Installation
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
[dependencies]
polars = { version = "x", features = ["lazy", ...]}
Big Index
By default, Polars dataframes are limited to 232 rows (~4.3 billion). Increase this limit to 264 (~18 quintillion) by enabling the big index extension:
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:
pip install polars-lts-cpu
Importing
To use the library, simply 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 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 valuesndarray
- Convert fromDataFrame
tondarray
temporal
- Conversions between Chrono and Polars for temporal data typestimezones
- Activate timezone support.strings
- Extra string utilities forStringChunked
:string_pad
- forpad_start
,pad_end
,zfill
.string_to_integer
- forparse_int
.
object
- Support for generic ChunkedArrays calledObjectChunked<T>
(generic overT
). 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 >> 232 rows. This allows polars to scale up way beyond that by usingu64
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 fromdataframes
. Also activatespivot
andtranspose
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 toUInt64Chunked
.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 serieszip_with
- Zip twoSeries
/ChunkedArray
sround_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 returningNone
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 withIterator<Item=Option<usize>>
.mode
- Return the most frequently occurring value(s).cum_agg
-cum_sum
,cum_min
, andcum_max
, aggregations.rolling_window
- rolling window functions, likerolling_mean
.interpolate
- interpolateNone
values.extract_jsonpath
- Runjsonpath
queries onStringChunked
.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
- ConvertList
toStruct
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.
-
Only needed if you are on Python < 3.9 or you are on Windows. ↩