Installation

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

Python Rust

pip install polars

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

cargo add polars -F lazy

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

Big Index

By default, Polars dataframes are limited to \(2^{32}\) rows (~4.3 billion). Increase this limit to \(2^{64}\) (~18 quintillion) by enabling the big index extension:

Python Rust

pip install polars-u64-idx

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:

Python

pip install polars-lts-cpu

Importing

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

Python Rust

import polars as pl

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

Tag	Description
all	Install all optional dependencies.

GPU

Tag	Description
gpu	Run queries on NVIDIA GPUs.

Note

To install the GPU engine, you need to pass --extra-index-url=https://pypi.nvidia.com to pip. See GPU support for more detailed instructions and prerequisites.

Interoperability

Tag	Description
pandas	Convert data to and from pandas dataframes/series.
numpy	Convert data to and from NumPy arrays.
pyarrow	Convert data to and from PyArrow tables/arrays.
pydantic	Convert data from Pydantic models to Polars.

Excel

Tag	Description
calamine	Read from Excel files with the calamine engine.
openpyxl	Read from Excel files with the openpyxl engine.
xlsx2csv	Read from Excel files with the xlsx2csv engine.
xlsxwriter	Write to Excel files with the XlsxWriter engine.
excel	Install all supported Excel engines.

Database

Tag	Description
adbc	Read from and write to databases with the Arrow Database Connectivity (ADBC) engine.
connectorx	Read from databases with the ConnectorX engine.
sqlalchemy	Write to databases with the SQLAlchemy engine.
database	Install all supported database engines.

Cloud

Tag	Description
fsspec	Read from and write to remote file systems.

Other I/O

Tag	Description
deltalake	Read from and write to Delta tables.
iceberg	Read from Apache Iceberg tables.

Other

Tag	Description
async	Collect LazyFrames asynchronously.
cloudpickle	Serialize user-defined functions.
graph	Visualize LazyFrames as a graph.
plot	Plot dataframes through the plot namespace.
style	Style dataframes through the style namespace.
timezone	Timezone support1.

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 >> \(2^{32}\) 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:
  • zip
  • gzip
• 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 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.

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1. Only needed if you are on Python < 3.9 or you are on Windows. ↩