parameters: (
          self, q=0.5, interpolation="linear", exact=True, quant_index=True
      )
return_statement: return Series(result, index=index, name=self.name)
docstring: Return values at the given quantile.
  
          Parameters
          ----------
  
          q : float or array-like, default 0.5 (50% quantile)
              0 <= q <= 1, the quantile(s) to compute
          interpolation : {’linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}
              This optional parameter specifies the interpolation method to use,
              when the desired quantile lies between two data points i and j:
          columns : list of str
              List of column names to include.
          exact : boolean
              Whether to use approximate or exact quantile algorithm.
          quant_index : boolean
              Whether to use the list of quantiles as index.
  
          Returns
          -------
          float or Series
              If ``q`` is an array, a Series will be returned where the
              index is ``q`` and the values are the quantiles, otherwise
              a float will be returned.
  
          Examples
          --------
          >>> import cudf
          >>> series = cudf.Series([1, 2, 3, 4])
          >>> series
          0    1
          1    2
          2    3
          3    4
          dtype: int64
          >>> series.quantile(0.5)
          2.5
          >>> series.quantile([0.25, 0.5, 0.75])
          0.25    1.75
          0.50    2.50
          0.75    3.25
          dtype: float64
docstring_summary: Return values at the given quantile.
docstring_tokens: Return values at the given quantile .
function: def quantile(
          self, q=0.5, interpolation="linear", exact=True, quant_index=True
      ):
          """
          Return values at the given quantile.
  
          Parameters
          ----------
  
          q : float or array-like, default 0.5 (50% quantile)
              0 <= q <= 1, the quantile(s) to compute
          interpolation : {’linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}
              This optional parameter specifies the interpolation method to use,
              when the desired quantile lies between two data points i and j:
          columns : list of str
              List of column names to include.
          exact : boolean
              Whether to use approximate or exact quantile algorithm.
          quant_index : boolean
              Whether to use the list of quantiles as index.
  
          Returns
          -------
          float or Series
              If ``q`` is an array, a Series will be returned where the
              index is ``q`` and the values are the quantiles, otherwise
              a float will be returned.
  
          Examples
          --------
          >>> import cudf
          >>> series = cudf.Series([1, 2, 3, 4])
          >>> series
          0    1
          1    2
          2    3
          3    4
          dtype: int64
          >>> series.quantile(0.5)
          2.5
          >>> series.quantile([0.25, 0.5, 0.75])
          0.25    1.75
          0.50    2.50
          0.75    3.25
          dtype: float64
          """
  
          result = self._column.quantile(q, interpolation, exact)
  
          if isinstance(q, Number):
              return result
  
          if quant_index:
              index = np.asarray(q)
              if len(self) == 0:
                  result = column_empty_like(
                      index, dtype=self.dtype, masked=True, newsize=len(index),
                  )
          else:
              index = None
  
          return Series(result, index=index, name=self.name)
function_tokens: def quantile ( self , q = 0.5 , interpolation = "linear" , exact = True , quant_index = True ) : result = self . _column . quantile ( q , interpolation , exact ) if isinstance ( q , Number ) : return result if quant_index : index = np . asarray ( q ) if len ( self ) == 0 : result = column_empty_like ( index , dtype = self . dtype , masked = True , newsize = len ( index ) , ) else : index = None return Series ( result , index = index , name = self . name )