How to Calculate a Rolling Mean in Pandas

A rolling mean is simply the mean of a certain number of previous periods in a time series.

To calculate the rolling mean for one or more columns in a pandas DataFrame, we can use the following syntax:

df['column_name'].rolling(rolling_window).mean()

This tutorial provides several examples of how to use this function in practice.

Example: Calculate the Rolling Mean in Pandas

Suppose we have the following pandas DataFrame:

import numpy as np
import pandas as pd

#make this example reproducible
np.random.seed(0)

#create dataset
period = np.arange(1, 101, 1)
leads = np.random.uniform(1, 20, 100)
sales = 60 + 2*period + np.random.normal(loc=0, scale=.5*period, size=100)
df = pd.DataFrame({'period': period, 'leads': leads, 'sales': sales})

#view first 10 rows
df.head(10)

   period	    leads	    sales
0	1	11.427457	61.417425
1	2	14.588598	64.900826
2	3	12.452504	66.698494
3	4	11.352780	64.927513
4	5	9.049441	73.720630
5	6	13.271988	77.687668
6	7	9.314157	78.125728
7	8	17.943687	75.280301
8	9	19.309592	73.181613
9	10	8.285389	85.272259

We can use the following syntax to create a new column that contains the rolling mean of ‘sales’ for the previous 5 periods:

#find rolling mean of previous 5 sales periods
df['rolling_sales_5'] = df['sales'].rolling(5).mean()

#view first 10 rows
df.head(10)

	period	    leads	    sales	rolling_sales_5
0	1	11.427457	61.417425	NaN
1	2	14.588598	64.900826	NaN
2	3	12.452504	66.698494	NaN
3	4	11.352780	64.927513	NaN
4	5	9.049441	73.720630	66.332978
5	6	13.271988	77.687668	69.587026
6	7	9.314157	78.125728	72.232007
7	8	17.943687	75.280301	73.948368
8	9	19.309592	73.181613	75.599188
9	10	8.285389	85.272259	77.909514

We can manually verify that the rolling mean sales displayed for period 5 is the mean of the previous 5 periods:

Rolling mean at period 5: (61.417+64.900+66.698+64.927+73.720)/5 = 66.33

We can use similar syntax to calculate the rolling mean of multiple columns:

#find rolling mean of previous 5 leads periods 
df['rolling_leads_5'] = df['leads'].rolling(5).mean() 

#find rolling mean of previous 5 leads periods
df['rolling_sales_5'] = df['sales'].rolling(5).mean()

#view first 10 rows
df.head(10)

	period	    leads	    sales	rolling_sales_5	 rolling_leads_5
0	1	11.427457	61.417425	NaN	NaN
1	2	14.588598	64.900826	NaN	NaN
2	3	12.452504	66.698494	NaN	NaN
3	4	11.352780	64.927513	NaN	NaN
4	5	9.049441	73.720630	66.332978	 11.774156
5	6	13.271988	77.687668	69.587026	 12.143062
6	7	9.314157	78.125728	72.232007	 11.088174
7	8	17.943687	75.280301	73.948368	 12.186411
8	9	19.309592	73.181613	75.599188	 13.777773
9	10	8.285389	85.272259	77.909514	 13.624963

We can also create a quick line plot using Matplotlib to visualize the raw sales compared to the rolling mean of sales:

import matplotlib.pyplot as plt
plt.plot(df['rolling_sales_5'], label='Rolling Mean')
plt.plot(df['sales'], label='Raw Data')
plt.legend()
plt.ylabel('Sales')
plt.xlabel('Period')
plt.show()