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In TensorFlow library, tensors are the building blocks as all computations are done using tensors. Google’s TensorFlow team says, "A tensor is a generalization of vectors and matrices to potentially higher dimensions. Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes."

Tensors can be of two types:
          i) Constant,
          ii) Variable.

A vector is understood as something that has a magnitude and a direction. Without the direction of a vector, a tensor becomes a scalar value that has only magnitude. A vector is used to represent n number of things and can represent area and different attributes, among other things.

If a vector is multiplyed with another vector, a scalar quantity is obtained, while if a vector is multiplyed with a scalar value, it just increases or decreases in the same proportion, in terms of its magnitude, without changing its direction. However, if a vector is multiplyed with a tensor, it then will result in a new vector that has a changed magnitude as well as a new direction.

In pandas, a series object represents a vector of data.

Script to output a vector:
         import numpy as np
         v = np.array([3,5,6])
         print(v)
     Output:     
         [3 5 6]

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