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-1.2.0.post20240718-cp38-abi3-win_amd64.whl (30.6 MB view details)

Uploaded CPython 3.8+ Windows x86-64

polars_nightly-1.2.0.post20240718-cp38-abi3-manylinux_2_24_aarch64.whl (28.4 MB view details)

Uploaded CPython 3.8+ manylinux: glibc 2.24+ ARM64

polars_nightly-1.2.0.post20240718-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.8 MB view details)

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

polars_nightly-1.2.0.post20240718-cp38-abi3-macosx_11_0_arm64.whl (26.1 MB view details)

Uploaded CPython 3.8+ macOS 11.0+ ARM64

polars_nightly-1.2.0.post20240718-cp38-abi3-macosx_10_12_x86_64.whl (29.4 MB view details)

Uploaded CPython 3.8+ macOS 10.12+ x86-64

File details

Details for the file polars_nightly-1.2.0.post20240718-cp38-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.0.post20240718-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 ec4f06d20c6a335344d2e799e328f2e7115d7786f4247f8385003d5a69f6a8d4
MD5 3633f579dde164df7ae05f713023f25b
BLAKE2b-256 0f3fe4ee1e25fc6a07f601ee1f402f71d42919d6947fb1c314cf4d205bb2ef98

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.0.post20240718-cp38-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.0.post20240718-cp38-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 2362d4cd5c7be4e4a3cee4b8880b27050bd6a871d005dccb7d7758576b70dd6b
MD5 6271771168262a53ae79044902d2a1a5
BLAKE2b-256 93d7f2cd208b1cee88c548380acc570b952c35c6a645ced1271c70bb4d1a60ba

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.0.post20240718-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.0.post20240718-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f9131d182379e040f0fd35aba53b4f0cba01c09c9d4081c5f10cb65e2d3edc48
MD5 c4f98b59fc99b7dd29d58a05cea8797a
BLAKE2b-256 8bffeb63db082c1718402fdcea6b0d65f60f25da1a1c6d21dabdab73ee837562

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.0.post20240718-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.0.post20240718-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7d2081a96acd66055312a6579d05d65464c066eaa21aab86306a547827ca9837
MD5 58a99068904d8b69875bd96e7b3321c1
BLAKE2b-256 2f7d19ff99383a96d85dae8add4c9a06fd3a9f3cf2e3143f45ebafb58d1b3db9

See more details on using hashes here.

File details

Details for the file polars_nightly-1.2.0.post20240718-cp38-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.2.0.post20240718-cp38-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 a0fc6a799c7798af91a0fc47632edb23754bcd5cb52cdf8e47bdc987e6077ea0
MD5 f19af883b86114ff2fb724b03e9b52e0
BLAKE2b-256 49ae66d66b0dc24f10b99041812f0323df3009b5d448227468c06b926116ecdf

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