def test_worldmap_i18n_clear():
set_countries(_COUNTRIES, True)
wmap = World()
wmap.add('countries', dict(fr=12))
set_countries({'fr': 'Frankreich'}, clear=True)
q = wmap.render_pyquery()
assert len(
q('.country.color-0')
) == 1
assert 'Frankreich' in q('.country.fr').text()
def test_worldmap():
set_countries(_COUNTRIES, True)
datas = {}
for i, ctry in enumerate(COUNTRIES):
datas[ctry] = i
wmap = World()
wmap.add('countries', datas)
q = wmap.render_pyquery()
assert len(
q('.country.color-0')
) == len(COUNTRIES)
assert 'France' in q('.country.fr').text()
from pygal_maps_world.maps import World
from pygal.style import Style
style = Style(font_family='googlefont:Raleway')
blue = Style(colors=('blue', ))
worldmap_chart = World(style=blue)
worldmap_chart.title = 'Country of residence'
worldmap_chart.add(
'Students', {
'by': 1,
'ca': 1,
'ch': 1,
'cn': 1,
'de': 4,
'eg': 1,
'in': 11,
'kr': 1,
'ru': 1,
'sg': 1,
'us': 4,
'gr': 1
})
worldmap_chart.render_to_png("./world.png")
import json
import pygal
from pygal_maps_world.maps import World
wm = World()
wm.title = 'Populations of Countries in North America'
wm.add('North America', {'ca': 3412600, 'us': 30934000, 'mx': 113423000})
wm.render_to_file('na_populations.svg')
#-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: HO0me
#
# Created: 21/04/2019
# Copyright: (c) HO0me 2019
# Licence: <your licence>
#-------------------------------------------------------------------------------
from pygal_maps_world.maps import World
wm = World()
wm.title = 'Populations of countries in North America'
wm.add('North America', {'ca': 34126000, 'mx': 113423000, 'us': 309349000})
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('na_americas.svg')
country_name = gdp_dict["Country Name"]
gdp = int(float(
gdp_dict["Value"])) # Armazenadas em um formato numérico.
code_country = get_country_code(country_name)
if code_country:
cc_gdps[code_country] = gdp
''' O dicionário armazena o código do país como chave e a população
como valor sempre que o código é devolvido. '''
# Agrupa os países em três níveis populacionais.
cc_gdps_1, cc_gdps_2, cc_gdps_3 = {}, {}, {}
for cc, gdp in cc_gdps.items():
if gdp < 5000000000:
cc_gdps_1[cc] = round(gdp / 1000000000)
elif gdp < 50000000000:
cc_gdps_2[cc] = round(gdp / 1000000000)
else:
cc_gdps_3[cc] = round(gdp / 1000000000)
# Vê quantos píses estão em cada nível.
print(len(cc_gdps_1), len(cc_gdps_2), len(cc_gdps_3))
wm_style = RotateStyle('#336699')
wm = World(style=wm_style) # Criamos uma instância da classe World().
wm.title = 'Global GDP in 2016, by Country (in billions USD)' # Definimos o atributo title() do mapa.
# usamos o método add() que aceita um rótulo (primiro argumento) e um dicionário de códigos de países (segundo argumento).
wm.add('0-5bn', cc_gdps_1)
wm.add('5bn-50bn', cc_gdps_2)
wm.add('>50bn', cc_gdps_3)
# O método render_to_file() cria um arquivo svg contendo o mapa, que poderá ser aberto no navegador.
wm.render_to_file('Global_gdp.svg')
def __init__(self, title):
'''
Constructor
'''
self.wmap = World()
self.wmap.title = title
def world_population():
filename = "population_data.json"
with open(filename) as f:
pop_data = json.load(f)
cc_pop = {}
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_pop[code] = population
#grouping countries into 3 pop levels
cc_pops1, cc_pops2, cc_pops3 = {}, {}, {}
for cc, pop in cc_pop.items():
if pop < 10000000:
cc_pops1[cc] = pop
elif pop < 1000000000:
cc_pops2[cc] = pop
else:
cc_pops3[cc] = pop
print(len(cc_pops1), len(cc_pops2), len(cc_pops3))
#building a world map
wm_style = RotateStyle("#336699", base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = "World Population in 2010, by Country"
wm.add('0-10m', cc_pops1)
wm.add('10m-1bn', cc_pops2)
wm.add('>1bn', cc_pops3)
wm.render_to_file('world_population.svg')
cc_pibs = {}
for pop_dict in pop_data:
if pop_dict['Year'] == 2016:
country_name = pop_dict['Country Name']
country_pib = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_pibs[code] = country_pib
# Agrupando os países pelo seu PIB.
cc_pibs_1, cc_pibs_2, cc_pibs_3 = {}, {}, {}
for cc, pib in cc_pibs.items():
if pib < 50_000_000_000:
cc_pibs_1[cc] = pib
elif pib < 51_000_000_000:
cc_pibs_2[cc]: pib
else:
cc_pibs_3[cc] = pib
# Vizualiza quantos países estão em cada nível.
print(len(cc_pibs_1), len(cc_pibs_2), len(cc_pibs_3))
w_style = RS('#902020', base_style=LCS)
w = World(style=w_style)
w.title = 'PIB Population in 2016, by Country'
w.add('0-50bn', cc_pibs_1)
w.add('>50bn', cc_pibs_3)
w.render_to_file("Estudos/PYTHON/Python-VisualizacaoDeDados/Dados-Gráficos/Dowload_de_dados/PIB_Population.svg")
import pandas as pd
import sqlite3
import sqlalchemy
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pygal_maps_world.maps import World
# df = pd.read_csv('tes.csv')
# listnama = list(df['nama'])
# # df = df.nama.replace(listnama,['Alpha','Bravo','Charlie','Delta','Echo'])
# listnama.remove('Andi')
# print(listnama)
# print(df.unique())
worldmap_chart = World()
worldmap_chart.title = 'Some countries'
worldmap_chart.add(
'', {
'af': 14,
'bd': 1,
'by': 3,
'cn': 1000,
'gm': 9,
'in': 1,
'ir': 314,
'iq': 129,
'jp': 7,
'kp': 6,
'pk': 1,
'ps': 6,
'sa': 79,
with open(filename) as file:
pop_data = json.load(file)
population_dict = {}
for pop_dict in pop_data:
if pop_dict['Year'] == 2016:
country_name = pop_dict['Country Name']
population = pop_dict['Value']
code = get_country_code(country_name)
if code:
population_dict[code] = population
sorted_pop = dict(sorted(population_dict.items(), key=operator.itemgetter(1)))
res = []
for item in chunks(sorted_pop, len(sorted_pop) // 10):
res.append(item)
map_style = RotateStyle('#336699', base_style=LightColorizedStyle)
wm = World(wm_style=map_style)
wm.title = 'World Population in 2016'
temp = 0
for group in res[:-1]:
mean = millify(int(sum(group.values()) / len(group)))
title = '%s - %s' % (temp, mean)
wm.add(str(title), group)
temp = mean
wm.add('> %s' % temp, res[-1])
wm.render_to_file('resources/world_population 2016.svg')
"""Americas"""
import os
from pygal_maps_world.maps import World
WM = World()
WM.title = 'North, Central and South America'
WM.add('North America', ['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'
])
SAVE_PATH = os.path.realpath(os.path.dirname(__file__)) + "\\svg"
WM.render_to_file(os.path.join(SAVE_PATH, 'americas.svg'))
from pygal_maps_world.maps import World
wm = World()
wm.force_uri_protocol = 'http'
wm.title = "Map of Central America"
wm.add('North America', {'ca': 84949494949, 'mx': 494794164, 'us': 99794616})
wm.render_to_file('map.svg')
filename = 'gdp_json.json'
with open(filename) as f:
gdp_data = json.load(f)
gdps = {}
for gdp_dict in gdp_data:
if gdp_dict['Year'] == 2016:
country_name = gdp_dict['Country Name']
value = int(float(gdp_dict['Value']))
code = get_country_code(country_name)
if code:
gdps[code] = value
gdp_1, gdp_2, gdp_3 = {}, {}, {}
for name, gdp in gdps.items():
if gdp < 50000000000:
gdp_1[name] = gdp
elif gdp < 100000000000:
gdp_2[name] = gdp
else:
gdp_3[name] = gdp
wm_style = RotateStyle('#338899', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = 'World Gdp in 2016, by Country'
wm.add('<50000000000', gdp_1)
wm.add('<100000000000', gdp_2)
wm.add('>100bn', gdp_3)
wm.render_to_file('gdp_2016.svg')
pop_data = json.load(f)
cc_populations = {}
for pop_dict in pop_data:
if pop_dict['Year'] == "1960":
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
# print(country_name + ": "+ str(population))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# print(cc_populations)
cc_pop_10yi, cc_pop_wan, cc_pop_less = {}, {}, {}
for cc, pop in cc_populations.items():
if pop > 100000000:
cc_pop_10yi[cc] = pop
elif pop < 10000000:
cc_pop_wan[cc] = pop
else:
cc_pop_less[cc] = pop
wm_style = LightColorizedStyle #RotateStyle('#336699')
wm = World(style=wm_style)
wm.title = 'World Population in 2010,by Country'
wm.add('1亿', cc_pop_10yi)
wm.add('千万', cc_pop_wan)
wm.add('少于一千万', cc_pop_less)
wm.render_to_file('world_population.svg')
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')
from pygal.style import RotateStyle
filename = 'population_data.json'
with open(filename) as f:
gdp_data = json.load(f)
def get_country_code(country):
for code, name in COUNTRIES.items():
if name == country:
return code
return None
gdp_all = {}
for gdp_dict in gdp_data:
if gdp_dict['Year'] == '2010':
country_name = gdp_dict['Country Name']
value = int(float(gdp_dict['Value']))
code = get_country_code(country_name)
if code:
gdp_all[code] = value
wm_style = RotateStyle('#336699')
wm = World(style=wm_style)
wm.title = 'World GDP in 2010'
wm.add('2010', gdp_all)
wm.render_to_file('gdp_world.svg')
cc_population[country_code] = population
else:
print('ERROR - ' + country_name)
cc_pop1, cc_pop2, cc_pop3 = {}, {}, {}
for cc, pop in cc_population.items():
if pop < 10000000:
cc_pop1[cc] = pop
elif pop < 1000000000:
cc_pop2[cc] = pop
else:
cc_pop3[cc] = pop
print(len(cc_pop1), len(cc_pop2), len(cc_pop3))
# Set graph format and preferences
wm_style = pygal.style.RotateStyle('#336699', base_style=pygal.style.LightColorizedStyle)
world = World(style=wm_style)
wm = pygal.maps.world.World(style=wm_style)
# To set contents shown on the graph.
wm.title = "World Population in 2010,by Country"
wm.add('0-10m', cc_pop1)
wm.add('10-1bn', cc_pop2)
wm.add('>1bn', cc_pop3)
wm.render_to_file('world_population.svg')
import json
from country_code import get_country_code
from pygal_maps_world.maps import World
filename = 'data_version/population_data.json'
with open(filename) as f:
data = json.load(f)
# print(isinstance(data, list))
cc_population = {}
for item in data:
if item['Year'] == '2010':
country_name = item['Country Name']
population = int(float(item['Value']))
code = get_country_code(country_name)
if code:
cc_population[code] = population
wm = World()
wm.title = 'World Population in 2010, by Country'
wm.add('2010', cc_population)
wm.render_to_file('world_population.svg')
file_name = 'population_data.json'
with open(file_name) as f:
pop_data = json.load(f)
populations = {}
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:
populations[code] = population
populations_group1, populations_group2, populations_group3 = {}, {}, {}
for code, population in populations.items():
if population < 10000000:
populations_group1[code] = population
elif population < 1000000000:
populations_group2[code] = population
else:
populations_group3[code] = population
print(len(populations_group1), len(populations_group2),
len(populations_group3))
world_map_style = RS('#336699', base_style=LCS)
world_map = World(style=world_map_style)
world_map.title = 'World Population in 2010, by Country'
world_map.add('2010,1-1千万', populations_group1)
world_map.add('2010,1千万-10亿', populations_group2)
world_map.add('2010,>10亿', populations_group3)
world_map.render_to_file('fill_world_map3.svg')
cc_populations = {}
# print the population of every country in 2010
for pop_dict in pop_data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
code = get_country_code(country_name)
population = int(float(pop_dict['Value']))
if code:
print(code + ': ' +
"{:,}".format(population)) # output as this format
cc_populations[code] = population
cc_pop_1, cc_pop_2, cc_pop_3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop < 10000000:
cc_pop_1[cc] = pop
elif pop < 100000000:
cc_pop_2[cc] = pop
else:
cc_pop_3[cc] = pop
print(len(cc_pop_1), len(cc_pop_2), len(cc_pop_3))
wm = World()
wm.title = "World Population in 2010, by Country"
wm.add('0-10m', cc_pop_1)
wm.add('10m-100m', cc_pop_2)
wm.add('>100m', cc_pop_3)
wm.render_to_file('world_population.svg')
class PWorldMap(object):
'''
classdocs
'''
def __init__(self, title):
'''
Constructor
'''
self.wmap = World()
self.wmap.title = title
def sample(self):
self.wmap.add('F countries', ['fr', 'fi'])
self.wmap.add('M countries', [
'ma', 'mc', 'md', 'me', 'mg', 'mk', 'ml', 'mm', 'mn', 'mo', 'mr',
'mt', 'mu', 'mv', 'mw', 'mx', 'my', 'mz'
])
self.wmap.add('U countries', ['ua', 'ug', 'us', 'uy', 'uz'])
self.wmap.add('North America', {
'ca': 84949494949,
'mx': 494794164,
'us': 99794616
})
def render(self, filename=None):
'''
render the map
see http://www.pygal.org/en/stable/documentation/output.html
'''
if filename is None:
self.wmap.render_in_browser()
else:
if filename.endswith(".png"):
self.wmap.render_to_png(filename)
else:
self.wmap.render_to_file(filename)
import json
from pygal_maps_world.maps import World
from country_codes import get_country_code
file_name = 'population_data.json'
with open(file_name) as f:
pop_data = json.load(f)
populations = {}
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:
populations[code] = population
world_map = World()
world_map.title = 'World Population in 2010, by Country'
world_map.add('2010', populations)
world_map.render_to_file('fill_world_map.svg')
# americas
# Created by JKChang
# 22/01/2018, 21:08
# Tag:
# Description:
from pygal_maps_world.maps import World
wm = World()
wm.title = 'North, Central, and South America'
wm.add('North America', ['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('resources/americas.svg')
from pygal_maps_world.maps import World
wm = World()
wm.title = "Population of North American Countries"
wm.add('North America', {'ca': 34126000, 'us': 30930000, 'mx': 113425670})
wm.render_to_file("Population map.svg")
'bo', 'ar','ir', 'np', 'cu', 'dk', 're', 'mt', 'mk', 'kr', 'al', 'ke', 'md',
'pk', 'na', 'uy', 'om', 'la', 'gf', 'hn', 'ml', 'ph', 'mk', 'sv', 'cr',
'gu', 'mc', 'ht', 'gt', 'ee', 'ec','tj','me', 'ba', 'kg', 'cy', 'id', 'jo',
'dj','cv', 'sc', 'lt', 'sm', 'sz', 'kz',
'sy', 'mo', 'tl', 'pr', 'bw', 'mn', 'do', 'ge', 'gl', 'lv', 'kp',
'am', 'lb', 'mw', 'ao', 'ye', 'ug','pa', 'lk', 'az', 'so', 'sg', 'li',
'gh', 'ng','ga', 'sa','by','uz', 'gm',
'aq', 'bh', 'tz', 'ci', 'sl', 'sr', 'tm', 'kh', 'mm', 'jm',
'gn', 'bj', 'mv', 'rw', 'st'
]
dfmap = df.country.replace(countrylist, pygalList)
dfmap = dfmap[dfmap.isin(pygalList)]
mappingDict = dict(dfmap.value_counts())
#============================================================================
#World map charting
wmChart = World()
wmChart.title = 'Number of Climbing Routes in Each Country According to 8a.nu Log Book'
wmChart.add('Number of Routes',mappingDict)
wmChart.render_to_file('routemap.svg')
#=================================================================================
#Number of grades in each country
#Split country into 3 groups with >100k route , with>2.5k route & country with less route
country1 = country.loc[country>25000]
country1 = list(country1.index)
country2 = country.loc[country.between(5000,25000)]
country2 = list(country2.index)
country3 = country.loc[country<5000]
country3 = list(country3.index)
filtered1 = df[df['country'].isin(country1)]
#==========================================================================
#Heatmap of route grades & country
# Utworzenie słownika danych dotyczących populacji
cc_populations = {}
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
# Podzieleniw państw na trzy grupy według liczebności populacji
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
# Przygotowanie wykresu
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.force_url_protocol = 'http'
wm.title = 'Populacja na świecie w 2010 roku. (dane dla poszczególnych państw)'
wm.add('0 - 10 mln', cc_pops_1)
wm.add('10 mln - 1 mld', cc_pops_2)
wm.add('> 1 mld', cc_pops_3)
wm.render_to_file('world_population.svg')
import pygal
from pygal_maps_world.maps import World
w = World()
w.title = 'Populations of Countries in North America'
w.add('North America', {'ca': 34126000, 'us': 30934900, 'mx': 113423000})
w.render_to_file('Estudos/PYTHON/Python-VisualizacaoDeDados/Dados-Gráficos/Dowload_de_dados/na_populations.svg')
def get_country_code(country):
for code, name in COUNTRIES.items():
if name == country:
return code
return None
filename = 'high_tech_exports.csv'
with open(filename) as f:
reader = csv.reader(f)
exports_by_country = {}
for row in reader:
if row[-4]:
if row[-4] != '2016':
exports_by_country[row[0]] = int(float(row[-4]))
data_to_plot = {}
for k, v in exports_by_country.items():
code = get_country_code(k)
data_to_plot[code] = v
wm_style = RotateStyle('#336699')
wm = World(style=wm_style)
wm.title = 'High Technology Exports in the world in 2015'
wm.add('2015', data_to_plot)
wm.render_to_file('high_tech_exports.svg')
cc_populations = {}
for pop_dict in pop_data:
# print(pop_dict['Year']==2016)
if pop_dict['Year'] == 2016:
# print(pop_dict['Country Name'])
country_name = pop_dict['Country Name']
# 有些值是小数，先转为float再转为int
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population #{'中国': '13亿'}
# 为了使颜色分层更加明显
cc_populations_1, cc_populations_2, cc_populations_3 = {}, {}, {}
for cc, population in cc_populations.items():
if population < 10000000:
cc_populations_1[cc] = population
elif population < 1000000000:
cc_populations_2[cc] = population
else:
cc_populations_3[cc] = population
wm_style = RotateStyle('#336699', base_style=LightColorizedStyle)
world = World(style=wm_style)
world.title = 'World Populations in 2015, By Country'
world.add('0-10m', cc_populations_1)
world.add('10m-1bn', cc_populations_2)
world.add('>1bn', cc_populations_3)
world.render_to_file('world_population_2015.svg')
#Print the 2010 population for each country
world_maps = {}
highest3 = {}
pop_list =[]
for pop_dict in data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = return_code(country_name)
pop_list.append(population)
#print(country_name+': ' + population+ '>>>'+ return_code(country_name))
if code:
world_maps[code] = population
else:
print('Error '+ country_name)
cc_pops1 = {}
cc_pops2 = {}
for cc, pop in world_maps.items():
if pop <10000000:
cc_pops1[cc] = pop
else:
cc_pops2[cc] = pop
wm = World()
wm_style = RotateStyle('#723ac3')
wm = World(style=wm_style)
wm.title = "World Population in 2010"
wm.add('World population', world_maps)
wm.add('Top 5 Most Populated', cc_pops1)
wm.add('toppp',cc_pops2)
wm.render_to_file('world_map.svg')
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')
from pygal_maps_world.maps import World
wm = World()
wm.title = 'North,Central,and South Amercia'
wm.add('North America', {'ca':34126000,'mx':309349000,'us':113423000})
wm.add('Central America',['bz','cr','gt','hn','ni','pa','sv'])
wm.add('South Ameirca',['ar','bo','br','cl','co','ec','gf','gy','pe','py','sr','uy','ve'])
wm.render_to_file('americas.svg')
else:
print('ERROR - ' + country_name)
# Group the countries into 3 population levels.
cc_pops_1 = {}
cc_pops_2 = {}
cc_pops_3 = {}
for cc, pop in cc_populations.items():
if pop < 100000000:
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# See how many countries are in each level.
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
# wm = World()
wm_style = RotateStyle('#336699', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = 'World Population in 2010, by Country'
# wm.add('2010', cc_populations)
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')
from pygal_maps_world.maps import World
wm = World()
wm.title = 'North, Central and South America'
wm.add('North America', ['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')