http://cxy.liuzhihengseo.com/462.htmljavascript
原文出处: 磁针石 css
本文摘要自Web Scraping with Python – 2015html
书籍下载地址:https://bitbucket.org/xurongzhong/python-chinese-library/downloadsjava
源码地址:https://bitbucket.org/wswp/codepython
演示站点:http://example.webscraping.com/程序员
演示站点代码:http://bitbucket.org/wswp/placesweb
推荐的python基础教程: http://www.diveintopython.net正则表达式
HTML和JavaScript基础:数据库
http://www.w3schools.comexpress
web抓取简介
为何要进行web抓取?
网购的时候想比较下各个网站的价格,也就是实现惠惠购物助手的功能。有API天然方便,可是一般是没有API,此时就须要web抓取。
web抓取是否合法?
抓取的数据,我的使用不违法,商业用途或从新发布则须要考虑受权,另外须要注意礼节。根据国外已经判决的案例,通常来讲位置和电话能够从新发布,可是原创数据不容许从新发布。
更多参考:
http://www.bvhd.dk/uploads/tx_mocarticles/S_-_og_Handelsrettens_afg_relse_i_Ofir-sagen.pdf
http://www.austlii.edu.au/au/cases/cth/FCA/2010/44.html
http://caselaw.findlaw.com/us-supreme-court/499/340.html
背景研究
robots.txt和Sitemap能够帮助了解站点的规模和结构,还可使用谷歌搜索和WHOIS等工具。
好比:http://example.webscraping.com/robots.txt
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# section 1 User-agent: BadCrawler Disallow: /
# section 2 User-agent: * Crawl-delay: 5 Disallow: /trap
# section 3 Sitemap: http://example.webscraping.com/sitemap.xml |
更多关于web机器人的介绍参见 http://www.robotstxt.org。
Sitemap的协议: http://www.sitemaps.org/protocol.html,好比:
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http://example.webscraping.com/view/Afghanistan-1 http://example.webscraping.com/view/Aland-Islands-2 http://example.webscraping.com/view/Albania-3 ... |
站点地图常常不完整。
站点大小评估:
经过google的site查询 好比:site:automationtesting.sinaapp.com
站点技术评估:
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# pip install builtwith # ipython In [1]: import builtwith
In [2]: builtwith.parse('http://automationtesting.sinaapp.com/') Out[2]: {u'issue-trackers': [u'Trac'], u'javascript-frameworks': [u'jQuery'], u'programming-languages': [u'Python'], u'web-servers': [u'Nginx']} |
分析网站全部者:
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# pip install python-whois # ipython In [1]: import whois
In [2]: print whois.whois('http://automationtesting.sinaapp.com') { "updated_date": "2016-01-07 00:00:00", "status": [ "serverDeleteProhibited https://www.icann.org/epp#serverDeleteProhibited", "serverTransferProhibited https://www.icann.org/epp#serverTransferProhibited", "serverUpdateProhibited https://www.icann.org/epp#serverUpdateProhibited" ], "name": null, "dnssec": null, "city": null, "expiration_date": "2021-06-29 00:00:00", "zipcode": null, "domain_name": "SINAAPP.COM", "country": null, "whois_server": "whois.paycenter.com.cn", "state": null, "registrar": "XIN NET TECHNOLOGY CORPORATION", "referral_url": "http://www.xinnet.com", "address": null, "name_servers": [ "NS1.SINAAPP.COM", "NS2.SINAAPP.COM", "NS3.SINAAPP.COM", "NS4.SINAAPP.COM" ], "org": null, "creation_date": "2009-06-29 00:00:00", "emails": null } |
抓取第一个站点
简单的爬虫(crawling)代码以下:
Python
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import urllib2
def download(url): print 'Downloading:', url try: html = urllib2.urlopen(url).read() except urllib2.URLError as e: print 'Download error:', e.reason html = None return html |
能够基于错误码重试。HTTP状态码:https://tools.ietf.org/html/rfc7231#section-6。4**不必重试,5**能够重试下。
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import urllib2
def download(url, num_retries=2): print 'Downloading:', url try: html = urllib2.urlopen(url).read() except urllib2.URLError as e: print 'Download error:', e.reason html = None if num_retries > 0: if hasattr(e, 'code') and 500 http://httpstat.us/500 会返回500,能够用它来测试下: >>> download('http://httpstat.us/500') Downloading: http://httpstat.us/500 Download error: Internal Server Error Downloading: http://httpstat.us/500 Download error: Internal Server Error Downloading: http://httpstat.us/500 Download error: Internal Server Error 设置 user agent: urllib2默认的user agent是“Python-urllib/2.7”,不少网站会对此进行拦截, 推荐使用接近真实的agent,好比 Mozilla/5.0 (X11; Linux x86_64; rv:38.0) Gecko/20100101 Firefox/38.0 为此咱们增长user agent设置: import urllib2
def download(url, user_agent='Mozilla/5.0 (X11; Linux x86_64; rv:38.0) Gecko/20100101 Firefox/38.0', num_retries=2): print 'Downloading:', url headers = {'User-agent': user_agent} request = urllib2.Request(url, headers=headers) try: html = urllib2.urlopen(request).read() except urllib2.URLError as e: print 'Download error:', e.reason html = None if num_retries > 0: if hasattr(e, 'code') and 500
爬行站点地图: def crawl_sitemap(url): # download the sitemap file sitemap = download(url) # extract the sitemap links links = re.findall('(.*?)', sitemap) # download each link for link in links: html = download(link) # scrape html here # ... ID循环爬行:• http://example.webscraping.com/view/Afghanistan-1• http://example.webscraping.com/view/Australia-2• http://example.webscraping.com/view/Brazil-3上面几个网址仅仅是最后面部分不一样,一般程序员喜欢用数据库的id,好比:http://example.webscraping.com/view/1 ,这样咱们就能够数据库的id抓取网页。 for page in itertools.count(1): url = 'http://example.webscraping.com/view/-%d' % page html = download(url) if html is None: break else: # success - can scrape the result pass 固然数据库有可能删除了一条记录,为此咱们改进成以下: # maximum number of consecutive download errors allowed max_errors = 5 # current number of consecutive download errors num_errors = 0 for page in itertools.count(1): url = 'http://example.webscraping.com/view/-%d' % page html = download(url) if html is None: # received an error trying to download this webpage num_errors += 1 if num_errors == max_errors: # reached maximum number of # consecutive errors so exit break else: # success - can scrape the result # ... num_errors = 0 有些网站不存在的时候会返回404,有些网站的ID不是这么有规则的,好比亚马逊使用ISBN。
分析网页 通常的浏览器都有"查看页面源码"的功能,在Firefox,Firebug尤为方便。以上工具均可以邮件点击网页调出。抓取网页数据主要有3种方法:正则表达式、BeautifulSoup和lxml。正则表达式示例: In [1]: import re
In [2]: import common
In [3]: url = 'http://example.webscraping.com/view/UnitedKingdom-239'
In [4]: html = common.download(url) Downloading: http://example.webscraping.com/view/UnitedKingdom-239
In [5]: re.findall('(.*?)', html) Out[5]: ['', '244,820 square kilometres', '62,348,447', 'GB', 'United Kingdom', 'London', 'EU', '.uk', 'GBP', 'Pound', '44', '@# #@@|@## #@@|@@# #@@|@@## #@@|@#@ #@@|@@#@ #@@|GIR0AA', '^(([A-Z]\d{2}[A-Z]{2})|([A-Z]\d{3}[A-Z]{2})|([A-Z]{2}\d{2}[A-Z]{2})|([A-Z]{2}\d{3}[A-Z]{2})|([A-Z]\d[A-Z]\d[A-Z]{2})|([A-Z]{2}\d[A-Z]\d[A-Z]{2})|(GIR0AA))$', 'en-GB,cy-GB,gd', 'IE ']
In [6]: re.findall('(.*?)', html)[1] Out[6]: '244,820 square kilometres' |
维护成本比较高。
Beautiful Soup:
Python
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In [7]: from bs4 import BeautifulSoup
In [8]: broken_html = '<ul class=country><li>Area<li>Population</ul>'
In [9]: # parse the HTML
In [10]: soup = BeautifulSoup(broken_html, 'html.parser')
In [11]: fixed_html = soup.prettify()
In [12]: print fixed_html <ul class="country"> <li> Area <li> Population </li> </li> </ul> In [13]: ul = soup.find('ul', attrs={'class':'country'})
In [14]: ul.find('li') # returns just the first match Out[14]: <li>Area<li>Population</li></li>
In [15]: ul.find_all('li') # returns all matches Out[15]: [<li>Area<li>Population</li></li>, <li>Population</li>] |
完整的例子:
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In [1]: from bs4 import BeautifulSoup
In [2]: url = 'http://example.webscraping.com/places/view/United-Kingdom-239'
In [3]: import common
In [5]: html = common.download(url) Downloading: http://example.webscraping.com/places/view/United-Kingdom-239
In [6]: soup = BeautifulSoup(html) /usr/lib/python2.7/site-packages/bs4/__init__.py:166: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.
To get rid of this warning, change this:
BeautifulSoup([your markup])
to this:
BeautifulSoup([your markup], "lxml")
markup_type=markup_type))
In [7]: # locate the area row
In [8]: tr = soup.find(attrs={'id':'places_area__row'})
In [9]: td = tr.find(attrs={'class':'w2p_fw'}) # locate the area tag
In [10]: area = td.text # extract the text from this tag
In [11]: print area 244,820 square kilometres |
Lxml基于 libxml2(c语言实现),更快速,可是有时更难安装。网址:http://lxml.de/installation.html。
Python
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In [1]: import lxml.html
In [2]: broken_html = '<ul class=country><li>Area<li>Population</ul>'
In [3]: tree = lxml.html.fromstring(broken_html) # parse the HTML
In [4]: fixed_html = lxml.html.tostring(tree, pretty_print=True)
In [5]: print fixed_html <ul class="country"> <li>Area</li> <li>Population</li> </ul> |
lxml的容错能力也比较强,少半边标签一般没事。
下面使用css选择器,注意安装cssselect。
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In [1]: import common
In [2]: import lxml.html
In [3]: url = 'http://example.webscraping.com/places/view/United-Kingdom-239'
In [4]: html = common.download(url) Downloading: http://example.webscraping.com/places/view/United-Kingdom-239
In [5]: tree = lxml.html.fromstring(html)
In [6]: td = tree.cssselect('tr#places_area__row > td.w2p_fw')[0]
In [7]: area = td.text_content()
In [8]: print area 244,820 square kilometres |
在 CSS 中,选择器是一种模式,用于选择须要添加样式的元素。
“CSS” 列指示该属性是在哪一个 CSS 版本中定义的。(CSS一、CSS2 仍是 CSS3。)
选择器 | 例子 | 例子描述 | CSS |
---|---|---|---|
.class | .intro | 选择 class=”intro” 的全部元素。 | 1 |
#id | #firstname | 选择 id=”firstname” 的全部元素。 | 1 |
* | * | 选择全部元素。 | 2 |
element | p | 选择全部元素。 | 1 |
element,element | div,p | 选择全部 元素和全部元素。 |
1 |
element element | div p | 选择 元素内部的全部元素。 |
1 |
element>element | div>p | 选择父元素为 元素的全部元素。 |
2 |
element+element | div+p | 选择紧接在 元素以后的全部元素。 |
2 |
[attribute] | [target] | 选择带有 target 属性全部元素。 | 2 |
[attribute=value] | [target=_blank] | 选择 target=”_blank” 的全部元素。 | 2 |
[attribute~=value] | [title~=flower] | 选择 title 属性包含单词 “flower” 的全部元素。 | 2 |
[attribute|=value] | [lang|=en] | 选择 lang 属性值以 “en” 开头的全部元素。 | 2 |
:link | a:link | 选择全部未被访问的连接。 | 1 |
:visited | a:visited | 选择全部已被访问的连接。 | 1 |
:active | a:active | 选择活动连接。 | 1 |
:hover | a:hover | 选择鼠标指针位于其上的连接。 | 1 |
:focus | input:focus | 选择得到焦点的 input 元素。 | 2 |
:first-letter | p:first-letter | 选择每一个元素的首字母。 | 1 |
:first-line | p:first-line | 选择每一个元素的首行。 | 1 |
:first-child | p:first-child | 选择属于父元素的第一个子元素的每一个元素。 | 2 |
:before | p:before | 在每一个元素的内容以前插入内容。 | 2 |
:after | p:after | 在每一个元素的内容以后插入内容。 | 2 |
:lang(language) | p:lang(it) | 选择带有以 “it” 开头的 lang 属性值的每一个元素。 | 2 |
element1~element2 | p~ul | 选择前面有元素的每一个
|
3 |
[attribute^=value] | a[src^="https"] | 选择其 src 属性值以 “https” 开头的每一个元素。 | 3 |
[attribute$=value] | a[src$=".pdf"] | 选择其 src 属性以 “.pdf” 结尾的全部 元素。 | 3 |
[attribute*=value] | a[src*="abc"] | 选择其 src 属性中包含 “abc” 子串的每一个元素。 | 3 |
:first-of-type | p:first-of-type | 选择属于其父元素的首个元素的每一个 元素。 |
3 |
:last-of-type | p:last-of-type | 选择属于其父元素的最后元素的每一个 元素。 |
3 |
:only-of-type | p:only-of-type | 选择属于其父元素惟一的元素的每一个 元素。 |
3 |
:only-child | p:only-child | 选择属于其父元素的惟一子元素的每一个元素。 | 3 |
:nth-child(n) | p:nth-child(2) | 选择属于其父元素的第二个子元素的每一个元素。 | 3 |
:nth-last-child(n) | p:nth-last-child(2) | 同上,从最后一个子元素开始计数。 | 3 |
:nth-of-type(n) | p:nth-of-type(2) | 选择属于其父元素第二个元素的每一个 元素。 |
3 |
:nth-last-of-type(n) | p:nth-last-of-type(2) | 同上,可是从最后一个子元素开始计数。 | 3 |
:last-child | p:last-child | 选择属于其父元素最后一个子元素每一个元素。 | 3 |
:root | :root | 选择文档的根元素。 | 3 |
:empty | p:empty | 选择没有子元素的每一个元素(包括文本节点)。 | 3 |
:target | #news:target | 选择当前活动的 #news 元素。 | 3 |
:enabled | input:enabled | 选择每一个启用的 <input>元素。 | 3 |
:disabled | input:disabled | 选择每一个禁用的 <input>元素 | 3 |
:checked | input:checked | 选择每一个被选中的 <input>元素。 | 3 |
:not(selector) | :not(p) | 选择非<p>元素的每一个元素。 | 3 |
::selection | ::selection | 选择被用户选取的元素部分。 | 3 |
CSS 选择器参见:http://www.w3school.com.cn/cssref/css_selectors.ASP 和 https://pythonhosted.org/cssselect/#supported-selectors。
下面经过提取以下页面的国家数据来比较性能:
比较代码:
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import urllib2 import itertools import re from bs4 import BeautifulSoup import lxml.html import time
FIELDS = ('area', 'population', 'iso', 'country', 'capital', 'continent', 'tld', 'currency_code', 'currency_name', 'phone', 'postal_code_format', 'postal_code_regex', 'languages', 'neighbours')
def download(url, user_agent='Mozilla/5.0 (X11; Linux x86_64; rv:38.0) Gecko/20100101 Firefox/38.0', num_retries=2): print 'Downloading:', url headers = {'User-agent': user_agent} request = urllib2.Request(url, headers=headers) try: html = urllib2.urlopen(request).read() except urllib2.URLError as e: print 'Download error:', e.reason html = None if num_retries > 0: if hasattr(e, 'code') and 500 (.*?)' % field, html.replace('n','')).groups()[0] return results
def bs_scraper(html): soup = BeautifulSoup(html, 'html.parser') results = {} for field in FIELDS: results[field] = soup.find('table').find('tr',id='places_%s__row' % field).find('td',class_='w2p_fw').text return results
def lxml_scraper(html): tree = lxml.html.fromstring(html) results = {} for field in FIELDS: results[field] = tree.cssselect('table > tr#places_%s__row> td.w2p_fw' % field)[0].text_content() return results
NUM_ITERATIONS = 1000 # number of times to test each scraper html = download('http://example.webscraping.com/places/view/United-Kingdom-239')
for name, scraper in [('Regular expressions', re_scraper),('BeautifulSoup', bs_scraper),('Lxml', lxml_scraper)]: # record start time of scrape start = time.time() for i in range(NUM_ITERATIONS): if scraper == re_scraper: re.purge() result = scraper(html) # check scraped result is as expected assert(result['area'] == '244,820 square kilometres')
# record end time of scrape and output the total end = time.time() print '%s: %.2f seconds' % (name, end - start) |
Windows执行结果:
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Downloading: http://example.webscraping.com/places/view/United-Kingdom-239 Regular expressions: 11.63 seconds BeautifulSoup: 92.80 seconds Lxml: 7.25 seconds |
Linux执行结果:
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Downloading: http://example.webscraping.com/places/view/United-Kingdom-239 Regular expressions: 3.09 seconds BeautifulSoup: 29.40 seconds Lxml: 4.25 seconds |
其中 re.purge() 用户清正则表达式的缓存。
推荐使用基于Linux的lxml,在同一网页屡次分析的状况优点更为明显。
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