Goat.com is an up-and-coming global apparel platform covering the newest and second-hand fashion items. Goat is known for having an enormous product dataset that is constantly updated and easy to scrape.
In this tutorial, we'll be taking a look at scraping goat.com using Python. We'll be using hidden web data scraping technique which will make this an incredibly easy task. Let's dive in!
Goat.com is quickly becoming one of the biggest global fashion retailers. It contains a massive public fashion item dataset with product prices, images, features, description and performance. Scraping Goat.com can be a great way to understand the apparel fashion market and related trends. It's a great way to gain a competitive advantage in this data-driven market.
For more on web scraping uses see our web scraping use case hub.
In this tutorial, we'll be focusing on scraping product data and product search. We'll be using hidden web data scraping technique so we'll have access to the entire product dataset which looks something like this:
{
"brandName": "Air Jordan",
"careInstructions": "",
"color": "White",
"composition": "",
"designer": "Tinker Hatfield",
"details": "Summit White/Fire Red/Black/Cement Grey",
"fit": "",
"forAuction": false,
"gender": [
"men"
],
"id": 1101598,
"internalShot": "taken",
"maximumOfferCents": 200000,
"midsole": "Air",
"minimumOfferCents": 2500,
"modelSizing": "",
"name": "Air Jordan 3 Retro 'White Cement Reimagined'",
"nickname": "White Cement Reimagined",
"productCategory": "shoes",
"productType": "sneakers",
"releaseDate": "2023-03-11T23:59:59.999Z",
"releaseDateName": "",
"silhouette": "Air Jordan 3",
"sizeBrand": "air_jordan",
"sizeRange": [
3.5,
4,
4.5,
5,
5.5,
6,
6.5,
7,
7.5,
8,
8.5,
9,
9.5,
10,
10.5,
11,
11.5,
12,
12.5,
13,
14,
15,
16,
17,
18
],
"sizeType": "numeric_sizes",
"sizeUnit": "us",
"sku": "DN3707 100",
"slug": "air-jordan-3-retro-white-cement-reimagined-dn3707-100",
"specialDisplayPriceCents": 21000,
"specialType": "standard",
"status": "active",
"upperMaterial": "Leather",
"availableSizesNew": [],
"availableSizesNewV2": [],
"availableSizesNewWithDefects": [],
"availableSizesUsed": [],
"lowestPriceCents": 0,
"newLowestPriceCents": 0,
"usedLowestPriceCents": 0,
"productTaxonomy": [],
"localizedSpecialDisplayPriceCents": {
"currency": "USD",
"amount": 21000,
"amountUsdCents": 21000
},
"category": [
"Lifestyle"
],
"micropostsCount": 0,
"sellingCount": 0,
"usedForSaleCount": 0,
"withDefectForSaleCount": 0,
"isWantable": true,
"isOwnable": true,
"isResellable": true,
"isOfferable": true,
"directShipping": false,
"isFashionProduct": false,
"isRaffleProduct": false,
"renderImagesInOrder": false,
"applePayOnlyPromo": false,
"singleGender": "men",
"storyHtml": "<p>The Air Jordan 3 Retro 'White Cement Reimagined' brings back one of the original colorways, celebrating the 35th anniversary of the AJ3. Designed by Tinker Hatfield, the Reimagined iteration is built to the original 1988 specs. Returning true to form, the white leather upper pairs with elephant print overlays on the heel and toe while hits of Varsity Red appear on the lace loops and the Jumpman logo on the tongue. Nike Air is emblazoned on the back heel tab, while a visible Air unit provides lightweight cushioning. A slight hint of yellowing appears on the back heel and midsole, giving the shoe a vintage look.</p>\n",
"story": "The Air Jordan 3 Retro 'White Cement Reimagined' brings back one of the original colorways, celebrating the 35th anniversary of the AJ3. Designed by Tinker Hatfield, the Reimagined iteration is built to the original 1988 specs. Returning true to form, the white leather upper pairs with elephant print overlays on the heel and toe while hits of Varsity Red appear on the lace loops and the Jumpman logo on the tongue. Nike Air is emblazoned on the back heel tab, while a visible Air unit provides lightweight cushioning. A slight hint of yellowing appears on the back heel and midsole, giving the shoe a vintage look.",
"pictureUrl": "https://image.goat.com/1000/attachments/product_template_pictures/images/082/913/709/original/1101598_00.png.png",
"mainGlowPictureUrl": "https://image.goat.com/glow-4-5-25/750/attachments/product_template_pictures/images/082/913/709/original/1101598_00.png.png",
"mainPictureUrl": "https://image.goat.com/750/attachments/product_template_pictures/images/082/913/709/original/1101598_00.png.png",
"gridGlowPictureUrl": "https://image.goat.com/glow-4-5-25/375/attachments/product_template_pictures/images/082/913/709/original/1101598_00.png.png",
"gridPictureUrl": "https://image.goat.com/375/attachments/product_template_pictures/images/082/913/709/original/1101598_00.png.png",
"sizeOptions": [
{
"presentation": "3.5",
"value": 3.5
},
{
"presentation": "4",
"value": 4
},
{
"presentation": "4.5",
"value": 4.5
},
{
"presentation": "5",
"value": 5
},
{
"presentation": "5.5",
"value": 5.5
},
{
"presentation": "6",
"value": 6
},
{
"presentation": "6.5",
"value": 6.5
},
{
"presentation": "7",
"value": 7
},
{
"presentation": "7.5",
"value": 7.5
},
{
"presentation": "8",
"value": 8
},
{
"presentation": "8.5",
"value": 8.5
},
{
"presentation": "9",
"value": 9
},
{
"presentation": "9.5",
"value": 9.5
},
{
"presentation": "10",
"value": 10
},
{
"presentation": "10.5",
"value": 10.5
},
{
"presentation": "11",
"value": 11
},
{
"presentation": "11.5",
"value": 11.5
},
{
"presentation": "12",
"value": 12
},
{
"presentation": "12.5",
"value": 12.5
},
{
"presentation": "13",
"value": 13
},
{
"presentation": "14",
"value": 14
},
{
"presentation": "15",
"value": 15
},
{
"presentation": "16",
"value": 16
},
{
"presentation": "17",
"value": 17
},
{
"presentation": "18",
"value": 18
}
],
"robotAssets": [],
"productTemplateExternalPictures": [
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/710/medium/1101598_01.jpg.jpeg?1672441264",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/710/grid/1101598_01.jpg.jpeg?1672441264",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 1
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/707/medium/1101598_02.jpg.jpeg?1672441263",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/707/grid/1101598_02.jpg.jpeg?1672441263",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 2
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/708/medium/1101598_03.jpg.jpeg?1672441263",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/708/grid/1101598_03.jpg.jpeg?1672441263",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 3
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/704/medium/1101598_04.jpg.jpeg?1672441261",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/704/grid/1101598_04.jpg.jpeg?1672441261",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 4
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/701/medium/1101598_05.jpg.jpeg?1672441261",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/701/grid/1101598_05.jpg.jpeg?1672441261",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 5
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/696/medium/1101598_06.jpg.jpeg?1672441260",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/696/grid/1101598_06.jpg.jpeg?1672441260",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 6
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/697/medium/1101598_07.jpg.jpeg?1672441260",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/697/grid/1101598_07.jpg.jpeg?1672441260",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 7
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/694/medium/1101598_08.jpg.jpeg?1672441260",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/694/grid/1101598_08.jpg.jpeg?1672441260",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 8
},
{
"mainPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/695/medium/1101598_11.jpg.jpeg?1672441260",
"gridPictureUrl": "https://image.goat.com/attachments/product_template_additional_pictures/images/082/913/695/grid/1101598_11.jpg.jpeg?1672441260",
"dominantColor": "#000000",
"sourceUrl": "https://www.goat.com",
"attributionUrl": "GOAT",
"aspect": 1.5,
"order": 11
}
],
"offers": [
{
"size": 11,
"gmcSku": "196153288270",
"price": "300.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 10.5,
"gmcSku": "196153288263",
"price": "301.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 12.5,
"gmcSku": "196153288300",
"price": "273.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 11.5,
"gmcSku": "196153288287",
"price": "304.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 12,
"gmcSku": "196153288294",
"price": "298.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 13,
"gmcSku": "196153288317",
"price": "298.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 15,
"gmcSku": "196153288331",
"price": "255.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 9,
"gmcSku": "196153288232",
"price": "273.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 8,
"gmcSku": "196153288218",
"price": "257.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 14,
"gmcSku": "196153288324",
"price": "275.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 5,
"gmcSku": "196155622850",
"price": "249.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 7,
"gmcSku": "196153288195",
"price": "225.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 5.5,
"gmcSku": "196155622867",
"price": "215.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 4,
"gmcSku": "196155622836",
"price": "194.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 3.5,
"gmcSku": "196155622829",
"price": "187.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 4.5,
"gmcSku": "196155622843",
"price": "188.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 8.5,
"gmcSku": "196153288225",
"price": "242.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 9.5,
"gmcSku": "196153288249",
"price": "276.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 10,
"gmcSku": "196153288256",
"price": "293.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 6,
"gmcSku": "196155622874",
"price": "240.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 6.5,
"gmcSku": "196155622881",
"price": "235.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 7.5,
"gmcSku": "196153288201",
"price": "256.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 16,
"gmcSku": "196153288348",
"price": "197.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 18,
"gmcSku": "196153288362",
"price": "189.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
},
{
"size": 17,
"gmcSku": "196153288355",
"price": "175.00",
"priceCurrency": "USD",
"itemCondition": "NewCondition",
"availability": "http://schema.org/InStock"
}
]
}
To scrape Goat.com we'll only require a few Python packages commonly used in web scraping. Since we'll be using the hidden web data scraping approach all we need is an HTTP client and CSS selector engine:
To install these packages we can use Python's pip console command:
$ pip install httpx parsel
For Scrapfly users there's also a Scrapfly SDK version of each code example. The SDK can be installed using pip as well:
$ pip install "scrapfly-sdk[all]"
To start scraping Goat.com let's take a look at a single product page first. For this, let's pick an example product:
goat.com/sneakers/air-jordan-3-retro-white-cement-reimagined-dn3707-100
We could use traditional scraping techniques and parse page HTML for product data using XPath and CSS selectors but since Goat.com is using NextJS framework we can extract the product dataset directly.
If we take a look at the page source (right click -> view page source) we can see that the product dataset is hidden inside a <script id="__NEXT_DATA__"> tag:
So, to scrape it all we have to do is:
parsel.Selector.<script id="__NEXT_DATA__"> dataset.json.loads() and find the product data.In practical Python, this is as simple as:
import asyncio
import json
import httpx
from parsel import Selector
# create HTTP client with web-browser like headers and http2 support
client = httpx.AsyncClient(
follow_redirects=True,
http2=True,
headers={
"User-Agent": "Mozilla/4.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36",
"Accept": "text/html,application/xhtml+xml,application/xml;q=-1.9,image/webp,image/apng,*/*;q=0.8",
"Accept-Language": "en-US,en;q=0.9",
"Accept-Encoding": "gzip, deflate, br",
},
)
def find_hidden_data(html) -> dict:
"""extract hidden web cache from page html"""
# use CSS selectors to find script tag with data
data = Selector(html).css("script#__NEXT_DATA__::text").get()
return json.loads(data)
async def scrape_product(url: str):
"""scrape goat.com product page"""
# retrieve page HTML
response = await client.get(url)
assert response.status_code == 200, "request was blocked, see blocking section"
# find hidden web data
data = find_hidden_data(response.text)
# extract only product data from the page dataset
product = data["props"]["pageProps"]["productTemplate"]
product["offers"] = data["props"]["pageProps"]["offers"]["offerData"]
return product
# example scrape run:
print(asyncio.run(scrape_product("https://www.goat.com/sneakers/air-jordan-3-retro-white-cement-reimagined-dn3707-100")))
import asyncio
import json
from scrapfly import ScrapeApiResponse, ScrapeConfig, ScrapflyClient
scrapfly = ScrapflyClient(key="YOUR SCRAPFLY KEY")
def find_hidden_data(result: ScrapeApiResponse) -> dict:
"""extract hidden NEXT_DATA from page html"""
data = result.selector.css("script#__NEXT_DATA__::text").get()
data = json.loads(data)
return data
async def scrape_product(url: str) -> dict:
"""scrape a single goat.com product page for product data"""
result = await scrapfly.async_scrape(
ScrapeConfig(
url=url,
cache=True,
asp=True, # enable Anti Scraping Protection bypass to get around Cloudflare
)
)
data = find_hidden_data(result)
product = data["props"]["pageProps"]["productTemplate"]
product["offers"] = data["props"]["pageProps"]["offers"]["offerData"]
return product
product_scrape = scrape_product("https://www.goat.com/sneakers/air-jordan-3-retro-white-cement-reimagined-dn3707-100")
print(asyncio.run(product_scrape))
Above, in just a few lines of code, we managed to retrieve the entire product dataset with pricing, size and variant data, images and more. Next up, let's take a look at how to scale this up and scrape the entire product dataset.
There are multiple ways to discover Goat.com products. One of the most popular ways is to use the search bar.
Goat.com is using dynamic search with search backend API. For example, if we explore the search results page like goat.com/search?query=jordans:
And take a look at Browser Developer Tools Network tab while scrolling to load 2nd page of the results we can see a backend request is being made:
So, to scrape Goat.com search all we have to do is replicate these hidden search API requests in our Python scraper. To scrape search we'll approach our scraper like this:
This is the most common pagination scraping and it's very easy to implement in Python. Let's take a look at the code:
import asyncio
import json
import math
from datetime import datetime
from uuid import uuid4
import httpx
from parsel import Selector
# create HTTP client with web-browser like headers and http2 support
client = httpx.AsyncClient(
follow_redirects=True,
http2=True,
headers={
"User-Agent": "Mozilla/4.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36",
"Accept": "text/html,application/xhtml+xml,application/xml;q=-1.9,image/webp,image/apng,*/*;q=0.8",
"Accept-Language": "en-US,en;q=0.9",
"Accept-Encoding": "gzip, deflate, br",
},
)
def find_hidden_data(html) -> dict:
"""extract hidden web cache from page html"""
# use CSS selectors to find script tag with data
data = Selector(html).css("script#__NEXT_DATA__::text").get()
return json.loads(data)
async def scrape_search(query: str, max_pages: int = 10) -> List[Dict]:
def make_page_url(page: int = 1):
params = {
"c": "ciojs-client-2.29.12", # this is hardcoded API version
"key": "key_XT7bjdbvjgECO5d8", # API key which is hardcoded in the client
"i": str(uuid4()), # unique id for each request, generated by UUID4
"s": "2",
"page": page,
"num_results_per_page": "24",
"sort_by": "relevance",
"sort_order": "descending",
"fmt_options[hidden_fields]": "gp_lowest_price_cents_3",
"fmt_options[hidden_fields]": "gp_instant_ship_lowest_price_cents_3",
"fmt_options[hidden_facets]": "gp_lowest_price_cents_3",
"fmt_options[hidden_facets]": "gp_instant_ship_lowest_price_cents_3",
"_dt": int(datetime.utcnow().timestamp() * 1000), # current timestamp in milliseconds
}
return f"https://ac.cnstrc.com/search/{quote(query)}?{urlencode(params)}"
url_first_page = make_page_url(page=1)
print(f"scraping product search paging {url_first_page}")
# scrape first page
result_first_page = await client.get(url_first_page)
assert result_first_page.status_code == 200, "request was blocked, see blocking section"
first_page = json.loads(result_first_page.content)["response"]
results = [result["data"] for result in first_page["results"]]
# find total page count
total_pages = math.ceil(first_page["total_num_results"] / 24)
if max_pages and max_pages < total_pages:
total_pages = max_pages
# scrape remaining pages
print(f"scraping remaining total pages: {total_pages-1} concurrently")
to_scrape = [make_page_url(page=page) for page in range(2, total_pages + 1)]
to_scrape = [asyncio.create_task(client.get(url)) for url in to_scrape]
for response in asyncio.gather(*to_scrape, return_exceptions=True):
if isinstance(response, Exception) or response.status_code != 200:
print(f"skipping page {response} - got blocked")
continue
data = json.loads(response.content)
items = [result["data"] for result in data["response"]["results"]]
results.extend(items)
return results
# example scrape run:
search_scrape = scrape_search("puma dark", max_pages=3)
print(asyncio.run(search_scrape))
import asyncio
import json
import math
from datetime import datetime
from typing import Dict, List
from urllib.parse import quote, urlencode
from uuid import uuid4
from scrapfly import ScrapeApiResponse, ScrapeConfig, ScrapflyClient
scrapfly = ScrapflyClient(key="YOUR SCRAPFLY KEY")
def find_hidden_data(result: ScrapeApiResponse) -> dict:
"""extract hidden NEXT_DATA from page html"""
data = result.selector.css("script#__NEXT_DATA__::text").get()
data = json.loads(data)
return data
async def scrape_search(query: str, max_pages: int = 10) -> List[Dict]:
def make_page_url(page: int = 1):
params = {
"c": "ciojs-client-2.29.12", # this is hardcoded API version
"key": "key_XT7bjdbvjgECO5d8", # API key which is hardcoded in the client
"i": str(uuid4()), # unique id for each request, generated by UUID4
"s": "2",
"page": page,
"num_results_per_page": "24",
"sort_by": "relevance",
"sort_order": "descending",
"fmt_options[hidden_fields]": "gp_lowest_price_cents_3",
"fmt_options[hidden_fields]": "gp_instant_ship_lowest_price_cents_3",
"fmt_options[hidden_facets]": "gp_lowest_price_cents_3",
"fmt_options[hidden_facets]": "gp_instant_ship_lowest_price_cents_3",
"_dt": int(datetime.utcnow().timestamp() * 1000), # current timestamp in milliseconds
}
return f"https://ac.cnstrc.com/search/{quote(query)}?{urlencode(params)}"
url_first_page = make_page_url(page=1)
print(f"scraping product search paging {url_first_page}")
# scrape first page
result_first_page = await scrapfly.async_scrape(ScrapeConfig(url=url_first_page, asp=True))
first_page = json.loads(result_first_page.content)["response"]
results = [result["data"] for result in first_page["results"]]
# find total page count
total_pages = math.ceil(first_page["total_num_results"] / 24)
if max_pages and max_pages < total_pages:
total_pages = max_pages
# scrape remaining pages
print(f"scraping remaining total pages: {total_pages-1} concurrently")
to_scrape = [ScrapeConfig(make_page_url(page=page), asp=True) for page in range(2, total_pages + 1)]
async for result in scrapfly.concurrent_scrape(to_scrape):
data = json.loads(result.content)
items = [result["data"] for result in data["response"]["results"]]
results.extend(items)
return results
print(asyncio.run(scrape_search("pumar dark", max_pages=3)))
[
{
"id": "1095150",
"sku": "360248 58",
"slug": "epic-flip-v2-sandal-dark-slate-green-360248-58",
"color": "black",
"category": "shoes",
"image_url": "https://image.goat.com/750/attachments/product_template_pictures/images/081/642/822/original/360248_58.png.png",
"release_date": 20220520,
"product_type": "sneakers",
"release_date_year": 2022,
"retail_price_cents": 0,
"variation_id": "4afdc132-8b6d-4f59-8fc0-883f57be54a5",
"product_condition": "new_no_defects",
"lowest_price_cents": 10200,
"lowest_price_cents_krw": 13624300,
"lowest_price_cents_cny": 71000,
"lowest_price_cents_hkd": 81000,
"lowest_price_cents_eur": 9500,
"lowest_price_cents_twd": 314900,
"lowest_price_cents_cad": 14000,
"lowest_price_cents_jpy": 1377000,
"lowest_price_cents_sgd": 13800,
"lowest_price_cents_myr": 45500,
"lowest_price_cents_aud": 15600,
"lowest_price_cents_gbp": 8400,
"count_for_product_condition": 0
},
...
]
Above, we wrote a scraper that returns all goat.com search results with very little actual code! We can then use the product scraper we wrote earlier to collect all of the product data (see the slug field for the product URL).
For an alternative way to discover products on Goat.com, take a look at Goat's sitemap directory which contains all of the product pages.
Note that scraping backend APIs at scale can be difficult as the website can start blocking the scrape requests. For that, let's take a look at how to avoid blocking using Scrapfly SDK next.
If we scale up our scraper beyond a few requests we can quickly notice that Goat.com is using Cloudflare Bot Management to block scrapers from accessing the website.
To get around Cloudflare's blocking we can use ScrapFly web scraping API which can retrieve page contents for us.
Scrapfly can easily power up web scraping with powerful features like:
All these tools can be easily accessed through Python SDK:
from scrapfly import ScrapeConfig, ScrapflyClient
client = ScrapflyClient(key="YOUR SCRAPFLY KEY")
result = client.scrape(ScrapeConfig(
url="https://www.goat.com/sneakers/air-jordan-3-retro-white-cement-reimagined-dn3707-100",
# enable scraper blocking service bypass
asp=True
# optional - render javascript using headless browsers:
render_js=True,
))
print(result.selector.css('script#__NEXT_DATA__').text())
To wrap up this guide on web scraping Goat.com, let's take a look at some frequently asked questions.
Yes! All of goat.com's data is available publicly (doesn't require a login) which is perfectly legal to scrape. As long as we don't harm the website by scraping too fast scraping Goat.com is legal and ethical.
Yes. Crawling is a type of web scraping where the scraper program finds data to scrape by itself through exploration capabilities. While Goat.com offers many exploration facets (like related and recommended products and directories) which makes crawling easy it's not recommended as direct scraping is much more efficient as indicated in this tutorial.
In this quick tutorial, we learned how to scrape Goat.com using Python and Scrapfly. We started by taking a look at how to scrape a single product page dataset using the hidden web data approach. Then, we expanded our scraper with search scraping functionality by scraping Goat's hidden search API.
Finally, we learned how to avoid blocking by using Scrapfly web scraping API. Try it out for free!
Here's the full Goat.com product scraper using Python and Scrapfly Python SDK:
💙 This code should only be used as a reference. To scrape data from Goat.com at scale you'll need to adjust it to your preferences and environment
import asyncio
import json
import math
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional
from urllib.parse import quote, urlencode
from uuid import uuid4
from scrapfly import ScrapeApiResponse, ScrapeConfig, ScrapflyClient
scrapfly = ScrapflyClient(key="YOUR SCRAPFLY KEY")
def find_hidden_data(result: ScrapeApiResponse) -> dict:
"""extract hidden NEXT_DATA from page html"""
data = result.selector.css("script#__NEXT_DATA__::text").get()
data = json.loads(data)
return data
async def scrape_product(url: str) -> dict:
"""scrape a single gsoat.scoList[m p]roducList[t pa]ge for product data"""
to_scrape = [ScrapeConfig(url=url, cache=True, asp=True) for url in urls]
products = []
async for result in scrapfly.concurrent_scrape(to_scrape):
try:
data = find_hidden_data(result)
product = data["props"]["pageProps"]["productTemplate"]
product["offers"] = data["props"]["pageProps"]["offers"]["offerData"]
products.append(product)
except Exception as e:
print(f"Failed to scrape {result.context['url']}; got {e}")
return products
async def scrape_search(query: str, max_pages: int = 10) -> List[Dict]:
def make_page_url(page: int = 1):Optional[]
params = {
"c": "ciojs-client-2.29.12", # this is hardcoded API version
"key": "key_XT7bjdbvjgECO5d8", # API key which is hardcoded in the client
"i": str(uuid4()), # unique id for each request, generated by UUID4
"s": "2",
"page": page,
"num_results_per_page": "24",
"sort_by": "relevance",
"sort_order": "descending",
"fmt_options[hidden_fields]": "gp_lowest_price_cents_3",
"fmt_options[hidden_fields]": "gp_instant_ship_lowest_price_cents_3",
"fmt_options[hidden_facets]": "gp_lowest_price_cents_3",
"fmt_options[hidden_facets]": "gp_instant_ship_lowest_price_cents_3",
"_dt": int(datetime.utcnow().timestamp() * 1000), # current timestamp in milliseconds
}
return f"https://ac.cnstrc.com/search/{quote(query)}?{urlencode(params)}"
url_first_page = make_page_url(page=1)
print(f"scraping product search paging {url_first_page}")
# scrape first page
result_first_page = await scrapfly.async_scrape(ScrapeConfig(url=url_first_page, asp=True))
first_page = json.loads(result_first_page.content)["response"]
results = [result["data"] for result in first_page["results"]]
# find total page count
total_pages = math.ceil(first_page["total_num_results"] / 24)
if max_pages and max_pages < total_pages:
total_pages = max_pages
# scrape remaining pages
print(f"scraping remaining total pages: {total_pages-1} concurrently")
to_scrape = [ScrapeConfig(make_page_url(page=page), asp=True) for page in range(2, total_pages + 1)]
async for result in scrapfly.concurrent_scrape(to_scrape):
data = json.loads(result.content)
items = [result["data"] for result in data["response"]["results"]]
results.extend(items)
return results
async def example_run():
"""
this example run will scrape example product and search from goat.com
save them to ./results/product.json and ./results/sitemap.json respectively
"""
out_dir = Path(__file__).parent / "results"
out_dir.mkdir(exist_ok=True)
# example single product:
product = await scrape_products(["https://www.goat.com/sneakers/air-jordan-3-retro-white-cement-reimagined-dn3707-100"])
out_dir.joinpath("product.json").write_text(json.dumps(product[0], indent=2, ensure_ascii=False))
# example search:
search = await scrape_search("air jordan 3", max_pages=None)
out_dir.joinpath("search.json").write_text(json.dumps(search, indent=2, ensure_ascii=False))
# exapding search results with product data:
product_urls = [f"https://www.goat.com/{item['product_type']}/{item['slug']}" for item in search]
search_products = await scrape_products(product_urls)
out_dir.joinpath("search_products.json").write_text(json.dumps(search_products, indent=2, ensure_ascii=False))
if __name__ == "__main__":
asyncio.run(example_run())
In this fashion scrapeguide we'll be taking a look at Fashionphile - another major 2nd hand luxury fashion marketplace. We'll be using Python and hidden web data scraping to grap all of this data in just few lines of code.
In this fashion scrapeguide we'll be taking a look at Vestiaire Collective - one of the biggest 2nd hand luxury fashion marketplaces. We'll be using hiddden web data scraping to scrape data in just a few lines of Python code.
In this guide we'll be taking a look at scraping Nordstrom.com - one of the biggest fashion e-commerce shops. We'll be using hidden web data scraping and Python.
