Python Pandas: How to GroupBy One Column and Get Mean, Min, and Max Values in Pandas

Learn how to use Pandas' groupby() with agg() to efficiently calculate mean, minimum, and maximum values for grouped data in Python DataFrames.

Grouping data by a category and computing summary statistics is one of the most fundamental operations in data analysis. Pandas' groupby() function lets you split a DataFrame into groups based on one or more columns, then apply aggregation functions like mean, min, and max to each group - all in a single, efficient operation.

In this guide, you will learn how to group a DataFrame by a column and calculate multiple aggregate statistics simultaneously using the agg() method.

Basic Syntax

result = df.groupby('column_name').agg({'value_column': ['mean', 'min', 'max']})

groupby('column_name'): Splits the DataFrame into groups based on unique values in the specified column.
agg(): Applies one or more aggregation functions to the specified columns.
['mean', 'min', 'max']: The list of functions to compute for each group.

Example 1: Vehicle Top Speeds by Type

import pandas as pd

df = pd.DataFrame({
    'Type': ['Bike', 'Bike', 'Car', 'Car', 'Bike', 'Car', 'Bike'],
    'Name': ['Kawasaki', 'Ducati Panigale', 'Bugatti Chiron',
             'Jaguar XJ220', 'Lightning LS-218',
             'Hennessey Venom GT', 'BMW S1000RR'],
    'TopSpeed_MPH': [186, 202, 304, 210, 218, 270, 188]
})

print("Original DataFrame:")
print(df)

Output:

Original DataFrame:
   Type                Name  TopSpeed_MPH
0  Bike            Kawasaki           186
1  Bike     Ducati Panigale           202
2   Car      Bugatti Chiron           304
3   Car        Jaguar XJ220           210
4  Bike    Lightning LS-218           218
5   Car  Hennessey Venom GT           270
6  Bike         BMW S1000RR           188

Now group by Type and compute the mean, min, and max of TopSpeed_MPH:

import pandas as pd

df = pd.DataFrame({
    'Type': ['Bike', 'Bike', 'Car', 'Car', 'Bike', 'Car', 'Bike'],
    'Name': ['Kawasaki', 'Ducati Panigale', 'Bugatti Chiron',
             'Jaguar XJ220', 'Lightning LS-218',
             'Hennessey Venom GT', 'BMW S1000RR'],
    'TopSpeed_MPH': [186, 202, 304, 210, 218, 270, 188]
})


result = df.groupby('Type').agg({'TopSpeed_MPH': ['mean', 'min', 'max']})

print("Top Speed statistics grouped by Vehicle Type:")
print(result)

Output:

Top Speed statistics grouped by Vehicle Type:
     TopSpeed_MPH          
             mean  min  max
Type                       
Bike   198.500000  186  218
Car    261.333333  210  304

Bikes average 198.5 mph while cars average 261.3 mph. The fastest vehicle overall is a car (Bugatti Chiron at 304 mph), while the fastest bike reaches 218 mph.

Example 2: Sales Data by Salesman

import pandas as pd

sales = pd.DataFrame({
    'customer_id': [3005, 3001, 3002, 3009, 3005, 3007,
                    3002, 3004, 3009, 3008, 3003, 3002],
    'salesman_id': [102, 105, 101, 103, 102, 101,
                    101, 106, 103, 102, 107, 101],
    'purchase_amt': [1500, 2700, 1525, 1100, 948, 2400,
                     5700, 2000, 1280, 2500, 750, 5050]
})

result = sales.groupby('salesman_id').agg({'purchase_amt': ['mean', 'min', 'max']})

print("Purchase Amount statistics grouped by Salesman ID:")
print(result)

Output:

Purchase Amount statistics grouped by Salesman ID:
            purchase_amt            
                    mean   min   max
salesman_id                         
101          3668.750000  1525  5700
102          1649.333333   948  2500
103          1190.000000  1100  1280
105          2700.000000  2700  2700
106          2000.000000  2000  2000
107           750.000000   750   750

Salesman 101 handles the largest transactions on average and has the widest range between min and max purchases.

Example 3: Player Ages by Team

import pandas as pd

df = pd.DataFrame({
    'Team': ['Radisson', 'Radisson', 'Gladiators', 'Blues',
             'Gladiators', 'Blues', 'Gladiators', 'Gladiators',
             'Blues', 'Blues', 'Radisson', 'Radisson'],
    'Position': ['Player', 'Extras', 'Player', 'Extras',
                 'Extras', 'Player', 'Player', 'Player',
                 'Extras', 'Player', 'Player', 'Extras'],
    'Age': [22, 24, 21, 29, 32, 20, 21, 23, 30, 26, 20, 31]
})

result = df.groupby('Team').agg({'Age': ['mean', 'min', 'max']})

print("Age statistics grouped by Team:")
print(result)

Output:

Age statistics grouped by Team:
              Age        
             mean min max
Team                     
Blues       26.25  20  30
Gladiators  24.25  21  32
Radisson    24.25  20  31

Adding More Aggregation Functions

You can include any number of aggregation functions - including built-in functions, NumPy functions, or even custom functions:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Type': ['Bike', 'Bike', 'Car', 'Car', 'Bike', 'Car', 'Bike'],
    'TopSpeed_MPH': [186, 202, 304, 210, 218, 270, 188]
})

result = df.groupby('Type').agg({
    'TopSpeed_MPH': ['mean', 'min', 'max', 'median', 'std', 'count']
})

print(result)

Output:

     TopSpeed_MPH                                  
             mean  min  max median        std count
Type                                               
Bike   198.500000  186  218  195.0  14.821156     4
Car    261.333333  210  304  270.0  47.595518     3

Flattening MultiIndex Columns

The agg() method produces a MultiIndex for column headers. To flatten them into simple column names:

import pandas as pd

df = pd.DataFrame({
    'Type': ['Bike', 'Bike', 'Car', 'Car', 'Bike', 'Car', 'Bike'],
    'TopSpeed_MPH': [186, 202, 304, 210, 218, 270, 188]
})

result = df.groupby('Type').agg({'TopSpeed_MPH': ['mean', 'min', 'max']})

result.columns = ['_'.join(col) for col in result.columns]
result = result.reset_index()

print(result)

Output:

   Type  TopSpeed_MPH_mean  TopSpeed_MPH_min  TopSpeed_MPH_max
0  Bike         198.500000               186               218
1   Car         261.333333               210               304

Aggregating Multiple Columns at Once

You can apply different aggregation functions to different columns in a single agg() call:

import pandas as pd

df = pd.DataFrame({
    'Team': ['Radisson', 'Radisson', 'Gladiators', 'Blues',
             'Gladiators', 'Blues', 'Gladiators', 'Radisson'],
    'Age': [22, 24, 21, 29, 32, 20, 21, 31],
    'Score': [88, 92, 75, 80, 95, 70, 85, 90]
})

result = df.groupby('Team').agg({
    'Age': ['mean', 'min', 'max'],
    'Score': ['mean', 'max']
})

print(result)

Output:

                  Age         Score    
                 mean min max  mean max
Team                                   
Blues       24.500000  20  29  75.0  80
Gladiators  24.666667  21  32  85.0  95
Radisson    25.666667  22  31  90.0  92

Using Named Aggregation (Pandas 0.25+)

For cleaner column naming, use named aggregation with tuples:

import pandas as pd

df = pd.DataFrame({
    'Type': ['Bike', 'Bike', 'Car', 'Car', 'Bike', 'Car', 'Bike'],
    'TopSpeed_MPH': [186, 202, 304, 210, 218, 270, 188]
})

result = df.groupby('Type').agg(
    avg_speed=('TopSpeed_MPH', 'mean'),
    min_speed=('TopSpeed_MPH', 'min'),
    max_speed=('TopSpeed_MPH', 'max'),
    count=('TopSpeed_MPH', 'count')
).reset_index()

print(result)

Output:

   Type   avg_speed  min_speed  max_speed  count
0  Bike  198.500000        186        218      4
1   Car  261.333333        210        304      3

Quick Reference

Task	Code
Mean, min, max of one column	`df.groupby('A').agg({'B': ['mean', 'min', 'max']})`
Multiple columns, different functions	`df.groupby('A').agg({'B': ['mean'], 'C': ['sum']})`
Named aggregation	`df.groupby('A').agg(avg_b=('B', 'mean'))`
Flatten MultiIndex columns	`result.columns = ['_'.join(col) for col in result.columns]`
Reset index after groupby	`result.reset_index()`

Conclusion

Pandas' groupby() combined with agg() provides a powerful and concise way to compute multiple summary statistics per group in a single operation.

Whether you need the mean, min, and max of a single column or different aggregations across multiple columns, the agg() method handles it efficiently.

For the cleanest results, use named aggregation (available in Pandas 0.25+) to produce flat, descriptive column names without manual post-processing.