pandas.core.groupby.DataFrameGroupBy.indices

property DataFrameGroupBy.indices

Dict {group name -> group indices}.

See also

core.groupby.DataFrameGroupBy.indices

Provides a mapping of group rows to positions of the elements.

core.groupby.SeriesGroupBy.indices

Provides a mapping of group rows to positions of the elements.

core.resample.Resampler.indices

Provides a mapping of group rows to positions of the elements.

Examples

For SeriesGroupBy:

>>> lst = ["a", "a", "b"]
>>> ser = pd.Series([1, 2, 3], index=lst)
>>> ser
a    1
a    2
b    3
dtype: int64
>>> ser.groupby(level=0).indices
{'a': array([0, 1]), 'b': array([2])}

For DataFrameGroupBy:

>>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
>>> df = pd.DataFrame(
...     data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"]
... )
>>> df
        a  b  c
owl     1  2  3
toucan  1  5  6
eagle   7  8  9
>>> df.groupby(by=["a"]).indices
{1: array([0, 1]), 7: array([2])}

For Resampler:

>>> ser = pd.Series(
...     [1, 2, 3, 4],
...     index=pd.DatetimeIndex(
...         ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"]
...     ),
... )
>>> ser
2023-01-01    1
2023-01-15    2
2023-02-01    3
2023-02-15    4
dtype: int64
>>> ser.resample("MS").indices
defaultdict(<class 'list'>, {Timestamp('2023-01-01 00:00:00'): [0, 1],
Timestamp('2023-02-01 00:00:00'): [2, 3]})