Why is Pandas `iterrows` So Slow, and How Can I Improve Performance?

Performance Issues with Pandas iterrows

iterrows, a pandas function for row-wise iteration, has been observed to exhibit performance deficiencies. While the issue may be linked to mixed dtypes in the dataframe, even simple scenarios without this issue demonstrate significant performance lags.

Vectorized operations, such as apply, often outperform iterrows, raising questions about the need for row-by-row iteration. However, there are instances where iterrows remains unavoidable.

Reasons for Iterrows Performance Issues

Generally, iterrows is less efficient than vectorization, apply, and itertuples due to performance characteristics:

• Vectorization: Vector operations allow for highly efficient computations.
• Apply: Apply is optimized by pandas, with some operations executed in Cython, offering marked performance gains.
• Itertuples: Itertuples avoids data boxing, retrieving data as tuples.
• Iterrows: Iterrows boxes data into Series objects, leading to performance penalties.

Guidelines for Optimal Performance

To optimize performance, consider the following guidelines:

1. Prioritize vectorization whenever possible.
2. Utilize apply when vectorization is not feasible.
3. Consider itertuples for non-boxing in specific scenarios.
4. Avoid iterrows when possible, as it introduces performance bottlenecks.
5. Build new structures and concatenate to avoid row-by-row updates on empty dataframes.

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