Writing Custom Code using RubiNotebook

To write your custom code using RubiNotebook, follow the steps given below.

1. Create your algorithm flow. Refer to Building Algorithm Flow in a Workbook Canvas.
2. Drag and drop RubiNotebook on your workbook canvas.
   
   
   
3. Connect RubiNotebook to the required node in your algorithm flow.
   
4. Select RubiNotebook and in the Properties pane, enter the jupyter URL and then click Configure
   
   
   
   The RubiNotebook configuration page is displayed.

   

   Note:

   The features in the above table can be used while running the entire node only. These features cannot be used while running individual cells of the Python program in the RubiNotebook editor.

5. Enter the Python code in the RubiNotebook code editor to perform various operations on the predecessor node.
   The RubiNotebook code editor is shown in the figure given below.
   
   

   The fields/icons on the RubiNotebook code editor are described in the table below.

   Field	Description
   File	It allows you to download python file (.py), python notebook (.ipynb), html file (.html), upload files, objects and notebooks
   	It helps you to Insert a new cell below the selected cell.
   	It helps you to Cut the selected cell.
   	It helps you to Copy the selected cell.
   	It helps you to Paste the copied or cut cell into the selected cell.
   	It helps you to execute the Python program.
   	It helps you to interrupt the kernel. The kernel resumes work when you start the execution of the program again.
   	It helps you to restart the kernel when it is shut down.
   	It helps you to restart the kernel and re-run the notebook.
   	Code cells– it allows you to write and edit new codes. Markdown- It allows you to format a text. It also documents the computational process in a literal way. Raw cells – It provides a place where we can write output directly.

6. Click Run to execute the code.
   After successful execution, the output is displayed.

7. After completing, click Close to close the RubiNotebook page and return to the workbook page.

Reading the Single Dataset Using the RubiNotebook

Reading Multiple Datasets using RubiNotebook

1. Create a workbook. Refer to Creating a Workbook.
2. Drag and drop the required datasets from the 'Reader section' in the Feature Studio. For example, we select the Employee Attrition, Female Birth, and IPL Records datasets.
3. Run the datasets.
4. Drag and drop the RubiNotebook from the 'Pro Code' section under the Model Studio in the Feature Studio.
5. Select RubiNotebook and in the Properties pane, enter the Jupyter URL and then click Configure.
   
   
   
6. Consider the following code 
   
   

   Code	Description
   _predecessorName_R_	It displays the predecessor name connected to the RubiNotebook.
   data = getRPredTaskResults(taskId_R=_predecessorName_R_['Female_Birth'])	Enter the task name to access the predecessor.
   data	Prints the data as data dictionary.
   import pandas as pd	Prompt to display the data in data frame
   new = pd.DataFrame.from_dict(data)	We save the framed data as new.
   new	Prints new result.
   returnData = getRPredTaskResults(taskId_R=_predecessorName_R_['Female_Birth']) ]]></ac:plain-text-body></ac:structured-macro> abc = rReturn(returnData)	It stores the metadata and results of RubiNotebook tasks. Shows all the available columns in the left side pane.

7. Click on the Save button.

   Notes

   The RubiNotebook allows you to connect multiple datasets; only one can be accessed at a time. To Run the RubiNotebook, Save the workbook and explore the RubiNotebook node.

Attaching a Successor Node in RubiNotebook

You can use the output from the RubiNotebook for the successor tasks using functions.

The Figure below shows the RubiNotebook node and its successor, the RubiPython node.

The figure given below shows the data in RubiNotebook. It contains data about City and Temperature.

1. For reading the dataset using the RubiNotebook please refer to Reading the dataset using the RubiNotebook
2. Select the Rubipython node from the task pane.
3. Click on Configure.
4. Consider the following code snippet

df = inputData[‘RubiNotebook’]

Output

Output[‘Category1’]= df[‘Category’]

Output[‘Discount1’]= df[‘Discount’]

return