Feature Engineering Cheat Sheet

Practical techniques for transforming raw data into model-ready features, including encoding, scaling, binning, and interaction terms with pandas and scikit-learn.

2 PagesIntermediateFeb 25, 2026

Encoding & Scaling

Convert categories and numeric ranges for modeling.

python

import pandas as pdfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler, MinMaxScaler# One-hot encoding (for nominal categories)df_encoded = pd.get_dummies(df, columns=["city"], drop_first=True)# Ordinal / label encoding (for ordered categories)le = LabelEncoder()df["size_encoded"] = le.fit_transform(df["size"])  # e.g. S, M, L -> 0, 1, 2# Target/mean encoding (compute within CV folds to avoid leakage!)means = df.groupby("category")["target"].mean()df["category_encoded"] = df["category"].map(means)# Scalingscaler = StandardScaler()               # mean=0, std=1df[["age_scaled"]] = scaler.fit_transform(df[["age"]])minmax = MinMaxScaler()                 # scales to [0, 1]df[["income_scaled"]] = minmax.fit_transform(df[["income"]])

Creating New Features

Date parts, bins, and interaction terms.

python

import pandas as pdimport numpy as np# Date/time featuresdf["date"] = pd.to_datetime(df["date"])df["day_of_week"] = df["date"].dt.dayofweekdf["month"] = df["date"].dt.monthdf["is_weekend"] = df["day_of_week"].isin([5, 6]).astype(int)# Binning a continuous variabledf["age_bucket"] = pd.cut(df["age"], bins=[0, 18, 35, 60, 100],                           labels=["teen", "young_adult", "adult", "senior"])# Interaction featuresdf["price_per_sqft"] = df["price"] / df["sqft"]df["income_x_education"] = df["income"] * df["education_years"]# Log transform for right-skewed datadf["log_income"] = np.log1p(df["income"])  # log1p handles zeros safely

Encoding Techniques

Ways to turn categorical variables into numbers.

One-hot encoding- creates a binary column per category; best for low-cardinality nominal features
Label/ordinal encoding- maps categories to integers; only valid when order is meaningful
Target encoding- replaces category with mean of target; must be computed within CV folds to avoid leakage
Frequency encoding- replaces category with its occurrence count/frequency
Hashing trick- hashes high-cardinality categories into a fixed number of buckets
Embeddings- learned dense vectors for categories, common in deep learning pipelines

Scaling Methods

How to rescale numeric features.

StandardScaler- centers to mean 0, std 1; assumes roughly Gaussian data, sensitive to outliers
MinMaxScaler- rescales to a fixed range (e.g. [0,1]); preserves shape but sensitive to outliers
RobustScaler- uses median and IQR; robust to outliers
Normalizer- scales each sample (row) to unit norm, not each feature
Log/Box-Cox transform- reduces right-skew and stabilizes variance
PolynomialFeatures- generates interaction and power terms from existing features

Pro Tip

Fit scalers and encoders only on the training fold, then transform validation/test data with those fitted parameters — fitting on the full dataset before splitting silently leaks information and inflates your validation score.

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