First of all, let me explain a bit what NumPy is and why you might need it. NumPy is a Python library that is used for working with arrays. It stands short for Numeric Python

This, of course, is still a bit vague. In general, it makes working with arrays (lists) about 50x faster than traditional python lists.

## Installing and using NumPy

To install NumPy, we must run a pip install command for it.

`pip install numpy`

Then we have to import it into our Python file.

`import numpy`

Now we can convert a list into a numpy array:

```
arr = numpy.array([1, 2, 3, 4, 5])
print(arr)
```

However, it’s quite often used to have the numpy imported as the `np`

alias.

We can do so like this:

```
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
```

This now does the exact same thing, but it’s easier to write.

If you ever wonder what version of numpy you have installed, you can simply print that out.

```
print(np.__version__)
# 1.20.3
```

## Types of arrays

The cool part about the NumPy arrays is that they can be built from all array-like data types of Python.

Which include the list, tuple, dictionary.

```
tuple = np.array((1, 2, 3, 4, 5))
print(tuple)
list = np.array(["dog", "cat", "penguin"])
print(list)
set = np.array({"dog", "cat", "penguin"})
print(set)
```

It’s super easy to convert this stuff to NumPy arrays since we can eventually do more stuff and faster!

In a follow-up article, I’ll go more in-depth about the options for the NumPy arrays.

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