Pandas is great — until it crashes with large data. Here’s what to use instead 🚀

In my latest post, I explore modern, high-performance Python libraries that can handle huge datasets faster and more efficiently than Pandas:

  • ⚡ Polars — written in Rust, ultra-fast with Arrow backend
  • 🧮 DuckDB — SQL-first analytics, no server needed
  • 🧠 Modin & Dask — scale Pandas-style workflows across all your CPU cores
  • 💾 Vaex — analyze 5–10 GB files even on low-memory machines
  • 🔧 Datatable — the R-style power tool for massive tabular data All with real examples, performance notes, and when to pick which one.

👉 Read the full article (with hands-on comparisons):
🔗 What to Use Instead of Pandas: Fast Python Libraries for Data Analysis

🛠 If your workflows involve filtering, grouping, joining or visualizing big data — stop fighting with memory errors and let these libraries do the heavy lifting.

💬 Got a favorite Pandas alternative? Share it below — I’m always up for discovering new tools in the ecosystem.