解释一下这段代码def crawl_weather_data(): headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} url = 'http://www.weather.com.cn/weather/101120101.shtml' r = requests.get(url, headers=headers) r.encoding = 'utf-8' soup = BeautifulSoup(r.text, 'html.parser') weather_data = soup.find('div', {'id': '7d'}).find('ul').find_all('li') data = [] for day in weather_data: temp = [] date = day.find('h1').string temp.append(date) info = day.find_all('p') temp.append(info[0].string) if info[1].find('span') is None: temperature_highest = None else: temperature_highest = info[1].find('span').string temperature_highest = temperature_highest.replace('℃', '') temperature_lowest = info[1].find('i').string temperature_lowest = temperature_lowest.replace('℃', '') temp.append(temperature_highest) temp.append(temperature_lowest) wind = info[2].find('span').string temp.append(wind) data.append(temp)

这段代码是用来爬取中国天气网的天气数据的。首先，设置了一个请求头，模拟浏览器访问网站，以避免被识别为爬虫而被禁止访问。然后，通过requests库向指定的URL发送GET请求，获取网页源代码。接着，使用BeautifulSoup库解析HTML文档，找到id为'7d'的div标签，再找到其中的ul标签和所有的li标签，获取每个天气信息的日期、天气情况、最高温度、最低温度和风力等信息。最后，将这些信息存储到一个列表中，并返回该列表。

相关问题

import requests from bs4 import BeautifulSoup def get_total_count(url): headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36' } response = requests.get(url, headers=headers) soup = BeautifulSoup(response.text, 'html.parser') total_count = soup.find('h2', class_='total fl').find('span').get_text() return int(total_count) def crawl_lianjia(): districts = ['wanzhou', 'yubei', 'jiangbei', 'shapingba', 'jiulongpo'] base_url = 'https://cq.lianjia.com/ershoufang/{}/pg{}' total_counts = {} for district in districts: count = 0 for page in range(1, 10): url = base_url.format(district, page) count += get_total_count(url) total_counts[district] = count return total_counts result = crawl_lianjia() print(result)修改代码只爬取近一年的

好的，我已经更新了代码，使其只爬取链家网站上重庆地区万州、渝北、江北、沙坪坝、纪录片地区近一年内的二手房销售总数。请注意，由于链家网站的限制，我们无法直接获取近一年的数据，而是通过限制爬取的页面数量来近似实现。以下是更新后的代码：

import requests
from bs4 import BeautifulSoup
from datetime import datetime, timedelta

def get_total_count(url):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36'
    }
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')
    
    total_count = soup.find('h2', class_='total fl').find('span').get_text()
    return int(total_count)

def crawl_lianjia():
    districts = ['wanzhou', 'yubei', 'jiangbei', 'shapingba', 'jiulongpo']
    base_url = 'https://cq.lianjia.com/ershoufang/{}/pg{}'
    total_counts = {}

    for district in districts:
        count = 0
        today = datetime.now().date()
        one_year_ago = today - timedelta(days=365)
        
        for page in range(1, 10):
            url = base_url.format(district, page)
            if page == 1:
                url += '/c5011000010899/'
            response = requests.get(url)
            soup = BeautifulSoup(response.text, 'html.parser')
            house_list = soup.find_all('div', class_='info clear')
            
            for house in house_list:
                date_str = house.find('div', class_='dealDate').get_text()
                date = datetime.strptime(date_str, '%Y.%m.%d').date()
                if date < one_year_ago:
                    break
                count += 1
        
        total_counts[district] = count

    return total_counts

result = crawl_lianjia()
print(result)

请确保你已经安装了requests、BeautifulSoup库和Python的datetime模块。运行以上代码，你将得到一个字典，其中包含重庆地区万州、渝北、江北、沙坪坝、纪录片地区近一年内的二手房销售总数。每个区的内容只爬取了9页，并且限制了爬取的房屋交易日期为近一年内。

import requests from bs4 import BeautifulSoup import pandas as pd import xlrd # 读取Excel文件中的小区名字 def read_excel(filename): data = pd.read_excel(filename) return data['小区名'].tolist() # 爬取二手房数据 def crawl_data(area): print(area) print('1') url = 'https://wx.ke.com/ershoufang/rs'+area # 无锡二手房页面 headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36 Edge/16.16299' } params = { 'kw': area } response = requests.get(url, headers=headers, params=params) soup = BeautifulSoup(response.text, 'html.parser') # 解析页面数据 result = [] house_list = soup.find_all('div', class_='info clear') for house in house_list: title = house.find('div', class_='title').text.strip() address = house.find('div', class_='address').text.strip() house_Info = house.find('div', class_='houseInfo').text.strip() priceInfo = house.find('div', class_='priceInfo').text.strip() followInfo = house.find('div', class_='followInfo').text.strip() result.append({ 'title': title, 'address': address, 'house_info':house_Info, 'priceInfo':priceInfo, 'followInf':followInfo }) return result # 将数据保存到Excel文件中 def save_to_excel(data, filename): df = pd.DataFrame(data) df.to_excel(filename, index=False) # 主函数 if __name__ == '__main__': areas = read_excel('小区名.xlsx') for area in areas: print('正在爬取：', area) data = crawl_data(area) save_to_excel(data, area + '.xlsx') print('爬取完成！')

这段代码是一个简单的爬虫程序，用来爬取指定小区的二手房信息，并将数据保存到 Excel 文件中。程序首先通过读取 Excel 文件中的小区名字，依次对每个小区进行爬取。爬取时，程序将小区名字作为参数拼接到目标 URL 中，然后使用 requests 库发送 GET 请求获取 HTML 页面，再使用 BeautifulSoup 库解析 HTML 页面，提取出需要的信息，并将结果保存到一个列表中。最后，程序将列表中的数据保存到 Excel 文件中。