The IDE that I'd Choose is...

The Temptation of New IDEs and the Reliability of VS Code

As a data scientist, I'm constantly enticed by the allure of new IDEs. PyCharm, Sublime Text, and Jupyter Notebooks each offer unique features and promise to enhance my workflow in different ways. The prospect of trying out these new tools is always exciting. However, despite the temptation, I invariably find myself returning to Visual Studio Code (VS Code). Here's why.

PyCharm: The Comprehensive Powerhouse

PyCharm is a fantastic IDE with an array of robust features:

Pros:

  • Feature-Rich: Excellent code completion, refactoring tools, and integrated debugging.
  • Integrated Tools: Built-in support for testing, version control, and scientific libraries.
  • Customization: Highly customizable with plugins to tailor it to specific needs.

Cons:

  • Resource Intensive: Can be heavy on system resources, which might slow down your computer.
  • Cost: The full-featured Professional edition is not free.

I actually haven't tried it. I know people who went from Jupyter Notebooks and swears by PyCharm. My only deterrance was cost. I know that I can get a free version as a regular person, but I figured when I eventually dip my toes in freelance work, it might be an added expense. If I ever start rolling in the money, maybe I'll buy winzip and PyCharm.

Sublime Text: The Speedy Editor

Sublime Text is known for its speed and simplicity:

Pros:

  • Lightweight: Extremely fast and responsive.
  • Customizable: Highly customizable with plugins and themes.
  • User-Friendly: Simple and clean interface that's easy to navigate.

Cons:

  • Limited Out-of-the-Box Features: Requires setup and plugins to match the functionality of more comprehensive IDEs.
  • Paid License: Not free for long-term use.

Same as PyCharm. Eventually, I would have to pay the piper.

Jupyter Notebooks: The Interactive Explorer

Jupyter Notebooks are perfect for interactive coding and data visualization:

Pros:

  • Interactivity: Ideal for writing and testing code in small, manageable chunks.
  • Visualization: Excellent support for inline visualizations.
  • Documentation: Combines code and documentation seamlessly with Markdown.

Cons:

  • Performance: As your skills grow, you will outlikely outgrow the notebooks. When I started into learning machine learning (wow, that sounds weird to write), I would have to wait minutes for one line of code to process.
  • Debugging: Limited debugging. I actually don't know how to debug in notebooks. I was the debugger. I just went line by line until it worked.

I think purely as data scientist, this is a great way to start. The greatness lies in it's name. It's a notebook. As I started learning, I would jot notes to explain why something would do something. When you're researching and see other notebooks, you can see their notes.

VS Code: The Reliable All-Rounder

In the end, VS Code consistently wins me over (because I'm too comfortable to leave):

Pros:

  • Lightweight Yet Powerful: Fast and responsive without sacrificing functionality.
  • Extensions: An extensive marketplace with a wide range of extensions, including those for Python, Jupyter, Docker, and more.
  • Customization: Highly customizable to suit any workflow.
  • Integrated Tools: Built-in terminal, debugging, and version control support.

Cons:

  • Initial Setup: Requires some setup and configuration, especially for data science workflows.
  • Learning Curve: Can be overwhelming for beginners due to the plethora of features and options.

I'm just a basic data scientist. I always go back to VS Code. When I watch videos, I usually see VS Code and they mention their extensions. I'm always, "I gotta have that."

Conclusion

I am not an authority on IDEs. If anything, if you read this, you realize how little knowledge I have of other IDEs. My advice is that nothing really matters. Just use whatever you're comfortable with.