<h1 align="center">
<img src="https://raw.githubusercontent.com/pola-rs/polars-static/master/banner/polars_github_banner.svg" alt="Polars logo">
<br>
</h1>
<div align="center">
<a href="https://crates.io/crates/polars">
<img src="https://img.shields.io/crates/v/polars.svg" alt="crates.io Latest Release"/>
</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>
<a href="https://rpolars.r-universe.dev">
<img src="https://rpolars.r-universe.dev/badges/polars" alt="R-universe Latest Release"/>
</a>
<a href="https://doi.org/10.5281/zenodo.7697217">
<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7697217.svg" alt="DOI Latest Release"/>
</a>
</div>
<p align="center">
<b>Documentation</b>:
<a href="https://docs.pola.rs/api/python/stable/reference/index.html">Python</a>
-
<a href="https://docs.rs/polars/latest/polars/">Rust</a>
-
<a href="https://pola-rs.github.io/nodejs-polars/index.html">Node.js</a>
-
<a href="https://pola-rs.github.io/r-polars/index.html">R</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://stackoverflow.com/questions/tagged/r-polars">R</a>
|
<a href="https://docs.pola.rs/">User guide</a>
|
<a href="https://discord.gg/4UfP5cfBE7">Discord</a>
</p>
## 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](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 | R | ...
To learn more, read the [user guide](https://docs.pola.rs/).
## Python
```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
```python
>>> 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:
```bash
# 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](https://github.com/pola-rs/polars-cli) for more information.
## Performance 🚀🚀
### Blazingly fast
Polars is very fast. In fact, it is one of the best performing solutions available. See the [PDS-H benchmarks](https://www.pola.rs/benchmarks.html) 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:
```sh
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.
```sh
pip install 'polars[all]'
```
You can also install a subset of all optional dependencies.
```sh
pip install 'polars[numpy,pandas,pyarrow]'
```
See the [User Guide](https://docs.pola.rs/user-guide/installation/#feature-flags) for more details on optional dependencies
To see the current Polars version and a full list of its optional dependencies, run:
```python
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.
```toml
polars = { git = "https://github.com/pola-rs/polars", rev = "<optional git tag>" }
```
Requires Rust version `>=1.80`.
## Contributing
Want to contribute? Read our [contributing guide](https://docs.pola.rs/development/contributing/).
## 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. `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](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) target
features.
## Sponsors
[<img src="https://www.jetbrains.com/company/brand/img/jetbrains_logo.png" height="50" alt="JetBrains logo" />](https://www.jetbrains.com)
Raw data
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"home_page": null,
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"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "dataframe, arrow, out-of-core",
"author": null,
"author_email": "Ritchie Vink <ritchie46@gmail.com>",
"download_url": null,
"platform": null,
"description": "<h1 align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pola-rs/polars-static/master/banner/polars_github_banner.svg\" alt=\"Polars logo\">\n <br>\n</h1>\n\n<div align=\"center\">\n <a href=\"https://crates.io/crates/polars\">\n <img src=\"https://img.shields.io/crates/v/polars.svg\" alt=\"crates.io Latest Release\"/>\n </a>\n <a href=\"https://pypi.org/project/polars/\">\n <img src=\"https://img.shields.io/pypi/v/polars.svg\" alt=\"PyPi Latest Release\"/>\n </a>\n <a href=\"https://www.npmjs.com/package/nodejs-polars\">\n <img src=\"https://img.shields.io/npm/v/nodejs-polars.svg\" alt=\"NPM Latest Release\"/>\n </a>\n <a href=\"https://rpolars.r-universe.dev\">\n <img src=\"https://rpolars.r-universe.dev/badges/polars\" alt=\"R-universe Latest Release\"/>\n </a>\n <a href=\"https://doi.org/10.5281/zenodo.7697217\">\n <img src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.7697217.svg\" alt=\"DOI Latest Release\"/>\n </a>\n</div>\n\n<p align=\"center\">\n <b>Documentation</b>:\n <a href=\"https://docs.pola.rs/api/python/stable/reference/index.html\">Python</a>\n -\n <a href=\"https://docs.rs/polars/latest/polars/\">Rust</a>\n -\n <a href=\"https://pola-rs.github.io/nodejs-polars/index.html\">Node.js</a>\n -\n <a href=\"https://pola-rs.github.io/r-polars/index.html\">R</a>\n |\n <b>StackOverflow</b>:\n <a href=\"https://stackoverflow.com/questions/tagged/python-polars\">Python</a>\n -\n <a href=\"https://stackoverflow.com/questions/tagged/rust-polars\">Rust</a>\n -\n <a href=\"https://stackoverflow.com/questions/tagged/nodejs-polars\">Node.js</a>\n -\n <a href=\"https://stackoverflow.com/questions/tagged/r-polars\">R</a>\n |\n <a href=\"https://docs.pola.rs/\">User guide</a>\n |\n <a href=\"https://discord.gg/4UfP5cfBE7\">Discord</a>\n</p>\n\n## Polars: Blazingly fast DataFrames in Rust, Python, Node.js, R, and SQL\n\nPolars is a DataFrame interface on top of an OLAP Query Engine implemented in Rust using\n[Apache Arrow Columnar Format](https://arrow.apache.org/docs/format/Columnar.html) as the memory model.\n\n- Lazy | eager execution\n- Multi-threaded\n- SIMD\n- Query optimization\n- Powerful expression API\n- Hybrid Streaming (larger-than-RAM datasets)\n- Rust | Python | NodeJS | R | ...\n\nTo learn more, read the [user guide](https://docs.pola.rs/).\n\n## Python\n\n```python\n>>> import polars as pl\n>>> df = pl.DataFrame(\n... {\n... \"A\": [1, 2, 3, 4, 5],\n... \"fruits\": [\"banana\", \"banana\", \"apple\", \"apple\", \"banana\"],\n... \"B\": [5, 4, 3, 2, 1],\n... \"cars\": [\"beetle\", \"audi\", \"beetle\", \"beetle\", \"beetle\"],\n... }\n... )\n\n# embarrassingly parallel execution & very expressive query language\n>>> df.sort(\"fruits\").select(\n... \"fruits\",\n... \"cars\",\n... pl.lit(\"fruits\").alias(\"literal_string_fruits\"),\n... pl.col(\"B\").filter(pl.col(\"cars\") == \"beetle\").sum(),\n... pl.col(\"A\").filter(pl.col(\"B\") > 2).sum().over(\"cars\").alias(\"sum_A_by_cars\"),\n... pl.col(\"A\").sum().over(\"fruits\").alias(\"sum_A_by_fruits\"),\n... pl.col(\"A\").reverse().over(\"fruits\").alias(\"rev_A_by_fruits\"),\n... pl.col(\"A\").sort_by(\"B\").over(\"fruits\").alias(\"sort_A_by_B_by_fruits\"),\n... )\nshape: (5, 8)\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 fruits \u2506 cars \u2506 literal_stri \u2506 B \u2506 sum_A_by_ca \u2506 sum_A_by_fr \u2506 rev_A_by_fr \u2506 sort_A_by_B \u2502\n\u2502 --- \u2506 --- \u2506 ng_fruits \u2506 --- \u2506 rs \u2506 uits \u2506 uits \u2506 _by_fruits \u2502\n\u2502 str \u2506 str \u2506 --- \u2506 i64 \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n\u2502 \u2506 \u2506 str \u2506 \u2506 i64 \u2506 i64 \u2506 i64 \u2506 i64 \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 \"apple\" \u2506 \"beetle\" \u2506 \"fruits\" \u2506 11 \u2506 4 \u2506 7 \u2506 4 \u2506 4 \u2502\n\u2502 \"apple\" \u2506 \"beetle\" \u2506 \"fruits\" \u2506 11 \u2506 4 \u2506 7 \u2506 3 \u2506 3 \u2502\n\u2502 \"banana\" \u2506 \"beetle\" \u2506 \"fruits\" \u2506 11 \u2506 4 \u2506 8 \u2506 5 \u2506 5 \u2502\n\u2502 \"banana\" \u2506 \"audi\" \u2506 \"fruits\" \u2506 11 \u2506 2 \u2506 8 \u2506 2 \u2506 2 \u2502\n\u2502 \"banana\" \u2506 \"beetle\" \u2506 \"fruits\" \u2506 11 \u2506 4 \u2506 8 \u2506 1 \u2506 1 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n## SQL\n\n```python\n>>> df = pl.scan_csv(\"docs/data/iris.csv\")\n>>> ## OPTION 1\n>>> # run SQL queries on frame-level\n>>> df.sql(\"\"\"\n...\tSELECT species,\n...\t AVG(sepal_length) AS avg_sepal_length\n...\tFROM self\n...\tGROUP BY species\n...\t\"\"\").collect()\nshape: (3, 2)\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 species \u2506 avg_sepal_length \u2502\n\u2502 --- \u2506 --- \u2502\n\u2502 str \u2506 f64 \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 Virginica \u2506 6.588 \u2502\n\u2502 Versicolor \u2506 5.936 \u2502\n\u2502 Setosa \u2506 5.006 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n>>> ## OPTION 2\n>>> # use pl.sql() to operate on the global context\n>>> df2 = pl.LazyFrame({\n... \"species\": [\"Setosa\", \"Versicolor\", \"Virginica\"],\n... \"blooming_season\": [\"Spring\", \"Summer\", \"Fall\"]\n...})\n>>> pl.sql(\"\"\"\n... SELECT df.species,\n... AVG(df.sepal_length) AS avg_sepal_length,\n... df2.blooming_season\n... FROM df\n... LEFT JOIN df2 ON df.species = df2.species\n... GROUP BY df.species, df2.blooming_season\n... \"\"\").collect()\n```\n\nSQL commands can also be run directly from your terminal using the Polars CLI:\n\n```bash\n# run an inline SQL query\n> 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;\"\n\n# run interactively\n> polars\nPolars CLI v0.3.0\nType .help for help.\n\n> 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;\n```\n\nRefer to the [Polars CLI repository](https://github.com/pola-rs/polars-cli) for more information.\n\n## Performance \ud83d\ude80\ud83d\ude80\n\n### Blazingly fast\n\nPolars is very fast. In fact, it is one of the best performing solutions available. See the [PDS-H benchmarks](https://www.pola.rs/benchmarks.html) results.\n\n### Lightweight\n\nPolars is also very lightweight. It comes with zero required dependencies, and this shows in the import times:\n\n- polars: 70ms\n- numpy: 104ms\n- pandas: 520ms\n\n### Handles larger-than-RAM data\n\nIf 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.\nThis drastically reduces memory requirements, so you might be able to process your 250GB dataset on your laptop.\nCollect with `collect(streaming=True)` to run the query streaming.\n(This might be a little slower, but it is still very fast!)\n\n## Setup\n\n### Python\n\nInstall the latest Polars version with:\n\n```sh\npip install polars\n```\n\nWe also have a conda package (`conda install -c conda-forge polars`), however pip is the preferred way to install Polars.\n\nInstall Polars with all optional dependencies.\n\n```sh\npip install 'polars[all]'\n```\n\nYou can also install a subset of all optional dependencies.\n\n```sh\npip install 'polars[numpy,pandas,pyarrow]'\n```\n\nSee the [User Guide](https://docs.pola.rs/user-guide/installation/#feature-flags) for more details on optional dependencies\n\nTo see the current Polars version and a full list of its optional dependencies, run:\n\n```python\npl.show_versions()\n```\n\nReleases 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.\n\n### Rust\n\nYou can take latest release from `crates.io`, or if you want to use the latest features / performance\nimprovements point to the `main` branch of this repo.\n\n```toml\npolars = { git = \"https://github.com/pola-rs/polars\", rev = \"<optional git tag>\" }\n```\n\nRequires Rust version `>=1.80`.\n\n## Contributing\n\nWant to contribute? Read our [contributing guide](https://docs.pola.rs/development/contributing/).\n\n## Python: compile Polars from source\n\nIf you want a bleeding edge release or maximal performance you should compile Polars from source.\n\nThis can be done by going through the following steps in sequence:\n\n1. Install the latest [Rust compiler](https://www.rust-lang.org/tools/install)\n2. Install [maturin](https://maturin.rs/): `pip install maturin`\n3. `cd py-polars` and choose one of the following:\n - `make build-release`, fastest binary, very long compile times\n - `make build-opt`, fast binary with debug symbols, long compile times\n - `make build-debug-opt`, medium-speed binary with debug assertions and symbols, medium compile times\n - `make build`, slow binary with debug assertions and symbols, fast compile times\n\n Append `-native` (e.g. `make build-release-native`) to enable further optimizations specific to\n your CPU. This produces a non-portable binary/wheel however.\n\nNote that the Rust crate implementing the Python bindings is called `py-polars` to distinguish from the wrapped\nRust crate `polars` itself. However, both the Python package and the Python module are named `polars`, so you\ncan `pip install polars` and `import polars`.\n\n## Using custom Rust functions in Python\n\nExtending Polars with UDFs compiled in Rust is easy. We expose PyO3 extensions for `DataFrame` and `Series`\ndata structures. See more in https://github.com/pola-rs/pyo3-polars.\n\n## Going big...\n\nDo you expect more than 2^32 (~4.2 billion) rows? Compile Polars with the `bigidx` feature\nflag or, for Python users, install `pip install polars-u64-idx`.\n\nDon't use this unless you hit the row boundary as the default build of Polars is faster and consumes less memory.\n\n## Legacy\n\nDo you want Polars to run on an old CPU (e.g. dating from before 2011), or on an `x86-64` build\nof Python on Apple Silicon under Rosetta? Install `pip install polars-lts-cpu`. This version of\nPolars is compiled without [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) target\nfeatures.\n\n## Sponsors\n\n[<img src=\"https://www.jetbrains.com/company/brand/img/jetbrains_logo.png\" height=\"50\" alt=\"JetBrains logo\" />](https://www.jetbrains.com)\n\n",
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