本文目录导读：
我来详细介绍Python中解析接口返回数据的常见方法和案例：
基础JSON解析
import requests
import json
# 发送请求
response = requests.get('https://api.example.com/users')
# 解析JSON数据
if response.status_code == 200:
# 方法1：直接使用.json()
data = response.json()
# 方法2：使用json模块
# data = json.loads(response.text)
print(f"数据类型: {type(data)}")
print(f"数据内容: {data}")
结构化数据解析案例
案例1：用户信息API
def parse_user_api():
"""解析用户信息API"""
response = requests.get('https://api.example.com/user/123')
if response.status_code == 200:
user_data = response.json()
# 安全地获取嵌套数据
user_info = {
'id': user_data.get('id'),
'name': user_data.get('profile', {}).get('name', 'Unknown'),
'email': user_data.get('contact', {}).get('email'),
'age': user_data.get('age', 0),
'address': user_data.get('address', {}).get('city', 'N/A')
}
return user_info
return None
# 使用示例
user = parse_user_api()
if user:
print(f"用户: {user['name']}, 邮箱: {user['email']}")
案例2：列表数据解析
def parse_users_list():
"""解析用户列表API"""
response = requests.get('https://api.example.com/users')
if response.status_code == 200:
data = response.json()
# 假设返回格式：{"users": [...], "total": 100}
users = data.get('users', [])
total = data.get('total', 0)
# 解析每个用户
parsed_users = []
for user in users:
parsed_user = {
'id': user.get('id'),
'name': user.get('name'),
'status': user.get('status', 'active'),
'created_at': user.get('created_at')
}
parsed_users.append(parsed_user)
return {
'total': total,
'users': parsed_users,
'count': len(parsed_users)
}
return None
复杂嵌套JSON解析
def parse_complex_api():
"""解析复杂嵌套JSON"""
response = requests.get('https://api.example.com/orders')
if response.status_code == 200:
data = response.json()
# 复杂嵌套结构
try:
orders = data['data']['orders']
for order in orders:
# 安全获取嵌套属性
order_id = order.get('id')
items = order.get('items', [])
# 解析商品
products = []
for item in items:
product = {
'sku': item.get('product', {}).get('sku'),
'name': item.get('product', {}).get('name'),
'price': item.get('price', 0),
'quantity': item.get('quantity', 1)
}
products.append(product)
# 解析收货地址
shipping = order.get('shipping', {})
address = {
'street': shipping.get('address', {}).get('street'),
'city': shipping.get('address', {}).get('city'),
'zip': shipping.get('address', {}).get('zipcode')
}
print(f"订单 {order_id}: {len(products)} 件商品")
except KeyError as e:
print(f"数据结构错误: {e}")
return None
return data
return None
错误处理和验证
import requests
from typing import Optional, Dict, Any
class APIResponseParser:
"""API响应解析器"""
def __init__(self, response: requests.Response):
self.response = response
self.raw_data = None
def parse(self) -> Optional[Dict[str, Any]]:
"""解析响应数据"""
try:
# 检查状态码
if self.response.status_code != 200:
raise ValueError(f"API返回错误状态码: {self.response.status_code}")
# 解析JSON
self.raw_data = self.response.json()
# 验证必要字段
if not self._validate_data():
raise ValueError("响应数据格式不正确")
return self.raw_data
except json.JSONDecodeError as e:
print(f"JSON解析错误: {e}")
return None
except ValueError as e:
print(f"数据验证错误: {e}")
return None
except Exception as e:
print(f"未知错误: {e}")
return None
def _validate_data(self) -> bool:
"""验证数据结构"""
if not self.raw_data:
return False
# 检查必要的顶级字段
required_fields = ['status', 'data']
for field in required_fields:
if field not in self.raw_data:
return False
return True
def extract_value(self, path: str, default=None):
"""通过点号路径提取值"""
keys = path.split('.')
value = self.raw_data
try:
for key in keys:
value = value[key]
return value
except (KeyError, TypeError):
return default
# 使用示例
response = requests.get('https://api.example.com/data')
parser = APIResponseParser(response)
parsed_data = parser.parse()
if parsed_data:
# 提取嵌套值
user_name = parser.extract_value('data.user.name', 'Unknown')
print(f"用户名: {user_name}")
分页数据处理
def process_paginated_api():
"""处理分页API"""
base_url = 'https://api.example.com/users'
page = 1
all_users = []
while True:
# 发送请求
response = requests.get(f"{base_url}?page={page}")
if response.status_code != 200:
break
data = response.json()
# 解析当前页数据
users = data.get('data', [])
all_users.extend(users)
# 检查是否有更多页
pagination = data.get('pagination', {})
total_pages = pagination.get('total_pages', 1)
if page >= total_pages:
break
page += 1
return all_users
使用数据类解析
from dataclasses import dataclass
from typing import List, Optional
import requests
@dataclass
class User:
id: int
name: str
email: str
age: Optional[int] = None
@classmethod
def from_dict(cls, data: dict):
return cls(
id=data.get('id'),
name=data.get('name'),
email=data.get('email'),
age=data.get('age')
)
def parse_with_dataclass():
"""使用数据类解析API响应"""
response = requests.get('https://api.example.com/users')
if response.status_code == 200:
users_data = response.json().get('users', [])
# 转换为User对象列表
users = [User.from_dict(user) for user in users_data]
for user in users:
print(f"用户: {user.name}, 邮箱: {user.email}")
return users
return None
- 始终使用
.get()方法：避免KeyError - 进行错误处理：检查状态码和JSON解析
- 验证数据结构：确保关键字段存在
- 使用类型提示：提高代码可读性
- 提取为函数/类：复用解析逻辑
这些方法可以帮助你安全、高效地解析各种API接口返回的数据。
