Advanced Pandas Patterns Most Data Scientists Don’t Use

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.

Meta Introduces Autodata: An Agentic Framework That Turns AI Models into Autonomous Data Scientists for High-Quality Training Data Creation The post Meta Introduces Autodata: An Agentic Framework That Turns AI Models into Autonomous Data Scientists for High-Quality Training Data Creation appeared first on MarkTechPost.

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.

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.

Cleancode, clean data: why Pyjanitor's method chaining approach is the pathway to reach this double goal.