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This article details the implementation of a Parquet file writer in Haskell for the DataHaskell/Dataframe library, enabling efficient data serialization and interoperability with the data science ecosystem.
The article argues that Pandas, a Python DataFrame library, should be replaced by more efficient alternatives like Polars and DuckDB due to its inefficiencies, based on analysis of data sizes and adoption patterns.
This cheatsheet provides a quick reference for using the Polars library in Python, covering installation, data structures, and methods for data analysis and transformation.
Man Group has open-sourced ArcticDB, a high-performance, serverless DataFrame database designed for Python data science and quantitative finance scenarios, supporting billions of rows of time-series data, S3/LMDB/Azure storage, time travel and snapshots, and C++ engine acceleration.
This article explains why and how fenic extended Polars with Rust expression plugins to perform text operations (chunking, prompt templating, fuzzy matching, etc.) natively in the engine, avoiding performance and composition issues of Python UDFs.