scipy.stats.mstats.tmean

scipy.stats.mstats.tmean(a, limits=None, inclusive=(True, True), axis=None)

Compute the trimmed mean.

Parameters:

a : array_like

Array of values.

limits : None 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).

axis : int or None, optional

Axis along which to operate. If None, compute over the whole array. Default is None.

Returns:

tmean : float

Notes

For more details on tmean, see stats.tmean.

Examples

>>> 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)