pytest
mypy
pytest | mypy | |
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30 | 112 | |
11,402 | 17,591 | |
1.3% | 1.0% | |
9.8 | 9.7 | |
6 days ago | 2 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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.
pytest
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Integrating Lab Equipment into pytest-Based Tests
In this blog post I want to demonstrate how my lab equipment such as a lab power supply or a digital multimeter (DMM) have been integrated into some pytest-based tests. Would love to get your feedback and thoughts! 🚀
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The Uncreative Software Engineer's Compendium to Testing
Pytest: is a third-party testing framework that supports fixtures, parameterized testing, and easy test discovery while having room to add plugins to extend its functionality.
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pytest VS vedro - a user suggested alternative
2 projects | 16 Jul 2023
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TDD vs BDD - A Detailed Guide
Next, you need to install a testing framework that will be used for performing unit testing in your project. Several testing frameworks are available depending on the programming language used to create an application. For example, JUnit is commonly used for Java apps, pytest for Python apps, NUnit for .NET apps, Jest for JavaScript apps, and so on. We’ll use the Jest framework for this tutorial since we are using JavaScript.
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Is there a way to automate testing in python? In my case :
Yea, read through the pytest docs.
- Testing an automation framework
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Pytest Tips and Tricks
I absolutely agree about fixtures-as-arguments thing. Ward does this a lot better, using default values for the fixture factory. There's a long issue on ideas to implement something like that as a pytest plugin (https://github.com/pytest-dev/pytest/issues/3834), but it seems the resulting plugin relies on something of a hack.
- 2023 Development Tool Map
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Is my merge sort right?
I recommend writing a few tests. py.test makes that quite simple:
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How to raise the quality of scientific Jupyter notebooks
Since ITK's inception in 1999, there has been a focus on engineering practices that result in high-quality software. High-quality scientific software is driven by regression testing. The ITK project supported the development of CTest and CDash unit testing and software quality dashboard tools for use with the CMake build system. In the Python programming language, the pytest test driver helps developers write small, readable scripts that ensure their software will continue to work as expected. However, pytest can only test Python scripts by default, and errors in untested computational notebooks are more common than well-tested Python code.
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?
nose2 - The successor to nose, based on unittest2
pyright - Static Type Checker for Python
Robot Framework - Generic automation framework for acceptance testing and RPA
ruff - An extremely fast Python linter and code formatter, written in Rust.
Behave - BDD, Python style.
pyre-check - Performant type-checking for python.
Slash - The Slash testing infrastructure
black - The uncompromising Python code formatter
hypothesis - Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
pytype - A static type analyzer for Python code
nose - nose is nicer testing for python
pydantic - Data validation using Python type hints