Step 1: Make a new Jupyter notebook.

Step 2: Using your CORGIS dataset in the form of a Pandas dataframe:

Choose a numerical column (which I'll call COL1) and calculate the mean, median, mode, and trimmed mean. For the trimmed mean, you can choose what to trim, but comment on the number used for trimming. Plot a histogram of COL1 data and comment on the relative locations of the mean, median, mode, and trimmed mean. Calculate the mean absolute deviation, the standard deviation, and the interquartile range (the difference of the 75% and 25% quantile) Comment as well on how they compare to each other and to the histogram plot. Plot a box plot of COL1 and check that the locations of the calculated median and the 25% and 75% quantiles match Choose a column with categorical values (COL2) Make a bar plot using the categorical values along the horizontal axis and values of your choose vertically Using both COL1 and COL2 data, make a plot that has the categorical values of COL2 on the horizontal axis and a box plot of grouped COL1 data in the vertical direction Choose another numerical column (COL3) Calculate the correlation between COL1 and COL3 Make a scatter plot and comment on whether the calculated correlation value makes sense. Make a hexagonal binning plot BONUS POINT (optional): Make a facet grid using the seaborn library, where you plot a hexagonal binning plot of COL1 and COL3 grouped into different facets based on the categorical values of COL2 Step 3: When you are done, commit the notebook to your "csx4501" repository on GitHub and submit the url for your repository.