利用 Python 进行数据分析实验（五）_python数据分析实验

一、实验目的

使用Python解决问题

二、实验要求

自主编写并运行代码，按照模板要求撰写实验报告

三、实验步骤

1 爬取并下载当当网某一本书的网页内容，并保存为html格式
2 在豆瓣网上爬取某本书的前50条短评内容并计算评分的平均值(自学正则表达式)
3 从https://cs.lianjia.com/上爬取长沙某小区的二手房信息（以名都花园为例），并将其保存到EXCEL文件当中

四、实验结果

T1

"""
爬取并下载当当网某一本书的网页内容，并保存为html格式
"""
import os
from urllib import request

header = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.221 Safari/537.36 SE 2.X MetaSr 1.0',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'}

url = 'http://product.dangdang.com/24029955.html'

req = request.Request(url, headers=header)

html = str(request.urlopen(req).read)

is_exist = os.path.exists('DangDang.html')

if not is_exist:
    with open('DangDang.html', 'w+') as f:
        f.write(html)

else:
    print('File already exsist')
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T2

"""
在豆瓣网上爬取某本书的前50条短评内容并计算评分的平均值(自学正则表达式)
"""
import re
from urllib import request

from bs4 import BeautifulSoup

comments = []
list = []


def get_commment(comment):
    count = 0
    for i in comment:
        count = count + 1
        # print(count, i.string) # 也可以使用正则
        comments.append(i.string)


def get_score(score):
    pattern = re.compile('<span class="user-stars allstar(.*?) rating"')
    res = re.findall(pattern, str(score))
    for irr in res:
        list.append(float(irr))


header = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.221 Safari/537.36 SE 2.X MetaSr 1.0',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'}

p = 0

for i in range(0, 3):
    url = f'https://book.douban.com/subject/26912767/comments/?start={i * 20}&limit={(i + 1) * 20}&status=P&sort=new_score'
    req = request.Request(url, headers=header)
    html = request.urlopen(req).read()
    soup = BeautifulSoup(html, 'html.parser')

    # get_commment(html.find_all("span", class_="short"))
    get_score(soup)
    get_commment(soup.find_all("span", class_="short"))

for j in range(0, 50):
    print(comments[j])

sum = 0.0
for j in range(0, 50):
    sum = sum + float(list[j])
print(sum / 50 * 2 / 10)
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T3

"""
从https://cs.lianjia.com/上爬取长沙某小区的二手房信息（以名都花园为例），并将其保存到EXCEL文件当中
"""
from urllib import request
import xlwt
from bs4 import BeautifulSoup


def getHouseList(url):
    house = []
    header = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.221 Safari/537.36 SE 2.X MetaSr 1.0',
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'}

    req = request.Request(url, headers = header)
    html = request.urlopen(req).read()
    soup = BeautifulSoup(html, 'html.parser')

    housename_divs = soup.find_all('div', class_='title')
    for housename_div in housename_divs:
        housename_as = housename_div.find_all('a')
        for housename_a in housename_as:
            housename = []

            housename.append(housename_a.get_text())

            housename.append(housename_a.get('href'))
            house.append(housename)
    huseinfo_divs = soup.find_all('div', class_='houseInfo')
    for i in range(len(huseinfo_divs)):
        info = huseinfo_divs[i].get_text()
        infos = info.split('|')
        # 小区名称
        house[i].append(infos[0])
        # 户型
        house[i].append(infos[1])
        # 平米
        house[i].append(infos[2])
    # 查询总价
    house_prices = soup.find_all('div', class_='totalPrice')
    for i in range(len(house_prices)):
        # 价格
        price = house_prices[i].get_text()
        house[i].append(price)
    return house


# 爬取房屋详细信息：所在区域、套内面积
def houseinfo(url):
    header = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.221 Safari/537.36 SE 2.X MetaSr 1.0',
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'}

    req = request.Request(url, headers=header)
    html = request.urlopen(req).read()
    soup = BeautifulSoup(html, 'html.parser')
    msg = []
    # 所在区域
    areainfos = soup.find_all('span', class_='info')
    for areainfo in areainfos:
        area = areainfo.find('a')
        if (not area):
            continue
        hrefStr = area['href']
        if (hrefStr.startswith('javascript')):
            continue
        msg.append(area.get_text())
        break
    infolist = soup.find_all('div', id='infoList')
    num = []
    for info in infolist:
        cols = info.find_all('div', class_='col')
        for i in cols:
            pingmi = i.get_text()
            try:
                a = float(pingmi[:-2])
                num.append(a)
            except ValueError:
                continue
    msg.append(sum(num))
    return msg


def writeExcel(excelPath, houses):
    workbook = xlwt.Workbook()
    sheet = workbook.add_sheet('git')
    row0 = ['标题', '链接地址', '户型', '面积', '朝向', '总价', '所属区域', '套内面积']
    for i in range(0, len(row0)):
        sheet.write(0, i, row0[i])
    for i in range(0, len(houses)):
        house = houses[i]
        print(house)
        for j in range(0, len(house)):
            sheet.write(i + 1, j, house[j])
    workbook.save(excelPath)


# 主函数
def main():
    data = []
    for i in range(1, 5):
        print('-----分隔符', i, '-------')
        if i == 1:
            url = 'https://cs.lianjia.com/ershoufang/c3511059937033rs%E5%90%8D%E9%83%BD%E8%8A%B1%E5%9B%AD/'
        else:
            url = 'https://cs.lianjia.com/ershoufang/pg' + str(
                i) + 'c3511059937033rs%E5%90%8D%E9%83%BD%E8%8A%B1%E5%9B%AD/'
        houses = getHouseList(url)
        for house in houses:
            link = house[1]
            if (not link or not link.startswith('http')):
                continue
            mianji = houseinfo(link)
            house.extend(mianji)
        data.extend(houses)
    writeExcel('C:/Users/Lunatic/Desktop/cs.xls', data)


if __name__ == '__main__':
    main()
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五、实验体会

爬虫是Python重要的应用场景，在使用相关技术时不仅仅需要熟悉相关的Python库，更要仔细分析网页，寻找其中规律进行爬取，达成自动化的初衷。