Combining Multiple Columns in Pandas groupby with Dictionary

Last Updated : 11 Jul, 2025

Combining multiple columns in Pandas groupby operation with a dictionary helps to aggregate and summarize the data in a custom manner. It is useful when you want to apply different aggregation functions to different columns of the same dataset.

Let's take an example of a sales dataset, where we need to group the data by Store column and then apply different aggregation functions to Sales and Quantity:

df.groupby('Column name'): Groups the data by the column.
.agg(agg_dict): Applies the aggregation functions specified in the dictionary. For Sales, it sums the values, and for Quantity, it calculates the mean.
.reset_index(): Resets the index to get a clean DataFrame after aggregation.

Python

import pandas as pd

# Sample DataFrame
data = {
    'Store': ['A', 'A', 'B', 'B', 'A', 'B'],
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple', 'Banana'],
    'Sales': [100, 150, 200, 100, 120, 180],
    'Quantity': [10, 20, 30, 40, 15, 35]
}

df = pd.DataFrame(data)
display(df)

# Grouping by 'Store' and applying different aggregation functions
agg_dict = {
    'Sales': 'sum',          # Sum the 'Sales' column
    'Quantity': 'mean'       # Find the mean of the 'Quantity' column
}

result = df.groupby('Store').agg(agg_dict).reset_index()

print("\n",result)

Output:

Combining Multiple Columns in Pandas groupby with Dictionary — Pandas DataFrame

Groupby-Store — Grouped by 'Store' and Applying Aggregation

Multiple Aggregations using GroupBy in Pandas DataFrame

You can also apply more complex or multiple aggregation functions to the same column. For example:

Python

agg_dict = {
    'Sales': ['sum', 'mean'],       # Sum and mean for 'Sales'
    'Quantity': ['max', 'min']      # Max and min for 'Quantity'
}

result = df.groupby('Store').agg(agg_dict).reset_index()

print(result)

Output:

  Store Sales             Quantity    
          sum        mean      max min
0     A   370  123.333333       20  10
1     B   480  160.000000       40  30

The aggregation dictionary specifies multiple functions for both Sales and Quantity. The result includes the sum and mean for Sales, and the maximum and minimum for Quantity.

Custom Aggregation Functions using GroupBy Function

If you want to use a custom aggregation function, you can pass a function name or lambda function inside the dictionary:

Python

agg_dict = {
    'Sales': lambda x: x.max() - x.min(),  # Custom function: range of 'Sales'
    'Quantity': 'sum'                     # Sum for 'Quantity'
}

result = df.groupby('Store').agg(agg_dict).reset_index()

print(result)

Output:

  Store  Sales  Quantity
0     A     50        45
1     B    100       105

The custom function lambda x: x.max() - x.min() computes the range of Sales, while Quantity is summed.

Using a dictionary with groupby in Pandas makes it easy to perform multiple aggregations on different columns in one go. It enhances code readability, reduces complexity, and provides a flexible way to manipulate your data.

Python | Pandas Merging, Joining and Concatenating

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Article Tags :

Combining Multiple Columns in Pandas groupby with Dictionary

Multiple Aggregations using GroupBy in Pandas DataFrame

Custom Aggregation Functions using GroupBy Function

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