parameters: (self, include=None, exclude=None)
return_statement: return res
docstring: Generate descriptive statistics that summarizes the central tendency,
          dispersion and shape of a dataset’s distribution, excluding NaN values.
  
          Analyzes numeric DataFrames only
  
          Parameters
          ----------
          include: ‘all’, list-like of dtypes or None (default), optional
              list of data types to include in the result.
              Ignored for Series.
  
          exclude: list-like of dtypes or None (default), optional,
              list of data types to omit from the result.
              Ignored for Series.
  
          Returns
          -------
          Series or DataFrame
              Summary statistics of the Dataframe provided.
  
          Examples
          --------
          >>> import cudf
          >>> gdf = cudf.DataFrame({"Speed": [380.0, 370.0, 24.0, 26.0],
                                    "Score": [50, 30, 90, 80]})
          >>> gdf
          Speed  Score
          0  380.0     50
          1  370.0     30
          2   24.0     90
          3   26.0     80
          >>> gdf.groupby('Score').describe()
               Speed
               count   mean   std    min    25%    50%    75%     max
          Score
          30        1  370.0  <NA>  370.0  370.0  370.0  370.0  370.0
          50        1  380.0  <NA>  380.0  380.0  380.0  380.0  380.0
          80        1   26.0  <NA>   26.0   26.0   26.0   26.0   26.0
          90        1   24.0  <NA>   24.0   24.0   24.0   24.0   24.0
docstring_summary: Generate descriptive statistics that summarizes the central tendency,
          dispersion and shape of a dataset’s distribution, excluding NaN values.
docstring_tokens: Generate descriptive statistics that summarizes the central tendency dispersion and shape of a dataset’s distribution excluding NaN values .
function: def describe(self, include=None, exclude=None):
          """
          Generate descriptive statistics that summarizes the central tendency,
          dispersion and shape of a dataset’s distribution, excluding NaN values.
  
          Analyzes numeric DataFrames only
  
          Parameters
          ----------
          include: ‘all’, list-like of dtypes or None (default), optional
              list of data types to include in the result.
              Ignored for Series.
  
          exclude: list-like of dtypes or None (default), optional,
              list of data types to omit from the result.
              Ignored for Series.
  
          Returns
          -------
          Series or DataFrame
              Summary statistics of the Dataframe provided.
  
          Examples
          --------
          >>> import cudf
          >>> gdf = cudf.DataFrame({"Speed": [380.0, 370.0, 24.0, 26.0],
                                    "Score": [50, 30, 90, 80]})
          >>> gdf
          Speed  Score
          0  380.0     50
          1  370.0     30
          2   24.0     90
          3   26.0     80
          >>> gdf.groupby('Score').describe()
               Speed
               count   mean   std    min    25%    50%    75%     max
          Score
          30        1  370.0  <NA>  370.0  370.0  370.0  370.0  370.0
          50        1  380.0  <NA>  380.0  380.0  380.0  380.0  380.0
          80        1   26.0  <NA>   26.0   26.0   26.0   26.0   26.0
          90        1   24.0  <NA>   24.0   24.0   24.0   24.0   24.0
  
          """
          if exclude is not None and include is not None:
              raise NotImplementedError
  
          res = self.agg(
              [
                  "count",
                  "mean",
                  "std",
                  "min",
                  _quantile_25,
                  _quantile_50,
                  _quantile_75,
                  "max",
              ]
          )
          res.rename(
              columns={
                  "_quantile_25": "25%",
                  "_quantile_50": "50%",
                  "_quantile_75": "75%",
              },
              level=1,
              inplace=True,
          )
          return res
function_tokens: def describe ( self , include = None , exclude = None ) : if exclude is not None and include is not None : raise NotImplementedError res = self . agg ( [ "count" , "mean" , "std" , "min" , _quantile_25 , _quantile_50 , _quantile_75 , "max" , ] ) res . rename ( columns = { "_quantile_25" : "25%" , "_quantile_50" : "50%" , "_quantile_75" : "75%" , } , level = 1 , inplace = True , ) return res