-think step by step
-i will tip $200 if you do well
-i have no fingers

根据以下 CURL 请求和返回的JSON文本完成爬虫代码，要求：
1、把CURL改写成Python requests代码
2、正确解析返回内容，**注意**必须将所有有意义的互动数据全部保存
3、写出循环，请求所有数据，每次循环停留 3s（offset从0开始）
4、将得到的所有数据保存到一个 CSV 文件中
你不需要解释代码，请一次性返回**所有代码**，你不需要运行代码，我会把代码保存到本地运行

（粘贴上面复制的 CURL 和响应内容）

import requests
import csv
import time

# 初始化变量
base_url = 'https://www.zhihu.com/api/v4/creators/creations/v2/all'
params = {
    'start': 0,
    'end': 0,
    'limit': 10,  # 你可以调整这个参数以获取更多或更少的记录
    'offset': 0,
    'need_co_creation': 1,
    'sort_type': 'created'
}
headers = {
    'authority': 'www.zhihu.com',
    'accept': '*/*',
    'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
    'referer': 'https://www.zhihu.com/creator/manage/creation/answer',
    'sec-ch-ua': '"Microsoft Edge";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
    'sec-ch-ua-mobile': '?0',
    'sec-ch-ua-platform': '"macOS"',
    'sec-fetch-dest': 'empty',
    'sec-fetch-mode': 'cors',
    'sec-fetch-site': 'same-origin',
    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0',
    'x-requested-with': 'fetch',
}
data_to_save = []

# 循环请求直到获取700条记录
while True:
    response = requests.get(base_url, headers=headers, params=params)
    data = response.json()
    
    # 追加数据
    for item in data['data']:
        answer = {}
        answer_data = item['data']
        answer['type'] = item['type']
        answer['id'] = answer_data['id']
        answer['created_time'] = answer_data['created_time']
        answer['updated_time'] = answer_data['updated_time']
        interaction_data = item['reaction']
        if answer['type'] == 'pin':
            answer['title'] = ''
        else:
            answer['title'] = answer_data['title']
        # 合并数据
        merged_data = {**answer, **interaction_data}
        data_to_save.append(merged_data)

    if data['paging']['is_end']:
        break

    # 获取下一页的URL并更新params
    next_url = data['paging']['next']
    next_offset = next_url.split('offset=')[1].split('&')[0]
    params['offset'] = int(next_offset)
# 保存数据到CSV文件
csv_columns = [
    'id', 'type', 'title', 'created_time', 'updated_time', 'comment_count', 'reviewing_comment_count', 'vote_up_count',
    'read_count', 'like_count', 'collect_count', 'play_count', 'repin_count',
    'reaction_count', 'view_count', 'answer_count', 'follow_count', "new_answer_count",
]

with open('zhihu_data_full.csv', 'w', newline='', encoding='utf-8') as file:
    writer = csv.DictWriter(file, fieldnames=csv_columns)
    writer.writeheader()

    # 写入数据
    for item in data_to_save:
        writer.writerow(item)

print("完整数据已保存为CSV文件")

-think step by step
-i will tip $200 if you do well
-i have no fingers
请扮演资深互联网企业活动运营策划，创建一份「知乎2023年终总结报告」模板，用于制作活动页面并分享到社交平台，这份报告应模仿Spotify、网易云音乐、QQ音乐等年终总结的风格。内容面向用户，语气亲切、活泼，富有创意。
知乎的创作分为回答、问题、想法、文章、视频五类，互动数据主要是点赞、喜欢、评论。
报告应该尽可能符合知乎的特点，并利用到创作数据和互动数据，适当地进行汇总分析，提供有趣的数据结果。

这是知乎用户「段小草」的创作数据，请分析数据并给出一份年终报告，注意：
1、要分类汇总回答、文章、想法、提问、视频的创作和互动数据；
2、你需要筛选出其中的2023年数据，并适当利用月份、时刻等给出分析结果；
3、不需要分析文本语义；
4、请返回中文。
{
  "comment_count": 5,   #评论数
  "reviewing_comment_count": null,  #无
  "vote_up_count": null,  #回答点赞
  "read_count": 2681,  #阅读数
  "like_count": null,  #回答喜欢
  "collect_count": null,  #收藏
  "play_count": null,  #视频播放量
  "repin_count": 0,   #想法转发
  "reaction_count": 14,  # 想法的点赞
  "view_count": 2681,   #浏览
  "answer_count": null,  #问题回答数
  "follow_count": null   #问题关注数
}

# Categorizing and summarizing the data for different types of content
# Types of content: answer, article, pin (idea), question, video

# Initializing dictionaries to store summary data
summary = {
    'answer': {'count': 0, 'comments': 0, 'likes': 0, 'reads': 0, 'votes': 0},
    'article': {'count': 0, 'comments': 0, 'likes': 0, 'reads': 0},
    'pin': {'count': 0, 'comments': 0, 'reactions': 0, 'repins': 0, 'views': 0},
    'question': {'count': 0, 'comments': 0, 'answers': 0, 'follows': 0},
    'video': {'count': 0, 'comments': 0, 'likes': 0, 'plays': 0, 'reads': 0, 'views': 0}
}

# Grouping and summarizing the data
for index, row in data_2023.iterrows():
    content_type = row['type']
    
    if content_type == 'answer':
        summary['answer']['count'] += 1
        summary['answer']['comments'] += row['comment_count']
        summary['answer']['likes'] += row['like_count'] if pd.notna(row['like_count']) else 0
        summary['answer']['reads'] += row['read_count'] if pd.notna(row['read_count']) else 0
        summary['answer']['votes'] += row['vote_up_count'] if pd.notna(row['vote_up_count']) else 0

    elif content_type == 'article':
        summary['article']['count'] += 1
        summary['article']['comments'] += row['comment_count']
        summary['article']['likes'] += row['like_count'] if pd.notna(row['like_count']) else 0
        summary['article']['reads'] += row['read_count'] if pd.notna(row['read_count']) else 0

    elif content_type == 'pin':
        summary['pin']['count'] += 1
        summary['pin']['comments'] += row['comment_count']
        summary['pin']['reactions'] += row['reaction_count'] if pd.notna(row['reaction_count']) else 0
        summary['pin']['repins'] += row['repin_count'] if pd.notna(row['repin_count']) else 0
        summary['pin']['views'] += row['view_count'] if pd.notna(row['view_count']) else 0

    elif content_type == 'question':
        summary['question']['count'] += 1
        summary['question']['comments'] += row['comment_count']
        summary['question']['answers'] += row['answer_count'] if pd.notna(row['answer_count']) else 0
        summary['question']['follows'] += row['follow_count'] if pd.notna(row['follow_count']) else 0

    elif content_type == 'video':
        summary['video']['count'] += 1
        summary['video']['comments'] += row['comment_count']
        summary['video']['likes'] += row['like_count'] if pd.notna(row['like_count']) else 0
        summary['video']['plays'] += row['play_count'] if pd.notna(row['play_count']) else 0
        summary['video']['reads'] += row['read_count'] if pd.notna(row['read_count']) else 0
        summary['video']['views'] += row['view_count'] if pd.notna(row['view_count']) else 0

# Analyzing active months and time of day
data_2023['month'] = data_2023['created_time'].dt.month
data_2023['hour'] = data_2023['created_time'].dt.hour

active_months = data_2023['month'].value_counts().idxmax()
active_hours = data_2023['hour'].value_counts().idxmax()

# Adding active month and hour to summary
summary['most_active_month'] = active_months
summary['most_active_hour'] = active_hours

summary

import requests
from bs4 import BeautifulSoup

url = "https://zhuanlan.zhihu.com/p/595050104"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

title = soup.find("h1", class_="Post-Title").text.strip()
body = soup.find("div", class_="Post-RichText").text.strip()

print("Title:", title)
print("Body:", body)