DataFrame – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-03T22:20:47Z http://www.open-lab.net/blog/feed/ Jess Nguyen <![CDATA[ICYMI: Unlocking the Power of GPU-Accelerated DataFrames?in Python]]> http://www.open-lab.net/blog/?p=68916 2023-08-24T18:03:51Z 2023-08-04T16:00:00Z Read this tutorial on how to tap into GPUs by importing cuDF instead of pandas�Cwith only a few code changes.]]> Read this tutorial on how to tap into GPUs by importing cuDF instead of pandas�Cwith only a few code changes.An illustration with 3 different colored squares labeled GPUs in a row.

Read this tutorial on how to tap into GPUs by importing cuDF instead of pandas�Cwith only a few code changes.

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Prachi Goel <![CDATA[Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF]]> http://www.open-lab.net/blog/?p=61837 2023-12-12T23:48:52Z 2023-03-14T14:01:00Z This post is part of a series on accelerated data analytics. Digital advancements in climate modeling, healthcare, finance, and retail are generating...]]> This post is part of a series on accelerated data analytics. Digital advancements in climate modeling, healthcare, finance, and retail are generating...

This post is part of a series on accelerated data analytics. Digital advancements in climate modeling, healthcare, finance, and retail are generating unprecedented volumes and types of data. IDC says that by 2025, there will be 180 ZB of data compared to 64 ZB in 2020, scaling up the need for data analytics to turn all that data into insights. NVIDIA provides the RAPIDS suite of��

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Prachi Goel <![CDATA[Accelerated Data Analytics: Faster Time Series Analysis with RAPIDS cuDF]]> http://www.open-lab.net/blog/?p=61790 2025-05-07T22:43:39Z 2023-03-14T14:00:00Z This post is part of a series on accelerated data analytics. [stextbox id="info"]Update: The below blog describes how to use GPU-only RAPIDS cuDF, which...]]> This post is part of a series on accelerated data analytics. [stextbox id="info"]Update: The below blog describes how to use GPU-only RAPIDS cuDF, which...Abstract bar graph

This post is part of a series on accelerated data analytics. Update: The below blog describes how to use GPU-only RAPIDS cuDF, which requires code changes. RAPIDS cuDF now has a CPU/GPU interoperability (cudf.pandas) that speeds up pandas code by up to 150x with zero code changes. At GTC 2024, NVIDIA announced that the cudf.pandas library is now GA. At Google I/O��

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Vukasin Milovanovic <![CDATA[Boosting Data Ingest Throughput with GPUDirect Storage and RAPIDS cuDF]]> http://www.open-lab.net/blog/?p=47682 2023-06-12T20:36:40Z 2022-05-27T21:45:32Z If you work in data analytics, you know that data ingest is often the bottleneck of data preprocessing workflows. Getting data from storage and decoding it can...]]> If you work in data analytics, you know that data ingest is often the bottleneck of data preprocessing workflows. Getting data from storage and decoding it can...

If you work in data analytics, you know that data ingest is often the bottleneck of data preprocessing workflows. Getting data from storage and decoding it can often be one of the most time-consuming steps in the workflow because of the data volume and the complexity of commonly used formats. Optimizing data ingest can greatly reduce this bottleneck for data scientists working on large data sets.

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Brandon Miller <![CDATA[Prototyping Faster with the Newest UDF Enhancements in the NVIDIA cuDF API]]> http://www.open-lab.net/blog/?p=47368 2024-07-12T21:35:48Z 2022-05-27T19:57:02Z Over the past few releases, the NVIDIA cuDF team has added several new features to user-defined functions (UDFs) that can streamline the development process...]]> Over the past few releases, the NVIDIA cuDF team has added several new features to user-defined functions (UDFs) that can streamline the development process...

Over the past few releases, the NVIDIA cuDF team has added several new features to user-defined functions (UDFs) that can streamline the development process while improving overall performance. In this post, I walk through the new UDF enhancements and show how you can take advantage of them within your own applications: If you��re not familiar with pandas, series apply is the main��

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Eryk Lewinson <![CDATA[Three Approaches to Encoding Time Information as Features for ML Models]]> http://www.open-lab.net/blog/?p=44228 2022-08-21T23:53:25Z 2022-02-17T16:00:00Z Imagine you have just started a new data science project. The goal is to build a model predicting Y, the target variable. You have already received some data...]]> Imagine you have just started a new data science project. The goal is to build a model predicting Y, the target variable. You have already received some data...

Imagine you have just started a new data science project. The goal is to build a model predicting Y, the target variable. You have already received some data from the stakeholders/data engineers, did a thorough EDA, and selected some variables you believe are relevant for the problem at hand. Then you finally built your first model. The score is acceptable, but you believe you can do much better.

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Tom Drabas <![CDATA[Dask Tutorial �C Beginner��s Guide to Distributed Computing with GPUs in Python]]> http://www.open-lab.net/blog/?p=24732 2022-08-21T23:41:08Z 2021-03-18T23:45:22Z This is the third installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its...]]> This is the third installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its...

This is the third installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform, Load) problems, build ML (Machine Learning) and DL (Deep Learning) models, explore expansive graphs, process geospatial, signal, and system log data, or use SQL language via BlazingSQL to process data.

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Tom Drabas <![CDATA[Python Pandas Tutorial: A Beginner��s Guide to GPU Accelerated DataFrames for Pandas Users]]> http://www.open-lab.net/blog/?p=24011 2024-05-15T16:09:08Z 2021-03-11T18:19:17Z This series on the RAPIDS ecosystem explores the various aspects that enable you to solve extract, transform, load (ETL) problems, build machine learning (ML)...]]> This series on the RAPIDS ecosystem explores the various aspects that enable you to solve extract, transform, load (ETL) problems, build machine learning (ML)...

This series on the RAPIDS ecosystem explores the various aspects that enable you to solve extract, transform, load (ETL) problems, build machine learning (ML) and deep learning (DL) models, explore expansive graphs, process signal and system logs, or use the SQL language through BlazingSQL to process data. For part 1, see Pandas DataFrame Tutorial: A Beginner��s Guide to GPU Accelerated DataFrames��

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Tom Drabas <![CDATA[Pandas DataFrame Tutorial �C Beginner��s Guide to GPU Accelerated DataFrames in Python]]> http://www.open-lab.net/blog/?p=23974 2024-05-15T16:07:38Z 2021-03-03T18:22:21Z This post is the first installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that...]]> This post is the first installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that...

This post is the first installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform, Load) problems, build ML (Machine Learning) and DL (Deep Learning) models, explore expansive graphs, process geospatial, signal, and system log data, or use SQL language via BlazingSQL to process��

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