
+ tm.assert_numpy_array_equal(np.asarray(df["labels"]), np.asarray(df.index))
+
+ # test with non-object block
+ df = DataFrame(
+ np.nan,
+ index=range(4),
+ columns=MultiIndex.from_tuples([("A", "1"), ("A", "2"), ("B", "1")]),
+ )
+ expected = df.copy()
+ df.loc[:, "B"] = np.arange(4)
+ expected.iloc[:, 2] = np.arange(4)
+ tm.assert_frame_equal(df, expected)
| Closes #27841
| https://api.github.com/repos/pandas-dev/pandas/pulls/27855 | 2019-08-10T19:42:15Z | 2019-08-13T01:04:21Z | 2019-08-13T01:04:21Z | 2019-08-14T07:08:40Z |
DOC:add append example in to_excel documentation | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 5a1b2a5dcd20b..71d5068e2e0fc 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -2179,6 +2179,12 @@ def _repr_data_resource_(self):
... df1.to_excel(writer, sheet_name='Sheet_name_1')
... df2.to_excel(writer, sheet_name='Sheet_name_2')
+ ExcelWriter can also be used to append to an existing Excel file:
+
+ >>> with pd.ExcelWriter('output.xlsx',
+ ... mode='a') as writer: # doctest: +SKIP
+ ... df.to_excel(writer, sheet_name='Sheet_name_3')
+
To set the library that is used to write the Excel file,
you can pass the `engine` keyword (the default engine is
automatically chosen depending on the file extension):
| closes #27051
| https://api.github.com/repos/pandas-dev/pandas/pulls/27852 | 2019-08-10T05:13:15Z | 2019-08-13T09:41:59Z | 2019-08-13T09:41:59Z | 2019-08-13T11:36:30Z |
DOC: Add CoC to the README | diff --git a/README.md b/README.md
index aeeea1464e1fd..3cde98d3145f2 100644
--- a/README.md
+++ b/README.md
@@ -233,3 +233,5 @@ You can also triage issues which may include reproducing bug reports, or asking
Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!
Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas).
+
+As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/pandas/blob/master/.github/CODE_OF_CONDUCT.md)
| @datapythonista : Add CoC to README file | https://api.github.com/repos/pandas-dev/pandas/pulls/27851 | 2019-08-10T04:43:03Z | 2019-08-13T14:12:45Z | 2019-08-13T14:12:45Z | 2019-08-13T14:12:48Z |
CLN: op-->opname in Categorical _cat_compare_op | diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 9862b4b530424..13058882084ff 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -77,7 +77,7 @@
)
-def _cat_compare_op(op):
+def _cat_compare_op(opname):
def f(self, other):
# On python2, you can usually compare any type to any type, and
# Categoricals can be seen as a custom type, but having different
@@ -90,7 +90,7 @@ def f(self, other):
other = lib.item_from_zerodim(other)
if not self.ordered:
- if op in ["__lt__", "__gt__", "__le__", "__ge__"]:
+ if opname in ["__lt__", "__gt__", "__le__", "__ge__"]:
raise TypeError(
"Unordered Categoricals can only compare equality or not"
)
@@ -117,7 +117,7 @@ def f(self, other):
other_codes = other._codes
mask = (self._codes == -1) | (other_codes == -1)
- f = getattr(self._codes, op)
+ f = getattr(self._codes, opname)
ret = f(other_codes)
if mask.any():
# In other series, the leads to False, so do that here too
@@ -127,38 +127,38 @@ def f(self, other):
if is_scalar(other):
if other in self.categories:
i = self.categories.get_loc(other)
- ret = getattr(self._codes, op)(i)
+ ret = getattr(self._codes, opname)(i)
# check for NaN in self
mask = self._codes == -1
ret[mask] = False
return ret
else:
- if op == "__eq__":
+ if opname == "__eq__":
return np.repeat(False, len(self))
- elif op == "__ne__":
+ elif opname == "__ne__":
return np.repeat(True, len(self))
else:
msg = (
"Cannot compare a Categorical for op {op} with a "
"scalar, which is not a category."
)
- raise TypeError(msg.format(op=op))
+ raise TypeError(msg.format(op=opname))
else:
# allow categorical vs object dtype array comparisons for equality
# these are only positional comparisons
- if op in ["__eq__", "__ne__"]:
- return getattr(np.array(self), op)(np.array(other))
+ if opname in ["__eq__", "__ne__"]:
+ return getattr(np.array(self), opname)(np.array(other))
msg = (
"Cannot compare a Categorical for op {op} with type {typ}."
"\nIf you want to compare values, use 'np.asarray(cat) "
"<op> other'."
)
- raise TypeError(msg.format(op=op, typ=type(other)))
+ raise TypeError(msg.format(op=opname, typ=type(other)))
- f.__name__ = op
+ f.__name__ = opname
return f
| In general we use `op` to refer to operator.foo, and `opname` to refer to the string, which is what is passed in this case. | https://api.github.com/repos/pandas-dev/pandas/pulls/27849 | 2019-08-10T01:13:28Z | 2019-08-12T18:53:36Z | 2019-08-12T18:53:36Z | 2019-08-12T19:04:48Z |
TST: parametrize arithmetic tests | diff --git a/pandas/tests/arithmetic/test_datetime64.py b/pandas/tests/arithmetic/test_datetime64.py
index 1fb93a182ba0b..5931cd93cc8c5 100644
--- a/pandas/tests/arithmetic/test_datetime64.py
+++ b/pandas/tests/arithmetic/test_datetime64.py
@@ -30,6 +30,54 @@
import pandas.util.testing as tm
+def assert_invalid_comparison(left, right, box):
+ """
+ Assert that comparison operations with mismatched types behave correctly.
+
+ Parameters
+ ----------
+ left : np.ndarray, ExtensionArray, Index, or Series
+ right : object
+ box : {pd.DataFrame, pd.Series, pd.Index, tm.to_array}
+ """
+ # Not for tznaive-tzaware comparison
+
+ # Note: not quite the same as how we do this for tm.box_expected
+ xbox = box if box is not pd.Index else np.array
+
+ result = left == right
+ expected = xbox(np.zeros(result.shape, dtype=np.bool_))
+
+ tm.assert_equal(result, expected)
+
+ result = right == left
+ tm.assert_equal(result, expected)
+
+ result = left != right
+ tm.assert_equal(result, ~expected)
+
+ result = right != left
+ tm.assert_equal(result, ~expected)
+
+ msg = "Invalid comparison between"
+ with pytest.raises(TypeError, match=msg):
+ left < right
+ with pytest.raises(TypeError, match=msg):
+ left <= right
+ with pytest.raises(TypeError, match=msg):
+ left > right
+ with pytest.raises(TypeError, match=msg):
+ left >= right
+ with pytest.raises(TypeError, match=msg):
+ right < left
+ with pytest.raises(TypeError, match=msg):
+ right <= left
+ with pytest.raises(TypeError, match=msg):
+ right > left
+ with pytest.raises(TypeError, match=msg):
+ right >= left
+
+
def assert_all(obj):
"""
Test helper to call call obj.all() the appropriate number of times on
@@ -47,7 +95,7 @@ def assert_all(obj):
class TestDatetime64ArrayLikeComparisons:
# Comparison tests for datetime64 vectors fully parametrized over
- # DataFrame/Series/DatetimeIndex/DateteimeArray. Ideally all comparison
+ # DataFrame/Series/DatetimeIndex/DatetimeArray. Ideally all comparison
# tests will eventually end up here.
def test_compare_zerodim(self, tz_naive_fixture, box_with_array):
@@ -59,36 +107,61 @@ def test_compare_zerodim(self, tz_naive_fixture, box_with_array):
other = np.array(dti.to_numpy()[0])
- # FIXME: ValueError with transpose on tzaware
- dtarr = tm.box_expected(dti, box, transpose=False)
+ dtarr = tm.box_expected(dti, box)
result = dtarr <= other
expected = np.array([True, False, False])
- expected = tm.box_expected(expected, xbox, transpose=False)
+ expected = tm.box_expected(expected, xbox)
tm.assert_equal(result, expected)
+ def test_dt64arr_cmp_date_invalid(self, tz_naive_fixture, box_with_array):
+ # GH#19800, GH#19301 datetime.date comparison raises to
+ # match DatetimeIndex/Timestamp. This also matches the behavior
+ # of stdlib datetime.datetime
+ tz = tz_naive_fixture
-class TestDatetime64DataFrameComparison:
- @pytest.mark.parametrize(
- "timestamps",
- [
- [pd.Timestamp("2012-01-01 13:00:00+00:00")] * 2,
- [pd.Timestamp("2012-01-01 13:00:00")] * 2,
- ],
- )
- def test_tz_aware_scalar_comparison(self, timestamps):
- # GH#15966
- df = pd.DataFrame({"test": timestamps})
- expected = pd.DataFrame({"test": [False, False]})
- tm.assert_frame_equal(df == -1, expected)
+ dti = pd.date_range("20010101", periods=10, tz=tz)
+ date = dti[0].to_pydatetime().date()
+
+ dtarr = tm.box_expected(dti, box_with_array)
+ assert_invalid_comparison(dtarr, date, box_with_array)
- def test_dt64_nat_comparison(self):
+ @pytest.mark.parametrize("other", ["foo", -1, 99, 4.0, object(), timedelta(days=2)])
+ def test_dt64arr_cmp_scalar_invalid(self, other, tz_naive_fixture, box_with_array):
+ # GH#22074, GH#15966
+ tz = tz_naive_fixture
+
+ rng = date_range("1/1/2000", periods=10, tz=tz)
+ dtarr = tm.box_expected(rng, box_with_array)
+ assert_invalid_comparison(dtarr, other, box_with_array)
+
+ @pytest.mark.parametrize("other", [None, np.nan])
+ def test_dt64arr_cmp_na_scalar_invalid(
+ self, other, tz_naive_fixture, box_with_array
+ ):
+ # GH#19301
+ tz = tz_naive_fixture
+ dti = pd.date_range("2016-01-01", periods=2, tz=tz)
+ dtarr = tm.box_expected(dti, box_with_array)
+ assert_invalid_comparison(dtarr, other, box_with_array)
+
+ def test_dt64arr_nat_comparison(self, tz_naive_fixture, box_with_array):
# GH#22242, GH#22163 DataFrame considered NaT == ts incorrectly
- ts = pd.Timestamp.now()
- df = pd.DataFrame([ts, pd.NaT])
- expected = pd.DataFrame([True, False])
+ tz = tz_naive_fixture
+ box = box_with_array
+ xbox = box if box is not pd.Index else np.ndarray
+
+ ts = pd.Timestamp.now(tz)
+ ser = pd.Series([ts, pd.NaT])
+
+ # FIXME: Can't transpose because that loses the tz dtype on
+ # the NaT column
+ obj = tm.box_expected(ser, box, transpose=False)
- result = df == ts
- tm.assert_frame_equal(result, expected)
+ expected = pd.Series([True, False], dtype=np.bool_)
+ expected = tm.box_expected(expected, xbox, transpose=False)
+
+ result = obj == ts
+ tm.assert_equal(result, expected)
class TestDatetime64SeriesComparison:
@@ -142,35 +215,17 @@ def test_nat_comparisons(self, dtype, box, reverse, pair):
expected = Series([False, False, True])
tm.assert_series_equal(left <= right, expected)
- def test_comparison_invalid(self, box_with_array):
+ def test_comparison_invalid(self, tz_naive_fixture, box_with_array):
# GH#4968
# invalid date/int comparisons
- xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
-
+ tz = tz_naive_fixture
ser = Series(range(5))
- ser2 = Series(pd.date_range("20010101", periods=5))
+ ser2 = Series(pd.date_range("20010101", periods=5, tz=tz))
ser = tm.box_expected(ser, box_with_array)
ser2 = tm.box_expected(ser2, box_with_array)
- for (x, y) in [(ser, ser2), (ser2, ser)]:
-
- result = x == y
- expected = tm.box_expected([False] * 5, xbox)
- tm.assert_equal(result, expected)
-
- result = x != y
- expected = tm.box_expected([True] * 5, xbox)
- tm.assert_equal(result, expected)
- msg = "Invalid comparison between"
- with pytest.raises(TypeError, match=msg):
- x >= y
- with pytest.raises(TypeError, match=msg):
- x > y
- with pytest.raises(TypeError, match=msg):
- x < y
- with pytest.raises(TypeError, match=msg):
- x <= y
+ assert_invalid_comparison(ser, ser2, box_with_array)
@pytest.mark.parametrize(
"data",
@@ -227,26 +282,6 @@ def test_series_comparison_scalars(self):
expected = Series([x > val for x in series])
tm.assert_series_equal(result, expected)
- def test_dt64ser_cmp_date_invalid(self, box_with_array):
- # GH#19800 datetime.date comparison raises to
- # match DatetimeIndex/Timestamp. This also matches the behavior
- # of stdlib datetime.datetime
-
- ser = pd.date_range("20010101", periods=10)
- date = ser[0].to_pydatetime().date()
-
- ser = tm.box_expected(ser, box_with_array)
- assert_all(~(ser == date))
- assert_all(ser != date)
- with pytest.raises(TypeError):
- ser > date
- with pytest.raises(TypeError):
- ser < date
- with pytest.raises(TypeError):
- ser >= date
- with pytest.raises(TypeError):
- ser <= date
-
@pytest.mark.parametrize(
"left,right", [("lt", "gt"), ("le", "ge"), ("eq", "eq"), ("ne", "ne")]
)
@@ -388,57 +423,6 @@ def test_dti_cmp_datetimelike(self, other, tz_naive_fixture):
expected = np.array([True, False])
tm.assert_numpy_array_equal(result, expected)
- def dt64arr_cmp_non_datetime(self, tz_naive_fixture, box_with_array):
- # GH#19301 by convention datetime.date is not considered comparable
- # to Timestamp or DatetimeIndex. This may change in the future.
- tz = tz_naive_fixture
- dti = pd.date_range("2016-01-01", periods=2, tz=tz)
- dtarr = tm.box_expected(dti, box_with_array)
-
- other = datetime(2016, 1, 1).date()
- assert not (dtarr == other).any()
- assert (dtarr != other).all()
- with pytest.raises(TypeError):
- dtarr < other
- with pytest.raises(TypeError):
- dtarr <= other
- with pytest.raises(TypeError):
- dtarr > other
- with pytest.raises(TypeError):
- dtarr >= other
-
- @pytest.mark.parametrize("other", [None, np.nan, pd.NaT])
- def test_dti_eq_null_scalar(self, other, tz_naive_fixture):
- # GH#19301
- tz = tz_naive_fixture
- dti = pd.date_range("2016-01-01", periods=2, tz=tz)
- assert not (dti == other).any()
-
- @pytest.mark.parametrize("other", [None, np.nan, pd.NaT])
- def test_dti_ne_null_scalar(self, other, tz_naive_fixture):
- # GH#19301
- tz = tz_naive_fixture
- dti = pd.date_range("2016-01-01", periods=2, tz=tz)
- assert (dti != other).all()
-
- @pytest.mark.parametrize("other", [None, np.nan])
- def test_dti_cmp_null_scalar_inequality(
- self, tz_naive_fixture, other, box_with_array
- ):
- # GH#19301
- tz = tz_naive_fixture
- dti = pd.date_range("2016-01-01", periods=2, tz=tz)
- dtarr = tm.box_expected(dti, box_with_array)
- msg = "Invalid comparison between"
- with pytest.raises(TypeError, match=msg):
- dtarr < other
- with pytest.raises(TypeError, match=msg):
- dtarr <= other
- with pytest.raises(TypeError, match=msg):
- dtarr > other
- with pytest.raises(TypeError, match=msg):
- dtarr >= other
-
@pytest.mark.parametrize("dtype", [None, object])
def test_dti_cmp_nat(self, dtype, box_with_array):
if box_with_array is tm.to_array and dtype is object:
@@ -728,34 +712,6 @@ def test_dti_cmp_str(self, tz_naive_fixture):
expected = np.array([True] * 10)
tm.assert_numpy_array_equal(result, expected)
- @pytest.mark.parametrize("other", ["foo", 99, 4.0, object(), timedelta(days=2)])
- def test_dt64arr_cmp_scalar_invalid(self, other, tz_naive_fixture, box_with_array):
- # GH#22074
- tz = tz_naive_fixture
- xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
-
- rng = date_range("1/1/2000", periods=10, tz=tz)
- rng = tm.box_expected(rng, box_with_array)
-
- result = rng == other
- expected = np.array([False] * 10)
- expected = tm.box_expected(expected, xbox)
- tm.assert_equal(result, expected)
-
- result = rng != other
- expected = np.array([True] * 10)
- expected = tm.box_expected(expected, xbox)
- tm.assert_equal(result, expected)
- msg = "Invalid comparison between"
- with pytest.raises(TypeError, match=msg):
- rng < other
- with pytest.raises(TypeError, match=msg):
- rng <= other
- with pytest.raises(TypeError, match=msg):
- rng > other
- with pytest.raises(TypeError, match=msg):
- rng >= other
-
def test_dti_cmp_list(self):
rng = date_range("1/1/2000", periods=10)
@@ -2576,24 +2532,3 @@ def test_shift_months(years, months):
raw = [x + pd.offsets.DateOffset(years=years, months=months) for x in dti]
expected = DatetimeIndex(raw)
tm.assert_index_equal(actual, expected)
-
-
-# FIXME: this belongs in scalar tests
-class SubDatetime(datetime):
- pass
-
-
-@pytest.mark.parametrize(
- "lh,rh",
- [
- (SubDatetime(2000, 1, 1), Timedelta(hours=1)),
- (Timedelta(hours=1), SubDatetime(2000, 1, 1)),
- ],
-)
-def test_dt_subclass_add_timedelta(lh, rh):
- # GH 25851
- # ensure that subclassed datetime works for
- # Timedelta operations
- result = lh + rh
- expected = SubDatetime(2000, 1, 1, 1)
- assert result == expected
diff --git a/pandas/tests/arithmetic/test_period.py b/pandas/tests/arithmetic/test_period.py
index 4b58c290c3cea..ed693d873efb8 100644
--- a/pandas/tests/arithmetic/test_period.py
+++ b/pandas/tests/arithmetic/test_period.py
@@ -573,12 +573,19 @@ def test_parr_add_sub_float_raises(self, op, other, box_with_array):
@pytest.mark.parametrize(
"other",
[
+ # datetime scalars
pd.Timestamp.now(),
pd.Timestamp.now().to_pydatetime(),
pd.Timestamp.now().to_datetime64(),
+ # datetime-like arrays
+ pd.date_range("2016-01-01", periods=3, freq="H"),
+ pd.date_range("2016-01-01", periods=3, tz="Europe/Brussels"),
+ pd.date_range("2016-01-01", periods=3, freq="S")._data,
+ pd.date_range("2016-01-01", periods=3, tz="Asia/Tokyo")._data,
+ # Miscellaneous invalid types
],
)
- def test_parr_add_sub_datetime_scalar(self, other, box_with_array):
+ def test_parr_add_sub_invalid(self, other, box_with_array):
# GH#23215
rng = pd.period_range("1/1/2000", freq="D", periods=3)
rng = tm.box_expected(rng, box_with_array)
@@ -595,23 +602,6 @@ def test_parr_add_sub_datetime_scalar(self, other, box_with_array):
# -----------------------------------------------------------------
# __add__/__sub__ with ndarray[datetime64] and ndarray[timedelta64]
- def test_parr_add_sub_dt64_array_raises(self, box_with_array):
- rng = pd.period_range("1/1/2000", freq="D", periods=3)
- dti = pd.date_range("2016-01-01", periods=3)
- dtarr = dti.values
-
- rng = tm.box_expected(rng, box_with_array)
-
- with pytest.raises(TypeError):
- rng + dtarr
- with pytest.raises(TypeError):
- dtarr + rng
-
- with pytest.raises(TypeError):
- rng - dtarr
- with pytest.raises(TypeError):
- dtarr - rng
-
def test_pi_add_sub_td64_array_non_tick_raises(self):
rng = pd.period_range("1/1/2000", freq="Q", periods=3)
tdi = pd.TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
diff --git a/pandas/tests/arithmetic/test_timedelta64.py b/pandas/tests/arithmetic/test_timedelta64.py
index 4f5e00bc5a37d..6af4ea18e63ca 100644
--- a/pandas/tests/arithmetic/test_timedelta64.py
+++ b/pandas/tests/arithmetic/test_timedelta64.py
@@ -18,6 +18,7 @@
Timestamp,
timedelta_range,
)
+from pandas.tests.arithmetic.test_datetime64 import assert_invalid_comparison
import pandas.util.testing as tm
@@ -61,42 +62,33 @@ def test_compare_timedelta64_zerodim(self, box_with_array):
# zero-dim of wrong dtype should still raise
tdi >= np.array(4)
-
-class TestTimedelta64ArrayComparisons:
- # TODO: All of these need to be parametrized over box
-
- def test_compare_timedelta_series(self):
+ @pytest.mark.parametrize(
+ "td_scalar",
+ [timedelta(days=1), Timedelta(days=1), Timedelta(days=1).to_timedelta64()],
+ )
+ def test_compare_timedeltalike_scalar(self, box_with_array, td_scalar):
# regression test for GH#5963
- s = pd.Series([timedelta(days=1), timedelta(days=2)])
- actual = s > timedelta(days=1)
+ box = box_with_array
+ xbox = box if box is not pd.Index else np.ndarray
+ ser = pd.Series([timedelta(days=1), timedelta(days=2)])
+ ser = tm.box_expected(ser, box)
+ actual = ser > td_scalar
expected = pd.Series([False, True])
- tm.assert_series_equal(actual, expected)
+ expected = tm.box_expected(expected, xbox)
+ tm.assert_equal(actual, expected)
- def test_tdi_cmp_str_invalid(self, box_with_array):
- # GH#13624
- xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
- tdi = TimedeltaIndex(["1 day", "2 days"])
- tdarr = tm.box_expected(tdi, box_with_array)
+ @pytest.mark.parametrize("invalid", [345600000000000, "a"])
+ def test_td64_comparisons_invalid(self, box_with_array, invalid):
+ # GH#13624 for str
+ box = box_with_array
+ rng = timedelta_range("1 days", periods=10)
+ obj = tm.box_expected(rng, box)
- for left, right in [(tdarr, "a"), ("a", tdarr)]:
- with pytest.raises(TypeError):
- left > right
- with pytest.raises(TypeError):
- left >= right
- with pytest.raises(TypeError):
- left < right
- with pytest.raises(TypeError):
- left <= right
-
- result = left == right
- expected = np.array([False, False], dtype=bool)
- expected = tm.box_expected(expected, xbox)
- tm.assert_equal(result, expected)
+ assert_invalid_comparison(obj, invalid, box)
- result = left != right
- expected = np.array([True, True], dtype=bool)
- expected = tm.box_expected(expected, xbox)
- tm.assert_equal(result, expected)
+
+class TestTimedelta64ArrayComparisons:
+ # TODO: All of these need to be parametrized over box
@pytest.mark.parametrize("dtype", [None, object])
def test_comp_nat(self, dtype):
@@ -191,10 +183,6 @@ def test_comparisons_coverage(self):
expected = np.array([True, True, True] + [False] * 7)
tm.assert_numpy_array_equal(result, expected)
- # raise TypeError for now
- with pytest.raises(TypeError):
- rng < rng[3].value
-
result = rng == list(rng)
exp = rng == rng
tm.assert_numpy_array_equal(result, exp)
@@ -835,19 +823,10 @@ def test_timedelta64_ops_nat(self):
# -------------------------------------------------------------
# Invalid Operations
- def test_td64arr_add_str_invalid(self, box_with_array):
- # GH#13624
+ @pytest.mark.parametrize("other", ["a", 3.14, np.array([2.0, 3.0])])
+ def test_td64arr_add_sub_invalid(self, box_with_array, other):
+ # GH#13624 for str
tdi = TimedeltaIndex(["1 day", "2 days"])
- tdi = tm.box_expected(tdi, box_with_array)
-
- with pytest.raises(TypeError):
- tdi + "a"
- with pytest.raises(TypeError):
- "a" + tdi
-
- @pytest.mark.parametrize("other", [3.14, np.array([2.0, 3.0])])
- def test_td64arr_add_sub_float(self, box_with_array, other):
- tdi = TimedeltaIndex(["-1 days", "-1 days"])
tdarr = tm.box_expected(tdi, box_with_array)
with pytest.raises(TypeError):
diff --git a/pandas/tests/scalar/timestamp/test_timestamp.py b/pandas/tests/scalar/timestamp/test_timestamp.py
index 401fc285424fe..652dd34ca7ce2 100644
--- a/pandas/tests/scalar/timestamp/test_timestamp.py
+++ b/pandas/tests/scalar/timestamp/test_timestamp.py
@@ -1047,3 +1047,23 @@ def test_to_numpy_alias(self):
# GH 24653: alias .to_numpy() for scalars
ts = Timestamp(datetime.now())
assert ts.to_datetime64() == ts.to_numpy()
+
+
+class SubDatetime(datetime):
+ pass
+
+
+@pytest.mark.parametrize(
+ "lh,rh",
+ [
+ (SubDatetime(2000, 1, 1), Timedelta(hours=1)),
+ (Timedelta(hours=1), SubDatetime(2000, 1, 1)),
+ ],
+)
+def test_dt_subclass_add_timedelta(lh, rh):
+ # GH#25851
+ # ensure that subclassed datetime works for
+ # Timedelta operations
+ result = lh + rh
+ expected = SubDatetime(2000, 1, 1, 1)
+ assert result == expected
| https://api.github.com/repos/pandas-dev/pandas/pulls/27847 | 2019-08-09T21:19:09Z | 2019-08-15T12:39:57Z | 2019-08-15T12:39:57Z | 2019-08-15T14:20:52Z | |
BUG: boxplot does not work when data has datetime column | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 974d14a4b424c..b35f230100f8d 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -165,6 +165,7 @@ Plotting
- Bug in :meth:`Series.plot` not able to plot boolean values (:issue:`23719`)
-
+- Bug in :meth:`DataFrame.plot` when ``kind='box'`` and data contains datetime or timedelta data. These types are now automatically dropped (:issue:`22799`)
Groupby/resample/rolling
^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index 50f0d16631a15..a262f89dcc79c 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -402,12 +402,20 @@ def _compute_plot_data(self):
# GH16953, _convert is needed as fallback, for ``Series``
# with ``dtype == object``
data = data._convert(datetime=True, timedelta=True)
- select_include_type = [np.number, "datetime", "datetimetz", "timedelta"]
+ include_type = [np.number, "datetime", "datetimetz", "timedelta"]
# GH23719, allow plotting boolean
if self.include_bool is True:
- select_include_type.append(np.bool_)
- numeric_data = data.select_dtypes(include=select_include_type)
+ include_type.append(np.bool_)
+
+ # GH22799, exclude datatime-like type for boxplot
+ exclude_type = None
+ if self._kind == "box":
+ # TODO: change after solving issue 27881
+ include_type = [np.number]
+ exclude_type = ["timedelta"]
+
+ numeric_data = data.select_dtypes(include=include_type, exclude=exclude_type)
try:
is_empty = numeric_data.empty
diff --git a/pandas/tests/plotting/test_boxplot_method.py b/pandas/tests/plotting/test_boxplot_method.py
index cab0efe53f1fc..5bbaff580c356 100644
--- a/pandas/tests/plotting/test_boxplot_method.py
+++ b/pandas/tests/plotting/test_boxplot_method.py
@@ -9,7 +9,7 @@
import pandas.util._test_decorators as td
-from pandas import DataFrame, MultiIndex, Series
+from pandas import DataFrame, MultiIndex, Series, date_range, timedelta_range
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
import pandas.util.testing as tm
@@ -160,6 +160,21 @@ def test_fontsize(self):
df.boxplot("a", fontsize=16), xlabelsize=16, ylabelsize=16
)
+ def test_boxplot_numeric_data(self):
+ # GH 22799
+ df = DataFrame(
+ {
+ "a": date_range("2012-01-01", periods=100),
+ "b": np.random.randn(100),
+ "c": np.random.randn(100) + 2,
+ "d": date_range("2012-01-01", periods=100).astype(str),
+ "e": date_range("2012-01-01", periods=100, tz="UTC"),
+ "f": timedelta_range("1 days", periods=100),
+ }
+ )
+ ax = df.plot(kind="box")
+ assert [x.get_text() for x in ax.get_xticklabels()] == ["b", "c"]
+
@td.skip_if_no_mpl
class TestDataFrameGroupByPlots(TestPlotBase):
| - [ ] closes #22799
- [ ] 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/27846 | 2019-08-09T19:19:08Z | 2019-08-13T12:44:11Z | 2019-08-13T12:44:10Z | 2019-08-13T12:44:11Z |
CI: pytest, xdist versions | diff --git a/ci/deps/azure-37-locale.yaml b/ci/deps/azure-37-locale.yaml
index 05adbf0c924dc..26dcd213bbfa0 100644
--- a/ci/deps/azure-37-locale.yaml
+++ b/ci/deps/azure-37-locale.yaml
@@ -26,8 +26,8 @@ dependencies:
- xlsxwriter
- xlwt
# universal
- - pytest>=4.0.2
- - pytest-xdist
+ - pytest>=5.0.1
+ - pytest-xdist>=1.29.0
- pytest-mock
- pytest-azurepipelines
- pip
diff --git a/ci/deps/azure-37-numpydev.yaml b/ci/deps/azure-37-numpydev.yaml
index 5cf897c98da10..65c92ec1dcf0d 100644
--- a/ci/deps/azure-37-numpydev.yaml
+++ b/ci/deps/azure-37-numpydev.yaml
@@ -6,7 +6,8 @@ dependencies:
- pytz
- Cython>=0.28.2
# universal
- - pytest>=4.0.2
+ # pytest < 5 until defaults has pytest-xdist>=1.29.0
+ - pytest>=4.0.2,<5.0
- pytest-xdist
- pytest-mock
- hypothesis>=3.58.0
diff --git a/ci/deps/azure-macos-35.yaml b/ci/deps/azure-macos-35.yaml
index 98859b596ab2a..cb2ac08cbf758 100644
--- a/ci/deps/azure-macos-35.yaml
+++ b/ci/deps/azure-macos-35.yaml
@@ -25,8 +25,8 @@ dependencies:
- pip:
- pyreadstat
# universal
- - pytest==4.5.0
- - pytest-xdist
+ - pytest>=5.0.1
+ - pytest-xdist>=1.29.0
- pytest-mock
- hypothesis>=3.58.0
# https://github.com/pandas-dev/pandas/issues/27421
diff --git a/ci/deps/azure-windows-36.yaml b/ci/deps/azure-windows-36.yaml
index b0f3f5389ac85..ff9264a36cb12 100644
--- a/ci/deps/azure-windows-36.yaml
+++ b/ci/deps/azure-windows-36.yaml
@@ -23,8 +23,8 @@ dependencies:
- xlwt
# universal
- cython>=0.28.2
- - pytest>=4.0.2
- - pytest-xdist
+ - pytest>=5.0.1
+ - pytest-xdist>=1.29.0
- pytest-mock
- pytest-azurepipelines
- hypothesis>=3.58.0
diff --git a/ci/deps/azure-windows-37.yaml b/ci/deps/azure-windows-37.yaml
index 08208d1e2d59a..075234a937035 100644
--- a/ci/deps/azure-windows-37.yaml
+++ b/ci/deps/azure-windows-37.yaml
@@ -26,8 +26,8 @@ dependencies:
- xlwt
# universal
- cython>=0.28.2
- - pytest>=4.0.2
- - pytest-xdist
+ - pytest>=5.0.0
+ - pytest-xdist>=1.29.0
- pytest-mock
- pytest-azurepipelines
- hypothesis>=3.58.0
diff --git a/ci/deps/travis-36-cov.yaml b/ci/deps/travis-36-cov.yaml
index a3f6d5b30f3e1..19002cbb8575e 100644
--- a/ci/deps/travis-36-cov.yaml
+++ b/ci/deps/travis-36-cov.yaml
@@ -39,8 +39,8 @@ dependencies:
- xlsxwriter
- xlwt
# universal
- - pytest
- - pytest-xdist
+ - pytest>=5.0.1
+ - pytest-xdist>=1.29.0
- pytest-cov
- pytest-mock
- hypothesis>=3.58.0
diff --git a/ci/deps/travis-37.yaml b/ci/deps/travis-37.yaml
index c9a8c274fb144..9e08c41a3d9c0 100644
--- a/ci/deps/travis-37.yaml
+++ b/ci/deps/travis-37.yaml
@@ -13,8 +13,8 @@ dependencies:
- pyarrow
- pytz
# universal
- - pytest>=4.0.2
- - pytest-xdist
+ - pytest>=5.0.0
+ - pytest-xdist>=1.29.0
- pytest-mock
- hypothesis>=3.58.0
- s3fs
| https://api.github.com/repos/pandas-dev/pandas/pulls/27845 | 2019-08-09T18:57:36Z | 2019-08-12T02:29:48Z | 2019-08-12T02:29:48Z | 2019-08-21T21:09:15Z | |
DOC: GroupBy.head()/tail() documentation | diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index c5e81e21e9fd5..d1da298412751 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2326,8 +2326,9 @@ def head(self, n=5):
"""
Return first n rows of each group.
- Essentially equivalent to ``.apply(lambda x: x.head(n))``,
- except ignores as_index flag.
+ Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows
+ from the original DataFrame with original index and order preserved
+ (``as_index`` flag is ignored).
Returns
-------
@@ -2338,10 +2339,6 @@ def head(self, n=5):
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
... columns=['A', 'B'])
- >>> df.groupby('A', as_index=False).head(1)
- A B
- 0 1 2
- 2 5 6
>>> df.groupby('A').head(1)
A B
0 1 2
@@ -2357,8 +2354,9 @@ def tail(self, n=5):
"""
Return last n rows of each group.
- Essentially equivalent to ``.apply(lambda x: x.tail(n))``,
- except ignores as_index flag.
+ Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows
+ from the original DataFrame with original index and order preserved
+ (``as_index`` flag is ignored).
Returns
-------
@@ -2373,10 +2371,6 @@ def tail(self, n=5):
A B
1 a 2
3 b 2
- >>> df.groupby('A').head(1)
- A B
- 0 a 1
- 2 b 1
"""
self._reset_group_selection()
mask = self._cumcount_array(ascending=False) < n
| - [x] closes #6721
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Update DOC to make it consistent with actual behavior | https://api.github.com/repos/pandas-dev/pandas/pulls/27844 | 2019-08-09T18:57:21Z | 2019-08-23T08:06:04Z | 2019-08-23T08:06:04Z | 2019-08-23T08:06:04Z |
PERF: Break reference cycle for all Index types | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index dfa216b1db56e..21f1fa7ddec1f 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -83,7 +83,7 @@ Indexing
^^^^^^^^
- Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']`` (:issue:`27516`)
-- Break reference cycle involving :class:`Index` to allow garbage collection of :class:`Index` objects without running the GC. (:issue:`27585`)
+- Break reference cycle involving :class:`Index` and other index classes to allow garbage collection of index objects without running the GC. (:issue:`27585`, :issue:`27840`)
- Fix regression in assigning values to a single column of a DataFrame with a ``MultiIndex`` columns (:issue:`27841`).
-
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 7272d4e2752be..d13e41eed7ad0 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -665,7 +665,7 @@ def _cleanup(self):
def _engine(self):
# property, for now, slow to look up
- # to avoid a refernce cycle, bind `_ndarray_values` to a local variable, so
+ # to avoid a reference cycle, bind `_ndarray_values` to a local variable, so
# `self` is not passed into the lambda.
_ndarray_values = self._ndarray_values
return self._engine_type(lambda: _ndarray_values, len(self))
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index 8bfa7e8d20b4f..82806c7351db6 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -446,9 +446,11 @@ def argsort(self, *args, **kwargs):
@cache_readonly
def _engine(self):
-
- # we are going to look things up with the codes themselves
- return self._engine_type(lambda: self.codes, len(self))
+ # we are going to look things up with the codes themselves.
+ # To avoid a reference cycle, bind `codes` to a local variable, so
+ # `self` is not passed into the lambda.
+ codes = self.codes
+ return self._engine_type(lambda: codes, len(self))
# introspection
@cache_readonly
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index b0cc386f7783d..5a2ca109597e8 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -1,5 +1,6 @@
from datetime import datetime, timedelta
import warnings
+import weakref
import numpy as np
@@ -441,7 +442,9 @@ def _formatter_func(self):
@cache_readonly
def _engine(self):
- return self._engine_type(lambda: self, len(self))
+ # To avoid a reference cycle, pass a weakref of self to _engine_type.
+ period = weakref.ref(self)
+ return self._engine_type(period, len(self))
@Appender(_index_shared_docs["contains"])
def __contains__(self, key):
diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py
index 9459069f0ea2d..0e74c87388682 100644
--- a/pandas/tests/indexes/common.py
+++ b/pandas/tests/indexes/common.py
@@ -1,3 +1,5 @@
+import gc
+
import numpy as np
import pytest
@@ -908,3 +910,10 @@ def test_is_unique(self):
# multiple NA should not be unique
index_na_dup = index_na.insert(0, np.nan)
assert index_na_dup.is_unique is False
+
+ def test_engine_reference_cycle(self):
+ # GH27585
+ index = self.create_index()
+ nrefs_pre = len(gc.get_referrers(index))
+ index._engine
+ assert len(gc.get_referrers(index)) == nrefs_pre
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index fe1eb96df1e97..d1ed79118d2fa 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -1,6 +1,5 @@
from collections import defaultdict
from datetime import datetime, timedelta
-import gc
from io import StringIO
import math
import operator
@@ -2425,13 +2424,6 @@ def test_deprecated_contains(self):
with tm.assert_produces_warning(FutureWarning):
index.contains(1)
- def test_engine_reference_cycle(self):
- # https://github.com/pandas-dev/pandas/issues/27585
- index = pd.Index([1, 2, 3])
- nrefs_pre = len(gc.get_referrers(index))
- index._engine
- assert len(gc.get_referrers(index)) == nrefs_pre
-
class TestMixedIntIndex(Base):
# Mostly the tests from common.py for which the results differ
| - [x] xref #27585 & #27607
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Generalizes the test in #27607 to be used for all index types. This finds ref cycle issues for ``PeriodIndex`` and ``CategoricalIndex`` which are solved in this PR.
| https://api.github.com/repos/pandas-dev/pandas/pulls/27840 | 2019-08-09T10:11:28Z | 2019-08-14T20:11:35Z | 2019-08-14T20:11:35Z | 2019-08-14T21:52:31Z |
Parametrize JSON tests | diff --git a/pandas/tests/io/json/conftest.py b/pandas/tests/io/json/conftest.py
new file mode 100644
index 0000000000000..4e848cd48b42d
--- /dev/null
+++ b/pandas/tests/io/json/conftest.py
@@ -0,0 +1,9 @@
+import pytest
+
+
+@pytest.fixture(params=["split", "records", "index", "columns", "values"])
+def orient(request):
+ """
+ Fixture for orients excluding the table format.
+ """
+ return request.param
diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py
index 9842a706f43d7..2ef8244f488ae 100644
--- a/pandas/tests/io/json/test_pandas.py
+++ b/pandas/tests/io/json/test_pandas.py
@@ -7,16 +7,14 @@
import numpy as np
import pytest
-from pandas.compat import is_platform_32bit
+from pandas.compat import PY35, is_platform_32bit
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, DatetimeIndex, Series, Timestamp, read_json
import pandas.util.testing as tm
from pandas.util.testing import (
- assert_almost_equal,
assert_frame_equal,
- assert_index_equal,
assert_series_equal,
ensure_clean,
network,
@@ -82,76 +80,79 @@ def setup(self, datapath):
del self.tsframe
del self.mixed_frame
- def test_frame_double_encoded_labels(self):
+ def test_frame_double_encoded_labels(self, orient):
df = DataFrame(
[["a", "b"], ["c", "d"]],
index=['index " 1', "index / 2"],
columns=["a \\ b", "y / z"],
)
- assert_frame_equal(df, read_json(df.to_json(orient="split"), orient="split"))
- assert_frame_equal(
- df, read_json(df.to_json(orient="columns"), orient="columns")
- )
- assert_frame_equal(df, read_json(df.to_json(orient="index"), orient="index"))
- df_unser = read_json(df.to_json(orient="records"), orient="records")
- assert_index_equal(df.columns, df_unser.columns)
- tm.assert_numpy_array_equal(df.values, df_unser.values)
-
- def test_frame_non_unique_index(self):
- df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"])
-
- msg = "DataFrame index must be unique for orient='index'"
- with pytest.raises(ValueError, match=msg):
- df.to_json(orient="index")
- msg = "DataFrame index must be unique for orient='columns'"
- with pytest.raises(ValueError, match=msg):
- df.to_json(orient="columns")
+ result = read_json(df.to_json(orient=orient), orient=orient)
+ expected = df.copy()
- assert_frame_equal(df, read_json(df.to_json(orient="split"), orient="split"))
- unser = read_json(df.to_json(orient="records"), orient="records")
- tm.assert_index_equal(df.columns, unser.columns)
- tm.assert_almost_equal(df.values, unser.values)
- unser = read_json(df.to_json(orient="values"), orient="values")
- tm.assert_numpy_array_equal(df.values, unser.values)
+ if orient == "records" or orient == "values":
+ expected = expected.reset_index(drop=True)
+ if orient == "values":
+ expected.columns = range(len(expected.columns))
- def test_frame_non_unique_columns(self):
- df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 2], columns=["x", "x"])
+ assert_frame_equal(result, expected)
- msg = "DataFrame columns must be unique for orient='index'"
- with pytest.raises(ValueError, match=msg):
- df.to_json(orient="index")
- msg = "DataFrame columns must be unique for orient='columns'"
- with pytest.raises(ValueError, match=msg):
- df.to_json(orient="columns")
- msg = "DataFrame columns must be unique for orient='records'"
- with pytest.raises(ValueError, match=msg):
- df.to_json(orient="records")
+ @pytest.mark.parametrize("orient", ["split", "records", "values"])
+ def test_frame_non_unique_index(self, orient):
+ df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"])
+ result = read_json(df.to_json(orient=orient), orient=orient)
+ expected = df.copy()
- assert_frame_equal(
- df, read_json(df.to_json(orient="split"), orient="split", dtype=False)
- )
- unser = read_json(df.to_json(orient="values"), orient="values")
- tm.assert_numpy_array_equal(df.values, unser.values)
+ if orient == "records" or orient == "values":
+ expected = expected.reset_index(drop=True)
+ if orient == "values":
+ expected.columns = range(len(expected.columns))
- # GH4377; duplicate columns not processing correctly
- df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 2], columns=["x", "y"])
- result = read_json(df.to_json(orient="split"), orient="split")
- assert_frame_equal(result, df)
+ assert_frame_equal(result, expected)
- def _check(df):
- result = read_json(
- df.to_json(orient="split"), orient="split", convert_dates=["x"]
- )
- assert_frame_equal(result, df)
+ @pytest.mark.parametrize("orient", ["index", "columns"])
+ def test_frame_non_unique_index_raises(self, orient):
+ df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"])
+ msg = "DataFrame index must be unique for orient='{}'".format(orient)
+ with pytest.raises(ValueError, match=msg):
+ df.to_json(orient=orient)
- for o in [
+ @pytest.mark.parametrize("orient", ["split", "values"])
+ @pytest.mark.parametrize(
+ "data",
+ [
[["a", "b"], ["c", "d"]],
[[1.5, 2.5], [3.5, 4.5]],
[[1, 2.5], [3, 4.5]],
[[Timestamp("20130101"), 3.5], [Timestamp("20130102"), 4.5]],
- ]:
- _check(DataFrame(o, index=[1, 2], columns=["x", "x"]))
+ ],
+ )
+ def test_frame_non_unique_columns(self, orient, data):
+ df = DataFrame(data, index=[1, 2], columns=["x", "x"])
+
+ result = read_json(
+ df.to_json(orient=orient), orient=orient, convert_dates=["x"]
+ )
+ if orient == "values":
+ expected = pd.DataFrame(data)
+ if expected.iloc[:, 0].dtype == "datetime64[ns]":
+ # orient == "values" by default will write Timestamp objects out
+ # in milliseconds; these are internally stored in nanosecond,
+ # so divide to get where we need
+ # TODO: a to_epoch method would also solve; see GH 14772
+ expected.iloc[:, 0] = expected.iloc[:, 0].astype(np.int64) // 1000000
+ elif orient == "split":
+ expected = df
+
+ assert_frame_equal(result, expected)
+
+ @pytest.mark.parametrize("orient", ["index", "columns", "records"])
+ def test_frame_non_unique_columns_raises(self, orient):
+ df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 2], columns=["x", "x"])
+
+ msg = "DataFrame columns must be unique for orient='{}'".format(orient)
+ with pytest.raises(ValueError, match=msg):
+ df.to_json(orient=orient)
def test_frame_from_json_to_json(self):
def _check_orient(
@@ -470,101 +471,93 @@ def _check_all_orients(
# force everything to have object dtype beforehand
_check_orient(df.transpose().transpose(), "index", dtype=False)
- def test_frame_from_json_bad_data(self):
- with pytest.raises(ValueError, match="Expected object or value"):
- read_json(StringIO('{"key":b:a:d}'))
-
- # too few indices
- json = StringIO(
- '{"columns":["A","B"],'
- '"index":["2","3"],'
- '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}'
- )
- msg = r"Shape of passed values is \(3, 2\), indices imply \(2, 2\)"
+ @pytest.mark.parametrize(
+ "data,msg,orient",
+ [
+ ('{"key":b:a:d}', "Expected object or value", "columns"),
+ # too few indices
+ (
+ '{"columns":["A","B"],'
+ '"index":["2","3"],'
+ '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
+ r"Shape of passed values is \(3, 2\), indices imply \(2, 2\)",
+ "split",
+ ),
+ # too many columns
+ (
+ '{"columns":["A","B","C"],'
+ '"index":["1","2","3"],'
+ '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
+ "3 columns passed, passed data had 2 columns",
+ "split",
+ ),
+ # bad key
+ (
+ '{"badkey":["A","B"],'
+ '"index":["2","3"],'
+ '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
+ r"unexpected key\(s\): badkey",
+ "split",
+ ),
+ ],
+ )
+ def test_frame_from_json_bad_data_raises(self, data, msg, orient):
with pytest.raises(ValueError, match=msg):
- read_json(json, orient="split")
+ read_json(StringIO(data), orient=orient)
- # too many columns
- json = StringIO(
- '{"columns":["A","B","C"],'
- '"index":["1","2","3"],'
- '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}'
+ @pytest.mark.parametrize("dtype", [True, False])
+ @pytest.mark.parametrize("convert_axes", [True, False])
+ @pytest.mark.parametrize("numpy", [True, False])
+ def test_frame_from_json_missing_data(self, orient, convert_axes, numpy, dtype):
+ num_df = DataFrame([[1, 2], [4, 5, 6]])
+ result = read_json(
+ num_df.to_json(orient=orient),
+ orient=orient,
+ convert_axes=convert_axes,
+ dtype=dtype,
)
- msg = "3 columns passed, passed data had 2 columns"
- with pytest.raises(ValueError, match=msg):
- read_json(json, orient="split")
+ assert np.isnan(result.iloc[0, 2])
- # bad key
- json = StringIO(
- '{"badkey":["A","B"],'
- '"index":["2","3"],'
- '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}'
+ obj_df = DataFrame([["1", "2"], ["4", "5", "6"]])
+ result = read_json(
+ obj_df.to_json(orient=orient),
+ orient=orient,
+ convert_axes=convert_axes,
+ dtype=dtype,
)
- with pytest.raises(ValueError, match=r"unexpected key\(s\): badkey"):
- read_json(json, orient="split")
-
- def test_frame_from_json_nones(self):
- df = DataFrame([[1, 2], [4, 5, 6]])
- unser = read_json(df.to_json())
- assert np.isnan(unser[2][0])
-
- df = DataFrame([["1", "2"], ["4", "5", "6"]])
- unser = read_json(df.to_json())
- assert np.isnan(unser[2][0])
- unser = read_json(df.to_json(), dtype=False)
- assert unser[2][0] is None
- unser = read_json(df.to_json(), convert_axes=False, dtype=False)
- assert unser["2"]["0"] is None
-
- unser = read_json(df.to_json(), numpy=False)
- assert np.isnan(unser[2][0])
- unser = read_json(df.to_json(), numpy=False, dtype=False)
- assert unser[2][0] is None
- unser = read_json(df.to_json(), numpy=False, convert_axes=False, dtype=False)
- assert unser["2"]["0"] is None
+ if not dtype: # TODO: Special case for object data; maybe a bug?
+ assert result.iloc[0, 2] is None
+ else:
+ assert np.isnan(result.iloc[0, 2])
+ @pytest.mark.parametrize("inf", [np.inf, np.NINF])
+ @pytest.mark.parametrize("dtype", [True, False])
+ def test_frame_infinity(self, orient, inf, dtype):
# infinities get mapped to nulls which get mapped to NaNs during
# deserialisation
df = DataFrame([[1, 2], [4, 5, 6]])
- df.loc[0, 2] = np.inf
- unser = read_json(df.to_json())
- assert np.isnan(unser[2][0])
- unser = read_json(df.to_json(), dtype=False)
- assert np.isnan(unser[2][0])
-
- df.loc[0, 2] = np.NINF
- unser = read_json(df.to_json())
- assert np.isnan(unser[2][0])
- unser = read_json(df.to_json(), dtype=False)
- assert np.isnan(unser[2][0])
+ df.loc[0, 2] = inf
+ result = read_json(df.to_json(), dtype=dtype)
+ assert np.isnan(result.iloc[0, 2])
@pytest.mark.skipif(
is_platform_32bit(), reason="not compliant on 32-bit, xref #15865"
)
- def test_frame_to_json_float_precision(self):
- df = pd.DataFrame([dict(a_float=0.95)])
- encoded = df.to_json(double_precision=1)
- assert encoded == '{"a_float":{"0":1.0}}'
-
- df = pd.DataFrame([dict(a_float=1.95)])
- encoded = df.to_json(double_precision=1)
- assert encoded == '{"a_float":{"0":2.0}}'
-
- df = pd.DataFrame([dict(a_float=-1.95)])
- encoded = df.to_json(double_precision=1)
- assert encoded == '{"a_float":{"0":-2.0}}'
-
- df = pd.DataFrame([dict(a_float=0.995)])
- encoded = df.to_json(double_precision=2)
- assert encoded == '{"a_float":{"0":1.0}}'
-
- df = pd.DataFrame([dict(a_float=0.9995)])
- encoded = df.to_json(double_precision=3)
- assert encoded == '{"a_float":{"0":1.0}}'
-
- df = pd.DataFrame([dict(a_float=0.99999999999999944)])
- encoded = df.to_json(double_precision=15)
- assert encoded == '{"a_float":{"0":1.0}}'
+ @pytest.mark.parametrize(
+ "value,precision,expected_val",
+ [
+ (0.95, 1, 1.0),
+ (1.95, 1, 2.0),
+ (-1.95, 1, -2.0),
+ (0.995, 2, 1.0),
+ (0.9995, 3, 1.0),
+ (0.99999999999999944, 15, 1.0),
+ ],
+ )
+ def test_frame_to_json_float_precision(self, value, precision, expected_val):
+ df = pd.DataFrame([dict(a_float=value)])
+ encoded = df.to_json(double_precision=precision)
+ assert encoded == '{{"a_float":{{"0":{}}}}}'.format(expected_val)
def test_frame_to_json_except(self):
df = DataFrame([1, 2, 3])
@@ -799,107 +792,92 @@ def test_series_non_unique_index(self):
unser = read_json(s.to_json(orient="records"), orient="records", typ="series")
tm.assert_numpy_array_equal(s.values, unser.values)
- def test_series_from_json_to_json(self):
- def _check_orient(
- series, orient, dtype=None, numpy=False, check_index_type=True
- ):
- series = series.sort_index()
- unser = read_json(
- series.to_json(orient=orient),
- typ="series",
- orient=orient,
- numpy=numpy,
- dtype=dtype,
- )
- unser = unser.sort_index()
- if orient == "records" or orient == "values":
- assert_almost_equal(series.values, unser.values)
- else:
- if orient == "split":
- assert_series_equal(
- series, unser, check_index_type=check_index_type
- )
- else:
- assert_series_equal(
- series,
- unser,
- check_names=False,
- check_index_type=check_index_type,
- )
-
- def _check_all_orients(series, dtype=None, check_index_type=True):
- _check_orient(
- series, "columns", dtype=dtype, check_index_type=check_index_type
- )
- _check_orient(
- series, "records", dtype=dtype, check_index_type=check_index_type
- )
- _check_orient(
- series, "split", dtype=dtype, check_index_type=check_index_type
- )
- _check_orient(
- series, "index", dtype=dtype, check_index_type=check_index_type
- )
- _check_orient(series, "values", dtype=dtype)
-
- _check_orient(
- series,
- "columns",
- dtype=dtype,
- numpy=True,
- check_index_type=check_index_type,
- )
- _check_orient(
- series,
- "records",
- dtype=dtype,
- numpy=True,
- check_index_type=check_index_type,
- )
- _check_orient(
- series,
- "split",
- dtype=dtype,
- numpy=True,
- check_index_type=check_index_type,
- )
- _check_orient(
- series,
- "index",
- dtype=dtype,
- numpy=True,
- check_index_type=check_index_type,
- )
- _check_orient(
- series,
- "values",
- dtype=dtype,
- numpy=True,
- check_index_type=check_index_type,
- )
-
- # basic
- _check_all_orients(self.series)
+ def test_series_default_orient(self):
assert self.series.to_json() == self.series.to_json(orient="index")
- objSeries = Series(
+ @pytest.mark.parametrize("numpy", [True, False])
+ def test_series_roundtrip_simple(self, orient, numpy):
+ data = self.series.to_json(orient=orient)
+ result = pd.read_json(data, typ="series", orient=orient, numpy=numpy)
+ expected = self.series.copy()
+
+ if not numpy and PY35 and orient in ("index", "columns"):
+ expected = expected.sort_index()
+ if orient in ("values", "records"):
+ expected = expected.reset_index(drop=True)
+ if orient != "split":
+ expected.name = None
+
+ tm.assert_series_equal(result, expected)
+
+ @pytest.mark.parametrize("dtype", [False, None])
+ @pytest.mark.parametrize("numpy", [True, False])
+ def test_series_roundtrip_object(self, orient, numpy, dtype):
+ # TODO: see why tm.makeObjectSeries provides back DTA
+ dtSeries = Series(
[str(d) for d in self.objSeries],
index=self.objSeries.index,
name=self.objSeries.name,
)
- _check_all_orients(objSeries, dtype=False)
+ data = dtSeries.to_json(orient=orient)
+ result = pd.read_json(
+ data, typ="series", orient=orient, numpy=numpy, dtype=dtype
+ )
+ if dtype is False:
+ expected = dtSeries.copy()
+ else:
+ expected = self.objSeries.copy()
+
+ if not numpy and PY35 and orient in ("index", "columns"):
+ expected = expected.sort_index()
+ if orient in ("values", "records"):
+ expected = expected.reset_index(drop=True)
+ if orient != "split":
+ expected.name = None
+
+ tm.assert_series_equal(result, expected)
+
+ @pytest.mark.parametrize("numpy", [True, False])
+ def test_series_roundtrip_empty(self, orient, numpy):
+ data = self.empty_series.to_json(orient=orient)
+ result = pd.read_json(data, typ="series", orient=orient, numpy=numpy)
+ expected = self.empty_series.copy()
+
+ # TODO: see what causes inconsistency
+ if not numpy and PY35 and orient == "index":
+ expected = expected.sort_index()
+ if orient in ("values", "records"):
+ expected = expected.reset_index(drop=True)
+ else:
+ expected.index = expected.index.astype(float)
+
+ tm.assert_series_equal(result, expected)
+
+ @pytest.mark.parametrize("numpy", [True, False])
+ def test_series_roundtrip_timeseries(self, orient, numpy):
+ data = self.ts.to_json(orient=orient)
+ result = pd.read_json(data, typ="series", orient=orient, numpy=numpy)
+ expected = self.ts.copy()
- # empty_series has empty index with object dtype
- # which cannot be revert
- assert self.empty_series.index.dtype == np.object_
- _check_all_orients(self.empty_series, check_index_type=False)
+ if orient in ("values", "records"):
+ expected = expected.reset_index(drop=True)
+ if orient != "split":
+ expected.name = None
- _check_all_orients(self.ts)
+ tm.assert_series_equal(result, expected)
- # dtype
+ @pytest.mark.parametrize("dtype", [np.float64, np.int])
+ @pytest.mark.parametrize("numpy", [True, False])
+ def test_series_roundtrip_numeric(self, orient, numpy, dtype):
s = Series(range(6), index=["a", "b", "c", "d", "e", "f"])
- _check_all_orients(Series(s, dtype=np.float64), dtype=np.float64)
- _check_all_orients(Series(s, dtype=np.int), dtype=np.int)
+ data = s.to_json(orient=orient)
+ result = pd.read_json(data, typ="series", orient=orient, numpy=numpy)
+
+ expected = s.copy()
+ if orient in ("values", "records"):
+ expected = expected.reset_index(drop=True)
+
+ tm.assert_series_equal(result, expected)
def test_series_to_json_except(self):
s = Series([1, 2, 3])
@@ -991,11 +969,9 @@ def test_convert_dates(self):
result = read_json(json, typ="series")
assert_series_equal(result, ts)
- def test_convert_dates_infer(self):
- # GH10747
- from pandas.io.json import dumps
-
- infer_words = [
+ @pytest.mark.parametrize(
+ "infer_word",
+ [
"trade_time",
"date",
"datetime",
@@ -1003,14 +979,18 @@ def test_convert_dates_infer(self):
"modified",
"timestamp",
"timestamps",
- ]
- for infer_word in infer_words:
- data = [{"id": 1, infer_word: 1036713600000}, {"id": 2}]
- expected = DataFrame(
- [[1, Timestamp("2002-11-08")], [2, pd.NaT]], columns=["id", infer_word]
- )
- result = read_json(dumps(data))[["id", infer_word]]
- assert_frame_equal(result, expected)
+ ],
+ )
+ def test_convert_dates_infer(self, infer_word):
+ # GH10747
+ from pandas.io.json import dumps
+
+ data = [{"id": 1, infer_word: 1036713600000}, {"id": 2}]
+ expected = DataFrame(
+ [[1, Timestamp("2002-11-08")], [2, pd.NaT]], columns=["id", infer_word]
+ )
+ result = read_json(dumps(data))[["id", infer_word]]
+ assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"date,date_unit",
@@ -1315,27 +1295,32 @@ def test_sparse(self):
expected = s.to_json()
assert expected == ss.to_json()
- def test_tz_is_utc(self):
+ @pytest.mark.parametrize(
+ "ts",
+ [
+ Timestamp("2013-01-10 05:00:00Z"),
+ Timestamp("2013-01-10 00:00:00", tz="US/Eastern"),
+ Timestamp("2013-01-10 00:00:00-0500"),
+ ],
+ )
+ def test_tz_is_utc(self, ts):
from pandas.io.json import dumps
exp = '"2013-01-10T05:00:00.000Z"'
- ts = Timestamp("2013-01-10 05:00:00Z")
- assert dumps(ts, iso_dates=True) == exp
- dt = ts.to_pydatetime()
- assert dumps(dt, iso_dates=True) == exp
-
- ts = Timestamp("2013-01-10 00:00:00", tz="US/Eastern")
assert dumps(ts, iso_dates=True) == exp
dt = ts.to_pydatetime()
assert dumps(dt, iso_dates=True) == exp
- ts = Timestamp("2013-01-10 00:00:00-0500")
- assert dumps(ts, iso_dates=True) == exp
- dt = ts.to_pydatetime()
- assert dumps(dt, iso_dates=True) == exp
-
- def test_tz_range_is_utc(self):
+ @pytest.mark.parametrize(
+ "tz_range",
+ [
+ pd.date_range("2013-01-01 05:00:00Z", periods=2),
+ pd.date_range("2013-01-01 00:00:00", periods=2, tz="US/Eastern"),
+ pd.date_range("2013-01-01 00:00:00-0500", periods=2),
+ ],
+ )
+ def test_tz_range_is_utc(self, tz_range):
from pandas.io.json import dumps
exp = '["2013-01-01T05:00:00.000Z","2013-01-02T05:00:00.000Z"]'
@@ -1345,7 +1330,6 @@ def test_tz_range_is_utc(self):
'"1":"2013-01-02T05:00:00.000Z"}}'
)
- tz_range = pd.date_range("2013-01-01 05:00:00Z", periods=2)
assert dumps(tz_range, iso_dates=True) == exp
dti = pd.DatetimeIndex(tz_range)
assert dumps(dti, iso_dates=True) == exp
@@ -1353,20 +1337,6 @@ def test_tz_range_is_utc(self):
result = dumps(df, iso_dates=True)
assert result == dfexp
- tz_range = pd.date_range("2013-01-01 00:00:00", periods=2, tz="US/Eastern")
- assert dumps(tz_range, iso_dates=True) == exp
- dti = pd.DatetimeIndex(tz_range)
- assert dumps(dti, iso_dates=True) == exp
- df = DataFrame({"DT": dti})
- assert dumps(df, iso_dates=True) == dfexp
-
- tz_range = pd.date_range("2013-01-01 00:00:00-0500", periods=2)
- assert dumps(tz_range, iso_dates=True) == exp
- dti = pd.DatetimeIndex(tz_range)
- assert dumps(dti, iso_dates=True) == exp
- df = DataFrame({"DT": dti})
- assert dumps(df, iso_dates=True) == dfexp
-
def test_read_inline_jsonl(self):
# GH9180
result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=True)
@@ -1458,14 +1428,10 @@ def test_latin_encoding(self):
[b"A\xf8\xfc", np.nan, b"", b"b", b"c"],
]
- def _try_decode(x, encoding="latin-1"):
- try:
- return x.decode(encoding)
- except AttributeError:
- return x
-
- # not sure how to remove latin-1 from code in python 2 and 3
- values = [[_try_decode(x) for x in y] for y in values]
+ values = [
+ [x.decode("latin-1") if isinstance(x, bytes) else x for x in y]
+ for y in values
+ ]
examples = []
for dtype in ["category", object]:
| lot more to be done here but split these up as some of the large functions are difficult to highlight failures while working on extension module. Improved coverage and identified some inconsistencies in process (marked with TODOs) which can be tackled as follow ups | https://api.github.com/repos/pandas-dev/pandas/pulls/27838 | 2019-08-09T08:56:09Z | 2019-09-17T21:49:45Z | 2019-09-17T21:49:45Z | 2020-01-16T00:33:52Z |
Backport PR #27818 on branch 0.25.x (COMPAT: restore shape for 'invalid' Index with nd array) | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index f1d3f152e503d..d48a003d56e4f 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -111,7 +111,7 @@ Plotting
^^^^^^^^
- Added a pandas_plotting_backends entrypoint group for registering plot backends. See :ref:`extending.plotting-backends` for more (:issue:`26747`).
--
+- Fix compatibility issue with matplotlib when passing a pandas ``Index`` to a plot call (:issue:`27775`).
-
Groupby/resample/rolling
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 17122d0981995..1d70232d08c94 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -5648,7 +5648,10 @@ def shape(self):
"""
Return a tuple of the shape of the underlying data.
"""
- return (len(self),)
+ # not using "(len(self), )" to return "correct" shape if the values
+ # consists of a >1 D array (see GH-27775)
+ # overridden in MultiIndex.shape to avoid materializing the values
+ return self._values.shape
Index._add_numeric_methods_disabled()
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index b673c119c0498..1acaa4ef5ecdc 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -616,6 +616,15 @@ def _values(self):
# We override here, since our parent uses _data, which we dont' use.
return self.values
+ @property
+ def shape(self):
+ """
+ Return a tuple of the shape of the underlying data.
+ """
+ # overriding the base Index.shape definition to avoid materializing
+ # the values (GH-27384, GH-27775)
+ return (len(self),)
+
@property
def array(self):
"""
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index c40a9bce9385b..d1ed79118d2fa 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -2805,3 +2805,17 @@ def test_deprecated_fastpath():
expected = pd.CategoricalIndex(["a", "b", "c"], name="test")
tm.assert_index_equal(idx, expected)
+
+
+def test_shape_of_invalid_index():
+ # Currently, it is possible to create "invalid" index objects backed by
+ # a multi-dimensional array (see https://github.com/pandas-dev/pandas/issues/27125
+ # about this). However, as long as this is not solved in general,this test ensures
+ # that the returned shape is consistent with this underlying array for
+ # compat with matplotlib (see https://github.com/pandas-dev/pandas/issues/27775)
+ a = np.arange(8).reshape(2, 2, 2)
+ idx = pd.Index(a)
+ assert idx.shape == a.shape
+
+ idx = pd.Index([0, 1, 2, 3])
+ assert idx[:, None].shape == (4, 1)
| Backport PR #27818: COMPAT: restore shape for 'invalid' Index with nd array | https://api.github.com/repos/pandas-dev/pandas/pulls/27836 | 2019-08-09T07:37:36Z | 2019-08-09T08:20:19Z | 2019-08-09T08:20:19Z | 2019-08-09T08:20:19Z |
Update sample() documentation | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index c38489c7b85ef..5a1b2a5dcd20b 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -4786,7 +4786,7 @@ def sample(
frac : float, optional
Fraction of axis items to return. Cannot be used with `n`.
replace : bool, default False
- Sample with or without replacement.
+ Allow or disallow sampling of the same row more than once.
weights : str or ndarray-like, optional
Default 'None' results in equal probability weighting.
If passed a Series, will align with target object on index. Index
| Clarify the definition of replace parameter
- [ ] 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/27833 | 2019-08-09T04:13:16Z | 2019-08-09T12:33:49Z | 2019-08-09T12:33:49Z | 2019-08-09T13:16:22Z |
Add example for Series.str.slice() | diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index 169a3a24c254d..25350119f9df5 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -1442,6 +1442,12 @@ def str_slice(arr, start=None, stop=None, step=None):
2 hameleon
dtype: object
+ >>> s.str.slice(start=-1)
+ 0 a
+ 1 x
+ 2 n
+ dtype: object
+
>>> s.str.slice(stop=2)
0 ko
1 fo
| Add example to show that negative values for start are allowed for Series.str.slice().
- [ X] closes #27802
- [ ] 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/27832 | 2019-08-09T03:26:12Z | 2019-08-09T12:55:23Z | 2019-08-09T12:55:23Z | 2019-08-09T13:16:10Z |
CLN: match standardized dispatch logic in IntegerArray | diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index 1f14bd169a228..069d661e6af34 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -21,7 +21,7 @@
is_scalar,
)
from pandas.core.dtypes.dtypes import register_extension_dtype
-from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
from pandas.core.dtypes.missing import isna, notna
from pandas.core import nanops, ops
@@ -592,25 +592,29 @@ def _values_for_argsort(self) -> np.ndarray:
@classmethod
def _create_comparison_method(cls, op):
- def cmp_method(self, other):
+ op_name = op.__name__
- op_name = op.__name__
- mask = None
+ def cmp_method(self, other):
- if isinstance(other, (ABCSeries, ABCIndexClass)):
+ if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
+ other = lib.item_from_zerodim(other)
+ mask = None
+
if isinstance(other, IntegerArray):
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other)
- if other.ndim > 0 and len(self) != len(other):
+ if other.ndim > 1:
+ raise NotImplementedError(
+ "can only perform ops with 1-d structures"
+ )
+ if len(self) != len(other):
raise ValueError("Lengths must match to compare")
- other = lib.item_from_zerodim(other)
-
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings():
@@ -683,31 +687,31 @@ def _maybe_mask_result(self, result, mask, other, op_name):
@classmethod
def _create_arithmetic_method(cls, op):
- def integer_arithmetic_method(self, other):
+ op_name = op.__name__
- op_name = op.__name__
- mask = None
+ def integer_arithmetic_method(self, other):
- if isinstance(other, (ABCSeries, ABCIndexClass)):
+ if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
- if getattr(other, "ndim", 0) > 1:
- raise NotImplementedError("can only perform ops with 1-d structures")
+ other = lib.item_from_zerodim(other)
+ mask = None
if isinstance(other, IntegerArray):
other, mask = other._data, other._mask
- elif getattr(other, "ndim", None) == 0:
- other = other.item()
-
elif is_list_like(other):
other = np.asarray(other)
- if not other.ndim:
- other = other.item()
- elif other.ndim == 1:
- if not (is_float_dtype(other) or is_integer_dtype(other)):
- raise TypeError("can only perform ops with numeric values")
+ if other.ndim > 1:
+ raise NotImplementedError(
+ "can only perform ops with 1-d structures"
+ )
+ if len(self) != len(other):
+ raise ValueError("Lengths must match")
+ if not (is_float_dtype(other) or is_integer_dtype(other)):
+ raise TypeError("can only perform ops with numeric values")
+
else:
if not (is_float(other) or is_integer(other)):
raise TypeError("can only perform ops with numeric values")
diff --git a/pandas/tests/arrays/test_integer.py b/pandas/tests/arrays/test_integer.py
index 50cd1469e5196..31a9a0483081e 100644
--- a/pandas/tests/arrays/test_integer.py
+++ b/pandas/tests/arrays/test_integer.py
@@ -280,7 +280,7 @@ def test_arith_coerce_scalar(self, data, all_arithmetic_operators):
other = 0.01
self._check_op(s, op, other)
- @pytest.mark.parametrize("other", [1.0, 1.0, np.array(1.0), np.array([1.0])])
+ @pytest.mark.parametrize("other", [1.0, np.array(1.0)])
def test_arithmetic_conversion(self, all_arithmetic_operators, other):
# if we have a float operand we should have a float result
# if that is equal to an integer
@@ -290,6 +290,15 @@ def test_arithmetic_conversion(self, all_arithmetic_operators, other):
result = op(s, other)
assert result.dtype is np.dtype("float")
+ def test_arith_len_mismatch(self, all_arithmetic_operators):
+ # operating with a list-like with non-matching length raises
+ op = self.get_op_from_name(all_arithmetic_operators)
+ other = np.array([1.0])
+
+ s = pd.Series([1, 2, 3], dtype="Int64")
+ with pytest.raises(ValueError, match="Lengths must match"):
+ op(s, other)
+
@pytest.mark.parametrize("other", [0, 0.5])
def test_arith_zero_dim_ndarray(self, other):
arr = integer_array([1, None, 2])
@@ -322,8 +331,9 @@ def test_error(self, data, all_arithmetic_operators):
ops(pd.Series(pd.date_range("20180101", periods=len(s))))
# 2d
- with pytest.raises(NotImplementedError):
- opa(pd.DataFrame({"A": s}))
+ result = opa(pd.DataFrame({"A": s}))
+ assert result is NotImplemented
+
with pytest.raises(NotImplementedError):
opa(np.arange(len(s)).reshape(-1, len(s)))
| Actual logic changes:
- return NotImplemented when operating with DataFrame (instead of raising NotImplementError)
- raise ValueError when operating with mismatched-length listlike
Everything else is just rearranging to use patterns that are more standard around the codebase, leading up to #23853 | https://api.github.com/repos/pandas-dev/pandas/pulls/27830 | 2019-08-08T21:47:05Z | 2019-08-13T12:13:17Z | 2019-08-13T12:13:16Z | 2019-08-13T13:59:35Z |
Backport PR #27607 on branch 0.25.x (BUG: break reference cycle in Index._engine) | diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py
index 6541ddcb0397d..49834ae94cc38 100644
--- a/asv_bench/benchmarks/index_object.py
+++ b/asv_bench/benchmarks/index_object.py
@@ -1,3 +1,4 @@
+import gc
import numpy as np
import pandas.util.testing as tm
from pandas import (
@@ -225,4 +226,21 @@ def time_intersection_both_duplicate(self, N):
self.intv.intersection(self.intv2)
+class GC:
+ params = [1, 2, 5]
+
+ def create_use_drop(self):
+ idx = Index(list(range(1000 * 1000)))
+ idx._engine
+
+ def peakmem_gc_instances(self, N):
+ try:
+ gc.disable()
+
+ for _ in range(N):
+ self.create_use_drop()
+ finally:
+ gc.enable()
+
+
from .pandas_vb_common import setup # noqa: F401
diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index f1d3f152e503d..443baa56374ca 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -83,6 +83,7 @@ Indexing
^^^^^^^^
- Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']`` (:issue:`27516`)
+- Break reference cycle involving :class:`Index` to allow garbage collection of :class:`Index` objects without running the GC. (:issue:`27585`)
-
-
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 17122d0981995..9297d02282a23 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -710,7 +710,11 @@ def _cleanup(self):
@cache_readonly
def _engine(self):
# property, for now, slow to look up
- return self._engine_type(lambda: self._ndarray_values, len(self))
+
+ # to avoid a refernce cycle, bind `_ndarray_values` to a local variable, so
+ # `self` is not passed into the lambda.
+ _ndarray_values = self._ndarray_values
+ return self._engine_type(lambda: _ndarray_values, len(self))
# --------------------------------------------------------------------
# Array-Like Methods
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index c40a9bce9385b..34d82525495fc 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -1,5 +1,6 @@
from collections import defaultdict
from datetime import datetime, timedelta
+import gc
from io import StringIO
import math
import operator
@@ -2424,6 +2425,13 @@ def test_deprecated_contains(self):
with tm.assert_produces_warning(FutureWarning):
index.contains(1)
+ def test_engine_reference_cycle(self):
+ # https://github.com/pandas-dev/pandas/issues/27585
+ index = pd.Index([1, 2, 3])
+ nrefs_pre = len(gc.get_referrers(index))
+ index._engine
+ assert len(gc.get_referrers(index)) == nrefs_pre
+
class TestMixedIntIndex(Base):
# Mostly the tests from common.py for which the results differ
| Backport PR #27607: BUG: break reference cycle in Index._engine | https://api.github.com/repos/pandas-dev/pandas/pulls/27828 | 2019-08-08T20:43:36Z | 2019-08-09T20:24:13Z | 2019-08-09T20:24:13Z | 2019-08-09T20:24:18Z |
BUG: Fixed groupby quantile for listlike q | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 94770a7aae676..4d385ef0ec166 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -117,6 +117,7 @@ Plotting
Groupby/resample/rolling
^^^^^^^^^^^^^^^^^^^^^^^^
+- Fixed regression in :meth:`pands.core.groupby.DataFrameGroupBy.quantile` raising when multiple quantiles are given (:issue:`27526`)
- Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.transform` where applying a timezone conversion lambda function would drop timezone information (:issue:`27496`)
- Bug in windowing over read-only arrays (:issue:`27766`)
- Fixed segfault in `pandas.core.groupby.DataFrameGroupBy.quantile` when an invalid quantile was passed (:issue:`27470`)
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index c5e81e21e9fd5..35c2dac3901a2 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1870,6 +1870,7 @@ def quantile(self, q=0.5, interpolation="linear"):
a 2.0
b 3.0
"""
+ from pandas import concat
def pre_processor(vals: np.ndarray) -> Tuple[np.ndarray, Optional[Type]]:
if is_object_dtype(vals):
@@ -1897,18 +1898,57 @@ def post_processor(vals: np.ndarray, inference: Optional[Type]) -> np.ndarray:
return vals
- return self._get_cythonized_result(
- "group_quantile",
- self.grouper,
- aggregate=True,
- needs_values=True,
- needs_mask=True,
- cython_dtype=np.float64,
- pre_processing=pre_processor,
- post_processing=post_processor,
- q=q,
- interpolation=interpolation,
- )
+ if is_scalar(q):
+ return self._get_cythonized_result(
+ "group_quantile",
+ self.grouper,
+ aggregate=True,
+ needs_values=True,
+ needs_mask=True,
+ cython_dtype=np.float64,
+ pre_processing=pre_processor,
+ post_processing=post_processor,
+ q=q,
+ interpolation=interpolation,
+ )
+ else:
+ results = [
+ self._get_cythonized_result(
+ "group_quantile",
+ self.grouper,
+ aggregate=True,
+ needs_values=True,
+ needs_mask=True,
+ cython_dtype=np.float64,
+ pre_processing=pre_processor,
+ post_processing=post_processor,
+ q=qi,
+ interpolation=interpolation,
+ )
+ for qi in q
+ ]
+ result = concat(results, axis=0, keys=q)
+ # fix levels to place quantiles on the inside
+ # TODO(GH-10710): Ideally, we could write this as
+ # >>> result.stack(0).loc[pd.IndexSlice[:, ..., q], :]
+ # but this hits https://github.com/pandas-dev/pandas/issues/10710
+ # which doesn't reorder the list-like `q` on the inner level.
+ order = np.roll(list(range(result.index.nlevels)), -1)
+ result = result.reorder_levels(order)
+ result = result.reindex(q, level=-1)
+
+ # fix order.
+ hi = len(q) * self.ngroups
+ arr = np.arange(0, hi, self.ngroups)
+ arrays = []
+
+ for i in range(self.ngroups):
+ arr = arr + i
+ arrays.append(arr)
+
+ indices = np.concatenate(arrays)
+ assert len(indices) == len(result)
+ return result.take(indices)
@Substitution(name="groupby")
def ngroup(self, ascending=True):
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 3794120281e1f..509d7c33b643b 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -1238,6 +1238,57 @@ def test_quantile(interpolation, a_vals, b_vals, q):
tm.assert_frame_equal(result, expected)
+def test_quantile_array():
+ # https://github.com/pandas-dev/pandas/issues/27526
+ df = pd.DataFrame({"A": [0, 1, 2, 3, 4]})
+ result = df.groupby([0, 0, 1, 1, 1]).quantile([0.25])
+
+ index = pd.MultiIndex.from_product([[0, 1], [0.25]])
+ expected = pd.DataFrame({"A": [0.25, 2.50]}, index=index)
+ tm.assert_frame_equal(result, expected)
+
+ df = pd.DataFrame({"A": [0, 1, 2, 3], "B": [4, 5, 6, 7]})
+ index = pd.MultiIndex.from_product([[0, 1], [0.25, 0.75]])
+
+ result = df.groupby([0, 0, 1, 1]).quantile([0.25, 0.75])
+ expected = pd.DataFrame(
+ {"A": [0.25, 0.75, 2.25, 2.75], "B": [4.25, 4.75, 6.25, 6.75]}, index=index
+ )
+ tm.assert_frame_equal(result, expected)
+
+
+def test_quantile_array_no_sort():
+ df = pd.DataFrame({"A": [0, 1, 2], "B": [3, 4, 5]})
+ result = df.groupby([1, 0, 1], sort=False).quantile([0.25, 0.5, 0.75])
+ expected = pd.DataFrame(
+ {"A": [0.5, 1.0, 1.5, 1.0, 1.0, 1.0], "B": [3.5, 4.0, 4.5, 4.0, 4.0, 4.0]},
+ index=pd.MultiIndex.from_product([[1, 0], [0.25, 0.5, 0.75]]),
+ )
+ tm.assert_frame_equal(result, expected)
+
+ result = df.groupby([1, 0, 1], sort=False).quantile([0.75, 0.25])
+ expected = pd.DataFrame(
+ {"A": [1.5, 0.5, 1.0, 1.0], "B": [4.5, 3.5, 4.0, 4.0]},
+ index=pd.MultiIndex.from_product([[1, 0], [0.75, 0.25]]),
+ )
+ tm.assert_frame_equal(result, expected)
+
+
+def test_quantile_array_multiple_levels():
+ df = pd.DataFrame(
+ {"A": [0, 1, 2], "B": [3, 4, 5], "c": ["a", "a", "a"], "d": ["a", "a", "b"]}
+ )
+ result = df.groupby(["c", "d"]).quantile([0.25, 0.75])
+ index = pd.MultiIndex.from_tuples(
+ [("a", "a", 0.25), ("a", "a", 0.75), ("a", "b", 0.25), ("a", "b", 0.75)],
+ names=["c", "d", None],
+ )
+ expected = pd.DataFrame(
+ {"A": [0.25, 0.75, 2.0, 2.0], "B": [3.25, 3.75, 5.0, 5.0]}, index=index
+ )
+ tm.assert_frame_equal(result, expected)
+
+
def test_quantile_raises():
df = pd.DataFrame(
[["foo", "a"], ["foo", "b"], ["foo", "c"]], columns=["key", "val"]
| Closes #27526
Just a WIP right now. This is under tested for
- [ ] `groupby(sort=False)`
- [ ] non-sorted `q`
- [ ] `groupby(multiple levels)` | https://api.github.com/repos/pandas-dev/pandas/pulls/27827 | 2019-08-08T20:36:24Z | 2019-08-22T11:28:37Z | 2019-08-22T11:28:37Z | 2019-08-22T11:28:40Z |
BUG: Fix groupby quantile segfault | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 637ac5c9c8bd1..7b39810a2cf49 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -120,7 +120,7 @@ Groupby/resample/rolling
- Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.transform` where applying a timezone conversion lambda function would drop timezone information (:issue:`27496`)
- Bug in windowing over read-only arrays (:issue:`27766`)
--
+- Fixed segfault in `pandas.core.groupby.DataFrameGroupBy.quantile` when an invalid quantile was passed (:issue:`27470`)
-
Reshaping
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index e3f18572abca1..3069bbbf34bb7 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -719,6 +719,11 @@ def group_quantile(ndarray[float64_t] out,
ndarray[int64_t] counts, non_na_counts, sort_arr
assert values.shape[0] == N
+
+ if not (0 <= q <= 1):
+ raise ValueError("'q' must be between 0 and 1. Got"
+ " '{}' instead".format(q))
+
inter_methods = {
'linear': INTERPOLATION_LINEAR,
'lower': INTERPOLATION_LOWER,
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index efc3142b25b82..3794120281e1f 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -1247,6 +1247,17 @@ def test_quantile_raises():
df.groupby("key").quantile()
+def test_quantile_out_of_bounds_q_raises():
+ # https://github.com/pandas-dev/pandas/issues/27470
+ df = pd.DataFrame(dict(a=[0, 0, 0, 1, 1, 1], b=range(6)))
+ g = df.groupby([0, 0, 0, 1, 1, 1])
+ with pytest.raises(ValueError, match="Got '50.0' instead"):
+ g.quantile(50)
+
+ with pytest.raises(ValueError, match="Got '-1.0' instead"):
+ g.quantile(-1)
+
+
# pipe
# --------------------------------
| Validate that q is between 0 and 1.
Closes #27470 | https://api.github.com/repos/pandas-dev/pandas/pulls/27826 | 2019-08-08T19:49:31Z | 2019-08-12T17:56:47Z | 2019-08-12T17:56:47Z | 2019-10-31T01:19:37Z |
Add tidelift sponsor to FUNDING | diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
index 6912d15abf3d6..944ce9b4fb1f6 100644
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1 +1,2 @@
custom: https://pandas.pydata.org/donate.html
+tidelift: pypi/pandas
| https://api.github.com/repos/pandas-dev/pandas/pulls/27823 | 2019-08-08T16:51:00Z | 2019-08-09T14:19:03Z | 2019-08-09T14:19:03Z | 2019-08-09T14:19:06Z | |
Add security policy | diff --git a/.github/SECURITY.md b/.github/SECURITY.md
new file mode 100644
index 0000000000000..f3b059a5d4f13
--- /dev/null
+++ b/.github/SECURITY.md
@@ -0,0 +1 @@
+To report a security vulnerability to pandas, please go to https://tidelift.com/security and see the instructions there.
| Closes #27821 | https://api.github.com/repos/pandas-dev/pandas/pulls/27822 | 2019-08-08T16:50:46Z | 2019-08-09T14:18:54Z | 2019-08-09T14:18:54Z | 2019-08-09T14:19:09Z |
Backport PR #27809 on branch 0.25.x (CI: remove pytest pins) | diff --git a/ci/deps/azure-36-locale.yaml b/ci/deps/azure-36-locale.yaml
index 8f8273f57c3fe..6a77b5dbedc61 100644
--- a/ci/deps/azure-36-locale.yaml
+++ b/ci/deps/azure-36-locale.yaml
@@ -20,8 +20,8 @@ dependencies:
- xlsxwriter=0.9.8
- xlwt=1.2.0
# universal
- - pytest>=4.0.2,<5.0.0
- - pytest-xdist
+ - pytest>=5.0.0
+ - pytest-xdist>=1.29.0
- pytest-mock
- pytest-azurepipelines
- hypothesis>=3.58.0
diff --git a/ci/deps/travis-36-slow.yaml b/ci/deps/travis-36-slow.yaml
index 538a82f66e4c8..9564bf5bb3a9f 100644
--- a/ci/deps/travis-36-slow.yaml
+++ b/ci/deps/travis-36-slow.yaml
@@ -25,8 +25,8 @@ dependencies:
- xlsxwriter
- xlwt
# universal
- - pytest>=4.0.2,<5.0.0
- - pytest-xdist
+ - pytest>=5.0.0
+ - pytest-xdist>=1.29.0
- pytest-mock
- moto
- hypothesis>=3.58.0
| Backport PR #27809: CI: remove pytest pins | https://api.github.com/repos/pandas-dev/pandas/pulls/27819 | 2019-08-08T11:00:57Z | 2019-08-08T12:13:54Z | 2019-08-08T12:13:54Z | 2019-08-08T12:13:54Z |
COMPAT: restore shape for 'invalid' Index with nd array | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index f1d3f152e503d..d48a003d56e4f 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -111,7 +111,7 @@ Plotting
^^^^^^^^
- Added a pandas_plotting_backends entrypoint group for registering plot backends. See :ref:`extending.plotting-backends` for more (:issue:`26747`).
--
+- Fix compatibility issue with matplotlib when passing a pandas ``Index`` to a plot call (:issue:`27775`).
-
Groupby/resample/rolling
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 356ae20b2240a..2ed9b91f9426d 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -5600,7 +5600,10 @@ def shape(self):
"""
Return a tuple of the shape of the underlying data.
"""
- return (len(self),)
+ # not using "(len(self), )" to return "correct" shape if the values
+ # consists of a >1 D array (see GH-27775)
+ # overridden in MultiIndex.shape to avoid materializing the values
+ return self._values.shape
Index._add_numeric_methods_disabled()
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 488107690fbd6..b614952ba1e04 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -622,6 +622,15 @@ def _values(self):
# We override here, since our parent uses _data, which we dont' use.
return self.values
+ @property
+ def shape(self):
+ """
+ Return a tuple of the shape of the underlying data.
+ """
+ # overriding the base Index.shape definition to avoid materializing
+ # the values (GH-27384, GH-27775)
+ return (len(self),)
+
@property
def array(self):
"""
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index c40a9bce9385b..d1ed79118d2fa 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -2805,3 +2805,17 @@ def test_deprecated_fastpath():
expected = pd.CategoricalIndex(["a", "b", "c"], name="test")
tm.assert_index_equal(idx, expected)
+
+
+def test_shape_of_invalid_index():
+ # Currently, it is possible to create "invalid" index objects backed by
+ # a multi-dimensional array (see https://github.com/pandas-dev/pandas/issues/27125
+ # about this). However, as long as this is not solved in general,this test ensures
+ # that the returned shape is consistent with this underlying array for
+ # compat with matplotlib (see https://github.com/pandas-dev/pandas/issues/27775)
+ a = np.arange(8).reshape(2, 2, 2)
+ idx = pd.Index(a)
+ assert idx.shape == a.shape
+
+ idx = pd.Index([0, 1, 2, 3])
+ assert idx[:, None].shape == (4, 1)
| closes #27775
| https://api.github.com/repos/pandas-dev/pandas/pulls/27818 | 2019-08-08T09:54:09Z | 2019-08-09T07:36:58Z | 2019-08-09T07:36:58Z | 2019-08-09T07:56:16Z |
REF: implement should_extension_dispatch | diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index 7229b3de4e9f0..843f12c20b07b 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -47,7 +47,7 @@
import pandas as pd
from pandas._typing import ArrayLike
-from pandas.core.construction import extract_array
+from pandas.core.construction import array, extract_array
from pandas.core.ops import missing
from pandas.core.ops.docstrings import (
_arith_doc_FRAME,
@@ -460,6 +460,33 @@ def masked_arith_op(x, y, op):
# Dispatch logic
+def should_extension_dispatch(left: ABCSeries, right: Any) -> bool:
+ """
+ Identify cases where Series operation should use dispatch_to_extension_op.
+
+ Parameters
+ ----------
+ left : Series
+ right : object
+
+ Returns
+ -------
+ bool
+ """
+ if (
+ is_extension_array_dtype(left.dtype)
+ or is_datetime64_dtype(left.dtype)
+ or is_timedelta64_dtype(left.dtype)
+ ):
+ return True
+
+ if is_extension_array_dtype(right) and not is_scalar(right):
+ # GH#22378 disallow scalar to exclude e.g. "category", "Int64"
+ return True
+
+ return False
+
+
def should_series_dispatch(left, right, op):
"""
Identify cases where a DataFrame operation should dispatch to its
@@ -564,19 +591,18 @@ def dispatch_to_extension_op(op, left, right):
apply the operator defined by op.
"""
+ if left.dtype.kind in "mM":
+ # We need to cast datetime64 and timedelta64 ndarrays to
+ # DatetimeArray/TimedeltaArray. But we avoid wrapping others in
+ # PandasArray as that behaves poorly with e.g. IntegerArray.
+ left = array(left)
+
# The op calls will raise TypeError if the op is not defined
# on the ExtensionArray
# unbox Series and Index to arrays
- if isinstance(left, (ABCSeries, ABCIndexClass)):
- new_left = left._values
- else:
- new_left = left
-
- if isinstance(right, (ABCSeries, ABCIndexClass)):
- new_right = right._values
- else:
- new_right = right
+ new_left = extract_array(left, extract_numpy=True)
+ new_right = extract_array(right, extract_numpy=True)
try:
res_values = op(new_left, new_right)
@@ -684,56 +710,27 @@ def wrapper(left, right):
res_name = get_op_result_name(left, right)
right = maybe_upcast_for_op(right, left.shape)
- if is_categorical_dtype(left):
- raise TypeError(
- "{typ} cannot perform the operation "
- "{op}".format(typ=type(left).__name__, op=str_rep)
- )
-
- elif is_datetime64_dtype(left) or is_datetime64tz_dtype(left):
- from pandas.core.arrays import DatetimeArray
-
- result = dispatch_to_extension_op(op, DatetimeArray(left), right)
- return construct_result(left, result, index=left.index, name=res_name)
-
- elif is_extension_array_dtype(left) or (
- is_extension_array_dtype(right) and not is_scalar(right)
- ):
- # GH#22378 disallow scalar to exclude e.g. "category", "Int64"
+ if should_extension_dispatch(left, right):
result = dispatch_to_extension_op(op, left, right)
- return construct_result(left, result, index=left.index, name=res_name)
- elif is_timedelta64_dtype(left):
- from pandas.core.arrays import TimedeltaArray
-
- result = dispatch_to_extension_op(op, TimedeltaArray(left), right)
- return construct_result(left, result, index=left.index, name=res_name)
-
- elif is_timedelta64_dtype(right):
- # We should only get here with non-scalar values for right
- # upcast by maybe_upcast_for_op
+ elif is_timedelta64_dtype(right) or isinstance(
+ right, (ABCDatetimeArray, ABCDatetimeIndex)
+ ):
+ # We should only get here with td64 right with non-scalar values
+ # for right upcast by maybe_upcast_for_op
assert not isinstance(right, (np.timedelta64, np.ndarray))
-
result = op(left._values, right)
- # We do not pass dtype to ensure that the Series constructor
- # does inference in the case where `result` has object-dtype.
- return construct_result(left, result, index=left.index, name=res_name)
-
- elif isinstance(right, (ABCDatetimeArray, ABCDatetimeIndex)):
- result = op(left._values, right)
- return construct_result(left, result, index=left.index, name=res_name)
+ else:
+ lvalues = extract_array(left, extract_numpy=True)
+ rvalues = extract_array(right, extract_numpy=True)
- lvalues = left.values
- rvalues = right
- if isinstance(rvalues, (ABCSeries, ABCIndexClass)):
- rvalues = rvalues._values
+ with np.errstate(all="ignore"):
+ result = na_op(lvalues, rvalues)
- with np.errstate(all="ignore"):
- result = na_op(lvalues, rvalues)
- return construct_result(
- left, result, index=left.index, name=res_name, dtype=None
- )
+ # We do not pass dtype to ensure that the Series constructor
+ # does inference in the case where `result` has object-dtype.
+ return construct_result(left, result, index=left.index, name=res_name)
wrapper.__name__ = op_name
return wrapper
diff --git a/pandas/tests/arrays/categorical/test_operators.py b/pandas/tests/arrays/categorical/test_operators.py
index 9a09ea8422b1f..22c1d5373372a 100644
--- a/pandas/tests/arrays/categorical/test_operators.py
+++ b/pandas/tests/arrays/categorical/test_operators.py
@@ -349,7 +349,9 @@ def test_numeric_like_ops(self):
("__mul__", r"\*"),
("__truediv__", "/"),
]:
- msg = r"Series cannot perform the operation {}".format(str_rep)
+ msg = r"Series cannot perform the operation {}|unsupported operand".format(
+ str_rep
+ )
with pytest.raises(TypeError, match=msg):
getattr(df, op)(df)
@@ -375,7 +377,9 @@ def test_numeric_like_ops(self):
("__mul__", r"\*"),
("__truediv__", "/"),
]:
- msg = r"Series cannot perform the operation {}".format(str_rep)
+ msg = r"Series cannot perform the operation {}|unsupported operand".format(
+ str_rep
+ )
with pytest.raises(TypeError, match=msg):
getattr(s, op)(2)
diff --git a/pandas/tests/extension/test_categorical.py b/pandas/tests/extension/test_categorical.py
index f7456d24ad6d3..0c0e8b0123c03 100644
--- a/pandas/tests/extension/test_categorical.py
+++ b/pandas/tests/extension/test_categorical.py
@@ -211,7 +211,7 @@ def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
def test_add_series_with_extension_array(self, data):
ser = pd.Series(data)
- with pytest.raises(TypeError, match="cannot perform"):
+ with pytest.raises(TypeError, match="cannot perform|unsupported operand"):
ser + data
def test_divmod_series_array(self):
| This uses should_extension_dispatch in _arith_method_SERIES. We'll be able to use it in _comp_method_SERIES after #27803. (this is orthogonal to 27803 so doesn't matter what order they go in) | https://api.github.com/repos/pandas-dev/pandas/pulls/27815 | 2019-08-08T03:20:05Z | 2019-08-14T20:39:15Z | 2019-08-14T20:39:15Z | 2019-08-14T21:34:32Z |
BUG: Series.rename raises error on values accepted by Series construc… | diff --git a/doc/source/whatsnew/v0.25.1.rst b/doc/source/whatsnew/v0.25.1.rst
index 680d69a9862cd..b307fae4fbdc1 100644
--- a/doc/source/whatsnew/v0.25.1.rst
+++ b/doc/source/whatsnew/v0.25.1.rst
@@ -108,6 +108,7 @@ Other
^^^^^
- Bug in :meth:`Series.replace` and :meth:`DataFrame.replace` when replacing timezone-aware timestamps using a dict-like replacer (:issue:`27720`)
+- Bug in :meth:`Series.rename` when using a custom type indexer. Now any value that isn't callable or dict-like is treated as a scalar. (:issue:`27814`)
.. _whatsnew_0.251.contributors:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 3f04970ee4e58..8b6c963e40e9d 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4165,12 +4165,10 @@ def rename(self, index=None, **kwargs):
"""
kwargs["inplace"] = validate_bool_kwarg(kwargs.get("inplace", False), "inplace")
- non_mapping = is_scalar(index) or (
- is_list_like(index) and not is_dict_like(index)
- )
- if non_mapping:
+ if callable(index) or is_dict_like(index):
+ return super().rename(index=index, **kwargs)
+ else:
return self._set_name(index, inplace=kwargs.get("inplace"))
- return super().rename(index=index, **kwargs)
@Substitution(**_shared_doc_kwargs)
@Appender(generic.NDFrame.reindex.__doc__)
diff --git a/pandas/tests/series/test_alter_axes.py b/pandas/tests/series/test_alter_axes.py
index 0a25d6ba203cb..5d74ad95be90d 100644
--- a/pandas/tests/series/test_alter_axes.py
+++ b/pandas/tests/series/test_alter_axes.py
@@ -267,6 +267,25 @@ def test_rename_axis_none(self, kwargs):
expected = Series([1, 2, 3], index=expected_index)
tm.assert_series_equal(result, expected)
+ def test_rename_with_custom_indexer(self):
+ # GH 27814
+ class MyIndexer:
+ pass
+
+ ix = MyIndexer()
+ s = Series([1, 2, 3]).rename(ix)
+ assert s.name is ix
+
+ def test_rename_with_custom_indexer_inplace(self):
+ # GH 27814
+ class MyIndexer:
+ pass
+
+ ix = MyIndexer()
+ s = Series([1, 2, 3])
+ s.rename(ix, inplace=True)
+ assert s.name is ix
+
def test_set_axis_inplace_axes(self, axis_series):
# GH14636
ser = Series(np.arange(4), index=[1, 3, 5, 7], dtype="int64")
| - closes #27813 | https://api.github.com/repos/pandas-dev/pandas/pulls/27814 | 2019-08-08T02:04:10Z | 2019-08-22T13:07:47Z | 2019-08-22T13:07:47Z | 2019-08-22T13:07:48Z |
REF: separate method-pinning functions | diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index 4ab1941e3493f..01de1428e290c 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -41,7 +41,6 @@
ABCIndex,
ABCIndexClass,
ABCSeries,
- ABCSparseArray,
ABCSparseSeries,
)
from pandas.core.dtypes.missing import isna, notna
@@ -57,6 +56,10 @@
_op_descriptions,
)
from pandas.core.ops.invalid import invalid_comparison
+from pandas.core.ops.methods import ( # noqa:F401
+ add_flex_arithmetic_methods,
+ add_special_arithmetic_methods,
+)
from pandas.core.ops.roperator import ( # noqa:F401
radd,
rand_,
@@ -587,224 +590,6 @@ def dispatch_to_extension_op(op, left, right):
return res_values
-# -----------------------------------------------------------------------------
-# Functions that add arithmetic methods to objects, given arithmetic factory
-# methods
-
-
-def _get_method_wrappers(cls):
- """
- Find the appropriate operation-wrappers to use when defining flex/special
- arithmetic, boolean, and comparison operations with the given class.
-
- Parameters
- ----------
- cls : class
-
- Returns
- -------
- arith_flex : function or None
- comp_flex : function or None
- arith_special : function
- comp_special : function
- bool_special : function
-
- Notes
- -----
- None is only returned for SparseArray
- """
- if issubclass(cls, ABCSparseSeries):
- # Be sure to catch this before ABCSeries and ABCSparseArray,
- # as they will both come see SparseSeries as a subclass
- arith_flex = _flex_method_SERIES
- comp_flex = _flex_method_SERIES
- arith_special = _arith_method_SPARSE_SERIES
- comp_special = _arith_method_SPARSE_SERIES
- bool_special = _bool_method_SERIES
- # TODO: I don't think the functions defined by bool_method are tested
- elif issubclass(cls, ABCSeries):
- # Just Series; SparseSeries is caught above
- arith_flex = _flex_method_SERIES
- comp_flex = _flex_method_SERIES
- arith_special = _arith_method_SERIES
- comp_special = _comp_method_SERIES
- bool_special = _bool_method_SERIES
- elif issubclass(cls, ABCDataFrame):
- # Same for DataFrame and SparseDataFrame
- arith_flex = _arith_method_FRAME
- comp_flex = _flex_comp_method_FRAME
- arith_special = _arith_method_FRAME
- comp_special = _comp_method_FRAME
- bool_special = _arith_method_FRAME
- return arith_flex, comp_flex, arith_special, comp_special, bool_special
-
-
-def _create_methods(cls, arith_method, comp_method, bool_method, special):
- # creates actual methods based upon arithmetic, comp and bool method
- # constructors.
-
- have_divmod = issubclass(cls, ABCSeries)
- # divmod is available for Series and SparseSeries
-
- # yapf: disable
- new_methods = dict(
- add=arith_method(cls, operator.add, special),
- radd=arith_method(cls, radd, special),
- sub=arith_method(cls, operator.sub, special),
- mul=arith_method(cls, operator.mul, special),
- truediv=arith_method(cls, operator.truediv, special),
- floordiv=arith_method(cls, operator.floordiv, special),
- # Causes a floating point exception in the tests when numexpr enabled,
- # so for now no speedup
- mod=arith_method(cls, operator.mod, special),
- pow=arith_method(cls, operator.pow, special),
- # not entirely sure why this is necessary, but previously was included
- # so it's here to maintain compatibility
- rmul=arith_method(cls, rmul, special),
- rsub=arith_method(cls, rsub, special),
- rtruediv=arith_method(cls, rtruediv, special),
- rfloordiv=arith_method(cls, rfloordiv, special),
- rpow=arith_method(cls, rpow, special),
- rmod=arith_method(cls, rmod, special))
- # yapf: enable
- new_methods["div"] = new_methods["truediv"]
- new_methods["rdiv"] = new_methods["rtruediv"]
- if have_divmod:
- # divmod doesn't have an op that is supported by numexpr
- new_methods["divmod"] = arith_method(cls, divmod, special)
- new_methods["rdivmod"] = arith_method(cls, rdivmod, special)
-
- new_methods.update(
- dict(
- eq=comp_method(cls, operator.eq, special),
- ne=comp_method(cls, operator.ne, special),
- lt=comp_method(cls, operator.lt, special),
- gt=comp_method(cls, operator.gt, special),
- le=comp_method(cls, operator.le, special),
- ge=comp_method(cls, operator.ge, special),
- )
- )
-
- if bool_method:
- new_methods.update(
- dict(
- and_=bool_method(cls, operator.and_, special),
- or_=bool_method(cls, operator.or_, special),
- # For some reason ``^`` wasn't used in original.
- xor=bool_method(cls, operator.xor, special),
- rand_=bool_method(cls, rand_, special),
- ror_=bool_method(cls, ror_, special),
- rxor=bool_method(cls, rxor, special),
- )
- )
-
- if special:
- dunderize = lambda x: "__{name}__".format(name=x.strip("_"))
- else:
- dunderize = lambda x: x
- new_methods = {dunderize(k): v for k, v in new_methods.items()}
- return new_methods
-
-
-def add_methods(cls, new_methods):
- for name, method in new_methods.items():
- # For most methods, if we find that the class already has a method
- # of the same name, it is OK to over-write it. The exception is
- # inplace methods (__iadd__, __isub__, ...) for SparseArray, which
- # retain the np.ndarray versions.
- force = not (issubclass(cls, ABCSparseArray) and name.startswith("__i"))
- if force or name not in cls.__dict__:
- setattr(cls, name, method)
-
-
-# ----------------------------------------------------------------------
-# Arithmetic
-def add_special_arithmetic_methods(cls):
- """
- Adds the full suite of special arithmetic methods (``__add__``,
- ``__sub__``, etc.) to the class.
-
- Parameters
- ----------
- cls : class
- special methods will be defined and pinned to this class
- """
- _, _, arith_method, comp_method, bool_method = _get_method_wrappers(cls)
- new_methods = _create_methods(
- cls, arith_method, comp_method, bool_method, special=True
- )
- # inplace operators (I feel like these should get passed an `inplace=True`
- # or just be removed
-
- def _wrap_inplace_method(method):
- """
- return an inplace wrapper for this method
- """
-
- def f(self, other):
- result = method(self, other)
-
- # this makes sure that we are aligned like the input
- # we are updating inplace so we want to ignore is_copy
- self._update_inplace(
- result.reindex_like(self, copy=False)._data, verify_is_copy=False
- )
-
- return self
-
- f.__name__ = "__i{name}__".format(name=method.__name__.strip("__"))
- return f
-
- new_methods.update(
- dict(
- __iadd__=_wrap_inplace_method(new_methods["__add__"]),
- __isub__=_wrap_inplace_method(new_methods["__sub__"]),
- __imul__=_wrap_inplace_method(new_methods["__mul__"]),
- __itruediv__=_wrap_inplace_method(new_methods["__truediv__"]),
- __ifloordiv__=_wrap_inplace_method(new_methods["__floordiv__"]),
- __imod__=_wrap_inplace_method(new_methods["__mod__"]),
- __ipow__=_wrap_inplace_method(new_methods["__pow__"]),
- )
- )
-
- new_methods.update(
- dict(
- __iand__=_wrap_inplace_method(new_methods["__and__"]),
- __ior__=_wrap_inplace_method(new_methods["__or__"]),
- __ixor__=_wrap_inplace_method(new_methods["__xor__"]),
- )
- )
-
- add_methods(cls, new_methods=new_methods)
-
-
-def add_flex_arithmetic_methods(cls):
- """
- Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
- to the class.
-
- Parameters
- ----------
- cls : class
- flex methods will be defined and pinned to this class
- """
- flex_arith_method, flex_comp_method, _, _, _ = _get_method_wrappers(cls)
- new_methods = _create_methods(
- cls, flex_arith_method, flex_comp_method, bool_method=None, special=False
- )
- new_methods.update(
- dict(
- multiply=new_methods["mul"],
- subtract=new_methods["sub"],
- divide=new_methods["div"],
- )
- )
- # opt out of bool flex methods for now
- assert not any(kname in new_methods for kname in ("ror_", "rxor", "rand_"))
-
- add_methods(cls, new_methods=new_methods)
-
-
# -----------------------------------------------------------------------------
# Series
diff --git a/pandas/core/ops/methods.py b/pandas/core/ops/methods.py
new file mode 100644
index 0000000000000..eba0a797a791f
--- /dev/null
+++ b/pandas/core/ops/methods.py
@@ -0,0 +1,249 @@
+"""
+Functions to generate methods and pin them to the appropriate classes.
+"""
+import operator
+
+from pandas.core.dtypes.generic import (
+ ABCDataFrame,
+ ABCSeries,
+ ABCSparseArray,
+ ABCSparseSeries,
+)
+
+from pandas.core.ops.roperator import (
+ radd,
+ rand_,
+ rdivmod,
+ rfloordiv,
+ rmod,
+ rmul,
+ ror_,
+ rpow,
+ rsub,
+ rtruediv,
+ rxor,
+)
+
+
+def _get_method_wrappers(cls):
+ """
+ Find the appropriate operation-wrappers to use when defining flex/special
+ arithmetic, boolean, and comparison operations with the given class.
+
+ Parameters
+ ----------
+ cls : class
+
+ Returns
+ -------
+ arith_flex : function or None
+ comp_flex : function or None
+ arith_special : function
+ comp_special : function
+ bool_special : function
+
+ Notes
+ -----
+ None is only returned for SparseArray
+ """
+ # TODO: make these non-runtime imports once the relevant functions
+ # are no longer in __init__
+ from pandas.core.ops import (
+ _arith_method_FRAME,
+ _arith_method_SERIES,
+ _arith_method_SPARSE_SERIES,
+ _bool_method_SERIES,
+ _comp_method_FRAME,
+ _comp_method_SERIES,
+ _flex_comp_method_FRAME,
+ _flex_method_SERIES,
+ )
+
+ if issubclass(cls, ABCSparseSeries):
+ # Be sure to catch this before ABCSeries and ABCSparseArray,
+ # as they will both come see SparseSeries as a subclass
+ arith_flex = _flex_method_SERIES
+ comp_flex = _flex_method_SERIES
+ arith_special = _arith_method_SPARSE_SERIES
+ comp_special = _arith_method_SPARSE_SERIES
+ bool_special = _bool_method_SERIES
+ # TODO: I don't think the functions defined by bool_method are tested
+ elif issubclass(cls, ABCSeries):
+ # Just Series; SparseSeries is caught above
+ arith_flex = _flex_method_SERIES
+ comp_flex = _flex_method_SERIES
+ arith_special = _arith_method_SERIES
+ comp_special = _comp_method_SERIES
+ bool_special = _bool_method_SERIES
+ elif issubclass(cls, ABCDataFrame):
+ # Same for DataFrame and SparseDataFrame
+ arith_flex = _arith_method_FRAME
+ comp_flex = _flex_comp_method_FRAME
+ arith_special = _arith_method_FRAME
+ comp_special = _comp_method_FRAME
+ bool_special = _arith_method_FRAME
+ return arith_flex, comp_flex, arith_special, comp_special, bool_special
+
+
+def add_special_arithmetic_methods(cls):
+ """
+ Adds the full suite of special arithmetic methods (``__add__``,
+ ``__sub__``, etc.) to the class.
+
+ Parameters
+ ----------
+ cls : class
+ special methods will be defined and pinned to this class
+ """
+ _, _, arith_method, comp_method, bool_method = _get_method_wrappers(cls)
+ new_methods = _create_methods(
+ cls, arith_method, comp_method, bool_method, special=True
+ )
+ # inplace operators (I feel like these should get passed an `inplace=True`
+ # or just be removed
+
+ def _wrap_inplace_method(method):
+ """
+ return an inplace wrapper for this method
+ """
+
+ def f(self, other):
+ result = method(self, other)
+
+ # this makes sure that we are aligned like the input
+ # we are updating inplace so we want to ignore is_copy
+ self._update_inplace(
+ result.reindex_like(self, copy=False)._data, verify_is_copy=False
+ )
+
+ return self
+
+ f.__name__ = "__i{name}__".format(name=method.__name__.strip("__"))
+ return f
+
+ new_methods.update(
+ dict(
+ __iadd__=_wrap_inplace_method(new_methods["__add__"]),
+ __isub__=_wrap_inplace_method(new_methods["__sub__"]),
+ __imul__=_wrap_inplace_method(new_methods["__mul__"]),
+ __itruediv__=_wrap_inplace_method(new_methods["__truediv__"]),
+ __ifloordiv__=_wrap_inplace_method(new_methods["__floordiv__"]),
+ __imod__=_wrap_inplace_method(new_methods["__mod__"]),
+ __ipow__=_wrap_inplace_method(new_methods["__pow__"]),
+ )
+ )
+
+ new_methods.update(
+ dict(
+ __iand__=_wrap_inplace_method(new_methods["__and__"]),
+ __ior__=_wrap_inplace_method(new_methods["__or__"]),
+ __ixor__=_wrap_inplace_method(new_methods["__xor__"]),
+ )
+ )
+
+ _add_methods(cls, new_methods=new_methods)
+
+
+def add_flex_arithmetic_methods(cls):
+ """
+ Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
+ to the class.
+
+ Parameters
+ ----------
+ cls : class
+ flex methods will be defined and pinned to this class
+ """
+ flex_arith_method, flex_comp_method, _, _, _ = _get_method_wrappers(cls)
+ new_methods = _create_methods(
+ cls, flex_arith_method, flex_comp_method, bool_method=None, special=False
+ )
+ new_methods.update(
+ dict(
+ multiply=new_methods["mul"],
+ subtract=new_methods["sub"],
+ divide=new_methods["div"],
+ )
+ )
+ # opt out of bool flex methods for now
+ assert not any(kname in new_methods for kname in ("ror_", "rxor", "rand_"))
+
+ _add_methods(cls, new_methods=new_methods)
+
+
+def _create_methods(cls, arith_method, comp_method, bool_method, special):
+ # creates actual methods based upon arithmetic, comp and bool method
+ # constructors.
+
+ have_divmod = issubclass(cls, ABCSeries)
+ # divmod is available for Series and SparseSeries
+
+ # yapf: disable
+ new_methods = dict(
+ add=arith_method(cls, operator.add, special),
+ radd=arith_method(cls, radd, special),
+ sub=arith_method(cls, operator.sub, special),
+ mul=arith_method(cls, operator.mul, special),
+ truediv=arith_method(cls, operator.truediv, special),
+ floordiv=arith_method(cls, operator.floordiv, special),
+ # Causes a floating point exception in the tests when numexpr enabled,
+ # so for now no speedup
+ mod=arith_method(cls, operator.mod, special),
+ pow=arith_method(cls, operator.pow, special),
+ # not entirely sure why this is necessary, but previously was included
+ # so it's here to maintain compatibility
+ rmul=arith_method(cls, rmul, special),
+ rsub=arith_method(cls, rsub, special),
+ rtruediv=arith_method(cls, rtruediv, special),
+ rfloordiv=arith_method(cls, rfloordiv, special),
+ rpow=arith_method(cls, rpow, special),
+ rmod=arith_method(cls, rmod, special))
+ # yapf: enable
+ new_methods["div"] = new_methods["truediv"]
+ new_methods["rdiv"] = new_methods["rtruediv"]
+ if have_divmod:
+ # divmod doesn't have an op that is supported by numexpr
+ new_methods["divmod"] = arith_method(cls, divmod, special)
+ new_methods["rdivmod"] = arith_method(cls, rdivmod, special)
+
+ new_methods.update(
+ dict(
+ eq=comp_method(cls, operator.eq, special),
+ ne=comp_method(cls, operator.ne, special),
+ lt=comp_method(cls, operator.lt, special),
+ gt=comp_method(cls, operator.gt, special),
+ le=comp_method(cls, operator.le, special),
+ ge=comp_method(cls, operator.ge, special),
+ )
+ )
+
+ if bool_method:
+ new_methods.update(
+ dict(
+ and_=bool_method(cls, operator.and_, special),
+ or_=bool_method(cls, operator.or_, special),
+ # For some reason ``^`` wasn't used in original.
+ xor=bool_method(cls, operator.xor, special),
+ rand_=bool_method(cls, rand_, special),
+ ror_=bool_method(cls, ror_, special),
+ rxor=bool_method(cls, rxor, special),
+ )
+ )
+
+ if special:
+ dunderize = lambda x: "__{name}__".format(name=x.strip("_"))
+ else:
+ dunderize = lambda x: x
+ new_methods = {dunderize(k): v for k, v in new_methods.items()}
+ return new_methods
+
+
+def _add_methods(cls, new_methods):
+ for name, method in new_methods.items():
+ # For most methods, if we find that the class already has a method
+ # of the same name, it is OK to over-write it. The exception is
+ # inplace methods (__iadd__, __isub__, ...) for SparseArray, which
+ # retain the np.ndarray versions.
+ force = not (issubclass(cls, ABCSparseArray) and name.startswith("__i"))
+ if force or name not in cls.__dict__:
+ setattr(cls, name, method)
| These are the ones that are executed at import-time and then never needed again. | https://api.github.com/repos/pandas-dev/pandas/pulls/27811 | 2019-08-07T22:26:00Z | 2019-08-08T12:33:12Z | 2019-08-08T12:33:12Z | 2019-08-08T13:33:55Z |
TST: skip test if running non-installed | diff --git a/pandas/tests/plotting/test_backend.py b/pandas/tests/plotting/test_backend.py
index e79e7b6239eb3..d126407cfd823 100644
--- a/pandas/tests/plotting/test_backend.py
+++ b/pandas/tests/plotting/test_backend.py
@@ -46,14 +46,18 @@ def test_backend_is_correct(monkeypatch):
@td.skip_if_no_mpl
def test_register_entrypoint():
+
+ dist = pkg_resources.get_distribution("pandas")
+ if dist.module_path not in pandas.__file__:
+ # We are running from a non-installed pandas, and this test is invalid
+ pytest.skip("Testing a non-installed pandas")
+
mod = types.ModuleType("my_backend")
mod.plot = lambda *args, **kwargs: 1
backends = pkg_resources.get_entry_map("pandas")
my_entrypoint = pkg_resources.EntryPoint(
- "pandas_plotting_backend",
- mod.__name__,
- dist=pkg_resources.get_distribution("pandas"),
+ "pandas_plotting_backend", mod.__name__, dist=dist
)
backends["pandas_plotting_backends"]["my_backend"] = my_entrypoint
# TODO: the docs recommend importlib.util.module_from_spec. But this works for now.
| If running the tests without the branch installed, this test fails. Skip it instead. | https://api.github.com/repos/pandas-dev/pandas/pulls/27810 | 2019-08-07T21:33:24Z | 2019-08-13T12:06:27Z | 2019-08-13T12:06:27Z | 2019-08-13T13:57:29Z |
CI: remove pytest pins | diff --git a/ci/deps/azure-36-locale.yaml b/ci/deps/azure-36-locale.yaml
index 8f8273f57c3fe..6a77b5dbedc61 100644
--- a/ci/deps/azure-36-locale.yaml
+++ b/ci/deps/azure-36-locale.yaml
@@ -20,8 +20,8 @@ dependencies:
- xlsxwriter=0.9.8
- xlwt=1.2.0
# universal
- - pytest>=4.0.2,<5.0.0
- - pytest-xdist
+ - pytest>=5.0.0
+ - pytest-xdist>=1.29.0
- pytest-mock
- pytest-azurepipelines
- hypothesis>=3.58.0
diff --git a/ci/deps/travis-36-slow.yaml b/ci/deps/travis-36-slow.yaml
index 538a82f66e4c8..9564bf5bb3a9f 100644
--- a/ci/deps/travis-36-slow.yaml
+++ b/ci/deps/travis-36-slow.yaml
@@ -25,8 +25,8 @@ dependencies:
- xlsxwriter
- xlwt
# universal
- - pytest>=4.0.2,<5.0.0
- - pytest-xdist
+ - pytest>=5.0.0
+ - pytest-xdist>=1.29.0
- pytest-mock
- moto
- hypothesis>=3.58.0
| https://api.github.com/repos/pandas-dev/pandas/pulls/27809 | 2019-08-07T21:20:42Z | 2019-08-08T11:00:23Z | 2019-08-08T11:00:23Z | 2019-08-08T16:22:48Z | |
VIS: Fix DataFrame.plot() produces incorrect legend markers when plotting multiple series on the same axis | diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index b35f230100f8d..82bba1ae22bf2 100644
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -165,6 +165,7 @@ Plotting
- Bug in :meth:`Series.plot` not able to plot boolean values (:issue:`23719`)
-
+- Bug in :meth:`DataFrame.plot` producing incorrect legend markers when plotting multiple series on the same axis (:issue:`18222`)
- Bug in :meth:`DataFrame.plot` when ``kind='box'`` and data contains datetime or timedelta data. These types are now automatically dropped (:issue:`22799`)
Groupby/resample/rolling
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index a262f89dcc79c..287cc2f4130f4 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -562,7 +562,7 @@ def _add_legend_handle(self, handle, label, index=None):
self.legend_labels.append(label)
def _make_legend(self):
- ax, leg = self._get_ax_legend(self.axes[0])
+ ax, leg, handle = self._get_ax_legend_handle(self.axes[0])
handles = []
labels = []
@@ -571,7 +571,8 @@ def _make_legend(self):
if not self.subplots:
if leg is not None:
title = leg.get_title().get_text()
- handles = leg.legendHandles
+ # Replace leg.LegendHandles because it misses marker info
+ handles.extend(handle)
labels = [x.get_text() for x in leg.get_texts()]
if self.legend:
@@ -581,6 +582,7 @@ def _make_legend(self):
handles += self.legend_handles
labels += self.legend_labels
+
if self.legend_title is not None:
title = self.legend_title
@@ -592,8 +594,14 @@ def _make_legend(self):
if ax.get_visible():
ax.legend(loc="best")
- def _get_ax_legend(self, ax):
+ def _get_ax_legend_handle(self, ax):
+ """
+ Take in axes and return ax, legend and handle under different scenarios
+ """
leg = ax.get_legend()
+
+ # Get handle from axes
+ handle, _ = ax.get_legend_handles_labels()
other_ax = getattr(ax, "left_ax", None) or getattr(ax, "right_ax", None)
other_leg = None
if other_ax is not None:
@@ -601,7 +609,7 @@ def _get_ax_legend(self, ax):
if leg is None and other_leg is not None:
leg = other_leg
ax = other_ax
- return ax, leg
+ return ax, leg, handle
@cache_readonly
def plt(self):
diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py
index 4929422d20e8a..5a591f72d7361 100644
--- a/pandas/tests/plotting/common.py
+++ b/pandas/tests/plotting/common.py
@@ -103,6 +103,28 @@ def _check_legend_labels(self, axes, labels=None, visible=True):
else:
assert ax.get_legend() is None
+ def _check_legend_marker(self, ax, expected_markers=None, visible=True):
+ """
+ Check ax has expected legend markers
+
+ Parameters
+ ----------
+ ax : matplotlib Axes object
+ expected_markers : list-like
+ expected legend markers
+ visible : bool
+ expected legend visibility. labels are checked only when visible is
+ True
+ """
+ if visible and (expected_markers is None):
+ raise ValueError("Markers must be specified when visible is True")
+ if visible:
+ handles, _ = ax.get_legend_handles_labels()
+ markers = [handle.get_marker() for handle in handles]
+ assert markers == expected_markers
+ else:
+ assert ax.get_legend() is None
+
def _check_data(self, xp, rs):
"""
Check each axes has identical lines
diff --git a/pandas/tests/plotting/test_frame.py b/pandas/tests/plotting/test_frame.py
index 65815bcedebfc..7fdc0252b71e3 100644
--- a/pandas/tests/plotting/test_frame.py
+++ b/pandas/tests/plotting/test_frame.py
@@ -1881,6 +1881,31 @@ def test_df_legend_labels(self):
self._check_legend_labels(ax, labels=["LABEL_b", "LABEL_c"])
assert df5.columns.tolist() == ["b", "c"]
+ def test_missing_marker_multi_plots_on_same_ax(self):
+ # GH 18222
+ df = pd.DataFrame(
+ data=[[1, 1, 1, 1], [2, 2, 4, 8]], columns=["x", "r", "g", "b"]
+ )
+ fig, ax = self.plt.subplots(nrows=1, ncols=3)
+ # Left plot
+ df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[0])
+ df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[0])
+ df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[0])
+ self._check_legend_labels(ax[0], labels=["r", "g", "b"])
+ self._check_legend_marker(ax[0], expected_markers=["o", "x", "o"])
+ # Center plot
+ df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[1])
+ df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[1])
+ df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[1])
+ self._check_legend_labels(ax[1], labels=["b", "r", "g"])
+ self._check_legend_marker(ax[1], expected_markers=["o", "o", "x"])
+ # Right plot
+ df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[2])
+ df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[2])
+ df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[2])
+ self._check_legend_labels(ax[2], labels=["g", "b", "r"])
+ self._check_legend_marker(ax[2], expected_markers=["x", "o", "o"])
+
def test_legend_name(self):
multi = DataFrame(
randn(4, 4),
| I found out that the reason this happens is because `subplots` is not updated accordingly when `plt.subplots()` is used and assign to `ax`, e.g. `fig, ax = plt.subplots(nrows=1, ncols=3)`. I tested my solution and things all get solved, and figure looks very correct like below:
BTW, i am not very sure how to properly test this, and would be nice to know!
- [x] closes #18222
- [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/27808 | 2019-08-07T20:10:10Z | 2019-08-16T11:38:47Z | 2019-08-16T11:38:47Z | 2021-01-31T10:02:37Z |
BUG: fix+test op(NaT, ndarray), also simplify | diff --git a/pandas/_libs/tslibs/nattype.pyx b/pandas/_libs/tslibs/nattype.pyx
index 6fab1b5c02be1..020d1acf0b4ce 100644
--- a/pandas/_libs/tslibs/nattype.pyx
+++ b/pandas/_libs/tslibs/nattype.pyx
@@ -92,6 +92,9 @@ cdef class _NaT(datetime):
# int64_t value
# object freq
+ # higher than np.ndarray and np.matrix
+ __array_priority__ = 100
+
def __hash__(_NaT self):
# py3k needs this defined here
return hash(self.value)
@@ -103,61 +106,102 @@ cdef class _NaT(datetime):
if ndim == -1:
return _nat_scalar_rules[op]
- if ndim == 0:
+ elif util.is_array(other):
+ result = np.empty(other.shape, dtype=np.bool_)
+ result.fill(_nat_scalar_rules[op])
+ return result
+
+ elif ndim == 0:
if is_datetime64_object(other):
return _nat_scalar_rules[op]
else:
raise TypeError('Cannot compare type %r with type %r' %
(type(self).__name__, type(other).__name__))
+
# Note: instead of passing "other, self, _reverse_ops[op]", we observe
# that `_nat_scalar_rules` is invariant under `_reverse_ops`,
# rendering it unnecessary.
return PyObject_RichCompare(other, self, op)
def __add__(self, other):
+ if self is not c_NaT:
+ # cython __radd__ semantics
+ self, other = other, self
+
if PyDateTime_Check(other):
return c_NaT
-
+ elif PyDelta_Check(other):
+ return c_NaT
+ elif is_datetime64_object(other) or is_timedelta64_object(other):
+ return c_NaT
elif hasattr(other, 'delta'):
# Timedelta, offsets.Tick, offsets.Week
return c_NaT
- elif getattr(other, '_typ', None) in ['dateoffset', 'series',
- 'period', 'datetimeindex',
- 'datetimearray',
- 'timedeltaindex',
- 'timedeltaarray']:
- # Duplicate logic in _Timestamp.__add__ to avoid needing
- # to subclass; allows us to @final(_Timestamp.__add__)
- return NotImplemented
- return c_NaT
+
+ elif is_integer_object(other) or util.is_period_object(other):
+ # For Period compat
+ # TODO: the integer behavior is deprecated, remove it
+ return c_NaT
+
+ elif util.is_array(other):
+ if other.dtype.kind in 'mM':
+ # If we are adding to datetime64, we treat NaT as timedelta
+ # Either way, result dtype is datetime64
+ result = np.empty(other.shape, dtype="datetime64[ns]")
+ result.fill("NaT")
+ return result
+
+ return NotImplemented
def __sub__(self, other):
# Duplicate some logic from _Timestamp.__sub__ to avoid needing
# to subclass; allows us to @final(_Timestamp.__sub__)
+ cdef:
+ bint is_rsub = False
+
+ if self is not c_NaT:
+ # cython __rsub__ semantics
+ self, other = other, self
+ is_rsub = True
+
if PyDateTime_Check(other):
- return NaT
+ return c_NaT
elif PyDelta_Check(other):
- return NaT
+ return c_NaT
+ elif is_datetime64_object(other) or is_timedelta64_object(other):
+ return c_NaT
+ elif hasattr(other, 'delta'):
+ # offsets.Tick, offsets.Week
+ return c_NaT
- elif getattr(other, '_typ', None) == 'datetimeindex':
- # a Timestamp-DatetimeIndex -> yields a negative TimedeltaIndex
- return -other.__sub__(self)
+ elif is_integer_object(other) or util.is_period_object(other):
+ # For Period compat
+ # TODO: the integer behavior is deprecated, remove it
+ return c_NaT
- elif getattr(other, '_typ', None) == 'timedeltaindex':
- # a Timestamp-TimedeltaIndex -> yields a negative TimedeltaIndex
- return (-other).__add__(self)
+ elif util.is_array(other):
+ if other.dtype.kind == 'm':
+ if not is_rsub:
+ # NaT - timedelta64 we treat NaT as datetime64, so result
+ # is datetime64
+ result = np.empty(other.shape, dtype="datetime64[ns]")
+ result.fill("NaT")
+ return result
+
+ # timedelta64 - NaT we have to treat NaT as timedelta64
+ # for this to be meaningful, and the result is timedelta64
+ result = np.empty(other.shape, dtype="timedelta64[ns]")
+ result.fill("NaT")
+ return result
+
+ elif other.dtype.kind == 'M':
+ # We treat NaT as a datetime, so regardless of whether this is
+ # NaT - other or other - NaT, the result is timedelta64
+ result = np.empty(other.shape, dtype="timedelta64[ns]")
+ result.fill("NaT")
+ return result
- elif hasattr(other, 'delta'):
- # offsets.Tick, offsets.Week
- neg_other = -other
- return self + neg_other
-
- elif getattr(other, '_typ', None) in ['period', 'series',
- 'periodindex', 'dateoffset',
- 'datetimearray',
- 'timedeltaarray']:
- return NotImplemented
- return NaT
+ return NotImplemented
def __pos__(self):
return NaT
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 9f2b31f23d2fa..08f2aa68eca9e 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -4,7 +4,7 @@
import numpy as np
-from pandas._libs import Timestamp, index as libindex, lib, tslib as libts
+from pandas._libs import NaT, Timestamp, index as libindex, lib, tslib as libts
import pandas._libs.join as libjoin
from pandas._libs.tslibs import ccalendar, fields, parsing, timezones
from pandas.util._decorators import Appender, Substitution, cache_readonly
@@ -1281,7 +1281,9 @@ def insert(self, loc, item):
raise ValueError("Passed item and index have different timezone")
# check freq can be preserved on edge cases
if self.size and self.freq is not None:
- if (loc == 0 or loc == -len(self)) and item + self.freq == self[0]:
+ if item is NaT:
+ pass
+ elif (loc == 0 or loc == -len(self)) and item + self.freq == self[0]:
freq = self.freq
elif (loc == len(self)) and item - self.freq == self[-1]:
freq = self.freq
diff --git a/pandas/tests/arithmetic/test_timedelta64.py b/pandas/tests/arithmetic/test_timedelta64.py
index 4f5e00bc5a37d..6cf90de86ad14 100644
--- a/pandas/tests/arithmetic/test_timedelta64.py
+++ b/pandas/tests/arithmetic/test_timedelta64.py
@@ -1631,7 +1631,7 @@ def test_td64arr_div_nat_invalid(self, box_with_array):
rng = timedelta_range("1 days", "10 days", name="foo")
rng = tm.box_expected(rng, box_with_array)
- with pytest.raises(TypeError, match="'?true_divide'? cannot use operands"):
+ with pytest.raises(TypeError, match="unsupported operand type"):
rng / pd.NaT
with pytest.raises(TypeError, match="Cannot divide NaTType by"):
pd.NaT / rng
diff --git a/pandas/tests/scalar/period/test_period.py b/pandas/tests/scalar/period/test_period.py
index b57b817461788..6da4d556ea07e 100644
--- a/pandas/tests/scalar/period/test_period.py
+++ b/pandas/tests/scalar/period/test_period.py
@@ -1298,23 +1298,13 @@ def test_add_offset_nat(self):
timedelta(365),
]:
assert p + o is NaT
-
- if isinstance(o, np.timedelta64):
- with pytest.raises(TypeError):
- o + p
- else:
- assert o + p is NaT
+ assert o + p is NaT
for freq in ["M", "2M", "3M"]:
p = Period("NaT", freq=freq)
for o in [offsets.MonthEnd(2), offsets.MonthEnd(12)]:
assert p + o is NaT
-
- if isinstance(o, np.timedelta64):
- with pytest.raises(TypeError):
- o + p
- else:
- assert o + p is NaT
+ assert o + p is NaT
for o in [
offsets.YearBegin(2),
@@ -1324,12 +1314,7 @@ def test_add_offset_nat(self):
timedelta(365),
]:
assert p + o is NaT
-
- if isinstance(o, np.timedelta64):
- with pytest.raises(TypeError):
- o + p
- else:
- assert o + p is NaT
+ assert o + p is NaT
# freq is Tick
for freq in ["D", "2D", "3D"]:
@@ -1343,12 +1328,7 @@ def test_add_offset_nat(self):
timedelta(hours=48),
]:
assert p + o is NaT
-
- if isinstance(o, np.timedelta64):
- with pytest.raises(TypeError):
- o + p
- else:
- assert o + p is NaT
+ assert o + p is NaT
for o in [
offsets.YearBegin(2),
@@ -1358,12 +1338,7 @@ def test_add_offset_nat(self):
timedelta(hours=23),
]:
assert p + o is NaT
-
- if isinstance(o, np.timedelta64):
- with pytest.raises(TypeError):
- o + p
- else:
- assert o + p is NaT
+ assert o + p is NaT
for freq in ["H", "2H", "3H"]:
p = Period("NaT", freq=freq)
@@ -1376,9 +1351,7 @@ def test_add_offset_nat(self):
timedelta(days=4, minutes=180),
]:
assert p + o is NaT
-
- if not isinstance(o, np.timedelta64):
- assert o + p is NaT
+ assert o + p is NaT
for o in [
offsets.YearBegin(2),
@@ -1388,12 +1361,7 @@ def test_add_offset_nat(self):
timedelta(hours=23, minutes=30),
]:
assert p + o is NaT
-
- if isinstance(o, np.timedelta64):
- with pytest.raises(TypeError):
- o + p
- else:
- assert o + p is NaT
+ assert o + p is NaT
def test_sub_offset(self):
# freq is DateOffset
diff --git a/pandas/tests/scalar/test_nat.py b/pandas/tests/scalar/test_nat.py
index e7ad76cf95ba0..5b1c4f92bf341 100644
--- a/pandas/tests/scalar/test_nat.py
+++ b/pandas/tests/scalar/test_nat.py
@@ -1,4 +1,5 @@
from datetime import datetime, timedelta
+import operator
import numpy as np
import pytest
@@ -21,6 +22,7 @@
isna,
)
from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
+from pandas.core.ops import roperator
from pandas.util import testing as tm
@@ -333,8 +335,9 @@ def test_nat_doc_strings(compare):
"value,val_type",
[
(2, "scalar"),
- (1.5, "scalar"),
- (np.nan, "scalar"),
+ (1.5, "floating"),
+ (np.nan, "floating"),
+ ("foo", "str"),
(timedelta(3600), "timedelta"),
(Timedelta("5s"), "timedelta"),
(datetime(2014, 1, 1), "timestamp"),
@@ -348,6 +351,14 @@ def test_nat_arithmetic_scalar(op_name, value, val_type):
# see gh-6873
invalid_ops = {
"scalar": {"right_div_left"},
+ "floating": {
+ "right_div_left",
+ "left_minus_right",
+ "right_minus_left",
+ "left_plus_right",
+ "right_plus_left",
+ },
+ "str": set(_ops.keys()),
"timedelta": {"left_times_right", "right_times_left"},
"timestamp": {
"left_times_right",
@@ -366,6 +377,16 @@ def test_nat_arithmetic_scalar(op_name, value, val_type):
and isinstance(value, Timedelta)
):
msg = "Cannot multiply"
+ elif val_type == "str":
+ # un-specific check here because the message comes from str
+ # and varies by method
+ msg = (
+ "can only concatenate str|"
+ "unsupported operand type|"
+ "can't multiply sequence|"
+ "Can't convert 'NaTType'|"
+ "must be str, not NaTType"
+ )
else:
msg = "unsupported operand type"
@@ -435,6 +456,28 @@ def test_nat_arithmetic_td64_vector(op_name, box):
tm.assert_equal(_ops[op_name](vec, NaT), box_nat)
+@pytest.mark.parametrize(
+ "dtype,op,out_dtype",
+ [
+ ("datetime64[ns]", operator.add, "datetime64[ns]"),
+ ("datetime64[ns]", roperator.radd, "datetime64[ns]"),
+ ("datetime64[ns]", operator.sub, "timedelta64[ns]"),
+ ("datetime64[ns]", roperator.rsub, "timedelta64[ns]"),
+ ("timedelta64[ns]", operator.add, "datetime64[ns]"),
+ ("timedelta64[ns]", roperator.radd, "datetime64[ns]"),
+ ("timedelta64[ns]", operator.sub, "datetime64[ns]"),
+ ("timedelta64[ns]", roperator.rsub, "timedelta64[ns]"),
+ ],
+)
+def test_nat_arithmetic_ndarray(dtype, op, out_dtype):
+ other = np.arange(10).astype(dtype)
+ result = op(NaT, other)
+
+ expected = np.empty(other.shape, dtype=out_dtype)
+ expected.fill("NaT")
+ tm.assert_numpy_array_equal(result, expected)
+
+
def test_nat_pinned_docstrings():
# see gh-17327
assert NaT.ctime.__doc__ == datetime.ctime.__doc__
| https://api.github.com/repos/pandas-dev/pandas/pulls/27807 | 2019-08-07T18:06:42Z | 2019-08-15T12:41:33Z | 2019-08-15T12:41:33Z | 2019-08-15T14:19:41Z |
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