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

Programming Tech Brief By HackerNoon

#HackerNoon #Tech #Podcasting Education #Programming #Code #Learning To Code #Coding Stories #Programming Stories #Technology #JavaScript #Python

26:18

How do you know when it’s time to make your next big career move? With International Women’s Day around the corner, we are excited to feature Avni Patel Thompson, Founder and CEO of Milo. Avni is building technology that directly supports the often overlooked emotional and logistical labor that falls on parents—especially women. Milo is an AI assistant designed to help families manage that invisible load more efficiently. In this episode, Avni shares her journey from studying chemistry to holding leadership roles at global brands like Adidas and Starbucks, to launching her own ventures. She discusses how she approaches career transitions, the importance of unpleasant experiences, and why she’s focused on making everyday life easier for parents. [01:26] Avni's University Days and Early Career [04:36] Non-Linear Career Paths [05:16] Pursuing Steep Learning Curves [11:51] Entrepreneurship and Safety Nets [15:22] Lived Experiences and Milo [19:55] Avni’s In Her Ellement Moment [20:03] Reflections Links: Avni Patel Thompson on LinkedIn Suchi Srinivasan on LinkedIn Kamila Rakhimova on LinkedIn Ipsos report on the future of parenting About In Her Ellement: In Her Ellement highlights the women and allies leading the charge in digital, business, and technology innovation. Through engaging conversations, the podcast explores their journeys—celebrating successes and acknowledging the balance between work and family. Most importantly, it asks: when was the moment you realized you hadn’t just arrived—you were truly in your element? About The Hosts: Suchi Srinivasan is an expert in AI and digital transformation. Originally from India, her career includes roles at trailblazing organizations like Bell Labs and Microsoft. In 2011, she co-founded the Cleanweb Hackathon, a global initiative driving IT-powered climate solutions with over 10,000 members across 25+ countries. She also advises Women in Cloud, aiming to create $1B in economic opportunities for women entrepreneurs by 2030. Kamila Rakhimova is a fintech leader whose journey took her from Tajikistan to the U.S., where she built a career on her own terms. Leveraging her English proficiency and international relations expertise, she discovered the power of microfinance and moved to the U.S., eventually leading Amazon's Alexa Fund to support underrepresented founders. Subscribe to In Her Ellement on your podcast app of choice to hear meaningful conversations with women in digital, business, and technology.…

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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

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

Programming Tech Brief By HackerNoon

published for ca. et år siden

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MP3•Episode hjem

346 episoder

#HackerNoon #Tech #Podcasting Education #Programming #Code #Learning To Code #Coding Stories #Programming Stories #Technology #JavaScript #Python

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1
Java vs. Scala: Comparative Analysis for Backend Development in Fintech 11:09

for 33 weeks siden11:09

11:09

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

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

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

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This story was originally published on HackerNoon at: https://hackernoon.com/building-a-level-viewer-for-the-legend-of-zelda-twilight-princess . I programmed a web BMD viewer for Twilight Princess because I am fascinated by analyzing levels and immersing myself in the details of how they were made. Check more stories related to programming at: https://hackernoon.com/c/programming . You can also check exclusive content about #reverse-engineering , #bmd , #game-development , #the-legend-of-zelda , #level-design , #web-bmd-viewer , #level-viewer-for-zelda-game , #hackernoon-top-story , and more. This story was written by: @hackerclz1yf3a00000356r1e6xb368 . Learn more about this writer by checking @hackerclz1yf3a00000356r1e6xb368's about page, and for more stories, please visit hackernoon.com . I started programming a web BMD viewer for Twilight Princess (Nintendo GameCube) because I love this game and as a game producer, I am fascinated by analyzing levels and immersing myself in the details of how they were made.…

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

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This story was originally published on HackerNoon at: https://hackernoon.com/how-to-simplify-state-management-with-reactjs-context-api-a-tutorial . Master state management in React using Context API. This guide provides practical examples and tips for avoiding prop drilling and enhancing app performance. Check more stories related to programming at: https://hackernoon.com/c/programming . You can also check exclusive content about #reactjs , #context-api , #react-tutorial , #javascript-tutorial , #frontend , #state-management , #hackernoon-top-story , #prop-drilling , and more. This story was written by: @codebucks . Learn more about this writer by checking @codebucks's about page, and for more stories, please visit hackernoon.com . This blog offers a comprehensive guide on managing state in React using the Context API. It explains how to avoid prop drilling, enhance performance, and implement the Context API effectively. With practical examples and optimization tips, it's perfect for developers looking to streamline state management in their React applications.…

1
Augmented Linked Lists: An Essential Guide 12:07

for 33 weeks siden12:07

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This story was originally published on HackerNoon at: https://hackernoon.com/augmented-linked-lists-an-essential-guide . While a linked list is primarily a write-only and sequence-scanning data structure, it can be optimized in different ways. Check more stories related to programming at: https://hackernoon.com/c/programming . You can also check exclusive content about #data-structures , #linked-lists , #memory-management , #linked-lists-explained , #how-does-a-linked-list-work , #hackernoon-top-story , #eviction-keys , #linked-list-guide , and more. This story was written by: @amoshi . Learn more about this writer by checking @amoshi's about page, and for more stories, please visit hackernoon.com . While a linked list is primarily a write-only and sequence-scanning data structure, it can be optimized in different ways. Augmentation is an approach that remains effective in some cases and provides extra capabilities in others.…

1
How to Write Tests for Free 10:35

for 33 weeks siden10:35

10:35

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

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

for 33 weeks siden4:11

4:11

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.…

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

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

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

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1
CSS Positions: Real Examples to Help You Learn 3:29

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