Registered Antennas scraper from keraies.eett.gr
by mpelas
registered antennas in the greek territory. It works slow and low. It is an ethical effort in the national goverment cloud. It will take almost 10hour...
Opens on Apify.com
About Registered Antennas scraper from keraies.eett.gr
registered antennas in the greek territory. It works slow and low. It is an ethical effort in the national goverment cloud. It will take almost 10hours to finish. Some patience. 24.000antennas approximately (2025) Every 50 antennas you get an output. Please be patient.
What does this actor do?
Registered Antennas scraper from keraies.eett.gr is a web scraping and automation tool available on the Apify platform. It's designed to help you extract data and automate tasks efficiently in the cloud.
Key Features
- Cloud-based execution - no local setup required
- Scalable infrastructure for large-scale operations
- API access for integration with your applications
- Built-in proxy rotation and anti-blocking measures
- Scheduled runs and webhooks for automation
How to Use
- Click "Try This Actor" to open it on Apify
- Create a free Apify account if you don't have one
- Configure the input parameters as needed
- Run the actor and download your results
Documentation
Greek Antenna Scraper This Actor scrapes antenna installation data from the Greek National Commission for Telecommunications and Post (EETT) website at https://keraies.eett.gr. ## What does this Actor do? The Greek Antenna Scraper collects detailed information about telecommunications antennas installed across Greek municipalities, including: - Antenna locations (address, coordinates) - Company/operator information - Permit status - Technical specifications - Associated documents and permits ## Input The Actor accepts the following input parameters: - municipalities (array of strings, required): List of Greek municipalities to scrape. Municipality names must be in Greek (e.g., "Αθηναίων", "Θεσσαλονίκης") - max_antennas (integer, optional): Maximum number of antennas to scrape. Default is 50. Useful for testing or limiting costs. ### Example Input json { "municipalities": [ "Αθηναίων", "Θεσσαλονίκης", "Αλεξανδρούπολης" ], "max_antennas": 100 } ## Output The Actor outputs a dataset with the following fields for each antenna: - antenna_id: Unique identifier - serial_number: Serial number - code: Antenna code - category: Antenna category - company: Operating company - address: Installation address - municipality: Municipality name - region: Region name - position_code: Position code - code_name: Code name - latitude: Geographic latitude (WGS84) - longitude: Geographic longitude (WGS84) - permit_status: Current permit status - measurements_eaee: EAEE measurements - documents: Array of associated documents with protocol numbers and file URLs ### Example Output json { "antenna_id": "12345", "serial_number": "001", "code": "ATH001", "category": "Mobile Telephony", "company": "Example Telecom", "address": "Example Street 1, Athens", "municipality": "Αθηναίων", "region": "Αττική", "position_code": 1001, "latitude": 37.9838, "longitude": 23.7275, "permit_status": "Approved", "documents": [ { "document_number": "DOC123", "protocol_number": "PROT456", "type": "Permit", "file_url": "https://example.com/doc.pdf" } ] } ## Performance - Speed: Approximately 1-2 seconds per antenna (including detailed information retrieval) - Default run: With 50 antennas (default), the Actor completes in under 5 minutes - Large runs: Scraping all municipalities can take several hours depending on the limit set ## Use Cases - Telecommunications Research: Analyze antenna distribution across Greece - Real Estate Analysis: Identify properties near telecommunications infrastructure - Regulatory Compliance: Track permits and documentation - Geographic Analysis: Map telecommunications coverage - Market Research: Understand operator presence by region ## Notes - The Actor respects the source website with appropriate delays between requests - Municipality names must be provided in Greek characters - Some antennas may have incomplete data depending on source availability - The Actor uses a two-stage process: first collecting antenna IDs, then fetching detailed information ## Support For issues or questions, please contact the Actor developer or use the Apify platform support. ## Python BeautifulSoup template A template for web scraping data from websites enqueued from starting URL using Python. The URL of the web page is passed in via input, which is defined by the input schema. The template uses the HTTPX to get the HTML of the page and the Beautiful Soup to parse the data from it. Enqueued URLs are available in request queue. The data are then stored in a dataset where you can easily access them. ## Included features - Apify SDK for Python - a toolkit for building Apify Actors and scrapers in Python - Input schema - define and easily validate a schema for your Actor's input - Request queue - queues into which you can put the URLs you want to scrape - Dataset - store structured data where each object stored has the same attributes - HTTPX - library for making asynchronous HTTP requests in Python - Beautiful Soup - a Python library for pulling data out of HTML and XML files ## How it works This code is a Python script that uses HTTPX and Beautiful Soup to scrape web pages and extract data from them. Here's a brief overview of how it works: - The script reads the input data from the Actor instance, which is expected to contain a start_urls key with a list of URLs to scrape and a max_depth key with the maximum depth of nested links to follow. - The script enqueues the starting URLs in the default request queue and sets their depth to 0. - The script processes the requests in the queue one by one, fetching the URL using HTTPX and parsing it using BeautifulSoup. - If the depth of the current request is less than the maximum depth, the script looks for nested links in the page and enqueues their targets in the request queue with an incremented depth. - The script extracts the desired data from the page (in this case, all the links) and pushes it to the default dataset using the push_data method of the Actor instance. - The script catches any exceptions that occur during the scraping process and logs an error message using the Actor.log.exception method. - This code demonstrates how to use Python and the Apify SDK to scrape web pages and extract specific data from them. ## Resources - BeautifulSoup Scraper - Beautifulsoup Scraper tutorial - Python tutorials in Academy - Web scraping with Beautiful Soup and Requests - Beautiful Soup vs. Scrapy for web scraping - Integration with Make, GitHub, Zapier, Google Drive, and other apps - Video guide on getting scraped data using Apify API - Video introduction to Python SDK - A short guide on how to build web scrapers using code templates: web scraper template ## Getting started For complete information see this article. In short, you will: 1. Build the Actor 2. Run the Actor ## Pull the Actor for local development If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI: 1. Install apify-cli Using Homebrew bash brew install apify-cli Using NPM bash npm -g install apify-cli 2. Pull the Actor by its unique <ActorId>, which is one of the following: - unique name of the Actor to pull (e.g. "apify/hello-world") - or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb") You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID. This command will copy the Actor into the current directory on your local machine. bash apify pull <ActorId> ## Documentation reference To learn more about Apify and Actors, take a look at the following resources: - Apify SDK for JavaScript documentation - Apify SDK for Python documentation - Apify Platform documentation - Join our developer community on Discord
Categories
Common Use Cases
Market Research
Gather competitive intelligence and market data
Lead Generation
Extract contact information for sales outreach
Price Monitoring
Track competitor pricing and product changes
Content Aggregation
Collect and organize content from multiple sources
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Start Free TrialActor Information
- Developer
- mpelas
- Pricing
- Paid
- Total Runs
- 41
- Active Users
- 3
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