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

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Manage episode 431877236 series 3474159
内容由HackerNoon提供。所有播客内容(包括剧集、图形和播客描述)均由 HackerNoon 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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.

  continue reading

346集单集

Artwork
icon分享
 
Manage episode 431877236 series 3474159
内容由HackerNoon提供。所有播客内容(包括剧集、图形和播客描述)均由 HackerNoon 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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.

  continue reading

346集单集

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