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nbconvert Documentation, Release 5.0.0.dev

2.2.6 reStructuredText

• --to rst

Basic reStructuredText output. Useful as a starting point for embedding notebooks in Sphinxdocs.

Note: nbconvert usespandoc to convert between various markup languages, so pandoc is a dependency when

converting to latex or reStructuredText.

2.2.7 Executable script

• --to script

Converta notebook toan executable script. This is the simplestway to get a Python (orother language,depend-

ing on the kernel) script out of a notebook. If there were any magics in an Jupyter notebook, this may only be

executable from a Jupyter session.

For example,to convert a Julia notebook to a Julia executable script:

jupyter nbconvert --to script my_julia_notebook.ipynb

2.2.8 Notebook and preprocessors

• --to notebook

New in version 3.0.

This doesn’t convert a notebook to a different format per se, instead it allows the running of nbconvert prepro-

cessors ona notebook, and/or conversion to other notebook formats. For example:

jupyter nbconvert --to notebook --execute mynotebook.ipynb

This will open the notebook, execute it, capture new

output, and save the result in

mynotebook.nbconvert.ipynb. By default, nbconvert will abort conversion if any exceptions occur

during execution of a cell. If you specify --allow-errors (in addition to the --execute ﬂag) then conversion

will continue and the output from any exception will be included in the cell output.

The following command:

jupyter nbconvert --to notebook --nbformat 3 mynotebook

will create a copy of mynotebook.ipynb in mynotebook.v3.ipynb in version 3 of the notebook format.

If you want to convert a notebookin-place, you can specify the ouptut ﬁle to be the same as the input ﬁle:

jupyter nbconvert --to notebook mynb --output mynb

Be careful with that, since it will replace the input ﬁle.

Note: nbconvert usespandoc to convert between various markup languages, so pandoc is a dependency when con-

verting to latex or reStructuredText.

The outputﬁle createdbynbconvert will have the samebase nameas thenotebook and willbe placedinthecurrent

working directory. Any supporting ﬁles (graphics, etc) will be placed in a new directory with the same base name as

the notebook, sufﬁxed with _files:

2.2. Supported output formats

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nbconvert Documentation, Release 5.0.0.dev

$ jupyter nbconvert notebook.ipynb

$ ls

notebook.ipynb

notebook.html

notebook_files/

For simple single-ﬁle output,such as html,markdown, etc., the output may be sent to standard output with:

$ jupyter nbconvert --to markdown notebook.ipynb --stdout

2.3 Converting multiple notebooks

Multiple notebooks can be speciﬁed fromthe command line:

$ jupyter nbconvert notebook

.ipynb

$ jupyter nbconvert notebook1.ipynb notebook2.ipynb

orvia a list in a conﬁguration ﬁle,say mycfg.py, containing the text:

c = get_config()

c.NbConvertApp.notebooks = ["notebook1.ipynb", , "notebook2.ipynb"]

and using the command:

$ jupyter nbconvert --config mycfg.py

Chapter 2. Using as a command line tool

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CHAPTER

Using nbconvert as a library

In this notebook, you will be introduced to the programmatic API of nbconvert and how it can be used in various

contexts.

Agreatblogpostby [@jakevdp](https://github.com/jakevdp) will be used to demonstrate. This notebook will not

focus on using the command line tool. The attentive reader will point-out that no data is read from or written to disk

during the conversion process. This is because nbconvert has been designed to work in memory so that it works well

in a database orweb-based environement too.

3.1 Quick overview

Credit: Jonathan Frederic (@jdfreder on github)

The main principle of nbconvert is to instantiate an Exporter that controls the pipeline through which notebooks

are converted.

First, download @jakevdp’s notebook (if you do not have requests, install it by running pip install

requests,or if youdon’t have pip installed,you canﬁndit on PYPI):

In [1]: from urllib.request import urlopen

url = 'http://jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb'

response = urlopen(url).read().decode()

response[0:60] + ' ...'

Out[1]: '{\n "metadata": {\n

"name": "XKCD_plots"\n },\n "nbformat": 3,\n ...'

The response is a JSON string which represents a Jupyternotebook.

Next, we will read the response using nbformat. Doing this will guarantee that the notebook structure is valid. Note

that the in-memory format and on disk format are slightly different. In particual, on disk, multiline strings might be

split into a list ofstrings.

In [2]: import nbformat

jake_notebook = nbformat.reads(response, as_version=4)

jake_notebook.cells[0]

Out[2]: {'cell_type': 'markdown',

'metadata': {},

'source': '# XKCD plots in Matplotlib'}

The nbformat API returns a special type of dictionary. For this examle, you don’t need to worry about the details of

the structure (if you are interested,please see thenbformatdocumentation).

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nbconvert Documentation, Release 5.0.0.dev

The nbconvert API exposes some basic exporters for common formats and defaults. You will start by using one of

them. First, you will import one of these exporters (speciﬁcally,the HTML exporter), then instantiate it using most of

the defaults, and then you will use it to process the notebook we downloaded earlier.

In [3]: from traitlets.config import Config

# 1. Import the exporter

from nbconvert import HTMLExporter

# 2. Instantiate the exporter. We use the `basic` template for now; we'll get into more details

# later about how to customize the exporter further.

html_exporter = HTMLExporter()

html_exporter.template_file = 'basic'

# 3. Process the notebook we loaded earlier

(body, resources) = html_exporter.from_notebook_node(jake_notebook)

The exporter returns a tuple containing the source of the converted notebook,as well as a resources dict. In this case,

the source is just raw HTML:

In [4]: print(body[:400] + '...')

</div>

<h1 id="XKCD-plots-in-Matplotlib">XKCD plots in Matplotlib<a class="anchor-link" href="#XKCD-plots-in-Matplotlib">¶</a></h1>

</div>

<div cl...

If you understand HTML, you’ll notice that some common tags are ommited, like the body tag. Those tags are

included in the default HtmlExporter, which is what would have been constructed if we had not modiﬁed the

template_file.

The resource dict contains (among many things)the extracted .png,.jpg, etc. fromthe notebook when applicable.

The basic HTML exporter leaves the ﬁgures as embedded base64, but you can conﬁgure it to extract the ﬁgures. So

for now, the resource dict should be mostly empty, except for a key containing CSS and a few others whose content

will be obvious:

In [5]: print("Resources:", resources.keys())

print("Metadata:", resources['metadata'].keys())

print("Inlining:", resources['inlining'].keys())

print("Extension:", resources['output_extension'])

Resources: dict_keys(['metadata', 'output_extension', 'raw_mimetypes', 'inlining'])

Metadata: dict_keys(['name'])

Inlining: dict_keys(['css'])

Extension: .html

Exporters are stateless, so you won’t be able to extract any useful information beyond their conﬁguration. You can

re-use an exporter instance to convert another notebook. In addition to the from_notebook_node used above,

each exporter exposes from_file and from_filename methods.

Chapter 3. Using nbconvert as a library

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nbconvert Documentation, Release 5.0.0.dev

3.2 Extracting Figures using the RST Exporter

When exporting, you may want to extract the base64 encoded ﬁgures as ﬁles. While the HTML exporter does not do

this by default, the RstExporter does:

In [6]: # Import the RST exproter

from nbconvert import RSTExporter

# Instantiate it

rst_exporter = RSTExporter()

# Convert the notebook to RST format

(body, resources) = rst_exporter.from_notebook_node(jake_notebook)

print(body[:970] + '...')

print('[.....]')

print(body[800:1200] + '...')

XKCD plots in Matplotlib

========================

This notebook originally appeared as a blog post at `Pythonic

Perambulations <http://jakevdp.github.com/blog/2012/10/07/xkcd-style-plots-in-matplotlib/>`__

by Jake Vanderplas.

.. raw:: html

Update: the matplotlib pull request has been merged! See

This

post

<http://jakevdp.github.io/blog/2013/07/10/XKCD-plots-in-matplotlib/>`__

for a description of the XKCD functionality now built-in to

matplotlib!

One of the problems I've had with typical matplotlib figures is that

everything in them is so precise, so perfect. For an example of what I

mean, take a look at this figure:

.. code:: python

from IPython.display import Image

Image('http://jakevdp.github.com/figures/xkcd_version.png')

.. image:: output_3_0.png

Sometimes when showing schematic plots, this is the type of figure I

want to display. But drawing it by hand is a pain: I'd rather just use

matp...

[.....]

image:: output_3_0.png

3.2. Extracting Figures using the RST Exporter

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nbconvert Documentation, Release 5.0.0.dev

Sometimes when showing schematic plots, this is the type of figure I

want to display. But drawing it by hand is a pain: I'd rather just use

matplotlib. The problem is, matplotlib is a bit too precise. Attempting

to duplicate this figure in matplotlib leads to something like this:

.. code:: python

Image('http://jakevdp.github.com/figures/mpl_version.png')

.. imag...

Notice that base64 images are not embeded, but instead there are ﬁlename-like strings, such as output_3_0.png.

The strings actually are (conﬁgurable) keys that map to the binary data in the resources dict.

Note, if you write an RST Plugin, you are responsible for writing all the ﬁles to the disk (or uploading, etc...) in the

right location. Of course,the naming scheme is conﬁgurable.

As an exercise, this notebook will show you how to get one of those images. First, take a look at the ’outputs’

of the returned resources dictionary. This is a dictionary that contains a key for each extracted resource, with values

corresponding to the actual base64 encoding:

In [7]: sorted(resources['outputs'].keys())

Out[7]: ['output_13_1.png',

'output_16_0.png',

'output_18_1.png',

'output_3_0.png',

'output_5_0.png']

In this case, there are 5 extractedbinaryﬁgures,allpngs. We can use the Image display object to actuallydisplay one

ofthe images:

In [8]: from IPython.display import Image

Image(data=resources['outputs']['output_3_0.png'], format='png')

Chapter 3. Using nbconvert as a library

nbconvert Documentation, Release 5.0.0.dev

Note that this image is being renderedwithout ever reading or writing to the disk.

3.3 Extracting Figures using the HTML Exporter

As mentioned above, by default, the HTML exporter does not extract images – it just leaves them as inline base64

encodings. However,this is not always what you might want. For example, here is a use case from @jakevdp:

Iwriteanawesomeblog usingJupyternotebooksconverted to HTML, andI wantthe images tobe cached.

Having one htmlﬁle with all ofthe images base64encoded inside it is nicewhensharingwith acoworker,

but for a website,not so much. I need an HTML exporter, and Iwant it to extract the ﬁgures!

3.3.1 Some theory

Before we get into actually extracting the ﬁgures, it will be helpful to give a high-level overview of the process of

converting a notebook to a anotherformat:

1. Retrieve the notebook and it’s accompanying resources (you are responsible forthis).

2. Feed the notebook into the Exporter, which:

(a) Sequentially feeds the notebook into an array of Preprocessors. Preprocessors only act on the struc-

ture of the notebook, and have unrestricted access to it.

(b) Feeds the notebook into the Jinja templating engine, whichconverts it to a particularformat depending on

which template is selected.

3. The exporter returns the converted notebook and other relevant resources as a tuple.

4. You write the data to the disk using the built-in FilesWriter (which writes the notebook and any extracted

ﬁles to disk), or elsewhere using a custom Writer.

3.3. Extracting Figures using the HTML Exporter

nbconvert Documentation, Release 5.0.0.dev

3.3.2 Using different preprocessors

To extract the ﬁgures when using the HTML exporter, we will want to change which Preprocessors

we are using.

There are several preprocessors that come with nbconvert, including one called the

ExtractOutputPreprocessor.

The ExtractOutputPreprocessor is responsible for crawling the notebook, ﬁnding all of the ﬁgures, and

putting them into the resources directory, as well as choosing the key (i.e. filename_xx_y.extension) that

can replace the ﬁgure inside the template. To enable the ExtractOutputPreprocessor, we must add it to the

exporter’s list of preprocessors:

In [9]: # create a configuration object that changes the preprocessors

from traitlets.config import Config

c = Config()

c.HTMLExporter.preprocessors = ['nbconvert.preprocessors.ExtractOutputPreprocessor']

# create the new exporter using the custom config

html_exporter_with_figs = HTMLExporter(config=c)

html_exporter_with_figs.preprocessors

Out[9]: ['nbconvert.preprocessors.ExtractOutputPreprocessor']

We can compare the resultofconvertingthe notebook using the originalHTML exporterandour new customized one:

In [10]: (_, resources)

= html_exporter.from_notebook_node(jake_notebook)

(_, resources_with_fig) = html_exporter_with_figs.from_notebook_node(jake_notebook)

print("resources without figures:")

print(sorted(resources.keys()))

print("\nresources with extracted figures (notice that there's one more field called 'outputs'):")

print(sorted(resources_with_fig.keys()))

print("\nthe actual figures are:")

print(sorted(resources_with_fig['outputs'].keys()))

resources without figures:

['inlining', 'metadata', 'output_extension', 'raw_mimetypes']

resources with extracted figures (notice that there's one more field called 'outputs'):

['inlining', 'metadata', 'output_extension', 'outputs', 'raw_mimetypes']

the actual figures are:

['output_13_1.png', 'output_16_0.png', 'output_18_1.png', 'output_3_0.png', 'output_5_0.png']

3.4 Custom Preprocessors

There are an endless number of transformations that you may want to apply to a notebook. In particularly complicated

cases,youmay want to actually create yourown Preprocessor. Above,when we customized the list ofpreproces-

sors accepted by the HTMLExporter, we passed in a string – this can be any valid module name. So, if you create

your own preprocessor, you can include it in that same list and it will be used by the exporter.

Tocreateyourownpreprocessor,youwillneedtosubclass fromnbconvert.preprocessors.Preprocessor

and overwrite either the preprocess and/or preprocess_cell methods.

Chapter 3. Using nbconvert as a library

nbconvert Documentation, Release 5.0.0.dev

3.5 Example

The following demonstration,as requested inaGitHubissue, adds the ability to exclude a cell by index.

Note: injecting cells is similar, and won’t be covered here. If you want to inject static content at the beginning/end of

anotebook,use a customtemplate.

In [11]: from traitlets import Integer

from nbconvert.preprocessors import Preprocessor

class PelicanSubCell(Preprocessor):

"""A Pelican specific preprocessor to remove some of the cells of a notebook"""

# I could also read the cells from nb.metadata.pelican if someone wrote a JS extension,

# but for now I'll stay with configurable value.

start = Integer(0, config=True, help="first cell of notebook to be converted")

end

= Integer(-1, config=True, help="last cell of notebook to be converted")

def preprocess(self, nb, resources):

self.log.info("I'll keep only cells from %d to %d", self.start, self.end)

nb.cells = nb.cells[self.start:self.end]

return nb, resources

/Users/benjaminrk/conda/lib/python3.5/site-packages/ipykernel/__main__.py:9: DeprecationWarning: metadata 'config': True was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')

/Users/benjaminrk/conda/lib/python3.5/site-packages/ipykernel/__main__.py:10: DeprecationWarning: metadata 'config': True was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')

Here a Pelican exporter is created that takes PelicanSubCell preprocessors and a config object as parameters.

This may seem redundant, but with the conﬁguration system you can register an inactive preprocessor on all of the

exporters and activate it from conﬁg ﬁles orthe command line.

In [12]: # Create a new config object that configures both the new preprocessor, as well as the exporter

c =

Config()

c.PelicanSubCell.start = 4

c.PelicanSubCell.end = 6

c.RSTExporter.preprocessors = [PelicanSubCell]

# Create our new, customized exporter that uses our custom preprocessor

pelican = RSTExporter(config=c)

# Process the notebook

print(pelican.from_notebook_node(jake_notebook)[0])

Sometimes when showing schematic plots, this is the type of figure I

want to display. But drawing it by hand is a pain: I'd rather just use

matplotlib. The problem is, matplotlib is a bit too precise. Attempting

to duplicate this figure in matplotlib leads to something like this:

.. code:: python

Image('http://jakevdp.github.com/figures/mpl_version.png')

.. image:: output_5_0.png

3.5. Example

nbconvert Documentation, Release 5.0.0.dev

3.6 Programatically creating templates

In [13]: from jinja2 import DictLoader

dl = DictLoader({'full.tpl':

"""

{%- extends 'basic.tpl' -%}

{% block footer %}

FOOOOOOOOTEEEEER

{% endblock footer %}

"""})

exportHTML = HTMLExporter(extra_loaders=[dl])

(body, resources) = exportHTML.from_notebook_node(jake_notebook)

for l in body.split('\n')[-4:]:

print(l)

</div>

FOOOOOOOOTEEEEER

3.7 Real World Uses

@jakevdp uses Pelican and Jupyter Notebook to blog. Pelicanwillusenbconvert programatically to generate blog

post. Have a look aPythonicPreambulationsfor Jake’s blog post.

@damianavila wrote the Nikola Plugin towriteblogpostasNotebooksand is developping a js-extension to publish

notebooks via one click fromthe web app.

As @Mbussonn requested... easieeeeer! Deploy your Nikola site with just a click in the IPython notebook!

http://t.co/860sJunZvjcc@ralsina

—Damián Avila (@damian_avila) August 21,2013

Chapter 3. Using nbconvert as a library

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