Polars
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Polars is a highly performant DataFrame library for manipulating structured data. The core is written in Rust, but the library is also available in Python. Its key features are:
Cost / License
- Free
- Open Source
EUPlatforms
- Mac
- Windows
- Linux
- BSD
- Python
Features
- Rust
- Scientific data analysis
- Data science
Tags
- Python
- Data Analysis
- data-structures
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Polars information
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Polars Licensing
Open Source and Free product.Written in
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19 alternatives listedSupported Languages
- English
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DevelopmentGitHub repository
- 36,560 Stars
- 2,514 Forks
- 2673 Open Issues
- Updated
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Polars is a highly performant DataFrame library for manipulating structured data. The core is written in Rust, but the library is also available in Python. Its key features are:
- Fast: Polars is written from the ground up, designed close to the machine and without external dependencies.
- I/O: First class support for all common data storage layers: local, cloud storage & databases.
- Easy to use: Write your queries the way they were intended. Polars, internally, will determine the most efficient way to execute using its query optimizer.
- Out of Core: Polars supports out of core data transformation with its streaming API. Allowing you to process your results without requiring all your data to be in memory at the same time
- Parallel: Polars fully utilises the power of your machine by dividing the workload among the available CPU cores without any additional configuration.
- Vectorized Query Engine: Polars uses Apache Arrow, a columnar data format, to process your queries in a vectorized manner. It uses SIMD to optimize CPU usage.