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BUG: fix isin with Series of tuples values (#16394) #16434

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Merged
merged 6 commits into from
May 23, 2017
Merged

BUG: fix isin with Series of tuples values (#16394) #16434

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db346e3
Swiched out "values = np.array(list(values), dtype='object')" for "va…
jaredsnyder May 22, 2017
4d192a0
- Added comment w/ issue number to test
jaredsnyder May 22, 2017
1a98fdc
Added line to whatsnew file
jaredsnyder May 22, 2017
4dc6bc5
Fixed formatting issue in whatsnew doc
jaredsnyder May 22, 2017
e71219d
Fixed PEP8 issues in test
jaredsnyder May 22, 2017
ce622e6
Fixed text in whatsnew
jaredsnyder May 23, 2017
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.20.2.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ Reshaping
^^^^^^^^^

- Bug in ``DataFrame.stack`` with unsorted levels in MultiIndex columns (:issue:`16323`)

- Bug in ``isin`` in core/algorithms.py with comparing lists of tuples
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Series.isin(..) with a list of tuples. add the issue number here as well.

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Thanks, think I got the format correct with the latest commit


Numeric
^^^^^^^
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2 changes: 1 addition & 1 deletion pandas/core/algorithms.py
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Expand Up @@ -388,7 +388,7 @@ def isin(comps, values):
"[{0}]".format(type(values).__name__))

if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
values = np.array(list(values), dtype='object')
values = lib.list_to_object_array(list(values))

comps, dtype, _ = _ensure_data(comps)
values, _, _ = _ensure_data(values, dtype=dtype)
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8 changes: 8 additions & 0 deletions pandas/tests/frame/test_analytics.py
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Expand Up @@ -1201,6 +1201,14 @@ def test_isin_df(self):
expected['B'] = False
tm.assert_frame_equal(result, expected)

def test_isin_tuples(self):
# GH16394
df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
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Can you add the original issue number as a comment on the first line ?
just like # GH16394

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Fixed

df['C'] = list(zip(df['A'], df['B']))
result = df['C'].isin([(1, 'a')])
tm.assert_series_equal(result,
Series([True,False,False],name="C"))

def test_isin_df_dupe_values(self):
df1 = DataFrame({'A': [1, 2, 3, 4], 'B': [2, np.nan, 4, 4]})
# just cols duped
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