# 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])
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.