for pop_dict in pop_data: if pop_dict['Year'] == '2010': country_name = pop_dict['Country Name'] population = int(float(pop_dict['Value'])) code = get_country_code(country_name) if code: cc_populations[code] = population # 根据人口数量将所有的国家分成三组 cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {} for cc, pop in cc_populations.items(): if pop < 10000000: cc_pops_1[cc] = pop elif pop < 1000000000: cc_pops_2[cc] = pop else: cc_pops_3[cc] = pop # 看看每组分别包含多少个国家 print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3)) wm_style = RS('#336699', base_style=LCS) wm = World(style=wm_style) wm._title = 'World Population in 2010, by Country' wm.add('0-10m', cc_pops_1) wm.add('10m-1bn', cc_pops_2) wm.add('>1bn', cc_pops_3) wm.render_to_file('world_population.svg')
""" @author: MrYang @contact: [email protected] @site: www.example.com @file: americas.py @time: 18/12/2017 10:35 PM """ from pygal_maps_world.maps import World wm = World() wm._title = 'North, Central, and South America' wm.add('North Amerca', ['ca', 'mx', 'us']) wm.add('Central America', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv']) wm.add('South America', ['ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf', 'gy', 'pe', 'py', 'sr', 'uy', 've']) wm.render_to_file('americas.svg')
from pygal_maps_world.maps import World world_map = World() world_map._title = 'North, Central, and South America' world_map.add('North America', ['ca', 'mx', 'us']) world_map.add('Central America', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv']) world_map.add('South America', ['ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf', 'gy', 'pe', 'py', 'sr', 'uy', 've']) world_map.render_to_file('americas.svg')
""" @author: MrYang @contact: [email protected] @site: www.example.com @file: na_populations.py @time: 18/12/2017 10:41 PM """ from pygal_maps_world.maps import World wm = World() wm._title = 'Populations of Countries in North America' wm.add('North America', {'ca':34126000, 'us':309349000, 'mx':113423000}) wm.render_to_file('na_populations.svg')