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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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3 changes: 2 additions & 1 deletion doc/source/whatsnew/v1.1.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -443,6 +443,7 @@ 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 Expand Up @@ -636,7 +637,7 @@ ExtensionArray
^^^^^^^^^^^^^^

- Fixed bug where :meth:`Serires.value_counts` would raise on empty input of ``Int64`` dtype (:issue:`33317`)
-
- Fixed bug where :meth:`StringArray.isna` would not return ``False`` for NA values when ``pandas.options.mode.use_inf_as_na`` was set to ``True`` (:issue:`33594`)


Other
Expand Down
7 changes: 6 additions & 1 deletion pandas/_libs/missing.pyx
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Expand Up @@ -140,6 +140,7 @@ def isnaobj_old(arr: ndarray) -> ndarray:
- INF
- NEGINF
- NaT
- NA

Parameters
----------
Expand All @@ -160,7 +161,11 @@ def isnaobj_old(arr: ndarray) -> ndarray:
result = np.zeros(n, dtype=np.uint8)
for i in range(n):
val = arr[i]
result[i] = val is NaT or _check_none_nan_inf_neginf(val)
result[i] = (
val is NaT
or _check_none_nan_inf_neginf(val)
or val is C_NA
)
return result.view(np.bool_)


Expand Down
6 changes: 5 additions & 1 deletion pandas/core/dtypes/missing.py
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Expand Up @@ -231,7 +231,11 @@ def _isna_ndarraylike(obj):


def _isna_ndarraylike_old(obj):
values = getattr(obj, "_values", obj)
if not isinstance(obj, np.ndarray):
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is the the same construct we use in isna_arraylike?

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@dsaxton dsaxton Apr 19, 2020 •
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In _isna_arraylike it looks like we use getattr, but then check if we have an ExtensionArray and call the isna method if so

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I agree the code should use the same structure as _isna_ndarraylike, refactoring out the common code maybe advantageous to avoid divergence occurring again.

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How would you recommend using a similar pattern here? We can't rely on isna to catch infinity (it only works if you construct the array after setting the option):

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

[ins] In [2]: pd.options.mode.use_inf_as_na = True

[ins] In [3]: cat.isna()
Out[3]: array([False, False, False])

[ins] In [4]: cat = pd.Categorical([1, 2, np.inf])

[ins] In [5]: cat.isna()
Out[5]: array([False, False,  True])

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so is there also a bug in Categorical.isna?

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I suppose you could call this behavior a bug, but I'm not sure if it's a problem with Categorical.isna (it's simply checking Categorical._codes, which makes total sense and shouldn't need to change in my opinion). IMHO the issue is more with trying to have the behavior of these constructors / methods respect hidden global variables instead of being well-defined in isolation (I could even see the merit of deprecating options like this rather than needing to patch every place where NA checking might happen)

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I would prefer to make this code substatially the same, difference only be the inf change.

values = obj.to_numpy()
else:
values = obj

dtype = values.dtype

if is_string_dtype(dtype):
Expand Down
26 changes: 25 additions & 1 deletion pandas/tests/arrays/categorical/test_missing.py
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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 +98,26 @@ 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])),
],
)
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 •
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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 •
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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)
22 changes: 22 additions & 0 deletions pandas/tests/arrays/string_/test_string.py
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Expand Up @@ -277,3 +277,25 @@ def test_value_counts_na():
result = arr.value_counts(dropna=True)
expected = pd.Series([2, 1], index=["a", "b"], dtype="Int64")
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
"values, expected",
[
(pd.array(["a", "b", "c"]), np.array([False, False, False])),
(pd.array(["a", "b", None]), np.array([False, False, True])),
],
)
def test_use_na_as_inf(values, expected):
# https://github.com/pandas-dev/pandas/issues/33594
with pd.option_context("mode.use_inf_as_na", True):
result = values.isna()
tm.assert_numpy_array_equal(result, expected)

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

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