Python网络数据抓取实战——Xpath解析豆瓣书评

数据小磨坊

发布于 2018-04-11 17:47:43

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发布于 2018-04-11 17:47:43

前两篇我详细的讲解了CSS和XPath表达式在网页解析中的用法，但是都是以列举和解释为主，并没有用于解决实战问题，今天这一篇，我使用urllib+lxml工具组合，结合XPath表达式来做一个小案例。

该案例是刘顺祥大神【公众号：每天进步一点点】中使用的爬虫实战案例，他用的request+BeautifulSoup，这样刚好扩展下XPath的用法，丰富一下该案例：

https://read.douban.com/search?q=Python

#! /usr/bin/env python
#coding=utf-8

from urllib.request import urlopen,Request
import pandas as pd
import numpy as np
from lxml import etree
url='https://read.douban.com/search?q=Python'
header ={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.79 Safari/537.36'}

构建解析函数：

def getcontent(url):
    i=0
    myresult={}
    title=[];subtitle=[];author=[];category=[];price=[];rating=[];eveluate_nums=[]
    for page in range(0,4):
        link=url+'&start='+str(page*10)
        content=urlopen(Request(link,headers=header)).read().decode('utf-8')
        result=etree.HTML(content)
        ###计算每一页有多少条书籍信息：
        length=len(result.xpath("//ol[@class='ebook-list column-list']/li"))
        ###提取图书标题信息：
        title.extend(result.xpath("//ol/li//div[@class='title']/a/text()| //ol/li//h4/a/text()"))
        ###考虑作者不唯一的情况：
        author_text=[np.nan]*length
        for i in range(1,length+1):
            author_text[i-1]=result.xpath("//li[{0}]//span[contains(text(),'作者')]/following-sibling::*[1]/a/text() | //li[{0}]//div[@class='author']/a/text()".format(i))
        author.extend(author_text)
        ###考虑分类，枚举出所有分类标签    
        category.extend(result.xpath("//span[@class='category']/span[2]/span/text() | //p[@class='category']/span[@class='labled-text']/text() | //div[@class='category']/text()"))
        ###考虑副标题是否存在
        subtext=[np.nan]*length
        for i in range(1,length+1):
            if result.xpath("//ol/li[{}]//p/text()".format(i)) != []:
                subtext[i-1]=result.xpath("//ol/li[{}]//p/text()".format(i))
        subtitle.extend(subtext)
        ###考虑评价是否存在：
        eveluate_text=[np.nan]*length
        for i in range(1,length+1):
            if result.xpath("//ol/li[{}]//a[@class='ratings-link']/span/text()".format(i)) != []:
                eveluate_text[i-1]=result.xpath("//ol/li[{}]//a[@class='ratings-link']/span/text()".format(i))
        eveluate_nums.extend(eveluate_text)
        ###考虑评分是否存在：
        rating_text=[np.nan]*length
        for i in range(1,length+1):
            if result.xpath("//ol/li[{}]//div[@class='rating list-rating']/span[2]/text()".format(i)) != []:
                rating_text[i-1]=result.xpath("//ol/li[{}]//div[@class='rating list-rating']/span[2]/text()".format(i))
        rating.extend(eveluate_text)
        ###考虑价格是否存在：
        price_text=[np.nan]*length
        for i in range(1,length+1):
            if result.xpath("//ol/li[{}]/div[@class='info']//span[@class='price-tag discount']/span/text()".format(i)) != []:
                price_text[i-1]=result.xpath("//ol/li[{}]/div[@class='info']//span[@class='price-tag discount']/span/text()".format(i))
        price.extend(eveluate_text)
        print("page {} is over!!!".format(page))
    print("everything is OK")
    myresult={"title":title,"subtitle":subtitle,"author":author,"category":category,"price":price,"rating":rating,"eveluate_nums":eveluate_nums}
    return myresult

运行函数：

url="https://read.douban.com/search?q=Python"
myresult=getcontent(url)

查看变量信息

for i,m in myresult.items():
    print(i+":"+str(len(m)))
title:39
subtitle:39
author:39
category:39
price:39
rating:39
eveluate_nums:39

铺平嵌套列表：

以上可以看到有几列是嵌套列表，会影响我们后期的数据分析，所以需要铺平列表，这里是一个我从网上找到的列表解除嵌套的代码。

def flatten(input_list):
    output_list = []
    while True:
            if input_list == []:
            break
        for index, i in enumerate(input_list):
            if type(i)== list:
                input_list = i + input_list[index+1:]
                break
            else:
                output_list.append(i)
                input_list.pop(index)
                break
    return output_list
myresult['eveluate_nums']=flatten(myresult['eveluate_nums'])
myresult['price']=flatten(myresult['price'])
myresult['rating']=flatten(myresult['rating'])
myresult['subtitle']=flatten(myresult['subtitle'])

批量修改变量格式：

mydata=pd.DataFrame(myresult)
mydata=mydata.astype({'author':'str','category':'str','eveluate_nums':'float', 'price':'float', 'rating':'float', 'subtitle':'str','title':'str'})
mydata.columns
mydata.dtypes
author            object
category          object
eveluate_nums       float64
price            float64
rating            float64
subtitle          object
title             object
dtype: object

参考资料： http://blog.csdn.net/vola9527/article/details/68964144 https://mp.weixin.qq.com/s?__biz=MzIxNjA2ODUzNg==&mid=2651435242&idx=1&sn=f9315b81911bbc4f83f41ddba23d054e

往期案例数据请移步本人GitHub： https://github.com/ljtyduyu/DataWarehouse/tree/master/File

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原始发表：2017-10-25，如有侵权请联系 cloudcommunity@tencent.com 删除

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