3w ago

How to Spot (and Fix) 5 Common Performance Bottlenecks in Pandas Workflows

developer.nvidia.com How to Spot (and Fix) 5 Common Performance Bottlenecks in pandas Workflows | NVIDIA Technical Blog

Slow data loads, memory-intensive joins, and long-running operations—these are problems every Python practitioner has faced. They waste valuable time and make iterating on your ideas harder than it…

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Use polars, or some framework that's been built with performance from the start?
I have optimized pandas notebooks before, but it seems a bit like a foundation of sand.
plus a GPU-powered drop-in accelerator, cudf.pandas, that delivers order-of-magnitude speedups with no code changes.
Don’t have a GPU on your machine? No problem—you can use cudf.pandas for free in Google Colab, where GPUs are available and the library comes pre-installed.
Ah, I see why NVIDIA is writing about it.