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DATA SCIENCE
How to Empower Pandas with GPUs
A quick introduction to cuDF, an NVIDIA framework for accelerating Pandas
Pandas remains a crucial tool in data analytics and machine learning endeavors, offering extensive capabilities for tasks such as data reading, transformation, cleaning, and writing. However, its efficiency with large datasets is somewhat limited, hindering its application in production environments or for constructing resilient data pipelines, despite its widespread use in data science projects.
Similar to Apache Spark, Pandas loads the data into memory for computation and transformation. But unlike Spark, Pandas is not a distributed compute platform, and therefore everything must be done on a single system CPU and memory (single-node processing). This feature limits the use of Pandas in two ways:
- Pandas on a single system cannot handle a large amount of data.
- Even for the data that fits into a single system memory, it may take considerable time to process a relatively small dataset.
Pandas on Steroid
The first issue is addressed by frameworks such as Dask. Dask DataFrame helps you process large tabular data by parallelizing Pandas on a distributed cluster of computers. In many ways…
