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we'll use Python to analyze data in Snowflake using the Posit\nWorkbench Native App. You'll learn how to launch the Posit Workbench Native App and use the available VS Code IDE. You'll also learn how to use the Ibis library to translate Python code into SQL, allowing you to run data operations directly in Snowflake's high-performance computing environment.\u003C/p\u003E\n","\u003Cp\u003EWe'll focus on a healthcare example by analyzing heart failure data. We'll guide you through accessing the data and performing data cleaning, transformation, and visualization. Finally, you'll see how to generate an HTML report, build an interactive Shiny app, and write data back to Snowflake—completing an end-to-end analysis in Python \u003Cem\u003Eentirely within Snowflake\u003C/em\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/overview/architecture.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to work in VS Code from Posit Workbench Native App.\u003C/li\u003E\u003Cli\u003EHow to connect to your Snowflake data from Python to create tables, visualizations, and more.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA VS Code environment to use within Snowflake.\u003C/li\u003E\u003Cli\u003EA Quarto document that contains plots and tables built with Python, using data stored in Snowflake.\u003C/li\u003E\u003Cli\u003EAn interactive Shiny for Python application built using data stored in Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAlong the way, you will use Python to analyze which variables are associated with survival among patients with heart failure.\nYou can follow along with this quickstart guide,\nor look at the materials provided in the accompanying repository:\n\u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-python\"\u003Ehttps://github.com/posit-dev/snowflake-posit-quickstart-python\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with appropriate access to databases and schemas.\u003C/li\u003E\u003Cli\u003EA Posit Workbench license and the ability to launch Posit Workbench from \u003Ca href=\"https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/\"\u003ESnowflake Native Applications\u003C/a\u003E. This can be provided by an administrator with the \u003Ccode\u003Eaccountadmin\u003C/code\u003E role.\u003C/li\u003E\u003Cli\u003EFamiliarity with Python.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EBefore we begin, let’s set up a few components. We need to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdd the heart failure data to Snowflake\u003C/li\u003E\u003Cli\u003ELaunch the Posit Workbench Native App\u003C/li\u003E\u003Cli\u003ECreate a VS Code session\u003C/li\u003E\u003Cli\u003ECreate a virtual environment and install the necessary libraries\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreate a warehouse, database, and schema\u003C/h3\u003E\n","\u003Cp\u003EFor this analysis, we'll use the \u003Ca href=\"https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records\"\u003EHeart Failure Clinical Records\u003C/a\u003E dataset. First, we need to create a warehouse, database, and schema.\u003C/p\u003E\n","\u003Cp\u003EIn Snowsight, open a SQL worksheet (\u003Ccode\u003ECreate\u003C/code\u003E > \u003Ccode\u003ESQL Worksheet\u003C/code\u003E). Then, paste in and run the following code, which creates the necessary database, schema, and warehouse. \u003Cstrong\u003EMake sure to change the role to your own role.\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE myrole; -- Replace with your actual Snowflake role (e.g., sysadmin)\n\nCREATE OR REPLACE DATABASE HEART_FAILURE;\n\nCREATE OR REPLACE SCHEMA PUBLIC;\n\nCREATE OR REPLACE WAREHOUSE HF_WH\n    WAREHOUSE_SIZE = 'xsmall'\n    WAREHOUSE_TYPE = 'standard'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates a database named \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E with a schema \u003Ccode\u003EPUBLIC\u003C/code\u003E, as well as a warehouse named \u003Ccode\u003EHF_WH\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ELoad data into Snowflake\u003C/h3\u003E\n","\u003Cp\u003ENext, we need to create a table to hold the heart failure dataset.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDownload the dataset from UCI:\u003C/strong\u003E\n\u003Ca href=\"https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records\"\u003Ehttps://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUnzip the downloaded file.\u003C/strong\u003E You should now see a file named \u003Ccode\u003Eheart_failure_clinical_records_dataset.csv\u003C/code\u003E. We’ll upload this CSV into Snowflake using the Snowsight UI.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn \u003Cstrong\u003ESnowsight\u003C/strong\u003E, click \u003Ccode\u003ECreate > Add Data\u003C/code\u003E, then select \u003Ccode\u003ELoad Data into a Table\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003EBrowse\u003C/code\u003E and choose \u003Ccode\u003Eheart_failure_clinical_records_dataset.csv\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003ESelect or create a database and schema\u003C/strong\u003E, choose:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003EPUBLIC\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003ESelect or create a table\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure \u003Cstrong\u003E+ Create new table\u003C/strong\u003E is selected.\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EName\u003C/strong\u003E, enter \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ENext\u003C/code\u003E, then \u003Ccode\u003ELoad\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EConfirm the database, data, and schema\u003C/h3\u003E\n","\u003Cp\u003EYou should now be able to see the heart failure data in Snowsight. Navigate to \u003Ccode\u003EData\u003C/code\u003E > \u003Ccode\u003EDatabases\u003C/code\u003E > \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E > \u003Ccode\u003EPUBLIC\u003C/code\u003E > \u003Ccode\u003ETables\u003C/code\u003E. You should now see the \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/04-confirm_data.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELaunch Posit Workbench\u003C/h3\u003E\n","\u003Cp\u003EWe can now start exploring the data using Posit Workbench.\nYou can find Posit Workbench as a Snowflake Native Application\nand use it to connect to your database.\u003C/p\u003E\n","\u003Ch4\u003EStep 1: Navigate to Apps\u003C/h4\u003E\n","\u003Cp\u003EIn your Snowflake account, go to \u003Ccode\u003EData Products\u003C/code\u003E > \u003Ccode\u003EApps\u003C/code\u003E to open the Native Apps collection. If Posit Workbench is not already installed, click \u003Ccode\u003EGet\u003C/code\u003E. Please note that the Native App must be \u003Ca href=\"https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/install.html\"\u003Einstalled and configured\u003C/a\u003E by an administrator.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/05-get_posit_workbench.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Open the Posit Workbench Native App\u003C/h4\u003E\n","\u003Cp\u003EOnce Posit Workbench is installed, click on the app under \u003Ccode\u003EInstalled Apps\u003C/code\u003E to launch the app. If you do not see the Posit Workbench app listed, ask your Snowflake account administrator for access to the app.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/06-open_app.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAfter clicking on the app, you will see a page with configuration instructions and a blue \u003Ccode\u003ELaunch app\u003C/code\u003E button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/09-launch-app.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Ccode\u003ELaunch app\u003C/code\u003E. This should take you to the webpage generated for the Workbench application. You may be prompted to first login to Snowflake using your regular credentials or authentication method.\u003C/p\u003E\n","\u003Ch3\u003ECreate a VS Code Session\u003C/h3\u003E\n","\u003Cp\u003EPosit Workbench provides several IDEs, including VS Code, RStudio Pro, and JupyterLab. For this analysis, we will use VS Code.\u003C/p\u003E\n","\u003Ch4\u003EStep 1: New Session\u003C/h4\u003E\n","\u003Cp\u003EWithin Posit Workbench, click \u003Ccode\u003ENew Session\u003C/code\u003E to launch a new session.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/01-start_new_session.png\" alt=\"assets/posit_workbench/01-start_new_session.png\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Select an IDE\u003C/h4\u003E\n","\u003Cp\u003EWhen prompted, select VS Code.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/02-create_new_session_snowflake.png\" alt=\"assets/posit_workbench/02-create_new_session_snowflake.png\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Log into your Snowflake account\u003C/h4\u003E\n","\u003Cp\u003ENext, connect to your Snowflake account from within Posit Workbench.\nUnder \u003Ccode\u003ESession Credentials\u003C/code\u003E, click the button with the Snowflake icon to sign in to Snowflake.\nFollow the sign in prompts.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/03-snowflake_login.png\" alt=\"assets/posit_workbench/03-snowflake_login.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen you're successfully signed in to Snowflake, the Snowflake button will turn blue\nand there will be a check mark in the upper-left corner.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/04-snowflake_login_success.png\" alt=\"assets/posit_workbench/04-snowflake_login_success.png\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 4: Launch VS Code\u003C/h4\u003E\n","\u003Cp\u003EClick \u003Ccode\u003EStart Session\u003C/code\u003E to launch VS Code.\u003C/p\u003E\n","\u003Cp\u003EYou will now be able to work with your Snowflake data\nin VS Code. Since the IDE is provided by the Posit Workbench Native App,\nyour entire analysis will occur securely within Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/vs_code/01-vscode.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EInstall Quarto, Shiny, and Jupyter Extensions\u003C/h3\u003E\n","\u003Cp\u003EThe Quarto and Shiny VS Code Extensions support the development\nof Quarto documents and Shiny apps in VS Code. The Jupyter extension provides\nsupport for running Python code in notebook cells\u003C/p\u003E\n","\u003Cp\u003EInstall these extensions:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EOpen the VS Code Extensions view.\u003C/strong\u003E On the right-hand side of VS Code, click\nthe Extensions icon in the Activity bar to open the Extensions view.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESearch for "Quarto"\u003C/strong\u003E to find the Quarto extension.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/vs_code/02-extensions_quarto.png\" alt=\"assets/vs_code/02-extensions_quarto.png\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\u003Cstrong\u003EInstall the Quarto extension.\u003C/strong\u003E Click on the Quarto extension, then click \u003Ccode\u003EInstall\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInstall the Shiny extension.\u003C/strong\u003E Search for the Shiny extension, then install\nthe extension in the same way.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInstall the Jupyter extension.\u003C/strong\u003E Search for the Jupyter extension, then install\nthe extension in the same way.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou can learn more about these extensions here: \u003Ca href=\"https://shiny.posit.co/blog/posts/shiny-vscode-1.0.0/\"\u003EShiny extension\u003C/a\u003E,\n\u003Ca href=\"https://quarto.org/docs/tools/vscode.html\"\u003EQuarto extension\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EOpen a new folder\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELaunch “Open Folder”:\u003C/strong\u003E\nPress \u003Ccode\u003ECtrl/Cmd+Shift+P\u003C/code\u003E to open the Command Palette, type \u003Cstrong\u003EFile: Open Folder\u003C/strong\u003E, and press Enter. In the dialog that appears, navigate to the directory where you want your work to live and click \u003Cstrong\u003EOk\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate a subfolder:\u003C/strong\u003E\nIn the Explorer pane (left sidebar), click the \u003Cstrong\u003ENew Folder\u003C/strong\u003E button (the folder icon) and name the folder \u003Ccode\u003Eheart_failure\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESwitch into the new folder:\u003C/strong\u003E\nPress \u003Ccode\u003ECtrl/Cmd+Shift+P\u003C/code\u003E again to reopen the Command Palette, select \u003Cstrong\u003EFile: Open Folder\u003C/strong\u003E, then navigate to \u003Ccode\u003Eheart_failure\u003C/code\u003E and click \u003Cstrong\u003EOk\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou now have an empty \u003Ccode\u003Eheart_failure\u003C/code\u003E folder open and ready for your work.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ECreate a virtual environment\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOpen the Command Palette\u003C/strong\u003E (\u003Ccode\u003ECmd/Ctrl+Shift+P\u003C/code\u003E), then search for and select \u003Cstrong\u003EPython: Create Environment\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EChoose \u003Ccode\u003EVenv\u003C/code\u003E\u003C/strong\u003E to create a \u003Ccode\u003E.venv\u003C/code\u003E virtual environment.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESelect the Python version\u003C/strong\u003E you want to use.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn a terminal, \u003Cstrong\u003Eactivate the virtual environment\u003C/strong\u003E by running \u003Ccode\u003Esource .venv/bin/activate\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you don't already see a terminal open, open the Command Palette (\u003Ccode\u003ECtrl/Cmd+Shift+P\u003C/code\u003E), then select \u003Ccode\u003ETerminal: Create New Terminal\u003C/code\u003E to open one.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAccess the Quickstart Materials\u003C/h3\u003E\n","\u003Cp\u003EThis Quickstart will walk you through the analysis contained in \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-python/blob/main/quarto.qmd\"\u003Ehttps://github.com/posit-dev/snowflake-posit-quickstart-python/blob/main/quarto.qmd\u003C/a\u003E.\nTo follow along, you can clone the \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-python/\"\u003EGitHub repo\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Egit clone https://github.com/posit-dev/snowflake-posit-quickstart-python/\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInstall requirements\u003C/h3\u003E\n","\u003Cp\u003EIn a terminal, run the following command to \u003Cstrong\u003Einstall the dependencies\u003C/strong\u003E listed in \u003Ccode\u003Erequirements.txt\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install -r requirements.txt\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Make sure your virtual environment is activated (\u003Ccode\u003Esource .venv/bin/activate\u003C/code\u003E) before installing.\u003C/p\u003E\n","\u003Ch2\u003EBuild Report and Dashboard\u003C/h2\u003E\n","\u003Cp\u003EBefore we dive into the specifics of the code, let's first discuss Quarto. We've written our analysis in a Quarto (\u003Ccode\u003E.qmd\u003C/code\u003E) document, \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-python/blob/main/quarto.qmd\"\u003Equarto.qmd\u003C/a\u003E. \u003Ca href=\"https://quarto.org/\"\u003EQuarto\u003C/a\u003E\nis an open-source publishing system that makes it easy to create\n\u003Ca href=\"https://quarto.org/docs/guide/\"\u003Edata products\u003C/a\u003E such as\n\u003Ca href=\"https://quarto.org/docs/output-formats/html-basics.html\"\u003Edocuments\u003C/a\u003E,\n\u003Ca href=\"https://quarto.org/docs/presentations/\"\u003Epresentations\u003C/a\u003E,\n\u003Ca href=\"https://quarto.org/docs/dashboards/\"\u003Edashboards\u003C/a\u003E,\n\u003Ca href=\"https://quarto.org/docs/websites/\"\u003Ewebsites\u003C/a\u003E,\nand\n\u003Ca href=\"https://quarto.org/docs/books/\"\u003Ebooks\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EBy placing our work in a Quarto document, we've interwoven all of our code, results, output, and prose text into a single literate programming document.\nThis way everything can travel together in a reproducible data product.\u003C/p\u003E\n","\u003Cp\u003EA Quarto document can be thought of as a regular markdown document,\nbut with the ability to run code chunks.\u003C/p\u003E\n","\u003Cp\u003EYou can run any of the code chunks by clicking the \u003Ccode\u003ERun Cell\u003C/code\u003E button above the chunk\nin VS Code.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/quarto/run-chunk.png\" alt=\"assets/quarto/run-chunk.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen you run a cell, cell output is displayed in the Jupyter interactive console.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/quarto/interactive-console.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo render and preview the entire document, click the \u003Ccode\u003EPreview\u003C/code\u003E button\nor run \u003Ccode\u003Equarto preview quarto.qmd\u003C/code\u003E from the terminal.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/quarto/preview.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will run all the code in the document from top to bottom and\ngenerate an HTML file, by default, for you to view and share.\u003C/p\u003E\n","\u003Ch3\u003ELearn More about Quarto\u003C/h3\u003E\n","\u003Cp\u003EYou can learn more about Quarto here: \u003Ca href=\"https://quarto.org/\"\u003Ehttps://quarto.org/\u003C/a\u003E,\nand the documentation for all the various Quarto outputs here: \u003Ca href=\"https://quarto.org/docs/guide/\"\u003Ehttps://quarto.org/docs/guide/\u003C/a\u003E.\nQuarto works with Python, R, and Javascript Observable code out-of-the box,\nand is a great tool to communicate your data science analyses.\u003C/p\u003E\n","\u003Ch2\u003EAccess Snowflake Data\u003C/h2\u003E\n","\u003Cp\u003ENow, let's take a closer look at the code in our Quarto document. Our code will run\nin our Python environment, but will use data stored in our database on Snowflake.\u003C/p\u003E\n","\u003Cp\u003ETo access this data, we'll use the Ibis library to connect to the database and\nquery the data from Python, without having to write raw SQL.\nLet's take a look at how this works.\u003C/p\u003E\n","\u003Ch3\u003EConnect to Database\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://ibis-project.org/\"\u003EIbis\u003C/a\u003E is an open source dataframe library that works with a wide variety of backends,\nincluding Snowflake.\u003C/p\u003E\n","\u003Cp\u003EFirst, we import \u003Ccode\u003Eibis\u003C/code\u003E, then use \u003Ccode\u003Eibis.snowflake.connect\u003C/code\u003E to connect to the Snowflake\ndatabase. We need to provide a \u003Ccode\u003Ewarehouse\u003C/code\u003E for compute and a \u003Ccode\u003Edatabase\u003C/code\u003E to connect to.\nWe can also provide a \u003Ccode\u003Eschema\u003C/code\u003E here to make connecting to specific tables easier.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport ibis\n\ncon = ibis.snowflake.connect(\n  warehouse="HF_WH",\n  database="HEART_FAILURE",\n  schema="PUBLIC",\n  connection_name="workbench"\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe variable \u003Ccode\u003Econ\u003C/code\u003E now stores our connection.\u003C/p\u003E\n","\u003Ch3\u003ECreate a table\u003C/h3\u003E\n","\u003Cp\u003EOnce we build a connection, we can use \u003Ccode\u003Etable()\u003C/code\u003E to create an Ibis \u003Ca href=\"https://ibis-project.org/reference/expression-tables#:~:text=A%20table%20expression%20contains%20an,new%20Table%20with%20those%20changes\"\u003Etable expression\u003C/a\u003E that represents the database table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eheart_failure = con.table("HEART_FAILURE")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETranslate Python to SQL\u003C/h3\u003E\n","\u003Cp\u003EWe can now use Ibis to interact with \u003Ccode\u003Eheart_failure\u003C/code\u003E. For example,\nwe can filter rows and rename and select columns.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eheart_failure_filtered = (\n    heart_failure.filter(heart_failure.AGE < 50)\n    .rename(\n        {\n            "age": "AGE",\n            "diabetes": "DIABETES",\n            "serum_sodium": "SERUM_SODIUM",\n            "serum_creatinine": "SERUM_CREATININE",\n            "sex": "SEX",\n            "death_event": "DEATH_EVENT",\n        }\n    )\n    .select(\n        ["age", "diabetes", "serum_sodium", "serum_creatinine", "sex", "death_event"]\n    )\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERight now, \u003Ccode\u003Eheart_failure_filtered\u003C/code\u003E is still a \u003Ca href=\"https://ibis-project.org/reference/expression-tables\"\u003Etable expression\u003C/a\u003E.\nIbis lazily evaluates commands, which means that the full query is never run on\nthe database unless explicitly requested.\u003C/p\u003E\n","\u003Cp\u003EUse \u003Ccode\u003E.execute()\u003C/code\u003E or \u003Ccode\u003E.to_pandas()\u003C/code\u003E to force Ibis to compile the table expression\ninto SQL and run that SQL on Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eheart_failure_filtered.execute()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf we want to see the SQL code that Ibis generates, we can run \u003Ccode\u003Eibis.to_sql()\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eibis.to_sql(heart_failure_filtered)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n  "t0"."AGE" AS "age",\n  "t0"."DIABETES" AS "diabetes",\n  "t0"."SERUM_SODIUM" AS "serum_sodium",\n  "t0"."SERUM_CREATININE" AS "serum_creatinine",\n  "t0"."SEX" AS "sex",\n  "t0"."DEATH_EVENT" AS "death_event"\nFROM "HEART_FAILURE" AS "t0"\nWHERE\n  "t0"."AGE" < 50\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESummary\u003C/h3\u003E\n","\u003Cp\u003EThis system:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EKeeps our data in the database, saving memory in the Python session.\u003C/li\u003E\u003Cli\u003EPushes computations to the database, saving compute in the Python session.\u003C/li\u003E\u003Cli\u003EEvaluates queries lazily, saving compute in the database.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EWe don't need to manage the process, it happens automatically behind the scenes.\u003C/p\u003E\n","\u003Cp\u003EYou can learn more about Ibis \u003Ca href=\"https://ibis-project.org/\"\u003Ehere\u003C/a\u003E. Take a look at the\n\u003Ca href=\"https://ibis-project.org/backends/snowflake\"\u003ESnowflake backend documentation\u003C/a\u003E to\nlearn more about using Ibis to interact with\nSnowflake specifically.\u003C/p\u003E\n","\u003Ch2\u003EWrite Snowflake Data\u003C/h2\u003E\n","\u003Cp\u003EYou can also use Ibis to create a new table in a database or append to an existing table.\u003C/p\u003E\n","\u003Cp\u003ETo add a new table, use \u003Ccode\u003Ecreate_table()\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econ.create_table("HEART_FAILURE_FILTERED", heart_failure_filtered)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo insert data into an existing table, use \u003Ca href=\"https://ibis-project.org/backends/snowflake#ibis.backends.snowflake.Backend.insert\"\u003E\u003Ccode\u003Einsert()\u003C/code\u003E\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ENow that we understand how to interact with our database, we can use Python to perform our analysis.\u003C/p\u003E\n","\u003Ch2\u003EClean Data\u003C/h2\u003E\n","\u003Cp\u003EWe want to understand which variables in \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E are associated with survival\nof patients with heart failure.\u003C/p\u003E\n","\u003Cp\u003EFirst, we convert the column names to lowercase so we won't need to worry about capitalization.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eheart_failure = heart_failure.rename(\n    {\n        "age": "AGE",\n        "diabetes": "DIABETES",\n        "serum_sodium": "SERUM_SODIUM",\n        "serum_creatinine": "SERUM_CREATININE",\n        "sex": "SEX",\n        "death_event": "DEATH_EVENT",\n    }\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFilter ages\u003C/h3\u003E\n","\u003Cp\u003EFor now, we'll focus on patients younger than 50. We also reduce the data to just the columns we're interested in.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eheart_failure_filtered = (\n    heart_failure\n    .filter(heart_failure.age < 50)  # Filter to age < 50\n    .select(["age", "diabetes", "serum_sodium", "serum_creatinine", "sex", "death_event"])\n)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EBy default, Ibis is in \u003Cem\u003Edeferred mode\u003C/em\u003E and lazily evaluates table expressions. To more easily interact with your tables and see the results of your code, you can turn on \u003Cem\u003Einteractive code\u003C/em\u003E. In \u003Ca href=\"https://ibis-project.org/how-to/configure/basics#interactive-mode\"\u003Einteractive mode\u003C/a\u003E, expressions are executed when printed to the console. Turn on interactive mode with \u003Ccode\u003Eibis.options.interactive = True\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EVisualize Data\u003C/h2\u003E\n","\u003Cp\u003EThe heart failure data provides important insights that can help us:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIdentify factors associated with increased risk of mortality after heart failure.\u003C/li\u003E\u003Cli\u003EPredict future survival outcomes based on historical clinical data.\u003C/li\u003E\u003Cli\u003EBenchmark patient outcomes based on clinical indicators like serum sodium levels.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EVisualizing clinical variables across different patient groups can help identify patterns.\u003C/p\u003E\n","\u003Ch3\u003EVisualize serum sodium levels\u003C/h3\u003E\n","\u003Cp\u003EWe can use \u003Ca href=\"https://plotnine.org/\"\u003Eplotnine\u003C/a\u003E to visually compare sodium levels across different patient groups. In this plot, we see the distribution of serum sodium based on whether the patients have diabetes and whether they survived (\u003Ccode\u003E0\u003C/code\u003E) or died (\u003Ccode\u003E1\u003C/code\u003E) during the follow-up period.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom plotnine import ggplot, aes, geom_boxplot, labs, theme\n\nheart_failure_plot = (\n    heart_failure_filtered\n    .mutate(\n        death_event=heart_failure_filtered["death_event"].cast("string"),\n        diabetes=heart_failure_filtered["diabetes"].cast("string")\n    )\n)\n\n(\n    ggplot(heart_failure_plot, aes(x="death_event", y="serum_sodium", color="diabetes")) +\n    geom_boxplot() +\n    labs(\n        title="Serum Sodium Levels by Diabetes Status and Survival Outcome",\n        x="Survival Outcome (0 = Survived, 1 = Died)",\n        y="Serum Sodium (mEq/L)",\n        color="Diabetes"\n    ) +\n    theme(legend_position="bottom")\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/analysis/plot-sodium.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ECreate tables for Analysis\u003C/h2\u003E\n","\u003Cp\u003ENext, we'll use Ibis to calculate the median values for various clinical metrics across different patient groups.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E(\n    heart_failure_filtered\n    .group_by(["death_event", "diabetes"])\n    .aggregate(\n        median_age=heart_failure_filtered["age"].median(),\n        median_serum_creatinine=heart_failure_filtered["serum_creatinine"].median(),\n        median_serum_sodium=heart_failure_filtered["serum_sodium"].median()\n    )\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is a useful way to examine the information for ourselves. However, if we wish to share the information with others, we might prefer to present the table in a more polished format. We can do this with the \u003Ca href=\"https://posit-dev.github.io/great-tables/articles/intro.html\"\u003EGreat Tables\u003C/a\u003E package.\u003C/p\u003E\n","\u003Cp\u003EThe following code prepares a table named \u003Ccode\u003Ecomparison\u003C/code\u003E, which we'll display with Great Tables.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ecomparison = (\n    heart_failure_filtered\n    .group_by(["death_event", "diabetes"])\n    .aggregate(\n        median_age=heart_failure_filtered["age"].median(),\n        median_serum_creatinine=heart_failure_filtered["serum_creatinine"].median(),\n        median_serum_sodium=heart_failure_filtered["serum_sodium"].median()\n    )\n    .mutate(\n        death_event=ibis.ifelse(heart_failure_filtered["death_event"] == 1, "Died", "Survived"),\n        diabetes=ibis.ifelse(heart_failure_filtered["diabetes"] == 1, "Yes", "No"),\n        median_serum_creatinine=heart_failure_filtered["serum_creatinine"].median().cast("float64")\n    )\n    .rename(\n        {\n            "Survival Outcome": "death_event",\n            "Diabetes Status": "diabetes",\n            "Median Age": "median_age",\n            "Median Serum Creatinine (mg/dL)": "median_serum_creatinine",\n            "Median Serum Sodium (mEq/L)": "median_serum_sodium"\n        }\n    )\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we use \u003Ccode\u003EGT()\u003C/code\u003E and other Great Tables functions to create and style a table that displays \u003Ccode\u003Ecomparison\u003C/code\u003E. Note that we need to evaluate \u003Ccode\u003Ecomparison\u003C/code\u003E with \u003Ccode\u003E.execute()\u003C/code\u003E first because \u003Ccode\u003EGT()\u003C/code\u003E only accepts Pandas or Polars DataFrames.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom great_tables import GT\n\n(\n    GT(comparison.execute())\n    .tab_header(title="Clinical Metrics by Survival Outcome and Diabetes Status")\n    .fmt_number(\n        columns=["Median Age", "Median Serum Creatinine (mg/dL)", "Median Serum Sodium (mEq/L)"],\n        decimals=1\n    )\n    .data_color(\n        columns=["Median Serum Creatinine (mg/dL)", "Median Serum Sodium (mEq/L)"],\n        palette=["white", "blue"]\n    )\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/gt/gt-table.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ECreate Shiny Application\u003C/h2\u003E\n","\u003Cp\u003EEarlier, we showed you how to render a report from our Quarto document. Another way\nto share our work and allow others to explore the heart failure dataset is to create an\ninteractive \u003Ca href=\"https://shiny.posit.co/\"\u003EShiny\u003C/a\u003E app.\u003C/p\u003E\n","\u003Cp\u003EOur GitHub repository contains an \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-python/tree/main/app\"\u003Eexample Shiny app\u003C/a\u003E.\nThis app allows the user\nto explore different clinical metrics in one place.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/shiny/shiny.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo run the app, open \u003Ccode\u003Eapp/app.py\u003C/code\u003E and then click the \u003Ccode\u003ERun Shiny App\u003C/code\u003E button at the\ntop of the script in VS Code.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/shiny/run.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAfter launching the app, use the sidebar to change the metric displayed.\u003C/p\u003E\n","\u003Ch3\u003ELearn More About Shiny\u003C/h3\u003E\n","\u003Cp\u003EYou can learn more about Shiny at: \u003Ca href=\"https://shiny.posit.co/\"\u003Ehttps://shiny.posit.co/\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf you're new to Shiny, you can try it online with\n\u003Ca href=\"https://shinylive.io/py/examples/\"\u003Eshinylive\u003C/a\u003E.\nShinylive is also available for \u003Ca href=\"https://shinylive.io/r/examples/\"\u003ER\u003C/a\u003E for Shiny for R.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EPython is a powerful, versatile tool for data science, and combined with Snowflake's high-performance data capabilities, it enables robust, end-to-end data workflows. Using the Posit Workbench Native Application, you can securely work with Python \u003Cem\u003Ewithin Snowflake\u003C/em\u003E while taking advantage of tools like Ibis, Quarto, and Shiny for Python to analyze, visualize, and share your results.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use VS Code within the Posit Workbench Native App.\u003C/li\u003E\u003Cli\u003EHow to connect to your Snowflake data from Python to create tables, visualizations, and more.\u003C/li\u003E\u003Cli\u003EHow to create a Quarto document containing plots and tables built in Python, using data stored in Snowflake.\u003C/li\u003E\u003Cli\u003EHow to build an interactive Shiny for Python application, working with data stored in Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-python\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://posit.co/products/enterprise/workbench/\"\u003EMore about Posit Workbench\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://ibis-project.org/\"\u003EIbis library website\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://plotnine.readthedocs.io/\"\u003Eplotnine package for plotting with a grammar of graphics in Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://posit-dev.github.io/great-tables/\"\u003EGreat Tables package for publication-ready tables in Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quarto.org/\"\u003EQuarto for reproducible documents, reports, and data products\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://shiny.posit.co/\"\u003EShiny for interactive dashboards and applications\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://shinylive.io/\"\u003ERun Shiny entirely in the browser with shinylive\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Analyze Snowflake data using Python in Posit Workbench for collaborative data science, ML development, and visualization.","title":"Analyze Data with Python using Posit Workbench and Snowflake","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"value":"## Overview\n\nIn this guide, we'll use Python to analyze data in Snowflake using the Posit\nWorkbench Native App. You'll learn how to launch the Posit Workbench Native App and use the available VS Code IDE. You'll also learn how to use the Ibis library to translate Python code into SQL, allowing you to run data operations directly in Snowflake's high-performance computing environment.\n\nWe'll focus on a healthcare example by analyzing heart failure data. We'll guide you through accessing the data and performing data cleaning, transformation, and visualization. Finally, you'll see how to generate an HTML report, build an interactive Shiny app, and write data back to Snowflake—completing an end-to-end analysis in Python _entirely within Snowflake_.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/overview/architecture.png)\n\n### What You Will Learn\n\n- How to work in VS Code from Posit Workbench Native App.\n- How to connect to your Snowflake data from Python to create tables, visualizations, and more.\n\n### What You Will Build\n\n- A VS Code environment to use within Snowflake.\n- A Quarto document that contains plots and tables built with Python, using data stored in Snowflake.\n- An interactive Shiny for Python application built using data stored in Snowflake.\n\nAlong the way, you will use Python to analyze which variables are associated with survival among patients with heart failure.\nYou can follow along with this quickstart guide,\nor look at the materials provided in the accompanying repository:\n\u003Chttps://github.com/posit-dev/snowflake-posit-quickstart-python\u003E.\n\n### Prerequisites\n\n- A [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with appropriate access to databases and schemas.\n- A Posit Workbench license and the ability to launch Posit Workbench from [Snowflake Native Applications](https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/). This can be provided by an administrator with the `accountadmin` role.\n- Familiarity with Python.\n\n\n## Setup\n\nBefore we begin, let’s set up a few components. We need to:\n\n- Add the heart failure data to Snowflake\n- Launch the Posit Workbench Native App\n- Create a VS Code session\n- Create a virtual environment and install the necessary libraries\n\n### Create a warehouse, database, and schema\n\nFor this analysis, we'll use the [Heart Failure Clinical Records](https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records) dataset. First, we need to create a warehouse, database, and schema.\n\nIn Snowsight, open a SQL worksheet (`Create` \u003E `SQL Worksheet`). Then, paste in and run the following code, which creates the necessary database, schema, and warehouse. **Make sure to change the role to your own role.**\n\n```sql\nUSE ROLE myrole; -- Replace with your actual Snowflake role (e.g., sysadmin)\n\nCREATE OR REPLACE DATABASE HEART_FAILURE;\n\nCREATE OR REPLACE SCHEMA PUBLIC;\n\nCREATE OR REPLACE WAREHOUSE HF_WH\n    WAREHOUSE_SIZE = 'xsmall'\n    WAREHOUSE_TYPE = 'standard'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE;\n```\n\nThis creates a database named `HEART_FAILURE` with a schema `PUBLIC`, as well as a warehouse named `HF_WH`.\n\n### Load data into Snowflake\n\nNext, we need to create a table to hold the heart failure dataset.\n\n1. **Download the dataset from UCI:**\n   [https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records](https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records)\n\n2. **Unzip the downloaded file.** You should now see a file named `heart_failure_clinical_records_dataset.csv`. We’ll upload this CSV into Snowflake using the Snowsight UI.\n\n3. In **Snowsight**, click `Create \u003E Add Data`, then select `Load Data into a Table`.\n\n4. Click `Browse` and choose `heart_failure_clinical_records_dataset.csv`.\n\n5. Under **Select or create a database and schema**, choose:\n   - **Database:** `HEART_FAILURE`\n   - **Schema:** `PUBLIC`\n\n6. Under **Select or create a table**:\n   - Ensure **+ Create new table** is selected.\n   - For **Name**, enter `HEART_FAILURE`.\n\n7. Click `Next`, then `Load`.\n\n### Confirm the database, data, and schema\n\nYou should now be able to see the heart failure data in Snowsight. Navigate to `Data` \u003E `Databases` \u003E `HEART_FAILURE` \u003E `PUBLIC` \u003E `Tables`. You should now see the `HEART_FAILURE` table.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/04-confirm_data.png)\n\n### Launch Posit Workbench\n\nWe can now start exploring the data using Posit Workbench.\nYou can find Posit Workbench as a Snowflake Native Application\nand use it to connect to your database.\n\n#### Step 1: Navigate to Apps\n\nIn your Snowflake account, go to `Data Products` \u003E `Apps` to open the Native Apps collection. If Posit Workbench is not already installed, click `Get`. Please note that the Native App must be [installed and configured ](https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/install.html) by an administrator.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/05-get_posit_workbench.png)\n\n#### Step 2: Open the Posit Workbench Native App\n\nOnce Posit Workbench is installed, click on the app under `Installed Apps` to launch the app. If you do not see the Posit Workbench app listed, ask your Snowflake account administrator for access to the app.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/06-open_app.png)\n\nAfter clicking on the app, you will see a page with configuration instructions and a blue `Launch app` button.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/snowflake/09-launch-app.png)\n\nClick on `Launch app`. This should take you to the webpage generated for the Workbench application. You may be prompted to first login to Snowflake using your regular credentials or authentication method.\n\n### Create a VS Code Session\n\nPosit Workbench provides several IDEs, including VS Code, RStudio Pro, and JupyterLab. For this analysis, we will use VS Code.\n\n#### Step 1: New Session\n\nWithin Posit Workbench, click `New Session` to launch a new session.\n\n![assets/posit_workbench/01-start_new_session.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/01-start_new_session.png)\n\n#### Step 2: Select an IDE\n\nWhen prompted, select VS Code.\n\n![assets/posit_workbench/02-create_new_session_snowflake.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/02-create_new_session_snowflake.png)\n\n#### Step 3: Log into your Snowflake account\n\nNext, connect to your Snowflake account from within Posit Workbench.\nUnder `Session Credentials`, click the button with the Snowflake icon to sign in to Snowflake.\nFollow the sign in prompts.\n\n![assets/posit_workbench/03-snowflake_login.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/03-snowflake_login.png)\n\nWhen you're successfully signed in to Snowflake, the Snowflake button will turn blue\nand there will be a check mark in the upper-left corner.\n\n![assets/posit_workbench/04-snowflake_login_success.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/posit_workbench/04-snowflake_login_success.png)\n\n\n#### Step 4: Launch VS Code\n\nClick `Start Session` to launch VS Code.\n\nYou will now be able to work with your Snowflake data\nin VS Code. Since the IDE is provided by the Posit Workbench Native App,\nyour entire analysis will occur securely within Snowflake.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/vs_code/01-vscode.png)\n\n### Install Quarto, Shiny, and Jupyter Extensions\n\nThe Quarto and Shiny VS Code Extensions support the development\nof Quarto documents and Shiny apps in VS Code. The Jupyter extension provides\nsupport for running Python code in notebook cells\n\nInstall these extensions:\n\n1. **Open the VS Code Extensions view.** On the right-hand side of VS Code, click\nthe Extensions icon in the Activity bar to open the Extensions view.\n2. **Search for \"Quarto\"** to find the Quarto extension.\n\n![assets/vs_code/02-extensions_quarto.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/vs_code/02-extensions_quarto.png)\n\n\n3. **Install the Quarto extension.** Click on the Quarto extension, then click `Install`.\n4. **Install the Shiny extension.** Search for the Shiny extension, then install\nthe extension in the same way.\n5. **Install the Jupyter extension.** Search for the Jupyter extension, then install\nthe extension in the same way.\n\nYou can learn more about these extensions here: [Shiny extension](https://shiny.posit.co/blog/posts/shiny-vscode-1.0.0/),\n[Quarto extension](https://quarto.org/docs/tools/vscode.html).\n\n### Open a new folder\n\n1. **Launch “Open Folder”:**\n   Press `Ctrl/Cmd+Shift+P` to open the Command Palette, type **File: Open Folder**, and press Enter. In the dialog that appears, navigate to the directory where you want your work to live and click **Ok**.\n\n2. **Create a subfolder:**\n   In the Explorer pane (left sidebar), click the **New Folder** button (the folder icon) and name the folder `heart_failure`.\n\n3. **Switch into the new folder:**\n   Press `Ctrl/Cmd+Shift+P` again to reopen the Command Palette, select **File: Open Folder**, then navigate to `heart_failure` and click **Ok**.\n\n4. You now have an empty `heart_failure` folder open and ready for your work.\n\n### Create a virtual environment\n\n1. **Open the Command Palette** (`Cmd/Ctrl+Shift+P`), then search for and select **Python: Create Environment**.\n\n2. **Choose `Venv`** to create a `.venv` virtual environment.\n\n3. **Select the Python version** you want to use.\n\n4. In a terminal, **activate the virtual environment** by running `source .venv/bin/activate`.\n\n\u003E \n\u003E\n\u003E If you don't already see a terminal open, open the Command Palette (`Ctrl/Cmd+Shift+P`), then select `Terminal: Create New Terminal` to open one.\n\n### Access the Quickstart Materials\n\nThis Quickstart will walk you through the analysis contained in \u003Chttps://github.com/posit-dev/snowflake-posit-quickstart-python/blob/main/quarto.qmd\u003E.\nTo follow along, you can clone the [GitHub repo](https://github.com/posit-dev/snowflake-posit-quickstart-python/):\n\n```\ngit clone https://github.com/posit-dev/snowflake-posit-quickstart-python/\n```\n\n\n### Install requirements\n\nIn a terminal, run the following command to **install the dependencies** listed in `requirements.txt`:\n\n```bash\npip install -r requirements.txt\n```\n\n**Note:** Make sure your virtual environment is activated (`source .venv/bin/activate`) before installing.\n\n## Build Report and Dashboard\n\nBefore we dive into the specifics of the code, let's first discuss Quarto. We've written our analysis in a Quarto (`.qmd`) document, [quarto.qmd](https://github.com/posit-dev/snowflake-posit-quickstart-python/blob/main/quarto.qmd). [Quarto](https://quarto.org/)\nis an open-source publishing system that makes it easy to create\n[data products](https://quarto.org/docs/guide/) such as\n[documents](https://quarto.org/docs/output-formats/html-basics.html),\n[presentations](https://quarto.org/docs/presentations/),\n[dashboards](https://quarto.org/docs/dashboards/),\n[websites](https://quarto.org/docs/websites/),\nand\n[books](https://quarto.org/docs/books/).\n\nBy placing our work in a Quarto document, we've interwoven all of our code, results, output, and prose text into a single literate programming document.\nThis way everything can travel together in a reproducible data product.\n\nA Quarto document can be thought of as a regular markdown document,\nbut with the ability to run code chunks.\n\nYou can run any of the code chunks by clicking the `Run Cell` button above the chunk\nin VS Code.\n\n![assets/quarto/run-chunk.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/quarto/run-chunk.png)\n\nWhen you run a cell, cell output is displayed in the Jupyter interactive console.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/quarto/interactive-console.png)\n\nTo render and preview the entire document, click the `Preview` button\nor run `quarto preview quarto.qmd` from the terminal.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/quarto/preview.png)\n\nThis will run all the code in the document from top to bottom and\ngenerate an HTML file, by default, for you to view and share.\n\n### Learn More about Quarto\n\nYou can learn more about Quarto here: \u003Chttps://quarto.org/\u003E,\nand the documentation for all the various Quarto outputs here: \u003Chttps://quarto.org/docs/guide/\u003E.\nQuarto works with Python, R, and Javascript Observable code out-of-the box,\nand is a great tool to communicate your data science analyses.\n\n## Access Snowflake Data\n\nNow, let's take a closer look at the code in our Quarto document. Our code will run\nin our Python environment, but will use data stored in our database on Snowflake.\n\nTo access this data, we'll use the Ibis library to connect to the database and\nquery the data from Python, without having to write raw SQL.\nLet's take a look at how this works.\n\n### Connect to Database\n\n[Ibis](https://ibis-project.org/) is an open source dataframe library that works with a wide variety of backends,\nincluding Snowflake.\n\nFirst, we import `ibis`, then use `ibis.snowflake.connect` to connect to the Snowflake\ndatabase. We need to provide a `warehouse` for compute and a `database` to connect to.\nWe can also provide a `schema` here to make connecting to specific tables easier.\n\n```python\nimport ibis\n\ncon = ibis.snowflake.connect(\n  warehouse=\"HF_WH\",\n  database=\"HEART_FAILURE\",\n  schema=\"PUBLIC\",\n  connection_name=\"workbench\"\n)\n```\n\nThe variable `con` now stores our connection.\n\n### Create a table\n\nOnce we build a connection, we can use `table()` to create an Ibis [table expression](https://ibis-project.org/reference/expression-tables#:~:text=A%20table%20expression%20contains%20an,new%20Table%20with%20those%20changes) that represents the database table.\n\n```python\nheart_failure = con.table(\"HEART_FAILURE\")\n```\n\n### Translate Python to SQL\n\nWe can now use Ibis to interact with `heart_failure`. For example,\nwe can filter rows and rename and select columns.\n\n```python\nheart_failure_filtered = (\n    heart_failure.filter(heart_failure.AGE \u003C 50)\n    .rename(\n        {\n            \"age\": \"AGE\",\n            \"diabetes\": \"DIABETES\",\n            \"serum_sodium\": \"SERUM_SODIUM\",\n            \"serum_creatinine\": \"SERUM_CREATININE\",\n            \"sex\": \"SEX\",\n            \"death_event\": \"DEATH_EVENT\",\n        }\n    )\n    .select(\n        [\"age\", \"diabetes\", \"serum_sodium\", \"serum_creatinine\", \"sex\", \"death_event\"]\n    )\n)\n```\n\nRight now, `heart_failure_filtered` is still a [table expression](https://ibis-project.org/reference/expression-tables).\nIbis lazily evaluates commands, which means that the full query is never run on\nthe database unless explicitly requested.\n\nUse `.execute()` or `.to_pandas()` to force Ibis to compile the table expression\ninto SQL and run that SQL on Snowflake.\n\n```python\nheart_failure_filtered.execute()\n```\n\nIf we want to see the SQL code that Ibis generates, we can run `ibis.to_sql()`.\n\n```python\nibis.to_sql(heart_failure_filtered)\n```\n\n```\nSELECT\n  \"t0\".\"AGE\" AS \"age\",\n  \"t0\".\"DIABETES\" AS \"diabetes\",\n  \"t0\".\"SERUM_SODIUM\" AS \"serum_sodium\",\n  \"t0\".\"SERUM_CREATININE\" AS \"serum_creatinine\",\n  \"t0\".\"SEX\" AS \"sex\",\n  \"t0\".\"DEATH_EVENT\" AS \"death_event\"\nFROM \"HEART_FAILURE\" AS \"t0\"\nWHERE\n  \"t0\".\"AGE\" \u003C 50\n```\n\n### Summary\n\nThis system:\n\n1. Keeps our data in the database, saving memory in the Python session.\n2. Pushes computations to the database, saving compute in the Python session.\n3. Evaluates queries lazily, saving compute in the database.\n\nWe don't need to manage the process, it happens automatically behind the scenes.\n\nYou can learn more about Ibis [here](https://ibis-project.org/). Take a look at the\n[Snowflake backend documentation](https://ibis-project.org/backends/snowflake) to\nlearn more about using Ibis to interact with\nSnowflake specifically.\n\n## Write Snowflake Data\n\nYou can also use Ibis to create a new table in a database or append to an existing table.\n\nTo add a new table, use `create_table()`.\n\n```python\ncon.create_table(\"HEART_FAILURE_FILTERED\", heart_failure_filtered)\n```\n\nTo insert data into an existing table, use [`insert()`](https://ibis-project.org/backends/snowflake#ibis.backends.snowflake.Backend.insert).\n\nNow that we understand how to interact with our database, we can use Python to perform our analysis.\n\n## Clean Data\n\nWe want to understand which variables in `HEART_FAILURE` are associated with survival\nof patients with heart failure.\n\nFirst, we convert the column names to lowercase so we won't need to worry about capitalization.\n\n```python\nheart_failure = heart_failure.rename(\n    {\n        \"age\": \"AGE\",\n        \"diabetes\": \"DIABETES\",\n        \"serum_sodium\": \"SERUM_SODIUM\",\n        \"serum_creatinine\": \"SERUM_CREATININE\",\n        \"sex\": \"SEX\",\n        \"death_event\": \"DEATH_EVENT\",\n    }\n)\n```\n\n\n### Filter ages\n\nFor now, we'll focus on patients younger than 50. We also reduce the data to just the columns we're interested in.\n\n```python\nheart_failure_filtered = (\n    heart_failure\n    .filter(heart_failure.age \u003C 50)  # Filter to age \u003C 50\n    .select([\"age\", \"diabetes\", \"serum_sodium\", \"serum_creatinine\", \"sex\", \"death_event\"])\n)\n```\n\n\u003E \n\u003E\n\u003E By default, Ibis is in _deferred mode_ and lazily evaluates table expressions. To more easily interact with your tables and see the results of your code, you can turn on _interactive code_. In [interactive mode](https://ibis-project.org/how-to/configure/basics#interactive-mode), expressions are executed when printed to the console. Turn on interactive mode with `ibis.options.interactive = True`.\n\n## Visualize Data\n\nThe heart failure data provides important insights that can help us:\n\n- Identify factors associated with increased risk of mortality after heart failure.\n- Predict future survival outcomes based on historical clinical data.\n- Benchmark patient outcomes based on clinical indicators like serum sodium levels.\n\nVisualizing clinical variables across different patient groups can help identify patterns.\n\n### Visualize serum sodium levels\n\nWe can use [plotnine](https://plotnine.org/) to visually compare sodium levels across different patient groups. In this plot, we see the distribution of serum sodium based on whether the patients have diabetes and whether they survived (`0`) or died (`1`) during the follow-up period.\n\n```python\nfrom plotnine import ggplot, aes, geom_boxplot, labs, theme\n\nheart_failure_plot = (\n    heart_failure_filtered\n    .mutate(\n        death_event=heart_failure_filtered[\"death_event\"].cast(\"string\"),\n        diabetes=heart_failure_filtered[\"diabetes\"].cast(\"string\")\n    )\n)\n\n(\n    ggplot(heart_failure_plot, aes(x=\"death_event\", y=\"serum_sodium\", color=\"diabetes\")) +\n    geom_boxplot() +\n    labs(\n        title=\"Serum Sodium Levels by Diabetes Status and Survival Outcome\",\n        x=\"Survival Outcome (0 = Survived, 1 = Died)\",\n        y=\"Serum Sodium (mEq/L)\",\n        color=\"Diabetes\"\n    ) +\n    theme(legend_position=\"bottom\")\n)\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/analysis/plot-sodium.png)\n\n## Create tables for Analysis\n\nNext, we'll use Ibis to calculate the median values for various clinical metrics across different patient groups.\n\n```python\n(\n    heart_failure_filtered\n    .group_by([\"death_event\", \"diabetes\"])\n    .aggregate(\n        median_age=heart_failure_filtered[\"age\"].median(),\n        median_serum_creatinine=heart_failure_filtered[\"serum_creatinine\"].median(),\n        median_serum_sodium=heart_failure_filtered[\"serum_sodium\"].median()\n    )\n)\n```\n\nThis is a useful way to examine the information for ourselves. However, if we wish to share the information with others, we might prefer to present the table in a more polished format. We can do this with the [Great Tables](https://posit-dev.github.io/great-tables/articles/intro.html) package.\n\nThe following code prepares a table named `comparison`, which we'll display with Great Tables.\n\n```python\ncomparison = (\n    heart_failure_filtered\n    .group_by([\"death_event\", \"diabetes\"])\n    .aggregate(\n        median_age=heart_failure_filtered[\"age\"].median(),\n        median_serum_creatinine=heart_failure_filtered[\"serum_creatinine\"].median(),\n        median_serum_sodium=heart_failure_filtered[\"serum_sodium\"].median()\n    )\n    .mutate(\n        death_event=ibis.ifelse(heart_failure_filtered[\"death_event\"] == 1, \"Died\", \"Survived\"),\n        diabetes=ibis.ifelse(heart_failure_filtered[\"diabetes\"] == 1, \"Yes\", \"No\"),\n        median_serum_creatinine=heart_failure_filtered[\"serum_creatinine\"].median().cast(\"float64\")\n    )\n    .rename(\n        {\n            \"Survival Outcome\": \"death_event\",\n            \"Diabetes Status\": \"diabetes\",\n            \"Median Age\": \"median_age\",\n            \"Median Serum Creatinine (mg/dL)\": \"median_serum_creatinine\",\n            \"Median Serum Sodium (mEq/L)\": \"median_serum_sodium\"\n        }\n    )\n)\n```\n\nNext, we use `GT()` and other Great Tables functions to create and style a table that displays `comparison`. Note that we need to evaluate `comparison` with `.execute()` first because `GT()` only accepts Pandas or Polars DataFrames.\n\n```python\nfrom great_tables import GT\n\n(\n    GT(comparison.execute())\n    .tab_header(title=\"Clinical Metrics by Survival Outcome and Diabetes Status\")\n    .fmt_number(\n        columns=[\"Median Age\", \"Median Serum Creatinine (mg/dL)\", \"Median Serum Sodium (mEq/L)\"],\n        decimals=1\n    )\n    .data_color(\n        columns=[\"Median Serum Creatinine (mg/dL)\", \"Median Serum Sodium (mEq/L)\"],\n        palette=[\"white\", \"blue\"]\n    )\n)\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/gt/gt-table.png)\n\n## Create Shiny Application\n\nEarlier, we showed you how to render a report from our Quarto document. Another way\nto share our work and allow others to explore the heart failure dataset is to create an\ninteractive [Shiny](https://shiny.posit.co/) app.\n\nOur GitHub repository contains an [example Shiny app](https://github.com/posit-dev/snowflake-posit-quickstart-python/tree/main/app).\nThis app allows the user\nto explore different clinical metrics in one place.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/shiny/shiny.png)\n\nTo run the app, open `app/app.py` and then click the `Run Shiny App` button at the\ntop of the script in VS Code.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-python-using-posit-workbench-and-snowflake/shiny/run.png)\n\nAfter launching the app, use the sidebar to change the metric displayed.\n\n### Learn More About Shiny\n\nYou can learn more about Shiny at: \u003Chttps://shiny.posit.co/\u003E.\n\nIf you're new to Shiny, you can try it online with\n[shinylive](https://shinylive.io/py/examples/).\nShinylive is also available for [R](https://shinylive.io/r/examples/) for Shiny for R.\n\n## Conclusion and Resources\n\n### Overview\n\nPython is a powerful, versatile tool for data science, and combined with Snowflake's high-performance data capabilities, it enables robust, end-to-end data workflows. Using the Posit Workbench Native Application, you can securely work with Python _within Snowflake_ while taking advantage of tools like Ibis, Quarto, and Shiny for Python to analyze, visualize, and share your results.\n\n### What You Learned\n\n- How to use VS Code within the Posit Workbench Native App.\n- How to connect to your Snowflake data from Python to create tables, visualizations, and more.\n- How to create a Quarto document containing plots and tables built in Python, using data stored in Snowflake.\n- How to build an interactive Shiny for Python application, working with data stored in Snowflake.\n\n### Resources\n\n- [Source Code on GitHub](https://github.com/posit-dev/snowflake-posit-quickstart-python)\n- [More about Posit Workbench](https://posit.co/products/enterprise/workbench/)\n- [Ibis library website](https://ibis-project.org/)\n- [plotnine package for plotting with a grammar of graphics in Python](https://plotnine.readthedocs.io/)\n- [Great Tables package for publication-ready tables in Python](https://posit-dev.github.io/great-tables/)\n- [Quarto for reproducible documents, reports, and data products](https://quarto.org/)\n- [Shiny for interactive dashboards and applications](https://shiny.posit.co/)\n- [Run Shiny entirely in the browser with shinylive](https://shinylive.io/)\n","title":"Quickstart Article Body","dataType":"string","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d45395d95e","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-05e2cf7cbf","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-1565138f57","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-7380d2fd28","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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