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

#  pandas.core.groupby.SeriesGroupBy.quantile  #

SeriesGroupBy.  quantile  (  _ q  =  0.5  _ , _ interpolation  =  'linear'  _
, _ numeric_only  =  False  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L4337-L4563) #

    

Return group values at the given quantile, a la numpy.percentile.

Parameters  :

    

**q** float or array-like, default 0.5 (50% quantile)

    

Value(s) between 0 and 1 providing the quantile(s) to compute.

**interpolation** {âlinearâ, âlowerâ, âhigherâ, âmidpointâ,
ânearestâ}

    

Method to use when the desired quantile falls between two points.

**numeric_only** bool, default False

    

Include only  float  ,  int  or  boolean  data.

New in version 1.5.0.

Changed in version 2.0.0:  numeric_only now defaults to ` False  ` .

Returns  :

    

Series or DataFrame

    

Return type determined by caller of GroupBy object.

See also

` Series.quantile  `

    

Similar method for Series.

` DataFrame.quantile  `

    

Similar method for DataFrame.

[ ` numpy.percentile  `
](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html#numpy.percentile
"\(in NumPy v1.26\)")

    

NumPy method to compute qth percentile.

Examples

    
    
    >>> df = pd.DataFrame([
    ...     ['a', 1], ['a', 2], ['a', 3],
    ...     ['b', 1], ['b', 3], ['b', 5]
    ... ], columns=['key', 'val'])
    >>> df.groupby('key').quantile()
        val
    key
    a    2.0
    b    3.0
    

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  * ` SeriesGroupBy.quantile()  `

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

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