Rolling window statistics are very frequently used in analyzing and smoothing time-series data. In this tutorial, we will look at how to get the rolling sum (over a specified rolling window) in pandas columns.

## Rolling sum using pandas `rolling().sum()`

You can use the pandas `rolling()`

function to get a rolling window over a pandas series and then apply the `sum()`

function to get the rolling sum over the window. The following is the syntax:

# s is pandas series, n is the window size s.rolling(n).sum()

Here, `n`

is the size of the moving window you want to use, that is, the number of observations you want to use to compute the rolling statistic, in our case, the sum. If you apply the above function on a pandas dataframe, it will result in a rolling sum for all the numerical columns in the dataframe.

## Examples

Let’s look at some examples of using the above syntax. First, let’s create a sample dataframe that we will be using throughout this tutorial.

import pandas as pd # create dataframe df = pd.DataFrame({'PageViews': [100, 120, 180, 200, 240, 160, 130], 'Revenue': [10, 15, 12, 20, 30, 22, 14]}, index = ['2020-03-01', '2020-03-02', '2020-03-03', '2020-03-04',\ '2020-03-05', '2020-03-06', '2020-03-07']) # print the dataframe print(df)

Output:

PageViews Revenue 2020-03-01 100 10 2020-03-02 120 15 2020-03-03 180 12 2020-03-04 200 20 2020-03-05 240 30 2020-03-06 160 22 2020-03-07 130 14

We have a dataframe containing the daily pageviews and revenue for a blogging website.

### Rolling sum of a pandas series

Let’s get the 3-day rolling sum of the “PageViews” columns. For this, we apply the `rolling()`

function with a window size of `3`

and then apply the `sum()`

function.

# 3-day rolling sum of PageViews df['PageViews'].rolling(3).sum()

Output:

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2020-03-01 NaN 2020-03-02 NaN 2020-03-03 400.0 2020-03-04 500.0 2020-03-05 620.0 2020-03-06 600.0 2020-03-07 530.0 Name: PageViews, dtype: float64

You can see that we get `NaN`

in the first two rows because we cannot calculate the rolling sum as there are no preceding values to make the three-day window complete. The third row is 400 which is the sum of pageviews over the three-day window containing 100, 120, and 180.

### Rolling sum of multiple columns

If you apply the same function directly on a dataframe instead of individual columns, it will compute the rolling sum for all the numerical columns in the dataframe. For example, let’s get the 3-day rolling sum of all columns in df

# 3-day rolling sum of entire dataframe df df.rolling(3).sum()

Output:

PageViews Revenue 2020-03-01 NaN NaN 2020-03-02 NaN NaN 2020-03-03 400.0 37.0 2020-03-04 500.0 47.0 2020-03-05 620.0 62.0 2020-03-06 600.0 72.0 2020-03-07 530.0 66.0

You can see that we get the 3-day rolling sum for both the column.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

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