AE 05: Wrangling College Majors

Application exercise

Goal

Our ultimate goal in this application exercise is to make the following data visualization.

Data

For this exercise you will work with data on the proportions of Bachelor’s degrees awarded in the US between 2005 and 2015. The dataset you will use is in your data/ folder and it’s called degrees.csv.

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import seaborn as sns

degrees = pd.read_csv("data/degrees.csv")

And let’s take a look at the data.

degrees.head()
degree 2019 2020 2021 2022 2023
0 Information Science & eSociety (BA) 63.0 61.0 67.0 71 38
1 Information Science (BS) NaN NaN NaN 16 57
2 Information (PhD) 2.0 3.0 1.0 1 1
3 Library & Information Science (MA) 47.0 57.0 72.0 42 58
4 Information (MS) 8.0 10.0 13.0 5 2

Pivoting

  • Pivot the degrees data frame longer such that each row represents a degree type / year combination and year and number of graduates for that year are columns in the data frame.
# add your code here
  • Question: What is the type of the year variable? Why? What should it be?

Add your response here.

  • Start over with pivoting, and this time also make sure year is a numerical variable in the resulting data frame.
# add your code here
  • Question: What would an NA mean in this context? Hint: The data come from the university registrars, and they have records on every single graduates, there shouldn’t be anything “unknown” to them about who graduated when.

Add your response here.

  • Add on to your pipeline that you started with pivoting and convert NAs in n to 0s.
# add your code here
  • In our plot the degree types are BA, BS, MA, MS, and PhD. This information is in our dataset, in the degree column, but this column also has additional characters we don’t need. Create a new column called degree_type with levels BA, BS, MA, MS, and PhD (in this order) based on degree. Do this by adding on to your pipeline from earlier.
# add your code here

  • Now we start making our plot, but let’s not get too fancy right away. Create the following plot, which will serve as the “first draft” on the way to our Goal. Do this by adding on to your pipeline from earlier.

  • Hint: Make sure to state ci=None when using sns.lineplot()

# add your code here
  • What aspects of the plot need to be updated to go from the draft you created above to the Goal plot at the beginning of this application exercise.

Add your response here.

  • Update x-axis scale such that the years displayed go from 2019 to 2023 in unique years. Do this by adding on to your pipeline from earlier.
# add your code here

  • Update line colors using the following level / color assignments. Once again, do this by adding on to your pipeline from earlier.

    • BA: “#53868B”

    • BS: “#7AC5CD”

    • MA: “#89a285”

    • MS: “#8B814C”

    • PhD: “#CDBE70”

# add your code here
  • Update the plot labels (title, x, and y) and use sns.set_style("white_grid"). Once again, do this by adding on to your pipeline from earlier.
# add your code here