Introduction to data

The Department of Education Statistics releases a data set annually containing the percentage of bachelor’s degrees granted to women from 1970 to 2012. The data set is broken up into 17 categories of degrees, with each column as a separate category.

Randal Olson, a data scientist at University of Pennsylvania, has cleaned the data set and made it available on his personal website. You can download the dataset Randal compiled here.

Goal

Explore how we can communicate the nuanced narrative of gender gap using effective data visualization.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline

women_degrees = pd.read_csv('../data/percent-bachelors-degrees-women-usa.csv')
women_degrees.head()

	Year	Agriculture	Architecture	Art and Performance	Biology	Business	Communications and Journalism	Computer Science	Education	Engineering	English	Foreign Languages	Health Professions	Math and Statistics	Physical Sciences	Psychology	Public Administration	Social Sciences and History
0	1970	4.229798	11.921005	59.7	29.088363	9.064439	35.3	13.6	74.535328	0.8	65.570923	73.8	77.1	38.0	13.8	44.4	68.4	36.8
1	1971	5.452797	12.003106	59.9	29.394403	9.503187	35.5	13.6	74.149204	1.0	64.556485	73.9	75.5	39.0	14.9	46.2	65.5	36.2
2	1972	7.420710	13.214594	60.4	29.810221	10.558962	36.6	14.9	73.554520	1.2	63.664263	74.6	76.9	40.2	14.8	47.6	62.6	36.1
3	1973	9.653602	14.791613	60.2	31.147915	12.804602	38.4	16.4	73.501814	1.6	62.941502	74.9	77.4	40.9	16.5	50.4	64.3	36.4
4	1974	14.074623	17.444688	61.9	32.996183	16.204850	40.5	18.9	73.336811	2.2	62.413412	75.3	77.9	41.8	18.2	52.6	66.1	37.3

a line chart that visualizes the historical percentage of Biology degrees awarded to women

fig, ax = plt.subplots(figsize=(20,10))
ax.plot(women_degrees.Year, women_degrees.Biology)
ax.set_title('The historical percentage of Biology degrees')
ax.set_ylabel('Biology')
ax.set_xlabel('Year')
plt.show()

Observation

From the plot, we can tell that Biology degrees increased steadily from 1970 and peaked in the early 2000’s. We can also tell that the percentage has stayed above 50% since around 1987.

Visualizing the gender gap

fig, ax = plt.subplots(figsize=(20,10))
ax.plot(women_degrees.Year, women_degrees.Biology, label='Women')
ax.plot(women_degrees.Year, 100-women_degrees.Biology, label='Men')
ax.set_title('Percentage of Biology Degrees Awarded By Gender')
ax.set_ylabel('Biology')
ax.set_xlabel('Year')
ax.set_xlim(left=1970)
ax.legend()
plt.show()

In the first period, from 1970 to around 1987, women were a minority when it came to majoring in Biology while in the second period, from around 1987 to around 2012, women became a majority

Data-ink ration

To improve the data-ink ratio, let’s make the following changes to the plot already created.

Remove all of the axis tick marks.
Hide the spines, which are the lines that connects the tick marks, on each axis.

fig, ax = plt.subplots(figsize=(20,10))
ax.plot(women_degrees.Year, women_degrees.Biology, label='Women')
ax.plot(women_degrees.Year, 100-women_degrees.Biology, label='Men')
ax.set_title('Percentage of Biology Degrees Awarded By Gender')
ax.set_ylabel('Biology')
ax.set_xlabel('Year')
ax.set_xlim(left=1970)
ax.tick_params(top=False, bottom=False, left=False, right=False)
ax.legend(loc='upper right')
plt.show()

Hiding spines

fig, ax = plt.subplots(figsize=(20,10))
ax.plot(women_degrees.Year, women_degrees.Biology, label='Women')
ax.plot(women_degrees.Year, 100-women_degrees.Biology, label='Men')
ax.set_title('Percentage of Biology Degrees Awarded By Gender')
ax.set_ylabel('Biology')
ax.set_xlabel('Year')
ax.set_xlim(left=1970)
for position in ['top', 'bottom', 'right', 'left']:
    ax.spines[position].set_visible(False)
ax.legend()
plt.show()

Comparing gender gap across degree categories

major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']
fig = plt.figure(figsize=(20, 20))

for sp in range(0,4):
    ax = fig.add_subplot(2,2,sp+1)
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=(0/255, 107/255, 164/255), label='Women', linewidth=3)
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=(255/255, 128/255, 14/255), linewidth=3, label='Men')
    # Add your code here.
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    for position in ['top', 'bottom', 'left', 'right']:
        ax.spines[position].set_visible(False)
    ax.tick_params(top=False, bottom=False, left=False, right=False)
    ax.set_title(major_cats[sp])
# Calling pyplot.legend() here will add the legend to the last subplot that was created.
plt.legend(loc='upper right')
plt.show()

Observation

we can conclude that the gender gap in Computer Science and Engineering have big gender gaps while the gap in Biology and Math and Statistics is quite small. In addition, the first two degree categories are dominated by men while the latter degree categories are much more balanced.

stem_cats = ['Engineering', 'Computer Science', 'Psychology', 'Biology', 'Physical Sciences', 'Math and Statistics']

fig = plt.figure(figsize=(20, 3))

for sp in range(0,6):
    ax = fig.add_subplot(1,6,sp+1)
    ax.plot(women_degrees['Year'], women_degrees[stem_cats[sp]], c=(0/255, 107/255, 164/255), label='Women', linewidth=3)
    ax.plot(women_degrees['Year'], 100-women_degrees[stem_cats[sp]], c=(255/255, 128/255, 14/255), label='Men', linewidth=3)
    for key,spine in ax.spines.items():
        spine.set_visible(False)
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    ax.set_title(stem_cats[sp])
    ax.tick_params(bottom=False, top=False, left=False, right=False)
    if sp==0:
        ax.text(2005,87,"Men")
        ax.text(2002,8, "Women")
    if sp==5:
        ax.text(2005,62,"Men")
        ax.text(2001,35, "Women")
plt.show()