In this post I show you how to calculate percent change in Pandas. You will also learn how to add a percentage change column to the dataframe.

Pandas offers in its wide range of functions one that calculates the percentage change. Use the pct_change function to calculate the percentage change.

## How to calculate percent change in Pandas dataframe

I will use the following dataframe example.

import pandas as pd my_df = pd.DataFrame({'Column1': [2, 5, 7, 46], 'Column2': [12, 21, 5, 22], 'Column3': [8, 22, 9, 12]}) print(f'This is my sample dataframe \n{my_df}')

I would like to calculate the percentage change for column number 3. To calculate the percentage change in column 3, I use the pct_change function.

import pandas as pd my_df = pd.DataFrame({'Column1': [2, 5, 7, 46], 'Column2': [12, 21, 5, 22 ], 'Column3': [8, 22, 9, 12]}) my_df['Coulmn3_Pct_Change'] = my_df['Column3'].pct_change().round(4)*100 print(f'This is the percent change of values \n{my_df}')

The proof that the code is very simple is that I didn’t even use any parameters to the pct_change function.

In my Python code, I created a new column in which Pandas calculated the percentage change in the data from column 3. For a neater notation, I multiplied the values by 100 and rounded to two decimal places.

You already know the method to calculate percentage changes in a Pandas dataframe. The pct_change function offers more possibilities. To learn about the possibilities of the pct_change function, I paste the link to the documentation below.

See also:

How to calculate sum of rows and columns in Pandas

Pct-change documentation

How to calculate standard error in Pandas

How to calculate business days

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