parameters: (a, axis=0, ddof=0, bias=True)
return_statement: return DescribeResult(n, mm, m, v, sk, kurt)
docstring: Computes several descriptive statistics of the passed array.
  
      Parameters
      ----------
      a : array_like
          Data array
      axis : int or None, optional
          Axis along which to calculate statistics. Default 0. If None,
          compute over the whole array `a`.
      ddof : int, optional
          degree of freedom (default 0); note that default ddof is different
          from the same routine in stats.describe
      bias : bool, optional
          If False, then the skewness and kurtosis calculations are corrected for
          statistical bias.
  
      Returns
      -------
      nobs : int
          (size of the data (discarding missing values)
  
      minmax : (int, int)
          min, max
  
      mean : float
          arithmetic mean
  
      variance : float
          unbiased variance
  
      skewness : float
          biased skewness
  
      kurtosis : float
          biased kurtosis
  
      Examples
      --------
      >>> from scipy.stats.mstats import describe
      >>> ma = np.ma.array(range(6), mask=[0, 0, 0, 1, 1, 1])
      >>> describe(ma)
      DescribeResult(nobs=3, minmax=(masked_array(data=0,
                   mask=False,
             fill_value=999999), masked_array(data=2,
                   mask=False,
             fill_value=999999)), mean=1.0, variance=0.6666666666666666,
             skewness=masked_array(data=0., mask=False, fill_value=1e+20),
              kurtosis=-1.5)
docstring_summary: Computes several descriptive statistics of the passed array.
docstring_tokens: Computes several descriptive statistics of the passed array .
function: def describe(a, axis=0, ddof=0, bias=True):
      """
      Computes several descriptive statistics of the passed array.
  
      Parameters
      ----------
      a : array_like
          Data array
      axis : int or None, optional
          Axis along which to calculate statistics. Default 0. If None,
          compute over the whole array `a`.
      ddof : int, optional
          degree of freedom (default 0); note that default ddof is different
          from the same routine in stats.describe
      bias : bool, optional
          If False, then the skewness and kurtosis calculations are corrected for
          statistical bias.
  
      Returns
      -------
      nobs : int
          (size of the data (discarding missing values)
  
      minmax : (int, int)
          min, max
  
      mean : float
          arithmetic mean
  
      variance : float
          unbiased variance
  
      skewness : float
          biased skewness
  
      kurtosis : float
          biased kurtosis
  
      Examples
      --------
      >>> from scipy.stats.mstats import describe
      >>> ma = np.ma.array(range(6), mask=[0, 0, 0, 1, 1, 1])
      >>> describe(ma)
      DescribeResult(nobs=3, minmax=(masked_array(data=0,
                   mask=False,
             fill_value=999999), masked_array(data=2,
                   mask=False,
             fill_value=999999)), mean=1.0, variance=0.6666666666666666,
             skewness=masked_array(data=0., mask=False, fill_value=1e+20),
              kurtosis=-1.5)
  
      """
      a, axis = _chk_asarray(a, axis)
      n = a.count(axis)
      mm = (ma.minimum.reduce(a), ma.maximum.reduce(a))
      m = a.mean(axis)
      v = a.var(axis, ddof=ddof)
      sk = skew(a, axis, bias=bias)
      kurt = kurtosis(a, axis, bias=bias)
  
      return DescribeResult(n, mm, m, v, sk, kurt)
function_tokens: def describe ( a , axis = 0 , ddof = 0 , bias = True ) : a , axis = _chk_asarray ( a , axis ) n = a . count ( axis ) mm = ( ma . minimum . reduce ( a ) , ma . maximum . reduce ( a ) ) m = a . mean ( axis ) v = a . var ( axis , ddof = ddof ) sk = skew ( a , axis , bias = bias ) kurt = kurtosis ( a , axis , bias = bias ) return DescribeResult ( n , mm , m , v , sk , kurt )