爬取博客园新闻版块里的新闻

爬取数据
数据展示
制作词云
彩蛋

爬取数据

import requests
from bs4 import BeautifulSoup
from multiprocessing import Pool
import sqlite3
import time
from tqdm import tqdm
import collections

your_cookie = "your_cookie"

headers = {
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
    'Accept-Encoding': 'gzip, deflate, br',
    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
    'Cache-Control': 'max-age=0',
    # 'Referer': 'https://news.cnblogs.com/n/104394/',
    'Sec-Ch-Ua': '"Microsoft Edge";v="117", "Not;A=Brand";v="8", "Chromium";v="117"',
    'Sec-Ch-Ua-Mobile': '?0',
    'Sec-Ch-Ua-Platform': '"Windows"',
    'Sec-Fetch-Dest': 'document',
    'Sec-Fetch-Mode': 'navigate',
    'Sec-Fetch-Site': 'same-origin',
    'Sec-Fetch-User': '?1',
    'Upgrade-Insecure-Requests': '1',
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36 Edg/117.0.2045.31',
    'Cookie': your_cookie
}


def get_news_info(news_id):
    url = f"https://news.cnblogs.com/n/{news_id}/"
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')

    news_title_div = soup.find('div', {'id': 'news_title'})
    news_info_div = soup.find('div', {'id': 'news_info'})

    if news_title_div and news_title_div.a:
        title = news_title_div.a.text
    else:
        title = 'Not Found'

    if news_info_div:
        # print(f"{str(news_info_div)=}")
        time_span = news_info_div.find('span', {'class': 'time'})
        view_span = news_info_div.find('span', {'class': 'view', 'id': 'News_TotalView'})

        if time_span:
            time_text = time_span.text.strip()
            # \.split(' ')[1]
        else:
            time_text = 'Not Found'

        if view_span:
            view_text = view_span.text
        else:
            view_text = 'Not Found'
    else:
        time_text = 'Not Found'
        view_text = 'Not Found'

    news_body = 'Not Found'
    news_body_div = soup.find('div', {'id': 'news_body'})
    if news_body_div:
        news_body = str(news_body_div)
    else:
        news_body = 'Not Found'

    return {
        'news_id': news_id,
        'title': title,
        'time': time_text,
        'views': view_text,
        'news_body': news_body,
        'url': url,
    }


def save_to_db(news_info, filename):
    conn = sqlite3.connect(f'news_{filename}.db')
    c = conn.cursor()
    c.execute('''CREATE TABLE IF NOT EXISTS news
                 (news_id INT PRIMARY KEY, title TEXT, time TEXT, views TEXT, news_body TEXT, url TEXT)''')

    c.execute('''INSERT INTO news VALUES (?,?,?,?,?,?)''',
              (news_info['news_id'], news_info['title'], news_info['time'], news_info['views'], news_info['news_body'],
               news_info['url']))
    conn.commit()
    conn.close()


# 定义一个 namedtuple 来存储你的列名和数据
News = collections.namedtuple('News', ['news_id', 'title', 'time', 'views', 'news_body', 'url'])


# 数据落盘，数据保存到sqlite数据库
def save_to_db_using_namedtuple(news_info, filename):
    news = News(**news_info)

    conn = sqlite3.connect(f'news_{filename}.db')
    c = conn.cursor()

    c.execute(f'''
        CREATE TABLE IF NOT EXISTS news
        ({', '.join(f'{field} TEXT' for field in News._fields)})
    ''')

    c.execute(f'''
        INSERT INTO news ({', '.join(News._fields)})
        VALUES ({', '.join('?' for _ in News._fields)})
    ''', news)

    conn.commit()
    conn.close()


if __name__ == '__main__':
    with Pool(20) as p:  # Number of parallel processes
        new_id_start = 753006 - 2000
        new_id_end = 753006
        news_ids = list(range(new_id_start, new_id_end))
        # new_id_start=41104
        # new_id_end=0
        # news_ids = list(range(new_id_start,new_id_end,-1))
        start = time.time()
        for news_info in tqdm(p.imap_unordered(get_news_info, news_ids), total=len(news_ids)):
            save_to_db_using_namedtuple(news_info=news_info, filename=f"{new_id_start}~{new_id_end}")
        end = time.time()
        print(f'Time taken: {end - start} seconds')

## https://zzk.cnblogs.com/s?Keywords=facebook&datetimerange=Customer&from=2010-10-01&to=2010-11-01  也可以用博客园的找找看

数据展示

后面可以考虑：洗数据，数据挖掘之类的。

制作词云

小教训，可以直接传文本进去generate_from_text。传频次表的话，频次表里不要出现换行符'\n'

import sqlite3
import pandas as pd
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from html.parser import HTMLParser

import jieba

jieba.setLogLevel(jieba.logging.INFO)

# 连接到SQLite数据库
conn = sqlite3.connect('news_79573~0.db')

# 从news表中读取数据
df = pd.read_sql_query("SELECT * from news", conn)

# 关闭数据库连接
conn.close()


# 创建一个HTML解析器来清洗news_body字段中的HTML标签
class MyHTMLParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.text = []

    def handle_data(self, data):
        self.text.append(data)

    def get_text(self):
        return ''.join(self.text)


# 清洗news_body字段中的HTML标签
def clean_html(html):
    parser = MyHTMLParser()
    parser.feed(html)
    return parser.get_text()


df['news_body'] = df['news_body'].apply(clean_html)

# 将time字段转换为日期类型，并提取出年月信息
df['time'] = pd.to_datetime(df['time'], format="发布于 %Y-%m-%d %H:%M", errors='coerce')
df['year_month'] = df['time'].dt.to_period('M')

# 将title和news_body字段合并
df['text'] = df['title'] + ' ' + df['news_body']

# 对每个月的数据进行分词和词云生成
grouped = df.groupby('year_month')
for name, group in grouped:
    text = ' '.join(group['text'].tolist())

    wordcloud = WordCloud(font_path='msyh.ttc').generate_from_text(text)
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis("off")
    plt.title(name)
    plt.show()