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

pip install polars

# Or for legacy CPUs without AVX2 support
pip install polars-lts-cpu
cargo add polars -F lazy

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

Big Index

By default, polars is limited to 2^32 (~4.2 billion rows). To increase this limit 2^64 (~18 quintillion) by enabling big index:

pip install polars-u64-idx
cargo add polars -F bigidx

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

Legacy CPU

To install polars on an old CPU without AVX support:

pip install polars-lts-cpu


To use the library import it into your project

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 is used that requires an additional dependency.


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


Tag Description
all Install all optional dependencies


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


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


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


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


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 support*

* Only needed if 1. you are on Python < 3.9 and/or 2. you are on Windows


# Cargo.toml
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 - pad_start, pad_end, zfill
    • string_to_integer - 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 has not been needed by anyone. 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. And 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 - Concat diagonally thereby combining different schemas.
    • dataframe_arithmetic - Arithmetic on (Dataframe and DataFrames) and (DataFrame on 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 N times, where N is given 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 - Concat 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 occurring value(s)
    • cum_agg - cum_sum, cum_min, cum_max aggregation.
    • 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 dtypes.
    • 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