Efficient Dataframe Systems: Lazy Fat Pandas on a Diet
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913649540464640 |
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| author | Singh, Bhushan Pal Kumar, Priyesh Bhattacharya, Chiranmoy Sudarshan, S. |
| author_facet | Singh, Bhushan Pal Kumar, Priyesh Bhattacharya, Chiranmoy Sudarshan, S. |
| contents | Pandas is widely used for data science applications, but users often run into problems when datasets are larger than memory. There are several frameworks based on lazy evaluation that handle large datasets, but the programs have to be rewritten to suit the framework, and the presence of multiple frameworks complicates the life of a programmer. In this paper we present a framework that allows programmers to code in plain Pandas; with just two lines of code changed by the user, our system optimizes the program using a combination of just-in-time static analysis, and runtime optimization based on a lazy dataframe wrapper framework. Moreover, our system allows the programmer to choose the backend. It works seamlessly with Pandas, Dask, and Modin, allowing the choice of the best-suited backend for an application based on factors such as data size. Performance results on a variety of programs show the benefits of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Dataframe Systems: Lazy Fat Pandas on a Diet Singh, Bhushan Pal Kumar, Priyesh Bhattacharya, Chiranmoy Sudarshan, S. Databases Pandas is widely used for data science applications, but users often run into problems when datasets are larger than memory. There are several frameworks based on lazy evaluation that handle large datasets, but the programs have to be rewritten to suit the framework, and the presence of multiple frameworks complicates the life of a programmer. In this paper we present a framework that allows programmers to code in plain Pandas; with just two lines of code changed by the user, our system optimizes the program using a combination of just-in-time static analysis, and runtime optimization based on a lazy dataframe wrapper framework. Moreover, our system allows the programmer to choose the backend. It works seamlessly with Pandas, Dask, and Modin, allowing the choice of the best-suited backend for an application based on factors such as data size. Performance results on a variety of programs show the benefits of our framework. |
| title | Efficient Dataframe Systems: Lazy Fat Pandas on a Diet |
| topic | Databases |
| url | https://arxiv.org/abs/2501.08207 |