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The goal of this project was to explore visualizing large datasets using Python.

To begin, we used a textbook that was outdated, is full of typos, and is difficult to correctly set up the software needed. After switching to the most recent textbook the Independent Study worked well.

I began by downloading Anaconda and installing the neccesarry libraries through it. All that was needed was one command to set everything up. The beginning of the book is a detailed introduction to Python and how to code in it. After, it goes in to stacks, queues, and other data-types as well as running times. Finding this in the book suprised me and made me think that this class should be taught in replacement of one or more of the classes we currently have here at Sewanee. The second half of the book is where the visualization was used. The plots mainly ran on pylab, or pyplot, and were simple to understand. The most difficult part about this book was having to use pieces of code from chapters before. The book gives code examples that require that you already have code from a few chapters before already typed in. Other than this, the examples, while rather lengthy, are great examples. Once the programmer understands how to use the built-in python IDLE, the examples run smoothly. The IDLE is not the easiest to run because of the constant freezing that occurs when attempting to exit out of programs, but it does not require a seperate download, which is always nice.

Overall, This class progressed smoothly with the exception of a few roadblocks at the start, The class increased my knowledge and proficciency in Python and I am confident that if I ever need to visualize a large dataset for a company in the future, that I can accurately portray it using the tools I have obtained over the course of this past semester.

cs444ara/ctve17.txt · Last modified: 2017/05/10 15:46 by auarnold3