.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)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.get_group  #

DataFrameGroupBy.  get_group  (  _ name  _ , _ obj  =  None  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L1022-L1127) #

    

Construct DataFrame from group with provided name.

Parameters  :

    

**name** object

    

The name of the group to get as a DataFrame.

**obj** DataFrame, default None

    

The DataFrame to take the DataFrame out of. If it is None, the object groupby
was called on will be used.

Deprecated since version 2.1.0:  The obj is deprecated and will be removed in
a future version. Do ` df.iloc[gb.indices.get(name)]  ` instead of `
gb.get_group(name,  obj=df)  ` .

Returns  :

    

same type as obj

    

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).get_group("a")
    a    1
    a    2
    dtype: int64
    

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"]).get_group((1,))
            a  b  c
    owl     1  2  3
    toucan  1  5  6
    

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').get_group('2023-01-01')
    2023-01-01    1
    2023-01-15    2
    dtype: int64
    

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