  In the 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. Evidence includes the
versatile set of machine learning, data visualization, and manipulation
packages used for the ever-growing size of available data. The Zen of Python is
a set of guiding design principles that developers use to write acceptable and
elegant Python code. Most principles revolve around simplicity. However, as the
need to compute large amounts of data, performance has become a necessity for
the Python programmer. The new idea in this paper is to confirm whether writing
the Pythonic way peaks performance at scale. 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. Our
results reveal that writing in Pythonic idioms may save memory and time. We
show that incorporating list comprehension, generator expression, zip, and
itertools.zip_longest idioms can save up to 7,000 MB and up to 32.25 seconds.
The results open more questions on how they could be utilized in a real-world
setting. The replication package includes all scripts, and the results are
available at https://doi.org/10.5281/zenodo.5712349
