The OrdinalEncoder() replaces the categories by digits, starting from 0 to k-1, where k is the number of different categories. If you select “arbitrary” in the encoding_method, then the encoder will assign numbers as the labels appear in the variable (first come first served). If you select “ordered”, the encoder will assign numbers following the mean of the target value for that label. So labels for which the mean of the target is higher will get the number 0, and those where the mean of the target is smallest will get the number k-1. This way, we create a monotonic relationship between the encoded variable and the target.

Arbitrary vs ordered encoding

Ordered ordinal encoding: for 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 1, red by 2 and grey by 0.

The motivation is to try and create a monotonic relationship between the target and the encoded categories. This tends to help improve performance of linear models.

Arbitrary ordinal encoding: the numbers will be assigned arbitrarily to the categories, on a first seen first served basis.

Let’s look at an example using 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.encoding import OrdinalEncoder

# Load dataset
def load_titanic():
        data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')
        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)

Now, we set up the OrdinalEncoder() to replace the categories by strings based on the target mean value and only in the 3 indicated variables:

# set up the encoder
encoder = OrdinalEncoder(encoding_method='ordered', variables=['pclass', 'cabin', 'embarked'])

# fit the encoder
encoder.fit(X_train, y_train)

With fit() the encoder learns the mappings for each category, which are stored in its encoder_dict_ parameter:


In the encoder_dict_ we find the integers that will replace each one of the categories of each variable that we want to encode. This way, we can map the original value of the variable to the new value.

{'pclass': {3: 0, 2: 1, 1: 2},
 'cabin': {'T': 0,
  'n': 1,
  'G': 2,
  'A': 3,
  'C': 4,
  'F': 5,
  'D': 6,
  'E': 7,
  'B': 8},
 'embarked': {'S': 0, 'Q': 1, 'C': 2}}

We can now go ahead and replace the original strings with the numbers:

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

More details#

In the following notebook, you can find more details into the OrdinalEncoder()’s functionality and example plots with the encoded variables:

All notebooks can be found in a dedicated repository.