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Grouping by year in a Pandas DataFrame is a way to organize and summarize data according to time periods. This can be done by using the Pandas groupby() function, which takes a column name as an argument and groups the DataFrame by that column. For example, if you have a DataFrame containing sales data for different years, you can group by the ‘Year’ column to get the total sales for each year. This can be useful for creating visualizations or for further analysis.
You can use the following basic syntax to group rows by year in a pandas DataFrame:
df.groupby(df.your_date_column.dt.year)['values_column'].sum()
This particular formula groups the rows by date in your_date_column and calculates the sum of values for the values_column in the DataFrame.
Note that the dt.year() function extracts the year from a date column in pandas.
The following example shows how to use this syntax in practice.
Example: How to Group by Year in Pandas
Suppose we have the following pandas DataFrame that shows the sales made by some company on various dates:
import pandas as pd
#create DataFrame
df = pd.DataFrame({'date': pd.date_range(start='1/1/2020', freq='3m', periods=10),
'sales': [6, 8, 9, 11, 13, 8, 8, 15, 22, 9],
'returns': [0, 3, 2, 2, 1, 3, 2, 4, 1, 5]})
#view DataFrame
print(df)
date sales returns
0 2020-01-31 6 0
1 2020-04-30 8 3
2 2020-07-31 9 2
3 2020-10-31 11 2
4 2021-01-31 13 1
5 2021-04-30 8 3
6 2021-07-31 8 2
7 2021-10-31 15 4
8 2022-01-31 22 1
9 2022-04-30 9 5 Related:
We can use the following syntax to calculate the sum of sales grouped by year:
#calculate sum of sales grouped by year
df.groupby(df.date.dt.year)['sales'].sum()
date
2020 34
2021 44
2022 31
Name: sales, dtype: int64 Here’s how to interpret the output:
- The total sales made during 2020 was 34.
- The total sales made during 2021 was 44.
- The total sales made during 2022 was 31.
We can use similar syntax to calculate the max of the sales values grouped by year:
#calculate max of sales grouped by year
df.groupby(df.date.dt.year)['sales'].max()
date
2020 11
2021 15
2022 22
Name: sales, dtype: int64 We can use similar syntax to calculate any value we’d like grouped by the year value of a date column.
Note: You can find the complete documentation for the GroupBy operation in pandas .