docs: def describe_1d(data, **kwargs):
      """Compute summary statistics of a variable (a Series).
  
      The description is different according to the type of the variable.
      However a set of common stats is also computed.
  
      Parameters
      ----------
      series : Series
          The variable to describe.
  
      Returns
      -------
      Series
          The description of the variable as a Series with index being stats keys.
      """
  
      # Replace infinite values with NaNs to avoid issues with
      # histograms later.
      data.replace(to_replace=[np.inf, np.NINF, np.PINF], value=np.nan, inplace=True)
  
      result = pd.Series({}, name=data.name)
  
      vartype = base.get_vartype(data)
  
      if vartype == base.S_TYPE_UNSUPPORTED:
          result = result.append(describe_unsupported(data))
      else:
          result = result.append(describe_supported(data))
  
          if vartype == base.S_TYPE_CONST:
              result = result.append(describe_constant_1d(data))
          elif vartype == base.TYPE_BOOL:
              result = result.append(describe_boolean_1d(data))
          elif vartype == base.TYPE_NUM:
              result = result.append(describe_numeric_1d(data, **kwargs))
          elif vartype == base.TYPE_DATE:
              result = result.append(describe_date_1d(data))
          elif vartype == base.S_TYPE_UNIQUE:
              result = result.append(describe_unique_1d(data))
          else:
              # TYPE_CAT
              result = result.append(describe_categorical_1d(data))
  
      return result