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scipy.stats.mstats.

# winsorize

scipy.stats.mstats.winsorize(a, limits=None, inclusive=(True, True), inplace=False, axis=None, nan_policy='propagate')

  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

      >>> 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])
      masked_array(data=[8, 4, 8, 8, 5, 3, 7, 2, 2, 6],
                   mask=False,
             fill_value=999999)