In this article, we have included information regarding why web scraping in python. And how to do web scraping in python using different python tools. That is beneficial for web scraping using python for beginners.
Nowadays, the importance and requirement of extracting information from the Web are growing more and more. However, Programmers find themselves in a situation where they want to extract data from the Web to create a machine learning model every few weeks.
Web scraping is an automatic process utilized to remove huge amounts of data from websites.
The website’s data are unorganized. Likewise, Web scraping assists in collecting these unorganized data and collecting it in a structured form. Also, there are several ways to scrape websites such as APIs, online Services, or writing your own code.
What is Web Scraping?
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Web scraping is a computer software procedure for obtaining information from websites. Mostly this method concentrates on the conversion of unorganized data on the Web into structured information. Likewise, Web scraping is an automatic process utilized to remove huge amounts of data from websites.
The website’s data are unorganized. Web scraping assists in collecting these unorganized data and collect it in a structured form. Likewise, there are several ways to scrape websites such as APIs, online Services, or writing your own code.
You can do web scraping in different ways, including using Google Docs to nearly all programming languages. The reason behind using Python for web scraping is that it is easy to understand and have a rich ecosystem. Also, it has different libraries that assist in this task. You will see the easiest way how to do web scraping in python programming.
Why Web Scraping In Python?
You have already learned how awesome Python is just like other programming languages. Then what is the reason for choosing Python over other programming languages for web scraping?
Here we have mentioned some of Python’s features, which makes it more fitting for web scraping.
- Simple to use: Python is easy to code. Also, there is no need to attach curly-braces “{}” or semi-colons “;” anywhere. However, this is one of the reasons why Python for web scraping.
- It Is Dynamically typed: In Python, for variables, you do not have to determine data types, you can instantly use the variables wherever needed. Also, this conserves time and does your job quicker.
- Large Selection of Libraries: Python has a large selection of libraries such as Pandas, Matlplotlib, Numpy, etc. Also, it gives opinions and assistance for different purposes. Hence, it is proper for web scraping and more use of removed data.
- Easily To Understand Syntax: Syntax of Python is easy to understand because understanding a Python code is very related to reading a statement in English. However, it is easily understandable, and the section practiced in Python. Also, it supports the user to differentiate between different blocks in the code.
- Small code, large task: You can write small codes in Python to do great tasks. Hence, you can save time even while you are writing the code.
Why Web Scraping?
Web scraping is utilized to assemble extensive website information. But what is the reason behind collecting such large pieces of information from websites.? Likewise, to understand that, let’s check some of the applications of web scraping:
Price Comparison:
Services such as ParseHub utilize web scraping. However, to accumulate information from online shopping websites and utilize it to contrast the product prices.
Social Media Scraping:
Web scraping is utilized to accumulate data from Social Media websites like Twitter to discover what’s trending.
Email address gathering:
Several organizations that utilize email as a means for marketing, utilize web scraping to accumulate email ID and then send bulk emails.
Research and Development:
From the website Web, scraping utilize to accumulate a huge set of data (Temperature, General Information, Statistics, etc.). Which are examined and utilized to carry out Surveys or for R&D.
Job listings:
Details related to job opportunities, interviews are accumulated from several websites. And then placed in one place so that it is simply available to the user.
Phone Number Scraping
Python’s libraries like BeautifulSoup and Scrapy make extracting phone numbers from websites easy for the purpose of lead generation or market research.
How To Do Web Scraping Using Python
For web scraping when you run the code, a request is forwarded to the URL that you have suggested. As a reply to the request you have forwarded, the server transmits the information and enables you to understand the XML and HTML pages. However, the code after that parses the XML or HTML page, obtains the information, and removes it.
To remove information utilizing web scraping in python, you require to follow these following steps:
- Discover the URL that you want to scrape
- Examining the Page
- Get the information you want to remove
- Write the code
- After that run the code and remove the information
- Collect the information in the needed format
Different Tools For Web Scraping In Python
- Urllib2
- BeautifulSoup
- Requests
- Selenium
- Lxml
- MechanicalSoup
From all the accessible tools, with Python, only urllib2 is pre-installed, and all different tools require to be installed if required. Let’s talk about all these tools in detail.
1.Urllib2 :
It is a python module utilized for getting URLs. Also, It gives a very easy interface in the form of urlopen function, which can fetch URLs utilizing various protocols like FTP, HTTP, etc.
| # Utilizing urllib2 module from urllib.request import urlopen html = urlopen(“https://www.codeavail.com/“) print(html.read()) |
2. BeautifulSoup:
It is a parsing library that can utilize various parsers. Also, it is a default parser originates from Python’s usual library. Likewise, it produces a parse tree that can be utilized to remove data from HTML; a toolkit for analyzing a document and removing what you want. Also, it automatically changes incoming records to Unicode and outgoing records to UTF-8.pip can be utilized to install BeautifulSoup :
# importing BeautifulSoup form
# bs4 module
from bs4 import BeautifulSoup
# importing requests
import requests
# get URL
r = requests.get(“https://www.codeavail.com/“)
data = r.text
soup = BeautifulSoup(data)
for link in soup.find_all(‘a’):
print(link.get(‘href’))
3. Requests:
Requests are not already installed in Python. Likewise, requests enable sending HTTP/1.1 requests. You can form data, attach headers, multipart files, and parameters with easy Python references and obtain the answer data in an identical way.
Such as Installing requests can be done using pip.
# Using requests module
import requests
# get URL
req = requests.get(‘https://www.codeavail.com/‘)
print(req.encoding)
print(req.status_code)
print(req.elapsed)
print(req.url)
print(req.history)
print(req.headers[‘Content-Type’])
4. Selenium
There are some websites that utilize javascript to assist content. For example, they might wait until you scroll down on the page or click a button before loading specific content. For such websites, selenium is required. Also, it is a tool used for browsers automation, also recognized as web-drivers. Likewise, it also comes with bindings of Python for checking it right from your application.
pip package is utilized to install selenium :
# importing webdriver from selenium module
from selenium import webdriver
# path for chromedriver
path_to_chromedriver =’/Users/Admin/Desktop/chromedriver’
browser = webdriver.Chrome(executable_path = path_to_chromedriver)
url = ‘https://www.codeavail.com/‘
browser.get(url)
5. Lmxl
It is the best performance XML and HTML parsing library. However, If the user requires speed, then Lxml is the best choice. Lxml has several modules, and etree is one of the modules responsible for generating elements and structure utilizing these elements.
You can begin utilizing Lxml by installing it as a python package utilizing pip tool :
| # importing etree from lxml module from lxml import etree root_elem = etree.Element(‘html’) etree.SubElement(root_elem, ‘head’) etree.SubElement(root_elem, ‘title’) etree.SubElement(root_elem, ‘body’) print(etree.tostring(root_elem, pretty_print = True).decode(“utf-8”)) |
6. MechanicalSoup
It is a Python library for automating communication with websites. Likewise, by itself, stores and transfers cookies follows redirects and can follow links and present forms. However, it does not execute JavaScript.
Such as one can utilize the following instruction to install MechanicalSoup :
# importing mechanicalsoup
import mechanicalsoup
browser = mechanicalsoup.StatefulBrowser()
value = browser.open(“https://www.codeavail.com/“)
print(value)
value1 = browser.get_url()
print(value1)
value2 = browser.follow_link(“forms”)
print(value2)
value = browser.get_url()
print(value)
Scrapy
Scrapy is an open-source and collaborative web crawling structure for obtaining the data required from websites. Also, it was basically intended for web scraping. Likewise, it can be practiced to handle requests, maintain user sessions follow redirects, and handle output pipelines.
There are 2 ways to install scrapy:
Using pip :
pip install scrapy
- Using Anaconda: First, install Anaconda or Miniconda and then use the following command to install scrapy :
conda install -c conda-forge scrapy
| # importing scrapy module import scrapy class CodeSpider(scrapy.Spider): name = “Code_spider” start_urls = [https://www.codeavail.com/‘] # Parse function def parse(self, response): SET_SELECTOR = ‘code’ for code in response.css(SET_SELECTOR): pass |
Use the following command to run a scrapy code :
scrapy runspider samplescapy.py
Conclusion:
This blog has given the information about what is a web scraping in python. Why there is a need for web scraping, what are the tools for web scraping in Python. That can be used as tips to do Web Scraping Using Python for beginners.
We explain the syntax and programming of each tool so that you can understand the ways of web scraping in python. These tools are Urllib2, BeautifulSoup, Requests, and others by which web scraping can be done easily.
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