這篇文章給出了如何繪制中國人口密度圖，但是運行存在一些問題，我在一些地方進行了修改。

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

conda install Basemap

新建文檔，導入需要的包

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中不包括中國省界，需要在下面網站下載中國省界，點擊Shapefile下載。

生成中國大陸省界圖片。

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)

去國家統計局網站下載人口各省，只需保留地區和總人口即可，保存為csv格式并改名為pop.csv。

讀取數據，儲存為dataframe格式，刪去地名之中的空格，并設置地名為dataframe的index。

df = pd.read_csv('pop.csv')new_index_list = []for i in df["地區"]: 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(["地區"], axis=1)df.set_index("region", inplace=True)

將Basemap中的地區與我們下載的csv中的人口數據對應起來，建立字典。注意，Basemap中的地名與csv文件中的地名并不完全一樣，需要進行一些處理。

provinces = m.states_infostatenames=[]colors = {}cmap = plt.cm.YlOrRdvmax = 100000000vmin = 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 = '黑龍江' if s == '內蒙':  s = '內蒙古' statenames.append(s) pop = df['人口數'][s] colors[s] = cmap(np.sqrt((pop - vmin) / (vmax - vmin)))[:3]

最后畫出圖片即可

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()

完整代碼如下

# -*- 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 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["地區"]: 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(["地區"], axis=1)df.set_index("region", inplace=True) provinces = m.states_infostatenames=[]colors = {}cmap = plt.cm.YlOrRdvmax = 100000000vmin = 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 = '黑龍江' if s == '內蒙':  s = '內蒙古' statenames.append(s) pop = df['人口數'][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()