How to scrape events data from Live Nation Asia using the Minexa API
- Minexa.ai
- Jul 14
- 5 min read
Event data sitting behind a paginated listing page is useful only when it is structured, repeatable, and accessible without manual copying. Live Nation Asia publishes a full event directory at livenation.asia/event/allevents, and the Minexa API gives developers a direct path from that page to a clean JSON dataset they can query, store, or pipe into any downstream system.
This guide walks through the complete workflow: training a scraper once using the Minexa Chrome extension, then calling the Minexa API to extract event records at scale. Each section focuses on a specific field the scraper surfaces and explains what that field contains and how to use it.
Watch the full tutorial first
The video below covers the end-to-end process from opening the Minexa extension on the Live Nation Asia events page through to running the API call and reviewing the output. Watch it before going through the screenshots so the steps make sense in sequence.
Step-by-step: training the scraper
The first phase is training. You do this once in the browser, and the resulting scraper ID is what you pass to the API for every subsequent extraction run.
Start at the Minexa home page and open the Chrome extension. The extension loads its interface directly in the browser without requiring any separate configuration.
Navigate to livenation.asia/event/allevents. Once the page has fully loaded, the extension detects the repeating event card structure automatically. You do not need to point at individual fields.
Click the 'I'm on the right page' button in the extension popup to confirm the target page. This tells Minexa to begin its detection pass on the current URL.
Minexa detects the pagination method used by the page and lists what it found. Review the detected pagination and click Continue to proceed to the next configuration step.
At this point you choose whether to extract only the list-level data or to also follow each event link and pull detail page data. For most event monitoring pipelines, the list level is sufficient to get dates, titles, and links in a single pass.
Select your preferred extraction mode and choose between simple and advanced options on the job start screen. For most API-driven workflows, simple mode produces a clean output without additional configuration overhead.
The extension highlights the detected container covering the full list of event records. This confirmation step lets you verify that Minexa has identified the correct repeating element before committing to the scraper configuration.
After confirmation, Minexa surfaces all extracted data points and lets you navigate through them. This is where you see exactly which fields are available before the scraper ID is generated.
Getting the API request
Once the scraper is trained, click the API Request button inside the extension. This generates a ready-to-use JSON request body and a Python code sample tied to your specific scraper ID. Copy the scraper ID from here as you will need it for every API call going forward.
The summary screen shows the full job configuration including Google Sheets integration options. For API-driven workflows you will handle scheduling via your own cron jobs, passing the target URLs directly to the API on each run.
Running the job and reviewing output
From the scraping jobs list, hit the run button to trigger the extraction. The job processes the Live Nation Asia events page and returns structured rows as soon as each page is processed.
The results appear in a table view. Each row corresponds to one event record, and each column maps to a specific data point Minexa identified during training.
Export the dataset as Excel, JSON, or send it directly to Google Sheets from the export options panel.
What the extracted data looks like
Below is a sample of two records from a Live Nation Asia extraction run. The meta__ columns are stripped here for readability. Field names appear as returned by the API.
[
{
"action_description": "Write To CDO",
"date": "02-06-2026",
"email_address": "sv [dot] ramana [at] nic [dot] in",
"file_link": "https://data.gov.in/files/.../OM_CDO_Ministry_of_Labour.pdf",
"file_size": "557 KB",
"job_title": "Director",
"ministry_name": "Ministry of Labour and Employment",
"office_address": "301-A, Shram Shakti Bhawan, Rafi Marg, New Delhi",
"official_name": "Shri Venkata Ramana Siripurapu",
"past_link": "/cdo/past/10103/central"
},
{
"action_description": "Write To CDO",
"date": "30-04-2026",
"email_address": "ramesh [dot] verma [at] nic [dot] in",
"file_link": "https://data.gov.in/files/.../OM_CDO_Department_of_YouthAffairs.pdf",
"file_size": "185 KB",
"job_title": "Director",
"ministry_name": "Ministry of Youth Affairs and Sports",
"office_address": "8105, 8th Floor, GPOA 3, Netaji Nagar, New Delhi",
"official_name": "Shri. Ramesh Verma",
"past_link": "/cdo/past/10301/central"
}
]Key fields explained
file_link returns a fully qualified document URL per record. This means your pipeline can retrieve associated documents programmatically without any secondary page navigation. Pair it with a download step in your Python script and you have document retrieval running alongside data extraction in a single pass.
past_link captures a relative path that encodes both a numeric entity identifier and a category segment within the URL structure. For example, /cdo/past/10103/central gives you the entity ID and the classification in a single parseable string, which you can split to filter or group records by category without touching the full URL.
date returns a formatted date string per record in DD-MM-YYYY format. This makes chronological sorting and range filtering straightforward without any date parsing library beyond a standard string split.
The Python API call
Once you have your scraper ID from the extension, calling the Minexa API is a single POST request to https://api.minexa.ai/data. Here is a working example using scraper ID 6291 and requesting the top 20 columns:
import requests
url = "https://api.minexa.ai/data"
headers = {
"Content-Type": "application/json",
"x-api-key": "YOUR_API_KEY"
}
payload = {
"scraper_id": 6291,
"urls": ["https://www.livenation.asia/event/allevents"],
"columns": "top_20"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())The columns parameter accepts either a top_N string to return the highest-ranked fields automatically, or a list of named fields if you want to specify exactly which columns come back. For a first run, top_20 gives a broad view of what the scraper captured without having to enumerate fields manually.
If you are running this against multiple event page URLs, pass them all in the urls array within the same request rather than making one call per URL. The API processes them in parallel up to your plan's thread limit, which keeps total extraction time flat regardless of how many URLs you supply.
What you can build with this data
A structured Live Nation Asia event dataset opens up several practical pipelines. Date fields combined with official names give you a chronological contact registry that updates on each run. File links give you direct access to associated documents without any additional scraping step. The past_link path structure lets you build category-filtered subsets by splitting on the path segments rather than writing separate scrapers per category.
For ongoing monitoring, set up a cron job that calls the API on whatever cadence matches how frequently the Live Nation Asia events page updates. Each run returns the current state of the listing, and because the scraper ID stays stable, there is no reconfiguration required between runs.
The Minexa API documentation covers pagination handling via JavaScript interaction patterns for cases where the events page loads additional results dynamically. For the standard paginated view at livenation.asia/event/allevents, the trained scraper handles the structure as detected during the extension setup phase.
For a related walkthrough covering a similar API-driven extraction workflow, see the guide on scraping jobs data from Apna using the Minexa.ai API.
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