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BUG: Fix Categorical use_inf_as_na bug #33629

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Merged
merged 19 commits into from
Apr 27, 2020
Merged

BUG: Fix Categorical use_inf_as_na bug #33629

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8985831
BUG: Fix use_inf_as_na bugs
dsaxton Apr 18, 2020
55ed2cf
Note
dsaxton Apr 18, 2020
9582927
Issue number
dsaxton Apr 18, 2020
365df7e
Oops
dsaxton Apr 18, 2020
1d05145
Different check
dsaxton Apr 19, 2020
a1b12ba
Merge remote-tracking branch 'upstream/master' into use-inf-as-na
dsaxton Apr 19, 2020
c7c03b8
Remove StringArray stuff
dsaxton Apr 19, 2020
dbf9830
Fix
dsaxton Apr 19, 2020
60c7625
Update
dsaxton Apr 20, 2020
6b413b4
Merge remote-tracking branch 'upstream/master' into use-inf-as-na
dsaxton Apr 20, 2020
ddff3d2
Use equality instead
dsaxton Apr 20, 2020
cbfdd6a
Delete _isna_ndarraylike_old
dsaxton Apr 21, 2020
81c310f
Merge remote-tracking branch 'upstream/master' into use-inf-as-na
dsaxton Apr 21, 2020
1838a33
Add some tests
dsaxton Apr 21, 2020
11d50a9
Fix test
dsaxton Apr 21, 2020
afa9448
Add docstring
dsaxton Apr 26, 2020
c7b65d5
Merge remote-tracking branch 'upstream/master' into use-inf-as-na
dsaxton Apr 26, 2020
a95f38c
Fix
dsaxton Apr 27, 2020
fe1648f
Nit
dsaxton Apr 27, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.1.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -480,6 +480,7 @@ Categorical
- :meth:`Categorical.fillna` now accepts :class:`Categorical` ``other`` argument (:issue:`32420`)
- Bug where :meth:`Categorical.replace` would replace with ``NaN`` whenever the new value and replacement value were equal (:issue:`33288`)
- Bug where an ordered :class:`Categorical` containing only ``NaN`` values would raise rather than returning ``NaN`` when taking the minimum or maximum (:issue:`33450`)
- Bug where :meth:`Series.isna` and :meth:`DataFrame.isna` would raise for categorical dtype when ``pandas.options.mode.use_inf_as_na`` was set to ``True`` (:issue:`33594`)

Datetimelike
^^^^^^^^^^^^
Expand Down
62 changes: 31 additions & 31 deletions pandas/core/dtypes/missing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,13 +134,13 @@ def _isna_new(obj):
elif isinstance(obj, type):
return False
elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
return _isna_ndarraylike(obj)
return _isna_ndarraylike(obj, old=False)
elif isinstance(obj, ABCDataFrame):
return obj.isna()
elif isinstance(obj, list):
return _isna_ndarraylike(np.asarray(obj, dtype=object))
return _isna_ndarraylike(np.asarray(obj, dtype=object), old=False)
elif hasattr(obj, "__array__"):
return _isna_ndarraylike(np.asarray(obj))
return _isna_ndarraylike(np.asarray(obj), old=False)
else:
return False

Expand All @@ -165,13 +165,13 @@ def _isna_old(obj):
elif isinstance(obj, type):
return False
elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
return _isna_ndarraylike_old(obj)
return _isna_ndarraylike(obj, old=True)
elif isinstance(obj, ABCDataFrame):
return obj.isna()
elif isinstance(obj, list):
return _isna_ndarraylike_old(np.asarray(obj, dtype=object))
return _isna_ndarraylike(np.asarray(obj, dtype=object), old=True)
elif hasattr(obj, "__array__"):
return _isna_ndarraylike_old(np.asarray(obj))
return _isna_ndarraylike(np.asarray(obj), old=True)
else:
return False

Expand Down Expand Up @@ -207,40 +207,40 @@ def _use_inf_as_na(key):
globals()["_isna"] = _isna_new


def _isna_ndarraylike(obj):
values = getattr(obj, "_values", obj)
dtype = values.dtype

if is_extension_array_dtype(dtype):
result = values.isna()
elif is_string_dtype(dtype):
result = _isna_string_dtype(values, dtype, old=False)

elif needs_i8_conversion(dtype):
# this is the NaT pattern
result = values.view("i8") == iNaT
else:
result = np.isnan(values)

# box
if isinstance(obj, ABCSeries):
result = obj._constructor(result, index=obj.index, name=obj.name, copy=False)

return result
def _isna_ndarraylike(obj, old: bool = False):
"""
Return an array indicating which values of the input array are NaN / NA.

Parameters
----------
obj: array-like
The input array whose elements are to be checked.
old: bool
Whether or not to treat infinite values as NA.

def _isna_ndarraylike_old(obj):
Returns
-------
array-like
Array of boolean values denoting the NA status of each element.
"""
values = getattr(obj, "_values", obj)
dtype = values.dtype

if is_string_dtype(dtype):
result = _isna_string_dtype(values, dtype, old=True)

if is_extension_array_dtype(dtype):
if old:
result = values.isna() | (values == -np.inf) | (values == np.inf)
else:
result = values.isna()
elif is_string_dtype(dtype):
result = _isna_string_dtype(values, dtype, old=old)
elif needs_i8_conversion(dtype):
# this is the NaT pattern
result = values.view("i8") == iNaT
else:
result = ~np.isfinite(values)
if old:
result = ~np.isfinite(values)
else:
result = np.isnan(values)

# box
if isinstance(obj, ABCSeries):
Expand Down
53 changes: 52 additions & 1 deletion pandas/tests/arrays/categorical/test_missing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,8 @@

from pandas.core.dtypes.dtypes import CategoricalDtype

from pandas import Categorical, Index, Series, isna
import pandas as pd
from pandas import Categorical, DataFrame, Index, Series, isna
import pandas._testing as tm


Expand Down Expand Up @@ -97,3 +98,53 @@ def test_fillna_array(self):
expected = Categorical(["A", "B", "C", "B", "A"], dtype=cat.dtype)
tm.assert_categorical_equal(result, expected)
assert isna(cat[-1]) # didnt modify original inplace

@pytest.mark.parametrize(
"values, expected",
[
([1, 2, 3], np.array([False, False, False])),
([1, 2, np.nan], np.array([False, False, True])),
([1, 2, np.inf], np.array([False, False, True])),
([1, 2, pd.NA], np.array([False, False, True])),
],
)
def test_use_inf_as_na(self, values, expected):
# https://github.com/pandas-dev/pandas/issues/33594
with pd.option_context("mode.use_inf_as_na", True):
cat = Categorical(values)
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Should we also test this with putting the Categorical creation outside of the option context?

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@dsaxton dsaxton Apr 20, 2020 •
edited
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It turns out that actually fails interestingly enough: #33629 (comment) (if you set the option after constructing the object it loses its effect). What's even more strange is the value displays as NaN:

[ins] In [3]: arr = pd.Categorical([1, 2, np.inf])                                                                                                                                                           

[ins] In [4]: pd.options.mode.use_inf_as_na = True                                                                                                                                                           

[ins] In [5]: arr                                                                                                                                                                                            
Out[5]: 
[1.0, 2.0, NaN]
Categories (3, float64): [1.0, 2.0, NaN]

[ins] In [6]: arr.isna()                                                                                                                                                                                     
Out[6]: array([False, False, False])

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What's even more strange is the value displays as NaN:

I think that is somewhat "expected", given the discussion above in #33629 (comment) (not that I like that behaviour though).

But regardless of how the categorical is created (with or without the option), I find it very strange that isna is then failing

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@jorisvandenbossche jorisvandenbossche Apr 20, 2020 •
edited
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Hmm, being a bit confused in the comment above :)
The Categorical.isna is failing, of course, since it just looks at -1 in the codes, and when inf is part of the categories, it is not -1 in the codes. Hence it is failing to detect it.

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@dsaxton so could you additionally test it for pd.isna ? In constract to Categorical.isna(), pd.isna should work even if the Categorical is created before the context, I think?

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@jorisvandenbossche Added some tests, let me know if this is roughly what you had in mind

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Yes, that looks good!

result = cat.isna()
tm.assert_numpy_array_equal(result, expected)

result = Series(cat).isna()
expected = Series(expected)
tm.assert_series_equal(result, expected)

result = DataFrame(cat).isna()
expected = DataFrame(expected)
tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize(
"values, expected",
[
([1, 2, 3], np.array([False, False, False])),
([1, 2, np.nan], np.array([False, False, True])),
([1, 2, np.inf], np.array([False, False, True])),
([1, 2, pd.NA], np.array([False, False, True])),
],
)
def test_use_inf_as_na_outside_context(self, values, expected):
# https://github.com/pandas-dev/pandas/issues/33594
# Using isna directly for Categorical will fail in general here
cat = Categorical(values)

with pd.option_context("mode.use_inf_as_na", True):
result = pd.isna(cat)
tm.assert_numpy_array_equal(result, expected)

result = pd.isna(Series(cat))
expected = Series(expected)
tm.assert_series_equal(result, expected)

result = pd.isna(DataFrame(cat))
expected = DataFrame(expected)
tm.assert_frame_equal(result, expected)