pandas.DataFrame.pivot#

DataFrame.pivot(*, index=typing.Literal[<no_default>], columns=typing.Literal[<no_default>], values=typing.Literal[<no_default>])[source]#

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
indexstr or object or a list of str, optional

Column to use to make new frame’s index. If None, uses existing index.

Changed in version 1.1.0: Also accept list of index names.

columnsstr or object or a list of str

Column to use to make new frame’s columns.

Changed in version 1.1.0: Also accept list of columns names.

valuesstr, object or a list 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