3.Consider a numpy 2-D array of the form [[50, 60, 70], [67, 88, 90], [60, 78, 97]] where the ith 1-D array contains the marks of the ith student in three subjects (in this order. E.g. 50, 60, 70 are the subject1, subject2, subject3 marks respectively of student-0. Use numpy sum function only to i) create a 1-D numpy array with the sum of the marks of individual students ii) create a 1-D numpy array with the sum of subject-wise marks. Also, do these operations to produce 2-D numpy arrays with the same content. Hint: use axis = 1, 0 etc
3.Consider a numpy 2-D array of the form [[50, 60, 70], [67, 88, 90], [60, 78, 97]]
where the ith 1-D array contains the marks of the ith student in three subjects (in
this order. E.g. 50, 60, 70 are the subject1, subject2, subject3 marks respectively
of student-0. Use numpy sum function only to i) create a 1-D numpy array with
the sum of the marks of individual students ii) create a 1-D numpy array with the
sum of subject-wise marks. Also, do these operations to produce 2-D numpy
arrays with the same content.
Hint: use axis = 1, 0 etc
4. Given two 1-D numpy arrays A and B, remove the elements in A which are also
in B and store the resulting array in C. Use numpy set operations.
5. Given two numpy 2-D arrays arr1 = np.array([[1, 2], [4, 5]]), arr2 = np.array([[3, 3],
[1,1]]) explore the difference between np.multiply(arr1, arr2) and np.matmul(arr1,
arr2
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