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# pandas.DataFrame.pivot_table

`DataFrame.``pivot_table`(*args, **kwargs)

  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 : a column, Grouper, array which has the same length as data, or list of them. 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 : a column, Grouper, array which has the same length as data, or list of them. 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, default numpy.mean, or list of functions 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 rows : kwarg only alias of index [deprecated] cols : kwarg only alias of columns [deprecated]
  Returns : | table : DataFrame

  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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