The Jupyter Notebook Format
Introduction
Jupyter (née IPython) notebook files are simple JSON documents, containing text, source code, rich media output, and metadata. each segment of the document is stored in a cell.
Some general points about the notebook format:
Note
All metadata fields are optional. While the type and values of some metadata are defined, no metadata values are required to be defined.
Top-level structure
At the highest level, a Jupyter notebook is a dictionary with a few keys:
- metadata (dict)
- nbformat (int)
- nbformat_minor (int)
- cells (list)
{ "metadata" : { "signature": "hex-digest", # used for authenticating unsafe outputs on load "kernel_info": { # if kernel_info is defined, its name and language fields are required. "name" : "the name of the kernel", "language" : "the programming language of the kernel", "codemirror_mode": "The name of the codemirror mode to use [optional]" }, }, "nbformat": 4, "nbformat_minor": 0, "cells" : [ # list of cell dictionaries, see below ], }
Cell Types
There are a few basic cell types for encapsulating code and text. All cells have the following basic structure:
{ "cell_type" : "name", "metadata" : {}, "source" : "single string or [list, of, strings]", }
Markdown cells
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" : ["some *markdown*"], }
Heading cells
Heading cells are single lines describing a section header (mapping onto h1-h6 tags in HTML). These cells indicate structure of the document, and are used for things like outline-views and automatically generating HTML anchors within the page for quick navigation. They have a level field, with an integer value from 1-6 (inclusive).
{ "cell_type" : "markdown", "metadata" : {}, "level" : 1, # An integer on [1-6] "source" : ["A simple heading"], }
Code cells
Code cells are the primary content of Jupyter notebooks. They contain source code int e language of the document's associated kernel, and a list of outputs associated with executing. 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 "autoscroll": False, # any of true, false or "auto" }, "source" : ["some code"], "outputs": [{ # list of output dicts (described below) "output_type": "stream", ... }], }
Code cell outputs
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.
stream output
{ "output_type" : "stream", "name" : "stdout", # or stderr "data" : ["multiline stream text"], }
display_data
Rich display messages (as created by display_data messages) contain data keyed by mime-type. All mime-type data should 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-png-data"], "application/json": { # JSON data is included as-is "json": "data", }, }, "metadata" : { "image/png": { "width": 640, "height": 480, }, }, }
execute_result
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 prompt_number field, which must be an integer.
{ "output_type" : "execute_result", "execute_result": 42, "data" : { "text/plain" : ["multiline text data"], "image/png": ["base64-encoded-png-data"], "application/json": { # JSON data is included as-is "json": "data", }, }, "metadata" : { "image/png": { "width": 640, "height": 480, }, }, }
error
Failed execution may show a traceback
{ '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, }
Raw NBConvert cells
A raw cell is defined as content that should be included unmodified in :ref:`nbconvert <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 mime-type data"] }