Gender Diversity in College Degrees

# import libraries import pandas as pd import matplotlib.pyplot as plt

# read in the data set women_degrees_df = pd.read_csv("http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv") # print tabular information women_degrees_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 42 entries, 0 to 41
Data columns (total 18 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   Year                           42 non-null     int64  
 1   Agriculture                    42 non-null     float64
 2   Architecture                   42 non-null     float64
 3   Art and Performance            42 non-null     float64
 4   Biology                        42 non-null     float64
 5   Business                       42 non-null     float64
 6   Communications and Journalism  42 non-null     float64
 7   Computer Science               42 non-null     float64
 8   Education                      42 non-null     float64
 9   Engineering                    42 non-null     float64
 10  English                        42 non-null     float64
 11  Foreign Languages              42 non-null     float64
 12  Health Professions             42 non-null     float64
 13  Math and Statistics            42 non-null     float64
 14  Physical Sciences              42 non-null     float64
 15  Psychology                     42 non-null     float64
 16  Public Administration          42 non-null     float64
 17  Social Sciences and History    42 non-null     float64
dtypes: float64(17), int64(1)
memory usage: 6.0 KB

women_degrees_df.describe()

# print row and column information women_degrees_df.head()

### code that we'll re-use throughout the analysis # matplotlib requires colour values in a range of 0-1, which is why we're dividing the RGB values by 255 PRIMARY_COLOR = (95/255, 158/255, 209/255) SECONDARY_COLOR = (255/255, 128/255, 14/255) SUPPORT_COLOR = (171/255, 171/255, 171/255) # setting a consistent line width LINE_WIDTH = 2 # function that adds a number of degree subplots def add_degrees_subplots(fig, degrees, rows, columns, annotations, ax_callback=None): for i in range(0, len(degrees)): # add the subplot ax = fig.add_subplot(rows, columns, i+1) # plot the trends in gender involvement over time ax.plot(women_degrees_df["Year"], women_degrees_df[degrees[i]], c=SECONDARY_COLOR, label="Women", linewidth=LINE_WIDTH) ax.plot(women_degrees_df["Year"], 100 - women_degrees_df[degrees[i]], c=PRIMARY_COLOR, label="Men", linewidth=LINE_WIDTH) # remove the x-axis tick marks ax.tick_params(bottom=False, top=False, left=False, right=False) # remove the borders around the charts for key, spine in ax.spines.items(): spine.set_visible(False) # restrict the range of x values and y values ax.set_xlim(1968, 2011) ax.set_ylim(0, 100) # set the title ax.set_title(degrees[i]) # add custom annotations for the first chart and the last if i == 0: ax.text(annotations[0]["x"], annotations[0]["y"], annotations[0]["label"]) ax.text(annotations[1]["x"], annotations[1]["y"], annotations[1]["label"]) elif i == len(degrees) - 1: ax.text(annotations[2]["x"], annotations[2]["y"], annotations[2]["label"]) ax.text(annotations[3]["x"], annotations[3]["y"], annotations[3]["label"]) # return a callback for increased customisation of each axis if ax_callback is not None: ax_callback(ax, degrees[i])

# group stem degrees stem_degrees = ['Engineering', 'Computer Science', 'Psychology', 'Biology', 'Physical Sciences', 'Math and Statistics'] # return the figure object which we'll use to add the subplots fig = plt.figure(figsize=(24, 4)) # add the degree subplots and annotations comparing male and female involvement add_degrees_subplots(fig, stem_degrees, 1, 6, [ { "x": 2005, "y": 87, "label": "Men" }, { "x": 2002, "y": 8, "label": "Women" }, { "x": 2005, "y": 62, "label": "Men" }, { "x": 2001, "y": 35, "label": "Women" } ]) # show the plots plt.show()

# group liberal arts arts_degrees = ['Foreign Languages', 'English', 'Communications and Journalism', 'Art and Performance', 'Social Sciences and History'] # return the figure object which we'll use to add the subplots fig = plt.figure(figsize=(24, 4)) # for each degree, add a subplot and annotations comparing male and female involvement add_degrees_subplots(fig, arts_degrees, 1, 5, [ { "x": 2005, "y": 20, "label": "Men" }, { "x": 2002, "y": 76, "label": "Women" }, { "x": 2005, "y": 58, "label": "Men" }, { "x": 2001, "y": 36, "label": "Women" } ]) # show the plots plt.show()

# group the remaining degrees other_degrees = ['Health Professions', 'Public Administration', 'Education', 'Agriculture','Business', 'Architecture'] # return the figure object which we'll use to add the subplots fig = plt.figure(figsize=(24, 4)) # for each degree, add a subplot and annotations comparing male and female involvement add_degrees_subplots(fig, other_degrees, 1, 6, [ { "x": 2005, "y": 6, "label": "Men" }, { "x": 2002, "y": 90, "label": "Women" }, { "x": 2005, "y": 64, "label": "Men" }, { "x": 2001, "y": 32, "label": "Women" } ]) # show the plots plt.show()

def ax_callback(ax, degree): if degree not in other_degrees: ax.tick_params(labelbottom=False) ax.set_yticks([0,100]) ax.axhline(50, c=(SUPPORT_COLOR), alpha=0.3) # group all the degrees all_degrees = stem_degrees + arts_degrees + other_degrees # return the figure object which we'll use to add the subplots fig = plt.figure(figsize=(24, 10)) # for each degree, add a subplot and annotations comparing male and female involvement add_degrees_subplots(fig, all_degrees, 3, 6, [ { "x": 2005, "y": 90, "label": "Men" }, { "x": 2002, "y": 6, "label": "Women" }, { "x": 2005, "y": 64, "label": "Men" }, { "x": 2001, "y": 32, "label": "Women" } ], ax_callback) # show the plots plt.show()