def test_worldmap():
wmap = world.World(print_values=True,
style=choice(list(styles.values())))
# wmap.js = ('http://l:2343/2.0.x/pygal-tooltips.js',)
# wmap.add('1st', [('fr', 100), {
# 'value': ('us', 10),
# 'node': {'style': 'fill: red'}
# }
# ])
# wmap.add('2nd', [('jp', 1), ('ru', 7), ('uk', 0)])
# wmap.add('3rd', ['ch', 'cz', 'ca', 'cn'])
# wmap.add('4th', {'jp': 12, 'bo': 1, 'bu': 23, 'fr': 34})
# wmap.add('5th', [{
# 'value': ('tw', 10),
# 'label': 'First label',
# 'xlink': 'http://google.com?q=tw',
# }, {
# 'value': ('bw', 20),
# 'label': 'Second one',
# 'xlink': 'http://google.com?q=bw',
# 'node': {'style': 'fill: blue'}
# }, {
# 'value': ('mw', 40),
# 'label': 'Last'
# }])
wmap.add('_', {'us': 1})
wmap.add('-', {'us': 2})
wmap.add('.', {'us': 3})
wmap.title = 'World Map !!'
wmap.value_formatter = lambda x: '%d%%' % x
return wmap.render_response()
def main(args):
filename = 'population_data.json'
with open(filename) as f:
pop_data = json.load(f)
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
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:
if population < 10000000:
cc_pops_1[code] = population
elif population < 1000000000:
cc_pops_2[code] = population
else:
cc_pops_3[code] = population
wm_style = RS('#336699', base_style=LCS)
wm = pywm.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')
return 0
def world_map():
# Get data from API
PAYLOAD = {"code": "ALL"}
URL = "https://api.statworx.com/covid"
RESPONSE = requests.post(url=URL, data=json.dumps(PAYLOAD))
# Convert to data frame
DFRAME = pd.DataFrame.from_dict(json.loads(RESPONSE.text))
# Return a list of codes of countries and Representation and cases and deaths
codes = [
code.lower()
for code in (
DFRAME.filter(items=["code", "cases"])
.groupby("code", as_index=False)
.sum()["code"]
.to_list()
)
]
Representation = ["cases", "cases_cum", "deaths", "deaths_cum"]
cases = (
DFRAME.filter(items=["code", "cases"]).groupby("code").sum().to_dict()["cases"]
)
deaths = (
DFRAME.filter(items=["code", "deaths"])
.groupby("code")
.sum()
.to_dict()["deaths"]
)
# Initial the World Map
WorldMap = world.World(
show_legend=False,
pretty_print=True,
human_readable=True,
style=DarkStyle,
tooltip_position=None,
)
# Bind the code of country to the number of its cases and deaths
dict_cases_deaths = {}
for code in codes:
dict_cases_deaths[code] = {
"Cases: {}, Deaths: {}".format(cases[code.upper()], deaths[code.upper()])
}
# Plot the data
WorldMap.title = "Current Statistics of Deaths and Cases; updated every day"
WorldMap.add("General Statistics", dict_cases_deaths)
fig = WorldMap.render_data_uri()
return render_template("world_map.html", fig=fig)
# Author: Bojan G. Kalicanin
# Date: 27-Dec-2016
# Plot population of the three countries in North America.
import pygal.maps.world as worldmap
wm = worldmap.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')
with open(data_path, "r") as f:
csvdt = reader(f)
for row in csvdt:
if len(row) > 56:
code = alpha_two(row[1])
if code is not None:
code = code.lower()
if code in cuty:
if isNum(row[55]):
co2[code] = float(row[55])
else:
print("Leaving out", row[0])
else:
print("Leaving out", row[0])
nodata = dict()
for code in cuty:
if code not in co2:
nodata[code]='?'
wmp = pw.World()
wmp.force_uri_protocol = "http"
wmp.title = "CO2 emissions in 2011"
wmp.add("Known data", co2)
wmp.add("No data", nodata)
wmp.style = Style(colors=("#B22222", "#A9A9A9"), legend_font_size=10, tooltip_font_size=8)
wmp.render()
wmp.render_to_file("CO2_emissions.svg")
#
# Open browser window
open_new_tab("file://" + path.realpath("CO2_emissions.svg"))
# 打印每个国家2016年的GDP
for gdp_dict in gdp_data:
if gdp_dict['Year'] == 2016:
country_name = gdp_dict['Country Name']
gdp = float(gdp_dict['Value'])
code = get_country_code(country_name)
if code:
cc_gdps[code] = gdp
# 根据gdp值将所有的国家分成三组
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 < 100000000000:
cc_gdps_2[cc] = gdp
else:
cc_gdps_3[cc] = gdp
# 看看每组分别包含多少个国家
# print(len(cc_gdps_1), len(cc_gdps_2), len(cc_gdps_3))
wm_style = RS('#d8886d', base_style=LCS) # 让Pygal使用一种基色
wm = pmw.World(style=wm_style)
wm.title = 'World GDP in 2016, by Country'
wm.add('0-10bn', cc_gdps_1)
wm.add('10bn-100bn', cc_gdps_2)
wm.add('>100bn', cc_gdps_3)
wm.render_to_file('diagram/Grouped_World_GDP_2016.svg')
import pygal.maps.world as maps
from populationData import getCountryCode
map = maps.World()
map.title = "Americas"
map.add("North America", ['us', 'mx', 'ca'])
map.add('Central America', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
map.add('South America', [
'ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf', 'gy', 'pe', 'py', 'sr', 'uy',
've'
])
map.render_to_file("americas.svg")
Сгруппируем страны по 3 уровням населения
1) Менее 10 миллионов
2) От 10 миллионов до миллиарда
3) Более миллиарда
"""
cc1, cc2, cc3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop < 10000000:
cc1[cc] = pop
elif pop < 1000000000:
cc2[cc] = pop
else:
cc3[cc] = pop
"""
RotateStyle принимает аргумент - цвет в ШЕСТНАДЦАТИРИЧНОМ ФОРМАТЕ RGB
Сначала пишется решется #
Затем шесть цифр
Первые две цифры - количество красного (Red)
Вторые две цифры - количество зеленого (Green)
Третьи две цифры - количество синего (Blue)
"""
style = RS(
"#336699", base_style=LCS
) #LCS - LightColorizedStyle из импорта - осветляет цфета оформления немного
wm = maps.World(style=style)
wm.title = "World population if 2010"
wm.add("0-10m", cc1)
wm.add("10m-1b", cc2)
wm.add("1b+", cc3)
wm.render_to_file("world_2010.svg")
import pygal.maps.world as pmw
wm = pmw.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')
filename = 'data/population_data.json'
with open(filename) as f:
pop_data = json.load(f)
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
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]
wm_style = LightColorizedStyle
wm = hehe.World(style=wm_style)
wm.title = 'just world map'
wm.add('0-10m', list(cc_pops_1))
wm.add('10m-1bn', list(cc_pops_2))
wm.add('>1bn', list(cc_pops_3))
wm.render_to_file('worldpopulations_map_2.svg')
from pygal.style import RotateStyle as RS, LightColorizedStyle as LCS
from get_country_code import get_cc
# Get data from json file
filename = 'chap16\\world_map\\world_data\\world_gdp.json'
with open(filename) as f:
pop_data = json.load(f)
# Puts country code and gdp in dict
country_gdps = {}
for pop_dict in pop_data:
if pop_dict['Year'] == 2016:
# Gets country code
country_name = pop_dict['Country Name']
code = get_cc(country_name)
# Gets gdp
gdp = int(float(pop_dict['Value']))
# Appends code and gdp
country_gdps[code] = gdp
# Creates map
wm = pywm.World()
wm.title = 'World GDP by Country'
wm.add('2016 GDP', country_gdps)
wm.render_to_file('world_gdp.svg')
#! /usr/bin/env python3.7
# -*- coding: utf-8 -*-
import pygal.maps.world as WMap
wm = WMap.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')
country_name = pop_dict['Country Name']
code = get_cc(country_name)
if not code:
print(country_name)
# Gets population
population = int(float(pop_dict['Value']))
# Appends code and population
country_populations[code] = population
# Adds coutntry to 1 of 3 dicts based on their population
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in country_populations.items():
if pop < 10000000:
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# Creates map
wm_style = RS('#336699', base_style=LCS)
wm = pywm.World(style=wm_style)
wm.title = 'World Population in 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')
import pygal.maps.world as wp
wm = wp.World()
wm.title = 'populatin of countries in north america'
wm.add('north america', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.render_to_file('na_population.svg')
import pygal
import pygal.maps.world as pw
from country_codes import get_country_code
import csv
#导入数据
filename = 'gdp.csv'
with open(filename, ) as f:
gdp_data = csv.reader(f)
for i in range(5):
header = next(gdp_data)
#获取每个国家2016年的GDP，做一个字典
gdps_dict = {}
for gdp_dict in gdp_data:
try:
country_name = gdp_dict[0]
gdp = int(gdp_dict[61])
print(int(gdp_dict[61]))
except ValueError:
print('Error')
else:
code = get_country_code(country_name)
if code:
gdps_dict[code] = gdp
wg = pw.World()
wg.title = '世界各国GDP'
wg.add('2016', gdps_dict)
wg.render_to_file('各国GDP.svg')
import pygal.maps.world as pyg
wm = pyg.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')
import pygal.maps.world as pyWorld
wm = pyWorld.World()
wm.title = 'North, Cenrtal,and Sounth America'
#add为添加突出标签，字符串为标签名字，列表为该标签的国家代码,如果用字典的话可以显示数值
wm.add('North America', {'ca': 3412600, 'mx': 113423000, 'us': 309349000})
wm.add('Central America', ['bz', 'cr', 'gt'])
wm.render_to_file('americas.svg')
import pygal
from pygal.maps import world as world
worldmap_chart = world.World()
worldmap_chart.title = "Some countries"
worldmap_chart.add("F countries", ['fr', 'fi'])
worldmap_chart.add('U countries', ['ua', 'ug', 'us', 'uy', 'uz'])
worldmap_chart.add('M countries', [
'ma', 'mc', 'md', 'me', 'mg', 'mk', 'ml', 'mm', 'mn', 'mo', 'mr', 'mt',
'mu', 'mv', 'mw', 'mx', 'my', 'mz'
])
worldmap_chart.render_to_file('bar_chart.svg')
import pygal.maps.world as maps
wm = maps.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('graphs/americas.svg')
'''Practice plotting numerical data on a map'''
import pygal.maps.world as py
wm = py.World()
wm.title = 'Populations of Countries in North America'
#pass a dictionary as the second argument
wm.add('North America', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
#pygal automatically use the numbers to shade the countries from light to dark
wm.render_to_file('na_populations.svg')
with open(filename) as f:
pop_data = json.load(f)
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 = pyworld.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('data_visualisation/results/world_population.svg')
import pygal.maps.world as pmap
wm = pmap.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')
for pop_dict in pop_data:
if pop_dict["Year"] == '2010':
country_name = pop_dict["Country Name"]
population = int(float(pop_dict["Value"]))
country_code = get_country_code(country_name)
if country_code:
cc_populations[country_code] = population
#根据人口数量，对国家进行分组:少于1000w,1000w~10亿,10亿以上
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for code, pops in cc_populations.items():
if pops < 10000000:
cc_pops_1[code] = pops
elif pops < 1000000000:
cc_pops_2[code] = pops
else:
cc_pops_3[code] = pops
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
#制定地图使用同一个颜色基调，我们选择一个style
wm_style = RotateStyle('#336699',
base_style=LCS) #16进制的RGB颜色，前两位表示red,中间两位绿，最后两位表示蓝
wm = WMap.World(style=wm_style)
wm.title = 'World Population in 2010, by country'
wm.add('10m', cc_pops_1)
wm.add('10m-1bn', cc_pops_2)
wm.add('>1bn', cc_pops_3)
wm.render_to_file('world_population.svg')
# -*- coding: utf-8 -*-
'''
Created on 2018年1月25日
@author: Jeff Yang
'''
# 绘制地图的模块，有改动，书上的pygal.Worldmap()已经无法使用
from pygal.maps import world
wm = world.World()
wm.title = 'North, Central, and South America'
# add()方法接收一个标签和一个列表，后者包含要突出的国家的国别码
# 每次调用add()都将为指定的国家选择一种新颜色，并在图标左边显示该颜色和指定的标签
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')
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
# 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 < 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 = pgl.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 pygal.maps.world as pygalW
wm = pygalW.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('Using_JSON/americas.svg')
with open(filename) as f:
gdp_data = json.load(f)
# Build a dictionary of GDP Data...
global_gdp = {}
for gdp_dict in gdp_data:
if gdp_dict['Year'] == '2014':
country_name = gdp_dict['Country Name']
gdp_value = int(float(gdp_dict['Value']))
code = get_country_code(country_name)
if code:
global_gdp[code] = gdp_value
# Group countries into 3 groups by GDP
gdp_pop1, gdp_pop2, gdp_pop3 = {}, {}, {}
for cc, gdp in global_gdp.items():
if gdp > 10**11:
gdp_pop1[cc] = gdp # 100 Billion
elif gdp > 10**10:
gdp_pop2[cc] = gdp # 10 Billion
else:
gdp_pop3 = gdp # Less than 10 Billion
# Display world map with GDP data
wm = WorldMap.World()
wm.title = 'World GDP in 2014, by Country (USD)'
wm.add('GDP > $100B', gdp_pop1)
wm.add('GDP > $10B', gdp_pop2)
wm.add('GDP < $10B', gdp_pop3)
wm.render_to_file('Global GDP 2014.svg')
#!/usr/bin/env python
# encoding=utf-8
import pygal.maps.world as hehe
wm = hehe.World()
wm.title = "just world"
wm.add('North American', ['ca', 'mx', 'us'])
wm.add('Central American', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add('South American', [
'ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf', 'gy', 'pe', 'py', 'sr', 'uy',
've'
])
wm.render_to_file('american.svg')
# build a dictionary of population population data
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: 0-10m, 10m-1bn, >1bn
cc_pop_1, cc_pop_2, cc_pop_3 = {}, {}, {}
for cc in cc_populations:
pop = cc_populations[cc]
if pop < 10000000:
cc_pop_1[cc] = pop
elif pop < 1000000000:
cc_pop_2[cc] = pop
else:
cc_pop_3[cc] = pop
# create population map of the world by get_country
wm_style = RotateStyle('#115599', base_style=LCS)
wm = py.World(style=wm_style)
wm.title = ('World population in 2010, by country')
wm.add('0-10m', cc_pop_1)
wm.add('10m-1bn', cc_pop_2)
wm.add('>1bn', cc_pop_3)
wm.render_to_file('world_population.svg')
import pygal.maps.world as pgl
# pygal.Worldmap()语句相应模块更改为pygal.maps.world。World()
wm = pgl.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')
