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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 as the memory model.

To learn more, read the User Guide.

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("literalstringfruits"), ... pl.col("B").filter(pl.col("cars") == "beetle").sum(), ... pl.col("A").filter(pl.col("B") > 2).sum().over("cars").alias("sumAbycars"), ... pl.col("A").sum().over("fruits").alias("sumAbyfruits"), ... pl.col("A").reverse().over("fruits").alias("revAbyfruits"), ... pl.col("A").sortby("B").over("fruits").alias("sortAbyBbyfruits"), ... ) shape: (5, 8) ┌──────────┬──────────┬──────────────┬─────┬─────────────┬─────────────┬─────────────┬─────────────┐ │ fruits ┆ cars ┆ literalstri ┆ B ┆ sumAbyca ┆ sumAbyfr ┆ revAbyfr ┆ sortAbyB │ │ --- ┆ --- ┆ ngfruits ┆ --- ┆ rs ┆ uits ┆ uits ┆ _byfruits │ │ 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

create a sql context

context = pl.SQLContext()

register a table

table = pl.scanipc("file.arrow") context.register("mytable", table)

the query we want to run

query = """ ... SELECT sum(v1) as sumv1, min(v2) as minv2 FROM my_table ... WHERE id1 = 'id016' ... LIMIT 10 ... """

OPTION 1

run query to materialization

context.query(query) shape: (1, 2) ┌────────┬────────┐ │ sumv1 ┆ minv2 │ │ --- ┆ --- │ │ i64 ┆ i64 │ ╞════════╪════════╡ │ 298268 ┆ 1 │ └────────┴────────┘

OPTION 2

Don't materialize the query, but return as LazyFrame

and continue in python

lf = context.execute(query) (lf.join(othertable) ... .groupby("foo") ... .agg( ... pl.col("sumv1").count() ... ).collect()) ```

SQL commands can also be ran directly from your terminal.

```bash

cargo install polars-cli --locked

run an inline sql query

polars -c "SELECT sum(v1) as sumv1, min(v2) as minv2 FROM read_ipc('file.arrow') WHERE id1 = 'id016' LIMIT 10"

run interactively

polars Polars CLI v0.1.0 Type .help for help.

SELECT sum(v1) as sumv1, min(v2) as minv2 FROM read_ipc('file.arrow') WHERE id1 = 'id016' LIMIT 10; ```

Refer to 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 results in DuckDB's db-benchmark.

In the TPCH benchmarks polars is orders of magnitudes faster than pandas, dask, modin and vaex on full queries (including IO).

Lightweight

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

Handles 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 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]' 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 | | deltalake | Support for reading from Delta Lake Tables | | timezone | Timezone support, only needed if are on Python<3.9 or you are on Windows |

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>" }

Required Rust version >=1.62

Contributing

Want to contribute? Read our contribution guideline.

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
  2. Install maturin: pip install maturin
  3. Choose any of:

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.

Use custom Rust function 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 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 target features.

Acknowledgements

Development of Polars is proudly powered by

Xomnia

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