Test: 2D Arrays - 2 - EmSAT Achieve MCQ

In Python, 2D arrays can have different data types in each row. This is because each row can be considered as a separate 1D array.

To access an element in a 2D array, we use the syntax array[i][j], where i represents the row index and j represents the column index.

Accessing an element in a 2D array takes constant time because the position of the element is known based on the row and column indices.

The numpy module provides efficient functions and data structures for working with arrays, including 2D arrays.

To iterate over all elements in a 2D array, we use nested loops. The outer loop iterates over the rows, and the inner loop iterates over the columns.

The code snippet accesses the element at row 1, column 2 in the 2D array and prints its value, which is 6.

The len() function returns the number of elements in the given list. In this case, it returns 2, which is the number of rows in the 2D array.

The sum() function calculates the sum of all elements in the given list. In this case, it calculates the sum of elements in the first row, which is 6.

The max() function returns the maximum value from the given list. In this case, it returns 6, which is the maximum value in the second row of the 2D array.

The code snippet accesses the last element of the last row in the 2D array and prints its value, which is 4.

To swap the elements of a matrix diagonally, we need to iterate over each element and swap it with its corresponding diagonal element.

To calculate the sum of all elements in a matrix, we can use nested loops to iterate over each element and add them together.

To find the row with the highest sum of elements, we need to iterate over each row, calculate the sum of elements in each row, and keep track of the row with the highest sum.

To check if a matrix is symmetric, we can iterate over each element and compare it with the corresponding element in the transposed matrix. If all corresponding elements are the same, the matrix is symmetric.

To rotate a matrix 90 degrees clockwise, we need to reverse each row and then transpose the matrix.