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# pandas.pivot

pandas.pivot(data, *, columns, index=<no_default>, values=<no_default>)

  Return reshaped DataFrame organized by given index / column values.

  Reshape data (produce a “pivot” table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the User Guide for more on reshaping.

  Parameters:
    data DataFrame

      Input pandas DataFrame object.

    columns Hashable or a sequence of the previous

      Column to use to make new frame’s columns.

    index Hashable or a sequence of the previous, optional

      Column to use to make new frame’s index. If not given, uses existing index.

    values Hashable or a sequence of the previous, optional

      Column(s) 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:
    DataFrame

      Returns reshaped DataFrame.

  Raises:
    ValueError:

      When there are any index, columns combinations with multiple values. DataFrame.pivot_table when you need to aggregate.

  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.

  `wide_to_long`

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

  Notes

  For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

  Reference the user guide for more examples.

  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],
      ...         "zoo": ["x", "y", "z", "q", "w", "t"],
      ...     }
      ... )
      >>> df
          foo   bar  baz  zoo
      0   one   A    1    x
      1   one   B    2    y
      2   one   C    3    z
      3   two   A    4    q
      4   two   B    5    w
      5   two   C    6    t

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

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

      >>> df.pivot(index="foo", columns="bar", values=["baz", "zoo"])
            baz       zoo
      bar   A  B  C   A  B  C
      foo
      one   1  2  3   x  y  z
      two   4  5  6   q  w  t

  You could also assign a list of column names or a list of index names.

      >>> df = pd.DataFrame(
      ...     {
      ...         "lev1": [1, 1, 1, 2, 2, 2],
      ...         "lev2": [1, 1, 2, 1, 1, 2],
      ...         "lev3": [1, 2, 1, 2, 1, 2],
      ...         "lev4": [1, 2, 3, 4, 5, 6],
      ...         "values": [0, 1, 2, 3, 4, 5],
      ...     }
      ... )
      >>> df
          lev1 lev2 lev3 lev4 values
      0   1    1    1    1    0
      1   1    1    2    2    1
      2   1    2    1    3    2
      3   2    1    2    4    3
      4   2    1    1    5    4
      5   2    2    2    6    5

      >>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
      lev2    1         2
      lev3    1    2    1    2
      lev1
      1     0.0  1.0  2.0  NaN
      2     4.0  3.0  NaN  5.0

      >>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
            lev3    1    2
      lev1  lev2
         1     1  0.0  1.0
               2  2.0  NaN
         2     1  4.0  3.0
               2  NaN  5.0

  A ValueError is raised if there are any duplicates.

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

  Notice that the first two rows are the same for our index and columns arguments.

      >>> df.pivot(index="foo", columns="bar", values="baz")
      Traceback (most recent call last):
         ...
      ValueError: Index contains duplicate entries, cannot reshape