Pandas is a powerful tool utilized by data scientists, programmers and analysts. The open-source Python package is great for data manipulation and analysis, making it easier than ever to make data-driven decisions. It's a robust and versatile tool that simplifies many tasks thanks to its various reliable functions.

One example is the ABS function. But what does the ABS function in pandas do?

Understanding the ABS Function

ABS is one of the simpler pandas DataFrame functions. Its purpose is to return a DataFrame or Series object with the absolute numeric value of each element.

In mathematics, the absolute value is a number's distance from zero. For example, the absolute value of the number 123 is 123. The same goes for -123! Absolute values are always positive. These values are important because they simplify calculations and lead to a better understanding of numeric magnitudes regardless of positive or negative signs.

The ABS function in pandas calculates the absolute value of every element in a series. It only works if the series objects have numerical aspects. Therefore, you can't perform the function if there are NaN values or missing details.

Using the ABS function requires a single argument. The argument must be a number, but it can be negative or positive. That value can be an integer, a floating-point number or a complex number.

Using the ABS Function

The syntax for this function is simple. It looks like this:

abs(NUMBER)

In place of "NUMBER," you'd place your argument. That's for a single number value. But your code would look different if you're looking to get absolute values for a series.

Suppose you have a series of data values for which you want to find the absolute values. You may have a table with values like:

• -275.3

• 45.2

• -850.2

• 52

Those numbers are all part of your series. Your Python package code would look something like this:

import numpy as np

import pandas as pd

s = pd.Series([-275.3, 45.2, -850.2, 52])

s.abs()

The output of that code after performing the ABS function would be:

275.3

45.2

850.2

52

Author Resource:-

Emily Clarke writes about business software and services like spreadsheets that automatically generate Python code and transform your data with AI etc. You can find her thoughts at Python workbook blog.