Extract Website Data with Just Two Lines of Python Code
Data extraction from websites doesn’t always require complex tools or extensive coding knowledge. With Python’s Pandas library, you can pull structured data from HTML tables with remarkable simplicity.
The powerful ReadHTML function in Pandas automatically identifies HTML tables on a webpage and converts them into DataFrames—Python’s efficient tabular data structure—making the information immediately ready for analysis.
How to Extract the PwC Global Top 100 Companies List
The PriceWaterhouseCoopers Global Top 100 list contains valuable information about the world’s largest companies. Extracting this data requires just two essential lines of code:
- Import the Pandas library:
import pandas as pd - Use the ReadHTML function with your target URL:
pd.read_html('your-url-here')[1]
The second line does all the heavy lifting. The ReadHTML function returns a list of all tables found on the webpage, and the index position [1] selects the second table (remember that Python uses zero-based indexing, so [0] would be the first table).
No Development Environment Required
One of the benefits of this approach is that you can execute this code directly in Google Colab without installing any development environment on your local machine. This makes it accessible even for beginners or those working on shared computers.
Important Limitation
It’s worth noting that this method only works when the table data is directly embedded in the HTML of the webpage. If the website loads its table data dynamically using JavaScript, the ReadHTML function won’t be able to capture it, as it only processes the initial HTML response.
For those cases, you might need more advanced web scraping techniques using libraries like Selenium that can interact with JavaScript-rendered content.