The energy consumption of machine learning applications and their impact on the environment has recently gained attention as a research area, focusing on the model creation and training/inference phases. However, the data-oriented stages of the machine learning pipeline, which involve preprocessing, cleaning, and exploratory analysis, are critical components. However, energy consumption during these stages has received limited attention. To fill this gap, as a first step, we aim to investigate the energy consumption of three popular dataframe processing libraries, namely Pandas, Vaex, and Dask. We perform experiments across 21 dataframe processing operations within four categories, utilizing three distinct datasets. Our results indicate that no single library is the most energy efficient for all tasks, and the choice of a library can have a significant impact on energy consumption based on the types and frequencies of operations performed. The findings of this study suggest the potential for optimization of the energy consumption of data-oriented stages in the machine learning pipeline and warrant further research in this area.