Speeding Up Pandas Row Iteration: Use itertuples() Instead of iterrows()

The author reflects on common complaints about Python's speed compared to compiled languages like C or Go, shares personal doubts about Python's performance, and notes attempts to use Go for data‑science tasks, which felt less pleasant due to static typing.

Motivated to improve daily Pandas work, the article focuses on speeding up row‑wise operations, specifically iterating over DataFrame rows.

A simple dataset is created: a single‑column DataFrame containing integers from 1 to 100,000, and its first few rows and shape are displayed to confirm the setup.

The author warns against the common but inefficient practice of using iterrows() for row iteration, explaining that it is slow by default and often overused.

A quick demonstration using %%time shows that iterating 100,000 rows with iterrows() and incrementing a variable takes around 15 seconds, highlighting the performance issue.

As a better alternative, the article introduces itertuples() , which returns named tuples for each row, allowing attribute‑style access (using the dot operator) and providing significant speed improvements.

Repeating the same task with itertuples() yields roughly a 154‑fold speed increase, illustrating how a small code change can dramatically reduce execution time when processing large numbers of rows.

The author concludes that while itertuples() may not always be 150× faster in every scenario, it generally offers a noticeable performance boost, emphasizing the importance of choosing efficient iteration methods in Pandas workflows.