
from itertools import product





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
				snake_case__						=				sides_number
				snake_case__						=				max_face_number * dice_number
				snake_case__						=				[0] * (max_total + 1)

				snake_case__						=				1
				snake_case__						=				range(__lowerCAmelCase    ,		max_face_number + 1							)
				for dice_numbers in product(__lowerCAmelCase    ,		repeat=__lowerCAmelCase							):
								snake_case__						=				sum(__lowerCAmelCase							)
								totals_frequencies[total] += 1

				return totals_frequencies





def 	SCREAMING_SNAKE_CASE__    (      ):
				snake_case__						=				total_frequency_distribution(
				    sides_number=4    ,		dice_number=9							)
				snake_case__						=				total_frequency_distribution(
				    sides_number=6    ,		dice_number=6							)

				snake_case__						=				0
				snake_case__						=				9
				snake_case__						=				4 * 9
				snake_case__						=				6
				for peter_total in range(__lowerCAmelCase    ,		max_peter_total + 1							):
								peter_wins_count += peter_totals_frequencies[peter_total] * sum(
								    colin_totals_frequencies[min_colin_total:peter_total]							)

				snake_case__						=				(4**9) * (6**6)
				snake_case__						=				peter_wins_count / total_games_number

				snake_case__						=				round(__lowerCAmelCase    ,		ndigits=7							)

				return rounded_peter_win_probability


if __name__ == "__main__":
					print(F'''{solution() = }''')





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = "laptop"							):
    snake_case__						=				F"""https://www.amazon.in/laptop/s?k={product}"""
    snake_case__						=				{
        "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
        "Accept-Language": "en-US, en;q=0.5",
    }
    snake_case__						=				BeautifulSoup(requests.get(__lowerCAmelCase    ,		headers=__lowerCAmelCase							).text							)
    # Initialize a Pandas dataframe with the column titles
    snake_case__						=				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:
            snake_case__						=				item.ha.text
            snake_case__						=				"https://www.amazon.in/" + item.ha.a["href"]
            snake_case__						=				item.find("span"    ,		attrs={"class": "a-offscreen"}							).text
            try:
                snake_case__						=				item.find("span"    ,		attrs={"class": "a-icon-alt"}							).text
            except AttributeError:
                snake_case__						=				"Not available"
            try:
                snake_case__						=				(
                    "₹"
                    + item.find(
                        "span"    ,		attrs={"class": "a-price a-text-price"}							).text.split("₹"							)[1]
                )
            except AttributeError:
                snake_case__						=				""
            try:
                snake_case__						=				float(
                    (
                        (
                            float(product_mrp.strip("₹"							).replace(","    ,		""							)							)
                            - float(product_price.strip("₹"							).replace(","    ,		""							)							)
                        )
                        / float(product_mrp.strip("₹"							).replace(","    ,		""							)							)
                    )
                    * 100							)
            except ValueError:
                snake_case__						=				float("nan"							)
        except AttributeError:
            pass
        snake_case__						=				[
            product_title,
            product_link,
            product_price,
            product_rating,
            product_mrp,
            discount,
        ]
    snake_case__						=				" "
    snake_case__						=				" "
    data_frame.index += 1
    return data_frame


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



