cli date and time processing - put in a query for some nlp time and it spits out the canonical iso version.

seems incredibly versatile (hundreds of examples on the github page) and could be incorporated into a wide variety of scripts

A way to extract the authenticator code from your steam account and add it as a OTP to other authenticator apps (aside from the using the official steam application)

Goes over advanced concepts of scraping (with Python):

• asynchronous loading pages / client-side rendering (Selenium)
• authentication
• blacklisting
  • header inspection
  • request frequency
  • pattern detection
  • honeypots
• captchas, redirects

Progress bars to implement either in python itself (as a library) or for shell operations (as a cli), seems fast and easy to implement.

An 8-part series on understanding the python pandas pipeline and concepts.

Amazing python plugin which makes interaction with local shell super simple.
Allows to import the 'local' machine and access commands from it, pipe them together, import commands from the PATH directly and even interact with other machines over ssh.

Can convert (and revert) jupyter notebooks to markdown and script files (i.e. plaintext files instead of singular json code files).

Could be useful for data tracking or converting between a jupyter-centric and a vim-centric data workflow.

Python library to parse timewarrior data. Has a simple example of creating a csv report, can be used for all sorts of fancy filtering and report creation ideas.

Third edition of the famous data analysis learning book for pandas (and numpy) by the pandas author.

Tool for converting markdown files into anki decks - GitHub - lukesmurray/markdown-anki-decks: Tool for converting markdown files into anki decks

Processing finally ported to python - a perfect fit.

How to specify versions in pyproject.toml

In-depth explanation and introduction of the requests standard library.

And another, more template-y version of 'modern' python development tooling.

Possibly the best overall introduction and reference I have seen for modern python tooling.

Goes a little into the details but expects you to generally look for additional information on your own if you're stuck.
Amazing, in-depth, concise.

easiest answer is with pandas as a library:

df = pd.read_json('inputfile.json')

df.to_csv('outputfile.csv', encoding='utf-8', index = False)

read_json converts a JSON string to a pandas object (either a series or dataframe).
to_csv can either return a string or write directly to a csv-file. See the docs for to_csv.

works best when json is an array of structured objects (unstructured data, see SO answer in link)

additional pandas to csv tips see this SO thread

Using python to write to a sqlite database, storing json data.

Pre-made models trained for natural language processing -- can summarize, generate, identify, answer questions, etc.

A whole library of interfaces to extract quantified self data through (more or less) simple python scripts.

powers ad is explained by many of the thoughts in beepboop

python testing resources:

• getting started with python testing: https://realpython.com/python-testing/#testing-your-code
• testing cli apps: https://stackoverflow.com/questions/13493288/python-cli-program-unit-testing
• techniques for cli testing: https://realpython.com/python-cli-testing/#unit-testing-with-pytest-and-mocks
• writing doctests: https://www.digitalocean.com/community/tutorials/how-to-write-doctests-in-python
• doctest documentation: https://docs.python.org/3/library/doctest.html