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一、数据文件准备
1、Info.csv
name,val,lat,lon
南京 ,4.23,32.04,118.78
徐州 ,4.13,34.26,117.2
睢宁 ,4.13,33.89,117.94
沛县 ,4.1,34.73,116.93
丰县 ,4.1,34.79,116.57
邳县 ,4.1,34.3,117.97
铜山 ,4.1,34.26,117.2
新沂 ,4.1,34.38,118.33
淮安 ,4.2,33.5,119.15
楚州,4.2,33.5,119.13
淮阴 ,4.1,33.62,119.02
涟水 ,4.1,33.77,119.26
新兴,4.05,33.46,120.09
步凤,4.05,33.34,120.32
盐城 ,4.1,33.38,120.13
阜宁 ,4.05,33.78,119.79
滨海 ,4.05,34.01,119.84
东台 ,4.05,32.84,120.31
盐都,4.05,33.33,120.15
建湖 ,4.05,33.46,119.77
射阳 ,4.1,33.77,120.26
大丰 ,4.1,33.19,120.45
宿迁 ,4.17,33.96,118.3
泗洪 ,4.17,33.46,118.23
沭阳 ,4.1,34.12,118.79
宿城,4.1,33.97,118.25
宿豫,4.1,33.95,118.32
泗洪 ,4.1,33.46,118.23
泰州 ,4.2,32.49,119.9
扬州 ,4.37,32.39,119.42
南通 ,4.15,32.01,120.86
如皋 ,4.15,32.39,120.56
海门 ,4.15,31.89,121.15
启东 ,4.15,31.8,121.66
海安 ,4.2,32.57,120.45
海安 ,4.2,32.57,120.45
通州,4.23,32.08,121.07
连云港 ,4.1,34.59,119.16
灌云 ,4.1,34.3,119.23
东海 ,4.1,34.54,118.75
赣榆 ,4.1,34.83,119.11
灌南 ,4.1,34.09,119.36
镇江 ,4.4,32.2,119.44
无锡 ,4.29,31.59,120.29
苏州 ,4.29,31.32,120.62
常州 ,4.29,31.79,119.95
昆山 ,4.29,31.39,120.952、CHN_adm/CHN_adm3
需要下载CHN_adm这个压缩文件，使用其画出地图，可以自行下载或者：
在这下载：https://download.csdn.net/download/mikasa3/10371748
二、导入模块包
可参考Windows下安装Python、matplotlib包 及相关
https://blog.csdn.net/mikasa3/article/details/78942650
1、numpy
2、pandas
3、matplotlib
4、Basemap
三、完整代码
如下：
# coding=utf-8
import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
def DrawPointMap(file_name):
fig = plt.figure()
ax1 = fig.add_axes([0.1,0.1,0.8,0.8])#[left,bottom,width,height]
map = Basemap(projection='mill',lat_0=36,lon_0=122,\
llcrnrlat=30.5 ,urcrnrlat=35.3,llcrnrlon=116.2,urcrnrlon=121.99,\
ax=ax1,rsphere=6371200.,resolution='h',area_thresh=1000000)
shp_info = map.readshapefile('CHN_adm/CHN_adm3','states',drawbounds=False)
for info, shp in zip(map.states_info, map.states):
proid = info['NAME_1']
if proid == 'Jiangsu':
poly = Polygon(shp,facecolor='w',edgecolor='k', lw=1.0, alpha=0.1)#注意设置透明度alpha，否则点会被地图覆盖
ax1.add_patch(poly)
parallels = np.arange(30.6,35.3,2)
map.drawparallels(parallels,labels=[1,0,0,0],fontsize=10) #parallels
meridians = np.arange(116.3,122,2)
map.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10) #meridians
posi = pd.read_csv(file_name)
lat = np.array(posi["lat"][0:48])#获取经纬度坐标，一共有48个数据
lon = np.array(posi["lon"][0:48])
val = np.array(posi["val"][0:48],dtype=float)#获取数值
size = (val-np.min(val)+0.05)*800#对点的数值作离散化，使得大小的显示明显
x,y = map(lon,lat)
map.scatter(x, y, s=size, color = 'r') #要标记的点的坐标、大小及颜色
for i in range(0,47):
plt.text(x[i]+5000,y[i]+5000,str(val[i]))
#plt.text(lat[i],lon[i],str(val[i]), family='serif', style='italic', ha='right', wrap=True)
#plt.annotate(s=3.33,xy=(x,y),xytext=None, xycoords='data',textcoords='offset points', arrowprops=None,fontsize=16)
map.drawmapboundary() #边界线
#map.fillcontinents()
map.drawstates()
#map.drawcoastlines() #海岸线
map.drawcountries()
map.drawcounties()
plt.title('Jiangsu in CHINA')#标题
plt.savefig('Jiangsu.png', dpi=100, bbox_inches='tight')#文件命名为Jiangsu.png存储
plt.show()
if __name__=='__main__':
DrawPointMap("Info.csv")
四、运行结果
如图：
