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BUG: pd.compare does not recognize differences when comparing values with null Int64 data type #48966

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Merged
merged 11 commits into from
Oct 14, 2022
Merged

BUG: pd.compare does not recognize differences when comparing values with null Int64 data type #48966

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f027ff9
BUG: df.compare returns incorrect result when comparing NA values
vamsi-verma-s Oct 5, 2022
0f3fcef
add bug fix note
vamsi-verma-s Oct 6, 2022
9c0a19b
add pd.compare tests specific to int64 null dtype
vamsi-verma-s Oct 6, 2022
0b5ec5c
change bug fix note to be specifically about int64 dtype null values
vamsi-verma-s Oct 6, 2022
d72e0e3
change bug fix note
vamsi-verma-s Oct 6, 2022
4412e33
add nullable int64 test and parameterize ea and np dtype test
vamsi-verma-s Oct 6, 2022
9b97320
fix test parametrization
vamsi-verma-s Oct 12, 2022
ffbb131
Merge branch 'master' into res-48939
vamsi-verma-s Oct 12, 2022
bae0808
Merge branch 'master' into res-48939
vamsi-verma-s Oct 13, 2022
4838dc0
rename variables, add gh refs
vamsi-verma-s Oct 14, 2022
3f739dd
change var name
vamsi-verma-s Oct 14, 2022
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2 changes: 2 additions & 0 deletions doc/source/whatsnew/v1.6.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -209,6 +209,8 @@ Indexing
- Bug in :meth:`DataFrame.reindex` filling with wrong values when indexing columns and index for ``uint`` dtypes (:issue:`48184`)
- Bug in :meth:`DataFrame.reindex` casting dtype to ``object`` when :class:`DataFrame` has single extension array column when re-indexing ``columns`` and ``index`` (:issue:`48190`)
- Bug in :func:`~DataFrame.describe` when formatting percentiles in the resulting index showed more decimals than needed (:issue:`46362`)
- Bug in :meth:`DataFrame.compare` does not recognize differences when comparing ``NA`` with value in nullable dtypes (:issue:`48939`)
-

Missing
^^^^^^^
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1 change: 1 addition & 0 deletions pandas/core/generic.py
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Expand Up @@ -9262,6 +9262,7 @@ def compare(
)

mask = ~((self == other) | (self.isna() & other.isna()))
mask.fillna(True, inplace=True)

if not keep_equal:
self = self.where(mask)
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72 changes: 60 additions & 12 deletions pandas/tests/frame/methods/test_compare.py
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Expand Up @@ -238,17 +238,65 @@ def test_invalid_input_result_names(result_names):
df1.compare(df2, result_names=result_names)


def test_compare_ea_and_np_dtype():
# GH#44014
df1 = pd.DataFrame({"a": [4.0, 4], "b": [1.0, 2]})
df2 = pd.DataFrame({"a": pd.Series([1, pd.NA], dtype="Int64"), "b": [1.0, 2]})
@pytest.mark.parametrize(
"df1,df2,expected",
[
(
pd.DataFrame({"a": [4.0, 4], "b": [1.0, 2]}),
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I meant parametrising single values, not the whole object. This is really hard to read

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@vamsi-verma-s vamsi-verma-s Oct 12, 2022 •
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I thought this would be easier to understand. I've changed it now. please take a look.

pd.DataFrame({"a": pd.Series([1, pd.NA], dtype="Int64"), "b": [1.0, 2]}),
pd.DataFrame(
{
("a", "self"): [4.0, 4],
("a", "other"): pd.Series([1, pd.NA], dtype="Int64"),
("b", "self"): np.nan,
("b", "other"): np.nan,
}
),
),
(
pd.DataFrame({"a": pd.Series([4, pd.NA], dtype="Int64"), "b": [1.0, 2]}),
pd.DataFrame({"a": [4, pd.NA], "b": [1.0, 2]}),
pd.DataFrame(
{
("a", "self"): pd.Series([4, pd.NA], dtype="Int64"),
("a", "other"): pd.Series([4, pd.NA]),
("b", "self"): np.nan,
("b", "other"): np.nan,
}
),
),
],
)
def test_compare_ea_and_np_dtype(df1, df2, expected):
result = df1.compare(df2, keep_shape=True)
tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
"df1,df2,expected",
[
(
pd.DataFrame({"a": pd.Series([4.0, pd.NA], dtype="Int64")}),
pd.DataFrame({"a": pd.Series([3, pd.NA], dtype="Int64")}),
pd.DataFrame(
{
("a", "self"): pd.Series([4, pd.NA], dtype="Int64"),
("a", "other"): pd.Series([3, pd.NA], dtype="Int64"),
}
),
),
(
pd.DataFrame({"a": pd.Series([4, pd.NA], dtype="Int64")}),
pd.DataFrame({"a": pd.Series([4, pd.NA], dtype="Int64")}),
pd.DataFrame(
{
("a", "self"): pd.Series([pd.NA, pd.NA], dtype="Int64"),
("a", "other"): pd.Series([pd.NA, pd.NA], dtype="Int64"),
}
),
),
],
)
def test_compare_nullable_int64_dtype(df1, df2, expected):
result = df1.compare(df2, keep_shape=True)
expected = pd.DataFrame(
{
("a", "self"): [4.0, np.nan],
("a", "other"): pd.Series([1, pd.NA], dtype="Int64"),
("b", "self"): np.nan,
("b", "other"): np.nan,
}
)
tm.assert_frame_equal(result, expected)