Show HN: Aiopandas – Async .apply() and .map() for Pandas, Faster API/LLMs Calls
(github.com/telekinesis-inc)
🚀 Async-Powered Pandas: Lightweight Pandas monkey-patch that adds async support to map, apply, applymap, aggregate, and transform, enabling seamless handling of async functions with controlled parallel execution (max_parallel).
🚀 Async-Powered Pandas: Lightweight Pandas monkey-patch that adds async support to map, apply, applymap, aggregate, and transform, enabling seamless handling of async functions with controlled parallel execution (max_parallel).
Non-elementary group-by aggregations in Polars vs pandas
(quansight.org)
I attended PyData Berlin 2024 in April, and it was a blast! I met so many colleagues, collaborators, and friends. There was quite some talk of Polars - some people even gathered together for a Polars-themed dinner! It's certainly nice to see people talking about it, and the focus tends to be on features such as:
I attended PyData Berlin 2024 in April, and it was a blast! I met so many colleagues, collaborators, and friends. There was quite some talk of Polars - some people even gathered together for a Polars-themed dinner! It's certainly nice to see people talking about it, and the focus tends to be on features such as:
FireDucks: Pandas but Faster
(bearblog.dev)
My main background is a hedge fund professional, so I deal with finance data all the time and so far the Pandas library has been an indispensable tool in my workflow and my most used Python library.
My main background is a hedge fund professional, so I deal with finance data all the time and so far the Pandas library has been an indispensable tool in my workflow and my most used Python library.