How to Spot (and Fix) 5 Common Performance Bottlenecks in Pandas Workflows
8 days ago
- #GPU-acceleration
- #pandas
- #data-processing
- Slow data loads, memory-intensive joins, and long-running operations are common issues in Python.
- Five common pandas bottlenecks are discussed with solutions including CPU tweaks and GPU-powered accelerators like cudf.pandas.
- cudf.pandas can be used for free in Google Colab, even without a local GPU.
- 1. Slow CSV parsing can be mitigated with PyArrow or cudf.pandas for faster reads.
- 2. Large joins or merges can be optimized with indexed joins or GPU acceleration.
- 3. String-heavy datasets can be managed by converting to category types or using GPU-optimized string operations.
- 4. Slow groupby operations can be sped up by reducing dataset size or using GPU acceleration.
- 5. Memory issues can be addressed by downcasting numeric types, converting strings to categories, or using Unified Virtual Memory on GPU.
- GPU acceleration can be used with Polars for similar performance improvements.
- A free course is available for deeper learning on GPU accelerators.