# Using Python and R in Notebooks

> How to use of Python libraries, the Notebook kernel, the SisenseHelper Functions, using Python code, and data visualization.

*Source: https://docs.sisense.com/main/SisenseLinux/using-python-and-r-in-notebooks.htm*

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Last updated: June 11, 2026

|  |  |
| --- | --- |
| [Tier](https://www.sisense.com/pricing/#pricing) | [Deployment](https://docs.sisense.com/main/SisenseLinux/introduction-to-sisense-cloud-managed-services.md#ComparisonofManagedCloudandSelfHosted) |
| Enterprise | Cloud     On-Prem |

This section covers the use of Python libraries, the Notebook kernel, the SisenseHelper Functions, using Python code,
and data visualization.

## Python Libraries

The libraries provided for the code language selected for the kernel are listed in the left navigation panel. The
libraries need to be imported from a code block at least once and can be used throughout the Notebook.
  
![Providedlibraries](https://docs.sisense.com/main/Resources/Images/providedlibraries_608x266.png)

### Adding External Python Libraries

You can add external Python libraries from code blocks using the pip install command.

![Pip install emoji](https://docs.sisense.com/main/Resources/Images/pip install emoji.png)

Once the installation of a library is complete, a note displays in the Console Output from pip stating that you may
need to restart the kernel to use the updated packages. This note can be ignored - you will not need to restart the
kernel to use the package.

**Syntax Errors for pip Installation**

Shell commands (like pip install) must be prefixed with an exclamation mark "!" when they are run from a
notebook cell. For example, if you get a syntax error using pip to install the emoji library, make sure your command
looks like this: `!pip install emoji`.

Watch this video about adding Python libraries:

## Notebook Kernel

The kernel is a virtual machine that performs the computations required to execute code. It can run in the background
to speed up computing.

It has three statuses:

- Stopped
- Starting
- Running with Elapsed Time

The kernel does not automatically start when a Notebook is created. The kernel status will read "Stopped" until the
very first Code block has been triggered to run.

When the first Code Block is triggered to run by a user clicking "Run Code", the kernel status changes to "Starting"
and the code block should show a status stating "Starting kernel" with a loading icon.

**Note:**

Starting the first code block run may take a few minutes. Subsequent code runs will be quicker.

  

Once the kernel has been started, the Kernel Status shows a timer with the Elapsed Time to indicate how long the
kernel has been running. You can click the square stop icon to stop the kernel. You will need to run a code block to
start the kernel again.

Switching languages, for example from Python 3.9 to Python 3.7, stops the current kernel and starts a new one.
  
![Switchlang](https://docs.sisense.com/main/Resources/Images/switchlang.png)

The kernel shuts down for 5 minutes after the notebook has been closed or after the browser tab has been closed.

**Note:**

If a notebook is left open, the kernel continues to run and may increase the CPU and memory use of your instance.

  

## SisenseHelper Functions

There are 3 SisenseHelper functions that must be used in order to get the most out of Notebooks. They are the
following:

| Function | Description |
| --- | --- |
| `SisenseHelper.load_dataframe('SQL_Cell_Name')` | Load the output table of a SQL cell named in the function. Define a variable using this function. |
| `SisenseHelper.save_dataframe(df)` | Save the dataframe into the Sisense Cloud so that the preview can be displayed and charts can be created using this output. |
| `SisenseHelper.save_image(plt)` | Save the image plot into the Sisense Cloud so that the preview can be displayed and chart cells can be created using this image. |

## Adding Python and R to a Notebook

### Adding a Code Cell

To gain further insights from the analysis done using SQL, you can use Python to do more complicated manipulations.

The outputs of a SQL block can be loaded into Code blocks using the SisenseHelper.load\_dataframe() function with the
name of the SQL block as the input. The SisenseHelper.save\_dataframe() function can be used to save the final output
as a dataframe:

- Click **+ Code**. The code block appears and already includes the SisenseHelper functions for
  reference. The load\_dataframe() function already has the most recent SQL block name as the input, ('data\_year' in
  the screenshot below).
- To create a Sisense chart using the output of the code, SisenseHelper.save\_dataframe() must be used with the
  desired dataframe used as the input.
    
  ![Addcodecell](https://docs.sisense.com/main/Resources/Images/addcodecell.png)  
  Once the Python output has been saved using SisenseHelper.save\_dataframe(), the output can be used to create a
  Sisense Chart by clicking the **Chart** button.

**Note:**

SQL block outputs can be loaded into and used in Code blocks **but Code blocks cannot be
loaded into other Code blocks as dataframes** at this time.

Watch this video on how to add Python code:

## Visualizing with Python and R Plotting Libraries

### Image Charts with Plotting Libraries

In addition to creating Sisense provided charts with the SisenseHelper.save\_dataframe() function, analysts can take
advantage of the plotting libraries available to Python and R by using a 3rd Sisense Helper function that will output
a static image of the custom plot.

- After creating a custom visualization using a plotting library like matplotlib, seaborn, ggplot and more.
- Use SisenseHelper.save\_image() to save and output your image chart

Only 1 image can be output per code block.
  
If the SisenseHelper.save\_dataframe() function and the SisenseHelper.save\_image() function are both found in the
code cell, the save\_dataframe() function will be the output.

Here is an example using the Chloroplethr plotting library with R:
  
![Imageplottinglibraries](https://docs.sisense.com/main/Resources/Images/imageplottinglibraries.png)

## Limitations

- Interactive libraries, such as Plot.ly, are not supported in the Notebooks tab for Python or R.
- Any chart created by Python or R cannot be used with the [Adding Notebook SQL Charts to Dashboards](https://docs.sisense.com/main/SisenseLinux/sharing-charts-to-dashboards.md) feature, which takes a chart from a Notebook and adds it directly to a Dashboard in
  the Analytics tab. This includes the Sisense provided charts created with Python or R and also the custom plot
  images.