# Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence

arXiv: https://arxiv.org/abs/2301.12633 · HTML: https://mlsanity.com/paper/2301.12633/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2023.

## Built on by (most cited first, 3 of 3)

- [On the Compression of Language Models for Code: An Empirical Study on CodeBERT](https://mlsanity.com/paper/2412.13737/) (2024)
- [GIST: Generated Inputs Sets Transferability in Deep Learning](https://mlsanity.com/paper/2311.00801/) (2023)
- [MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code](https://mlsanity.com/paper/2408.01354/) (2024)

## Abstract (arXiv)

The usage of Python idioms is popular among Python developers in a formative study of 101 performance-related questions of Python idioms on Stack Overflow, we find that developers often get confused about the performance impact of Python idioms and use anecdotal toy code or rely on personal project experience which is often contradictory in performance outcomes. There has been no large-scale, systematic empirical evidence to reconcile these performance debates. In the paper, we create a large synthetic dataset with 24,126 pairs of non-idiomatic and functionally-equivalent idiomatic code for the nine unique Python idioms identified in Zhang et al., and reuse a large real-project dataset of 54,879 such code pairs provided by Zhang et al. We develop a reliable performance measurement method to compare the speedup or slowdown by idiomatic code against non-idiomatic counterpart, and analyze the performance discrepancies between the synthetic and real-project code, the relationships between code features and performance changes, and the root causes of performance changes at the bytecode level. We summarize our findings as some actionable suggestions for using Python idioms.