Web Scraping Tables With Python Pandas | Beginner-Friendly Tutorial

Are you new to web scraping? Today, we’ll dive into Python Pandas and show you a simple way to extract and visualize web data. So, let’s get started!

By the end of this video, you’ll learn how to:
✅ Use Pandas for easy web scraping of HTML tables.
✅ Clean and organize scraped data.
✅ Visualize data with matplotlib to create insightful bar charts.

FAQs:
❓ What is Pandas?
Pandas is an open-source Python library that provides powerful tools for data analysis and data manipulation. It is one of the most popular libraries in data science and is widely used for working with structured data.

❓ What can I use Pandas for?
Pandas is commonly used for:
- Data Cleaning: Handle missing data, fill or remove null values, and filter data.
- Data Wrangling: Transform and reorganize data, merge or join datasets.
- Data Analysis: Perform statistical calculations, group data, and summarize insights.
- Data Visualization (with Matplotlib or Seaborn): Create visual representations like charts, graphs, and tables.
- Importing and Exporting Data: Work with data from various sources such as CSV, Excel, SQL databases, and HTML tables.

❓ Can I use Pandas to scrape any website?
Pandas' read_html() function can only extract tables from static web pages. For dynamic pages, you’ll need a more advanced tool like BeautifulSoup or Selenium.

❓ Why do I get an error when reading the HTML?
This could happen if the webpage doesn’t have any HTML tables or if the content is dynamic. Make sure the target website is static or look into using another scraping method.

⌨️ Full code:

import pandas as pd # For data manipulation and web scraping

# web scraping
happiness_table = tables[0]

# tidy data

# save the file

# plot data