The DropCorrelatedFeatures() finds and removes correlated variables from a dataframe. Correlation is calculated with pandas.corr(). All correlation methods supported by pandas.corr() can be used in the selection, including Spearman, Kendall, or Spearman. You can also pass a bespoke correlation function, provided it returns a value between -1 and 1.

Features are removed on first found first removed basis, without any further insight. That is, the first feature will be retained an all subsequent features that are correlated with this, will be removed.

The transformer will examine all numerical variables automatically. Note that you could pass a dataframe with categorical and datetime variables, and these will be ignored automatically. Alternatively, you can pass a list with the variables you wish to evaluate.


Let’s create a toy dataframe where 4 of the features are correlated:

import pandas as pd
from sklearn.datasets import make_classification
from feature_engine.selection import DropCorrelatedFeatures

# make dataframe with some correlated variables
def make_data():
    X, y = make_classification(n_samples=1000,

    # trasform arrays into pandas df and series
    colnames = ['var_'+str(i) for i in range(12)]
    X = pd.DataFrame(X, columns =colnames)
    return X

X = make_data()

Now, we set up DropCorrelatedFeatures() to find and remove variables which (absolute) correlation coefficient is bigger than 0.8:

tr = DropCorrelatedFeatures(variables=None, method='pearson', threshold=0.8)

With fit() the transformer finds the correlated variables and with transform() it drops them from the dataset:

Xt = tr.fit_transform(X)

The correlated feature groups are stored in the transformer’s attributes:

[{'var_0', 'var_8'}, {'var_4', 'var_6', 'var_7', 'var_9'}]

We can identify from each group which feature will be retained and which ones removed by inspecting the dictionary:


In the dictionary below we see that from the first correlated group, var_0 is a key, hence it will be retained, whereas var_8 is a value, which means that it is correlated to var_0 and will therefore be removed.

{'var_0': {'var_8'}, 'var_4': {'var_6', 'var_7', 'var_9'}}

Similarly, var_4 is a key and will be retained, whereas the variables 6, 7 and 8 were found correlated to var_4 and will therefore be removed.

The features that will be removed from the dataset are stored in a different attribute as well:

['var_8', 'var_6', 'var_7', 'var_9']

If we now go ahead and print the transformed data, we see that the correlated features have been removed.

          var_0     var_1     var_2     var_3     var_4     var_5    var_10  \
0  1.471061 -2.376400 -0.247208  1.210290 -3.247521  0.091527  2.070526
1  1.819196  1.969326 -0.126894  0.034598 -2.910112 -0.186802  1.184820
2  1.625024  1.499174  0.334123 -2.233844 -3.399345 -0.313881 -0.066448
3  1.939212  0.075341  1.627132  0.943132 -4.783124 -0.468041  0.713558
4  1.579307  0.372213  0.338141  0.951526 -3.199285  0.729005  0.398790

0 -1.989335
1 -1.309524
2 -0.852703
3  0.484649
4 -0.186530

More details#

In this notebook, we show how to use DropCorrelatedFeatures() with a different relation metric:

All notebooks can be found in a dedicated repository.

For more details about this and other feature selection methods check out these resources: