How to Iterate Over Rows in pandas DataFrame

To iterate over rows in a pandas DataFrame, use iterrows() to get each row as an index and Series, or itertuples() for faster access as named tuples. For applying a function to each row, use apply() with axis=1.

Syntax

Here are common ways to iterate over rows in a pandas DataFrame:

• for index, row in df.iterrows(): — loops over rows, each row is a Series.
• for row in df.itertuples(): — loops over rows as named tuples, faster than iterrows.
• df.apply(function, axis=1) — applies a function to each row.

python

for index, row in df.iterrows():
    # use index and row (Series)

for row in df.itertuples():
    # use row as namedtuple

def my_func(row):
    # process row
    return row['column'] * 2

result = df.apply(my_func, axis=1)

Example

This example shows how to iterate over rows using iterrows() and itertuples(), printing values from each row.

python

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)

print('Using iterrows():')
for index, row in df.iterrows():
    print(f"Index: {index}, Name: {row['Name']}, Age: {row['Age']}")

print('\nUsing itertuples():')
for row in df.itertuples():
    print(f"Index: {row.Index}, Name: {row.Name}, Age: {row.Age}")

Output

Using iterrows():
Index: 0, Name: Alice, Age: 25
Index: 1, Name: Bob, Age: 30
Index: 2, Name: Charlie, Age: 35

Using itertuples():
Index: 0, Name: Alice, Age: 25
Index: 1, Name: Bob, Age: 30
Index: 2, Name: Charlie, Age: 35

Common Pitfalls

Common mistakes when iterating rows in pandas include:

• Modifying the DataFrame inside the loop does not change the original DataFrame.
• iterrows() returns a copy of each row, so changes to row do not affect df.
• itertuples() is faster but returns named tuples, so you cannot assign new values to rows.
• For large DataFrames, row iteration is slow; vectorized operations or apply() are better.

python

import pandas as pd

data = {'A': [1, 2, 3]}
df = pd.DataFrame(data)

# Wrong: trying to modify df inside iterrows
for index, row in df.iterrows():
    row['A'] = row['A'] * 2  # This does NOT change df

print(df)  # Original df unchanged

# Right: use loc to modify df
for index, row in df.iterrows():
    df.loc[index, 'A'] = row['A'] * 2

print(df)  # df updated correctly

Output

   A
0  1
1  2
2  3
   A
0  2
1  4
2  6

Quick Reference

Method	Description	Return Type	Performance
iterrows()	Iterate rows as (index, Series)	Tuple (index, Series)	Slower
itertuples()	Iterate rows as named tuples	Namedtuple	Faster
apply(func, axis=1)	Apply function to each row	Depends on function	Moderate

Key Takeaways

Use iterrows() to loop over rows as index and Series pairs.

Use itertuples() for faster iteration with named tuples.

Modifying rows inside iterrows() does not change the original DataFrame; use df.loc to update.

For better performance, prefer vectorized operations or apply() over explicit loops.

Iterating rows is simple but slow for large data; avoid if possible.