【Python爬虫2】网页数据提取

我们让这个爬虫比每个网页中抽取一些数据，然后实现某些事情，这种做法也被称为提取（scraping）。

1 提取数据方法

• 正则表达式
• BeautifulSoup模块（流行）
• Lxml（强大）

1.1 正则表达式

下面是用正则表达式提取国家面积数据的例子。
正则表达式文档：https://docs.python.org/3/howto/regex.html

# -*- coding: utf-8 -*-import urllib2import redef scrape(html):    area = re.findall('<tr id="places_area__row">.*?<td\s*class=["\']w2p_fw["\']>(.*?)</td>', html)[0]    return areaif __name__ == '__main__':    html = urllib2.urlopen('http://example.webscraping.com/view/China-47').read()    print scrape(html)

正则表达式容易适应未来网站的变化，但难以构造、可读性差，难于适应布局微小的变化。

1.2 流行的BeautifulSoup模块

安装：pip install beautifulsoup4
有些网页不具备良好的HTML格式，如下面HTML就存在属性两侧引号缺失和标签未闭合问题。

<ul class=country>    <li>Area    <li>Population</ul>

这样提取数据往往不能得到预期结果，但可以Beautiful Soup来处理。

>>> from bs4 import BeautifulSoup>>> brocken_html='<ul class=country><Li>Area<li>Population</ul>'>>> soup=BeautifulSoup(brocken_html,'html.parser')>>> fixed_html=soup.prettify()>>> print fixed_html<ul class="country"> <li>  Area  <li>   Population  </li> </li></ul>>>> >>> ul=soup.find('ul',attrs={'class':'country'})>>> ul.find('li')<li>Area<li>Population</li></li>>>> ul.find_all('li')[<li>Area<li>Population</li></li>, <li>Population</li>]>>> 

BeautifulSoup官方文档：https://www.crummy.com/software/BeautifulSoup/bs4/doc/
下面是用BeautifulSoup提取国家面积数据的例子。

# -*- coding: utf-8 -*-import urllib2from bs4 import BeautifulSoupdef scrape(html):    soup = BeautifulSoup(html)     tr = soup.find(attrs={'id':'places_area__row'}) # locate the area row    # 'class' is a special python attribute so instead 'class_' is used    td = tr.find(attrs={'class':'w2p_fw'})  # locate the area tag    area = td.text  # extract the area contents from this tag    return areaif __name__ == '__main__':    html = urllib2.urlopen('http://example.webscraping.com/view/United-Kingdom-239').read()    print scrape(html)

虽然BeautifulSoup正则表达式更加复杂，但容易构造和理解，而且无须担心多余空格和标签属性这样布局上的小变化。

1.3 强大的Lxml模块

Lxml是基于libxml2这个XML解析库的Python封装。该模块用C语言编写的，解析速度比Beautiful Soup更快，不过安装过程也更为复杂。最新的安装说明可以参考http://Lxml.de/installation.html 。
和Beautiful Soup一样，使用lxml模块的第一步也是将有可能不合法的HTML解析为统一格式。

>>> import lxml.html>>> broken_html='<ul class=country><li>Area<li>Population</ul>'>>> tree=lxml.html.fromstring(broken_html) #parse the HTML>>> fixed_html=lxml.html.tostring(tree,pretty_print=True)>>> print fixed_html<ul class="country"><li>Area</li><li>Population</li></ul>

lxml也可以正确解析属性两侧缺失的引号，并闭合标签。解析完输入内容之后，进入选择元素的步骤，此时lxml有几种不用的方法：
- XPath选择器（类似Beautiful Soup的find()方法）
- CSS选择器（类似jQuery选择器）

这里选用CSS选择器，它更加简洁，也可以用在解析动态内容。

>>> li=tree.cssselect('ul.country > li')[0]>>> area=li.text_content()>>> print areaArea>>> 

说明 示例 选择所有标签 * 选择<a>标签 a 选择所有class="link"的标签 .link 选择class="link"的<a>标签 a.link 选择id="home"的<a>标签 a#home 选择父元素为<a>标签的所有<span>标签 a > span 选择<a>标签内部的所有<span>标签 a span 选择title属性为”Home”的所有<a>标签 a[title=Home]

下面是用CSS选择器提取国家面积数据的例子。

# -*- coding: utf-8 -*-import urllib2import lxml.htmldef scrape(html):    tree = lxml.html.fromstring(html)    td = tree.cssselect('tr#places_area__row > td.w2p_fw')[0]    area = td.text_content()    return areaif __name__ == '__main__':    html = urllib2.urlopen('http://127.0.0.1:8000/places/default/view/China-47').read()    print scrape(html)

W3C已提出CSS3规范，其网址是http://www.w3c.org/TR/2011/REC-css3-selectors-20110929/ 。
Lxml已经实现了大部分CSS3属性，其不支持的功能可以参见http://pythonhosted.org/cssselect/#supported-selectors 。
需要注意的是，lxml在内部实现中，实际上是将CSS选择器转换为等价的XPath选择器。

2 性能对比

# -*- coding: utf-8 -*-import csvimport timeimport urllib2import reimport timeitfrom bs4 import BeautifulSoupimport lxml.htmlFIELDS = ('area', 'population', 'iso', 'country', 'capital', 'continent', 'tld', 'currency_code', 'currency_name', 'phone', 'postal_code_format', 'postal_code_regex', 'languages', 'neighbours')def regex_scraper(html):    results = {}    for field in FIELDS:        results[field] = re.search('<tr id="places_{}__row">.*?<td class="w2p_fw">(.*?)</td>'.format(field), html).groups()[0]    return resultsdef beautiful_soup_scraper(html):    soup = BeautifulSoup(html, 'html.parser')     results = {}    for field in FIELDS:        results[field] = soup.find('table').find('tr', id='places_{}__row'.format(field)).find('td', class_='w2p_fw').text    return resultsdef lxml_scraper(html):    tree = lxml.html.fromstring(html)    results = {}    for field in FIELDS:        results[field] = tree.cssselect('table > tr#places_{}__row > td.w2p_fw'.format(field))[0].text_content()    return resultsdef main():    times = {}    html = urllib2.urlopen('http://127.0.0.1:8000/places/default/view/China-47').read()    NUM_ITERATIONS = 1000 # number of times to test each scraper    for name, scraper in ('Regular expressions', regex_scraper), ('Beautiful Soup', beautiful_soup_scraper), ('Lxml', lxml_scraper):        times[name] = []        # record start time of scrape        start = time.time()        for i in range(NUM_ITERATIONS):            if scraper == regex_scraper:                # the regular expression module will cache results                # so need to purge this cache for meaningful timings                re.purge()             result = scraper(html)            # check scraped result is as expected            assert(result['area'] == '9596960 square kilometres')            times[name].append(time.time() - start)        # record end time of scrape and output the total        end = time.time()        print '{}: {:.2f} seconds'.format(name, end - start)    writer = csv.writer(open('times.csv', 'w'))    header = sorted(times.keys())    writer.writerow(header)    for row in zip(*[times[scraper] for scraper in header]):        writer.writerow(row)if __name__ == '__main__':    main()

这段代码每个爬虫执行1000次，每次都有会检查结果是否正确，然后打印用时，并把所有记录存入csv文件中。正则表达式模块会用缓存搜索结果，我们用re.purge()方法清除第次的缓存。

wu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/2.数据抓取$ python 2performance.py Regular expressions: 6.65 secondsBeautiful Soup: 61.61 secondsLxml: 8.57 seconds

提取方法 性能 使用难度 安装难度 正则表达式 快 困难 简单（内置模块） Beautiful Soup 慢 简单 简单（纯Python） Lxml 快 简单 相对困难

3 为链接爬虫添加抓取回调

要想把提取数据代码集成到上章链接爬虫代码中，我们需要添加一个回调函数callback，该函数就是调入参数处理用于提取数据行为。本例中，网页下载后调用回调函数，数据提取函数包含url和html两个参数，并返回一个待爬取的URL列表。

def link_crawler(seed_url, link_regex=None,... scrape_callback=None):    ...    html = download(url, headers, proxy=proxy, num_retries=num_retries)    links = []    if scrape_callback:        links.extend(scrape_callback(url, html) or [])##这里没有返回一个待爬取的URL列表    ...

3.1 回调函数一

现在我们只需对传入的scrape_callback函数定制化处理。

# -*- coding: utf-8 -*-import csvimport reimport urlparseimport lxml.htmlfrom link_crawler import link_crawlerFIELDS = ('area', 'population', 'iso', 'country', 'capital', 'continent', 'tld', 'currency_code', 'currency_name', 'phone', 'postal_code_format', 'postal_code_regex', 'languages', 'neighbours')def scrape_callback(url, html):    if re.search('/view/', url):        tree = lxml.html.fromstring(html)        row = [tree.cssselect('table > tr#places_{}__row > td.w2p_fw'.format(field))[0].text_content() for field in FIELDS]        print url, rowif __name__ == '__main__':    link_crawler('http://example.webscraping.com/', '/(index|view)', scrape_callback=scrape_callback)

用第一种回调输出：

wu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/2.数据抓取$ python 3scrape_callback1.py Downloading: http://example.webscraping.com/Downloading: http://example.webscraping.com/index/1...Downloading: http://example.webscraping.com/index/25Downloading: http://example.webscraping.com/view/Zimbabwe-252http://example.webscraping.com/view/Zimbabwe-252 ['390,580 square kilometres', '11,651,858', 'ZW', 'Zimbabwe', 'Harare', 'AF', '.zw', 'ZWL', 'Dollar', '263', '', '', 'en-ZW,sn,nr,nd', 'ZA MZ BW ZM ']Downloading: http://example.webscraping.com/view/Zambia-251http://example.webscraping.com/view/Zambia-251 ['752,614 square kilometres', '13,460,305', 'ZM', 'Zambia', 'Lusaka', 'AF', '.zm', 'ZMW', 'Kwacha', '260', '#####', '^(\\d{5})$', 'en-ZM,bem,loz,lun,lue,ny,toi', 'ZW TZ MZ CD NA MW AO ']Downloading: http://example.webscraping.com/view/Yemen-250...

3.2 回调函数二

下面我们对功能进行扩展，把得到的结果数据保存到CSV表格中。这里我们使用了回调类，以便保持csv的writer属性的状态。csv的writer属性在构造方法中进行了实现化处理，然后在call方法中多次写操作。注意，call是一个特殊方法，也是链接接爬虫中scrape_callback的调用方法。也就是说scrape_callback(url,html)和scrape_callback.__call__(url,html)是等价的。可以参考https://docs.python.org/2/reference/datamodel.html#special-method-names .。

# -*- coding: utf-8 -*-import csvimport reimport urlparseimport lxml.htmlfrom link_crawler import link_crawlerclass ScrapeCallback:    def __init__(self):        self.writer = csv.writer(open('countries.csv', 'w'))        self.fields = ('area', 'population', 'iso', 'country', 'capital', 'continent', 'tld', 'currency_code', 'currency_name', 'phone', 'postal_code_format', 'postal_code_regex', 'languages', 'neighbours')        self.writer.writerow(self.fields)    def __call__(self, url, html):        if re.search('/view/', url):            tree = lxml.html.fromstring(html)            row = []            for field in self.fields:                row.append(tree.cssselect('table > tr#places_{}__row > td.w2p_fw'.format(field))[0].text_content())            self.writer.writerow(row)if __name__ == '__main__':    link_crawler('http://127.0.0.1:8000/places', '/places/default/(index|view)', scrape_callback=ScrapeCallback())    #link_crawler('http://example.webscraping.com/', '/(index|view)', scrape_callback=ScrapeCallback())

3.3 复用上章的链接爬虫代码

# -*- coding: utf-8 -*-import reimport urlparseimport urllib2import timefrom datetime import datetimeimport robotparserimport Queuedef link_crawler(seed_url, link_regex=None, delay=0, max_depth=-1, max_urls=-1, headers=None, user_agent='wswp', proxy=None, num_retries=1, scrape_callback=None):    """Crawl from the given seed URL following links matched by link_regex    """    # the queue of URL's that still need to be crawled    crawl_queue = [seed_url]    # the URL's that have been seen and at what depth    seen = {seed_url: 0}    # track how many URL's have been downloaded    num_urls = 0    rp = get_robots(seed_url)    throttle = Throttle(delay)    headers = headers or {}    if user_agent:        headers['User-agent'] = user_agent    while crawl_queue:        url = crawl_queue.pop()        depth = seen[url]        # check url passes robots.txt restrictions        if rp.can_fetch(user_agent, url):            throttle.wait(url)            html = download(url, headers, proxy=proxy, num_retries=num_retries)            links = []            if scrape_callback:                links.extend(scrape_callback(url, html) or [])##这里没有返回一个待爬取的URL列表            if depth != max_depth:                # can still crawl further                if link_regex:                    # filter for links matching our regular expression                    links.extend(link for link in get_links(html) if re.match(link_regex, link))                for link in links:                    link = normalize(seed_url, link)                    # check whether already crawled this link                    if link not in seen:                        seen[link] = depth + 1                        # check link is within same domain                        if same_domain(seed_url, link):                            # success! add this new link to queue                            crawl_queue.append(link)            # check whether have reached downloaded maximum            num_urls += 1            if num_urls == max_urls:                break        else:            print 'Blocked by robots.txt:', urlclass Throttle:    """Throttle downloading by sleeping between requests to same domain    """    def __init__(self, delay):        # amount of delay between downloads for each domain        self.delay = delay        # timestamp of when a domain was last accessed        self.domains = {}    def wait(self, url):        """Delay if have accessed this domain recently        """        domain = urlparse.urlsplit(url).netloc        last_accessed = self.domains.get(domain)        if self.delay > 0 and last_accessed is not None:            sleep_secs = self.delay - (datetime.now() - last_accessed).seconds            if sleep_secs > 0:                time.sleep(sleep_secs)        self.domains[domain] = datetime.now()def download(url, headers, proxy, num_retries, data=None):    print 'Downloading:', url    request = urllib2.Request(url, data, headers)    opener = urllib2.build_opener()    if proxy:        proxy_params = {urlparse.urlparse(url).scheme: proxy}        opener.add_handler(urllib2.ProxyHandler(proxy_params))    try:        response = opener.open(request)        html = response.read()        code = response.code    except urllib2.URLError as e:        print 'Download error:', e.reason        html = ''        if hasattr(e, 'code'):            code = e.code            if num_retries > 0 and 500 <= code < 600:                # retry 5XX HTTP errors                html = download(url, headers, proxy, num_retries-1, data)        else:            code = None    return htmldef normalize(seed_url, link):    """Normalize this URL by removing hash and adding domain    """    link, _ = urlparse.urldefrag(link) # remove hash to avoid duplicates    return urlparse.urljoin(seed_url, link)def same_domain(url1, url2):    """Return True if both URL's belong to same domain    """    return urlparse.urlparse(url1).netloc == urlparse.urlparse(url2).netlocdef get_robots(url):    """Initialize robots parser for this domain    """    rp = robotparser.RobotFileParser()    rp.set_url(urlparse.urljoin(url, '/robots.txt'))    rp.read()    return rpdef get_links(html):    """Return a list of links from html     """    # a regular expression to extract all links from the webpage    webpage_regex = re.compile('<a[^>]+href=["\'](.*?)["\']', re.IGNORECASE)    # list of all links from the webpage    return webpage_regex.findall(html)if __name__ == '__main__':    link_crawler('http://example.webscraping.com', '/(index|view)', delay=0, num_retries=1, user_agent='BadCrawler')    link_crawler('http://example.webscraping.com', '/(index|view)', delay=0, num_retries=1, max_depth=1, user_agent='GoodCrawler')

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【Python爬虫系列】《【Python爬虫2】网页数据提取》http://blog.csdn.net/u014134180/article/details/55506973
Python爬虫系列的GitHub代码文件：https://github.com/1040003585/WebScrapingWithPython

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