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I am learning Python for data science, but my problem is that I still don't understand the difference between Spyder and Jupyter!
I would like you guys to help me to understand the difference, please; I would appreciate that.
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Anaconda is a Python distribution with many software tools in it. Spyder is an IDE and Jupyter Notebook is a web-based program to code Python for scientific purposes in Anaconda.
When I work with large datasets, I never use Jupyter as Spyder seems to run much faster. The only way to truly compare this would be to run/time the same script on both Spyder and Jupyter a couple of times, but in my experience Spyder always beats Jupyter when it comes to computation time.
Spyder-notebook is a plugin that allows you to open, edit and interact with Jupyter Notebooks right inside Spyder. Using notebooks inside Spyder allows you to take advantage of their web interface alongside Spyder's powerful features such as the Variable explorer, console and debugger.
Spyder is a free and open source scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.
Here's just a basic summary of the two tools.
Jupyter is a very popular application used for data analysis. It's an IPython notebook ("interactive python"). You can run each block of code separately. For example, I can print a graph using matplotlib. Create a new block of code and print another graph. There are also cool functions like %timeit that test the speed of your code.
Spyder is an Integrated Development Environment (IDE) for Python like Atom, Visual Studio, etc. I use VS Code and I suggest you install it as well. It's easier to learn and get running. There's also tons of helpful youtube videos due to its popularity.
I prefer to use Jupyter notebook to analyze data whether it be in pandas dataframes or plots. When I'm developing a program or implementing new code on data I already analyzed, I use a text editor like VS Code.
There's a lot more to it, but I think that's all you need to know for now. As you gain more experience you'll learn more about the tools and find your preferences. If you want to know more, there a ton of information about them online with people who can probably explain this much better than I can.
I hope your journey into data science goes well! Just be patient and remember struggling is part of learning. Good luck!
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