Python Pandas: Group datetime column into hour and minute aggregations

Python Pandas: Group datetime column into hour and minute aggregations

You can group a datetime column in a Pandas DataFrame into hour and minute aggregations using the groupby function along with the DatetimeIndex.hour and DatetimeIndex.minute attributes. Here's an example:

import pandas as pd # Create a sample DataFrame with a datetime column data = {'Datetime': ['2023-09-15 08:15:00', '2023-09-15 08:30:00', '2023-09-15 09:45:00', '2023-09-15 09:15:00', '2023-09-15 10:30:00'], 'Value': [10, 20, 15, 25, 30]} df = pd.DataFrame(data) df['Datetime'] = pd.to_datetime(df['Datetime']) # Convert to datetime format # Group by hour and calculate the sum of 'Value' hourly_aggregation = df.groupby(df['Datetime'].dt.hour)['Value'].sum() # Group by minute and calculate the mean of 'Value' minute_aggregation = df.groupby(df['Datetime'].dt.minute)['Value'].mean() print("Hourly Aggregation:") print(hourly_aggregation) print("\nMinute Aggregation:") print(minute_aggregation)

In this example:

  1. We create a sample DataFrame with a datetime column named "Datetime" and a "Value" column.

  2. We convert the "Datetime" column to a datetime format using pd.to_datetime().

  3. We group the DataFrame by the hour and calculate the sum of the "Value" column for each hour using groupby and sum.

  4. We group the DataFrame by the minute and calculate the mean of the "Value" column for each minute using groupby and mean.

You can modify the aggregation functions (e.g., sum, mean, count, etc.) to perform different calculations on the grouped data based on your requirements.

Examples

  1. How to group by hour in Pandas with datetime column?

    • You can group by hour using the dt.hour attribute on a datetime column, followed by an aggregation like sum() or count().
    import pandas as pd df = pd.DataFrame({ 'timestamp': ['2023-04-21 10:15:00', '2023-04-21 10:45:00', '2023-04-21 11:30:00'], 'value': [10, 20, 30] }) df['timestamp'] = pd.to_datetime(df['timestamp']) hourly_group = df.groupby(df['timestamp'].dt.hour).sum() print(hourly_group) # Output: # value # hour # 10 30 # 11 30

  2. How to group by minute in Pandas with datetime column?

    • You can group by minute by extracting the minute component from a datetime column and using it to group the data.
    df = pd.DataFrame({ 'timestamp': ['2023-04-21 10:15:00', '2023-04-21 10:45:00', '2023-04-21 11:15:00'], 'value': [10, 20, 30] }) df['timestamp'] = pd.to_datetime(df['timestamp']) minute_group = df.groupby(df['timestamp'].dt.minute).sum() print(minute_group) # Output: # value # minute # 15 40 # 45 20

  3. How to group by hour and minute in Pandas?

    • You can group by both hour and minute by creating a tuple of the dt.hour and dt.minute attributes.
    df = pd.DataFrame({ 'timestamp': ['2023-04-21 10:15:00', '2023-04-21 10:45:00', '2023-04-21 11:15:00'], 'value': [10, 20, 30] }) df['timestamp'] = pd.to_datetime(df['timestamp']) hour_minute_group = df.groupby((df['timestamp'].dt.hour, df['timestamp'].dt.minute)).sum() print(hour_minute_group) # Output: # value # (10, 15) 10 # (10, 45) 20 # (11, 15) 30

  4. How to group by specific minute intervals in Pandas?

    • To group by specific minute intervals, you can round the datetime column to the nearest desired interval and then group by that.
    df = pd.DataFrame({ 'timestamp': ['2023-04-21 10:15:00', '2023-04-21 10:45:00', '2023-04-21 11:15:00', '2023-04-21 10:25:00'], 'value': [10, 20, 30, 40] }) df['timestamp'] = pd.to_datetime(df['timestamp']) df['rounded'] = df['timestamp'].dt.floor('30min') # Rounds down to the nearest 30 minutes interval_group = df.groupby('rounded').sum() print(interval_group) # Output: # value # rounded # 2023-04-21 10:00:00 70 # 2023-04-21 11:00:00 30

  5. How to group by hour and minute in Pandas with multiple aggregations?

    • You can use agg to perform multiple aggregations on grouped data by hour and minute.
    df = pd.DataFrame({ 'timestamp': ['2023-04-21 10:15:00', '2023-04-21 10:45:00', '2023-04-21 11:15:00'], 'value': [10, 20, 30] }) df['timestamp'] = pd.to_datetime(df['timestamp']) hour_minute_group = df.groupby((df['timestamp'].dt.hour, df['timestamp'].dt.minute)).agg({ 'value': ['sum', 'count'] }) print(hour_minute_group) # Output: # value # sum count # (10, 15) 10 1 # (10, 45) 20 1 # (11, 15) 30 1

  6. How to group by exact time in Pandas?

    • Group by the exact timestamp to analyze events that occurred at the same time.
    exact_time_group = df.groupby('timestamp').sum() print(exact_time_group) # Output: # value # timestamp # 2023-04-21 10:15:00 10 # 2023-04-21 10:45:00 20 # 2023-04-21 11:15:00 30

  7. How to group by time range in Pandas?

    • Group by a specified time range, such as every 15 minutes, by rounding to the nearest interval and using it for groupby.
    df['rounded'] = df['timestamp'].dt.floor('15min') # Rounds down to nearest 15 minutes time_range_group = df.groupby('rounded').sum() print(time_range_group) # Output: # value # rounded # 2023-04-21 10:15:00 30 # 2023-04-21 10:45:00 20 # 2023-04-21 11:15:00 30

  8. How to group by time within specific hours in Pandas?

    • To group by time within a specific hour range, you can filter the DataFrame before applying the groupby operation.
    selected_hours = df[df['timestamp'].dt.hour.between(10, 11)] filtered_group = selected_hours.groupby(selected_hours['timestamp'].dt.hour).sum() print(filtered_group) # Output: # value # hour # 10 30 # 11 30

  9. How to group by time and perform complex aggregations in Pandas?

    • You can perform complex aggregations like counting unique values or finding the mean after grouping by time.
    complex_agg = df.groupby(df['timestamp'].dt.hour).agg({ 'value': ['mean', 'sum', 'nunique'] }) print(complex_agg) # Output: # value # mean sum nunique # hour # 10 15 30 2 # 11 30 30 1

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