A comparative overview

What’s cuDF Pandas?

Should you’re a person of the Pandas library in Python, and also you need or must maximise your program run instances, then you have got a number of choices out there to you. Most of those choices revolve round using exterior libraries that supplant current Pandas operations and are optimised for knowledge processing at scale and velocity. Examples of those libraries are VAEX, POLARS, DuckDB and others. The difficulty with these is that usually, they require you to re-write your code to a higher or lesser extent which might not be one thing you need, or have the flexibility, to do.

In case you are fortunate sufficient to have a GPU in your system then one other, newer choice which has turn out to be out there is known as cuDF.pandas.

cuDF.pandas is constructed upon cuDF, a Python GPU DataFrame library (based mostly on the Apache Arrow columnar reminiscence format) for loading, becoming a member of, aggregating, filtering, and in any other case manipulating knowledge.

To make use of cuDF Pandas, you merely provide a flag if operating Python from the command line or load an extension if operating Python by way of a Jupyter Pocket book. When GPU computation is supported (e.g. there’s an NVIDIA GPU out there, and cuDF is aware of find out how to run the Pandas code), your code will run on the GPU. In circumstances the place this isn’t attainable, cuDF mechanically switches to operating on the CPU. You don’t want to jot down two variations of your code, and also you don’t must deal with switching between GPU and CPU manually.

cuDF is a part of RAPIDS, an open-source suite of software program libraries and APIs created and maintained by NVIDIA, the pioneering firm in GPU (Graphics Processing Unit) know-how and the creator of CUDA (Compute Unified Gadget Structure). RAPIDS is designed to allow knowledge scientists and engineers to leverage the facility of GPU acceleration for knowledge exploration and analytics pipelines. It does this by utilizing the parallel processing functionality of GPUs and permits for important efficiency enhancements in comparison with conventional CPU-based knowledge processing.

On this article, we’ll pit common Pandas towards cuDF Pandas and see what all of the fuss is about. We’ll supply a big enter file, learn it right into a pandas dataframe and carry out some manipulations on the dataframe. At every step…