1 # Read input 2 num rows = int(input()) 3 samples_list = [] 4 for i in range(num_rows): 5 samples_list.append([int(x) for x in input().split()]) 6 7 min_list = # Your code goes here 8 9 print (f'All numbers: {samples_list}') 10 print (f'Smallest value in each row: {min_list}')
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Integer num_rows is read from input, representing the number of rows in a two-dimensional list. List samples_list is a two-dimensional list containing the remaining integers read from input. Assign min_list with the new list containing the smallest value in each row of samples_list.
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- Python: 2048 games Board = list[list[str]]# Checks whether a given board has any# possible move left. If no more moves,# return True. Otherwise return False.def isGameOver(board: Board) -> bool:return False# Returns a tuple (changed, new_board)# where:# changed - a boolean indicating if# the board has changed.# new_board - the board after the user# presses the 'Up' key.def doKeyUp(board: Board) -> tuple[bool, Board]:return False, board# Returns a tuple (changed, new_board)# where:# changed - a boolean indicating if# the board has changed.# new_board - the board after the user# presses the 'Down' key.def doKeyDown(board: Board) -> tuple[bool, Board]:return False, board# Returns a tuple (changed, new_board)# where:# changed - a boolean indicating if# the board has changed.# new_board - the board after the user# presses the 'Left' key.def doKeyLeft(board: Board) -> tuple[bool, Board]:return False, board# Returns a tuple (changed, new_board)# where:# changed - a boolean…List pressure_data contains integers read from input, representing data samples from an experiment. Initialize variable sum_passed with 0. Then, for each element in pressure_data that is both at an odd-numbered index and greater than 65: Output 'Index ', followed by the element's index, ': ', and the element's value. Increase sum_passed by each such element's value. # Read input and split input into tokenstokens = input().split() pressure_data = []for token in tokens: pressure_data.append(int(token)) print(f'All data: {pressure_data}') ''' Your code goes here ''' print(f'Sum of selected elements is: {sum_passed}')12. Write a for loop that sums the odd values from the LIST_SIZE element array list. For example, the sum for this list would be 113 (51 + 17 + 45). Array 11at liat (0)1iat tal 1nt ta) liat (11iet14] 1iat ( 30 12 51 17 45 62
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- In C program language, You are to read 10 numbers from a data file into an array named List. Create another 10 element array named Reverse that is to contain the same items as List but in reverse order. For example, the first element in List will be placed in the last position of Reverse, the second element in List will be placed in the second-to-last position in Reverse, etc. After Reverse has been created, output the contents of both arrays.# convert_revised_1.py # We will try to print a table without using "string formatting"def main():print("celsius", "fahrenheit") for celsius in range (-20, 110, 10):fahrenheit = 9/5 * celsius + 32print(celsius, fahrenheit)main() Your turn: Please revise the above program convert_revised_1.py with "string formatting" and try to print a better-formatted table(required width for Celsius value is 35, width Fahrenheit value is 45, both should be center-aligned/justified)Calculate cosine similarity between given two 1d list.Two list must have the same length.Example:cosine_similarity([1, 1, 1], [1, 2, -1]) # output : 0.47140452079103173"""import math def _l2_distance(vec): """ Calculate l2 distance from two given vectors. """ norm = 0. for element in vec: norm += element * element norm = math.sqrt(norm) return norm def cosine_similarity(vec1, vec2): """ Calculate cosine similarity between given two vectors :type vec1: list :type vec2: list """ if len(vec1) != len(vec2): raise ValueError("The two vectors must be the same length. Got shape " + str(len(vec1)) + " and " + str(len(vec2))) norm_a = _l2_distance(vec1) norm_b = _l2_distance(vec2) similarity = 0. # Calculate the dot product of two vectors for vec1_element, vec2_element in zip(vec1, vec2): similarity += vec1_element * vec2_element similarity /= (norm_a * norm_b).
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