papermill 0.5.1

Map Reduce for Notebooks

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

The goals for Papermill are:

• Parametrizing notebooks

• Executing and collecting metrics across the notebooks

• Summarizing collections of notebooks

Installation

pip install papermill

Usage

Parameterizing a notebook.

### template.ipynb
# This cell has a "preface" tag. These values will be overwritten by Papermill.
alpha = 0.5
ratio = 0.1

Recording values to be saved with the notebook.

### template.ipynb
import random
import papermill as pm

rand_value = random.randint(1, 10)
pm.record("random_value", rand_value)
pm.record("foo", "bar")

Displaying outputs to be saved with the notebook.

### template.ipynb
# Import plt and turn off interactive plotting to avoid double plotting.
import papermill as pm
import matplotlib.pyplot as plt; plt.ioff()
from ggplot import mpg

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

Executing a parameterized Jupyter notebook

import papermill as pm

pm.execute_notebook(
    notebook="template.ipynb",
    output="output.ipynb",
    params=dict(alpha=0.1, ratio=0.001)
)

Analyzing a single notebook

### summary.ipynb
import papermill as pm

nb = pm.read_notebook('output.ipynb')
nb.dataframe.head()

# Show named plot from 'output.ipynb'
nb.display_output('matplotlib_hist')

Analyzing a collection of notebooks

### summary.ipynb
import papermill as pm

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

# Show named plot from 'output1.ipynb'
nbs.display_output('output1.ipynb', 'matplotlib_hist')

# Dataframe for all notebooks in collection
df = nbs.dataframe
df.head()

# Show histograms from notebooks with the highest random value.
pivoted_df = df.pivot('key', 'name', 'value').sort_values(by='name')
pivoted_df.head()

nbs.display_output(pivoted_df[:3], 'matplotlib_hist')