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Polars: Implement suggestions by CodeRabbit
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docs/integrate/polars/index.md

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:::{rubric} About
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:::
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[Polars] is a blazingly fast DataFrames library with language bindings for
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Rust, Python, Node.js, R, and SQL. Polars is powered by a multithreaded,
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vectorized query engine, it is open source, and written in Rust.
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[Polars] is a high‑performance DataFrames library with interfaces for
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Rust, Python, Node.js, and R, plus a SQL context. It is powered by a
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multithreaded, vectorized query engine and written in Rust.
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- **Fast:** Written from scratch in Rust and with performance in mind,
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designed close to the machine, and without external dependencies.
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- **Out of Core:** The streaming API allows you to process your results without
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requiring all your data to be in memory at the same time.
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- **Parallel:** Polars' multi-threaded query engine utilises the power of your
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- **Parallel:** Polars' multi-threaded query engine utilizes the power of your
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machine by dividing the workload among the available CPU cores without any
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additional configuration.
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Polars supports reading and writing to many common data formats.
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This allows you to easily integrate Polars into your existing data stack.
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- Text: CSV & JSON
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- Binary: Parquet, Delta Lake, AVRO & Excel
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- IPC: Feather, Arrow
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- Databases: MySQL, Postgres, SQL Server, Sqlite, Redshift & Oracle
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- Cloud Storage: S3, Azure Blob & Azure File
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- Text: CSV, JSON
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- Binary: Parquet, Delta Lake, Avro, Excel
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- IPC: Feather, Arrow IPC
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- Databases: MySQL, PostgreSQL, SQLite, Redshift, SQL Server, (others via ConnectorX)
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- Cloud storage: Amazon S3, Azure Blob/ADLS (via fsspec‑compatible backends)
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:::{rubric} Learn
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:::
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- [Polars code examples]
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[Apache Arrow]: https://arrow.apache.org/
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[Polars]: https://pola.rs/
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[Polars code examples]: https://github.com/crate/cratedb-examples/tree/main/by-dataframe/polars

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