Showing 7 open source projects for "write"

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    Scrapy

    Scrapy

    A fast, high-level web crawling and web scraping framework

    Scrapy is a fast, open source, high-level framework for crawling websites and extracting structured data from these websites. Portable and written in Python, it can run on Windows, Linux, macOS and BSD. Scrapy is powerful, fast and simple, and also easily extensible. Simply write the rules to extract the data, and add new functionality if you wish without having to touch the core. Scrapy does the rest, and can be used in a number of applications. It can be used for data mining, monitoring and automated testing.
    Downloads: 17 This Week
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  • 2
    Grab Framework Project

    Grab Framework Project

    Web Scraping Framework

    ...The single request/response API that allows you to build network request, perform it and work with the received content. The API is built on top of urllib3 and lxml libraries. The Spider API to build asynchronous web crawlers. You write classes that define handlers for each type of network request. Each handler is able to spawn new network requests. Network requests are processed concurrently with a pool of asynchronous web sockets. Grab provides interface called Spider to develop multithreaded web-site scrapers.
    Downloads: 0 This Week
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  • 3
    JobFunnel

    JobFunnel

    Scrape job websites into a single spreadsheet with no duplicates.

    ...Run funnel with your settings YAML to populate your master CSV file with jobs from available providers. JobFunnel can be easily automated to run nightly with crontab. If you have a job website you'd like to write a scraper for, you are welcome to implement it, Review the Base Scraper for implementation details. JobFunnel supports scraping jobs from the same job website across locales & domains. If you are interested in adding support, you may only need to define session headers and domain strings, Review the Base Scraper for further implementation details.
    Downloads: 0 This Week
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  • 4
    mlscraper

    mlscraper

    ML-based HTML scraper that learns extraction rules from examples

    mlscraper is a Python library designed to automatically extract structured data from HTML pages without requiring developers to manually write CSS selectors or XPath rules. Instead of defining extraction logic by hand, users provide a few examples of the data they want to retrieve from a webpage. It analyzes those examples within the HTML document and determines patterns or rules that can be used to extract the same type of information from similar pages. Once trained, the generated scraper can process new pages and return the extracted data in structured formats such as dictionaries or lists. ...
    Downloads: 7 This Week
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  • 5
    ruia

    ruia

    Async Python framework for fast and flexible web scraping spiders

    ...Ruia is powered by Python’s asyncio library along with aiohttp, enabling developers to perform concurrent network requests efficiently and scrape data from websites with minimal overhead. Ruia follows a “write less, run faster” philosophy, emphasizing concise code and streamlined spider development. It provides a structured approach to building scraping projects through components such as data items, spiders, middleware, and plugins. Developers can define structured fields to extract information from HTML content and process responses asynchronously to improve crawling performance. ...
    Downloads: 8 This Week
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  • 6
    Twitter Intelligence

    Twitter Intelligence

    Twitter Intelligence OSINT project performs tracking and analysis

    ...Tweet data is stored on the Tweet, User, Location, Hashtag, HashtagTweet tables. The database is created automatically. analysis.py performs analysis processing. User, hashtag, and location analyzes are performed. You must write Google Map API Key in setting.py to display Google Maps.
    Downloads: 0 This Week
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  • 7
    pyspider

    pyspider

    A powerful Spider(Web Crawler) system in Python

    pyspider is a powerful Spider(Web Crawler) system in Python. Components are connected by message queue. Every component, including message queue, is running in their own process/thread, and replaceable. That means, when process is slow, you can have many instances of processor and make full use of multiple CPUs, or deploy to multiple machines. This architecture makes pyspider really fast. benchmarking. Since pyspider has various components, you can just run pyspider to start a standalone and...
    Downloads: 0 This Week
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