YouTube Channel Scraper in Python: Filter Active Creators

A YouTube channel scraper in Python can collect channel names, subscriber counts, and URLs. It gets the data. That solves extraction, not qualification.

For outreach, the expensive mistake is not missing one channel. It is spending time on channels that stopped publishing, no longer reach viewers, use the wrong content format, or expose no public business-contact route.

This tutorial builds a bounded Python workflow that returns active creator prospects instead of a raw channel dump.

Define active before scraping

“Active” needs a rule. A practical first pass might require:

at least one video per month;
a public upload within the last 60 days;
at least 1,000 median views across a recent sample;
5,000–250,000 subscribers;
long-form or Shorts content that matches the offer.

Those values are not universal. A thumbnail agency, affiliate manager, recruiter, and mobile-app sponsor will use different thresholds. The important part is to write the rule before seeing the results.

Install the Apify client

pip install apify-client

Load your token from an environment variable. Never place it in the script or commit it to Git.

export APIFY_API_TOKEN="..."

Run a qualified channel search

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,
)

dataset = client.dataset(run["defaultDatasetId"])
creators = dataset.list_items().items

The Actor discovers candidates from the requested niches and queries. Channel URLs remain optional. It deduplicates output by channelId inside the run and writes one Dataset row per qualified channel.

Inspect recent performance, not lifetime totals

For each returned creator, the most useful review fields are recent:

for creator in creators:
    print({
        "channel": creator["channelName"],
        "url": creator["channelUrl"],
        "subscribers": creator.get("subscriberCount"),
        "last_upload": creator.get("lastUploadAt"),
        "videos_per_month": creator.get("estimatedVideosPerMonth"),
        "median_views": creator.get("recentMedianViews"),
        "score": creator.get("opportunityScore"),
        "reasons": creator.get("opportunityReasons", []),
        "warnings": creator.get("warnings", []),
    })

Median recent views are especially useful because one viral upload can distort an average. The sample is still bounded, so keep its warnings and do not present the metric as a complete channel history.

Match the content format to the offer

A channel can be active and still be the wrong prospect.

A long-form editing service may prioritize longFormRatio.
A vertical-video agency may prioritize shortsRatio.
A webinar or streaming product may prioritize liveRatio.

The output also includes a format confidence value. Shorts classification uses a duration heuristic from available public API data, so uncertain cases should remain visible rather than being forced into a perfect category.

Keep geography and language evidence separate

Search region, declared country, inferred country, and detected language are different facts.

The Dataset keeps them separate through fields such as:

declaredCountry;
inferredCountries;
countryConfidence;
primaryLanguage;
detectedLanguages;
languageConfidence.

If the brief asks for French-speaking creators worldwide, leave countries empty and set worldwideLanguageMode to True. Do not silently turn the search region into the creator’s declared location.

Export the shortlist to CSV

import csv

columns = [
    "channelId",
    "channelName",
    "channelUrl",
    "subscriberCount",
    "lastUploadAt",
    "estimatedVideosPerMonth",
    "recentMedianViews",
    "shortsRatio",
    "longFormRatio",
    "publicBusinessEmail",
    "contactSourceUrl",
    "opportunityScore",
]

with open("qualified-youtube-creators.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=columns, extrasaction="ignore")
    writer.writeheader()
    writer.writerows(creators)

Keep contactSourceUrl, opportunityReasons, and warnings in the production export. They are the audit trail behind the row.

Public contacts are evidence, not enrichment

The workflow can collect a professional email, website, social profile, or management route only when the creator exposed it in accessible public channel text.

It does not reveal YouTube’s protected business-email field, bypass CAPTCHA, sign in, guess private addresses, or fetch linked websites. An empty contact field is preferable to an invented address.