5 Best Ways to Iterate Over Rows in a Pandas DataFrame : Emily Rosemary Collins

5 Best Ways to Iterate Over Rows in a Pandas DataFrame
by: Emily Rosemary Collins
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💡 Problem Formulation: When working with data in Python, a common task is iterating over rows in a pandas DataFrame to perform operations on each row. For example, you may have a DataFrame containing stock prices and would like to calculate the daily return for each stock. You need efficient ways to loop through rows to compute the desired result. Here, we will discuss some best methods for row iteration, including their syntax and best-use scenarios.

Method 1: Using iterrows()

Iterating through a DataFrame can be done using iterrows(), which returns an iterator yielding index and row data as pairs. This method is straightforward and useful for iterating while considering the index.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Using iterrows to iterate
for index, row in df.iterrows():
    print(f'Index: {index}, A: {row["A"]}, B: {row["B"]}')


Index: 0, A: 1, B: 4
Index: 1, A: 2, B: 5
Index: 2, A: 3, B: 6

This code snippet creates a pandas DataFrame and iterates over each row using iterrows(). The loop prints the index along with the values in columns ‘A’ and ‘B’ for each row. It’s a convenient method for row-wise operations where index plays an important role.

Method 2: Using itertuples()

The itertuples() method for data frames is faster than iterrows() and returns a namedtuple for each row, which makes it more memory efficient and typically better for performance.

Here’s an example:

# Using itertuples to iterate
for row in df.itertuples():
    print(f'Index: {row.Index}, A: {row.A}, B: {row.B}')


Index: 0, A: 1, B: 4
Index: 1, A: 2, B: 5
Index: 2, A: 3, B: 6

In this snippet, itertuples() is used to iterate over the DataFrame rows as namedtuples. This method improves readability and performance, especially in large DataFrames.

Method 3: Using Vectorization with pandas Series and DataFrame methods

Vectorization is the use of operations on complete arrays instead of individual elements, which is the optimal way to perform operations in pandas. It is the most efficient way to work with pandas and should be your first choice before considering iteration.

Here’s an example:

# Vectorized operation
df['C'] = df['A'] + df['B']



   A  B  C
0  1  4  5
1  2  5  7
2  3  6  9

This code uses vectorization to add columns ‘A’ and ‘B’ to create a new column ‘C’. It avoids explicit iteration and is usually the fastest method when performing calculations across rows.

Method 4: Applying a Function with apply()

For more complex operations that may not be vectorizable, or when you want to use a custom function across rows, the apply() method can be a lifesaver.

Here’s an example:

# Custom function to apply
def custom_operation(row):
    return row['A'] * row['B']

# Applying the function to each row
df['D'] = df.apply(custom_operation, axis=1)



   A  B  C   D
0  1  4  5   4
1  2  5  7  10
2  3  6  9  18

The custom function custom_operation is applied to each row thanks to apply(). The function multiplies elements in columns ‘A’ and ‘B’, storing the result in a new column ‘D’.

Bonus One-Liner Method 5: Using List Comprehensions

While list comprehensions aren’t specifically part of pandas, they can be used to iterate over DataFrame rows quickly. They’re concise and can be written in a single line of code.

Here’s an example:

# List comprehension to create a list of sums
column_sum = [row.A + row.B for row in df.itertuples()]



[5, 7, 9]

The list comprehension iterates over each row, accessed via itertuples(), and computes the sum of columns ‘A’ and ‘B’ for each row, storing the results in a list.


  • Method 1: iterrows(). Easy to use. Suitable for operations where the index is significant. It’s less memory efficient and slower than other methods.
  • Method 2: itertuples(). Faster and more memory-efficient than iterrows(). Best for per-row operations where index matters.
  • Method 3: Vectorization. The most efficient way to operate across a DataFrame. Avoids explicit iteration. Use when possible.
  • Method 4: apply(). Flexible for complex operations. More efficient than row-wise iteration but typically slower than vectorization.
  • Bonus Method 5: List Comprehensions. A Pythonic, readable approach. Quick for simple operations, but lacks the direct access to DataFrame features.

February 19, 2024 at 02:53AM
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