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adding a binder example
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‎README.md

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[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jupyter/nbclient/master?filepath=binder%2Frun_nbclient.ipynb)
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[![Travis Build Status](https://travis-ci.org/jupyter/nbclient.svg?branch=master)](https://travis-ci.org/jupyter/nbclient)
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[![image](https://codecov.io/github/jupyter/nbclient/coverage.svg?branch=master)](https://codecov.io/github/jupyter/nbclient?branch=master)
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[![Python 3.5](https://img.shields.io/badge/python-3.5-blue.svg)](https://www.python.org/downloads/release/python-350/)

‎binder/empty_notebook.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Show a pandas dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import scrapbook as sb"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.DataFrame(np.random.randn(20, 2), columns=['a', 'b'])\n",
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"data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use scrapbook to store this data in the notebook\n",
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"sb.glue('dataframe', data.to_dict())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Make a matplotlib plot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Make and display a plot\n",
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"fig, ax = plt.subplots()\n",
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"ax.scatter(data['a'], data['b'])\n",
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"sb.glue('plot', fig, 'display')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎binder/requirements.txt

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numpy
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pandas
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scrapbook
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nbformat
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./

‎binder/run_nbclient.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import nbclient\n",
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"import nbformat as nbf\n",
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"import pandas as pd\n",
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"import scrapbook as sb"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Background\n",
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"\n",
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"This notebook uses `nbclient` to read and execute an *empty* notebook.\n",
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"The empty notebook generates some fake data, makes a plot, and stores\n",
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"both the data and the plot inside the notebook using the\n",
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"[scrapbook package](https://github.com/nteract/scrapbook). We will\n",
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"then be able to access the generated contents of the notebook here.\n",
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"\n",
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"You can see the empty notebook by clicking this button:\n",
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"\n",
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"<a href=\"empty_notebook.ipynb\"><button>Empty notebook</button></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Read and execute the empty notebook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# We use nbformat to represent our empty notebook in-memory\n",
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"nb = nbf.read('./empty_notebook.ipynb', nbf.NO_CONVERT)\n",
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"\n",
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"# The Executor class handles execution of the notebook\n",
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"executor = nbclient.execute.Executor(nb)\n",
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"\n",
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"# Update our in-memory notebook, which will now have outputs\n",
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"nb = executor.execute()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Inspect the new notebook for its contents"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# First we'll convert our nbformat NotebokNote into a *scrapbook* NotebookNode\n",
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"nb = sb.read_notebook(nb)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# We can access the dataframe that was created and glued into the empty notebook\n",
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"pd.DataFrame.from_dict(nb.scraps.get('dataframe').data).head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# We can also access the generated plot by \"re-gluing\" the notebook here\n",
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"nb.reglue('plot')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎docs/index.rst

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Similar in nature to jupyter_client, as the jupyter_client is to the jupyter
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protocol nbclient is to notebooks allowing for execution contexts to be run.
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To demo **NBClient** interactively, click the Binder link below:
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.. image:: https://mybinder.org/badge_logo.svg
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:target: https://mybinder.org/v2/gh/jupyter/nbclient/master?filepath=binder%2Frun_nbclient.ipynb
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Origins
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-------
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