We put Pandas, Polars, and DuckDB to the test! which comes out on top? | Data Tools
Data Speed Showdown!
We put Pandas, Polars, and DuckDB to the test: which one comes out on top for data read and transformation speed?
The results are in: DuckDB takes the lead, with Polars a close second, and Pandas trailing slightly behind.
So, what's the takeaway? If you're working with smaller datasets and prioritize ease of use, Pandas is still an excellent choice. For larger datasets in the Python ecosystem, Polars offers a significant speed advantage. And if you prefer SQL for your transformations, DuckDB provides a great balance.
Each tool has its strengths, and it's essential to choose the right one for your specific task and workflow. #DataScience #Python #DuckDB #Polars #pandas
Link to Docs:
DuckDB: https://duckdb.org/docs/
Polars: https://docs.pola.rs/
Pandas: https://pandas.pydata.org/docs/
Link to Channel's site:
https://hnawaz007.github.io/
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Topics in this video (click to jump around):
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0:00 - Introduction to Data Tools
0:35 - Pandas Overview
1:03 - Polars
1:33 - DuckDB
2:05 - Speed Benchmarks
3:00 - Summary & Recap