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Calculating a rolling maximum in Pandas can be done using the Pandas rolling() function with the max() aggregate function. The rolling() function requires the window size and the min_periods as arguments. The window size is the number of observations used for calculating the statistic and the min_periods is the minimum number of observations in the window required to have a value. This calculation can be applied to a Series or DataFrame along either axis. Examples of rolling maximum calculations are provided to illustrate how the rolling() function can be used.
You can use the following methods to calculate a rolling maximum value in a pandas DataFrame:
Method 1: Calculate Rolling Maximum
df['rolling_max'] = df.values_column.cummax()
Method 2: Calculate Rolling Maximum by Group
df['rolling_max'] = df.groupby('group_column').values_column.cummax()
The following examples show how to use each method in practice.
Example 1: Calculate Rolling Maximum
Suppose we have the following pandas DataFrame that shows the sales made each day at some store:
import pandas as pd #create DataFrame df = pd.DataFrame({'day': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'sales': [4, 6, 5, 8, 14, 13, 13, 12, 9, 8, 19, 14]}) #view DataFrame print(df) day sales 0 1 4 1 2 6 2 3 5 3 4 8 4 5 14 5 6 13 6 7 13 7 8 12 8 9 9 9 10 8 10 11 19 11 12 14
We can use the following syntax to create a new column that displays the rolling maximum value of sales:
#add column that displays rolling maximum of sales df['rolling_max'] = df.sales.cummax() #view updated DataFrame print(df) day sales rolling_max 0 1 4 4 1 2 6 6 2 3 5 6 3 4 8 8 4 5 14 14 5 6 13 14 6 7 13 14 7 8 12 14 8 9 9 14 9 10 8 14 10 11 19 19 11 12 14 19
The new column titled rolling_max displays the rolling maximum value of sales.
Example 2: Calculate Rolling Maximum by Group
Suppose we have the following pandas DataFrame that shows the sales made each day at two different stores:
import pandas as pd #create DataFrame df = pd.DataFrame({'store': ['A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B'], 'day': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'sales': [4, 6, 5, 8, 14, 13, 13, 12, 9, 8, 19, 14]}) #view DataFrame print(df) store day sales 0 A 1 4 1 A 2 6 2 A 3 5 3 A 4 8 4 A 5 14 5 A 6 13 6 B 7 13 7 B 8 12 8 B 9 9 9 B 10 8 10 B 11 19 11 B 12 14
We can use the following syntax to create a new column that displays the rolling maximum value of sales grouped by store:
#add column that displays rolling maximum of sales grouped by store df['rolling_max'] = df.groupby('store').sales.cummax() #view updated DataFrame print(df) store day sales rolling_max 0 A 1 4 4 1 A 2 6 6 2 A 3 5 6 3 A 4 8 8 4 A 5 14 14 5 A 6 13 14 6 B 7 13 13 7 B 8 12 13 8 B 9 9 13 9 B 10 8 13 10 B 11 19 19 11 B 12 14 19