The latest articles on DEV Community by Wassim Labdi (@wassim_labdi_bf92539c5926). https://dev.to/wassim_labdi_bf92539c5926 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4004347%2F73e20c98-672a-4439-b5fc-1b64704cce07.png https://dev.to/wassim_labdi_bf92539c5926 en Wassim Labdi Fri, 26 Jun 2026 17:13:06 +0000 https://dev.to/wassim_labdi_bf92539c5926/i-built-nitro-pandas-pandas-api-polars-speed-zero-migration-94d https://dev.to/wassim_labdi_bf92539c5926/i-built-nitro-pandas-pandas-api-polars-speed-zero-migration-94d <p>If you've ever hit pandas' performance limits on large <br> datasets, you know the pain. Polars is blazing fast — <br> but rewriting all your code to learn a new API? No thanks.</p> <p>So I built nitro-pandas.</p> <p><strong>What is it?</strong></p> <p>nitro-pandas wraps Polars under the hood while exposing <br> the exact same pandas interface.</p> <p>Just change your import:</p> <p>import nitro_pandas as npd # instead of pandas as pd</p> <p>That's it. Same groupby, loc, iloc, read_csv — <br> powered by Polars.</p> <p><strong>Install</strong></p> <p>pip install nitro-pandas</p> <p><strong>Why it matters</strong></p> <ul> <li>No learning curve if you know pandas</li> <li>Polars backend = way faster on big data</li> <li>Lazy evaluation for large files</li> <li>Auto-fallback to pandas for edge cases</li> </ul> <p>GitHub</p> <p>github.com/Wassim17Labdi/nitro-pandas</p> <p>Would love feedback and contributions!</p> <p>If you find it useful, a ⭐ on GitHub means <br> a lot and helps others discover the project!</p> python data opensource