Choosing the Right IDE: My Journey from Visual Studio to VS Code for Python Development

For Python development, Visual Studio Code is the better choice for most day-to-day work, while Visual Studio shines in large-scale, Windows-centric solutions. I switched after a year of juggling both tools, and the change cut my build time in half and let me focus on code, not configuration.

In 2024, 72% of developers reported that VS Code is their primary editor for Python projects (Aikido Security). That number mattered to me because I was trying to decide whether to keep paying for the full Visual Studio license or adopt the free, lightweight VS Code.

Why the IDE choice matters for Python developers

When I first started coding in Python, I used the full Visual Studio suite because my employer already had a site license. The IDE felt powerful: integrated debugging, designers for Windows Forms, and a rich project system. However, the installation was 5 GB, the startup took 30 seconds, and every new library required a heavyweight project file.

Think of an IDE as a toolbox. Visual Studio is a full carpenter’s workshop with every power tool you could imagine; VS Code is a well-organized utility belt that lets you add only the tools you need. For a Python script that runs in a container or a cloud function, the extra weight of the workshop slows you down.

My experience aligns with industry trends: developers value speed, extensibility, and a low barrier to entry. When a tool loads quickly, you spend more time writing and testing code. When the same tool can be customized with extensions, you get the best of both worlds.

In my next section I walk through the exact steps I took to migrate a mid-size Flask API from Visual Studio to VS Code, illustrating how each decision impacted productivity.

My case study: Migrating a Python project from Visual Studio to VS Code

Back in March 2023, my team maintained a Flask-based microservice that handled user authentication. The codebase sat in a Visual Studio solution with a .vcxproj file, even though it was pure Python. The build process looked like this:

1. Open Visual Studio → Load the .sln file
2. Run the integrated Python environment → Wait for the interpreter to start
3. Hit F5 to debug → Attach to the Flask process

We spent roughly 15 minutes each morning just getting the environment ready. I decided to try VS Code because the team was already experimenting with containerized development.

Here’s the migration checklist I used:

• Export requirements: pip freeze > requirements.txt
• Set up a .vscode folder with launch.json and settings.json
• Install the Python extension (Microsoft’s official extension)
• Configure a dev container using the Remote-Containers extension
• Validate by running the test suite inside the container

After the migration, my "run-debug-repeat" cycle dropped from 15 minutes to under 3 minutes. The reduced friction also meant junior developers could onboard faster.

Key Takeaways

• VS Code’s lightweight core speeds up start-up time.
• Extensions let you add only the features you need.
• Docker-based dev containers isolate environments.
• Visual Studio remains useful for Windows-only GUI apps.
• Migration is a checklist, not a rewrite.

Below is a side-by-side comparison of the two environments for this project.

From the table you can see that VS Code wins on speed, size, and cost, while Visual Studio still offers deep integration for Windows-specific workloads.

Step-by-step VS Code setup for Python

Below is the exact process I follow when I set up a fresh machine for Python development. Feel free to copy-paste each command.

1. Download VS Code from code.visualstudio.com (the installer is ~200 MB).
2. Install the Python extension (search "Python" by Microsoft in the Extensions view). This adds IntelliSense, linting, debugging, and testing support.
3. Select the interpreter via the Command Palette (Ctrl+Shift+P → "Python: Select Interpreter") and choose the .venv path.
4. Test the debugger by pressing F5. Breakpoints set in app.py pause execution, and the VARIABLES pane shows the request context.

Create a launch configuration for Flask debugging. In the .vscode folder, add launch.json:

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Python: Flask",
      "type": "python",
      "request": "launch",
      "module": "flask",
      "env": {"FLASK_APP": "app.py", "FLASK_ENV": "development"},
      "args": ["run", "--no-debugger", "--no-reload"],
      "jinja": true
    }
  ]
}

Configure a virtual environment:

python -m venv .venv
source .venv/bin/activate  # macOS/Linux
.\\venv\\Scripts\\activate  # Windows

Pro tip: Add "python.linting.pylintEnabled": true to settings.json to get on-the-fly linting without extra configuration.

With these steps, the IDE is ready for both interactive development and automated testing.

Python testing tutorial in VS Code: unittest → pytest

My team originally used the built-in unittest framework because it ships with Python. While reliable, the test discovery in VS Code required a lot of boilerplate. Switching to pytest gave us a cleaner syntax and richer output.

Here’s how I migrated a single test file:

# test_auth.py (unittest version)
import unittest
from auth import login

class TestAuth(unittest.TestCase):
    def test_login_success(self):
        self.assertTrue(login('user', 'pass'))

if __name__ == '__main__':
    unittest.main

After migration to pytest:

# test_auth.py (pytest version)
from auth import login

def test_login_success:
    assert login('user', 'pass')

To enable pytest in VS Code, open the Command Palette → "Python: Configure Tests" → select "pytest" and point to the test folder. VS Code then shows a Test Explorer pane where you can run, debug, or rerun failing tests with a single click.

When I ran the suite for the first time, the test run time dropped from 12 seconds to 5 seconds, and the output highlighted the exact line where an assertion failed. That speed gain mattered during continuous integration runs.

According to Augment Code, the top AI coding assistants now integrate directly with VS Code to suggest test cases (Augment Code). Pairing those assistants with pytest can auto-generate skeleton tests for new functions, further reducing manual effort.

Extending VS Code with AI assistants and productivity extensions

Once the core Python workflow was smooth, I started adding extensions that boost daily productivity. Aikido Security recently published a list of the Top 14 VS Code extensions for 2026, highlighting tools for testing, security, and AI assistance. I installed three that changed how I code:

• GitHub Copilot - an AI pair programmer that suggests whole functions as you type.
• Python Test Explorer UI - gives a visual hierarchy of tests and quick run buttons.
• Bracket Pair Colorizer 2 - colors matching brackets, which reduces syntax errors in complex list comprehensions.

The table below summarizes the extensions I consider essential for a Python developer in 2026.

According to Visual Studio Magazine, the AI Toolkit for VS Code now includes a catalog of tools that can automatically generate Dockerfiles, CI pipelines, and even API client stubs (Visual Studio Magazine). Leveraging those tools allowed my team to spin up a new microservice in under an hour - a task that previously took a day.

When Visual Studio still wins: Scenarios that demand the full IDE

Despite the advantages of VS Code, there are still pockets where Visual Studio is the pragmatic choice. In my career, I encountered two such scenarios:

• Enterprise Windows desktop apps built with WinForms or WPF. The designers and drag-and-drop UI editors are exclusive to Visual Studio.
• Complex C++/CLI interop projects that embed Python via native extensions. Visual Studio’s debugger can step across the managed/unmanaged boundary without extra configuration.

In a 2023 client engagement, we needed to integrate a Python analytics engine into an existing .NET WPF application. The team kept the Python code in a separate VS Code workspace for rapid iteration, but the final build and deployment were orchestrated within Visual Studio because the solution required MSBuild and the Windows Installer project type.

Below is a concise comparison of when each IDE shines:

My rule of thumb now is: start every new Python project in VS Code, and only pull in Visual Studio when you hit a Windows-specific requirement that VS Code cannot satisfy.

Frequently Asked Questions

Q: Is VS Code truly free for commercial use?

A: Yes. VS Code is released under the MIT license and can be used in any commercial project without paying a license fee. The only cost may come from paid extensions such as GitHub Copilot.

Q: How does Python debugging differ between the two IDEs?

A: Visual Studio provides a heavyweight debugger with built-in UI designers and automatic breakpoints for Windows applications. VS Code relies on the Python extension’s debug adapter, which is lightweight but fully featured for script-level debugging, supporting breakpoints, watch expressions, and remote debugging via containers.

Q: Can I run pytest inside VS Code’s integrated terminal?

A: Absolutely. After configuring pytest via the "Python: Configure Tests" command, you can invoke it from the integrated terminal with pytest, or use the Test Explorer UI to run individual tests or the entire suite with a click.

Q: When should I consider buying a Visual Studio license?

A: Purchase Visual Studio when you need advanced Windows desktop designers, extensive C++/CLI debugging, or enterprise-level profiling tools that are not available in VS Code. For most pure-Python workloads, the free VS Code setup suffices.

Q: Do AI coding assistants work with both IDEs?

A: Yes. Extensions like GitHub Copilot and the VS Code AI Toolkit integrate with both VS Code and Visual Studio, though the experience is smoother in VS Code because the extension marketplace is more centralized. The AI Toolkit described by Visual Studio Magazine now supports code generation directly in the editor for both products.