Python 网络爬虫工具：httpx 和 parsel（对比测评）

Python 网络爬虫领域两个最新的比较火的工具莫过于 httpx 和 parsel 了。

httpx 号称下一代的新一代的网络请求库，不仅支持 requests 库的所有操作，还能发送异步请求，为编写异步爬虫提供了便利。parsel 最初集成在著名 Python 爬虫框架 Scrapy 中，后独立出来成立一个单独的模块，支持 XPath 选择器, CSS 选择器和正则表达式等多种解析提取方式, 据说相比于 BeautifulSoup，parsel 的解析效率更高。

今天我们就以爬取链家网上的二手房在售房产信息为例，来测评下 httpx 和 parsel 这两个库。为了节约时间，我们以爬取上海市浦东新区 500 万元 -800 万元以上的房产为例。

requests + BeautifulSoup 组合

首先上场的是 Requests + BeautifulSoup 组合，这也是大多数人刚学习 Python 爬虫时使用的组合。本例中爬虫的入口 url 是https://sh.lianjia.com/ershoufang/pudong/a3p5/, 先发送请求获取最大页数，然后循环发送请求解析单个页面提取我们所要的信息（比如小区名，楼层，朝向，总价，单价等信息)，最后导出csv文件。如果你正在阅读本文，相信你对Python 爬虫已经有了一定了解，所以我们不会详细解释每一行代码。

整个项目代码如下所示：

Python 网络爬虫领域两个最新的比较火的工具莫过于 httpx 和 parsel 了。httpx 号称下一代的新一代的网络请求库，不仅支持 requests 库的所有操作，还能发送异步请求，为编写异步爬虫提供了便利。parsel 最初集成在著名 Python 爬虫框架 Scrapy 中，后独立出来成立一个单独的模块，支持 XPath 选择器, CSS 选择器和正则表达式等多种解析提取方式, 据说相比于 BeautifulSoup，parsel 的解析效率更高。
今天我们就以爬取链家网上的二手房在售房产信息为例，来测评下 httpx 和 parsel 这两个库。为了节约时间，我们以爬取上海市浦东新区 500 万元 -800 万元以上的房产为例。requests + BeautifulSoup 组合首先上场的是 Requests + BeautifulSoup 组合，这也是大多数人刚学习 Python 爬虫时使用的组合。本例中爬虫的入口 url 是https://sh.lianjia.com/ershoufang/pudong/a3p5/, 先发送请求获取最大页数，然后循环发送请求解析单个页面提取我们所要的信息（比如小区名，楼层，朝向，总价，单价等信息)，最后导出csv文件。如果你正在阅读本文，相信你对Python 爬虫已经有了一定了解，所以我们不会详细解释每一行代码。
整个项目代码如下所示：
# homelink_requests.py
# Author: 大江狗from fake_useragent import UserAgentimport requestsfrom bs4 import BeautifulSoupimport csvimport reimport timeclass HomeLinkSpider(object):def __init__(self):self.ua = UserAgent()self.headers = {"User-Agent": self.ua.random}self.data = list()self.path = "浦东_三房_500_800万.csv"self.url = "https://sh.lianjia.com/ershoufang/pudong/a3p5/"def get_max_page(self):response = requests.get(self.url, headers=self.headers)if response.status_code == 200:soup = BeautifulSoup(response.text, 'html.parser')a = soup.select('div[class="page-box house-lst-page-box"]')#使用eval是字符串转化为字典格式max_page = eval(a[0].attrs["page-data"])["totalPage"] return max_pageelse:print("请求失败 status:{}".format(response.status_code))return Nonedef parse_page(self):max_page = self.get_max_page()for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)response = requests.get(url, headers=self.headers)soup = BeautifulSoup(response.text, 'html.parser')ul = soup.find_all("ul", class_="sellListContent")li_list = ul[0].select("li")for li in li_list:detail = dict()detail['title'] = li.select('div[class="title"]')[0].get_text()#  2室1厅 | 74.14平米 | 南 | 精装 | 高楼层(共6层) | 1999年建 | 板楼house_info = li.select('div[class="houseInfo"]')[0].get_text()house_info_list = house_info.split(" | ")detail['bedroom'] = house_info_list[0]detail['area'] = house_info_list[1]detail['direction'] = house_info_list[2]floor_pattern = re.compile(r'\d{1,2}')# 从字符串任意位置匹配match1 = re.search(floor_pattern, house_info_list[4])  if match1:detail['floor'] = match1.group()else:detail['floor'] = "未知"# 匹配年份year_pattern = re.compile(r'\d{4}')match2 = re.search(year_pattern, house_info_list[5])if match2:detail['year'] = match2.group()else:detail['year'] = "未知"# 文兰小区 - 塘桥， 提取小区名和哈快position_info = li.select('div[class="positionInfo"]')[0].get_text().split(' - ')detail['house'] = position_info[0]detail['location'] = position_info[1]# 650万，匹配650price_pattern = re.compile(r'\d+')total_price = li.select('div[class="totalPrice"]')[0].get_text()detail['total_price'] = re.search(price_pattern, total_price).group()# 单价64182元/平米， 匹配64182unit_price = li.select('div[class="unitPrice"]')[0].get_text()detail['unit_price'] = re.search(price_pattern, unit_price).group()self.data.append(detail)def write_csv_file(self):head = ["标题", "小区", "房厅", "面积", "朝向", "楼层", "年份","位置", "总价(万)", "单价(元/平方米)"]keys = ["title", "house", "bedroom", "area", "direction","floor", "year", "location","total_price", "unit_price"]try:with open(self.path, 'w', newline='', encoding='utf_8_sig') as csv_file:writer = csv.writer(csv_file, dialect='excel')if head is not None:writer.writerow(head)for item in self.data:row_data = []for k in keys:row_data.append(item[k])# print(row_data)writer.writerow(row_data)print("Write a CSV file to path %s Successful." % self.path)except Exception as e:print("Fail to write CSV to path: %s, Case: %s" % (self.path, e))if __name__ == '__main__':start = time.time()home_link_spider = HomeLinkSpider()home_link_spider.parse_page()home_link_spider.write_csv_file()end = time.time()print("耗时：{}秒".format(end-start))
注意：我们使用了 fake_useragent, requests和BeautifulSoup，这些都需要通过 pip 事先安装好才能用。
现在我们来看下爬取结果，耗时约 18.5 秒，总共爬取 580 条数据。
图片requests + parsel 组合
这次我们同样采用 requests 获取目标网页内容，使用 parsel 库(事先需通过 pip 安装)来解析。Parsel 库的用法和 BeautifulSoup 相似，都是先创建实例，然后使用各种选择器提取 DOM 元素和数据，但语法上稍有不同。Beautiful 有自己的语法规则，而 Parsel 库支持标准的 css 选择器和 xpath 选择器, 通过 get 方法或 getall 方法获取文本或属性值，使用起来更方便。# BeautifulSoup的用法from bs4 import BeautifulSoupsoup = BeautifulSoup(response.text, 'html.parser')ul = soup.find_all("ul", class_="sellListContent")[0]# Parsel的用法, 使用Selector类from parsel import Selectorselector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]# Parsel获取文本值或属性值案例selector.css('div.title span::text').get()selector.css('ul li a::attr(href)').get()>>> for li in selector.css('ul > li'):...     print(li.xpath('.//@href').get())
注：老版的 parsel 库使用extract()或extract_first()方法获取文本或属性值，在新版中已被get()和getall()方法替代。
全部代码如下所示：# homelink_parsel.py# Author: 大江狗from fake_useragent import UserAgentimport requestsimport csvimport reimport timefrom parsel import Selectorclass HomeLinkSpider(object):def __init__(self):self.ua = UserAgent()self.headers = {"User-Agent": self.ua.random}self.data = list()self.path = "浦东_三房_500_800万.csv"self.url = "https://sh.lianjia.com/ershoufang/pudong/a3p5/"def get_max_page(self):response = requests.get(self.url, headers=self.headers)if response.status_code == 200:# 创建Selector类实例selector = Selector(response.text)# 采用css选择器获取最大页码div Boxla = selector.css('div[class="page-box house-lst-page-box"]')# 使用eval将page-data的json字符串转化为字典格式max_page = eval(a[0].xpath('//@page-data').get())["totalPage"]print("最大页码数:{}".format(max_page))return max_pageelse:print("请求失败 status:{}".format(response.status_code))return Nonedef parse_page(self):max_page = self.get_max_page()for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)response = requests.get(url, headers=self.headers)selector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]li_list = ul.css('li')for li in li_list:detail = dict()detail['title'] = li.css('div.title a::text').get()#  2室1厅 | 74.14平米 | 南 | 精装 | 高楼层(共6层) | 1999年建 | 板楼house_info = li.css('div.houseInfo::text').get()house_info_list = house_info.split(" | ")detail['bedroom'] = house_info_list[0]detail['area'] = house_info_list[1]detail['direction'] = house_info_list[2]floor_pattern = re.compile(r'\d{1,2}')match1 = re.search(floor_pattern, house_info_list[4])  # 从字符串任意位置匹配if match1:detail['floor'] = match1.group()else:detail['floor'] = "未知"# 匹配年份year_pattern = re.compile(r'\d{4}')match2 = re.search(year_pattern, house_info_list[5])if match2:detail['year'] = match2.group()else:detail['year'] = "未知"# 文兰小区 - 塘桥    提取小区名和哈快position_info = li.css('div.positionInfo a::text').getall()detail['house'] = position_info[0]detail['location'] = position_info[1]# 650万，匹配650price_pattern = re.compile(r'\d+')total_price = li.css('div.totalPrice span::text').get()detail['total_price'] = re.search(price_pattern, total_price).group()# 单价64182元/平米， 匹配64182unit_price = li.css('div.unitPrice span::text').get()detail['unit_price'] = re.search(price_pattern, unit_price).group()self.data.append(detail)def write_csv_file(self):head = ["标题", "小区", "房厅", "面积", "朝向", "楼层", "年份", "位置", "总价(万)", "单价(元/平方米)"]keys = ["title", "house", "bedroom", "area", "direction", "floor", "year", "location","total_price", "unit_price"]try:with open(self.path, 'w', newline='', encoding='utf_8_sig') as csv_file:writer = csv.writer(csv_file, dialect='excel')if head is not None:writer.writerow(head)for item in self.data:row_data = []for k in keys:row_data.append(item[k])# print(row_data)writer.writerow(row_data)print("Write a CSV file to path %s Successful." % self.path)except Exception as e:print("Fail to write CSV to path: %s, Case: %s" % (self.path, e))if __name__ == '__main__':start = time.time()home_link_spider = HomeLinkSpider()home_link_spider.parse_page()home_link_spider.write_csv_file()end = time.time()print("耗时：{}秒".format(end-start))
现在我们来看下爬取结果，爬取 580 条数据耗时约 16.5 秒，节省了 2 秒时间。可见 parsel 比 BeautifulSoup 解析效率是要高的，爬取任务少时差别不大，任务多的话差别可能会大些。图片httpx 同步 + parsel 组合
我们现在来更进一步，使用 httpx 替代 requests 库。httpx 发送同步请求的方式和 requests 库基本一样，所以我们只需要修改上例中两行代码，把 requests 替换成 httpx 即可, 其余代码一模一样。
from fake_useragent import UserAgentimport csvimport reimport timefrom parsel import Selectorimport httpxclass HomeLinkSpider(object):def __init__(self):self.ua = UserAgent()self.headers = {"User-Agent": self.ua.random}self.data = list()self.path = "浦东_三房_500_800万.csv"self.url = "https://sh.lianjia.com/ershoufang/pudong/a3p5/"def get_max_page(self):# 修改这里把requests换成httpxresponse = httpx.get(self.url, headers=self.headers)if response.status_code == 200:# 创建Selector类实例selector = Selector(response.text)# 采用css选择器获取最大页码div Boxla = selector.css('div[class="page-box house-lst-page-box"]')# 使用eval将page-data的json字符串转化为字典格式max_page = eval(a[0].xpath('//@page-data').get())["totalPage"]print("最大页码数:{}".format(max_page))return max_pageelse:print("请求失败 status:{}".format(response.status_code))return Nonedef parse_page(self):max_page = self.get_max_page()for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)# 修改这里把requests换成httpxresponse = httpx.get(url, headers=self.headers)selector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]li_list = ul.css('li')for li in li_list:detail = dict()detail['title'] = li.css('div.title a::text').get()#  2室1厅 | 74.14平米 | 南 | 精装 | 高楼层(共6层) | 1999年建 | 板楼house_info = li.css('div.houseInfo::text').get()house_info_list = house_info.split(" | ")detail['bedroom'] = house_info_list[0]detail['area'] = house_info_list[1]detail['direction'] = house_info_list[2]floor_pattern = re.compile(r'\d{1,2}')match1 = re.search(floor_pattern, house_info_list[4])  # 从字符串任意位置匹配if match1:detail['floor'] = match1.group()else:detail['floor'] = "未知"# 匹配年份year_pattern = re.compile(r'\d{4}')match2 = re.search(year_pattern, house_info_list[5])if match2:detail['year'] = match2.group()else:detail['year'] = "未知"# 文兰小区 - 塘桥    提取小区名和哈快position_info = li.css('div.positionInfo a::text').getall()detail['house'] = position_info[0]detail['location'] = position_info[1]# 650万，匹配650price_pattern = re.compile(r'\d+')total_price = li.css('div.totalPrice span::text').get()detail['total_price'] = re.search(price_pattern, total_price).group()# 单价64182元/平米， 匹配64182unit_price = li.css('div.unitPrice span::text').get()detail['unit_price'] = re.search(price_pattern, unit_price).group()self.data.append(detail)def write_csv_file(self):head = ["标题", "小区", "房厅", "面积", "朝向", "楼层", "年份", "位置", "总价(万)", "单价(元/平方米)"]keys = ["title", "house", "bedroom", "area", "direction", "floor", "year", "location","total_price", "unit_price"]try:with open(self.path, 'w', newline='', encoding='utf_8_sig') as csv_file:writer = csv.writer(csv_file, dialect='excel')if head is not None:writer.writerow(head)for item in self.data:row_data = []for k in keys:row_data.append(item[k])# print(row_data)writer.writerow(row_data)print("Write a CSV file to path %s Successful." % self.path)except Exception as e:print("Fail to write CSV to path: %s, Case: %s" % (self.path, e))if __name__ == '__main__':start = time.time()home_link_spider = HomeLinkSpider()home_link_spider.parse_page()home_link_spider.write_csv_file()end = time.time()print("耗时：{}秒".format(end-start))整个爬取过程耗时 16.1 秒，可见使用 httpx 发送同步请求时效率和 requests 基本无差别。
图片注意：Windows 上使用 pip 安装 httpx 可能会出现报错，要求安装 Visual Studio C++, 这个下载安装好就没事了。
接下来，我们就要开始王炸了，使用 httpx 和 asyncio 编写一个异步爬虫看看从链家网上爬取 580 条数据到底需要多长时间。
httpx 异步+ parsel 组合
Httpx 厉害的地方就是能发送异步请求。整个异步爬虫实现原理时，先发送同步请求获取最大页码，把每个单页的爬取和数据解析变为一个 asyncio 协程任务(使用async定义)，最后使用 loop 执行。
大部分代码与同步爬虫相同，主要变动地方有两个：# 异步 - 使用协程函数解析单页面，需传入单页面url地址async def parse_single_page(self, url):# 使用httpx发送异步请求获取单页数据async with httpx.AsyncClient() as client:response = await client.get(url, headers=self.headers)selector = Selector(response.text)# 其余地方一样def parse_page(self):max_page = self.get_max_page()loop = asyncio.get_event_loop()# Python 3.6之前用ayncio.ensure_future或loop.create_task方法创建单个协程任务# Python 3.7以后可以用户asyncio.create_task方法创建单个协程任务tasks = []for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)tasks.append(self.parse_single_page(url))# 还可以使用asyncio.gather(*tasks)命令将多个协程任务加入到事件循环loop.run_until_complete(asyncio.wait(tasks))loop.close()整个项目代码如下所示：from fake_useragent import UserAgentimport csvimport reimport timefrom parsel import Selectorimport httpximport asyncioclass HomeLinkSpider(object):def __init__(self):self.ua = UserAgent()self.headers = {"User-Agent": self.ua.random}self.data = list()self.path = "浦东_三房_500_800万.csv"self.url = "https://sh.lianjia.com/ershoufang/pudong/a3p5/"def get_max_page(self):response = httpx.get(self.url, headers=self.headers)if response.status_code == 200:# 创建Selector类实例selector = Selector(response.text)# 采用css选择器获取最大页码div Boxla = selector.css('div[class="page-box house-lst-page-box"]')# 使用eval将page-data的json字符串转化为字典格式max_page = eval(a[0].xpath('//@page-data').get())["totalPage"]print("最大页码数:{}".format(max_page))return max_pageelse:print("请求失败 status:{}".format(response.status_code))return None# 异步 - 使用协程函数解析单页面，需传入单页面url地址async def parse_single_page(self, url):async with httpx.AsyncClient() as client:response = await client.get(url, headers=self.headers)selector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]li_list = ul.css('li')for li in li_list:detail = dict()detail['title'] = li.css('div.title a::text').get()#  2室1厅 | 74.14平米 | 南 | 精装 | 高楼层(共6层) | 1999年建 | 板楼house_info = li.css('div.houseInfo::text').get()house_info_list = house_info.split(" | ")detail['bedroom'] = house_info_list[0]detail['area'] = house_info_list[1]detail['direction'] = house_info_list[2]floor_pattern = re.compile(r'\d{1,2}')match1 = re.search(floor_pattern, house_info_list[4])  # 从字符串任意位置匹配if match1:detail['floor'] = match1.group()else:detail['floor'] = "未知"# 匹配年份year_pattern = re.compile(r'\d{4}')match2 = re.search(year_pattern, house_info_list[5])if match2:detail['year'] = match2.group()else:detail['year'] = "未知"# 文兰小区 - 塘桥    提取小区名和哈快position_info = li.css('div.positionInfo a::text').getall()detail['house'] = position_info[0]detail['location'] = position_info[1]# 650万，匹配650price_pattern = re.compile(r'\d+')total_price = li.css('div.totalPrice span::text').get()detail['total_price'] = re.search(price_pattern, total_price).group()# 单价64182元/平米， 匹配64182unit_price = li.css('div.unitPrice span::text').get()detail['unit_price'] = re.search(price_pattern, unit_price).group()self.data.append(detail)def parse_page(self):max_page = self.get_max_page()loop = asyncio.get_event_loop()# Python 3.6之前用ayncio.ensure_future或loop.create_task方法创建单个协程任务# Python 3.7以后可以用户asyncio.create_task方法创建单个协程任务tasks = []for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)tasks.append(self.parse_single_page(url))# 还可以使用asyncio.gather(*tasks)命令将多个协程任务加入到事件循环loop.run_until_complete(asyncio.wait(tasks))loop.close()def write_csv_file(self):head = ["标题", "小区", "房厅", "面积", "朝向", "楼层","年份", "位置", "总价(万)", "单价(元/平方米)"]keys = ["title", "house", "bedroom", "area", "direction","floor", "year", "location","total_price", "unit_price"]try:with open(self.path, 'w', newline='', encoding='utf_8_sig') as csv_file:writer = csv.writer(csv_file, dialect='excel')if head is not None:writer.writerow(head)for item in self.data:row_data = []for k in keys:row_data.append(item[k])writer.writerow(row_data)print("Write a CSV file to path %s Successful." % self.path)except Exception as e:print("Fail to write CSV to path: %s, Case: %s" % (self.path, e))if __name__ == '__main__':start = time.time()home_link_spider = HomeLinkSpider()home_link_spider.parse_page()home_link_spider.write_csv_file()end = time.time()print("耗时：{}秒".format(end-start))
现在到了见证奇迹的时刻了。从链家网上爬取了 580 条数据，使用 httpx 编写的异步爬虫仅仅花了 2.5 秒!!
图片对比与总结
爬取同样的内容，采用不同工具组合耗时是不一样的。httpx 异步+parsel 组合毫无疑问是最大的赢家, requests 和 BeautifulSoup 确实可以功成身退啦。
requests + BeautifulSoup: 18.5 秒
requests + parsel: 16.5 秒
httpx 同步 + parsel: 16.1 秒
httpx 异步 + parsel: 2.5 秒

注意：我们使用了 fake_useragent, requests和BeautifulSoup，这些都需要通过 pip 事先安装好才能用。

现在我们来看下爬取结果，耗时约 18.5 秒，总共爬取 580 条数据。

requests + parsel 组合

这次我们同样采用 requests 获取目标网页内容，使用 parsel 库(事先需通过 pip 安装)来解析。Parsel 库的用法和 BeautifulSoup 相似，都是先创建实例，然后使用各种选择器提取 DOM 元素和数据，但语法上稍有不同。Beautiful 有自己的语法规则，而 Parsel 库支持标准的 css 选择器和 xpath 选择器, 通过 get 方法或 getall 方法获取文本或属性值，使用起来更方便。

# BeautifulSoup的用法from bs4 import BeautifulSoupsoup = BeautifulSoup(response.text, 'html.parser')ul = soup.find_all("ul", class_="sellListContent")[0]# Parsel的用法, 使用Selector类from parsel import Selectorselector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]# Parsel获取文本值或属性值案例selector.css('div.title span::text').get()selector.css('ul li a::attr(href)').get()>>> for li in selector.css('ul > li'):...     print(li.xpath('.//@href').get())
注：老版的 parsel 库使用extract()或extract_first()方法获取文本或属性值，在新版中已被get()和getall()方法替代。
全部代码如下所示：# homelink_parsel.py# Author: 大江狗from fake_useragent import UserAgentimport requestsimport csvimport reimport timefrom parsel import Selectorclass HomeLinkSpider(object):def __init__(self):self.ua = UserAgent()self.headers = {"User-Agent": self.ua.random}self.data = list()self.path = "浦东_三房_500_800万.csv"self.url = "https://sh.lianjia.com/ershoufang/pudong/a3p5/"def get_max_page(self):response = requests.get(self.url, headers=self.headers)if response.status_code == 200:# 创建Selector类实例selector = Selector(response.text)# 采用css选择器获取最大页码div Boxla = selector.css('div[class="page-box house-lst-page-box"]')# 使用eval将page-data的json字符串转化为字典格式max_page = eval(a[0].xpath('//@page-data').get())["totalPage"]print("最大页码数:{}".format(max_page))return max_pageelse:print("请求失败 status:{}".format(response.status_code))return Nonedef parse_page(self):max_page = self.get_max_page()for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)response = requests.get(url, headers=self.headers)selector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]li_list = ul.css('li')for li in li_list:detail = dict()detail['title'] = li.css('div.title a::text').get()#  2室1厅 | 74.14平米 | 南 | 精装 | 高楼层(共6层) | 1999年建 | 板楼house_info = li.css('div.houseInfo::text').get()house_info_list = house_info.split(" | ")detail['bedroom'] = house_info_list[0]detail['area'] = house_info_list[1]detail['direction'] = house_info_list[2]floor_pattern = re.compile(r'\d{1,2}')match1 = re.search(floor_pattern, house_info_list[4])  # 从字符串任意位置匹配if match1:detail['floor'] = match1.group()else:detail['floor'] = "未知"# 匹配年份year_pattern = re.compile(r'\d{4}')match2 = re.search(year_pattern, house_info_list[5])if match2:detail['year'] = match2.group()else:detail['year'] = "未知"# 文兰小区 - 塘桥    提取小区名和哈快position_info = li.css('div.positionInfo a::text').getall()detail['house'] = position_info[0]detail['location'] = position_info[1]# 650万，匹配650price_pattern = re.compile(r'\d+')total_price = li.css('div.totalPrice span::text').get()detail['total_price'] = re.search(price_pattern, total_price).group()# 单价64182元/平米， 匹配64182unit_price = li.css('div.unitPrice span::text').get()detail['unit_price'] = re.search(price_pattern, unit_price).group()self.data.append(detail)def write_csv_file(self):head = ["标题", "小区", "房厅", "面积", "朝向", "楼层", "年份", "位置", "总价(万)", "单价(元/平方米)"]keys = ["title", "house", "bedroom", "area", "direction", "floor", "year", "location","total_price", "unit_price"]try:with open(self.path, 'w', newline='', encoding='utf_8_sig') as csv_file:writer = csv.writer(csv_file, dialect='excel')if head is not None:writer.writerow(head)for item in self.data:row_data = []for k in keys:row_data.append(item[k])# print(row_data)writer.writerow(row_data)print("Write a CSV file to path %s Successful." % self.path)except Exception as e:print("Fail to write CSV to path: %s, Case: %s" % (self.path, e))if __name__ == '__main__':start = time.time()home_link_spider = HomeLinkSpider()home_link_spider.parse_page()home_link_spider.write_csv_file()end = time.time()print("耗时：{}秒".format(end-start))

现在我们来看下爬取结果，爬取 580 条数据耗时约 16.5 秒，节省了 2 秒时间。可见 parsel 比 BeautifulSoup 解析效率是要高的，爬取任务少时差别不大，任务多的话差别可能会大些。

httpx 同步 + parsel 组合

我们现在来更进一步，使用 httpx 替代 requests 库。httpx 发送同步请求的方式和 requests 库基本一样，所以我们只需要修改上例中两行代码，把 requests 替换成 httpx 即可, 其余代码一模一样。

整个爬取过程耗时 16.1 秒，可见使用 httpx 发送同步请求时效率和 requests 基本无差别。

from fake_useragent import UserAgentimport csvimport reimport timefrom parsel import Selectorimport httpxclass HomeLinkSpider(object):def __init__(self):self.ua = UserAgent()self.headers = {"User-Agent": self.ua.random}self.data = list()self.path = "浦东_三房_500_800万.csv"self.url = "https://sh.lianjia.com/ershoufang/pudong/a3p5/"def get_max_page(self):# 修改这里把requests换成httpxresponse = httpx.get(self.url, headers=self.headers)if response.status_code == 200:# 创建Selector类实例selector = Selector(response.text)# 采用css选择器获取最大页码div Boxla = selector.css('div[class="page-box house-lst-page-box"]')# 使用eval将page-data的json字符串转化为字典格式max_page = eval(a[0].xpath('//@page-data').get())["totalPage"]print("最大页码数:{}".format(max_page))return max_pageelse:print("请求失败 status:{}".format(response.status_code))return Nonedef parse_page(self):max_page = self.get_max_page()for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)# 修改这里把requests换成httpxresponse = httpx.get(url, headers=self.headers)selector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]li_list = ul.css('li')for li in li_list:detail = dict()detail['title'] = li.css('div.title a::text').get()#  2室1厅 | 74.14平米 | 南 | 精装 | 高楼层(共6层) | 1999年建 | 板楼house_info = li.css('div.houseInfo::text').get()house_info_list = house_info.split(" | ")detail['bedroom'] = house_info_list[0]detail['area'] = house_info_list[1]detail['direction'] = house_info_list[2]floor_pattern = re.compile(r'\d{1,2}')match1 = re.search(floor_pattern, house_info_list[4])  # 从字符串任意位置匹配if match1:detail['floor'] = match1.group()else:detail['floor'] = "未知"# 匹配年份year_pattern = re.compile(r'\d{4}')match2 = re.search(year_pattern, house_info_list[5])if match2:detail['year'] = match2.group()else:detail['year'] = "未知"# 文兰小区 - 塘桥    提取小区名和哈快position_info = li.css('div.positionInfo a::text').getall()detail['house'] = position_info[0]detail['location'] = position_info[1]# 650万，匹配650price_pattern = re.compile(r'\d+')total_price = li.css('div.totalPrice span::text').get()detail['total_price'] = re.search(price_pattern, total_price).group()# 单价64182元/平米， 匹配64182unit_price = li.css('div.unitPrice span::text').get()detail['unit_price'] = re.search(price_pattern, unit_price).group()self.data.append(detail)def write_csv_file(self):head = ["标题", "小区", "房厅", "面积", "朝向", "楼层", "年份", "位置", "总价(万)", "单价(元/平方米)"]keys = ["title", "house", "bedroom", "area", "direction", "floor", "year", "location","total_price", "unit_price"]try:with open(self.path, 'w', newline='', encoding='utf_8_sig') as csv_file:writer = csv.writer(csv_file, dialect='excel')if head is not None:writer.writerow(head)for item in self.data:row_data = []for k in keys:row_data.append(item[k])# print(row_data)writer.writerow(row_data)print("Write a CSV file to path %s Successful." % self.path)except Exception as e:print("Fail to write CSV to path: %s, Case: %s" % (self.path, e))if __name__ == '__main__':start = time.time()home_link_spider = HomeLinkSpider()home_link_spider.parse_page()home_link_spider.write_csv_file()end = time.time()print("耗时：{}秒".format(end-start))

注意：Windows 上使用 pip 安装 httpx 可能会出现报错，要求安装 Visual Studio C++, 这个下载安装好就没事了。

接下来，我们就要开始王炸了，使用 httpx 和 asyncio 编写一个异步爬虫看看从链家网上爬取 580 条数据到底需要多长时间。

httpx 异步+ parsel 组合

Httpx 厉害的地方就是能发送异步请求。整个异步爬虫实现原理时，先发送同步请求获取最大页码，把每个单页的爬取和数据解析变为一个 asyncio 协程任务(使用async定义)，最后使用 loop 执行。

大部分代码与同步爬虫相同，主要变动地方有两个：

 # 异步 - 使用协程函数解析单页面，需传入单页面url地址async def parse_single_page(self, url):# 使用httpx发送异步请求获取单页数据async with httpx.AsyncClient() as client:response = await client.get(url, headers=self.headers)selector = Selector(response.text)# 其余地方一样def parse_page(self):max_page = self.get_max_page()loop = asyncio.get_event_loop()# Python 3.6之前用ayncio.ensure_future或loop.create_task方法创建单个协程任务# Python 3.7以后可以用户asyncio.create_task方法创建单个协程任务tasks = []for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)tasks.append(self.parse_single_page(url))# 还可以使用asyncio.gather(*tasks)命令将多个协程任务加入到事件循环loop.run_until_complete(asyncio.wait(tasks))loop.close()

整个项目代码如下所示：

 from fake_useragent import UserAgentimport csvimport reimport timefrom parsel import Selectorimport httpximport asyncioclass HomeLinkSpider(object):def __init__(self):self.ua = UserAgent()self.headers = {"User-Agent": self.ua.random}self.data = list()self.path = "浦东_三房_500_800万.csv"self.url = "https://sh.lianjia.com/ershoufang/pudong/a3p5/"def get_max_page(self):response = httpx.get(self.url, headers=self.headers)if response.status_code == 200:# 创建Selector类实例selector = Selector(response.text)# 采用css选择器获取最大页码div Boxla = selector.css('div[class="page-box house-lst-page-box"]')# 使用eval将page-data的json字符串转化为字典格式max_page = eval(a[0].xpath('//@page-data').get())["totalPage"]print("最大页码数:{}".format(max_page))return max_pageelse:print("请求失败 status:{}".format(response.status_code))return None# 异步 - 使用协程函数解析单页面，需传入单页面url地址async def parse_single_page(self, url):async with httpx.AsyncClient() as client:response = await client.get(url, headers=self.headers)selector = Selector(response.text)ul = selector.css('ul.sellListContent')[0]li_list = ul.css('li')for li in li_list:detail = dict()detail['title'] = li.css('div.title a::text').get()#  2室1厅 | 74.14平米 | 南 | 精装 | 高楼层(共6层) | 1999年建 | 板楼house_info = li.css('div.houseInfo::text').get()house_info_list = house_info.split(" | ")detail['bedroom'] = house_info_list[0]detail['area'] = house_info_list[1]detail['direction'] = house_info_list[2]floor_pattern = re.compile(r'\d{1,2}')match1 = re.search(floor_pattern, house_info_list[4])  # 从字符串任意位置匹配if match1:detail['floor'] = match1.group()else:detail['floor'] = "未知"# 匹配年份year_pattern = re.compile(r'\d{4}')match2 = re.search(year_pattern, house_info_list[5])if match2:detail['year'] = match2.group()else:detail['year'] = "未知"# 文兰小区 - 塘桥    提取小区名和哈快position_info = li.css('div.positionInfo a::text').getall()detail['house'] = position_info[0]detail['location'] = position_info[1]# 650万，匹配650price_pattern = re.compile(r'\d+')total_price = li.css('div.totalPrice span::text').get()detail['total_price'] = re.search(price_pattern, total_price).group()# 单价64182元/平米， 匹配64182unit_price = li.css('div.unitPrice span::text').get()detail['unit_price'] = re.search(price_pattern, unit_price).group()self.data.append(detail)def parse_page(self):max_page = self.get_max_page()loop = asyncio.get_event_loop()# Python 3.6之前用ayncio.ensure_future或loop.create_task方法创建单个协程任务# Python 3.7以后可以用户asyncio.create_task方法创建单个协程任务tasks = []for i in range(1, max_page + 1):url = 'https://sh.lianjia.com/ershoufang/pudong/pg{}a3p5/'.format(i)tasks.append(self.parse_single_page(url))# 还可以使用asyncio.gather(*tasks)命令将多个协程任务加入到事件循环loop.run_until_complete(asyncio.wait(tasks))loop.close()def write_csv_file(self):head = ["标题", "小区", "房厅", "面积", "朝向", "楼层","年份", "位置", "总价(万)", "单价(元/平方米)"]keys = ["title", "house", "bedroom", "area", "direction","floor", "year", "location","total_price", "unit_price"]try:with open(self.path, 'w', newline='', encoding='utf_8_sig') as csv_file:writer = csv.writer(csv_file, dialect='excel')if head is not None:writer.writerow(head)for item in self.data:row_data = []for k in keys:row_data.append(item[k])writer.writerow(row_data)print("Write a CSV file to path %s Successful." % self.path)except Exception as e:print("Fail to write CSV to path: %s, Case: %s" % (self.path, e))if __name__ == '__main__':start = time.time()home_link_spider = HomeLinkSpider()home_link_spider.parse_page()home_link_spider.write_csv_file()end = time.time()print("耗时：{}秒".format(end-start))

现在到了见证奇迹的时刻了。从链家网上爬取了 580 条数据，使用 httpx 编写的异步爬虫仅仅花了 2.5 秒!!

对比与总结

爬取同样的内容，采用不同工具组合耗时是不一样的。httpx 异步+parsel 组合毫无疑问是最大的赢家, requests 和 BeautifulSoup 确实可以功成身退啦。

requests + BeautifulSoup: 18.5 秒
requests + parsel: 16.5 秒
httpx 同步 + parsel: 16.1 秒
httpx 异步 + parsel: 2.5 秒