Pandas DataFrame | Percentage Change Calculation

Introduction

The pct_change() method in the Pandas DataFrame calculates the percent change in the DataFrame between the current and previous element. It is useful for analyzing data and calculating differences in sales, month-to-month or year-to-year.

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Skills Graph

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Calculate the Percentage Change in a Pandas DataFrame

To calculate the percent change in a Pandas DataFrame, follow these steps:

Import the pandas library.

import pandas as pd

Create a DataFrame with a time series index and desired data.

Values = pd.date_range('2021-01-01', periods=3, freq='5W')
df = pd.DataFrame({'coffee': [755.2, 751.23, 852.21], 'Tea': [700.21, 695.21, 726.21], 'Pepper': [900.14, 8254.1, 455.27]}, index=Values)

Print the original DataFrame.

print("----------The dataset is----------")
print(df)

Use the pct_change() method to calculate the percentage change in the DataFrame.

print("-------percentage change in the dataset-------")
print(df.pct_change())

Calculate the Percentage Change along the Column Axis

To calculate the percent change along the column axis, modify the code in step 1 as follows:

Import the pandas library.

import pandas as pd

Create a DataFrame with a time series index and desired data.

Values = pd.date_range('2021-01-01', periods=3, freq='5W')
df = pd.DataFrame({'coffee': [755.2, 751.23, 852.21], 'Tea': [700.21, 695.21, 726.21], 'Pepper': [900.14, 8254.1, 455.27]}, index=Values)

Print the original DataFrame.

print("----------The dataset is----------")
print(df)

Use the pct_change() method with axis=1 to calculate the percentage change along the column axis.

print("-------percentage change in the dataset-------")
print(df.pct_change(axis=1))

Calculate the Percentage Change with a Specified Period

To calculate the percent change with a specified period, modify the code in step 1 as follows:

Import the pandas library.

import pandas as pd

Create a DataFrame with a time series index and desired data.

Values = pd.date_range('2021-01-01', periods=3, freq='5W')
df = pd.DataFrame({'coffee': [755.2, 751.23, 852.21], 'Tea': [700.21, 695.21, 726.21], 'Pepper': [900.14, 8254.1, 455.27]}, index=Values)

Print the original DataFrame.

print("----------The dataset is----------")
print(df)

Use the pct_change() method with periods=2 to calculate the percentage change with a specified period.

print("-------percentage change in the dataset-------")
print(df.pct_change(periods=2))

Handle Missing Values before Calculation

To handle missing values before calculating the percent change, modify the code in step 1 as follows:

Import the pandas library.

import pandas as pd

Create a DataFrame with a time series index and desired data.

Values = pd.date_range('2021-01-01', periods=3, freq='5W')
df = pd.DataFrame({'coffee': [755.2, 751.23, 852.21], 'Tea': [700.21, 695.21, 726.21], 'Pepper': [900.14, 8254.1, 455.27]}, index=Values)

Print the original DataFrame.

print("----------The dataset is----------")
print(df)

Use the pct_change() method with fill_method='ffill' to handle missing values before calculation.

print("-------percentage change in the dataset-------")
print(df.pct_change(fill_method='ffill'))

Summary

The pct_change() method in the Pandas DataFrame calculates the percent change between the current and previous element. It can be used to analyze data and calculate differences, and it has parameters to handle missing values and specify a period for calculation. By following the steps in this tutorial, you can use the pct_change() method effectively in your data analysis tasks.

Pandas DataFrame Pct_change Method