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Above libraries I use in almost all the Python libraries we build. Apart from these I had use other Python frameworks and libraries for very specific purposes like FastAPI for web frameworks, tensorflow, pandas, numpy, etc. for AI/ML/DL based projects. TBH I prefer looking at awesome-python GitHub repository anytime I have to work in some new area.
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Your post has so many good elements that I've saved them for study and prompted feedback about the applications in Natural Language Processing and ML in Finance/BioTech. Most of my work lately has been NLP analysis research so devops and other GOF software concepts in your repo https://github.com/faif/python-patterns has been challenging.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Poetry for dependency management and packaging.
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pytest
The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Pytest for unit testing. Hypothesis to generate dummy data for test. mutmut for mutation testing.
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Hypothesis to generate dummy data for test.
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mutmut for mutation testing.
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Flake8
flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
flake8 for linting along with following plugin (list of awesome plugin can be found here, but me and my teammates have selected the below one. Have linting but don't make it too hard.) flake8-black which uses black for code formatting check. flake8-isort which uses isort for separation of import in section and formatting them alphabetically. flake8-bandit which uses bandit for security linting. flake8-bugbear for finding likely bugs and design problems in your program. flake8-bugbear - Finding likely bugs and design problems in your program. pep8-naming for checking the PEP-8 naming conventions. mccabe for Ned’s script to check McCabe complexity flake8-comprehensions for writing better list/set/dict comprehensions.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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awesome-flake8-extensions
:octocat: A curated awesome list of flake8 extensions. Feel free to contribute! :mortar_board:
flake8 for linting along with following plugin (list of awesome plugin can be found here, but me and my teammates have selected the below one. Have linting but don't make it too hard.) flake8-black which uses black for code formatting check. flake8-isort which uses isort for separation of import in section and formatting them alphabetically. flake8-bandit which uses bandit for security linting. flake8-bugbear for finding likely bugs and design problems in your program. flake8-bugbear - Finding likely bugs and design problems in your program. pep8-naming for checking the PEP-8 naming conventions. mccabe for Ned’s script to check McCabe complexity flake8-comprehensions for writing better list/set/dict comprehensions.
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flake8-black which uses black for code formatting check.
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flake8-black which uses black for code formatting check.
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flake8-isort which uses isort for separation of import in section and formatting them alphabetically.
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flake8-isort which uses isort for separation of import in section and formatting them alphabetically.
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flake8-bandit which uses bandit for security linting.
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flake8-bugbear
A plugin for Flake8 finding likely bugs and design problems in your program. Contains warnings that don't belong in pyflakes and pycodestyle.
flake8-bugbear for finding likely bugs and design problems in your program. flake8-bugbear - Finding likely bugs and design problems in your program.
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pep8-naming for checking the PEP-8 naming conventions.
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mccabe for Ned’s script to check McCabe complexity
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flake8-comprehensions for writing better list/set/dict comprehensions.
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CSV – csv Reader or dataclass-csv
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Lark
Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
STDOUT: Lark or pyparsing
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STDOUT: Lark or pyparsing
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click to create command line interface
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Sphinx along with MyST-parser to write documentation in markdown. I recently discovered portray which seems like a nice alternative as it supports markdown by default for both generic documentation and docstring in modules, class, methods and functions.
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Sphinx along with MyST-parser to write documentation in markdown. I recently discovered portray which seems like a nice alternative as it supports markdown by default for both generic documentation and docstring in modules, class, methods and functions.
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Sphinx along with MyST-parser to write documentation in markdown. I recently discovered portray which seems like a nice alternative as it supports markdown by default for both generic documentation and docstring in modules, class, methods and functions.
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cookiecutter
A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.
I maintain cookiecutter templates (can't share. It's in companies private repository) which have all these tool included along with some CI/CD pipelines. In case the template changes, we use cruft to update existing project which was using that template.
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cruft
Allows you to maintain all the necessary cruft for packaging and building projects separate from the code you intentionally write. Built on-top of, and fully compatible with, CookieCutter.
I maintain cookiecutter templates (can't share. It's in companies private repository) which have all these tool included along with some CI/CD pipelines. In case the template changes, we use cruft to update existing project which was using that template.
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Above libraries I use in almost all the Python libraries we build. Apart from these I had use other Python frameworks and libraries for very specific purposes like FastAPI for web frameworks, tensorflow, pandas, numpy, etc. for AI/ML/DL based projects. TBH I prefer looking at awesome-python GitHub repository anytime I have to work in some new area.
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Above libraries I use in almost all the Python libraries we build. Apart from these I had use other Python frameworks and libraries for very specific purposes like FastAPI for web frameworks, tensorflow, pandas, numpy, etc. for AI/ML/DL based projects. TBH I prefer looking at awesome-python GitHub repository anytime I have to work in some new area.
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Textual to diagram: kroki(this has integration for other popular tools like Mermaid, GraphViz, Excalidraw, PlantUML etc.).
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Textual to diagram: kroki(this has integration for other popular tools like Mermaid, GraphViz, Excalidraw, PlantUML etc.).
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Textual to diagram: kroki(this has integration for other popular tools like Mermaid, GraphViz, Excalidraw, PlantUML etc.).
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Add TestSlide to testing toolkits, it’s really good https://github.com/facebook/TestSlide
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I think we use these - https://github.com/openapi-generators/openapi-python-client
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Our team has transferred from Sphinx for documentation to JupyterBook. There have been some growing pains with it but I prefer the look of the output and being able to play with the examples on Colab or Binder at the click of a button is a great feature.
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So the Poetry maintainer has explained here about why they don’t have the better scripts support you mentioned. However, someone has already built this plugin for Poetry to achieve it.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives