CST-383 Week 3

This week, I learned to visualize variables by creating plots using Python. These plots include box plots, histograms, density plots, and scatterplots. Something I was not expecting was how easy it was to create plots in Python for data. For example, if we had a Data Frame (df) that contains a column called "age", we could simply call,

df['age'].plot.hist()

to create a visualization containing the frequency of the age column.

Something that was a bit confusing at first for me was determining the probability of a value from a given PDF. However, it became more intuitive for me by thinking of it in terms of writing an expression using Pandas to determine the probability of getting a certain value. For example, we could create mask for a specific column then call .mean() using this mask to get a probability.

Something I found interesting was how I have recently seen scatterplots being used for plotting real world data containing polling data. Before taking this course, I was not very familiar with understanding how scatterplots can be useful in visualizing data. Now, I can understand how it is used give information on the distribution of two quantitative variables. I can determine if there are any patterns in the data by looking at the shape and direction of the pattern.

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