Tag: jupyter_notebook (Page 10 of 17)

import requests
from bs4 import BeautifulSoup
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
from matplotlib import cm
import math

# grab the page
result = requests.get("https://www.collegechoice.net/rankings/best-colleges-in-ohio/")
soup = BeautifulSoup(result.content, 'lxml')

# parse out just the top 10 schools and their popular majors
ranking_divs = soup.find_all('div', 'ranking-box')
top_10_schools = {}

for ranking_div in ranking_divs[:10]:
    school = ranking_div.select_one('div.rb-list-title h3').text
    majors = [maj.text for maj in ranking_div.select('div.rb-ranking-body ul li')]
    top_10_schools[school] = majors

for school, majors in top_10_schools.items():
    if 'Visual and Performing Arts' in majors:
        top_10_schools[school] = ['Visual & Performing Arts' if maj=='Visual and Performing Arts' else maj for maj in majors]

# if, say, you have a count of 11 and you want to round up to the nearest 5, this will return 15
def roundupto(your_count, round_up_to_nearest):
    return int(math.ceil(your_count / round_up_to_nearest)) * round_up_to_nearest

# draws a line between points
def newline(p1, p2, color='black'):
    ax = plt.gca()
    l = mlines.Line2D([p1[0], p2[0]], [p1[1], p2[1]], color=color, marker='o', markersize=6)
    ax.add_line(l)
    return l
    

fig, ax = plt.subplots(1, 1, figsize=(14, 14))

# get school and major lists and calculate the scale of the chart
school_list = list(top_10_schools.keys())
school_list.reverse()  # matplotlib will then put the #1 school at the top of the chart
major_list = list(set(sum(top_10_schools.values(), [])))
major_list.sort(); major_list.reverse()  # to help matplotlib list majors alphabetically down the chart
scale = roundupto(max(len(school_list), len(major_list)), 5)

# write the vertical lines
ax.vlines(x=1, ymin=0, ymax=scale, color='black', alpha=0.7, linewidth=1, linestyles='dotted')
ax.vlines(x=3, ymin=0, ymax=scale, color='black', alpha=0.7, linewidth=1, linestyles='dotted')

# plot the points; unlike the slope chart in the MachineLearningPlus.com article, my Y axis has no meaning, so
# I use numpy's linspace function to help me evenly space each point
school_y_vals = np.linspace(1, scale-1, num=len(school_list))
major_y_vals = np.linspace(1, scale-1, num=len(major_list))
ax.scatter(y=school_y_vals, x=np.repeat(1, len(school_list)), s=10, color='black', alpha=0.7)
ax.scatter(y=major_y_vals, x=np.repeat(3, len(major_list)), s=10, color='black', alpha=0.7)

# write the lines and annotation
for school, school_y_val in zip(school_list, school_y_vals):
    ax.text(1-0.05, school_y_val, school, horizontalalignment='right', verticalalignment='center', fontdict={'size':10})
    for major in top_10_schools[school]:
        major_y_val = major_y_vals[major_list.index(major)]
        newline([1, school_y_val], [3, major_y_val], color=cm.get_cmap('tab20')(school_list.index(school)))
        
for major, major_y_val in zip(major_list, major_y_vals):
    ax.text(3+0.05, major_y_val, major, horizontalalignment='left', verticalalignment='center', fontdict={'size':10})
    
# vertical line annotations
ax.text(1-0.05, scale-0.25, 'College', horizontalalignment='right', verticalalignment='center', 
        fontdict={'size':14, 'weight':700})
ax.text(3+0.05, scale-0.25, 'Major', horizontalalignment='left', verticalalignment='center', 
        fontdict={'size':14, 'weight':700})

# misc cleanup
ax.set(xlim=(0, 4), ylim=(0, scale))
ax.axis('off')

plt.title("Most Popular Majors at Ohio's Top 10 Colleges")
plt.show()