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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.
>>> 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 │
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘>>> 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:
# 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 for more information.
Polars is very fast. In fact, it is one of the best performing solutions available. See the PDS-H benchmarks results.
Polars is also very lightweight. It comes with zero required dependencies, and this shows in the import times:
- polars: 70ms
- numpy: 104ms
- pandas: 520ms
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!)
Install the latest Polars version with:
pip install polarsWe 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]'See the User Guide for more details on optional dependencies
To see the current Polars version and a full list of its optional dependencies, run:
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.
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>" }Requires Rust version >=1.80.
Want to contribute? Read our contributing guide.
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:
-
Install the latest Rust compiler
-
Install maturin:
pip install maturin -
cd py-polarsand choose one of the following:make build-release, fastest binary, very long compile timesmake build-opt, fast binary with debug symbols, long compile timesmake build-debug-opt, medium-speed binary with debug assertions and symbols, medium compile timesmake 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.
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.
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.
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.
