Preliminaries
# Load libraries
import pandas as pd
import numpy as np
Load Data
# Create feature matrix with two highly correlated features
X = np.array([[1, 1, 1],
[2, 2, 0],
[3, 3, 1],
[4, 4, 0],
[5, 5, 1],
[6, 6, 0],
[7, 7, 1],
[8, 7, 0],
[9, 7, 1]])
# Convert feature matrix into DataFrame
df = pd.DataFrame(X)
# View the data frame
df

0 
1 
2 
0 
1 
1 
1 
1 
2 
2 
0 
2 
3 
3 
1 
3 
4 
4 
0 
4 
5 
5 
1 
5 
6 
6 
0 
6 
7 
7 
1 
7 
8 
7 
0 
8 
9 
7 
1 
Identify Highly Correlated Features
# Create correlation matrix
corr_matrix = df.corr().abs()
# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
# Find index of feature columns with correlation greater than 0.95
to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]
Drop Marked Features
# Drop features
df.drop(df.columns[to_drop], axis=1)

0 
2 
0 
1 
1 
1 
2 
0 
2 
3 
1 
3 
4 
0 
4 
5 
1 
5 
6 
0 
6 
7 
1 
7 
8 
0 
8 
9 
1 