Using Polars Instead of Pandas: Performance Deep Dive

A beginner's tutorial on exploratory data analysis using Pandas, Matplolib, and Seaborn The post Exploring Patterns of Survival from the Titanic Dataset appeared first on Towards Data Science.

From 61 seconds to 0.20 seconds — and the mental model shift I didn't expect The post I Rewrote a Real Data Workflow in Polars. Pandas Didn’t Stand a Chance. appeared first on Towards Data Science.

Windmill raised $12M in seed funding to build AI-powered workforce tools that help HR teams track performance, development, and team effectiveness.

Most slow Pandas code "works", until it doesn't. Learn how to spot hidden bottlenecks, avoid costly row-wise operations, and know when Pandas is no longer enough. The post I Reduced My Pandas Runtime by 95% — Here’s What I Was Doing Wrong appeared first on Towards Data Science.

A guide to bridging the gap between ease of use and raw performance. The post How to Call Rust from Python appeared first on Towards Data Science.

Learn method chaining, pipe(), efficient joins, optimized groupby operations, and vectorized logic to write faster and cleaner pandas code

This one simple Python stack will make your projects faster, cleaner, and easier to maintain.

In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with the fundamentals of connection management and data generation, then move into real analytical workflows, including querying Pandas, Polars, and Arrow objects without manual loading, transforming results across multiple formats, and writing […] The post An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling appeared first on MarkTechPost.