NumPy Tutorial - NumPy Multidimensional Array-Ndarray

1. Ndarray Definition
2. Ndarray Attributes

NumPy is a library that uses multidimensional arrays as the basic data structure. The only data structure in NumPy is ndarray but not Python primitive list data type, because list runs relatively slowly.

After you have learned ndarray that is cornerstone of NumPy, you will understand why NumPy can achieve high-speed computing.

Ndarray Definition

ndarray is the abbreviation of n-dimension array, or in other words - multidimensional arrays. ndarray is an array object representing a multidimensional, homogeneous array of fixed-size items.

The dimensions and the number of elements are defined by the shape, that is a tuple of N integers that represents the number of elements in each dimension. The element type in the array is defined by dtype- data-type object.

Let’s explain the sentences above in layman language. All the elements stored in the ndarray object must have the same data type and size.

The characteristics of ndarray data type are summarized as follows.

1. Can only store elements of the same type
2. The amount of data in each dimension must be the same, for example 2D ndarray must have the same amount of elements in every column, and of course also in each row.
3. It is written in C language and it could execute matrix operation optimally

Ndarray Attributes

Let’s list the attributes of ndarray.

When you access the attributes of ndarray, the data of ndarray instance is not modified, even if when you use .T to get the transpose of the object. You get a new ndarray object but not modified original data.

Let’s take a look at the specific meaning of each attribute through example codes.

>>> import numpy as np
>>> a = np.array([1, 2, 3])

We need to import NumPy library and create a new 1-D array. You could check its data type and the data type of its element.

>>> type(a)
numpy.ndarray
>>> a.dtype
dtype('int32')

Let’s create a new 2-D array and then check its attributes.

>>> b = np.array([[4, 5, 6], [7, 8, 9]])
>>> b
array([[4, 5, 6],
       [7, 8, 9]])
>>> b.T     # get the transpose of b
array([[4, 7],
       [5, 8],
       [6, 9]])
>>> b       # b keeps unmodified
array([[4, 5, 6],
       [7, 8, 9]])
>>> a.size  # a has 3 elements
3
>>> b.size  # b has 6 elements
6
>>> a.itemsize # The size of element in a. The data type here is int64 - 8 bytes
8
>>> b.nbytes  # check how many bytes in b. It is 48, where 6x8 = 48
48
>>> b.shape # The shape of b
(2, 3)
>>> b.dnim # The dimensions of b
2