{ "source_file": "0930", "source_platform": "Medium", "author": "Naser Tamimi", "title_listed": "How to Empower Pandas with GPUs", "keywords": "", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/how-to-empower-pandas-with-gpus-43909ad59e75/", "publish_date": "2024-04-07", "corpus_stage": "", "notes": "", "id": "5cd6a960edeb", "collected_at": "2026-10-02T04:59:32", "title": "How to Empower Pandas with GPUs | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# How to Empower Pandas with GPUs\nA quick introduction to cuDF, an NVIDIA framework for accelerating Pandas\n[Naser Tamimi](https://towardsdatascience.com/author/tamimi-naser/)\nApril 7, 20247 min read\n### DATA SCIENCE\n![Photo by BoliviaInteligente on Unsplash](../images/000.jpg)\nPhoto by [BoliviaInteligente](https://unsplash.com/@boliviainteligente?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nPandas remains a crucial tool in data analytics and machine learning endeavors, offering extensive capabilities for tasks such as data reading, transformation, cleaning, and writing. However, its efficiency with large datasets is somewhat limited, hindering its application in production environments or for constructing resilient data pipelines, despite its widespread use in data science projects.\nSimilar to Apache Spark, Pandas loads the data into memory for computation and transformation. But unlike Spark, Pandas is not a distributed compute platform, and therefore everything must be done on a single system CPU and memory (single-node processing). This feature limits the use of Pandas in two ways:\n 1. Pandas on a single system cannot handle a large amount of data.\n 2. Even for the data that fits into a single system memory, it may take considerable time to process a relatively small dataset.\n\n\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Pandas on Steroid\nThe first issue is addressed by frameworks such as Dask. Dask DataFrame helps you process large tabular data by parallelizing Pandas on a distributed cluster of computers. In many ways, Pandas empowered by Dask is similar to Apache Spark (however, still Spark can handle large datasets more efficiently and that's why it is a preferred tool among data engineers).\nAlthough Dask enables parallel processing of large datasets across a cluster of machines, in reality, the data for most machine learning projects can be accommodated within a single system's memory. Consequently, employing a cluster of machines for such projects might be excessive. Thus, there is a need for a tool that efficiently executes Pandas operations in parallel on a single machine, addressing the second issue mentioned earlier.\nWhenever someone talks about parallel processing, the first word that comes to most engineers' minds is GPU. For a long time, it was a wish to run Pandas on GPU for efficient parallel computing. The wish came true with the introduction of NVIDIA RAPIDS cuDF. cuDF (pronounced \"KOO-dee-eff\") is a GPU DataFrame library for processing data in parallel using GPU.\nAt NVIDIA GTC 2024, it was announced that RAPIDS cuDF can now bring GPU acceleration to 9.5M million pandas users without requiring them to change their code.\n## Installation\nBefore proceeding with the installation, ensure that you have access to an NVIDIA GPU on your system. If you possess an NVIDIA graphics card locally, you can execute cuDF on your machine. Alternatively, if you lack such hardware, you can still run your cuDF code on cloud platforms like Google Colab, which offers sufficient GPU resources for your use case. Personally, I have a GeForce RTX 3090 installed on my system, hence I opted to conduct this test locally. However, beginners may find it more convenient to run the cuDF codes in this tutorial on a Google Colab instance.\nIn addition, as of April 2024, cuDF only runs on Linux OS and Python 3.9 or higher. Please visit [this page](https://docs.rapids.ai/install#system-recommendations) for system requirements, before starting the installation.\nTo facilitate easier comprehension for readers, I've divided the installation part into two sections. The first section helps users who want to install cuDF on their local system, and the second section guides the Google Colab users through the installation.\n### Installing on a System with a GPU\nAs the cuDF name suggests, it works on the CUDA platform (developed by NVIDIA). Therefore, you need to install the CUDA toolkit on your system first. I suggest to install CUDA 12.0 if you don't have it installed. Otherwise, any CUDA 11 or newer should work fine.\nIf you are planning to run cuDF on your local system, after installing the CUDA toolkit, you need to install the RAPIDS framework. Installing RAPIDS using the UI command builder is easy and straightforward. You need to navigate to and select your system CUDA and Python versions. You can choose the Standard Package (which includes `cuDF`, `cuML`, `cuGraph`, `cuSpatial`, `cuProj`, `cuxfilter`, `cuCIM`, `RAFT`) or choose cuDF only through \"Choose Specific Package\". The following screenshot is what I chose for my system.\n![](../images/body-601eebe10b499c29a711.png)\n### Google Colab\nIf you are planning to run cuDF on Google Colab, you have an easier path. First, Google Colab and many similar services have the CUDA toolkit already installed on their GPU instances.\nFor installing RAPIDS, as [this document](https://colab.google/articles/cudf) from Google explains in detail, you need to take two steps. Step 1 is to choose a Colab runtime that uses a GPU accelerator. Step 2 is running the following commands in your notebook to install RAPIDS on your Colab instance via pip.\nbash\n\n```\n !pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com\n !rm -rf /usr/local/lib/python3.8/dist-packages/cupy*\n !pip install cupy-cuda11x \n```\n\nPlease read the \"Getting Started with RAPIDS on Colab\" section of the mentioned [document](https://colab.google/articles/cudf) for more details.\n## An Example to Test\nTo compare the capabilities of CuDF Pandas and standard Pandas, I ran a test using a fake dataset with 10 million records. You can find all the required files in this repository: \nFirst, we need to create a large test dataset. If you have access to a big dataset that you can load into Pandas, you can go ahead with your dataset. I decided to create a fake CSV dataset with 10 million rows and use it for this test. I used the following Python code ([link to repo](https://github.com/tamiminaser/cudf-examples/blob/master/example1/fake_data.py)) to create my fake dataset and called it `data_10000000.csv`.\npython\n\n```\nimport csv\nfrom faker import Faker\nimport datetime\nfrom tqdm import tqdm\n\ndef datagenerate(records, headers):\n fake = Faker('en_US')\n with open(\"data_10000000.csv\", 'wt') as csvFile:\n writer = csv.DictWriter(csvFile, fieldnames=headers)\n writer.writeheader()\n for i in tqdm(range(records)):\n full_name = fake.name()\n FLname = full_name.split(\" \")\n Fname = FLname[0]\n Lname = FLname[1]\n domain_name = \"@testDomain.com\"\n userId = Fname +\".\"+ Lname + domain_name\n\n writer.writerow({\n \"user_id\": fake.unique.random_int(min=1111111111111, max=9999999999999),\n \"name\": fake.name(),\n \"date_of_birth\" : fake.date(pattern=\"%d-%m-%Y\", end_datetime=datetime.date(2000, 1,1)),\n \"country_of_birth\": fake.country(),\n \"email\": fake.email(),\n \"record_year\": fake.year(),\n \"score\": fake.random_int(min=1, max=999),\n })\n\nrecords = 100000000\nheaders = [\"user_id\", \"name\", \"date_of_birth\", \"country_of_birth\", \"email\", \"record_year\", \"score\"]\ndatagenerate(records, headers)\nprint(\"CSV generation complete!\")\n```\n\nIt will take a long time to create such a 10 million rows csv file and the output file size will be about 8 GB. You can lower the `records` number (for example to 3M records) in the code to produce smaller datasets. Also, I used my NVIDIA GeForce RTX 3090, which has 24 GB memory and I had no issue loading such a large dataset, but if you are using GPUs with smaller memories, you might need to work with a smaller dataset.\nAfter producing the fake CSV file, you can do the following test on the file. First, let's begin with standard Pandas and observe the processing time for this dataset. Below is the Jupyter Notebook we utilized for the initial phase of the test. (Note, I limited my code to 3M records here to loop the test 10 times for a more accurate comparison).\nNow, let's do the same processing using cuDF Pandas.\nAs you see, both codes are similar and the only difference is in the first cell of the cuDF notebook. In Jupiter Notebook, simply by adding `%load_ext cudf.pandas` before importing Pandas we could use cudf.pandas instead of standard Pandas and enjoy all of its benefits (it could not be simpler).\nAs you see, the group by process (cell #3) took about 1.06 seconds via standard Pandas, and it took only 122 ms via cuDF Pandas. It means that cuDF Pandas is about 8 times faster than the standard Pandas on CPU.\nIn Cell #4, we did a more complicated series of joining and transformations. For this one, it took 6 seconds for the standard Pandas to process the data, but it took about 109 ms for cuDF Pandas to do the same process. Therefore, cuDF Pandas could process the same data 55 times faster.\n## Summary\nAs you see, cuDF provides an API that enables us to transform Pandas dataframes on GPUs with minimum effort. The performance of cuDF compared to standard Pandas on a CPU is 10–100 times faster according to our test.\nAs mentioned at the beginning, Dask is another popular tool that enables us to run Pandas transformations and jobs on a cluster of machines. cuDF and Dask could be combined to even enhance the processing further and take advantage of both distributed compute systems as well as GPUs to process the data faster ([read more](https://docs.rapids.ai/api/cudf/nightly/user_guide/10min/)).\n## Update\nAt Google I/O 2024, Laurence Moroney, the Head of AI Advocacy at Google, announced the integration of RAPIDS cuDF into Google Colab. This enables developers to significantly speed up pandas code, achieving up to a 50x performance boost on Google Colab GPU instances, all while allowing them to continue using pandas seamlessly as their data scales. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# How to Empower Pandas with GPUs\nA quick introduction to cuDF, an NVIDIA framework for accelerating Pandas\n[Naser Tamimi](https://towardsdatascience.com/author/tamimi-naser/)\nApril 7, 20247 min read\n### DATA SCIENCE\n![Photo by BoliviaInteligente on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/0QZesVFOEipPavhpy-scaled.jpg)\nPhoto by [BoliviaInteligente](https://unsplash.com/@boliviainteligente?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nPandas remains a crucial tool in data analytics and machine learning endeavors, offering extensive capabilities for tasks such as data reading, transformation, cleaning, and writing. However, its efficiency with large datasets is somewhat limited, hindering its application in production environments or for constructing resilient data pipelines, despite its widespread use in data science projects.\nSimilar to Apache Spark, Pandas loads the data into memory for computation and transformation. But unlike Spark, Pandas is not a distributed compute platform, and therefore everything must be done on a single system CPU and memory (single-node processing). This feature limits the use of Pandas in two ways:\n 1. Pandas on a single system cannot handle a large amount of data.\n 2. Even for the data that fits into a single system memory, it may take considerable time to process a relatively small dataset.\n\n\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Pandas on Steroid\nThe first issue is addressed by frameworks such as Dask. Dask DataFrame helps you process large tabular data by parallelizing Pandas on a distributed cluster of computers. In many ways, Pandas empowered by Dask is similar to Apache Spark (however, still Spark can handle large datasets more efficiently and that's why it is a preferred tool among data engineers).\nAlthough Dask enables parallel processing of large datasets across a cluster of machines, in reality, the data for most machine learning projects can be accommodated within a single system's memory. Consequently, employing a cluster of machines for such projects might be excessive. Thus, there is a need for a tool that efficiently executes Pandas operations in parallel on a single machine, addressing the second issue mentioned earlier.\nWhenever someone talks about parallel processing, the first word that comes to most engineers' minds is GPU. For a long time, it was a wish to run Pandas on GPU for efficient parallel computing. The wish came true with the introduction of NVIDIA RAPIDS cuDF. cuDF (pronounced \"KOO-dee-eff\") is a GPU DataFrame library for processing data in parallel using GPU.\nAt NVIDIA GTC 2024, it was announced that RAPIDS cuDF can now bring GPU acceleration to 9.5M million pandas users without requiring them to change their code.\n## Installation\nBefore proceeding with the installation, ensure that you have access to an NVIDIA GPU on your system. If you possess an NVIDIA graphics card locally, you can execute cuDF on your machine. Alternatively, if you lack such hardware, you can still run your cuDF code on cloud platforms like Google Colab, which offers sufficient GPU resources for your use case. Personally, I have a GeForce RTX 3090 installed on my system, hence I opted to conduct this test locally. However, beginners may find it more convenient to run the cuDF codes in this tutorial on a Google Colab instance.\nIn addition, as of April 2024, cuDF only runs on Linux OS and Python 3.9 or higher. Please visit [this page](https://docs.rapids.ai/install#system-recommendations) for system requirements, before starting the installation.\nTo facilitate easier comprehension for readers, I've divided the installation part into two sections. The first section helps users who want to install cuDF on their local system, and the second section guides the Google Colab users through the installation.\n### Installing on a System with a GPU\nAs the cuDF name suggests, it works on the CUDA platform (developed by NVIDIA). Therefore, you need to install the CUDA toolkit on your system first. I suggest to install CUDA 12.0 if you don't have it installed. Otherwise, any CUDA 11 or newer should work fine.\nIf you are planning to run cuDF on your local system, after installing the CUDA toolkit, you need to install the RAPIDS framework. Installing RAPIDS using the UI command builder is easy and straightforward. You need to navigate to and select your system CUDA and Python versions. You can choose the Standard Package (which includes `cuDF`, `cuML`, `cuGraph`, `cuSpatial`, `cuProj`, `cuxfilter`, `cuCIM`, `RAFT`) or choose cuDF only through \"Choose Specific Package\". The following screenshot is what I chose for my system.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1j3OuscSYA19cb5NmLCmZbw.png)\n### Google Colab\nIf you are planning to run cuDF on Google Colab, you have an easier path. First, Google Colab and many similar services have the CUDA toolkit already installed on their GPU instances.\nFor installing RAPIDS, as [this document](https://colab.google/articles/cudf) from Google explains in detail, you need to take two steps. Step 1 is to choose a Colab runtime that uses a GPU accelerator. Step 2 is running the following commands in your notebook to install RAPIDS on your Colab instance via pip.\nbash\n\n```\n !pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com\n !rm -rf /usr/local/lib/python3.8/dist-packages/cupy*\n !pip install cupy-cuda11x \n```\n\nPlease read the \"Getting Started with RAPIDS on Colab\" section of the mentioned [document](https://colab.google/articles/cudf) for more details.\n## An Example to Test\nTo compare the capabilities of CuDF Pandas and standard Pandas, I ran a test using a fake dataset with 10 million records. You can find all the required files in this repository: \nFirst, we need to create a large test dataset. If you have access to a big dataset that you can load into Pandas, you can go ahead with your dataset. I decided to create a fake CSV dataset with 10 million rows and use it for this test. I used the following Python code ([link to repo](https://github.com/tamiminaser/cudf-examples/blob/master/example1/fake_data.py)) to create my fake dataset and called it `data_10000000.csv`.\npython\n\n```\nimport csv\nfrom faker import Faker\nimport datetime\nfrom tqdm import tqdm\n\ndef datagenerate(records, headers):\n fake = Faker('en_US')\n with open(\"data_10000000.csv\", 'wt') as csvFile:\n writer = csv.DictWriter(csvFile, fieldnames=headers)\n writer.writeheader()\n for i in tqdm(range(records)):\n full_name = fake.name()\n FLname = full_name.split(\" \")\n Fname = FLname[0]\n Lname = FLname[1]\n domain_name = \"@testDomain.com\"\n userId = Fname +\".\"+ Lname + domain_name\n\n writer.writerow({\n \"user_id\": fake.unique.random_int(min=1111111111111, max=9999999999999),\n \"name\": fake.name(),\n \"date_of_birth\" : fake.date(pattern=\"%d-%m-%Y\", end_datetime=datetime.date(2000, 1,1)),\n \"country_of_birth\": fake.country(),\n \"email\": fake.email(),\n \"record_year\": fake.year(),\n \"score\": fake.random_int(min=1, max=999),\n })\n\nrecords = 100000000\nheaders = [\"user_id\", \"name\", \"date_of_birth\", \"country_of_birth\", \"email\", \"record_year\", \"score\"]\ndatagenerate(records, headers)\nprint(\"CSV generation complete!\")\n```\n\nIt will take a long time to create such a 10 million rows csv file and the output file size will be about 8 GB. You can lower the `records` number (for example to 3M records) in the code to produce smaller datasets. Also, I used my NVIDIA GeForce RTX 3090, which has 24 GB memory and I had no issue loading such a large dataset, but if you are using GPUs with smaller memories, you might need to work with a smaller dataset.\nAfter producing the fake CSV file, you can do the following test on the file. First, let's begin with standard Pandas and observe the processing time for this dataset. Below is the Jupyter Notebook we utilized for the initial phase of the test. (Note, I limited my code to 3M records here to loop the test 10 times for a more accurate comparison).\nNow, let's do the same processing using cuDF Pandas.\nAs you see, both codes are similar and the only difference is in the first cell of the cuDF notebook. In Jupiter Notebook, simply by adding `%load_ext cudf.pandas` before importing Pandas we could use cudf.pandas instead of standard Pandas and enjoy all of its benefits (it could not be simpler).\nAs you see, the group by process (cell #3) took about 1.06 seconds via standard Pandas, and it took only 122 ms via cuDF Pandas. It means that cuDF Pandas is about 8 times faster than the standard Pandas on CPU.\nIn Cell #4, we did a more complicated series of joining and transformations. For this one, it took 6 seconds for the standard Pandas to process the data, but it took about 109 ms for cuDF Pandas to do the same process. Therefore, cuDF Pandas could process the same data 55 times faster.\n## Summary\nAs you see, cuDF provides an API that enables us to transform Pandas dataframes on GPUs with minimum effort. The performance of cuDF compared to standard Pandas on a CPU is 10–100 times faster according to our test.\nAs mentioned at the beginning, Dask is another popular tool that enables us to run Pandas transformations and jobs on a cluster of machines. cuDF and Dask could be combined to even enhance the processing further and take advantage of both distributed compute systems as well as GPUs to process the data faster ([read more](https://docs.rapids.ai/api/cudf/nightly/user_guide/10min/)).\n## Update\nAt Google I/O 2024, Laurence Moroney, the Head of AI Advocacy at Google, announced the integration of RAPIDS cuDF into Google Colab. This enables developers to significantly speed up pandas code, achieving up to a 50x performance boost on Google Colab GPU instances, all while allowing them to continue using pandas seamlessly as their data scales. [Read more here](https://developer.nvidia.com/blog/rapids-cudf-instantly-accelerates-pandas-up-to-50x-on-google-colab/).\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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