Jupyter Notebook Basics

Some notes to keep handy as a reminder of the basics in using a Jupyter notebook

What is a Jupyter notebook?

According to the Web App's main page: _The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more._

In my own words: _Application for creating and sharing documents that contain:_

live code
equations
visualizations
explanatory text

Home page: http://jupyter.org/

Notebook tutorials

_Handy write ups on specific skills or applications of Jupyter Notebooks_

Quick Start Guide
Brief tutorial on installing and running notebooks on a local computer with no python familiarity assumed
User Documentation
User documentation on setup and configuration
Examples Documentation
Jupyter docs with Notebook examples for definitions
Cal Tech
Content intro from Markdown and Latex to interactive plotting with bokeh

Notebook Users

students, readers, viewers, learners
read a digital book
interact with a "live" book
notebook developers
create notebooks for students, readers, ...

Notebooks cells

_Notebooks are composed of cells. These can be modified for myriad forms of content_

Code cells
execute interactively on computer (Python, or many other languages)
memory across notebook (variable from one cell can be used in a later)
Markdown cells
documentation / "narrative" cells
Can be used to decorate and guide a reader through a notebook

All cells have the following basic structure:

{
  "cell_type" : "name",
  "metadata" : {},
  "source" : "single string or [list, of, strings]",
}

Markdown cells

markdown, as defined in GitHub-flavored markdown, and implemented in marked

{
  "cell_type" : "markdown",
  "metadata" : {},
  "source" : ["some *markdown*"],
}

Code cells

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",
      ...
  }],
}

Code cell outputs

code cell outputs correspond to messages produced as a result of executing the cell

stream output

{
  "output_type" : "stream",
  "name" : "stdout", # or stderr
  "text" : ["multiline stream text"],
}

_the keys stream key was changed to name to match the stream message in nbformat: 4.0_

display_data

Rich display outputs, as created by display_data messages, contain data keyed by mime-type

{
  "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,
    },
  },
}

Following cells are "live" cells

print ("Hello Jupyter World!; You are helping me learn")

Hello Jupyter World!; You are helping me learn

(5+7)/4

import numpy as np
my_first_array = np.arange(11)
print (my_first_array)

[ 0 1 2 3 4 5 6 7 8 9 10]