python绘制*人口热力图

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，点击shapefile下载。

生成*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，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()

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。