The official Jupyter Notebook format is defined with this JSON schema, which is used by Jupyter tools to validate notebooks.
This page contains a human-readable description of the notebook format.
Note
All metadata fields are optional. While the types and values of some metadata fields are defined, no metadata fields are required to be defined. Any metadata field may also be ignored.
At the highest level, a Jupyter notebook is a dictionary with a few keys:
- metadata (dict)
- nbformat (int)
- nbformat_minor (int)
- cells (list)
{
"metadata": {
"kernel_info": {
# if kernel_info is defined, its name field is required.
"name": "the name of the kernel"
},
"language_info": {
# if language_info is defined, its name field is required.
"name": "the programming language of the kernel",
"version": "the version of the language",
"codemirror_mode": "The name of the codemirror mode to use [optional]",
},
},
"nbformat": 4,
"nbformat_minor": 0,
"cells": [
# list of cell dictionaries, see below
],
}Some fields, such as code input and text output, are characteristically multi-line strings.
When these fields are written to disk, they may be written as a list of strings,
which should be joined with '' when reading back into memory.
In programmatic APIs for working with notebooks (Python, Javascript),
these are always re-joined into the original multi-line string.
If you intend to work with notebook files directly,
you must allow multi-line string fields to be either a string or list of strings.
There are a few basic cell types for encapsulating code and text. All cells have the following basic structure:
{
"cell_type": "type",
"metadata": {},
"source": "single string or [list, of, strings]",
}Note
On disk, multi-line strings MAY be split into lists of strings. When read with the nbformat Python API, these multi-line strings will always be a single string.
Markdown cells are used for body-text, and contain markdown, as defined in GitHub-flavored markdown, and implemented in marked.
{
"cell_type": "markdown",
"metadata": {},
"source": "[multi-line *markdown*]",
}.. versionchanged:: nbformat 4.0
Heading cells have been removed in favor of simple headings in markdown.
Code cells are the primary content of Jupyter notebooks.
They contain source code in the language of the document's associated kernel,
and a list of outputs associated with executing that code.
They also have an execution_count, which must be an integer or null.
{
"cell_type": "code",
"execution_count": 1, # integer or null
"metadata": {
"collapsed": True, # whether the output of the cell is collapsed
"scrolled": False, # any of true, false or "auto"
},
"source": "[some multi-line code]",
"outputs": [
{
# list of output dicts (described below)
"output_type": "stream",
# ...
}
],
}.. versionchanged:: nbformat 4.0
``input`` was renamed to ``source``, for consistency among cell types.
.. versionchanged:: nbformat 4.0
``prompt_number`` renamed to ``execution_count``
A code cell can have a variety of outputs (stream data or rich mime-type output). These correspond to :ref:`messages <messaging>` produced as a result of executing the cell.
All outputs have an output_type field,
which is a string defining what type of output it is.
{
"output_type": "stream",
"name": "stdout", # or stderr
"text": "[multiline stream text]",
}.. versionchanged:: nbformat 4.0
The ``stream`` key was changed to ``name`` to match
the stream message.
Rich display outputs, as created by display_data messages,
contain data keyed by mime-type. This is often called a mime-bundle,
and shows up in various locations in the notebook format and message spec.
The metadata of these messages may be keyed by mime-type as well.
{
"output_type": "display_data",
"data": {
"text/plain": "[multiline text data]",
"image/png": "[base64-encoded-multiline-png-data]",
"application/json": {
# JSON data is included as-is
"key1": "data",
"key2": ["some", "values"],
"key3": {"more": "data"},
},
"application/vnd.exampleorg.type+json": {
# JSON data, included as-is, when the mime-type key ends in +json
"key1": "data",
"key2": ["some", "values"],
"key3": {"more": "data"},
},
},
"metadata": {
"image/png": {
"width": 640,
"height": 480,
},
},
}.. versionchanged:: nbformat 4.0
``application/json`` output is no longer double-serialized into a string.
.. versionchanged:: nbformat 4.0
mime-types are used for keys, instead of a combination of short names (``text``)
and mime-types, and are stored in a ``data`` key, rather than the top-level.
i.e. ``output.data['image/png']`` instead of ``output.png``.
Results of executing a cell (as created by displayhook in Python)
are stored in execute_result outputs.
execute_result outputs are identical to display_data,
adding only a execution_count field, which must be an integer.
{
"output_type": "execute_result",
"execution_count": 42,
"data": {
"text/plain": "[multiline text data]",
"image/png": "[base64-encoded-multiline-png-data]",
"application/json": {
# JSON data is included as-is
"json": "data",
},
},
"metadata": {
"image/png": {
"width": 640,
"height": 480,
},
},
}.. versionchanged:: nbformat 4.0
``pyout`` renamed to ``execute_result``
.. versionchanged:: nbformat 4.0
``prompt_number`` renamed to ``execution_count``
Failed execution may show an error:
{
'output_type': 'error',
'ename' : str, # Exception name, as a string
'evalue' : str, # Exception value, as a string
# The traceback will contain a list of frames,
# represented each as a string.
'traceback' : list,
}
.. versionchanged:: nbformat 4.0
``pyerr`` renamed to ``error``
A raw cell is defined as content that should be included unmodified in nbconvert output. For example, this cell could include raw LaTeX for nbconvert to pdf via latex, or restructured text for use in Sphinx documentation.
The notebook authoring environment does not render raw cells.
The only logic in a raw cell is the format metadata field.
If defined, it specifies which nbconvert output format is the intended target
for the raw cell. When outputting to any other format,
the raw cell's contents will be excluded.
In the default case when this value is undefined,
a raw cell's contents will be included in any nbconvert output,
regardless of format.
{
"cell_type": "raw",
"metadata": {
# the mime-type of the target nbconvert format.
# nbconvert to formats other than this will exclude this cell.
"format": "mime/type"
},
"source": "[some nbformat output text]",
}Markdown and raw cells can have a number of attachments, typically inline
images that can be referenced in the markdown content of a cell. The attachments
dictionary of a cell contains a set of mime-bundles (see :ref:`display_data`)
keyed by filename that represents the files attached to the cell.
Note
The attachments dictionary is an optional field and can be undefined or empty if the cell does not have any attachments.
{
"cell_type": "markdown",
"metadata": {},
"source": ["Here is an *inline* image "],
"attachments": {"test.png": {"image/png": "base64-encoded-png-data"}},
}Since the 4.5 schema release, all cells have an id field which must be a string of length
1-64 with alphanumeric, -, and _ as legal characters to use. These ids must be unique to
any given Notebook following the nbformat spec.
The full rules and guidelines for using cells ids is captured in the corresponding JEP Proposal.
If attempting to add similar support to other languages supporting notebooks specs, this Example PR can be used as a reference to follow.
The notebook format is an evolving format. When backward-compatible changes are made, the notebook format minor version is incremented. When backward-incompatible changes are made, the major version is incremented.
As of nbformat 4.x, backward-compatible changes include:
- new fields in any dictionary (notebook, cell, output, metadata, etc.)
- new cell types
- new output types
New cell or output types will not be rendered in versions that do not recognize them, but they will be preserved.
Because the nbformat python package used to be less strict about validating notebook files, two features have been backported from nbformat 4.x to nbformat 4.0. These are:
attachmenttop-level keys in the Markdown and raw cell types (backported from nbformat 4.1)- Mime-bundle attributes are JSON data if the mime-type key ends in
+json(backported from nbformat 4.2)
These backports ensure that any valid nbformat 4.4 file is also a valid nbformat 4.0 file.
Metadata is a place that you can put arbitrary JSONable information about
your notebook, cell, or output. Because it is a shared namespace,
any custom metadata should use a sufficiently unique namespace,
such as metadata.kaylees_md.foo = "bar".
Metadata fields officially defined for Jupyter notebooks are listed here:
The following metadata keys are defined at the notebook level:
| Key | Value | Interpretation |
|---|---|---|
| kernelspec | dict | A :ref:`kernel specification <kernelspecs>` |
| authors | list of dicts | A list of authors of the document |
A notebook's authors is a list of dictionaries containing information about each author of the notebook. Currently, only the name is required. Additional fields may be added.
nb.metadata.authors = [
{
"name": "Fernando Perez",
},
{
"name": "Brian Granger",
},
]Official Jupyter metadata, as used by Jupyter frontends should be placed in the
metadata.jupyter namespace, for example metadata.jupyter.foo = "bar".
The following metadata keys are defined at the cell level:
| Key | Value | Interpretation |
|---|---|---|
| collapsed | bool | Whether the cell's output container should be collapsed |
| scrolled | bool or 'auto' | Whether the cell's output is scrolled, unscrolled, or autoscrolled |
| deletable | bool | If False, prevent deletion of the cell |
| editable | bool | If False, prevent editing of the cell (by definition, this also prevents deleting the cell) |
| format | 'mime/type' | The mime-type of a :ref:`Raw NBConvert Cell <raw nbconvert cells>` |
| name | str | A name for the cell. Should be unique across the notebook. Uniqueness must be verified outside of the json schema. |
| tags | list of str | A list of string tags on the cell. Commas are not allowed in a tag |
| jupyter | dict | A namespace holding jupyter specific fields. See docs below for more details |
| execution | dict | A namespace holding execution specific fields. See docs below for more details |
The following metadata keys are defined at the cell level within the jupyter namespace
| Key | Value | Interpretation |
|---|---|---|
| source_hidden | bool | Whether the cell's source should be shown |
| outputs_hidden | bool | Whether the cell's outputs should be shown |
The following metadata keys are defined at the cell level within the execution namespace.
These are lower level fields capturing common kernel message timestamps for better visibility
in applications where needed. Most users will not look at these directly.
| Key | Value | Interpretation |
|---|---|---|
| iopub.execute_input | ISO 8601 format | Indicates the time at which the kernel broadcasts an execute_input message. This represents the time when request for work was received by the kernel. |
| iopub.status.busy | ISO 8601 format | Indicates the time at which the iopub channel's kernel status message is 'busy'. This represents the time when work was started by the kernel. |
| shell.execute_reply | ISO 8601 format | Indicates the time at which the shell channel's execute_reply status message was created. This represents the time when work was completed by the kernel. |
| iopub.status.idle | ISO 8601 format | Indicates the time at which the iopub channel's kernel status message is 'idle'. This represents the time when the kernel is ready to accept new work. |
The following metadata keys are defined for code cell outputs:
| Key | Value | Interpretation |
|---|---|---|
| isolated | bool | Whether the output should be isolated into an IFrame |