https://mp./s/Bh-L3syzJSHhLPZqzHvIyA
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 pltfrom mpl_toolkits.basemap import Basemapfrom matplotlib.patches import Polygonfrom matplotlib.colors import rgb2heximport numpy as npimport 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(/download_country_v3.html)，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(http://www./tjsj/pcsj/rkpc/6rp/indexce.htm)，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_infostatenames=[]colors = {}cmap = plt.cm.YlOrRdvmax = 100000000vmin = 3000000for 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 pltfrom mpl_toolkits.basemap import Basemapfrom matplotlib.patches import Polygonfrom matplotlib.colors import rgb2heximport numpy as npimport pandas as pdplt.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_infostatenames=[]colors = {}cmap = plt.cm.YlOrRdvmax = 100000000vmin = 3000000for 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()
