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HeidiSteenHeidiSteen
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Updated CAB downloads page, plus minor edits on a python tutorial
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‎docs/advanced-analytics/install/sql-ml-cab-downloads.md‎

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@@ -4,7 +4,7 @@ description: R and Python CAB and package downloads for SQL Server 2017 Machine
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ms.prod: sql
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ms.technology: machine-learning
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ms.date: 01/19/2019
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ms.date: 04/01/2019
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ms.topic: conceptual
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author: dphansen
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ms.author: davidph
@@ -31,8 +31,13 @@ CAB files are listed in reverse chronological order. When you download the CAB f
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|Release |Component | Download link | Issues addressed |
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|---------|----------|----------------|------------------|
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|**[SQL Server 2017 CU14](https://support.microsoft.com/help/4484710/)** | | | |
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| | Microsoft R Open | [SRO_3.3.3.1400_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2073898&clcid=1033)| Packages are signed. |
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| | R Server |[SRS_9.2.0.1400_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2069739&clcid=1033)| Packages are signed. |
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| | Microsoft Python Open | [SPO_9.2.0.1400_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2073897&clcid=1033)| Packages are signed. |
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| | Python Server |[SPS_9.2.0.1400_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2071421&clcid=1033)| Packages are signed. |
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|**[SQL Server 2017 CU13](https://support.microsoft.com/help/4466404)** | | | |
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| | Microsoft R Open | [SRO_3.3.3.300_1033.cab](https://go.microsoft.com/fwlink/?LinkId=863894)| No change from previous versions. |
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| | Microsoft R Open | [SRO_3.3.3.1300_1033.cab](https://go.microsoft.com/fwlink/?LinkId=863894)| No change from previous versions. |
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| | R Server |[SRS_9.2.0.1300_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2038263&clcid=1033)| Contains a fix for upgrading an [operationalized standalone R Server](https://docs.microsoft.com/machine-learning-server/what-is-operationalization), as installed through SQL Server Setup. Use the CU13 CABs and follow [these instructions](sql-machine-learning-standalone-windows-install.md#apply-cu) to apply the update. |
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| | Microsoft Python Open | [SPO_9.2.0.24_1033.cab](https://go.microsoft.com/fwlink/?LinkId=851502)| No change from previous versions. |
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| | Python Server |[SPS_9.2.0.1300_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2038197&clcid=1033)| Contains a fix for upgrading an [operationalized standalone Python Server](https://docs.microsoft.com/machine-learning-server/what-is-operationalization), as installed through SQL Server Setup. Use the CU13 CABs and follow [these instructions](sql-machine-learning-standalone-windows-install.md#apply-cu) to apply the update. |
@@ -86,14 +91,19 @@ For SQL Server 2016 R Services, baseline releases are either the RTM version or
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|Release |Download link |
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|---------|---------------|
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|**SQL Server 2016 SP2 CU1-CU4** |
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|**SQL Server 2016 SP2 CU6** |
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|Microsoft R Open |[SRO_3.2.2.20100_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2079936&clcid=1033)|
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|Microsoft R Server |[SRS_8.0.3.20100_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2079933&clcid=1033)|
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|**SQL Server 2016 SP2 CU1-CU5** |
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|Microsoft R Open |[SRO_3.2.2.16000_1033.cab](https://go.microsoft.com/fwlink/?LinkId=836819)|
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|Microsoft R Server |[SRS_8.0.3.20000_1033.cab](https://go.microsoft.com/fwlink/?LinkId=866038)|
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|**SQL Server 2016 SP1 CU4-CU10** |
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|**SQL Server 2016 SP2** |
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|Microsoft R Open |[SRO_3.2.2.16000_1033.cab](https://go.microsoft.com/fwlink/?LinkId=836819)|
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|Microsoft R Server |[SRS_8.0.3.17000_1033.cab](https://go.microsoft.com/fwlink/?LinkId=850317)|
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|**SQL Server 2016 SP1 CU1-CU3** |
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|**SQL Server 2016 SP1 CU14** |
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|Microsoft R Open |[SRO_3.2.2.16100_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2080130&clcid=1033)|
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|Microsoft R Server |[SRS_8.0.3.17200_1033.cab](https://go.microsoft.com/fwlink/?LinkId=2079935&clcid=1033)|
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|**SQL Server 2016 SP1 CU1-CU13** |
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|Microsoft R Open |[SRO_3.2.2.16000_1033.cab](https://go.microsoft.com/fwlink/?LinkId=836819)|
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|Microsoft R Server |[SRS_8.0.3.16000_1033.cab](https://go.microsoft.com/fwlink/?LinkId=836818)|
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|**SQL Server 2016 SP1** |

‎docs/advanced-analytics/tutorials/sqldev-py3-explore-and-visualize-the-data.md‎

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@@ -76,56 +76,56 @@ The stored procedure returns a serialized Python `figure` object as a stream of
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DROP PROCEDURE IF EXISTS PyPlotMatplotlib;
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GO
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CREATE PROCEDURE [dbo].[PyPlotMatplotlib]
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AS
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BEGIN
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SET NOCOUNT ON;
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DECLARE @query nvarchar(max) =
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N'SELECT cast(tipped as int) as tipped, tip_amount, fare_amount FROM [dbo].[nyctaxi_sample]'
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EXECUTE sp_execute_external_script
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@language = N'Python',
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@script = N'
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import pandas as pd
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import pickle
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fig_handle = plt.figure()
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plt.hist(InputDataSet.tipped)
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plt.xlabel("Tipped")
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plt.ylabel("Counts")
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plt.title("Histogram, Tipped")
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plot0 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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plt.hist(InputDataSet.tip_amount)
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plt.xlabel("Tip amount ($)")
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plt.ylabel("Counts")
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plt.title("Histogram, Tip amount")
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plot1 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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plt.hist(InputDataSet.fare_amount)
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plt.xlabel("Fare amount ($)")
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plt.ylabel("Counts")
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plt.title("Histogram, Fare amount")
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plot2 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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plt.scatter( InputDataSet.fare_amount, InputDataSet.tip_amount)
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plt.xlabel("Fare Amount ($)")
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plt.ylabel("Tip Amount ($)")
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plt.title("Tip amount by Fare amount")
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plot3 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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OutputDataSet = plot0.append(plot1, ignore_index=True).append(plot2, ignore_index=True).append(plot3, ignore_index=True)
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',
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CREATE PROCEDURE [dbo].[PyPlotMatplotlib]
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AS
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BEGIN
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SET NOCOUNT ON;
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DECLARE @query nvarchar(max) =
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N'SELECT cast(tipped as int) as tipped, tip_amount, fare_amount FROM [dbo].[nyctaxi_sample]'
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EXECUTE sp_execute_external_script
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@language = N'Python',
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@script = N'
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import pandas as pd
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import pickle
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fig_handle = plt.figure()
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plt.hist(InputDataSet.tipped)
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plt.xlabel("Tipped")
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plt.ylabel("Counts")
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plt.title("Histogram, Tipped")
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plot0 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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plt.hist(InputDataSet.tip_amount)
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plt.xlabel("Tip amount ($)")
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plt.ylabel("Counts")
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plt.title("Histogram, Tip amount")
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plot1 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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plt.hist(InputDataSet.fare_amount)
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plt.xlabel("Fare amount ($)")
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plt.ylabel("Counts")
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plt.title("Histogram, Fare amount")
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plot2 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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plt.scatter( InputDataSet.fare_amount, InputDataSet.tip_amount)
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plt.xlabel("Fare Amount ($)")
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plt.ylabel("Tip Amount ($)")
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plt.title("Tip amount by Fare amount")
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plot3 = pd.DataFrame(data =[pickle.dumps(fig_handle)], columns =["plot"])
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plt.clf()
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OutputDataSet = plot0.append(plot1, ignore_index=True).append(plot2, ignore_index=True).append(plot3, ignore_index=True)
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',
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@input_data_1 = @query
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WITH RESULT SETS ((plot varbinary(max)))
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END
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GO
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WITH RESULT SETS ((plot varbinary(max)))
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END
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GO
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```
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2. Now run the stored procedure with no arguments to generate a plot from the data hard-coded as the input query.
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import pyodbc
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import pickle
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import os
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cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER={SERVER_NAME};DATABASE={DB_NAME};UID={USER_NAME};PWD={PASSWORD}')
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cnxn = pyodbc.connect('DRIVER=SQL Server;SERVER={SERVER_NAME};DATABASE={DB_NAME};UID={USER_NAME};PWD={PASSWORD}')
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cursor = cnxn.cursor()
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cursor.execute("EXECUTE [dbo].[PyPlotMatplotlib]")
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tables = cursor.fetchall()
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import pyodbc
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import pickle
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import os
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cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER={SERVER_NAME};DATABASE={DB_NAME};Trusted_Connection=True;')
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cnxn = pyodbc.connect('DRIVER=SQL Server;SERVER={SERVER_NAME};DATABASE={DB_NAME};Trusted_Connection=True;')
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cursor = cnxn.cursor()
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cursor.execute("EXECUTE [dbo].[PyPlotMatplotlib]")
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tables = cursor.fetchall()

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