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):
  title = Field()
  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 Spider
from getblog.items import BlogItem
from 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，csv
FEED_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, django
2014-08-21 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, SpiderState
2014-08-21 04:09:11+0800 [scrapy] INFO: Enabled downloader middlewares: HttpAuthMiddleware, DownloadTimeoutMiddleware, UserAgentMiddleware, RetryMiddleware, DefaultHeadersMiddleware, MetaRefreshMiddleware, HttpCompressionMiddleware, RedirectMiddleware, CookiesMiddleware, ChunkedTransferMiddleware, DownloaderStats
2014-08-21 04:09:11+0800 [scrapy] INFO: Enabled spider middlewares: HttpErrorMiddleware, OffsiteMiddleware, RefererMiddleware, UrlLengthMiddleware, DepthMiddleware
2014-08-21 04:09:11+0800 [scrapy] INFO: Enabled item pipelines:
2014-08-21 04:09:11+0800 [scrapy] DEBUG: Telnet console listening on 127.0.0.1:6024
2014-08-21 04:09:11+0800 [scrapy] DEBUG: Web service listening on 127.0.0.1:6081
2014-08-21 04:09:11+0800 [default] INFO: Spider opened
2014-08-21 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 to
query the response, and convenient shortcuts like response.xpath() and response.css() mapping to
response.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 Spider
from 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-8
from scrapy.contrib.spiders import CrawlSpider, Rule
from scrapy.contrib.linkextractors import LinkExtractor
import 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。