Scientific Python and Plotting
The Python programming language is quite suited for scientific work. This is due to it being really easy to program for while being powerful enough to do almost anything you need. This combination has spawned a whole bunch of (very large) Python projects, such as numpy, scipy, matplotlib, pandas, and so on, over the years. While these libraries are all large enough to warrant entire books for themselves, we can offer a little insight into where and when they can be useful so you have an idea of where to start.
The major topics and libraries covered in this chapter are split into three sections:
- Arrays and matrices: NumPy, Numba, SciPy, Pandas, statsmodels, and xarray
- Mathematics and precise calculations: gmpy2, Sage, mpmath, SymPy, and Patsy
- Plotting, graphing, and charting: Matplotlib, Seaborn, Yellowbrick, Plotly, Bokeh, and Datashader
It is very likely that not all libraries in this chapter are relevant to you,...
