Q1. Create a 1D array of 10 numbers from 0 to 9. Extract the first 3 elements, the last 2 elements, and every 2nd element starting from index 2.
arr = np.arange(10)
first3 = arr[:3] # [0,1,2]
last2 = arr[-2:] # [8,9]
step2 = arr[2::2] # [2,4,6,8]Q2. You have a 2D array (3 rows, 4 columns). Select the second row, the third column, and the submatrix consisting of rows 0-1 and columns 1-3.
arr = np.arange(12).reshape(3,4)
row1 = arr[1, :] # second row: [4,5,6,7]
col2 = arr[:, 2] # third column: [2,6,10]
sub = arr[0:2, 1:4] # rows 0-1, columns 1-3: [[1,2,3], [5,6,7]]Q3. Using boolean indexing, select all elements greater than 5 from array [1,6,2,7,3,8,4,9]. Then replace all elements less than 5 with 0.
arr = np.array([1,6,2,7,3,8,4,9])
gt5 = arr[arr > 5] # [6,7,8,9]
arr[arr < 5] = 0 # arr becomes [0,6,0,7,0,8,0,9]Q4. You have a 5x5 identity matrix. Use fancy indexing to set the first and third rows to zeros. Then set the last column to 5.
eye = np.eye(5)
eye[[0,2], :] = 0 # set rows 0 and 2 to zeros
eye[:, -1] = 5 # set last column to 5Q5. Given a 2D array of shape (4,4), extract the diagonal elements using two methods: diagonal() and fancy indexing.
arr = np.arange(16).reshape(4,4)
diag1 = np.diagonal(arr)
diag2 = arr[np.arange(4), np.arange(4)] # both give [0,5,10,15]