Ayesha Published: June 14, 2023 · 5 minutes read

In today’s data-driven world, extracting information from websites has become crucial for businesses and researchers. Web scraping in Python is the process of automating data extraction from websites, and Python is the most suitable for this task. In this blog, we will explore what Python web scraping is and why Python is an excellent choice for it.

We will also delve into the techniques of scraping data from websites using Python. Additionally, we’ll discuss the possibility of using APIs for Python web scraping and introduce you to Zenscrape, a powerful web scraping tool. Let’s get started with our Python web scraping tutorial!

What Is Web Scraping?

Web scraping helps us in extracting data from websites automatically. It involves retrieving HTML code from web pages and parsing it to extract relevant information.

Examples of web scraping include extracting product details and prices from e-commerce websites, gathering news articles from news websites, aggregating data from social media platforms, or scraping weather data from weather websites.

By using specialized tools and libraries, web scraping enables us to collect and analyze vast amounts of data quickly and efficiently. It has numerous applications in business intelligence, market research, data analysis, and academic research.

Why Is Web Scraping Python Important?

Web scraping is important for several reasons. Firstly, it allows us to access and collect data that may not be readily available through traditional means. It enables businesses to gather market intelligence, track competitors, monitor pricing trends, and gather customer reviews.

Researchers can utilize web scraping to collect data for analysis, study online behavior, or track social media sentiment.

Secondly, web scraping automates data extraction, saving time and effort compared to manual data collection.

Python’s robust ecosystem makes it a popular choice for web scraping projects, allowing developers to efficiently navigate, extract, and process data from websites. Let’s explore why we need to scrape websites in Python.

Why Is Python Good for Web Scraping?

Firstly, Python offers many powerful libraries and frameworks specifically designed for web scraping. Libraries like BeautifulSoup and Scrapy provide easy-to-use tools for parsing and extracting data from HTML or XML documents. Hence, making the scraping process more efficient.

Secondly, Python has a simple and intuitive syntax, making it beginner-friendly and easy to learn. This reduces the barrier to entry for those interested in web scraping.

Additionally, Python has excellent support for handling various data formats commonly encountered during web scrapings, such as JSON and CSV. Its robust ecosystem also offers libraries for handling HTTP requests, managing cookies and sessions, and handling proxies. Hence, enabling developers to build robust and scalable web scraping applications.

How to Scrape Data From a Website Through Python Web Scraping?

Let’s suppose we want to scrape the Flipkart website by using Python. We must follow the below steps:

Get The URL

First of all, we will get the URL of our target website. Here is the URL: https://www.flipkart.com/laptops/~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5g&uniqBStoreParam1=val1&wid=11.productCard.PMU_V2

Page Source Code Inspection

We must know that our data is mostly nested in HTML tags. Therefore, we must perform the inspection of tags. To perform the inspection, we must right-click on our desired page and navigate to the inspect. It will bring us to a Browser Inspector Box.

Look For The Desired Data

Suppose you want to check a product page’s rating, name, and price. You will find it inside the “div” tag.

Generate Code

To begin, we will generate a Python file. To accomplish this, launch the terminal in Ubuntu and enter the command “gedit <your file name>” along with the .py extension.

You can perform the same function in the Command prompt or Terminal when using Windows or MacOS respectively.

Linux

gedit scrape.py

Windows

notepad scrape.py

MacOS

open -e scrape.py

Then, import requests library and other necessary libraries.

Linux

from selenium import webdriver
from bs4 import BeautifulSoup as bs
import pandas as pd

Windows & MacOS

from selenium import webdriver
from bs4 import BeautifulSoup as bs
import pandas as pd

# Set the path to the chromedriver executable for Windows
driver_path = "path/to/chromedriver.exe"

# Create a Chrome webdriver instance
driver = webdriver.Chrome(executable_path=driver_path)

# Rest of your code

Beautiful Soup is the most used Python library for scraping.

Then, you must configure the web driver to the Chrome browser to get all the data.

Linux

driver = webdriver.Chrome("/usr/lib/chromium-browser/chromedriver")

The following code can help you open the URL.

product_names = [] # List to store names of the products
product_prices = [] # List to store prices of the products
product_ratings = [] # List to store ratings of the products
driver.get("https://www.flipkart.com/laptops/~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5g&uniq")

The next step is to find all the div tags containing our data in an HTML document.

content = driver.page_source
soup = BeautifulSoup(content)
for a in soup.findAll('a', href=True, attrs={'class': '_31qSD5'}):
    product_names = a.find('div', attrs={'class': '_3wU53n'})
    products.append(name.text)
    product_price = a.find('div', attrs={'class': '_1vC4OE _2rQ-NK'})
    prices.append(price.text)
    product_rating = a.find('div', attrs={'class': 'hGSR34 _2beYZw'})
    ratings.append(rating.text)

Then, use the following command to run your code.

python scrape.py

Finally, you must parse HTML & store the data in a format such as a CSV file.

df = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings}) 
df.to_csv('result.csv', index=False, encoding='utf-8')

Can We Use APIs For Web Scraping?

Many websites provide APIs (Application Programming Interfaces) that allow developers to extract data in a structured manner. Instead of parsing and scraping the raw HTML of a webpage, you can make requests to the API endpoints and receive data in a more organized format, such as JSON.

Using APIs for web scraping has several advantages:

• APIs typically provide data in a structured format like JSON or XML, making extracting and processing the desired information easier.
• The API responses are often more compact than full HTML pages, leading to faster and more efficient data retrieval.
• APIs often offer a consistent data structure, reducing the chances of scraping errors due to website HTML changes.
• Some APIs require authentication, ensuring that you have authorized access to the data.

Let’s explore Zenscrape, one of the most reliable APIs.

Zenscrape

Zenscrape helps you in scraping websites without getting blocked. This is what you get from Zenscrape:

• 100% of what users see
• Javascript Rendering
• Free Plan
• Fair Pricing

10,000+ customers trust this lightning-fast API. Moreover, it supports all programming languages. Zenscrape also offers the following features:

• Location Based
• Proxy Pool with Millions of IPs
• Automatic Proxy Rotation
• High Concurrency

Here is a Python code example of scraping websites with Zenscrape:

import requests

headers = { 
  "apikey": "YOUR-APIKEY"}

params = (
   ("url","https://httpbin.org/ip"),
   ("premium","true"),
   ("country","de"),
   ("render","true"),
);

response = requests.get('https://app.zenscrape.com/api/v1/get', headers=headers, params=params);
print(response.text)

Also learn advanced techniques for web scraping with Python and proxies.

Conclusion

Web scraping using Python provides a practical and powerful approach to gathering valuable data from websites. With the abundance of information available online, web scraping enables users to extract specific data elements efficiently.

Python’s rich ecosystem of libraries, such as BeautifulSoup and Scrapy, empowers developers to handle HTML parsing, data extraction, and automation tasks seamlessly. By harnessing the flexibility and versatility of Python, web scraping becomes accessible to individuals and businesses alike.

As with any technology, it is crucial to adhere to ethical guidelines and respect website terms of service while engaging in web scraping activities. Moreover, you can use Zenscrape to unlock the potential of web scraping in Python, PHP, JavaScript code, and other languages.

Visit APILayer Marketplace for more APIs to streamline your Web Developments.

FAQs

Is Python Web Scraping Legal?

Python web scraping is generally legal, but it’s important to comply with website terms of service and legal regulations.

What Is Web Scraping in Python?

Web scraping in Python involves extracting data from websites using code to automate data retrieval and analysis tasks.

How Difficult Is Web Scraping in Python?

Web scraping in Python can be challenging, but it becomes manageable and rewarding with the right libraries and knowledge of web scrapers.

Is Web Scraping API Legal?

The legality of web scraping using APIs depends on the website’s terms of service and the API provider’s policies.

Unlock the power of web scraping with Zenscrape today and gain valuable insights from website data! Try it now!