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Grouping by quarter in a Pandas DataFrame is a useful way to analyze time-series data. To do this, use the Pandas resample() method and pass ‘Q’ as the argument to group the data by quarter. The resample() method will then return a new DataFrame with the dates grouped into different quarter periods. An example of this would be to resample a DataFrame with monthly data into quarters by using the resample(‘Q’) method. This can be useful for plotting the data or performing further analysis.
You can use the following basic syntax to group rows by quarter in a pandas DataFrame:
#convert date column to datetime df['date'] = pd.to_datetime(df['date']) #calculate sum of values, grouped by quarter df.groupby(df['date'].dt.to_period('Q'))['values'].sum()
This particular formula groups the rows by quarter in the date column and calculates the sum for the values column in the DataFrame.
The following example shows how to use this syntax in practice.
Example: How to Group by Quarter 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='1/1/2022', freq='M', periods=12),
'sales': [6, 8, 10, 5, 4, 8, 8, 3, 5, 14, 8, 3]})
#view DataFrame
print(df)
date sales
0 2022-01-31 6
1 2022-02-28 8
2 2022-03-31 10
3 2022-04-30 5
4 2022-05-31 4
5 2022-06-30 8
6 2022-07-31 8
7 2022-08-31 3
8 2022-09-30 5
9 2022-10-31 14
10 2022-11-30 8
11 2022-12-31 3 Related:
We can use the following syntax to calculate the sum of sales grouped by quarter:
#convert date column to datetime and subtract one week df['date'] = pd.to_datetime(df['date']) #calculate sum of sales, grouped by quarter df.groupby(df['date'].dt.to_period('Q'))['sales'].sum() date 2022Q1 24 2022Q2 17 2022Q3 16 2022Q4 25 Freq: Q-DEC, Name: sales, dtype: int64
Here’s how to interpret the output:
- There were 24 total sales made during the first quarter.
- There were 17 total sales made during the second quarter.
- There were 16 total sales made during the third quarter.
- There were 25 total sales made during the fourth quarter.
We can use similar syntax to calculate some other metric, grouped by quarter.
For example, we could instead calculate the max value of sales, grouped by quarter:
#convert date column to datetime df['date'] = pd.to_datetime(df['date']) #calculate max of sales, grouped by quarter df.groupby(df['date'].dt.to_period('Q'))['sales'].max() date 2022Q1 10 2022Q2 8 2022Q3 8 2022Q4 14 Freq: Q-DEC, Name: sales, dtype: int64
Here’s how to interpret the output:
- The max sales on an individual month during the first quarter was 10.
- The max sales on an individual month during the second quarter was 8.
- The max sales on an individual month during the third quarter was 8.
- The max sales on an individual month during the fourth quarter was 14.