Open Source Python Web Scrapers - Page 3
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Python Web Scrapers
View 201 business solutionsBrowse free open source Python Web Scrapers and projects below. Use the toggles on the left to filter open source Python Web Scrapers by OS, license, language, programming language, and project status.
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DecryptLogin
Python library providing APIs for automated website login workflows
DecryptLogin is a Python library designed to simplify automated login processes for many popular websites by providing ready-to-use APIs that simulate authentication behavior. It focuses on implementing login mechanisms through HTTP requests, allowing developers to programmatically authenticate with supported services without manually replicating complex login flows. It includes modules that handle different authentication modes such as PC login, mobile login, and QR code login depending on what the target platform supports. DecryptLogin supports a wide variety of online services and platforms, including social media sites, developer platforms, cloud services, and other web portals. Developers can integrate these login routines into automation scripts, crawlers, or data collection tools that require authenticated sessions. It also provides example utilities and automation scripts demonstrating how the login APIs can be used in practical scenarios. -
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Webhunter is a distributed, multi-threaded web crawler designed for both general indexing and crawling the web for focused content.
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Eskalations-Radar
polizei, afd, linke, linke gewalt, polizeigewalt, demo gegen rechts
Dieses Programm kann man mehr als Scherzprogramm betrachten, ein Scherz den nicht alle vertragen. -
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FEAPDER
Powerful Python crawler framework for scalable web scraping tasks
feapder is a Python-based web crawling framework designed to simplify the process of building scalable and efficient web scrapers. It focuses on providing a developer-friendly environment that makes it easier to create, run, and manage crawlers for a variety of data collection tasks. It includes several built-in spider types, such as AirSpider, Spider, TaskSpider, and BatchSpider, which address different crawling scenarios ranging from lightweight scraping to distributed and batch-based jobs. feapder supports features such as breakpoint resume, allowing crawlers to continue from where they stopped without losing progress. It also integrates monitoring and alerting capabilities to help developers track crawler performance and detect issues during execution. feapder includes browser rendering support for handling dynamic web pages and provides mechanisms for large-scale data deduplication during crawling. -
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FinalRecon
All-in-one Python web reconnaissance tool for fast target analysis
FinalRecon is an all-in-one web reconnaissance tool written in Python that helps security professionals gather information about a target website quickly and efficiently. It combines multiple reconnaissance techniques into a single command-line utility so users do not need to run several separate tools to collect similar data. FinalRecon focuses on providing a fast overview of a web target while maintaining accuracy in the collected results. It includes modules for gathering server information, analyzing SSL certificates, performing WHOIS lookups, and crawling website resources. FinalRecon can also enumerate DNS records, discover subdomains, search for directories and files, and scan common network ports. Historical URLs and resources can be retrieved from archived sources to help analyze changes in a website over time. Designed primarily for penetration testers and security researchers, FinalRecon simplifies the reconnaissance phase of security assessments. -
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Gerapy
Distributed Crawler Management Framework Based on Scrapy
Distributed Crawler Management Framework Based on Scrapy, Scrapyd, Scrapyd-Client, Scrapyd-API, Django and Vue.js. Someone who has worked as a crawler with Python may use Scrapy. Scrapy is indeed a very powerful crawler framework. It has high crawling efficiency and good scalability. It is basically a necessary tool for developing crawlers using Python. If you use Scrapy as a crawler, then of course we can use our own host to crawl when crawling, but when the crawl is very large, we can’t run the crawler on our own machine, a good one. The method is to deploy Scrapy to a remote server for execution. At this time, you might use Scrapyd. With it, we only need to install Scrapyd on the remote server and start the service. We can deploy the Scrapy project we wrote. Go to the remote host. In addition, Scrapyd provides a variety of operations API, which gives you free control over the operation of the Scrapy project. -
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Grab Framework Project
Web Scraping Framework
Grab is a python framework for building web scrapers. With Grab you can build web scrapers of various complexity, from simple 5-line scripts to complex asynchronous website crawlers processing millions of web pages. Grab provides an API for performing network requests and for handling the received content e.g. interacting with DOM tree of the HTML document. 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. -
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JobFunnel
Scrape job websites into a single spreadsheet with no duplicates.
Scrape job websites into a single spreadsheet with no duplicates. Automated tool for scraping job postings into a .csv file. You can search for jobs with YAML configuration files or by passing command arguments. By performing regular scraping and reviewing, you can cut through the noise of even the busiest job markets. 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. -
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MechanicalSoup
A Python library for automating interaction with websites
A Python library for automating interaction with websites. MechanicalSoup automatically stores and sends cookies, follows redirects, and can follow links and submit forms. It doesn't do JavaScript. MechanicalSoup was created by M Hickford, who was a fond user of the Mechanize library. Unfortunately, Mechanize was incompatible with Python 3 until 2019 and its development stalled for several years. MechanicalSoup provides a similar API, built on Python giants Requests (for HTTP sessions) and BeautifulSoup (for document navigation). Since 2017 it is a project actively maintained by a small team. -
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PAMIE
A Python class to allow the user to automate Internet Explorer
Python Automation Module (class) for Internet Explorer (PAM.py). Originally written as a simple Python module. This new Python class starting with 2.0 allows the user to automate Internet Explorer browser for QA testing, development testing, or web scraping. This python class only runs on Windows (only) and automates Internet Explorer using the COM object, there is no support for Firefox, Chrome, Safari or Flex at this time. This is not an Application. Also check out the original "SAMIE" (Perl module) written by Henry Wasserman. -
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Provas aplicadas - Concursos
Baixar provas aplicadas de bancas
Um aplicativo desktop que permite o download de provas aplicadas em concursos públicos no Brasil. O aplicativo oferece filtros personalizáveis para pesquisa de provas e gabaritos de acordo com a banca organizadora, ano e nível de escolaridade. -
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Python Crawler Library
Python Web Crawler Library
A simple library for crawling the web. This library will give you the ability to create macros for crawling web site and preforming simple actions like preforming "log in" and other simple actions in web sites. -
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Requests-HTML
Pythonic HTML Parsing for Humans
This library intends to make parsing HTML (e.g. scraping the web) as simple and intuitive as possible. When using this library you automatically get full JavaScript support! (Using Chromium, thanks to puppeteer) CSS Selectors (a.k.a jQuery-style, thanks to PyQuery). XPath Selectors, for the faint of heart. Mocked user-agent (like a real web browser). Automatic following of redirects. Connection–pooling and cookie persistence. The Requests experience you know and love, with magical parsing abilities, and async support. The rest of the code operates the same way as the synchronous version except that results is a list containing multiple response objects however the same basic processes can be applied as above to extract the data you want. -
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Scrapy-Redis
Redis-based components for Scrapy
You can start multiple spider instances that share a single redis queue. Best suitable for broad multi-domain crawls. Scraped items gets pushed into a redis queued meaning that you can start as many as needed post-processing processes sharing the items queue. Scheduler + Duplication Filter, Item Pipeline, Base Spiders. Default requests serializer is pickle, but it can be changed to any module with loads and dumps functions. Note that pickle is not compatible between python versions. Version 0.3 changed the requests serialization from marshal to cPickle, therefore persisted requests using version 0.2 will not able to work on 0.3. The class scrapy_redis.spiders.RedisSpider enables a spider to read the urls from redis. The urls in the redis queue will be processed one after another, if the first request yields more requests, the spider will process those requests before fetching another url from redis. -
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Scrapyd
A service daemon to run Scrapy spiders
Scrapyd can manage multiple projects and each project can have multiple versions uploaded, but only the latest one will be used for launching new spiders. A common (and useful) convention to use for the version name is the revision number of the version control tool you’re using to track your Scrapy project code. For example: r23. The versions are not compared alphabetically but using a smarter algorithm (the same packaging uses) so r10 compares greater to r9, for example. Scrapyd is an application (typically run as a daemon) that listens to requests for spiders to run and spawns a process for each one. Scrapyd also runs multiple processes in parallel, allocating them in a fixed number of slots given by the max_proc and max_proc_per_cpu options, starting as many processes as possible to handle the load. -
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ScrapydWeb
Web app for Scrapyd cluster management
Web app for Scrapyd cluster management, with support for Scrapy log analysis & visualization. Make sure that Scrapyd has been installed and started on all of your hosts. Start ScrapydWeb via command scrapydweb. (a config file would be generated for customizing settings on the first startup.) Add your Scrapyd servers, both formats of string and tuple are supported, you can attach basic auth for accessing the Scrapyd server, as well as a string for grouping or labeling. You can select any number of Scrapyd servers by grouping and filtering, and then invoke the HTTP JSON API of Scrapyd on the cluster with just a few clicks. -
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Scweet
Scrape tweets, profiles, followers and following from Twitter/X
Scweet is a Python-based Twitter/X scraping library and CLI designed to collect tweets, profile timelines, followers, following lists, and user profile data without requiring the official Twitter/X API or a developer account. Instead of depending on deprecated unauthenticated scraping methods, it works by using X’s web GraphQL API together with authenticated browser cookies, which gives it a more current and practical approach for data extraction. The project supports a broad set of collection patterns, including searches by keyword, hashtag, user, date range, engagement thresholds, language, and location, making it useful for research, monitoring, and data gathering workflows. It is built for both local use and higher-volume runs, with support for proxies, dedicated accounts, and multi-account cookie handling to improve reliability at scale. Scweet also includes asynchronous method variants, a command-line interface, automatic credential persistence in a local database, etc. -
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Snoop Project
This is the most powerful software taking into account CIS location
Snoop is an open data intelligence tool (OSINT world). Snoop Project is one of the most promising OSINT tools for finding nicknames. This is the most powerful software taking into account the CIS location. Is your life slideshow? Ask Snoop. Snoop project is developed without taking into account the opinions of the NSA and their friends, that is, it is available to the average user. Snoop is a research work (own database / closed bugbounty) in the field of searching and processing public data on the Internet. In terms of specialized search, Snoop is able to compete with traditional search engines. -
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Twitter Intelligence
Twitter Intelligence OSINT project performs tracking and analysis
A project written in Python for Twitter tracking and analysis without using Twitter API. This project is a Python 3.x application. The package dependencies are in the file requirements.txt. Run that command to install the dependencies. SQLite is used as the database. 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. -
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VIT Marks Display
A small program that accesses VIT marks of a specific student
A small attempt while learning interfacing with the web while learning python to get the marks of a specific valid VIT student using basic web scraping techniques -
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WeChatSogou
Python library to crawl and retrieve data from WeChat accounts
WechatSogou is an open source Python library designed to retrieve data from WeChat official accounts by using the Sogou WeChat search service as its data source. It provides developers with a programmatic way to search for public accounts and collect article information without manually browsing the search interface. It functions as a crawler interface that sends requests to the search engine, retrieves results, and converts the returned pages into structured data that can be used in applications or analysis pipelines. Internally, the library separates its functionality into several layers including an API interface, request handling, and response parsing components to organize the crawling workflow. These components work together to process HTTP requests, handle verification mechanisms, and transform HTML or JSON responses into usable objects. Developers can integrate the library into scripts or larger data collection systems to automate gathering content from public accounts. -
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Web Crawler Security Tool
A web crawler oriented to information security.
Last update on tue mar 26 16:25 UTC 2012 The Web Crawler Security is a python based tool to automatically crawl a web site. It is a web crawler oriented to help in penetration testing tasks. The main task of this tool is to search and list all the links (pages and files) in a web site. The crawler has been completely rewritten in v1.0 bringing a lot of improvements: improved the data visualization, interactive option to download files, increased speed in crawling, exports list of found files into a separated file (useful to crawl a site once, then download files and analyse them with FOCA), generate an output log in Common Log Format (CLF), manage basic authentication and more! Many of the old features has been reimplemented and the most interesting one is the capability of the crawler to search for directory indexing. -
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YouTube video web scraper 2 [ISA]
YouTube video web scraper 2 [Improved.Simplified.Alternative]
'YouTube video web scraper 2' is an desktop application developed using python 3.11.4 and other add-on libaries. Finds YouTube video based on user request and view as table. Export the table as excel. Compatible only for windows OS. -
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bilili
Command-line Bilibili video and danmaku downloader with batch support
bilili is a command-line tool designed to download videos and related content from the Bilibili video platform. It focuses on enabling users to retrieve user-uploaded videos as well as serialized content such as bangumi episodes directly from the terminal environment. It provides automated downloading capabilities that handle video streams and associated data efficiently while minimizing manual interaction. bilili supports retrieving both the video files and danmaku comments, which are the scrolling overlay comments commonly associated with the platform’s videos. These danmaku comments can be automatically converted into ASS subtitle format for playback compatibility with media players. bilili also implements multi-threaded and segmented downloading techniques to improve download performance and reliability. Additionally, it supports resuming interrupted downloads so partially downloaded content can continue without restarting from the beginning. -
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diskover-community
Open source file indexing & storage analytics powered by Elasticsearch
Diskover Community Edition is an open source file system indexing and storage analytics platform designed to help organizations understand and manage large volumes of file data. It crawls file systems and indexes metadata using Elasticsearch, enabling fast search, analysis, and organization of files stored across different storage systems. It allows administrators and users to explore file structures, monitor storage usage, and gain insights into how data is distributed across infrastructure. By indexing file metadata from sources such as local file systems, network shares like NFS and SMB, and cloud storage, the tool provides a centralized way to analyze heterogeneous storage environments. Diskover also helps identify outdated or unused files, duplicate data, and inefficient storage usage that can waste resources or increase operational costs. A Python-based indexing engine performs the scanning and indexing tasks.
