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.
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