Python的Scrapy爬虫框架简单学习笔记

一、简单配置，获取单个网页上的内容。
（1）创建scrapy项目

scrapy startproject getblog

（2）编辑 items.py

# -*- coding: utf-8 -*- # Define here the models for your scraped items## See documentation in:# http://doc.scrapy.org/en/latest/topics/items.html from scrapy.item import Item, Field class BlogItem(Item):  desc = Field()

（3）在 spiders 文件夹下，创建 blog_spider.py

需要熟悉下xpath选择，感觉跟JQuery选择器差不多，但是不如JQuery选择器用着舒服（ w3school教程： http://www.w3school.com.cn/xpath/ ）。

# coding=utf-8 from scrapy.spider import Spiderfrom getblog.items import BlogItemfrom scrapy.selector import Selector  class BlogSpider(Spider):  # 标识名称  name = 'blog'  # 起始地址  start_urls = ['http://www.cnblogs.com/']   def parse(self, response):    sel = Selector(response) # Xptah 选择器    # 选择所有含有class属性，值为‘post_item'的div 标签内容    # 下面的 第2个div 的 所有内容    sites = sel.xpath('//div[@class="post_item"]/div[2]')    items = []    for site in sites:      item = BlogItem()      # 选取h3标签下，a标签下，的文字内容 ‘text()'      item['title'] = site.xpath('h3/a/text()').extract()      # 同上，p标签下的 文字内容 ‘text()'      item['desc'] = site.xpath('p[@class="post_item_summary"]/text()').extract()      items.append(item)    return items

（4）运行，

scrapy crawl blog # 即可

（5）输出文件。

在 settings.py 中进行输出配置。

# 输出文件位置FEED_URI = 'blog.xml'# 输出文件格式 可以为 json，xml，csvFEED_FORMAT = 'xml'

输出位置为项目根文件夹下。

二、基本的 -- scrapy.spider.Spider

（1）使用交互shell

dizzy@dizzy-pc:~$ scrapy shell "http://www.baidu.com/"

2014-08-21 04:09:11+0800 [scrapy] INFO: Scrapy 0.24.4 started (bot: scrapybot)2014-08-21 04:09:11+0800 [scrapy] INFO: Optional features available: ssl, http11, django2014-08-21 04:09:11+0800 [scrapy] INFO: Overridden settings: {'LOGSTATS_INTERVAL': 0}2014-08-21 04:09:11+0800 [scrapy] INFO: Enabled extensions: TelnetConsole, CloseSpider, WebService, CoreStats, SpiderState2014-08-21 04:09:11+0800 [scrapy] INFO: Enabled downloader middlewares: HttpAuthMiddleware, DownloadTimeoutMiddleware, UserAgentMiddleware, RetryMiddleware, DefaultHeadersMiddleware, MetaRefreshMiddleware, HttpCompressionMiddleware, RedirectMiddleware, CookiesMiddleware, ChunkedTransferMiddleware, DownloaderStats2014-08-21 04:09:11+0800 [scrapy] INFO: Enabled spider middlewares: HttpErrorMiddleware, OffsiteMiddleware, RefererMiddleware, UrlLengthMiddleware, DepthMiddleware2014-08-21 04:09:11+0800 [scrapy] INFO: Enabled item pipelines: 2014-08-21 04:09:11+0800 [scrapy] DEBUG: Telnet console listening on 127.0.0.1:60242014-08-21 04:09:11+0800 [scrapy] DEBUG: Web service listening on 127.0.0.1:60812014-08-21 04:09:11+0800 [default] INFO: Spider opened2014-08-21 04:09:12+0800 [default] DEBUG: Crawled (200) <GET http://www.baidu.com/> (referer: None)[s] Available Scrapy objects:[s]  crawler  <scrapy.crawler.Crawler object at 0xa483cec>[s]  item    {}[s]  request  <GET http://www.baidu.com/>[s]  response  <200 http://www.baidu.com/>[s]  settings  <scrapy.settings.Settings object at 0xa0de78c>[s]  spider   <Spider 'default' at 0xa78086c>[s] Useful shortcuts:[s]  shelp()      Shell help (print this help)[s]  fetch(req_or_url) Fetch request (or URL) and update local objects[s]  view(response)  View response in a browser >>>   # response.body 返回的所有内容  # response.xpath('//ul/li') 可以测试所有的xpath内容    More important, if you type response.selector you will access a selector object you can use toquery the response, and convenient shortcuts like response.xpath() and response.css() mapping toresponse.selector.xpath() and response.selector.css()

也就是可以很方便的，以交互的形式来查看xpath选择是否正确。之前是用FireFox的F12来选择的，但是并不能保证每次都能正确的选择出内容。

也可使用：

scrapy shell 'http://scrapy.org' --nolog# 参数 --nolog 没有日志

（2）示例

from scrapy import Spiderfrom scrapy_test.items import DmozItem  class DmozSpider(Spider):  name = 'dmoz'  allowed_domains = ['dmoz.org']  start_urls = ['http://www.dmoz.org/Computers/Programming/Languages/Python/Books/',         'http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/,'         '']   def parse(self, response):    for sel in response.xpath('//ul/li'):      item = DmozItem()      item['title'] = sel.xpath('a/text()').extract()      item['link'] = sel.xpath('a/@href').extract()      item['desc'] = sel.xpath('text()').extract()      yield item

（3）保存文件

可以使用，保存文件。格式可以 json，xml，csv

scrapy crawl -o 'a.json' -t 'json'

（4）使用模板创建spider

scrapy genspider baidu baidu.com # -*- coding: utf-8 -*-import scrapy  class BaiduSpider(scrapy.Spider):  name = "baidu"  allowed_domains = ["baidu.com"]  start_urls = (    'http://www.baidu.com/',  )   def parse(self, response):    pass

这段先这样吧，记得之前5个的，现在只能想起4个来了. :-(

千万记得随手点下保存按钮。否则很是影响心情的(⊙o⊙)！

三、高级 -- scrapy.contrib.spiders.CrawlSpider

例子

#coding=utf-8from scrapy.contrib.spiders import CrawlSpider, Rulefrom scrapy.contrib.linkextractors import LinkExtractorimport scrapy  class TestSpider(CrawlSpider):  name = 'test'  allowed_domains = ['example.com']  start_urls = ['http://www.example.com/']  rules = (    # 元组    Rule(LinkExtractor(allow=('category/.php', ), deny=('subsection/.php', ))),    Rule(LinkExtractor(allow=('item/.php', )), callback='pars_item'),  )   def parse_item(self, response):    self.log('item page : %s' % response.url)    item = scrapy.Item()    item['id'] = response.xpath('//td[@id="item_id"]/text()').re('ID：(/d+)')    item['name'] = response.xpath('//td[@id="item_name"]/text()').extract()    item['description'] = response.xpath('//td[@id="item_description"]/text()').extract()    return item

其他的还有 XMLFeedSpider

class scrapy.contrib.spiders.XMLFeedSpider
class scrapy.contrib.spiders.CSVFeedSpider
class scrapy.contrib.spiders.SitemapSpider

四、选择器

  >>> from scrapy.selector import Selector  >>> from scrapy.http import HtmlResponse

可以灵活的使用 .css() 和 .xpath() 来快速的选取目标数据

关于选择器，需要好好研究一下。xpath() 和 css() ，还要继续熟悉正则.

当通过class来进行选择的时候，尽量使用 css() 来选择，然后再用 xpath() 来选择元素的熟悉

五、Item Pipeline

Typical use for item pipelines are:
    • cleansing HTML data # 清除HTML数据
    • validating scraped data (checking that the items contain certain fields) # 验证数据
    • checking for duplicates (and dropping them) # 检查重复
    • storing the scraped item in a database # 存入数据库
    （1）验证数据

from scrapy.exceptions import DropItem class PricePipeline(object):  vat_factor = 1.5  def process_item(self, item, spider):    if item['price']:      if item['price_excludes_vat']:        item['price'] *= self.vat_factor    else:      raise DropItem('Missing price in %s' % item)

（2）写Json文件

import json class JsonWriterPipeline(object):  def __init__(self):    self.file = open('json.jl', 'wb')  def process_item(self, item, spider):    line = json.dumps(dict(item)) + '/n'    self.file.write(line)    return item

（3）检查重复

from scrapy.exceptions import DropItem class Duplicates(object):  def __init__(self):    self.ids_seen = set()  def process_item(self, item, spider):    if item['id'] in self.ids_seen:      raise DropItem('Duplicate item found : %s' % item)    else:      self.ids_seen.add(item['id'])      return item

至于将数据写入数据库，应该也很简单。在 process_item 函数中，将 item 存入进去即可了。