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# scipy.stats.describe

`scipy.stats.``describe`(a, axis=0, ddof=1, bias=True, nan_policy='propagate')

  Computes 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 Number of observations (length of data along axis). 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=9.1666666666666661,
                     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.]))