pyupgrade
Schemathesis
pyupgrade | Schemathesis | |
---|---|---|
23 | 23 | |
3,330 | 2,096 | |
- | 1.8% | |
7.9 | 9.7 | |
8 days ago | 8 days ago | |
Python | Python | |
MIT License | MIT License |
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.
pyupgrade
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
pyupgrade and flynt are examples of tools that modify your code base from earlier python versions into the newest python syntax, rewriting all string formats into f-strings and similar things.
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Conversion from the f-string literals to format method in python
pyupgrade - A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.
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Which is your favourite or go-to YouTube channel for being up-to-date on Python?
He made yesqa and pyupgrade (among others), and also works on flake8. His main job is for https://sentry.io/.
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Reasons Python Sucks
That's a decade to make a 30 second change. Add something like https://github.com/asottile/pyupgrade to your pre-commit hooks and you won't even need 25 of those seconds.
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Is there a way to convert python 2 to 3 without finding every single line and fix it?
There is also pyupgrade: https://github.com/asottile/pyupgrade
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I've recently learned about better support for type-hinting. What other 'best practices' have been introduced in Python 3.10 or newer?
pyupgrade is a useful tool that can help you find some of these things.
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Flake8 took down the gitlab repository in favor of github
and last a little plug or two -- because I do this all for free and despite millions benefiting I receive zero proportional benefit from the maintenance work I put in -- consider sponsoring or maybe check out pre-commit.ci which would have automatically fixed this problem for you a year and a half ago
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Django upgrade services?
Running https://github.com/adamchainz/django-upgrade with https://github.com/asottile/pyupgrade recursively will give an idea about how much work is there on Django side. Still, there may have dependency on third party libraries (both django+python). Another thing to consider is which role Django performing here, serving APIs or html views. As good test coverage is already there, you are on lucky side.
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It’s Time to Say Goodbye to These Obsolete Python Libraries
Such goodness here and even points to an interesting project I’d never heard of for automated “de-deprecation”
https://github.com/asottile/pyupgrade
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Is there a linter which would suggest using elif rather than an else in an if clause?
I do would like to recommend pyupgrade. Just pip install as expected and then run pyupgrade --py310-plus to drag your code kicking and screaming into $CURRENT_YEAR. Or at least into whatever version you're using :)
Schemathesis
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Ask HN: Any Good Fuzzer for gRPC?
I am not aware of any tools like that, but eventually, I plan to add support for gRPC fuzzing to Schemathesis. There were already some discussions and it is more or less clear how to move forward. See https://github.com/schemathesis/schemathesis/discussions/190...
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Show HN: Auto-generate load tests/synthetic test data from OpenAPI spec/HAR file
Why is AI needed for this at all? Have you heard about Schemathesis (https://github.com/schemathesis/schemathesis)?
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
SchemaThesis is a powerful tool, especially when working with web APIs, and here's how it can enhance your testing capabilities:
- Hurl 4.0.0
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OpenAPI v4 Proposal
I'm sorry, but you have completely misunderstood the purpose of Open API.
It is not a specification to define your business logic classes and objects -- either client or server side. Its goal is to define the interface of an API, and to provide a single source of truth that requests and responses can be validated against. It contains everything you need to know to make requests to an API; code generation is nice to have (and I use it myself, but mainly on the server side, for routing and validation), but not something required or expected from OpenAPI
For what it's worth, my personal preferred workflow to build an API is as follows:
1. Build the OpenAPI spec first. A smaller spec could easily be done by hand, but I prefer using a design tool like Stoplight [0]; it has the best Web-based OpenAPI (and JSON Schema) editor I have encountered, and integrates with git nearly flawlessly.
2. Use an automated tool to generate the API code implementation. Again, a static generation tool such as datamodel-code-generator [1] (which generates Pydantic models) would suffice, but for Python I prefer the dynamic request routing and validation provided by pyapi-server [2].
3. Finally, I use automated testing tools such as schemathesis [3] to test the implementation against the specification.
[0] https://stoplight.io/
[1] https://koxudaxi.github.io/datamodel-code-generator/
[2] https://pyapi-server.readthedocs.io
[3] https://schemathesis.readthedocs.io
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Faster time-to-market with API-first
Consolidating the API specification with OpenAPI was a turning point for the project. From that moment we were able to run mock servers to build and test the UI before integrating with the backend, and we were able to validate the backend implementation against the specification. We used prism to run mock servers, and Dredd to validate the server implementation (these days I’d rather use schemathesis).
- Show HN: Step CI – API Testing and Monitoring Made Simple
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API-first development maturity framework
In this approach, you produce an API specification first, then you build the API against the specification, and then you validate your implementation against the specification using automated API testing tools. This is the most reliable approach for building API servers, since it’s the only one that holds the server accountable and validates the implementation against the source of truth. Unfortunately, this approach isn’t as common as it should be. One of the reasons why it isn’t so common is because it requires you to produce the API specification first, which, as we saw earlier, puts off many developers who don’t know how to work with OpenAPI. However, like I said before, generating OpenAPI specifications doesn’t need to be painful since you can use tools for that. In this approach, you use automated API testing tools to validate your implementation. Tools like Dredd and schemathesis. These tools work by parsing your API specification and automatically generating tests that ensure your implementation complies with the specification. They look at every aspect of your API implementation, including use of headers, status codes, compliance with schemas, and so on. The most advanced of these tools at the moment is schemathesis, which I highly encourage you to check out.
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How do you manage microservices API versions and branching strategies?
Keep all API versions in the code Another strategy is to have all the different API versions in the same code. So you may have a folder structure that looks like this: api ├── v1 └── v2 Within the API folder, you have one folder for v1 and another one for v2. Each folder has its own schemas and routes as required by the API version they implement. If you use URL-based versioning, v1 is accessible through the example.com/v1 endpoint or the v1.example.com subdomain (whichever strategy you use), and same for v2. Deprecating a version is a simple as its corresponding folder. In any case, I'd recommend you also validate your API implementations in the CI using something like schemathesis. Schemathesis looks at the API documentation and automatically generates hundreds of tests to make sure you're using the right schemas, status codes, and so on. It works best if you design and document the API before implementing, which allows you to include OpenAPI links and other features.
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This Week in Python
schemathesis – Run generated test scenarios based on your OpenAPI specification
What are some alternatives?
pre-commit-hooks - Some out-of-the-box hooks for pre-commit
dredd - Language-agnostic HTTP API Testing Tool
flynt - A tool to automatically convert old string literal formatting to f-strings
Robot Framework - Generic automation framework for acceptance testing and RPA
black - The uncompromising Python code formatter
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
pep585-upgrade - Pre-commit hook for upgrading type hints
coverage
autoflake - Removes unused imports and unused variables as reported by pyflakes
drf-openapi-tester - Test utility for validating OpenAPI documentation
black - The uncompromising Python code formatter [Moved to: https://github.com/psf/black]
tox - Command line driven CI frontend and development task automation tool.