Build a YouTube Channel Finder in Python That Qualifies Creators

A developer built a Python workflow using Apify's YouTube creator lead finder Actor to qualify channels for creator outreach. The tool discovers channels by niche, filters by recent activity and view counts, and outputs structured data rows for review. It emphasizes qualifying recent channel state rather than just subscriber counts.

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 input should describe the opportunity, not a pile of channel URLs: run input = { "preset": "qualified shortlist", "niches": "AI automation", "B2B SaaS" , "languages": "en" , "countries": "US", "CA", "GB" , "worldwideLanguageMode": False, "minSubscribers": 5 000, "maxSubscribers": 250 000, "minVideosPerMonth": 1, "lastUploadWithinDays": 60, "minRecentMedianViews": 1 000, "contentFormats": "long-form", "Shorts" , "requirePublicBusinessContact": False, "maxChannels": 10, "videosAnalyzedPerChannel": 10, "maximumSearchCalls": 12, } The output should be reviewable without opening every channel manually. Useful fields include: That is the difference between a channel scraper and a creator-prospecting dataset. 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: A useful YouTube creator finder therefore has to qualify the recent channel state, not only copy lifetime totals. Install the Apify client: pip install 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. export APIFY API TOKEN="..." Then call the Actor and retrieve its default Dataset: python import os from apify client import ApifyClient client = ApifyClient os.environ "APIFY API TOKEN" run input = { "preset": "qualified shortlist", "niches": "AI automation", "B2B SaaS" , "languages": "en" , "countries": "US", "CA", "GB" , "worldwideLanguageMode": False, "minSubscribers": 5 000, "maxSubscribers": 250 000, "minVideosPerMonth": 1, "lastUploadWithinDays": 60, "minRecentMedianViews": 1 000, "contentFormats": "long-form", "Shorts" , "requirePublicBusinessContact": False, "maxChannels": 10, "videosAnalyzedPerChannel": 10, "maximumSearchCalls": 12, } run = client.actor "kazkn/youtube-creator-lead-finder" .call run input=run input items = client.dataset run "defaultDatasetId" .list items .items for creator in items: print creator "channelName" , creator "channelUrl" , creator.get "recentMedianViews" , creator.get "opportunityScore" , The Actor writes one deduplicated row per channelId within the run. Separate runs use separate Datasets, so the same channel can legitimately appear again in a later search. 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: run input = { "preset": "quick leads", "niches": "AI agents" , "languages": "fr" , "countries": , "worldwideLanguageMode": True, "maxChannels": 5, "videosAnalyzedPerChannel": 5, "maximumSearchCalls": 5, } 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. For outreach, “active” should be explicit. Three inputs do most of the work: { "minVideosPerMonth": 2, "lastUploadWithinDays": 45, "minRecentMedianViews": 2 000, } 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. 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. { "contentFormats": "long-form" } The output exposes shortsRatio , longFormRatio , liveRatio , and formatConfidence . 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. A YouTube email finder can mean two very different things: This workflow only does the first. { "preset": "contact ready", "requirePublicBusinessContact": True, "contactTypes": "business email", "website", "social" , } Each returned contact keeps its type, value, source URL, collection time, public status, and confidence. The current provider reads public channel text. It does not access YouTube’s protected business-email field, bypass CAPTCHA, sign in, guess private addresses, or fetch linked websites. If a protected email is unavailable, that absence is a real result. It should not be replaced by a guessed pattern. Apify can export the Dataset directly to CSV, JSON, or Excel. If you need a small CSV for manual review, keep the qualification and provenance columns: python import csv columns = "channelId", "channelName", "channelUrl", "primaryLanguage", "declaredCountry", "subscriberCount", "lastUploadAt", "estimatedVideosPerMonth", "recentMedianViews", "publicBusinessEmail", "contactSourceUrl", "opportunityScore", with open "youtube-creators.csv", "w", newline="", encoding="utf-8" as file: writer = csv.DictWriter file, fieldnames=columns, extrasaction="ignore" writer.writeheader writer.writerows items Do not drop contactSourceUrl , opportunityReasons , or warnings in the production pipeline. They are what let a human review why a creator qualified and where a contact came from. YouTube discovery has quota and latency costs. A sensible workflow performs cheap discovery first, then enriches only the best candidates. Start small: Then tighten the filters. Requiring a contact, high cadence, high median views, a narrow country, and a small subscriber band all at once can correctly return zero rows. The quickest path is the public YouTube Channel Finder by Niche Task. It opens with a bounded, editable input and the qualified-creator Dataset view: Run the YouTube Channel Finder by Niche https://apify.com/kazkn/youtube-creator-lead-finder/examples/youtube-channel-finder-by-niche?utm source=devto&utm medium=organic content&utm campaign=youtube creator finder seo 20260825&utm content=python channel finder For all filters, output fields, limits, and live pricing, use the full YouTube Creator Lead Finder https://apify.com/kazkn/youtube-creator-lead-finder?utm source=devto&utm medium=organic content&utm campaign=youtube creator finder seo 20260825&utm content=python channel finder actor . The useful output is not a longer list. It is a list you can explain, review, and act on.