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.
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), reStructeredText, LaTeX, PDF, and slide shows, via
the new nbconvert command.
Furthermore, any .ipynb notebook document available from a public
URL can be shared via the IPython 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 IPython 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.
Starting the notebook server
You can start running a notebook server from the command line using the
following command:
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
IPython kernel, 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. 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 in the notebook.
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.
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.
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.
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.
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.
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.)
Ctrl-m:
This is the prefix for all other shortcuts, which consist of Ctrl-m
followed by a single letter or character. For example, if you type
Ctrl-m h (that is, the sole letter h after Ctrl-m),
IPython will show you all the available keyboard shortcuts.
Here is the complete set of keyboard shortcuts available:
| Shortcut |
Action |
| Shift-Enter |
run cell |
| Ctrl-Enter |
run cell in-place |
| Alt-Enter |
run cell, insert below |
| Ctrl-m x |
cut cell |
| Ctrl-m c |
copy cell |
| Ctrl-m v |
paste cell |
| Ctrl-m d |
delete cell |
| Ctrl-m z |
undo last cell deletion |
| Ctrl-m - |
split cell |
| Ctrl-m a |
insert cell above |
| Ctrl-m b |
insert cell below |
| Ctrl-m o |
toggle output |
| Ctrl-m O |
toggle output scroll |
| Ctrl-m l |
toggle line numbers |
| Ctrl-m s |
save notebook |
| Ctrl-m j |
move cell down |
| Ctrl-m k |
move cell up |
| Ctrl-m y |
code cell |
| Ctrl-m m |
markdown cell |
| Ctrl-m t |
raw cell |
| Ctrl-m 1-6 |
heading 1-6 cell |
| Ctrl-m p |
select previous |
| Ctrl-m n |
select next |
| Ctrl-m i |
interrupt kernel |
| Ctrl-m . |
restart kernel |
| Ctrl-m h |
show keyboard shortcuts |
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 magic command. 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:
A particularly interesting backend, provided by IPython, is the inline
backend. This is available only for the IPython Notebook and the
IPython QtConsole. It can be invoked as follows:
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.
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:
To create a new set of default configuration files, with lots of information
on available options, use:
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
Ctrl-m - keyboard shortcut.
Note that .py scripts obtained from a notebook document using nbconvert_
maintain the structure of the notebook in comments. Reimporting such a
script back into a notebook will preserve this structure.
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.
