scipy.stats.mstats.variation#

scipy.stats.mstats.variation(a, axis=0, ddof=0)[source]#

Compute the coefficient of variation.

The coefficient of variation is the standard deviation divided by the mean. This function is equivalent to:

np.std(x, axis=axis, ddof=ddof) / np.mean(x)

The default for ddof is 0, but many definitions of the coefficient of variation use the square root of the unbiased sample variance for the sample standard deviation, which corresponds to ddof=1.

Parameters:

aarray_like: Input array.
axisint or None, optional: Axis along which to calculate the coefficient of variation. Default is 0. If None, compute over the whole array a.
ddofint, optional: Delta degrees of freedom. Default is 0.

Returns:

variationndarray: The calculated variation along the requested axis.

Notes

For more details about variation, see scipy.stats.variation.

Examples

>>> import numpy as np
>>> from scipy.stats.mstats import variation
>>> a = np.array([2,8,4])
>>> variation(a)
0.5345224838248487
>>> b = np.array([2,8,3,4])
>>> c = np.ma.masked_array(b, mask=[0,0,1,0])
>>> variation(c)
0.5345224838248487

In the example above, it can be seen that this works the same as scipy.stats.variation except ‘stats.mstats.variation’ ignores masked array elements.