Pandas DataFrame diff() Method – Be on the Right Side of Change (original) (raw)

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

The diff() method calculates the difference between a DataFrame element compared with another element in the same DataFrame. The default is the element in the previous row.

The syntax for this method is as follows:

DataFrame.diff(periods=1, axis=0)

Parameter	Description
axis	If zero (0) or index is selected, apply to each column. Default 0.If one (1) apply to each row.
periods	The periods to shift for calculating differences. This parameter accepts negative values.

Code – Example 1

This example reflects the difference in regard to the previous row.

df_teams = pd.DataFrame({'Bruins': [4, 5, 9], 'Oilers': [3, 6, 10], 'Leafs': [2, 7, 11], 'Flames': [1, 8, 12]})

result = df_teams.diff() print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
Line [2] uses the diff() method to determine the difference from the previous row and saves it to the result variable.
Line [3] outputs the result to the terminal.

Output

| | Bruins | Oilers | Leafs | Flames | | | -------- | ------ | ----- | ------ | --- | | 0 | NaN | NaN | NaN | NaN | | 1 | 1.0 | 3.0 | 5.0 | 7.0 | | 2 | 4.0 | 4.0 | 4.0 | 4.0 |

Code – Example 2

This example reflects the difference in regard to the previous column.

df_teams = pd.DataFrame({'Bruins': [4, 5, 9], 'Oilers': [3, 6, 10], 'Leafs': [2, 7, 11], 'Flames': [1, 8, 12]})

result = df_teams.diff(axis=1) print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
Line [2] uses the diff() method to determine the difference from the previous column and saves it to the result variable.
Line [3] outputs the result to the terminal.

Output

| | Bruins | Oilers | Leafs | Flames | | | -------- | ------ | ----- | ------ | --- | | 0 | NaN | -1 | -1 | -1 | | 1 | NaN | 1 | 1 | 1 | | 2 | NaN | 1 | 1 | 1 |

Code – Example 3

This example reflects the difference in regard to the previous rows.

df_teams = pd.DataFrame({'Bruins': [4, 5, 9], 'Oilers': [3, 6, 10], 'Leafs': [2, 7, 11], 'Flames': [1, 8, 12]})

result = df_teams.diff(periods=1) print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
Line [2] uses the diff() method to determine the difference from the previous column and with periods set to 1 and saves to the result variable.
Line [3] outputs the result to the terminal.

Output

| | Bruins | Oilers | Leafs | Flames | | | -------- | ------ | ----- | ------ | --- | | 0 | NaN | NaN | NaN | NaN | | 1 | 1.0 | 3.0 | 5.0 | 7.0 | | 2 | 4.0 | 4.0 | 4.0 | 4.0 |

More Pandas DataFrame Methods

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

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