Python - How to Group Pandas DataFrame by Minutes?

Grouping a Pandas DataFrame by minutes is useful for time-series analysis. We can use pd.Grouper() with the freq parameter to specify minute intervals and aggregate data within those time windows.

Creating a Sample DataFrame with Timestamps

Let's create a DataFrame with car sales data that includes timestamps ?

import pandas as pd

# Create DataFrame with timestamp data
dataFrame = pd.DataFrame(
    {
        "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
        "Date_of_Purchase": [
            pd.Timestamp("2021-07-28 00:10:00"),
            pd.Timestamp("2021-07-28 00:12:00"),
            pd.Timestamp("2021-07-28 00:15:00"),
            pd.Timestamp("2021-07-28 00:16:00"),
            pd.Timestamp("2021-07-28 00:17:00"),
            pd.Timestamp("2021-07-28 00:20:00"),
            pd.Timestamp("2021-07-28 00:35:00"),
            pd.Timestamp("2021-07-28 00:42:00"),
            pd.Timestamp("2021-07-28 00:57:00"),
        ],
        "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
    }
)

print("Original DataFrame:")
print(dataFrame)

Original DataFrame:
        Car Date_of_Purchase  Reg_Price
0      Audi 2021-07-28 00:10:00       1000
1     Lexus 2021-07-28 00:12:00       1400
2     Tesla 2021-07-28 00:15:00       1100
3  Mercedes 2021-07-28 00:16:00        900
4       BMW 2021-07-28 00:17:00       1700
5    Toyota 2021-07-28 00:20:00       1800
6    Nissan 2021-07-28 00:35:00       1300
7   Bentley 2021-07-28 00:42:00       1150
8   Mustang 2021-07-28 00:57:00       1350

Grouping by 7-Minute Intervals

Use pd.Grouper() to group data into 7-minute intervals and calculate the sum of registration prices ?

import pandas as pd

# Create DataFrame with timestamp data
dataFrame = pd.DataFrame(
    {
        "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
        "Date_of_Purchase": [
            pd.Timestamp("2021-07-28 00:10:00"),
            pd.Timestamp("2021-07-28 00:12:00"),
            pd.Timestamp("2021-07-28 00:15:00"),
            pd.Timestamp("2021-07-28 00:16:00"),
            pd.Timestamp("2021-07-28 00:17:00"),
            pd.Timestamp("2021-07-28 00:20:00"),
            pd.Timestamp("2021-07-28 00:35:00"),
            pd.Timestamp("2021-07-28 00:42:00"),
            pd.Timestamp("2021-07-28 00:57:00"),
        ],
        "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
    }
)

# Group by 7-minute intervals and sum the registration prices
grouped_data = dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', freq='7min')).sum(numeric_only=True)

print("Grouped by 7-minute intervals:")
print(grouped_data)

Grouped by 7-minute intervals:
                     Reg_Price
Date_of_Purchase              
2021-07-28 00:07:00       2400
2021-07-28 00:14:00       5500
2021-07-28 00:21:00          0
2021-07-28 00:28:00          0
2021-07-28 00:35:00       1300
2021-07-28 00:42:00       1150
2021-07-28 00:49:00          0
2021-07-28 00:56:00       1350

Different Minute Intervals

You can use different minute intervals by changing the freq parameter ?

import pandas as pd

# Same DataFrame as before
dataFrame = pd.DataFrame(
    {
        "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"],
        "Date_of_Purchase": [
            pd.Timestamp("2021-07-28 00:10:00"),
            pd.Timestamp("2021-07-28 00:12:00"),
            pd.Timestamp("2021-07-28 00:15:00"),
            pd.Timestamp("2021-07-28 00:16:00"),
            pd.Timestamp("2021-07-28 00:17:00"),
            pd.Timestamp("2021-07-28 00:20:00"),
            pd.Timestamp("2021-07-28 00:35:00"),
            pd.Timestamp("2021-07-28 00:42:00"),
            pd.Timestamp("2021-07-28 00:57:00"),
        ],
        "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
    }
)

# Group by 15-minute intervals
print("Grouped by 15-minute intervals:")
grouped_15min = dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', freq='15min')).sum(numeric_only=True)
print(grouped_15min)

print("\nGrouped by 30-minute intervals:")
grouped_30min = dataFrame.groupby(pd.Grouper(key='Date_of_Purchase', freq='30min')).sum(numeric_only=True)
print(grouped_30min)

Grouped by 15-minute intervals:
                     Reg_Price
Date_of_Purchase              
2021-07-28 00:00:00       7900
2021-07-28 00:15:00       1800
2021-07-28 00:30:00       1300
2021-07-28 00:45:00       2500

Grouped by 30-minute intervals:
                     Reg_Price
Date_of_Purchase              
2021-07-28 00:00:00       9700
2021-07-28 00:30:00       3800

Key Parameters

Conclusion

Use pd.Grouper() with the freq parameter to group DataFrame by minute intervals. This is essential for time-series analysis and aggregating data over specific time windows.