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FIX: pivot_table dropna=False drops index level names #12327

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FIX: pivot_table dropna=False drops index level names #12327

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e2e18ee
pivot_table dropna=False drops index level names
Winand Feb 15, 2016
b4c58c6
test level names
Winand Feb 15, 2016
472cbd8
Update test_pivot.py
Winand Feb 16, 2016
539e74e
pep8: 79 char.
Winand Feb 16, 2016
5428d28
issue 12133
Winand Feb 16, 2016
8f96971
formatting
Winand Feb 16, 2016
bff8b21
Update v0.18.0.txt
Winand Feb 16, 2016
feb35bb
reference issue
Winand Feb 16, 2016
4b1ac6b
Merge pull request #3 from Winand/temp
Winand Feb 16, 2016
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4 changes: 2 additions & 2 deletions pandas/tools/pivot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,13 +126,13 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',

if not dropna:
try:
m = MultiIndex.from_arrays(cartesian_product(table.index.levels))
m = MultiIndex.from_arrays(cartesian_product(table.index.levels), names=table.index.names)
table = table.reindex_axis(m, axis=0)
except AttributeError:
pass # it's a single level

try:
m = MultiIndex.from_arrays(cartesian_product(table.columns.levels))
m = MultiIndex.from_arrays(cartesian_product(table.columns.levels), names=table.columns.names)
table = table.reindex_axis(m, axis=1)
except AttributeError:
pass # it's a single level or a series
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15 changes: 9 additions & 6 deletions pandas/tools/tests/test_pivot.py
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Expand Up @@ -75,11 +75,10 @@ def test_pivot_table_dropna(self):
'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'},
'quantity': {0: 2000000, 1: 500000,
2: 1000000, 3: 1000000}})
pv_col = df.pivot_table('quantity', 'month', [
'customer', 'product'], dropna=False)
pv_ind = df.pivot_table(
'quantity', ['customer', 'product'], 'month', dropna=False)

names1, names2 = ['month'], ['customer', 'product']
pv_col = df.pivot_table('quantity', names1, names2, dropna=False)
pv_ind = df.pivot_table('quantity', names2, names1, dropna=False)

m = MultiIndex.from_tuples([(u('A'), u('a')),
(u('A'), u('b')),
(u('A'), u('c')),
Expand All @@ -92,7 +91,11 @@ def test_pivot_table_dropna(self):
(u('C'), u('b')),
(u('C'), u('c')),
(u('C'), u('d'))])


assert_equal(pv_col.index.names, names1)
assert_equal(pv_ind.columns.names, names1)
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construct an expected frame, and use assert_frame_equal

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@jreback do you mean i need to fully rewrite test_pivot_table_dropna or just make a separate DataFrame for this case?

assert_equal(pv_col.columns.names, names2)
assert_equal(pv_ind.index.names, names2)
assert_equal(pv_col.columns.values, m.values)
assert_equal(pv_ind.index.values, m.values)

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