Pandas DataFrame abs() Method – Be on the Right Side of Change

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

The Pandas library enables access to/from a DataFrame.
The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required libraries.

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import numpy as np

DataFrame abs()

The abs() method converts each element in a DataFrame containing a negative value to a positive (absolute) value. This method has no parameters. Another option aside from the abs() method is to use numpy.absolute().

The syntax for this method is as follows:

DataFrame.abs()

For this example, the Sales Manager of Rivers Clothing noticed that some of their inventory contained negative pricing. To resolve this issue, the Sales Manager ran the following code.

Code – Example 1

df_inv = pd.DataFrame({'Tops':     [36, 23, 19],
                       'Tanks':    [44, 43, -20],
                       'Pants':    [61, -33, 67],
                       'Sweats':   [88, 38, 13]})

index_ = ['Small', 'Medium', 'Large']
df_inv.index = index_

result = df_inv.abs()
print(result)

Line [1] creates a DataFrame from a dictionary of lists and saves it to df_inv.
Line [2-3] creates and sets the index for the DataFrame (Small/Medium/Large).
Line [4] uses the abs() method to convert negative values to positive (absolute) values. The output saves to the result variable.
Line [5] outputs the result to the terminal.

Output

	Tops	Tanks	Pants	Sweats
Small	36	44	61	88
Medium	23	43	33	38
Large	19	20	67	13

This example is similar to the above. However, it calls numpy.absolute() to change negative values to positive (absolute) values. The output remains the same.

Code – Example 2

df_inv = pd.DataFrame({'Tops':     [36, 23, 19],
                       'Tanks':    [44, 43, -20],
                       'Pants':    [61, -33, 67],
                       'Sweats':   [88, 38, 13]})

index_ = ['Small', 'Medium', 'Large']
df_inv.index = index_

result = np.absolute(df_inv)
print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_inv.
Line [2-3] creates and sets the index for the DataFrame (Small/Medium/Large).
Line [4] uses np.absolute() to convert any negative values to positive (absolute) values. The output saves to the result variable.
Line [5] outputs the result to the terminal. The output is identical to the example above.

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

👈 df.update()

df.add_prefix() 👉

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.