DEV Community: Azzam

How to Scrape Instagram Comments in Python Without Logging In (2026 Guide)

Azzam — Tue, 13 Oct 2026 14:20:03 +0000

You have a few Instagram posts and you want every comment under them in a spreadsheet: username, text, likes and date. Maybe it's a giveaway, maybe a client wants to know what people said about a launch. This guide shows how to scrape Instagram comments in Python without an Instagram account, using a hosted Instagram comment scraper on Apify.

Quick start

Install the client:

pip install apify-client

Then run this with your Apify API token:

import csv
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("automly/instagram-comments-scraper").call(run_input={
    "postUrls": [
        "https://www.instagram.com/p/DdG4RIxIPyf/",
        "https://www.instagram.com/reel/DdHyaYAifb6/",
    ],
    "resultsPerPost": 500,
})

comments = list(client.dataset(run["defaultDatasetId"]).iterate_items())

with open("instagram_comments.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(
        f,
        fieldnames=["postUrl", "ownerUsername", "text", "likes", "createdAt"],
        extrasaction="ignore",
    )
    writer.writeheader()
    writer.writerows(comments)

print(f"Saved {len(comments)} comments")

That's the whole thing. You get up to 500 comments from each post, newest first, in instagram_comments.csv. No Instagram login, cookies or Meta developer app.

Why not the official API?

Meta does have an Instagram API, and if you only need comments on posts from accounts you manage, it's a fine choice. For anyone else's posts it doesn't help much.

Option	Access needed	Cost	What you get
Instagram API, `/comments` edge	An Instagram professional (Business or Creator) account, a token from the user who created the post, `instagram_business_basic` + `instagram_business_manage_comments` permissions. App Review and Business Verification for accounts you don't own or manage	No fee listed by Meta	Comments on your own media. Up to 50 per query, top-level only by default, no date filter
Business Discovery	Your own professional account and a Meta app	No fee listed by Meta	Follower count, media count and `comments_count` for other professional accounts. Not the comment text, and age-gated accounts return nothing
Instagram Basic Display API	n/a	n/a	Shut down on 4 December 2024
Instagram Comments Scraper	An Apify account	$0.40 per 1,000 comments	Comments on any public post or reel, with likes, date and commenter

So the official route gives you comment counts for other accounts, and comment text only for your own. If the posts belong to someone else, you need a different tool.

Step 1: Get an Apify token

Create a free account at apify.com (no credit card). Your token is in Apify Console under Settings > API & Integrations. Put it in an environment variable rather than in your code:

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

Step 2: Collect the post links

postUrls takes post links (https://www.instagram.com/p/...) and reel links (https://www.instagram.com/reel/...). Links copied from the app with extra parameters on the end work too. You can pass as many as you want in one run, and every comment row tells you which post it belongs to.

Step 3: Choose how many comments

resultsPerPost is the most comments to get from each post. The default is 100 and it goes up to 100,000. Comments come newest first, and the scraper stops when it has that many or the post runs out.

One thing to know before you compare numbers: the count Instagram shows on a post includes replies to comments. This scraper collects the comments themselves, not the replies under them, so you'll usually get fewer rows than the number on the post.

Step 4: Only get new comments

This one matters when you follow a post over several days: without it, every run pays again for comments you already have. commentsPostedAfter keeps only comments posted on or after a date (UTC if you don't give an offset):

run_input = {
    "postUrls": ["https://www.instagram.com/p/DdG4RIxIPyf/"],
    "resultsPerPost": 10000,
    "commentsPostedAfter": "2026-10-01",
}

It also takes a time, like 2026-10-01T12:00:00Z. Because comments arrive newest first, the run stops as soon as it reaches older ones.

Step 5: Read the output

Each row is one comment. Here's a trimmed one (names changed):

{
  "id": "17995635840044270",
  "text": "Amazing shot!",
  "createdAt": "2026-10-02T10:24:54Z",
  "likes": 3,
  "ownerUsername": "travel.with.jane",
  "ownerId": "1234567890",
  "ownerVerified": false,
  "ownerProfilePicUrl": "https://scontent.cdninstagram.com/v/...jpg",
  "postShortCode": "DdG4RIxIPyf",
  "postId": "3983118388705688735",
  "postUrl": "https://www.instagram.com/p/DdG4RIxIPyf/",
  "source": "https://www.instagram.com/p/DdG4RIxIPyf/",
  "fetchedAt": "2026-10-02T10:31:42Z"
}

source is the exact link you passed in, and postUrl and postShortCode identify the post. If you'd rather work in pandas, the same list goes straight into a DataFrame:

import pandas as pd

df = pd.DataFrame(comments)
df["createdAt"] = pd.to_datetime(df["createdAt"])
df.to_excel("instagram_comments.xlsx", index=False)
print(df.groupby("postShortCode")["likes"].sum())

What else can you scrape?

Most comment jobs start before the comments: you need to find the posts, or you want to know more about who wrote them.

Instagram Posts & Reels Scraper gets a profile's posts and reels, so you can pick the ones worth scraping comments from.
Instagram Post Scraper returns full details of posts and reels from their links.
Instagram Profile Scraper gives bio, followers, following and post count for usernames, such as your commenters.
Instagram Scraper does posts, profiles, hashtags and comments in one tool, if you'd rather learn how to scrape Instagram data from a single place.

Scaling up

Put all your links in one run instead of starting a run per post. In my test, 30 posts gave 6,000 comments in 41 seconds.

For monitoring, save the input as a task in Apify Console, add a daily schedule, and move commentsPostedAfter forward each time (or set it from your own code before calling). Results can go to Google Sheets, Slack, Make or Zapier through Apify integrations.

Each run also saves a short report: how many posts and comments it covered, and any link it couldn't scrape with the reason, such as a deleted or private post. Read it from Python:

store = client.key_value_store(run["defaultKeyValueStoreId"])
report = store.get_record("report-summary")
print(report["value"])

You can also set a maximum cost on a run in Console, and the run stops when it reaches it.

No code? Use the Console

Open Instagram Comments Scraper and click Try for free.
Paste post or reel links into Post or reel URLs, one per line.
Set Comments per post and, if you want, Comments posted after.
Click Start, then export the Instagram comments as CSV, Excel, JSON, HTML or XML.

What it costs

You pay per comment you get: $0.40 per 1,000 comments, the same on every Apify plan, plus $0.00005 when a run starts. 10,000 comments cost about $4. Every Apify account gets $5 of free usage each month, which covers roughly 12,000 comments at that price.

Responsible use

Only scrape public posts, and only for a real reason. Comments can contain personal data that laws like GDPR and CCPA protect, so follow Instagram's terms and ask a lawyer if you're not sure. The tool isn't affiliated with Instagram or Meta.

How to Scrape the Facebook Ad Library in Python (2026 Guide)

Azzam — Mon, 12 Oct 2026 13:49:05 +0000

The Meta Ad Library shows every active ad running on Facebook, Instagram and the rest of Meta's apps, but the website only lets you scroll through it one card at a time. This guide shows how to scrape Facebook ads from the library with Python, using a Facebook Ad Library scraper on Apify, and get one clean row per ad: text, headline, images, videos, landing page, call to action, dates, platforms and the advertiser's Page. You don't need a Facebook account or a Meta developer app.

Quick start

pip install apify-client

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("automly/facebook-ad-library-scraper").call(run_input={
    "searchTerms": ["meal kit"],
    "country": "US",
    "activeStatus": "active",
    "maxAds": 100,
    "outputFormat": "clean",
})

for ad in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(ad["pageName"], ad["startDate"], ad["activeDays"], ad["linkUrl"])

Get your token from the Apify Console under Settings > API & Integrations. That's the whole thing. The rest of this post covers the inputs, the output, and when Meta's own API is the better choice.

Why not the official Meta Ad Library API?

Meta does have an Ad Library API. Before you can call it you have to confirm your identity and location at Facebook.com/ID (Meta says this "can take a few days"), create a Meta for Developers account and set up an app.

The bigger issue is what it returns. Per Meta's own page, the API covers ads about social issues, elections or politics from anywhere in the world over the past 7 years, plus ads of any type delivered to the EU or UK during the past year. The EU part comes from the Digital Services Act: in 2023 Meta expanded the library to archive every ad shown to people in the EU for a year. The ads_archive reference spells out the catch: ads that did not reach any EU location only come back if they are political or issue ads. So a US-only ad for a skincare brand isn't in the API at all.

The fields are mostly text lists (bodies, link titles, captions, descriptions), dates, the Page, platforms and an ad_snapshot_url that opens the ad. Spend, impressions and demographics are only filled for political and issue ads; reach and targeting only for EU/UK ads. There is no field for image or video files, the CTA button or the landing page URL.

Option	Access needed	Cost	What you get
Ad Library website	None. Anyone can view and search it	Free	All active ads across Meta apps, one at a time, no export
Meta Ad Library API	ID confirmation, developer account, app	No fee listed by Meta	Political/issue ads worldwide (7 years); other ads only if delivered to the EU/UK (past year); text fields and a snapshot link
Facebook Ads Scraper	Free Apify account	$0.70 per 1,000 ads on the Free plan, less on paid plans	Any ad the library shows, in any country, with media links, landing page, CTA and Page data

If you only research political ads, or only care about EU ads, the official API is a good fit. For commercial ads elsewhere, a Facebook ads library scraper is the practical route.

Step 1: Pick your input

There are three ways in, and you can mix them in one run.

Keywords. searchTerms takes a list, and each term runs as its own search. Set searchType to keyword_exact_phrase if you want the words in order.

Advertiser Pages. Put Page links in urls to get every ad one advertiser runs:

run_input = {
    "urls": [{"url": "https://www.facebook.com/nike"}],
    "country": "ALL",
    "activeStatus": "all",
    "maxAds": 1000,
    "outputFormat": "clean",
}

Ad Library search links. If you already set up a search on the Ad Library website, copy the link from the address bar and paste it into urls. Its own filters are used as they are. A single ad link (https://www.facebook.com/ads/library/?id=...) works too.

Step 2: Set the filters

country: a two-letter code (US, GB, DE), a country name, or ALL. Defaults to US.
activeStatus: active (default), inactive or all.
mediaType: all, image, video, meme or none (text only).
adType: all, or a special category such as political_and_issue_ads or housing_ads.
startDateMin / startDateMax: only ads that started in that window, as YYYY-MM-DD.
maxAds: cap per search term or URL. Default 100, maximum 10,000.

Always pass "outputFormat": "clean" from code. Runs that leave it out get the older field layout so existing integrations don't break.

Step 3: Read the output

Here is a real row from a netflix keyword search, trimmed (media links shortened, emoji removed from the ad text):

{
  "adArchiveId": "742409480628500",
  "adLibraryUrl": "https://www.facebook.com/ads/library/?id=742409480628500",
  "isActive": true,
  "startDate": "2023-03-21",
  "endDate": null,
  "activeDays": 1145,
  "adFormat": "video",
  "mediaType": "video",
  "bodyText": "Wow! These are the most comfortable headphones for relaxing...",
  "headline": "Get Your CozyBand Today",
  "linkUrl": "https://www.cozyband.com/products/cozyband",
  "linkDomain": "cozyband.com",
  "ctaText": "Shop now",
  "videos": [{"hdUrl": "https://video.xx.fbcdn.net/...mp4", "sdUrl": "...", "previewImageUrl": "..."}],
  "platforms": ["facebook", "instagram", "audience_network", "messenger"],
  "pageName": "CozyBand",
  "pageUrl": "https://www.facebook.com/getcozyband/",
  "pageCategory": "Electronics",
  "searchTerm": "netflix"
}

Image ads carry imageUrls, and carousels come with a cards list where each card has its own headline, link, CTA and image. activeDays is the column I sort by first. An ad that has kept running for months is usually one that pays for itself.

Step 4: Save it to CSV

import csv

cols = ["pageName", "headline", "bodyText", "linkUrl", "ctaText",
        "startDate", "activeDays", "mediaType", "adLibraryUrl"]

with open("ads.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=cols, extrasaction="ignore")
    writer.writeheader()
    for ad in client.dataset(run["defaultDatasetId"]).iterate_items():
        writer.writerow(ad)

You can also skip the code and download the dataset from the Console as CSV, Excel, JSON, HTML or XML.

What else can you scrape?

There are three optional switches, all off by default. Set includeAdDetails to true and ads shown in the EU or UK come back with their reach, targeted ages, gender and places, and who paid for them. Political and issue ads get their audience breakdown and the spend range Meta publishes.

includePageTransparency adds when the advertiser's Page was created, its past names and how many ads it runs. includeAdvertiserDetails adds the advertiser's Instagram account and follower count, verified badge and About text.

A note on spend, since people ask: Meta only publishes spend ranges for political and issue ads. For ordinary commercial ads no tool reading the Ad Library can give you a real spend figure.

Scaling up

Add more terms or Page links to the same run, and raise maxAds up to 10,000 per search. For ongoing competitor tracking, save your input as a task in the Console and put it on a schedule. Each run can push its dataset to Google Sheets, Slack or a webhook, and n8n, Make and Zapier can start runs too.

No-code option: the Apify Console

Open the actor page and click Try for free.
Fill in Search Terms or paste links into Ad Library URLs.
Pick Country, Ad Status and Max Ads per Search. Output format is already set to Clean.
Click Start, then export the results.

What it costs

You pay per ad saved: $0.70 per 1,000 ads on the Free plan, with lower rates on paid Apify plans. Each run also has a $0.05 start fee at the default settings. Every Apify account gets $5 of free usage each month, which covers roughly 7,000 ads.

Responsible use

Stick to public ad data and a clear purpose for collecting it. Advertiser Pages and political ad disclaimers can contain personal data, which laws like GDPR and CCPA may protect, so follow Meta's terms and check with a lawyer if you're unsure. The scraper isn't affiliated with Meta.

Links

If you want your competitors' Facebook and Instagram ads in a spreadsheet today, try the Facebook Ads Scraper on Apify. The free monthly credit covers a few thousand ads, which is plenty for a first look at three or four brands.

How to Scrape Amazon Product Data in Python (2026 Guide)

Azzam — Mon, 12 Oct 2026 13:48:45 +0000

If you sell on Amazon, track competitors or research a niche, you probably want the same few numbers for a list of products: price, rating, Best Sellers Rank and some idea of how many units move each month. This guide gets you all of that in Python with one function call, from keywords, product links or ASINs, on 20 Amazon stores, with no Amazon account or API key.

Quick start

# pip install apify-client
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("automly/amazon-products-scraper---fast-efficient-with-sales-data").call(
    run_input={"keywords": ["garlic press"], "maxResultsPerKeyword": 20}
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    bsr = item["best_sellers_rank"][0]["rank"] if item.get("best_sellers_rank") else None
    print(item["asin"], item["price"], bsr, item.get("estimated_monthly_sales"))

That prints the ASIN, price, main-category rank and estimated monthly sales for the top 20 garlic presses on amazon.com. The rest of this post covers the inputs, the output, cost and limits.

Why not the official Amazon API?

Amazon has two developer APIs, and both come with conditions. The Product Advertising API (PA-API 5.0) that most old tutorials use is gone: Amazon deprecated it, and calls to it now return 403 Forbidden. Its replacement is the Creators API.

Option	Access needed	Cost	What you get
Creators API (replaces PA-API)	Amazon Associates account with at least 10 qualifying sales in the past 30 days. Access is lost after 30 straight days without referred sales (source)	No fee listed. Quota starts at 1 request/second and 8,640/day, then grows with the sales you refer (source)	Search, item details, variations, categories, offers and sales rank. No reviews or ratings resource (resources)
Selling Partner API	A Professional seller account (Individual accounts can't build private apps) plus developer registration	Professional plan is $39.99/month. Amazon dropped the developer fees it had planned for 2026	Catalog details and sales ranks (Catalog Items API), but its use policy limits it to helping sellers run their Amazon business
This scraper	Free Apify account	$5.55 per 1,000 results	Price, rating, reviews count, BSR per category, "bought in past month", monthly sales and revenue estimates

So the official route works if you're an active affiliate who links back to Amazon, or a seller managing your own business. For product research or price monitoring across any product, it's a poor fit. I also couldn't find Amazon's "bought in past month" figure or any sales estimate in either API's docs.

Step 1: Install the client and get a token

pip install apify-client

Sign up at apify.com, then copy your API token from Settings > API & Integrations. Keep it in an environment variable rather than in your code.

Step 2: Scrape Amazon search results by keyword

keywords takes a list. Each keyword returns up to maxResultsPerKeyword products (default 50, max 500). marketplace picks the store and defaults to com.

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
ACTOR = "automly/amazon-products-scraper---fast-efficient-with-sales-data"

run = client.actor(ACTOR).call(run_input={
    "keywords": ["garlic press", "yoga mat"],
    "maxResultsPerKeyword": 100,
    "marketplace": "com",
})

Each row keeps search_keyword, search_position and a sponsored flag, so you can see where a product sits in the results as well as what it is.

Step 3: Scrape Amazon prices by ASIN

If you already have product IDs, pass them in asins. Product links go in startUrls. You can mix all three inputs in one run.

run = client.actor(ACTOR).call(run_input={
    "asins": ["B0DJ28XG7Z", "B00HEZ888K", "B098WNQ44M"],
    "startUrls": [{"url": "https://www.amazon.com/dp/B09QBDRCRK"}],
    "marketplace": "com",
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["asin"], item["price"], item["list_price"], item["discount_percent"])

Run that on a schedule and you have a basic Amazon price tracker.

Step 4: Read the output

One row per product. Here's a real one, shortened:

{
  "asin": "B0DJ28XG7Z",
  "url": "https://www.amazon.com/dp/B0DJ28XG7Z",
  "title": "Kitchen Garlic Press, Rust Proof & Dishwasher Safe Garlic Mincer...",
  "brand": "SRISE",
  "price": "$6.99",
  "list_price": "$7.99",
  "discount_percent": 13,
  "rating": 4.6,
  "review_count": 1478,
  "best_sellers_rank": [
    {"rank": 886, "category": "Kitchen & Dining"},
    {"rank": 3, "category": "Garlic Presses"}
  ],
  "bought_in_past_month": "3K+",
  "estimated_monthly_sales": 3149,
  "estimated_monthly_revenue": "$22,011.51",
  "availability": "In Stock",
  "is_prime": true,
  "badges": ["Amazon's Choice"],
  "search_keyword": "garlic press",
  "search_position": 2
}

The full row also has the star breakdown, category path, bullet points, description, specs, variants with their own ASINs, images, coupon, seller, related ASINs and a scraped_at timestamp.

Step 5: Save to CSV

import csv

fields = ["asin", "title", "price", "rating", "review_count",
          "bought_in_past_month", "estimated_monthly_sales", "estimated_monthly_revenue"]

with open("amazon_products.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=fields + ["bsr"], extrasaction="ignore")
    writer.writeheader()
    for item in client.dataset(run["defaultDatasetId"]).iterate_items():
        top = item["best_sellers_rank"][0] if item.get("best_sellers_rank") else {}
        writer.writerow({**item, "bsr": top.get("rank")})

If you prefer pandas, pd.DataFrame(client.dataset(run["defaultDatasetId"]).list_items().items) does the same in one line.

How far to trust the sales estimate

The monthly sales number is derived from the product's Best Sellers Rank, and revenue is units times the current price. I use it to compare products against each other and to size a niche, and that's where it's good. For a single product, real sales can be about 55% above or below it, so don't treat it as an exact figure.

Estimates exist on amazon.com, .co.uk, .de, .fr, .it, .es, .ca, .com.mx and .in, for most main categories. On the other stores you get every other field, just not the estimate. Products with no BSR, like brand-new listings, have none either.

What else can you scrape?

The same code works on 20 Amazon stores: set marketplace to co.uk, de, fr, it, es, ca, com.mx, in, com.br, co.jp, com.au, ae, sa, nl, se, pl, com.tr, sg or eg. Prices and text come back in that store's language and currency.

For other jobs there are three related scrapers: Amazon Search Scraper for keyword rank tracking, Amazon Best Sellers Scraper for the top 100 of any category, and Amazon Reviews Scraper for review text. The first two give you ASINs you can feed straight into Step 3.

Scaling

Raise maxResultsPerKeyword up to 500 and add more keywords.
Set "fullDetails": false when you only need what the search list shows (ASIN, title, price, rating, image, sponsored flag). It's much faster, but you lose BSR and the sales estimates.
Want every variant priced? Each row lists its variants' ASINs; pass them back in through asins.
For daily tracking, save your input as a task in Apify and attach a schedule. Set a maximum cost per run if you want a hard budget.

No-code option: the Apify Console

You don't have to write Python at all. Open the actor page, click Try for free, type your keywords or ASINs, pick a marketplace and click Start. When it finishes, download the results as CSV, Excel or JSON, or send them to Google Sheets.

What it costs

You pay $5.55 per 1,000 results on every Apify plan, plus a start fee of $0.05 per GB of memory (that's $0.10 per run at the default 2 GB) and the run's platform usage. Every Apify account gets $5 of free usage each month, which covers several hundred products. That's enough to test a niche or two before paying anything.

Responsible use

Stick to public product data and follow Amazon's terms. Some fields, like seller names, can count as personal data under GDPR or CCPA, so have a legitimate reason to collect them and check with a lawyer if you're unsure.

Links

If you want Amazon prices, ranks and sales estimates without building and maintaining your own scraper, try Amazon Scraper - Product Prices, BSR & Sales Estimates on Apify. If a field you need is missing, request it on the actor's Issues tab.