scipy.stats.mstats.trimmed_var(a, limits=(0.1, 0.1), inclusive=(1, 1), relative=True, axis=None, ddof=0)[source]#

Returns the trimmed variance of the data along the given axis.


Input array

limits{None, tuple}, optional

If relative is False, tuple (lower limit, upper limit) in absolute values. Values of the input array lower (greater) than the lower (upper) limit are masked.

If relative is True, tuple (lower percentage, upper percentage) to cut on each side of the array, with respect to the number of unmasked data.

Noting n the number of unmasked data before trimming, the (n*limits[0])th smallest data and the (n*limits[1])th largest data are masked, and the total number of unmasked data after trimming is n*(1.-sum(limits)) In each case, the value of one limit can be set to None to indicate an open interval.

If limits is None, no trimming is performed

inclusive{(bool, bool) tuple}, optional

If relative is False, tuple indicating whether values exactly equal to the absolute limits are allowed. If relative is True, tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False).

relativebool, optional

Whether to consider the limits as absolute values (False) or proportions to cut (True).

axisint, optional

Axis along which to trim.

ddof{0,integer}, optional

Means Delta Degrees of Freedom. The denominator used during computations is (n-ddof). DDOF=0 corresponds to a biased estimate, DDOF=1 to an un- biased estimate of the variance.