Python Web Scraping - Data Extraction

Analyzing a web page means understanding its sructure . Now, the question arises why it is important for web scraping? In this chapter, let us understand this in detail.
Web page analysis is important because without analyzing we are not able to know in which form we are going to receive the data from (structured or unstructured) that web page after extraction. We can do web page analysis in the following ways −
This is a way to understand how a web page is structured by examining its source code. To implement this, we need to right click the page and then must select the View page source option. Then, we will get the data of our interest from that web page in the form of HTML. But the main concern is about whitespaces and formatting which is difficult for us to format.
This is another way of analyzing web page. But the difference is that it will resolve the issue of formatting and whitespaces in the source code of web page. You can implement this by right clicking and then selecting the Inspect or Inspect element option from menu. It will provide the information about particular area or element of that web page.
The following methods are mostly used for extracting data from a web page −
They are highly specialized programming language embedded in Python. We can use it through re module of Python. It is also called RE or regexes or regex patterns. With the help of regular expressions, we can specify some rules for the possible set of strings we want to match from the data.
If you want to learn more about regular expression in general, go to the link https://www.tutorialspoint.com/automata_theory/regular_expressions.htm and if you want to know more about re module or regular expression in Python, you can follow the
link https://www.tutorialspoint.com/python/python_reg_expressions.htm.
In the following example, we are going to scrape data about India from
http://example.webscraping.com  after matching the contents of <td> with the help of regular expression.
import re
import urllib.request
response =
   urllib.request.urlopen('http://example.webscraping.com/places/default/view/India-102')
html = response.read()
text = html.decode()
re.findall('<td class="w2p_fw">(.*?)</td>',text)

The corresponding output will be as shown here −
[
   '<img src="/places/static/images/flags/in.png" />',
   '3,287,590 square kilometres',
   '1,173,108,018',
   'IN',
   'India',
   'New Delhi',
   '<a href="/places/default/continent/AS">AS</a>',
   '.in',
   'INR',
   'Rupee',
   '91',
   '######',
   '^(\\d{6})$',
   'enIN,hi,bn,te,mr,ta,ur,gu,kn,ml,or,pa,as,bh,sat,ks,ne,sd,kok,doi,mni,sit,sa,fr,lus,inc',
   '<div>
      <a href="/places/default/iso/CN">CN </a>
      <a href="/places/default/iso/NP">NP </a>
      <a href="/places/default/iso/MM">MM </a>
      <a href="/places/default/iso/BT">BT </a>
      <a href="/places/default/iso/PK">PK </a>
      <a href="/places/default/iso/BD">BD </a>
   </div>'
]

Observe that in the above output you can see the details about country India by using regular expression.
Suppose we want to collect all the hyperlinks from a web page, then we can use a parser called BeautifulSoup which can be known in more detail at https://www.crummy.com/software/BeautifulSoup/bs4/doc/. In simple words, BeautifulSoup is a Python library for pulling data out of HTML and XML files. It can be used with requests, because it needs an input (document or url) to create a soup object asit cannot fetch a web page by itself. You can use the following Python script to gather the title of web page and hyperlinks.
Using the pip command, we can install beautifulsoup either in our virtual environment or in global installation.
(base) D:\ProgramData>pip install bs4
Collecting bs4
   Downloading
https://files.pythonhosted.org/packages/10/ed/7e8b97591f6f456174139ec089c769f89
a94a1a4025fe967691de971f314/bs4-0.0.1.tar.gz
Requirement already satisfied: beautifulsoup4 in d:\programdata\lib\sitepackages
(from bs4) (4.6.0)
Building wheels for collected packages: bs4
   Running setup.py bdist_wheel for bs4 ... done
   Stored in directory:
C:\Users\gaurav\AppData\Local\pip\Cache\wheels\a0\b0\b2\4f80b9456b87abedbc0bf2d
52235414c3467d8889be38dd472
Successfully built bs4
Installing collected packages: bs4
Successfully installed bs4-0.0.1

Note that in this example, we are extending the above example implemented with requests python module. we are using r.text for creating a soup object which will further be used to fetch details like title of the webpage.
First, we need to import necessary Python modules −
import requests
from bs4 import BeautifulSoup

In this following line of code we use requests to make a GET HTTP requests for the url:
https://authoraditiagarwal.com/ by making a GET request.
r = requests.get('https://authoraditiagarwal.com/')

Now we need to create a Soup object as follows −
soup = BeautifulSoup(r.text, 'lxml')
print (soup.title)
print (soup.title.text)

The corresponding output will be as shown here −
<title>Learn and Grow with Aditi Agarwal</title>
Learn and Grow with Aditi Agarwal

Another Python library we are going to discuss for web scraping is lxml. It is a highperformance HTML and XML parsing library. It is comparatively fast and straightforward. You can read about it more on https://lxml.de/.
Using the pip command, we can install lxml either in our virtual environment or in global installation.
(base) D:\ProgramData>pip install lxml
Collecting lxml
   Downloading
https://files.pythonhosted.org/packages/b9/55/bcc78c70e8ba30f51b5495eb0e
3e949aa06e4a2de55b3de53dc9fa9653fa/lxml-4.2.5-cp36-cp36m-win_amd64.whl
(3.
6MB)
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 3.6MB 64kB/s
Installing collected packages: lxml
Successfully installed lxml-4.2.5

In the following example, we are scraping a particular element of the web page from authoraditiagarwal.com by using lxml and requests −
First, we need to import the requests and html from lxml library as follows −
import requests
from lxml import html 

Now we need to provide the url of web page to scrap
url = 'https://authoraditiagarwal.com/leadershipmanagement/'

Now we need to provide the path (Xpath) to particular element of that web page −
path = '//*[@id="panel-836-0-0-1"]/div/div/p[1]'
response = requests.get(url)
byte_string = response.content
source_code = html.fromstring(byte_string)
tree = source_code.xpath(path)
print(tree[0].text_content()) 
The corresponding output will be as shown here −
The Sprint Burndown or the Iteration Burndown chart is a powerful tool to communicate
daily progress to the stakeholders. It tracks the completion of work for a given sprint
or an iteration. The horizontal axis represents the days within a Sprint. The vertical 
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