notebook/docs/source/notebook.rst

The Jupyter Notebook

Introduction

The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process: developing, documenting, and executing code, as well as communicating the results. The Jupyter notebook combines two components:

A web application: a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output.

Notebook documents: a representation of all content visible in the web application, including inputs and outputs of the computations, explanatory text, mathematics, images, and rich media representations of objects.

See the installation guide <jupyter:install> on how to install the notebook and its dependencies.

Main features of the web application

• In-browser editing for code, with automatic syntax highlighting, indentation, and tab completion/introspection.
• The ability to execute code from the browser, with the results of computations attached to the code which generated them.
• Displaying the result of computation using rich media representations, such as HTML, LaTeX, PNG, SVG, etc. For example, publication-quality figures rendered by the matplotlib library, can be included inline.
• In-browser editing for rich text using the Markdown markup language, which can provide commentary for the code, is not limited to plain text.
• The ability to easily include mathematical notation within markdown cells using LaTeX, and rendered natively by MathJax.

Notebook documents

Notebook documents contains the inputs and outputs of a interactive session as well as additional text that accompanies the code but is not meant for execution. In this way, notebook files can serve as a complete computational record of a session, interleaving executable code with explanatory text, mathematics, and rich representations of resulting objects. These documents are internally JSON files and are saved with the .ipynb extension. Since JSON is a plain text format, they can be version-controlled and shared with colleagues.

Notebooks may be exported to a range of static formats, including HTML (for example, for blog posts), reStructuredText, LaTeX, PDF, and slide shows, via the nbconvert command.

Furthermore, any .ipynb notebook document available from a public URL can be shared via the Jupyter Notebook Viewer (nbviewer). This service loads the notebook document from the URL and renders it as a static web page. The results may thus be shared with a colleague, or as a public blog post, without other users needing to install the Jupyter notebook themselves. In effect, nbviewer is simply nbconvert as a web service, so you can do your own static conversions with nbconvert, without relying on nbviewer.

Details on the notebook JSON file format <nbformat:notebook_file_format>

Starting the notebook server

You can start running a notebook server from the command line using the following command:

jupyter notebook

This will print some information about the notebook server in your console, and open a web browser to the URL of the web application (by default, http://127.0.0.1:8888).

The landing page of the Jupyter notebook web application, the dashboard, shows the notebooks currently available in the notebook directory (by default, the directory from which the notebook server was started).

You can create new notebooks from the dashboard with the New Notebook button, or open existing ones by clicking on their name. You can also drag and drop .ipynb notebooks and standard .py Python source code files into the notebook list area.

When starting a notebook server from the command line, you can also open a particular notebook directly, bypassing the dashboard, with jupyter notebook my_notebook.ipynb. The .ipynb extension is assumed if no extension is given.

When you are inside an open notebook, the File | Open... menu option will open the dashboard in a new browser tab, to allow you to open another notebook from the notebook directory or to create a new notebook.

Note

You can start more than one notebook server at the same time, if you want to work on notebooks in different directories. By default the first notebook server starts on port 8888, and later notebook servers search for ports near that one. You can also manually specify the port with the --port option.

Creating a new notebook document

A new notebook may be created at any time, either from the dashboard, or using the File | New menu option from within an active notebook. The new notebook is created within the same directory and will open in a new browser tab. It will also be reflected as a new entry in the notebook list on the dashboard.

Opening notebooks

An open notebook has exactly one interactive session connected to an IPython kernel <ipythonzmq>, which will execute code sent by the user and communicate back results. This kernel remains active if the web browser window is closed, and reopening the same notebook from the dashboard will reconnect the web application to the same kernel. In the dashboard, notebooks with an active kernel have a Shutdown button next to them, whereas notebooks without an active kernel have a Delete button in its place.

Other clients may connect to the same underlying IPython kernel. The notebook server always prints to the terminal the full details of how to connect to each kernel, with messages such as the following:

[NotebookApp] Kernel started: 87f7d2c0-13e3-43df-8bb8-1bd37aaf3373

This long string is the kernel's ID which is sufficient for getting the information necessary to connect to the kernel. You can also request this connection data by running the %connect_info magic <magics_explained>. This will print the same ID information as well as the content of the JSON data structure it contains.

You can then, for example, manually start a Qt console connected to the same kernel from the command line, by passing a portion of the ID:

$ ipython qtconsole --existing 87f7d2c0

Without an ID, --existing will connect to the most recently started kernel. This can also be done by running the %qtconsole magic <magics_explained> in the notebook.

ipythonzmq

Notebook user interface

When you create a new notebook document, you will be presented with the notebook name, a menu bar, a toolbar and an empty code cell.

notebook name: The name of the notebook document is displayed at the top of the page, next to the IP[y]: Notebook logo. This name reflects the name of the .ipynb notebook document file. Clicking on the notebook name brings up a dialog which allows you to rename it. Thus, renaming a notebook from "Untitled0" to "My first notebook" in the browser, renames the Untitled0.ipynb file to My first notebook.ipynb.

menu bar: The menu bar presents different options that may be used to manipulate the way the notebook functions.

toolbar: The tool bar gives a quick way of performing the most-used operations within the notebook, by clicking on an icon.

code cell: the default type of cell, read on for an explanation of cells

Structure of a notebook document

The notebook consists of a sequence of cells. A cell is a multi-line text input field, and its contents can be executed by using Shift-Enter, or by clicking either the "Play" button the toolbar, or Cell | Run in the menu bar. The execution behavior of a cell is determined the cell's type. There are four types of cells: code cells, markdown cells, raw cells and heading cells. Every cell starts off being a code cell, but its type can be changed by using a dropdown on the toolbar (which will be "Code", initially), or via keyboard shortcuts <keyboard-shortcuts>.

For more information on the different things you can do in a notebook, see the collection of examples.

Code cells

A code cell allows you to edit and write new code, with full syntax highlighting and tab completion. By default, the language associated to a code cell is Python, but other languages, such as Julia and R, can be handled using cell magic commands <magics_explained>.

When a code cell is executed, code that it contains is sent to the kernel associated with the notebook. The results that are returned from this computation are then displayed in the notebook as the cell's output. The output is not limited to text, with many other possible forms of output are also possible, including matplotlib figures and HTML tables (as used, for example, in the pandas data analysis package). This is known as IPython's rich display capability.

Rich Output example notebook

Markdown cells

You can document the computational process in a literate way, alternating descriptive text with code, using rich text. In IPython this is accomplished by marking up text with the Markdown language. The corresponding cells are called Markdown cells. The Markdown language provides a simple way to perform this text markup, that is, to specify which parts of the text should be emphasized (italics), bold, form lists, etc.

When a Markdown cell is executed, the Markdown code is converted into the corresponding formatted rich text. Markdown allows arbitrary HTML code for formatting.

Within Markdown cells, you can also include mathematics in a straightforward way, using standard LaTeX notation: $...$ for inline mathematics and $$...$$ for displayed mathematics. When the Markdown cell is executed, the LaTeX portions are automatically rendered in the HTML output as equations with high quality typography. This is made possible by MathJax, which supports a large subset of LaTeX functionality

Standard mathematics environments defined by LaTeX and AMS-LaTeX (the amsmath package) also work, such as \begin{equation}...\end{equation}, and \begin{align}...\end{align}. New LaTeX macros may be defined using standard methods, such as \newcommand, by placing them anywhere between math delimiters in a Markdown cell. These definitions are then available throughout the rest of the IPython session.

Markdown Cells example notebook

Raw cells

Raw cells provide a place in which you can write output directly. Raw cells are not evaluated by the notebook. When passed through nbconvert, raw cells arrive in the destination format unmodified. For example, this allows you to type full LaTeX into a raw cell, which will only be rendered by LaTeX after conversion by nbconvert.

Heading cells

You can provide a conceptual structure for your computational document as a whole using different levels of headings; there are 6 levels available, from level 1 (top level) down to level 6 (paragraph). These can be used later for constructing tables of contents, etc. As with Markdown cells, a heading cell is replaced by a rich text rendering of the heading when the cell is executed.

Basic workflow

The normal workflow in a notebook is, then, quite similar to a standard IPython session, with the difference that you can edit cells in-place multiple times until you obtain the desired results, rather than having to rerun separate scripts with the %run magic command.

Typically, you will work on a computational problem in pieces, organizing related ideas into cells and moving forward once previous parts work correctly. This is much more convenient for interactive exploration than breaking up a computation into scripts that must be executed together, as was previously necessary, especially if parts of them take a long time to run.

At certain moments, it may be necessary to interrupt a calculation which is taking too long to complete. This may be done with the Kernel | Interrupt menu option, or the Ctrl-m i keyboard shortcut. Similarly, it may be necessary or desirable to restart the whole computational process, with the Kernel | Restart menu option or Ctrl-m . shortcut.

A notebook may be downloaded in either a .ipynb or .py file from the menu option File | Download as. Choosing the .py option downloads a Python .py script, in which all rich output has been removed and the content of markdown cells have been inserted as comments.

Running Code in the Jupyter Notebook example notebook

Notebook Basics example notebook

a warning about doing "roundtrip" conversions <note_about_roundtrip>.

Keyboard shortcuts

All actions in the notebook can be performed with the mouse, but keyboard shortcuts are also available for the most common ones. The essential shortcuts to remember are the following:

• Shift-Enter: run cell

  Execute the current cell, show output (if any), and jump to the next cell below. If Shift-Enter is invoked on the last cell, a new code cell will also be created. Note that in the notebook, typing Enter on its own never forces execution, but rather just inserts a new line in the current cell. Shift-Enter is equivalent to clicking the Cell | Run menu item.

• Ctrl-Enter: run cell in-place

  Execute the current cell as if it were in "terminal mode", where any output is shown, but the cursor remains in the current cell. The cell's entire contents are selected after execution, so you can just start typing and only the new input will be in the cell. This is convenient for doing quick experiments in place, or for querying things like filesystem content, without needing to create additional cells that you may not want to be saved in the notebook.

• Alt-Enter: run cell, insert below

  Executes the current cell, shows the output, and inserts a new cell between the current cell and the cell below (if one exists). This is thus a shortcut for the sequence Shift-Enter, Ctrl-m a. (Ctrl-m a adds a new cell above the current one.)

• Esc and Enter: Command mode and edit mode

  In command mode, you can easily navigate around the notebook using keyboard shortcuts. In edit mode, you can edit text in cells.

For the full list of available shortcuts, click Help, Keyboard Shortcuts in the notebook menus.

Plotting

One major feature of the Jupyter notebook is the ability to display plots that are the output of running code cells. The IPython kernel is designed to work seamlessly with the matplotlib plotting library to provide this functionality. Specific plotting library integration is a feature of the kernel.

Installing kernels

For information on how to install a Python kernel, refer to the IPython install page.

Kernels for other languages can be found in the IPython wiki. They usually come with instruction what to run to make the kernel available in the notebook.

Signing Notebooks

To prevent untrusted code from executing on users' behalf when notebooks open, we have added a signature to the notebook, stored in metadata. The notebook server verifies this signature when a notebook is opened. If the signature stored in the notebook metadata does not match, javascript and HTML output will not be displayed on load, and must be regenerated by re-executing the cells.

Any notebook that you have executed yourself in its entirety will be considered trusted, and its HTML and javascript output will be displayed on load.

If you need to see HTML or Javascript output without re-executing, you can explicitly trust notebooks, such as those shared with you, or those that you have written yourself prior to IPython 2.0, at the command-line with:

$ jupyter trust mynotebook.ipynb [other notebooks.ipynb]

This just generates a new signature stored in each notebook.

You can generate a new notebook signing key with:

$ jupyter trust --reset

Browser Compatibility

The Jupyter Notebook is officially supported on the following browsers:

• Chrome ≥ 13
• Safari ≥ 5
• Firefox ≥ 6

The is mainly due to the notebook's usage of WebSockets and the flexible box model.

The following browsers are unsupported:

• Safari < 5
• Firefox < 6
• Chrome < 13
• Opera (any): CSS issues, but execution might work
• Internet Explorer < 10
• Internet Explorer ≥ 10 (same as Opera)

Using Safari with HTTPS and an untrusted certificate is known to not work (websockets will fail).