这篇文章给出了如何绘制中国人口密度图，但是运行存在一些问题，我在一些地方进行了修改。

本人使用的IDE是anaconda，因此事先在anaconda prompt 中安装Basemap包

conda install Basemap

新建文档，导入需要的包

import matplotlib.pyplot as plt

from mpl_toolkits.basemap import Basemap

from matplotlib.patches import Polygon

from matplotlib.colors import rgb2hex

import numpy as np

import pandas as pd

Basemap中不包括中国省界，需要在下面网站下载中国省界，点击Shapefile下载。

生成中国大陆省界图片。

plt.figure(figsize=(16,8))

m = Basemap(

llcrnrlon=77,

llcrnrlat=14,

urcrnrlon=140,

urcrnrlat=51,

projection='lcc',

lat_1=33,

lat_2=45,

lon_0=100

)

m.drawcountries(linewidth=1.5)

m.drawcoastlines()

m.readshapefile('gadm36_CHN_shp/gadm36_CHN_1', 'states', drawbounds=True)

去国家统计局网站下载人口各省，只需保留地区和总人口即可，保存为csv格式并改名为pop.csv。

读取数据，储存为dataframe格式，删去地名之中的空格，并设置地名为dataframe的index。

df = pd.read_csv('pop.csv')

new_index_list = []

for i in df["地区"]:

i = i.replace(" ","")

new_index_list.append(i)

new_index = {"region": new_index_list}

new_index = pd.DataFrame(new_index)

df = pd.concat([df,new_index], axis=1)

df = df.drop(["地区"], axis=1)

df.set_index("region", inplace=True)

将Basemap中的地区与我们下载的csv中的人口数据对应起来，建立字典。注意，Basemap中的地名与csv文件中的地名并不完全一样，需要进行一些处理。

provinces = m.states_info

statenames=[]

colors = {}

cmap = plt.cm.YlOrRd

vmax = 100000000

vmin = 3000000

for each_province in provinces:

province_name = each_province['NL_NAME_1']

p = province_name.split('|')

if len(p) > 1:

s = p[1]

else:

s = p[0]

s = s[:2]

if s == '黑龍':

s = '黑龙江'

if s == '内蒙':

s = '内蒙古'

statenames.append(s)

pop = df['人口数'][s]

colors[s] = cmap(np.sqrt((pop - vmin) / (vmax - vmin)))[:3]

最后画出图片即可

ax = plt.gca()

for nshape, seg in enumerate(m.states):

color = rgb2hex(colors[statenames[nshape]])

poly = Polygon(seg, facecolor=color, edgecolor=color)

ax.add_patch(poly)

plt.show()

完整代码如下

# -*- coding: utf-8 -*-

import matplotlib.pyplot as plt

from mpl_toolkits.basemap import Basemap

from matplotlib.patches import Polygon

from matplotlib.colors import rgb2hex

import numpy as np

import pandas as pd

plt.figure(figsize=(16,8))

m = Basemap(

llcrnrlon=77,

llcrnrlat=14,

urcrnrlon=140,

urcrnrlat=51,

projection='lcc',

lat_1=33,

lat_2=45,

lon_0=100

)

m.drawcountries(linewidth=1.5)

m.drawcoastlines()

m.readshapefile('gadm36_CHN_shp/gadm36_CHN_1', 'states', drawbounds=True)

df = pd.read_csv('pop.csv')

new_index_list = []

for i in df["地区"]:

i = i.replace(" ","")

new_index_list.append(i)

new_index = {"region": new_index_list}

new_index = pd.DataFrame(new_index)

df = pd.concat([df,new_index], axis=1)

df = df.drop(["地区"], axis=1)

df.set_index("region", inplace=True)

provinces = m.states_info

statenames=[]

colors = {}

cmap = plt.cm.YlOrRd

vmax = 100000000

vmin = 3000000

for each_province in provinces:

province_name = each_province['NL_NAME_1']

p = province_name.split('|')

if len(p) > 1:

s = p[1]

else:

s = p[0]

s = s[:2]

if s == '黑龍':

s = '黑龙江'

if s == '内蒙':

s = '内蒙古'

statenames.append(s)

pop = df['人口数'][s]

colors[s] = cmap(np.sqrt((pop - vmin) / (vmax - vmin)))[:3]

ax = plt.gca()

for nshape, seg in enumerate(m.states):

color = rgb2hex(colors[statenames[nshape]])

poly = Polygon(seg, facecolor=color, edgecolor=color)

ax.add_patch(poly)

plt.show()

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