Select row with maximum and minimum value in Pandas dataframe

Last Updated : 06 Jan, 2019

Let’s see how can we select row with maximum and minimum value in Pandas dataframe with help of different examples.

Consider this dataset.

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
print(df.head(10)) 

Output:

Using max on Dataframe –

Code #1: Shows max on Driver, Points, Age columns.

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
  
# the result shows max on 
# Driver, Points, Age columns. 
print(df.max()) 

Output:

Code #2: Who scored max points

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
  
# Who scored more points ? 
print(df[df.Points == df.Points.max()]) 

Output:

Code #3: What is the maximum age

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
  
# what is the maximum age ? 
print(df.Age.max()) 

Output:

Code #4: Which row has maximum age in the dataframe | who is the oldest driver ?

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
  
# Which row has maximum age | 
# who is the oldest driver ? 
print(df[df.Age == df.Age.max()]) 

Output:

Using min on Dataframe –

Code #1: Shows min on Driver, Points, Age columns.

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
  
# the result shows min on  
# Driver, Points, Age columns. 
print(df.min()) 

Output:

Code #2: Who scored less points

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
  
# Who scored less points ? 
print(df[df.Points == df.Points.min()]) 

Output:

Code #3: Which row has minimum age in the dataframe | who is the youngest driver

# importing pandas and numpy 
import pandas as pd 
import numpy as np 
  
# data of 2018 drivers world championship 
dict1 ={'Driver':['Hamilton', 'Vettel', 'Raikkonen', 
                  'Verstappen', 'Bottas', 'Ricciardo', 
                  'Hulkenberg', 'Perez', 'Magnussen',  
                  'Sainz', 'Alonso', 'Ocon', 'Leclerc', 
                  'Grosjean', 'Gasly', 'Vandoorne', 
                  'Ericsson', 'Stroll', 'Hartley', 'Sirotkin'], 
                    
        'Points':[408, 320, 251, 249, 247, 170, 69, 62, 56, 
                   53, 50, 49, 39, 37, 29, 12, 9, 6, 4, 1], 
                     
        'Age':[33, 31, 39, 21, 29, 29, 31, 28, 26, 24, 37, 
                      22, 21, 32, 22, 26, 28, 20, 29, 23]} 
                        
# creating dataframe using DataFrame constructor 
df = pd.DataFrame(dict1) 
  
# Which row has maximum age |  
# who is the youngest driver ? 
print(df[df.Age == df.Age.min()]) 