Different ways to iterate over rows in Pandas Dataframe

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Let’s see the Different ways to iterate over rows in Pandas Dataframe :

Method #1 : Using index attribute of the Dataframe .



filter_none

edit
close

play_arrow

link
brightness_4
code

# import pandas package as pd
import pandas as pd
  
# Define a dictionary containing students data
data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'],
                'Age': [21, 19, 20, 18],
                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'],
                'Percentage': [88, 92, 95, 70]}
  
# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage'])
  
print("Given Dataframe :\n", df)
  
print("\nIterating over rows using index attribute :\n")
  
# iterate through each row and select 
# 'Name' and 'Stream' column respectively.
for ind in df.index:
     print(df['Name'][ind], df['Stream'][ind])

chevron_right






                        filter_none









Output:


Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using index attribute :

Ankit Math
Amit Commerce
Aishwarya Arts
Priyanka Biology





 

Method #2 : Using loc[] function of the Dataframe.











                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas package as pd 


import pandas as pd 


  


# Define a dictionary containing students data 


data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'], 


                'Age': [21, 19, 20, 18], 


                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'], 


                'Percentage': [88, 92, 95, 70]} 


  


# Convert the dictionary into DataFrame 


df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage']) 


  


print("Given Dataframe :\n", df) 


  


print("\nIterating over rows using loc function :\n") 


  


# iterate through each row and select  


# 'Name' and 'Age' column respectively. 


for i in range(len(df)) : 


  print(df.loc[i, "Name"], df.loc[i, "Age"]) 



















                        chevron_right







                        filter_none









Output:


Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using loc function :

Ankit 21
Amit 19
Aishwarya 20
Priyanka 18





 

Method #3 : Using iloc[] function of the DataFrame. 











                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas package as pd 


import pandas as pd 


  


# Define a dictionary containing students data 


data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'], 


                'Age': [21, 19, 20, 18], 


                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'], 


                'Percentage': [88, 92, 95, 70]} 


  


# Convert the dictionary into DataFrame 


df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage']) 


  


print("Given Dataframe :\n", df) 


  


print("\nIterating over rows using iloc function :\n") 


  


# iterate through each row and select  


# 0th and 2nd index column respectively. 


for i in range(len(df)) : 


  print(df.iloc[i, 0], df.iloc[i, 2]) 



















                        chevron_right







                        filter_none









Output:


Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using iloc function :

Ankit Math
Amit Commerce
Aishwarya Arts
Priyanka Biology





 

Method #4 : Using iterrows() method of the Dataframe.











                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas package as pd 


import pandas as pd 


  


# Define a dictionary containing students data 


data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'], 


                'Age': [21, 19, 20, 18], 


                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'], 


                'Percentage': [88, 92, 95, 70]} 


  


# Convert the dictionary into DataFrame 


df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage']) 


  


print("Given Dataframe :\n", df) 


  


print("\nIterating over rows using iterrows() method :\n") 


  


# iterate through each row and select  


# 'Name' and 'Age' column respectively. 


for index, row in df.iterrows(): 


    print (row["Name"], row["Age"]) 



















                        chevron_right







                        filter_none









Output:


Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using iterrows() method :

Ankit 21
Amit 19
Aishwarya 20
Priyanka 18





 

Method #5 : Using itertuples() method of the Dataframe.











                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas package as pd 


import pandas as pd 


  


# Define a dictionary containing students data 


data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'], 


                'Age': [21, 19, 20, 18], 


                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'], 


                'Percentage': [88, 92, 95, 70]} 


  


# Convert the dictionary into DataFrame 


df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage']) 


  


print("Given Dataframe :\n", df) 


  


print("\nIterating over rows using itertuples() method :\n") 


  


# iterate through each row and select  


# 'Name' and 'Percentage' column respectively. 


for row in df.itertuples(index = True, name ='Pandas'): 


    print (getattr(row, "Name"), getattr(row, "Percentage")) 



















                        chevron_right







                        filter_none









Output:


Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using itertuples() method :

Ankit 88
Amit 92
Aishwarya 95
Priyanka 70





 

Method #6 : Using apply() method of the Dataframe. 











                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas package as pd 


import pandas as pd 


  


# Define a dictionary containing students data 


data = {'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka'], 


                'Age': [21, 19, 20, 18], 


                'Stream': ['Math', 'Commerce', 'Arts', 'Biology'], 


                'Percentage': [88, 92, 95, 70]} 


  


# Convert the dictionary into DataFrame 


df = pd.DataFrame(data, columns = ['Name', 'Age', 'Stream', 'Percentage']) 


  


print("Given Dataframe :\n", df) 


  


print("\nIterating over rows using apply function :\n") 


  


# iterate through each row and concatenate 


# 'Name' and 'Percentage' column respectively. 


print(df.apply(lambda row: row["Name"] + " " + str(row["Percentage"]), axis = 1)) 



















                        chevron_right







                        filter_none









Output:


Given Dataframe :
         Name  Age    Stream  Percentage
0      Ankit   21      Math          88
1       Amit   19  Commerce          92
2  Aishwarya   20      Arts          95
3   Priyanka   18   Biology          70

Iterating over rows using apply function :

0        Ankit 88
1         Amit 92
2    Aishwarya 95
3     Priyanka 70
dtype: object






          
          
          
          

            

    

        My Personal Notes
        arrow_drop_up
    

    

        
        

            
            
        

    


Recommended Posts:
AnkitRai01
Check out this Author's contributed articles.
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on  the "Improve Article" button below.