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Classe « DataFrame »

Méthode pandas.DataFrame.explode

Signature de la méthode explode

def explode(self, column: 'IndexLabel', ignore_index: 'bool' = False) -> 'DataFrame' 

Description

help(DataFrame.explode)

Transform each element of a list-like to a row, replicating index values.

Parameters
----------
column : IndexLabel
    Column(s) to explode.
    For multiple columns, specify a non-empty list with each element
    be str or tuple, and all specified columns their list-like data
    on same row of the frame must have matching length.

    .. versionadded:: 1.3.0
        Multi-column explode

ignore_index : bool, default False
    If True, the resulting index will be labeled 0, 1, ..., n - 1.

Returns
-------
DataFrame
    Exploded lists to rows of the subset columns;
    index will be duplicated for these rows.

Raises
------
ValueError :
    * If columns of the frame are not unique.
    * If specified columns to explode is empty list.
    * If specified columns to explode have not matching count of
      elements rowwise in the frame.

See Also
--------
DataFrame.unstack : Pivot a level of the (necessarily hierarchical)
    index labels.
DataFrame.melt : Unpivot a DataFrame from wide format to long format.
Series.explode : Explode a DataFrame from list-like columns to long format.

Notes
-----
This routine will explode list-likes including lists, tuples, sets,
Series, and np.ndarray. The result dtype of the subset rows will
be object. Scalars will be returned unchanged, and empty list-likes will
result in a np.nan for that row. In addition, the ordering of rows in the
output will be non-deterministic when exploding sets.

Reference :ref:`the user guide <reshaping.explode>` for more examples.

Examples
--------
>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
...                    'B': 1,
...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode('A')
     A  B          C
0    0  1  [a, b, c]
0    1  1  [a, b, c]
0    2  1  [a, b, c]
1  foo  1        NaN
2  NaN  1         []
3    3  1     [d, e]
3    4  1     [d, e]

Multi-column explode.

>>> df.explode(list('AC'))
     A  B    C
0    0  1    a
0    1  1    b
0    2  1    c
1  foo  1  NaN
2  NaN  1  NaN
3    3  1    d
3    4  1    e

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