pandas.DataFrame.explode¶
- DataFrame.explode(column, ignore_index=False)[source]¶
Transform each element of a list-like to a row, replicating index values.
New in version 0.25.0.
- Parameters
- columnIndexLabel
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
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.unstackPivot a level of the (necessarily hierarchical) index labels.
DataFrame.meltUnpivot a DataFrame from wide format to long format.
Series.explodeExplode 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