The NumPy array object
NumPy has a multidimensional array object called ndarray. It consists of two parts as follows:
The actual data
Some metadata describing the data
The majority of array operations leave the raw data untouched. The only aspect that changes is the metadata.
We have already learned in the previous chapter how to create an array using the arange() function. Actually, we created a one-dimensional array that contained a set of numbers. The ndarray object can have more than one dimension.
The advantages of using NumPy arrays
A NumPy array is a general homogeneous array—the items in an array have to be of the same type (there is a special array type that is heterogeneous). The advantage is that if we know that the items in an array are of the same type, it is easy to determine the storage size required for the array. NumPy arrays can perform vectorized operations working on a whole array. Contrast this to Python lists, where normally you have to loop through the list and perform operations...
