Though SageMath depends on other software packages for the heavy lifting, SageMath offers a centralized interface for working with many subfields of mathematics.
SageMath does not do much mathematics by itself. Instead, it depends on many CAS and non-CAS software packages, which it calls based on the desired operations. Installing SageMath pulls in all those packages as mandatory prerequisites. There is no lightweight GUI either.
Representation of data still remains highly fragmented technically and one fumbles between data types and stumbles on strange assignment statements to attempt conversions of meaning.
When you create and market software, do not claim you are better than anything you cannot even come close to. Robs the bank for no bloody reason and does not provide anything.
If you're using CAS within another program, it's great that SymPy is written in Python.
But most of the time you're just trying to do some math, and having to declare all your variables and write equations in obscure ways so they fit Python syntax can be annoying.
Click continuously to calculate calculus, solve equations, give analytical solutions and numerical solutions and diagrams, interactively zoom in the drawing, and zoom in with the mouse wheel.
Because the editor is a web app (the Jupyter Notebook program is a web server that you run on the host machine), it is possible to use this on quite literally any machine. Morever, you can have Jupyter Notebook run on one machine (like a VM that you have provisioned in the cloud) and access the web page / do your editing from a different machine (like a Chromebook).
Because it is open source, you can review the source code and also propose extensions and fixes to it. It is also possible to fork the repository and make changes to it to customize it for your specific use case.
Jupyter Notebook, formerly known as ipython, used to be specific to Python; however, in recent iterations, it has become capable of general purpose usage for any programming language. Thus it is possible to use this and have a consistent developer workflow, regardless of language.
The interactive editor is able to display complex equations, charts, graphs, etc. making this particular editor very well-regarded among data scientists.
Most IDEs require you to separately run Python to see the output of a particular piece of code. By contrast, Jupyter Notebook can evaluate Python statements inline, giving you the immediate feedback of interactive use of the interpreter while keeping your changes saved.