blocks|key|2103139|text|实际上，Python中的真实世界HTML解析器是美汤。目前，Python库很受欢迎(尽管标准库具有类似的功能，但使用的请求相当繁琐)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2103140|好斗和收割机项目是真实世界里的爬虫机，只是为了爬行而定制的。|2103141|entityMap|0|LINK|mutability|MUTABLE|url|http://www.crummy.com/software/BeautifulSoup/|1|http://docs.python-requests.org/en/latest/index.html^0|O|2|0|1N|2|1|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

The de facto real world HTML parser in Python is <a href="http://www.crummy.com/software/BeautifulSoup/" rel="nofollow">beautiful soup</a>. The Python <a href="http://docs.python-requests.org/en/latest/index.html" rel="nofollow">requests</a> library is popular these days for HTTP (although the standard library has similar functionality but with a rather cumbersome API). 

The scrappy and harvestman projects are real world crawlers that have been custom built just for the purpose of crawling.

blocks|key|1625746|text|在lxml和漂亮的汤之间，lxml更等同于nokogiri，因为它基于libxml2，并且支持xpath/css。|type|ordered-list-item|depth|inlineStyleRanges|entityRanges|data|1625747|网络/http的等价物是urllib2|1625748|unstyled|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|H|8|@]|9|@]|A|$]]|$1|D|3|-4|5|E|7|I|8|@]|9|@]|A|$]]]|F|$]]

<ol>
<li>Between lxml and beautiful soup, lxml is more equivalent to nokogiri
because it is based on libxml2 and it has xpath/css support.</li>
<li>The equivalent of net/http is urllib2</li>
</ol>

blocks|key|1625796|text|井|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1625797|主要是您必须分离“刮刀器”/爬虫--+python+/program/函数，它将从webserver和解析器中下载文件/数据，解析器将读取这些数据并解释数据。在我的例子中，我不得不放弃并获得一些“开放”的政府信息，但不是对下载/数据友好的。在这个项目中，我使用了scrapy1。|1625798|主要是设置'starter_urls‘，这是我的机器人将爬行/获取的urls，在我使用一个函数’解析器‘检索/解析这个数据之后。|1625799|为了进行解析/检索，您将需要一些html、lxml提取器，因为您90%25的数据都是这样。|1625800|现在集中在你的问题上：|1625801|用于数据爬行|1625802|刮痕|ordered-list-item|1625803|请求2|1625804|Urllib+3|1625805|用于分析数据|1625806|Scrapy/lxml或scrapy%2Bother|1625807|lxml4|1625808|丰盛的汤5|1625809|请记住，“爬行”和“报废”不仅适用于网络，还包括电子邮件。你可以在这里检查另一个问题|1625810|1=+http://scrapy.org/|offset|length|1625811|2-+http://docs.python-requests.org/en/latest/|1625812|3-+http://docs.python.org/library/urllib.html|1625813|4-+http://lxml.de/|1625814|5-+http://www.crummy.com/software/BeautifulSoup/|1625815|6-+Python读取我的outlook电子邮件邮箱并解析消息|1625816|entityMap|0|LINK|mutability|MUTABLE|url|http://scrapy.org/|1|http://docs.python-requests.org/en/latest/|2|http://docs.python.org/library/urllib.html|3|http://lxml.de/|4|http://www.crummy.com/software/BeautifulSoup/|5|https://stackoverflow.com/questions/12291294/python-read-my-outlook-email-mailbox-and-parse-messages/12342273#12342273^0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|3|I|0|0|3|16|1|0|3|16|2|0|3|F|3|0|3|19|4|0|3|S|5|0^^$0|@$1|2|3|4|5|6|7|1Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1Z|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|20|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|21|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|22|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|23|8|@]|9|@]|A|$]]|$1|L|3|M|5|N|7|24|8|@]|9|@]|A|$]]|$1|O|3|P|5|N|7|25|8|@]|9|@]|A|$]]|$1|Q|3|R|5|N|7|26|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|27|8|@]|9|@]|A|$]]|$1|U|3|V|5|N|7|28|8|@]|9|@]|A|$]]|$1|W|3|X|5|N|7|29|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|N|7|2A|8|@]|9|@]|A|$]]|$1|10|3|11|5|6|7|2B|8|@]|9|@]|A|$]]|$1|12|3|13|5|6|7|2C|8|@]|9|@$14|2D|15|2E|1|2F]]|A|$]]|$1|16|3|17|5|6|7|2G|8|@]|9|@$14|2H|15|2I|1|2J]]|A|$]]|$1|18|3|19|5|6|7|2K|8|@]|9|@$14|2L|15|2M|1|2N]]|A|$]]|$1|1A|3|1B|5|6|7|2O|8|@]|9|@$14|2P|15|2Q|1|2R]]|A|$]]|$1|1C|3|1D|5|6|7|2S|8|@]|9|@$14|2T|15|2U|1|2V]]|A|$]]|$1|1E|3|1F|5|6|7|2W|8|@]|9|@$14|2X|15|2Y|1|2Z]]|A|$]]|$1|1G|3|-4|5|6|7|30|8|@]|9|@]|A|$]]]|1H|$1I|$5|1J|1K|1L|A|$1M|1N]]|1O|$5|1J|1K|1L|A|$1M|1P]]|1Q|$5|1J|1K|1L|A|$1M|1R]]|1S|$5|1J|1K|1L|A|$1M|1T]]|1U|$5|1J|1K|1L|A|$1M|1V]]|1W|$5|1J|1K|1L|A|$1M|1X]]]]

Well

Mainly you have to separate the 'scraper'/crawler the python lib/program/function that will download the files/data from the webserver and the Parser that will read this data and interpret the data.
In my case I had to scrap and get some govt info that is 'open' but not download/data friendly.
For this project I used scrapy[1]. 

Mainly I set the 'starter_urls' that are the urls my robot will crawl/get and after I use a function 'parser' to retrieve/parse this data.

For parsing/retrieving you are going to need some html,lxml extractor as the 90% of your data will be that.

Now focusing in your question:

For data crawling

<ol>
<li>Scrapy</li>
<li>Requests [2]</li>
<li>Urllib [3] </li>
</ol>

For parsing data

<ol>
<li>Scrapy/lxml or scrapy+other</li>
<li>lxml[4]</li>
<li>beatiful soup [5] </li>
</ol>

And please remember 'crawling' and scrapping is not only for web, emails too. you can check another question about that here [6] 

[1] = <a href="http://scrapy.org/" rel="nofollow noreferrer">http://scrapy.org/</a>

[2] - <a href="http://docs.python-requests.org/en/latest/" rel="nofollow noreferrer">http://docs.python-requests.org/en/latest/</a>

[3] - <a href="http://docs.python.org/library/urllib.html" rel="nofollow noreferrer">http://docs.python.org/library/urllib.html</a>

[4] - <a href="http://lxml.de/" rel="nofollow noreferrer">http://lxml.de/</a>

[5] - <a href="http://www.crummy.com/software/BeautifulSoup/" rel="nofollow noreferrer">http://www.crummy.com/software/BeautifulSoup/</a>

[6] - <a href="https://stackoverflow.com/questions/12291294/python-read-my-outlook-email-mailbox-and-parse-messages/12342273#12342273">Python read my outlook email mailbox and parse messages</a>

blocks|key|2103269|text|我还使用了美汤，这是一种非常简单的解析方法。当我爬行一些网页时，我也使用ElementTree+XML。就我个人而言，我非常喜欢ElementTree库(它易于解析)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2103270|entityMap|0|LINK|mutability|MUTABLE|url|http://www.crummy.com/software/BeautifulSoup/|1|http://docs.python.org/library/xml.etree.elementtree.html^0|5|2|0|10|F|1|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]|$A|R|B|S|1|T]]|C|$]]|$1|D|3|-4|5|6|7|U|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]]]

I also use <a href="http://www.crummy.com/software/BeautifulSoup/" rel="nofollow">Beautiful Soup</a>, its very easy way how to parse HTML. When i was crawling some web pages i also use <a href="http://docs.python.org/library/xml.etree.elementtree.html" rel="nofollow">The ElementTree XML API</a>. Personally, i really like The ElementTree library(its easy to parse XML).

I've started to learn python the past couple of days. I want to know the equivalent way of writing crawlers in python.

so In ruby I use:

<ol>
<li><code>nokogiri</code> for crawling html and getting content through css tags</li>
<li><code>Net::HTTP</code> and <code>Net::HTTP::Get.new(uri.request_uri).body</code> for getting JSON data from a url</li>
</ol>

what are equivalents of these in python?

Going from Ruby to Python : Crawlers

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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在过去的几天里我开始学习蟒蛇。我想知道用python编写爬虫的等效方法。所以在红宝石里我用：nokogiri，用于爬行html并通过css标记获取内容。用于从url获取JSON数据的Net::HTTP和Net::HTTP::Get.new(uri.request_uri).body在python中，它们的等价物是什么？

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