Python

# NumPy Np.Atleast_1d()

This function allows you to convert input values into an array of at least one dimension.

Let us explore how this function works.

## Function Syntax

The function syntax is expressed as shown:

 1 numpy.atleast_1d(*arys)

## Parameters

The function accepts the following parameters:

1. array1, array2, array3… – refers to one or more input arrays or array_like objects.

## Return Value

The function returns an array or a list of arrays, each with a dimension greater than or equal to 1.

If the input is a scalar value, the function converts it into a one-dimensional array while N-dimensional inputs are conserved.

## Example #1

The example below shows how to use the atleast_1d function to convert a scalar value into a one-dimensional array.

 1234 # import numpy import numpy as np print(f"array: {np.atleast_1d(10)}") print(f"shape: {np.atleast_1d(10).shape}")

In the code above, we pass a scalar value to the atleast_1d function, which returns a 1D array as shown:

 12 array:  shape: (1,)

## Example #2

The example below demonstrates how the function operates on a 2-dimensional array.

 12 arr = np.array([[1,2,3], [4,5,6]]) print(np.atleast_1d(arr))

The function does not alter the input value as it contains at least one dimension. This means that the input value is preserved.

## Example #3

You can also check if the input value is at least one dimension, as shown in the example code below:

 12 arr = np.array([[1,2,3], [4,5,6]]) print(np.atleast_1d(arr) is arr)

Here, we test if the input array is at least 1D. The code above should return:

 1 True

## Closing

This article taught us how to convert an input value into at least one dimension using the np.atleast_1d() function. 