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ExtractKit

what is this??

This is a Easy Scrapper for scrapping essential data from web

What can it Scrape?

How to use this??

So simple!! Just go and type

pip install ExtractKit

Bam!! You just installed a great scraping tool

How to use in python??

import ExtractKit as ekit #or whatever

Detailed documentation

Scrap Car Data

This scrapes car specification from Carwale Website

To Scrap car Specification,

from ExtractKit import carExtractor as car

data = car.extractCar('hyundai',"creta")
print(data)

extractCar(brand, model),

Parameters, brand - string data type, enter the brand name of the car you want to search model - string data type, enter the model name of the car you want to search

Result Data Looks like this:

    {
    'product': 'Hyundai Creta', 
    'price': '₹ 10.00 Lakh'
    }

Scrap Bike Data

This scrapes bike specification from Bikewale Website

To Scrap Bike Specification,

from ExtractKit import bikeExtractor as bike

data = bike.extractBike('yamaha',"R15")
print(data)

extractBike(brand, model)

Parameters, brand - string data type, enter the brand name of the bike you want to search model - string data type, enter the model name of the bike you want to search

Result Data Looks like this:

    {
    "product": "Yamaha YZF R15 V3", 
    "price": "1,55,334", 
    "Engine Capacity": "155 cc", 
    "Mileage": "43 kmpl", 
    "Transmission": "6 Speed Manual ", 
    "Kerb Weight": "142 kg", 
    "Fuel Tank Capacity": "11 litres", 
    "Seat Height": "815 mm"
    }

Scrap Mobile Specifications

This scraps data from 91 Mobiles Website

from ExtractKit import gadgetsExtractor as gadgets
x = gadgets.extractMobile('samsung','galaxy a52')
print(x)

In extractMobile(brand, model),

  • Brand and model must be passed as string

Result data looks like this:

{
    "name": "Samsung Galaxy A52",
    "price": " 23,499",
    "variants": [
        "128GB Storage, 8GB RAM\n"
    ],
    "specs": [
        [
            "RAM","6GB"
        ],
        [   
            "Processor","QualcommSnapdragon720G"
        ],
        [   
            "Rear Camera","64MP+12MP+5MP+5MP"
        ],
        [
            "Front Camera","32MP"
        ],
        [
            "Battery","4500mAh"
        ],
        [...]
        [...]
    ]
}

Scrap Flipkart

This Scraps data from flipkart.com

To Scrap Flipkart products,

from ExtractKit import flipkartExtractor as flipkart
x = flipkart.extractFlipkart('XBOX Series S')
print(x)

In extractFlipkart(product),

  • It must be a valid product.
  • Product must be in string format.

Result data looks like this:

{
    "product": "MICROSOFT Xbox Series S 512 GB\u00a0\u00a0(White)",
    "price": "\u20b934,990",
    "url": "https://www.flipkart.com/microsoft-xbox-series-s-512-gb/p/itm13c51f9047da8?pid=GMCFVPFCNHHZQBNA&lid=LSTGMCFVPFCNHHZQBNAH2FMXL&marketplace=FLIPKART&q=XBOX+Series+S&store=4rr&srno=s_1_1&otracker=search&fm=organic&iid=a98ea799-22c1-440d-b211-9835eb1790eb.GMCFVPFCNHHZQBNA.SEARCH&ppt=None&ppn=None&ssid=l55b2xbdfk0000001625828211339&qH=fd75188725591a70"
}

You can also use extractFlipkartUrl(url) to scrap data from the given flipkart link

from ExtractKit import flipkartExtractor as flipkart
x = flipkart.extractFlipkartUrl('https://www.flipkart.com/cyberpunk-2077-limited-edition/p/itm6e7fc6515a3b3?pid=GAMG2Z8UGZHTYHAF&lid=LSTGAMG2Z8UGZHTYHAFHTIMQD&marketplace=FLIPKART&q=cyberpunk+2077&store=4rr%2Ffa6%2F27p%2Fmtr&spotlightTagId=TrendingId_4rr%2Ffa6%2F27p%2Fmtr&srno=s_1_1&otracker=AS_QueryStore_OrganicAutoSuggest_2_9_na_na_na&otracker1=AS_QueryStore_OrganicAutoSuggest_2_9_na_na_na&fm=SEARCH&iid=0fa0a337-bb8d-4126-9fe6-9421bcbdb3ee.GAMG2Z8UGZHTYHAF.SEARCH&ppt=sp&ppn=sp&ssid=rg6sh1i72o0000001625828417574&qH=65fc51bd3f81e7c2')
print(x)

The result for this function is:

{
    "product": "Cyberpunk 2077 (Limited Edition) (for PS4)",
    "price": "₹2,899"
}

Scrap PC Components

This Scraps data from PC Studio

To Scrap PC Components,

from ExtractKit import pcBuildExtractor as pcBuild
x = pcBuild.extractProcessors()
print(x)

Result data looks like this:

{
    "0": {
        "title": "AMD RYZEN 3 3200G OPEN BOX OEM PROCESSOR WITH RX VEGA 8 GRAPHICS (UP TO 4.0GHZ 6 MB CACHE) ",
        "price": "\u20b911,300.00",
        "details": "https://www.pcstudio.in/product/amd-ryzen-3-3200g-open-box-oem-processor-with-rx-vega-8-graphics-up-to-4-0ghz-6-mb-cache/",
        "img": "https://www.pcstudio.in/wp-content/uploads/2021/07/AMD-RYZEN-3-3200G-OPEN-BOX-OEM-PROCESSOR-WITH-RX-VEGA-8-GRAPHICS-1.jpg"
    },
    "1":{...},
    "2":{...},
}

other functions available are:

print(pcBuild.extractProcessors())    #Extract Processors Data
print(extractMotherboards())          #Extract Motherboard Data 
print(extractRams())                  #Extract RAM Data
print(extractStorage1())              #Extract Storages Data
print(extractStorage2())              #Extract Storages Data too
print(extractCabinets())              #Extract Cabinet Data
print(extractCoolers())               #Extract Coolers Data
print(extractGPUs())                  #Extract GPU Data
print(extractPowerSupplys())          #Extract PowerSupplyUnit Data
print(extractDisplays())              #Extract Display Data

Scrap News Data

This scraps live and archive news data from One India News

To Scrap news,

from ExtractKit import newsExtractor as news

data = news.extractlNewsArchives('english',2021,7,1)
print(data)

Parameters, language - string data type, enter the language in which you want to search Avaliable Languages are

  • tamil
  • telugu
  • malayalam
  • bengali
  • gujarati
  • hindi
  • kannada

year - integer data type, enter the year in which you want to search month - integer data type, enter the month in which you want to search date - integer data type, enter the date in which you want to search

Result Data Looks like this:

    {
    "language": "english",
    "1": {
        "category": "bengaluru",
        "news": "No interviews to fill up vacancies to post of assistant professors, principles in Karnataka\u2019s higher education"
    },
    "2":{...},
    "3":{...},
    ....

Credits

Karthik Raja K

Vignesh R    


Source Code Available in Github

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