  Background: The energy consumption of machine learning and its impact on the
environment has made energy efficient ML an emerging area of research. However,
most of the attention stays focused on the model creation and the training and
inferencing phase. Data oriented stages like preprocessing, cleaning and
exploratory analysis form a critical part of the machine learning workflow.
However, the energy efficiency of these stages have gained little attention
from the researchers. Aim: Our study aims to explore the energy consumption of
different dataframe processing libraries as a first step towards studying the
energy efficiency of the data oriented stages of the machine learning pipeline.
Method: We measure the energy consumption of 3 popular libraries used to work
with dataframes, namely Pandas, Vaex and Dask for 21 different operations
grouped under 4 categories on 2 datasets. Results: The results of our analysis
show that for a given dataframe processing operation, the choice of library can
indeed influence the energy consumption with some libraries consuming 202 times
lesser energy over others. Conclusion: The results of our study indicates that
there is a potential for optimizing the energy consumption of the data oriented
stages of the machine learning pipeline and further research is needed in the
direction.
