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 toddof=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
, seescipy.stats.variation
.Examples
>>> 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.