
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str = "laptop"       ) ->       DataFrame:

						"""simple docstring"""







						A__  :	List[Any]         =							F"https://www.amazon.in/laptop/s?k={product}"
						A__  :	Dict         =							{
						    '''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''',
						}
						A__  :	Any         =							BeautifulSoup(requests.get(__UpperCamelCase							,						headers=__UpperCamelCase       ).text       )
						# Initialize a Pandas dataframe with the column titles
						A__  :	Optional[Any]         =							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:
																		A__  :	Optional[int]         =							item.ha.text
																		A__  :	Optional[Any]         =							'''https://www.amazon.in/''' + item.ha.a['''href''']
																		A__  :	List[Any]         =							item.find('''span'''							,						attrs={'''class''': '''a-offscreen'''}       ).text
																		try:
																								A__  :	Optional[int]         =							item.find('''span'''							,						attrs={'''class''': '''a-icon-alt'''}       ).text
																		except AttributeError:
																								A__  :	str         =							'''Not available'''
																		try:
																								A__  :	Optional[int]         =							(
																								    '''₹'''
																								    + item.find(
																								        '''span'''							,						attrs={'''class''': '''a-price a-text-price'''}       ).text.split('''₹'''       )[1]
																								)
																		except AttributeError:
																								A__  :	Any         =							''''''
																		try:
																								A__  :	Union[str, Any]         =							float(
																								    (
																								        (
																								            float(product_mrp.strip('''₹'''       ).replace(''','''							,						''''''       )       )
																								            - float(product_price.strip('''₹'''       ).replace(''','''							,						''''''       )       )
																								        )
																								        / float(product_mrp.strip('''₹'''       ).replace(''','''							,						''''''       )       )
																								    )
																								    * 1_00       )
																		except ValueError:
																								A__  :	Any         =							float('''nan'''       )
												except AttributeError:
																		pass
												A__  :	Dict         =							[
												    product_title,
												    product_link,
												    product_price,
												    product_rating,
												    product_mrp,
												    discount,
												]
						A__  :	Tuple         =							''' '''
						A__  :	Union[str, Any]         =							''' '''
						data_frame.index += 1
						return data_frame


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

import numpy as np

_SCREAMING_SNAKE_CASE    :      Any          =			[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self		):
					A__  :	List[Any]         =							np.array(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__       ,							A__  :	Any         =							np.where(letter == self.SQUARE		)
					A__  :	int         =							np.concatenate([indexa + 1, indexa + 1]		)
					return indexes





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Union[str, Any]         =							self.SQUARE[indexa - 1, indexa - 1]
					return letter





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							message.lower()
					A__  :	str         =							message.replace(''' '''				,       ''''''		)
					A__  :	Union[str, Any]         =							message.replace('''j'''				,       '''i'''		)

					A__  :	List[Any]         =							np.empty((2, len(UpperCamelCase__		))		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	Any         =							self.letter_to_numbers(message[letter_index]		)

									A__  :	Optional[Any]         =							numbers[0]
									A__  :	List[str]         =							numbers[1]

					A__  :	List[str]         =							first_step.reshape(2 * len(UpperCamelCase__		)		)
					A__  :	List[Any]         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Dict         =							int(second_step[numbers_index * 2]		)
									A__  :	List[str]         =							int(second_step[(numbers_index * 2) + 1]		)
									A__  :	Dict         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							encoded_message + letter

					return encoded_message





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	str         =							message.lower()
					message.replace(''' '''				,       ''''''		)
					A__  :	List[Any]         =							np.empty(2 * len(UpperCamelCase__		)		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	List[str]         =							self.letter_to_numbers(message[letter_index]		)
									A__  :	Dict         =							numbers[0]
									A__  :	int         =							numbers[1]

					A__  :	Optional[Any]         =							first_step.reshape((2, len(UpperCamelCase__		))		)
					A__  :	int         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Tuple         =							int(second_step[0, numbers_index]		)
									A__  :	Dict         =							int(second_step[1, numbers_index]		)
									A__  :	List[str]         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							decoded_message + letter

					return decoded_message