Packages
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
Hypothesis is a property-based testing library that generates random test inputs within specified ranges to find edge cases and bugs, then simplifies failing cases to their minimal form for easier debugging.
Install it if you want to catch bugs your manual tests miss.
progressbar2 renders text-based progress bars in the terminal, handling custom widgets, concurrent bars, unknown-length progress, and clean output around logs and prints.
Represents and manipulates IPv4, IPv6, MAC addresses and related network objects; supports CIDR notation, subnetting, set operations, IANA lookups, and DNS reverse generation.
Generates shell tab completion scripts for Python CLI applications built with argparse, supporting bash, zsh, fish, and tcsh without runtime overhead.
Install it if you build Python command-line tools and want to offer your users fast, reliable shell completion.
Generates random JSON data that conforms to a given JSON schema, for use as test input in property-based testing with Hypothesis.
Solves the linear assignment problem using the Jonker-Volgenant (LAPJV) or Volgenant-Mordecai (LAPMOD) algorithm, returning optimal row-to-column assignments for dense or sparse cost matrices.
Install it if you need to solve linear assignment problems and prefer a specialized implementation over a general-purpose optimizer.
Converts written-out number words (like "twenty one") into numeric digits (21), supporting positive integers and decimals up to 999,999,999,999.
No—install only if you have a legacy codebase already using it.
A dict subclass that adds dot-notation access, keypath queries, and normalized I/O for formats like JSON, YAML, CSV, XML, and others.
Install it if you work frequently with nested dicts, multi-format data loading, or want cleaner syntax for accessing deeply nested values—the keypath and I/O features…
Solves linear assignment problems using Jonker-Volgenant and related algorithms, supporting single and batch operations on square and rectangular cost matrices.
Install it if you need to solve assignment problems and want more flexibility or batch processing capabilities.
Hegel-core provides property-based testing data generation and shrinking built on Hypothesis, implementing a universal testing protocol for discovering edge cases through randomized input generation.
No, not for new projects.
radish is a Behavior Driven Development (BDD) test framework written in Python that executes Gherkin-style feature files and supports standard BDD patterns plus extensions like preconditions, scenario loops, and constants.