Features of the IPython Notebook web app

Some of the main features of the IPython Notebook app include:

• Display rich data representations (e.g. HTML / LaTeX / SVG) in the browser as a result of computations.
• Compose text cells using Markdown and HTML.
• Include mathematical equations, rendered directly in the browser by MathJax.
• Import standard Python scripts
• In-browser editing, syntax highlighting, tab completion and autoindentation.
• Inline figures rendered by the matplotlib library with publication quality, in a range of formats (SVG / PDF / PNG).

If you have ever used the Mathematica or SAGE notebooks (the latter is also web-based) you should feel right at home. If you have not, you will be able to learn how to use the IPython Notebook in just a few minutes.

Notebook documents

Notebook document files are just standard text files with the extension .ipynb, stored in the working directory on your computer. This file can be easily put under version control and shared with colleagues.

Despite the fact that the notebook documents are plain text files, they use the JSON format in order to store a complete, reproducible copy of the state of the computation as it is inside the Notebook app. That is, they record all computations carried out and the results obtained in a literate way; inputs and outputs of computations can be freely mixed with descriptive text, mathematics, and HTML 5 objects.

Notebooks may easily be exported to a range of static formats, including HTML (for example, for blog posts), PDF and slide shows. Furthermore, any publicly available notebook may be shared via the IPython Notebook Viewer service, which will provide it as a static web page. The results may thus be shared without having to install anything.

See :ref:`our installation documentation <install_index>` for directions on how to install the notebook and its dependencies.

Notebook user interface

When you finally start editing a notebook document in the Notebook, you will be presented with the title of the notebook, a menu bar, a toolbar and an empty input cell.

Input cells

Input cells are the core of the functionality of the IPython Notebook. They are regions in the document where you can enter different types of text and commands. These regions are then executed using :kbd:`Shift-Enter`, at which point the Notebook executes the current input cell, displays the resulting output beneath it, and adds a new input cell below.

The notebook consists of a sequence of input cells, providing the means to direct the computational process.

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. (Magic commands do, however, also work in the notebook; see below). Typically, you'll work on a problem in pieces, organizing related pieces into cells and moving forward as previous parts work correctly. This is much more convenient for interactive exploration than breaking up a computation into scripts that must be executed together, especially if parts of them take a long time to run

The only significant limitation that the notebook currently has, compared to the Qt console, is that it cannot run any code that expects input from the kernel (such as scripts that call :func:`raw_input`). Very importantly, this means that the %debug magic does not currently work in the notebook! This limitation will be overcome in the future, but in the meantime, there is a way to debug problems in the notebook: you can attach a Qt console to your existing notebook kernel, and run %debug from the Qt console. If your notebook is running on a local computer (i.e. if you are accessing it via your localhost address at 127.0.0.1), you can just type %qtconsole in the notebook and a Qt console will open up, connected to that same kernel.

At certain moments, it may be necessary to interrupt a particularly long calculation, or even to kill the entire computational process. This may be achieved by interrupting or restarting the kernel, respectively. After a restart, all relevant cells must be re-evaluated

Saveing a notebook

The Download button lets you save a notebook file to the Download area configured by your web browser (particularly useful if you are running the notebook server on a remote host and need a file locally). But you can always export the input part of a notebook to a plain python script by choosing Python format in the Download drop list. This removes all output and saves the text cells in comment areas. See ref:below <notebook_format> for more details on the notebook format.

Importing or executing a notebook as a normal Python file

The native format of the notebook, a file with a .ipynb `extension, is a JSON container of all the input and output of the notebook, and therefore not valid Python by itself. This means that by default, you cannot directly import a notebook from Python, nor execute it as a normal python script.

But if you want to be able to use notebooks also as regular Python files, you can start the notebook server with:

ipython notebook --script

or you can set this option permanently in your configuration file with:

c.NotebookManager.save_script=True

This will instruct the notebook server to save the .py export of each notebook, in addition to the .ipynb, at every save. These are standard .py files, and so they can be %run, imported from regular IPython sessions or other notebooks, or executed at the command-line. Since we export the raw code you have typed, for these files to be importable from other code you will have to avoid using syntax such as %magics and other IPython-specific extensions to the language.

In regular practice, the standard way to differentiate importable code from the 'executable' part of a script is to put at the bottom:

if __name__ == '__main__':
  # rest of the code...

Since all cells in the notebook are run as top-level code, you'll need to similarly protect all cells that you do not want executed when other scripts try to import your notebook. A convenient shortand for this is to define early on:

script = __name__ == '__main__'

and then on any cell that you need to protect, use:

if script:
  # rest of the cell...

Cell types

Each IPython input cell has a cell type. There is a restricted number of possible cell types, which may be set by using the cell type dropdown on the toolbar, or via the following keyboard shortcuts:

• code: :kbd:`Ctrl-m y`
• markdown: :kbd:`Ctrl-m m`
• raw: :kbd:`Ctrl-m t`
• heading: :kbd:`Ctrl-m 1` - :kbd:`Ctrl-m 6`

Magic commands

Magic commands, or magics, are one-word commands beginning with the symbol %, which send commands to IPython itself (as opposed to standard Python commands which are exported to be run in a Python interpreter).

Magics control different elements of the way that the IPython notebook operates. They are entered into standard code cells and executed as usual with :kbd:`Shift-Enter`.

There are two types of magics: line magics, which begin with a single % and operate on a single line of the code cell; and cell magics, which begin with %% and operate on the entire contents of the cell.

Line magics ˜˜˜˜˜˜˜˜˜˜˜ Some of the available line magics are the following:

• %load: Loads a file and places its content into a new code cell.
• %timeit: A simple way to time how long a single line of code takes to run
• %config: Configuration of the IPython Notebook
• %lsmagic: Provides a list of all available magic commands

Cell magics ˜˜˜˜˜˜˜˜˜˜˜

• %%bash: Send the contents of the code cell to be executed by bash
• %%file: Writes a file with with contents of the cell. Caution: The file is ovewritten without asking.
• %%R: Execute the contents of the cell using the R language.
• %%cython: Execute the contents of the cell using Cython.

Plotting

One major feature of the Notebook is the ability to capture the result of plots as inline output. IPython is designed to work seamlessly together with the %matplotlib plotting library. In order to set this up, the %matplotlib magic command must be run before any plotting takes place.

Note that %matplotlib only sets up IPython to work correctly with matplotlib; it does not actually execute any import commands and does not add anything to the namespace.

There is an alternative magic, %pylab, which, in addition, also executes a sequence of standard import statements required for working with the %matplotlib library. In particular, it automatically imports all names in the numpy and matplotlib packages to the namespace. A less invasive solution is %pylab --no-import-all, which imports just the standard names np for the numpy module and plt for the matplotlib.pyplot module.

When the default %matplotlib or %pylab magics are used, the output of a plotting command is captured in a separate window. An alternative is to use::

%matplotlib inline

which captures the output inline within the notebook format. This has the benefit that the resulting plots will be stored in the notebook document.

Converting notebooks to other formats using nbconvert

Configuration

The IPython 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; ipython locate will show you where it is located.

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

$ ipython profile create

Keyboard shortcuts

All actions in the notebook can be achieved with the mouse, but we have also added keyboard shortcuts for the most common ones, so that productive use of the notebook can be achieved with minimal mouse intervention. The main key bindings you need to remember are:

• :kbd:`Shift-Enter`: execute the current cell (similar to the Qt console), show output (if any) and jump to the next cell below. If :kbd:`Shift-Enter` was invoked on the last input line, a new code cell will also be created. Note that in the notebook, simply using :kbd:`Enter` never forces execution, it simply inserts a new line in the current cell. Therefore, in the notebook you must always use :kbd:`Shift-Enter` to get execution (or use the mouse and click on the Run Selected button).
• :kbd:`Alt-Enter`: this combination is similar to the previous one, with the exception that, if the next cell below is not empty, a new code cell will be added to the notebook, even if the cell execution happens not in the last cell. In this regard, :kbd:`Alt-Enter`: is simply a shortcut for the :kbd:`Shift-Enter`, :kbd:`Ctrl-m a` sequence.
• :kbd:`Ctrl-Enter`: execute the current cell in "terminal mode", where any output is shown but the cursor stays in the current cell, whose input area is flushed empty. This is convenient to do quick in-place experiments or query things like filesystem content without creating additional cells you may not want saved in your notebook.
• :kbd:`Ctrl-m`: this is the prefix for all other keybindings, which consist of an additional single letter. Type :kbd:`Ctrl-m h` (that is, the sole letter :kbd:`h` after :kbd:`Ctrl-m`) and IPython will show you the remaining available keybindings.

Connecting to an existing kernel

The notebook server always prints to the terminal the full details of how to connect to each kernel, with lines like:

[IPKernelApp] To connect another client to this kernel, use:
[IPKernelApp] --existing kernel-3bb93edd-6b5a-455c-99c8-3b658f45dde5.json

This is the name of a JSON file that contains all the port and validation information necessary to connect to the kernel. You can manually start a Qt console with:

ipython qtconsole --existing kernel-3bb93edd-6b5a-455c-99c8-3b658f45dde5.json

and if you only have a single kernel running, simply typing:

ipython qtconsole --existing

will automatically find it (it will always find the most recently started kernel if there is more than one). You can also request this connection data by typing %connect_info; this will print the same file information as well as the content of the JSON data structure it contains.

Running a public notebook server

If you want to access your notebook server remotely with just a web browser, here is a quick set of instructions. Start by creating a certificate file and a hashed password as explained above. Then, create a custom profile for the notebook. At the command line, type:

ipython profile create nbserver

In the profile directory, edit the file ipython_notebook_config.py. By default the file has all fields commented, the minimum set you need to uncomment and edit is here:

c = get_config()

# Kernel config
c.IPKernelApp.pylab = 'inline'  # if you want plotting support always

# Notebook config
c.NotebookApp.certfile = u'/absolute/path/to/your/certificate/mycert.pem'
c.NotebookApp.ip = '*'
c.NotebookApp.open_browser = False
c.NotebookApp.password = u'sha1:bcd259ccf...your hashed password here'
# It's a good idea to put it on a known, fixed port
c.NotebookApp.port = 9999

You can then start the notebook and access it later by pointing your browser to https://your.host.com:9999 with ipython notebook --profile=nbserver.

Running with a different URL prefix

The notebook dashboard (i.e. the default landing page with an overview of all your notebooks) typically lives at a URL path of "http://localhost:8888/". If you want to have it, and the rest of the notebook, live under a sub-directory, e.g. "http://localhost:8888/ipython/", you can do so with configuration options like these (see above for instructions about modifying ipython_notebook_config.py):

c.NotebookApp.base_project_url = '/ipython/'
c.NotebookApp.base_kernel_url = '/ipython/'
c.NotebookApp.webapp_settings = {'static_url_prefix':'/ipython/static/'}

Using a different notebook store

By default the notebook server stores notebooks as files in the working directory of the notebook server, also known as the notebook_dir. This logic is implemented in the :class:`FileNotebookManager` class. However, the server can be configured to use a different notebook manager class, which can store the notebooks in a different format. Currently, we ship a :class:`AzureNotebookManager` class that stores notebooks in Azure blob storage. This can be used by adding the following lines to your ipython_notebook_config.py file:

c.NotebookApp.notebook_manager_class = 'IPython.html.services.notebooks.azurenbmanager.AzureNotebookManager'
c.AzureNotebookManager.account_name = u'paste_your_account_name_here'
c.AzureNotebookManager.account_key = u'paste_your_account_key_here'
c.AzureNotebookManager.container = u'notebooks'

In addition to providing your Azure Blob Storage account name and key, you will have to provide a container name; you can use multiple containers to organize your Notebooks.