Python Modules for Web Scraping

Joseph Birla

10 months ago

Python Modules for Web Scraping | Insideaiml
Python Modules for Web Scraping | Insideaiml
In this chapter, let us learn various Python modules that we can use for web scraping.

Python Development Environments using virtualenv

Virtualenv is a tool to create isolated Python environments. With the help of virtualenv, we can create a folder that contains all necessary executables to use the packages that our Python project requires. It also allows us to add and modify Python modules without access to the global installation.
You can use the following command to install virtualenv

(base) D:\ProgramData>pip install virtualenv
Collecting virtualenv
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 1.9MB 86kB/s
Installing collected packages: virtualenv
Successfully installed virtualenv-16.0.0
Now, we need to create a directory which will represent the project with the help of the following command −
(base) D:\ProgramData>mkdir webscrap
Now, enter into that directory with the help of this following command −
(base) D:\ProgramData>cd webscrap
Now, we need to initialize the virtual environment folder of our choice as follows −

(base) D:\ProgramData\webscrap>virtualenv websc
Using base prefix 'd:\\programdata'
New python executable in D:\ProgramData\webscrap\websc\Scripts\python.exe
Installing setuptools, pip, wheel...done.
Now, activate the virtual environment with the command given below. Once successfully activated, you will see the name of it on the left-hand side in brackets.
(base) D:\ProgramData\webscrap>websc\scripts\activate
We can install any module in this environment as follows −

(websc) (base) D:\ProgramData\webscrap>pip install requests
Collecting requests
c4c3fdf57a284257925dfc12bda/requests-2.19.1-py2.py3-none-any.whl (9
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 92kB 148kB/s
Collecting chardet<3.1.0,>=3.0.2 (from requests)
55ec7510b22e4c51f14098443b8/chardet-3.0.4-py2.py3-none-any.whl (133
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 143kB 369kB/s
Collecting certifi>=2017.4.17 (from requests)
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 153kB 527kB/s
Collecting urllib3<1.24,>=1.21.1 (from requests)
3851ef4f56f62a3486e6a7d8ffb/urllib3-1.23-py2.py3-none-any.whl (133k
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 143kB 517kB/s
Collecting idna<2.8,>=2.5 (from requests)
a97fab05a372e4a2c6a6b876165/idna-2.7-py2.py3-none-any.whl (58kB)
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 61kB 339kB/s
Installing collected packages: chardet, certifi, urllib3, idna, requests
Successfully installed certifi-2018.8.24 chardet-3.0.4 idna-2.7 requests-2.19.1
For deactivating the virtual environment, we can use the following command −
(websc) (base) D:\ProgramData\webscrap>deactivate
(base) D:\ProgramData\webscrap>
You can see that (websc) has been deactivated.

Python Modules for Web Scraping

Web scraping is the process of constructing an agent which can extract, parse, download and organize useful information from the web automatically. In other words, instead of manually saving the data from websites, the web scraping software will automatically load and extract data from multiple websites as per our requirement.
In this section, we are going to discuss about useful Python libraries for web scraping.


It is a simple python web scraping library. It is an efficient HTTP library used for accessing web pages. With the help of Requests, we can get the raw HTML of web pages which can then be parsed for retrieving the data. Before using requests, let us understand its installation.

Installing Requests

We can install it in either on our virtual environment or on the global installation. With the help of pip command, we can easily install it as follows −

(base) D:\ProgramData> pip install requests
Collecting requests
Using cached
Requirement already satisfied: idna<2.8,>=2.5 in d:\programdata\lib\sitepackages
(from requests) (2.6)
Requirement already satisfied: urllib3<1.24,>=1.21.1 in
d:\programdata\lib\site-packages (from requests) (1.22)
Requirement already satisfied: certifi>=2017.4.17 in d:\programdata\lib\sitepackages
(from requests) (2018.1.18)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in
d:\programdata\lib\site-packages (from requests) (3.0.4)
Installing collected packages: requests
Successfully installed requests-2.19.1


In this example, we are making a GET HTTP request for a web page. For this, we need to first import requests library as follows −
In [1]: import requests
In this following line of code, we use requests to make a GET HTTP requests for the URL: by making a GET request.
In [2]: r = requests.get('')
Now we can retrieve the content by using .text property as follows −
In [5]: r.text[:200]
Observe that in the following output, we got the first 200 characters.
Out[5]: '\n\n\n\t\n\t


It is another Python library that can be used for retrieving data from URLs similar to the requests library. You can read more on this at its technical documentation at

Installing Urllib3

Using the pip command, we can install urllib3 either in our virtual environment or in global installation.

(base) D:\ProgramData>pip install urllib3
Collecting urllib3
Using cached
Installing collected packages: urllib3
Successfully installed urllib3-1.23

Example: Scraping using Urllib3 and BeautifulSoup

In the following example, we are scraping the web page by using Urllib3 and BeautifulSoup. We are using Urllib3 at the place of requests library for getting the raw data (HTML) from the web page. Then we are using BeautifulSoup for parsing that HTML data.

import urllib3
from bs4 import BeautifulSoup
http = urllib3.PoolManager()
r = http.request('GET', '')
soup = BeautifulSoup(, 'lxml')
print (soup.title)
print (soup.title.text)
This is the output you will observe when you run this code −
Learn and Grow with Aditi Agarwal
Learn and Grow with Aditi Agarwal


It is an open source automated testing suite for web applications across different browsers and platforms. It is not a single tool but a suite of software. We have selenium bindings for Python, Java, C#, Ruby and JavaScript. Here we are going to perform web scraping by using selenium and its Python bindings. You can learn more about Selenium with Java on the link Selenium.
Selenium Python bindings provide a convenient API to access Selenium WebDrivers like Firefox, IE, Chrome, Remote etc. The current supported Python versions are 2.7, 3.5 and above.

Installing Selenium

Using the pip command, we can install urllib3 either in our virtual environment or in global installation.
pip install selenium
As selenium requires a driver to interface with the chosen browser, we need to download it. The following table shows different browsers and their links for downloading the same.
 Different Browsers and their links for Downloading | Insideaiml
Different Browsers and their links for Downloading | Insideaiml



This example shows web scraping using selenium. It can also be used for testing which is called selenium testing.
After downloading the particular driver for the specified version of the browser, we need to do programming in Python.
First, need to import web driver from selenium as follows −
from selenium import webdriver
Now, provide the path of web driver which we have downloaded as per our requirement −
path = r'C:\\Users\\gaurav\\Desktop\\Chromedriver'
browser = webdriver.Chrome(executable_path = path)
Now, provide the URL which we want to open in that web browser now controlled by our Python script.
We can also scrape a particular element by providing the XPath as provided in XML.
You can check the browser, controlled by Python script, for output.


Scrapy is a fast, open-source web crawling framework written in Python, used to extract the data from the web page with the help of selectors based on XPath. Scrapy was first released on June 26, 2008, licensed under BSD, with a milestone 1.0 releasing in June 2015. It provides us all the tools we need to extract, process, and structure the data from websites.

Installing Scrapy

Using the pip command, we can install urllib3 either in our virtual environment or in global installation.
pip install scrapy
For more detail study of Scrapy you can go to the link Scrapy.
To know more about python click here and select option python in the article section.

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