# Benefit of NumPy arrays over Python arrays

• Difficulty Level : Hard
• Last Updated : 05 Sep, 2020

The need for NumPy arises when we are working with multi-dimensional arrays. The traditional array module does not support multi-dimensional arrays.

Let’s first try to create a single-dimensional array (i.e one row & multiple columns) in Python without installing NumPy Package to get a more clear picture.

## Python3

 `from` `array ``import` `*` ` `  ` `  `arr ``=` `array(``'i'``, [``25``, ``16``, ``3``]) ` `print``(arr)`

Output:

```array('i', [25, 16, 3])
```

Now, Let’s try to create a multi-dimensional array by using the array module.

## Python3

 `from` `array ``import` `*` ` `  ` `  `arr ``=` `array(``'i'``, [``25``, ``16``, ``3``], [``5``, ``19``, ``28``]) ` `print``(arr)`

Output:

```TypeError: array() takes at most 2 arguments (3 given)
```

We see that the array module does not support multi-dimensional array, this is where we require NumPy. NumPy supports large, multi-dimensional arrays and has a large collection of high-level math functions that can operate on those arrays.

Let’s use NumPy to create a multi-dimensional array.

## Python3

 `from` `numpy ``import` `*` ` `  ` `  `arr ``=` `array ([[``25``, ``31``, ``3``], [``5``, ``19``, ``28``]]) ` `print``(arr)`

Output:

```[[25 31  3]
[ 5 19 28]]
```

My Personal Notes arrow_drop_up
Recommended Articles
Page :