Boosting Performance and Memory Efficiency with PyArrow and Pandas for Clinical Trial Data

1. Introduction

In the world of data analysis and manipulation, efficiency and memory usage play crucial roles, especially when dealing with large datasets. Clinical trials generate vast amounts of data, making it imperative to employ tools that optimize both processing time and memory utilization. One such strategy involves combining the power of Pandas and PyArrow, two popular Python libraries for data manipulation and in-memory columnar storage, respectively.

In this blog, we'll delve into how PyArrow can be integrated with Pandas …

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