For example, we can build 1-dimensional arrays, 2-dimensional arrays, and n-dimensional arrays.Īdditionally, we can create Numpy arrays where the Numbers have a variety of different properties. Numpy arrays can come in a variety of shapes and sizes. So for example, a 2-dimensional Numpy array looks something like this: In particular, Numpy creates and operates on Numpy arrays.Ī Numpy array is a data structure that stores numerical data in a row and column structure. Numpy is a Package for Working with Numeric Data in Python We mostly use Numpy for data manipulation and scientific computing, but we use Numpy on specific types of data in specific data structures. Numpy dot operates on Numpy arraysĪs you’re probably aware, Numpy is an add-on package for the Python programming language. So let’s quickly review some basics about Numpy and about dot products. If you’re a little new to Numpy though, or if you don’t completely understand dot products, that might not entirely make sense. Having said that, if you’re new to Numpy, or need a quick refresher about mathematical dot products, you should probably read the whole tutorial.įirst of all, let’s start with the basics.Īt a high level, Numpy dot computes the dot product of two Numpy arrays. If you need something specific, you can click on any of the above links, and it will take you to the appropriate section of the tutorial. I’ll explain exactly what the function does, how the syntax works, and I’ll show you clear examples of how to use np.dot. $$s = \mathbf)=0$, it implies that the dot product of any two orthogonal vectors must be $0$.In this tutorial, I’ll show you how to use the Numpy dot function (np.dot), to compute dot products in Numpy. The dot product is represented by a dot operator: In Euclidean geometry, the dot product between the Cartesian components of two vectors is often referred to as the inner product.
The dot product is an algebraic operation which takes two equal-sized vectors and returns a single scalar (which is why it is sometimes referred to as the scalar product).
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