# Aggregate pandas DataFrame by Group Column Value in Python (2 Examples)

In this tutorial you’ll learn how to aggregate a pandas DataFrame by a group indicator in the Python programming language.

## Creation of Example Data

`import pandas as pd # Import pandas`
```my_df = pd.DataFrame({'A':range(19, 28), # Constructing a pandas DataFrame 'B':[6, 7, 3, 9, 1, 3, 8, 8, 9], 'C':range(20, 11, - 1), 'GRP_a':['gr1', 'gr1', 'gr2', 'gr3', 'gr1', 'gr2', 'gr2', 'gr3', 'gr3'], 'GRP_b':['a', 'b', 'c', 'c', 'a', 'b', 'b', 'a', 'a']}) print(my_df) # A B C GRP_a GRP_b # 0 19 6 20 gr1 a # 1 20 7 19 gr1 b # 2 21 3 18 gr2 c # 3 22 9 17 gr3 c # 4 23 1 16 gr1 a # 5 24 3 15 gr2 b # 6 25 8 14 gr2 b # 7 26 8 13 gr3 a # 8 27 9 12 gr3 a```

## Example 1: Calculate Mean by One Group Indicator

```print(my_df.groupby('GRP_a').mean()) # Computing the column mean by group # A B C # GRP_a # gr1 20.666667 4.666667 18.333333 # gr2 23.333333 4.666667 15.666667 # gr3 25.000000 8.666667 14.000000```

## Example 2: Calculate Sum by Multiple Group Indicators

```print(my_df.groupby(['GRP_a', 'GRP_b']).sum()) # Computing the column sum by multiple groups # A B C # GRP_a GRP_b # gr1 a 42 7 36 # b 20 7 19 # gr2 b 49 11 29 # c 21 3 18 # gr3 a 53 17 25 # c 22 9 17```

## Further Resources & Related Articles

In the following, you may find some further resources on topics such as variables and dates: