In this tutorial, we will look at how to compute the rolling maximum in a pandas column.

## How to get rolling maximum in pandas?

You can use the pandas `rolling()`

function to get a rolling window of your desired size over the series and then apply the pandas `max()`

function to get the rolling maximum. The following is the syntax –

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

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 maximum. If you apply the above function on a pandas dataframe, it will result in a rolling max for all the numerical columns in the dataframe.

## Examples

Let’s look at some examples of using the above syntax to get the rolling maximum in pandas. 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 now have a dataframe containing the daily “PageViews” and “Revenue” of a blogging website.

### Rolling Max of a Pandas Series

Let’s get the maximum “PageViews” over a 3-day rolling window from the above data. For this, we apply the `rolling()`

function with a window size of `3`

and then apply the `max()`

function to get the maximum value over that window.

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

Output:

2020-03-01 NaN 2020-03-02 NaN 2020-03-03 180.0 2020-03-04 200.0 2020-03-05 240.0 2020-03-06 240.0 2020-03-07 240.0 Name: PageViews, dtype: float64

You can see that we get `NaN`

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

### Rolling max of multiple columns

If you apply the above function directly on a dataframe, it will compute the rolling maximum for all the numerical columns in the dataframe. For example, let’s get the 3-day rolling maximum over df

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

Output:

PageViews Revenue 2020-03-01 NaN NaN 2020-03-02 NaN NaN 2020-03-03 180.0 15.0 2020-03-04 200.0 20.0 2020-03-05 240.0 30.0 2020-03-06 240.0 30.0 2020-03-07 240.0 30.0

You can see that we get the rolling maximum for both the columns.

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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