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Comparison with other tools

These are several libraries and tools that share similar functionalities with Polars. This often leads to questions from data experts about what the differences are. Below is a short comparison between some of the more popular data processing tools and Polars, to help data experts make a deliberate decision on which tool to use.

You can find performance benchmarks (h2oai benchmark) of these tools here: Polars blog post or a more recent benchmark done by DuckDB


Pandas stands as a widely-adopted and comprehensive tool in Python data analysis, renowned for its rich feature set and strong community support. However, due to its single threaded nature, it can struggle with performance and memory usage on medium and large datasets.

In contrast, Polars is optimised for high-performance multithreaded computing on single nodes, providing significant improvements in speed and memory efficiency, particularly for medium to large data operations. Its more composable and stricter API results in greater expressiveness and fewer schema-related bugs.


Dask extends Pandas' capabilities to large, distributed datasets. Dask mimics Pandas' API, offering a familiar environment for Pandas users, but with the added benefit of parallel and distributed computing.

While Dask excels at scaling Pandas workflows across clusters, it only supports a subset of the Pandas API and therefore cannot be used for all use cases. Polars offers a more versatile API that delivers strong performance within the constraints of a single node.

The choice between Dask and Polars often comes down to familiarity with the Pandas API and the need for distributed processing for extremely large datasets versus the need for efficiency and speed in a vertically scaled environment for a wide range of use cases.


Similar to Dask. In 2023, Snowflake acquired Ponder, the organisation that maintains Modin.


Spark (specifically PySpark) represents a different approach to large-scale data processing. While Polars has an optimised performance for single-node environments, Spark is designed for distributed data processing across clusters, making it suitable for extremely large datasets.

However, Spark's distributed nature can introduce complexity and overhead, especially for small datasets and tasks that can run on a single machine. Another consideration is collaboration between data scientists and engineers. As they typically work with different tools (Pandas and Pyspark), refactoring is often required by engineers to deploy data scientists' data processing pipelines. Polars offers a single syntax that, due to vertical scaling, works in local environments and on a single machine in the cloud.

The choice between Polars and Spark often depends on the scale of data and the specific requirements of the processing task. If you need to process TBs of data, Spark is a better choice.


Polars and DuckDB have many similarities. However, DuckDB is focused on providing an in-process SQL OLAP database management system, while Polars is focused on providing a scalable DataFrame interface to many languages. The different front-ends lead to different optimisation strategies and different algorithm prioritisation. The interoperability between both is zero-copy. DuckDB offers a guide on how to integrate with Polars.