Do you want to find the absolute value of the items in an array? In this NumPy article, I will provide a comprehensive overview of the **np.abs() in Python**, its syntax, parameters required, functionality, and applications.

**To effectively calculate the absolute values of elements in different types of arrays, np.abs() in Python is a versatile tool. It handles arrays of integers, floats, complex numbers, and even 2D arrays, converting all elements into their non-negative equivalents.**

## np.abs() in Python

The **np.abs() in Python** is essentially an alias or shorthand for **np.absolute()** in Python. It is designed to calculate the absolute value of each element in an array.

The absolute value of a number refers to its distance from zero on the number line, regardless of direction, making all negative numbers positive.

### Syntax and Parameters required for Python np.abs() function

The syntax of the Python np.abs function:

`numpy.abs(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)`

Here,

x | Input array in Python. Can contain complex values as well. |

out | A location where the result in Python is stored. |

where | A condition on which to apply the operation in Python. |

casting, order, dtype, subok | These parameters control the type casting rules, memory layout, data type, and sub-classing behavior respectively. |

## np.abs function in Python use cases

Let’s see some demonstrative examples related to the **np.abs()** in Python:

### 1. NumPy absolute value of an integer array in Python

The **np.abs() in Python** is used to convert each element in an array of integers, including negative values, to their positive counterparts, effectively calculating the absolute value of each number.

```
import numpy as np
temperature_fluctuations = np.array([-5, 32, -3, 28, 45, -10])
print(np.abs(temperature_fluctuations))
```

**Output:**

`[ 5 32 3 28 45 10]`

After executing the code in Pycharm, one can see the output in the below screenshot.

### 2. np.abs in Python on an array with float values

To effectively compute the absolute value of each floating-point number present in an array, removing any negative signs and leaving the magnitude intact.

Here’s how np.abs() in Python can be used in this context:

```
import numpy as np
portfolio_changes = np.array([-1.5, 2.3, -0.8, 1.7, -2.0])
absolute_changes = np.abs(portfolio_changes)
print(absolute_changes)
```

**Output:**

`[1.5 2.3 0.8 1.7 2. ]`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

### 3. absolute value NumPy array of complex

For an array of complex numbers, **np.abs() in Python** calculates the magnitude (or the absolute value) of each complex number, disregarding the phase or direction of the number in the complex plane.

```
import numpy as np
complex_impedances = np.array([1+2j, 3+4j, 5-6j])
print(np.abs(complex_impedances))
```

**Output:**

`[2.23606798 5. 7.81024968]`

After executing the code in Pycharm, one can see the output in the below screenshot.

### 4. absolute value Python NumPy of 2D array

When we apply **np.abs() in Python** to a 2D array, it computes the absolute value for each element, turning all negative numbers in the array into their positive equivalents.

```
import numpy as np
daily_financials = np.array([[20, -15], [-25, 30], [10, -5]])
print(np.abs(daily_financials))
```

**Output:**

```
[[20 15]
[25 30]
[10 5]]
```

After the implementation of the code in the Pycharm editor, the screenshot is mentioned below.

## Conclusion

Here, we have learned how the **np.abs() in Python** is a highly useful function for getting the absolute, or positive, values from different types of data, whether they are simple numbers, complex numbers, or elements in a multi-dimensional array.

You may also like to read:

- Check if NumPy Array is Empty in Python
- Python NumPy zeros
- Python NumPy max
- NumPy average function in Python

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.