#### Previous topic

scipy.stats.mstats.count_tied_groups

#### Next topic

scipy.stats.mstats.f_oneway

# scipy.stats.mstats.describe

`scipy.stats.mstats.``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=array(3), minmax=(masked_array(data = 0,
                   mask = False,
             fill_value = 999999)
      , masked_array(data = 2,
                   mask = False,
             fill_value = 999999)
      ), mean=1.0, variance=0.66666666666666663, skewness=masked_array(data = 0.0,
                   mask = False,
             fill_value = 1e+20)
      , kurtosis=-1.5)