Sure. Python-specific extensions: Jupyter - for working with Jupyter notebooks (if needed) Mypy - type checking Pylance - alternative tool for type checking Ruff - the best and only linter you'll need Generic extensions: CodeSnap - useful for sharing code in image form, primarily for social media. Basically https://carbon.now.sh but as an extension. Even Better TOML - for pyproject.toml files file-tree-generator - for sharing your directory structure but being too lazy to make one by hand Hex Editor - for the rare occasion you need to dig into binary files HexInspector - see numbers in other number bases on hover Live Preview - lets you render web pages within VS Code with auto-refresh Markdown All in One - because you'll generally see plenty of Markdown in projects Markdown Preview Enhanced Snyk Security - helps find common security issues WSL - Better WSL integration Theme-wise I'm also fond of Material Icon Theme, Monokai Charcoal High Contrast, and Doki-Theme, but those aren't exactly useful. More on reddit.com

*cracks knuckles* autoDocstring - because I want future me to not be angry at present me Error Lens - because I'm blind Even Better TOML - because anyone who hasn't switched over to pyproject.toml format should Jupyter - because data science pylens - because I'd like to know what dependencies have updated Python Indent - because I value my sanity Ruff - nice performant linter + isort functionality View Image for Python Debugging - if your data science work sometimes strays away from Jupyter notebooks, you'll understand why More on reddit.com

I Use Python As My Main Language, And I Want To Make A VS Code Extension, But I Cant Find Any Docs/Resources, Is Possible To Make One In Python? More on stackoverflow.com

Python has different formatters and linters... For formatters there is autopep8 which as the name suggests makes code compliant with pep8 . Its main purpose is to address spacing disparities: import numpy as np x = np.array([0,1,2,3, 4]) y= 2 *x z = 1 import pandas as pd df= pd.DataFrame({'x':x, "y": y}) import matplotlib.pyplot as plt plt.plot(df['x'],df["y"]) import datetime time = datetime.datetime.now() print('hello world', sep='\t', end="\n\n") import collections counts = collections.Counter([1, 1,1, 2,2,3, 3,3,4]) Microsoft have an autopep8 extension. Pressing Ctrl, ⇧ and p to open the command palette and using the Format Document command or Format Document with command which allows selection of the default formatter. And the code formatted with autopep8 below: import collections import datetime import matplotlib.pyplot as plt import pandas as pd import numpy as np x = np.array([0, 1, 2, 3, 4]) y = 2 * x z = 1 df = pd.DataFrame({'x': x, "y": y}) plt.plot(df['x'], df["y"]) time = datetime.datetime.now() print('hello world', sep='\t', end="\n\n") counts = collections.Counter([1, 1, 1, 2, 2, 3, 3, 3, 4]) PEP8 is a bit vague on the sorting of imports: For imports Imports should be grouped in the following order: Standard library imports. Related third party imports. Local application/library specific imports. So the import organiser isort is a separate operation which separates these two groups out with a space and organises imports within these two groups alphabetically. Microsoft have an isort extension. Pressing Ctrl, ⇧ and p to open the command palette and using the Organize Imports command. import collections import datetime import matplotlib.pyplot as plt import numpy as np import pandas as pd x = np.array([0, 1, 2, 3, 4]) y = 2 * x z = 1 df = pd.DataFrame({'x': x, "y": y}) plt.plot(df['x'], df["y"]) time = datetime.datetime.now() print('hello world', sep='\t', end="\n\n") counts = collections.Counter([1, 1, 1, 2, 2, 3, 3, 3, 4]) PEP8 is a bit vague on the string style stating: In Python, single-quoted strings and double-quoted strings are the same. This PEP does not make a recommendation for this. Pick a rule and stick to it. When a string contains single or double quote characters, however, use the other one to avoid backslashes in the string. It improves readability. There are therefore opinionated formatters that attempt to make the string styles consistent as well as other changes. Microsoft have a black extension. Pressing Ctrl, ⇧ and p to open the command palette and using the Format Document command or Format Document with command which allows selection of the default formatter. import collections import datetime import matplotlib.pyplot as plt import numpy as np import pandas as pd x = np.array([0, 1, 2, 3, 4]) y = 2 * x z = 1 df = pd.DataFrame({"x": x, "y": y}) plt.plot(df["x"], df["y"]) time = datetime.datetime.now() print("hello world", sep="\t", end="\n\n") counts = collections.Counter([1, 1, 1, 2, 2, 3, 3, 3, 4]) Notice that all the str quotations are now consistently double quotations... In my VSCode black does not seem to work well unless the code has previously been formatted with autopep8 and isort. Unfortunately blacks formatting differs from the preference of the Python interpreter, the formal Python documentation and the way that the PEP8 document itself is written. For example inputting: "hello world!" 'The string is '\hello world!\'' Will display in the Python interpreter as: 'hello world!' "The string is 'hello world!'" Notice that single quotations are preferred unless the str includes a str literal. Triple quotes are preferred for docstrings as they often include str literals or could be later modified to have a str literal. This has led to some developers not really liking black... and wanting some options. The Rust Fast Formatter Ruff (VSCode has a third-party extension made by the developer) largely mimics the behaviour of black. However it is easy to make a ruff.toml file in the project folder and configure some of the options: [format] # Use single quotes for strings. quote-style = "single" The developer Astral Software has a ruff extension. Pressing Ctrl, ⇧ and p to open the command palette and using the Format Document with Ruff command or Format Notebook with Ruff command which allows selection of the default formatter. This gives the following which looks more similar to the style in PEP8. import collections import datetime import matplotlib.pyplot as plt import numpy as np import pandas as pd x = np.array([0, 1, 2, 3, 4]) y = 2 * x z = 1 df = pd.DataFrame({'x': x, 'y': y}) plt.plot(df['x'], df['y']) time = datetime.datetime.now() print('hello world', sep='\t', end='\n\n') counts = collections.Counter([1, 1, 1, 2, 2, 3, 3, 3, 4]) The pylint, pylance and pyflake8 linters all behave slightly differently and there is an extension for each of these from Microsoft. Installation of Ruff will also perform additional linting. While ruff is the fastest, all of these linters will pick up on slightly different things. pylint will for example pick up on some semantics that ruff won't but is much slower to load/process changes. For example in the code above it complains that the z is in lower case and recommends using uppercase to make it a constant, it complains about a missing module docstring and it complains about a missing end of line at the end of the script... Some of these things are minor, that personally I wouldn't change in the simple code above and essentially its a trade off between how much warnings you want and how through you want to be over simple semantics. More on reddit.com