Pandas DataFrame.itertuples

Pandas DataFrame.itertuples

The DataFrame.itertuples method in pandas is used to iterate over rows of a DataFrame as named tuples. This is a fast and memory-efficient way to access row data, particularly for large DataFrames.

Syntax

The syntax for DataFrame.itertuples is:

DataFrame.itertuples(index=True, name='Pandas')

Here, DataFrame refers to the pandas DataFrame whose rows are being iterated.

Parameters

Returns

An iterator yielding named tuples of row data. Each tuple contains the index (if index=True) and the values of the row.

Examples

Iterating Over Rows with Named Tuples

Use itertuples to iterate over rows and access their data as named tuples.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Iterate over rows as named tuples
print("Iterating with Named Tuples:")
for row in df.itertuples():
    print(row)

Output

Iterating with Named Tuples:
Pandas(Index=0, Name='Arjun', Age=25, Salary=70000)
Pandas(Index=1, Name='Ram', Age=30, Salary=80000)
Pandas(Index=2, Name='Priya', Age=35, Salary=90000)

Accessing Specific Columns in Named Tuples

Access specific columns in each row using attribute-style access on named tuples.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Access specific columns
print("Accessing Specific Columns:")
for row in df.itertuples():
    print(f"Name: {row.Name}, Age: {row.Age}")

Output

Accessing Specific Columns:
Name: Arjun, Age: 25
Name: Ram, Age: 30
Name: Priya, Age: 35

Excluding the Index

Set index=False to exclude the index from the tuples.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Iterate over rows without including the index
print("Iterating Without Index:")
for row in df.itertuples(index=False):
    print(row)

Output

Iterating Without Index:
Pandas(Name='Arjun', Age=25, Salary=70000)
Pandas(Name='Ram', Age=30, Salary=80000)
Pandas(Name='Priya', Age=35, Salary=90000)

Returning Regular Tuples

Set name=None to return regular tuples instead of named tuples.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Return regular tuples
print("Iterating with Regular Tuples:")
for row in df.itertuples(name=None):
    print(row)

Output

Iterating with Regular Tuples:
(0, 'Arjun', 25, 70000)
(1, 'Ram', 30, 80000)
(2, 'Priya', 35, 90000)

Summary

In this tutorial, we explored the DataFrame.itertuples method in pandas. Key takeaways include:

• Using itertuples to iterate over rows as named tuples.
• Accessing specific columns using attribute-style access.
• Excluding the index or returning regular tuples for flexibility.

The DataFrame.itertuples method is a fast and efficient way to iterate through row data in pandas.