python_repos_hormigo.py

'''
An interactive, informative visualization of data retrieved through an API: best rated Python projects currently hosted on GitHub.
'''


import requests
import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS

# Make an API call, and store the response.
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print("Status code:", r.status_code)

# Store API response in a variable.
response_dict = r.json()
print("Total repositories:", response_dict['total_count'])

# Explore information about the repositories.
repo_dicts = response_dict['items']
# print('Repos colected:', len(repo_dicts))

# print("\nSelected information about each repository:")
# for repo_dict in repo_dicts:
#     print('\nName:', repo_dict['name'])
#     print('Owner:', repo_dict['owner']['login'])
#     print('Stars:', repo_dict['stargazers_count'])
#     print('Repository:', repo_dict['html_url'])
#     print('Created:', repo_dict['created_at'])
#     print('Updated:', repo_dict['updated_at'])
#     print('Description:', repo_dict['description'])

names, plot_dicts = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])

    # Get the project description, if one is available.
    description = repo_dict['description']
    if not description:
        description = "No description provided."

    plot_dict = {
        'value': repo_dict['stargazers_count'],
        'label': repo_dict['description'],
        'xlink': repo_dict['html_url']
    }
    plot_dicts.append(plot_dict)

# Make visualization.
my_style = LS('#333366', base_style=LCS)
my_style.title_font_size = 24
my_style.label_font_size = 14
my_style.major_label_font_size = 18

my_config = pygal.Config()
my_config.x_label_rotation = 45
my_config.show_legend = False
my_config.truncate_label = 15
my_config.show_y_guides = False
my_config.width = 1000

chart = pygal.Bar(my_config, style=my_style)
chart.title = 'Most-Starred Python Projects on GitHub'
chart.x_labels = names

chart.add('', plot_dicts)
chart.render_to_file('python_repos.svg')