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DOC: correct DataFrame.pivot docstring #14430

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Oct 22, 2016
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DOC: correct DataFrame.pivot docstring #14430

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48 changes: 30 additions & 18 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3868,9 +3868,8 @@ def last_valid_index(self):
def pivot(self, index=None, columns=None, values=None):
"""
Reshape data (produce a "pivot" table) based on column values. Uses
unique values from index / columns to form axes and return either
DataFrame or Panel, depending on whether you request a single value
column (DataFrame) or all columns (Panel)
unique values from index / columns to form axes of the resulting
DataFrame.

Parameters
----------
Expand All @@ -3880,7 +3879,20 @@ def pivot(self, index=None, columns=None, values=None):
columns : string or object
Column name to use to make new frame's columns
values : string or object, optional
Column name to use for populating new frame's values
Column name to use for populating new frame's values. If not
specified, all remaining columns will be used and the result will
have hierarchically indexed columns

Returns
-------
pivoted : DataFrame

See also
--------
DataFrame.pivot_table : generalization of pivot that can handle
duplicate values for one index/column pair
DataFrame.unstack : pivot based on the index values instead of a
column

Notes
-----
Expand All @@ -3889,30 +3901,30 @@ def pivot(self, index=None, columns=None, values=None):

Examples
--------

>>> df = pd.DataFrame({'foo': ['one','one','one','two','two','two'],
'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
'baz': [1, 2, 3, 4, 5, 6]})
>>> df
foo bar baz
0 one A 1.
1 one B 2.
2 one C 3.
3 two A 4.
4 two B 5.
5 two C 6.

>>> df.pivot('foo', 'bar', 'baz')
0 one A 1
1 one B 2
2 one C 3
3 two A 4
4 two B 5
5 two C 6

>>> df.pivot(index='foo', columns='bar', values='baz')
A B C
one 1 2 3
two 4 5 6

>>> df.pivot('foo', 'bar')['baz']
>>> df.pivot(index='foo', columns='bar')['baz']
A B C
one 1 2 3
two 4 5 6

Returns
-------
pivoted : DataFrame
If no values column specified, will have hierarchically indexed
columns

"""
from pandas.core.reshape import pivot
return pivot(self, index=index, columns=columns, values=values)
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