Python Scientific Computing with NumPy Mastery Hub: The Indu

In NumPy, when performing element-wise multiplication between two arrays `a` and `b` with compatible shapes, what fundamental operation is occurring at the computational level that distinguishes it from matrix multiplication?

Consider a NumPy array `data` with shape (10, 5) and a boolean mask `mask` of shape (10, 5) where `True` indicates elements to be selected. What is the most efficient and idiomatic NumPy approach to create a new array containing only the elements from `data` where `mask` is `True`?

You have a NumPy array `matrix` of shape (N, M) and you want to compute the sum of elements along each column. Which of the following NumPy functions or methods, when applied with the appropriate `axis` argument, achieves this efficiently?