Instead, if A is a NumPy array it’s much simpler # List comprehension - nicer but still slow To do this we’d have to either write a for loop or a list comprehension. Let’s say we have a Python list and want to add 5 to every element. This is one advantage NumPy arrays have over standard Python lists. The default behavior for any mathematical function in NumPy is element-wise operations. Now you know why it’s so important, let’s get to the code. If you are working with numbers, you will use matrices, arrays and matrix multiplication at some point. This includes machine learning, computer vision and neuroscience to name a few. Matrices and arrays are the basis of almost every area of research. If you don’t know what matrix multiplication is, or why it’s useful, check out this short article.
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