Exploring Patterns of Survival from the Titanic Dataset

In this article, we explore three real data problems using real questions where Polars outpaces Pandas on every metric.

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.

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.

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

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.

Master method chaining, assign(), and pipe() to write cleaner, testable, production-ready Pandas code The post Write Pandas Like a Pro With Method Chaining Pipelines appeared first on Towards Data Science.

On February 10, 2026, Scott Shambaugh—a volunteer maintainer for Matplotlib, one of the world’s most popular open source software libraries—rejected a proposed code change. Why? Because an AI agent wrote it. Standard policy. What happened next wasn’t standard, though. The AI agent autonomously researched Shambaugh’s code contribution history and published a highly personalized hit piece […]