Metadata-Version: 2.1
Name: polars
Version: 0.10.1
Requires-Dist: numpy
Requires-Dist: pyarrow>=4.0.*; extra == 'pandas'
Requires-Dist: pandas; extra == 'pandas'
Requires-Dist: pyarrow>=4.0.*; extra == 'pyarrow'
Requires-Dist: connectorx; extra == 'connectorx'
Requires-Dist: fsspec; extra == 'fsspec'
Provides-Extra: pandas
Provides-Extra: pyarrow
Provides-Extra: connectorx
Provides-Extra: fsspec
Home-Page: https://github.com/pola-rs/polars
Author: ritchie46 <ritchie46@gmail.com>
Author-email: ritchie46 <ritchie46@gmail.com>
License: MIT
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# Polars
[![rust docs](https://docs.rs/polars/badge.svg)](https://docs.rs/polars/latest/polars/)
[![Build and test](https://github.com/pola-rs/polars/workflows/Build%20and%20test/badge.svg)](https://github.com/pola-rs/polars/actions)
[![](https://img.shields.io/crates/v/polars.svg)](https://crates.io/crates/polars)
[![Gitter](https://badges.gitter.im/polars-rs/community.svg)](https://gitter.im/polars-rs/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

## Blazingly fast DataFrames in Rust & Python

Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow(2) as memory model.

* Lazy | eager execution
* Multi-threaded
* SIMD
* Query optimization
* Powerful expression API
* Rust | Python | ...

To learn more, read the [User Guide](https://pola-rs.github.io/polars-book/).

```python
>>> 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",
    lit("fruits").alias("literal_string_fruits"),
    col("B").filter(col("cars") == "beetle").sum(),
    col("A").filter(col("B") > 2).sum().over("cars").alias("sum_A_by_cars"),       # groups by "cars"
    col("A").sum().over("fruits").alias("sum_A_by_fruits"),                        # groups by "fruits"
    col("A").reverse().over("fruits").flatten().alias("rev_A_by_fruits"),          # groups by "fruits
    col("A").sort_by("B").over("fruits").flatten().alias("sort_A_by_B_by_fruits")  # groups 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           │
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘

```



## Performance 🚀🚀
Polars is very fast, and in fact is one of the best performing solutions available. 
See the results in [h2oai's db-benchmark](https://h2oai.github.io/db-benchmark/).

## Rust setup
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.

```toml
polars = { git = "https://github.com/pola-rs/polars", rev = "<optional git tag>" }
```
## Rust version
Required Rust version `>=1.52`

## Python users read this!
Polars is currently transitioning from `py-polars` to `polars`. Some docs may still refer the old name. 

Install the latest polars version with: 
`$ pip3 install polars`

## Documentation
Want to know about all the features Polars support? Read the docs!

#### Rust
* [Documentation (master branch)](https://pola-rs.github.io/polars/polars/index.html). 
* [User Guide](https://pola-rs.github.io/polars-book/)
    
#### Python
* installation guide: `$ pip3 install polars`
* [User Guide](https://pola-rs.github.io/polars-book/)
* [Reference guide](https://pola-rs.github.io/polars/py-polars/html/reference/index.html)

## Contribution
Want to contribute? Read our [contribution guideline](https://github.com/pola-rs/polars/blob/master/CONTRIBUTING.md).

## \[Python\] compile py-polars from source
If you want a bleeding edge release or maximal performance you should compile **py-polars** from source.

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

1. install the latest [Rust compiler](https://www.rust-lang.org/tools/install)
2. `$ pip3 install maturin`
4.  Choose any of:
  * Very long compile times, fastest binary: `$ cd py-polars && maturin develop --rustc-extra-args="-C target-cpu=native" --release`
  * Shorter compile times, fast binary: `$ cd py-polars && maturin develop --rustc-extra-args="-C codegen-units=16 -C lto=thin -C target-cpu=native" --release
    `

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` (previously, these were called `py-polars` and `pypolars`).

## Arrow2
Polars has transitioned to [arrow2](https://crates.io/crates/arrow2). Arrow2 is a faster and safer implementation of the arrow spec. 
Arrow2 also has a more granular code base, helping to reduce the compiler bloat.
There is still a maintained `arrow-rs` branch for users who want to use another backend.

## Acknowledgements
Development of Polars is proudly powered by

[![Xomnia](https://raw.githubusercontent.com/pola-rs/polars-static/master/sponsors/xomnia.png)](https://www.xomnia.com/)

## Sponsors
* [Xomnia](https://www.xomnia.com/)
* [JetBrains](https://www.jetbrains.com/company/brand/img/jetbrains_logo.png)

