Does Coding in Pythonic Zen Peak Performance? Preliminary Experiments of Nine Pythonic Idioms at Scale

<strong>Link to pre-print: </strong>https://arxiv.org/abs/2203.14484<br> <br> <strong>How to run</strong> Extract <strong>pythonnic_performance.zip </strong>and move into the extracted directory. Execute <strong>install.sh</strong> to install required dependencies. Execute <strong>run.sh </strong>to start the experiment. Execute <strong>python statistic.py </strong>to perform the statistical test and show data statistics. Note that files in <strong>submitted_output</strong> are our experimental results shown in the paper. <br> <strong>Abstract</strong> In field of data science, and for academics in general, the Python programming language is a popular choice, mainly because of its libraries for storing, manipulating, and gaining insight from data.<br> Evidence includes the versatile set of machine learning, data visualization, and manipulation packages used for the ever-growing size of available data.<br> The <em>Zen of Python</em> is a set of guiding design principles that developers use to write acceptable and elegant Python code.<br> Most principles revolve around simplicity.<br> However, as the need to compute on large amounts of data, performance has become a necessity for the Python programmer. The new idea in this paper is to empirically confirm whether writing the Pythonic way peaks performance at scale.<br> As a starting point, we conduct a set of preliminary experiments to evaluate nine Pythonic code examples by comparing the performance of both Pythonic and Non-Pythonic code snippets.<br> Our results reveal that writing in Pythonic idioms may save memory and time.<br> We show that incorporating list comprehension, generator expression, zip, and itertools.zip\_longest can save up to 7,000 MB and 32.25 seconds.<br> The results open more questions on how they could be utilized in a real-world setting.