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Pandas – GroupBy One Column and Get Mean, Min, and Max values

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We can use Groupby function to split dataframe into groups and apply different operations on it. One of them is Aggregation. Aggregation i.e. computing statistical parameters for each group created example – mean, min, max, or sums. Let’s have a look at how we can group a dataframe by one column and get their mean, min, and max values. Example 1:
import pandas as pd
  
  
# creating a dataframe
df = pd.DataFrame([('Bike', 'Kawasaki', 186),
                   ('Bike', 'Ducati Panigale', 202),
                   ('Car', 'Bugatti Chiron', 304), 
                   ('Car', 'Jaguar XJ220', 210),
                   ('Bike', 'Lightning LS-218', 218), 
                   ('Car', 'Hennessey Venom GT', 270),
                   ('Bike', 'BMW S1000RR', 188)],
                  columns =('Type', 'Name', 'top_speed(mph)'))
  
df

                    
Output : Finding mean, min and max values.
# using groupby function with aggregation
# to get mean, min and max values
result = df.groupby('Type').agg({'top_speed(mph)': ['mean', 'min', 'max']})
  
print("Mean, min, and max values of Top Speed grouped by Vehicle Type")
print(result)

                    
Output : Example 2:
import pandas as pd
  
  
# creating a dataframe
sales_data = 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]})
  
sales_data

                    
Output : Finding mean, min and max values.
# using groupby function with aggregation 
# to get mean, min and max values
result = sales_data.groupby('salesman_id').agg({'purchase_amt': ['mean', 'min', 'max']})
  
print("Mean, min, and max values of Purchase Amount grouped by Salesman id")
print(result)

                    
Output : Example 3:
import pandas as pd
  
  
# creating a dataframe
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]})
df

                    
Output : Finding mean, min and max values.
# using groupby function with aggregation 
# to get mean, min and max values
result = df.groupby('Team').agg({'Age': ['mean', 'min', 'max']})
  
print("Mean, min, and max values of Age grouped by Team")
print(result)

                    
Output :

Last Updated : 25 Aug, 2020
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