How to Group Pandas DataFrame Entries by Year in a Non-Unique Date Column

When working with a Pandas DataFrame containing datetime values in a non-unique “date” column, it’s common to need grouping based on specific time units like years. While the `groupby` function is a powerful tool, grouping directly by the “date” column splits the data by individual datetime values, which might not be what you desire, especially when aiming to aggregate data by year.

To achieve grouping by year in such cases, you need to extract the year component from the datetime values in the “date” column. This can be done by applying the `dt.year` attribute to the datetime values. Let’s walk through how to accomplish this with a practical example using a sample dataset.

1. Example Dataset.

1. Consider the following example dataset:

   |   date    |  value1  |  value2  |
   |-----------|----------|----------|
   |2022-01-15 |   10     |   20     |
   |2022-03-21 |   15     |   25     |
   |2023-02-08 |   12     |   18     |
   |2023-04-17 |   8      |   22     |
   

2. Solution.

1. We aim to group the DataFrame entries by the year component of the “date” column.
2. Here’s how you can achieve this using Pandas:

   import pandas as pd
   
   # Sample DataFrame
   data = pd.DataFrame({
       'date': ['2022-01-15', '2023-02-08', '2022-03-21', '2023-04-17'],
       'value1': [10, 15, 12, 8],
       'value2': [20, 25, 18, 22]
   })
   
   print('Original data:')
   print(data)
   print('')
   
   # Convert 'date' column to datetime dtype
   data['date'] = pd.to_datetime(data['date'])
   
   print('Data after converter:')
   print(data)
   print('')
   
   # Group by year extracted from 'date' column
   grouped_data = data.groupby(data['date'].dt.year)
   
   # Iterate over groups and perform desired operations
   for year, group in grouped_data:
       print(f"Year: {year}")
       print(group)
       print()
   

3. Output:
   

   Original data:
            date  value1  value2
   0  2022-01-15      10      20
   1  2023-02-08      15      25
   2  2022-03-21      12      18
   3  2023-04-17       8      22
   
   Data after converter:
           date  value1  value2
   0 2022-01-15      10      20
   1 2023-02-08      15      25
   2 2022-03-21      12      18
   3 2023-04-17       8      22
   
   Year: 2022
           date  value1  value2
   0 2022-01-15      10      20
   2 2022-03-21      12      18
   
   Year: 2023
           date  value1  value2
   1 2023-02-08      15      25
   3 2023-04-17       8      22

4. In this solution, we first ensure the “date” column is of datetime type. Then, we use `groupby` along with `dt.year` to group the DataFrame entries by the year component.
5. Finally, we iterate over the groups to perform any desired operations on each group.
6. By following these steps, you can effectively group Pandas DataFrame entries by year, even when dealing with non-unique datetime values in the “date” column.