Pandas - Applying a custom Rolling average function

Last update on April 09 2025 12:53:38 (UTC/GMT +8 hours)

18. Rolling Window Calculation Using apply()

Write a Pandas program that applies apply() to perform a rolling window calculation.

This exercise shows how to use apply() to calculate a custom rolling window average over a DataFrame column.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [5, 6, 7, 8, 9]
})

# Define a custom function to calculate the rolling average
def rolling_average(series, window):
    return series.rolling(window=window).mean()

# Apply the function to column 'A' with a window of 2
df['A_rolling_avg'] = rolling_average(df['A'], window=2)

# Output the result
print(df)

Output:

   A  B  A_rolling_avg
0  1  5            NaN
1  2  6            1.5
2  3  7            2.5
3  4  8            3.5
4  5  9            4.5                              

Explanation:

• Created a DataFrame with two columns 'A' and 'B'.
• Defined a custom function rolling_average() to compute the rolling average with a specified window size.
• Applied the rolling average function to column 'A' with a window size of 2.
• Added the rolling average as a new column 'A_rolling_avg' in the DataFrame.

For more Practice: Solve these Related Problems:

• Write a Pandas program to compute a rolling average on a time series column using apply() with a custom window function.
• Write a Pandas program to use apply() to perform a rolling window calculation and then compare the result with the built-in rolling().
• Write a Pandas program to create a custom function for a rolling sum and apply it via apply() to a DataFrame column.
• Write a Pandas program to calculate a rolling standard deviation on a DataFrame column using a custom function applied with apply().

Python-Pandas Code Editor:

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