Skip to main content

Blazingly fast DataFrame library

Project description

Polars logo

Documentation: Python - Rust - Node.js - R | StackOverflow: Python - Rust - Node.js - R | User guide | Discord

Polars: Blazingly fast DataFrames in Rust, Python, Node.js, R, and SQL

Polars is a DataFrame interface on top of an OLAP Query Engine implemented in Rust using Apache Arrow Columnar Format as the memory model.

  • Lazy | eager execution
  • Multi-threaded
  • SIMD
  • Query optimization
  • Powerful expression API
  • Hybrid Streaming (larger-than-RAM datasets)
  • Rust | Python | NodeJS | R | ...

To learn more, read the user guide.

Python

>>> import polars as pl
>>> df = pl.DataFrame(
...     {
...         "A": [1, 2, 3, 4, 5],
...         "fruits": ["banana", "banana", "apple", "apple", "banana"],
...         "B": [5, 4, 3, 2, 1],
...         "cars": ["beetle", "audi", "beetle", "beetle", "beetle"],
...     }
... )

# embarrassingly parallel execution & very expressive query language
>>> df.sort("fruits").select(
...     "fruits",
...     "cars",
...     pl.lit("fruits").alias("literal_string_fruits"),
...     pl.col("B").filter(pl.col("cars") == "beetle").sum(),
...     pl.col("A").filter(pl.col("B") > 2).sum().over("cars").alias("sum_A_by_cars"),
...     pl.col("A").sum().over("fruits").alias("sum_A_by_fruits"),
...     pl.col("A").reverse().over("fruits").alias("rev_A_by_fruits"),
...     pl.col("A").sort_by("B").over("fruits").alias("sort_A_by_B_by_fruits"),
... )
shape: (5, 8)
┌──────────┬──────────┬──────────────┬─────┬─────────────┬─────────────┬─────────────┬─────────────┐
 fruits    cars      literal_stri  B    sum_A_by_ca  sum_A_by_fr  rev_A_by_fr  sort_A_by_B 
 ---       ---       ng_fruits     ---  rs           uits         uits         _by_fruits  
 str       str       ---           i64  ---          ---          ---          ---         
                     str                i64          i64          i64          i64         
╞══════════╪══════════╪══════════════╪═════╪═════════════╪═════════════╪═════════════╪═════════════╡
 "apple"   "beetle"  "fruits"      11   4            7            4            4           
 "apple"   "beetle"  "fruits"      11   4            7            3            3           
 "banana"  "beetle"  "fruits"      11   4            8            5            5           
 "banana"  "audi"    "fruits"      11   2            8            2            2           
 "banana"  "beetle"  "fruits"      11   4            8            1            1           
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘

SQL

>>> df = pl.scan_csv("docs/data/iris.csv")
>>> ## OPTION 1
>>> # run SQL queries on frame-level
>>> df.sql("""
...	SELECT species,
...	  AVG(sepal_length) AS avg_sepal_length
...	FROM self
...	GROUP BY species
...	""").collect()
shape: (3, 2)
┌────────────┬──────────────────┐
 species     avg_sepal_length 
 ---         ---              
 str         f64              
╞════════════╪══════════════════╡
 Virginica   6.588            
 Versicolor  5.936            
 Setosa      5.006            
└────────────┴──────────────────┘
>>> ## OPTION 2
>>> # use pl.sql() to operate on the global context
>>> df2 = pl.LazyFrame({
...    "species": ["Setosa", "Versicolor", "Virginica"],
...    "blooming_season": ["Spring", "Summer", "Fall"]
...})
>>> pl.sql("""
... SELECT df.species,
...     AVG(df.sepal_length) AS avg_sepal_length,
...     df2.blooming_season
... FROM df
... LEFT JOIN df2 ON df.species = df2.species
... GROUP BY df.species, df2.blooming_season
... """).collect()

SQL commands can also be run directly from your terminal using the Polars CLI:

# run an inline SQL query
> polars -c "SELECT species, AVG(sepal_length) AS avg_sepal_length, AVG(sepal_width) AS avg_sepal_width FROM read_csv('docs/data/iris.csv') GROUP BY species;"

# run interactively
> polars
Polars CLI v0.3.0
Type .help for help.

> SELECT species, AVG(sepal_length) AS avg_sepal_length, AVG(sepal_width) AS avg_sepal_width FROM read_csv('docs/data/iris.csv') GROUP BY species;

Refer to the Polars CLI repository for more information.

Performance 🚀🚀

Blazingly fast

Polars is very fast. In fact, it is one of the best performing solutions available. See the TPC-H benchmarks results.

Lightweight

Polars is also very lightweight. It comes with zero required dependencies, and this shows in the import times:

  • polars: 70ms
  • numpy: 104ms
  • pandas: 520ms

Handles larger-than-RAM data

If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a streaming fashion. This drastically reduces memory requirements, so you might be able to process your 250GB dataset on your laptop. Collect with collect(streaming=True) to run the query streaming. (This might be a little slower, but it is still very fast!)

Setup

Python

Install the latest Polars version with:

pip install polars

We also have a conda package (conda install -c conda-forge polars), however pip is the preferred way to install Polars.

Install Polars with all optional dependencies.

pip install 'polars[all]'

You can also install a subset of all optional dependencies.

pip install 'polars[numpy,pandas,pyarrow]'

See the User Guide for more details on optional dependencies

To see the current Polars version and a full list of its optional dependencies, run:

pl.show_versions()

Releases happen quite often (weekly / every few days) at the moment, so updating Polars regularly to get the latest bugfixes / features might not be a bad idea.

Rust

You can take latest release from crates.io, or if you want to use the latest features / performance improvements point to the main branch of this repo.

polars = { git = "https://github.com/pola-rs/polars", rev = "<optional git tag>" }

Requires Rust version >=1.71.

Contributing

Want to contribute? Read our contributing guide.

Python: compile Polars from source

If you want a bleeding edge release or maximal performance you should compile Polars from source.

This can be done by going through the following steps in sequence:

  1. Install the latest Rust compiler

  2. Install maturin: pip install maturin

  3. cd py-polars and choose one of the following:

    • make build-release, fastest binary, very long compile times
    • make build-opt, fast binary with debug symbols, long compile times
    • make build-debug-opt, medium-speed binary with debug assertions and symbols, medium compile times
    • make build, slow binary with debug assertions and symbols, fast compile times

    Append -native (e.g. make build-release-native) to enable further optimizations specific to your CPU. This produces a non-portable binary/wheel however.

Note that the Rust crate implementing the Python bindings is called py-polars to distinguish from the wrapped Rust crate polars itself. However, both the Python package and the Python module are named polars, so you can pip install polars and import polars.

Using custom Rust functions in Python

Extending Polars with UDFs compiled in Rust is easy. We expose PyO3 extensions for DataFrame and Series data structures. See more in https://github.com/pola-rs/pyo3-polars.

Going big...

Do you expect more than 2^32 (~4.2 billion) rows? Compile Polars with the bigidx feature flag or, for Python users, install pip install polars-u64-idx.

Don't use this unless you hit the row boundary as the default build of Polars is faster and consumes less memory.

Legacy

Do you want Polars to run on an old CPU (e.g. dating from before 2011), or on an x86-64 build of Python on Apple Silicon under Rosetta? Install pip install polars-lts-cpu. This version of Polars is compiled without AVX target features.

Sponsors

JetBrains logo

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

polars_nightly-1.2.1.post20240727-cp38-abi3-win_amd64.whl (30.9 MB view details)

Uploaded CPython 3.8+ Windows x86-64

polars_nightly-1.2.1.post20240727-cp38-abi3-manylinux_2_24_aarch64.whl (28.6 MB view details)

Uploaded CPython 3.8+ manylinux: glibc 2.24+ ARM64

polars_nightly-1.2.1.post20240727-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (31.1 MB view details)

Uploaded CPython 3.8+ manylinux: glibc 2.17+ x86-64

polars_nightly-1.2.1.post20240727-cp38-abi3-macosx_11_0_arm64.whl (26.2 MB view details)

Uploaded CPython 3.8+ macOS 11.0+ ARM64

polars_nightly-1.2.1.post20240727-cp38-abi3-macosx_10_12_x86_64.whl (29.7 MB view details)

Uploaded CPython 3.8+ macOS 10.12+ x86-64

File details

Details for the file polars_nightly-1.2.1.post20240727-cp38-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.1.post20240727-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 6041bc578b780f6ee58d14406a5b01ef959c4a23cddad447f4aff90494c24f45
MD5 bbcdacf023fbcab93a84f30b6748cf25
BLAKE2b-256 e07dd1c1d2398aeee10c5f150a74f8cdcdee45e230ec1e838f6c200ede755a5c

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.1.post20240727-cp38-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.1.post20240727-cp38-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 0382e7e49d6c08d9895f753dea0a0e803d02c6f1a7177e1e36d09f416acacbec
MD5 74f8a61fdad5af27017bf62be751014e
BLAKE2b-256 5f2984029b509f19ec54803d1096e0402daf634ab96086ce377fd5d2fe902800

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.1.post20240727-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.1.post20240727-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2595ca658132a15c45a2cf3b183f03e57091ba7d9766ca4a492b3f5efb0d8fa3
MD5 5e8bb8029e639d4f723fe066b7affa77
BLAKE2b-256 0d475a63fb70abae3934506ce6f0fa335261b0aac25299d11e6afb5923670944

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.1.post20240727-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.1.post20240727-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e46a445c9a7ce720a018df7fe41bae620fef2738e674c62b5a36f9d978417d8a
MD5 bb80cd440ad6ea839e33647c76162c13
BLAKE2b-256 64a834f9bfd1de411c08a4fe97d7bcd9175274bb28358543fce2d19e9b3d795e

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.1.post20240727-cp38-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.1.post20240727-cp38-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 4ea578fa923cf6f266bccb8198ea5761d2d922a06e9412c7ad4254a324507cab
MD5 c21451eb775be0efb7d243dcbcc92539
BLAKE2b-256 50c21d52edd0b0948986094643b8a70a488efb47bb11be2b7ffdf681fcdb1db0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page