# How to install numpy in Python

## Introduction

To start our exploration into the world of programming, we'll be learning about a useful tool in the Python programming language called numpy. Numpy, which stands for 'Numerical Python', is a library in Python that is used for working with arrays. It also has functions to work in domain of linear algebra, fourier transform and matrices. But, what does that all mean? Don't worry, we'll explain each of these terms as we go along. Let's start with the basics.

## What is a library in Python?

In simple terms, a library in Python is a collection of modules, which are just files containing Python code. These libraries save us from writing extra code. Just like how you might go to a library to borrow a book instead of writing one yourself, you can use a Python library to borrow code that accomplishes a specific task.

## Understanding Numpy

Numpy is a Python library that is a great tool for anyone who is dealing with numerical data in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

A multidimensional array is like a list of lists. If you're a fan of board games, you can think of a regular array (or a list) as a game of tic-tac-toe, where you have rows and columns to fill in. A multidimensional array is like a game of 3D tic-tac-toe, where you have multiple boards stacked on top of each other.

## Installing Numpy

Before we can utilize the power of Numpy, we need to install it. Thankfully, Python makes it easy for us to install libraries with the help of a tool called pip. Pip is like a delivery service that brings the libraries from the Python warehouse straight to your computer.

### The Installation Process

To install Numpy, you first need to open your computer's command line interface (CLI). The CLI is like the steering wheel of your computer. It allows you to give direct instructions to your computer in the form of commands.

If you are using Windows, you can access the CLI by searching for 'Command Prompt' in the start menu. If you are using MacOS, you can find it by searching for 'Terminal' in Spotlight.

Once you have your CLI open, you can install numpy by typing in the following command:

```
pip install numpy
```

Then press the Enter key to execute the command. Your computer will then reach out to the Python warehouse (known as PyPi), find the numpy library, and install it on your computer.

## Verifying the Installation

After the installation is complete, we can verify it by trying to import the numpy library into our Python code. To do this, we can use the `import`

command in Python. Importing is like telling your code, "Hey, I want to use this library in my code." You can do this by typing the following in your Python file:

```
import numpy as np
```

If you don't see any error messages after running this command, congratulations! You have successfully installed numpy on your computer.

## Using Numpy

Now that we have numpy installed, let's see it in action. Remember the 3D tic-tac-toe analogy for multidimensional arrays? Let's create one using numpy.

```
import numpy as np
# Creating a 3D numpy array
array_3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(array_3d)
```

When you run this code, you should see the following output:

```
[[[ 1 2 3]
[ 4 5 6]]
[[ 7 8 9]
[10 11 12]]]
```

This is a 3D array (or a list of lists of lists) that you've just created using numpy.

## Conclusion

We've just scratched the surface of what you can do with numpy. As you continue your journey into programming, you'll find that this library is a powerful tool in your toolkit. Whether you're building a complex machine learning model or just need to do some quick calculations, numpy is there to help.

Remember, learning to code is like learning a new language. It might feel strange and confusing at first, but with practice, you'll start to understand the logic behind it. It's okay to not understand everything right away. Keep experimenting, keep making mistakes, and keep learning. Happy coding!