Merge pull request #16386 from garyericson/07-31-rename-python-tutorial-files

SQLML: Renaming Python tutorial files

Author: ShannonLeavitt

.openpublishing.redirection.json

@@ -42533,6 +42533,11 @@
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       "redirect_url": "/sql/machine-learning/tutorials/sqldev-in-database-python-for-sql-developers",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "docs/machine-learning/tutorials/sqldev-in-database-python-for-sql-developers.md",
+      "redirect_url": "/sql/machine-learning/tutorials/python-taxi-classification-introduction",
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@@ -42548,21 +42553,41 @@
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       "redirect_url": "/sql/machine-learning/tutorials/sqldev-py3-explore-and-visualize-the-data",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "docs/machine-learning/tutorials/sqldev-py3-explore-and-visualize-the-data.md",
+      "redirect_url": "/sql/machine-learning/tutorials/python-taxi-classification-explore-data",
       "redirect_document_id": true
     },
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       "source_path": "docs/advanced-analytics/tutorials/sqldev-py4-create-data-features-using-t-sql.md",
       "redirect_url": "/sql/machine-learning/tutorials/sqldev-py4-create-data-features-using-t-sql",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "docs/machine-learning/tutorials/sqldev-py4-create-data-features-using-t-sql.md",
+      "redirect_url": "/sql/machine-learning/tutorials/python-taxi-classification-create-features",
       "redirect_document_id": true
     },
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       "source_path": "docs/advanced-analytics/tutorials/sqldev-py5-train-and-save-a-model-using-t-sql.md",
       "redirect_url": "/sql/machine-learning/tutorials/sqldev-py5-train-and-save-a-model-using-t-sql",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "docs/machine-learning/tutorials/sqldev-py5-train-and-save-a-model-using-t-sql.md",
+      "redirect_url": "/sql/machine-learning/tutorials/python-taxi-classification-train-model",
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       "redirect_url": "/sql/machine-learning/tutorials/sqldev-py6-operationalize-the-model",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "docs/machine-learning/tutorials/sqldev-py6-operationalize-the-model.md",
+      "redirect_url": "/sql/machine-learning/tutorials/python-taxi-classification-deploy-model",
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docs/machine-learning/python/ref-py-revoscalepy.md

@@ -99,7 +99,7 @@ When running locally, you typically run a Python script from the command line, o
 
 When you are ready to encapsulate Python script inside a stored procedure, [sp_execute_external_script](https://docs.microsoft.com/sql/relational-databases/system-stored-procedures/sp-execute-external-script-transact-sql), we recommend rewriting the code as a single function that has clearly defined inputs and outputs. 
 
-Inputs and outputs must be **pandas** data frames. When this is done, you can call the stored procedure from any client that supports T-SQL, easily pass SQL queries as inputs, and save the results to SQL tables. For an example, see [Learn in-database Python analytics for SQL developers](../tutorials/sqldev-in-database-python-for-sql-developers.md).
+Inputs and outputs must be **pandas** data frames. When this is done, you can call the stored procedure from any client that supports T-SQL, easily pass SQL queries as inputs, and save the results to SQL tables. For an example, see [Learn in-database Python analytics for SQL developers](../tutorials/python-taxi-classification-introduction.md).
 
 ### Using revoscalepy with microsoftml
 

docs/machine-learning/toc.yml

@@ -102,15 +102,15 @@
     - name: Python for SQL developers
       items:
       - name: 1 - Introduction
-        href: ../machine-learning/tutorials/sqldev-in-database-python-for-sql-developers.md
+        href: ../machine-learning/tutorials/python-taxi-classification-introduction.md
       - name: 2 - Data exploration
-        href: ../machine-learning/tutorials/sqldev-py3-explore-and-visualize-the-data.md
+        href: ../machine-learning/tutorials/python-taxi-classification-explore-data.md
       - name: 3 - Feature engineering
-        href: ../machine-learning/tutorials/sqldev-py4-create-data-features-using-t-sql.md
+        href: ../machine-learning/tutorials/python-taxi-classification-create-features.md
       - name: 4 - Train and deploy
-        href: ../machine-learning/tutorials/sqldev-py5-train-and-save-a-model-using-t-sql.md
+        href: ../machine-learning/tutorials/python-taxi-classification-train-model.md
       - name: 5 - Predictions
-        href: ../machine-learning/tutorials/sqldev-py6-operationalize-the-model.md
+        href: ../machine-learning/tutorials/python-taxi-classification-deploy-model.md
   - name: R
     items:
     - name: R tutorials

docs/machine-learning/tutorials/demo-data-nyctaxi-in-sql.md

@@ -21,7 +21,7 @@ To complete this exercise, you should have [SQL Server Management Studio](https:
 Tutorials and quickstarts using this data set include the following:
 
 + [Learn in-database analytics using R in SQL Server](sqldev-in-database-r-for-sql-developers.md)
-+ [Learn in-database analytics using Python in SQL Server](sqldev-in-database-python-for-sql-developers.md)
++ [Learn in-database analytics using Python in SQL Server](python-taxi-classification-introduction.md)
 
 ## Download files
 
@@ -111,4 +111,4 @@ Results should be similar to those showing in the following screenshot.
 NYC Taxi sample data is now available for hands-on learning.
 
 + [Learn in-database analytics using R in SQL Server](sqldev-in-database-r-for-sql-developers.md)
-+ [Learn in-database analytics using Python in SQL Server](sqldev-in-database-python-for-sql-developers.md)
++ [Learn in-database analytics using Python in SQL Server](python-taxi-classification-introduction.md)

…-py4-create-data-features-using-t-sql.md …n-taxi-classification-create-features.mddocs/machine-learning/tutorials/sqldev-py4-create-data-features-using-t-sql.md renamed to docs/machine-learning/tutorials/python-taxi-classification-create-features.md docs/machine-learning/tutorials/sqldev-py4-create-data-features-using-t-sql.md renamed to docs/machine-learning/tutorials/python-taxi-classification-create-features.md

@@ -25,13 +25,13 @@ In this article, you'll:
 > + Modify a custom function to calculate trip distance
 > + Save the features using another custom function
 
-In [part one](sqldev-in-database-python-for-sql-developers.md), you installed the prerequisites and restored the sample database.
+In [part one](python-taxi-classification-introduction.md), you installed the prerequisites and restored the sample database.
 
-In [part two](sqldev-py3-explore-and-visualize-the-data.md), you explored the sample data and generated some plots.
+In [part two](python-taxi-classification-explore-data.md), you explored the sample data and generated some plots.
 
-In [part four](sqldev-py5-train-and-save-a-model-using-t-sql.md), you'll load the modules and call the necessary functions to create and train the model using a SQL Server stored procedure.
+In [part four](python-taxi-classification-train-model.md), you'll load the modules and call the necessary functions to create and train the model using a SQL Server stored procedure.
 
-In [part five](sqldev-py6-operationalize-the-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
+In [part five](python-taxi-classification-deploy-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
 
 ## Define the Function
 
@@ -130,4 +130,4 @@ In this article, you:
 > + Saved the features using another custom function
 
 > [!div class="nextstepaction"]
-> [Python tutorial: Train and save a Python model using T-SQL](sqldev-py5-train-and-save-a-model-using-t-sql.md)
+> [Python tutorial: Train and save a Python model using T-SQL](python-taxi-classification-train-model.md)

…s/sqldev-py6-operationalize-the-model.md …thon-taxi-classification-deploy-model.mddocs/machine-learning/tutorials/sqldev-py6-operationalize-the-model.md renamed to docs/machine-learning/tutorials/python-taxi-classification-deploy-model.md docs/machine-learning/tutorials/sqldev-py6-operationalize-the-model.md renamed to docs/machine-learning/tutorials/python-taxi-classification-deploy-model.md

@@ -32,13 +32,13 @@ In this article, you'll:
 > + Create and use stored procedures for batch scoring
 > + Create and use stored procedures for scoring a single row
 
-In [part one](sqldev-in-database-python-for-sql-developers.md), you installed the prerequisites and restored the sample database.
+In [part one](python-taxi-classification-introduction.md), you installed the prerequisites and restored the sample database.
 
-In [part two](sqldev-py3-explore-and-visualize-the-data.md), you explored the sample data and generated some plots.
+In [part two](python-taxi-classification-explore-data.md), you explored the sample data and generated some plots.
 
-In [part three](sqldev-py4-create-data-features-using-t-sql.md), you learned how to create features from raw data by using a Transact-SQL function. You then called that function from a stored procedure to create a table that contains the feature values.
+In [part three](python-taxi-classification-create-features.md), you learned how to create features from raw data by using a Transact-SQL function. You then called that function from a stored procedure to create a table that contains the feature values.
 
-In [part four](sqldev-py5-train-and-save-a-model-using-t-sql.md), you loaded the modules and called the necessary functions to create and train the model using a SQL Server stored procedure.
+In [part four](python-taxi-classification-train-model.md), you loaded the modules and called the necessary functions to create and train the model using a SQL Server stored procedure.
 
 ## Batch scoring
 
@@ -183,9 +183,9 @@ In this section, you'll learn how to create single predictions by calling two st
 
 + [PredictTipSingleModeSciKitPy](#predicttipsinglemodescikitpy) is designed for single-row scoring using the scikit-learn model.
 + [PredictTipSingleModeRxPy](#predicttipsinglemoderxpy) is designed for single-row scoring using the revoscalepy model.
-+ If you haven't trained a model yet, return to [part five](sqldev-py5-train-and-save-a-model-using-t-sql.md)!
++ If you haven't trained a model yet, return to [part five](python-taxi-classification-train-model.md)!
 
-Both models take as input a series of single values, such as passenger count, trip distance, and so forth. A table-valued function, `fnEngineerFeatures`, is used to convert latitude and longitude values from the inputs to a new feature, direct distance. [Part four](sqldev-py4-create-data-features-using-t-sql.md) contains a description of this table-valued function.
+Both models take as input a series of single values, such as passenger count, trip distance, and so forth. A table-valued function, `fnEngineerFeatures`, is used to convert latitude and longitude values from the inputs to a new feature, direct distance. [Part four](python-taxi-classification-create-features.md) contains a description of this table-valued function.
 
 Both stored procedures create a score based on the Python model.
 

…ev-py3-explore-and-visualize-the-data.md …thon-taxi-classification-explore-data.mddocs/machine-learning/tutorials/sqldev-py3-explore-and-visualize-the-data.md renamed to docs/machine-learning/tutorials/python-taxi-classification-explore-data.md docs/machine-learning/tutorials/sqldev-py3-explore-and-visualize-the-data.md renamed to docs/machine-learning/tutorials/python-taxi-classification-explore-data.md

@@ -23,13 +23,13 @@ In this article, you'll:
 > + Review the sample data
 > + Create plots using Python in T-SQL
 
-In [part one](sqldev-in-database-python-for-sql-developers.md), you installed the prerequisites and restored the sample database.
+In [part one](python-taxi-classification-introduction.md), you installed the prerequisites and restored the sample database.
 
-In [part three](sqldev-py4-create-data-features-using-t-sql.md), you'll learn how to create features from raw data by using a Transact-SQL function. You'll then call that function from a stored procedure to create a table that contains the feature values.
+In [part three](python-taxi-classification-create-features.md), you'll learn how to create features from raw data by using a Transact-SQL function. You'll then call that function from a stored procedure to create a table that contains the feature values.
 
-In [part four](sqldev-py5-train-and-save-a-model-using-t-sql.md), you'll load the modules and call the necessary functions to create and train the model using a SQL Server stored procedure.
+In [part four](python-taxi-classification-train-model.md), you'll load the modules and call the necessary functions to create and train the model using a SQL Server stored procedure.
 
-In [part five](sqldev-py6-operationalize-the-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
+In [part five](python-taxi-classification-deploy-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
 
 ## Review the data
 
@@ -194,4 +194,4 @@ In this article, you:
 > + Created plots using Python in T-SQL
 
 > [!div class="nextstepaction"]
-> [Python tutorial: Create Data Features using T-SQL](sqldev-py4-create-data-features-using-t-sql.md)
+> [Python tutorial: Create Data Features using T-SQL](python-taxi-classification-create-features.md)

…in-database-python-for-sql-developers.md …thon-taxi-classification-introduction.mddocs/machine-learning/tutorials/sqldev-in-database-python-for-sql-developers.md renamed to docs/machine-learning/tutorials/python-taxi-classification-introduction.md docs/machine-learning/tutorials/sqldev-in-database-python-for-sql-developers.md renamed to docs/machine-learning/tutorials/python-taxi-classification-introduction.md

@@ -39,13 +39,13 @@ In this article, you'll:
 > + Install prerequisites
 > + Restore the sample database
 
-In [part two](sqldev-py3-explore-and-visualize-the-data.md), you'll explore the sample data and generate some plots.
+In [part two](python-taxi-classification-explore-data.md), you'll explore the sample data and generate some plots.
 
-In [part three](sqldev-py4-create-data-features-using-t-sql.md), you'll learn how to create features from raw data by using a Transact-SQL function. You'll then call that function from a stored procedure to create a table that contains the feature values.
+In [part three](python-taxi-classification-create-features.md), you'll learn how to create features from raw data by using a Transact-SQL function. You'll then call that function from a stored procedure to create a table that contains the feature values.
 
-In [part four](sqldev-py5-train-and-save-a-model-using-t-sql.md), you'll load the modules and call the necessary functions to create and train the model using a SQL Server stored procedure.
+In [part four](python-taxi-classification-train-model.md), you'll load the modules and call the necessary functions to create and train the model using a SQL Server stored procedure.
 
-In [part five](sqldev-py6-operationalize-the-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
+In [part five](python-taxi-classification-deploy-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
 
 > [!NOTE]
 > This tutorial is available in both R and Python. For the R version, see [R tutorial: Predict NYC taxi fares with binary classification](sqldev-in-database-r-for-sql-developers.md).
@@ -88,4 +88,4 @@ In this article, you:
 > + Restored the sample database
 
 > [!div class="nextstepaction"]
-> [Python tutorial: Explore and visualize data](sqldev-py3-explore-and-visualize-the-data.md)
+> [Python tutorial: Explore and visualize data](python-taxi-classification-explore-data.md)

…y5-train-and-save-a-model-using-t-sql.md …ython-taxi-classification-train-model.mddocs/machine-learning/tutorials/sqldev-py5-train-and-save-a-model-using-t-sql.md renamed to docs/machine-learning/tutorials/python-taxi-classification-train-model.md docs/machine-learning/tutorials/sqldev-py5-train-and-save-a-model-using-t-sql.md renamed to docs/machine-learning/tutorials/python-taxi-classification-train-model.md

@@ -25,13 +25,13 @@ In this article, you'll:
 > + Create and train a model using a SQL stored procedure
 > + Save the trained model to a SQL table
 
-In [part one](sqldev-in-database-python-for-sql-developers.md), you installed the prerequisites and restored the sample database.
+In [part one](python-taxi-classification-introduction.md), you installed the prerequisites and restored the sample database.
 
-In [part two](sqldev-py3-explore-and-visualize-the-data.md), you explored the sample data and generated some plots.
+In [part two](python-taxi-classification-explore-data.md), you explored the sample data and generated some plots.
 
-In [part three](sqldev-py4-create-data-features-using-t-sql.md), you learned how to create features from raw data by using a Transact-SQL function. You then called that function from a stored procedure to create a table that contains the feature values.
+In [part three](python-taxi-classification-create-features.md), you learned how to create features from raw data by using a Transact-SQL function. You then called that function from a stored procedure to create a table that contains the feature values.
 
-In [part five](sqldev-py6-operationalize-the-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
+In [part five](python-taxi-classification-deploy-model.md), you'll learn how to operationalize the models that you trained and saved in part four.
 
 ## Split the sample data into training and testing sets
 
@@ -216,4 +216,4 @@ In this article, you:
 > + Saved the trained model to a SQL table
 
 > [!div class="nextstepaction"]
-> [Python tutorial: Run predictions using Python embedded in a stored procedure](sqldev-py6-operationalize-the-model.md)
+> [Python tutorial: Run predictions using Python embedded in a stored procedure](python-taxi-classification-deploy-model.md)

docs/machine-learning/tutorials/python-tutorials.md

@@ -33,7 +33,7 @@ This article describes the Python tutorials and quickstarts for [Azure SQL Manag
 | [Predict ski rental with linear regression](python-ski-rental-linear-regression.md) | Use Python and linear regression to predict the number of ski rentals. Use notebooks in Azure Data Studio for preparing data and training the model, and T-SQL for model deployment. |
 | [Categorizing customers using k-means clustering](python-clustering-model.md) | Use Python to develop and deploy a K-Means clustering model to categorize customers. Use notebooks in Azure Data Studio for preparing data and training the model, and T-SQL for model deployment. |
 | [Create a model using revoscalepy](use-python-revoscalepy-to-create-model.md) | Demonstrates how to run code from a remote Python client using SQL Server as compute context. The tutorial creates a model using **rxLinMod** from the **revoscalepy** library. |
-| [Python data analytics for SQL developers](sqldev-in-database-python-for-sql-developers.md) | This end-to-end walkthrough demonstrates the process of building a complete Python solution using T-SQL. |
+| [Python data analytics for SQL developers](python-taxi-classification-introduction.md) | This end-to-end walkthrough demonstrates the process of building a complete Python solution using T-SQL. |
 ::: moniker-end
 ::: moniker range="=azuresqldb-mi-current"
 | Tutorial | Description |