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Background: The energy consumption of machine learning and its impact on theenvironment has made energy efficient ML an emerging area of research. However,most of the attention stays focused on the model creation and the training andinferencing phase. Data oriented stages like preprocessing, cleaning andexploratory analysis form a critical part of the machine learning workflow.However, the energy efficiency of these stages have gained little attentionfrom the researchers. Aim: Our study aims to explore the energy consumption ofdifferent dataframe processing libraries as a first step towards studying theenergy efficiency of the data oriented stages of the machine learning pipeline.Method: We measure the energy consumption of 3 popular libraries used to workwith dataframes, namely Pandas, Vaex and Dask for 21 different operationsgrouped under 4 categories on 2 datasets. Results: The results of our analysisshow that for a given dataframe processing operation, the choice of library canindeed influence the energy consumption with some libraries consuming 202 timeslesser energy over others. Conclusion: The results of our study indicates thatthere is a potential for optimizing the energy consumption of the data orientedstages of the machine learning pipeline and further research is needed in thedirection.

On the Energy Consumption of Different Dataframe Processing Libraries -- An Exploratory Study