Python之路【第二十三篇】爬虫

difference between urllib and urllib2

自己翻译的装逼必备 

What is the difference between urllib and urllib2 modules of Python?
#python的urllib2模块和urllib模块之间有什么不同呢?
You might be intrigued by the existence of two separate URL modules in Python - urllib and urllib2. Even more intriguing: they are not alternatives for each other. 
So what is the difference between urllib and urllib2, and do we need them both?
#这两个模块你可能好奇,他们不是互相替代的模块。所以什么是他们之间的不同呢?什么时候我们使用他们?

urllib and urllib2 are both Python modules that do URL request related stuff but offer different functionalities. Their two most significant differences are listed below:
#urlib和urlib2他们都是访问URL相关请求功能的模块,下面列出了他们之间的重要差异:
urllib2 can accept a Request object to set the headers for a URL request, urllib accepts only a URL. That means, you cannot masquerade your User Agent string etc.
#urlib2 可以接受请求对象去设置这个请求的头部,urlib仅能接收一个URL意思是你不能伪装你的用户代理字符串。
urllib provides the urlencode method which is used for the generation of GET query strings, urllib2 doesn't have such a function. This is one of the reasons why urllib is often used along with urllib2.
#urlib 提供了 urlencode 方法用户生成和查询字符串,urlib2不支持这个功能,这是为什么常常urlib和urlib2一起使用的原因
For other differences between urllib and urllib2 refer to their documentations, the links are given in the References section.
#看下面的链接
Tip: if you are planning to do HTTP stuff only, check out httplib2, it is much better than httplib or urllib or urllib2.
#如果你仅仅是要获取http页面的东西的话,看看httplib2,它是比httplib or urlib or urlib2 更好的~~

在查询的时候看到的文章很不错:

http://www.hacksparrow.com/python-difference-between-urllib-and-urllib2.html

References

  1. urllib
  2. urllib2

在Python3中合并了 urllib 和 urllib2, 统一命名为 urllib 了

urllib

整个Urllib的源码也就1000来行可以自己看下源码~~,并且urllib2和urllib一样也就一个文件~

1、urllib.urlopen(url, data=None, proxies=None, context=None)

打开一个url的方法,返回一个文件对象,然后可以进行类似文件对象的操作。 

import urllib

f = urllib.urlopen('http://www.baidu.com/')

content = f.readlines()
print content

对象返回的对象提供的方法如下:

#这些方法的使用方式与文件对象完全一样
read() , readline() ,readlines() , fileno() , close() 

#返回一个请求头信息
content = f.info()
print content
'''
info方法内部调用的是headers方法
    def info(self):
        return self.headers
'''
#返回请求的状态码信息
content = f.getcode()
print content

#返回请求的url信息
content = f.geturl()
print content

 2、urllib.urlencode(query) 将URL中的键值对一链接符&划分

>>> urllib.urlencode({'word':'luotianshuai','age':18})
'age=18&word=luotianshuai'

所以我们可以结合urllib.urlopen来实现GET和POST请求

GET

import urllib

params = urllib.urlencode({'word':'luotianshuai','age':18})
'''
>>> urllib.urlencode({'word':'luotianshuai','age':18})
'age=18&word=luotianshuai'
'''
f = urllib.urlopen('http://zhidao.baidu.com/search?%s' % params)
print f.read()

POST 

import urllib

params = urllib.urlencode({'word':'luotianshuai','age':18})
'''
>>> urllib.urlencode({'word':'luotianshuai','age':18})
'age=18&word=luotianshuai'
'''
f = urllib.urlopen('http://zhidao.baidu.com/search',params)
for i in f.read().split('\n'):
    print i

urllib2

import urllib2
import json
import cookielib


def urllib2_request(url, method="GET", cookie="", headers={}, data=None):
    """
    :param url: 要请求的url
    :param cookie: 请求方式,GET、POST、DELETE、PUT..
    :param cookie: 要传入的cookie,cookie= 'k1=v1;k1=v2'
    :param headers: 发送数据时携带的请求头,headers = {'ContentType':'application/json; charset=UTF-8'}
    :param data: 要发送的数据GET方式需要传入参数,data={'d1': 'v1'}
    :return: 返回元祖,响应的字符串内容 和 cookiejar对象
    对于cookiejar对象,可以使用for循环访问:
        for item in cookiejar:
            print item.name,item.value
    """
    if data:
        data = json.dumps(data)

    cookie_jar = cookielib.CookieJar()
    handler = urllib2.HTTPCookieProcessor(cookie_jar)
    opener = urllib2.build_opener(handler)
    opener.addheaders.append(['Cookie', 'k1=v1;k1=v2'])
    request = urllib2.Request(url=url, data=data, headers=headers)
    request.get_method = lambda: method

    response = opener.open(request)
    origin = response.read()

    return origin, cookie_jar


# GET
result = urllib2_request('http://127.0.0.1:8001/index/', method="GET")

# POST
result = urllib2_request('http://127.0.0.1:8001/index/',  method="POST", data= {'k1': 'v1'})

# PUT
result = urllib2_request('http://127.0.0.1:8001/index/',  method="PUT", data= {'k1': 'v1'})

封装urllib请求

requests

上面是吧urllib2进行了封装并没有实现上传文件要是上传文件的话就更麻烦了,所以又出现了一个模块requests上面的操作就相当于底层的东西了,requests对其进行了封装!

所以我们只需安装个包就OK了~

# 1、基本POST实例
 
import requests
 
payload = {'key1': 'value1', 'key2': 'value2'}
ret = requests.post("http://httpbin.org/post", data=payload)
 
print ret.text
 
 
# 2、发送请求头和数据实例
 
import requests
import json
 
url = 'https://api.github.com/some/endpoint'
payload = {'some': 'data'}
headers = {'content-type': 'application/json'}
 
ret = requests.post(url, data=json.dumps(payload), headers=headers)
 
print ret.text
print ret.cookies

#向https://api.github.com/some/endpoint发送一个POST请求,将请求和相应相关的内容封装在 ret 对象中。

二、其他请求

requests.get(url, params=None, **kwargs)
requests.post(url, data=None, json=None, **kwargs)
requests.put(url, data=None, **kwargs)
requests.head(url, **kwargs)
requests.delete(url, **kwargs)
requests.patch(url, data=None, **kwargs)
requests.options(url, **kwargs)
 
# 以上方法均是在此方法的基础上构建
requests.request(method, url, **kwargs)

requests模块已经将常用的Http请求方法为用户封装完成,用户直接调用其提供的相应方法即可,其中方法的所有参数有:

def request(method, url, **kwargs):
    """Constructs and sends a :class:`Request <Request>`.

    :param method: method for the new :class:`Request` object.
    :param url: URL for the new :class:`Request` object.
    :param params: (optional) Dictionary or bytes to be sent in the query string for the :class:`Request`.
    :param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
    :param json: (optional) json data to send in the body of the :class:`Request`.
    :param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.
    :param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
    :param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': ('filename', fileobj)}``) for multipart encoding upload.
    :param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.
    :param timeout: (optional) How long to wait for the server to send data
        before giving up, as a float, or a :ref:`(connect timeout, read
        timeout) <timeouts>` tuple.
    :type timeout: float or tuple
    :param allow_redirects: (optional) Boolean. Set to True if POST/PUT/DELETE redirect following is allowed.
    :type allow_redirects: bool
    :param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.
    :param verify: (optional) whether the SSL cert will be verified. A CA_BUNDLE path can also be provided. Defaults to ``True``.
    :param stream: (optional) if ``False``, the response content will be immediately downloaded.
    :param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.
    :return: :class:`Response <Response>` object
    :rtype: requests.Response

    Usage::

      >>> import requests
      >>> req = requests.request('GET', 'http://httpbin.org/get')
      <Response [200]>
    """

    # By using the 'with' statement we are sure the session is closed, thus we
    # avoid leaving sockets open which can trigger a ResourceWarning in some
    # cases, and look like a memory leak in others.
    with sessions.Session() as session:
        return session.request(method=method, url=url, **kwargs)

更多requests模块相关的文档见:http://cn.python-requests.org/zh_CN/latest/ 

结合reques可以进行浏览器一模一样的工作!

#!/usr/bin/env python
#-*- coding:utf-8 -*-
__author__ = 'luotianshuai'

import requests
import json


login_dic = {
    'email':'shuaige@qq.com',
    'password':'shuaige!',
    '_ref':'frame',
}

login_ret = requests.post(url='https://huaban.com/auth/',
                          data=login_dic,
                          )
print login_ret.text

print '*' * 50

check_my_info = requests.get(url='http://huaban.com/ugb8cx9ky3/following/')
print check_my_info.text

举例来说如果是在web上聊天原理上也是通过get或者post发送数据过去那么我们就可以通过reques来进行发送消息访问各种url 大赞~~

scrapy

Scrapy是一个为了爬取网站数据,提取结构性数据而编写的应用框架。 其可以应用在数据挖掘,信息处理或存储历史数据等一系列的程序中。
其最初是为了页面抓取 (更确切来说, 网络抓取 )所设计的, 也可以应用在获取API所返回的数据(例如 Amazon Associates Web Services ) 或者通用的网络爬虫。Scrapy用途广泛,可以用于数据挖掘、监测和自动化测试。

requests本质就是就是发送http请求,如果在requests基础上做个封装,我去某个网站或者某个域名一直去发送请求找到所有的url,下载东西的请求在写个方法源源不断的下载东西!这样我们就写了个框架。

Scrapy 使用了 Twisted异步网络库来处理网络通讯。整体架构大致如下

Scrapy主要包括了以下组件:

  • 引擎(Scrapy)
    用来处理整个系统的数据流处理, 触发事务(框架核心)
  • 调度器(Scheduler)
    用来接受引擎发过来的请求, 压入队列中, 并在引擎再次请求的时候返回. 可以想像成一个URL(抓取网页的网址或者说是链接)的优先队列, 由它来决定下一个要抓取的网址是什么, 同时去除重复的网址
  • 下载器(Downloader)
    用于下载网页内容, 并将网页内容返回给蜘蛛(Scrapy下载器是建立在twisted这个高效的异步模型上的)
  • 爬虫(Spiders)
    爬虫是主要干活的, 用于从特定的网页中提取自己需要的信息, 即所谓的实体(Item)。用户也可以从中提取出链接,让Scrapy继续抓取下一个页面
  • 项目管道(Pipeline)
    负责处理爬虫从网页中抽取的实体,主要的功能是持久化实体、验证实体的有效性、清除不需要的信息。当页面被爬虫解析后,将被发送到项目管道,并经过几个特定的次序处理数据。
  • 下载器中间件(Downloader Middlewares)
    位于Scrapy引擎和下载器之间的框架,主要是处理Scrapy引擎与下载器之间的请求及响应。
  • 爬虫中间件(Spider Middlewares)
    介于Scrapy引擎和爬虫之间的框架,主要工作是处理蜘蛛的响应输入和请求输出。
  • 调度中间件(Scheduler Middewares)
    介于Scrapy引擎和调度之间的中间件,从Scrapy引擎发送到调度的请求和响应。

 

Scrapy中的数据流由执行引擎控制,其过程如下:

  1. 引擎打开一个网站(open a domain),找到处理该网站的Spider并向该spider请求第一个要爬取的URL(s)。
  2. 引擎从Spider中获取到第一个要爬取的URL并在调度器(Scheduler)以Request调度。
  3. 引擎向调度器请求下一个要爬取的URL。
  4. 调度器返回下一个要爬取的URL给引擎,引擎将URL通过下载中间件(请求(request)方向)转发给下载器(Downloader)。
  5. 一旦页面下载完毕,下载器生成一个该页面的Response,并将其通过下载中间件(返回(response)方向)发送给引擎。
  6. 引擎从下载器中接收到Response并通过Spider中间件(输入方向)发送给Spider处理。
  7. Spider处理Response并返回爬取到的Item及(跟进的)新的Request给引擎。
  8. 引擎将(Spider返回的)爬取到的Item给Item Pipeline,将(Spider返回的)Request给调度器。
  9. (从第二步)重复直到调度器中没有更多地request,引擎关闭该网站。

一、安装

pip install Scrapy
#windows平台需要依赖pywin32,请根据自己系统32/64位选择下载安装,https://sourceforge.net/projects/pywin32/

在MAC安装的时候遇到了个有趣的问题自己总结了下面的文档~~,顺便赞下Google

I resolved a problem ,when you you install scrapy-----{mac os system}, maybe you will get error like:

'''
sted>=10.0.0->Scrapy)
Installing collected packages: six, w3lib, parsel, PyDispatcher, Twisted, Scrapy
  Found existing installation: six 1.4.1
    DEPRECATION: Uninstalling a distutils installed project (six) has been deprecated and will be removed in a future version. This is due to the fact that uninstalling a distutils project will only partially uninstall the project.
    Uninstalling six-1.4.1:
Exception:
Traceback (most recent call last):
  File "/Library/Python/2.7/site-packages/pip-8.1.1-py2.7.egg/pip/basecommand.py", line 209, in main
    status = self.run(options, args)
  File "/Library/Python/2.7/site-packages/pip-8.1.1-py2.7.egg/pip/commands/install.py", line 317, in run
    prefix=options.prefix_path,
  File "/Library/Python/2.7/site-packages/pip-8.1.1-py2.7.egg/pip/req/req_set.py", line 726, in install
    requirement.uninstall(auto_confirm=True)
  File "/Library/Python/2.7/site-packages/pip-8.1.1-py2.7.egg/pip/req/req_install.py", line 746, in uninstall
    paths_to_remove.remove(auto_confirm)
  File "/Library/Python/2.7/site-packages/pip-8.1.1-py2.7.egg/pip/req/req_uninstall.py", line 115, in remove
    renames(path, new_path)
  File "/Library/Python/2.7/site-packages/pip-8.1.1-py2.7.egg/pip/utils/__init__.py", line 267, in renames
    shutil.move(old, new)
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/shutil.py", line 302, in move
    copy2(src, real_dst)
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/shutil.py", line 131, in copy2
    copystat(src, dst)
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/shutil.py", line 103, in copystat
    os.chflags(dst, st.st_flags)
OSError: [Errno 1] Operation not permitted: '/tmp/pip-ZVi5QO-uninstall/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/six-1.4.1-py2.7.egg-info'
You are using pip version 8.1.1, however version 8.1.2 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
LuoTimdeMacBook-Pro-2:~ luotim$ sudo pip install Scrapy --ingnore-installed six

'''

Six is a Python 2 and 3 compatibility library.

frist thanks google and what's fuck baidu ! so you should be do this to resolved the problem:
1、Download the six-1.10.0.tar.gz package
wget https://pypi.python.org/packages/b3/b2/238e2590826bfdd113244a40d9d3eb26918bd798fc187e2360a8367068db/six-1.10.0.tar.gz#md5=34eed507548117b2ab523ab14b2f8b55

2、UnZip software package
tar -zxvf six-1.10.0.tar.gz

3、Use this command to install it.
cd cd six-1.10.0
sudo python setup.py install

http://stackoverflow.com/questions/29485741/unable-to-upgrade-python-six-package-in-mac-osx-10-10-2

二、基本使用

1、创建项目

运行命令他和Django一样要想穿件Project必须执行下面的命令:

scrapy startproject your_project_name

将会在执行命令的目录自动创建如下文件:

LuoTimdeMacBook-Pro-2:day26 luotim$ tree meinv/
meinv/
├── meinv
│   ├── __init__.py
│   ├── items.py
│   ├── pipelines.py
│   ├── settings.py
│   └── spiders
│       └── __init__.py
└── scrapy.cfg

2 directories, 6 files
  • scrapy.cfg  项目的配置信息,主要为Scrapy命令行工具提供一个基础的配置信息。(真正爬虫相关的配置信息在settings.py文件中)
  • items.py    设置数据存储模板,用于结构化数据,如:Django的Model
  • pipelines    数据处理行为,如:一般结构化的数据持久化
  • settings.py 配置文件,如:递归的层数、并发数,延迟下载等
  • spiders      爬虫目录,如:创建文件,编写爬虫规则

 2、编写爬虫

注意:一般创建爬虫文件时,以网站域名命名

在spiders目录中新建 xiaohuar_spider.py 文件

#!/usr/bin/env python
#-*- coding:utf-8 -*-
__author__ = 'luotianshuai'

import scrapy


#定义一个类
class XiaoHuarSpider(scrapy.spiders.Spider):
    #这个类是有名字的可以随便定义
    name = "xiaohuar"
    #定义限制只能在这个域名下爬
    allowed_domains = ["xiaohuar.com"]
    #起始URL
    start_urls = [
        "http://www.xiaohuar.com/hua/",
    ]

    '''
    #当程序运行的时候,会自动执行我们定义的上面的类,并访问start_urls并下载里面的内容封装起来传给parese中的"response"
    这个都是scrapy内部干的
    '''

    def parse(self, response):
        # print(response, type(response))
        # from scrapy.http.response.html import HtmlResponse
        # print(response.body_as_unicode())

        '''然后就可以通过response获取这次请求的相关信息'''
        current_url = response.url
        body = response.body
        unicode_body = response.body_as_unicode()

3、运行

进入project_name目录,运行命令!

#进入scrapy项目目录里
cd meinv

#执行命令,这个spider_name就是在我们定义爬虫的那个类里的name字段
scrapy crawl spider_name --nolog

4、递归的访问

以上的爬虫仅仅是爬去初始页,而我们爬虫是需要源源不断的执行下去,直到所有的网页被执行完毕

 

#!/usr/bin/env python
#-*- coding:utf-8 -*-
__author__ = 'luotianshuai'

import scrapy
from scrapy.http import Request
from scrapy.selector import HtmlXPathSelector
import re
import urllib
import os


class XiaoHuarSpider(scrapy.spiders.Spider):
    name = "xiaohuar"
    allowed_domains = ["xiaohuar.com"]
    start_urls = [
        "http://www.xiaohuar.com/list-1-1.html",
    ]

    def parse(self, response):
        '''
        1 分析页面
        2 找到页面中符合规则的内容(校花图片),保存
        3 找到所有的a标签,再访问其他a标签,一层一层的搞下去
        '''



        hxs = HtmlXPathSelector(response)
        '''
        hxs = HtmlXPathSelector(response)
        #格式化源码
        #以前咱们从html页面中去获取某些数据的时候需要用正则,现在不用了scrapy给咱们提供了类选择器
        #只要创建一个对象然后他就会页面中去找,他支持  --链式编程--  类似于找:
        div[@class='xxx]的标签 如果在加个/a  就是div[@class='xxx]/a 就是div下的class='xxx'的下面的a标签
        '''

        # 如果url是 http://www.xiaohuar.com/list-1-\d+.html通过正则去判断,这里首选需要了解的是
        # 这个网站的URL设计就可以了,这是符合URL的
        if re.match('http://www.xiaohuar.com/list-1-\d+.html', response.url):

            #这里是调用hxs然后去找到div下class='item_list infinite_scroll'下的div,
            #这个同样也是需要看下网页的设计结构,校花网的设计结构就是这样的嘿嘿....
            items = hxs.select('//div[@class="item_list infinite_scroll"]/div')


            for i in range(len(items)):
                #这个校花里的DIV是可以通过索引去取值的
                src = hxs.select(
                    '//div[@class="item_list infinite_scroll"]/div[%d]//div[@class="img"]/a/img/@src' % i).extract()
                    #@表示取里面的属性
                name = hxs.select(
                    '//div[@class="item_list infinite_scroll"]/div[%d]//div[@class="img"]/span/text()' % i).extract()
                school = hxs.select(
                    '//div[@class="item_list infinite_scroll"]/div[%d]//div[@class="img"]/div[@class="btns"]/a/text()' % i).extract()
                if src:
                    ab_src = "http://www.xiaohuar.com" + src[0]
                    file_name = "%s_%s.jpg" % (school[0].encode('utf-8'), name[0].encode('utf-8'))
                    
                    #这个方法下载文件,并且file_name为文件
                    urllib.urlretrieve(ab_src, file_name)

        # 获取所有的url,继续访问,并在其中寻找相同的url
        all_urls = hxs.select('//a/@href').extract()  #查找所有的A标签有href属性的URL
        #去循环他
        for url in all_urls:
            #并且这里在加了一个判断,也可以不加,并且符合
            if url.startswith('http://www.xiaohuar.com/list-1-'):
                #如果你返回了一个URL并且有callback就会去递归,还去执行self.parse
                yield Request(url, callback=self.parse)

以上代码将符合规则的页面中的图片保存在指定目录,并且在HTML源码中找到所有的其他 a 标签的href属性,从而“递归”的执行下去,直到所有的页面都被访问过为止。以上代码之所以可以进行“递归”的访问相关URL,关键在于parse方法使用了 yield Request对象。

执行效果,哇哦·

如果上面执行的话会下载很多层,我已我们可以设置层数:可以修改settings.py 中的配置文件,以此来指定“递归”的层数,如: DEPTH_LIMIT = 1

#!/usr/bin/env python
# -*- coding:utf-8 -*-

import scrapy
import hashlib
from tutorial.items import JinLuoSiItem
from scrapy.http import Request
from scrapy.selector import HtmlXPathSelector


class JinLuoSiSpider(scrapy.spiders.Spider):
    count = 0
    url_set = set()

    name = "jluosi"
    domain = 'http://www.jluosi.com'
    allowed_domains = ["jluosi.com"]

    start_urls = [
        "http://www.jluosi.com:80/ec/goodsDetail.action?jls=QjRDNEIzMzAzOEZFNEE3NQ==",
    ]

    def parse(self, response):
        md5_obj = hashlib.md5()
        md5_obj.update(response.url)
        md5_url = md5_obj.hexdigest()
        if md5_url in JinLuoSiSpider.url_set:
            pass
        else:
            JinLuoSiSpider.url_set.add(md5_url)
            hxs = HtmlXPathSelector(response)
            if response.url.startswith('http://www.jluosi.com:80/ec/goodsDetail.action'):
                item = JinLuoSiItem()
                item['company'] = hxs.select('//div[@class="ShopAddress"]/ul/li[1]/text()').extract()
                item['link'] = hxs.select('//div[@class="ShopAddress"]/ul/li[2]/text()').extract()
                item['qq'] = hxs.select('//div[@class="ShopAddress"]//a/@href').re('.*uin=(?P<qq>\d*)&')
                item['address'] = hxs.select('//div[@class="ShopAddress"]/ul/li[4]/text()').extract()

                item['title'] = hxs.select('//h1[@class="goodsDetail_goodsName"]/text()').extract()

                item['unit'] = hxs.select('//table[@class="R_WebDetail_content_tab"]//tr[1]//td[3]/text()').extract()
                product_list = []
                product_tr = hxs.select('//table[@class="R_WebDetail_content_tab"]//tr')
                for i in range(2,len(product_tr)):
                    temp = {
                        'standard':hxs.select('//table[@class="R_WebDetail_content_tab"]//tr[%d]//td[2]/text()' %i).extract()[0].strip(),
                        'price':hxs.select('//table[@class="R_WebDetail_content_tab"]//tr[%d]//td[3]/text()' %i).extract()[0].strip(),
                    }
                    product_list.append(temp)

                item['product_list'] = product_list
                yield item

            current_page_urls = hxs.select('//a/@href').extract()
            for i in range(len(current_page_urls)):
                url = current_page_urls[i]
                if url.startswith('http://www.jluosi.com'):
                    url_ab = url
                    yield Request(url_ab, callback=self.parse)
选择器demo

更多选择器规则:http://scrapy-chs.readthedocs.io/zh_CN/latest/topics/selectors.html

 

5、格式化处理

上述实例只是简单的图片处理,所以在parse方法中直接处理。如果对于想要获取更多的数据(获取页面的价格、商品名称、QQ等),则可以利用Scrapy的items将数据格式化,然后统一交由pipelines来处理。

在items.py中创建类:

# -*- coding: utf-8 -*-
 
# Define here the models for your scraped items
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/items.html
 
import scrapy
 
class JieYiCaiItem(scrapy.Item):
 
    company = scrapy.Field()
    title = scrapy.Field()
    qq = scrapy.Field()
    info = scrapy.Field()
    more = scrapy.Field()

上述定义模板,以后对于从请求的源码中获取的数据同意按照此结构来获取,所以在spider中需要有一下操作:

 

#!/usr/bin/env python
# -*- coding:utf-8 -*-

import scrapy
import hashlib
from beauty.items import JieYiCaiItem
from scrapy.http import Request
from scrapy.selector import HtmlXPathSelector
from scrapy.spiders import CrawlSpider, Rule
from scrapy.linkextractors import LinkExtractor


class JieYiCaiSpider(scrapy.spiders.Spider):
    count = 0
    url_set = set()

    name = "jieyicai"
    domain = 'http://www.jieyicai.com'
    allowed_domains = ["jieyicai.com"]

    start_urls = [
        "http://www.jieyicai.com",
    ]

    rules = [
        #下面是符合规则的网址,但是不抓取内容,只是提取该页的链接(这里网址是虚构的,实际使用时请替换)
        #Rule(SgmlLinkExtractor(allow=(r'http://test_url/test?page_index=\d+'))),
        #下面是符合规则的网址,提取内容,(这里网址是虚构的,实际使用时请替换)
        #Rule(LinkExtractor(allow=(r'http://www.jieyicai.com/Product/Detail.aspx?pid=\d+')), callback="parse"),
    ]

    def parse(self, response):
        md5_obj = hashlib.md5()
        md5_obj.update(response.url)
        md5_url = md5_obj.hexdigest()
        if md5_url in JieYiCaiSpider.url_set:
            pass
        else:
            JieYiCaiSpider.url_set.add(md5_url)
            
            hxs = HtmlXPathSelector(response)
            if response.url.startswith('http://www.jieyicai.com/Product/Detail.aspx'):
                item = JieYiCaiItem()
                item['company'] = hxs.select('//span[@class="username g-fs-14"]/text()').extract()
                item['qq'] = hxs.select('//span[@class="g-left bor1qq"]/a/@href').re('.*uin=(?P<qq>\d*)&')
                item['info'] = hxs.select('//div[@class="padd20 bor1 comard"]/text()').extract()
                item['more'] = hxs.select('//li[@class="style4"]/a/@href').extract()
                item['title'] = hxs.select('//div[@class="g-left prodetail-text"]/h2/text()').extract()
                yield item

            current_page_urls = hxs.select('//a/@href').extract()
            for i in range(len(current_page_urls)):
                url = current_page_urls[i]
                if url.startswith('/'):
                    url_ab = JieYiCaiSpider.domain + url
                    yield Request(url_ab, callback=self.parse)

此处代码的关键在于:

  • 将获取的数据封装在了Item对象中
  • yield Item对象 (一旦parse中执行yield Item对象,则自动将该对象交个pipelines的类来处理)

 

# -*- coding: utf-8 -*-

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html

import json
from twisted.enterprise import adbapi
import MySQLdb.cursors
import re

mobile_re = re.compile(r'(13[0-9]|15[012356789]|17[678]|18[0-9]|14[57])[0-9]{8}')
phone_re = re.compile(r'(\d+-\d+|\d+)')

class JsonPipeline(object):

    def __init__(self):
        self.file = open('/Users/wupeiqi/PycharmProjects/beauty/beauty/jieyicai.json', 'wb')


    def process_item(self, item, spider):
        line = "%s  %s\n" % (item['company'][0].encode('utf-8'), item['title'][0].encode('utf-8'))
        self.file.write(line)
        return item

class DBPipeline(object):

    def __init__(self):
        self.db_pool = adbapi.ConnectionPool('MySQLdb',
                                             db='DbCenter',
                                             user='root',
                                             passwd='123',
                                             cursorclass=MySQLdb.cursors.DictCursor,
                                             use_unicode=True)

    def process_item(self, item, spider):
        query = self.db_pool.runInteraction(self._conditional_insert, item)
        query.addErrback(self.handle_error)
        return item

    def _conditional_insert(self, tx, item):
        tx.execute("select nid from company where company = %s", (item['company'][0], ))
        result = tx.fetchone()
        if result:
            pass
        else:
            phone_obj = phone_re.search(item['info'][0].strip())
            phone = phone_obj.group() if phone_obj else ' '

            mobile_obj = mobile_re.search(item['info'][1].strip())
            mobile = mobile_obj.group() if mobile_obj else ' '

            values = (
                item['company'][0],
                item['qq'][0],
                phone,
                mobile,
                item['info'][2].strip(),
                item['more'][0])
            tx.execute("insert into company(company,qq,phone,mobile,address,more) values(%s,%s,%s,%s,%s,%s)", values)

    def handle_error(self, e):
        print 'error',e

上述中的pipelines中有多个类,到底Scapy会自动执行那个?哈哈哈哈,当然需要先配置了,不然Scapy就蒙逼了。。。

在settings.py中做如下配置:

ITEM_PIPELINES = {
    'beauty.pipelines.DBPipeline': 300,
    'beauty.pipelines.JsonPipeline': 100,
}
# 每行后面的整型值,确定了他们运行的顺序,item按数字从低到高的顺序,通过pipeline,通常将这些数字定义在0-1000范围内。

 

更多请参见

武sir博客:http://www.cnblogs.com/wupeiqi/articles/5354900.html    

Scrapy文档:http://scrapy-chs.readthedocs.io/zh_CN/latest/index.html

posted @ 2016-05-19 10:19  天帅  阅读(4097)  评论(0编辑  收藏  举报