Our function checks if a is even and returns True or False.
#Pycharm command line arguments code
There're some ways to separate type annotations from the code itself - with stubs, but it's a different story, let's check a simple example: def foo ( a : int ) -> bool : return str ( a % 2 = 0 ) Type annotations is a double-edged sword - you're going to find some errors in your code in advance but you're also going to introduce a lot of redundant code to your project which is completely useless in the runtime. This is a huge topic and you should read some docs to make yourself familiar with type annotations in Python, good start is PEP 484. As we all know Python is a dynamic language but you still can type-annotate your code and have it type-checked before runtime with external tools, e.g. Or mute all inspections for a specific function: def main(): # pragma: no cover mute invalid name inspection for specific file: # pylint: disable=invalid-name You can also disable a warning per file with comments on top of it, e.g. missing docstring - I rely on pydocstyle to check my docstrings. This will reduce the size of output (I don't really care about my code assessment rate), enforce some style options like 80 characters per line and disable few inspections globally, e.g. # False positive for test file classes and methods
# False positive for OK test methods names and few other places # False positive for type annotations with typing module Just create a pylintrc file in the project root and add some options, my example: To mute such false positives and customize the report one may use pylintrc file, this is in fact a very simple and useful. As I said pylint is super strict and some errors may be just false positives, in the case above I really known what I'm doing with the variable name a and wrong type in slice is just a Python 3.6 style type annotation that is not supported by pylint at all at least right now. These two tell us that I used a wrong type inside a slice and an invalid name of the variable (in terms of PEP8). The most important here is warnings (top of the report), e.g.: ************* Module 圓bĮ: 27, 0: Sequence index is not an int, slice, or instance with _index_ (invalid-sequence-index)Ĭ: 44, 8: Invalid variable name "a" (invalid-name) To run pylint against your code just type: python -m pylint īy default it generates a voluminous output with code assessment, different metrics (tables) and so on. It's very strict and highly customizable. This is however only conceptual, and does not have a practical difference at the moment.Īs for other servers specifically, Waitress supports this invocation method.Pylint is a well known Python linter and one of my favorite tools which I use extensively in almost every project I have. Tools in the latter category is tightly coupled with the Python environment it is installed in, and python -m is thus preferred to make this obvious. pytest and pip), with the difference being whether the installation method matters to how the tool behaves. mypy and black) and in-environment commands (e.g.
There seems to be a recent trend in Python packaging to attempt to distinguish between standalone (e.g. There is also virtually no downside to providing such an entry point, even if the command is preferred. path/to/python -m uvicorn (or py -m uvicorn on Windows) is by contrast much more obvious. Uvicorn is by design to be installed into the same environment as the target application, and if there are multiple Python installations on PATH, it may not be immediately apparent what a given uvicorn command is tied to, even if I use the absolute path. The only significant benefit is that it is easier to tell which Python interpreter I’m running against.
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