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pola-rs
GitHub Repository: pola-rs/polars
 Path: blob/main/docs/source/user-guide/installation.md
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Installation

Polars is a library and installation is as simple as invoking the package manager of the corresponding programming language.

=== ":fontawesome-brands-python: Python"

``` bash
pip install polars

# Or for legacy CPUs without AVX2 support
pip install polars-lts-cpu
```

=== ":fontawesome-brands-rust: Rust"

``` shell
cargo add polars -F lazy

# Or Cargo.toml
[dependencies]
polars = { version = "x", features = ["lazy", ...]}
```

Big Index

By default, Polars dataframes are limited to 2322^{32} rows (~4.3 billion). Increase this limit to 2642^{64} (~18 quintillion) by enabling the big index extension:

=== ":fontawesome-brands-python: Python"

``` bash
pip install polars-u64-idx
```

=== ":fontawesome-brands-rust: Rust"

``` shell
cargo add polars -F bigidx

# Or Cargo.toml
[dependencies]
polars = { version = "x", features = ["bigidx", ...] }
```

Legacy CPU

To install Polars for Python on an old CPU without AVX support, run:

=== ":fontawesome-brands-python: Python"

``` bash
pip install polars-lts-cpu
```

Importing

To use the library, simply import it into your project:

=== ":fontawesome-brands-python: Python"

``` python
import polars as pl
```

=== ":fontawesome-brands-rust: Rust"

``` rust
use polars::prelude::*;
```

Feature flags

By using the above command you install the core of Polars onto your system. However, depending on your use case, you might want to install the optional dependencies as well. These are made optional to minimize the footprint. The flags are different depending on the programming language. Throughout the user guide we will mention when a functionality used requires an additional dependency.

Python

# For example
pip install 'polars[numpy,fsspec]'

All

TagDescription
allInstall all optional dependencies.

GPU

TagDescription
gpuRun queries on NVIDIA GPUs.

!!! note

See [GPU support](gpu-support.md) for more detailed instructions and
prerequisites.

Interoperability

TagDescription
pandasConvert data to and from pandas dataframes/series.
numpyConvert data to and from NumPy arrays.
pyarrowConvert data to and from PyArrow tables/arrays.
pydanticConvert data from Pydantic models to Polars.

Excel

TagDescription
calamineRead from Excel files with the calamine engine.
openpyxlRead from Excel files with the openpyxl engine.
xlsx2csvRead from Excel files with the xlsx2csv engine.
xlsxwriterWrite to Excel files with the XlsxWriter engine.
excelInstall all supported Excel engines.

Database

TagDescription
adbcRead from and write to databases with the Arrow Database Connectivity (ADBC) engine.
connectorxRead from databases with the ConnectorX engine.
sqlalchemyWrite to databases with the SQLAlchemy engine.
databaseInstall all supported database engines.

Cloud

TagDescription
fsspecRead from and write to remote file systems.

Other I/O

TagDescription
deltalakeRead from and write to Delta tables.
icebergRead from Apache Iceberg tables.

Other

TagDescription
asyncCollect LazyFrames asynchronously.
cloudpickleSerialize user-defined functions.
graphVisualize LazyFrames as a graph.
plotPlot dataframes through the plot namespace.
styleStyle dataframes through the style namespace.
timezoneTimezone support[^note].

[^note]: Only needed if you are on Windows.

Rust

# Cargo.toml
[dependencies]
polars = { version = "0.26.1", features = ["lazy", "temporal", "describe", "json", "parquet", "dtype-datetime"] }

The opt-in features are:

  • Additional data types:

    • dtype-date

    • dtype-datetime

    • dtype-time

    • dtype-duration

    • dtype-i8

    • dtype-i16

    • dtype-u8

    • dtype-u16

    • dtype-categorical

    • dtype-struct

  • lazy - Lazy API:

    • regex - Use regexes in column selection.

    • dot_diagram - Create dot diagrams from lazy logical plans.

  • sql - Pass SQL queries to Polars.

  • streaming - Be able to process datasets that are larger than RAM.

  • random - Generate arrays with randomly sampled values

  • ndarray- Convert from DataFrame to ndarray

  • temporal - Conversions between Chrono and Polars for temporal data types

  • timezones - Activate timezone support.

  • strings - Extra string utilities for StringChunked:

    • string_pad - for pad_start, pad_end, zfill.

    • string_to_integer - for parse_int.

  • object - Support for generic ChunkedArrays called ObjectChunked<T> (generic over T). These are downcastable from Series through the Any trait.

  • Performance related:

    • nightly - Several nightly only features such as SIMD and specialization.

    • performant - more fast paths, slower compile times.

    • bigidx - Activate this feature if you expect >> 2322^{32} rows. This allows polars to scale up way beyond that by using u64 as an index. Polars will be a bit slower with this feature activated as many data structures are less cache efficient.

    • cse - Activate common subplan elimination optimization.

  • IO related:

    • serde - Support for serde serialization and deserialization. Can be used for JSON and more serde supported serialization formats.

    • serde-lazy - Support for serde serialization and deserialization. Can be used for JSON and more serde supported serialization formats.

    • parquet - Read Apache Parquet format.

    • json - JSON serialization.

    • ipc - Arrow's IPC format serialization.

    • decompress - Automatically infer compression of csvs and decompress them. Supported compressions:

      • gzip

      • zlib

      • zstd

  • Dataframe operations:

    • dynamic_group_by - Group by based on a time window instead of predefined keys. Also activates rolling window group by operations.

    • sort_multiple - Allow sorting a dataframe on multiple columns.

    • rows - Create dataframe from rows and extract rows from dataframes. Also activates pivot and transpose operations.

    • join_asof - Join ASOF, to join on nearest keys instead of exact equality match.

    • cross_join - Create the Cartesian product of two dataframes.

    • semi_anti_join - SEMI and ANTI joins.

    • row_hash - Utility to hash dataframe rows to UInt64Chunked.

    • diagonal_concat - Diagonal concatenation thereby combining different schemas.

    • dataframe_arithmetic - Arithmetic between dataframes and other dataframes or series.

    • partition_by - Split into multiple dataframes partitioned by groups.

  • Series/expression operations:

    • is_in - Check for membership in Series.

    • zip_with - Zip two Series / ChunkedArrays.

    • round_series - round underlying float types of series.

    • repeat_by - Repeat element in an array a number of times specified by another array.

    • is_first_distinct - Check if element is first unique value.

    • is_last_distinct - Check if element is last unique value.

    • checked_arithmetic - checked arithmetic returning None on invalid operations.

    • dot_product - Dot/inner product on series and expressions.

    • concat_str - Concatenate string data in linear time.

    • reinterpret - Utility to reinterpret bits to signed/unsigned.

    • take_opt_iter - Take from a series with Iterator<Item=Option<usize>>.

    • mode - Return the most frequently occurring value(s).

    • cum_agg - cum_sum, cum_min, and cum_max, aggregations.

    • rolling_window - rolling window functions, like rolling_mean.

    • interpolate - Interpolate intermediate None values.

    • extract_jsonpath - Run jsonpath queries on StringChunked.

    • list - List utils:

      • list_gather - take sublist by multiple indices.

    • rank - Ranking algorithms.

    • moment - Kurtosis and skew statistics.

    • ewma - Exponential moving average windows.

    • abs - Get absolute values of series.

    • arange - Range operation on series.

    • product - Compute the product of a series.

    • diff - diff operation.

    • pct_change - Compute change percentages.

    • unique_counts - Count unique values in expressions.

    • log - Logarithms for series.

    • list_to_struct - Convert List to Struct data types.

    • list_count - Count elements in lists.

    • list_eval - Apply expressions over list elements.

    • cumulative_eval - Apply expressions over cumulatively increasing windows.

    • arg_where - Get indices where condition holds.

    • search_sorted - Find indices where elements should be inserted to maintain order.

    • offset_by - Add an offset to dates that take months and leap years into account.

    • trigonometry - Trigonometric functions.

    • sign - Compute the element-wise sign of a series.

    • propagate_nans - NaN-propagating min/max aggregations.

  • Dataframe pretty printing:

    • fmt - Activate dataframe formatting.