Page 190 - Robotics and AI class 10
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//                      import numpy as np                            [ 5  5 10  8]
          (floor division)        marks1 = np.array([10,20,30,40])              2 marks divided  using

                                  marks2=np.array([2,4,3,5])                    floor division: [ 5 10 15
                                                                                20]
                                  print(marks1//marks2)
                                  print("2  marks  divided  using  floor
                                  division:",marks1//2)
          %                       import numpy as np                            [0 0 0 0]

          (Remainder of           marks1 = np.array([10,20,30,40])              remainder  of division:
          division)               marks2=np.array([1,2,3,4])                    [1 2 0 1]

                                  print(marks1%marks2)
                                  print("remainder of
                                  division:",marks1%3)
        Some of the basic mathematical operations can also be done by using built-in functions like:
        1.  Addition: np.add()

        2.  Subtraction: np.subtract()

        3.  Multiplication: np.multiply()
        4.  Division: np.divide()

        5.  Remainder: np.remainder()
        Some important attributes and functions of NumPy Array:

                 Type             Function                   Example                          Output
           Type  of  an  object  type(ARR)      import numpy as np                  <class           'numpy.
           i.e. array                           ARR = np.array([1,2,3,4])           ndarray'>

                                                print(type(ARR))
           gives the          ARR.ndim          import numpy as np                  2
           dimensions of an                     ARR = np.array([[1,2,3,4],
           array as an integer                  [3,4,5,6]])
           value. Arrays can                    print(ARR.ndim)
           be 1-D, 2-D or n-D.

           Shape  of an  array  ARR.shape       import numpy as np                  (2, 4)
           i.e.  length  of the                 ARR =
           array   of   each                    np.array([[1,2,3,4],
           dimension                            [3,4,5,6]])

                                                print(ARR.shape)
           Size of  an  array  ARR.size         import numpy as np                  8
           i.e.  counts  the                    ARR =
           total number of                      np.array([[1,2,3,4],
           elements                             [3,4,5,6]])
                                                print(ARR.size)



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