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This paper presents a comparative performance analysis of three popular Python data manipulation libraries—Pandas, Polars, and Dask—within the context of deep learning training pipelines. The existing studies in this area do not embed the libraries inside a full deep-learning training pipeline where data loading, preprocessing, and batch feeding interact tightly with GPU workloads. To bridge this gap, we integrate Pandas, Polars, and Dask into representative deep learning training and inference pipelines and conduct experi- ments across a wide range of various machine learning models and datasets, measuring key performance indicators such as runtime, memory usage, disk usage, and energy consumption (CPU and GPU). Our comprehensive analysis reveals that Polars consistently minimizes CPU energy consumption on larger workloads, while Pandas remains competitive for moderate sizes. Dask’s overhead can lead to higher energy usage on small to moderate datasets. All three libraries achieve similar runtimes for heavy GPU workloads (ResNet, Mask R-CNN). Polars and Pandas maintain lower CPU memory footprints than Dask, but Dask offers easier scalability if data truly exceeds available RAM. Polars shows marginal energy savings on the CPU during preprocessing.
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This content will become publicly available on April 14, 2027
Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines – A Comparative Analysis
This paper presents a comparative performance analysis of three popular Python data manipulation libraries—Pandas, Polars, and Dask—within the context of deep learning training pipelines. The existing studies in this area do not embed the libraries inside a full deep-learning training pipeline where data loading, preprocessing, and batch feeding interact tightly with GPU workloads. To bridge this gap, we integrate Pandas, Polars, and Dask into representative deep learning training and inference pipelines and conduct experiments across a wide range of various machine learning models and datasets, measuring key performance indicators such as runtime, memory usage, disk usage, and energy consumption (CPU and GPU). Our comprehensive analysis reveals that Polars consistently minimizes CPU energy consumption on larger workloads, while Pandas remains competitive for moderate sizes. Dask’s overhead can lead to higher energy usage on small to moderate datasets. All three libraries achieve similar runtimes for heavy GPU workloads (ResNet, Mask R-CNN). Polars and Pandas maintain lower CPU memory footprints than Dask, but Dask o"ers easier scalability if data truly exceeds available RAM. Polars shows marginal energy savings on the CPU during preprocessing.
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- Award ID(s):
- 2343284
- PAR ID:
- 10693912
- Publisher / Repository:
- IEEE/ACM International Conference on Software Engineering, 2026
- Date Published:
- Format(s):
- Medium: X
- Location:
- Rio de Janeiro, Brazil
- Sponsoring Org:
- National Science Foundation
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