Use the boxplot() method of college to produce side-by-side boxplots of Outstate versus Private. (f) Create a new qualitative variable, called Elite, by binning the Top10perc variable into two groups based on whether or not the proportion of students coming from the top 10% of their high school classes exceeds 50%. college['Elite '] = pd.cut(college['Top10perc '], [0,0.5,1], labels =['No', 'Yes']) Use the value_counts() method of college['Elite'] to see how many elite universities there are. Finally, use the boxplot() method again to produce side-by-side boxplots of Outstate versus Elite. (g) Use the plot.hist() method of college to produce some histograms with differing numbers of bins for a few of the quantitative variables. The command plt.subplots(2, 2) may be useful: it will divide the plot window into four regions so that four plots can be made simultaneously. By changing the arguments you can divide the screen up in other combinations. (h) Continue exploring the data, and provide a brief summary of what you discover