1.熟悉、梳理、总结项目研发实战中的Python开发日常使用中的问题、知识点等，如获取省市等边界区域经纬度进行可视化，从而辅助判断、决策。
2.欢迎点赞、关注、批评、指正，互三走起来，小手动起来！
3.欢迎点赞、关注、批评、指正，互三走起来，小手动起来！

• 如获取省市等特定区域经纬度进行可视化，从而辅助判断、决策
• 如获取省市等特定区域经纬度进行可视化，从而辅助判断、决策
• 如获取省市等特定区域经纬度进行可视化，从而辅助判断、决策

1.省市边界经纬度json获取并解析

• 经纬度点位初步压缩

  import re
  import os
  import sys
  import json
  import nltk
  import time
  import pickle
  import random
  import base64
  import datetime
  import requests
  import openpyxl
  import readline
  import itertools
  import numpy as np
  import pandas as pd
  from PIL import Image
  from tqdm import tqdm, trange
  from bs4 import BeautifulSoup
  import matplotlib.pyplot as plt
  from collections import Counter
  from pypinyin import lazy_pinyin, Style
  from joblib import Parallel, delayed
  from sklearn.linear_model import LinearRegression
  import warnings
  warnings.filterwarnings('ignore')
  
  pd.set_option('display.width', 500)
  pd.set_option('display.max_rows', 200)
  pd.set_option('display.max_columns', 200)
  pd.set_option('display.max_colwidth', 1000)
  
  # step 1： 浙江省边界数据
  zjbj_url = "https://up.caup.net/guihuayun/json/330000.json"
  zjbj_datas = requests.get( zjbj_url )
  zjbj_datas_json = json.loads( zjbj_datas.content )
  zjbj_datas_json_list = zjbj_datas_json['geometry']['coordinates'][-1]
  
  zjbj_points = zjbj_datas_json_list[0]
  zjbj_merged_points = merge_points( zjbj_points, threshold=5000 )
  print( len(zjbj_merged_points), len( zjbj_points ) )
  
  # step 2： 杭州市边界数据
  hzbj_url = "https://up.caup.net/guihuayun/json/330100.json"
  datas = requests.get( hzbj_url )
  datas_json = json.loads( datas.content )
  datas_json_list = datas_json['geometry']['coordinates'][0]
  
  points = datas_json_list
  merged_points = merge_points(points, threshold=4000)
  print(len(merged_points), len(points))
  

2.读取特定区域经纬度点位execl解析并自动生成html文件

• import folium
  import pandas as pd
  
  _titles = 'http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=7&x={x}&y={y}&z={z}'
  _gd_tiles='http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=7&x={x}&y={y}&z={z}'
  san_map=folium.Map(location=[30.245853, 120.209947], zoom_start=14, tiles= _gd_tiles, attr='default')
  
  data=pd.read_excel(r'.\gsddw_youli.xlsx')
  data2 = data[['youli_dz', 'youli_jd', 'youli_wd']]
  
  data2.columns = ['qymc','lng','lat']
  data2 = data2[(data2.lng.notna()) & (data2.lng>118) & (data2.lng<121)]
  # data=pd.read_csv('./stlz.csv',encoding='gbk')
  data2.head(3)
  
  for ii in data2.iterrows():
      qymc, lon, lat = ii[1][0], ii[1][1], ii[1][2]
      folium.Marker([lat,lon], popup=folium.Popup(qymc, max_width=100), tooltip=qymc, icon=folium.Icon(icon='cloud', color='green')).add_to(san_map)
      folium.Circle([lat,lon], 500, color='yellow', fill_color='yellow', fillOpacity=0.3).add_to(san_map)
      
  for ii in merged_points:
      lon, lat = ii[0], ii[1]
      folium.Marker([lat, lon], popup=folium.Popup(qymc, max_width=100), tooltip=qymc, icon=folium.Icon(color='red', icon='info-sign')).add_to(san_map)
      folium.Circle([lat, lon], 500, color='red', fill_color='red', fillOpacity=0.3).add_to(san_map)
      
  for ii in zjbj_merged_points:
      lon, lat = ii[0], ii[1]
      folium.Marker([lat, lon], popup=folium.Popup(qymc, max_width=100), tooltip=qymc, icon=folium.Icon(color='blue', icon='info-sign')).add_to(san_map)
      folium.Circle([lat, lon], 500, color='blue', fill_color='blue', fillOpacity=0.3).add_to(san_map)
      
  san_map.save('sy_youli_vis.html')
  
  html_lis_vis = open( r'./sy_youli_vis.html', encoding='utf8').readlines()
  html_str_vis = ''.join(html_lis_vis)
  html_str_vis2 = html_str_vis.replace( 'https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js','./leaflet.js' ) \
                               .replace( 'https://code.jquery.com/jquery-3.7.1.min.js','./jquery-3.7.1.min.js' )
  #                              .replace( 'https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js','./bootstrap.bundle.min.js' ) \
  #                              .replace( 'https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js','./leaflet.awesome-markers.js' ) \
  #                              .replace( 'https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css','./leaflet.css' ) \
  #                              .replace( 'https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css','./bootstrap.min.css' ) \
  #                              .replace( 'https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css','./all.min.css' ) \
  #                              .replace( 'https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css','./leaflet.awesome-markers.css' ) \
  #                              .replace( 'https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css','./bootstrap.min.css' )
  
  html_str_vis2_html = r"sy_youli_vis_new.html" 
  f = open( html_str_vis2_html,'w', encoding='utf8')
  
  f.write( html_str_vis2 ) 
  f.close()
  

3.可视效果