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-1.3.0.post20240729-cp38-abi3-win_amd64.whl (30.9 MB view details)

Uploaded CPython 3.8+ Windows x86-64

polars_nightly-1.3.0.post20240729-cp38-abi3-manylinux_2_24_aarch64.whl (28.6 MB view details)

Uploaded CPython 3.8+ manylinux: glibc 2.24+ ARM64

polars_nightly-1.3.0.post20240729-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (31.0 MB view details)

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

polars_nightly-1.3.0.post20240729-cp38-abi3-macosx_11_0_arm64.whl (26.2 MB view details)

Uploaded CPython 3.8+ macOS 11.0+ ARM64

polars_nightly-1.3.0.post20240729-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-1.3.0.post20240729-cp38-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.3.0.post20240729-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 f54d3f11e2b302dc171f03de71b789cd341321e95c31e836727cec6750e98e96
MD5 f8dae542455c5b37e0188a32a40406d0
BLAKE2b-256 f3838c0feb305ba53720a907e94cbefbc42971157ec609239b3cba2dde4cd0cc

See more details on using hashes here.

File details

Details for the file polars_nightly-1.3.0.post20240729-cp38-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.3.0.post20240729-cp38-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 a526cd33c986826ad88b443bffd47f3a06d9086edbca9d31a6b0b290555b320b
MD5 cdf54e6ea59d1bb665914e6d747ca5b8
BLAKE2b-256 bd49f33232d99483574606bcdcfaa73029c2e4eca69b85d43ea4772f64018540

See more details on using hashes here.

File details

Details for the file polars_nightly-1.3.0.post20240729-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.3.0.post20240729-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 40155c86a2dea0a6a7fc7d8ba28ca61cccae414571fab7ac0f634e8b2e0e7747
MD5 312f596d21ebe6778d6c09831029de98
BLAKE2b-256 53c7e7da64589ee12a3c03648c1c94c32336f7afccb65348eaa03f2626370e4a

See more details on using hashes here.

File details

Details for the file polars_nightly-1.3.0.post20240729-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.3.0.post20240729-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 891c274dbe52c606961600bd22fff21468e5f9a0583f1ddc4f4c4eb5d970829d
MD5 396e0ff7ab24b4c41e62e401aa05a743
BLAKE2b-256 1cfbb282e5633039ff36e7d53eea91dddf9a6604a1cb96ca192c885be223dc86

See more details on using hashes here.

File details

Details for the file polars_nightly-1.3.0.post20240729-cp38-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.3.0.post20240729-cp38-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 2727eb4a6fb28d83f853a16ca13a69dd941cb8b12ec30639cf962bae0792ad27
MD5 cf922de1c9521b8b3719a528cdc76af7
BLAKE2b-256 b2f290c6efdae8209b79b8df2a64dc1763565912fa902eb02e2d0310f8831e11

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