Arrays are important because they enable you to express batch operations on data without writing any for loops. This is usually called vectorization. Any arithmetic operations between equal-size arrays applies the operation elementwise: In [45]: arr = np.array([[1., 2., 3.], [4., 5.,...
NumPy arrays also use much less memory than built-in Python sequences. NumPy operations perform complex computations on entire arrays without the need for Python for loops. To give you an idea of the performance difference, consider a NumPy array of one million integers, and the equivalent ...
Vectorization with NumPy The NumPy package for numerical computing (cf. NumPy home page) introduces vectorization to Python. The major class provided by NumPy is the ndarray class, which stands for n-dimensional array. An instance of such an object can be created, for example, on the basis of...