豆瓣影评

Use Requests and regular expression

正则表达式-重要的事情说两遍

[c,m,f]an: can, man, fan; [^b]og to skip bog
[^a-z]\w+ skip lower case begined string; \w means [A-Za-z0-9]; \d means [0-9]
z{3} match z three times: uzzz, wuzzzz; .{2,6} match string with length of 2-6
? match ?
whitespace: space (␣), the tab (\t), the new line (\n) and the carriage return (\r)
\s will match any of the specific whitespaces above
\D represents any non-digit character, \S any non-whitespace character, and \W any non-alphanumeric
^Mission: successful$ ^为字符串开始 and $为字符串结尾
^(file_\w+) can match file_record_transcript in file_record_transcript.pdf
^([A-Z]\w{2} (\d{4})) 括号中为提取的信息，此处不但提取Jan 1987，还提取1987
^I love cats|I love dogs$ match "I love cats"或"I love dogs"
^The.* match string starting with "The"

正则表达式的练习在线网址: https://regexone.com/

二话不说上代码，晕~

import re
import requests
import matplotlib.pyplot as plt
url = "https://movie.douban.com/review/best/"
total1 = requests.get(url).text

pattern1 = re.compile("https://movie.douban.com/review/\d+/")
review_url_list = pattern1.findall(total1)
review_url_list = list(set(review_url_list))#remove duplicates

import pandas as pd
review = []

for url in review_url_list:
    total = requests.get(url).text
    pattern2 = re.compile(".*")
    review0 = str(pattern2.findall(total))
    review.append(review0) 

DF1 = pd.DataFrame({"影评": review, "网址": review_url_list})
DF1#可以看到影评中还有许多源码需要去除

获取图片地址

import re
import requests
import matplotlib.pyplot as plt
url = "https://movie.douban.com/"
total = requests.get(url).text
pattern1 = re.compile("https://movie.douban.com/subject/\d+/\?from=showing")
pattern2 = re.compile("https://img3.doubanio.com/view/photo/s_ratio_poster/public/p\d+.jpg")
pattern2.findall(total)[:5]

有了图片地址，就意味着我们可以得到图片了

同时获取图片及命名

Use BeautifulSoup

import urllib.request
import urllib
import os
from bs4 import BeautifulSoup

import string
table=str.maketrans({key:None for key in string.punctuation})#防止文件命名中出现标点符号，移除 ? / , .

def getAllImageLink():
    for i in range(0,2):#可以设为100页或更高
        if i==0:
            url="http://www.dbmeinv.com"
        else:
            url="https://www.dbmeinv.com/?pager_offset="+str(i+1)#自己寻找翻页后网址变化规律
        html = urllib.request .urlopen(url).read()
        soup = BeautifulSoup(html)
        liResult = soup.findAll('li',attrs={"class":"span3"})

        for li in liResult:
            imageEntityArray = li.findAll('img')
            for image in imageEntityArray:
                link = image.get('src')
                imageName = image.get('title')
                imageName=imageName.translate(table)
                filesavepath = "%s.png" % imageName
                urllib.request.urlretrieve(link,filesavepath)

getAllImageLink()

辣么多&不正经

伪装Header，爬取百度百科中北京地铁站

大boss之一

import requests
import re
from bs4 import BeautifulSoup
import collections

headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.84 Safari/537.36'
}

def craw(url):
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.content, 'html.parser')
    return soup

soup = craw('https://baike.baidu.com/item/%E5%8C%97%E4%BA%AC%E5%9C%B0%E9%93%81/408485')
lines = soup.findAll('table')[4].findAll('a')[:-1]

stations_connection = collections.defaultdict(list)
dist = collections.defaultdict(int)
node_in_line = collections.defaultdict(set)

pattern = re.compile('([\w|\d]+)相邻站间距信息统计表')

for line in lines[:-3]:
    link = 'https://baike.baidu.com' + line.get('href')
    soup = craw(link)

    for caption in soup.find_all('caption'):
        line_name = re.findall(pattern, caption.get_text())
        if line_name:
            print('\n----The information of {} is following...'.format(line_name[0]))
            table = caption.find_parent('table')
            for neigbor in table.find_all('tr')[1:]:
                start, end = re.findall(re.compile('([\w|\d]+)——([\w|\d]+)'), neigbor.th.text)[0]
                distance = re.findall(re.compile('([\d]+)米*'), neigbor.td.text)[0]

                stations_connection[start].append(end)
                stations_connection[end].append(start)

                dist[(start,end)] = dist[(end,start)] = int(distance)

                node_in_line[start].add(line_name[0])
                node_in_line[end].add(line_name[0])

                print('{}--{}: {}m'.format(start, end, distance))

            break

有了这些数据，意味着你可以作出个北京地铁路线图（如图1所示）

图1

也意味着你可以做个北京地铁换乘的simple APP，示意如图2所示。

图2

手动+自动的Selenium 本章终极boss “Website Nightmare”

既然可以手动，那就代表：

你可以手动关闭弹窗
手动输入账号密码跨过大部分网站门槛
手动输入验证码跨过进入壁垒
还一个优势就是可视化你的爬取过程
虽然速度无法与Scrapy媲美，但操作性+适用性还是挺有优势的

selenium入门教学请点击Christopher's Github

Example: 爬取兰蔻天猫旗舰店的消费者评论

import re
import requests
import time
from bs4 import BeautifulSoup
from selenium import webdriver

url = 'https://detail.tmall.com/item.htm?spm=a1z10.3-b-s.w4011-14640892229.94.3c6f3c22ejqhZA&id=556028888955&rn=f74615e5cda8e547b07f67e1eb384119&abbucket=16&on_comment=1'
#这里更换目标产品评论网页（天猫兰蔻旗舰店）
driver1 = webdriver.Chrome()#谷歌自动打开之后请手动登陆
driver1.get(url) # 打开网页

import pandas as pd
time=[0]*100
review=[0]*100

#前四页的“下一页”Xpath不一样；所以先“手动”爬前四页
j=1#爬完一页，手动点击下一页，再j变2，变3，变4；这对于没规律的网站是很难爬，爬取大批量数据建议找有规律的网站，通过循环爬取
for i in range(1,21):
    success =False
    while not success:
        try:
            time[(j-1)*20+i-1]=driver1.find_element_by_xpath('//*[@id="J_Reviews"]/div/div[6]/table/tbody/tr['+str(i)+']/td[1]/div[2]').text
            review[(j-1)*20+i-1]=driver1.find_element_by_xpath('//*[@id="J_Reviews"]/div/div[6]/table/tbody/tr['+str(i)+']/td[1]/div[1]/div[1]').text
            success = True
        except:
            success=True
            pass 

DF1 = pd.DataFrame({"time": time, "reviews": review})
DF1.head()#由于追加评论导致格式不一致，后期数据清洗也很重要

有了消费者评论数据，你可以做

评论情感打分
产品属性提及率及好评率交叉分析
竞品Attributes比较分析
Digital Marketing Strategy及市场营销策略

Conclusion

爬虫不是我们的终点与重点
爬虫只是我们获取数据的一种方法
运用爬虫的同时请注意法律法规
通过分析数据得到我们的Insights才是重点

Reference

支线任务-Python爬虫

豆瓣影评

获取图片地址

同时获取图片及命名

伪装Header，爬取百度百科中北京地铁站

手动+自动的Selenium 本章终极boss “Website Nightmare”

Conclusion

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