Собрали в одном месте самые важные ссылкии сделали Тренажер IT-инцидентов для DevOps/SRE
This article is a beginner-to-intermediate-level walkthrough on Python and matplotlib that mixes theory with example.
How Parquet’s columnar layout, encodings, zstd compression, and row group statistics make it smaller and faster to read than CSV, measured on NYC taxi trips data with Polars, DuckDB, and PyArrow.
Instead of testing every hyperparameter combination, Bayesian optimization uses results from earlier trials to decide what to try next. This hands-on tutorial builds it step by step with scikit-learn’s GaussianProcessRegressor and a custom Expected Improvement function, plotting the model’s predictions and uncertainty along with each point the search picks.
A recap of Pratham’s Quansight internship porting NumPy extension modules to the Limited API: heap types, swapping macros for function calls, and the side quest it started, getting NumPy ready for subinterpreters.
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Learn how Datadog made Python profiling async-aware while reducing profiler overhead and preserving context across asyncio tasks.
Monte Carlo simulations (or methods) is the technique of applying randomness and the Law of large numbers to the solution of various scientific and engineering problems.