scipy.stats.mstats.

describe

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

Computes several descriptive statistics of the passed array.

Deprecated since version 2.0.0: scipy.stats.mstats.describe is deprecated as of SciPy 2.0.0 and will be removed, along with the scipy.stats.mstats namespace, in SciPy 2.4.0. For similar functionality, use scipy.stats.describe with MArray(s) instead of NumPy masked array(s).

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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