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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.

• 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.

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

>>> df = pl.scan_ipc("file.arrow")
>>> # create a sql context, registering the frame as a table
>>> sql = pl.SQLContext(my_table=df)
>>> # create a sql query to execute
>>> query = """
...   SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM my_table
...   WHERE id1 = 'id016'
...   LIMIT 10
... """
>>> ## OPTION 1
>>> # run the query, materializing as a DataFrame
>>> sql.execute(query, eager=True)
 shape: (1, 2)
 ┌────────┬────────┐
 │ sum_v1 ┆ min_v2 │
 │ ---    ┆ ---    │
 │ i64    ┆ i64    │
 ╞════════╪════════╡
 │ 298268 ┆ 1      │
 └────────┴────────┘
>>> ## OPTION 2
>>> # run the query but don't immediately materialize the result.
>>> # this returns a LazyFrame that you can continue to operate on.
>>> lf = sql.execute(query)
>>> (lf.join(other_table)
...      .group_by("foo")
...      .agg(
...     pl.col("sum_v1").count()
... ).collect())

SQL commands can also be run directly from your terminal using the Polars CLI:

# run an inline sql query
> polars -c "SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM read_ipc('file.arrow') WHERE id1 = 'id016' LIMIT 10"

# run interactively
> polars
Polars CLI v0.3.0
Type .help for help.

> SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM read_ipc('file.arrow') WHERE id1 = 'id016' LIMIT 10;

Refer to the Polars CLI repository 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:

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

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:

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.

pip install 'polars[all]'

You can also install a subset of all optional dependencies.

pip install 'polars[numpy,pandas,pyarrow]'

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
openpyxl	Support for reading from Excel files with native types
deltalake	Support for reading and writing Delta Lake Tables
pyiceberg	Support for reading from Apache Iceberg tables
plot	Support for plot functions on Dataframes
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.

polars = { git = "https://github.com/pola-rs/polars", rev = "<optional git tag>" }

Required Rust version >=1.71.

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. 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.

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), 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 target features.

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