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Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as the memory model.
- Lazy | eager execution
- Multi-threaded
- SIMD
- Query optimization
- Powerful expression API
- Rust | Python | ...
To learn more, read the User Guide.
>>> 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 │
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘Polars is very fast. In fact, it is one of the best performing solutions available. See the results in h2oai's db-benchmark.
Install the latest polars version with:
pip install polarsWe also have a conda package (conda install polars), however pip is the preferred way to install Polars.
pip install 'polars[all]'
pip install 'polars[numpy,pandas,pyarrow]' # install a subset of all optional dependenciesYou can also install the dependencies directly.
| Tag | Description |
|---|---|
| all | Install all optional dependencies (all of the following) |
| pandas | Install with Pandas for converting data to and from Pandas Dataframes/Series |
| numpy | Install with numpy for converting data to and from numpy arrays |
| pyarrow | Reading data formats using PyArrow |
| fsspec | Support for reading from remote file systems |
| connectorx | Support for reading from SQL databases |
| xlsx2csv | Support for reading from Excel files |
| timezone | Timezone support, only needed if 1. you are on Python < 3.9 and/or 2. you are on Windows, otherwise no dependencies will be installed |
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.
You can take latest release from crates.io, or if you want to use the latest features / performance improvements
point to the master branch of this repo.
polars = { git = "https://github.com/pola-rs/polars", rev = "<optional git tag>" }Required Rust version >=1.58
Want to know about all the features Polars supports? Read the docs!
- Installation guide:
pip install polars - Python documentation
- User guide
- Installation guide:
yarn add nodejs-polars - Node documentation
- User guide
- Github
Want to contribute? Read our contribution guideline.
[Python]: compile polars from source
< 896C a id="user-content-python-compile-polars-from-source" class="anchor" aria-label="Permalink: [Python]: compile polars from source" href="#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:
- Install the latest Rust compiler
- Install maturin:
pip install maturin - Choose any of:
- Fastest binary, very long compile times:
$ cd py-polars && maturin develop --release -- -C target-cpu=native
- Fast binary, Shorter compile times:
$ cd py-polars && maturin develop --release -- -C codegen-units=16 -C lto=thin -C target-cpu=native
- Fastest binary, very long compile times:
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.
Polars has transitioned to arrow2. Arrow2 is a faster and safer implementation of the Apache Arrow Columnar Format. Arrow2 also has a more granular code base, helping to reduce the compiler bloat.
See this example.
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 polars is faster and consumes less memory.
Do you want polars to run on an old CPU (e.g. dating from before 2011)? Install pip polars-lts-cpu. This polars project is
compiled without avx target features.
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