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| Comment | ||||
|---|---|---|---|---|
| pandas | The classic Python dataframe library, offering flexible in-memory data structures and a broad analysis toolkit. | 19.9M | 11 | 44,807 |
| polars | Fast dataframe library built on Apache Arrow and written in Rust, with lazy query optimization and multithreaded execution. | 1.6M | 30 | 34,177 |
| dask | Parallel computing library that scales pandas-like dataframes across cores or clusters for larger-than-memory data. | 875K | 15 | 13,728 |
| pyarrow | Python bindings for Apache Arrow, providing a columnar in-memory format and Table structures used by many dataframe libraries. | 11.0M | 6 | 16,669 |
| pyspark | Python API for Apache Spark, including a distributed DataFrame API for large-scale data processing. | 1.6M | 17 | 49,079 |
| narwhals | Lightweight compatibility layer providing a unified API across pandas, Polars, and other dataframe libraries. | 2.8M | 25 | 1,092 |
| ibis-framework | Portable dataframe API that compiles deferred expressions to SQL or other engines like DuckDB, Spark, and BigQuery. | 66K | 18 | 5,314 |
| datatable | H2O's dataframe library focused on fast, memory-efficient processing of large flat datasets. | 2K | 0 | 1,982 |
| modin | Drop-in replacement for pandas that parallelizes operations across multiple cores or a Ray/Dask cluster. | 57K | 2 | 10,711 |
| vaex | Out-of-core dataframe library for exploring and visualizing datasets larger than available memory. | 798 | 1 | — |