HW 6 (Plotting climate data)

There are Python packages that allow us to plot graphs, just like what you might do in Excel. In this assignment, you'll practice using lists to create coordinates for a set of 2-dimensional data points, and then plot these points. I'll provide the starter code that constructs a plot (given data that's properly formatted), your responsibilities is (1) to load in the data from a file and get it into the correct format for plotting, and (2) using my code as a template, create another plot of your choice.

Specifically, we'll be looking at historic climate data from the MSP airport.

Mount the COURSES drive and create a folder called hw6 in your STUWORK folder. Open the new folder in VSCode. If you need a refresher on how to complete these steps, refer back to the in-class lab from the first day of class.

Download the starter code and move the two files, climate.py and mspData.csv into your hw6 folder.

If you are working on your own computer, you'll need to install the Python package matplotlib. To do so, run the following command from the terminal:

pip3 install matplotlib

If that doesn't work, you can try pip install matplotlib instead. If that still doesn't work, reach out for help, and in the meantime, work on the lab computers.

I recommend creating a smaller data file (e.g., using only the first 5-6 lines) to use during testing.

Fill in the missing code in the parseData function. The core of this function should be a loop using readlines that iterates over each line in the data file. Within the loop body, you'll need to use string manipulation to separate out the different parts of the data, and then use list indexing and mutation to add the correct pieces of data to each of the lists we return.

Note that the first line in mspData is a header, meaning it doesn't contain data. You'll have to figure out a way to skip over this line when populating the lists in parseData. One way (but certainly not the only way) to do this is to make use of the python keyword continue, as illustrated in the following for loop:

# example of continue. Code will print 0, 1, 2, 4, 5
for i in range(5):
    if i == 3:
       continue # go to the next loop iteration now
    print(i)

After writing parseData, be sure to test it. Creating a smaller data file and then printing the variables and manually checking is a great way to do this. Another option is to print out the length of the variables years, months, minTemps, and maxTemps in main (they should each have 1057 items) and to print out the first few elements of each variable and check that they match the first few items in in the data file.

Note that while there exist libraries for automating CSV processing in Python, you should not use those libraries here.

In the next part of this assignment, I am going to ask you to plot the data for all the years in the dataset, but only for the month of May. That means we have to create new versions of the years, minTemps, and maxTemps list that only contain data for the month of May. The starter code calls getMonthData from main to create new variables for you, but you have to fill in the missing code in getMonthData.

Your basic strategy will be to iterate through monthList, and whenever monthList[i] (where i is your loop variable) is the specified month, you should also add otherList[i] to newList.

I've already written plotMonthTemps for you. Run the code after completing the previous two parts and check that the following image is produced:

The graph should show up in a popup window. You need to close this window for the code to continue executing.

What else could we plot with the data? For this part of the assignment, you'll create one more plot of your choice. Feel free to be creative, but here are some ideas:

• Plot the average snowfall, snow depth, or rainfall per year (could be the average per year, or the average for a given month)
• Plot the average temperature, snowfall, or rainfall by month for a given year.
• Plot the average temperature each month for a multi-year period (I recommend limiting to a 5-10 year period so that your graph is still readable)
• Plot the change in temperature between all successive months (again, you'd likely want to limit yourself to a few-year timespan or else do the change in the average across all months)

All of these ideas are (likely) line plots, but you can also create bar plots or other graph styles if you wish! Depending on what plot you create, it may be sufficient to only slightly modify the code from plotMonthTemps. But if you have more ambitious ideas (or just want to understand what is going on), feel free to reference and cite the matplotlib documentation. A good strategy is to find an image of a graph similar to the one you want to create, and then look at that example code to try to figure out how to do a similar thing for your situation.

It's possible that you'll need to edit parseData to create and return additional variables. Feel free to do so! You will likely also want to create a new helper function to do data preprocessing (similar to getMonthData) and a new function to plot the graph (similar to plotMonthTemps).

When you create your new plot, make sure you update the title and the axis labels to be descriptive.

Create more plots!

When you're finished, make sure to complete the usual documentation steps. This includes adding comments, writing function docstrings, and adding a top-level comment, acknowledgements, and a reflection to the header.

You should also think about coding style. Have you written everything in a consistent way that is easy to read? Does your code have any unnecessary print statements? (Remove them.) Is there any repetitive code that could be rewritten to use loops or functions? Review the style document on Moodle for the expectations for this assignment.

This assignment was adapted from assignments used by Layla Oesper and Tanya Amert. Thanks for sharing!