DEV Community: Serenido

Find best-selling AliExpress products by exact order count with Python

Serenido — Sat, 17 Oct 2026 09:24:44 +0000

AliExpress search pages round sales into buckets like "10,000+ sold". That hides the difference between a product with 10,100 orders and one with 19,900. For product research you want the real number.

This post shows how to search AliExpress from Python and get the exact order count, price, rating and a few useful flags for every product, then pick the best sellers.

Disclosure: I built the tool used below. It runs on Apify and costs $0.25 per 1,000 products.

What you get for each product

Price, crossed-out price, discount and currency
Exact order count, plus the rounded text AliExpress shows
Star rating, ships-from country and delivery estimate
Flags: AliExpress Choice, free shipping, sponsored (ad) and new-shopper deal
Product link, image, category id and its rank in the search results

24 ship-to countries, each with its local currency. Sort by best match, orders, or price (low or high).

Step 1: Install the client

pip install apify-client

You need a free Apify account for the API token. The free plan gives $5 of usage every month, no credit card needed. Find the token in Apify Console under Settings > API & Integrations.

Step 2: Search and pick the best sellers

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("serenido/aliexpress-search-reliable").call(run_input={
    "searchTerms": ["yoga mat"],
    "maxItems": 120,
    "sortBy": "orders",
    "country": "US",
    "minRating": 4.5,
})

products = list(client.dataset(run["defaultDatasetId"]).iterate_items())
normal = [p for p in products if not p["isNewShopperDeal"]]
print(len(products), "products,", len(normal), "with normal prices")

for p in sorted(normal, key=lambda p: p["orders"] or 0, reverse=True)[:5]:
    print(f'{p["orders"]:>6} orders  ${p["price"]:<6} {p["rating"]}  {p["title"][:50]}')

The result

From a real run on October 14. The 120 products took 7 seconds:

120 products, 35 with normal prices
 12544 orders  $6.19   4.9  Yoga Mat Pilates Fitness Mat 3/4/6mm Thicknes Non
  9056 orders  $1.33   4.9  Yoga Kneeling Mat Thickened Flat Support Mat Knee
  4380 orders  $14.59  4.9  Foldable Yoga Mat Mute  Eco Friendly Folding Trave
  2776 orders  $12.95  4.9  Balance Pad Non-Slip Foam Mat Ankles Knee Cushion
  2739 orders  $9.59   4.9  8/16 Pcs Foam Fitness Non Slip Yoga Mat Fitness Fl

The top product's search card said "10,000+ sold". The exact count is 12,544.

Two things to know

1. Most prices you see logged out are first-order prices. AliExpress shows very low "welcome" prices to visitors who are not logged in, valid for a first order only. In this run, 85 of 120 products were flagged isNewShopperDeal. The code keeps only the other 35, so the prices are ones most buyers would pay.

2. You get what AliExpress search returns. That includes the odd unrelated item: in this run, one of the 120 "yoga mat" results was a glow-in-the-dark rug. Filter by title if you need a clean list.

Useful filters

All of these go in run_input:

minPrice, maxPrice, minOrders, minRating
freeShippingOnly, choiceOnly, skipSponsored
shipFromCountry (for example, only products that ship from a US warehouse)
startUrls: paste AliExpress search or category links instead of keywords

Next step: the full product

Search results show one price per product. For every variant (color, size) with its own price and stock, plus shipping to your country, see my earlier post: How to get AliExpress product variants, stock and shipping as JSON with Python.

Cost

$0.25 per 1,000 products, plus a small start fee of $0.00005 per GB of memory, which is $0.0002 per run at the default 4 GB. AliExpress blocks datacenter IPs, so the tool uses residential IPs and retries blocked pages on its own.

Use it from an AI assistant

The tool also works through Apify's MCP server, so Claude, ChatGPT, Cursor or VS Code can run it for you. Add this server URL to your app: https://mcp.apify.com?tools=serenido/aliexpress-search-reliable

A pytrends alternative in 2026: Google Trends data in Python without 429 errors

Serenido — Fri, 16 Oct 2026 09:22:23 +0000

For years, pytrends was the default way to get Google Trends data in Python. Today its GitHub repository is archived (read-only), and its last release on PyPI was in April 2023. Its issue tracker has 28 issues with "429" in the title, the "too many requests" error Google sends when it blocks you.

Google announced an official Trends API in July 2025, but access is by application while it is in alpha.

This post shows another way: a hosted tool that fetches Trends data, retries blocked requests on a fresh IP, and returns clean JSON.

Disclosure: I built the tool used below. It runs on Apify and costs $1 per 1,000 results.

What you get for each keyword

Interest over time (the 0 to 100 curve), plus a summary: average, latest week, peak week, change in % and a direction (rising, stable or falling)
Rising and top related queries
Interest by region
Optional: today's Trending Now searches for any country, with traffic and news links

Search types: web, images, news, YouTube and Google Shopping. Time ranges: from the past hour to all time (2004).

Step 1: Install the client

pip install apify-client

You need a free Apify account for the API token. The free plan gives $5 of usage every month, no credit card needed. Find the token in Apify Console under Settings > API & Integrations.

Step 2: Get the trends

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("serenido/google-trends-reliable").call(run_input={
    "searchTerms": ["gym wear", "vw golf"],
    "geo": "US",
    "timeRange": "past_12_months",
    "includeRawData": True,
})

for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    if row["type"] != "keyword":
        continue
    change = row["trendChangePct"]
    change_text = f" ({change:+}%)" if change is not None else ""
    print(f'{row["keyword"]}: {row["trendDirection"]}{change_text}, peak week {row["peakDate"]}')
    for q in row["relatedQueries"]["rising"][:3]:
        print("   rising:", q["query"], q["value"])
    full_weeks = [p["value"] for p in row["interestOverTime"] if not p["isPartial"]]
    print("   last 4 full weeks:", full_weeks[-4:])

includeRawData adds the full arrays (interestOverTime, interestByRegion, relatedQueries). Without it you get one flat row per keyword, which is easier to open in Excel.

The result

From a real run on October 14:

gym wear: stable (+10%), peak week 2026-04-12
   rising: thriving gym wear +350%
   rising: gym wear men's +250%
   rising: women's gym wear +200%
   last 4 full weeks: [64, 68, 61, 58]
vw golf: stable (-12%), peak week 2026-04-12
   rising: vw golf 2026 +2,250%
   rising: 2026 vw golf r +900%
   rising: vw golf plus +250%
   last 4 full weeks: [47, 50, 50, 48]

The change in % compares the average of the last quarter of the period with the first quarter. +15% or more is "rising", -15% or less is "falling".

Three things to know about Trends data

1. The numbers are relative, not search counts. 100 is the highest point for that keyword in the chosen time and place. A keyword at 50 had half the searches of its own peak, not 50 searches.

2. Google samples the data. I ran the same request twice, about two hours apart. The first run said "gym wear" peaked in the week of February 8; the second said April 12. The average moved from 74.7 to 74.5. Look at the shape of the curve and the direction, not at one exact week or value.

3. The last week is often unfinished. It is marked isPartial, and it can look like a jump. The code above leaves it out.

Blocks and billing

Google blocks Trends scrapers a lot. The tool retries a blocked request with a fresh session and a fresh residential IP, so one block does not end the run. Keywords that are still blocked after all retries, or that have too little search volume for Google to show data, are skipped and not charged. The run's status message lists them.

Cost

$1 per 1,000 results (one result = one keyword, or one Trending Now search), plus a small start fee of $0.00005 per GB of memory, which is $0.0002 per run at the default 4 GB.

Use it from an AI assistant

How to get AliExpress product variants, stock and shipping as JSON with Python

Serenido — Wed, 14 Oct 2026 06:44:56 +0000

If you do dropshipping, price tracking or product research, the AliExpress product page has the data you need, but not in a form you can use. Each variant (color, size, plug type) has its own price and stock. Shipping cost and delivery time change with the country. And some prices are not what most buyers pay.

This post shows how to get all of it as clean JSON, then turn it into a CSV with one row per variant.

Disclosure: I built the tool used below. It runs on Apify and costs $1 per 1,000 products.

Why not just scrape the page?

You can, but it gets hard fast. In my tests, AliExpress showed a robot check after about 9 to 10 requests from the same IP address, and datacenter IPs were blocked from the first request. So you need residential IPs, a way to notice the block, and a retry on a fresh IP. The tool does that for you. It never tries to solve the robot check; it just moves to a new IP.

What you get for each product

Every variant with its own price, crossed-out price, stock and image
Shipping fee, free shipping, delivery days and the shipping method for the country you choose
Rating, review count, sold count
Store name and store rating
A status for products that are missing, removed or not sold to your country

You can pick from 89 ship-to countries, 41 currencies and 16 languages.

Step 1: Install the client

pip install apify-client

You need a free Apify account for the API token. The free plan gives $5 of usage every month, no credit card needed. Find the token in Apify Console under Settings > API & Integrations.

Step 2: Get the products and save one row per variant

import csv

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("serenido/aliexpress-product-details").call(run_input={
    "productUrls": [
        "https://www.aliexpress.com/item/1005007857239353.html",
        "3256811374070548",
    ],
    "shipTo": "US",
    "currency": "USD",
})

rows = []
for product in client.dataset(run["defaultDatasetId"]).iterate_items():
    if product["status"] != "ok":
        print("skipped", product.get("productId"), product["status"])
        continue
    if not product["canShip"]:
        print("does not ship to", product["shipTo"], product["productId"])
        continue
    for sku in product["skus"]:
        rows.append({
            "productId": product["productId"],
            "variant": sku["name"],
            "price": sku["price"],
            "stock": sku["stock"],
            "available": sku["available"],
            "newShopperPrice": sku["isNewShopperDeal"],
            "shippingFee": product["shippingFee"],
            "deliveryDays": f'{product["deliveryDaysMin"]}-{product["deliveryDaysMax"]}',
        })

with open("variants.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
    writer.writeheader()
    writer.writerows(rows)

print(len(rows), "variants saved to variants.csv")

You can paste full product links or just the product ids.

The result

These two products (a yoga mat and a wig) gave 104 variants. The first lines of variants.csv, from a real run on October 14:

productId,variant,price,stock,available,newShopperPrice,shippingFee,deliveryDays
1005007857239353,0.6cm Black,5.09,11,True,True,0,6-10
1005007857239353,0.6cm Purple,5.53,9,True,True,0,6-10
1005007857239353,0.3cm Dark Blue,1.97,17,True,True,0,6-10
1005007857239353,0.4cm Black,2.51,25,True,True,0,6-10
1005007857239353,0.4cm Dark Blue,3.1,3,True,True,0,6-10

Same product, same page: prices from $1.09 to $11.19 and stock from 0 to 28, depending on the variant. That is why one row per variant matters.

Three traps to watch for

1. New-shopper prices. AliExpress shows very low "welcome" prices to visitors who are not logged in, and those prices are for a first order only. In my US tests, about 60% of products showed them. Every product and every variant has an isNewShopperDeal flag, so you can keep these prices apart from normal ones. All the rows above are flagged, so treat them as first-order prices.

2. Not every product is sold everywhere. In one test of 250 products shipped to Germany, 92 were not sold there. Those come back as free rows with the status unavailable_in_region. A product can also be on sale but not deliverable to your address; then canShip is false. The code above skips both.

3. Dead links. Removed products, wrong ids and products that no longer exist come back as rows with a status like not_found, no_longer_sold or invalid_link. These rows are free.

Other countries and currencies

Change three fields:

run_input = {
    "productUrls": ["1005007857239353"],
    "shipTo": "GB",
    "currency": "GBP",
    "language": "en",
}

Shipping fee, delivery days and the canShip flag then follow the UK. Prices come back in pounds.

Cost and speed

$1 per 1,000 products. Rows for missing, removed, not-sold or failed products cost nothing.
1,000 products took about 5 minutes in my test.

DEV Community: Serenido

Find best-selling AliExpress products by exact order count with Python

What you get for each product

Step 1: Install the client

Step 2: Search and pick the best sellers

The result

Two things to know

Useful filters

Next step: the full product

Cost

Use it from an AI assistant

Links

A pytrends alternative in 2026: Google Trends data in Python without 429 errors

What you get for each keyword

Step 1: Install the client

Step 2: Get the trends

The result

Three things to know about Trends data

Blocks and billing

Cost

Use it from an AI assistant

Links

How to get AliExpress product variants, stock and shipping as JSON with Python

Why not just scrape the page?

What you get for each product

Step 1: Install the client

Step 2: Get the products and save one row per variant

The result

Three traps to watch for

Other countries and currencies

Cost and speed

Use it from an AI assistant

Links