Evaluation of Dataframe Libraries for Data Preparation on a Single Machine

Data preparation is a trial-and-error process that typically involves countless iterations over the data to define the best pipeline of operators for a given task. With tabular data, practitioners often perform that burdensome activity on local machines by writing ad hoc scripts with libraries based on the Pandas dataframe API and testing them on samples of the entire dataset--the faster the library, the less idle time its users have. In this paper, we evaluate the most popular Python dataframe libraries in general data preparation use cases to assess how they perform on a single machine. To do so, we employ 4 real-world datasets and pipelines with distinct characteristics, covering a variety of scenarios. The insights gained with this experimentation are useful to data scientists who need to choose which of the dataframe libraries best suits their data preparation task at hand. In a nutshell, we found that: for small datasets, Pandas consistently proves to be the best choice with the richest API; when RAM is limited and there is no need to complete compatibility with Pandas API, Polars is the go-to choice thanks to its resource and query optimization; when a GPU is available, CuDF often yields the best performance, while for very large datasets that cannot fit in the GPU memory and RAM, PySpark (thanks to a multi-thread execution and a query optimizer) and Vaex (exploiting a columnar data format) are the best options.