如何使用 Stack Exchange API 高效获取问题正文内容

import requests

# 请替换为您的 Stack Exchange API 密钥
stack_exchange_api_key = 'your_stack_exchange_api_key'

# Stack Exchange API 端点
stack_exchange_endpoint = 'https://api.stackexchange.com/2.3/questions'

# 设置基本参数
stack_exchange_params = {
    'site': 'stackoverflow',
    'key': stack_exchange_api_key,
    'order': 'desc',
    'sort': 'creation',
    'tagged': 'python',
    'answers': 0,  # 过滤未回答的问题
}

# 发送 API 请求
stack_exchange_response = requests.get(stack_exchange_endpoint, params=stack_exchange_params)

if stack_exchange_response.status_code == 200:
    stack_exchange_data = stack_exchange_response.json()
    for question in stack_exchange_data.get('items', []):
        print(f"问题标题: {question.get('title')}")
        # 此时，question 字典中通常不包含 'body' 字段
else:
    print(f"请求失败: {stack_exchange_response.status_code} - {stack_exchange_response.text}")

import requests

# 请替换为您的 Stack Exchange API 密钥
stack_exchange_api_key = 'your_stack_exchange_api_key'

# Stack Exchange API 端点
stack_exchange_endpoint = 'https://api.stackexchange.com/2.3/questions'

# 设置参数，关键在于添加 'filter': 'withbody'
stack_exchange_params = {
    'site': 'stackoverflow',
    'key': stack_exchange_api_key,
    'filter': 'withbody',  # 添加此过滤器以获取问题正文
    'order': 'desc',
    'sort': 'creation',
    'tagged': 'python',
    'answers': 0,  # 过滤未回答的问题
}

# 发送 API 请求
stack_exchange_response = requests.get(stack_exchange_endpoint, params=stack_exchange_params)

# 检查请求是否成功
if stack_exchange_response.status_code == 200:
    # 解析响应 JSON
    stack_exchange_data = stack_exchange_response.json()

    # 遍历并打印问题标题和正文
    print("成功获取问题正文：")
    for i, question in enumerate(stack_exchange_data.get('items', [])):
        print(f"\n--- 问题 {i+1} ---")
        print(f"问题标题: {question.get('title', 'N/A')}")
        print(f"问题正文: {question.get('body', '正文内容不可用')}")
        # 为了演示，只打印前几个问题
        if i >= 2:
            break
else:
    print(f"请求失败: {stack_exchange_response.status_code} - {stack_exchange_response.text}")

<p>I created a neural network model that predicts certain properties from coordinates.</p>
<p>Using that model, I want to find the coordinates that minimize the properties in optuna's NSGA-II sampler.</p>
<code>import optuna
# ... (更多代码)
</code>