python 世界人口预期寿命分析

from pygal_maps_world.i18n import COUNTRIES
import pygal.maps.world
from pandas import DataFrame
import pandas as pd
import matplotlib.pyplot as plt
from pylab import mpl
def get_country_name(country_name):
for code, name in COUNTRIES.items():
if name == country_name:
return code
return None


# #mpl.rcParams['font.sans-serif'] = ['SimHei']
# mpl.rcParams['font.sans-serif'] = ['Microsoft YaHei']
# 指定默认字体：解决plot不能显示中文问题
# mpl.rcParams['axes.unicode_minus'] = False

res = pd.read_csv(r'C:UsersAdministratorDesktopdata.csv')
res.rename(columns={'Unnamed: 0': 'country'}, inplace=True)
res.rename(columns={'Unnamed: 1': 'year'}, inplace=True)
newdata = DataFrame()
newdata['Country'] = res.country[1:]
newdata['Year'] = res.year[1:]
newdata['bothsex'] = res['Life expectancy at birth (years)'][1:]
newdata['male'] = res['Life expectancy at birth (years).1'][1:]
newdata['female'] = res['Life expectancy at birth (years).2'][1:]
# finaldata = DataFrame(newdata[newdata['Country'] == 'China'][['bothsex', 'male', 'female']], dtype='float64')
finaldata = DataFrame(newdata[newdata['Year']==' 2016'][['bothsex']], dtype='float64')
finaldata['Country'] = newdata[newdata['Year']==' 2016'][['Country']]
wm = pygal.maps.world.World()
age = {}
age1 = {}
age2 = {}
age3 = {}
for i in finaldata.values:
age[get_country_name(i[1])] = i[0]
for cc, pop in age.items():
if pop < 70.0:
age1[cc] = pop
elif pop < 78.0:
age2[cc] = pop
else:
age3[cc] = pop
wm.add('年龄小于70岁', age1)
wm.add('年龄小于78岁', age2)
wm.add('年龄大于78岁', age3)
wm.render_to_file('age.svg')
下载数据链接：http://apps.who.int/gho/data/node.main.1?lang=en

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