Packages FOR This Process is

pip install jupyter
pip install requests
pip install pandas
pip install sqlalchemy

After Installation given Packages We use THis E-Commerce Site for data https://www.currys.co.uk/ Now load this site and after That Use this cmd On Chrome ANd inspect the Given page ctrl+shift+s And Click on Network And after it Looks For File start with name navigation and copy as a CURL By Right clicking on it

now go to google and search for https://www.onlinedevtools.in/curl and paste the given file and convert It into Python and AFter that Open Jupyter imports Given Libraries, which We installed earlier

import requests
import pandas as pd
import sqlalchemy

after that Paste the Given Code that we get from devtools site Now, Create a JSON object and output data using this code

result_json  =  response.json()
result_json.keys()
listing =  result_json['resultsets']['default']['results']

After all, We Put everything together That the KEys WE HAve By running Upper Code

product_title = []
brand= []
price= []
review_score= []
review_count= []
description= []

for result in listing:
product_title.append(result['title'])
brand.append(result['brand'])
price.append(result['price'])
review_score.append(result['reevoo_score'])
review_count.append(result['reevoo_count'])
description.append(result['short_description'])
speakers_df = pd.DataFrame({'product_title':product_title,'brand':brand,'price':price,'review_score':review_score,
'review_count':review_count,'description':description})
speakers_df

after running all these cell you get Data from only One Page

So What we have t do if we need data from multiple pages

for that you have to make for loop suppose i want data from 20 pages so we can us eloop that look like this for i in range(1,21): print (i) Here the Complete Code for Multiple Pages `

headers = {
'authority': 'api.cdn.dcg-search.com',
'sec-ch-ua': '^\^',
'sec-ch-ua-mobile': '?0',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36',
'sec-ch-ua-platform': '^\^Windows^\^',
'accept': '/',
'origin': 'https://www.currys.co.uk',
'sec-fetch-site': 'cross-site',
'sec-fetch-mode': 'cors',
'sec-fetch-dest': 'empty',
'referer': 'https://www.currys.co.uk/',
'accept-language': 'en-US,en;q=0.9',
}
product_title = []
brand= []
price= []
review_score= []
review_count= []
description= []

for i in range(1,21):
params = (
('do', 'json'),
('m_results_per_page', '20'),
('page', str(i)),
('q', 'speaker^%%5E20docks'),
('sort', 'relevance'),
('sp_cs', 'UTF-8'),
)

response = requests.get('https://api.cdn.dcg-search.com/currys/navigation', headers=headers, params=params)

result_json  =  response.json()
listing =  result_json['resultsets']['default']['results']

for result in listing:
    #Product title
    try:
     product_title.append(result['title'])
    except:
        product_title.append('')

    #brand
    try:
        brand.append(result['brand'])
    except:
        brand.append('')
    try:

        price.append(float (result['price']))
    except:
        price.append('')
    try:
        review_score.append(float (result['reevoo_score']))
    except:
        review_score.append('')

    try:

        review_count.append(int (result['reevoo_count']))
    except:
        review_count.append('')
    try:

        description.append(result['short_description'])
    except:
        description.append('')
speakers_multiple_df = pd.DataFrame({'product_title':product_title,'brand':brand,'price':price,'review_score':review_score,
'review_count':review_count,'description':description})
speakers_multiple_df

`

after all we have to save data in excel file so for that we use this logic

speakers_multiple_df.to_excel('speakers_multiple.xlsx',index = False)

And also if we want to Save data and use in PostgreSQL for that we use this logic For Multiple Pages

engine = sqlalchemy.create_engine('postgresql://postgres:"yourPassword"@localhost:5432')
#hAnii :(
speakers_multiple_df.to_sql('e-commerce',engine, index=False)

for getting this Data In PostgreSQL You HAve To Install this In Your Machine

That's It For Now.