The objects used to manipulate vectors, matrices, and more general tensors in NumPy are called arrays. In this section, we examine their essential properties, how to create them, and how to access their information.
Arrays are essentially characterized by three properties, which is given in the following table (Table 4.2):
|
Name |
Description |
|
|
It describes how the data should be interpreted, as a vector, a matrix or as a higher order tensor, and it gives the corresponding dimension. It is accessed with the |
|
|
It gives the type of the underlying data (float, complex, integer, and so on). |
|
|
This attribute specifies in which order the data should be read. For instance, a matrix could be stored in memory contiguously column by column (the FORTRAN convention), or row by row (the C convention). The attribute is a tuple with the numbers of bytes that have to be skipped in memory to reach the next row and the number of bytes... |
