


Comprehensive understanding of Python crawler xlml parsing library
The following editor will bring you an article about the xlml parsing library of Python crawler (comprehensive understanding). The editor thinks it’s pretty good, so I’ll share it with you now and give it as a reference. Let’s follow the editor to take a look
1.Xpath
Xpath is a language for finding information in XML, which can be used to Traverse elements and attributes in XML documents. Both XQuery and xpoint are built on xpath expression
2. Node
Parent (parent), child (children), brother ( sibling), ancestor (ancetstor), descendant (Decendant)
3. Select node
Path expression
Expression | Description | Path expression | Result |
nodename | Select all child nodes on this node | bookstore | Select all child nodes of the bookstore element |
/ | Select | /bookstore from the root node | Select the root element bookstore, which is the absolute path |
// | Select nodes in the document from the current node matching the selection, regardless of position | //book | Select all book child elements regardless of their position in the document |
. | Select the current node | bookstore//book | Select all book elements in the bookstore descendants |
.. | Select the parent node of the current node | ||
Select attributes | //@lang | Select all attributes named lang |
Predicate
The predicate is used tofind a specific node or a node containing a specified value
The predicate is embedded in square bracketsResult | |
Select the bookstore child The first book element of the element | |
Select the last book element that belongs to the bookstore child element | |
Select the penultimate book element that belongs to the bookstore child element | |
Select all title elements that have a lang attribute with a value of eng | |
Select all book elements of the bookstore element, and the price value is greater than 35.0 |
* Match any element node
@* Match any attribute nodenode() Match any type of node 4.lxml usage#!/usr/bin/python #_*_coding:utf-8_*_ from lxml import etree text=''' <p> <ul> <li class="item-0"><a href="link1.html" rel="external nofollow" rel="external nofollow" >first item</a></li> <li class="item-1"><a href="link2.html" rel="external nofollow" >second item</a></li> <li class="item-inactive"><a href="link3.html" rel="external nofollow" >third item</a></li> <li class="item-1"><a href="link4.html" rel="external nofollow" >fourth item</a></li> <li class="item-0"><a href="link5.html" rel="external nofollow" >fifth item</a> </ul> </p> ''' # html=etree.HTML(text) #html对象,存储在地址中,有自动修正功能 # result=etree.tostring(html) #将html对象转化为字符串 html=etree.parse('hello.html') # result=etree.tostring(html,pretty_print=True) # print result print type(html) result= html.xpath('//li') print result print len(result) print type(result) print type(result[0]) print html.xpath('//li/@class') # 获取li标签下的所有的class print html.xpath('//li/a[@href="link1.html" rel="external nofollow" rel="external nofollow" ]') #获取li标签下href为link1的<a>标签 print html.xpath('//li//span') #获取li标签下所有的span标签 print html.xpath('//li[last()-1]/a')[0].text #获取倒数第二个元素的内容
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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