7 Drop-In Replacements to Instantly Speed Up Your Python Data Science Workflows

You’ve been there. You wrote a perfect Python script, tested it on a small CSV, and it ran smoothly. But when you tried it on a 10-million-row dataset, your laptop fan kicked into overdrive, the console froze, and you had time to brew three coffees before seeing any output. What if you could speed up these exact workflows with just a single line of code or a simple parameter change? The good news is you can. Many popular Python data science libraries now support GPU acceleration with minimal effort. Tools like NVIDIA cuDF, cuML, and cuGraph let you keep your existing code while unlocking dramatic performance gains—especially on large datasets. Here are seven drop-in replacements that can supercharge your data science workflows with little to no code changes. Use pandas with GPU power via cuDF Pandas is the go-to library for data manipulation, but it slows down quickly with big data. With the cudf.pandas extension, you can run your pandas code on the GPU without rewriting anything. Simply add %%load_ext cudf.pandas at the top of your notebook or script. cuDF automatically handles GPU execution for supported operations, making data loading, filtering, and aggregation much faster. Try it: Run a stock analysis on 18 million rows and see the difference in speed between CPU and GPU. Make Polars even faster with GPU support Polars is already known for its speed, but it can go even further. By enabling the GPU-powered execution engine with .collect(engine="gpu"), you can process massive datasets like 100 million transaction records in under two seconds. This works seamlessly with Polars’ query optimization and allows you to scale beyond what’s possible on CPU alone. Accelerate scikit-learn with GPU support Scikit-learn is widely used for machine learning, but training on large datasets can take minutes or hours. With cuml.accel, you can run common scikit-learn models like Random Forest on the GPU with zero code changes. Just load the extension with %load_ext cuml.accel and keep writing your scikit-learn code. Under the hood, cuML handles GPU execution—training that once took minutes now runs in seconds. Note: Not all models are supported yet, but coverage is growing fast. Speed up XGBoost with one parameter XGBoost has built-in GPU support. All you need to do is set device="cuda" when initializing your model. This enables full GPU acceleration for training and inference, drastically reducing iteration time during hyperparameter tuning and feature engineering. Try it with real-world taxi fare data and see how quickly you can build and improve models. Speed up UMAP visualizations UMAP is excellent for dimensionality reduction and visualization, but it can be painfully slow on large datasets. With cuML’s accelerator mode, you can generate UMAP projections in under a second instead of minutes. Just load %load_ext cuml.accel and use the same umap.UMAP code. No changes needed—your visualization runs on the GPU automatically. Accelerate HDBSCAN clustering HDBSCAN is powerful for finding clusters in complex data, but CPU execution can take 30–60 seconds even on small datasets. With cuml.accel, you can run HDBSCAN in under two seconds on high-dimensional data. Same code. Same import. Just load the extension and let the GPU do the work. Scale NetworkX graphs with GPU power NetworkX is a standard for graph analysis, but its pure-Python implementation struggles with large graphs. The new nx-cugraph backend lets you use the same NetworkX syntax while running on the GPU. Set the environment variable NX_CUGRAPH_AUTOCONFIG=True and your code automatically runs on cuGraph. No rewrites, no conversions—just faster graph analytics. Conclusion You don’t need to be a CUDA expert to get massive speedups. With these tools, you can keep your existing workflows and unlock GPU performance with just a few lines of code. All examples, notebooks, and starter code are available on GitHub. Start building faster today.