# pandas.DataFrame.pivot_table

DataFrame.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True)

  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
    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, list of functions, dict, 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) If dict is passed, the key is column to aggregate and value is function or list of functions.

    fill_value scalar, default None

      Value to replace missing values with (in the resulting pivot table, after aggregation).

    margins bool, default False

      Add all row / columns (e.g. for subtotal / grand totals).

    dropna bool, default True

      Do not include columns whose entries are all NaN.

    margins_name str, default ‘All’

      Name of the row / column that will contain the totals when margins is True.

    observed bool, default False

      This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

      Changed in version 0.25.0.

    sort bool, default True

      Specifies if the result should be sorted.

      New in version 1.3.0.

  Returns
    DataFrame

      An Excel style pivot table.

  See also

  `DataFrame.pivot`

    Pivot without aggregation that can handle non-numeric data.

  `DataFrame.melt`

    Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

  `wide_to_long`

    Wide panel to long format. Less flexible but more user-friendly than melt.

  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],
      ...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
      >>> df
           A    B      C  D  E
      0  foo  one  small  1  2
      1  foo  one  large  2  4
      2  foo  one  large  2  5
      3  foo  two  small  3  5
      4  foo  two  small  3  6
      5  bar  one  large  4  6
      6  bar  one  small  5  8
      7  bar  two  small  6  9
      8  bar  two  large  7  9

  This first example aggregates values by taking the sum.

      >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
      ...                     columns=['C'], aggfunc=np.sum)
      >>> table
      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

  We can also fill missing values using the fill_value parameter.

      >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
      ...                     columns=['C'], aggfunc=np.sum, fill_value=0)
      >>> table
      C        large  small
      A   B
      bar one      4      5
          two      7      6
      foo one      4      1
          two      0      6

  The next example aggregates by taking the mean across multiple columns.

      >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
      ...                     aggfunc={'D': np.mean,
      ...                              'E': np.mean})
      >>> table
                      D         E
      A   C
      bar large  5.500000  7.500000
          small  5.500000  8.500000
      foo large  2.000000  4.500000
          small  2.333333  4.333333

  We can also calculate multiple types of aggregations for any given value column.

      >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
      ...                     aggfunc={'D': np.mean,
      ...                              'E': [min, max, np.mean]})
      >>> table
                      D    E
                  mean  max      mean  min
      A   C
      bar large  5.500000  9.0  7.500000  6.0
          small  5.500000  9.0  8.500000  8.0
      foo large  2.000000  5.0  4.500000  4.0
          small  2.333333  6.0  4.333333  2.0