mutmut | yapf | |
---|---|---|
4 | 21 | |
860 | 13,663 | |
- | 0.3% | |
6.9 | 8.0 | |
19 days ago | 13 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mutmut
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Scientist: A Ruby library for carefully refactoring critical paths
I wrote one (https://github.com/boxed/scientist) as I found the existing ones very complicated and that just gives me a bad feeling. Since I'm the author of mutmut (https://github.com/boxed/mutmut), I also made sure my implementation was 100% mutation tested before I used it in production.
I used my implementation to replace number parsing in my work project: https://kodare.net/2021/04/04/safe_number_parsing.html
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Mutmut introduces a clever approach to scrutinizing your tests. It evaluates the effectiveness of your test suite by slightly altering the code after the tests have been written. If a test fails after a minor change, that's a good sign; it means the test is robust enough to catch those changes. But if the test passes even after the code change, it indicates that the test isn't effectively detecting that alteration – this is what Mutmut terms a "surviving mutant."
- Boring Python: Code Quality
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Python toolkits
mutmut for mutation testing.
yapf
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Enhance Your Project Quality with These Top Python Libraries
YAPF (Yet Another Python Formatter): YAPF takes a different approach in that it’s based off of ‘clang-format’, a popular formatter for C++ code. YAPF reformats Python code so that it conforms to the style guide and looks good.
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Why is Prettier rock solid?
I think I agree about the testing and labor of complicated translation rules.
But it doesn't appear that almost every pretty printer uses the Wadler pretty printing paper. It seems like MOST of them don't?
e.g. clang-format is one of the biggest and best, and it has a model that includes "unwrapped lines", a "layouter", a line break cost function, exhaustive search with memoization, and Dijikstra's algorithm:
https://llvm.org/devmtg/2013-04/jasper-slides.pdf
The YAPF Python formatter is based on this same algorithm - https://github.com/google/yapf
The Dart formatter used a model of "chunks, rules, and spans"
https://journal.stuffwithstuff.com/2015/09/08/the-hardest-pr...
It almost seems like there are 2 camps -- the functional algorithms for functional/expression-based languages, and other algorithms for more statement-based languages.
Though I guess Prettier/JavaScript falls on the functional side.
I just ran across this survey on lobste.rs and it seems to cover the functional pretty printing languages influenced by Wadler, but functional style, but not the other kind of formatter ("Google" formatters perhaps)
https://arxiv.org/pdf/2310.01530.pdf
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
To get all your code into a consistent format the next step is to run a formatter. I recommend black, the well-known uncompromising code formatter, which is the most popular choice. Alternatives to black are autoflake, prettier and yapf, if you do not agree with blacks constraints.
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Front page news headline scraping data engineering project
Use yapf to format code -> https://github.com/google/yapf
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Confused by Google's docstring "Attributes" section.
Google is surprisingly rigorous when it comes to code formatting. I have been a software engineer at Amazon and it was nothing like what the book says happens at Google. So the conventions you see for python docstring formatting are primarily designed to integrate with Google's internal tooling. By using docstrings following the Google conventions, you will ultimately end up with automated documentation and other fancy automated things (like type checking which they did in the docstring before there were type hints). Also notably, Google has an open source python formatting tool that they use internally called YAPF (which stands for "Yet Another Python Formatter". So if you really want to go all-in on Google python style, grab that, too.
- Alternate python spacing.
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Not sure if this is the worst or most genius indentation I've seen
https://github.com/google/yapf has configs, do ctrl+f SPLIT_COMPLEX_COMPREHENSION in the readme
- Google Python Style Guide
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Enable hyphenation only for code blocks
Only as recommendation: If the lines of the source code (here: you C code you aim to document) are kept short, in manageable bytes (similar to entries parser.add_argument in Clark's "Tiny Python Projects", example seldomly pass beyond the frequently recommended threshold of 80 characters/line), reporting with listings becomes easier (equally, the reading of the difference logs/views by git and vimdiff), than with lines of say 120 characters per line. Though we no longer are constrained to 80 characters per line by terminals/screens and punch cards (when Fortran still was FORTRAN), this is a reason e.g., yapf for Python allows you to choose between 4 spaces/indentation (PEP8 style), or 2 spaces/indentation (Google style).
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3 popular Python style guides that will help your team write better code
There is also a formatter for Python files called yapf that your team can use to avoid arguing over formatting conventions. Plus, Google also provides a settings file for Vim, noting that the default settings should be enough if you're using Emacs.
What are some alternatives?
cookiecutter-hypermodern-python - Hypermodern Python Cookiecutter
black - The uncompromising Python code formatter
reorder-python-imports - Rewrites source to reorder python imports
isort - A Python utility / library to sort imports.
go-scientist
flake8
Poetry - Python packaging and dependency management made easy
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
hypothesis - Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
awesome-python-typing - Collection of awesome Python types, stubs, plugins, and tools to work with them.
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
pyright - Static Type Checker for Python