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move pivot_table doc-string to DataFrame #17174

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Merged
merged 8 commits into from
Aug 11, 2017
Merged

move pivot_table doc-string to DataFrame #17174

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773c5b7
Define pivot_table in DataFrame instead of
jbrockmendel Aug 4, 2017
23b1cb1
Fix NameError caused by referencing DataFrame incorrectly;
jbrockmendel Aug 4, 2017
082715e
Fix import from wrong module
jbrockmendel Aug 6, 2017
3d6f3de
Fix broken test by checking isinstance(by, Index) instead of ABCIndex
jbrockmendel Aug 6, 2017
fce2a38
Move pivot_table.__doc__ into _shared_docs
jbrockmendel Aug 7, 2017
fd17936
Add pivot_table to _shared_docs in frame,
jbrockmendel Aug 7, 2017
bf0f4fd
Fix missing import if pivot_table
jbrockmendel Aug 7, 2017
ea334c5
Move import of _shared_docs to end of reshape.pivot imports
jbrockmendel Aug 9, 2017
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84 changes: 84 additions & 0 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4146,6 +4146,90 @@ def pivot(self, index=None, columns=None, values=None):
from pandas.core.reshape.reshape import pivot
return pivot(self, index=index, columns=columns, values=values)

def pivot_table(self, values=None, index=None, columns=None,
aggfunc='mean', fill_value=None, margins=False,
dropna=True, margins_name='All'):
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in
the pivot table will be stored in MultiIndex objects (hierarchical
indexes) on the index and columns of the result DataFrame

Parameters
----------
data : DataFrame
values : column to aggregate, optional
index : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table index. If an array is passed,
it is being used as the same manner as column values.
columns : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table column. If an array is passed,
it is being used as the same manner as column values.
aggfunc : function or list of functions, default numpy.mean
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names
(inferred from the function objects themselves)
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.

Examples
--------
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
... "bar", "bar", "bar", "bar"],
... "B": ["one", "one", "one", "two", "two",
... "one", "one", "two", "two"],
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7]})
>>> df
A B C D
0 foo one small 1
1 foo one large 2
2 foo one large 2
3 foo two small 3
4 foo two small 3
5 bar one large 4
6 bar one small 5
7 bar two small 6
8 bar two large 7

>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
... # doctest: +NORMALIZE_WHITESPACE
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0

Returns
-------
table : DataFrame

See also
--------
DataFrame.pivot : pivot without aggregation that can handle
non-numeric data
"""
from pandas.core.reshape.reshape import pivot_table
return pivot_table(self, values=values, index=index, columns=columns,
aggfunc=aggfunc, fill_value=fill_value,
margins=margins, dropna=dropna,
margins_name=margins_name)

def stack(self, level=-1, dropna=True):
"""
Pivot a level of the (possibly hierarchical) column labels, returning a
Expand Down
79 changes: 1 addition & 78 deletions pandas/core/reshape/pivot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,81 +16,6 @@
def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
fill_value=None, margins=False, dropna=True,
margins_name='All'):
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in the
pivot table will be stored in MultiIndex objects (hierarchical indexes) on
the index and columns of the result DataFrame

Parameters
----------
data : DataFrame
values : column to aggregate, optional
index : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The list
can contain any of the other types (except list).
Keys to group by on the pivot table index. If an array is passed, it
is being used as the same manner as column values.
columns : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The list
can contain any of the other types (except list).
Keys to group by on the pivot table column. If an array is passed, it
is being used as the same manner as column values.
aggfunc : function or list of functions, default numpy.mean
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names (inferred
from the function objects themselves)
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.

Examples
--------
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
... "bar", "bar", "bar", "bar"],
... "B": ["one", "one", "one", "two", "two",
... "one", "one", "two", "two"],
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7]})
>>> df
A B C D
0 foo one small 1
1 foo one large 2
2 foo one large 2
3 foo two small 3
4 foo two small 3
5 bar one large 4
6 bar one small 5
7 bar two small 6
8 bar two large 7

>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
... # doctest: +NORMALIZE_WHITESPACE
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0

Returns
-------
table : DataFrame

See also
--------
DataFrame.pivot : pivot without aggregation that can handle
non-numeric data
"""
index = _convert_by(index)
columns = _convert_by(columns)

Expand Down Expand Up @@ -202,9 +127,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
table = table.dropna(how='all', axis=1)

return table


DataFrame.pivot_table = pivot_table
pivot_table.__doc__ = DataFrame.pivot_table.__doc__


def _add_margins(table, data, values, rows, cols, aggfunc,
Expand Down