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\"]
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。
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
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:
# 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\"]
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。
from fake_useragent import UserAgent
import csv
import re
import time
from parsel import Selector
import httpx
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):
# 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]
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\"]
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。
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
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 = 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]
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 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
对于Python爬虫，你还有喜欢的库吗?
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