Python Running Mean with Pandas

As a programmer, you might come across a situation where you need to calculate a running mean (also known as moving average) of a data series. This can be achieved in Python using the Pandas library.

What is a Running Mean?

A running mean is a calculation of the mean of a series of data points over time. It is useful in smoothing out fluctuations in the data and providing a clearer trend. The calculation involves taking a window of a fixed size (usually odd), moving it across the data series, and calculating the mean of the values within the window at each position.

For example, if we have a series of data points [1, 2, 3, 4, 5, 6, 7, 8, 9] and a window size of 3, the running mean calculation would look like this:

[1, 2, 3]
  [2, 3, 4]
    [3, 4, 5]
      [4, 5, 6]
        [5, 6, 7]
          [6, 7, 8]
            [7, 8, 9]

The resulting running mean series would be [2, 3, 4, 5, 6, 7, 8].

Calculating a Running Mean with Pandas

To calculate a running mean in Python using Pandas, we first need to create a DataFrame object containing our data series. For example:

import pandas as pd

data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
df = pd.DataFrame(data, columns=['value'])

Next, we can use the rolling() method of the DataFrame object to define our window and calculate the mean. This method takes several arguments, such as the window size and the type of calculation (e.g. mean, median, etc.). For example, to calculate the running mean with a window size of 3, we can do:

rolling_mean = df['value'].rolling(window=3).mean()

The resulting rolling_mean variable will contain the running mean series.

Conclusion

In this brief introduction, we covered what a running mean is and how to calculate it in Python using the Pandas library. This technique can be useful in many data analysis and visualization tasks.