After you create a notebook to use in the notebook editor, you need to add libraries, code, and data so you can do your analysis.

To develop analytic applications in a notebook, follow these general steps:

1. Open the notebook in edit mode: click the edit icon (). If the notebook is locked, you might be able to unlock and edit it.

2. If the notebook is marked as being untrusted, tell the Jupyter service to trust your notebook content and allow executing all cells by:

   1. Clicking Not Trusted in the upper right corner of the notebook.
   2. Clicking Trust to execute all cells.

3. Determine if the environment template that is associated with the notebook has the correct hardware size for the anticipated analysis processing throughput.

   1. Check the size of the environment by clicking the View notebook info icon () from the notebook toolbar and selecting the Environments page.

   2. If you need to change the environment, select another one from the list or, if none fits your needs, create your own environment template. See Creating emvironment template.

      If you create an environment template, you can add your own libraries to the template that are preinstalled at the time the environment is started. See Customize your environment for Python and R.

4. Import preinstalled libraries. See Libraries and scripts for notebooks.

5. Load and access data. You can access data from project assets by running code that is generated for you when you select the asset or programmatically by using preinstalled library functions. See Load and access data.

6. Prepare and analyze the data with the appropriate methods:

   • Build watsonx.ai Runtime models
   • Build Decision Optimization models
   • Use Watson Natural Language Processing
   • Use SPSS predictive analytics algorithms
   • Use geospatial location analysis methods
   • Use Data skipping for Spark SQL
   • Apply Parquet encryption
   • Use Time series analysis methods

7. If necessary, schedule the notebook to run at a regular time. See Schedule a notebook.

   1. Monitor the status of your job runs from the project's Jobs page.
   2. Click your job to open the job's details page to view the runs for your job and the status of each run. If a run failed, you can select the run and view the log tail or download the entire log file to troubleshoot the run.

8. If you have problems running your code, use the Debugger to go through a code cell in your notebook. To activate the debugger:

   1. Click on the Enable Debugger icon () in the notebook toolbar.
   2. Click next to any line of code to add a breakpoint.

   To debug your code from the View menu, select Right Sidebar, and then select Show Debugger.

9. When you're not actively working on the notebook, go to Kernel and click Shut Down Kernel to stop the notebook kernel and free up resources.

10. Stop the active runtime (and unnecessary capacity unit consumption) if no other notebook kernels are active under Tool runtimes on the Environments page on the Manage tab of your project.

Watch this short video to see how to create a Jupyter notebook and custom environment.

Watch this short video to see how to run basic SQL queries on Db2 Warehouse data in a Python notebook.

Find videos showing additional examples of Python notebooks on the Videos page.

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