Personal Python Preferences

note: These are recommendations, not hard and fast rules. There are trade-offs in every decision in software.

While your set up is largely up to personal preference, some factors to consider are:

  • How many different computers you use (will your configuration transfer easily?)
  • How many different OS's you use (will your configuration work between Windows / Linux?)
  • How many projects or versions of python / python packages do you need to use
  • How invested are you in learning the command line (would GUI make your life much less stressful?)

The following set up has worked well for me doing working with multiple python versions on both Windows and Ubuntu/Debian systems.


Download and Install Python Conda

  • Use Miniconda, the little sibling of Anaconda. It manages downloading and using different python versions and has some unique "virtual environment" features that are especially nice in data science work.

    • For the most up to date information (i.e. if the following doesn't work), see the official guide and click the link for your OS
    • Browser / GUI method:
      • Download the installer for Python 3.9 for your computer's OS (or 3.10 if available).
        • note most Windows computers are 64-bit these days, if you're unsure just double check
      • Run the file that was downloaded (probably in your ~/Downloads folder)
        • Ask it to add python to your account's PATH variable.
        • Installing just for yourself should be fine for most users
    • Command line method:

      • note right click links on website to get specific base version of Python (latest is fine in most cases)
      • Windows Powershell

        # Download the installer exe
          curl -uri -outfile MinicondaInstaller.exe
          # Install
          & .\MinicondaInstaller.exe /AddToPath=0 /InstallationType=JustMe /RegisterPython=0 /S /D=%UserProfile%\Miniconda3
      • Linux bash (MacOS needs different download link)

        # Download
          curl -url --output
          # Install

Initialize Conda Base Environment

note This glosses over what virtual environments actually are entirely. This is intended to keep the document short. In essence they are folders that each hold a specific version of python and specific versions of python packages.

2 Goals:

  • Python can make new virtual environments for different projects
  • Allows your code editor to utilize tools like black and flake8 to automatically format your code, show you what variables and functions you can use, and catch simple errors and tpyos before you run your code

Conda Init

After Installing Miniconda

  • On windows open Anaconda Powershell Prompt on Windows, terminal for linux
    • Enter conda init powershell then close and move on to the next step
      • note if Windows fails to do the initialization and instead makes a pop-up about "Folder Security", click the pop up then take action to allow the changes. You'll need to re-run the conda init command.
  • open up a new terminal
    • note if you installed via command line method, you may need to close your current terminal and reopen
    • If you see (base) on the left of your current line try running the following command
      python --version
    • If you don't see (base), try running the following command. Then open a new terminal window and try something like the python --version command again.
      conda init

Python Base Environment

  • Install some packages in the (base) conda virtual environment that are useful for code formatting and testing:

    # Windows Powershell
      # Download the requirements file
      (base)$> curl -uri -outfile base.requirements.txt
      # Install
      (base)$> python -m pip install -r base.requirements.txt
      # Unix
      # Download the requirements file
      (base)$> curl -url --output base.requirements.txt
      # Install
      (base)$> python -m pip install -r base.requirements.txt
# formatter
# % testing coverage
# linter + static checker
# interact with jupyter notebooks
# sort imports in a consistent fashion
# let flake8 nag you about object naming
# test your code
# make pytest and coverage play nicely

VS Code Text Editor Setup

VS Code is a very popular code editor with powerful features, active community extensions, and gets useful updates frequently. It's by no means the only way to program with Python, but it's one of the most beginner friendly ways to work on a variety of projects.

Set up test project

Make a folder called something like test_python and a file inside it called

mkdir test_python
cd test_python

Download and Install VS Code

  • Download from your OS link
  • Install with downloaded file
  • Open VS Code for the first time
  • If it won't work or can't open, try reading their getting started guides

Open your test project in VS Code

  • File -> Open Folder
    • Select the test_python folder
    • If a "Trust this workspace" message pops up, hit agree / trust
    • Open the file
      • Write something like print('Hello There') and save the file
  • Go back to your terminal and run python

Install Extensions for Python

In VS Code, open the extensions menu on the left (or with ctrl+shift+x)

  • Search for and install the following:
    • Python (Microsoft)
  • Reload the window now (close and reopen VS Code) or install some more of the things below then reload (all are optional)

Some other nice extensions (not necessary off the bat)

  • Python Specific
    • Python Docstring Generator (Nils Werner)
    • Even Better TOML (tamasfe)
  • General VS Code
    • indent-rainbow (oderwat): Visualize deeply indented blocks more easily
    • GitLens (Eric Amodio): Quickly check git history of files, branches, lines, etc.
  • Various File Types
    • Markdown TOC (AlanWalk)
    • markdownlint (David Anson)
    • XML (Red Hat)
    • SQL Formatter (adpyke)

Bonus: For Emacs users, "Awesome Emacs Keymap" will get you most of the way to familiar text editing keybindings

VS Code Set Python Env and Formatter

Recommended settings in VS Code

  • Type ctrl+shift+p then type "settings json" and select the entry, add these key-value pairs to file
    • Edit the CHANGEME:USERNAME with your Windows user. (On Ubuntu based systems use /home/username/miniconda3/bin/python, similar for other Unix systems but with relevant user home directory)

note: you can instead use ctrl+shift+p and search "settings ui" (or use ctrl+,) and then search for each of the keynames below to explore what other settings you might like to try.

    "python.pythonPath": "C:\\Users\\CHANGEME:USERNAME\\miniconda3\\python.exe",
    "files.autoSave": "onFocusChange",
    "editor.wordWrap": "on",
    "editor.wordWrapColumn": 88,
    "jupyter.alwaysTrustNotebooks": true,
    "python.linting.flake8Enabled": true,
    "python.linting.flake8Args": [
    "python.analysis.typeCheckingMode": "basic",
    "workbench.editorAssociations": [
            "viewType": "jupyter.notebook.ipynb",
            "filenamePattern": "*.ipynb"
    "python.linting.pylintEnabled": false,
    "python.formatting.provider": "black",
    "python.linting.ignorePatterns": [
    "python.testing.pytestEnabled": true,
    "python.venvPath": "${workspaceFolder}"

Get on to Getting Things Done

That's the bare-bones Python setup.

Feel free to try out / research the following (Python related):

  • Typing error-filled python code and see how flake8 warns you
  • Start your .py document with # %% to make it a VS Code "Python Interactive" document (really awesome way to experiment with code blocks and debug small chunks)
  • Use alt+shift+f or ctrl+shift+p and search 'Format Document' to format python according to black standard
  • Text Editing and Searching Guide including stuff like using multiple cursors, search and replace over multiple files, auto save (which is included in my json settings above)
  • Make a new virtual environment for your project with python -m venv venv or look into using conda to manage your environments
  • Look into VS Code's live code sharing and git integrations to collaborate better

Or expand your environment by setting up a few more tools (tangential to Python):

  • Look into Emmet the auto completer for html files built into VS Code
  • Install Node if you're going to be doing some web development or otherwise need npm
  • Install Docker if you want to explore modern container based deployment
  • Find some good Python Reading (Ok one more Python recommendation; some of these are available for low-to-no cost from the authors)