coveragepy
mypy
coveragepy | mypy | |
---|---|---|
7 | 112 | |
2,838 | 17,569 | |
- | 0.9% | |
9.6 | 9.7 | |
about 21 hours ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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coveragepy
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An Introduction to Testing with Django for Python
Coverage.py is the go-to tool for measuring code coverage of Python programs. Once installed, you can use it with either unittest or pytest.
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The Uncreative Software Engineer's Compendium to Testing
Code Coverage Analysis assess the code portions tested by the current test suites without altering the code.
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Slipcover: Near Zero-Overhead Python Code Coverage
The PLASMA lab @ UMass Amherst (home of the Scalene profiler) has released a new version of Slipcover, a super fast code coverage tool for Python. It is by far the fastest code coverage tool: in our tests, its average slowdown is just 5% (compare to the widely used coverage.py, average slowdown 218%!). The latest release performs both line and branch coverage with virtually no overhead. Use it to dramatically speed up your tests and continuous integration!
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Unit Tests - what’s the point?
Tests ensure the tested behavior is maintained. It's up to the developers to write tests with sufficient coverage. Determining which lines of code on your project are covered by tests is easily quantifiable using tooling. E.g. https://coverage.readthedocs.io/
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How to make Django package smaller for Serverless deployment
Taking the idea further, if you build robust tests for your API, you could use a dynamic code analyzer like coverage or figleaf to identify and delete unused functions.
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Comparison of Python TOML parser libraries
coverage
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New Ways to Be Told That Your Python Code Is Bad
FWIW, ternary expressions aren't properly detected by coverage: https://github.com/nedbat/coveragepy/issues/509
mypy
- The GIL can now be disabled in Python's main branch
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Polars – A bird's eye view of Polars
It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282
- Static Typing for Python
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Python 3.13 Gets a JIT
There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.
And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.
And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.
[0] https://nuitka.net/
[1] https://github.com/python/mypy/tree/master/mypyc
- Introducing Flask-Muck: How To Build a Comprehensive Flask REST API in 5 Minutes
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WeveAllBeenThere
In Python there is MyPy that can help with this. https://www.mypy-lang.org/
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It's Time for a Change: Datetime.utcnow() Is Now Deprecated
It's funny you should say this.
Reading this article prompted me to future-proof a program I maintain for fun that deals with time; it had one use of utcnow, which I fixed.
And then I tripped over a runtime type problem in an unrelated area of the code, despite the code being green under "mypy --strict". (and "100% coverage" from tests, except this particular exception only occured in a "# pragma: no-cover" codepath so it wasn't actually covered)
It turns out that because of some core decisions about how datetime objects work, `datetime.date.today() < datetime.datetime.now()` type-checks but gives a TypeError at runtime. Oops. (cause discussed at length in https://github.com/python/mypy/issues/9015 but without action for 3 years)
One solution is apparently to use `datetype` for type annotations (while continuing to use `datetime` objects at runtime): https://github.com/glyph/DateType
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What's New in Python 3.12
PEP 695 is great. I've been using mypy every day at work in last couple years or so with very strict parameters (no any type etc) and I have experience writing real life programs with Rust, Agda, and some Haskell before, so I'm familiar with strict type systems. I'm sure many will disagree with me but these are my very honest opinions as a professional who uses Python types every day:
* Some types are better than no types. I love Python types, and I consider them required. Even if they're not type-checked they're better than no types. If they're type-checked it's even better. If things are typed properly (no any etc) and type-checked that's even better. And so on...
* Having said this, Python's type system as checked by mypy feels like a toy type system. It's very easy to fool it, and you need to be careful so that type-checking actually fails badly formed programs.
* The biggest issue I face are exceptions. Community discussed this many times [1] [2] and the overall consensus is to not check exceptions. I personally disagree as if you have a Python program that's meticulously typed and type-checked exceptions still cause bad states and since Python code uses exceptions liberally, it's pretty easy to accidentally go to a bad state. E.g. in the linked github issue JukkaL (developer) claims checking things like "KeyError" will create too many false positives, I strongly disagree. If a function can realistically raise a "KeyError" the program should be properly written to accept this at some level otherwise something that returns type T but 0.01% of the time raises "KeyError" should actually be typed "Raises[T, KeyError]".
* PEP 695 will help because typing things particularly is very helpful. Often you want to pass bunch of Ts around but since this is impractical some devs resort to passing "dict[str, Any]"s around and thus things type-check but you still get "KeyError" left and right. It's better to have "SomeStructure[T]" types with "T" as your custom data type (whether dataclass, or pydantic, or traditional class) so that type system has more opportunities to reject bad programs.
* Overall, I'm personally very optimistic about the future of types in Python!
[1] https://github.com/python/mypy/issues/1773
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Mypy 1.6 Released
# is fixed: https://github.com/python/mypy/issues/12987.
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Ask HN: Why are all of the best back end web frameworks dynamically typed?
You probably already know but you can add type hints and then check for consistency with https://github.com/python/mypy in python.
Modern Python with things like https://learnpython.com/blog/python-match-case-statement/ + mypy + Ruff for linting https://github.com/astral-sh/ruff can get pretty good results.
I found typed dataclasses (https://docs.python.org/3/library/dataclasses.html) in python using mypy to give me really high confidence when building data representations.
What are some alternatives?
global-chem - A Knowledge Graph of Common Chemical Names to their Molecular Definition
pyright - Static Type Checker for Python
slipcover - Near Zero-Overhead Python Code Coverage
ruff - An extremely fast Python linter and code formatter, written in Rust.
Zappa - Serverless Python
pyre-check - Performant type-checking for python.
pytomlpp - A python wrapper for tomlplusplus
black - The uncompromising Python code formatter
flit - Simplified packaging of Python modules
pytype - A static type analyzer for Python code
toml - Python lib for TOML
pydantic - Data validation using Python type hints