Gå offline med appen Player FM !
How to Scrape Data Off Wikipedia: Three Ways (No Code and Code)
Manage episode 431877236 series 3474159
This story was originally published on HackerNoon at: https://hackernoon.com/how-to-scrape-data-off-wikipedia-three-ways-no-code-and-code.
Get your hands on excellent manually annotated datasets with Google Sheets or Python
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #python, #google-sheets, #data-analysis, #pandas, #data-scraping, #web-scraping, #wikipedia-data, #scraping-wikipedia-data, and more.
This story was written by: @horosin. Learn more about this writer by checking @horosin's about page, and for more stories, please visit hackernoon.com.
For a side project, I turned to Wikipedia tables as a data source. Despite their inconsistencies, they proved quite useful. I explored three methods for extracting this data: - Google Sheets: Easily scrape tables using the =importHTML function. - Pandas and Python: Use pd.read_html to load tables into dataframes. - Beautiful Soup and Python: Handle more complex scraping, such as extracting data from both tables and their preceding headings. These methods simplify data extraction, though some cleanup is needed due to inconsistencies in the tables. Overall, leveraging Wikipedia as a free and accessible resource made data collection surprisingly easy. With a little effort to clean and organize the data, it's possible to gain valuable insights for any project.
346 episoder
Manage episode 431877236 series 3474159
This story was originally published on HackerNoon at: https://hackernoon.com/how-to-scrape-data-off-wikipedia-three-ways-no-code-and-code.
Get your hands on excellent manually annotated datasets with Google Sheets or Python
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #python, #google-sheets, #data-analysis, #pandas, #data-scraping, #web-scraping, #wikipedia-data, #scraping-wikipedia-data, and more.
This story was written by: @horosin. Learn more about this writer by checking @horosin's about page, and for more stories, please visit hackernoon.com.
For a side project, I turned to Wikipedia tables as a data source. Despite their inconsistencies, they proved quite useful. I explored three methods for extracting this data: - Google Sheets: Easily scrape tables using the =importHTML function. - Pandas and Python: Use pd.read_html to load tables into dataframes. - Beautiful Soup and Python: Handle more complex scraping, such as extracting data from both tables and their preceding headings. These methods simplify data extraction, though some cleanup is needed due to inconsistencies in the tables. Overall, leveraging Wikipedia as a free and accessible resource made data collection surprisingly easy. With a little effort to clean and organize the data, it's possible to gain valuable insights for any project.
346 episoder
All episodes
×
1 Java vs. Scala: Comparative Analysis for Backend Development in Fintech 11:09

1 A Simplified Guide for the"Dockerazition" of Ruby and Rails With React Front-End App 11:50

1 Step-by-Step Guide to Publishing Your First Python Package on PyPI Using Poetry: Lessons Learned 4:05

1 Building a Level Viewer for The Legend Of Zelda - Twilight Princess 8:24

1 How to Simplify State Management With React.js Context API - A Tutorial 9:05

1 Augmented Linked Lists: An Essential Guide 12:07

1 Five Questions to Ask Yourself Before Creating a Web Project 13:54

1 Declarative Shadow DOM: The Magic Pill for Server-Side Rendering and Web Components 3:08

1 How to Scrape Data Off Wikipedia: Three Ways (No Code and Code) 4:11

1 Deploying Airflow on Kubernetes Using ArgoCD and Terraform: Modern GitOps approach 5:38

1 Automating App Architecture Diagrams: How I Built a Tool to Map Codebases from the Source 8:40

1 Keyword-Based Anomaly Detection in Log Files 5:45

1 Why Open Source AI is Good For Developers, Meta, and the World 13:11

1 CSS Positions: Real Examples to Help You Learn 3:29
Velkommen til Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.