Polars — DataFrames for the new era Docs User guide Rust Python Resources Our services Academy Blog About us We're hiring Polars Cloud We're hiring Docs User guide Rust Python Resources Our services Academy Blog About us We're hiring DataFrames for the new era 675M+ Downloads to date 39k+ GitHub stars Get started Available now -> Polars Cloud Scale your Polars queries from laptop to production workloads in the Cloud or On-prem. Available now -> Polars Cloud Scale your Polars queries from laptop to production workloads in the cloud or on-premise. Polars is an open-source library for data manipulation, known for being one of the fastest data processing solutions on a single machine. It features a well-structured, typed API that is both expressive and easy to use. Benefits 01 Fast Polars is written from the ground up with performance in mind. Its multi-threaded query engine is written in Rust and designed for effective parallelism. Its vectorized and columnar processing enables cache-coherent algorithms and high performance on modern processors. 02 Easy to use You will feel right at home with Polars if you are familiar with data wrangling. Its expressions are intuitive and empower you to write code which is readable and performant at the same time. 03 Open source Polars is and always will be open source. Driven by an active community of developers, everyone is encouraged to add new features and contribute. Polars is free to use under the MIT license. Our services Polars at any scale Start building with Polars open source, then seamlessly scale your workloads with Polars Cloud when your datasets require it. Open source library Polars Open source forever High-performance DataFrame library Free to use and simple to install Get started Managed solution Polars Cloud Same API, zero code changes Cloud or On-Prem deployment Only pay for query execution Try for free Built by developers for developers to achieve up to 50x performance Why use Polars Polars was benchmarked in a derived version of the independent TPC-H benchmark against several other solutions. This benchmark aims to replicate data wrangling operations used in practice. Polars easily trumps other solutions due to its parallel execution engine, efficient algorithms and use of vectorization with SIMD (Single Instruction, Multiple Data). Compared to pandas, it can achieve more than 30x performance gains. Learn more → The benchmark was performed on a c3-highmem-22 with scale factor 10 including I/O. The queries are open source. Quick install Polars is easy to install using your standard package manager. Select your programming language and get started! Python Rust JavaScript pip install polars [dependencies] polars = { version = "x", features = ["lazy", ...]} const pl = require('nodejs-polars'); Leading companies using Polars "Polars revolutionizes data analysis, completely replacing pandas in my setup. It offers massive performance boosts, effortlessly handling data frames with millions of rows. Allowing us to focus on crafting simpler, cleaner code. This efficiency drastically cuts down iteration time, driving improved trading decisions. And speed? Polars isn't just fast—it's lightning-fast." Matt Whitehead Quantitative Researcher @ Optiver " Migrating from pandas to Polars was surprisingly easy. For us, the results speak for themselves. Polars not only solved our initial problem but opened the door to new possibilities. We are excited to use Polars on future data engineering projects. " Paul Duvenage Senior Data Engineer @ Check "The speedup of Polars compared to Pandas is massively noticeable. I generally enjoy writing code that I know is fast." Casey H. Machine Learning Engineer @ G-Research Support Support for all common data formats Polars supports reading and writing to all common data formats. This allows you to easily integrate Polars with your existing data stack. Text: CSV & JSON Binary: Parquet, Delta Lake, AVRO & Excel IPC: Feather, Arrow Databases: MySQL, Postgres, SQL Server, Sqlite, Redshift & Oracle Cloud storage: S3, Azure Blob & Azure File How Polars will make your life easier 01 Easy to use Write your queries the way they were intended. Polars will determine the most efficient way to execute them using its query optimizer. 02 Embarrassingly parallel Complete your queries faster! Polars fully utilizes the power of your machine by dividing the workload among the available CPU cores without any additional configuration or serialization overhead. 03 Apache Arrow Polars utilizes the Apache Arrow memory model allowing you to easily integrate with existing tools in the data landscape. It supports zero-copy data sharing for efficient collaboration. 04 Close to the metal Polars is written from the ground up, designed close to the machine and without external dependencies. This allows for full control of the ecosystem (API, memory & execution). 05 Written in Rust The core of Polars is written in Rust, one of the fastest growing programming languages in the world. Rust allows for high performance with fine-grained control over memory. 06 Out of core Want to process large data sets that are bigger than your memory? Our streaming API allows you to process your results efficiently, eliminating the need to keep all data in memory. 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