parameters: (cls)
docstring: Add the operations to the cls; evaluate the doc strings again
docstring_summary: Add the operations to the cls; evaluate the doc strings again
docstring_tokens: Add the operations to the cls ; evaluate the doc strings again
function: def _add_numeric_operations(cls):
          """
          Add the operations to the cls; evaluate the doc strings again
          """
  
          axis_descr, name, name2 = _doc_parms(cls)
  
          cls.any = _make_logical_function(
              cls,
              "any",
              name,
              name2,
              axis_descr,
              _any_desc,
              nanops.nanany,
              _any_see_also,
              _any_examples,
              empty_value=False,
          )
          cls.all = _make_logical_function(
              cls,
              "all",
              name,
              name2,
              axis_descr,
              _all_desc,
              nanops.nanall,
              _all_see_also,
              _all_examples,
              empty_value=True,
          )
  
          @Substitution(
              desc="Return the mean absolute deviation of the values "
              "for the requested axis.",
              name1=name,
              name2=name2,
              axis_descr=axis_descr,
              min_count="",
              see_also="",
              examples="",
          )
          @Appender(_num_doc)
          def mad(self, axis=None, skipna=None, level=None):
              if skipna is None:
                  skipna = True
              if axis is None:
                  axis = self._stat_axis_number
              if level is not None:
                  return self._agg_by_level("mad", axis=axis, level=level, skipna=skipna)
  
              data = self._get_numeric_data()
              if axis == 0:
                  demeaned = data - data.mean(axis=0)
              else:
                  demeaned = data.sub(data.mean(axis=1), axis=0)
              return np.abs(demeaned).mean(axis=axis, skipna=skipna)
  
          cls.mad = mad
  
          cls.sem = _make_stat_function_ddof(
              cls,
              "sem",
              name,
              name2,
              axis_descr,
              "Return unbiased standard error of the mean over requested "
              "axis.\n\nNormalized by N-1 by default. This can be changed "
              "using the ddof argument",
              nanops.nansem,
          )
          cls.var = _make_stat_function_ddof(
              cls,
              "var",
              name,
              name2,
              axis_descr,
              "Return unbiased variance over requested axis.\n\nNormalized by "
              "N-1 by default. This can be changed using the ddof argument",
              nanops.nanvar,
          )
          cls.std = _make_stat_function_ddof(
              cls,
              "std",
              name,
              name2,
              axis_descr,
              "Return sample standard deviation over requested axis."
              "\n\nNormalized by N-1 by default. This can be changed using the "
              "ddof argument",
              nanops.nanstd,
          )
  
          cls.cummin = _make_cum_function(
              cls,
              "cummin",
              name,
              name2,
              axis_descr,
              "minimum",
              np.minimum.accumulate,
              "min",
              np.inf,
              np.nan,
              _cummin_examples,
          )
          cls.cumsum = _make_cum_function(
              cls,
              "cumsum",
              name,
              name2,
              axis_descr,
              "sum",
              np.cumsum,
              "sum",
              0.0,
              np.nan,
              _cumsum_examples,
          )
          cls.cumprod = _make_cum_function(
              cls,
              "cumprod",
              name,
              name2,
              axis_descr,
              "product",
              np.cumprod,
              "prod",
              1.0,
              np.nan,
              _cumprod_examples,
          )
          cls.cummax = _make_cum_function(
              cls,
              "cummax",
              name,
              name2,
              axis_descr,
              "maximum",
              np.maximum.accumulate,
              "max",
              -np.inf,
              np.nan,
              _cummax_examples,
          )
  
          cls.sum = _make_min_count_stat_function(
              cls,
              "sum",
              name,
              name2,
              axis_descr,
              """Return the sum of the values for the requested axis.\n
              This is equivalent to the method ``numpy.sum``.""",
              nanops.nansum,
              _stat_func_see_also,
              _sum_examples,
          )
          cls.mean = _make_stat_function(
              cls,
              "mean",
              name,
              name2,
              axis_descr,
              "Return the mean of the values for the requested axis.",
              nanops.nanmean,
          )
          cls.skew = _make_stat_function(
              cls,
              "skew",
              name,
              name2,
              axis_descr,
              "Return unbiased skew over requested axis.\n\nNormalized by N-1.",
              nanops.nanskew,
          )
          cls.kurt = _make_stat_function(
              cls,
              "kurt",
              name,
              name2,
              axis_descr,
              "Return unbiased kurtosis over requested axis.\n\n"
              "Kurtosis obtained using Fisher's definition of\n"
              "kurtosis (kurtosis of normal == 0.0). Normalized "
              "by N-1.",
              nanops.nankurt,
          )
          cls.kurtosis = cls.kurt
          cls.prod = _make_min_count_stat_function(
              cls,
              "prod",
              name,
              name2,
              axis_descr,
              "Return the product of the values for the requested axis.",
              nanops.nanprod,
              examples=_prod_examples,
          )
          cls.product = cls.prod
          cls.median = _make_stat_function(
              cls,
              "median",
              name,
              name2,
              axis_descr,
              "Return the median of the values for the requested axis.",
              nanops.nanmedian,
          )
          cls.max = _make_stat_function(
              cls,
              "max",
              name,
              name2,
              axis_descr,
              """Return the maximum of the values for the requested axis.\n
              If you want the *index* of the maximum, use ``idxmax``. This is
              the equivalent of the ``numpy.ndarray`` method ``argmax``.""",
              nanops.nanmax,
              _stat_func_see_also,
              _max_examples,
          )
          cls.min = _make_stat_function(
              cls,
              "min",
              name,
              name2,
              axis_descr,
              """Return the minimum of the values for the requested axis.\n
              If you want the *index* of the minimum, use ``idxmin``. This is
              the equivalent of the ``numpy.ndarray`` method ``argmin``.""",
              nanops.nanmin,
              _stat_func_see_also,
              _min_examples,
          )
function_tokens: def _add_numeric_operations ( cls ) : axis_descr , name , name2 = _doc_parms ( cls ) cls . any = _make_logical_function ( cls , "any" , name , name2 , axis_descr , _any_desc , nanops . nanany , _any_see_also , _any_examples , empty_value = False , ) cls . all = _make_logical_function ( cls , "all" , name , name2 , axis_descr , _all_desc , nanops . nanall , _all_see_also , _all_examples , empty_value = True , ) @ Substitution ( desc = "Return the mean absolute deviation of the values " "for the requested axis." , name1 = name , name2 = name2 , axis_descr = axis_descr , min_count = "" , see_also = "" , examples = "" , ) @ Appender ( _num_doc ) def mad ( self , axis = None , skipna = None , level = None ) : if skipna is None : skipna = True if axis is None : axis = self . _stat_axis_number if level is not None : return self . _agg_by_level ( "mad" , axis = axis , level = level , skipna = skipna ) data = self . _get_numeric_data ( ) if axis == 0 : demeaned = data - data . mean ( axis = 0 ) else : demeaned = data . sub ( data . mean ( axis = 1 ) , axis = 0 ) return np . abs ( demeaned ) . mean ( axis = axis , skipna = skipna ) cls . mad = mad cls . sem = _make_stat_function_ddof ( cls , "sem" , name , name2 , axis_descr , "Return unbiased standard error of the mean over requested " "axis.\n\nNormalized by N-1 by default. This can be changed " "using the ddof argument" , nanops . nansem , ) cls . var = _make_stat_function_ddof ( cls , "var" , name , name2 , axis_descr , "Return unbiased variance over requested axis.\n\nNormalized by " "N-1 by default. This can be changed using the ddof argument" , nanops . nanvar , ) cls . std = _make_stat_function_ddof ( cls , "std" , name , name2 , axis_descr , "Return sample standard deviation over requested axis." "\n\nNormalized by N-1 by default. This can be changed using the " "ddof argument" , nanops . nanstd , ) cls . cummin = _make_cum_function ( cls , "cummin" , name , name2 , axis_descr , "minimum" , np . minimum . accumulate , "min" , np . inf , np . nan , _cummin_examples , ) cls . cumsum = _make_cum_function ( cls , "cumsum" , name , name2 , axis_descr , "sum" , np . cumsum , "sum" , 0.0 , np . nan , _cumsum_examples , ) cls . cumprod = _make_cum_function ( cls , "cumprod" , name , name2 , axis_descr , "product" , np . cumprod , "prod" , 1.0 , np . nan , _cumprod_examples , ) cls . cummax = _make_cum_function ( cls , "cummax" , name , name2 , axis_descr , "maximum" , np . maximum . accumulate , "max" , - np . inf , np . nan , _cummax_examples , ) cls . sum = _make_min_count_stat_function ( cls , "sum" , name , name2 , axis_descr , """Return the sum of the values for the requested axis.\n
              This is equivalent to the method ``numpy.sum``.""" , nanops . nansum , _stat_func_see_also , _sum_examples , ) cls . mean = _make_stat_function ( cls , "mean" , name , name2 , axis_descr , "Return the mean of the values for the requested axis." , nanops . nanmean , ) cls . skew = _make_stat_function ( cls , "skew" , name , name2 , axis_descr , "Return unbiased skew over requested axis.\n\nNormalized by N-1." , nanops . nanskew , ) cls . kurt = _make_stat_function ( cls , "kurt" , name , name2 , axis_descr , "Return unbiased kurtosis over requested axis.\n\n" "Kurtosis obtained using Fisher's definition of\n" "kurtosis (kurtosis of normal == 0.0). Normalized " "by N-1." , nanops . nankurt , ) cls . kurtosis = cls . kurt cls . prod = _make_min_count_stat_function ( cls , "prod" , name , name2 , axis_descr , "Return the product of the values for the requested axis." , nanops . nanprod , examples = _prod_examples , ) cls . product = cls . prod cls . median = _make_stat_function ( cls , "median" , name , name2 , axis_descr , "Return the median of the values for the requested axis." , nanops . nanmedian , ) cls . max = _make_stat_function ( cls , "max" , name , name2 , axis_descr , """Return the maximum of the values for the requested axis.\n
              If you want the *index* of the maximum, use ``idxmax``. This is
              the equivalent of the ``numpy.ndarray`` method ``argmax``.""" , nanops . nanmax , _stat_func_see_also , _max_examples , ) cls . min = _make_stat_function ( cls , "min" , name , name2 , axis_descr , """Return the minimum of the values for the requested axis.\n
              If you want the *index* of the minimum, use ``idxmin``. This is
              the equivalent of the ``numpy.ndarray`` method ``argmin``.""" , nanops . nanmin , _stat_func_see_also , _min_examples , )