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Iterating over rows and columns in Pandas DataFrame

  • Difficulty Level : Easy
  • Last Updated : 09 Aug, 2021

Iteration is a general term for taking each item of something, one after another. Pandas DataFrame consists of rows and columns so, in order to iterate over dataframe, we have to iterate a dataframe like a dictionary. In a dictionary, we iterate over the keys of the object in the same way we have to iterate in dataframe.
 

In this article, we are using “nba.csv” file to download the CSV, click here.
In Pandas Dataframe we can iterate an element in two ways: 
 

  • Iterating over rows
  • Iterating over columns

 

Iterating over rows :

In order to iterate over rows, we can use three function iteritems(), iterrows(), itertuples() . These three function will help in iteration over rows. 
 



Iteration over rows using iterrows()

In order to iterate over rows, we apply a iterrows() function this function return each index value along with a series containing the data in each row.
Code #1:

Python3




# importing pandas as pd
import pandas as pd
   
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
  
# creating a dataframe from a dictionary 
df = pd.DataFrame(dict)
  
print(df)


Now we apply iterrows() function in order to get a each element of rows. 
 

Python3




# importing pandas as pd
import pandas as pd
   
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
  
# creating a dataframe from a dictionary 
df = pd.DataFrame(dict)
  
# iterating over rows using iterrows() function 
for i, j in df.iterrows():
    print(i, j)
    print()

Output: 
 

  
Code #2: 
 

Python




# importing pandas module 
import pandas as pd 
     
# making data frame from csv file 
data = pd.read_csv("nba.csv"
  
# for data visulaization we filter first 3 datasets
data.head(3)



Now we apply a iterrows to get each element of rows in dataframe 
 

Python




# importing pandas module 
import pandas as pd 
     
# making data frame from csv file 
data = pd.read_csv("nba.csv"
  
for i, j in data.iterrows():
    print(i, j)
    print()

Output: 
 

 

Iteration over rows using iteritems()

In order to iterate over rows, we use iteritems() function this function iterates over each column as key, value pair with label as key and column value as a Series object.
Code #1:

Python3




# importing pandas as pd
import pandas as pd
   
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
  
# creating a dataframe from a dictionary 
df = pd.DataFrame(dict)
  
print(df)


Now we apply a iteritems() function in order to retrieve an rows of dataframe. 
 

Python3




# importing pandas as pd
import pandas as pd
   
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
  
# creating a dataframe from a dictionary 
df = pd.DataFrame(dict)
  
# using iteritems() function to retrieve rows
for key, value in df.iteritems():
    print(key, value)
    print()

Output: 
 



Code #2: 
 

Python




# importing pandas module 
import pandas as pd 
     
# making data frame from csv file 
data = pd.read_csv("nba.csv"
  
# for data visualization we filter first 3 datasets
data.head(3)

Output: 
 

Now we apply a iteritems() in order to retrieve rows from a dataframe 
 

Python




# importing pandas module 
import pandas as pd 
     
# making data frame from csv file 
data = pd.read_csv("nba.csv"
  
for key, value in data.iteritems():
    print(key, value)
    print()

Output: 
 

 

Iteration over rows using itertuples()

In order to iterate over rows, we apply a function itertuples() this function return a tuple for each row in the DataFrame. The first element of the tuple will be the row’s corresponding index value, while the remaining values are the row values.
Code #1:



Python3




# importing pandas as pd
import pandas as pd
   
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
  
# creating a dataframe from a dictionary 
df = pd.DataFrame(dict)
  
print(df)


Now we apply a itertuples() function inorder to get tuple for each row
 

Python3




# importing pandas as pd
import pandas as pd
   
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
  
# creating a dataframe from dictionary 
df = pd.DataFrame(dict)
  
# using a itertuples() 
for i in df.itertuples():
    print(i)

Output: 
 

Code #2: 
 

Python




# importing pandas module 
import pandas as pd 
     
# making data frame from csv file 
data = pd.read_csv("nba.csv"
  
# for data visualization we filter first 3 datasets
data.head(3)

Now we apply an itertuples() to get atuple of each rows 
 

Python




# importing pandas module 
import pandas as pd 
     
# making data frame from csv file 
data = pd.read_csv("nba.csv"
  
for i in data.itertuples():
    print(i)

Output: 
 



 

Iterating over Columns :

In order to iterate over columns, we need to create a list of dataframe columns and then iterating through that list to pull out the dataframe columns.
Code #1:

Python3




# importing pandas as pd
import pandas as pd
    
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}
   
# creating a dataframe from a dictionary 
df = pd.DataFrame(dict)
  
print(df)


Now we iterate through columns in order to iterate through columns we first create a list of dataframe columns and then iterate through list. 
 

Python




# creating a list of dataframe columns
columns = list(df)
  
for i in columns:
  
    # printing the third element of the column
    print (df[i][2])

Output: 
 

  
Code #2: 
 

Python






# importing pandas module 
import pandas as pd 
     
# making data frame from csv file 
data = pd.read_csv("nba.csv"
  
# for data visualization we filter first 3 datasets
 col = data.head(3)
  
col

Now we iterate over columns in CSV file in order to iterate over columns we create a list of dataframe columns and iterate over list 
 

Python




# creating a list of dataframe columns
clmn = list(col)
  
for i in clmn:
    # printing a third element of column
    print(col[i][2])

Output: 
 

 

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