How to Iterate over rows in Pandas Dataframe – Definitive Guide

Pandas DataFrame is a two-dimensional data structure used to store the data in the tabular format. It is similar to spreadsheets or a database table.

You can iterate over the pandas dataframe using the df.itertuples() method.

If You’re in Hurry…

You can use the below code to iterate over rows in pandas dataframe.

This is one of the fastest methods available to iterate over rows in the pandas dataframe.

Example

for tuple in df.itertuples():
    print(tuple)

You’ll see the below output.

Each row in the dataframe will be iterated over and printed using the print statement.

Output

    Pandas(Index=0, Lang='Java', Difficulty='Medium', Difficulty_Score=5, Type='Statically Typed')
    Pandas(Index=1, Lang='Python', Difficulty='Easy', Difficulty_Score=2, Type='Dynamically Typed')
    Pandas(Index=2, Lang='Cobol', Difficulty='Hard', Difficulty_Score=10, Type='NA')
    Pandas(Index=3, Lang='Javascript', Difficulty='Medium', Difficulty_Score=8, Type='Dynamically typed')

If You Want to Understand Details, Read on…

In this tutorial, you’ll learn the various methods available to iterate over rows in the Pandas Dataframe.

Sample DataFrame

import pandas as pd

data = {
    "Lang":["Java","Python","Cobol","Javascript"],        
    "Difficulty":["Medium","Easy","Hard","Medium"],
    "Difficulty_Score":[5,2,10,8],
    "Type":["Statically Typed","Dynamically Typed","NA","Dynamically typed"],

}

df = pd.DataFrame(data)

print(df)

DataFrame Visualization

             Lang Difficulty  Difficulty_Score               Type
    0        Java     Medium                 5   Statically Typed
    1      Python       Easy                 2  Dynamically Typed
    2       Cobol       Hard                10                 NA
    3  Javascript     Medium                 8  Dynamically typed

Now let’s discuss the various methods available to iterate over the rows in the pandas dataframe.

Using Itertuples() Function

In this section, you’ll learn how to iterate over rows in Pandas dataframe using the Itertuples() method.

Itertuples() method iterates over the dataframe rows and returns a named tuple.

It accepts two parameters.

• Index – If true, it’ll include the index of the row as the first element of the tuple. If false, it’ll not in include the index of the row in the tuple. Default is set to true.
• name – You can give a name to each tuple. By Default, its Pandas.

This is the fastest method to iterate over rows in Pandas dataframe.

Example

for tuple in df.itertuples():
    print(tuple)

You’ve not passed the index parameter or the name parameter. Hence, the default value for both the parameter is used.

In each tuple displayed below, it’s named Pandas and it contains the index element for each row.

Output

    Pandas(Index=0, Lang='Java', Difficulty='Medium', Difficulty_Score=5, Type='Statically Typed')
    Pandas(Index=1, Lang='Python', Difficulty='Easy', Difficulty_Score=2, Type='Dynamically Typed')
    Pandas(Index=2, Lang='Cobol', Difficulty='Hard', Difficulty_Score=10, Type='NA')
    Pandas(Index=3, Lang='Javascript', Difficulty='Medium', Difficulty_Score=8, Type='Dynamically typed')

This is how you can iterate over rows in Pandas DataFrame using itertuples().

Next, you’ll see how to iterate over rows by column name.

Pandas Iterate Over Rows by Column Name

In this section, you’ll learn how to iterate over rows by column name. For this also you can use the itertuples() and when iterating over each tuple, you can access the column in each tuple by using the column name as shown in the example.

Example

for tuple in df.itertuples():
    print(tuple.Lang)

In this iteration, you’ll iterate over rows and access only the column Lang.

Output

Java
Python
Cobol
Javascript

This is how you can iterate over rows by column name.

Next, you’ll learn how to use the apply() method in Dataframe.

Using Apply() Function

In this section, you’ll learn how to use the apply() method to iterate over rows in pandas dataframe.

Apply() method is used to apply a function to a row or a column in the dataframe.

A function can be of anything. You can define a function to perform any mathematical operation such as addition, subtraction, or average, and so on.

When to Use

You can use this method if you wish to modify the rows while iterating it over.
For eg.

• Iterating and appending a new value to a string based on a condition.
• Iterating and updating a new value for a column based on a condition.

Example 1

The below example shows how to iterate over the dataframe and concatenate two columns to display it together. This is achieved by defining a lambda function that will concatenate two columns of each row.

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

Output

    0          Java 5
    1        Python 2
    2        Cobol 10
    3    Javascript 8
    dtype: object

Example 2

The below example shows how to iterate over the dataframe and add 10 to the difficulty score of each language and display it…

…This is achieved by defining a lambda function which will add 10 to the difficulty score of each language.

print(df.apply(lambda row:  row["Difficulty_Score"] + 10, axis = 1))

Output

    0    15
    1    12
    2    20
    3    18
    dtype: int64

This is how you can use the apply() method to iterate over rows in a dataframe and apply a function to each row or column.

Next, you’ll learn about the iterrows() function.

Using Iterrows() Function

In this section, you’ll learn how to use iterrows() function to iterate through rows in a pandas dataframe.

iterrows() method iterates over dataframe as (index, series) pairs.

• Index is the name of each row
• series is a set of data in each row.

Example

for index, row in df.iterrows():
    print(index)
    print("*****")
    print(row)

Output

   0
*****
Lang                            Java
Difficulty                    Medium
Difficulty_Score                   5
Type                Statically Typed
Name: 0, dtype: object
1
*****
Lang                           Python
Difficulty                       Easy
Difficulty_Score                    2
Type                Dynamically Typed
Name: 1, dtype: object
2
*****
Lang                Cobol
Difficulty           Hard
Difficulty_Score       10
Type                   NA
Name: 2, dtype: object
3
*****
Lang                       Javascript
Difficulty                     Medium
Difficulty_Score                    8
Type                Dynamically typed
Name: 3, dtype: object

This is how you can use the iterrows() method to iterate through the pandas dataframe and access the index and series of data in the dataframe.

Next, you’ll see the index attribute of the Dataframe.

Using Index Attribute

In this section, you’ll learn how to iterate over rows in the dataframe using the index attribute.

Index attribute is an immutable sequence used of indexing elements in a dataframe.

You can use this attribute and iterate over rows by using the index values and each column name.

Dataframe is two-dimensional.

• First dimension specifies the index of the columns
• Second Dimension specifies the index of the columns

df["lang"][i] will yield value of the lang column in the position i.

Example

In the example, you’re iterating the dataframe index, and accessing the rows with this index, and specifying the column name in the first dimension to access the row of these columns.

for i in df.index:
    print(df["Lang"][i],df["Difficulty"][i] )

Output

    Java Medium
    Python Easy
    Cobol Hard
    Javascript Medium

This is how you can iterate through the pandas dataframe using the index attribute.

Next, you’ll see about the loc function.

Using LOC[] Function

In this section, you’ll learn how to use the LOC[] attribute to iterate over the dataframe.

LOC[] attribute is primarily label based and you can access the particular labels(Column name) from the specified index.

Example

In the below example, you’ll access the dataframe using the loc attribute and the column name lang and Difficulty during each iteration to access the values of these columns.

for i in range(len(df)):
    print(df.loc[i,"Lang"], df.loc[i,"Difficulty"])

Output

    Java Medium
    Python Easy
    Cobol Hard
    Javascript Medium

This is how the loc[] attribute is used to iterate through the dataframe.

Next, you’ll see how to use the iLOC[] function.

Using iLOC[] Fuction Of DataFrame

In this section, you’ll learn how to use the iLOC[] attribute to iterate over the dataframe.

iLOC[] attribute is primarily integer-based and you can access the particular index by specifying the integer.

Example

In the below example, you’ll access the dataframe using the iloc attribute.

During each row iteration,

• Use the column index 0 to access the first column.
• Use the column index 1 to access the second column.

for i in range(len(df)):
    print(df.iloc[i,0], df.iloc[i,1])

Output

    Java Medium
    Python Easy
    Cobol Hard
    Javascript Medium

This is how you can iterate over the dataframe using the iLOC[] attribute.

Next, you’ll see the iteritems() function to iterate over the dataframe.

Using Iteritems() Function

In this section, you’ll use the Iteritems() function to iterate over the dataframe.

iteritems() function iterates over the dataframe columns and returns a tuple with column name and content as a series.

iteritems() is deprecated and will be removed in the future pandas version. You can use the items() method instead.

Example

for item in df.iteritems():
    print(item)

Output

    ('Lang', 0          Java
    1        Python
    2         Cobol
    3    Javascript
    Name: Lang, dtype: object)
    ('Difficulty', 0    Medium
    1      Easy
    2      Hard
    3    Medium
    Name: Difficulty, dtype: object)
    ('Difficulty_Score', 0     5
    1     2
    2    10
    3     8
    Name: Difficulty_Score, dtype: int64)
    ('Type', 0     Statically Typed
    1    Dynamically Typed
    2                   NA
    3    Dynamically typed
    Name: Type, dtype: object)

This is how you can use the iteritems() method.

Using Items() Function

In this section, you’ll use the items() method in the dataframe to iterate over the rows.

items() method iterate over the dataframe and returns a tuple with the column name and content as a series of data.

Example

for item in df.items():
    print(item)

Output

    ('Lang', 0          Java
    1        Python
    2         Cobol
    3    Javascript
    Name: Lang, dtype: object)
    ('Difficulty', 0    Medium
    1      Easy
    2      Hard
    3    Medium
    Name: Difficulty, dtype: object)
    ('Difficulty_Score', 0     5
    1     2
    2    10
    3     8
    Name: Difficulty_Score, dtype: int64)
    ('Type', 0     Statically Typed
    1    Dynamically Typed
    2                   NA
    3    Dynamically typed
    Name: Type, dtype: object)

pandas iterate over rows by column name

In this subsection, you’ll use the iteritems() to iterate over the dataframe and use the columnName and columnData fields to access the column data.

Example

for (columnName, columnData) in df.iteritems():
    print('Column Name : ', columnName)
    print('Column Contents : ', columnData.values)

Output

    Column Name :  Lang
    Column Contents :  ['Java' 'Python' 'Cobol' 'Javascript']
    Column Name :  Difficulty
    Column Contents :  ['Medium' 'Easy' 'Hard' 'Medium']
    Column Name :  Difficulty_Score
    Column Contents :  [ 5  2 10  8]
    Column Name :  Type
    Column Contents :  ['Statically Typed' 'Dynamically Typed' 'NA' 'Dynamically typed']

This is also known as Pandas Iterate Over Columns.

pandas iterate over rows with condition

In this subsection, you’ll use the iteritems() to iterate over the dataframe and use an if condition to check if the current column is a specific column and access the column data if the condition is true. Else, the column will be skipped.

Example

for (columnName, columnData) in df.iteritems():
    if(columnName == "Lang"):
        print('Column Name : ', columnName)
        print('Column Contents : ', columnData.values)

Output

    Column Name :  Lang
    Column Contents :  ['Java' 'Python' 'Cobol' 'Javascript']

Pandas Iterate Over Columns

Example

for (columnName, columnData) in df.iteritems():
    print('Column Name : ', columnName)
    print('Column Contents : ', columnData.values)

Output

    Column Name :  Lang
    Column Contents :  ['Java' 'Python' 'Cobol' 'Javascript']
    Column Name :  Difficulty
    Column Contents :  ['Medium' 'Easy' 'Hard' 'Medium']
    Column Name :  Difficulty_Score
    Column Contents :  [ 5  2 10  8]
    Column Name :  Type
    Column Contents :  ['Statically Typed' 'Dynamically Typed' 'NA' 'Dynamically typed']

Conclusion

To summarize, you’ve learned how to iterate over rows in Pandas dataframe using the different methods available in the Dataframe.

Among all the methods available, itertuples() is the fastest method available to iterate over the pandas dataframe.

If you have any questions, feel free to comment below.

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