Python - How to Group Pandas DataFrame by Year?

We will group Pandas DataFrame using the groupby(). Select the column to be used using the grouper function. We will group year-wise and calculate sum of Registration Price with year interval for our example shown below for Car Sale Records.

At first, let’s say the following is our Pandas DataFrame with three columns −

# dataframe with one of the columns as Date_of_Purchase
dataFrame = pd.DataFrame(
   {
      "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],

      "Date_of_Purchase": [pd.Timestamp("2021-06-10"),
         pd.Timestamp("2019-07-11"),
         pd.Timestamp("2016-06-25"),
         pd.Timestamp("2021-06-29"),
         pd.Timestamp("2020-03-20"),
         pd.Timestamp("2019-01-22"),
         pd.Timestamp("2011-01-06"),
         pd.Timestamp("2013-01-04"),
         pd.Timestamp("2014-05-09")
      ],
      "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
   }
)

Next, use the Grouper to select Date_of_Purchase column within groupby function. The frequency is set as 3Y i.e. interval of 3 Years grouped.

Example

Following is the code −

import pandas as pd

# dataframe with one of the columns as Date_of_Purchase
dataFrame = pd.DataFrame(
   {
      "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],

      "Date_of_Purchase": [pd.Timestamp("2021-06-10"),
         pd.Timestamp("2019-07-11"),
         pd.Timestamp("2016-06-25"),
         pd.Timestamp("2021-06-29"),
         pd.Timestamp("2020-03-20"),
         pd.Timestamp("2019-01-22"),
         pd.Timestamp("2011-01-06"),
         pd.Timestamp("2013-01-04"),
         pd.Timestamp("2014-05-09")
      ],

      "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
   }
)

print("DataFrame...\n",dataFrame)

# Grouper to select Date_of_Purchase column within groupby function
print("\nGroup Dataframe by 3 years...\n",dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', axis=0, freq='3Y')).sum())

Output

This will produce the following output −

DataFrame...
        Car   Date_of_Purchase Reg_Price
0      Audi         2021-06-10 1000
1     Lexus         2019-07-11 1400
2     Tesla         2016-06-25 1100
3  Mercedes         2021-06-29 900
4       BMW         2020-03-20 1700
5    Toyota         2019-01-22 1800
6    Nissan         2011-01-06 1300
7   Bentley         2013-01-04 1150
8   Mustang         2014-05-09 1350

Group Dataframe by 3 years...
Reg_Price
Date_of_Purchase
2011-12-31 1300
2014-12-31 2500
2017-12-31 1100
2020-12-31 4900
2023-12-31 1900