Rolling window estimates can be very useful when working with time-series data. They are quite frequently used in finance, for example, to smooth out a value over a rolling window using a rolling mean. In this tutorial, we will look at how to calculate rolling estimates like the rolling mean in a pandas dataframe.

## The pandas `rolling()`

function

You can use the pandas `rolling()`

function to get a rolling window for computing the rolling estimates. The following is the syntax:

# get rolling mean df.rolling(n).mean()

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 a rolling statistic. Also, note that the above will result in a rolling mean for all the numerical columns of the dataframe df. If you want to compute the rolling mean of a specific column, use the following syntax:

# get rolling mean for Col1 df['Col1'].rolling(n).mean()

## Examples

Let’s look at some examples of using the pandas `rolling()`

function to compute rolling window estimates. First, we’ll create a sample dataframe with just one column.

import pandas as pd # create dataframe df = pd.DataFrame({'PageViews': [100, 120, 180, 200, 240, 160, 130]}, 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 2020-03-01 100 2020-03-02 120 2020-03-03 180 2020-03-04 200 2020-03-05 240 2020-03-06 160 2020-03-07 130

The dataframe `df`

stores the daily pageviews of a blogging website.

### Rolling mean on pandas dataframe

Let’s calculate the 3 day rolling mean of the number of page views the website gets.

# rolling mean of pageviews print(df.rolling(3).mean())

Output:

PageViews 2020-03-01 NaN 2020-03-02 NaN 2020-03-03 133.333333 2020-03-04 166.666667 2020-03-05 206.666667 2020-03-06 200.000000 2020-03-07 176.666667

You can see that we get `NaN`

in the first two rows because we cannot calculate the rolling mean as there are no preceding values to make the three-day window complete. The third row is 133.33 which is the average pageviews over the previous three days.

You can similarly use the `rolling()`

function to calculate other rolling window estimates like the rolling median, rolling sum, etc. Let’s calculate the rolling the sum of the 3 day window on the above dataframe.

# rolling sum of pageviews print(df.rolling(3).sum())

Output:

PageViews 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

Similar to the rolling mean example above, we get `NaN`

for the first two rows. From the third row, the value in each row is a rolling sum of the previous three values.

### Rolling mean on dataframe with multiple columns

What happens if we apply a rolling window function to a dataframe with multiple columns? Let’s find out. First, we’ll add an additional column representing the Revenue (in dollars) generated by the website on the corresponding date.

# add column for revenue df['Revenue'] = [10, 15, 12, 20, 30, 22, 14] # 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

Now, let’s apply a rolling mean function on the dataframe for a three-day window.

# rolling mean print(df.rolling(3).mean())

Output:

PageViews Revenue 2020-03-01 NaN NaN 2020-03-02 NaN NaN 2020-03-03 133.333333 12.333333 2020-03-04 166.666667 15.666667 2020-03-05 206.666667 20.666667 2020-03-06 200.000000 24.000000 2020-03-07 176.666667 22.000000

Applying the pandas `rolling()`

function on the entire dataframe results in the rolling window estimate of all the columns for which it can be calculated (that is, all the numerical columns for rolling mean).

If you want to calculate the rolling estimate for a specific column you can apply the rolling function on the column itself instead of the entire dataframe.

# rolling mean of Revenue print(df['Revenue'].rolling(3).mean())

Output:

2020-03-01 NaN 2020-03-02 NaN 2020-03-03 12.333333 2020-03-04 15.666667 2020-03-05 20.666667 2020-03-06 24.000000 2020-03-07 22.000000 Name: Revenue, dtype: float64

For more on the pandas `rolling()`

function, refer to its documentation.

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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