Stick with pandas if your code relies on libraries built around it and your datasets fit comfortably in memory. Move to Polars if you're hitting speed or memory limits, because it runs multi-threaded by default, can optimise whole queries lazily and can stream data bigger than your RAM. Polars' own migration guide is the clearest map of what changes when you switch.
I read pandas' home, about and package overview pages, and Polars' home page, user guide and its Coming from pandas guide. Here's what each library says about how it works.
pandas and Polars compared
| pandas | Polars | |
|---|---|---|
| Written in | Python, built on top of NumPy | Rust, with Python, R and NodeJS APIs |
| Row index | Every row gets a label | No index; rows by integer position |
| Memory format | NumPy arrays by default | Apache Arrow memory format |
| Parallelism | Core is single-threaded; Dask to parallelise | Multi-threaded by default |
| Evaluation | Eager only | Eager and lazy, with a query optimiser |
| Data types | Converts loosely, such as integers to floats | Strict |
| Free option (as of October 2026) | Yes, open source | Yes, open source |
Why pandas is still everywhere
pandas began in 2008 at AQR Capital Management, was open sourced in 2009 and has been supported by NumFOCUS since 2015. Its overview describes two main structures, the one-dimensional Series and the two-dimensional DataFrame, which it says handle the vast majority of typical use cases in finance, statistics, social science and engineering. It's built on NumPy and designed to integrate well with many other third-party scientific Python libraries.
pandas' overview lists the kinds of data it handles well: tabular data with mixed column types, as in an SQL table or Excel spreadsheet, ordered and unordered time series, arbitrary matrix data with row and column labels, and other observational or statistical data sets. For anyone coming from R, it says the pandas DataFrame provides everything R's data.frame does and much more.
What Polars changes
Polars' docs describe it as a DataFrame library written from scratch in Rust. The Coming from pandas guide is unusually direct about the differences. Polars doesn't use an index: each row is identified by its position, and the team says that makes queries more predictable because results don't change with the state of an index or a reset_index call.
Performance comes from a few places. Polars uses the Apache Arrow memory format, while pandas uses NumPy arrays by default. Polars runs many operations in parallel, whereas pandas' core is single-threaded and needs a library such as Dask to parallelise. And Polars supports lazy evaluation, where it builds a query plan and optimises it before running, which pandas doesn't do.
The strict types detail
Polars says this causes subtle bugs. Its guide notes that pandas converts types loosely, for example new missing data can turn an integer column into floats. Polars is strict about data types, which its team says leads to fewer bugs and more predictable behaviour. Polars also offers a streaming engine for data that doesn't fit in memory and an optional engine that runs queries on NVIDIA GPUs.
Should you switch? Five checks
- Do your datasets fit easily in memory and run fast enough? pandas is fine; don't change for the sake of it.
- Are jobs slow on a multi-core machine? Polars uses all cores by default.
- Does your data outgrow your RAM? Polars' streaming engine is built for that.
- Do you depend on libraries that expect pandas? Check them before switching; Polars can convert data to NumPy.
- Is your code full of index juggling? Polars' no-index approach removes that whole category of work.
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My advice on pandas or Polars
Learn pandas first if you're new to data analysis, because it's the long-established library, around since 2008. If you already know pandas and your jobs are slow or running out of memory, try Polars on one pipeline and compare; its lazy queries and multi-threading are where the gains show. You can also use both: Polars for heavy processing and pandas where another library needs it.
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Frequently asked questions
Is Polars faster than pandas?
Polars' docs say it's multi-threaded by default and uses a query optimiser, while pandas' core is single-threaded. Polars also says it's faster than all open source solutions that parallelise pandas code.
Does Polars have an index like pandas?
No. Polars doesn't use an index; each row is indexed by its integer position in the table.
Can Polars handle data bigger than memory?
Yes. Polars has a streaming engine for out-of-core processing, so all your data doesn't need to be in memory at the same time.
What is pandas built on?
pandas is built on top of NumPy and is designed to integrate with other scientific Python libraries.
Is Polars only for Python?
No. Polars' core is written in Rust and it's available for Python, R and NodeJS.
