DrawDB: Visual Schema Design to Production SQL in Minutes
Plus pd.col for cleaner pandas 3.0 code
Grab your coffee. Here are this week’s highlights.
📅 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.
📖 View the full article | 🧪 Run code | ⭐ View GitHub
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
☕️ 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
📚 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.
Before You Go
🔍 Explore More on CodeCut
Tool Selector - Discover 70+ Python tools for AI and data science
Production Ready Data Science - A practical book for taking projects from prototype to production
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