Arrays in Numpy

Overview

Time: min

Objectives

Create a NumPy ndarray Object

We can create a NumPy ndarray object by using the array() function.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr)

print(type(arr))

Dimensions in Arrays

A dimension in arrays is the level of array depth (nested arrays).

0-D Arrays

0-D arrays, or Scalars, are the elements in an array. Each value in an array is a 0-D array.

import numpy as np

arr = np.array(42)

print(arr)

1-D Arrays

An array that has 0-D arrays as its elements is called uni-dimensional or 1-D array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr)

2-D Arrays

An array that has 1-D arrays as its elements is called a 2-D array.These are often used to represent matrix or 2nd order tensors.

 import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr)

3-D arrays

An array that has 2-D arrays (matrices) as its elements is called 3-D array.These are often used to represent a 3rd order tensor.

import numpy as np

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])

print(arr)

Higher Dimensional Arrays

We can create higher dimension arrays by using the ndmin argument.

import numpy as np

arr = np.array([1, 2, 3, 4], ndmin=5)

print(arr)
print('number of dimensions :', arr.ndim)

Find the dimension of the Array

To find the number of dimensions of an array, we can print the ndim attribute of that array.

print(arr.ndim)

Key Points