If you work in data science, you definitely know about data frame libraries. Pandas is certainly the most popular, but there are others such as cuDF, Modin, Polars, Dask, and more. They are all similar but definitely not the same APIs and Polars is quite different. But here's the problem. If you want to write a library that is for users of more than one of these data frame frameworks, how do you do that? Or if you want to leave open the possibility of changing yours after the app is built, same problem. That's the problem that Narwhals solves. We have Marco Gorelli on the show to tell us all about it.
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Links from the show
Marco Gorelli: @marcogorelli)
Marco on LinkedIn: linkedin.com)
Narwhals: github.io)
Narwhals on Github: github.com)
DuckDB: duckdb.org)
Ibis: ibis-project.org)
modin: readthedocs.io)
Pandas and Beyond with Wes McKinney: talkpython.fm)
Polars: A Lightning-fast DataFrame for Python: talkpython.fm)
Polars: pola.rs)
Pandas: pandas.pydata.org)
Watch this episode on YouTube: youtube.com)
Episode transcripts: talkpython.fm)
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