pytoolz/toolz
Toolz
Toolz is a Python library that implements functional programming utilities for iterable transformation, dictionary manipulation, function composition, and lazy evaluation. It provides a set of pure functions designed to work with Python's built-in data structures, enabling concise and composable data processing workflows.
What distinguishes toolz is its support for curried partial application, allowing functions to be incrementally applied and reused. It includes dictionary-centric operations that handle nested structures, and offers iterable chain transformers that combine mapping, filtering, grouping, and reducing steps into efficient pipelines without intermediate collections. The library also provides lazy generator iteration for memory-efficient processing of large or infinite sequences, and supports parallel computation through serialization-based distribution across multiple cores.
Beyond these core differentiators, toolz covers a broad range of data manipulation tasks, including transforming and aggregating iterables, merging and updating dictionaries, and composing functions into pipelines. It also enables partial application to pre-fill arguments, and facilitates parallel data processing. The library is built around these functional programming primitives, making it a comprehensive toolkit for constructing data pipelines in Python.
Features
- Python Functional Programming Libraries - A Python library providing functional tools including iterable transformations, function composition, and lazy evaluation.
- Lazy Infinite Sequences - Handles large or infinite sequences efficiently by processing elements on demand without loading all into memory.
- Iterator Transformation Helpers - Groups, deduplicates, interposes, and transforms sequences for processing iterable data.
- Iterable Transformation Utilities - Offers functions to map, filter, group, and combine iterables with a functional style for data processing.
- Filtering and Transformations - Reshapes and summarizes collections through mapping, filtering, grouping, and reducing operations.
- Dictionary Extraction and Cleaning - Provides functions to extract, omit, compact, and join dictionary entries for data cleaning.
- Lazy Iterators - Processes sequences on-demand using generators for memory-efficient handling of large data streams.
- Transformation Chains - Combines mapping, filtering, and reducing into reusable pipelines that avoid intermediate collections.
- Composable Dictionary Operations - Ships composable functions for extracting, merging, and updating nested dictionaries without mutation.
- Dictionary Manipulation Utilities - Ships comprehensive dictionary manipulation utilities for merging, extracting, and cleaning nested data.
- Python Data Pipeline Frameworks - Builds chains of data transformations using functional composition and lazy evaluation in Python.
- Lazy Sequences - Enables processing sequences one element at a time, only when demanded, for efficient handling of large data.
- Dictionary Merging - Merges multiple dictionaries and updates values at any depth within nested structures.
- Expressive Manipulation Utilities - Provides expressive utility functions for extracting, merging, and cleaning dictionary and iterable data.
- Function Argument Pre-setting - Fixes some arguments of a function upfront to create a new function accepting the remaining arguments.
- Functional - Combines multiple functions into pipelines where each function's output feeds into the next.
- Function Currying - Provides curried versions of functions, enabling incremental argument binding for reusable logic.
- Functional Pipeline Composition - Supports chaining functions into pipelines and partially applying arguments to build reusable logic.
- Serialization-Based Parallelization - Provides parallel computation through serialization-based distribution across multiple cores.
- Functional Programming - Offers a collection of functional utilities for data processing.
- Functional Utility Libraries - Provides a standard library of functional utilities for Python.
Star history
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