1. 前期准备及环境安装
1.1 前期准备
python语法基础，html，css基础。
1.2 环境安装
官网下载python3.5以上版本，傻瓜安装。查看环境变量是否配好，cmd键入python，可查看python版本，并进入python编译环境，逐条执python代码，ctrl+z退出python编译环境。
安装pycharm，可直接在seting default中搜索需要安装的类库安装之。做python网络爬虫需要的类库有：lxml, BeautidulSoup4, Requests。
2. 爬取网页入门
2.1 爬取本地网页信息
1）使用beautifulSoup解析网页
Soup = BeautifulSoup(html,'lxml')
-
描述爬取信息位置
XXX = Soup.select('???')
两种描述元素字啊网页中位置的方法：
css Selector:
body > div.main-content > ul > li:nth-child(1) > img
XPath:
/html/body/div[2]/ul/li[1]/img
BeautifulSoup只能使用css Selector定位
3)从爬取标签中定位所需要信息
<p> Something </p>
并把获取到的有用信息装到合适的数据容器中方便查询。
示例代码如下：
# coding=utf-8
from bs4 import BeautifulSoup
info = []
path = './web/new_index.html'#本地网页文件路径
with open(path, 'r') as web_data: #打开本地网页文件
Soup = BeautifulSoup(web_data,'lxml') #新生成一个BeautifulSoup对象
#定位需求元素位置，BeautifulSoup只能使用css selector
images = Soup.select('body > div.main-content > ul > li > img')
titles = Soup.select("body > div.main-content > ul > li > div.article-info > h3 > a")
descs = Soup.select('body > div.main-content > ul > li > div.article-info > p.description')
rates = Soup.select('body > div.main-content > ul > li > div.rate > span')
cates = Soup.select('body > div.main-content > ul > li > div.article-info > p.meta-info')
#cates因为有一对多关系，所以需要取到父级标签
#依靠循环取出标签中文本，并存入字典，图片取出图片地址
for title,image,desc,rate,cate in zip(titles,images,descs,rates,cates):
data = {
"title":title.get_text(),
"image":image.get('src'),
"desc":desc.get_text(),
"rate":rate.get_text(),
"cate":list(cate.stripped_strings) #取出标签下子标签文本，并用存入list
}
info.append(data)
for i in info:
if float(i['rate'])>3:
print(i['title'],':',i['cate'],i["rate"])
2.2 爬取实际网页
Get的请求方法:
请求格式：
GET /page_one.html HTTP/1.1
HOST：www.sample.com
1).输入实际网页网址并使用BeautifulSoup获取
info = []
url = 'https://www.liaoxuefeng.com/category/0013738748415562fee26e070fa4664ad926c8e30146c67000'
wb_data = requests.get(url)
soup = BeautifulSoup(wb_data.text,'lxml')
2)定位所需元素的位置，注意将css Selector转换成BeautifulSoup所需要的格式(-of-type)
titles = soup.select('body > div > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > div > h3 > a')
authors = soup.select('#main > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > div > p:nth-of-type(1) > a:nth-of-type(1)')
texts = soup.select('#main > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > div > p:nth-of-type(2)')
images = soup.select('#main > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > a > img')
3)将爬出数据存入适宜格式本地变量，方便查取
for title,author,text,image in zip(titles,authors,texts,images):
data={
'title':title.get_text(),
'author':author.get_text(),
'text':text.get_text(),
'image':image.get('src')
}
info.append(data)
爬取廖雪峰个人网站首页的教程信息实例：
from bs4 import BeautifulSoup
import requests
info = []
url = 'https://www.liaoxuefeng.com/category/0013738748415562fee26e070fa4664ad926c8e30146c67000'
wb_data = requests.get(url)
soup = BeautifulSoup(wb_data.text,'lxml')
titles = soup.select('body > div > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > div > h3 > a')
authors = soup.select('#main > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > div > p:nth-of-type(1) > a:nth-of-type(1)')
texts = soup.select('#main > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > div > p:nth-of-type(2)')
images = soup.select('#main > div.uk-container.x-container > div > div > div.x-center > div.x-content > div > a > img')
#print(images)
for title,author,text,image in zip(titles,authors,texts,images):
data={
'title':title.get_text(),
'author':author.get_text(),
'text':text.get_text(),
'image':image.get('src')
}
info.append(data)
print(info)
2.3 爬取网页动态数据
1)异步加载概念
一个网页，不跳转新页面，部分内容动态加载显示。一般用Ajax实现。
2）爬取异步信息
检点例子，分页异步：
爬取一页数据函数：
def get_page(url,data=None):
wb_data = requests.get(url)
soup = BeautifulSoup(wb_data.text,'lxml')
imgs = soup.select('a.cover-inner > img')
titles = soup.select('section.content > h4 > a')
links = soup.select('section.content > h4 > a')
if data==None:
for img,title,link in zip(imgs,titles,links):
data = {
'img':img.get('src'),
'title':title.get('title'),
'link':link.get('href')
}
print(data)
页面动态增加函数：
def get_more_pages(start,end):
for one in range(start,end):
get_page(url+str(one))
time.sleep(2)
爬取动态分页（异步分页网站）实例代码：
from bs4 import BeautifulSoup
import requests
import time
url = 'https://knewone.com/discover?page='
def get_page(url,data=None):
wb_data = requests.get(url)
soup = BeautifulSoup(wb_data.text,'lxml')
imgs = soup.select('a.cover-inner > img')
titles = soup.select('section.content > h4 > a')
links = soup.select('section.content > h4 > a')
if data==None:
for img,title,link in zip(imgs,titles,links):
data = {
'img':img.get('src'),
'title':title.get('title'),
'link':link.get('href')
}
print(data)
def get_more_pages(start,end):
for one in range(start,end):
get_page(url+str(one))
time.sleep(2)
get_more_pages(1,10)
- 爬取大规模数据，使用数据库(mongoDB)存储
3.1 前期环境准备
1).安装并启动mongoDB数据库
2). 安装第三方库pymongo
3). 安装mongoDB插件
4). 连接好pychram和本地mongoDB
3.2 使用python对mongoDB进行操作
第一步，导入pymongo库
import pymongo
第二步，连接mongodb,创建数据库（集合库）
client = pymongo.MongoClient('localhost',27017)
walden = client['walden']
sheet_lines = walden['sheet_lines']
第三步，导入文件中数据进入mongodb
path = 'G:/tzsfile/walden.txt'
with open(path,'r') as f:
lines = f.readlines()
for index, line in enumerate(lines):
data = {
'index':index,
'line':line,
'words':len(line.split())
}
sheet_lines.insert_one(data)
第四步，根据条件筛选出所需要的数据
for item in sheet_lines.find():
print(item)
for item in sheet_lines.find({'words':0}):
print(item)
#$lt/$lte/$gt/$gte/$ne,依次等价于<,<=,>,>=,!= (l表示less g表示greater e表示equal n表示not)
for item in sheet_lines.find({'words':{'$lt':5}}):
print(item)
3.3 爬取大规模数据的工作流分析
工作流分析：
工作流主要分为两部分，首先先获取网页url并存入数据库：
def get_links_from(channel, pages, who_sells=0):
# td.t 没有这个就终止
list_view = '{}{}/pn{}/'.format(channel, str(who_sells), str(pages))
wb_data = requests.get(list_view)
time.sleep(1)
soup = BeautifulSoup(wb_data.text, 'lxml')
if soup.find('td', 't'):
for link in soup.select('td.t a.t'):
item_link = link.get('href').split('?')[0]
url_list.insert_one({'url': item_link})
print(item_link)
# return urls
else:
# It's the last page !
pass
再从数据库获取网页url，并解析出各网页元素：
def get_item_info(url):
wb_data = requests.get(url)
soup = BeautifulSoup(wb_data.text, 'lxml')
no_longer_exist = '404' in soup.find('script', type="text/javascript").get('src').split('/')
if no_longer_exist:
pass
else:
title = soup.title.text
price = soup.select('span.price.c_f50')[0].text
date = soup.select('.time')[0].text
area = list(soup.select('.c_25d a')[0].stripped_strings) if soup.find_all('span', 'c_25d') else None
item_info.insert_one({'title': title, 'price': price, 'date': date, 'area': area, 'url': url})
print({'title': title, 'price': price, 'date': date, 'area': area, 'url': url})
