.. _htmlnotebook:

The IPython 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 IPython 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>` command.

Furthermore, any  ``.ipynb`` notebook document available from a public 
URL can be shared via the `IPython 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 IPython themselves.
In effect, nbviewer_ is simply :ref:`nbconvert <nbconvert>` 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>` 
   

Starting the notebook server
----------------------------

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

    ipython 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 IPython 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 ``ipython 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 Display System`_  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>`, 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 IPython Notebook`_ example notebook

    `Basic Output`_ 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 notebook is the ability to display plots that are the
output of running code cells. IPython is designed to work seamlessly with the
matplotlib_ plotting library to provide this functionality. 

To set this up, before any plotting is performed you must execute the
``%matplotlib``  :ref:`magic command <magics_explained>`. This performs the
necessary behind-the-scenes setup for IPython to work correctly hand in hand
with ``matplotlib``; it does *not*, however, actually execute any Python
``import`` commands, that is, no names are added to the namespace.

If the ``%matplotlib`` magic is called without an argument, the 
output of a plotting command is displayed using the default ``matplotlib`` 
backend in a separate window. Alternatively, the backend can be explicitly 
requested using, for example::

  %matplotlib gtk

A particularly interesting backend, provided by IPython, is the ``inline``
backend.  This is available only for the IPython Notebook and the
:ref:`IPython QtConsole <qtconsole>`.  It can be invoked as follows::

  %matplotlib inline

With this backend, the output of plotting commands is displayed *inline*
within the notebook, directly below the code cell that produced it. The
resulting plots will then also be stored in the notebook document. 

.. seealso::

    `Plotting with Matplotlib`_  example notebook


Configuring the IPython Notebook
--------------------------------
The notebook server can be run with a variety of command line arguments. 
To see a list of available options enter::

  $ ipython notebook --help 

Defaults for these options can also be set by creating a file named 
``ipython_notebook_config.py`` in your IPython *profile folder*. The profile 
folder is a subfolder of your IPython directory; to find out where it is 
located, run::

  $ ipython locate

To create a new set of default configuration files, with lots of information 
on available options, use::

  $ ipython profile create

.. seealso::

    :ref:`config_overview`, in particular :ref:`Profiles`.
    
    :ref:`notebook_server_security`

    :ref:`notebook_public_server`

Installing new kernels
----------------------

Running the notebook makes the current python installation available as a kernel. Other
python installations (different python versions, virtualenv or conda environments) can
be installed as kernels by following these steps:

* make sure that the desired python installation is active (e.g. activate the environment)
  and ipython is installed
* run once ``ipython kernelspec install-self --user`` (or ``ipython2 ...`` or ``ipython3 ...``
  if you want to install specific python versions)

The last command installs a kernel spec file for the current python installation in
``~/.ipython/kernels/``. Kernel spec files are JSON files, which can be viewed and changed with a
normal text editor.

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::

    $ ipython 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::

    $ ipython trust --reset


Importing ``.py`` files
-----------------------

``.py`` files will be imported as a notebook with 
the same basename, but an ``.ipynb`` extension, located in the notebook 
directory. The notebook created will have just one cell, which will contain 
all the code in the ``.py`` file. You can later manually partition this into 
individual cells using the ``Edit | Split Cell`` menu option, or the 
:kbd:`Ctrl-m -` keyboard shortcut.

Note that ``.py`` scripts obtained from a notebook document using :doc:`nbconvert <nbconvert>`
maintain the structure of the notebook in comments. Reimporting such a
script back into a notebook will preserve this structure.

.. _note_about_roundtrip:

.. warning::

   While in simple cases you can "roundtrip" a notebook to Python, edit the
   Python file, and then import it back without loss of main content, this is 
   in general *not guaranteed to work*.  First, there is extra metadata
   saved in the notebook that may not be saved to the ``.py`` format.  And as
   the notebook format evolves in complexity, there will be attributes of the
   notebook that will not survive a roundtrip through the Python form.  You
   should think of the Python format as a way to output a script version of a
   notebook and the import capabilities as a way to load existing code to get 
   a notebook started.  But the Python version is *not* an alternate notebook
   format.

.. seealso::
    :ref:`nbformat`

.. include:: ../links.txt
