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- | Throughout this class, we'll be using several python packages extensively. If you've never used them before, we **strongly** encourage you to play around with them and get comfortable. | ||
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- | They are: | ||
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- | * ''numpy'' - Python's package that provides high-performance matrix, vector and linear algebra operations | ||
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- | * ''matplotlib'' - a package for basic plotting and visualization of data | ||
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- | * ''seaborn'' - a nice visualization package that builds on matplotlib. Produces nicer visualizations, and has some built-in data analytics | ||
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- | All assignments will be turned in using ipython notebooks. If you've never used ipython before, you can either run it in a browser using something like ''jupyter'', or you can run it interactively from a terminal using: | ||
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- | ''ipython --pylab'' | ||
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- | (One disadvantage of running ''ipython --pylab'' is that it automatically imports numpy and matplotlib. This pollutes the IDE's namespace, and sometimes makes it difficult to port code to non-ipython interpreters, because you won't have the right imports in place...) | ||
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- | I often find it easier to do my code development interactively in the terminal, then save a final notebook to turn in. | ||
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- | If you're more comfortable with IDEs, you may also want to check out ''spyder'' or ''pycharm''. | ||
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