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title Quickstart: Python functions
description In this quickstart, you'll learn how to use Python mathematical and utility functions with SQL Server Machine Learning Services.
ms.prod sql
ms.technology machine-learning
ms.date 04/15/2020
ms.topic quickstart
author cawrites
ms.author chadam
ms.reviewer garye
ms.custom seo-lt-2019
monikerRange >=sql-server-2017||>=sql-server-linux-ver15||=sqlallproducts-allversions

Quickstart: Python functions with SQL Server Machine Learning Services

[!INCLUDEappliesto-ss-xxxx-xxxx-xxx-md]

In this quickstart, you'll learn how to use Python mathematical and utility functions with SQL Server Machine Learning Services. Statistical functions are often complicated to implement in T-SQL, but can be done in Python with only a few lines of code.

Prerequisites

  • This quickstart requires access to an instance of SQL Server with SQL Server Machine Learning Services with the Python language installed.

    Your SQL Server instance can be in an Azure virtual machine or on-premises. Just be aware that the external scripting feature is disabled by default, so you might need to enable external scripting and verify that SQL Server Launchpad service is running before you start.

  • You also need a tool for running SQL queries that contain Python scripts. You can run these scripts using any database management or query tool, as long as it can connect to a SQL Server instance, and run a T-SQL query or stored procedure. This quickstart uses Azure Data Studio).

Create a stored procedure to generate random numbers

For simplicity, let's use the Python numpy package, that's installed and loaded by default in SQL Server Machine Learning Services with Python installed. The package contains hundreds of functions for common statistical tasks, among them the random.normal function, which generates a specified number of random numbers using the normal distribution, given a standard deviation and mean.

For example, the following Python code returns 100 numbers on a mean of 50, given a standard deviation of 3.

numpy.random.normal(size=100, loc=50, scale=3)

To call this line of Python from T-SQL, add the Python function in the Python script parameter of sp_execute_external_script. The output expects a data frame, so use pandas to convert it.

EXECUTE sp_execute_external_script @language = N'Python'
    , @script = N'
import numpy
import pandas
OutputDataSet = pandas.DataFrame(numpy.random.normal(size=100, loc=50, scale=3));
'
    , @input_data_1 = N'   ;'
WITH RESULT SETS(([Density] FLOAT NOT NULL));

What if you'd like to make it easier to generate a different set of random numbers?

That's easy when combined with SQL Server. You define a stored procedure that gets the arguments from the user, then pass those arguments into the Python script as variables.

CREATE PROCEDURE MyPyNorm (
      @param1 INT
    , @param2 INT
    , @param3 INT
    )
AS
EXECUTE sp_execute_external_script @language = N'Python'
    , @script = N'
import numpy
import pandas
OutputDataSet = pandas.DataFrame(numpy.random.normal(size=mynumbers, loc=mymean, scale=mysd));
'
    , @input_data_1 = N'   ;'
    , @params = N' @mynumbers int, @mymean int, @mysd int'
    , @mynumbers = @param1
    , @mymean = @param2
    , @mysd = @param3
WITH RESULT SETS(([Density] FLOAT NOT NULL));

  • The first line defines each of the SQL input parameters that are required when the stored procedure is executed.

  • The line beginning with @params defines all variables used by the Python code, and the corresponding SQL data types.

  • The lines that immediately follow map the SQL parameter names to the corresponding Python variable names.

Now that you've wrapped the Python function in a stored procedure, you can easily call the function and pass in different values, like this:

EXECUTE MyPyNorm @param1 = 100,@param2 = 50, @param3 = 3

Use Python utility functions for troubleshooting

Python packages provide a variety of utility functions for investigating the current Python environment. These functions can be useful if you're finding discrepancies in the way your Python code performs in SQL Server and in outside environments.

For example, you might use system timing functions in the time package to measure the amount of time used by Python processes and analyze performance issues.

EXECUTE sp_execute_external_script
      @language = N'Python'
    , @script = N'
import time
start_time = time.time()

# Run Python processes

elapsed_time = time.time() - start_time
'
    , @input_data_1 = N' ;';

Next steps

To create a machine learning model using Python in SQL Server, follow this quickstart:

[!div class="nextstepaction"] Quickstart: Create and score a predictive model in Python with SQL Server Machine Learning Services

For more information on SQL Server Machine Learning Services, see: