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
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conda install Basemap |
新建文档，导入需要的包
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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，点击Shapefile下载。
生成中国大陆省界图片。
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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。
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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。
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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
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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
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# -*- 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 = 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() |
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_41816368/article/details/80787415
