# pandas.pivot_table¶

pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')[source]

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. table : DataFrame

See also

DataFrame.pivot
pivot without aggregation that can handle non-numeric data

Examples

>>> 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
small  large
foo  one  1      4
two  6      NaN
bar  one  5      4
two  6      7

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