oupby.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.SeriesGroupBy.nunique  #

SeriesGroupBy.  nunique  (  _ dropna  =  True  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L635-L709) #

    

Return number of unique elements in the group.

Returns  :

    

Series

    

Number of unique values within each group.

Examples

For SeriesGroupby:

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

For Resampler:

    
    
    >>> ser = pd.Series([1, 2, 3, 3], 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    3
    dtype: int64
    >>> ser.resample('MS').nunique()
    2023-01-01    2
    2023-02-01    1
    Freq: MS, dtype: int64
    

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  * ` SeriesGroupBy.nunique()  `

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](../../_sources/reference/api/pandas.core.groupby.SeriesGroupBy.nunique.rst.txt)

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