Notebook Usage.ipynb

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   "display_name": "IPython (Python 2)",
   "language": "python",
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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "How are people using the Jupyter Notebook and IPython?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display, Image, HTML\n",
      "from talktools import website, nbviewer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Cam Davidson-Pilon's book"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Cam Davidson-Pilon has written an entire book on Bayesian Statistics as a set of Jupyter+IPython Notebooks that are hosted on GitHub and viewed on https://nbviewer.jupyter.org."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website('http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Mining the Social Web"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Matthew Russell has written an O'Reilly published book, [Mining The Social Web](http://shop.oreilly.com/product/0636920030195.do), that includes Jupyter+IPython Notebooks for all examples."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website('http://shop.oreilly.com/product/0636920030195.do')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"http://shop.oreilly.com/product/0636920030195.do\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Python for Signal Processing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Jose Unpingco has written a series of [blog posts](http://python-for-signal-processing.blogspot.com/) on Signal Processing Jupyter+IPython Notebooks. These blog posts were the basis of a full length book [Python for Signal Processing](http://www.springer.com/engineering/signals/book/978-3-319-01341-1) published by Springer in 2013.\n",
      "\n",
      "<img src=\"images/p4sp.png\">"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Technical blogging"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Jake Vanderplas at the University of Washington blogs using the IPython Notebook:\n",
      "\n",
      "* [Pythonic Perambulations](http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/), Personal blog.\n",
      "* [Visualizing 4 Dimensional Asteroids](http://blogs.scientificamerican.com/sa-visual/2014/09/16/visualizing-4-dimensional-asteroids/), Scientific American, Sept. 2014.\n",
      "\n",
      "<img src=\"images/jakevdp_sa.png\">"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Social media"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Many individuals are now using http://nbviewer.jupyter.org with [Twitter](http://twitter.com) to *speak* about a wide range of technical work."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('images/twitter_post.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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/L/tj4+IclMvjdIqlMpfdKURiyuOiT1vCqgEcdBjgrFYvaAmvG/xBf7go0A5M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\n",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.Image object>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Peter Norvig"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Peter Norvig (Director of Research at Google) has been using the Jupyter Notebook to explore interesting algorithmic problems. He began with a notebook inspired by a regular expression golf question from xkcd [#1313](http://xkcd.com/1313/):\n",
      "\n",
      "> Can I come up with an algorithm to find a short regex that matches the [presidential] winners and not the losers?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website('https://nbviewer.jupyter.org/url/norvig.com/ipython/xkcd1313.ipynb')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"https://nbviewer.jupyter.org/url/norvig.com/ipython/xkcd1313.ipynb\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebok spawned a huge amount of interest in this problem. A number of individuals started to interact with Peter over email about the algorithmic questions involved. This led to a second notebook on the topic:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website(\"https://nbviewer.jupyter.org/url/norvig.com/ipython/xkcd1313-part2.ipynb\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"https://nbviewer.jupyter.org/url/norvig.com/ipython/xkcd1313-part2.ipynb\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Notebook makes its easy to have detailed technical discussions involving code and data that are open and reproducible."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "NASA/JPL"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "NASA/JPL is using the Jupyter Notebook on on various teams and is running nbviewer internally to share Jupyter Notebooks:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('images/NASA-nbviewer.jpg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Data-driven journalism"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Notebook is being used in reproducible, data-driven journalism."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "538"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is an example from [538](http://fivethirtyeight.com)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "On April 1, 2014, Walter Hickey of 538, published an article entitled [The Dollars-and-Cents Case Against Hollywood's Exclusion of Women](http://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/) about the [Bechdel test](http://www.npr.org/templates/story/story.php?storyId=94202522), which provides a simple way to model the prominence of women in movies.\n",
      "\n",
      "On April 7, 2014, Brian Keegan, a post-doctoral research fellow in computational social science Northeastern University, published a blog post entitled [The Need for Openness in Data Journalism](http://www.brianckeegan.com/2014/04/the-need-for-openness-in-data-journalism/). Along with the blog post, he published a replication of Walter Hickey's analysis using the Jupyter Notebook and nbviewer."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website(\"https://nbviewer.jupyter.org/github/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"https://nbviewer.jupyter.org/github/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This caught the attention of Walter Hickey:\n",
      "\n",
      "<img src=\"images/walt_hickey.png\">\n",
      "\n",
      "and led to a [follow up article](http://fivethirtyeight.com/datalab/the-bechdel-test-checking-our-work/) on 538 along with the posting of Walter Hickey's data and code on GitHub. We followed that up by creating an Jupyter+IR Notebook version of the original data analysis posted in the 538 GitHub repository:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website(\"https://nbviewer.jupyter.org/github/ellisonbg/data/blob/r-notebook/bechdel/analyze-bechdel.ipynb\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"https://nbviewer.jupyter.org/github/ellisonbg/data/blob/r-notebook/bechdel/analyze-bechdel.ipynb\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "BuzzFeed"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[BuzzFeed](http://www.buzzfeed.com/) has started to publish data and Jupyter Notebooks for their articles in their [BuzzFeedNews](https://github.com/BuzzFeedNews) repository on GitHub. Here are a few highlights:\n",
      "\n",
      "On August 7, 2014, [BuzzFeed](http://www.buzzfeed.com/) published an article by John Templon, entitled [Tax Collection Scams Skyrocket](http://www.buzzfeed.com/johntemplon/tax-collection-scams-skyrocket). Here is their notebook that replicates the analysis:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website(\"https://nbviewer.jupyter.org/github/buzzfeednews/2014-08-irs-scams/blob/master/notebooks/irs-scams.ipynb\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"https://nbviewer.jupyter.org/github/buzzfeednews/2014-08-irs-scams/blob/master/notebooks/irs-scams.ipynb\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On August 20, 2014, [BuzzFeed](http://www.buzzfeed.com/) published an article by Jeremy Singer-Vine, entitled [The Ferguson Area Is Even More Segregated Than You Probably Guessed](http://www.buzzfeed.com/jsvine/the-ferguson-area-is-even-more-segregated-than-you-thought#44ydjhp). Here is their notebook that replicates the analysis:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website('http://nbviewer.ipython.org/github/BuzzFeedNews/2014-08-st-louis-county-segregation/blob/master/notebooks/segregation-analysis.ipynb')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"http://nbviewer.ipython.org/github/BuzzFeedNews/2014-08-st-louis-county-segregation/blob/master/notebooks/segregation-analysis.ipynb\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Reproducible academic publications"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Publications in traditional academic journals are being accompanied by notebooks that reproduce and expand upon the main computational results of the work. See [this section](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#reproducible-academic-publications) of the [Notebook Gallery](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) for a full list. Here is one notable example from Nature Genetics:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website(\"http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3051.html\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3051.html\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The authors published a GitHub repository with notebooks that replicate the results:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "website(\"https://nbviewer.jupyter.org/github/theandygross/TCGA/tree/master/Analysis_Notebooks/\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=\"https://nbviewer.jupyter.org/github/theandygross/TCGA/tree/master/Analysis_Notebooks/\"  width=\"100%\" height=\"450\">\n",
        "</iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.HTML object>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Teaching"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Jupyter Notebook is being used for lecture materials and student work in a large number of university and high school courses on scientific computing and data science. Most of these courses are being developed publicly on GitHub. Here are a few of the ones we know about:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file courses.csv\n",
      "\"Course\",\"University\",\"Instructor\"\n",
      "\"Python for Data Science\",\"UC Berkeley\",\"Josh Bloom\"\n",
      "\"Introduction to Data Science\",\"UC Berkeley\",\"Michael Franklin\"\n",
      "\"Working with Open Data\",\"UC Berkeley\",\"Raymond Yee\"\n",
      "\"Introduction to Signal Processing\",\"UC Berkeley\",\"Miki Lustig\"\n",
      "\"Data Science (CS 109)\",\"Harvard University\",\"Pfister and Blitzstein\"\n",
      "\"Practical Data Science\",\"NYU\",\"Josh Attenberg\"\n",
      "\"Scientific Computing (ASTR 599)\",\"University of Washington\",\"Jake Vanderplas\"\n",
      "\"Computational Physics\",\"Cal Poly\",\"Jennifer Klay\"\n",
      "\"Introduction to Programming\",\"Alaskan High School\",\"Eric Matthes\"\n",
      "\"Aerodynamics-Hydrodynamics (MAE 6226)\",\"George Washington University\",\"Lorena Barba\"\n",
      "\"HyperPython: hyperbolic conservation laws\",\"KAUST\",\"David Ketcheson\"\n",
      "\"Quantitative Economics\",\"NYU\",\"Sargent and Stachurski\"\n",
      "\"Practical Numerical Methods with Python\",\"4 separate universities + MOOC\",\"Barba, et al.\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting courses.csv\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pandas.read_csv('courses.csv'); df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Notebook gallery"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The examples shown here of how the Jupyter Notebook is being used are merely representative. We maintain a longer [gallery of interesting Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) that contains a curated list of notebooks on various topics."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}