`scipy.stats._mstats_basic:winsorize`

source: /scipy/stats/_mstats_basic.py :2557

Signature

   def     winsorize (    a    ,    limits    =  None   ,    inclusive    =  (True, True)   ,    inplace    =  False   ,    axis    =  None   ,    nan_policy    =  propagate     )

Summary

Returns a Winsorized version of the input array.

Extended Summary

The (limits[0])th lowest values are set to the (limits[0])th percentile, and the (limits[1])th highest values are set to the (1 - limits[1])th percentile. Masked values are skipped.

Parameters

a : sequence

Input array.

limits : {None, tuple of float}, optional

Tuple of the percentages to cut on each side of the array, with respect to the number of unmasked data, as floats between 0. and 1. 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)) The value of one limit can be set to None to indicate an open interval.

inclusive : {(True, True) tuple}, optional

Tuple indicating whether the number of data being masked on each side should be truncated (True) or rounded (False).

inplace : {False, True}, optional

Whether to winsorize in place (True) or to use a copy (False)

axis : {None, int}, optional

Axis along which to trim. If None, the whole array is trimmed, but its shape is maintained.

nan_policy : {'propagate', 'raise', 'omit'}, optional

Defines how to handle when input contains nan. The following options are available (default is 'propagate'):

'propagate': allows nan values and may overwrite or propagate them
'raise': throws an error
'omit': performs the calculations ignoring nan values

Notes

This function is applied to reduce the effect of possibly spurious outliers by limiting the extreme values.

Examples

import numpy as np
from scipy.stats.mstats import winsorize

✓

A shuffled array contains integers from 1 to 10.

a = np.array([10, 4, 9, 8, 5, 3, 7, 2, 1, 6])

✓

The 10% of the lowest value (i.e., ``1``) and the 20% of the highest values (i.e., ``9`` and ``10``) are replaced.

winsorize(a, limits=[0.1, 0.2])

✓

Aliases

scipy.stats._mstats_basic.winsorize