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• Post category:NumPy / Python

Python NumPy `absolute()` function is used to return the element-wise absolute values of the input array. In other words, this mathematical function helps the user to calculate the absolute value element in the given array. An absolute value is a positive value of a given negative value, so it just removes the negative sign from the input value.

The NumPy `absolute()` function takes four parameters `arr`, `out`, `where`, and `dtype`, and returns a new array with an argument value as the absolute of the source array elements. In this article, I will explain how to use `numpy.absolute()` function on single, multi-dimension arrays of float elements, and complex numbers.

## 1. Quick Examples of Absolute Value

If you are in a hurry, below are some quick examples of how to use Python NumPy absolute value.

``````
# Quick examples of absolute value

# Example 1: Use numpy.absolute()
# To get the absolute value
arr = np.array([-46])
arr2 = np.absolute(arr)

# Example 2: Get the absolute values
# Of multiple elements of 1-d array
arr = [4, -9, 14, -23, 32, -56]
arr2 = np.absolute(arr)

# Example 3: Use numpy.absolute() function
# To get the absolute values of floating point
arr = [-15.7, 8.6, -7.1, 8.7, -19.2, 43.8]
arr2 = np.absolute(arr)

# Example 4: Use numpy.absolute()
# Get the absolute values of 2-D array elements
arr = np.array([[-12, 8, -23, 32], [49, -74, -92, 106]])
arr2 = np.absolute(arr)

# Example 5: Use numpy.absolute() function
# With complex numbers
arr = [4+7j, -5-9j, 16+13j, -8+17j]
arr2 = np.absolute(arr)

# Example 6: Use np.absolute function
# To graphical representation
arr = np.linspace(start = -18, stop = 13, num = 12, endpoint = True)
arr2 = np.absolute(arr)
print(arr2)
plt.plot(arr, np.absolute(arr))
plt.plot(arr, arr, color = 'orange')
plt.show()
``````

## 2. Syntax of NumPy absolute()

Following is the syntax of the `numpy.absolute()` function.

``````
# Syntax of numpy.absolute()
numpy.absolute(arr, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None)
``````

### 2.1 Parameters of absolute()

The absolute() function allows the following parameters.

• `arr -` Input array or object whose elements’ absolute values are to be computed.
• `/` – Indicates the end of positional arguments
• `out` – optional) Output array where the result will be placed. If not provided, a new array will be created.
• `*` – Indicates the start of keyword-only arguments.
• `where` -(optional) This parameter is used to define a condition that selects which elements to calculate. By default, it is set to True, meaning that all elements will be considered.
• `casting` – (optional) Controls what kind of data casting may occur during the operation. Default is ‘same_kind’.
• `order` – (optional) Controls the memory layout of the result. Default is ‘K’.
• `dtype` – (optional) Data type of the output array. If not specified, the data type is inferred from the input array.

### 2.2 Return Value of absolute()

It returns an array with the absolute value for each element of an array.

## 3. Usage of NumPy absolute() Function

This mathematical Python NumPy in-built function `absolute()` takes the input array as a param and returns the absolute value of each element in the input array. An absolute value is a positive value of a given negative value, so it just removes the negative sign from the input value. For positive value, it just returns the same value.

### 3.1 Get the Absolute Value of a Single Element

To get the absolute value of a single element rather than an entire array, you can use the `numpy.absolute()` function. For instance, it creates a NumPy array with a single element (`[-46]`), and then uses the `numpy.absolute()` function to obtain the absolute value of that element.

``````
# Import numpy
import numpy as np

# Create array
arr = np.array([-46])
print("Original array:\n", arr)

# Use numpy.absolute()
# To get the absolute value
arr2 = np.absolute(arr)
print("Absolute value:\n", arr2)
``````

Yields below output.

It shows that the absolute value of -46 is 46. Your usage of `numpy.absolute()` is correct for getting the absolute value of a single element in a NumPy array.

### 3.2 Get the Absolute Values of Multiple Elements of 1-D Array

If you have a 1-dimensional NumPy array and you want to get the absolute values of all its elements, you can use the `numpy.absolute()` function.

The below example defines a 1-dimensional NumPy array `arr` with multiple elements, and then it is used `numpy.absolute()` to get the absolute values of all elements. The resulting `arr2` will contain the absolute values.

``````
import numpy as np

# Create an 1D input array
arr = [4, -9, 14, -23, 32, -56]
print("Original array:", arr)

# Get the absolute values
# Of multiple elements of 1-d array
arr2 = np.absolute(arr)
print("Absolute values:", arr2)
``````

Yields below output.

## 4. Get the absolute Values of Float Elements of 1-D NumPy Array

Similarly, let’s calculate the absolute value of the float elements of an array by using the `numpy.absolute()` function. For float values, the absolute returns the positive float value.

The below example `arr` is a 1-dimensional NumPy array with float elements and `numpy.absolute()` is used to calculate the absolute values of those elements. The results `arr2` will contain the absolute values.

``````
import numpy as np

# Create an 1D input array
arr = [-15.7, 8.6, -7.1, 8.7, -19.2, 43.8]
print("Original array:", arr)

# Use numpy.absolute() function to get
# The absolute values of floating point
arr2 = np.absolute(arr)
print("Absolute float values:", arr2)

# Output:
# Original array: [-15.7, 8.6, -7.1, 8.7, -19.2, 43.8]
# Absolute float values: [15.7  8.6  7.1  8.7 19.2 43.8]
``````

## 5. Get the Absolute Values of 2-D NumPy Array Elements

To get absolute values of Two-dimensional array elements, just pass the two-dimensional array to the `absolute()` function. It returns the array in the same shape as the input.

In this example, `arr` is a 2-dimensional NumPy array, and `numpy.absolute()` is used to calculate the absolute values of all elements. The resulting `arr2` will contain the absolute values.

``````
import numpy as np

# Creating an 2D input array
arr = np.array([[-12, 8, -23, 32], [49, -74, -92, 106]])
print("Original 2-D array:\n", arr)

# Use numpy.absolute() function
# Get the absolute values of 2-D array elements
arr2 = np.absolute(arr)
print("Absolute values of 2-D array:\n", arr2)

# Output:
# Original 2-D array:
#  [[-12   8 -23  32]
#  [ 49 -74 -92 106]]
# Absolute values of 2-D array:
#  [[ 12   8  23  32]
#  [ 49  74  92 106]]
``````

## 6. Get the Absolute Values of Complex Numbers

You can use the `numpy.absolute()` function to get the absolute values of complex elements in a NumPy array. For instance, it creates a list of complex numbers and then uses the `numpy.absolute()` function to obtain the absolute values of each complex number in the list. However, if you want to work with NumPy arrays instead of lists, you should create a NumPy array using `np.array()`.

``````
import numpy as np

# Create an array with complex values
arr = [4+7j, -5-9j, 16+13j, -8+17j]
print("Original array with complex numbers:\n", arr)

# Use numpy.absolute() function
# With complex numbers
arr2 = np.absolute(arr)
print("Absolute values of complex numbers:\n", arr2)

# Output:
# Original array with complex numbers:
#  [(4+7j), (-5-9j), (16+13j), (-8+17j)]
# Absolute values of complex numbers:
#  [ 8.06225775 10.29563014 20.61552813 18.78829423]
``````

## 7. Get the Absolute Values on Graphical Representation

By using the matplotlib.pyplot, you can represent the absolute values in a graphical representation.

``````
import numpy as np
import matplotlib.pyplot as plt

# Use np.absolute function to graphical representation
arr = np.linspace(start = -18, stop = 13,
num = 12, endpoint = True)

arr2 = np.absolute(arr)
print(arr2)

plt.plot(arr, np.absolute(arr))
plt.plot(arr, arr, color = 'orange')
plt.show()

# Output:
# [18.         15.18181818 12.36363636  9.54545455  6.72727273  3.90909091
#   1.09090909  1.72727273  4.54545455  7.36363636 10.18181818 13.        ]
``````

Yields below output.

You can see the Parabolic graph of the absolute() method in Numpy.

How do I calculate the absolute value of elements in a NumPy array?

To calculate the absolute value of elements in a NumPy array, you can use the `numpy.absolute()` function or its equivalent `numpy.abs()` function.

Can I get the absolute values of a NumPy array containing complex numbers?

You can get the absolute values of a NumPy array containing complex numbers using the `numpy.absolute()` function or its equivalent `numpy.abs()` function.

Can I calculate the absolute values of elements in a 2D NumPy array?

You can calculate the absolute values of elements in a 2D NumPy array using the `np.abs()` function.

How do I get the absolute values of float elements in a NumPy array?

To get the absolute values of float elements in a NumPy array, you can use the `numpy.absolute()` function or its equivalent `numpy.abs()` function

What is the difference between numpy.absolute() and numpy.abs()?

There is no functional difference between `numpy.absolute()` and `numpy.abs()`. They are two different names for the same function. Both functions are used to compute the absolute values of elements in a NumPy array, whether they are real, complex, or of any other numeric type.

Can I compute the absolute values along a specific axis in a multi-dimensional array?

You can compute the absolute values along a specific axis in a multi-dimensional array using the `axis` parameter with the `np.abs()` function. The `axis` parameter allows you to specify along which axis the absolute values should be calculated.

## Conclusion

In this article, I have explained how to use Python `numpy.absolute()` function and how to calculate the absolute value of every element in the given array with examples.

Happy Learning!!