A **one-dimensional array** is a collection of elements, **all of the same type**, indexed by sequences of natural numbers. 

Let's have a look at an example of creating a 1-D array:

import numpy as np

# Creating array
arr = np.array([1, 2, 0, 0])

# Displaying array
print(arr)

>Note
>
>We should use the `np.array()` function and enclose your 1-dimensional array in square brackets. For example: `np.array([value1, value2, value3])`.

Select all options with the INCORRECT code for creating a 1-D array.

NumPy is one of the basic packages for scientific computing in Python. The 'NumPy in a Nutshell' course will introduce you to such a powerful tool as NumPy, which is convenient for working with arrays of different sizes. After completing this course, you will be able to easily work with matrices, using various functions. In addition, during the course, you will learn basic methods for working with arrays that simplify code writing.

In this section we will get acquainted with what the NumPy library is, as well as learn how to create an array.

In this section we will get acquainted with arrays of different dimensions, and understand the difference between them.

In this section we should recall what slices are and learn how to make them for arrays of different dimensions. We shall also learn to refer to elements in an array using their indexes.

In this section we will learn how to reshape arrays and also how to concatenate an array. Moreover we will learn how to sort an array. Also we will learn about such an interesting method that is often used for arrays, namely: copy().

1-D Arrays