https://github.com/cgevans/polars

Fast multi-threaded, hybrid-out-of-core DataFrame library in Rust | Python | Node.js

https://github.com/cgevans/polars

Repository

Fast multi-threaded, hybrid-out-of-core DataFrame library in Rust | Python | Node.js

https://github.com/cgevans/polars/blob/main/

  
    
  



  
    
  
  
    
  
  
    
  
  
    
  
  
    
  



  Documentation:
  Python
  -
  Rust
  -
  Node.js
  -
  R
  |
  StackOverflow:
  Python
  -
  Rust
  -
  Node.js
  -
  R
  |
  User guide
  |
  Discord


## 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 = "" }
```

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

[](https://www.jetbrains.com)

Owner

GitHub Events