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Bug: Logical operator of Series with Index (#22092) #22293

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Merged
merged 9 commits into from
Sep 18, 2018
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Bug: Logical operator of Series with Index (#22092) #22293

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2c1bb1a
Fix bug #GH22092
makbigc Aug 12, 2018
3607834
Update v0.24.0.txt
makbigc Aug 13, 2018
9f7b586
Update v0.24.0.txt
makbigc Aug 14, 2018
fd0f381
Update ops.py
makbigc Aug 14, 2018
f9f63fd
Update test_operators.py
makbigc Aug 14, 2018
ba456e7
Update v0.24.0.txt
makbigc Aug 22, 2018
84ac158
Update test_operators.py
makbigc Aug 22, 2018
aaf8349
Merge branch 'master' into master
makbigc Sep 9, 2018
d671f99
Merge branch 'master' into master
makbigc Sep 16, 2018
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.24.0.txt
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Expand Up @@ -497,6 +497,6 @@ Other
- :meth: `~pandas.io.formats.style.Styler.background_gradient` now takes a ``text_color_threshold`` parameter to automatically lighten the text color based on the luminance of the background color. This improves readability with dark background colors without the need to limit the background colormap range. (:issue:`21258`)
- Require at least 0.28.2 version of ``cython`` to support read-only memoryviews (:issue:`21688`)
- :meth: `~pandas.io.formats.style.Styler.background_gradient` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` (:issue:`15204`)
-
- Bug in the logical operator of :class:`Series` with :class:`Index` (:issue:`22092`)
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@gfyoung gfyoung Aug 13, 2018

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Perhaps something like:

Bug in logical operators handling :class:`Series` and :class:`Index` together (:issue:`22092`)

-
-
2 changes: 1 addition & 1 deletion pandas/core/ops.py
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Expand Up @@ -1383,7 +1383,7 @@ def na_op(x, y):
if isinstance(y, list):
y = construct_1d_object_array_from_listlike(y)

if isinstance(y, (np.ndarray, ABCSeries)):
if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
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@jbrockmendel jbrockmendel Aug 13, 2018

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This should be ABCIndexClass, not ABCIndex.

if (is_bool_dtype(x.dtype) and is_bool_dtype(y.dtype)):
result = op(x, y) # when would this be hit?
else:
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17 changes: 17 additions & 0 deletions pandas/tests/series/test_operators.py
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Expand Up @@ -537,6 +537,23 @@ def test_comparison_flex_alignment_fill(self):
exp = pd.Series([True, True, False, False], index=list('abcd'))
assert_series_equal(left.gt(right, fill_value=0), exp)

def test_comparison_with_index(self):
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@jreback jreback Aug 14, 2018

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can you check this on Int64Index as well. I think some other index types might work.

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@jbrockmendel jbrockmendel Aug 22, 2018

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"comparison" is a misnomer here. Consider "binary_ops" or "logical_ops"

# GH22092
ser = Series([True, True, False, False])
idx = Index([True, False, True, False])

expected = Series([True, False, False, False])
result = ser & idx
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@jreback jreback Aug 14, 2018

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cc @jbrockmendel I think we DO allow this on index types which don't makes sense, but we DO raise on Series, e.g.

not sure what to make of [1] and [2]

In [1]: dti = pd.date_range('20130101', periods=3)

In [2]: dti ^ dti
Out[2]: DatetimeIndex([], dtype='datetime64[ns]', freq=None)

In [3]: dti & dti
Out[3]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03'], dtype='datetime64[ns]', freq='D')
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

# I think this PR fixes this one?
In [4]: pd.Series(dti) & dti

# this is good
In [5]: pd.Series(dti) & pd.Series(dti)
TypeError: cannot astype a datetimelike from [datetime64[ns]] to [bool]

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@jbrockmendel jbrockmendel Aug 14, 2018

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I think we DO allow this on index types which don't makes sense, but we DO raise on Series,

Yah I've opened a couple of Issues about this. The existing Series ops don't handle dt64/td64 at all, and are not super-clear for some other dtypes. The reversed op(Index, Series) ops are a separate can of worms because those are set operations.

I don't think this PR fixes [4] (yet), needs to chance ABCIndex to ABCIndexClass (like #22173)

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@jreback jreback Aug 14, 2018 •
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ok this is fine then (for now as you have other issues about this)

assert_series_equal(result, expected)

expected = Series([True, True, True, False])
result = ser | idx
assert_series_equal(result, expected)

expected = Series([False, True, True, False])
result = ser ^ idx
assert_series_equal(result, expected)

def test_ne(self):
ts = Series([3, 4, 5, 6, 7], [3, 4, 5, 6, 7], dtype=float)
expected = [True, True, False, True, True]
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