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BUG: Crosstab bug in #18321 #19326

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BUG: Crosstab bug in #18321 #19326

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6140d5a
Add test
Jan 20, 2018
6bcb2d7
Add logic to fix CI failure from new test
Jan 20, 2018
c1e87e6
fix CI failure
Jan 20, 2018
af90130
Added documentation for the bug fix
Jan 20, 2018
8f4380f
added more test and fixed up code based on comments of first pull req…
Jan 22, 2018
451792f
Update tests
Jan 22, 2018
30da7f4
Update logic
Jan 22, 2018
0970999
fix pep8
Jan 22, 2018
52072ae
pull request 3
Jan 23, 2018
ba9006d
merge from main and solve merge conflict
Jan 23, 2018
cc0b9ba
fix pep8
Jan 23, 2018
ae55326
pull request 4 with updated doc, parametrized tests and comments in c…
Jan 24, 2018
a914b15
fix pep8
Jan 24, 2018
81dd6c2
fix pep8 again
Jan 24, 2018
c0f1d29
fix test
Jan 24, 2018
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.23.0.txt
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Expand Up @@ -509,6 +509,7 @@ Reshaping
- Bug in :func:`DataFrame.merge` in which merging using ``Index`` objects as vectors raised an Exception (:issue:`19038`)
- Bug in :func:`DataFrame.stack`, :func:`DataFrame.unstack`, :func:`Series.unstack` which were not returning subclasses (:issue:`15563`)
- Bug in timezone comparisons, manifesting as a conversion of the index to UTC in ``.concat()`` (:issue:`18523`)
- Bug in :func:`crosstab` where the added column is removed incorrectly (:issue:`18321`)
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can you make this more informative. from a user perspective the bug was that you had an incorrectly named output index when you were crosstabbing 2 series with tuples names.

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noted.

-

Numeric
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8 changes: 7 additions & 1 deletion pandas/core/reshape/pivot.py
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Expand Up @@ -455,17 +455,23 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None,

from pandas import DataFrame
df = DataFrame(data, index=common_idx)
common_cols_idx = df.columns

if values is None:
df['__dummy__'] = 0
kwargs = {'aggfunc': len, 'fill_value': 0}
else:
df['__dummy__'] = values
kwargs = {'aggfunc': aggfunc}

table = df.pivot_table('__dummy__', index=rownames, columns=colnames,
table = df.pivot_table(['__dummy__'], index=rownames, columns=colnames,
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Passing array so that pivot_table won't try to remove the extra dummy layers

margins=margins, margins_name=margins_name,
dropna=dropna, **kwargs)

if not table.empty:
added_cols_idx = list(df.columns.difference(common_cols_idx))[0]
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comment here on why you are removing this column

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noted.

table = table[added_cols_idx]

# Post-process
if normalize is not False:
table = _normalize(table, normalize=normalize, margins=margins,
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31 changes: 30 additions & 1 deletion pandas/tests/reshape/test_pivot.py
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Expand Up @@ -1628,7 +1628,8 @@ def test_crosstab_dup_index_names(self):
pytest.raises(ValueError, pd.crosstab, s, s)

@pytest.mark.parametrize("names", [['a', ('b', 'c')],
[('a', 'b'), 'c']])
[('a', 'b'), 'c'],
[('a', 'b'), ('c', 'd')]])
def test_crosstab_tuple_name(self, names):
s1 = pd.Series(range(3), name=names[0])
s2 = pd.Series(range(1, 4), name=names[1])
Expand All @@ -1638,3 +1639,31 @@ def test_crosstab_tuple_name(self, names):

result = pd.crosstab(s1, s2)
tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize("names", [['a', 'b'],
[('a', 'b'), 'c'],
[('a', 'b'), ('c', 'd')],
[(1, 2, 3), ('a', 'b', 'c')]])
def test_crosstab_cols_output(self, names):
rows = [[1, 2, 3, 4], [1, 1, 2, 2], [1, 3, 1, 4]]
cols = [[1, 1, 1, 1], [3, 2, 2, 3], []]

expected_ct1 = pd.DataFrame(
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put the expected IN the parameterize, e.g.

@pytest.mark.parametrize(
"names, expected",
[
([['a', 'b'], pd.DataFrame(......)),
(.....)
(.....)
])

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@ghost ghost Jan 23, 2018 •
edited by ghost
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@jreback This might not be possible as for each "name", there are 3 different expected-s. The only parameter here is "name". Am I missing anything?

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make that a parameter as well if needed
this is just a convenient way of writing out the cases

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@ghost ghost Jan 23, 2018 •
edited by ghost
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Ok. I'll do something like parametrize( "name, expected1, expected2, expected3", [...])

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no

""name,input,expected", [(....), (...)]

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@ghost ghost Jan 23, 2018 •
edited by ghost
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Uhm. Ok. Thanks for the pointer. I'm still new at pytest. Will do that then.

[1, 1, 1, 1],
index=pd.Index([1, 2, 3, 4], name=names[0]),
columns=pd.Index([1], name=names[1])
)
expected_ct2 = pd.DataFrame(
[[1, 1], [1, 1]],
index=pd.Index([1, 2], name=names[0]),
columns=pd.Index([2, 3], name=names[1])
)
expected_ct3 = pd.DataFrame([])
expected_arr = [expected_ct1, expected_ct2, expected_ct3]

for row, col, expected_data in zip(rows, cols, expected_arr):
s1 = pd.Series(row, name=names[0])
s2 = pd.Series(col, name=names[1])
result = pd.crosstab(s1, s2)
tm.assert_frame_equal(result, expected_data,
check_column_type=True)