How to calculate MOVING AVERAGE in Pandas DataFrame in Python

Calculating a moving average in a Pandas DataFrame in Python can be done easily using the rolling() function. This function allows you to calculate the average of a certain number of rows in a DataFrame.

First, you need to import the Pandas library and create a DataFrame. For example, you can create a DataFrame with random numbers using the numpy library.

import pandas as pd import numpy as np df = pd.DataFrame(np.random.randint(0,100,size=(100, 1)), columns=[‘data’])

Next, you can use the rolling() function to calculate the moving average. The function takes two parameters: the window size and the type of average (mean, sum, etc.). For example, to calculate the moving average of the last 3 rows, you can use the following code:

df[‘moving_average’] = df[‘data’].rolling(window=3).mean()

The window size can be changed to any number you want. For example, if you want to calculate the moving average of the last 5 rows, you can change the window size to 5.

df[‘moving_average’] = df[‘data’].rolling(window=5).mean()

You can also calculate the moving average for different types of averages, such as median or sum. To calculate the moving median, you can use the following code:

df[‘moving_median’] = df[‘data’].rolling(window=3).median()

To calculate the moving sum, you can use the following code:

df[‘moving_sum’] = df[‘data’].rolling(window=3).sum()

In this way, you can easily calculate the moving average of a Pandas DataFrame in Python using the rolling() function. You can adjust the window size and type of average to suit your needs.

 

In this Learn through Codes example, you will learn: How to calculate MOVING AVERAGE in Pandas DataFrame in Python.

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