Celtics Income Statements and Kellogg Company Balance Sheet Analysis Report Program 5 Learning Outcomes Design and implement a solution from scratch. U

Celtics Income Statements and Kellogg Company Balance Sheet Analysis Report Program 5

Learning Outcomes
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.
Assignment
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, in the matplotlib activity 3 video, we saw that Montana’s population growth slowed in the 1920s through 1940s and posited that this was due to drought and depression. 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.