Переходьте в офлайн за допомогою програми Player FM !
Подкасти, які варто послухати
РЕКЛАМА


1 Battle Camp S1: Reality Rivalries with Dana Moon & QT 1:00:36
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 епізодів
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 епізодів
Усі епізоди
×
1 Step-by-Step Guide to Publishing Your First Python Package on PyPI Using Poetry: Lessons Learned 4:05




1 AOSP and Linux Cross Border Convergence! Look at OpenFDE, New Open Source Linux Desktop Environment 3:16




1 Is Your Reporting Software WCAG Compliant? Make Data Accessible to Everyone with Practical Steps 14:36











1 TypeScript SDK Development: A 5-Year-Old Could Follow This Step-By-Step ~ Part 1: Our First MVP 4:15




1 Load Balancing For High Performance Computing
Using Quantum Annealing: Grid Based Application 12:00

1 Load Balancing For High Performance Computing
Using Quantum Annealing: Adaptive Mesh Refinement 4:57



Ласкаво просимо до Player FM!
Player FM сканує Інтернет для отримання високоякісних подкастів, щоб ви могли насолоджуватися ними зараз. Це найкращий додаток для подкастів, який працює на Android, iPhone і веб-сторінці. Реєстрація для синхронізації підписок між пристроями.