scipy.stats.mstats.winsorize¶

scipy.stats.mstats.
winsorize
(a, limits=None, inclusive=True, True, inplace=False, axis=None, nan_policy='propagate')[source]¶ Returns a Winsorized version of the input array.
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
 asequence
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
>>> 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]) masked_array(data=[8, 4, 8, 8, 5, 3, 7, 2, 2, 6], mask=False, fill_value=999999)