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=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)