NumPy Array Function

Introduction

In this lab, we will learn about the array() function in the NumPy library. The array() function is used to create an array in NumPy and can be used to create a homogeneous multidimensional array which is the main object in the NumPy library.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

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Basic array() example

Below is an example that shows how to create an array having only one dimension using the array() function:

import numpy as np

a = np.array([1, 4, 9])
print("The Array is:")
print(a)

Output:

The Array is:
[1 4 9]

Creating an array with more than one dimension

Now, let's learn how to create an array with more than one dimension using the array() function. The code for the same is as follows:

import numpy as np

a = np.array([[1, 7], [6, 4]])
print("The Array with more than one dimension:")
print(a)

Output:

The Array with more than one dimension:
[[1 7]
[6 4]]

Using the dtype parameter

The dtype parameter is used to define the desired data type for the array. Below is an example where we will use the dtype parameter:

import numpy as np

a = np.array([1, 7, 9], dtype='complex')
print("The Array with more than one dimension:")
print(a)

Output:

The Array with more than one dimension:
[1.+0.j 7.+0.j 9.+0.j]

Note: The output of the above code snippet indicates the values of the array elements in the form of complex numbers.

Summary

In this lab, we learned about the array() function in the NumPy library. We saw how to use the array() function with its syntax, its parameters, and the values returned by this function. We also saw examples of creating an array having only one dimension, multi-dimensional arrays, and using the dtype parameter.