.. _htmlnotebook:

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.

.. seealso::

    See the :ref:`installation documentation <installnotebook>` for directions
    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_.



.. _MathJax: http://www.mathjax.org/


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.

.. _JSON: http://en.wikipedia.org/wiki/JSON

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 new :ref:`nbconvert <nbconvert:index>` command.

Furthermore, any  ``.ipynb`` notebook document available from a public
URL can be shared via the `Jupyter Notebook Viewer <nbviewer>`_ (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 :ref:`nbconvert <nbconvert:index>` as 
a web service, so you can do your own static conversions with nbconvert, 
without relying on nbviewer.



.. seealso::

    :ref:`Details on the notebook JSON file format <nbformat:format_description>`


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
:ref:`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`` :ref:`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``
:ref:`magic <magics_explained>` in the notebook.

.. seealso::

    :ref:`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
:kbd:`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 :ref:`keyboard shortcuts
<keyboard-shortcuts>`.

For more information on the different things you can do in a notebook,
see the `collection of examples
<http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Notebook/Index.ipynb>`_.

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 :ref:`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.

.. seealso::

    `Basic Output`_  example notebook

    `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 <mathjax_tex>`_ of LaTeX functionality

.. _mathjax_tex: http://docs.mathjax.org/en/latest/tex.html

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.

.. seealso::

    `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 :ref:`nbconvert <nbconvert:index>`, 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 :kbd:`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 :kbd:`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.

.. seealso::

    `Running Code in the Jupyter Notebook`_ example notebook

    `Notebook Basics`_ example notebook

    :ref:`a warning about doing "roundtrip" conversions <note_about_roundtrip>`.

.. _keyboard-shortcuts:

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:

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

* :kbd:`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.

* :kbd:`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 :kbd:`Shift-Enter`, :kbd:`Ctrl-m a`.
    (:kbd:`Ctrl-m a` adds a new cell above the current one.)

* :kbd:`Esc` and :kbd:`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 :guilabel:`Help`,
:guilabel:`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.  See the
:ref:`plotting document <ipykernel:plotting>` of the IPython kernel for more
information.

Installing kernels
------------------

For information on how to install a Python kernel, refer to :ref:`IPython kernel's 
installation document <ipykernel:kernel_install>`

Kernels for other languages can be found in the `IPython wiki
<https://github.com/ipython/ipython/wiki/IPython%20kernels%20for%20other%20languages>`_.
They usually come with instruction what to run to make the kernel available in the notebook.


.. _signing_notebooks:

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

.. include:: links.txt
