The Jupyter Notebook¶
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 documentation 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.
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
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.
.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.
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,
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
button, or open existing ones by clicking on their name. You can also drag
.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
.ipynb extension is assumed if no extension is
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.
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
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.
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
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
.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.
For more information on the different things you can do in a notebook, see the collection of examples.
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
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.
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
New LaTeX macros may be defined using standard methods,
\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 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.
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.
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
A notebook may be downloaded in either a
.py file from the
menu option File | Download as. Choosing the
.py option downloads a
.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
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-Enteris invoked on the last cell, a new code cell will also be created. Note that in the notebook, typing
Enteron its own never forces execution, but rather just inserts a new line in the current cell.
Shift-Enteris equivalent to clicking the
Cell | Runmenu 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
Ctrl-m a. (
Ctrl-m aadds a new cell above the current one.)
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.
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.
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.
$ 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
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).