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

Try it in your browser!

>>> 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))

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