Adventures in Machine Learning

Mastering Rolling Mean Calculation in Pandas for Time Series Analysis

Rolling Mean Calculation in Pandas

Are you familiar with the concept of a rolling mean calculation in Pandas? If not, fear not, because we’re about to dive into this data manipulation technique and show you how to use it effectively to analyze your data.

Syntax for Calculating Rolling Mean in Pandas

The rolling mean calculation in Pandas is an essential tool for time series analysis. It allows you to calculate the average value of a set of data over a specified rolling window, which is a sliding time interval.

The syntax for this calculation in Pandas is as follows:


Where `dataframe` is the name of your data set, and `window_size` is the number of data points to use to calculate the moving average.

Example: Calculating the Rolling Mean

Let’s consider an example to help you understand how to calculate the rolling mean.

Suppose we have a Pandas DataFrame that contains daily sales records for a company, and we want to calculate the moving average of sales over a week.


import pandas as pd

data = {‘Date’: [‘2021-06-01’, ‘2021-06-02’, ‘2021-06-03’, ‘2021-06-04’, ‘2021-06-05’, ‘2021-06-06’, ‘2021-06-07’, ‘2021-06-08’],

‘Sales’: [100, 120, 80, 90, 110, 120, 150, 200]}

df = pd.DataFrame(data)

df[‘Date’] = pd.to_datetime(df[‘Date’])

df.set_index(‘Date’, inplace=True)

weekly_mean = df[‘Sales’].rolling(window=7).mean()



In this example, we first import the necessary pandas library. Then we create a dictionary named `data` that contains the sales records, which we then convert into a Pandas DataFrame named `df`.

To set the date column as the index, we use the `pd.to_datetime` function and `set_index`. Finally, we calculate the rolling mean over a week using the `rolling()` method and store it in a new DataFrame named `weekly_mean`.

We print the `weekly_mean` DataFrame to verify our results.

Manual Verification of the Rolling Mean

As you can see in the output, the rolling mean calculation has been successfully applied, and the moving average values have been computed. However, we can double-check the results manually.

We take the first data point of June 8th, which has the value of 200. The seven observations preceding this data point (June 1 to June 7) have the following sales values: 100, 120, 80, 90, 110, 120, and 150.

When we calculate the average of these seven points (which is the same as the rolling mean value of June 8th), we get 112.86, which corresponds to the value displayed in the DataFrame.

Creating Rolling Mean for Multiple Columns

You can apply the rolling mean calculation to multiple columns in your DataFrame by specifying the columns’ names inside the brackets. For example, if you wanted to calculate the rolling mean of both sales and profit columns, you would modify the calculation as follows:


dataframe[[‘Sales’, ‘Profit’]].rolling(window_size).mean()


Visualization of Rolling Mean Using Matplotlib

Now that we’ve discussed the rolling mean calculation in Pandas, let’s move on to visualizing the results using Matplotlib.

Creating a Line Plot Using Matplotlib

To create a line plot showing the rolling mean over time, we first import the necessary libraries and plot the original data before plotting the rolling mean values on top:


import matplotlib.pyplot as plt

plt.plot(df[‘Sales’], label=’Sales’)

plt.plot(weekly_mean, label=’Weekly Mean’)

plt.legend(loc=’upper left’)


In this example, we first import `Matplotlib` and plot the original sales data using the `plt.plot()` method. We then plot the rolling mean on top of the original data using the same method.

We add a legend and display the plot using the `` method.

Interpreting the Line Plot

The line plots show the original sales data in blue and the rolling mean values in orange. We can see that the sales data fluctuates significantly from day to day, which makes it hard to identify any trends.

However, when we plot the rolling mean, we can see a smoother line that more accurately reflects the overall trend. In this case, we can see that sales have been steadily increasing over time.


In conclusion, calculating the rolling mean in Pandas can be an effective tool to analyze time series data. It enables you to calculate the moving average of values over a specific window and generate more reliable trend lines.

Furthermore, Matplotlib provides an excellent tool to visualize the results using line plots. We hope that this guide has provided you with the knowledge required to use these tools effectively in your data analysis endeavors.

Additional Resources

Now that we’ve covered the basics of rolling mean calculations in Pandas and visualizing them using Matplotlib, let’s take a look at some further reading materials that can help you deepen your understanding of these concepts and learn more advanced techniques.

Further Readings on Rolling Mean

1. Time Series Analysis and Data Wrangling with Pandas by Armando Fandango

This book provides a comprehensive guide to working with time series data in Pandas, including calculating rolling statistics, resampling, and handling missing data.

It covers the fundamental concepts of time series analysis and walks you through common techniques for data wrangling, visualization, and modeling. 2.

Mastering Pandas by Femi Anthony

This book offers an in-depth exploration of Pandas and its capabilities for data manipulation and analysis, including rolling mean calculations. It covers advanced topics such as time series analysis, aggregation, and grouping data, and other data manipulation techniques.

3. Python for Data Analysis by Wes McKinney

This book offers a comprehensive guide to data analysis using Python, with an emphasis on Pandas.

It covers a wide range of topics, including reading and writing data, manipulation, data cleaning, visualization, and analysis. The chapter on time series data covers the basics of rolling statistics and provides examples of how to use them in practice.

4. Data Wrangling with Pandas by Kevin Markham

This online course covers the basics of data wrangling with Pandas, including calculating rolling statistics.

It contains video lectures, coding exercises, and quizzes to help you master the material. The course is suitable for beginners and offers a solid foundation for working with time series data in Pandas.

5. Pandas for Time Series Data Analysis by David Taieb

This tutorial series provides a detailed guide to using Pandas for time series data analysis, including calculating rolling window functions.

It covers topics such as resampling, shifting, and rolling calculations. The series contains Jupyter Notebooks with code examples and interactive widgets to help you get hands-on experience with the material.

In conclusion, Pandas and Matplotlib offer powerful tools for analyzing time series data and visualizing it effectively. There are plenty of resources available online to help you deepen your understanding of these concepts and apply them to real-world problems.

With the resources listed above and a willingness to learn and experiment, you can become proficient in these techniques and gain valuable insights from your data. In this article, we explored the concept of rolling mean calculation in Pandas and visualizing the results using Matplotlib.

By calculating the moving average of a set of data over a specified rolling window, we can identify trends and patterns that may not be apparent in the original data. Furthermore, line plots generated by Matplotlib provide a great tool for visualizing the results.

To deepen your understanding of these concepts and learn more advanced techniques, there are numerous resources available, including books, online courses, and tutorials. In conclusion, data analysis using Pandas and Matplotlib is an important and powerful technique in time series analysis.

With the right resources and a willingness to learn, anyone can become proficient in these techniques and gain valuable insights from their data.

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