SciPy

scipy.stats.mstats.describe

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

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=0.6666666666666666,
       skewness=masked_array(data=0., mask=False, fill_value=1e+20),
        kurtosis=-1.5)

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