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Installation

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

pip install polars
cargo add polars -F lazy

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

Importing

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.

Python

# For example
pip install 'polars[numpy,fsspec]'
Tag Description
all Install all optional dependencies (all of the following)
pandas Install with Pandas for converting data to and from Pandas Dataframes/Series
numpy Install with numpy for converting data to and from numpy arrays
pyarrow Reading data formats using PyArrow
fsspec Support for reading from remote file systems
connectorx Support for reading from SQL databases
xlsx2csv Support for reading from Excel files
deltalake Support for reading from Delta Lake Tables
plot Support for plotting Dataframes
timezone Timezone support, only needed if 1. you are on Python < 3.9 and/or 2. you are on Windows, otherwise no dependencies will be installed

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 - 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.
    • group_by_list - Allow group by operation on keys of type List.
    • 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.
    • date_offset 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