8000 Pandas pivot_table MultiIndex and dropna=False generates all combinations of modalities instead of keeping existing one only by randolf-scholz · Pull Request #45994 · pandas-dev/pandas · GitHub
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Pandas pivot_table MultiIndex and dropna=False generates all combinations of modalities instead of keeping existing one only #45994

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Pandas pivot_table MultiIndex and dropna=False generates all combinations of modalities instead of keeping existing one only #45994

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b63e452
fix #18030
Feb 15, 2022
b016463
restored behaviour for COLUMNS
Feb 15, 2022
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fix #18030
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Randolf Scholz committed Feb 15, 2022
commit b63e452a54827670a4f4e3fdfa32194c0d977d8f
38 changes: 26 additions & 12 deletions pandas/core/reshape/pivot.py
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Expand Up @@ -203,18 +203,32 @@ def __internal_pivot_table(
to_unstack.append(name)
table = agged.unstack(to_unstack)

if not dropna:
if isinstance(table.index, MultiIndex):
m = MultiIndex.from_arrays(
cartesian_product(table.index.levels), names=table.index.names
)
table = table.reindex(m, axis=0)

if isinstance(table.columns, MultiIndex):
m = MultiIndex.from_arrays(
cartesian_product(table.columns.levels), names=table.columns.names
)
table = table.reindex(m, axis=1)
# BEGIN Issue #18030
# Commenting these lines in view of #18030.
# The cartesian product makes little sense and deprecation of this behaviour should be considered.
# It is unexpected that a pivot operation returns a larger index than is given.
# Secondly, this behaviour was never documented. The documentation says
#
# dropna:bool, default True
#
# Do not include columns whose entries are all NaN.
#
# However the code below creates rows.

# if not dropna:
# if isinstance(table.index, MultiIndex):
# m = MultiIndex.from_arrays(
# cartesian_product(table.index.levels), names=table.index.names
# )
# table = table.reindex(m, axis=0)
#
# if isinstance(table.columns, MultiIndex):
# m = MultiIndex.from_arrays(
# cartesian_product(table.columns.levels), names=table.columns.names
# )
# table = table.reindex(m, axis=1)

# END Issue #18030

if isinstance(table, ABCDataFrame):
table = table.sort_index(axis=1)
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