What are the Steps to Get Website Data using Python?

Lovely
Jun 30, 2022

Web scraping, also known as data harvesting or extraction, refers to the automated workflow of collecting data from websites using APIs, tools, copy-pasting, and scripting codes in languages like Python.

This blog is particularly dedicated to defining the steps for Python-driven web extraction to gather valuable insights into competitor products, pricing, keywords, and ad market trends in a few minutes.

Steps to Getting Web Data Extracted Using Python

The scraping of a website involves a code-run process, which involves sending a request to the URL, the server responds by sending the data and permitting it to go through its HTML or XML pages. The written code or script parses those pages, finds data, and finally extracts it.

Step 1: Prerequisites: Ensure you have Python installed, along with these libraries.

To get ready, Python web scraping experts prepare the right Python ecosystem. So, up next are some libraries that make navigating links, parsing, and managing web data like a walkover.

Selenium: This library allows web testing while automating browser activities. It is ideally fit to extract data from dynamic websites.
BeautifulSoup: It’s a package that allows parsing HTML and XML documents while creating parse trees for defining the entire flow of extraction.
Pandas: This is a library defining all controls to manipulate data for analysis. Besides, you may extract and save data herein in the desired format like CSV.
Installation required: Install Python 2.x or Python 3.x with the aforesaid libraries and Ubuntu operating system

Let’s get started discovering how the Python language can help with it.

Step 2: Figure Out the URL of the Target Website

This can be any website’s URL that you want to collect data from. It needs authorization from the website for automated access via its robots.txt file.

Step 3: Examine the Page Structure

It’s noteworthy that most web content is put inside tags. So, we have to go through them to see under which tag the desirable information is nested.

For this purpose,

Just right-click the element (e.g., a product price) that you want to discover.
Select “Inspect” from the dropdown options.
The selection will open a “Browser Inspector Box”. It carries the specific HTML codes.

Step 4: Decide the Content Categories to Extract

Before moving to scripting a code, you should categorize the details that you want to get from the web page. The target data like product names, prices, and ratings are available in the

tag, which defines specific class names.

Step 5: Write a Python Script

Now, you need to create a Python file & import your libraries. For this, move to configuring a web drive like (like chromedriver for Google Chrome) to simulate user behavior.

from selenium import webdriver

from bs4 import BeautifulSoup

import pandas as pd

# Configure Chrome driver

driver = webdriver.Chrome("/path/to/chromedriver")

# Initialize lists to store data

products = []

prices = []

# Open the target URL

driver.get("https://www.example.com/")

# Extract data

content = driver.page_source

soup = BeautifulSoup(content, 'html.parser')

# Find elements by their class tags

for a in soup.find_all('a', href=True, attrs={'class':'your-class-name'}):

name = a.find('div', attrs={'class':'product-name-class'})

price = a.find('div', attrs={'class':'price-class'})

products.append(name.text)

prices.append(price.text)

Step 6: Run the Script to Get Data

Now, you need to run the aforesaid code or script. For this purpose, type this syntax:

python your_script_name.py

Your code will launch a browser, navigate to your target URL, and start extracting the data that you have specified.

Step 7: Save and Export Data

This is the last step for scraping web content using Python, which pushes to store data in a readable format. You may export this data to a CSV file using Pandas:

Python

df = pd.DataFrame({'Product Name': products, 'Price': prices})

df.to_csv('product_data.csv', index=False, encoding='utf-8')

Wrap Up

Web scraping using Python involves the aforesaid steps and resources, which can be libraries & the Chrome browser. The process begins with calling the resources. Call the URL, examine what you need to extract, decide, write the scraping code, run it, and then store the data. Expert scrapers always respect robots.txt to ensure ethical extraction from the website. Maintain frequency by delaying its use. It prevents your IP from being blocked and also reduces server load. You may switch user-agent headers to imitate browsers and devices While capturing data, smart professionals responsibly comply with the target website’s terms of service.