Merge pull request #106142 from PeterCLu/plu-designer-python

Plu designer python

Author: shawnmariejones

articles/machine-learning/how-to-designer-python.md

@@ -0,0 +1,83 @@
+---
+title: Python
+titleSuffix: Azure Machine Learning
+description: Learn how to use Python in Azure Machine Learning designer to transform data.
+services: machine-learning
+ms.service: machine-learning
+ms.subservice: core
+ms.topic: how-to
+
+author: peterclu
+ms.author: peterlu
+ms.date: 02/28/2020
+---
+
+# Execute Python code in Azure Machine Learning designer
+
+In this article, you learn how to use the [Execute Python Script](algorithm-module-reference/execute-python-script.md) module to add custom logic to  Azure Machine Learning designer. In the following how-to, you use the Pandas library to do simple feature engineering.
+
+You can use the in-built code editor to quickly add simple Python logic. If you want to add more complex code or upload additional Python libraries, you should use the zip file method.
+
+The default execution environment uses the Anacondas distribution of Python. For a complete list of pre-installed packages, see the [Execute Python Script module reference](algorithm-module-reference/execute-python-script.md) page.
+
+![Execute Python input map](media/how-to-designer-python/execute-python-map.png)
+
+## Execute Python written in the designer
+
+### Add the Execute Python Script module
+
+1. Find the **Execute Python Script** module in the designer palette. It can be found in the **Python Language** section.
+
+1. Drag and drop the module onto the pipeline canvas.
+
+### Connect input datasets
+
+This article uses the sample dataset, **Automobile price data (Raw)**. 
+
+1. Drag and drop your dataset to the pipeline canvas.
+
+1. Connect the output port of the dataset to the top-left input port of the **Execute Python Script** module. The designer exposes the input as a parameter to the entry point script.
+    
+    The right input port is reserved for zipped python libraries.
+
+    ![Connect datasets](media/how-to-designer-python/connect-dataset.png)
+        
+
+1. Take note of which input port you use. The designer assigns the left input port to the variable `dataset1` and the middle input port to `dataset2`. 
+
+Input modules are optional since you can generate or import data directly in the **Execute Python Script** module.
+
+### Write your Python code
+
+The designer provides an initial entry point script for you to edit and enter your own Python code. 
+
+In this example, you use Pandas to combine two columns found in the automobile dataset, **Price** and **Horsepower**, to create a new column, **Dollars per horsepower**. This column represents how much you pay for each horsepower, which could be a useful feature to decide if a car is a good deal for the money. 
+
+1. Select the **Execute Python Script** module.
+
+1. In the pane that appears to the right of the canvas, select the **Python script** text box.
+
+1. Copy and paste the following code into the text box.
+
+    ```python
+    import pandas as pd
+    
+    def azureml_main(dataframe1 = None, dataframe2 = None):
+        dataframe1['Dollar/HP'] = dataframe1.price / dataframe1.horsepower
+        return dataframe1
+    ```
+    Your pipeline should look the following image:
+    
+    ![Execute Python pipeline](media/how-to-designer-python/execute-python-pipeline.png)
+
+    The entry point script must contain the function `azureml_main`. There are two function parameters that map to the two input ports for the **Execute Python Script** module.
+
+    The return value must be a Pandas Dataframe. You can return up to two dataframes as module outputs.
+    
+1. Run the pipeline.
+
+Now, you have a dataset with the new feature **Dollars/HP**, which could be useful in training a car recommender. This is an example of feature extraction and dimensionality reduction. 
+
+## Next steps
+
+Learn how to [import your own data](how-to-designer-import-data.md) in Azure Machine Learning designer.

articles/machine-learning/media/how-to-designer-python/connect-dataset.png

@@ -367,18 +367,21 @@
     - name: 'Azure Pipelines for CI/CD'
       displayName: continuous, integration, delivery
       href: /azure/devops/pipelines/targets/azure-machine-learning?context=azure/machine-learning/service/context/ml-context
-    - name: 'Designer: Retrain using published pipelines'
+    - name: 'Designer retrain using published pipelines'
       displayName: retrain, designer, published pipeline
       href: how-to-retrain-designer.md
+    - name: Designer batch predictions
+      displayName: score scoring asynchronous consume pipeline parallelrunstep inference designer
+      href: how-to-run-batch-predictions-designer.md
+    - name: Designer execute Python code
+      displayName: feature extraction, feature engineering
+      href: how-to-designer-python.md
     - name: Use parallel run step
       displayName: score scoring batch consume pipeline parallelrunstep inference
       href: how-to-use-parallel-run-step.md
     - name: Debug & troubleshoot parallel run step
       displayName: debug_batch consume pipeline parallelrunstep inference
       href: how-to-debug-parallel-run-step.md
-    - name: Designer batch predictions
-      displayName: score scoring asynchronous consume pipeline parallelrunstep inference designer
-      href: how-to-run-batch-predictions-designer.md
   - name: Manage resource quotas
     displayName: limits
     href: how-to-manage-quotas.md