27 - Comments

The Invoke-SqlNotebook cmdlet executes a SQL Notebook file (.ipynb) and outputs the materialized notebook. The Notebook will be executed on the ServerInstance and Database provided. When the cmdlet is run, the resulting Notebook file will be in the location the user defines or in the same directory of the input notebook file. The cmdlet outfile may be omitted: if that's the case, it will be. The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

With Jupyter Notebook integration available in PyCharm, you can easily edit, execute, and debug notebook source code and examine execution outputs including stream data, images, and other media.

Notebook support in PyCharm includes:

  • Editing and preview:

    • Ability to present a notebook as source code with textual cell definitions and manipulate cells as regular code.

    • Live preview of the cell execution output and Markdown content.

    • Auto-saving changes that you make in your files. Saving is triggered by various events, for example, closing a file or a project, or quitting the IDE.

  • Coding assistance:

    • Error and syntax highlighting.

    • Code completion.

    • Ability to create line commentsCtrl+/.

  • Ability to run cells and preview execution results.

  • Dedicated Jupyter Notebook Debugger.

  • Shortcuts for basic operations with Jupyter notebooks.

  • Ability to recognize .ipynb files and mark them with the icon.

Quick start with the Jupyter notebook in PyCharm

To start working with Jupyter notebooks in PyCharm:

  1. Create a new Python project, specify a virtual environment, and install the jupyter package.

  2. Open files in your.

  3. If needed, configure or create a new virtual environment.

  4. Open or create an .ipynb file.

  5. Add and edit source cells.

  6. Execute any of the code cells to launch the Jupyter server.

  7. Analyze execution results in the Preview pane.

Get familiar with the user interface

Mind the following user interface features when working with Jupyter notebooks in PyCharm.

Notebook editor

In PyCharm, you can work with Jupyter notebook files using three viewing modes:

In this mode, you can add notebook cells and edit them.

In this mode, you can both edit cells and preview their output. This is the default viewing mode for Jupyter notebooks in PyCharm.

In this mode, you can preview markdown and raw cells as well as code cell execution results.

Editing and preview modes reflect the currently selected PyCharm user interface appearance.

Notebook toolbar

The Jupyter notebook toolbar provides quick access to all basic operations with notebooks:

Opens the Jupyter Quick List for easy access to the basic notebook operations:

  • Run Cell: Executes the current cell Ctrl+Enter.

  • Run All Above: Executes all cells above excluding the current cell.

  • Run All Below: Executes the current cell and all cells below.

  • Run Cell and Select Below: Executes the current cell and navigates to the below cell ( ).

  • Debug Cell: Starts the debugging session for the current cell (Alt + Shift + Enter for Windows or ⌥⇧↩ for macOS).

  • Add Code Cell Above: Adds a new code cell above the current cell (Alt + Shift + A for Windows or ⌥ ⇧ A for macOS).

  • Add Code Cell Below: Adds a new code cell below the current cell (Alt + Shift + B for Windows or ⌥ ⇧ B for macOS).

  • Select Cell Above: Selects the cell above the current cell.

  • Select Cell Below: Selects the cell below the current cell.

  • Clear Outputs: Erases all execution output in the Preview area.

  • Start Jupyter Server: Launches the Jupyter server.

  • Stop Jupyter Server: Stops the Jupyter notebook.

  • Configure Shortcuts: Opens the Settings/Preferences Keymap Python dialog to alter the default keymaps.

Executes all cells in the notebook.
Click this icon if you want to interrupt any cell execution.
Click this icon to restart the currently running kernel
Upload to Datalore/Update uploaded notebook. Enables sharing the selected Jupyter notebook using Datalore, an intelligent web application for data analysis. Click this button to start sharing the current notebook file. Once you modify the notebook file, this button enables updating the shared notebook in Datalore.
The Jupyter Server widget that shows the currently used Jupyter server. Click the widget and select Configure Jupyter Server to setup another local or remote Jupyter server.
List of the available Jupyter kernels.
Select this checkbox to allow executing JavaScript in your Jupyter notebook.
This actions moves the current cell up.
This actions moves the current cell down.
You can preview the notebook in a browser.
Enables auto scrolling from the source cell in the Editor to the corresponding output in the Preview pane.
Enables auto scrolling from the cell output in the Preview pane to the corresponding source cell in the Editor.
Click this icon to show source code fragments in the Preview pane.
Click this icon to switch into the editor only viewing mode.
Click this icon to show both Editor and the Preview pane.
Click this icon to switch into the preview only mode.

Tool windows

The Server Log tab of the Jupyter tool window appears when you have any of the Jupyter server launched. The Server log tab of this window shows the current state of the Jupyter server and the link to the notebook in a browser.

It also provides controls to stop the running server () and launch the stopped server ( ).

The Variables tab provides the detailed report about variable values of the executed cell.

You can use the icon to manage the variables loading policy.

Latest version


Imports A.ipynb into B.ipynb

Project description


Suppose you want to import the contents of A.ipynb into B.ipynb.

How to use

Place both ipynb files in the same directory. Then, in the B.ipynb:

Congratulations! You can now run any functions defined in A.ipynb fromB.ipynb!

How it works

The code within import_ipynb.py defines a “notebook loader” that allowsyou to ‘import’ other ipynb files into your current ipynb file. Thisentails:

  1. load the notebook document into memory
  2. create an empty Module
  3. execute every cell in the Module namespace

Note that since every cell in the A.ipynb is executed when you importthe the file, A.ipynb should only contain classes and functiondefinitions (otherwise you’ll end up loading all variables and data intomemory).


The code within imoprt_ipynb.py comes fromhttp://jupyter-notebook.readthedocs.io/en/latest/examples/Notebook/Importing%20Notebooks.html.

Riley F. Edmunds (@rileyedmunds) wrote instructions on how to use itand Lev Maximov (@axil) packaged it.

Release historyRelease notifications RSS feed






Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Ipynb file
Files for import-ipynb, version 0.1.3
Filename, sizeFile typePython versionUpload dateHashes
Filename, size import-ipynb-0.1.3.tar.gz (4.0 kB) File type Source Python version None Upload dateHashes

Ipynb Api


Hashes for import-ipynb-0.1.3.tar.gz

Ipynb App

Hashes for import-ipynb-0.1.3.tar.gz
AlgorithmHash digest

Recent Pages