oupby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
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    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
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    * [ 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)
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  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.head  #

DataFrameGroupBy.  head  (  _ n  =  5  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L5461-L5497) #

    

Return first n rows of each group.

Similar to ` .apply(lambda  x:  x.head(n))  ` , but it returns a subset of
rows from the original DataFrame with original index and order preserved ( `
as_index  ` flag is ignored).

Parameters  :

    

**n** int

    

If positive: number of entries to include from start of each group. If
negative: number of entries to exclude from end of each group.

Returns  :

    

Series or DataFrame

    

Subset of original Series or DataFrame as determined by n.

See also

` Series.groupby  `

    

Apply a function groupby to a Series.

` DataFrame.groupby  `

    

Apply a function groupby to each row or column of a DataFrame.

Examples

    
    
    >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
    ...                   columns=['A', 'B'])
    >>> df.groupby('A').head(1)
       A  B
    0  1  2
    2  5  6
    >>> df.groupby('A').head(-1)
       A  B
    0  1  2
    

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  * ` DataFrameGroupBy.head()  `

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](../../_sources/reference/api/pandas.core.groupby.DataFrameGroupBy.head.rst.txt)

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