---
title: 10 Python One-Liners Simplify Feature Engineering
url: https://www.dataloco.com/en/10-python-one-liners-simplify-feature-engineering
published: 2026-09-13T17:10:47+00:00
language: en
section: Data
source: https://machinelearningmastery.com/10-python-one-liners-that-will-simplify-feature-engineering/
publisher: Dataloco
---

# 10 Python One-Liners Simplify Feature Engineering

A new article presents 10 Python one-liners aimed at simplifying feature engineering processes in data analysis workflows. Feature engineering is essential for enhancing machine learning models by creating new features from existing data to improve analytical insights and model performance. The article introduces concise lines of code that can be utilized in various situations to optimize data preparation workflows.

The article begins by mentioning the necessity of importing essential Python libraries and two datasets from Scikit-learn: the wine dataset and the Boston housing dataset. These datasets are loaded into two Pandas dataframes, named df_wine and df_boston.

One of the highlighted techniques is the standardization of numerical features using Z-score scaling, which transforms numerical values to follow a standard normal distribution. This approach is facilitated by Scikit-learn’s StandardScaler class, which standardizes the features of a dataframe.

Another method discussed is min-max scaling, suitable for features that vary uniformly. This technique normalizes feature values to lie within the interval [0,1] using a specific formula. The Boston housing dataset is utilized to demonstrate this process, excluding the target variable MEDV from normalization.

The article also emphasizes the importance of adding polynomial features to capture nonlinear relationships in data. The PolynomialFeatures class is used to generate new features based on original attributes, such as alcohol and malic acid properties from the wine dataset.

One-hot encoding of categorical variables is another key process explained. This method creates binary features for categorical variables, significantly aiding in data analysis and machine learning tasks. The example provided assumes the CHAS attribute is categorical and applies the get_dummies function for encoding.

Additionally, discretizing continuous variables into equal-width bins is presented as a common analysis technique, which assists in visualizations. Logarithmic transformations for skewed features and the creation of ratios between features are also covered, demonstrating their practical applications in data processing.