NumPy has a multidimensional array object called ndarray. It consists of two parts:

The majority of array operations leave the raw data untouched. The only aspect that changes is the metadata.

In the previous chapter, we have already learned 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 NumPy array is in general homogeneous (there is a special array type that is heterogeneous as described in the Time for action – creating a record data type section)—the items in the array have to be of the same type. The advantage is that, if we know that the items in the array are of the same type, it is easy to determine the storage size required for the array.

NumPy arrays are indexed starting from 0, just like in Python. Data types are represented by special objects. We will discuss these objects comprehensively...