Data Scraping with Python: A Beginner’s Guide

Introduction

Data Scraping with Python referred to as web scraping is the pulling of information from websites for other uses.This can turn out to be a very effective tool especially for the business world, researchers and analysts. In the sense that once they need to embark on data collection for a certain period, they can easily do so within a short span of time. Python is widely used for web scraping because of the simple, clear language, as well as useful libraries.

In this blog, we will explain Data Scraping with Python also, we are going to explain the web scraping process in python in full detail. We will also give example code to assist the reader through this process.

Data Scraping with Python step by step.

 Step 1: Choosing a target website

First of all, you have to decide which website you are going to scrape. We need to avoid any legal cases at any cost; so, scrape only the websites that allow it or those that offer Application Programming Interfaces (APIs).

Step 2: Inspecting the HTML

For you to scrape data with python from a particular website, it is important for you to analyze the HTML of that site. In your browser, right click on the page and choose “Inspect” or “Inspect Element”. This will open the developer tools, and you will be able to identify the HTML elements to scrape as the below image refers.

 Step 3: As a starting note, installing required libraries

There are some libraries which are necessary for web scraping in order to extract the data from webpages. Install the required Python packages using pip:

The second one is using python libraries, so you can use it by simply clicking on the terminal or command prompt and write:

 pip install requests beautifulsoup4

Requests is a library for use while making a request to the server and Beautiful Soup is a library used in scraping for the HTML and XML documents.

Step 4: Making a request

Now, let’s use the Requests library to make a GET request to the target website:

#python
import requests

url = 'https://example.com'
response = requests.get(url)

Check if the request was successful:

#python code
if response.status_code == 200:
    print('Request successful.')
else:
    print('Request failed.')

Step 5: Parsing HTML

But as soon as we have a piece of HTML content, we can parse it using Beautiful Soup. First, let’s create a Beautiful Soup object:

from bs4 import BeautifulSoup

     soup = BeautifulSoup(response.content, “html.parser”)

Now you can explore the objects of the soup and find the required elements. For example, to find all ‘a‘ tags:

links = soup.find_all('a')
for link in links:
    print(link.get('href'))

Step 6: Extracting data

We have now continued the search for the desired elements, and let’s move on to the extraction of data. It is important part of data scraping with python. Let’s say we want to extract the text and href of all ‘a’ tags:

for link in links:
    When true, print(link.text, link.get(‘href’))

Step 7: Storing extracted data

This extraction is done to a File or Database where the obtained data is stored based on your interest. Here’s an example of writing the data to a CSV file:

import csv

with open('output.csv', 'w', newline='', encoding='utf-8') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow( [‘Text’, ‘URL’)

    for link in links:
        writer.writerow([link.text, link.get(’href’)])

 Conclusion

That’s it! You now know how to scrape a website using the Python programming language and have just finished your first web scraper. Of course, this is the simplest example; however, the knowledge will be adequate when considering the more intricate projects and the websites. Nevertheless, be sure to always follow the terms of service of the website and avoid bringing out too much load. Happy Data Scraping with Python!