Python爬虫最强组合httpx+parsel测评

Python猫 2022-04-12

The following article is from Python Web与Django开发 Author 大江狗

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作者：大江狗

来源：Python Web与Django开发

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 UserAgent import requests from bs4 import BeautifulSoup import csv import re import time

class 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_page else: print("请求失败 status:{}".format(response.status_code)) return None

def 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650 price_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64182 unit_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"]

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。

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 BeautifulSoup

soup = BeautifulSoup(response.text, 'html.parser') ul = soup.find_all("ul", class_="sellListContent")[0]

# Parsel的用法, 使用Selector类 from parsel import Selector selector = 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 UserAgent import requests import csv import re import time from parsel import Selector

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 Boxl a = 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_page else: print("请求失败 status:{}".format(response.status_code)) return None

def 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650 price_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64182 unit_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"]

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。

from fake_useragent import UserAgent import csv import re import time from parsel import Selector import httpx

def get_max_page(self):

# 修改这里把requests换成httpx 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 Boxl a = 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_page else: print("请求失败 status:{}".format(response.status_code)) return None

def 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httpx response = 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]

# 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650 price_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64182 unit_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"]

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。

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 UserAgent import csv import re import time from parsel import Selector import httpx import asyncio

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 Boxl a = 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_page else: 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]

# 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650 price_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64182 unit_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()

# 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。