Module « scipy.stats.mstats »

Fonction winsorize - module scipy.stats.mstats

Signature de la fonction winsorize

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

Description

winsorize.__doc__

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
    ----------
    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
    --------
    >>> 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)