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Yuliia Barabash - Data Harvest: Unlocking Insights with Python Web Scraping | PyData Global 2023
Here is the meta description: "Discover how to unlock insights with Python web scraping using Scrappy, a powerful library that extracts data from websites, and learn best practices for web scraping and data analysis."
- Data Harvest with Python Web Scraping: The talk introduces Scrappy, a web scraping library for Python that helps extract data from websites.
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Setting Up Scrappy: Scrappy can be set up by running
pip install Scrappyand creating a project withstart project. - Parsing HTML: Scrappy uses CSS selectors and expressions to extract data from HTML pages.
- Scrapy Pipelines: Pipelines can be used to process data, validate data, and store data in various formats, such as JSON, CSV, or MySQL.
- Concurrency: Scrappy allows for concurrency processing, making it faster and more efficient.
- Middlewares: Middlewares can be used to customize the scraping process, such as handling HTTP requests and responses.
- JSON Output: Scrappy can output data in JSON format, making it easy to work with the extracted data.
- Web Scraping Examples: Examples of web scraping use cases include extracting data from real estate websites, weather websites, and e-commerce websites.
- Scrapy Framework: Scrapy has a framework that allows for easy configuration and customization of the scraping process.
- Best Practices: Best practices for web scraping include following the website’s terms of service, respecting robots.txt files, and not overwhelming the website with requests.
- Scrapy Plugins: Scrapy has various plugins available that can be used to extend its functionality, such as support for PostgreSQL, SQS, and S3.
- Authentication: Scrappy supports authentication, allowing for scraping of websites that require login credentials.
- Anti-Scrapping Measures: Websites may use anti-scrapping measures, such as CAPTCHAs, rate limiting, and blocking of IP addresses, to prevent web scraping.
- Scrapy Monitoring: Scrappy provides monitoring tools to help track the scraping process and identify issues.
- Scrapy Cloud: Scrappy can be used in cloud environments, allowing for scalability and ease of deployment.
- Scrapy Use Cases: Scrappy has various use cases, including data mining, market research, and lead generation.