polarscnj 1.0.4
Fork from polars repository, faster conversions to python. All of this was made possible by the incredible work of ritchie46.
License: MIT
Requires Python: >=3.7

<h1 align="center">
  <img src="https://raw.githubusercontent.com/pola-rs/polars-static/master/logos/polars_github_logo_rect_dark_name.svg">
  <br>
</h1>

<div align="center">
  <a href="https://docs.rs/polars/latest/polars/">
    <img src="https://docs.rs/polars/badge.svg" alt="rust docs"/>
  </a>
  <a href="https://github.com/pola-rs/polars/actions">
    <img src="https://github.com/pola-rs/polars/workflows/Build%20and%20test/badge.svg" alt="Build and test"/>
  </a>
  <a href="https://crates.io/crates/polars">
    <img src="https://img.shields.io/crates/v/polars.svg"/>
  </a>
  <a href="https://pypi.org/project/polars/">
    <img src="https://img.shields.io/pypi/v/polars.svg" alt="PyPi Latest Release"/>
  </a>
  <a href="https://www.npmjs.com/package/nodejs-polars">
    <img src="https://img.shields.io/npm/v/nodejs-polars.svg" alt="NPM Latest Release"/>
  </a>
</div>

<p align="center">
  <b>Documentation</b>:
  <a href="https://pola-rs.github.io/polars/py-polars/html/reference/index.html">Python</a>
  -
  <a href="https://pola-rs.github.io/polars/polars/index.html">Rust</a>
  -
  <a href="https://pola-rs.github.io/nodejs-polars/index.html">Node.js</a>
  |
  <b>StackOverflow</b>:
  <a href="https://stackoverflow.com/questions/tagged/python-polars">Python</a>
  -
  <a href="https://stackoverflow.com/questions/tagged/rust-polars">Rust</a>
  -
  <a href="https://stackoverflow.com/questions/tagged/nodejs-polars">Node.js</a>
  |
  <a href="https://pola-rs.github.io/polars-book/">User Guide</a>
  |
  <a href="https://discord.gg/4UfP5cfBE7">Discord</a>
</p>

## Polars: Blazingly fast DataFrames in Rust, Python & Node.js

Polars is a blazingly fast DataFrames library implemented in Rust using
[Apache Arrow Columnar Format](https://arrow.apache.org/docs/format/Columnar.html) as the memory model.

- Lazy | eager execution
- Multi-threaded
- SIMD
- Query optimization
- Powerful expression API
- Hybrid Streaming (larger than RAM datasets)
- Rust | Python | NodeJS | ...

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

```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           │
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘
```

## Performance 🚀🚀

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

In the [TPCH benchmarks](https://www.pola.rs/benchmarks.html) polars is orders of magnitudes faster than pandas, dask, modin and vaex
on full queries (including IO).

Besides fast, polars is also very lightweight. It comes with zero required dependencies, and this shows in the import times:

### import time measurements:

- polars: 70ms
- numpy: 104ms
- pandas: 520ms

## Python setup

Install the latest polars version with:

```sh
pip install polars
```

We also have a conda package (`conda install polars`), however pip is the preferred way to install Polars.

### Install Polars with all optional dependencies.

```sh
pip install 'polars[all]'
pip install 'polars[numpy,pandas,pyarrow]'  # install a subset of all optional dependencies
```

You 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.

## 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 `>=1.58`

## Documentation

Want to know about all the features Polars supports? Read the docs!

## Larger than RAM data

If you have data that does not fit into memory, polars lazy is able to process your query (or parts of your query) in a
streaming fashion, this drastically reduces memory requirements you might be able to process your 250GB dataset on your
laptop. Collect with `collect(allow_streaming=True)` to run the query streaming. (This might be a little slower, but
it is still very fast!)

#### Python

- Installation guide: `pip install polars`
- [Python documentation](https://pola-rs.github.io/polars/py-polars/html/reference/index.html)
- [User guide](https://pola-rs.github.io/polars-book/)

#### Rust

- [Rust documentation (master branch)](https://pola-rs.github.io/polars/polars/index.html)
- [User guide](https://pola-rs.github.io/polars-book/)

#### Node

- Installation guide: `yarn add nodejs-polars`
- [Node documentation](https://pola-rs.github.io/nodejs-polars/index.html)
- [User guide](https://pola-rs.github.io/polars-book/)
- [Github](https://github.com/pola-rs/nodejs-polars)

## Contribution

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

## \[Python\]: 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](https://www.rust-lang.org/tools/install)
2. Install [maturin](https://maturin.rs/): `pip install maturin`
3. Choose any of:
   - Fastest binary, very long compile times:
     ```sh
     $ cd py-polars && maturin develop --release -- -C target-cpu=native
     ```
   - Fast binary, Shorter compile times:
     ```sh
     $ cd py-polars && maturin develop --release -- -C codegen-units=16 -C lto=thin -C target-cpu=native
     ```

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`.

## Arrow2

Polars has transitioned to [arrow2](https://crates.io/crates/arrow2).
Arrow2 is a faster and safer implementation of the [Apache Arrow Columnar Format](https://arrow.apache.org/docs/format/Columnar.html).
Arrow2 also has a more granular code base, helping to reduce the compiler bloat.

## Use custom Rust function in python?

See [this example](./examples/python_rust_compiled_function).

# 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 polars is faster and consumes less memory.

# Legacy

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](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) target features.

## 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

[<img src="https://raw.githubusercontent.com/pola-rs/polars-static/master/sponsors/xomnia.png" height="40" />](https://www.xomnia.com/) &emsp; [<img src="https://www.jetbrains.com/company/brand/img/jetbrains_logo.png" height="50" />](https://www.jetbrains.com)