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📅 Today’s Picks

pd.col: Polars-Like Column References in pandas 3.0

Problem

Before pandas 3.0, creating columns meant:

• Bracket notation: repeats DataFrame name, breaks chaining
• assign() with lambdas: verbose syntax, scoping bugs from variable capture

Solution

pandas 3.0 solves this with pd.col expressions: clean column references that chain naturally, with syntax as readable as Polars and PySpark.

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DrawDB: Visual Schema Design to Production SQL in Minutes

Problem

Have you ever sketched a database schema on a whiteboard, then spent hours converting it to SQL?

There’s a faster way to go from diagram to production-ready code.

Solution

With DrawDB, your database diagram becomes the code. Just drag tables onto a canvas, connect them visually, and export SQL for 6 databases.

Key benefits:

• Draw tables and relationships on a visual canvas
• Export production-ready SQL for MySQL, PostgreSQL, SQLite, MariaDB, MSSQL, and Oracle
• No account or subscription required
• Share diagrams with your team instantly

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☕️ Weekly Finds

timescaledb [Data Engineer] – A time-series database for high-performance real-time analytics packaged as a Postgres extension

rembg [Python Utils] – A tool to remove images background with Python

grip [Python Utils] – Preview GitHub README.md files locally before committing them

Looking for a specific tool? Explore 70+ Python tools →

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📚 Latest Deep Dives

What’s New in pandas 3.0: Expressions, Copy-on-Write, and Faster Strings – Learn what’s new in pandas 3.0: pd.col expressions for cleaner code, Copy-on-Write for predictable behavior, and PyArrow-backed strings for 5-10x faster operations.

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