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scipy.stats.mstats.

# describe

scipy.stats.mstats.describe(a, axis=0, ddof=0, bias=True)

  Computes several descriptive statistics of the passed array.

  Parameters:
    aarray_like

      Data array

    axisint or None, optional

      Axis along which to calculate statistics. Default 0. If None, compute over the whole array a.

    ddofint, optional

      degree of freedom (default 0); note that default ddof is different from the same routine in stats.describe

    biasbool, optional

      If False, then the skewness and kurtosis calculations are corrected for statistical bias.

  Returns:
    nobsint

      (size of the data (discarding missing values)

    minmax(int, int)

      min, max

    meanfloat

      arithmetic mean

    variancefloat

      unbiased variance

    skewnessfloat

      biased skewness

    kurtosisfloat

      biased kurtosis

  Examples

      >>> import numpy as np
      >>> from scipy.stats.mstats import describe
      >>> ma = np.ma.array(range(6), mask=[0, 0, 0, 1, 1, 1])
      >>> describe(ma)
      DescribeResult(nobs=np.int64(3), minmax=(masked_array(data=0,
                   mask=False,
             fill_value=999999), masked_array(data=2,
                   mask=False,
             fill_value=999999)), mean=np.float64(1.0),
             variance=np.float64(0.6666666666666666),
             skewness=masked_array(data=0., mask=False, fill_value=1e+20),
              kurtosis=np.float64(-1.5))