Plotting in PythonΒΆ

Python has many nice and useful modules that can be used for plotting, such as:


Explore the galleries and examples of different visualization libraries above to learn what’s possible to do in Python.

As you can see from the examples, the plotting possibilities in Python are numerous and rich. Do you need to know them all? Of course not. Not even we do. It is not even rational for trying to use them all, instead you should start by learning to use one of them that suits your needs and then later extend your knowledge and skills to other visualizing libraries when necessary. In our courses, we will be start our plotting experiments with Matplotlib and Plotly that makes it possible to store and show our interactive plots in the web.


Later, in the Automating GIS processes course, we will be learning a little bit of Bokeh as well. We will work in Matplotlib in the Introduction to Quantitative Geology course.


For interactive visualizations in Python, it can be extremely useful to use a specific software called Jupyter that is extensively used nowadays for documenting, presenting and visualizing interactive plots in Python using specific Notebooks. Jupyter Notebook is also installed in our cloud computer instances.