How to do Web Scraping in Python?

How to do Web Scraping in Python?




Python can be a well-suited language for web scraping. It has a creed design which emphasizes code readability and a syntax which allows programmers to express their concepts in fewer lines of code than possible in languages such as C++ or Java.

Python has several modules that make it easy to scrape web pages using python some of the commonly used modules are Requests, urllib2, BeautifulSoup, Selenium

Introduction

As business technology has advanced exponentially in recent years, data-driven decision making has become a much more fundamental part of every industry. In the past, the business decision was solely made based on one’s intuition, well-informed guesswork or past experience of handling similar situations.

Then came an era where key decisions were complemented with data to back the decision. In today’s fast-changing world, the decision making needs far more complex data analysis coupled with external factors like social behavior, economic trends etc. The access to more relevant data would mean deeper insights and better business decisions.

 

Need for Web scraping

Some of the data needed for such analysis is available on the web in a format that makes it easier to collect and use it, for example in the form of downloadable comma-separated values (CSV) datasets that can then be imported in a spreadsheet or loaded into a data analysis script or leverage the API’s to access data in a specified structure but this data is not sufficient for complex problems that business is trying to address and that’s where web scrapping helps business to access a large amount of data quickly and extract insights from data.

 

Is web scraping legal?

This has been one of the highly debated topics in recent past and there are opinions which are for and against web scraping.

Web scraping is not illegal by itself but if one plays on someone else’s turf on their own terms without respecting the rules of the game then the chances are you are inviting trouble. It is important to remember that as with most thing in real life, there is a fine line which should not be crossed.

Legal

If the scraped data is being used for personal and private use, and within fair use of copyright laws, there is usually no problem. However, if the data is going to be republished, if the scraping is aggressive enough to take down the site, or if the content is copyrighted and the scraper violates the terms of service, then there are several legal precedents to note.

Many websites have a file called robots.txt intended for web crawlers and scrapers, this file instructs batswhat parts of the site they may access It may also define how fast is too fast. The web scraper should respect websites’ robots.txt file some packages have built-in support for reading and adhering to a robots.txt file

 

Prerequisites for web scraping: Knowledge of web development is not mandatory but basic understanding web structure(DOM) and its core elements like HTML, CSS, JavaScript, can help in creating robust web scraper and knowledge of python is essential to building effective web scraper or crawler.

Interaction with websites: The browsersChrome, Firefox has their own developer tool where we can see each websites underlying code. We can use developer mode to know the structure of website thereby we can select a suitable class or id to extract the information we needed.

Web Scraping using Python

Python modules for web scraping:

Python can be a well-suited language for web scraping. It has a creed design which emphasizes code readability and a syntax which allows programmers to express their concepts in fewer lines of code than possible in languages such as C++ or Java.

Python has several modules that make it easy to scrape web pages using python some of the commonly used modules are Requests, urllib2, BeautifulSoup, Selenium

web browser module comes with python which can be used to open a specific page in the browser

Requests: Requests module can be used to download a web page from the internet

GET: to request data from the server.

POST: to submit data to be processed to the server.

Requests will allow us to send HTTP/1.1 requests using python, it has two main methods. POST method is used to add content like headers, form data, multipart files, and parameters via simple Python libraries. Similarly using GET method It also allows us to access the response data of Python in the same way.

 

Installation and importing Requests library:

Coding picture

pip install requests

import requests

response=requests.get(URL)

To know more about Requests module, refer official documentation: Click Here

 

Beautiful Soup:Beautiful Soup is a module for extracting information from an HTML page. The BeautifulSoup module’s name is bs4 (for Beautiful Soup, version 4)

Beautiful Soup creates a parse tree from parsed HTML and XML documents

Installation and demo:

pip install BeautifulSoup4or

conda install -c anaconda beautifulsoup4 

frombs4import BeautifulSoup

importurllib

r = urllib.urlopen(‘URL’).read()

soup = BeautifulSoup(r)

print type(soup)

For official documentation: Click here

 

Selenium: The selenium module lets Python directly control the browser. We can write a python script to click links and filling in login information using selenium module. Unlike to other python modules selenium can be used to scrape dynamic websites.

Installation and demo:

pip install selenium  or

conda install -c conda-forge selenium

from selenium import webdriver

driver=webdriver.Chrome(“chrome driver location”)

driver.get(“URL”)

For official documentation: Click here

 


 

Interested to Learn Python and Web Scraping? Learn Python for Machine Learning from Basics to Advanced concepts from the Best faculties with industry relevant case studies. To know more Click here: Python for Machine Learning

 

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BestinTown Analytics is a training and consulting firm founded in 2009 and located in Bangaluru, India and Toronto, Canada. We have trained and developed solutions for Cisco, PWC, KPMG, VISA, Tesco, Brillio and many more.

 

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