How can I iterate over rows in a Pandas DataFrame?

How to iterate over rows in a Pandas DataFrame?

Iterating over rows in a Pandas DataFrame can be done in several ways, depending on the use case and performance requirements. Here are the most common methods:

1. Using iterrows()

This method returns an iterator generating pairs of index and Series for each row.

import pandas as pd

# Sample DataFrame
data = {'ID': [101, 102, 103], 'Name': ['Alice', 'Bob', 'Charlie'], 'Score': [85, 90, 78]}
df = pd.DataFrame(data)

# Iterate over rows
for index, row in df.iterrows():
    print(f"Index: {index}, Name: {row['Name']}, Score: {row['Score']}")

Output:

Index: 0, Name: Alice, Score: 85
Index: 1, Name: Bob, Score: 90
Index: 2, Name: Charlie, Score: 78

2. Using itertuples()

This method returns named tuples of the rows, which is faster than iterrows().

# Iterate using itertuples
for row in df.itertuples(index=True):
    print(f"ID: {row.ID}, Name: {row.Name}, Score: {row.Score}")

Output:

ID: 101, Name: Alice, Score: 85
ID: 102, Name: Bob, Score: 90
ID: 103, Name: Charlie, Score: 78

3. Using apply()

For row-wise operations, you can use the apply() function.

# Apply a function to each row
df['Status'] = df.apply(lambda row: 'Pass' if row['Score'] >= 80 else 'Fail', axis=1)
print(df)

Output:

    ID     Name  Score Status
0  101    Alice     85   Pass
1  102      Bob     90   Pass
2  103  Charlie     78   Fail

4. Using zip() with DataFrame Columns

For simpler DataFrames, you can iterate directly over the columns using zip().

# Iterate using zip
for id_, name, score in zip(df['ID'], df['Name'], df['Score']):
    print(f"ID: {id_}, Name: {name}, Score: {score}")

Output:

ID: 101, Name: Alice, Score: 85
ID: 102, Name: Bob, Score: 90
ID: 103, Name: Charlie, Score: 78

5. Using to_dict()

You can convert the DataFrame to a dictionary and iterate over its rows.

# Convert DataFrame to dictionary
data_dict = df.to_dict(orient='records')
for row in data_dict:
    print(row)

Output:

{'ID': 101, 'Name': 'Alice', 'Score': 85}
{'ID': 102, 'Name': 'Bob', 'Score': 90}
{'ID': 103, 'Name': 'Charlie', 'Score': 78}

When to Use Each Method

• itertuples() for faster iteration when reading rows without modification.
• iterrows() when you need row data as Series (but avoid for performance-critical tasks).
• apply() for vectorized operations without explicit iteration.
• zip() for iterating specific columns.
• to_dict() for working with row data as dictionaries.