y.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.__iter__  #

DataFrameGroupBy.  __iter__  (  )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L1129-L1218) #

    

Groupby iterator.

Returns  :

    

Generator yielding sequence of (name, subsetted object)

    
for each group

    

Examples

For SeriesGroupBy:

    
    
    >>> lst = ['a', 'a', 'b']
    >>> ser = pd.Series([1, 2, 3], index=lst)
    >>> ser
    a    1
    a    2
    b    3
    dtype: int64
    >>> for x, y in ser.groupby(level=0):
    ...     print(f'{x}\n{y}\n')
    a
    a    1
    a    2
    dtype: int64
    b
    b    3
    dtype: int64
    

For DataFrameGroupBy:

    
    
    >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
    >>> df = pd.DataFrame(data, columns=["a", "b", "c"])
    >>> df
       a  b  c
    0  1  2  3
    1  1  5  6
    2  7  8  9
    >>> for x, y in df.groupby(by=["a"]):
    ...     print(f'{x}\n{y}\n')
    (1,)
       a  b  c
    0  1  2  3
    1  1  5  6
    (7,)
       a  b  c
    2  7  8  9
    

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
    >>> for x, y in ser.resample('MS'):
    ...     print(f'{x}\n{y}\n')
    2023-01-01 00:00:00
    2023-01-01    1
    2023-01-15    2
    dtype: int64
    2023-02-01 00:00:00
    2023-02-01    3
    2023-02-15    4
    dtype: int64
    

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pandas.core.groupby.SeriesGroupBy.__iter__  __
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__ On this page

  * ` DataFrameGroupBy.__iter__()  `

[ __ Show Source
](../../_sources/reference/api/pandas.core.groupby.DataFrameGroupBy.__iter__.rst.txt)

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