pandas.DataFrame.explode#

DataFrame.explode(column,ignore_index=False)[source]#

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

Parameters:

columnIndexLabel

Column(s) to explode.For multiple columns, specify a non-empty list with each elementbe str or tuple, and all specified columns their list-like dataon same row of the frame must have matching length.

Added in version 1.3.0:Multi-column explode

ignore_indexbool, 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 ofelements 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 willbe object. Scalars will be returned unchanged, and empty list-likes willresult in a np.nan for that row. In addition, the ordering of rows in theoutput will be non-deterministic when exploding sets.

Referencethe user guide 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          C0  [0, 1, 2]  1  [a, b, c]1        foo  1        NaN2         []  1         []3     [3, 4]  1     [d, e]

Single-column explode.

>>>df.explode('A')     A  B          C0    0  1  [a, b, c]0    1  1  [a, b, c]0    2  1  [a, b, c]1  foo  1        NaN2  NaN  1         []3    3  1     [d, e]3    4  1     [d, e]

Multi-column explode.

>>>df.explode(list('AC'))     A  B    C0    0  1    a0    1  1    b0    2  1    c1  foo  1  NaN2  NaN  1  NaN3    3  1    d3    4  1    e