dask.dataframe.DataFrame.explode

DataFrame.explode(column)[source]

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

This docstring was copied from pandas.core.frame.DataFrame.explode.

Some inconsistencies with the Dask version may exist.

New in version 0.25.0.

Parameters
columnstr or tuple or list thereof

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.

New in version 1.3.0: Multi-column explode

ignore_indexbool, default False (Not supported in Dask)

If True, the resulting index will be labeled 0, 1, …, n - 1.

New in version 1.1.0.

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.

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