First, you'll need to download the dataset "Top American Colleges 2022" from Kaggle.com and get it into this directory. You'll need to make an account first. Below is a list of useful functions. Part of this homework is practicing reading the documentation, so you'll want to look them up as you go. I'd recommend starting with this: https://pandas.pydata.org/docs/user_guide/10min.html. Once you've read that, in general you can find the API for any of these functions by searching their name plus pandas. List of helpful functions: read_csv > head unique value_counts df.columns ('columns' is a dataframe variable that tracks the columns) groupby apply (An important note about this one--pay careful attention to the weird axis argument. When you apply over a series, you often don't need it, but when you apply over a dataframe axis=1 and axis=0 will do very different things.) isin fillna etc. astype hist The Basics First, read the dataframe in. Store it in a variable called "df". Let's get a feel for our dataframe. Print out a list of columns Now print out the first ten elements. There's a single function that does it by default. Exploration Now let's learn to do some exploration. Try printing out the median "medianBaseSalary" Making it a little more complicated--print out the median "medianBaseSalary" only for urban colleges. Print out the number of colleges by state. Your results should look something like: NY 63 CA 55 Now, still using one statement, let's print out median "medianBaseSalary" for all different possible values of "campusSetting". You'll need a statement we haven't used yet. + + Markdown Display just the line for University of Maryland. (There are a couple of ways of doing this.) Take the website column and change it so that no string includes "http://" or "www" Create a new column called "faculty" that computes the number of faculty at each university Graphs Markdown Let's do some very basic graphing here! Create a histogram for the student population. Python Python Python Python Python Modifications Let's start modifying our dataframe! Remember, dataframe operations return a copy by default, so you'll either need to use the inplace=True, or just assign the dataframe back into itself (as in, df = df.someFunction ()). Start by filling in all blank phone numbers with "no number" Python Python Python Python Python Python Python