Comparison with other tools

There 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 make a deliberate decision on which tool to use.

You can find performance benchmarks of these tools in this Polars blog post or in the db-benchmark maintained by DuckDB Labs. You can also run the benchmarks yourself using the polars-benchmark repository.

pandas

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 a single machine, 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. For a hands-on guide, see Coming from pandas.

Note

The name pandas is written in lowercase, even at the start of a sentence, as requested by the pandas project.

Dask

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 on a single machine, and scales across a cluster with the distributed engine.

Dask also supports workloads beyond DataFrames, such as distributed arrays and custom task graphs, which are outside the scope of Polars.

Modin

Modin provides a drop-in replacement for the pandas API that parallelises work across multiple cores or a cluster, using Ray or Dask as the execution backend. The same considerations as for Dask apply. In 2023, Snowflake acquired Ponder, the organisation that maintains Modin.

Spark

Spark (specifically PySpark) represents a different approach to large-scale data processing. Spark was designed for distributed data processing across clusters, making it suitable for extremely large datasets, but its architecture carries the overhead of distributed execution into every workload, including those that fit on a single machine. Polars is a suite of engines behind a single API: the in-memory and streaming engines are built for optimal performance on a single machine, while the distributed engine scales the same syntax across a cluster, available as Polars Cloud on AWS and as Polars On-Prem on Kubernetes.

With Spark, data scientists and engineers typically work with different tools (pandas and PySpark), so deploying a pipeline often requires refactoring by engineers. With Polars, the same code runs in local environments, on a single machine in the cloud, and across a cluster, so scaling a pipeline does not require rewriting it. For a hands-on guide, see Coming from Apache Spark.

DuckDB

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.