Skip to main content
Join the official Python Developers Survey 2018 and win valuable prizes: Start the survey!

Parametrize and run Jupyter and nteract Notebooks

Project description

<a href=""><img src="" height="48px" /></a>

[![Build Status](](
[![Documentation Status](](
[![Code style: black](](

**Papermill** is a tool for parameterizing, executing, and analyzing
Jupyter Notebooks.

Papermill lets you:

- **parameterize** notebooks
- **execute** and **collect** metrics across the notebooks
- **summarize collections** of notebooks

This opens up new opportunities for how notebooks can be used. For

- Perhaps you have a financial report that you wish to run with
different values on the first or last day of a month or at the
beginning or end of the year, **using parameters** makes this task
- Do you want to run a notebook and depending on its results, choose a
particular notebook to run next? You can now programmatically
**execute a workflow** without having to copy and paste from
notebook to notebook manually.
- Do you have plots and visualizations spread across 10 or more
notebooks? Now you can choose which plots to programmatically
display a **summary** **collection** in a notebook to share with


From the command line:

``` {.sourceCode .bash}
pip install papermill

Installing In-Notebook bindings

- [Python]( (included in this repo)
- [R]( (**experimentally** available in the
**papermillr** project)

Other language bindings welcome if someone would like to maintain parallel implementations!


### Parameterizing a Notebook

To parameterize your notebook designate a cell with the tag ``parameters``.

Papermill looks for the ``parameters`` cell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged with ``injected-parameters`` with input parameters in order to overwrite the values in ``parameters``. If no cell is tagged with ``parameters`` the injected cell will be inserted at the top of the notebook.

Additionally, if you rerun notebooks through papermill and it will reuse the ``injected-parameters`` cell from the prior run. In this case Papermill will replace the old ``injected-parameters`` cell with the new run's inputs.


### Executing a Notebook

The two ways to execute the notebook with parameters are: (1) through
the Python API and (2) through the command line interface.

#### Execute via the Python API

``` {.sourceCode .python}
import papermill as pm

parameters = dict(alpha=0.6, ratio=0.1)

#### Execute via CLI

Here's an example of a local notebook being executed and output to an
Amazon S3 account:

``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1

In the above example, two parameters are set: ``alpha`` and ``l1_ratio`` using ``-p`` (``--parameters`` also works). Parameter values that look like booleans or numbers will be interpreted as such. Here are the different ways users may set parameters:

``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -r version 1.0

Using ``-r`` or ``--parameters_raw``, users can set parameters one by one. However, unlike ``-p``, the parameter will remain a string, even if it may be interpreted as a number or boolean.

``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -f parameters.yaml

Using ``-f`` or ``--parameters_file``, users can provide a YAML file from which parameter values should be read.

``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
alpha: 0.6
l1_ratio: 0.1"

Using ``-y`` or ``--parameters_yaml``, users can directly provide a YAML string containing parameter values.

``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -b YWxwaGE6IDAuNgpsMV9yYXRpbzogMC4xCg==

Using ``-b`` or ``--parameters_base64``, users can provide a YAML string, base64-encoded, containing parameter values.

When using YAML to pass arguments, through ``-y``, ``-b`` or ``-f``, parameter values can be arrays or dictionaries:

``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
- 0.0
- 1.0
- 2.0
- 3.0
slope: 3.0
intercept: 1.0"

Python In-notebook Bindings

### Recording Values to the Notebook

Users can save values to the notebook document to be consumed by other

Recording values to be saved with the notebook.

``` {.sourceCode .python}
import papermill as pm

pm.record("hello", "world")
pm.record("number", 123)
pm.record("some_list", [1, 3, 5])
pm.record("some_dict", {"a": 1, "b": 2})

Users can recover those values as a Pandas dataframe via the
`read_notebook` function.

``` {.sourceCode .python}
import papermill as pm

nb = pm.read_notebook('notebook.ipynb')


### Displaying Plots and Images Saved by Other Notebooks

Display a matplotlib histogram with the key name `matplotlib_hist`.

``` {.sourceCode .python}
import papermill as pm
from ggplot import mpg
import matplotlib.pyplot as plt

# turn off interactive plotting to avoid double plotting

f = plt.figure()
plt.hist('cty', bins=12, data=mpg)
pm.display('matplotlib_hist', f)


Read in that above notebook and display the plot saved at

``` {.sourceCode .python}
import papermill as pm

nb = pm.read_notebook('notebook.ipynb')


### Analyzing a Collection of Notebooks

Papermill can read in a directory of notebooks and provides the
`NotebookCollection` interface for operating on them.

``` {.sourceCode .python}
import papermill as pm

nbs = pm.read_notebooks('/path/to/results/')

# Show named plot from 'notebook1.ipynb'
# Accept a key or list of keys to plot in order.
nbs.display_output('train_1.ipynb', 'matplotlib_hist')


``` {.sourceCode .python}
# Dataframe for all notebooks in collection


Development Guide

Read for guidelines on how to setup a local development environment and make code changes back to Papermill.

For development guidelines look in the file. This should inform you on how to make particular additions to the code base.


We host the [Papermill documentation](
on ReadTheDocs.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
papermill-0.15.1-py2.py3-none-any.whl (31.8 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Oct 2, 2018
papermill-0.15.1.tar.gz (190.8 kB) Copy SHA256 hash SHA256 Source None Oct 2, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page