The MeanCategoricalEncoder() replaces categories with the mean of the target per category. For example, if we are trying to predict default rate, and our data has the variable city, with categories, London, Manchester and Bristol, and the default rate per city is 0.1, 0.5, and 0.3, respectively, the encoder will replace London by 0.1, Manchester by 0.5 and Bristol by 0.3.

The MeanCategoricalEncoder() works only with categorical variables. A list of variables can be indicated, or the encoder will automatically select all categorical variables in the train set.

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

from feature_engine import categorical_encoders as ce

# 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)
        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)

# set up the encoder
encoder = ce.MeanCategoricalEncoder(variables=['cabin', 'pclass', 'embarked'])

# fit the encoder, y_train)

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

{'cabin': {'A': 0.5294117647058824,
  'B': 0.7619047619047619,
  'C': 0.5633802816901409,
  'D': 0.71875,
  'E': 0.71875,
  'F': 0.6666666666666666,
  'G': 0.5,
  'T': 0.0,
  'n': 0.30484330484330485},
 'pclass': {1: 0.6173913043478261,
  2: 0.43617021276595747,
  3: 0.25903614457831325},
 'embarked': {'C': 0.5580110497237569,
  'Q': 0.37349397590361444,
  'S': 0.3389570552147239}}

API Reference

class feature_engine.categorical_encoders.MeanCategoricalEncoder(variables=None)[source]

The MeanCategoricalEncoder() replaces categories by the mean value of the target for each category.

For example in the variable colour, if the mean of the target for blue, red and grey is 0.5, 0.8 and 0.1 respectively, blue is replaced by 0.5, red by 0.8 and grey by 0.1.

The encoder will encode only categorical variables (type ‘object’). A list of variables can be passed as an argument. If no variables are passed as argument, the encoder will find and encode all categorical variables (object type).

The encoder first maps the categories to the numbers for each variable (fit).

The encoder then transforms the categories to the mapped numbers (transform).


variables (list, default=None) – The list of categorical variables that will be encoded. If None, the encoder will find and select all object type variables.

fit(X, y)[source]

Learns the mean value of the target for each category of the variable.

  • X (pandas dataframe of shape = [n_samples, n_features]) – The training input samples. Can be the entire dataframe, not just the variables to be encoded.

  • y (pandas series) – The target.


The dictionary containing the {category: target mean} pairs used to replace categories in every variable.




Convert the data back to the original representation.


X_transformed (pandas dataframe of shape = [n_samples, n_features]) – The transformed dataframe.


X – The un-transformed dataframe, that is, containing the original values of the categorical variables.

Return type

pandas dataframe of shape = [n_samples, n_features]


Replaces categories with the learned parameters.


X (pandas dataframe of shape = [n_samples, n_features]) – The input samples.


X_transformed – The dataframe containing categories replaced by numbers.

Return type

pandas dataframe of shape = [n_samples, n_features]