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Python quickstart rewrite, TOC change, and file move
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‎.openpublishing.redirection.json‎

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{
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"redirect_url": "/sql/advanced-analytics/r/managing-and-monitoring-r-solutions",

‎docs/advanced-analytics/toc.yml‎

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- name: R
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items:
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- name: Verify R exists in SQL Server
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href: tutorials/rtsql-verify-r-exists.md
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href: tutorials/quickstart-r-verify.md
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- name: Hello World in R and SQL
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href: tutorials/rtsql-using-r-code-in-transact-sql-quickstart.md
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href: tutorials/quickstart-r-run-using-tsql.md
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- name: Handle inputs and outputs
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href: tutorials/rtsql-working-with-inputs-and-outputs.md
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href: tutorials/quickstart-r-inputs-and-outputs.md
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- name: Handle data types and objects
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href: tutorials/rtsql-r-and-sql-data-types-and-data-objects.md
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href: tutorials/quickstart-r-data-types-and-objects.md
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- name: Using R functions
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href: tutorials/rtsql-using-r-functions-with-sql-server-data.md
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href: tutorials/quickstart-r-functions.md
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- name: Create a predictive model
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href: tutorials/rtsql-create-a-predictive-model-r.md
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href: tutorials/quickstart-r-create-predictive-model.md
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- name: Predict from model
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href: tutorials/rtsql-predict-from-model.md
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href: tutorials/quickstart-r-predict-from-model.md
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- name: Python
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items:
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- name: Verify Python exists in SQL Server
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href: tutorials/pythontsql-verify-python-exists.md
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- name: Hello world Python script in SQL Server
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href: tutorials/pythontsql-run-python-using-t-sql.md
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href: tutorials/quickstart-python-verify.md
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- name: Hello World in Python and SQL
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href: tutorials/quickstart-python-run-using-t-sql.md
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- name: Handle inputs and outputs
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href: tutorials/pythontsql-working-with-inputs-and-outputs.md
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- name: Python data structures in SQL Server
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href: tutorials/pythontsql-python-data-structures-in-sql-server.md
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href: tutorials/quickstart-python-inputs-and-outputs.md
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- name: Python data structures and SQL
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href: tutorials/quickstart-python-data-structures.md
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- name: Train and use your first model
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href: tutorials/train-score-using-python-in-tsql.md
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href: tutorials/quickstart-python-train-score-in-tsql.md
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- name: Tutorials
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href: tutorials/demo-data-iris-in-sql.md
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- name: NYC Taxi data set
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- name: Python
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href: tutorials/sql-server-python-tutorials.md
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items:
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- name: Create a Python model using revoscalepy
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href: tutorials/use-python-revoscalepy-to-create-model.md
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- name: Learn in-database analytics
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href: tutorials/sqldev-in-database-python-for-sql-developers.md
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items:
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- name: 1 - Data exploration
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href: tutorials/sqldev-py3-explore-and-visualize-the-data.md
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- name: 2 - Feature engineering
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href: tutorials/sqldev-py4-create-data-features-using-t-sql.md
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- name: 3 - Train and deploy
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href: tutorials/sqldev-py5-train-and-save-a-model-using-t-sql.md
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- name: 4 - Predictions
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href: tutorials/sqldev-py6-operationalize-the-model.md
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- name: R
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items:
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href: tutorials/deepdive-move-data-between-sql-server-and-xdf-file.md
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- name: Send custom R using rxExec
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href: tutorials/r-tutorial-custom-r-functions.md
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- name: Python
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href: tutorials/sql-server-python-tutorials.md
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items:
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- name: Create a Python model using revoscalepy
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href: tutorials/use-python-revoscalepy-to-create-model.md
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- name: Learn in-database analytics
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href: tutorials/sqldev-in-database-python-for-sql-developers.md
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items:
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- name: 1 - Data exploration
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href: tutorials/sqldev-py3-explore-and-visualize-the-data.md
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- name: 2 - Feature engineering
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href: tutorials/sqldev-py4-create-data-features-using-t-sql.md
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- name: 3 - Train and deploy
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href: tutorials/sqldev-py5-train-and-save-a-model-using-t-sql.md
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- name: 4 - Predictions
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href: tutorials/sqldev-py6-operationalize-the-model.md
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- name: Samples
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href: https://github.com/Microsoft/sql-server-samples
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- name: Solutions
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href: r/installing-and-managing-r-packages.md
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- name: Get package information
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- name: Install new Python packages
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- name: Install new R packages
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items:
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- name: Tips for using R packages
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- name: Install new Python packages
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href: python/install-additional-python-packages-on-sql-server.md
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- name: Data exploration and modeling
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items:
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- name: R-to-SQL data type conversions

docs/advanced-analytics/tutorials/pythontsql-python-data-structures-in-sql-server.md renamed to docs/advanced-analytics/tutorials/quickstart-python-data-structures.md

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ms.prod: sql
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ms.date: 12/31/2018
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ms.date: 01/04/2019
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ms.topic: quickstart
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author: dphansen
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ms.author: davidph

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---
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# Quickstart: Python data structures in SQL Server
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# Quickstart: Handle inputs and outputs using Python in SQL Server
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[!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-winonly](../../includes/appliesto-ss-xxxx-xxxx-xxx-md-winonly.md)]
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This quickstart shows how to handle inputs and outputs when using Python in SQL Server Machine Learning Services.

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---
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title: Python models for training and predictions using stored procedures - SQL Server Machine Learning
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title: Quickstart for Python models for training and predictions using stored procedures - SQL Server Machine Learning
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description: Embed Python code in SQL Server stored procedures to create, train, and use a Python model with the classic Iris data set. Save a trained model to SQL Server, and then use it to generate predicted outcomes.
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ms.date: 10/23/2018
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---
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# Create, train, and use a Python model with stored procedures in SQL Server
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# Quickstart: Create, train, and use a Python model with stored procedures in SQL Server
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[!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-winonly](../../includes/appliesto-ss-xxxx-xxxx-xxx-md-winonly.md)]
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This exercise demonstrates Python integration with SQL Server when you add the [Machine Learning Services](../install/sql-machine-learning-services-windows-install.md) feature to a database engine instance. On such an instance, you can wrap Python code inside a [stored procedure](../../relational-databases/stored-procedures/stored-procedures-database-engine.md) to operationalize your script for production workloads. The ability to embed code in a stored procedure has tangible benefits in how you design, test, and manage data science and machine learning tasks. It makes your script and models accessible to any application that can connect to SQL Server.
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In this Python exercise, you will create and execute two stored procedures. The first one uses the classic Iris flower data set and generates a Naïve Bayes model to predict an Iris species based on flower characteristics. The second procedure is for scoring. It calls the model generated in the first procedure to output a set of predictions. By placing code in a stored procedure, operations are contained, reusable, and callable by other stored procedures and client applications.
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In this quickstart using Python, you will create and execute two stored procedures. The first one uses the classic Iris flower data set and generates a Naïve Bayes model to predict an Iris species based on flower characteristics. The second procedure is for scoring. It calls the model generated in the first procedure to output a set of predictions. By placing code in a stored procedure, operations are contained, reusable, and callable by other stored procedures and client applications.
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By completing this tutorial, you will learn:
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## Prerequisites
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Sample data used in this exercise is the [**irissql**](demo-data-iris-in-sql.md) database.
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A previous quickstart, [Verify Python exists in SQL Server](pythontsql-verify-python-exists.md), provides information and links for setting up the Python environment required for this quickstart.
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You also need a T-SQL editor, such as [SQL Server Management Studio](https://docs.microsoft.com/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017).
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The sample data used in this exercise is the [**irissql**](demo-data-iris-in-sql.md) database.
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## Create a stored procedure that generates models
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import pickle
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trained_model = pickle.dumps(GNB.fit(iris_data[[0,1,2,3]], iris_data[[4]].values.ravel()))
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'
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, @input_data_1 = N'select "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "SpeciesId" from iris_data'
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, @input_data_1_name = N'iris_data'
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```sql
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DECLARE @model varbinary(max);
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EXEC generate_iris_model @model OUTPUT;
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2. Verify that the model was inserted Another way to return a list of models is
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```sql
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SELECT * FROM dbo.iris_models
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```
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**Results**
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| | (no column name |
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| model_name | model |
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|---|-----------------|
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| 1 | Naive Bayes |
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| Naive Bayes | 0x800363736B6C6561726E2E6E616976655F62617965730A... |
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## Create and execute a stored procedure for generating predictions
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docs/advanced-analytics/tutorials/pythontsql-verify-python-exists.md renamed to docs/advanced-analytics/tutorials/quickstart-python-verify.md

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docs/advanced-analytics/tutorials/rtsql-create-a-predictive-model-r.md renamed to docs/advanced-analytics/tutorials/quickstart-r-create-predictive-model.md

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docs/advanced-analytics/tutorials/rtsql-r-and-sql-data-types-and-data-objects.md renamed to docs/advanced-analytics/tutorials/quickstart-r-data-types-and-objects.md

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# Quickstart: Handle data types and objects using R in SQL Server

docs/advanced-analytics/tutorials/rtsql-using-r-functions-with-sql-server-data.md renamed to docs/advanced-analytics/tutorials/quickstart-r-functions.md

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title: Quickstart showing R functions with SQL Server data - SQL Server Machine Learning
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description: In this quickstart, learn how to write an R function that uses SQL Server data.
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title: Quickstart showing R functions- SQL Server Machine Learning
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description: In this quickstart, learn how to write an R function for advanced statistical computation.
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# Quickstart: Using R functions with SQL Server data
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# Quickstart: Using R functions
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[!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-winonly](../../includes/appliesto-ss-xxxx-xxxx-xxx-md-winonly.md)]
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If you completed the previous quickstarts, you're familiar with basic operations and ready for something more complex, such as statistical functions. Advanced statistical functions that are complicated to implement in T-SQL can be done in R with only a single line of code.

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