This tool uses SerpApi as a tool to parse data.
You can use provided API key that will be available after installation, however, it's purely for testing purposes to see if the tool fits your needs. If you'll be using it for your own purpose (personal or commercial), you have to use your own SerpApi key.
$ pip install ecommerce-scraper-pyfrom google_shopping import GoogleShoppingSearch
scraper = GoogleShoppingSearch(
api_key='<your_serpapi_api_key>',
query='coffee',
domain='google.de',
country='de',
language='de',
price_from=20,
price_to=200,
results_limit=150
)
products = scraper.get_products()
scraper.print(products)
scraper.save_to_json(products)from google_shopping import GoogleShoppingProduct
scraper = GoogleShoppingProduct(
api_key='<your_serpapi_api_key>',
product_id=14019378181107046593,
reviews_limit=125
)
product = scraper.get_product()
scraper.print(product)
scraper.save_to_json(product)from google_shopping import GoogleShoppingSearch, GoogleShoppingProduct
search_scraper = GoogleShoppingSearch(
api_key='<your_serpapi_api_key>',
query='Sony PlayStation 5',
price_from=400,
price_to=1000,
results_limit=10,
domain='google.de',
country='de',
language='de'
)
data = []
products = search_scraper.get_products()
for product in products:
product_scraper = GoogleShoppingProduct(
api_key='<your_serpapi_api_key>',
product_id=product['product_id'],
reviews_limit=15,
domain='google.de',
country='de',
language='de'
)
product_data = product_scraper.get_product()
data.append(product_data)
search_scraper.print(data)
search_scraper.save_to_json(data)from walmart import WalmartSearch
scraper = WalmartSearch(
api_key='<your_serpapi_api_key>',
query='coffee starbucks',
price_from=20,
price_to=200,
results_limit=150,
# store="356"
)
products = scraper.get_products()
scraper.print(products)
scraper.save_to_json(products)from walmart import WalmartProduct
scraper = WalmartProduct(
api_key='<your_serpapi_api_key>',
product_id=520468661,
reviews_limit=125
)
product = scraper.get_product()
scraper.print(product)
scraper.save_to_json(product)from walmart import WalmartSearch, WalmartProduct
search_scraper = WalmartSearch(
api_key='<your_serpapi_api_key>',
query='coffee starbucks',
price_from=20,
price_to=200,
results_limit=10
)
data = []
products = search_scraper.get_products()
for product in products:
product_scraper = WalmartProduct(
api_key='<your_serpapi_api_key>',
product_id=product['product_id'],
reviews_limit=15
)
product_data = product_scraper.get_product()
data.append(product_data)
search_scraper.print(data)
search_scraper.save_to_json(data)from home_depot import HomeDepotSearch
scraper = HomeDepotSearch(
api_key='<your_serpapi_api_key>',
query='chair',
price_from=20,
price_to=200,
results_limit=150,
# zip_code='04401' # zip code must be in the format '12345' or '12345-6789'
)
products = scraper.get_products()
scraper.print(products)
scraper.save_to_json(products)from home_depot import HomeDepotProduct
scraper = HomeDepotProduct(
api_key='<your_serpapi_api_key>',
product_id=202054749
)
product = scraper.get_product()
scraper.print(product)
scraper.save_to_json(product)from home_depot import HomeDepotSearch, HomeDepotProduct
search_scraper = HomeDepotSearch(
api_key='<your_serpapi_api_key>',
query='chair',
price_from=20,
price_to=200,
results_limit=10,
zip_code='04401' # zip code must be in the format '12345' or '12345-6789'
)
data = []
products = search_scraper.get_products()
for product in products:
product_scraper = HomeDepotProduct(
api_key='<your_serpapi_api_key>',
product_id=product['product_id']
)
product_data = product_scraper.get_product()
data.append(product_data)
search_scraper.print(data)
search_scraper.save_to_json(data)from amazon import AmazonSearch
scraper = AmazonSearch(
query='coffee',
results_limit=125,
price_from=20,
price_to=50,
currency='USD',
language='en_US',
customer_reviews_rating=4,
multiplier=1
)
products = scraper.get_products()
scraper.print(products)
scraper.save_to_json(products)from amazon import AmazonProduct
scraper = AmazonProduct(
link='https://www.amazon.com/McCafe-Premium-Roast-Decaf-Coffee/dp/B07GCNDL91/ref=sr_1_1?currency=USD&keywords=coffee&qid=1684849762&refinements=p_36%3A2000-5000%2Cp_72%3A1248897011&rnid=1248895011&s=grocery&sr=1-1&th=1',
reviews_limit=35,
multiplier=1,
currency='USD',
language='en_US'
)
product = scraper.get_product()
scraper.print(product)
scraper.save_to_json(product)from amazon import AmazonSearch, AmazonProduct
search_scraper = AmazonSearch(
query='coffee',
results_limit=5,
price_from=20,
price_to=50,
currency='USD',
language='en_US',
customer_reviews_rating=4,
multiplier=1
)
data = []
products = search_scraper.get_products()
for product in products:
product_scraper = AmazonProduct(
link=product['link'],
reviews_limit=15,
multiplier=1,
currency='USD',
language='en_US'
)
product_data = product_scraper.get_product()
data.append(product_data)
search_scraper.print(data)
search_scraper.save_to_json(data)from ebay import EbaySearch
scraper = EbaySearch(
api_key='<your_serpapi_api_key>',
query='coffee starbucks',
price_from=20,
price_to=200,
results_limit=250,
domain='ebay.com'
)
products = scraper.get_products()
scraper.print(products)
scraper.save_to_json(products)from ebay import EbayProduct
scraper = EbayProduct(
link='https://www.ebay.com/itm/2-Bags-STARBUCKS-French-Roast-DARK-Whole-Bean-100-Arabica-Coffee-40oz-ea-09-23/144356021636',
reviews_limit=125,
multiplier=1
)
product = scraper.get_product()
scraper.print(product)
scraper.save_to_json(product)from ebay import EbaySearch, EbayProduct
search_scraper = EbaySearch(
api_key='<your_serpapi_api_key>',
query='coffee',
results_limit=5,
domain='ebay.com'
)
data = []
products = search_scraper.get_products()
for product in products:
product_scraper = EbayProduct(
link=product['link'],
reviews_limit=15,
multiplier=1
)
product_data = product_scraper.get_product()
data.append(product_data)
search_scraper.print(data)
search_scraper.save_to_json(data)Feel free to open bug issue, something isn't working, what feature to add, or anything else.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent