parameters: (a, axis=0, ddof=1, bias=True, nan_policy='propagate')
return_statement: return DescribeResult(n, mm, m, v, sk, kurt)
docstring: Compute several descriptive statistics of the passed array.
  
      Parameters
      ----------
      a : array_like
         Input data.
      axis : int or None, optional
         Axis along which statistics are calculated. Default is 0.
         If None, compute over the whole array `a`.
      ddof : int, optional
          Delta degrees of freedom (only for variance).  Default is 1.
      bias : bool, optional
          If False, then the skewness and kurtosis calculations are corrected for
          statistical bias.
      nan_policy : {'propagate', 'raise', 'omit'}, optional
          Defines how to handle when input contains nan. 'propagate' returns nan,
          'raise' throws an error, 'omit' performs the calculations ignoring nan
          values. Default is 'propagate'.
  
      Returns
      -------
      nobs : int or ndarray of ints
         Number of observations (length of data along `axis`).
         When 'omit' is chosen as nan_policy, each column is counted separately.
      minmax: tuple of ndarrays or floats
         Minimum and maximum value of data array.
      mean : ndarray or float
         Arithmetic mean of data along axis.
      variance : ndarray or float
         Unbiased variance of the data along axis, denominator is number of
         observations minus one.
      skewness : ndarray or float
         Skewness, based on moment calculations with denominator equal to
         the number of observations, i.e. no degrees of freedom correction.
      kurtosis : ndarray or float
         Kurtosis (Fisher).  The kurtosis is normalized so that it is
         zero for the normal distribution.  No degrees of freedom are used.
  
      See Also
      --------
      skew, kurtosis
  
      Examples
      --------
      >>> from scipy import stats
      >>> a = np.arange(10)
      >>> stats.describe(a)
      DescribeResult(nobs=10, minmax=(0, 9), mean=4.5, variance=9.166666666666666,
                     skewness=0.0, kurtosis=-1.2242424242424244)
      >>> b = [[1, 2], [3, 4]]
      >>> stats.describe(b)
      DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, 4])),
                     mean=array([2., 3.]), variance=array([2., 2.]),
                     skewness=array([0., 0.]), kurtosis=array([-2., -2.]))
docstring_summary: Compute several descriptive statistics of the passed array.
docstring_tokens: Compute several descriptive statistics of the passed array .
function: def describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate'):
      """
      Compute several descriptive statistics of the passed array.
  
      Parameters
      ----------
      a : array_like
         Input data.
      axis : int or None, optional
         Axis along which statistics are calculated. Default is 0.
         If None, compute over the whole array `a`.
      ddof : int, optional
          Delta degrees of freedom (only for variance).  Default is 1.
      bias : bool, optional
          If False, then the skewness and kurtosis calculations are corrected for
          statistical bias.
      nan_policy : {'propagate', 'raise', 'omit'}, optional
          Defines how to handle when input contains nan. 'propagate' returns nan,
          'raise' throws an error, 'omit' performs the calculations ignoring nan
          values. Default is 'propagate'.
  
      Returns
      -------
      nobs : int or ndarray of ints
         Number of observations (length of data along `axis`).
         When 'omit' is chosen as nan_policy, each column is counted separately.
      minmax: tuple of ndarrays or floats
         Minimum and maximum value of data array.
      mean : ndarray or float
         Arithmetic mean of data along axis.
      variance : ndarray or float
         Unbiased variance of the data along axis, denominator is number of
         observations minus one.
      skewness : ndarray or float
         Skewness, based on moment calculations with denominator equal to
         the number of observations, i.e. no degrees of freedom correction.
      kurtosis : ndarray or float
         Kurtosis (Fisher).  The kurtosis is normalized so that it is
         zero for the normal distribution.  No degrees of freedom are used.
  
      See Also
      --------
      skew, kurtosis
  
      Examples
      --------
      >>> from scipy import stats
      >>> a = np.arange(10)
      >>> stats.describe(a)
      DescribeResult(nobs=10, minmax=(0, 9), mean=4.5, variance=9.166666666666666,
                     skewness=0.0, kurtosis=-1.2242424242424244)
      >>> b = [[1, 2], [3, 4]]
      >>> stats.describe(b)
      DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, 4])),
                     mean=array([2., 3.]), variance=array([2., 2.]),
                     skewness=array([0., 0.]), kurtosis=array([-2., -2.]))
  
      """
      a, axis = _chk_asarray(a, axis)
  
      contains_nan, nan_policy = _contains_nan(a, nan_policy)
  
      if contains_nan and nan_policy == 'omit':
          a = ma.masked_invalid(a)
          return mstats_basic.describe(a, axis, ddof, bias)
  
      if a.size == 0:
          raise ValueError("The input must not be empty.")
      n = a.shape[axis]
      mm = (np.min(a, axis=axis), np.max(a, axis=axis))
      m = np.mean(a, axis=axis)
      v = np.var(a, axis=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 = 1 , bias = True , nan_policy = 'propagate' ) : a , axis = _chk_asarray ( a , axis ) contains_nan , nan_policy = _contains_nan ( a , nan_policy ) if contains_nan and nan_policy == 'omit' : a = ma . masked_invalid ( a ) return mstats_basic . describe ( a , axis , ddof , bias ) if a . size == 0 : raise ValueError ( "The input must not be empty." ) n = a . shape [ axis ] mm = ( np . min ( a , axis = axis ) , np . max ( a , axis = axis ) ) m = np . mean ( a , axis = axis ) v = np . var ( a , axis = axis , ddof = ddof ) sk = skew ( a , axis , bias = bias ) kurt = kurtosis ( a , axis , bias = bias ) return DescribeResult ( n , mm , m , v , sk , kurt )