# A Comprehensive Guide to NumPy Creating Array: Effortlessly Create Arrays | tutorials 4

**NumPy Creating array:** When it comes to scientific computing in Python, one of the most popular libraries used is NumPy. NumPy is a powerful library that enables efficient numerical operations on arrays, making it a popular choice for scientific computing and data analysis. In this article, we’ll explore how to create arrays in NumPy.

**NumPy Creating array**

To create a NumPy array, we need to import the NumPy library first. We can do this by typing the following code:

import numpy as np

This line of code imports the NumPy library and gives it an alias of “np” to make it easier to use in our code.

Now that we have NumPy imported, let’s create our first array. We can create a NumPy array from a list by using the np.array() function. Let’s create an array of integers:

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

print(arr)

The output will be:

[1 2 3 4 5]

We can also create a NumPy array from a tuple:

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

print(arr)

The output will be the same as before:

[1 2 3 4 5]

**Creating a Multi-Dimensional Array**

In addition to creating a one-dimensional array, we can also create multi-dimensional arrays. We can create a two-dimensional array by passing a list of lists to the np.array() function:

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

print(arr)

The output will be:

[[1 2 3]

[4 5 6]]

We can also create multi-dimensional arrays with more than two dimensions. For example, we can create a three-dimensional array with the following code:

arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

print(arr)

The output will be:

[[[1 2]

[3 4]]

[[5 6]

[7 8]]]

**Creating an Empty Array**

Sometimes, we may want to create an array without initializing it with any values. We can create an empty array by using the np.empty() function. For example:

arr = np.empty((3, 2))

print(arr)

The output will be:

[[1.04602945e-311 9.58487353e-322]

[0.00000000e+000 0.00000000e+000]

[0.00000000e+000 0.00000000e+000]]

Notice that the values in the array are not initialized, and can contain any arbitrary values.

**Creating an Array of Zeros or Ones**

We can also create arrays that are initialized with zeros or ones. To create an array of zeros, we can use the np.zeros() function:

arr = np.zeros((3, 4))

print(arr)

The output will be:

[[0. 0. 0. 0.]

[0. 0. 0. 0.]

[0. 0. 0. 0.]]

To create an array of ones, we can use the np.ones() function:

arr = np.ones((2, 3))

print(arr)

The output will be:

[[1. 1. 1.]

[1. 1. 1.]]

**Creating an Array with a Range of Values**

We can also create an array with a range of values using the np.arange() function. This function takes three arguments: start, stop, and step.

arr = np.arange(0, 10, 2)

print(arr)

The output will be:

[0 2 4 6 8]

In this example, we created an array with values ranging from 0 to 10 with a step of 2.

We can also create an array with evenly spaced values using the np.linspace() function. This function takes three arguments: start, stop, and the number of evenly spaced values to generate.

arr = np.linspace(0, 1, 5)

print(arr)

The output will be:

[0. 0.25 0.5 0.75 1. ]

In this example, we created an array with 5 evenly spaced values ranging from 0 to 1.