Parts of a notebook
A notebook comprises cells that contain code or textual information and various other elements that help you interact with the code. Depending on whether a notebook is in edit mode, the list of interactive elements might include the menu bar, the main toolbar, the action bar, and the progress bar.
When you open a notebook in view mode, the only available elements are the cells and the action bar. You can use it to run, schedule, share, download, or view information about your notebook. To access additional options, click the Edit icon to switch to the edit mode.
When you open a notebook in edit mode, the notebook editor includes the following elements:
- Menu bar and toolbar
- Notebook action bar
- Cells that contain code or textual information
- Spark job progress bar (for notebooks that run in a Spark environment)
Menu bar and toolbar
From the menu bar and the main toolbar, you can select notebook features that affect the way that the notebook functions and perform the most-used operations within the notebook.
Notebook action bar
You can select features that enhance notebook collaboration. From the action bar, you can:
- Publish your notebook as a gist or on GitHub
- Create a permanent URL so that anyone with the link can view your notebook
- Create jobs in which to run your notebook. See Scheduling a notebook
- Download your notebook
- Add a project token so that code can access the project resources. See Adding code to set the project token
- Generate code snippets to add data from a data asset or a connection to a notebook cell
- View your notebook information and interact with the environment it is running in
- Save versions of your notebook
- Upload assets to your project
Cells in a Jupyter notebook
A Jupyter notebook consists of a sequence of cells. The flow of a notebook is sequential. You enter code into an input cell, and when you run the cell, the notebook executes the code and prints the output of the computation to an output cell.
You can change the code in an input cell and re-run the cell as often as you like. This way, the notebook follows a read-evaluate-print loop paradigm.
The behavior of a cell is determined by a cell’s type. The different types of cells include:
Jupyter code cells
In Jupyter code cells you can edit and write new code.
Jupyter markdown cells
In Jupyter markdown cells you can document the computational process. You can input headings to structure your notebook hierarchically.
You can also add and edit image files as attachments to the notebook. The markdown code and images are rendered when the cell is run.
Raw Jupyter NBConvert cells
Raw cells are not evaluated by the notebook. A raw cell is useful if you're looking for a place to keep content that you don't want to run, like generated output.
Spark job progress bar
When you run code in a notebook that triggers Spark jobs, it is often challenging to determine why your code is not running efficiently.
To help you better understand what your code is doing and assist you in code debugging, a notebook can monitor the execution of the Spark jobs for a code cell.
To enable Spark monitoring for a cell in a notebook:
- Select the code cell that you want to monitor.
- Click the Enable Spark Monitoring icon
in the notebook toolbar.
The progress bars that you see display the real time runtime progress of your jobs on the Spark cluster. Each Spark job runs on the cluster in one or more stages, where each stage is a list of tasks that can be run in parallel. The monitoring pane can become very large if the Spark job has many stages.
The job monitoring pane also displays the duration of each job and the status of the job stages. A stage can have one of the following statuses:
Running: Stage is active and startedCompleted: Stage is completedSkipped: The results of this stage were cached from an earlier operation and so the task doesn't have to run againPending: Stage not started yet
Click the icon again to disable monitoring in a cell.
Parent topic: Jupyter Notebook editor