Polars apply alternative: when/then/otherwise for 80x speedups
Eight worked examples that swap pandas-style .apply() in Polars for native when/then/otherwise expressions, plus benchmarks showing the real speedup on a 50M-row dataset.
Articles by Marcus Holloway
Hypothesis Testing in Python: t-Tests, Chi-Square, ANOVA, and Non-Parametric Tests with SciPy
A practical, code-driven guide to hypothesis testing in Python using SciPy 1.17. Covers t-tests, chi-square, ANOVA, Mann-Whitney U, and Kruskal-Wallis with working examples, assumption checking, and a decision framework for choosing the right test.
Automated EDA in Python: YData Profiling, SweetViz, DataPrep, and D-Tale Compared
Find out which automated EDA tool fits your Python workflow. We compare YData Profiling, SweetViz, DataPrep, and D-Tale with code examples, benchmarks, and a practical decision framework.
Cross-Validation in Python: Every Scikit-Learn Strategy Explained with Code
Master every cross-validation strategy in scikit-learn 1.8 — KFold, StratifiedKFold, GroupKFold, TimeSeriesSplit, and more. Includes working code, a decision flowchart, and tips for avoiding data leakage.
SHAP Values in Python: A Practical Guide to Explaining ML Models
Learn how to use SHAP values to explain machine learning predictions in Python. Practical guide with scikit-learn, XGBoost, and LightGBM code examples, visualization techniques, and production-ready interpretability patterns.
Gradient Boosting in Python: XGBoost, LightGBM, and CatBoost Compared
A hands-on guide to XGBoost 3.2, LightGBM 4.6, and CatBoost 1.2.10 in Python. Covers benchmarks, categorical feature handling, Optuna hyperparameter tuning, SHAP interpretability, and production deployment.
Time Series Analysis and Forecasting in Python: From Pandas to Foundation Models
A practical guide to time series forecasting in Python — from pandas fundamentals and classical ARIMA models to deep learning with NeuralForecast and zero-shot predictions with Amazon Chronos-2 foundation models.
Python Data Visualization in 2026: A Practical Guide to Matplotlib 3.10, Seaborn, and Plotly 6
A hands-on guide to Python data visualization in 2026 covering Matplotlib 3.10's accessible color cycles and Colorizer API, Seaborn's mature objects interface, and Plotly 6's Narwhals-powered zero-copy DataFrame support. Includes code examples, performance tips, and a library comparison.
Beyond Pandas: A Practical Guide to Polars and DuckDB for Python Data Science
Master the modern Python data stack by learning when and how to use Polars and DuckDB alongside pandas. This hands-on guide covers real code examples, performance benchmarks, and hybrid workflow patterns for 2026.