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title Work with Python and SQL data types and objects
titleSuffix SQL Server Machine Learning Services
description In this quickstart, learn how to work with data types and data objects in Python and SQL Server with SQL Server Machine Learning Services.
ms.prod sql
ms.technology machine-learning
ms.date 09/17/2019
ms.topic quickstart
author garyericson
ms.author garye
ms.reviewer davidph
monikerRange >=sql-server-2017||>=sql-server-linux-ver15||=sqlallproducts-allversions

Quickstart: Handle data types and objects using Python in SQL Server Machine Learning Services

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

This quickstart shows how to use data structures when using Python in SQL Server Machine Learning Services.

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

Concepts to know up front include:

  • A data frame is a table with multiple columns.
  • A single column of a data frame is a list-like object called a series.
  • A single value of a data frame is called a cell and is accessed by index.

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.

Prerequisites

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

  • 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 SQL Server Management Studio (SSMS).

Scalar value as a series

This example does some simple math and converts a scalar into a series.

  1. 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

  2. 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, an index is generated that has 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

  3. 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 the scalar math results to a tabular structure, you still need to convert them to a format that SQL Server can handle.

  1. 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))

    The result is shown below. Even if you use the index to get specific values from the data.frame, the index values aren't part of the output.

    Results

    ResultValue
    0.5
    2

Output values into data.frame

Now you'll output specific values from two series of math results in a data.frame. The first has an index of sequential values generated by Python. The second uses an arbitrary index of string values.

  1. The following example gets a value from the series using 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))

    Results

    ResultValue
    2.0

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

  2. Now get a single value from the other data frame using a string index.

    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

    If you try to use a numeric index to get a value from this series, you get an error.

Next steps

Next, you'll build a predictive model using Python in SQL Server.

[!div class="nextstepaction"] Create and score a predictive model in Python

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