Feature selection using variance threshold in Pandas

Last update on May 06 2025 13:19:29 (UTC/GMT +8 hours)

12. Selecting Features Using Variance Threshold

Write a Pandas program to select feature selection using variance threshold.

This exercise demonstrates how to select features based on their variance using Scikit-learn's VarianceThreshold.

Sample Solution :

Code :

import pandas as pd
from sklearn.feature_selection import VarianceThreshold

# Load the dataset
df = pd.read_csv('data.csv')

# Select only the numeric columns for feature selection
numeric_cols = df.select_dtypes(include=[float, int])

# Initialize the VarianceThreshold with a threshold of 0.1
selector = VarianceThreshold(threshold=0.1)

# Apply feature selection based on variance
X_selected = selector.fit_transform(numeric_cols)

# Output the selected features
print(X_selected)

Output:

[[1.0e+00 2.5e+01 5.0e+04 0.0e+00]
 [2.0e+00 3.0e+01 6.0e+04 1.0e+00]
 [3.0e+00 2.2e+01 7.0e+04 0.0e+00]
 [4.0e+00 3.5e+01 8.0e+04 1.0e+00]
 [5.0e+00     nan 5.5e+04 0.0e+00]
 [6.0e+00 2.9e+01     nan 1.0e+00]]

Explanation:

Import Libraries:

pandas is imported for handling data in DataFrame format.
VarianceThreshold from Scikit-learn is imported for performing feature selection based on variance.

Load the Dataset:

The dataset data.csv is loaded using pd.read_csv() and stored in the DataFrame df.

Select Numeric Columns:

select_dtypes(include=[float, int]) is used to select only the numeric columns from the dataset (e.g., Age, Salary) and exclude non-numeric columns like Name and Gender.

Initialize VarianceThreshold:

VarianceThreshold is initialized with a threshold of 0.1. Features with variance lower than this threshold will be removed.

Apply VarianceThreshold:

fit_transform() is applied to the numeric columns to perform feature selection, keeping only the features that have a variance greater than 0.1.

Output the Selected Features:

The resulting selected features are printed after the variance-based filtering.

For more Practice: Solve these Related Problems:

Write a Pandas program to perform feature selection by removing columns with variance below a specified threshold.
Write a Pandas program to calculate variance for each feature and drop those with low variability.
Write a Pandas program to apply a variance threshold and then compare the shape of the DataFrame before and after feature selection.
Write a Pandas program to automate variance thresholding and output the names of features that were dropped.

Go to:

Previous: Imputing Missing Values Using K-Nearest Neighbours.
Next: Handling Class Imbalance Using Random Oversampling.

Python-Pandas Code Editor:

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