All you should know about Python Numpy 1D Arrays

In this article, we are going to discuss about one dimensional numpy arrays.

Most of you might know it, but I wanted to share my learning here.

Photo by Andrew Moca on Unsplash

Let’s get started.

What is an array?

We can say that array is a data structure which stores data of similar data type.

One Dimensional Array is nothing but a list having only one row without columns

Wait, what?

You may think that we do have Lists in Python which will store different kinds of data. Then why use arrays?

Well, We will cover that going forward.

Firstly, let’s jump into our topic.

How to create arrays in Python?

Python provides us with the best library numpy to work with arrays. We need to install numpy library before we start using it.

Installation:

pip install numpy

Creating numpy arrays:

Numpy arrays are created from lists.

import numpy as np
list1 = [1,2,3,4,5]
arr1 = np.array(list1)
arr1

In the above code,

import numpy as np: imports the numpy module

np.array(list1): creates an array from list1

Why Use Numpy Arrays?

The main reason for using arrays is that their execution speed. Arrays are more faster when compared to lists.

Speaking of lists, we cannot perform arithmetic operations on elements from two lists. Of course, we can do. But we need to iterate through each and every element in the list.

Array make this easy. Wondering how? Look at the following code.

import numpy as np
arr1 = np.array([1,2,3,5])
arr2 = np.array([6,7,9,1])
print(arr1 + arr2)
print(arr1 * arr2)
print(arr1/arr2)
print(arr1 ** arr2)

output:

[ 7 9 12 6]

[ 6 14 27 5]

[0.16666667 0.28571429 0.33333333 5. ]

[ 1 128 19683 5]

That’s how arrays make it easy.

Numpy is a major part of Data Science

Accessing array elements:

This part is similar to that of lists. We will provide the index of the element which we want to access.

array[index]

There is a lot more to know about 1D arrays. I had given a clear explanation with code in my notebook linked with my Github repo. You can check them out.

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Happy Learning :)