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| Comment | ||||
|---|---|---|---|---|
| pandas | The classic Python dataframe library, offering flexible in-memory data structures and a broad analysis toolkit. | 20.8M | 13 | 44,825 |
| polars | Fast dataframe library built on Apache Arrow and written in Rust, with lazy query optimization and multithreaded execution. | 1.7M | 28 | 34,181 |
| dask | Parallel computing library that scales pandas-like dataframes across cores or clusters for larger-than-memory data. | 915K | 13 | 13,731 |
| pyarrow | Python bindings for Apache Arrow, providing a columnar in-memory format and Table structures used by many dataframe libraries. | 11.8M | 7 | 16,674 |
| pyspark | Python API for Apache Spark, including a distributed DataFrame API for large-scale data processing. | 1.6M | 20 | 49,087 |
| narwhals | Lightweight compatibility layer providing a unified API across pandas, Polars, and other dataframe libraries. | 3.2M | 21 | 1,093 |
| ibis-framework | Portable dataframe API that compiles deferred expressions to SQL or other engines like DuckDB, Spark, and BigQuery. | 78K | 11 | 5,314 |
| datatable | H2O's dataframe library focused on fast, memory-efficient processing of large flat datasets. | 1K | 0 | 1,982 |
| modin | Drop-in replacement for pandas that parallelizes operations across multiple cores or a Ray/Dask cluster. | 54K | 0 | 10,711 |
| vaex | Out-of-core dataframe library for exploring and visualizing datasets larger than available memory. | 721 | 2 | — |