# numpy.atleast_1d() – The NumPy atleast_1d Python Function

## Introduction to NumPy atleast_1d Python Function

Guide to NumPy atleast_1d Python Function – When we wish to convert inputs to arrays with at least one dimension. We utilize the numpy.atleast_1d() function. Higher-dimensional inputs are maintained while scalar inputs are transformed into 1-dimensional arrays.

Syntax:

`numpy.atleast_1d(*arrays)`

Parameters:

• arrays1, arrays2, … : [array_like] One or more input arrays.

Return: [ndarray] An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.

## Code Examples of NumPy atleast_1d Python Function

Example 01:

``````# welcome to softhunt.net
# Python program explaining
# numpy.atleast_1d() function

import numpy as np
num = 15

print ("Input number : ", num)

out_arr = np.atleast_1d(num)
print ("output 1d array from input number : ", out_arr)``````

Output:

```Input number :  15
output 1d array from input number :  [15]```

Example 02:

``````# welcome to softhunt.net
# Python program explaining
# numpy.atleast_1d() function

import numpy as np

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

print ("Input list : \n", my_list)

out_arr = np.atleast_1d(my_list)
print ("output array : \n", out_arr)``````

Output:

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

Example 03:

``````# welcome to softhunt.net
# Python program explaining
# numpy.atleast_1d() function
# when inputs are in high dimension

import numpy as np

in_arr = np.arange(9).reshape(3, 3)
print ("Input array :\n ", in_arr)

out_arr = np.atleast_1d(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]]
output array :
[[0 1 2]
[3 4 5]
[6 7 8]]
True```

## FAQs

### What is Python NumPy package?

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.

### Why NumPy is so fast?

NumPy is a Python core module for manipulating and operating on high-level mathematical functions, multi-dimensional arrays, linear algebra, Fourier transformations, and random number capabilities, among other things. It offers Python with tools for integrating C, C++, and Fortran programs. NumPy is mostly used for scientific computing in Python.

NumPy Arrays are faster than Python Lists because of the following reasons:

• A collection of homogenous data types store in contiguous memory spaces is refer to as an array. A list, on the other hand, is a collection of disparate data types store in non-contiguous memory regions in Python.
• The NumPy package divides a job into many pieces, which are subsequently process in parallel.
• NumPy is a Python library that combines C, C++, and Fortran code. In comparison to Python, these programming languages have an extremely short execution time.

## 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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