NumPy Operations

The point of putting data into a NumPy array is to do arithmetic on it without writing a for loop. Element-wise math, aggregations along axes, broadcasting two differently-shaped arrays together, and linear-algebra calls into compiled BLAS routines. This is the core of NumPy. A loop that took 40 milliseconds in pure Python now takes less than a millisecond, and the code that does it is shorter and clearer. This lesson covers the operations that show up in most NumPy code, plus the broadcasting rules required to avoid shape-mismatch surprises.