# numpy.atleast_3d() – The NumPy atleast_3d Python Function

## Introduction to NumPy atleast_3d

When we wish to convert inputs to arrays with at least three dimensions, we utilize the numpy.atleast_3d() function. Higher-dimensional inputs are maintained while scalar, 1, and 2-dimensional inputs are transformed to 3-dimensional arrays.

Scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays are all possible inputs.

Syntax:

`numpy.atleast_3d(*arrays)`

Parameters:

• arrays1, arrays2, … : [array_like] One or more array-like sequences. Non-array inputs are convert to arrays. Arrays that already have three or more dimensions are preserved.

Return: An array, or list of arrays, each with arr.ndim >= 3. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape (N, ) becomes a view of shape (1, N, 1), and a 2-D array of shape (M, N) becomes a view of shape (M, N, 1).

## Code Examples of NumPy atleast_3d

Example 01:

``````# welcome to softhunt.net
# Python program explaining
# numpy.atleast_3d() function

import numpy as np
num = 15

print ("Input number : ", num)

out_arr = np.atleast_3d(num)
print ("output 3d array from input number : ", out_arr)``````

Output:

```Input number :  15
output 3d array from input number :  [[]]```

Example 02:

``````# welcome to softhunt.net
# Python program explaining
# numpy.atleast_3d() function

import numpy as np

my_list = [[1, 2, 3],
[4, 5, 6]]

print ("Input list : ", my_list)

out_arr = np.atleast_3d(my_list)
print ("output array : \n", out_arr)``````

Output:

```Input list :  [[1, 2, 3], [4, 5, 6]]
output array :
[[

]

[

]]```

Example 03:

``````# welcome to softhunt.net
# Python program explaining
# numpy.atleast_3d() function
# when inputs are in high dimension

import numpy as np

in_arr = np.arange(16).reshape(1, 4, 4)
print ("Input array :\n ", in_arr)

out_arr = np.atleast_3d(in_arr)
print ("output array :\n ", out_arr)
print(in_arr is out_arr)``````

Output:

```Input array :
[[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]
[12 13 14 15]]]
output array :
[[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]
[12 13 14 15]]]
True```

## FAQs

### Difference between numpy.atleast_1d(), numpy.atleast_2d(), and numpy.atleast_3d()

numpy.atleast_1d(): When we wish to convert inputs to arrays with at least one dimension. We utilize the numpy.atleast_1d() function. Higher-dimensional inputs are maintain while scalar inputs are transform into 1-dimensional arrays.

numpy.atleast_2d(): When we wish to convert inputs to arrays with at least two dimensions, we utilize the numpy.atleast_2d() function. Higher-dimensional inputs are maintain while scalar and 1-dimensional inputs are transform into 2-dimensional arrays.

numpy.atleast_3d(): When we wish to convert inputs to arrays with at least three dimensions, we utilize the numpy.atleast_3d() function. Higher-dimensional inputs are maintain while scalar, 1, and 2-dimensional inputs are transform to 3-dimensional arrays.

### What is NumPy?

NumPy is the most important Python module for scientific computing. It’s a Python library that includes a multidimensional array object, derived objects (such as masked arrays and matrices), and a variety of routines for performing fast array operations, such as mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and more.

## Conclusion

That’s all for this article, if you have any confusion contact us through our website or email us at [email protected] or by using LinkedIn. And make sure you check out our NumPy tutorials.

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