Polars

This release focuses heavily on performance improvements across various operations, especially in streaming contexts, and introduces significant enhancements to SQL support and data type handling, including new Extension types.

This release introduces the ability to retrieve a byte from binary data using get() and includes numerous bug fixes across query optimization, schema inference, and data handling. Several internal cleanups and documentation updates were also performed.

This release focuses heavily on performance improvements across streaming, I/O, and core computations, alongside numerous bug fixes for stability and correctness. A key change is the deprecation of the `retries` argument in favor of using `storage_options`.

This release focuses on performance improvements, particularly for SQL UNION clauses, and includes several bug fixes related to IPC slicing and error handling. Dependency updates were also performed.

This release focuses on performance improvements, new features like Object literal creation, and numerous bug fixes across various components including binary operations and DataFrame methods.

This release introduces significant enhancements across SQL support (including window functions and QUALIFY), new DataFrame/Expr methods like bin slicing and improved rolling calculations, and numerous performance optimizations, especially for streaming and Parquet I/O. Numerous bugs related to panics, dtype handling, and SQL expression resolution have also been fixed.

This release introduces significant enhancements across SQL support, expression capabilities (like rolling windows and aggregation contexts), and adds support for Extension types. Numerous performance improvements and bug fixes address stability and correctness across various operations, especially in streaming and Parquet handling.

This release focuses on stability and performance, primarily addressing several bugs related to group_by operations and fixing a wide-table join performance regression.

This release focuses heavily on performance improvements across lazy evaluation, group-by operations, and I/O, alongside numerous bug fixes, especially in SQL handling and streaming aggregations. New features include batch collection methods for LazyFrames and enhanced streaming support for various functions.

This release focuses on performance improvements, including optimizing rolling moment window computation and IPC stream reads. It also introduces support for BYTE_ARRAY backed Decimals in Parquet and fixes numerous bugs across SQL handling, group-by operations, and predicate pushdown.

This release focuses heavily on performance improvements across group-by operations, native aggregations, and data parsing, while stabilizing Decimal support and introducing several new features like ewm_mean in streaming and enhanced list/array aggregations. Two functions, Expr.agg_groups() and pl.groups(), have been deprecated.

This release focuses heavily on performance improvements across group-by operations, data serialization, and string parsing. Numerous enhancements were added, including new aggregation methods and improved ADBC engine integration, alongside fixes for various edge cases and regressions.

This release introduces new batch collection methods for LazyFrames and significant performance optimizations across various operations, including native streaming support for gather_every and mode(). Numerous bug fixes address issues related to CSV parsing, streaming engine consistency, and cloud storage scanning.

This release introduces new lazy sink/collect batch methods, enhances performance across various scan and expression operations, and fixes numerous bugs related to data types, streaming, and cloud storage interactions.

This release introduces new batch collection methods (`LazyFrame.{sink,collect}_batches`), significant performance optimizations across scanning and expressions, and numerous bug fixes, especially around Iceberg and streaming operations.

This release introduces new batch collection methods for LazyFrames and enhances performance across various scan types, alongside numerous bug fixes for stability and correctness in streaming and aggregation operations.

This release introduces new batch collection methods for LazyFrames and significant performance improvements across various scan types. It also includes numerous bug fixes, especially around cloud storage interactions and data type handling.

This release introduces significant performance improvements across the streaming engine, new features like array support for unique counts and AWS role assumption guides, and addresses numerous bugs, notably around serialization and type handling. The most critical change is the transition of some eager Expressions to be lazy compatible.

This release focuses heavily on performance improvements, particularly around Parquet decoding and memory allocation. It also introduces several enhancements, including S3 URI support and a new security policy, alongside numerous bug fixes.

This release focuses heavily on performance improvements by lowering many operations natively to the streaming engine and optimizing the query planner. A significant breaking change involves making previously eager Exprs lazy compatible.

This release focuses heavily on performance improvements by lowering various operations to the streaming engine and enhancing lazy evaluation capabilities, alongside numerous bug fixes and the removal of eager expression behavior.

This release focuses heavily on performance improvements by lowering operations like `.sort(maintain_order=True).head()` and `rle` to the streaming engine. Numerous bug fixes address issues across data types, serialization, and expression evaluation.

This release primarily focuses on fixing an issue related to returning the correct Python package version and includes a documentation update.

This release focuses heavily on performance improvements by lowering more operations to the streaming engine and optimizing internal parsing. Numerous bug fixes address issues across Iceberg/Delta scans, data type handling, and aggregation queries.

This release focuses heavily on performance improvements by lowering more operations to the streaming engine and optimizing various internal processes. It also introduces significant enhancements around Categorical/Enum types and fixes numerous bugs across expressions, I/O, and joins.

This release focuses heavily on performance improvements across the streaming engine, expression lowering, and various I/O operations. Key enhancements include the formalization of `Selector` in the DSL and a rework of Categorical/Enum handling using (Frozen)Categories.

This release focuses heavily on performance improvements across the streaming engine, including lowering various operations and optimizing predicate pushdown. Key enhancements include the formalization of `Selector` in the DSL and reworking Categorical/Enum handling using (Frozen)Categories.

This release focuses on performance optimizations, enhanced functionality for padding methods, and fixes related to time zone handling in date/time expressions.

This release removes the old streaming engine, introduces native Iceberg positional delete support, and includes numerous performance optimizations and bug fixes across various data types and operations.

This release removes the old streaming engine, introduces DataType expressions and Iceberg positional delete support, and includes numerous performance optimizations and bug fixes across various operations.

This release removes the old streaming engine, introduces DataType expressions and Iceberg positional delete support, and includes numerous performance optimizations and bug fixes across various components.

This release focuses heavily on performance improvements across various operations, including optimizer casts, parallelism, and elementwise execution. It introduces several new features like list.filter, LazyFrame.match_to_schema, and enhanced type inference, alongside numerous bug fixes addressing panics and incorrect outputs.

This release focuses on performance improvements by switching eligible casts to non-strict in the optimizer and includes fixes for serialization errors and build system issues.

This release introduces several performance improvements, new features like list.filter and schema matching for LazyFrames, and addresses numerous bugs, including panics and incorrect outputs in various operations. A breaking change involves updating how time zone information is stored internally.

This release focuses heavily on performance improvements, including optimized initialization and parallelism, alongside numerous bug fixes across SQL, I/O, and expression evaluation. New features include enhanced schema matching, improved time-string parsing, and better support for various data types and operations.

This release introduces significant performance improvements across the streaming engine, new SQL functions, and enhanced support for array types and various sink configurations. A breaking change was made to how the bottom interval is handled in `hist`.

This release focuses on performance improvements, notably avoiding alloc_zeroed in decompression, and introduces several new features including SQL support for array checks and DataFrame initialization from torch Tensors. Several bugs related to joins, parquet reading, and date/datetime conversions have also been fixed.

This release focuses on bug fixes related to Parquet reading and predicate filtering, alongside minor build system updates and documentation improvements.

This release focuses heavily on performance improvements, particularly within the streaming engine, and introduces several new features like enhanced rolling statistics and GPU support for sink APIs. Numerous bug fixes address issues across data types, I/O operations, and streaming execution.

This release focuses on stability and correctness, fixing several bugs related to joins, caching, JSON writing, and deadlocks, alongside an enhancement to expression autocomplete in interactive environments.

This release introduces several performance improvements, new SQL functions, and enhances compatibility with Python I/O classes. It also contains several bug fixes, notably around streaming engine stability and type handling, alongside breaking changes to the `hist` function and Partition API.

This release focuses heavily on performance improvements, including optimizations for binary hash tables and join operations, alongside numerous bug fixes across various functionalities like CSV parsing and aggregation.

This release introduces significant performance enhancements, including common subplan elimination and linear-time rolling operations, alongside new features like lazy sinks and expanded support for streaming engines.

This release focuses heavily on performance improvements, particularly in the new streaming engine, and introduces several enhancements like lossy decoding for CSVs and DataFrame.write_iceberg. Numerous bug fixes address stability issues across various operations including rolling statistics and sink phases.

This release focuses heavily on performance improvements, especially around rolling operations and group-by scenarios, alongside numerous bug fixes across data types, I/O, and SQL functionality. New features include SQL DELETE support and enhanced streaming capabilities.

This release focuses heavily on performance improvements across various operations, especially within the new streaming engine, and introduces significant enhancements to I/O capabilities, including better support for Unity Catalog and IO plugins. Several bugs related to type handling, aggregations, and specific functions like `Expr.over` and `top_k` have also been resolved.

This release introduces the new Int128Type and brings significant performance enhancements across various operations, especially within the new streaming engine. Numerous bug fixes address issues related to Decimal types, serialization, and cloud storage interactions.

This release focuses heavily on performance improvements through increased use of BitmapBuilder and enhancements across the streaming engine, including new CSV and NDJson sinks. Numerous bug fixes address issues related to Decimal types, slicing, joins, and error handling.

This release focuses heavily on performance improvements across various areas, including streaming engine aggregations, serialization, and Rust/Python data conversion. It also introduces several new features like SQL support for NORMALIZE and enhancements to Parquet handling and cloud storage integration.