Multiple aggregations of the same column using pandas GroupBy.agg()

Multiple aggregations of the same column using pandas GroupBy.agg()

You can perform multiple aggregations of the same column using the agg() method in pandas' GroupBy object. To do this, you can pass a dictionary to the agg() method, where the keys are the column names, and the values are the aggregation functions you want to apply to each column. Here's an example:

import pandas as pd

# Create a sample DataFrame
data = {
    'Category': ['A', 'A', 'B', 'B', 'A'],
    'Value': [10, 20, 5, 15, 30]
}

df = pd.DataFrame(data)

# Group by 'Category' and perform multiple aggregations on 'Value'
result = df.groupby('Category')['Value'].agg({
    'Total': 'sum',
    'Average': 'mean',
    'Max': 'max',
    'Min': 'min'
}).reset_index()

print(result)

In this example, we group the DataFrame df by the 'Category' column and then use the agg() method to perform multiple aggregations on the 'Value' column. We create a dictionary where the keys are the names of the new aggregated columns, and the values are the aggregation functions applied to the 'Value' column.

The result will be a DataFrame with the 'Category' column and the aggregated columns 'Total', 'Average', 'Max', and 'Min':

  Category  Total  Average  Max  Min
0        A     60     20.0   30   10
1        B     20     10.0   15    5

You can customize the aggregation functions and the names of the aggregated columns in the dictionary as needed for your specific use case.

Examples

  1. "Pandas GroupBy multiple aggregations"

    • Description: Users often seek ways to apply multiple aggregation functions to the same column when using Pandas' GroupBy object. This query suggests they are looking for a solution to perform various aggregations simultaneously.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data['target_column'].agg(['sum', 'mean', 'max'])

  2. "Pandas GroupBy agg with different functions"

    • Description: This query indicates users want to utilize different aggregation functions within the GroupBy.agg() method in Pandas.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data['target_column'].agg({'sum': sum, 'mean': np.mean, 'max': max})

  3. "Pandas GroupBy multiple aggregation columns"

    • Description: Users seek information on how to apply multiple aggregations across different columns simultaneously.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data.agg({'column1': 'sum', 'column2': 'mean'})

  4. "Combine multiple aggregate functions in Pandas"

    • Description: This query implies users are interested in combining various aggregation functions into a single operation when using Pandas.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data['target_column'].agg(['sum', np.mean, 'max'])

  5. "Perform multiple aggregations on Pandas DataFrame"

    • Description: Users are looking for ways to perform multiple aggregations on a Pandas DataFrame, likely using the GroupBy.agg() method.
    # Code Implementation
aggregated_data = df.groupby('column_name').agg({'target_column': ['sum', 'mean', 'max']})

  6. "GroupBy agg with custom functions"

    • Description: Users are interested in applying custom aggregation functions within the Pandas GroupBy.agg() method.
    # Code Implementation
def custom_function(arr):
    return arr.max() - arr.min()

grouped_data = df.groupby('column_name')
aggregated_data = grouped_data['target_column'].agg(['sum', custom_function])

  7. "Pandas GroupBy multiple aggregations on one column"

    • Description: This query suggests users want to apply multiple aggregation functions to a single column using Pandas' GroupBy functionality.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data['target_column'].agg(['sum', 'mean', 'max'])

  8. "Apply sum and mean to same column in GroupBy"

    • Description: Users are specifically interested in applying both the sum and mean aggregation functions to the same column within a GroupBy operation.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data['target_column'].agg({'sum': 'sum', 'mean': 'mean'})

  9. "GroupBy agg multiple functions to multiple columns"

    • Description: This query indicates users want to apply multiple aggregation functions to different columns simultaneously using Pandas' GroupBy.agg() method.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data.agg({'column1': ['sum', 'mean'], 'column2': ['max', 'min']})

  10. "Combine sum and max functions in Pandas GroupBy"

    • Description: Users are interested in combining the sum and max aggregation functions within a GroupBy operation using Pandas.
    # Code Implementation
grouped_data = df.groupby('column_name')
aggregated_data = grouped_data['target_column'].agg({'sum': sum, 'max': max})

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