Note

This documentation is for a development version of IPython. There may be significant differences from the latest stable release.

The Jupyter Notebook Format

Introduction

Jupyter (né 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 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.

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*"],
}

Changed in version nbformat: 4.0

Heading cells have been removed, in favor of simple headings in markdown.

Code cells

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
      "autoscroll": False, # any of true, false or "auto"
  },
  "source" : ["some code"],
  "outputs": [{
      # list of output dicts (described below)
      "output_type": "stream",
      ...
  }],
}

Changed in version nbformat: 4.0

input was renamed to source, for consistency among cell types.

Changed in version nbformat: 4.0

prompt_number renamed to execution_count

Code cell outputs

A code cell can have a variety of outputs (stream data or rich mime-type output). These correspond to messages 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"],
}

Changed in version nbformat: 4.0

The keys stream and text were changed to name and data to match the stream message specification.

display_data

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-png-data"],
    "application/json": {
      # JSON data is included as-is
      "json": "data",
    },
  },
  "metadata" : {
    "image/png": {
      "width": 640,
      "height": 480,
    },
  },
}

Changed in version nbformat: 4.0

application/json output is no longer double-serialized into a string.

Changed in version 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.

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 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-png-data"],
    "application/json": {
      # JSON data is included as-is
      "json": "data",
    },
  },
  "metadata" : {
    "image/png": {
      "width": 640,
      "height": 480,
    },
  },
}

Changed in version nbformat: 4.0

pyout renamed to execute_result

Changed in version nbformat: 4.0

prompt_number renamed to execution_count

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,
}

Changed in version nbformat: 4.0

pyerr renamed to error

Raw NBConvert cells

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 mime-type data"]
}

Metadata

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:

Notebook metadata

The following metadata keys are defined at the notebook level:

Key Value Interpretation
kernelspec dict A kernel specification
signature str A hashed signature of the notebook

Cell metadata

The following metadata keys are defined at the cell level:

Key Value Interpretation
collapsed bool Whether the cell’s output container should be collapsed
autoscroll bool or ‘auto’ Whether the cell’s output is scrolled, unscrolled, or autoscrolled
deletable bool If False, prevent deletion of the cell
format ‘mime/type’ The mime-type of a Raw NBConvert Cell
name str A name for the cell. Should be unique
tags list of str A list of string tags on the cell. Commas are not allowed in a tag

Output metadata

The following metadata keys are defined for code cell outputs:

Key Value Interpretation
isolated bool Whether the output should be isolated into an IFrame