The article discusses the limitations of Pandas when dealing with large datasets, as it is a single-node processing framework that loads data into memory for computation and transformation. This can hinder its use in production environments or for building robust data pipelines.
To address the first issue of Pandas' inability to handle large amounts of data, the author introduces Dask DataFrame, a framework that helps process large tabular data by parallelizing Pandas on a distributed cluster of computers.
However, the article focuses on cuDF, an NVIDIA framework that can further accelerate Pandas-based data processing by leveraging the power of GPUs. cuDF provides a Pandas-like API, allowing users to seamlessly integrate it into their existing Pandas-based workflows.
The key highlights and insights from the article are:
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by Naser Tamimi 게시일 towardsdatascience.com 04-07-2024
https://towardsdatascience.com/how-to-empower-pandas-with-gpus-43909ad59e75더 깊은 질문