def world_map_test():
map_style = RotateStyle('#44ff77')
map = World(style=map_style,
value_formatter=lambda x: '{}'.format(x) + u'国家')
map.title = 'world map scope'
country_list = []
chinese_name = {
'United States': u'大美利坚',
'United Kingdom': u'大英帝国',
'France': u'法兰西',
'Italy': u'意大利',
'Japan': u'东瀛',
'Germany': u'德意志',
'Canada': u'加拿大'
}
detail_dict = {}
print(type(chinese_name))
for code, name in COUNTRIES.items():
if name in ('United States', 'United Kingdom', 'France', 'Italy',
'Japan', 'Germany', 'Canada'):
country_list.append(code)
detail_dict[code] = chinese_name[name]
print(code)
#if name == 'Russian Federation':
# unique=code
for x in detail_dict.items():
map.add(u'G7国家', )
#map.add(u'G8国家',country_list+[unique])
map.render_to_file('/Users/qmp/Desktop/map1.svg')
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')
def main():
# Get dict of code to population at year 2010 for plotting
world_pop_2010 = build_pop_dict("population_data.json", 2010)
# Divide dict into three smaller dicts corresponding to population levels (low, medium, high)
low_pop_2010, medium_pop_2010, high_pop_2010 = {}, {}, {}
for code, pop in world_pop_2010.items():
if pop < 10000000:
low_pop_2010[code] = pop
elif pop < 1000000000:
medium_pop_2010[code] = pop
else:
high_pop_2010[code] = pop
# Create pygal maps plotting object
map_style = ps.DarkSolarizedStyle
wm = World(style=map_style)
wm.title = 'World populations'
# Add the shading for countries in each population level
wm.add('0-10m', low_pop_2010)
wm.add('10m-1b', medium_pop_2010)
wm.add('>1b', high_pop_2010)
# Render graph to svg file
wm.render_to_file('world.svg')
def one_country_map():
'''一国地图'''
wm_o = World()
wm_o.force_uri_protocol = 'http'
wm_o.title = '世界地图(中国)'
wm_o.add('China', {'中国': 'cn'})
wm_o.render_to_file('country_map.svg')
def world_country_map():
'''世界各国地图'''
wm_c = World()
wm_c.force_uri_protocol = 'http'
wm_c.title = '世界地图'
for code, name in COUNTRIES.items():
# print(name, code)
wm_c.add(name, code)
wm_c.add('Yemen', {'ye': 'Yemen'})
wm_c.render_to_file('world_map.svg')
def csv_data_handle():
with open(
'/Users/qmp/Downloads/API_TX.VAL.TECH.MF.ZS_DS2_en_csv_v2/API_TX.VAL.TECH.MF.ZS_DS2_en_csv_v2.csv'
) as f:
reader = csv.reader(f)
#print(next(reader))
map_style = RotateStyle('#4455ee', base_style=LightStyle)
map = World()
info_dict = {}
for x in reader:
if len(x) == 62:
print(x[0], x[1], x[-2])
code = counrty_list_show(x[0])
info_dict[code] = float((x[-2])) if x[-2] != '' else None
map.title = 'High-technology exports (% of manufactured exports)'
map.add('', info_dict)
map.render_to_file('/Users/qmp/Desktop/High_Tech_map.svg')
def json_data_handle():
gdp_style = RotateStyle('#5555ff')
map = World(style=gdp_style)
package = Package('/Users/qmp/Downloads/gdp.zip')
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
#print(resourceList)
data = package.resources[0].read()
show_dict = {}
for x in data:
if x[2] == 2016:
code = counrty_list_show(x[0])
#print(code,x)
gdp = long(x[-1])
print(gdp)
show_dict[code] = gdp
print(show_dict)
map.title = u'2016世界所有经济体GDP'
map.add(u'经济体', show_dict)
map.render_to_file('/Users/qmp/Desktop/map_gdp.svg')
def clickedBtn(self):
try:
global capitala
capitala = self.label_capitala.text()
if capitala:
path = easygui.filesavebox(msg="",
title="Salvati datele",
default=selected_country + ".txt",
filetypes=None)
# salvare date in fisier txt
file = open(path, "w")
file.write("Tara: " + tara + "\n")
file.write("Continent: " + continent + "\n")
file.write("Capitala: " + capitala + "\n")
file.write("Populatie: " + populatie + "\n")
file.write("Suprafata: " + suprafata + "\n")
file.write("Moneda: " + moneda + "\n")
file.write("Ora GMT: " + timezone + "\n")
file.write("Provincii: " + str(provincii) + "\n")
file.write(
"All data: " +
json.dumps(countries.get_all_info_for_country(tara)) +
"\n")
file.close()
# desenam harta lumii si o salvam
worldmap_chart = World()
worldmap_chart.title = tara
worldmap_chart.add(
'Tara aleasa',
[countries.get_country_code_from_country(tara)])
path = path[:len(path) - 4]
print(path)
worldmap_chart.render_to_file(path + ".svg")
else:
self.prompt_message("Trebuie sa selectati o tara!", "Eroare")
except Exception as ex:
print(ex)
def render_world_map(gdpinfo, plot_countries, year, map_file):
"""
Inputs:
gdpinfo - A GDP information dictionary
plot_countries - Dictionary whose keys are plot library country codes
and values are the corresponding country name
year - String year to create GDP mapping for
map_file - Name of output file to create
Output:
Returns None.
Action:
Creates a world map plot of the GDP data for the given year and
writes it to a file named by map_file.
"""
worldmap_chart = World()
worldmap_chart.title = 'GDP by Country (Log Scale) for ' + year
gdp_dict, missing_data, missing_data_year = build_map_dict_by_name(gdpinfo, plot_countries, year)
worldmap_chart.add('GDP for '+year, gdp_dict)
worldmap_chart.add('Missing '+year+' data', missing_data_year)
worldmap_chart.add('No record', missing_data)
worldmap_chart.render_to_file(map_file)
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)
'gh': 24392000,
'gn': 9982000,
'gw': 1515000,
'ci': 19738000,
'lr': 3994000,
'ml': 15370000,
'mr': 3460000,
'ne': 15512000,
'ng': 158423000,
'sn': 12434000,
'sl': 5867000,
'tg': 6028000
}
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for pop in population.values():
if pop < 10000000:
cc_pops_1[pop] = pop
elif pop < 90000000:
cc_pops_2[pop] = pop
else:
cc_pops_3[pop] = pop
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
wm.add('Africa', population)
wm.add('0-10m', cc_pops_1)
wm.add('10-90m', cc_pops_2)
wm.add('>90m', cc_pops_3)
wm.render_to_file('africa.svg')
for pop_dict in pop_data:
#print(pop_dict)
if int(pop_dict['Year']) == 2010:
country_name = pop_dict['Country Name']
population = int(pop_dict['Value'])
code = get_country_code(country_name)
if code:
cc_populations[code] = population
#print(code + ": " + str(population))
else:
print('ERROR - ' + country_name)
#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 < 100000000:
cc_pops_1[cc] = pop
elif pop < 100000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
#看看每组包含了多少个国家
wm = World()
print(len(cc_pops_1),len(cc_pops_2),len(cc_pops_3))
wm_style = RS('#336699', base_style=NS)
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')
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')
# See how many countries are in each level.
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
"""
Essa saída mostra que há 85 países com menos de 10 milhões de pessoas,
69 países com população entre 10 milhões e 1 bilhão de pessoas e dois
países extremos com mais de 1 bilhão.
"""
from pygal.style import LightColorizedStyle as LG
dc = {'ca'}
wm_style2 = LG
wm_style = RS('#336699', base_style=LCS)
#wm = pygal.Worldmap(style=wm_style) #NÃO FUNCIONA MAIS ATUALMENTE.
#wm = wm(style=wm_style) #GAMBIARRA QUE EU FIZ, MAS TAMBÉM NÃO SURTIU O EFEITO ESPERADO.
wm = WD(style=wm_style2)
#wm.force_uri_protocol = 'http'
wm.title = 'World Population in 2010, by Country'
#Aqui Iluminou a America Do Norte toda. O problema é que; estes .adds NÃO estão recebendo Estes dicionarios 'cc_pops_2'
#wm.add('0 a 10', ['ca', 'mx', 'us'])
wm.add('0-10m', cc_pops_1) #Se eu colocar a Var 'dc = {'ca'}' .Funciona. Mas cc_pops NÃO.
wm.add('10m-1bn', cc_pops_2)
wm.add('>1bn', cc_pops_3)
wm.render_to_file('Pagina_519_20_População Mundial.svg')
"""
Não Obtive os mesmos Resultados dos mostrados no Livro, pois, o método de Import
mostrado no livro esta ultrapassado. E o metodo atual não obtive a mesma reação.
"""
#Soluções dos Exercicios:
#https://ehmatthes.github.io/pcc/solutions/chapter_16.html
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('world_map.svg')
#python2
#使用pygal.Worldmap()对象生成地图
#python3
#使用下面的对象生成地图
# from pygal_maps_world.maps import World
# world_map = World()
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： NorthAmericaPopulation
Description :
Author : Liangz
Date： 2018/10/26
-------------------------------------------------
Change Activity:
2018/10/26:
-------------------------------------------------
"""
__author__ = 'Liangz'
from pygal_maps_world.maps import World
world_map = World()
world_map.title = 'Populations Of Countries In North America'
world_map.add('North America', {
'ca': 34126000,
'us': 309349000,
'mx': 113423000
})
world_map.render_to_file('NA_population.svg')
cc_populations = {}
for pop_dict in pop_data:
if str(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
# 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 < 10000000:
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_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")
import json
from pygal_maps_world.maps import World
from country_codes import get_country_code
from pygal.style import LightColorizedStyle as LCS, RotateStyle as RS
filename = 'gdp_json.json'
with open(filename) as f:
pop_data = json.load(f)
gdp = {}
for gdp_dict in pop_data:
if gdp_dict['Year'] == 2010:
country_name = gdp_dict['Country Name']
gdp_val = float(gdp_dict['Value'])
code = get_country_code(country_name)
if code:
gdp[code] = gdp_val
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.title = "World GDP in 2010, by Country"
wm.add("GDP", gdp)
wm.render_to_file('world_gdp.svg')
with open(filename) as f:
reader = csv.reader(f)
for row in reader:
if row[2] == '2000':
value = int(float(row[3]))
country_name = row[0]
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 2000, by Country'
wm.add('<50000000000', gdp_1)
wm.add('<100000000000', gdp_2)
wm.add('>100bn', gdp_3)
wm.render_to_file('gdp_2000.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_population.svg')
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
#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 < 10000000:
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_style = RotateStyle('#336699', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = 'World Population 2010, by Country'
wm.add('0 - 10m', cc_pops_1)
wm.add('10m - 1b', cc_pops_2)
wm.add('>1b', cc_pops_3)
wm.render_to_file('World_Population.svg')
filename = 'gdp_json.json'
with open(filename) as f:
gdp_data = json.load(f)
cc_gdps = {}
for gdp_dict in gdp_data:
if gdp_dict['Year'] == 2010:
country_name = gdp_dict['Country Name']
gdp = int(gdp_dict['Value'])
code = get_country_code(country_name)
if code:
cc_gdps[code] = gdp
#根据gdp数值对国家进行分组
cc_gdps_1, cc_gdps_2, cc_gdps_3 = {}, {}, {}
for cc, gdp in cc_gdps.items():
if gdp < 10000000000:
cc_gdps_1[cc] = gdp
elif gdp < 1000000000000:
cc_gdps_2[cc] = gdp
else:
cc_gdps_3[cc] = gdp
#绘制世界地图
wm = World()
wm.title = 'World GDP in 2010'
wm.add('0-10bn', cc_gdps_1)
wm.add('10bn-1000bn', cc_gdps_2)
wm.add('>1000bn', cc_gdps_3)
wm.render_to_file('World_GDP.svg')
# population of per country
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_name(country_name)
if code:
cc_population[code] = population
else:
print("Error - " + country_name)
# grouping countries by 3 types of pipulations
cc_pop_1, cc_pop_2, cc_pop_3 = {}, {}, {}
for cc, pop in cc_population.items():
if pop < 10000000: # 10 000 000
cc_pop_1[cc] = pop
elif pop < 1000000000: # 1 000 000 000
cc_pop_2[cc] = pop
else:
cc_pop_3[cc] = pop
wm_style = RotateStyle('#336699')
wm = World(style=wm_style)
wm.title = 'World population in 2010, By countries'
wm.add('0-10m', cc_pop_1)
wm.add('10m-1bn', cc_pop_2)
wm.add('>1bn', cc_pop_3)
wm.render_to_file('word_pop_by_groups_wm_style.svg')
code = get_country_code(country_name)
if code:
tmp = {code: export_value_index}
tx_dic.update(tmp)
else:
print("Błąd - " + country_name)
#print(tx_dic)
# Podzielenie państw na trzy grupy według wielkości indexu
tx_dic_100, tx_dic_300, tx_dic_more = {}, {}, {}
for cc, pop in tx_dic.items():
if pop < 100:
tx_dic_100[cc] = pop
elif pop < 300:
tx_dic_300[cc] = pop
else:
tx_dic_more[cc] = pop
# Przygotowanie wykresu
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.force_url_protocol = 'http'
wm.title = 'Export value index w 2017 roku. (dane dla poszczególnych państw)'
wm.add('< 100', tx_dic_100)
wm.add('100 < 300', tx_dic_300)
wm.add('< 300', tx_dic_more)
wm.render_to_file('Export_value_index_2017.svg')
cc_populations = {}
for pop_dict in pop_data:
if pop_dict['Year'] == 2015:
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
# 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')
# https://www.jianshu.com/p/6fcd47b3528b
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
# print(code + ':' + str(population))
whole_population[code] = population
# else:
# print('Error - ' + country_name)
# 根据人口数量将所有的国家分成三组
world_population_1, world_population_2, world_population_3 = {}, {}, {}
for world_code, population in whole_population.items():
if population < 10000000:
world_population_1[world_code] = population
elif population < 1000000000:
world_population_2[world_code] = population
else:
world_population_3[world_code] = population
# 看看每组分别包含多少个国家
print(len(world_population_1), len(world_population_2),
len(world_population_3))
world_map_style = RotateStyle('#336699', base_style=LightColorizedStyle)
# world_map_style = LightColorizedStyle
world_map = World(style=world_map_style)
world_map.title = 'World Population In 2010, By Country'
world_map.add('0 - 10M', world_population_1)
world_map.add('10M - 1BN', world_population_2)
world_map.add('>1BN', world_population_3)
world_map.render_to_file('threelevel_light_style.svg')
import pandas as pd
from pygal.style import *
from pygal_maps_world.maps import World
from country_codes import get_country_code
csv = pd.read_csv("co2_emissions.csv", keep_default_na=False, skiprows=4)
csv['code'] = csv['Country Name'].apply(get_country_code)
# 需要把CO2排放量转换为数字格式
csv['digit_2013'] = pd.to_numeric(csv['2013'])
# 把CO2排放量分3个层次
# 并把国家码和对应的2013年CO2排放量取出来放在字典中
co2_1, co2_2, co2_3 = {}, {}, {}
for code, co2 in zip(csv['code'], csv['digit_2013']):
if co2 <= 5:
co2_1[code] = co2
elif co2 <= 10:
co2_2[code] = co2
else:
co2_3[code] = co2
wm = World(fill=True, style=RedBlueStyle)
wm.title = "World's CO2 emissions at 2013"
wm.add("CO2 <= 5", co2_1)
wm.add("CO2 <= 10", co2_2)
wm.add("CO2 > 10", co2_3)
wm.render_to_file("practice_16-7.svg")
from pygal_maps_world.maps import World
wm = World()
wm.title = 'Americas'
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')
cc_population = {}
# population of per country
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_name(country_name)
if code:
cc_population[code] = population
else:
print("Error - " + country_name)
# grouping countries by 3 types of pipulations
cc_pop_1, cc_pop_2, cc_pop_3 = {}, {}, {}
for cc, pop in cc_population.items():
if pop < 10000000: # 10 000 000
cc_pop_1[cc] = pop
elif pop < 1000000000 : # 1 000 000 000
cc_pop_2[cc] = pop
else:
cc_pop_3[cc] = pop
wm = World()
wm.title = 'World population in 2010, By countries'
wm.add('0-10m', cc_pop_1)
wm.add('10m-1bn', cc_pop_2)
wm.add('>1bn', cc_pop_3)
wm.render_to_file('word_pop_by_groups.svg')
# 为了使颜色分层更加明显
cc_level_1, cc_level_2, cc_level_3,cc_level_4,cc_level_5,cc_level_6,cc_level_7 = {}, {}, {},{}, {}, {},{}
for cc, number1 in cc_movie_number.items():
if number1 > 3000:
cc_level_7[cc] = number1
elif number1 < 3000 and number1 > 2000:
cc_level_6[cc] = number1
elif number1 < 2000 and number1 > 900:
cc_level_5[cc] = number1
elif number1 < 900 and number1 > 675:
cc_level_4[cc] = number1
elif number1 < 675 and number1 > 450:
cc_level_3[cc] = number1
elif number1 < 450 and number1 > 225:
cc_level_2[cc] = number1
else:
cc_level_1[cc] = number1
wm_style = RotateStyle(color='#fde0dc', base_style=LightColorizedStyle)
world = World(style=wm_style)
world.title = '近10年世界范围各个地区的电影数量'
world.add('不足225部', cc_level_1)
world.add('225——450部', cc_level_2)
world.add('450——675部', cc_level_3)
world.add('675——900部', cc_level_4)
world.add('900——2000部', cc_level_5)
world.add('2000——3000部', cc_level_6)
world.add('超过3000部', cc_level_7)
world.render_to_file('近10年世界范围各个地区的电影数量.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')
f = open("gdp.json")
gdp = json.load(f)
gdps = {}
# 把所有国家的名称通过get_country_code函数以得到其国别码
# 并把其GDP存入字典
for item in gdp:
if item["Year"] == "2016":
code = get_country_code(item["Country Name"])
if code:
gdps[code] = int(float(item["Value"])) / 100000000
# 根据GDP数量级别不同把国家分成三组
gdp_1, gdp_2, gdp_3 = {}, {}, {}
for c, g in gdps.items():
if g < 1000:
gdp_1[c] = g
elif g < 10000:
gdp_2[c] = g
else:
gdp_3[c] = g
wm_style = RotateStyle('#336699', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = "GDP of the Whold World - 2016"
wm.add("0-100bn", gdp_1)
wm.add("100-1000bn", gdp_2)
wm.add("1000-10000bn", gdp_3)
wm.render_to_file("gdps.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')
