How to Scrape Pinterest Profiles in Python (Followers, Pins, Boards Without Login)

Pinterest is one of the highest-intent social platforms for e-commerce, fashion, food, home decor, and DIY content. With no public API for profile metrics, scraping is the standard method for influencer research and audience analysis. Here is the complete guide.

Try it live: Pinterest Scraper, 4 Modes, 17-Field Pins, No Login. Pay per result delivered. Failed and empty results are never charged.

What each profile contains

• Follower count and monthly views
• Board count and total pin count
• Profile bio and external website link
• Recent pins: save count, image URL, caption, and board name

Common use cases

Influencer vetting. Before a partnership, pull follower count, monthly views, and recent pin engagement to assess reach and authenticity.

Competitor content research. Monitor the pins, boards, and save counts for competitors or category leaders to identify high-performing content angles.

Brand monitoring. Track what pins featuring your products get shared and saved by checking for your URL in profiles that link back.

E-commerce product research. High save counts on pins indicate purchase intent. Use this to identify what visual styles or products are trending in a niche.

Using the Apify actor

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "username": "anthropic",
    "maxItems": 20,
}

run = client.actor('themineworks/pinterest-profile-scraper').call(run_input=run_input)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    if item.get('_type') == 'profile':
        print(f"Profile: {item.get('full_name')} (@{item.get('username')})")
        print(f"  Followers: {item.get('follower_count', 0):,}")
        print(f"  Monthly views: {item.get('monthly_views', 0):,}")
        print(f"  Boards: {item.get('board_count', 0)} | Pins: {item.get('pin_count', 0)}")
        print(f"  Website: {item.get('website', 'N/A')}")
    elif item.get('_type') == 'pin':
        print(f"  Pin: {item.get('description', '')[:60]}...")
        print(f"    Saves: {item.get('save_count', 0)} | Board: {item.get('board_name', 'N/A')}")

Bulk influencer vetting

ACCOUNTS = ['joannagaines', 'thepioneerwoman', 'minimalistbaker', 'halfbakedharvest']

run_input = {
    "usernames": ACCOUNTS,
    "maxItems": 10,
}

run = client.actor('themineworks/pinterest-profile-scraper').call(run_input=run_input)
profiles = [i for i in client.dataset(run['defaultDatasetId']).iterate_items()
            if i.get('_type') == 'profile']

# Sort by monthly views
for p in sorted(profiles, key=lambda x: x.get('monthly_views', 0), reverse=True):
    ratio = p.get('monthly_views', 0) / max(p.get('follower_count', 1), 1)
    print(f"{p.get('full_name', p.get('username'))}")
    print(f"  {p.get('monthly_views', 0):,} monthly views | {p.get('follower_count', 0):,} followers")
    print(f"  Views-to-follower ratio: {ratio:.1f}x")

Top pins by save count

Find the content resonating most with an audience:

run_input = {
    "username": "target",
    "includePins": True,
    "maxItems": 50,
}

run = client.actor('themineworks/pinterest-profile-scraper').call(run_input=run_input)
pins = [i for i in client.dataset(run['defaultDatasetId']).iterate_items()
        if i.get('_type') == 'pin' and i.get('save_count')]

top_pins = sorted(pins, key=lambda x: x.get('save_count', 0), reverse=True)[:10]
print("Top 10 pins by saves:")
for pin in top_pins:
    print(f"  [{pin.get('save_count', 0):,} saves] {pin.get('description', '')[:80]}")
    print(f"    Board: {pin.get('board_name', 'N/A')} | {pin.get('image_url', '')[:60]}")

Board analysis

from collections import defaultdict

board_stats = defaultdict(lambda: {'pins': 0, 'total_saves': 0})
for pin in pins:
    board = pin.get('board_name', 'Unknown')
    board_stats[board]['pins'] += 1
    board_stats[board]['total_saves'] += pin.get('save_count', 0)

print("Boards ranked by total saves:")
for board, stats in sorted(board_stats.items(), key=lambda x: -x[1]['total_saves'])[:10]:
    avg = stats['total_saves'] / max(stats['pins'], 1)
    print(f"  {board}: {stats['pins']} pins | {stats['total_saves']:,} saves | {avg:.0f} avg")

Pricing

$0.002 per profile, failed results are never charged. Pins are included when includePins: true is set.