Python可视化-县市按经纬度坐标在地图标记数值

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  ,4.29,31.39,120.95

第一列是城市名称，第二列是数值，第三四列是城市对应的真实纬度和经度。

2、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")

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四、运行结果

如图：

版权声明：本文为CSDN博主「kewlgrl」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。

原文链接：https://blog.csdn.net/MIKASA3/article/details/80071177