A basic YouTube channel finder returns channels that match a name or topic.
That is useful for lookup.
It is not enough for creator outreach.
If you are building a sponsor list, recruiting educators, or selling a service to creators, the real question is narrower: Which channels match my niche, still publish, reach enough viewers, and expose a public route for business contact?
This tutorial builds that workflow in Python.
It starts without a seed list, discovers candidates from a niche, qualifies recent public performance, and returns one structured Dataset row per channel.
The result we want The input should describe the opportunity, not a pile of channel URLs: The output should be reviewable without opening every channel manually.
Useful fields include: channel identity and URL; subscriber count and recent upload date; videos published in the last 30 and 90 days; estimated videos per month; recent median and average views; engagement when the public counts make it calculable; Shorts, long-form, and live ratios; declared country, inferred countries, and confidence kept separate; public business email, website, or social route when explicitly exposed; an opportunity score plus the reasons behind it.
That is the difference between a channel scraper and a creator-prospecting dataset.
Why subscriber count is a weak filter by itself Subscriber count is easy to retrieve, so many YouTube scraping tutorials stop there.
But two channels with 50,000 subscribers can be completely different prospects: one published yesterday and regularly reaches 8,000 views; the other last uploaded nine months ago; one is mostly Shorts while your offer is for long-form editing; one covers your niche consistently while the other mentioned it once; one publishes a business email in the channel description while the other does not expose a public contact.
A useful YouTube creator finder therefore has to qualify the recent channel state, not only copy lifetime totals.
Run the Actor from Python Install the Apify client: Store your Apify token in an environment variable.
Do not paste it into source code, a notebook, a screenshot, or a public repository.
Then call the Actor and retrieve its default Dataset: The Actor writes one deduplicated row per within the run.
Separate runs use separate Datasets, so the same channel can legitimately appear again in a later search.
Search by language worldwide Country and language are different filters.
A French-speaking creator might be based in France, Canada, Belgium, Switzerland, Morocco, or somewhere else entirely.
To find French-speaking creators without forcing one country: The discovery hints guide the search.
The output still keeps confirmed language evidence and confidence separate from geography.
A search region is not silently presented as a creator’s declared country.
Require activity and recent performance For outreach, “active” should be explicit.
Three inputs do most of the work: The Actor samples a bounded number of recent videos per candidate.
It calculates cadence over 30 and 90 days, then computes recent median and average views from the available sample.
Median views are especially useful because one viral upload can inflate the average.
You should still inspect the sample size and warnings before treating any metric as complete.
Filter by content format The same niche can contain very different production workflows.
A thumbnail agency may want long-form channels.
A vertical-video editor may want Shorts-heavy creators.
A webinar platform may prefer live streams.
The output exposes , , , and .
Shorts detection from the official API uses a documented duration heuristic, so the Dataset can include a warning instead of pretending the classification is exact.
Treat public contacts as evidence, not enrichment A YouTube email finder can mean two very different things: collect a business email the creator explicitly published; or reveal or guess an address the creator did not expose.
This workflow only does th