Seeding random number generators and NumPy print options
For reproducible data analysis, we should prefer deterministic algorithms. Some algorithms use random numbers, but in practice we rarely use perfectly random numbers. The algorithms provided in numpy.random allow us to specify a seed value. For reproducibility, it is important to always provide a seed value but it is easy to forget. A utility function in sklearn.utils provides a solution for this issue.
NumPy has a set_printoptions() function, which controls how NumPy prints arrays. Obviously, printing should not influence the quality of your analysis too much. However, readability is important if you want people to understand and reproduce your results.
Getting ready
Install NumPy using the ...
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