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.
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dbt Incremental Model Not Working? The 5 Silent Full-Refresh Traps
A field-tested walkthrough of the five reasons your dbt incremental model silently runs a full refresh, with a reproducible debug recipe using compiled SQL, run results, and warehouse query history.
Polars vs pandas in 2026: a real 14GB benchmark, not a toy CSV
Every Polars-vs-pandas comparison I've read in the last year uses a 500MB CSV and announces that Polars is faster. I wanted to know what happens on the workload I actually run at work.
ML Model Serving in Python: BentoML, Ray Serve, FastAPI, and Triton Compared (2026)
BentoML, Ray Serve, FastAPI, and Triton compared for production ML model serving in Python: latency overhead, GPU batching, autoscaling, and cost per prediction with working code examples.
Conformal Prediction in Python with MAPIE 1.0: Distribution-Free Uncertainty Quantification (2026)
A practical 2026 guide to conformal prediction in Python with MAPIE 1.0: split conformal, CQR, APS/RAPS classification, alpha selection, and how to keep coverage under drift.
Marimo vs Jupyter Notebooks in 2026: Reactive Python Notebooks Compared
Marimo replaces Jupyter's manual execution with a reactive DAG, plain Python files, and one-command WASM deploys. Here's how the two notebooks compare in 2026.
Data Drift Detection in Python: Evidently, NannyML, and Alibi Detect Compared (2026)
Compare the three leading open-source Python drift detection libraries — Evidently, NannyML, and Alibi Detect — with runnable code, statistical test guidance, and production patterns for ML monitoring in 2026.
Imbalanced Classification in Python: SMOTE, ADASYN, and Threshold Tuning Compared (2026)
A practical 2026 guide to imbalanced classification in Python: when to reach for SMOTE, ADASYN, BorderlineSMOTE, class_weight, or threshold tuning — with runnable scikit-learn 1.8 and imbalanced-learn 0.14 code, common pitfalls, and a clear decision framework.
A/B Testing in Python: Power Analysis, Frequentist and Bayesian Methods with SciPy and PyMC
Learn how to run A/B tests in Python from start to finish — power analysis with statsmodels, frequentist hypothesis testing with SciPy 1.17, and Bayesian analysis with PyMC 5.28. Includes working code, decision frameworks, and common pitfalls to avoid.
Speed Up Pandas with Parallel Processing: Dask, Modin, Joblib, and multiprocessing Compared
Learn five practical ways to parallelize pandas operations — multiprocessing, Joblib, Dask, Modin, and Swifter — with working code examples, benchmarks, and a decision guide to pick the right tool.
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.