scipy.stats.mstats.

tmean#

scipy.stats.mstats.tmean(a, limits=None, inclusive=(True, True), axis=None)[source]#

Compute the trimmed mean.

Parameters:

aarray_like: Array of values.
limitsNone or (lower limit, upper limit), optional: Values in the input array less than the lower limit or greater than the upper limit will be ignored. When limits is None (default), then all values are used. Either of the limit values in the tuple can also be None representing a half-open interval.
inclusive(bool, bool), optional: A tuple consisting of the (lower flag, upper flag). These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True).
axisint or None, optional: Axis along which to operate. If None, compute over the whole array. Default is None.

Returns:

tmeanfloat

Notes

For more details on tmean, see scipy.stats.tmean.

Examples

>>> import numpy as np
>>> from scipy.stats import mstats
>>> a = np.array([[6, 8, 3, 0],
...               [3, 9, 1, 2],
...               [8, 7, 8, 2],
...               [5, 6, 0, 2],
...               [4, 5, 5, 2]])
...
...
>>> mstats.tmean(a, (2,5))
3.3
>>> mstats.tmean(a, (2,5), axis=0)
masked_array(data=[4.0, 5.0, 4.0, 2.0],
             mask=[False, False, False, False],
       fill_value=1e+20)