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title Run Python using T-SQL | Microsoft Docs
ms.custom
ms.date 02/28/2018
ms.reviewer
ms.suite sql
ms.prod machine-learning-services
ms.prod_service machine-learning-services
ms.component
ms.technology
ms.tgt_pltfrm
ms.topic tutorial
applies_to
SQL Server 2017
dev_langs
Python
caps.latest.revision 2
author jeannt
ms.author jeannt
manager cgronlund

Run Python using T-SQL

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

This tutorial explains how you can run Python code in SQL Server 2017. It walks you through the process of moving data between SQL Server and Python, and explains how to wrap well-formed Python code in a stored procedure sp_execute_external_script to build, train, and use machine learning models in SQL Server.

Prerequisites

To complete this tutorial, you must first install SQL Server 2017 and enable Machine Learning Services on the instance, as described in this article.

You should also install SQL Server Management Studio. Alternatively, you can use another database management or query tool, as long as it can connect to a server and database, and run a T-SQL query or stored procedure.

After you have completed setup, return to this tutorial, to learn how to execute Python code in the context of a stored procedure.

Overview

This tutorial includes four lessons:

  • The basics of moving data between SQL Server and Python: learn the basic requirements, data structures, inputs, and outputs.
  • Practice using stored procedures for simple Python tasks, like loading sample data.
  • Use stored procedures to create a Python machine learning model, and generate scores from the model.
  • An optional lesson for users who intend to run Python from a remote client, using SQL Server as the compute context. Includes code for building a model; however, requires that you are already somewhat familiar with Python environments and Python tools.

Additional Python samples specific to SQL Server 2017 are provided here: SQL Server Python tutorials

Verify that Python is enabled and the Launchpad is running

  1. In Management Studio, run this statement to make sure the service has been enabled.

    sp_configure 'external scripts enabled'

    If run_value is 1, the machine learning feature is installed and ready to use.

    A common cause of errors is that the Launchpad, which manages communication between SQL Server and Python, has stopped. You can view the Launchpad status by using the Windows Services panel, or by opening SQL Server Configuration Manager. If the service has stopped, restart it.

  2. Next, verify that the Python runtime is working and communicating with SQL Server. To do this, open a new Query window in SQL Server Management Studio, and connect to the instance where Python was installed.

    EXEC sp_execute_external_script @language = N'Python', 
@script = N'print(3+4)'

    If all is well, you should see a result message like this one

    STDOUT message(s) from external script: 
7

    If you get errors, there are a variety of things you can do to ensure that the server and Python can communicate. For example, typically you must add the Windows user group SQLRUserGroup as a login on the instance, to ensure that Launchpad can provide communication between Python and SQL Server. (The same group is used for both R and Python code execution.) For more information, see Enabled implied authentication.

    Additionally, you might need to enable network protocols that have been disabled, or open the firewall so that SQL Server can communicate with external clients. For more information, see Troubleshooting setup.

Basic Python interaction

There are two ways to run Python code in SQL Server:

  • Add a Python script as an argument of the system stored procedure, sp_execute_external_script
  • From a remote Python client, connect to SQL Server, and execute code using the SQL Server as the compute context. This requires revoscalepy.

The primary goal of this tutorial is to ensure that you can use Python in a stored procedure.

For now, assuming that you have everything set up correctly, and Python and SQL Server are talking to each other, run some simple code to see how data is passed back and forth between SQL Server and Python.

execute sp_execute_external_script 
@language = N'Python', 
@script = N'
a = 1
b = 2
c = a/b
d = a*b
print(c, d)
'
  • Everything inside the @script argument must be valid Python code. That means following all Pythonic rules regarding indentation, variable names, and so forth. When you get an error, check your white space and casing.
  • If you are using any libraries not loaded by default, you must use an import statement at the beginning of your script to load them. If the library is not installed, stop, and install the Python package outside of SQL Server, as described here: Install new Python packages on SQL Server

SQL Server passes the code to Python, and returns results and messages.

Results

STDOUT message(s) from external script: 
0.5 2

Although the correct result was calculated, the Python print function just returns the result to the Messages windows. this can be handy when testing your code, but what you really want is to return your result in tabular format, so that you can use it in an application or write it to a table.

Inputs and outputs

By default, sp_execute_external_script accepts a single input dataset, which you might supply in the form of a valid SQL query. You can pass other types of input as a SQL variable: for example, you can pass a trained model as a variable, using a serialization function such as pickle or rx_serialize_model to write the model in a binary format.

The stored procedure can return a single Python pandas data frame as output. However you can add other types of outputs as variables, in addition to this single data frame. For example, you might output a trained model as a variable and use T-SQL to immediately save that model to a table. You can also generate plots or scalars.

For now, let's look at just the default input and output variables, InputDataSet and OutputDataSet. The following code does some math and outputs the results.

execute sp_execute_external_script 
@language = N'Python', 
@script = N'
a = 1
b = 2
c = a/b
print(c)
OutputDataSet = c
'
WITH RESULT SETS ((ResultValue float))

You should get an error, because the Python code generates a scalar, not a data frame.

Results

 line 43, in transform
    raise TypeError('OutputDataSet should be of type pandas.DataFrame')

Now see what happens when you pass a tabular dataset to Python, using the default input variable InputDataSet. The stored procedure returns a data.frame automatically, without you having to do anything extra in your Python code.

EXECUTE sp_execute_external_script 
@language = N'Python', 
@script = N'
OutputDataSet = InputDataSet
',
@input_data_1 = N'SELECT 1 as Col1'

Results

no columnname
1

A couple of notes on this stored procedure:

  • The variable for the input dataset has the default name, @input_data_1, but you can change that name by adding a line like his: @input_data_1_name = N'myResultName'.
  • Column names used by Python are never preserved in the output. So even if your query specified the column name Col1, that name is not returned, nor would any column headings used by your Python script. To specify a column name and data type when you return the data to SQL Server, use the T-SQL WITH RESULT SETS clause.

Here's an example that names a new input and output variable and provides a schema for the output.

execute sp_execute_external_script 
@language = N'Python', 
@script = N'
MyOutput = MyInput
',
@input_data_1_name = N'MyInput',
@input_data_1 = N'SELECT 1 as Col1',
@output_data_1_name = N'MyOutput'
WITH RESULT SETS ((ResultValue int))

Results

ResultValue
1

Finally, just for fun, go back and change the line @input_data_1_name = N'MyInput' to @input_data_1_name = N'myinput'.

This change results in an error from Python, passed on to you by the satellite service used by SQL Server. The key message is in this line:

MyOutput = MyInput
NameError: name 'MyInput' is not defined

Recall that Python, like R, is case-sensitive. Therefore, when you get any kind of error, be sure to check your variable names, and look for issues with spacing, indentation, and data types.

Python data structures

SQL Server relies on the Python pandas package, which is great for working with tabular data. However, you've already seen that you cannot pass a scalar from Python to SQL Server and expect it to "just work".

In this section, we'll review some basic data type definitions, to prepare you for additional issues that you might run across when passing tabular data between Python and SQL Server.

  • A data frame is a table with multiple columns.
  • A single column of a DataFrame, is a list-like object called a Series.
  • A single value is a cell of a data frame and has to be called by index.

So how would you expose the single result of a calculation as a data frame, if a data.frame requires a tabular structure? One answer is to represent the single scalar value as a series, which is easily converted to a data frame.

Convert scalar to series

This example does some simple math and converts the scalar into a series. A series requires an index, which you can assign manually, as shown here, or programmatically.

execute sp_execute_external_script 
@language = N'Python', 
@script = N'
a = 1
b = 2
c = a/b
print(c)
s = pandas.Series(c, index =["simple math example 1"])
print(s)
'

Because the series hasn't been converted to a data.frame, the values are returned in the Messages window, but you can see that the results are in a more tabular format.

Results

STDOUT message(s) from external script: 
0.5
simple math example 1    0.5
dtype: float64

To increase the length of the series, you can add new values, using an array.

execute sp_execute_external_script 
@language = N'Python', 
@script = N'
a = 1
b = 2
c = a/b
d = a*b
s = pandas.Series([c,d])
print(s)
'

If you do not specify an index, pandas generates an index having values starting with 0 and ending with the length of the array.

Results

STDOUT message(s) from external script: 
0    0.5
1    2.0
dtype: float64

If you increase the number of index values, but don't add new data values, the data values are repeated to fill the series.

execute sp_execute_external_script 
@language = N'Python', 
@script = N'
a = 1
b = 2
c = a/b
s = pandas.Series(c, index =["simple math example 1", "simple math example 2"])
print(s)
'

Results

STDOUT message(s) from external script: 
0.5
simple math example 1    0.5
simple math example 2    0.5
dtype: float64

Convert series to data frame

Having converted our scalar math results to a tabular structure, we still need to convert them to a format that SQL Server can handle. To convert a series to a data.frame, call the pandas DataFrame method.

execute sp_execute_external_script 
@language = N'Python', 
@script = N'
import pandas as pd
a = 1
b = 2
c = a/b
d = a*b
s = pandas.Series([c,d])
print(s)
df = pd.DataFrame(s)
OutputDataSet = df
'
WITH RESULT SETS (( ResultValue float ))

*Results

ResultValue
0.5
2

Note that the index values aren't output, even if you use the index to get specific values from the data.frame.

Output values into data.frame using an index

Let's see how this works with our two series containing the results of simple math operations:

  • The first has an index of sequential values generated by Python
  • The second uses an arbitrary index of string values.

This example gets a value from the series that uses an integer index.

EXECUTE sp_execute_external_script 
@language = N'Python', 
@script = N'
import pandas as pd
a = 1
b = 2
c = a/b
d = a*b
s = pandas.Series([c,d])
print(s)
df = pd.DataFrame(s, index=[1])
OutputDataSet = df
'
WITH RESULT SETS (( ResultValue float ))

Remember that the auto-generated index starts at 0. Try using an out of range index value and see what happens.

Now let's get a single value from the other data frame that has a string index. If you try to reference a numeric index on this series, you get an error.

EXECUTE sp_execute_external_script 
@language = N'Python', 
@script = N'
import pandas as pd
a = 1
b = 2
c = a/b
s = pandas.Series(c, index =["simple math example 1", "simple math example 2"])
print(s)
df = pd.DataFrame(s, index=["simple math example 1"])
OutputDataSet = df
'
WITH RESULT SETS (( ResultValue float ))

Results

ResultValue
0.5

From this exercise, you can see that outputting a single value as a data frame might be more trouble than its worth. Fortunately, you can easily pass all kinds of values in and out of the stored procedure as variables. That's covered in the next lesson.

Tips

  • Among programming languages, Python is one of the most flexible with regard to single quotes vs. double quotation marks; they're pretty much interchangeable.

    However, T-SQL uses single quotes for only certain things, and the @script argument uses single quotes to enclose the Python code as a Unicode string. Therefore, you might need to review your Python code and change some single quotes to double quotes.

  • Can't find the stored procedure, sp_execute_external_script? It means you probably haven't finished configuring the instance to support external script execution. After running SQL Server 2017 setup and selecting Python as the machine learning language, you must also explicitly enable the feature using sp_configure, and then restart the instance.

    For details, see Setup Machine Learning Services with Python.

Next steps

Wrap Python code in a SQL stored procedure