The Winsorizer() caps maximum and/or minimum values of a variable at automatically determined values. The minimum and maximum values can be calculated in 1 of 3 different ways:

Gaussian limits:

  • right tail: mean + 3* std

  • left tail: mean - 3* std

IQR limits:

  • right tail: 75th quantile + 3* IQR

  • left tail: 25th quantile - 3* IQR

where IQR is the inter-quartile range: 75th quantile - 25th quantile.

MAD limits:

  • right tail: median + 3* MAD

  • left tail: median - 3* MAD

where MAD is the median absoulte deviation from the median.

percentiles or quantiles:

  • right tail: 95th percentile

  • left tail: 5th percentile


Let’s cap some outliers in the Titanic Dataset. First, let’s load the data and separate it into train and test:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from feature_engine.outliers import Winsorizer

# Load dataset
def load_titanic():
    data = pd.read_csv('')
    data = data.replace('?', np.nan)
    data['cabin'] = data['cabin'].astype(str).str[0]
    data['pclass'] = data['pclass'].astype('O')
    data['embarked'].fillna('C', inplace=True)
    data['fare'] = data['fare'].astype('float')
    data['fare'].fillna(data['fare'].median(), inplace=True)
    data['age'] = data['age'].astype('float')
    data['age'].fillna(data['age'].median(), inplace=True)
    return data

data = load_titanic()

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
            data.drop(['survived', 'name', 'ticket'], axis=1),
            data['survived'], test_size=0.3, random_state=0)

Now, we will set the Winsorizer() to cap outliers at the right side of the distribution only (param tail). We want the maximum values to be determined using the mean value of the variable (param capping_method) plus 3 times the standard deviation (param fold). And we only want to cap outliers in 2 variables, which we indicate in a list.

# set up the capper
capper = Winsorizer(capping_method='gaussian', tail='right', fold=3, variables=['age', 'fare'])

# fit the capper

With fit(), the Winsorizer() finds the values at which it should cap the variables. These values are stored in its attribute:

{'age': 67.49048447470315, 'fare': 174.78162171790441}

We can now go ahead and censor the outliers:

# transform the data
train_t= capper.transform(X_train)
test_t= capper.transform(X_test)

If we evaluate now the maximum of the variables in the transformed datasets, they should coincide with the values observed in the attribute right_tail_caps_:

train_t[['fare', 'age']].max()
fare    174.781622
age      67.490484
dtype: float64

More details#

You can find more details about the Winsorizer() functionality in the following notebook:

All notebooks can be found in a dedicated repository.