Grouping results and removing unwanted ones
Here we want to scrape product name, price and rating from ebay product pages:
url = 'https://www.ebay.com/itm/Sony-PlayStation-4-PS4-Pro-1TB-4K-Console-Black/203084236670'
wanted_list = ['Sony PlayStation 4 PS4 Pro 1TB 4K Console - Black', 'US $349.99', '4.8']
scraper.build(url, wanted_list)
The items which we wanted have been on multiple sections of the page and the scraper tries to catch them all. So it may retrieve some extra information compared to what we have in mind. Let's run it on a different page:
scraper.get_result_exact('https://www.ebay.com/itm/Acer-Predator-Helios-300-15-6-144Hz-FHD-Laptop-i7-9750H-16GB-512GB-GTX-1660-Ti/114183725523')
The result:
[
"Acer Predator Helios 300 15.6'' 144Hz FHD Laptop i7-9750H 16GB 512GB GTX 1660 Ti",
'ACER Predator Helios 300 i7-9750H 15.6" 144Hz FHD GTX 1660Ti 16GB 512GB SSD⚡RGB',
'US $1,229.49',
'5.0'
]
As we can see we have one extra item here. We can run the get_result_exact
or get_result_similar
method with grouped=True
parameter. It will group all results per its scraping rule:
scraper.get_result_exact('https://www.ebay.com/itm/Acer-Predator-Helios-300-15-6-144Hz-FHD-Laptop-i7-9750H-16GB-512GB-GTX-1660-Ti/114183725523', grouped=True)
Output:
{
'rule_sks3': ["Acer Predator Helios 300 15.6'' 144Hz FHD Laptop i7-9750H 16GB 512GB GTX 1660 Ti"],
'rule_d4n5': ['ACER Predator Helios 300 i7-9750H 15.6" 144Hz FHD GTX 1660Ti 16GB 512GB SSD⚡RGB'],
'rule_fmrm': ['ACER Predator Helios 300 i7-9750H 15.6" 144Hz FHD GTX 1660Ti 16GB 512GB SSD⚡RGB'],
'rule_2ydq': ['US $1,229.49'],
'rule_buhw': ['5.0'],
'rule_vpfp': ['5.0']
}
Now we can use keep_rules
or remove_rules
methods to prune unwanted rules:
scraper.keep_rules(['rule_sks3', 'rule_2ydq', 'rule_buhw'])
scraper.get_result_exact('https://www.ebay.com/itm/Acer-Predator-Helios-300-15-6-144Hz-FHD-Laptop-i7-9750H-16GB-512GB-GTX-1660-Ti/114183725523')
And now the result only contains the ones which we want:
[
"Acer Predator Helios 300 15.6'' 144Hz FHD Laptop i7-9750H 16GB 512GB GTX 1660 Ti",
'US $1,229.49',
'5.0'
]
Building a scraper to work with multiple websites with incremental learning
Suppose we want to make a price scraper to work with multiple websites. Here we consider ebay.com, walmart.com and etsy.com.
We create some sample data for each website and then feed it to the scraper. By using update=True
parameter when calling the build
method, all previously learned rules will be kept and new rules will be added to them:
from autoscraper import AutoScraper
data = [
# some Ebay examples
('https://www.ebay.com/itm/Sony-PlayStation-4-PS4-Pro-1TB-4K-Console-Black/193632846009', ['US $349.99']),
('https://www.ebay.com/itm/Acer-Predator-Helios-300-15-6-FHD-Gaming-Laptop-i7-10750H-16GB-512GB-RTX-2060/303669272117', ['US $1,369.00']),
('https://www.ebay.com/itm/8-TAC-FORCE-SPRING-ASSISTED-FOLDING-STILETTO-TACTICAL-KNIFE-Blade-Pocket-Open/331625445801', ['US $8.95']),
#some Walmart examples
('https://www.walmart.com/ip/8mm-Classic-Sterling-Silver-Plain-Wedding-Band-Ring/113651182', ['US $8.95']),
('https://www.walmart.com/ip/Apple-iPhone-11-64GB-Red-Fully-Unlocked-A-Grade-Refurbished/806414606', ['$659.99']),
#some Etsy examples
('https://www.etsy.com/listing/805075149/starstruck-silk-face-mask-black-silk', ['$12.50+']),
('https://www.etsy.com/listing/851553172/apple-macbook-pro-i9-32gb-500gb-radeon', ['$1,500.00']),
]
scraper = AutoScraper()
for url, wanted_list in data:
scraper.build(url=url, wanted_list=wanted_list, update=True)
Now hopefully the scraper has learned to scrape all 3 websites. Let's check some new pages:
>>> scraper.get_result_exact('https://www.ebay.com/itm/PUMA-Mens-Turino-Sneakers/274324387149')
['US $24.99', "PUMA Men's Turino Sneakers | eBay"]
>>> scraper.get_result_exact('https://www.walmart.com/ip/Pack-of-8-Gerber-1st-Foods-Baby-Food-Peach-2-2-oz-Tubs/267133209')
['$8.71', '(Pack of 8) Gerber 1st Foods Baby Food, Peach, 2-2 oz Tubs - Walmart.com']
>>> scraper.get_result_exact('https://www.etsy.com/listing/863615551/matte-black-smart-wireless-bluetooth')
['$60.00']
Almost done! But's there's some extra info, let's fix it:
>>> scraper.get_result_exact('https://www.walmart.com/ip/Pack-of-8-Gerber-1st-Foods-Baby-Food-Peach-2-2-oz-Tubs/267133209', grouped=True)
{'rule_cqhs': [],
'rule_h4sy': [],
'rule_jqtb': [],
'rule_r9qd': ['$8.71'],
'rule_6lt7': ['$8.71'],
'rule_2nrk': ['$8.71'],
'rule_wy9j': ['$8.71'],
'rule_v395': [],
'rule_4ej6': ['(Pack of 8) Gerber 1st Foods Baby Food, Peach, 2-2 oz Tubs - Walmart.com']}
>>> scraper.remove_rules(['rule_4ej6'])
>>> scraper.get_result_exact('https://www.ebay.com/itm/PUMA-Mens-Turino-Sneakers/274324387149')
['US $24.99']
>>> scraper.get_result_exact('https://www.walmart.com/ip/Pack-of-8-Gerber-1st-Foods-Baby-Food-Peach-2-2-oz-Tubs/267133209')
['$8.71']
>>> scraper.get_result_exact('https://www.etsy.com/listing/863615551/matte-black-smart-wireless-bluetooth')
['$60.00']
Now we have a scraper which works with Ebay, Walmart and Etsy!
Fuzzy matching for html tag attributes
Some websites use different tag values for different pages (like different styles for the same element). In these cases you can adjust attr_fuzz_ratio
parameter when getting the results. See this issue for a sample usage.
Using regular expressions
You can use regular expressions for wanted items:
wanted_list = [re.compile('Lorem ipsum.+est laborum')]
This is a great tool!
I'm trying to perform a scrape of the device brand, device name, monthly price (..../mo.), down payment ($0) for most, and savings (i.e., "Save up to $450") from the following website:
https://www.telus.com/en/mobility/phones
When I try to scrape the rules, the rule ID's corresponding to the data change so I am unable to save rules. Was wondering if I could get your help on this. My code is below.
`from autoscraper import AutoScraper
url = 'https://www.telus.com/en/mobility/phones'
wanted_list = ['Apple', 'iPhone 12 Pro', '41', '.25', '0', 'Save up to $450']
scraper = AutoScraper()
scraper.build(url=url, wanted_list=wanted_list)
scraper.get_result_exact(url, grouped=True)`