Pandas

The three Reasons Why I Have Permanently Switched From Pandas To Polars

I got here for the speed, but I stayed for the syntaxAnd that brings us to .scan_parquet() and .sink_parquet().Through the use of .scan_parquet() as your data input function, LazyFrame as your dataframe, and .sink_parquet()...

Measuring The Speed of Recent Pandas 2.0 Against Polars and Datatable — Still Not Good Enough

Though the brand new PyArrow backend for Pandas is bringing exciting features, it still looks disappointing when it comes to speed.Things are changingFor years now, Pandas have stood on the shoulders of NumPy because...

Measuring The Speed of Latest Pandas 2.0 Against Polars and Datatable — Still Not Good Enough

Though the brand new PyArrow backend for Pandas is bringing exciting features, it still looks disappointing when it comes to speed.Things are changingFor years now, Pandas have stood on the shoulders of NumPy because...

Pandas 2.0: A Faster Version of Pandas with Apache Arrow Backend

Here’s every thing it's worthwhile to know concerning the recent Pandas 2.0Pandas 2.0 was recently released. This version mainly includes bug fixes, performance improvements, and the addition of the Apache Arrow backend.If you happen...

Use Regex Patterns in Pandas to Work With Complex Strings Conclusion

simplifies pattern-matching tasks on large amounts of text — Pandas makes it elegantBesides readability, each methods aren’t too different. However the difference becomes significant if you happen to’re working with an unlimited dataset.Also,...

Polars: The Super Fast Dataframe Library for Python — Goodbye Pandas?

Here’s why you need to select polars over pandas (and the right way to learn polars very quickly!).How can polars outperform pandas?Unlike pandas, polars is lazy and semi-lazy. In lazy Polars, we are able...

Recent posts

Popular categories

ASK ANA