How 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 how to iterate over rows in Pandas Dataframe using inerrows() and itertuples() :

Method #1: Using the DataFrame.iterrows() method

This method iterated over the rows as (index, series) pairs.

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# importing pandas
import pandas as pd
  
# list of dicts
input_df = [{'name':'Sujeet', 'age':10},
            {'name':'Sameer', 'age':11},
            {'name':'Sumit', 'age':12}]
  
df = pd.DataFrame(input_df)
print('Original DataFrame: \n', df)
  
  
print('\nRows iterated using iterrows() : ')
for index, row in df.iterrows():
    print(row['name'], row['age'])

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Output:



Original DataFrame: 
    age    name
0   10  Sujeet
1   11  Sameer
2   12   Sumit

Rows iterated using iterrows() : 
Sujeet 10
Sameer 11
Sumit 12

 
Method #2: Using the DataFrame.itertuples() method

This method returns a named tuple for every row. getattr() function can be used to get the row attribute in the returned tuple. This method is faster than Method #1.

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# importing pandas
import pandas as pd
  
# list of dicts
input_df = [{'name':'Sujeet', 'age':10},
            {'name':'Sameer', 'age':110},
            {'name':'Sumit', 'age':120}]
  
df = pd.DataFrame(input_df)
print('Original DataFrame: \n', df)
  
print('\nRows iterated using itertuples() : ')
for row in df.itertuples():
    print(getattr(row, 'name'), getattr(row, 'age'))

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Output:

Original DataFrame: 
    age    name
0   10  Sujeet
1  110  Sameer
2  120   Sumit

Rows iterated using itertuples() : 
Sujeet 10
Sameer 110
Sumit 120

There are few other ways we can iterate over rows in Pandas Dataframe. See Different ways to iterate over rows in Pandas Dataframe.



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