pandas.pivot¶

pandas.pivot(data: ‘DataFrame’, index=None, columns=None, values=None) → ’DataFrame’[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

dataDataFrame
indexstr or object, optional: Column to use to make new frame’s index. If None, uses existing index.
columnsstr or object: Column to use to make new frame’s columns.
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.

Changed in version 0.23.0: Also accept list of column names.

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.

Notes

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

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

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

pandas.melt pandas.pivot_table