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DEPR: DataFrame dropna accepting multiple axes #20995

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jreback merged 4 commits into from
May 10, 2018
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DEPR: DataFrame dropna accepting multiple axes #20995

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depr multiple axes to dropna
kunalgosar May 9, 2018
02d7c00
resolving comments
kunalgosar May 10, 2018
d7c6e01
resolving addition comments
kunalgosar May 10, 2018
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jreback May 10, 2018
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.23.0.txt
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Expand Up @@ -1005,6 +1005,7 @@ Deprecations
- Setting ``PeriodIndex.freq`` (which was not guaranteed to work correctly) is deprecated. Use :meth:`PeriodIndex.asfreq` instead (:issue:`20678`)
- ``Index.get_duplicates()`` is deprecated and will be removed in a future version (:issue:`20239`)
- The previous default behavior of negative indices in ``Categorical.take`` is deprecated. In a future version it will change from meaning missing values to meaning positional indices from the right. The future behavior is consistent with :meth:`Series.take` (:issue:`20664`).
- Passing multiple axes to the ``axis`` parameter in :func: `DataFrame.dropna` has been deprecated and will not be suppoted in a future version (:issue:`20987`)
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@WillAyd WillAyd May 10, 2018

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Small typo of "supported"

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@kunalgosar kunalgosar May 10, 2018

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Thanks!



.. _whatsnew_0230.prior_deprecations:
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9 changes: 7 additions & 2 deletions pandas/core/frame.py
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Expand Up @@ -4168,14 +4168,14 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None,

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, or tuple/list thereof
axis : {0 or 'index', 1 or 'columns'}
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@WillAyd WillAyd May 10, 2018

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I believe the standard for this is axis : {0 or ‘index’, 1 or ‘columns’, None}, default None

See end of pandas docstring guide section on Parameters:

https://python-sprints.github.io/pandas/guide/pandas_docstring.html#section-3-parameters

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@kunalgosar kunalgosar May 10, 2018

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I've added the default option here. dropna, however, does not support None as an input to axis.

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@WillAyd WillAyd May 10, 2018

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Hmm if that's the case then just go with axis : {0 or 'index', 1 or 'columns'}, default 0. That's what I see everywhere else in the code anyway

@datapythonista - something we should update in the docstring standard?

Determine if rows or columns which contain missing values are
removed.

* 0, or 'index' : Drop rows which contain missing values.
* 1, or 'columns' : Drop columns which contain missing value.

Pass tuple or list to drop on multiple axes.
deprecated:: 0.23.0: Pass tuple or list to drop on multiple axes.
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@WillAyd WillAyd May 10, 2018

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The directive should start with a ".. " here, no?

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@kunalgosar kunalgosar May 10, 2018

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Fixed.

how : {'any', 'all'}, default 'any'
Determine if row or column is removed from DataFrame, when we have
at least one NA or all NA.
Expand Down Expand Up @@ -4259,6 +4259,11 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None,
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if isinstance(axis, (tuple, list)):
# GH20987
msg = ("supplying multiple axes to axis is deprecated and "
"will be removed in a future version.")
warnings.warn(msg, FutureWarning, stacklevel=2)

result = self
for ax in axis:
result = result.dropna(how=how, thresh=thresh, subset=subset,
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11 changes: 8 additions & 3 deletions pandas/tests/frame/test_missing.py
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Expand Up @@ -174,16 +174,21 @@ def test_dropna_multiple_axes(self):
[np.nan, np.nan, np.nan, np.nan],
[7, np.nan, 8, 9]])
cp = df.copy()
result = df.dropna(how='all', axis=[0, 1])
result2 = df.dropna(how='all', axis=(0, 1))

# GH20987
with tm.assert_produces_warning(FutureWarning):
result = df.dropna(how='all', axis=[0, 1])
with tm.assert_produces_warning(FutureWarning):
result2 = df.dropna(how='all', axis=(0, 1))
expected = df.dropna(how='all').dropna(how='all', axis=1)

assert_frame_equal(result, expected)
assert_frame_equal(result2, expected)
assert_frame_equal(df, cp)

inp = df.copy()
inp.dropna(how='all', axis=(0, 1), inplace=True)
with tm.assert_produces_warning(FutureWarning):
inp.dropna(how='all', axis=(0, 1), inplace=True)
assert_frame_equal(inp, expected)

def test_dropna_tz_aware_datetime(self):
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