【行政区编码对应表及生态等级数据的制作】-python

使用python借助高德的api接口实现

获取高德的行政区数据-district_data.json

得到高德的api_key

（服务平台得是web服务）

使用request发送带参的get请求

# 定义高德地图行政区查询接口URL
url = "https://restapi.amap.com/v3/config/district"

# 请求参数
params = {
    'key': api_key,
    'keywords': '中国',
    'subdistrict': 3,  # 获取到区县级别
    'extensions': 'all'  # 获取详细信息
}

# 发送请求
response = requests.get(url, params=params)

完整代码：请求中国的整体数据，保存成json文件

# -*- coding: utf-8 -*-
# @Author  : longbhu
# @Time    : 2024/12/11 16:40
# @Function:
import requests
import json

# 高德地图API密钥
api_key = 'ac2c3314c22api_key082a9903'  # 替换为你的高德地图API密钥

# 定义高德地图行政区查询接口URL
url = "https://restapi.amap.com/v3/config/district"

# 请求参数
params = {
    'key': api_key,
    'keywords': '中国',
    'subdistrict': 3,  # 获取到区县级别
    'extensions': 'all'  # 获取详细信息
}

# 发送请求
response = requests.get(url, params=params)

# 检查响应状态码
if response.status_code == 200:
    data = response.json()
    if data['status'] == '1':
        # 获取行政区数据
        district_data = data['districts']

        # 保存到JSON文件
        output_file_path = 'district_data.json'
        with open(output_file_path, 'w', encoding='utf-8') as f:
            json.dump(district_data, f, ensure_ascii=False, indent=4)

        print(f"行政区数据已保存到 {output_file_path}")
    else:
        print(f"请求失败: {data['info']}")
else:
    print(f"请求失败: {response.status_code}")

输出为output_file_path = ‘district_data.json’

制作行政区名信息表-district_data.xlsx

根据上面的district_data.json得到district_data.xlsx（行政区名信息表）

包含如下字段adcode,province_name,city_name,district_name,full_name

完整代码

# -*- coding: utf-8 -*-
# @Author  : longbhu
# @Time    : 2024/12/11 17:12
# @Function: 输出district_data.xlsx 行政区名信息表 包含如下字段adcode,province_name,city_name,district_name,full_name
import json
import pandas as pd

def extract_district_info(data):
    extracted_data = []

    def recursive_extract(items, province_name=None, city_name=None, district_name=None):
        for item in items:
            adcode = item.get('adcode')
            name = item.get('name')
            level = item.get('level')

            if level == 'province':
                province_name = name
                city_name = None  # Reset city_name when entering a new province
                district_name = None  # Reset district_name when entering a new province
            elif level == 'city':
                city_name = name
                district_name = None  # Reset district_name when entering a new city
            elif level == 'district':
                district_name = name

            # 拼接省市县名称
            full_name = f"{province_name}{' ' + city_name if city_name else ''}{' ' + district_name if district_name else ''}"

            extracted_data.append({
                'adcode': adcode,
                'province_name': province_name,
                'city_name': city_name,
                'district_name': district_name if level == 'district' else None,
                'full_name': full_name  # 新增的 full_name 列
            })

            if 'districts' in item and isinstance(item['districts'], list):
                recursive_extract(item['districts'], province_name, city_name, district_name)

    recursive_extract(data)
    return extracted_data

# 读取 JSON 文件
with open('F:\\code\\test\\Gui\\calculator\\dat_to_excel\\calc_percent_excel\\行政区代码对应表\\district_data.json',
          'r', encoding='utf-8') as file:
    data = json.load(file)

# 提取 adcode 和 name 信息
extracted_data = extract_district_info(data)

# 创建 DataFrame
df = pd.DataFrame(extracted_data)

# 保存到 Excel 文件
output_file = 'F:\\code\\test\\Gui\\calculator\\dat_to_excel\\calc_percent_excel\\行政区代码对应表\\district_data.xlsx'
df.to_excel(output_file, index=False)

print(f"数据已成功保存到 {output_file}")

制作行政区生态等级数据-districtLevels.json

根据district_data.xlsx（行政区名信息表）和行政区生态区等级排名的excel文件（包含 code2014 和 rank 列的 Excel 文件）生成一个json文件（districtLevels.json）

districtLevels.json的格式

{		
    "provinces": [],
    "cities": [],
    "districts": [
        {
            "adcode": 410323,
            "name": "新安县",
            "value": 3,
            "eco_level": 3
        },
        ]
}

生态等级分级

按照0-100、101-500、501-1500及1500以下分为四个等级

eco_level_dict = {}
for index, row in eco_excel_data.iterrows():
    code2014 = number(row['CODE2014'])
    rank = row['Rank']
    if rank < 100:
        eco_level = 1
    elif 101 <= rank <= 500:
        eco_level = 2
    elif 501 <= rank <= 1500:
        eco_level = 3
    else:
        eco_level = 4
    eco_level_dict[code2014] = eco_level

（其他分级方法待开发···）

完整代码

县级_code_sort_3.xlsx

# -*- coding: utf-8 -*-
# @Author  : longbhu
# @Time    : 2024/12/12 11:47
# @Function: 根据district_data.xlsx（行政区名信息表）生成一个json文件， 形式如下：
#
# "districts": [
#         {
#             "adcode": 410323,
#             "name": "新安县",
#             "value": 3,
#             "eco_level": 3
#         },


import pandas as pd
import json  # 导入 json 模块

from PIL.ImImagePlugin import number

# 读取 Excel 文件
excel_path = 'F:\\code\\test\\Gui\\calculator\\dat_to_excel\\calc_percent_excel\\行政区代码对应表\\district_data.xlsx'
excel_data = pd.read_excel(excel_path)

# 读取包含 code2014 和 rank 列的 Excel 文件
eco_excel_path = './县级_code_sort_3.xlsx'
eco_excel_data = pd.read_excel(eco_excel_path)

# 将 code2014 和 rank 列的数据映射到一个字典中
eco_level_dict = {}
for index, row in eco_excel_data.iterrows():
    code2014 = number(row['CODE2014'])
    rank = row['Rank']
    if rank < 100:
        eco_level = 1
    elif 101 <= rank <= 500:
        eco_level = 2
    elif 501 <= rank <= 1500:
        eco_level = 3
    else:
        eco_level = 4
    eco_level_dict[code2014] = eco_level

# 初始化三个空列表来存储不同级别的 JSON 数据
provinces = []
cities = []
districts = []

# 遍历每一行数据
for index, row in excel_data.iterrows():
    adcode = number(row['adcode'])
    province_name = row['province_name']
    city_name = row['city_name']
    district_name = row['district_name']

    # 获取 eco_level，默认为 None
    # print(eco_level_dict[adcode])
    eco_level = eco_level_dict.get(adcode, 4)

    # 根据不同的级别设置 name 和 value
    if pd.notna(district_name) and district_name != "":
        districts.append({
            "adcode": adcode,
            "name": district_name,
            "value": 3 , # 区级行政区对应的 value 为 3
            "eco_level": eco_level
        })
    elif pd.notna(city_name) and city_name != "":
        cities.append({
            "adcode": adcode,
            "name": city_name,
            "value": 2  # 市级行政区对应的 value 为 2
        })
    elif pd.notna(province_name) and province_name != "":
        provinces.append({
            "adcode": adcode,
            "name": province_name,
            "value": 1  # 省级行政区对应的 value 为 1
        })

# 创建最终的 JSON 数据结构
json_data = {
    "provinces": provinces,
    "cities": cities,
    "districts": districts
}

# 保存 JSON 数据到文件
json_output_path = 'districtLevels.json'
with open(json_output_path, 'w', encoding='utf-8') as json_file:
    json.dump(json_data, json_file, ensure_ascii=False, indent=4)

print(f"JSON 数据已成功保存到 {json_output_path}")