AI prompts
base on Dataframes powered by a multithreaded, vectorized query engine, written in Rust <h1 align="center">
<a href="https://pola.rs">
<img src="https://raw.githubusercontent.com/pola-rs/polars-static/master/banner/polars_github_banner.svg" alt="Polars logo">
</a>
</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://community.r-multiverse.org/polars">
<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fcommunity.r-multiverse.org%2Fapi%2Fpackages%2Fpolars&query=%24.Version&label=r-multiverse" alt="R-multiverse 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>
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<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/assets/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/assets/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/assets/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(engine='streaming')` 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`, slow binary with debug assertions and symbols, fast compile times
- `make build-release`, fast binary without debug assertions, minimal debug symbols, long compile
times
- `make build-nodebug-release`, same as build-release but without any debug symbols, slightly
faster to compile
- `make build-debug-release`, same as build-release but with full debug symbols, slightly slower
to compile
- `make build-dist-release`, fastest binary, extreme compile times
By default the binary is compiled with optimizations turned on for a modern CPU. Specify `LTS_CPU=1`
with the command if your CPU is older and does not support e.g. AVX2.
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/polars/tree/main/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)
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