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_ipc("file.arrow")
>>> # create a SQL context, registering the frame as a table
>>> sql = pl.SQLContext(my_table=df)
>>> # create a SQL query to execute
>>> query = """
...   SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM my_table
...   WHERE id1 = 'id016'
...   LIMIT 10
... """
>>> ## OPTION 1
>>> # run the query, materializing as a DataFrame
>>> sql.execute(query, eager=True)
 shape: (1, 2)
 ┌────────┬────────┐
  sum_v1  min_v2 
  ---     ---    
  i64     i64    
 ╞════════╪════════╡
  298268  1      
 └────────┴────────┘
>>> ## OPTION 2
>>> # run the query but don't immediately materialize the result.
>>> # this returns a LazyFrame that you can continue to operate on.
>>> lf = sql.execute(query)
>>> (lf.join(other_table)
...      .group_by("foo")
...      .agg(
...     pl.col("sum_v1").count()
... ).collect())

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

# run an inline SQL query
> polars -c "SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM read_ipc('file.arrow') WHERE id1 = 'id016' LIMIT 10"

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

> SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM read_ipc('file.arrow') WHERE id1 = 'id016' LIMIT 10;

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-1.1.0.post20240709-cp38-abi3-win_amd64.whl (30.5 MB view details)

Uploaded CPython 3.8+ Windows x86-64

polars_nightly-1.1.0.post20240709-cp38-abi3-manylinux_2_24_aarch64.whl (28.4 MB view details)

Uploaded CPython 3.8+ manylinux: glibc 2.24+ ARM64

polars_nightly-1.1.0.post20240709-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.7 MB view details)

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

polars_nightly-1.1.0.post20240709-cp38-abi3-macosx_11_0_arm64.whl (26.0 MB view details)

Uploaded CPython 3.8+ macOS 11.0+ ARM64

polars_nightly-1.1.0.post20240709-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-1.1.0.post20240709-cp38-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.1.0.post20240709-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 b8ac0da6ed3309989a1038fdacdcf8cb3f660e8b721e0df6860b77e371559bd8
MD5 23d9b4a30ff8f259eabc3a74f164918f
BLAKE2b-256 ac6a0bd468518915542b1323ae1ac099cd25c7e8ae92ace2f2df09fe9047a348

See more details on using hashes here.

File details

Details for the file polars_nightly-1.1.0.post20240709-cp38-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.1.0.post20240709-cp38-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 ec796f7bf09a9c3fcd581e2eed400374bafaf7b0c554b21c5c85d7647a40c516
MD5 a7b1da756864a91473940b0fb5b96133
BLAKE2b-256 675a9b9126af985f5d2af61cc4890d28e1c2766011a2682ec80741dabe29192a

See more details on using hashes here.

File details

Details for the file polars_nightly-1.1.0.post20240709-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.1.0.post20240709-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ed8896a0023b4b60201de05541fe1d9314ff0e946507fa357ecd3477739fd29a
MD5 96a1bca4f8915497c8b5569801d77d0b
BLAKE2b-256 3d4f5db97f92f9d0a302c5bbb2cb1bbbea22af5d9797089822414ddf53030ade

See more details on using hashes here.

File details

Details for the file polars_nightly-1.1.0.post20240709-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.1.0.post20240709-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5d63d9c7b8e79036a8df9272b7a272685a6ef47c6f15b47d7a3c6048f4c4ea94
MD5 21e426ab4af4321f478a4a58e8320fb9
BLAKE2b-256 57022736f263218e94b04bd9d2bfc46b66fe7b5c776ee8d612c7ab36d7040e3f

See more details on using hashes here.

File details

Details for the file polars_nightly-1.1.0.post20240709-cp38-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for polars_nightly-1.1.0.post20240709-cp38-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 e17fde770371cae6171a46fb9f3e0ec9e0b104ce80fe45423ea48b80db47b8f3
MD5 5253acc6e7714a083ee72d4be23c821a
BLAKE2b-256 a01e0e96ec3b1b6ae4fb0f4e5390dced8ef6710fb5d3c7d4a7d92f0bbd359fc1

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