如何用Python从复杂字符串中提取数值范围与费用值？

问题分析 #

你手里的字符串本质是标准JSON格式，没必要用复杂的字符串替换和正则解析，直接用Python的json模块就能轻松拿到结构化数据，后续导出Excel、做用户分类都会更便捷。

优化后的代码 #

import json
import pandas as pd

# 补全原字符串末尾缺失的闭合"}"，否则JSON解析会报错
fee_str = '{"row1":{"from":"0","to":"500","fee":"23100    "},"row2":{"from":"500","to":"1000","fee":"24100    "},"row3":{"from":"1000","to":"1500","fee":"25200    "},"row4":{"from":"1500","to":"2000","fee":"26200    "},"row5":{"from":"2000","to":"2500","fee":"27200    "},"row6":{"from":"2500","to":"3000","fee":"28300    "},"row7":{"from":"3000","to":"3500","fee":"29300    "},"row8":{"from":"3500","to":"4000","fee":"30400    "},"row9":{"from":"4000","to":"4500","fee":"31400    "},"row10":{"from":"4500","to":"5000","fee":"32400    "},"row11":{"from":"5000","to":"5500","fee":"33500    "},"row12":{"from":"5500","to":"6000","fee":"34600    "},"row13":{"from":"6000","to":"6500","fee":"35500  "},"row14":{"from":"6500","to":"7000","fee":"36600    "},"row15":{"from":"7000","to":"7500","fee":"37700    "},"row16":{"from":"7500","to":"8000","fee":"38600    "},"row17":{"from":"8000","to":"8500","fee":"39700    "},"row18":{"from":"8500","to":"9000","fee":"40300    "},"row19":{"from":"9000","to":"9500","fee":"41400    "},"row20":{"from":"9500","to":"10000","fee":"42700    "},"row21":{"from":"10000","to":"10500","fee":"43500    "},"row22":{"from":"10500","to":"11000","fee":"44500    "},"row23":{"from":"11000","to":"11500","fee":"45600    "},"row24":{"from":"11500","to":"12000","fee":"46600    "},"row25":{"from":"12000","to":"12500","fee":"47700    "},"row26":{"from":"12500","to":"13000","fee":"48700    "},"row27":{"from":"13000","to":"13500","fee":"49700    "},"row28":{"from":"13500","to":"14000","fee":"50800    "},"row29":{"from":"14000","to":"14500","fee":"51900    "},"row30":{"from":"14500","to":"15000","fee":"52800    "},"row31":{"from":"15000","to":"15500","fee":"52800    "},"row32":{"from":"15500","to":"16000","fee":"52800    "},"row33":{"from":"16000","to":"70000","fee":"52800    "}}'

# 解析JSON字符串为字典
data = json.loads(fee_str)

# 整理成结构化列表，方便后续处理
fee_list = []
for row_info in data.values():
    # 清理费用字段中的特殊空格和空白字符，转换为整数
    clean_fee = int(row_info['fee'].replace(' ', '').strip())
    fee_list.append({
        '起始公里': int(row_info['from']),
        '结束公里': int(row_info['to']),
        '费用': clean_fee
    })

# 直接导出到Excel文件，无需手动处理格式
df = pd.DataFrame(fee_list)
df.to_excel('公里费用对照表.xlsx', index=False)

# 用于判断用户公里数对应费用的函数
def get_user_fee(user_km):
    for item in fee_list:
        # 采用左闭右开逻辑，符合常规区间划分规则
        if item['起始公里'] <= user_km < item['结束公里']:
            return item['费用']
    # 超出最大范围时返回最高档费用
    return fee_list[-1]['费用']

# 测试分类函数
print(get_user_fee(300))   # 输出：23100
print(get_user_fee(16000)) # 输出：52800

为什么不用原正则方法？ #

• 原字符串是标准JSON，用json模块解析更稳定，不会因为字符串格式微小变化（比如空格、特殊字符）导致匹配失败
• 结构化后的列表可直接通过pandas导出Excel，无需手动拼接格式
• 分类函数逻辑清晰，维护和修改成本更低

关键处理点 #

• 补全原字符串末尾缺失的}，否则JSON解析会抛出语法错误
• 清理fee字段中的 和多余空格，确保转换为整数类型时不报错
• 分类函数采用左闭右开的判断逻辑，符合实际场景中区间划分的常规规则

内容的提问来源于stack exchange，提问作者Feiznia