by.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
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    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
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    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
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  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.indices  #

_ property  _ DataFrameGroupBy.  indices  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L858-L906) #

    

Dict {group name -> group indices}.

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]})
    

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