- Due date: Friday, July 5th no later than 11:59 p.m.
- Submission instructions: upload your solution, entitled YourFirstName-YourLastName-Program6.py to the BrightSpace Program 6 Dropbox.
- Deadline Reminder: Once the submission deadline passes, BrightSpace will no longer accept your Python submission and you will no longer be able to earn credit. Thus, if you are not able to fully complete the assignment, submit whatever you have before the deadline so that partial credit can be earned.
- Design and implement a solution from scratch.
- Use the pandas library to load, manipulate, and analyze data.
- Use the matplotlib library to create visualizations highlighting a specific insight
- Use the Airlines CSV for this assignment.
- Use the pandas library and the matplotlib library to create two different visualizations to help a user gain insight into the airlines.csv file.
- Optional: you can make one of your visualizations about predictions generated using the sklearn package.
- In order to save your visualizations, you can use the
plt.savefig(output_filename.png)command, as in Lab 11.
Grading - 100 points
10 points - pandas is used to load and manipulate the data.
10 points - matplotlib is used to create the two visualizations.
10 points - Each matplotlib visualization is a different type. For example, one visualization could be a bar chart and the other could be a line chart.
10 points - The visualizations are saved in the same directory as the Python file as visualization1.png and visualization2.png.
20 points - All aspects of each visualization are clearly labeled (titles, axes, etc). 3 points for each type of improvement up to 20 points.
15 points - The first visualization contains at least two insights that are explained carefully in a comment at the top of your program. For example, when we did the Montana population example in class, the graph enabled us to see that something interesting (e.g. drought and depression!) happened from 1920 through 1940 that caused the population growth to flatten or even decrease. 5 points per explained insight.
15 points - same as above for the second visualization.
10 points - The Python solution is properly commented, easy to understand and does not contain unnecessary code. 3 points for each type of improvement up to 20 points.