Install Python and VS Code, then add the Microsoft Python extension for full functionality. 2. Set up a virtual environment using python -m venv venv, activate it based on your OS, and select the interpreter via Ctrl Shift P to ensure correct environment usage. 3. Create a .py file, write code like print("Hello from VS Code!"), and run it using the right-click option, the play button, or the integrated terminal with python app.py. 4. Enable linting by installing tools like pylint and configuring them in Settings to receive real-time code quality feedback. 5. Debug code by setting breakpoints, pressing F5 to launch the debugger, allowing creation of launch.json, and using debug controls to inspect variables and execution flow. 6. Use Jupyter Notebooks by installing Jupyter, opening or creating .ipynb files, and running cells with Shift Enter for interactive computing. 7. Customize your workspace by setting up code formatting with black, enabling auto-save, choosing a theme and font, and optionally using a local settings.json file for project-specific configurations, resulting in a powerful, adaptable Python development environment.

Using Visual Studio Code (VS Code) for Python development is a popular choice because of its lightweight design, strong extension ecosystem, and excellent debugging tools. Here’s a practical guide to get you up and running efficiently.

1. Install Python and VS Code

First, make sure you have both Python and VS Code installed:

• Python: Download from python.org and ensure it's added to your system PATH. Verify installation by running python --version or python3 --version in your terminal.
• VS Code: Download and install from the official site.

After installation, open VS Code and install the official Python extension by Microsoft (search for "Python" in the Extensions marketplace). This adds support for IntelliSense, linting, debugging, code navigation, and more.

2. Set Up Your Python Environment

VS Code supports virtual environments, which help manage dependencies per project.

• Create a project folder and open it in VS Code.
• In the terminal (inside VS Code), set up a virtual environment:

  python -m venv venv

• Activate it:
  • On Windows:

    venv\Scripts\activate

  • On macOS/Linux:

    source venv/bin/activate

• Once activated, select the interpreter: press Ctrl Shift P, type "Python: Select Interpreter", and choose the one inside your venv folder (e.g., ./venv/bin/python).

You’ll know it’s working when you see the environment name in the bottom-left of the status bar.

3. Write and Run Python Code

• Create a .py file (e.g., app.py).
• Write some code:

  print("Hello from VS Code!")

• Run it by right-clicking the file and selecting "Run Python File in Terminal", or use the play button in the top-right corner (if the Python extension is active).

For more control, use the integrated terminal (`Ctrl ``) and run:

python app.py

4. Use IntelliSense and Linting

The Python extension provides smart code completion, parameter info, and error detection.

• Install a linter like pylint or flake8:

  pip install pylint

• VS Code will prompt you to enable linting. Go to Settings (Ctrl ,) and search for "Python Linting" to enable and select your preferred linter.

You’ll get real-time feedback on code quality and potential bugs.

5. Debug Your Code

VS Code has a powerful debugger.

• Set a breakpoint by clicking to the left of a line number (a red dot will appear).
• Press F5 to start debugging.
• The first time, you’ll be prompted to create a launch.json file under .vscode/. Choose "Python File" as the configuration.
• Debug controls (pause, step over, step into, etc.) appear at the top, and variables and call stack show in the sidebar.

You can inspect variables, evaluate expressions, and step through your code line by line.

6. Use Jupyter Notebooks (Optional)

VS Code supports .ipynb files natively.

• Install Jupyter: pip install jupyter
• Open a .ipynb file or create one.
• You can run cells with Shift Enter, just like in Jupyter Notebook.

This is great for data analysis or prototyping.

7. Customize Your Workspace

• Formatting: Use black or autopep8. Install via pip and configure in settings:

  "python.formatting.provider": "black"

• Auto-save: Enable in File > Auto Save.
• Theme and Font: Pick a comfortable theme (e.g., "Dark ", "Solarized") and adjust font size in Settings.

You can also create a settings.json file in your project’s .vscode folder to keep configurations local.

Basically, with the right setup, VS Code becomes a powerful, flexible environment for Python—whether you're writing scripts, building web apps, or doing data science. The key is installing the right extensions and using virtual environments to keep things clean.

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