# Accelerating Data Science Workflows with GPUs The benefits and applications of GPU usage in data preparation processes - **Source:** [Towards Data Science](https://towardsdatascience.com) - **Published:** 2026-07-21 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/077812d5-8592-4e59-9b6c-6dbf79461b3e ## The Role of GPUs in Data Science Traditionally, data science workloads ran on CPUs. However, when working with large datasets, this approach can lead to performance issues. GPUs can provide significant acceleration not just for deep learning but also for tabular data processing. ## GPU Support in Python Ecosystem Increasing GPU support in Python's data science stack enables significant acceleration of existing workflows. Tools like NVIDIA's RAPIDS project allow data scientists to leverage GPUs without major code changes. ## cuDF: GPU DataFrame Library cuDF provides a pandas-like API for data processing on GPUs. New projects can create GPU-native workflows using cuDF's API. Many familiar pandas operations can be executed on GPUs through cuDF. ## Migrating Existing Code to GPUs The cudf.pandas module enables running existing pandas code on GPUs with minimal changes. This provides significant time savings for data scientists working with large datasets. ## Data Loading and Transformation cuDF offers GPU versions of pandas' read_* functions. Data can be loaded from formats like Parquet and CSV using cudf.read_parquet. Once loaded into GPU memory, all operations execute on the GPU. ## Basic DataFrame Operations cuDF supports fundamental pandas operations like column selection, row filtering, new column creation, and summary statistics. For example, filtering like trips[trips['fare_amount'] > 20] can be done directly on the GPU. ## Conversion Between pandas and cuDF It's possible to convert cuDF DataFrames to pandas and vice versa. This feature is particularly useful when working with libraries that only support pandas or when gradually migrating workflows to GPUs. ## Performance Comparison Tests on NYC Taxi data show that operations like groupby and agg run significantly faster on GPUs with cuDF compared to pandas. The difference becomes more pronounced with larger datasets. ## Installation and Compatibility cuDF comes pre-installed in Google Colab. For local installation, the package matching the system's CUDA version must be selected. While cuDF closely follows the pandas API, some behavioral differences may exist. ## Real-World Scenarios Real data processing workflows rarely consist of single operations. cuDF enables running the entire pipeline - cleaning, feature engineering, and preprocessing - on GPUs, saving considerable time. ## GPU Revolution in Data Science Using GPUs for data preparation allows data scientists to spend less time waiting and more time analyzing. This technology has the potential to transform data science workflows in the era of big data. ## Sources covering this story - https://towardsdatascience.com/how-much-of-a-data-science-workflow-can-run-on-a-gpu-today-part-1-accelerating-data-preparation/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._