Some general recommendations in the Python ecosystem¶
If you are new to Python, here are some tools you should look into:
The Interactive Python shell (IPython). It enriches the REPL interaction with syntax highlighting, tab completion, comprehensive object introspection, input and output history, much more readable stack traces, etc. (see list of features).
Jupyter and JupyterLab. They bring a Matlab-like notebook-oriented interface which allows for writing high-quality documents with mixture of code, output and Latex/Rich text documentation. These notebooks can be rendered as websites and shared easily. Most noteworthly, they allow interactivity in cells, such as sliders and animations. There is a whole universe to explore once you look for Jupyter notebooks. And you can easily host your own notebooks in the cloud.
The Python debugger can come in handy in case of errors. With IPython, it’s just the four letters
If you look for plotting, Matplotlib is the defacto standard. Being part of Scipy, it depends on Numpy, which provides N-dimensional arrays, linear algebra and input/output. When it comes to scientific computing, Numpy got some kind of hub and it’s website lists dozens of related projects within all sciences.