



from random import randint
from tempfile import TemporaryFile

import numpy as np






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Dict	)  ->						List[Any]:
					__lowerCAmelCase	   =0
					if start < end:
										__lowerCAmelCase	   =randint(__lowerCamelCase				,   __lowerCamelCase	)
										__lowerCAmelCase	   =a[end]
										__lowerCAmelCase	   =a[pivot]
										__lowerCAmelCase	   =temp

										__lowerCAmelCase   ,	__lowerCAmelCase	   =_in_place_partition(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
										count += _in_place_quick_sort(__lowerCamelCase				,   __lowerCamelCase				,   p - 1	)
										count += _in_place_quick_sort(__lowerCamelCase				,   p + 1				,   __lowerCamelCase	)
					return count






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    str				,   __lowerCamelCase	:    Union[str, Any]	)  ->						Any:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =randint(__lowerCamelCase				,   __lowerCamelCase	)
					__lowerCAmelCase	   =a[end]
					__lowerCAmelCase	   =a[pivot]
					__lowerCAmelCase	   =temp
					__lowerCAmelCase	   =start - 1
					for index in range(__lowerCamelCase				,   __lowerCamelCase	):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															__lowerCAmelCase	   =new_pivot_index + 1
															__lowerCAmelCase	   =a[new_pivot_index]
															__lowerCAmelCase	   =a[index]
															__lowerCAmelCase	   =temp

					__lowerCAmelCase	   =a[new_pivot_index + 1]
					__lowerCAmelCase	   =a[end]
					__lowerCAmelCase	   =temp
					return new_pivot_index + 1, count


lowercase_	       =			TemporaryFile()
lowercase_	       =			1_00  # 1000 elements are to be sorted


lowercase_       ,       lowercase_	       =			0, 1  # mean and standard deviation
lowercase_	       =			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
lowercase_	       =			np.load(outfile)
lowercase_	       =			len(M) - 1
lowercase_	       =			_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)


print(z)






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str = "laptop"	)  ->						DataFrame:
					__lowerCAmelCase	   =f"""https://www.amazon.in/laptop/s?k={product}"""
					__lowerCAmelCase	   ={
					    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
					    """Accept-Language""": """en-US, en;q=0.5""",
					}
					__lowerCAmelCase	   =BeautifulSoup(requests.get(__lowerCamelCase				,   headers=__lowerCamelCase	).text	)
					# Initialize a Pandas dataframe with the column titles
					__lowerCAmelCase	   =DataFrame(
					    columns=[
					        """Product Title""",
					        """Product Link""",
					        """Current Price of the product""",
					        """Product Rating""",
					        """MRP of the product""",
					        """Discount""",
					    ]	)
					# Loop through each entry and store them in the dataframe
					for item, _ in zip_longest(
					    soup.find_all(
					        """div"""				,   attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""}				,   )				,   soup.find_all("""div"""				,   attrs={"""class""": """a-row a-size-base a-color-base"""}	)				,   ):
										try:
															__lowerCAmelCase	   =item.ha.text
															__lowerCAmelCase	   ="""https://www.amazon.in/""" + item.ha.a["""href"""]
															__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-offscreen"""}	).text
															try:
																				__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-icon-alt"""}	).text
															except AttributeError:
																				__lowerCAmelCase	   ="""Not available"""
															try:
																				__lowerCAmelCase	   =(
																				    """₹"""
																				    + item.find(
																				        """span"""				,   attrs={"""class""": """a-price a-text-price"""}	).text.split("""₹"""	)[1]
																				)
															except AttributeError:
																				__lowerCAmelCase	   =""""""
															try:
																				__lowerCAmelCase	   =float(
																				    (
																				        (
																				            float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				            - float(product_price.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				        )
																				        / float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				    )
																				    * 100	)
															except ValueError:
																				__lowerCAmelCase	   =float("""nan"""	)
										except AttributeError:
															pass
										__lowerCAmelCase	   =[
										    product_title,
										    product_link,
										    product_price,
										    product_rating,
										    product_mrp,
										    discount,
										]
					__lowerCAmelCase	   =""" """
					__lowerCAmelCase	   =""" """
					data_frame.index += 1
					return data_frame


if __name__ == "__main__":
				lowercase_	       =			'''headphones'''
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")




