如何让你在众多二手车中挑中满意的？python帮你实现（附源码）

前言

老司机带你去看车，网上的几千条的二手车数据，只需几十行代码，就可以统统获取，保存数据到我们本地电脑上

知识点：

1.python基础知识2.函数3.requests库4.xpath适合零基础的同学

环境：

windows + pycharm + python3

爬虫流程：

1.目标网址2. 发送请求，获取响应3. 解析网页提取数据4. 保存数据

步骤：

1.导入工具

import io
import sys
import requests   # pip install requests
from lxml import etree   # pip

2.获取汽车详情页面的url，解析网站

def get_detail_urls(url):
    # 目标网址
    # url = 'https://www.guazi.com/cs/buy/o3/'
    # 发送请求，获取响应
    resp = requests.get(url,headers=headers)
    text = resp.content.decode('utf-8')
    # 解析网页
    html = etree.HTML(text)
    ul = html.xpath('//ul[@class="carlist clearfix js-top"]')[0]
    # print(ul)
    lis = ul.xpath('./li')
    detail_urls = []
    for li in lis:
        detail_url = li.xpath('./a/@href')
        # print(detail_url)
        detail_url = 'https://www.guazi.com' + detail_url[0]
        # print(detail_url)
        detail_urls.append(detail_url)

    return detail_urls

3.添加请求头

headers = {
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36',
    'Cookie':'uuid=5a823c6f-3504-47a9-8360-f9a5040e5f23; ganji_uuid=4238534742401031078259; lg=1; Hm_lvt_936a6d5df3f3d309bda39e92da3dd52f=1590045325; track_id=79952087417704448; antipas=q7222002m3213k0641719; cityDomain=cs; clueSourceCode=%2A%2300; user_city_id=204; sessionid=38afa34e-f972-431b-ce65-010f82a03571; close_finance_popup=2020-05-23; cainfo=%7B%22ca_a%22%3A%22-%22%2C%22ca_b%22%3A%22-%22%2C%22ca_s%22%3A%22pz_baidu%22%2C%22ca_n%22%3A%22pcbiaoti%22%2C%22ca_medium%22%3A%22-%22%2C%22ca_term%22%3A%22-%22%2C%22ca_content%22%3A%22%22%2C%22ca_campaign%22%3A%22%22%2C%22ca_kw%22%3A%22-%22%2C%22ca_i%22%3A%22-%22%2C%22scode%22%3A%22-%22%2C%22keyword%22%3A%22-%22%2C%22ca_keywordid%22%3A%22-%22%2C%22ca_transid%22%3A%22%22%2C%22platform%22%3A%221%22%2C%22version%22%3A1%2C%22track_id%22%3A%2279952087417704448%22%2C%22display_finance_flag%22%3A%22-%22%2C%22client_ab%22%3A%22-%22%2C%22guid%22%3A%225a823c6f-3504-47a9-8360-f9a5040e5f23%22%2C%22ca_city%22%3A%22cs%22%2C%22sessionid%22%3A%2238afa34e-f972-431b-ce65-010f82a03571%22%7D; preTime=%7B%22last%22%3A1590217273%2C%22this%22%3A1586866452%2C%22pre%22%3A1586866452%7D',
}

4.提取每辆汽车详情页面的数据

def parse_detail_page(url):
    resp = requests.get(url,headers=headers)
    text = resp.content.decode('utf-8')
    html = etree.HTML(text)
    # 标题
    title = html.xpath('//div[@class="product-textbox"]/h2/text()')[0]
    title = title.strip()
    print(title)
    # 信息
    info = html.xpath('//div[@class="product-textbox"]/ul/li/span/text()')
    # print(info)

    infos = {}
    cardtime = info[0]
    km = info[1]
    displacement = info[2]
    speedbox = info[3]

    infos['title'] = title
    infos['cardtime'] = cardtime
    infos['km'] = km
    infos['displacement'] = displacement
    infos['speedbox'] = speedbox
    print(infos)
    return infos

5.保存数据

def save_data(infos, f):
    f.write('{},{},{},{},{}n'.format(infos['title'],infos['cardtime'],infos['km'],infos['displacement'],infos['speedbox']))



if __name__ == '__main__':
    
    base_url = 'https://www.guazi.com/cs/buy/o{}/'
    with open('guazi.csv','a',encoding='utf-8') as f:
        for x in range(1,51):
            url = base_url.format(x)
            detail_urls = get_detail_urls(url)
            for detail_url in detail_urls:
                infos = parse_detail_page(detail_url)
                save_data(infos, f)

最后运行代码，效果如下图