responses
Poetry
responses | Poetry | |
---|---|---|
12 | 377 | |
4,045 | 29,552 | |
0.4% | 1.3% | |
7.2 | 9.7 | |
3 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | 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.
responses
- Please recommend a good API Mocking tool
-
Useful libraries for integration/api testing
I use responses for this (for HTTP/HTTPS requests), to great affect. It's API is very nice. We have an API layer at work that's basically just a proxy to micro services, and responses is what we use to test it.
-
How I start every new Python backend API project
responses
-
Mocking api request?
Please format your code appropriately. But if you are using python requests you can use responses for mocking requests: https://github.com/getsentry/responses
- We Just Gave $260,028 to Open Source Maintainers
-
best way to test requests and responses in pytest?
You can use this https://github.com/getsentry/responses
- What's the use case for Responses library?
-
Am new to Testing, Should I test Functions that Return a Queryset?
The repsonses library is designed for mocking requests during tests https://github.com/getsentry/responses
-
Testing for API data (python)
I like using responses for mocking API calls in unit tests. It's easy to use and has less boilerplate than doing the mocks yourself. The bulk of your unit tests should be around any data transformation you do, and unit testing them should be fairly straightforward. I'd recommend writing integration tests as well, especially as IME the most common reason why data pipelines with API sources break is due to breaking API changes out of your control. I'd make them as high level as possible and make real API calls, preferably using credentials for a designated testing account. Assert the response from the API call conforms to the contract you expect. Additionally to integration test your code, you can test the main entrypoint to your script and make sure it spits out a file with the shape and contents you expect.
-
Two Methods for Testing HTTPS API Calls with Python and pytest and also Communicating with the In-laws
HTTP client library flexibility. Yes, requests pairs well with responses or requests-mock, and HTTPX has RESPX or pytest_httpx. Those testing helpers are an excellent match for the corresponding library, and should certainly be recommended. However, I don't always want to face the risk of rewriting all my tests if I replace the client library some day. And sometimes I am using an altogether different tool (even though both requests and HTTPX are quite awesome) such as urlopen, urllib3, httplib2, tornado, or aiohttp.
Poetry
-
Understanding Dependencies in Programming
You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.
-
Implementing semantic image search with Amazon Titan and Supabase Vector
Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
-
From Kotlin Scripting to Python
Poetry
-
How to Enhance Content with Semantify
The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:
-
Uv: Python Packaging in Rust
Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry?
https://github.com/python-poetry/poetry/issues/6409
-
Boring Python: dependency management (2022)
Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.
Not sure where I would stand if I fully investigated it tho.
[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...
-
Fun with Avatars: Crafting the core engine | Part. 1
We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.
-
Python Packaging, One Year Later: A Look Back at 2023 in Python Packaging
Here are the two main packaging issues I run into, specifically when using Poetry:
1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.
2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.
I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.
Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.
[0] https://github.com/python-poetry/poetry/issues/2740
[1] https://github.com/python-poetry/poetry/issues/2184
[2] https://pypi.org/project/pypiserver/
-
Introducing Flama for Robust Machine Learning APIs
We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.
-
How do you resolve dependency conflicts?
I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github
What are some alternatives?
httpretty - Intercept HTTP requests at the Python socket level. Fakes the whole socket module
Pipenv - Python Development Workflow for Humans.
VCR.py - Automatically mock your HTTP interactions to simplify and speed up testing
PDM - A modern Python package and dependency manager supporting the latest PEP standards
httmock - A mocking library for requests
hatch - Modern, extensible Python project management
Selenium Wire - Extends Selenium's Python bindings to give you the ability to inspect requests made by the browser.
pyenv - Simple Python version management
pytest-recording - A pytest plugin that allows recording network interactions via VCR.py
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
Moto - A library that allows you to easily mock out tests based on AWS infrastructure.
virtualenv - Virtual Python Environment builder