DropDuplicateFeatures#

The DropDuplicateFeatures() finds and removes duplicated variables from a dataframe. Duplicated features are identical features, regardless of the variable or column name. If they show the same values for every observation, then they are considered duplicated.

The transformer will automatically evaluate all variables, or alternatively, you can pass a list with the variables you wish to have examined. And it works with numerical and categorical features.

Example

Let’s see how to use DropDuplicateFeatures() in an example with the Titanic dataset. These dataset does not have duplicated features, so we will add a few manually:

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

from feature_engine.selection import DropDuplicateFeatures

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 = data[['pclass', 'survived', 'sex', 'age', 'sibsp', 'parch', 'cabin', 'embarked']]
        data = pd.concat([data, data[['sex', 'age', 'sibsp']]], axis=1)
        data.columns = ['pclass', 'survived', 'sex', 'age', 'sibsp', 'parch', 'cabin', 'embarked',
                        'sex_dup', 'age_dup', 'sibsp_dup']
        return data

# load data as pandas dataframe
data = load_titanic()
data.head()

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

Now, we set up DropDuplicateFeatures() to find the duplications:

# set up the transformer
transformer = DropDuplicateFeatures()

With fit() the transformer finds the duplicated features, With transform() it removes them:

# fit the transformer
transformer.fit(X_train)

# transform the data
train_t = transformer.transform(X_train)

If we examine the variable names of the transformed dataset, we see that the duplicated features are not present:

train_t.columns

Index(['pclass', 'sex', 'age', 'sibsp', 'parch', 'cabin', 'embarked'], dtype='object')

The features that are removed are stored in the transformer’s attribute:

transformer.features_to_drop_

{'age_dup', 'sex_dup', 'sibsp_dup'}

And the transformer also stores the groups of duplicated features, which could be useful if we have groups where more than 2 features are identical.

transformer.duplicated_feature_sets_

[{'sex', 'sex_dup'}, {'age', 'age_dup'}, {'sibsp', 'sibsp_dup'}]

More details#

In this Kaggle kernel we use DropDuplicateFeatures() together with other feature selection algorithms:

Kaggle kernel

All notebooks can be found in a dedicated repository.

DropConstantFeatures

DropCorrelatedFeatures