# 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](https://pola.rs/posts/benchmarks/) or a more recent benchmark
[done by DuckDB](https://duckdblabs.github.io/db-benchmark/)

### 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 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

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.

### Modin

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

### Spark

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.

### 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](https://duckdb.org/docs/guides/python/polars.html).
