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Merged
merged 6 commits into from
Jun 8, 2018
Merged

REGR: allow merging on object boolean columns #21310

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REGR: allow merging on object boolean columns
jorisvandenbossche Jun 4, 2018
1630072
add more test cases
jorisvandenbossche Jun 8, 2018
fe45b89
add comment
jorisvandenbossche Jun 8, 2018
1bd8661
Merge remote-tracking branch 'upstream/master' into merge-dtype-warning
jorisvandenbossche Jun 8, 2018
58b72c5
add whatsnew
jorisvandenbossche Jun 8, 2018
ad845d7
Merge branch 'master' into merge-dtype-warning
jorisvandenbossche Jun 8, 2018
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.23.1.txt
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Expand Up @@ -31,7 +31,7 @@ Fixed Regressions
- Bug in :meth:`Categorical.fillna` incorrectly raising a ``TypeError`` when `value` the individual categories are iterable and `value` is an iterable (:issue:`21097`, :issue:`19788`)
- Regression in :func:`pivot_table` where an ordered ``Categorical`` with missing
values for the pivot's ``index`` would give a mis-aligned result (:issue:`21133`)

- Fixed regression in merging on boolean index/columns (:issue:`21119`).

.. _whatsnew_0231.performance:

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10 changes: 8 additions & 2 deletions pandas/core/reshape/merge.py
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Expand Up @@ -28,6 +28,7 @@
is_int_or_datetime_dtype,
is_dtype_equal,
is_bool,
is_bool_dtype,
is_list_like,
is_datetimelike,
_ensure_int64,
Expand Down Expand Up @@ -974,9 +975,14 @@ def _maybe_coerce_merge_keys(self):

# Check if we are trying to merge on obviously
# incompatible dtypes GH 9780, GH 15800
elif is_numeric_dtype(lk) and not is_numeric_dtype(rk):

# boolean values are considered as numeric, but are still allowed
# to be merged on object boolean values
elif ((is_numeric_dtype(lk) and not is_bool_dtype(lk))
and not is_numeric_dtype(rk)):
raise ValueError(msg)
elif not is_numeric_dtype(lk) and is_numeric_dtype(rk):
elif (not is_numeric_dtype(lk)
and (is_numeric_dtype(rk) and not is_bool_dtype(rk))):
raise ValueError(msg)
elif is_datetimelike(lk) and not is_datetimelike(rk):
raise ValueError(msg)
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23 changes: 23 additions & 0 deletions pandas/tests/reshape/merge/test_merge.py
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Expand Up @@ -1526,6 +1526,27 @@ def test_merge_on_ints_floats_warning(self):
result = B.merge(A, left_on='Y', right_on='X')
assert_frame_equal(result, expected[['Y', 'X']])

def test_merge_incompat_infer_boolean_object(self):
# GH21119: bool + object bool merge OK
df1 = DataFrame({'key': Series([True, False], dtype=object)})
df2 = DataFrame({'key': [True, False]})
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you need to have a NaN in the object dtype as well, this should test inferred and bool on both sides (with and w/o NaN).


expected = DataFrame({'key': [True, False]}, dtype=object)
result = pd.merge(df1, df2, on='key')
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need to test the error case (e.g. bool / inferred bool) on 1 side and numeric on the other (I think this will raise), prob have an existing test, just need to add to it

assert_frame_equal(result, expected)
result = pd.merge(df2, df1, on='key')
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blank lines between cases

assert_frame_equal(result, expected)
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ideally use the how fixture

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This is also not used for the other tests on this, so leaving this for now


# with missing value
df1 = DataFrame({'key': Series([True, False, np.nan], dtype=object)})
df2 = DataFrame({'key': [True, False]})

expected = DataFrame({'key': [True, False]}, dtype=object)
result = pd.merge(df1, df2, on='key')
assert_frame_equal(result, expected)
result = pd.merge(df2, df1, on='key')
assert_frame_equal(result, expected)

@pytest.mark.parametrize('df1_vals, df2_vals', [
([0, 1, 2], ["0", "1", "2"]),
([0.0, 1.0, 2.0], ["0", "1", "2"]),
Expand All @@ -1538,6 +1559,8 @@ def test_merge_on_ints_floats_warning(self):
pd.date_range('20130101', periods=3, tz='US/Eastern')),
([0, 1, 2], Series(['a', 'b', 'a']).astype('category')),
([0.0, 1.0, 2.0], Series(['a', 'b', 'a']).astype('category')),
# TODO ([0, 1], pd.Series([False, True], dtype=bool)),
([0, 1], pd.Series([False, True], dtype=object))
])
def test_merge_incompat_dtypes(self, df1_vals, df2_vals):
# GH 9780, GH 15800
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