msund · June 6, 2016 12:08 · May 8, 2014 · May 6, 2014 · May 6, 2014 · May 6, 2014
diff --git a/Graphing b/Graphing
@@ -9,9 +9,9 @@
    "cells": [
     {
      "cell_type": "heading",
-     "level": 1,
+     "level": 2,
      "metadata": {},
-     "source": "21 Interactive Plots from matplotlib, ggplot for Python, prettyplotlib, Stack Overflow, and seaborn"
+     "source": "21 Interactive Plots from matplotlib, ggplot for Python,<br>prettyplotlib, Stack Overflow, and seaborn"
     },
     {
      "cell_type": "markdown",
@@ -169,7 +169,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. Check out our [getting started guide](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb) for a full background on these features."
+     "source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. Check out our [getting started guide](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s0_getting-started/s0_getting-started.ipynb) for a full background on these features."
     },
     {
      "cell_type": "code",
@@ -284,7 +284,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly graphs are always interactive, and you can even [stream data to the browser](http://nbviewer.ipython.org/github/plotly/Streaming-Demos/blob/master/IPython%20examples/Real-Time%20Time%20Series.ipynb). You can also embed them in the browser with an iframe snippet."
+     "source": "Plotly graphs are always interactive, and you can even [stream data to the browser](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s7_streaming/s7_streaming.ipynb). You can also embed them in the browser with an iframe snippet."
     },
     {
      "cell_type": "code",
@@ -953,6 +953,23 @@
       }
      ],
      "prompt_number": 51
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "# CSS styling within IPython notebook\nfrom IPython.core.display import HTML\nimport urllib2\ndef css_styling():\n    url = 'https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css'\n    styles = urllib2.urlopen(url).read()\n    return HTML(styles)\n\ncss_styling()",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<style>\n    /*body {\n        background-color: #F5F5F5;\n    }*/\n    div.cell{\n        width: 850px;\n        margin-left: 10% !important;\n        margin-right: auto;\n    }\n    h1 {\n        font-family: 'Open sans',verdana,arial,sans-serif;\n    }\n    .text_cell_render h1 {\n        font-weight: 200;\n        font-size: 40pt;\n        line-height: 100%;\n        color:#447adb;\n        margin-bottom: 0em;\n        margin-top: 0em;\n        display: block;\n        white-space: nowrap;\n    } \n    h2 {\n        font-family: 'Open sans',verdana,arial,sans-serif;\n        text-indent:1em;\n    }\n    .text_cell_render h2 {\n        font-weight: 200;\n        font-size: 20pt;\n        font-style: italic;\n        line-height: 100%;\n        color:#447adb;\n        margin-bottom: 1.5em;\n        margin-top: 0.5em;\n        display: block;\n        white-space: nowrap;\n    } \n    h3 {\n        font-family: 'Open sans',verdana,arial,sans-serif;\n    }\n    .text_cell_render h3 {\n        font-weight: 300;\n        font-size: 18pt;\n        line-height: 100%;\n        color:#447adb;\n        margin-bottom: 0.5em;\n        margin-top: 2em;\n        display: block;\n        white-space: nowrap;\n    }\n    h4 {\n        font-family: 'Open sans',verdana,arial,sans-serif;\n    }\n    .text_cell_render h4 {\n        font-weight: 300;\n        font-size: 16pt;\n        color:#447adb;\n        margin-bottom: 0.5em;\n        margin-top: 0.5em;\n        display: block;\n        white-space: nowrap;\n    }\n    h5 {\n        font-family: 'Open sans',verdana,arial,sans-serif;\n    }\n    .text_cell_render h5 {\n        font-weight: 300;\n        font-style: normal;\n        color: #1d3b84;\n        font-size: 16pt;\n        margin-bottom: 0em;\n        margin-top: 1.5em;\n        display: block;\n        white-space: nowrap;\n    }\n    div.text_cell_render{\n        font-family: 'Open sans',verdana,arial,sans-serif;\n        line-height: 135%;\n        font-size: 125%;\n        width:750px;\n        margin-left:auto;\n        margin-right:auto;\n        text-align:justify;\n        text-justify:inter-word;\n    }\n    div.output_subarea.output_text.output_pyout {\n        overflow-x: auto;\n        overflow-y: scroll;\n        max-height: 300px;\n    }\n    div.output_subarea.output_stream.output_stdout.output_text {\n        overflow-x: auto;\n        overflow-y: scroll;\n        max-height: 300px;\n    }\n    code{\n      font-size: 78%;\n    }\n    .rendered_html code{\n    background-color: transparent;\n    }\n    ul{\n        /* color:#447adb; */  // colors text too\n        margin: 2em;\n    }\n    ul li{\n        padding-left: 0.5em; \n        margin-bottom: 0.5em; \n        margin-top: 0.5em; \n    }\n    ul li li{\n        padding-left: 0.2em; \n        margin-bottom: 0.2em; \n        margin-top: 0.2em; \n    }\n    ol{\n        /* color:#447adb; */  // colors text too\n        margin: 2em;\n    }\n    ol li{\n        padding-left: 0.5em; \n        margin-bottom: 0.5em; \n        margin-top: 0.5em; \n    }\n    /*.prompt{\n        display: None;\n    } */\n    ul li{\n        padding-left: 0.5em; \n        margin-bottom: 0.5em; \n        margin-top: 0.2em; \n    }\n    a:link{\n       font-weight: bold;\n       color:#447adb;\n    }\n    a:visited{\n       font-weight: bold;\n       color: #1d3b84;\n    }\n    a:hover{\n       font-weight: bold;\n       color: #1d3b84;\n    }\n    a:focus{\n       font-weight: bold;\n       color:#447adb;\n    }\n    a:active{\n       font-weight: bold;\n       color:#447adb;\n    }\n    .rendered_html :link {\n       text-decoration: none; \n    }\n    .rendered_html :hover {\n       text-decoration: none; \n    }\n    .rendered_html :visited {\n      text-decoration: none;\n    }\n    .rendered_html :focus {\n      text-decoration: none;\n    }\n    .rendered_html :active {\n      text-decoration: none;\n    }\n    .warning{\n        color: rgb( 240, 20, 20 )\n    } \n    hr {\n      color: #f3f3f3;\n      background-color: #f3f3f3;\n      height: 1px;\n    }\n    blockquote{\n      display:block;\n      background: #f3f3f3;\n      font-family: 'Open sans',verdana,arial,sans-serif;\n      width:610px;\n      padding: 15px 15px 15px 15px;\n      text-align:justify;\n      text-justify:inter-word;\n      }\n      blockquote p {\n        margin-bottom: 0;\n        line-height: 125%;\n        font-size: 100%;\n      }\n   /* element.style {\n    } */  \n</style>\n<script>\n    MathJax.Hub.Config({\n                        TeX: {\n                           extensions: [\"AMSmath.js\"]\n                           },\n                tex2jax: {\n                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n                },\n                displayAlign: 'center', // Change this to 'center' to center equations.\n                \"HTML-CSS\": {\n                    styles: {'.MathJax_Display': {\"margin\": 4}}\n                }\n        });\n</script>\n",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1,
+       "text": "<IPython.core.display.HTML at 0x102c04110>"
+      }
+     ],
+     "prompt_number": 1
     }
    ],
    "metadata": {}

diff --git a/Graphing b/Graphing
@@ -16,7 +16,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python plotting libraries. The matplotlylib project is a collaboration with [mpld3](http://mpld3.github.io/index.html) and [Jake Vanderplas](https://github.com/jakevdp). We've put together a [User Guide](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s00_homepage/s00_homepage.ipynb#Installation-guidelines) that outlines the full extent of Plotly's APIs.\n\nFor best results, you can copy and paste this Notebook and key. Run `$ pip install plotly` inside a terminal then start up a Notebook. Let's get started."
+     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python plotting libraries. The matplotlylib project is a collaboration with [mpld3](http://mpld3.github.io/index.html) and [Jake Vanderplas](https://github.com/jakevdp). We've put together a [User Guide](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s00_homepage/s00_homepage.ipynb#Installation-guidelines) that outlines the full extent of Plotly's APIs.\n\nFor best results, you can copy and paste this Notebook and key. Run `$ pip install plotly` inside a terminal then start up a Notebook. We'll also be using ggplot, seaborn, and prettyplotlib, which you can also all install form pip. Let's get started."
     },
     {
      "cell_type": "code",
@@ -98,7 +98,7 @@
        "metadata": {},
        "output_type": "display_data",
        "png": 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11ajXqPU3pxk7BVJGmbzRCT4hNy+X4hPFMBT7lxlrT/cQcBkiPo+gfZv2XFpzibprLDtG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\nCB4jSkMQBEHwGFEagiAIgsf8f6/NeKkjAey4AAAAAElFTkSuQmCC\n",
-       "text": "<matplotlib.figure.Figure at 0x1061c4750>"
+       "text": "<matplotlib.figure.Figure at 0x106091750>"
       }
      ],
      "prompt_number": 5
@@ -116,10 +116,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3535\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3560\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106311310>"
+       "text": "<IPython.core.display.HTML at 0x101ad27d0>"
       }
      ],
      "prompt_number": 6
@@ -137,10 +137,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3536\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3561\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x105d9d3d0>"
+       "text": "<IPython.core.display.HTML at 0x1060a17d0>"
       }
      ],
      "prompt_number": 7
@@ -158,10 +158,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3537\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3562\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1063bf550>"
+       "text": "<IPython.core.display.HTML at 0x10639a550>"
       }
      ],
      "prompt_number": 8
@@ -233,7 +233,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 12,
-       "text": "<IPython.core.display.Image at 0x106314f10>"
+       "text": "<IPython.core.display.Image at 0x10043b910>"
       }
      ],
      "prompt_number": 12
@@ -246,7 +246,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/Mq490fb.png')",
+     "input": "Image(url='https://i.imgur.com/Mq490fb.png')",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -255,7 +255,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 13,
-       "text": "<IPython.core.display.Image at 0x106330290>"
+       "text": "<IPython.core.display.Image at 0x10639a650>"
       }
      ],
      "prompt_number": 13
@@ -276,7 +276,7 @@
        "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~MattSundquist/1307\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1061c4410>"
+       "text": "<IPython.core.display.HTML at 0x1060bff90>"
       }
      ],
      "prompt_number": 14
@@ -316,7 +316,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 17,
-       "text": "<IPython.core.display.HTML at 0x1061c4410>"
+       "text": "<IPython.core.display.HTML at 0x1060bf350>"
       }
      ],
      "prompt_number": 17
@@ -329,7 +329,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "from IPython.display import Image\nImage(url='http://i.imgur.com/XjvtYMr.png')",
+     "input": "Image(url='http://i.imgur.com/XjvtYMr.png')",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -338,7 +338,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 18,
-       "text": "<IPython.core.display.Image at 0x106330e10>"
+       "text": "<IPython.core.display.Image at 0x10639ad90>"
       }
      ],
      "prompt_number": 18
@@ -351,7 +351,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "from IPython.display import HTML\nHTML('<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>')",
+     "input": "HTML('<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>')",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -360,7 +360,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 19,
-       "text": "<IPython.core.display.HTML at 0x106330210>"
+       "text": "<IPython.core.display.HTML at 0x1060a3150>"
       }
      ],
      "prompt_number": 19
@@ -373,7 +373,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/CxIYtzG.png')",
+     "input": "Image(url='https://i.imgur.com/CxIYtzG.png')",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -382,7 +382,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 20,
-       "text": "<IPython.core.display.Image at 0x1061b2e90>"
+       "text": "<IPython.core.display.Image at 0x1060bff90>"
       }
      ],
      "prompt_number": 20
@@ -395,7 +395,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/gUC4ajR.png')",
+     "input": "Image(url='https://i.imgur.com/gUC4ajR.png')",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -404,7 +404,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 21,
-       "text": "<IPython.core.display.Image at 0x106330110>"
+       "text": "<IPython.core.display.Image at 0x1060bfc90>"
       }
      ],
      "prompt_number": 21
@@ -422,10 +422,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3538\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3563\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1063d2110>"
+       "text": "<IPython.core.display.HTML at 0x106435a90>"
       }
      ],
      "prompt_number": 22
@@ -443,10 +443,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3539\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3564\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1064c9d50>"
+       "text": "<IPython.core.display.HTML at 0x106488d50>"
       }
      ],
      "prompt_number": 23
@@ -469,10 +469,10 @@
        "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
       },
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3540\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3565\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10659cfd0>"
+       "text": "<IPython.core.display.HTML at 0x106d446d0>"
       }
      ],
      "prompt_number": 24
@@ -490,10 +490,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3541\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3566\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10647add0>"
+       "text": "<IPython.core.display.HTML at 0x106cf8d10>"
       }
      ],
      "prompt_number": 25
@@ -506,7 +506,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/HEJEnjQ.png')",
+     "input": "Image(url='https://i.imgur.com/HEJEnjQ.png')",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -515,7 +515,7 @@
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 26,
-       "text": "<IPython.core.display.Image at 0x1063b2cd0>"
+       "text": "<IPython.core.display.Image at 0x106372890>"
       }
      ],
      "prompt_number": 26
@@ -567,10 +567,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3557\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3582\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x113983990>"
+       "text": "<IPython.core.display.HTML at 0x1126eb490>"
       }
      ],
      "prompt_number": 52
@@ -597,10 +597,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3543\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3568\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10649c610>"
+       "text": "<IPython.core.display.HTML at 0x10645a490>"
       }
      ],
      "prompt_number": 31
@@ -627,10 +627,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3544\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3569\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1116a5bd0>"
+       "text": "<IPython.core.display.HTML at 0x1115a4b50>"
       }
      ],
      "prompt_number": 33
@@ -652,10 +652,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3545\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3570\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110b97c90>"
+       "text": "<IPython.core.display.HTML at 0x110c96f90>"
       }
      ],
      "prompt_number": 35
@@ -691,10 +691,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3546\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3571\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110b92ad0>"
+       "text": "<IPython.core.display.HTML at 0x110c1c210>"
       }
      ],
      "prompt_number": 38
@@ -708,7 +708,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "The gallery of [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) from [prettyplotlib](https://github.com/olgabot/prettyplotlib), a library by [Olga Botvinnik](https://github.com/olgabot), can be a fun one to make interactive. Here's a scatter; let us know if you make others. You'll note that not all elements of the styling come through. "
+     "source": "The lovely gallery of [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) from [prettyplotlib](https://github.com/olgabot/prettyplotlib), a matplotlib enhnacing library by [Olga Botvinnik](https://github.com/olgabot), is a fun one to make interactive. Here's a scatter; let us know if you make others. You'll note that not all elements of the styling come through. Head over to [the homepage](http://olgabot.github.io/prettyplotlib/) for documentation."
     },
     {
      "cell_type": "code",
@@ -718,10 +718,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3547\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3572\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110bfc390>"
+       "text": "<IPython.core.display.HTML at 0x111596390>"
       }
      ],
      "prompt_number": 39
@@ -739,10 +739,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3548\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3573\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x111b21f50>"
+       "text": "<IPython.core.display.HTML at 0x110171b50>"
       }
      ],
      "prompt_number": 40
@@ -756,7 +756,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Another library we really dig is [seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), by Michael Waskom. You may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip. The styling isn't yet translated to Plotly, so we'll go to Plotly's default settings. "
+     "source": "Another library we really dig is [seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), a library to maximize aesthetics of matplotlib plots. It's by by [Michael Waskom](http://stanford.edu/~mwaskom/). You'll need to install it with ` $ pip install seaborn`, and may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip. The styling isn't yet translated to Plotly, so we'll go to Plotly's default settings. "
     },
     {
      "cell_type": "code",
@@ -784,10 +784,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3549\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3574\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1116a1e10>"
+       "text": "<IPython.core.display.HTML at 0x110c84f50>"
       }
      ],
      "prompt_number": 43
@@ -805,10 +805,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3550\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3575\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110be3150>"
+       "text": "<IPython.core.display.HTML at 0x110ce3150>"
       }
      ],
      "prompt_number": 44
@@ -835,10 +835,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3551\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3576\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110b7bf90>"
+       "text": "<IPython.core.display.HTML at 0x110cf2550>"
       }
      ],
      "prompt_number": 46
@@ -862,10 +862,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3552\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3577\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110053510>"
+       "text": "<IPython.core.display.HTML at 0x11157f510>"
       }
      ],
      "prompt_number": 47
@@ -883,10 +883,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3553\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3578\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f920750>"
+       "text": "<IPython.core.display.HTML at 0x110cec910>"
       }
      ],
      "prompt_number": 48
@@ -904,10 +904,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3554\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3579\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110615610>"
+       "text": "<IPython.core.display.HTML at 0x10fade810>"
       }
      ],
      "prompt_number": 49
@@ -925,10 +925,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3555\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3580\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f9da110>"
+       "text": "<IPython.core.display.HTML at 0x10f9f0110>"
       }
      ],
      "prompt_number": 50
@@ -946,10 +946,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3556\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3581\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x11053ce10>"
+       "text": "<IPython.core.display.HTML at 0x10f9f9d10>"
       }
      ],
      "prompt_number": 51

diff --git a/Graphing b/Graphing
@@ -16,7 +16,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python libraries. The matplotlylib project is a collaboration with [mpld3](http://mpld3.github.io/index.html) and [Jake Vanderplas](https://github.com/jakevdp).\n\nFor best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. Let's set up our environment and packages."
+     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python plotting libraries. The matplotlylib project is a collaboration with [mpld3](http://mpld3.github.io/index.html) and [Jake Vanderplas](https://github.com/jakevdp). We've put together a [User Guide](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s00_homepage/s00_homepage.ipynb#Installation-guidelines) that outlines the full extent of Plotly's APIs.\n\nFor best results, you can copy and paste this Notebook and key. Run `$ pip install plotly` inside a terminal then start up a Notebook. Let's get started."
     },
     {
      "cell_type": "code",
@@ -25,7 +25,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 35
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -39,7 +39,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 36
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -48,7 +48,12 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 37
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "You'll want to have version 1.0.0. If not, run `$ pip install plotly --upgrade` in a terminal. Check out our User Guide for more details on [where to get your key](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/s00_homepage/s00_homepage.ipynb#Installation-guidelines). Problems or questions? Email feedback@plot.ly or find us on [Twitter](https://twitter.com/plotlygraphs)."
     },
     {
      "cell_type": "code",
@@ -60,15 +65,15 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 38,
+       "prompt_number": 4,
        "text": "'1.0.0'"
       }
      ],
-     "prompt_number": 38
+     "prompt_number": 4
     },
     {
      "cell_type": "heading",
-     "level": 1,
+     "level": 2,
      "metadata": {},
      "source": "I. matplotlib Gallery graphs"
     },
@@ -93,10 +98,10 @@
        "metadata": {},
        "output_type": "display_data",
        "png": 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qK+Gzz2D9enjkESgthdtv10J7b78d4uOdv++r6rChSLgmxYXrebVGAqY0du/e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\nCB4jSkMQBEHwGFEagiAIgsf8f6/NeKkjAey4AAAAAElFTkSuQmCC\n",
-       "text": "<matplotlib.figure.Figure at 0x11091ab90>"
+       "text": "<matplotlib.figure.Figure at 0x1061c4750>"
       }
      ],
-     "prompt_number": 39
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -111,13 +116,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3486\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3535\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f07f190>"
+       "text": "<IPython.core.display.HTML at 0x106311310>"
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -132,13 +137,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3487\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3536\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1108e6390>"
+       "text": "<IPython.core.display.HTML at 0x105d9d3d0>"
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -153,13 +158,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3488\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3537\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f144390>"
+       "text": "<IPython.core.display.HTML at 0x1063bf550>"
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -176,11 +181,11 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 43,
+       "prompt_number": 9,
        "text": "[{'name': '_line0',\n  'x': [0.0,\n   0.20000000000000001,\n   0.40000000000000002,\n   0.60000000000000009,\n   0.80000000000000004,\n   1.0,\n   1.2000000000000002,\n   1.4000000000000001,\n   1.6000000000000001,\n   1.8,\n   2.0,\n   2.2000000000000002,\n   2.4000000000000004,\n   2.6000000000000001,\n   2.8000000000000003,\n   3.0,\n   3.2000000000000002,\n   3.4000000000000004,\n   3.6000000000000001,\n   3.8000000000000003,\n   4.0,\n   4.2000000000000002,\n   4.4000000000000004,\n   4.6000000000000005,\n   4.8000000000000007],\n  'y': [1.0,\n   0.25300171651849518,\n   -0.54230030891302927,\n   -0.44399794031078654,\n   0.13885028597711233,\n   0.36787944117144233,\n   0.09307413008823949,\n   -0.19950113459002566,\n   -0.16333771416280363,\n   0.051080165611754998,\n   0.1353352832366127,\n   0.034240058964379601,\n   -0.073392365906047419,\n   -0.060088587008433003,\n   0.018791342780197139,\n   0.049787068367863944,\n   0.012596213757493282,\n   -0.026999542555766767,\n   -0.022105355809443925,\n   0.0069129486808399343,\n   0.018315638888734179,\n   0.0046338880779826647,\n   -0.0099325766273000524,\n   -0.0081321059420741033,\n   0.0025431316975542792]}]"
       }
      ],
-     "prompt_number": 43
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -194,7 +199,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 44
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -209,7 +214,7 @@
        "text": "Figure(\n    data=Data([\n        Scatter(\n            x=[0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000...],\n            y=[1.0, 0.2530017165184952, -0.5423003089130293, -0.44399794031...],\n            name='_line0',\n            mode='markers',\n            marker=Marker(\n                symbol='dot',\n                line=Line(\n                    color='#000000',\n                    width=0.5\n                ),\n                size=30,\n                color='#0000FF',\n                opacity=1\n            )\n        )\n    ]),\n    layout=Layout(\n        xaxis=XAxis(\n            domain=[0.0, 1.0],\n            range=[0.0, 5.0],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='y',\n            mirror=True\n        ),\n        yaxis=YAxis(\n            domain=[0.0, 1.0],\n            range=[-0.6000000000000001, 1.2],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='x',\n            mirror=True\n        ),\n        hovermode='closest',\n        showlegend=False\n    )\n)\n"
       }
      ],
-     "prompt_number": 45
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -219,24 +224,46 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/RusH4k2.png?1')",
+     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/WG0gb9J.png')",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<img src=\"https://i.imgur.com/RusH4k2.png?1\"/>",
+       "html": "<img src=\"https://i.imgur.com/WG0gb9J.png\"/>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 46,
-       "text": "<IPython.core.display.Image at 0x10f098a90>"
+       "prompt_number": 12,
+       "text": "<IPython.core.display.Image at 0x106314f10>"
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "I can now call that graph into the NB. And if I want to see the data for the fit or access the figure styling, I can  run the same commands, but now on this graph."
+     "source": "We also keep the data and graph together. You can analyze it, share it, or add to other plots. You can [append data](https://plot.ly/python/add-append-extend) to your plots, copy and paste, import, or upload data. Take-away: a Python user could make plots with an Excel user, [ggplot2 Ploty package](ropensci.org/blog/2014/04/17/plotly/), and [MATLAB](plot.ly/MATLAB) user. That's collaboration. "
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/Mq490fb.png')",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<img src=\"https://i.imgur.com/Mq490fb.png\"/>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": "<IPython.core.display.Image at 0x106330290>"
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "I can now call that graph into the NB. I can keep the styling, re-use that styling on future graphs, and save styles from other graphs. And if I want to see the data for the fit or access the figure styling, I can  run the same commands, but on the updated figure and data for this graph. I don't need to re-code it, and I can save and share this version. "
     },
     {
      "cell_type": "code",
@@ -249,15 +276,77 @@
        "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~MattSundquist/1307\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f0985d0>"
+       "text": "<IPython.core.display.HTML at 0x1061c4410>"
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Plotly graphs are always interactive, and you can even [stream data to the browser](http://nbviewer.ipython.org/github/plotly/Streaming-Demos/blob/master/IPython%20examples/Real-Time%20Time%20Series.ipynb). You can also embed them in the browser with an iframe snippet."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from IPython.display import HTML",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "s = \"\"\"<pre style=\"background:#f1f1f1;color:#000\">&lt;iframe src=<span style=\"color:#c03030\">\"https://plot.ly/~etpinard/176/650/550\"</span> width=<span style=\"color:#c03030\">\"650\"</span> height=550<span style=\"color:#c03030\">\" frameBorder=\"</span>0<span style=\"color:#c03030\">\" seamless=\"</span>seamless<span style=\"color:#c03030\">\" scrolling=\"</span>no<span style=\"color:#c03030\">\">&lt;/iframe></span></pre>\"\"\"",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "h = HTML(s); h",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<pre style=\"background:#f1f1f1;color:#000\">&lt;iframe src=<span style=\"color:#c03030\">\"https://plot.ly/~etpinard/176/650/550\"</span> width=<span style=\"color:#c03030\">\"650\"</span> height=550<span style=\"color:#c03030\">\" frameBorder=\"</span>0<span style=\"color:#c03030\">\" seamless=\"</span>seamless<span style=\"color:#c03030\">\" scrolling=\"</span>no<span style=\"color:#c03030\">\">&lt;/iframe></span></pre>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 17,
+       "text": "<IPython.core.display.HTML at 0x1061c4410>"
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Where it remains interactive. That means your for-free defaults are: D3 graphs, drawn with JavaScript, and shared data. Here's how it looks in the Washington Post."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from IPython.display import Image\nImage(url='http://i.imgur.com/XjvtYMr.png')",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<img src=\"http://i.imgur.com/XjvtYMr.png\"/>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": "<IPython.core.display.Image at 0x106330e10>"
+      }
+     ],
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "And Plotly graphs are interactive. "
+     "source": "It's fun to zoom. Then double-click to re-size. "
     },
     {
      "cell_type": "code",
@@ -270,11 +359,55 @@
        "html": "<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 48,
-       "text": "<IPython.core.display.HTML at 0x10f098b10>"
+       "prompt_number": 19,
+       "text": "<IPython.core.display.HTML at 0x106330210>"
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Plots can be collaboratively edited and shared with others."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/CxIYtzG.png')",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<img src=\"https://i.imgur.com/CxIYtzG.png\"/>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": "<IPython.core.display.Image at 0x1061b2e90>"
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "So you can keep all your plots for your project, team, or personal work in one plce, you get a profile, like this: https://plot.ly/~jackp/. "
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/gUC4ajR.png')",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<img src=\"https://i.imgur.com/gUC4ajR.png\"/>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": "<IPython.core.display.Image at 0x106330110>"
+      }
+     ],
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -289,13 +422,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3489\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3538\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110939e90>"
+       "text": "<IPython.core.display.HTML at 0x1063d2110>"
       }
      ],
-     "prompt_number": 49
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -310,13 +443,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3490\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3539\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110d22510>"
+       "text": "<IPython.core.display.HTML at 0x1064c9d50>"
       }
      ],
-     "prompt_number": 50
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -336,13 +469,13 @@
        "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
       },
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3491\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3540\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f073250>"
+       "text": "<IPython.core.display.HTML at 0x10659cfd0>"
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -357,13 +490,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3492\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3541\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1108f6e50>"
+       "text": "<IPython.core.display.HTML at 0x10647add0>"
       }
      ],
-     "prompt_number": 52
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -381,11 +514,11 @@
        "html": "<img src=\"https://i.imgur.com/HEJEnjQ.png\"/>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 53,
-       "text": "<IPython.core.display.Image at 0x10f668090>"
+       "prompt_number": 26,
+       "text": "<IPython.core.display.Image at 0x1063b2cd0>"
       }
      ],
-     "prompt_number": 53
+     "prompt_number": 26
     },
     {
      "cell_type": "heading",
@@ -405,7 +538,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 54
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -419,7 +552,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 55
+     "prompt_number": 28
     },
     {
      "cell_type": "markdown",
@@ -434,13 +567,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3493\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3557\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1109693d0>"
+       "text": "<IPython.core.display.HTML at 0x113983990>"
       }
      ],
-     "prompt_number": 56
+     "prompt_number": 52
     },
     {
      "cell_type": "markdown",
@@ -454,7 +587,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 57
+     "prompt_number": 30
     },
     {
      "cell_type": "code",
@@ -464,13 +597,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3494\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3543\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f0d9690>"
+       "text": "<IPython.core.display.HTML at 0x10649c610>"
       }
      ],
-     "prompt_number": 58
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -484,7 +617,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 59
+     "prompt_number": 32
     },
     {
      "cell_type": "code",
@@ -494,13 +627,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3495\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3544\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1109b3750>"
+       "text": "<IPython.core.display.HTML at 0x1116a5bd0>"
       }
      ],
-     "prompt_number": 60
+     "prompt_number": 33
     },
     {
      "cell_type": "code",
@@ -509,7 +642,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 61
+     "prompt_number": 34
     },
     {
      "cell_type": "code",
@@ -519,13 +652,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3496\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3545\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1109379d0>"
+       "text": "<IPython.core.display.HTML at 0x110b97c90>"
       }
      ],
-     "prompt_number": 62
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -539,7 +672,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 63
+     "prompt_number": 36
     },
     {
      "cell_type": "code",
@@ -548,7 +681,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 64
+     "prompt_number": 37
     },
     {
      "cell_type": "code",
@@ -558,13 +691,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3497\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3546\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1061c8d10>"
+       "text": "<IPython.core.display.HTML at 0x110b92ad0>"
       }
      ],
-     "prompt_number": 65
+     "prompt_number": 38
     },
     {
      "cell_type": "heading",
@@ -585,13 +718,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3498\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3547\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f1d7210>"
+       "text": "<IPython.core.display.HTML at 0x110bfc390>"
       }
      ],
-     "prompt_number": 66
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
@@ -606,13 +739,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3499\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3548\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10fd5d550>"
+       "text": "<IPython.core.display.HTML at 0x111b21f50>"
       }
      ],
-     "prompt_number": 67
+     "prompt_number": 40
     },
     {
      "cell_type": "heading",
@@ -632,7 +765,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 68
+     "prompt_number": 41
     },
     {
      "cell_type": "code",
@@ -641,7 +774,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 69
+     "prompt_number": 42
     },
     {
      "cell_type": "code",
@@ -651,13 +784,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3500\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3549\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x111e96650>"
+       "text": "<IPython.core.display.HTML at 0x1116a1e10>"
       }
      ],
-     "prompt_number": 70
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -672,13 +805,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3501\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3550\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e81f890>"
+       "text": "<IPython.core.display.HTML at 0x110be3150>"
       }
      ],
-     "prompt_number": 71
+     "prompt_number": 44
     },
     {
      "cell_type": "markdown",
@@ -692,7 +825,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 72
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
@@ -702,17 +835,17 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3502\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3551\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10fd75490>"
+       "text": "<IPython.core.display.HTML at 0x110b7bf90>"
       }
      ],
-     "prompt_number": 73
+     "prompt_number": 46
     },
     {
      "cell_type": "heading",
-     "level": 1,
+     "level": 2,
      "metadata": {},
      "source": "V. Stack Overflow Answers"
     },
@@ -729,13 +862,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3503\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3552\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f5c0250>"
+       "text": "<IPython.core.display.HTML at 0x110053510>"
       }
      ],
-     "prompt_number": 74
+     "prompt_number": 47
     },
     {
      "cell_type": "markdown",
@@ -750,13 +883,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3504\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3553\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f55cd10>"
+       "text": "<IPython.core.display.HTML at 0x10f920750>"
       }
      ],
-     "prompt_number": 75
+     "prompt_number": 48
     },
     {
      "cell_type": "markdown",
@@ -771,13 +904,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3505\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3554\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10fd63190>"
+       "text": "<IPython.core.display.HTML at 0x110615610>"
       }
      ],
-     "prompt_number": 76
+     "prompt_number": 49
     },
     {
      "cell_type": "markdown",
@@ -792,13 +925,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3506\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3555\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1108bde90>"
+       "text": "<IPython.core.display.HTML at 0x10f9da110>"
       }
      ],
-     "prompt_number": 77
+     "prompt_number": 50
     },
     {
      "cell_type": "markdown",
@@ -813,13 +946,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3507\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3556\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f488510>"
+       "text": "<IPython.core.display.HTML at 0x11053ce10>"
       }
      ],
-     "prompt_number": 78
+     "prompt_number": 51
     }
    ],
    "metadata": {}

diff --git a/Graphing b/Graphing
@@ -27,6 +27,11 @@
      "outputs": [],
      "prompt_number": 35
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "You can use our public key and username or sign up for an account on [Plotly](plot.ly/ssi). Plotly is free for public use, you own your data, and you control the privacy. "
+    },
     {
      "cell_type": "code",
      "collapsed": false,

diff --git a/Graphing b/Graphing
@@ -16,7 +16,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python libraries. For best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. \n\nLet's set up our environment and packages."
+     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python libraries. The matplotlylib project is a collaboration with [mpld3](http://mpld3.github.io/index.html) and [Jake Vanderplas](https://github.com/jakevdp).\n\nFor best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. Let's set up our environment and packages."
     },
     {
      "cell_type": "code",

diff --git a/Graphing b/Graphing
@@ -11,7 +11,7 @@
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "21 Interactive Plots from matplotlib, prettyplotlib, Stack Overflow, and seaborn"
+     "source": "21 Interactive Plots from matplotlib, ggplot for Python, prettyplotlib, Stack Overflow, and seaborn"
     },
     {
      "cell_type": "markdown",
@@ -75,7 +75,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly also reads the label types in this [damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html) graph."
+     "source": "Let's get started with this [damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html) graph."
     },
     {
      "cell_type": "code",
@@ -467,6 +467,11 @@
      ],
      "prompt_number": 58
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Histograms are also fun to hover over to get the exact data. "
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -520,7 +525,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "You can also use more advanced plotting types in collaboration with pandas. Just add a geom."
+     "source": "You can also use more advanced plotting types in collaboration with pandas. You can add a geom."
     },
     {
      "cell_type": "code",

diff --git a/Graphing b/Graphing
@@ -11,12 +11,12 @@
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "17 Interactive Plots from matplotlib, prettyplotlib, Stack Overflow, and seaborn"
+     "source": "21 Interactive Plots from matplotlib, prettyplotlib, Stack Overflow, and seaborn"
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "In this Notebook, we'll create interactive Plotly graphs from different Python libraries. Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. You can also always access the data from your graphs or any public Plotly graph. And it's free.\n\nFor a full walk-through and documentation, check out our [getting started Notebook](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb). Let's set up our environment and packages.\n\nFor best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. "
+     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python libraries. For best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. \n\nLet's set up our environment and packages."
     },
     {
      "cell_type": "code",
@@ -25,7 +25,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 35
     },
     {
      "cell_type": "code",
@@ -34,7 +34,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 36
     },
     {
      "cell_type": "code",
@@ -43,7 +43,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 37
     },
     {
      "cell_type": "code",
@@ -55,11 +55,11 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 38,
        "text": "'1.0.0'"
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 38
     },
     {
      "cell_type": "heading",
@@ -80,29 +80,44 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig1 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nplt.show()\npy.iplot_mpl(fig1)",
+     "input": "fig1 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nplt.show()",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "metadata": {},
        "output_type": "display_data",
        "png": 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11ajXqPU3pxk7BVJGmbzRCT4hNy+X4hPFMBT7lxlrT/cQcBkiPo+gfZv2XFpzibprLDtG\n4CSbAMXri8nNyw1Z2WyueSmQpqVgIeDRU77AVQRAXR188AG8+CJ07ap9/vCHvlUe6XelUzi0sF5J\nmLG/IQ0QXRHNc1PqfRVOvg+9grF+PwK6XOnCst8tC9mbUwgs1h5wWXSZk7kJM3AIjJeNJPdO5qUn\nXyJrXJaz7wPCTjaDKXIpGAi76KnmEBEBkybBvn0wYwZkZ8PNN8P69b4xW9ne4qC+d2HCpjCMl42k\nx6bz/vz37ZzbWeOyKMgt4MVpLxK9Ptp1j2MslGVpPQ4xVQneYtcDdpTNT8Gwx0B6XDorX1nJnn/t\nsT38rbK58qWVpHVIw1BuecMyEzayKeal5hO2PQ1Hamth5Ur47W8hJkbreWRlNa3n4fQWB7a3Nwye\nv4nl5uUy9fmplGWVOfdWLP6RtA5pFOQWeN9IoVVik80fOciURTZRIa29ZzLl1JOGkO9xlFeVt3rz\nkp6QTO7zBd6ceF2d5iR/6SUtPPfFF+Guu7SeiSfYolHKS2AATjZf43ojK19a6fGN5OvjCa0XO1ka\ni0ufRGJBIgufXOiRPHl0vMJEFs707HjNJZgyqcMFURpeUFcHa9dqyuPqVZgzB+69t2Hl4cu3OKfj\nPj+VMmNZ2LzRCf7Hbc/Ayx6wnmDqDYs/wveIT8MLIiLg7rvhiy+0KKv58yElBd5+G77/3nl/p0gp\nva34VmAsJMYl8tunfut1W7LGZbHsd8swXjFqK8yEjQ1Z8A8ufWxgk8/E2MQmvXhYZTOxMNE5omqs\n9ll8rtgvsin+iOCg1fY0HFFV2LhRUx4HDsB//Af8/OfQoYO2ffyj49lg2uDTtzhHXL4p6hh/eDzr\n/9/6Jh9fCF/GPzqeDSUbWkw2XfaG9b/fiGz6wrQk/gjfIz2NZmAwaCG5GzbAmjWwezcMGADPPw/L\n389lV/EubUcfvsU58ttZv61/o7NixpZcteurXdLbEJzIzbPIpwvZNFYbfSKbTr1h8Eo2fTFokDWT\nWhRGYAnqMiKBIj0d/vEPOHQIZj2TyytrZ6PGlWsbTZadPsX2Frfwd75xBFqPMfX5qZRR5uR4PM95\nSfwT7LCaTcvblTvJJioM6jzIZ7KSNS6LQYsHUUih17IppqXwQXoaDZCQAFdjF6HeV9KiPQw9djZk\nh0gVgJK0Eha/u9hnvyeENotWLNLKhPjQx9YQtt6wl7IZyEGDBN8iPY1GOFF2QrsRTZYVlre4yJOd\nuP+BhYwf6/s3fqsSenjOw5znvFO0yrH2x3z+m0LoYTNLmXCSz06XO7Hw977pATv6IxaysF42wU4+\nd13e5bLMSCCL9Am+RXoaDZCbl0vJEd34HSZsb3GDrx3B9k1ZJCRozvPz533721njshg+aLjLaJVD\n5w+Jb6OVY2eWsmLCJp8jBo/wWQ/Y0R9hk01wks/zPzovkX5hjiiNBli0YhGVaZX1ZikL0eujefmp\nWWzZAqtXw8dvTmNSt0zGRsdza5t2PNCmDWMNBn5iMPBAmzY80K4d0+LjUTIzUaZN8/j3sx/MJrow\n2skMUDmhUkxUrRwns5SOxIJEZk2Z5bPfcuWPyH4wW0yorRQxTzVAtVqtZWmDnXMxoVMCWeOyNAVg\nNtO3rIic2gqUWm1XRT/V1mo1TE6fhtOnMRcVoWRmgsmEkpPT4O9njcsisV8i+9jntK2qrsoXpyiE\nKNVqtTZjsqxoAbOUFVeVXZ1MqA6IfIYv0tNwQ25eLvv2Wx7WJmzdfm6FPvF9UKZNw7x6NcrmzZgq\nKjw+rqmiAjZv1r7rQa+jV5de9QtmbCGO+w7sExNAK+bCuQv1CyYaNEv9fO3PyczJ5M537qS8qhxv\ncRfq6mSmssgmG+FC6QWE8ESUhgts2d8pZU5d/5S32jPwi+OYV6/2SlkoDsumigpNcTRisrKZAcxI\nlrgAaPJ5suKkk2zGb413aZbyRY6EO7IfzCb+03gnv9vJmpMim2GKKA0X2OzFJmzlpNkEXXK7kNnJ\nxOKv9nmlMPQounm7Xocb5ZE1LouFMxfSZX8XsR0LgCafp249ZSebfAo92/X0e45E1rgsesb2dJLN\nUzefEtkMU8Sn4YBdGCPYwhn7r4LUU1e5WHXUJ7+j6BcsykNxvStZ47JIeSeFzWjF2jwJcRTCE6cw\nW1P9to7fdXT5nZYeba5jZ93vmhHZDHNEaehwFcbYfxWYyiHuFKyurnD7YFeAIrThwH8ClFo+AZLd\n7O/Id4VFKNOmuXSQRxmitBkzkiXeitDnSEztPJXn33zePsxWhzHC6HJ9S+dIiGy2LsQ8pcNVGKOp\nHPIPQ2q16+8ouvlUoE9sLMlTp7JJVXlPVUmeOhVzbKzb7+qnZRcq+DbPzJdfOu8rIY6tE70/Yvam\n2X4Ls/UGkc3WhfQ0dDiGMfb/C8SVefZdc2wsptRUTA6htEpOjtZ7MJsxFxVppigdisNxrp4t4rkR\nmZxtb+KJ3+UwZQrExkqIY2tF74+I+jyK09ec9kuYrTeIbLYuRGnouHDuQn1ehglM0ZBa67yfopt3\npyzs9res1ysPvSNdfzy+rwA2k91bK9X+619rIws+/jjc+cMshq8YzgY2OJUWudBBQhzDEas/4r72\n9zHzq5nQ37LBhE15jDjsu+zvppI1TiebYCef+67sE99GGCFKw4JdGKOL8QKsKI7Lqako+fke/YZe\neZhXr7brdTge98KRIq7vnEniBBPdU3P45S+huhpG3pJNtw17KY08ZdfOk9tOyo0ZhsQZ45gaN9U+\nBNxh+NZZTwbGLOVI9oPZlLxRQkmnEjtTVRll4tsII2QQJgu2QZbM0P9DMFVD3EVIrav3Oeix9jA8\nyex2hZKZiWKJmNJPTvtlZKDk56Oq2hgff/87LFmbTu2jhc7nIIM0BRW+GtPaJpvg88GVfI3d8LAO\niHwGH015dkpPw4Len2GKhvwz9Q9xxWFfc2wspnvuaZKysGEyoYCdn8Pxd7Bst0ZUjRgBI0bAlxUd\n+czFvleuiu04mLA6sUFTIE2NYLLJJtiZpVK+SwkqhQEuwsN1iG8jPBClYSHKEGUXXmtFcbGvkpra\nPIWBzlSVmQmb628wp9+rqEAxm+1WRUdGuTzmji1GnnwSHn5YUy4GQ7OaKDQTXyXV2UJaHXAXYhto\nQq29gneI0kDrUpeeKWVACWy6ZP/g1s/rTVI+Q9fjMLnpcTgWObTZjtNKbA5H4xUjCUlnOH85l4cf\nzuLqVXjgAW0aPFgUSCDwVVLdqEGj+GzVZ1ROqLStCyZfhiNOvo0IiC6P5sYHbwx00wRfoIYBzTmN\ndRvWqYl3J6ooqBk9UFVQ56J9Ok5zMzJ812gH5mZk2H7Xk99ft2GdmnZnmmq80aiiYJsS705U1368\nTi0oUNXnnlPVfv1UddAgVZ03T1W//rrFmh92PLHmCTXj7Qz1juV3qOcrzwesHTb5nIbKGFQyUKNT\no9W5f5gbsDZ5wtw/zFWjb4x2ks11G9YFummCjqY8O1t9cp81oa//KojzvgCo7zCZ3CYBuiJrXBbd\nunejaoK9nbgkrYQ//2MxaWnwhz/Ad9/B0qVQVgYZGdr45/Pnw+HDvj6B8KIli/x5g10dNEsl28p7\nKtlxcEfA2uQJnxd/btczAkn0CxdavXnK6mQ0ldtnfSu6fVrELOWAkpPTqH/D0Uxl5yDVoXc4RkTA\nqFHa9N//DVu2wD/+AUOHwsCBcP/9cO+9MGCAy0O1WlqyyJ83ePI/DkZCtd1C47RqpWEbM8Phgak4\n7OdNLkazaCyiyqGwoZ3D0UyjyVSRkTB2rDb9+c9a8uA//wkjR0KfPpryuPdeSHZVLKuV0dJF/jzB\nnXxC8DuVvZVNIXRotUrD3ZgZisN+5thYTC3Yw9DjLqLKHc1JpmrbFiZM0KYlS2DrVk2BjB8P7dvX\nK5D09NbpRG/pIn+N4SSfQZrQ5w5J9AtfWm1ynzVhqv8qMJ2CuFJYXee8nzW5zp9Yh5F1LDdiRZ8n\n4utkKmsS4T//CR98ADU1mvK47z7NxBURIl4wXyXWBYpQSuhzhyT6BT9NeXaGyCPA9+h9Gfmntczv\nYEHJyUHJz9f8KDhXw82pqABL7kbWuCxSklNcHqcp9mODQcvxePVV+OYbWLtWK5g4Ywb07AmPPQar\nVsGlS14f2q8EiyO7qTgl9Fmc4CnJwZfQ5w5fy6YQHLRa85SrBCRFN+8P57evaKlkKoNBy/EYPBjm\nzoVDh2DdOvjLX2DqVLj5Zpg4EX70I+jXr1k/5XOCxZHdVMIlQS5czkOop9Uqjei9pxn7gYGO32td\nM8Vhu9+c3w1hMtmc4orDJn0kVfZP/ZNMlZAA2dnadOECfPyxpkTmzoXevesVyPDhgTdjBYMju6lY\nk00jD0RSe2d9meVQ8GU44i4R9UyvM+IQD1V8nCviFZs3b1aTkpLUa6+9Vl20aJHT9k2bNqkdO3ZU\nU1NT1dTUVPW3v/2ty+N4exrrNqxTJ3QxBiyRzxvmZmR41M5AJlNdvaqqW7eq6n/+p6omJ6tqjx6q\n+thjqrpypaqeD1xeXEiiTza1JvQZhxrV9LvSQzYxzpqI2m5kO0n2CzKaogIC+j44e/Zs3nzzTT75\n5BPeeOMNzp4967RPRkYGhYWFFBYW8sILL/jkdxetWERl+/CyqQYymSoyUjNVvfoq7N8P27fDDTfA\nW29pZqvRo+Hll+GLL6DOA9/Rz9f+nMycTO58507KqwKZcel/bMl8YPNlVE2soluXbiH7Vm5NRP3+\nju/t1kuyX2gSMPNUhSUqaMyYMQDcfvvt7Ny5k6ws+xtDbYHgLsfEI0U3H3S+DIfcDcVhs9VMVXHs\nYP2IbjoC4XDUm7EqK+Gzz2D9enjkESgthdtv10J7b78d4uOdv++r6rChSLgmxYXrebVGAqY0du/e\nTVJSkm05OTmZHTt22CkNg8HA9u3bSU1N5dZbb2XmzJkkJiY2+7e/3/oN3S2VbBWHbUHhy9DhKndD\n0e9gSfj7sodru32gHY7R0ZpyuP12LSP9yBHNF/Kvf8Hs2ZpunjBBUyKjRkFUVOg7sZtDuDqOw/W8\nWiNBHXKbnp7O0aNH2b17N8nJycyePbvZx8zNy6XzuQt2JUPCgT7d+5BYaFGoZmAjGNcYOXNWczgG\nC/36wRNPaDkgZ87A4sWaeevZZ6FrV015jDy2gh/2nMT6B/NCzondXEYNGkX0+mi7dYkFicyaEloO\ncEeyH8zW5NOMlqy4CaJXRXNjklS+DTUC1tMYPnw4zz77rG15//79TJgwwW6fDh062OYff/xxnn/+\neaqrq4mKchEuqyi2+czMTDIzM532sWbZ9ul4Gc4HNvvba3RmKsVFwt/FI0fJiB9Kx10dKT5XTNWE\nKqqoopDCoM3AbdtW83dYfR7nz0N+PmzcGMexnPcY+GvIzITbbtOm664L7+z03Lxclm9fTmVSJXwK\nGCC6IpqHpjwUdP87b8kal8Xuwt3MXzXf5nurpJLl25czPG94yJ9fqJCfn09+My0pAc0IT0tLY+HC\nhfTr148JEyawdetWunbtatt++vRpunfvjsFgYM2aNSxevJi8vDyn43ia1WjNss14G/JdVHkNRPa3\nt1jNVIqrbRkZfD4gqj6TWEcoZuCeOAGffqrVyNq4EWpr6xXIrbdC376BbqFvscsC168Pwf+dK8L9\n/EKRkBvudcGCBUyfPp2amhqys7Pp2rUrb775JgDTp09n5cqVLFmyhDZt2jBkyBBef/31Zv1exWdf\nkrHJfmS+cCPQDkdflu/o1QseekibVBVKSjTlkZsLzzwDHTrAmDHalJGhOeBDuScS6P9dSxPu59da\nCKjSyMjIoLi42G7d9OnTbfMzZ85k5syZPvu9npeqWXXa9RCuIUMjCX+dDhroHw+Hf2y/zV8Ox5aK\nfDIY4NprtWn6dE2JHDyolXr/5BOYM0fbT69EBg0KLSUS7s7icD+/1kKryQjPzcul5vsa27Ki2/Zt\nTAwDhw8PnjDbBnAcd0PRb6yogAq446qRw1QFJAPXX5FPBoOmFAYNqlci332nKZHNm+GPf9Sy1m+5\nRVMit9wCQ4ZofpRgJDcvl1OnT8E+4Ef160MxC9wdrrLDoy5Hcaa3ZIeHEq1CaTTmAFeGDw96X4Y3\nXNvrWtIe2EekAAAgAElEQVR2teVA2QGq76j2q0M8UOU7DAbNPJWQANOmaeuOHdNyRDZv1kYvNJu1\nwaduukmbRo2CLl381kS3WOWzZKTlYfopGC8bSe6dzEtPvhQ2D1PrecxZMMcWrFFNdVAHawjOtIrS\n6Ddd24t2V08SdwpWuzCrhoIDXI++dHqOKzNVbCwVRgOF8eVOZqrW7HQsL4edO7WM9c8/1+Z79rRX\nIoMG+b9uVmtzELe28w1mQs4R7i+6X7rC6lD3ZejwKOGvAjKN4Bgk1pDTMdTHoGiMuDgtD2T8eG25\ntra+7MmWLVoZlLIyuPHGeiUybJj2vZaktTmIW9v5hhutQmkYqPeGKrr1+9pGknLT6JDwZfiKhpyO\nra18R2Sk5ucYMgR+8Qtt3ZkzWi9k+3aYNw+KirQoruHDtXFGhg+H1FQt091XtDYHcWs733CjVSiN\n2JhYoNyppzEraVBImaWccDGmuJ7oS0YwV3lcLr01l++w0r073H23NgFcvQrFxdpohrt2wbJl2nJS\nkqZArMokORnaNOFuspZBNx40UjWh/k07nBzgjki59NAm7H0auXm5/HnK/XxU5tz1DTVfhjvcjSl+\nMCqK3R1qOPRkfWnZxMJEFs5c6PLGLK8qD9kxKPxJVZXWA9m9u16ZHDum9UBGjNDGVU9P1zLYIyPd\nH8fmALc+PA+FpwPcFbl5uXYOcSsNyafge5ri0wh7pXHTtb3ofuxkWDjA3dFQlnhmf9j8qP06cTj6\nnooKrfT77t1QUACFhXDyJKSkQFqapkTS0rRlo8UK09odwq39/IOBFnGEX7p0iejoaCIjIzl9+jQl\nJSXcdNNNTW6kv+l+6Qqp1c5O8H1tI0kJF1+GLuHPE8Th6HtiY+tLnFi5cEHrkRQWwrZt8Oc/a+Ou\nDxyoKZCS49VBU84+EIhDPDRpVGmMGTOGrVu3cvXqVUaOHElSUhJJSUksWLDAH+1rNgYMLt/A7+3c\n0RaFFOroE/4Uh21xpyDjbTDH1WeJi8PRP3TsWJ+hbqWqCvbt0xTJpqWuHcLlpUYOHtSy35viJwkV\nxCEemjQqknV1dcTExPDnP/+Zxx57jBdffJERI0b4o23NxjELXE/v7r393Br/oegXqoHDkFkFhzeK\nwzHQGI1aGO/p87l07VLK6Y/a2Y1o1ykvEWPkLLKy4NQpzeE+eLA2DRmiffboEVrlUdxhc4i38Nj2\ngm9pVGl06dKFjRs3smzZMv7v//4PgMrKyka+FXgev/02ThRsJ+qK665ul85BkArsRyKuGOA2NejL\npbcGbA7wES4ywOfWO8AvXtTySL76SpvWrdM+DQZ7RZKSoiUlduwY0NPyGimXHpo0qjRef/11FixY\nwM9+9jMSEhIoKSlh7Nix/mhbszi3t4CPyqrCJqGvUUwmXqi7ytdf7ITKq06b6zrbO7us4zPLjel/\nnMYBN0EVVXQ7bD8OeIcOWqLhjboXb1XVeiB792oKZMsW+Mtf4OuvtSTEQYOgzbFp9K01c+zsQaov\nnqObWsfp2lq6AaWACnS3zHfTfUa0bYsxJgaMRkxJSVpIdwubcBsa215kMzhpVGl888035OgEJzEx\nkdGjR7dkm3yCSv1DUtGtD9eEPscsccVhuyvfhjgcA0NzHMAGg1b6pGfP+sx2gLlTp1Hw4Xo6bq+i\npvIS/6vW2mRAcZjcraOmhqKKCuIqKjCfPk3V1q1MW7++RRWIOMNDj0aVxiuvvMJPfvKTRtcFG9Ys\ncMVh/b2dO4ZFmK0nKPoFq2+D+tIi4nAMDL50AFvrkB0uKmKopQ6Z0sR2KQ5TUW0txtOn6xXI6tVa\nL2TCBJ8pEHGGhx5ulcZHH33Ehx9+yPHjx8nOzrbF8paWltKrVy+/NbAptFYHONBoljhVaOOHi0M8\nYIwaNIrPVn1mZ5bxNgO8saKVviAVXU+kttZS06wC8+rVWo/WB70PyQ4PPdwqjV69ejF06FD+9a9/\nMXToUJvSMJlMjBo1ym8N9JZQdYD7qlig3kyluMgSjyvFMkiTOMQDQXPGAbcqiqKDBzGePcs/amv9\n5rNTqO/BmCoqYPNmDmwv4vHPMsFk4ulFOQwYADEx3h3XVbl0CdYIbtwqjRtuuIEbbriBn/70p7QN\n1pFrXKB3gCu69cHuy2ipYoGK44o6yCyvN1GJ09G/ODnB0SKGdhzc0fiXzWYUN5n/gSC5pgIObWbf\n4SIevGka33yfQ1xc/bgmjlPPnq7LzmeNy2LRikUUjii0Wy+yGZy4VRqDBw92+yWDwcDevXtbpEHN\nxeoAVxzW39O5fVD7MgJZLFCcjv6jKY5fvSnKUxSgCLgE/AQtQsr6qbpY19XjI+P0QpZSW0F7w2ru\nGZHJle4m7srO4dAhOHRIG4rXOl9err2zuVIol2vEIR4quFUaa9eu9Wc7fIa+DLqeCPw8so6X+HzE\nOy9Ki4jT0X946/hVpk3DvHq1R36LImCaZb4qMpK49u2J89BxrUybhmIxfU2rqqLq0iWSamsb/g4O\nJqstmymNLWJjaSaYTMxz+M3Ll7XRE61K5NAh+PRTKCmBry9FwUDn3yg/Y2TnTujfX6tA7O8BsgRn\n3CoNk4MZZ+fOnRgMhqDPBu/TvQ+cLndaH+wO8DhjnE/Hr/C0tEib/uFbgjsYsXP8WnDlBNf3LkyN\nKH7F8pmKNmqjKTXVaye1477KtGkUrV9vUyA0okCsWP0d5qIiTRHpjnvNNXD99drkyLoN2cxaXIJ5\nWP11id2QSEy7WTz5JBw+rNXy6ttXUyD9+mmf+vk+fSDKtU4WfEijIbf5+fk88cQT/OAHPwDg22+/\n5a9//SsZGRkt3jhvUaZN41zJIZfbgtUB7i8U/YIl/Pa2sgjO9wuxNOIQJjcvl0UrFnG5/DKxa2Lp\n27svvbv2ZtaTs2x2e2+iohy3mWNjMd1zj0/CYfXHsPZCGlJgikN7TF5GWf3o9iwMBlj87mKKSooo\nryinb79oOnRZRPaDmt/jyhU4elRTIIcPw5EjWk/lyBFt+cQJ6NxZUx69ezt/WuevuaZ516a106jS\neO2111i3bh3XXXcdoCX7PfXUU0GpNMoK9jDwyhWnm+nbmBgGBqkDvEVpJPy2tksdhUMlSsUf2I2d\nYdLWdS3syqwps+yveyPObgXNDBWHZoIytm9vy+A2tVACni0iz2Iqa8jkqeAcZWUuKvJIeVivwy8X\n/ZLqW6vZZ/kreaPEtv2667RxSlxRW6tlyx8/ro1vYv3cv99+OSrKvUKxfnbpEh71vVqCRsfTuOmm\nm/joo4+IjY0FoKKigjvuuIPt27f7pYGeYK0J/+P4TqxyYZq6t0cn/nnqXIv9frCPre0u/FY/1oaM\nYdCyeDJ2hKP/wjqBc68CAjMejLuekOIwucKTnlBLj7GhqnD+vL0S0X9a569cgfh4LeIrPt5+Xv/Z\nvTu0a9fsZgWMFhlPY+rUqdxxxx3cf//9qKrKqlWrmDZtWlPb2KLoS4foqaPO5XpfESpjaysOy3rf\nRlWqRKm0JA1FTXniv1Acls2xsU5+R3+g73V4YrKyo6ICxWxu8PgtXVbEYNBMWJ07a8Ue3XHlitZr\nOXVKG0zL+rlrV/3yqVPamPJxcQ0rFutnx47h0XvxaBCmV199lW3btgGwZMmSBsNxA0mgIqdCaWxt\nRb+gKy0iEVQtS4NRU4c8z7+wOrpbyhTlKZ6arBSH5cZMVcFSViQmpj4cuCFqa6GszF6xnDql+Vh2\n7Khfd/IkfP+91jPp1k37tE76Zf28t4mS/qJRpXHx4kVmzJhBp06dmDx5Mt27d/dHu7wmkKVDfB4u\n62sa8W1ElBk4c1bKNrQUuXm5lJ4pJepgFNUT6t+kU95qz8BOxzEfOerye4puPliUhSNKTo5dr8OV\nfCl47ucItTE2IiPrH/I33NDwvpWVUFqq9U6sn9apuLh+3rotMtK9QunWTZu6dIGuXbXPDh3805Px\neIzwL7/8kvfee4+VK1fSp08fNm7c2NJt8xiDwcCELkbOV1URXw2GOi2B6WqbSAZcN4gu6UOD6kYL\nFNYQXEeKoqAoCdqYElk4c6EoDh9i5wA3A4fqx8646bvvWfzVPp/4AoIBRzObQtP8HMp8xW6MDYDE\nwtYlm6oKly7ZKxdHRXP2rNbLsX5WV9srEf2n47rrrtPMai3i07DSvXt34uPj6dKlC6WlpV5fhJbm\nozJnm+e9nTqyeO9XAWhNcKM4rqjWSotslrINPsfV2Bk9VlXRf+d3XKxy9rUpLo6hpKYGvcIAz0xW\nCo2H5soYG1qPoUMHbWrMRGalutpeieg/Dx+GgoL6dS+9BBMmNK1tjSqNv/zlL7z33nucOXOGSZMm\n8dZbb5GcnNy0X/MzLe0AD0ekbINvceXYNZXDP0+fd60gdPP6RL1QwlOTlQ2Lycq6TsbYaBpRUdCr\nlza1JI0qjaNHj7JgwQJSU1NbtiUtQLCXDvE7HpQWEYe4b3F07PZfpUWtOaLo5oPVf+ENjoOCudzH\nYdnq5/j+2DcwwHl/kc3gwKNBmEIVTxzgwZ5j4UsaKy0yYZGRXkNdBxMITcOxbIipHFIdXqQVh+8o\nqalBXVzTK3RBGI2VQ7E6yQfExHBxaXv2PX7Jts3b8UaElsNjn0Yo4knpkFDJsWgJFP1CNVBdxY/3\nFkgUlY/pWNORaxe1oX+NSuwVFXRmU0W3X6iaoxqiKX6OgVeuADEMXNKJksha9na6gDHByKIViwCp\nXBBowkZpKLr5b2NiGDh8uEc3XyjlWPiERsJvzxvLpayIj8jNy+XXjz9Al4hL9LsMq6vt5VRx2D+s\nehgOeO3nuHIFrlzhji5G9k6C/ZY/fUkRITB4HHIbzBgMBrtccG/KhpRXlQd3jkULYS0tojisL4qC\n8nioadOLbf8+HoimhQ3jHx1P9aYN5B/2TYmNcMGb0FyrPJrj4PCPtXVS8sZ3tGjIbSjhTdSUr0uS\nhyKKfsGSJX5Pj8uBaUwYUfHZl8Q34vS2rQuRsFpf4ImT3EqqRR6LTgGrNMUhUVSBJaDhRVu2bGHQ\noEEMHDiQxYsXu9znN7/5DQkJCQwdOpSDBw96dFyJmmo+cg2bT89L1U5Ob7B/q54WG4uSkRFWfgyP\nMZkwWwqhOqI4LKdWQ+pBrVZazdZvW7xpgnsC2tOYPXs2b775Jv3792f8+PFMmTKFrl3rB57ctWsX\nn332GV988QUff/wxzzzzDOvWrWv0uME+4FJQYPFtfLt7t2Y/dkCuYfNxNSCY4rBPOPsxGsNrP4el\n1zFrcGc/tVBwRcCURoVFQMaMGQPA7bffzs6dO8nKqndw7dy5k/vvv5/OnTszZcoUXnjhBbfH+3EE\nRBgiaNuhA0npQ1u28WGA3kTgqmz6uZJDTiOvCZ6hTJtGWcEeTh4sRh+/p+jmwzFSqil4UjVXcfjO\nyYPFzBoyWMoDeYnVl9RcAqY0du/eTVJSkm05OTmZHTt22CmNXbt28fDDD9uWu3XrRklJCYmJiU7H\nW1UHUMesvn1FkJqA4rjiyhVmFewJQEtCn7KCPbaaUuCmrHkrcXp7ijd+jpSaWvhqH9+WHNLMfHId\nG8RxDBQ985pwvKB2hKuq6uTZNzRSxvH4GYn48QqTiX3bt0KN8xjQci29w3pznjxYXL/O1X6tyOnt\nNQ1ULVBwzufwZkjZ1oY3Qwd7Q8CUxvDhw3n22Wdty/v372eCQwWtkSNHcuDAAcaPHw9AaWkpCW6q\ndymWzwOXrpCfn09mZmYLtDr8UHJyuGf9ajjtLFQR5y7IDekNLoZq1c/vaxtJyk2jW71JqiF8XWq9\ntWFVFEUHD2I8e5Z/1NbayWC+ZWoOAVMa1uFjt2zZQr9+/cjLy2Pu3Ll2+4wcOZKnn36aRx55hI8/\n/phBgwa5PZ5i+dzbPkYUhpfoB69S9Btqal2WHBGcUaZN0x5y+nUO+9zbuWOrdXp7g6OfY9/2rZpJ\nynE/RHlY8bRXkWmZrISceWrBggVMnz6dmpoasrOz6dq1K2+++SYA06dPZ8SIEYwePZphw4bRuXNn\nli9f3ugxJerHe1xF+QheYjY3WltJZNM7rA/9WUMGw1f7PPqOnfJoJYEcjmPLtzQBVRoZGRkUFxfb\nrZs+fbrd8quvvsqrr77a6LHu7dGJ3t1700Uip7ymS/pQZgEnDhyA2jonwft29+5W+fbmCXbZzfr1\nuvmvIiPolZwsstlEuqQP5eA332oDRrhBofFxOsINT8aWd/qObr4oCi2M2UuC2hHuDZ6WDRGcsd5Q\nP47vZOtxKPodrlwRM5U7GvFjANzbNVYGA2sGSk4ON23dQObVk1pZ+QYedArhbbJSpk2jaP164qqq\nqLp0ycln4fZ7unl9aRa+9L4NYaM0hOajEvJlyPyG/i3Pbr3DfkVRcKp9O381K2xpN/oHbB5wkv6r\ntFEmG1MeesLBZKWXt1Rdva5Gv+ewbB3a2VrHS5SG0CzOtI8h01hB3ClQXNyQoXzT+RwPehgAmfEQ\nNyb0BjALNqyDWR3+MRwGm/LoeiLSpZMcwsNk1ZSwWQUoAuKAqshIjO3bg9FIBdUUxZfXK4wmIkpD\nsPH8kr9qZdGLSuCwi4dgRQWKDzJKQx1XkVLg2gRQVtueV6fI4EHNxXEwq8M/hjYFiST+oD/mXXsa\nHI1SIbRMVo2Fzbr9nm4+FS2JNEmXRJqbl8vsP88GSprVPlEagg3rGAVLH34YOB/YxgQzLiKlFIdd\nftgukos33MCrT74kYz/4AOs1XPzuYo6dPMax0mNE94rmWO829BkxFOX72kaHMtYTbCarpvoqXOFq\nuODcvFwWrViEscZIl9wu9IzvSe+uvfmYj70+vigNwY6scVn8c0gaezdtgquq23Gcg/ENraXxJFKq\n3slYSxuDZw8wwTOsimP2G7OpmFhBBRXsYx+J5xNZ+OxCeOd9t/WrrCjU/7+KgLiKCg4uX8601avB\naMSUlORX2W6qr8L2fd28u7Hlc/Nymf3GbK2XZtLWxRXGMWvKLD5+W5SG4AOO9W5DdW9VMx7jIMSW\nNzTF+Wvhjwd+jMx42PyodamExe8ulp6GD1m0YpHNRGWlJE27zutztIGZGhpa1oqC7n9XW0tRRQVx\nFRWYT5+mautWpq1f32IKpLm9iiJgmmVe77MwTZjgsq0NXbOmIEpDcKJabULwditEcVguirKEMeqQ\nAYN8izvZ1F9nT0qR6FEcpqLaWoynT9crEGsvxM1DucFj6xTEsStX6FpXR11tLanQ5F6F1V9hrZLc\nWJs8uWbeIEpDcCLKEMXXcVq5AWtoo+KwT2syU7kKr1Vc7JcZj1NkijHC2JJNa3VYo6gccbzOnpRc\nd4fdA93SC7lUUcGVZcsYu2wZ3YBSQAW6W+bdrXM8nu24XuC4v7dVkj29Zp4iw7MJTmQ/mE0bUyKb\nx0K5Tt4U3ZRTUaGNw9EaoqksZin9Q0fRTT+NieFHna7h6GX72ymxIJFZEjnlU7IfzCax0DI0ghnY\nCMa1Rs6cPUNuXq7T/kpODkp+PqZ77nE7SmBDKGgP/dHAe0CG7jPTg3XJXv9i/e8WoZ3iwchIzLGx\nmHv0gIwMrxRGbl4upWdKafeRfa5Qc2RTehqCE1njsthduJv5q+ZDp0q4EOgWBQ5PChHeEV3H+llV\n2h3+KRgvG0nuncxLEjnlc6zXc86CORw4d4DqCdVUUUUhhVq4uG4fPd6arAKBo68izuKrSGqCWQx0\nDvARJT6VTVEagks+L/6cygmVmFfVm6laZcKfQ3itot8UG0uF0UBxvKXYo0mbqqii2+FuojBaiKxx\nWSxasYjqEfYCaXXuurvujiYrax4Eta6TA/2FYvn01lfRGHYOcBM+k01RGoJLrM4zawZuxtu0qoQ/\nV+G1iuM+qankm+DwAOeR5sQB3rI0x7mrfxjrFcg0SzRTkh+UiLcRUE3B1w5wK6I0BJe4c561GlyE\n17rC105GwTN8dd0dH9DWaCerAvFFL8SqII4BPwEiIiMxRkRA584tmhfSUrIpSkNwiWPZBnMc7D1m\ngNrwTvjztBChOTYWk8lE9oOTOLjoIEeGHbFtSyxIZNaT4gBvSRzlE3xz3d31Qi6dO8cDdXWcrq3l\nJ9RHSlnn3a3TK4jRfk4cbKlrJEpDcIld2YZTxzjV7hTVsZVw7goQxgl/HhYiVFJTGf7TSZpt/UI1\nUauiGNhvIL279mbWk7PEn9HC6OWzqKSIykuVRPeOZtGKRXbbm0OovwABdKzpSKd1nVAjVRJ6JPgk\nOEOUhuAWfdmGsqwyLr8NhPGwJZ4UIrQ6Ko+2i2S5Q2mGysJKZk0RheEv9PJ5+tbT7LP8lbxRYre9\nNWIXOWWhotA3EWMGVVVDfhAFg8FAGJxGUDL+0fFsMG0AtHLUJstYBqtdJfz5MPIjECiZmbYek3Vy\n2icjAyU/3+666Bl/eDzr/9/6lmymoEP+D67x9Lo05dkpPQ2hQfQRGI6RVBAeZipPChHqFSK0XGSK\n4B3yf3BNS14XURpCgzQWRaW4WBdyuRse+DGU1FSU/HzbskRNBQfyf3BNS14XURpCg9hFYJiBEjh6\nMYIfdY4mtkpl4JUrIZu74W2klJ5Rg0bx2arPqJxQaVsnUVP+xyafnUq0sYUiILo8mhsfvDHQTQsY\n1tIhbQ604eqdV23rfSWfojSEBrEr21B2gOo7qjl0Wx2HuMyExUbQgqlCMwzXi0gpx/EJlm9fTmVS\nJXwKGCC6IpqHpjzUqp2vgUBf8saqwCupZPn25QzPG97q/h8tVTpEjygNoVHclW2obF8FZfXLin5j\nkPs3vImUwqGX4VSeAe1BtePgjpZoqtAI1pI3ehorKRKutFTpED2iNASPcOVYM8fBvhORUFOfNas4\n7hNkPQ47p3cjQ7Y6+jGsiPM1uJD/Rz3+uBaiNASPcOVYO/xjSD/VEU7bjyeu6BeCrcfhpjyIftld\nD8OKOF+DC/l/1OOPayFKQ/AIdw7Hut79UJKGOJWbVhy+H+gehzunN3jew4B6J2PkgUhq76zvYYkT\nPHC4CtYwXjFyppc2xkZrMVFZZTOqOIrqO+p7HL6WTVEagke4czjuK7zEEzN/B797DTbbV3tV9AuB\n7nF40MMA15FSVvzhZBS8Rx+ssb9sP9/f8b1HY2yEE/6UTckIFzymoSzTG+vibW/yORUVbpWDt0NV\n+gJl2jTMq1fb2qWfnPa1ZHy7QrKPg5vW/P9p6rlLRrjQojTkZLMNcGMpxWFFcdzZjzkcDTm9wTs/\nBojDNdhpzf8ff567KA3BYzxysplMKGD3oFYc9m9p/4ZeWbjq9eiXrcrC5EFbxOEa3LTm/48/zz3C\n50cUwpbsB7NJLEy0W+c4QL2Sk4OSn6+9tetQdPMmi3/DvHo1Smam9pD3JRb/hbvehX4yWZzenigv\nT85fCByt+f/jz3OXnobgMfoxDI6cOELxsWLaDWjnegwDk8kposoRm/LwYa0qd0l74J3T25HcvFwW\nrViE8XsjXXK70DO+p4ydEWQ4jQFz9hTR8b4dYyOYiamOoePajkS0ifDZ2BmuEKUheIV+DAMmQbHl\nz3EMAyUnx8m/YUVxXOEDP4c3/gvbOofyIO6wRaboxs6IK4yTsTOCEJt8/lkbA6aMsrAfY8Mmnzf6\nfuwMV4jSELzGrlSBBZdlG3T+Dcceh2L5LALigKqtW5kWFwdGo1fjJjfmv3D8PfDM6a3H4/MVgoJF\nKxZRkt56/l/+lk9RGoLXeBqp4S6iyrYd3cO8tlZTLBUVcPp0gyYrq6IoOngQ49mz/KO21itl4YnT\nW09rjsoJRVrb/8vf5ytKQ/AaryM1XERUOaJg3/u4VFHBlWXLGLtsGd2AUkAFulv2eQ/XJif98fQ0\nJz+kNUflhCKt7f/l7/MNSPTUxYsXufvuu+nXrx/33HMPly5dcrmfyWRiyJAhpKWlMWLECD+3UnCH\nXaSGGdgIxrVGzpzVyjY4YououucezLGxDR5bAVKB0WiKIUP3mWmZT26kfYqLyeSh/8IVowaNwrje\n/gZsLVE5oYhNPs3ARmATRK+K5sak8Btjw1o6xJBrsFvfkvIZEKWxZMkS+vXrx7fffkufPn34n//5\nH5f7GQwG8vPzKSwsZNeuXX5upeCOrHFZLJy5kLRdaRgPGuE2qJpYReFQrWyDK8UBmvIw3XMPSkZG\no8qjOSi6aVpsLEpGhsf+C0esY2dUJVVpY2dsgujV0Tx0s4ydEaxkjcvioZseIvpgNNwGjIXKH2tj\nbLiTzVDE6gAvHFGIer2qlQ5ZayS9IJ2FTy5sMfkMiNLYtWsXjz/+OFFRUTz22GPs3LnT7b5SHiQ4\nyRqXRbfu3aiaYG83tTrg3OEuj8MXKLp5c2wsZGRoSsrDPAxX2JyMJuBWtAfQPTJ2RrDT0Bgb4YLT\n2Bm3ai9v3br4buwMVwTEp7F7926SkpIASEpKctuLMBgM3HrrrQwYMIDHHnuMu+66y5/NFBqhWQ44\ni5/D6symtraxb7hFcVj2ZX2r1uZUDRdaw/8tUOfYYkpj3LhxnDp1ymn97373O497D9u2baNnz54U\nFxczceJERowYQXx8vMt9FUWxzWdmZpKZmdmUZgteYHPAmbGVS6cOLnS40Oh39Q90Zdo0FGvZ8gaS\nAe2+jy5cNzISY/v2tnBdb6Oj3JGbl8u+/ftggPO2cHWqhgt2zmEzNvncd2VfWJRLb6ps5ufnk++m\nIKenBKTK7X333ccLL7xAWloae/bs4ZVXXmHlypUNfufpp59m0KBBPPHEE07bpMptYMjNy+VnL/+M\nU+opzXZsIX5bPG89+5bXN6Y+lPbSuXN0ravjdG2tU/RURGQkRERgjInRFMWECT6vYWVLmLKOH6I7\nv8SCxBa1GQvNp8H/X2EiC2eG7v/Pl7LZlGdnQJTG/PnzOXr0KPPnz+eZZ55hwIABPPPMM3b7XLly\nhdraWjp06EBpaSmZmZmsX7+evn37Oh1PlEbgSL8rncKhhU7rQ70ctV2paTNwCDBAlytdWPa7ZSH7\nwAuMX1wAAA4RSURBVGlN5OblMvX5qZRllTltC2X59KVsNuXZGRBH+IwZMzhy5AjXXXcdx48f5xe/\n+AUAJ06cICtLO+FTp05xyy23kJqaygMPPMCvfvUrlwpDCCwdO3d0uT7Ubcd29mITNid4SnKKKIwQ\nIWtcFinJKS63hbJ8Blo2A+II79ChA//617+c1vfq1YvcXC0kLiEhgSI3heeE4CFcE6nC9bxaG+H4\nfwz0OUlpdKFZeJvoFwpYE6bafdTObr0k9IUe4SifowaNInp9tN06f8qmlBERmoV+fObic8VUTagK\n6fGZZRzw8EIvn3vP7qX2ztqQl8/l25dTmVSpJZsaILoimoem+C/ZVMYIF3xCuIzPHC7nIdgTLv9X\nX59HyDjChfAjXJKpwuU8BHvC5f8aDOch5inBJ4RLMtWFcxckmS8MaU4iarAQLMmm0tMQfIJdZVFr\nwtFYKMsqa7CIYTCRm5fLyYqTWmVUHfFb48UBHuJkP5hN/KfxdrLJbXCy5mTIyObsN2ZTllLmJJ/+\nDtAQn4bgM0I9mcpmLzZjS5hChbT2aRTkFgS2cUKzCeVE1JZKNm3Ks1PMU4LPyBqXRco7KWzGeZS+\nULAd2+zFJmxjgQN0/M51AqMQWoRyIqpTQp9Jm035zv/JpmKeEnxKoBOPmkMot11onFD+/wZT20Vp\nCD4lVJOprAl9MkJf+BLqshksyabi0xB8Tm5eLnMWzOGrs19x9c6rtvXBWl3UltCXVmKzF0tCX3hi\nlU1rIqqV1iqbIVPl1teI0gg+QimZKpTaKjSfUPp/t3RbJblPCBqCIQnJU0KprULzCaX/dzC2VaKn\nhBYhVJKpgiVhSvAfoZSIGozJptLTEFqEUEimCqaEKcF/hEoiarAmm4pPQ2gxgj2ZSkbna72EQiKq\nP5JNJblPCCrskqnM2MwAuy7vCgozwImyE/VJfCYCmjAl+Be7RFQzdibUY+2PBbZxaEptV/Guerk0\n1W8LdLKpmKeEFsPJr2ExA5z/0fmAmwFy83IpOVLicpv4MloHUYYoJ9nkNjh0/lDAZXP2G7Mpb1fu\ncnug5VOUhtBi2GzH1ptSR0laCYvfXRyQdgEsWrGIyrRKJ3tx9Ppo8WW0ErIfzCa6MNpJNisnVAZc\nNkvSSiCRoPS1iXlKaDGsJp6H5zzMec47bQ9k2GC1Wl0flWIZAQ0VEjoliGmqlZA1LovEfonsY5/T\ntoDLJtSbpCzy2elyJxb+PvAJiNLTEFqUrHFZDB80vH6FGe3taRPsO7AvIGYAW5gtaDfmrWimiVuh\nT3wfv7dHCBy9uvSqXzATXLIJdvI5YvCIgCsMEKUh+IFgCnGUMFtBj8im90jIreAXgiXEUcJsBUda\ns2xKGREhaMkal0VKcoq2YMZmBmAjHDvlnxBHWxijFRO2rn9KsoTZtlbsZBPs5HPXV7v81ts4UXai\nfsFE0MqmKA3BbwQyxDHYwxiFwBLo8PBQCgEXpSH4jUCGOAZ7GKMQWAIdHh5KIeASciv4DacQRzN+\nyxK3ZX+bLCuCLIxRCCxO4eFm/JYlbjOb3mFZEeQh4NLTEPyKLcTRjN/MAE5dfxNBF8YoBB5beLgZ\nv5lQncymJoI+BFyUhuBXXJoBzMBGKCkvYerzU316c+bm5TL1v6aGTNdfCCxOJlQzsBEqoypbTDZD\nzWwq5inBr7g1A1hu0jK0+Hj9vk3FFvd+TZmTWSpYu/5CYLEzoZrxj2xCSJlNpach+B27LPEWdDza\nnN91lhUmgr7rLwQemwnVn7IJIWM2FaUhBASbmcoqgWZ8mrthl5MRQl1/IfA4ySb4PHfDlpMRgrIp\nSkMICFnjslg4cyFdrnSxNwNYiggeOHaA9LvSm3RzKvMVJr04yd65mIjW9d8EXXK7sPDJ4Oz6C4HH\nTjahXj4TgTo4f815Jj07CWW+4vWxc/NySc9KZ/+/92srTIScbEoZESGg5OblMunZSVT+uNLJhgyQ\nWJjIwpme30SNHq8gMehvSiE4sPodSs5ZHNUOshS9Ppr3X3rfK9n05fF8QVOenaI0hIAz+K7B7Bu6\nT+um66NWLHHyntbesUajlF1TpvktrMex1PDpdLkTf//930VhCB6Tm5erBW20O+8b2fxRmWaCHYvT\nMK7XX3M9+z50LtPekoRM7an333+f66+/nsjISAoK3I91u2XLFgYNGsTAgQNZvDhwg6IILYvN8aj3\nb+jMAWUxZQ2aA6xd/vtfvF9TGCHoXBSCE1vQhqNsWkypZcYy7v/N/Q2aUq3mUlukVIgHZgREaQwe\nPJhVq1YxZsyYBvebPXs2b775Jp988glvvPEGZ8+e9VMLWy/5+fl+/02b49F6M1kVhu7mrOxYyUvL\nX3K6Oa03ZOHFQqomVGnHcOFcDFRORiCuZzgTKPmMLo/WFqwyacYmp1UxVRReKHR6sbG+zLyU8xKV\nEyrr5TsEnd96AqI0kpKS+MEPftDgPhUVFQCMGTOG/v37c/vtt7Nz505/NK9VE4ib0up4TOuQhnG9\nUZNKx5vzNlCHqhQeK2Ri9kSMyUZikmKYlzNPuyGtkmxVNjrnYvTqaJ778XMB6WWI0vAtgZLP5x58\njuj10fVy5ubFZt5b84hJiiE6KZqJv55I4cVC1O4W849VWZiwyadxrZH0gvSQ8rMFbfTU7t27SUpK\nsi0nJyezY8eOALZIaEmyxmVRkFvAypdWalEr+pvTqjy+BNqDeqNKdddqKrtXQnfLfvoufyJ2YxG8\nP/99lOcUv52LEH4ozym8/9L79RFVrl5sEoH2UNm9kqruVah3qbbaVYB9pNR30KWyCytfWcmef+0J\nGYUBLag0xo0bx+DBg52mtWvXttRPCmFA1rgslv1uWb05QK882qPdpNabVX9D6rv8JuBWSIxNlIGV\nBJ9hlU2bKdXxxUYvo9ZtjuZSE3ArRFdHh65sqgEkMzNT3bNnj8tt5eXlampqqm35ySefVNetW+dy\n38TERBWQSSaZZJLJiykxMdHr53bAa0+pbsK9YmNjAS2Cql+/fuTl5TF37lyX+/773/9usfYJgiAI\n9QTEp7Fq1Sr69u3Ljh07yMrK4o47tELyJ06cICurvru2YMECpk+fzg9/+EN++ctf0rVr10A0VxAE\nQbAQFsl9giAIgn8I2ugpRzxJ9PvNb35DQkICQ4cO5eDBg35uYWjR2PXMz88nNjaWtLQ00tLSePnl\nlwPQytDgscceo0ePHgwePNjtPiKbntPY9RTZ9JyjR48yduxYrr/+ejIzM1mxYoXL/byST6+9IAEi\nNTVV3bx5s2o2m9XrrrtOLS0ttdu+c+dO9eabb1bLysrUFStWqFlZWQFqaWjQ2PXctGmTOnHixAC1\nLrTYsmWLWlBQoKakpLjcLrLpHY1dT5FNzzl58qRaWFioqqqqlpaWqgMGDFAvXLhgt4+38hkSPQ1P\nEv127tzJ/fffT+fOnZkyZQrFxcWBaGpI4GnipCqWS4+45ZZb6NSpk9vtIpve0dj1BJFNT4mPjyc1\nNRWArl27cv311/PFF1/Y7eOtfIaE0vAk0W/Xrl0kJyfblrt160ZJSQmCM55cT4PBwPbt20lNTeXp\np5+Wa9kMRDZ9i8hm0/j3v//N/v37GTFihN16b+UzJJSGJ6iq6vT2YTAYAtSa0Cc9PZ2jR4+ye/du\nkpOTmT17dqCbFLKIbPoWkU3vuXjxIpMnT+ZPf/oT11xzjd02b+UzJJTG8OHD7Zwz+/fv58Ybb7Tb\nZ+TIkRw4cMC2XFpaSkJCgt/aGEp4cj07dOhATEwMbdu25fHHH2f37t1UV1f7u6lhgcimbxHZ9I6a\nmhruu+8+Hn74Ye6++26n7d7KZ0goDX2in9lsJi8vj5EjR9rtM3LkSD744APKyspYsWIFgwYNCkRT\nQwJPrufp06dtbx9r165lyJAhREVF+b2t4YDIpm8R2fQcVVV5/PHHSUlJ4amnnnK5j7fyGfCMcE+x\nJvrV1NSQnZ1N165defPNNwGYPn06I0aMYPTo0QwbNozOnTuzfPnyALc4uGnseq5cuZIlS5bQpk0b\nhgwZwuuvvx7gFgcvU6ZMYfPmzZw9e5a+ffsyb948ampqAJHNptDY9RTZ9Jxt27axfPlyhgwZQlpa\nGgC///3vOXLkCNA0+ZTkPkEQBMFjQsI8JQiCIAQHojQEQRAEjxGlIQiCIHiMKA1BEATBY0RpCIIg\nCB4jSkMQBEHwGFEaguAlFRUVLFmyBICTJ08yadKkALdIEPyH5GkIgpeYzWYmTpzIV199FeimCILf\nkZ6GIHjJr3/9a0pKSkhLS+MnP/mJbbCgnJwcJk+ezO23305CQgLLli1jyZIlDBkyhClTpnDx4kUA\njh8/zrPPPsuoUaOYOnUq3333XSBPRxC8QpSGIHjJH/7wBxITEyksLOS1116z27ZlyxaWL1/Opk2b\nmDFjBufOnWPv3r1ER0ezYcMGAF588UUeeOABPv/8cyZPnsz8+fMDcRqC0CRCpvaUIAQLeouuo3X3\nhz/8Id27dwegU6dOTJkyBYBRo0bx+eefc/fdd/Phhx9SUFDgvwYLgg8RpSEIPiQuLs42365dO9ty\nu3btqK6upq6ujoiICHbs2CGVWYWQRMxTguAlPXr04MKFC159x9ojadeuHXfeeSdLliyhtrYWVVXZ\nu3dvSzRTEFoEURqC4CXR0dFMnjyZ9PR0nnvuOdsoZwaDwW7EM8d56/K8efM4deoUw4YNIyUlhTVr\n1vj3BAShGUjIrSAIguAx0tMQBEEQPEaUhiAIguAxojQEQRAEjxGlIQiCIHiMKA1BEATBY0RpCIIg\nCB4jSkMQBEHwGFEagiAIgsf8f6/NeKkjAey4AAAAAElFTkSuQmCC\n",
-       "text": "<matplotlib.figure.Figure at 0x106257450>"
-      },
+       "text": "<matplotlib.figure.Figure at 0x11091ab90>"
+      }
+     ],
+     "prompt_number": 39
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Now, to convert it to a Plotly figure, this is all it takes:"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "py.iplot_mpl(fig1)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3389\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3486\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106316390>"
+       "text": "<IPython.core.display.HTML at 0x10f07f190>"
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Notice the difference. You can hover, zoom, and pan on the figure. You can also strip out the matplotlib styling, and use Plotly's default styling."
+     "source": "You can hover, zoom, and pan on the figure. You can also strip out the matplotlib styling, and use Plotly's default styling."
     },
     {
      "cell_type": "code",
@@ -112,13 +127,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3390\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3487\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106332f10>"
+       "text": "<IPython.core.display.HTML at 0x1108e6390>"
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",
@@ -133,13 +148,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3391\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3488\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1063d4950>"
+       "text": "<IPython.core.display.HTML at 0x10f144390>"
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -156,11 +171,11 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 43,
        "text": "[{'name': '_line0',\n  'x': [0.0,\n   0.20000000000000001,\n   0.40000000000000002,\n   0.60000000000000009,\n   0.80000000000000004,\n   1.0,\n   1.2000000000000002,\n   1.4000000000000001,\n   1.6000000000000001,\n   1.8,\n   2.0,\n   2.2000000000000002,\n   2.4000000000000004,\n   2.6000000000000001,\n   2.8000000000000003,\n   3.0,\n   3.2000000000000002,\n   3.4000000000000004,\n   3.6000000000000001,\n   3.8000000000000003,\n   4.0,\n   4.2000000000000002,\n   4.4000000000000004,\n   4.6000000000000005,\n   4.8000000000000007],\n  'y': [1.0,\n   0.25300171651849518,\n   -0.54230030891302927,\n   -0.44399794031078654,\n   0.13885028597711233,\n   0.36787944117144233,\n   0.09307413008823949,\n   -0.19950113459002566,\n   -0.16333771416280363,\n   0.051080165611754998,\n   0.1353352832366127,\n   0.034240058964379601,\n   -0.073392365906047419,\n   -0.060088587008433003,\n   0.018791342780197139,\n   0.049787068367863944,\n   0.012596213757493282,\n   -0.026999542555766767,\n   -0.022105355809443925,\n   0.0069129486808399343,\n   0.018315638888734179,\n   0.0046338880779826647,\n   -0.0099325766273000524,\n   -0.0081321059420741033,\n   0.0025431316975542792]}]"
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -174,7 +189,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 44
     },
     {
      "cell_type": "code",
@@ -189,7 +204,7 @@
        "text": "Figure(\n    data=Data([\n        Scatter(\n            x=[0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000...],\n            y=[1.0, 0.2530017165184952, -0.5423003089130293, -0.44399794031...],\n            name='_line0',\n            mode='markers',\n            marker=Marker(\n                symbol='dot',\n                line=Line(\n                    color='#000000',\n                    width=0.5\n                ),\n                size=30,\n                color='#0000FF',\n                opacity=1\n            )\n        )\n    ]),\n    layout=Layout(\n        xaxis=XAxis(\n            domain=[0.0, 1.0],\n            range=[0.0, 5.0],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='y',\n            mirror=True\n        ),\n        yaxis=YAxis(\n            domain=[0.0, 1.0],\n            range=[-0.6000000000000001, 1.2],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='x',\n            mirror=True\n        ),\n        hovermode='closest',\n        showlegend=False\n    )\n)\n"
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 45
     },
     {
      "cell_type": "markdown",
@@ -207,11 +222,11 @@
        "html": "<img src=\"https://i.imgur.com/RusH4k2.png?1\"/>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
-       "text": "<IPython.core.display.Image at 0x106332810>"
+       "prompt_number": 46,
+       "text": "<IPython.core.display.Image at 0x10f098a90>"
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 46
     },
     {
      "cell_type": "markdown",
@@ -229,10 +244,10 @@
        "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~MattSundquist/1307\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106250c10>"
+       "text": "<IPython.core.display.HTML at 0x10f0985d0>"
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 47
     },
     {
      "cell_type": "markdown",
@@ -250,11 +265,11 @@
        "html": "<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 17,
-       "text": "<IPython.core.display.HTML at 0x106332b90>"
+       "prompt_number": 48,
+       "text": "<IPython.core.display.HTML at 0x10f098b10>"
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 48
     },
     {
      "cell_type": "markdown",
@@ -269,13 +284,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3392\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3489\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1063f5310>"
+       "text": "<IPython.core.display.HTML at 0x110939e90>"
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 49
     },
     {
      "cell_type": "markdown",
@@ -290,13 +305,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3393\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3490\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1068c4650>"
+       "text": "<IPython.core.display.HTML at 0x110d22510>"
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 50
     },
     {
      "cell_type": "markdown",
@@ -316,13 +331,13 @@
        "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
       },
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3394\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3491\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1068fed50>"
+       "text": "<IPython.core.display.HTML at 0x10f073250>"
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 51
     },
     {
      "cell_type": "markdown",
@@ -337,124 +352,209 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3395\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3492\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1068bf090>"
+       "text": "<IPython.core.display.HTML at 0x1108f6e50>"
+      }
+     ],
+     "prompt_number": 52
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Want to see more matplotlylib graphs? Head over to our [API](https://plot.ly/python) and copy and paste away."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/HEJEnjQ.png')",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<img src=\"https://i.imgur.com/HEJEnjQ.png\"/>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 53,
+       "text": "<IPython.core.display.Image at 0x10f668090>"
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 53
     },
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "II. Stack Overflow Answers"
+     "source": "II. ggplot for Python"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "An exciting package by [Greg Lamp](https://github.com/glamp) and the team at [\u0177hat](https://yhathq.com/) is [ggplot for Python](https://github.com/yhat/ggplot). You can draw figures with ggplot's wonderful syntax and share them with Plotly. You'll want to run `$ pip install ggplot` to get started."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from ggplot import *",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 54
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "We'll start out with a plot from the diamonds dataset. "
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "a = ggplot(aes(x='price'), data=diamonds) + geom_histogram() + facet_wrap(\"cut\") ",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 55
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "We love Stack Overflow, so wanted to show answers to a few questions from there, in Plotly. If you want to plot data you already have as a [histogram](http://stackoverflow.com/questions/5328556/histogram-matplotlib) and make it interactive, try this one out."
+     "source": "Then share it to Plotly."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig7 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\nhist, bins = np.histogram(x, bins=50)\nwidth = 0.7 * (bins[1] - bins[0])\ncenter = (bins[:-1] + bins[1:]) / 2\nplt.bar(center, hist, align='center', width=width)\n\npy.iplot_mpl(fig7, strip_style = True)",
+     "input": "fig = a.draw() \npy.iplot_mpl(fig, strip_style=True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3396\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3493\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1063e12d0>"
+       "text": "<IPython.core.display.HTML at 0x1109693d0>"
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 56
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Here is how to create a [density plot](http://stackoverflow.com/questions/4150171/how-to-create-a-density-plot-in-matplotlib/4152016#4152016) like you might in R, but in matplotlib."
+     "source": "Line charts can be interactive (drag your mouse along the line to see the data on the hover)."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "b = ggplot(aes(x='date', y='beef'), data=meat) + \\\n    geom_line() ",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 57
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig8 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\ndata = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8\ndensity = gaussian_kde(data)\nxs = np.linspace(0,8,200)\ndensity.covariance_factor = lambda : .25\ndensity._compute_covariance()\nplt.plot(xs,density(xs))\n\npy.iplot_mpl(fig8, strip_style = True)",
+     "input": "fig = b.draw() \npy.iplot_mpl(fig)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3397\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3494\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e6b6a10>"
+       "text": "<IPython.core.display.HTML at 0x10f0d9690>"
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 58
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "c = ggplot(aes(x='price'), data=diamonds) + geom_histogram() + ggtitle('My Diamond Histogram')",
+     "language": "python",
      "metadata": {},
-     "source": "Drawing a simple example of [different lines for different plots](http://stackoverflow.com/questions/4805048/how-to-get-different-lines-for-different-plots-in-a-single-figure/4805456#4805456) looks like this..."
+     "outputs": [],
+     "prompt_number": 59
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig9 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10)\n\nplt.plot(x, x)\nplt.plot(x, 2 * x)\nplt.plot(x, 3 * x)\nplt.plot(x, 4 * x)\n\npy.iplot_mpl(fig9, strip_style = True)",
+     "input": "fig = c.draw() \npy.iplot_mpl(fig, strip_style=True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3398\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3495\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10eaa6450>"
+       "text": "<IPython.core.display.HTML at 0x1109b3750>"
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 60
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "d = ggplot(aes(x='x', y='y', color='z'), data=diamonds.head(1000)) +\\\n    geom_point() ",
+     "language": "python",
      "metadata": {},
-     "source": "...and can get more exciting like this."
+     "outputs": [],
+     "prompt_number": 61
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig10 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 10\n\n# Have a look at the colormaps here and decide which one you'd like:\n# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\ncolormap = plt.cm.gist_ncar\nplt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])\n\n# Plot several different functions...\nx = np.arange(10)\nlabels = []\nfor i in range(1, num_plots + 1):\n    plt.plot(x, i * x + 5 * i)\n    labels.append(r'$y = %ix + %i$' % (i, 5*i))\n\npy.iplot_mpl(fig10, strip_style = True)",
+     "input": "fig = d.draw() \npy.iplot_mpl(fig, strip_style=True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3399\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3496\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10ed9c450>"
+       "text": "<IPython.core.display.HTML at 0x1109379d0>"
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 62
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly also lets you draw [variables as subscripts in math mode](http://stackoverflow.com/questions/23276918/writing-variables-as-subscripts-in-math-mode)."
+     "source": "You can also use more advanced plotting types in collaboration with pandas. Just add a geom."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "import pandas as pd",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 63
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig11 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\n\nmean = [10,12,16,22,25]\nvariance = [3,6,8,10,12]\n\nx = np.linspace(0,40,1000)\n\nfor i in range(4):\n    sigma = np.sqrt(variance[i])\n    y = mlab.normpdf(x,mean[i],sigma)\n    plt.plot(x,y, label=r'$v_{}$'.format(i+1))\n\nplt.xlabel(\"X\")\nplt.ylabel(\"P(X)\")        \n\npy.iplot_mpl(fig11, strip_style = True)",
+     "input": "random_walk1 = pd.DataFrame({\n  \"x\": np.arange(100),\n  \"y\": np.cumsum(np.random.choice([-1, 1], 100))\n})\nrandom_walk2 = pd.DataFrame({\n  \"x\": np.arange(100),\n  \"y\": np.cumsum(np.random.choice([-1, 1], 100))\n})\ne = ggplot(aes(x='x', y='y'), data=random_walk1) + \\\n    geom_step() + \\\n    geom_step(aes(x='x', y='y'), data=random_walk2)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 64
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig = e.draw() \npy.iplot_mpl(fig, strip_style=True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3400\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3497\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10ef9afd0>"
+       "text": "<IPython.core.display.HTML at 0x1061c8d10>"
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 65
     },
     {
      "cell_type": "heading",
@@ -465,7 +565,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "The gallery of [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) we really like from [prettyplotlib](https://github.com/olgabot/prettyplotlib) can be a fun one to make interactive. Here's a scatter; let us know if you make others. You'll note that not all elements of the styling come through. "
+     "source": "The gallery of [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) from [prettyplotlib](https://github.com/olgabot/prettyplotlib), a library by [Olga Botvinnik](https://github.com/olgabot), can be a fun one to make interactive. Here's a scatter; let us know if you make others. You'll note that not all elements of the styling come through. "
     },
     {
      "cell_type": "code",
@@ -475,13 +575,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3401\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3498\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10eaaa990>"
+       "text": "<IPython.core.display.HTML at 0x10f1d7210>"
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 66
     },
     {
      "cell_type": "markdown",
@@ -496,13 +596,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3402\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3499\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f00ba90>"
+       "text": "<IPython.core.display.HTML at 0x10fd5d550>"
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 67
     },
     {
      "cell_type": "heading",
@@ -522,7 +622,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 29
+     "prompt_number": 68
     },
     {
      "cell_type": "code",
@@ -531,23 +631,23 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 30
+     "prompt_number": 69
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig14 = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\npy.iplot_mpl(fig15, strip_style = True)",
+     "input": "fig14 = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\npy.iplot_mpl(fig14, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3403\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3500\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10ef99e10>"
+       "text": "<IPython.core.display.HTML at 0x111e96650>"
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 70
     },
     {
      "cell_type": "markdown",
@@ -557,18 +657,18 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig16 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n    plt.subplot(211)\n    sinplot()\nplt.subplot(212)\nsinplot(-1)\n\npy.iplot_mpl(fig16, strip_style = True)",
+     "input": "fig15 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n    plt.subplot(211)\n    sinplot()\nplt.subplot(212)\nsinplot(-1)\n\npy.iplot_mpl(fig15, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3404\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3501\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110a99ed0>"
+       "text": "<IPython.core.display.HTML at 0x10e81f890>"
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 71
     },
     {
      "cell_type": "markdown",
@@ -582,23 +682,134 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 72
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig16 = plt.figure()\n\nsns.set_palette(\"hls\")\nmpl.rc(\"figure\", figsize=(8, 4))\ndata = randn(200)\nsns.distplot(data);\n\npy.iplot_mpl(fig16, strip_style = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3502\" height=\"525\" width=\"100%\"></iframe>",
+       "metadata": {},
+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x10fd75490>"
+      }
+     ],
+     "prompt_number": 73
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": "V. Stack Overflow Answers"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "We love Stack Overflow, so wanted answer a few questions from there, in Plotly. If you want to plot data you already have as a [histogram](http://stackoverflow.com/questions/5328556/histogram-matplotlib) and make it interactive, try this one out."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig17 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\nhist, bins = np.histogram(x, bins=50)\nwidth = 0.7 * (bins[1] - bins[0])\ncenter = (bins[:-1] + bins[1:]) / 2\nplt.bar(center, hist, align='center', width=width)\n\npy.iplot_mpl(fig17, strip_style = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3503\" height=\"525\" width=\"100%\"></iframe>",
+       "metadata": {},
+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x10f5c0250>"
+      }
+     ],
+     "prompt_number": 74
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Here is how to create a [density plot](http://stackoverflow.com/questions/4150171/how-to-create-a-density-plot-in-matplotlib/4152016#4152016) like you might in R, but in matplotlib."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig18 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\ndata = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8\ndensity = gaussian_kde(data)\nxs = np.linspace(0,8,200)\ndensity.covariance_factor = lambda : .25\ndensity._compute_covariance()\nplt.plot(xs,density(xs))\n\npy.iplot_mpl(fig18, strip_style = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3504\" height=\"525\" width=\"100%\"></iframe>",
+       "metadata": {},
+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x10f55cd10>"
+      }
+     ],
+     "prompt_number": 75
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Drawing a simple example of [different lines for different plots](http://stackoverflow.com/questions/4805048/how-to-get-different-lines-for-different-plots-in-a-single-figure/4805456#4805456) looks like this..."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig19 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10)\n\nplt.plot(x, x)\nplt.plot(x, 2 * x)\nplt.plot(x, 3 * x)\nplt.plot(x, 4 * x)\n\npy.iplot_mpl(fig19, strip_style = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3505\" height=\"525\" width=\"100%\"></iframe>",
+       "metadata": {},
+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x10fd63190>"
+      }
+     ],
+     "prompt_number": 76
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "...and can get more exciting like this."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig20 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 10\n\n# Have a look at the colormaps here and decide which one you'd like:\n# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\ncolormap = plt.cm.gist_ncar\nplt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])\n\n# Plot several different functions...\nx = np.arange(10)\nlabels = []\nfor i in range(1, num_plots + 1):\n    plt.plot(x, i * x + 5 * i)\n    labels.append(r'$y = %ix + %i$' % (i, 5*i))\n\npy.iplot_mpl(fig20, strip_style = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3506\" height=\"525\" width=\"100%\"></iframe>",
+       "metadata": {},
+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x1108bde90>"
+      }
+     ],
+     "prompt_number": 77
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Plotly also lets you draw [variables as subscripts in math mode](http://stackoverflow.com/questions/23276918/writing-variables-as-subscripts-in-math-mode)."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig17 = plt.figure()\n\nsns.set_palette(\"hls\")\nmpl.rc(\"figure\", figsize=(8, 4))\ndata = randn(200)\nsns.distplot(data);\n\npy.iplot_mpl(fig17, strip_style = True)",
+     "input": "fig21 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\n\nmean = [10,12,16,22,25]\nvariance = [3,6,8,10,12]\n\nx = np.linspace(0,40,1000)\n\nfor i in range(4):\n    sigma = np.sqrt(variance[i])\n    y = mlab.normpdf(x,mean[i],sigma)\n    plt.plot(x,y, label=r'$v_{}$'.format(i+1))\n\nplt.xlabel(\"X\")\nplt.ylabel(\"P(X)\")        \n\npy.iplot_mpl(fig21, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3405\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3507\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x11095bc10>"
+       "text": "<IPython.core.display.HTML at 0x10f488510>"
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 78
     }
    ],
    "metadata": {}

diff --git a/Graphing b/Graphing
@@ -11,7 +11,7 @@
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "18 Interactive Plots from matplotlib, prettyplotlib, Stack Overflow, and seaborn"
+     "source": "17 Interactive Plots from matplotlib, prettyplotlib, Stack Overflow, and seaborn"
     },
     {
      "cell_type": "markdown",
@@ -80,7 +80,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig3 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nplt.show()\npy.iplot_mpl(fig3)",
+     "input": "fig1 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nplt.show()\npy.iplot_mpl(fig1)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -107,7 +107,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig = tls.mpl_to_plotly(fig3)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
+     "input": "fig = tls.mpl_to_plotly(fig1)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -128,7 +128,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig4 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\npy.iplot_mpl(fig4)",
+     "input": "fig2 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\npy.iplot_mpl(fig2)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -149,7 +149,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "tls.mpl_to_plotly(fig4).get_data()",
+     "input": "tls.mpl_to_plotly(fig2).get_data()",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -264,7 +264,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig5 = plt.figure()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# make a little extra space between the subplots\nplt.subplots_adjust(wspace=0.5)\n\ndt = 0.01\nt = np.arange(0, 30, dt)\nnse1 = np.random.randn(len(t))                 # white noise 1\nnse2 = np.random.randn(len(t))                 # white noise 2\nr = np.exp(-t/0.05)\n\ncnse1 = np.convolve(nse1, r, mode='same')*dt   # colored noise 1\ncnse2 = np.convolve(nse2, r, mode='same')*dt   # colored noise 2\n\n# two signals with a coherent part and a random part\ns1 = 0.01*np.sin(2*np.pi*10*t) + cnse1\ns2 = 0.01*np.sin(2*np.pi*10*t) + cnse2\n\nplt.subplot(211)\nplt.plot(t, s1, 'b-', t, s2, 'g-')\nplt.xlim(0,5)\nplt.xlabel('time')\nplt.ylabel('s1 and s2')\nplt.grid(True)\n\nplt.subplot(212)\ncxy, f = plt.csd(s1, s2, 256, 1./dt)\nplt.ylabel('CSD (db)')\n\npy.iplot_mpl(fig5)",
+     "input": "fig3 = plt.figure()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# make a little extra space between the subplots\nplt.subplots_adjust(wspace=0.5)\n\ndt = 0.01\nt = np.arange(0, 30, dt)\nnse1 = np.random.randn(len(t))                 # white noise 1\nnse2 = np.random.randn(len(t))                 # white noise 2\nr = np.exp(-t/0.05)\n\ncnse1 = np.convolve(nse1, r, mode='same')*dt   # colored noise 1\ncnse2 = np.convolve(nse2, r, mode='same')*dt   # colored noise 2\n\n# two signals with a coherent part and a random part\ns1 = 0.01*np.sin(2*np.pi*10*t) + cnse1\ns2 = 0.01*np.sin(2*np.pi*10*t) + cnse2\n\nplt.subplot(211)\nplt.plot(t, s1, 'b-', t, s2, 'g-')\nplt.xlim(0,5)\nplt.xlabel('time')\nplt.ylabel('s1 and s2')\nplt.grid(True)\n\nplt.subplot(212)\ncxy, f = plt.csd(s1, s2, 256, 1./dt)\nplt.ylabel('CSD (db)')\n\npy.iplot_mpl(fig3)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -285,7 +285,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig6 = plt.figure()\n\nfrom pylab import figure, show\nfrom numpy import arange, sin, pi\n\nt = arange(0.0, 1.0, 0.01)\n\nfig = figure(1)\n\nax1 = fig.add_subplot(211)\nax1.plot(t, sin(2*pi*t))\nax1.grid(True)\nax1.set_ylim( (-2,2) )\nax1.set_ylabel('1 Hz')\nax1.set_title('A sine wave or two')\n\nfor label in ax1.get_xticklabels():\n    label.set_color('r')\n\n\nax2 = fig.add_subplot(212)\nax2.plot(t, sin(2*2*pi*t))\nax2.grid(True)\nax2.set_ylim( (-2,2) )\nl = ax2.set_xlabel('Hi mom')\nl.set_color('g')\nl.set_fontsize('large')\n\npy.iplot_mpl(fig6, strip_style = True)",
+     "input": "fig4 = plt.figure()\n\nfrom pylab import figure, show\nfrom numpy import arange, sin, pi\n\nt = arange(0.0, 1.0, 0.01)\n\nfig = figure(1)\n\nax1 = fig.add_subplot(211)\nax1.plot(t, sin(2*pi*t))\nax1.grid(True)\nax1.set_ylim( (-2,2) )\nax1.set_ylabel('1 Hz')\nax1.set_title('A sine wave or two')\n\nfor label in ax1.get_xticklabels():\n    label.set_color('r')\n\n\nax2 = fig.add_subplot(212)\nax2.plot(t, sin(2*2*pi*t))\nax2.grid(True)\nax2.set_ylim( (-2,2) )\nl = ax2.set_xlabel('Hi mom')\nl.set_color('g')\nl.set_fontsize('large')\n\npy.iplot_mpl(fig4, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -306,7 +306,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig7 = plt.figure()\n\nfrom __future__ import print_function\n\"\"\"\nEdward Tufte uses this example from Anscombe to show 4 datasets of x\nand y that have the same mean, standard deviation, and regression\nline, but which are qualitatively different.\n\nmatplotlib fun for a rainy day\n\"\"\"\n\nfrom pylab import *\n\nx =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\ny1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\ny2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\ny3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\nx4 = array([8,8,8,8,8,8,8,19,8,8,8])\ny4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])\n\ndef fit(x):\n    return 3+0.5*x\n\n\n\nxfit = array( [amin(x), amax(x) ] )\n\nsubplot(221)\nplot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'I', fontsize=20)\n\nsubplot(222)\nplot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))\ntext(3,12, 'II', fontsize=20)\n\nsubplot(223)\nplot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\ntext(3,12, 'III', fontsize=20)\nsetp(gca(), yticks=(4,8,12), xticks=(0,10,20))\n\nsubplot(224)\n\nxfit = array([amin(x4),amax(x4)])\nplot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'IV', fontsize=20)\n\n#verify the stats\npairs = (x,y1), (x,y2), (x,y3), (x4,y4)\nfor x,y in pairs:\n    print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))\n\npy.iplot_mpl(fig7, strip_style = True)",
+     "input": "fig5 = plt.figure()\n\nfrom __future__ import print_function\n\"\"\"\nEdward Tufte uses this example from Anscombe to show 4 datasets of x\nand y that have the same mean, standard deviation, and regression\nline, but which are qualitatively different.\n\nmatplotlib fun for a rainy day\n\"\"\"\n\nfrom pylab import *\n\nx =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\ny1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\ny2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\ny3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\nx4 = array([8,8,8,8,8,8,8,19,8,8,8])\ny4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])\n\ndef fit(x):\n    return 3+0.5*x\n\n\n\nxfit = array( [amin(x), amax(x) ] )\n\nsubplot(221)\nplot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'I', fontsize=20)\n\nsubplot(222)\nplot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))\ntext(3,12, 'II', fontsize=20)\n\nsubplot(223)\nplot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\ntext(3,12, 'III', fontsize=20)\nsetp(gca(), yticks=(4,8,12), xticks=(0,10,20))\n\nsubplot(224)\n\nxfit = array([amin(x4),amax(x4)])\nplot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'IV', fontsize=20)\n\n#verify the stats\npairs = (x,y1), (x,y2), (x,y3), (x4,y4)\nfor x,y in pairs:\n    print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))\n\npy.iplot_mpl(fig5, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -332,7 +332,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig8 = plt.figure()\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\n\n\n# example data\nmu = 100 # mean of distribution\nsigma = 15 # standard deviation of distribution\nx = mu + sigma * np.random.randn(10000)\n\nnum_bins = 50\n# the histogram of the data\nn, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n# add a 'best fit' line\ny = mlab.normpdf(bins, mu, sigma)\nplt.plot(bins, y, 'r--')\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\npy.iplot_mpl(fig8, strip_style = True)",
+     "input": "fig6 = plt.figure()\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\n\n\n# example data\nmu = 100 # mean of distribution\nsigma = 15 # standard deviation of distribution\nx = mu + sigma * np.random.randn(10000)\n\nnum_bins = 50\n# the histogram of the data\nn, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n# add a 'best fit' line\ny = mlab.normpdf(bins, mu, sigma)\nplt.plot(bins, y, 'r--')\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\npy.iplot_mpl(fig6, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -359,7 +359,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig9 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\nhist, bins = np.histogram(x, bins=50)\nwidth = 0.7 * (bins[1] - bins[0])\ncenter = (bins[:-1] + bins[1:]) / 2\nplt.bar(center, hist, align='center', width=width)\n\npy.iplot_mpl(fig9, strip_style = True)",
+     "input": "fig7 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\nhist, bins = np.histogram(x, bins=50)\nwidth = 0.7 * (bins[1] - bins[0])\ncenter = (bins[:-1] + bins[1:]) / 2\nplt.bar(center, hist, align='center', width=width)\n\npy.iplot_mpl(fig7, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -380,7 +380,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig10 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\ndata = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8\ndensity = gaussian_kde(data)\nxs = np.linspace(0,8,200)\ndensity.covariance_factor = lambda : .25\ndensity._compute_covariance()\nplt.plot(xs,density(xs))\n\npy.iplot_mpl(fig10, strip_style = True)",
+     "input": "fig8 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\ndata = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8\ndensity = gaussian_kde(data)\nxs = np.linspace(0,8,200)\ndensity.covariance_factor = lambda : .25\ndensity._compute_covariance()\nplt.plot(xs,density(xs))\n\npy.iplot_mpl(fig8, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -401,7 +401,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig11 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10)\n\nplt.plot(x, x)\nplt.plot(x, 2 * x)\nplt.plot(x, 3 * x)\nplt.plot(x, 4 * x)\n\npy.iplot_mpl(fig11, strip_style = True)",
+     "input": "fig9 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10)\n\nplt.plot(x, x)\nplt.plot(x, 2 * x)\nplt.plot(x, 3 * x)\nplt.plot(x, 4 * x)\n\npy.iplot_mpl(fig9, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -422,7 +422,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig12 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 10\n\n# Have a look at the colormaps here and decide which one you'd like:\n# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\ncolormap = plt.cm.gist_ncar\nplt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])\n\n# Plot several different functions...\nx = np.arange(10)\nlabels = []\nfor i in range(1, num_plots + 1):\n    plt.plot(x, i * x + 5 * i)\n    labels.append(r'$y = %ix + %i$' % (i, 5*i))\n\npy.iplot_mpl(fig12, strip_style = True)",
+     "input": "fig10 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 10\n\n# Have a look at the colormaps here and decide which one you'd like:\n# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\ncolormap = plt.cm.gist_ncar\nplt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])\n\n# Plot several different functions...\nx = np.arange(10)\nlabels = []\nfor i in range(1, num_plots + 1):\n    plt.plot(x, i * x + 5 * i)\n    labels.append(r'$y = %ix + %i$' % (i, 5*i))\n\npy.iplot_mpl(fig10, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -443,7 +443,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig13 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\n\nmean = [10,12,16,22,25]\nvariance = [3,6,8,10,12]\n\nx = np.linspace(0,40,1000)\n\nfor i in range(4):\n    sigma = np.sqrt(variance[i])\n    y = mlab.normpdf(x,mean[i],sigma)\n    plt.plot(x,y, label=r'$v_{}$'.format(i+1))\n\nplt.xlabel(\"X\")\nplt.ylabel(\"P(X)\")        \n\npy.iplot_mpl(fig13, strip_style = True)",
+     "input": "fig11 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\n\nmean = [10,12,16,22,25]\nvariance = [3,6,8,10,12]\n\nx = np.linspace(0,40,1000)\n\nfor i in range(4):\n    sigma = np.sqrt(variance[i])\n    y = mlab.normpdf(x,mean[i],sigma)\n    plt.plot(x,y, label=r'$v_{}$'.format(i+1))\n\nplt.xlabel(\"X\")\nplt.ylabel(\"P(X)\")        \n\npy.iplot_mpl(fig11, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -470,7 +470,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig14 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    x = np.random.normal(loc=i, size=800)\n    y = np.random.normal(loc=i, size=800)\n    ax = ppl.scatter(x, y, label=str(i))\n    \nppl.legend(ax)\nax.set_title('prettyplotlib `scatter`')\nax.legend().set_visible(False)\n\npy.iplot_mpl(fig14)",
+     "input": "fig12 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    x = np.random.normal(loc=i, size=800)\n    y = np.random.normal(loc=i, size=800)\n    ax = ppl.scatter(x, y, label=str(i))\n    \nppl.legend(ax)\nax.set_title('prettyplotlib `scatter`')\nax.legend().set_visible(False)\n\npy.iplot_mpl(fig12)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -491,7 +491,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig15 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    y = np.random.normal(size=1000).cumsum()\n    x = np.arange(1000)\n\n    # Specify both x and y\n    ppl.plot(x, y, label=str(i), linewidth=0.75)\n        \npy.iplot_mpl(fig15)",
+     "input": "fig13 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    y = np.random.normal(size=1000).cumsum()\n    x = np.arange(1000)\n\n    # Specify both x and y\n    ppl.plot(x, y, label=str(i), linewidth=0.75)\n        \npy.iplot_mpl(fig13)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -536,7 +536,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig16 = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\npy.iplot_mpl(fig16, strip_style = True)",
+     "input": "fig14 = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\npy.iplot_mpl(fig15, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -557,7 +557,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig17 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n    plt.subplot(211)\n    sinplot()\nplt.subplot(212)\nsinplot(-1)\n\npy.iplot_mpl(fig17, strip_style = True)",
+     "input": "fig16 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n    plt.subplot(211)\n    sinplot()\nplt.subplot(212)\nsinplot(-1)\n\npy.iplot_mpl(fig16, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
@@ -587,7 +587,7 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig18 = plt.figure()\n\nsns.set_palette(\"hls\")\nmpl.rc(\"figure\", figsize=(8, 4))\ndata = randn(200)\nsns.distplot(data);\n\npy.iplot_mpl(fig18, strip_style = True)",
+     "input": "fig17 = plt.figure()\n\nsns.set_palette(\"hls\")\nmpl.rc(\"figure\", figsize=(8, 4))\ndata = randn(200)\nsns.distplot(data);\n\npy.iplot_mpl(fig17, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [

diff --git a/Graphing b/Graphing
@@ -16,7 +16,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "In this Notebook, we'll create interactive Plotly graphs from different Python libraries. It's easy, lets you collaborate, makes a D3 graph with a URL for you, and stores your data and graphs together. You can also always access the data from your graphs or any public Plotly graph. For a full walk-through and documentation, check out our [getting started Notebook](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb). Let's set up our environment and packages."
+     "source": "In this Notebook, we'll create interactive Plotly graphs from different Python libraries. Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. You can also always access the data from your graphs or any public Plotly graph. And it's free.\n\nFor a full walk-through and documentation, check out our [getting started Notebook](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb). Let's set up our environment and packages.\n\nFor best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. "
     },
     {
      "cell_type": "code",
@@ -25,16 +25,16 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 5
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "import plotly.plotly as py\nimport plotly.tools as tls\nfrom plotly.graph_objs import *\n# py.sign_in(\"IPython.Demo\", \"1fw3zw2o13\")",
+     "input": "import plotly.plotly as py\nimport plotly.tools as tls\nfrom plotly.graph_objs import *\npy.sign_in(\"IPython.Demo\", \"1fw3zw2o13\")",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -43,7 +43,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -55,204 +55,206 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 4,
+       "prompt_number": 8,
        "text": "'1.0.0'"
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 8
     },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 1,
      "metadata": {},
-     "source": "I. Plotly for Teaching: Software Carpentry Notebook"
+     "source": "I. matplotlib Gallery graphs"
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "These first two are drawn from the [Software Carpentry repo](http://nbviewer.ipython.org/github/swcarpentry/notebooks/blob/master/matplotlib.ipynb). First, we'll draw a matplotlib figure and show both the matplotlib figure and the Plotly graph."
+     "source": "For matplotlib experts, you'll recognize these graphs from the [matplotlib gallery](matplotlib.org/gallery.html). \n\nIn addition to matplotlib and Plotly's own [Python API](https://plot.ly/python), You can also use Plotly's other [APIs](https://plot.ly/api) for MATLAB, R, Perl, Julia, and REST to write to graphs. That means you and I could edit the same graph with any language. We can even edit the graph and data from the GUI, so technical and non-technical teams can work together. And all the graphs go to your profile, like this: https://plot.ly/~IPython.Demo.\n\nYou control [the privacy](http://plot.ly/python/privacy) by setting `world_readable` to False or True, and can control your [sharing](http://plot.ly/python/file-sharing)."
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Plotly also reads the label types in this [damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html) graph."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig1 = plt.figure()\n\n#generate some data\nx = np.array(range(20))\ny = 3 + 0.5 * x + np.random.randn(20)\n\n#plot the data\nplt.plot(x, y, 'bo')\n\n#generate a mpl figure and Plotly figure\nplt.show()\npy.iplot_mpl(fig1)",
+     "input": "fig3 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nplt.show()\npy.iplot_mpl(fig3)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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-       "text": "<matplotlib.figure.Figure at 0x1060cb6d0>"
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qK+Gzz2D9enjkESgthdtv10J7b78d4uOdv++r6rChSLgmxYXrebVGAqY0du/e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\nCB4jSkMQBEHwGFEagiAIgsf8f6/NeKkjAey4AAAAAElFTkSuQmCC\n",
+       "text": "<matplotlib.figure.Figure at 0x106257450>"
       },
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3353\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3389\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1060f93d0>"
+       "text": "<IPython.core.display.HTML at 0x106316390>"
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Now let's draw another figure. This time, we'll only print the converted Plotly version of the figure. "
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": "x = np.array(range(20))\ny = 3 + 0.5 * x + np.random.randn(20)\nz = 2 + 0.9 * x + np.random.randn(20)",
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
+     "source": "Notice the difference. You can hover, zoom, and pan on the figure. You can also strip out the matplotlib styling, and use Plotly's default styling."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig2 = plt.figure()\n\n#plot the data\nplt.plot(x, y, 'bo')\nplt.hold(True)\nplt.plot(x, z, 'r^')\n\npy.iplot_mpl(fig2)",
+     "input": "fig = tls.mpl_to_plotly(fig3)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3354\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3390\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1061318d0>"
+       "text": "<IPython.core.display.HTML at 0x106332f10>"
       }
      ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": "One special Plotly feature is that you'll get a URL with your data and graph. You can then go to Plotly's GUI and edit, tweak, and share your graph. Here's the URL:\n\nhttps://plot.ly/~IPython.Demo/3080\n\nAnd I've gone in to make a copy of the graph, and shared the data here:\n\nhttps://plot.ly/~MattSundquist/1190."
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": "II. matplotlib Gallery graphs"
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": "For matplotlib experts, you'll recognize these graphs from the [matplotlib gallery](matplotlib.org/gallery.html). \n\nIn addition to matplotlib and Plotly's own Python API, You can also use Plotly's other [APIs](https://plot.ly/api) for MATLAB, R, Perl, Julia, and REST to write to graphs. That means you and I could edit the same graph with any language. We can even edit the graph and data from the GUI, so technical and non-technical teams can work together. And all the graphs go to your profile, like this: https://plot.ly/~IPython.Demo.\n\nYou control [the privacy](http://plot.ly/python/privacy) by setting `world_readable` to False or True, and can control your [sharing](http://plot.ly/python/file-sharing)."
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly also reads the label types in this [damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html) graph."
+     "source": "Next up, an example from [pylab](http://matplotlib.org/examples/pylab_examples/arctest.html)."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig3 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\n\npy.iplot_mpl(fig3)",
+     "input": "fig4 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\npy.iplot_mpl(fig4)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3355\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3391\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106516ad0>"
+       "text": "<IPython.core.display.HTML at 0x1063d4950>"
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "You can also strip out the matplotlib styling, and use Plotly's default styling."
+     "source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. Check out our [getting started guide](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb) for a full background on these features."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig = tls.mpl_to_plotly(fig3)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
+     "input": "tls.mpl_to_plotly(fig4).get_data()",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3356\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
-       "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106131990>"
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": "[{'name': '_line0',\n  'x': [0.0,\n   0.20000000000000001,\n   0.40000000000000002,\n   0.60000000000000009,\n   0.80000000000000004,\n   1.0,\n   1.2000000000000002,\n   1.4000000000000001,\n   1.6000000000000001,\n   1.8,\n   2.0,\n   2.2000000000000002,\n   2.4000000000000004,\n   2.6000000000000001,\n   2.8000000000000003,\n   3.0,\n   3.2000000000000002,\n   3.4000000000000004,\n   3.6000000000000001,\n   3.8000000000000003,\n   4.0,\n   4.2000000000000002,\n   4.4000000000000004,\n   4.6000000000000005,\n   4.8000000000000007],\n  'y': [1.0,\n   0.25300171651849518,\n   -0.54230030891302927,\n   -0.44399794031078654,\n   0.13885028597711233,\n   0.36787944117144233,\n   0.09307413008823949,\n   -0.19950113459002566,\n   -0.16333771416280363,\n   0.051080165611754998,\n   0.1353352832366127,\n   0.034240058964379601,\n   -0.073392365906047419,\n   -0.060088587008433003,\n   0.018791342780197139,\n   0.049787068367863944,\n   0.012596213757493282,\n   -0.026999542555766767,\n   -0.022105355809443925,\n   0.0069129486808399343,\n   0.018315638888734179,\n   0.0046338880779826647,\n   -0.0099325766273000524,\n   -0.0081321059420741033,\n   0.0025431316975542792]}]"
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Next up, an example from [pylab](http://matplotlib.org/examples/pylab_examples/arctest.html)."
+     "source": "Or you can get the figure makeup. Here, we're using 'IPython.Demo', which is the username and '3357' which is the figure number. You can use this command on Plotly graphs to interact with them from the console. You can access  graphs via a URL. For example, for this plot, it's:\n\nhttps://plot.ly/~IPython.Demo/3357/\n"
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig4 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\npy.iplot_mpl(fig4)",
+     "input": "pylab = py.get_figure('IPython.Demo', '3357')",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "#print figure\nprint pylab.to_string()",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3357\" height=\"525\" width=\"100%\"></iframe>",
-       "metadata": {},
-       "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1060cb150>"
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": "Figure(\n    data=Data([\n        Scatter(\n            x=[0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000...],\n            y=[1.0, 0.2530017165184952, -0.5423003089130293, -0.44399794031...],\n            name='_line0',\n            mode='markers',\n            marker=Marker(\n                symbol='dot',\n                line=Line(\n                    color='#000000',\n                    width=0.5\n                ),\n                size=30,\n                color='#0000FF',\n                opacity=1\n            )\n        )\n    ]),\n    layout=Layout(\n        xaxis=XAxis(\n            domain=[0.0, 1.0],\n            range=[0.0, 5.0],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='y',\n            mirror=True\n        ),\n        yaxis=YAxis(\n            domain=[0.0, 1.0],\n            range=[-0.6000000000000001, 1.2],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='x',\n            mirror=True\n        ),\n        hovermode='closest',\n        showlegend=False\n    )\n)\n"
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. "
+     "source": "Now let's suppose we wanted to add a fit to the graph (see our [fits post](http://blog.plot.ly/post/84402951992/contour-plots-error-bars-chocolate-beer-meat) to learn more), and re-style it a bit. We can go into the web app, fork a copy, and edit the image in our GUI. No coding required."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "tls.mpl_to_plotly(fig4).get_data()",
+     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/RusH4k2.png?1')",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
+       "html": "<img src=\"https://i.imgur.com/RusH4k2.png?1\"/>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
-       "text": "[{'name': '_line0',\n  'x': [0.0,\n   0.20000000000000001,\n   0.40000000000000002,\n   0.60000000000000009,\n   0.80000000000000004,\n   1.0,\n   1.2000000000000002,\n   1.4000000000000001,\n   1.6000000000000001,\n   1.8,\n   2.0,\n   2.2000000000000002,\n   2.4000000000000004,\n   2.6000000000000001,\n   2.8000000000000003,\n   3.0,\n   3.2000000000000002,\n   3.4000000000000004,\n   3.6000000000000001,\n   3.8000000000000003,\n   4.0,\n   4.2000000000000002,\n   4.4000000000000004,\n   4.6000000000000005,\n   4.8000000000000007],\n  'y': [1.0,\n   0.25300171651849518,\n   -0.54230030891302927,\n   -0.44399794031078654,\n   0.13885028597711233,\n   0.36787944117144233,\n   0.09307413008823949,\n   -0.19950113459002566,\n   -0.16333771416280363,\n   0.051080165611754998,\n   0.1353352832366127,\n   0.034240058964379601,\n   -0.073392365906047419,\n   -0.060088587008433003,\n   0.018791342780197139,\n   0.049787068367863944,\n   0.012596213757493282,\n   -0.026999542555766767,\n   -0.022105355809443925,\n   0.0069129486808399343,\n   0.018315638888734179,\n   0.0046338880779826647,\n   -0.0099325766273000524,\n   -0.0081321059420741033,\n   0.0025431316975542792]}]"
+       "prompt_number": 15,
+       "text": "<IPython.core.display.Image at 0x106332810>"
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Or you can get the data about the figure. Here, we're using 'IPython.Demo', which is the username and '3357' which is the figure number. You can use this command on any graph you see and have access to."
+     "source": "I can now call that graph into the NB. And if I want to see the data for the fit or access the figure styling, I can  run the same commands, but now on this graph."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "pylab = py.get_figure('IPython.Demo', '3357')",
+     "input": "tls.embed('MattSundquist', '1307')",
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
+     "outputs": [
+      {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~MattSundquist/1307\" height=\"525\" width=\"100%\"></iframe>",
+       "metadata": {},
+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x106250c10>"
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "And Plotly graphs are interactive. "
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "#print figure\npylab",
+     "input": "from IPython.display import HTML\nHTML('<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>')",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
+       "html": "<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
-       "text": "{'data': [{'marker': {'color': '#0000FF',\n    'line': {'color': '#000000', 'width': 0.5},\n    'opacity': 1,\n    'size': 30,\n    'symbol': 'dot'},\n   'mode': 'markers',\n   'name': '_line0',\n   'type': 'scatter',\n   'x': [0.0,\n    0.2,\n    0.4,\n    0.6000000000000001,\n    0.8,\n    1.0,\n    1.2000000000000002,\n    1.4000000000000001,\n    1.6,\n    1.8,\n    2.0,\n    2.2,\n    2.4000000000000004,\n    2.6,\n    2.8000000000000003,\n    3.0,\n    3.2,\n    3.4000000000000004,\n    3.6,\n    3.8000000000000003,\n    4.0,\n    4.2,\n    4.4,\n    4.6000000000000005,\n    4.800000000000001],\n   'y': [1.0,\n    0.2530017165184952,\n    -0.5423003089130293,\n    -0.44399794031078654,\n    0.13885028597711233,\n    0.36787944117144233,\n    0.09307413008823949,\n    -0.19950113459002566,\n    -0.16333771416280363,\n    0.051080165611755,\n    0.1353352832366127,\n    0.0342400589643796,\n    -0.07339236590604742,\n    -0.060088587008433,\n    0.01879134278019714,\n    0.049787068367863944,\n    0.012596213757493282,\n    -0.026999542555766767,\n    -0.022105355809443925,\n    0.006912948680839934,\n    0.01831563888873418,\n    0.004633888077982665,\n    -0.009932576627300052,\n    -0.008132105942074103,\n    0.0025431316975542792]}],\n 'layout': {'hovermode': 'closest',\n  'showlegend': False,\n  'xaxis': {'anchor': 'y',\n   'domain': [0.0, 1.0],\n   'mirror': True,\n   'range': [0.0, 5.0],\n   'showgrid': False,\n   'showline': True,\n   'ticks': 'inside',\n   'zeroline': False},\n  'yaxis': {'anchor': 'x',\n   'domain': [0.0, 1.0],\n   'mirror': True,\n   'range': [-0.6000000000000001, 1.2],\n   'showgrid': False,\n   'showline': True,\n   'ticks': 'inside',\n   'zeroline': False}}}"
+       "prompt_number": 17,
+       "text": "<IPython.core.display.HTML at 0x106332b90>"
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -267,13 +269,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3358\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3392\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1061dc210>"
+       "text": "<IPython.core.display.HTML at 0x1063f5310>"
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -288,13 +290,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3359\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3393\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106578110>"
+       "text": "<IPython.core.display.HTML at 0x1068c4650>"
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -314,13 +316,13 @@
        "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
       },
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3360\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3394\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x106fe8e50>"
+       "text": "<IPython.core.display.HTML at 0x1068fed50>"
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -335,19 +337,19 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3361\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3395\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1075a9c10>"
+       "text": "<IPython.core.display.HTML at 0x1068bf090>"
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 21
     },
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "III. Stack Overflow Answers"
+     "source": "II. Stack Overflow Answers"
     },
     {
      "cell_type": "markdown",
@@ -362,13 +364,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3362\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3396\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1075a5f50>"
+       "text": "<IPython.core.display.HTML at 0x1063e12d0>"
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -383,13 +385,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3363\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3397\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e94f110>"
+       "text": "<IPython.core.display.HTML at 0x10e6b6a10>"
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -404,13 +406,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3364\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3398\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10ec37b50>"
+       "text": "<IPython.core.display.HTML at 0x10eaa6450>"
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -425,18 +427,18 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3365\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3399\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10eee2f90>"
+       "text": "<IPython.core.display.HTML at 0x10ed9c450>"
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Plotly also shows LaTeX if you want to draw [variables as subscripts in math mode](http://stackoverflow.com/questions/23276918/writing-variables-as-subscripts-in-math-mode)."
+     "source": "Plotly also lets you draw [variables as subscripts in math mode](http://stackoverflow.com/questions/23276918/writing-variables-as-subscripts-in-math-mode)."
     },
     {
      "cell_type": "code",
@@ -446,19 +448,19 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3366\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3400\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10ef14390>"
+       "text": "<IPython.core.display.HTML at 0x10ef9afd0>"
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 26
     },
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "IV. Prettyplotlib graphs in Plotly"
+     "source": "III. Prettyplotlib graphs in Plotly"
     },
     {
      "cell_type": "markdown",
@@ -473,13 +475,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3367\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3401\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10ee04990>"
+       "text": "<IPython.core.display.HTML at 0x10eaaa990>"
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -494,10 +496,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3368\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3402\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f94fb10>"
+       "text": "<IPython.core.display.HTML at 0x10f00ba90>"
       }
      ],
      "prompt_number": 28
@@ -506,7 +508,7 @@
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "V. Plotting with seaborn"
+     "source": "IV. Plotting with seaborn"
     },
     {
      "cell_type": "markdown",
@@ -539,10 +541,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3369\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3403\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f21f990>"
+       "text": "<IPython.core.display.HTML at 0x10ef99e10>"
       }
      ],
      "prompt_number": 31
@@ -560,10 +562,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3370\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3404\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x111743490>"
+       "text": "<IPython.core.display.HTML at 0x110a99ed0>"
       }
      ],
      "prompt_number": 32
@@ -590,10 +592,10 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3371\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3405\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x111766710>"
+       "text": "<IPython.core.display.HTML at 0x11095bc10>"
       }
      ],
      "prompt_number": 34

diff --git a/Graphing b/Graphing
@@ -25,7 +25,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -34,7 +34,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -43,7 +43,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -55,11 +55,11 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 4,
        "text": "'1.0.0'"
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 4
     },
     {
      "cell_type": "heading",
@@ -82,17 +82,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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wBwBkHjYNAYABCHMAMABhDgAGIMwBwAApnc2SjLfeekuHDx9Wbm6u3nzzTY0fP96tp8oK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\n",
-       "text": "<matplotlib.figure.Figure at 0x10e073fd0>"
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGOz/Avh2jouZHyYAAAAASUVORK5CYII=\n",
+       "text": "<matplotlib.figure.Figure at 0x1060cb6d0>"
       },
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3312\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3353\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e088410>"
+       "text": "<IPython.core.display.HTML at 0x1060f93d0>"
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -106,7 +106,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -116,13 +116,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3313\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3354\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e09c4d0>"
+       "text": "<IPython.core.display.HTML at 0x1061318d0>"
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -153,13 +153,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3347\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3355\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110f2f710>"
+       "text": "<IPython.core.display.HTML at 0x106516ad0>"
       }
      ],
-     "prompt_number": 59
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -174,13 +174,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3315\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3356\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e5f1210>"
+       "text": "<IPython.core.display.HTML at 0x106131990>"
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -195,13 +195,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3316\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3357\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e6ba410>"
+       "text": "<IPython.core.display.HTML at 0x1060cb150>"
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -218,32 +218,41 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 61,
+       "prompt_number": 11,
        "text": "[{'name': '_line0',\n  'x': [0.0,\n   0.20000000000000001,\n   0.40000000000000002,\n   0.60000000000000009,\n   0.80000000000000004,\n   1.0,\n   1.2000000000000002,\n   1.4000000000000001,\n   1.6000000000000001,\n   1.8,\n   2.0,\n   2.2000000000000002,\n   2.4000000000000004,\n   2.6000000000000001,\n   2.8000000000000003,\n   3.0,\n   3.2000000000000002,\n   3.4000000000000004,\n   3.6000000000000001,\n   3.8000000000000003,\n   4.0,\n   4.2000000000000002,\n   4.4000000000000004,\n   4.6000000000000005,\n   4.8000000000000007],\n  'y': [1.0,\n   0.25300171651849518,\n   -0.54230030891302927,\n   -0.44399794031078654,\n   0.13885028597711233,\n   0.36787944117144233,\n   0.09307413008823949,\n   -0.19950113459002566,\n   -0.16333771416280363,\n   0.051080165611754998,\n   0.1353352832366127,\n   0.034240058964379601,\n   -0.073392365906047419,\n   -0.060088587008433003,\n   0.018791342780197139,\n   0.049787068367863944,\n   0.012596213757493282,\n   -0.026999542555766767,\n   -0.022105355809443925,\n   0.0069129486808399343,\n   0.018315638888734179,\n   0.0046338880779826647,\n   -0.0099325766273000524,\n   -0.0081321059420741033,\n   0.0025431316975542792]}]"
       }
      ],
-     "prompt_number": 61
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Or you can get the string to produce the graph using Plotly."
+     "source": "Or you can get the data about the figure. Here, we're using 'IPython.Demo', which is the username and '3357' which is the figure number. You can use this command on any graph you see and have access to."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "tls.mpl_to_plotly(fig4).to_string(pretty = False)",
+     "input": "pylab = py.get_figure('IPython.Demo', '3357')",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "#print figure\npylab",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 62,
-       "text": "\"Figure(\\n    data=Data([\\n        Scatter(\\n            x=[0.0, 0.20000000000000001, 0.40000000000000002, 0.60000000000000009, 0.80000000000000004, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6000000000000001, 1.8, 2.0, 2.2000000000000002, 2.4000000000000004, 2.6000000000000001, 2.8000000000000003, 3.0, 3.2000000000000002, 3.4000000000000004, 3.6000000000000001, 3.8000000000000003, 4.0, 4.2000000000000002, 4.4000000000000004, 4.6000000000000005, 4.8000000000000007],\\n            y=[1.0, 0.25300171651849518, -0.54230030891302927, -0.44399794031078654, 0.13885028597711233, 0.36787944117144233, 0.09307413008823949, -0.19950113459002566, -0.16333771416280363, 0.051080165611754998, 0.1353352832366127, 0.034240058964379601, -0.073392365906047419, -0.060088587008433003, 0.018791342780197139, 0.049787068367863944, 0.012596213757493282, -0.026999542555766767, -0.022105355809443925, 0.0069129486808399343, 0.018315638888734179, 0.0046338880779826647, -0.0099325766273000524, -0.0081321059420741033, 0.0025431316975542792],\\n            name='_line0',\\n            mode='markers',\\n            marker=Marker(\\n                symbol='dot',\\n                line=Line(\\n                    color='#000000',\\n                    width=0\\n                ),\\n                size=30,\\n                color='#0000FF',\\n                opacity=1\\n            )\\n        )\\n    ]),\\n    layout=Layout(\\n        xaxis=XAxis(\\n            domain=[0.0, 1.0],\\n            range=(0.0, 5.0),\\n            showline=True,\\n            ticks='inside',\\n            showgrid=True,\\n            zeroline=False,\\n            anchor='y',\\n            mirror=True\\n        ),\\n        yaxis=YAxis(\\n            domain=[0.0, 1.0],\\n            range=(-0.60000000000000009, 1.2),\\n            showline=True,\\n            ticks='inside',\\n            showgrid=True,\\n            zeroline=False,\\n            anchor='x',\\n            mirror=True\\n        ),\\n        width=480,\\n        height=320,\\n        autosize=False,\\n        margin=Margin(\\n            l=60,\\n            r=47,\\n            b=40,\\n            t=31,\\n            pad=0\\n        ),\\n        hovermode='closest',\\n        showlegend=False\\n    )\\n)\""
+       "prompt_number": 16,
+       "text": "{'data': [{'marker': {'color': '#0000FF',\n    'line': {'color': '#000000', 'width': 0.5},\n    'opacity': 1,\n    'size': 30,\n    'symbol': 'dot'},\n   'mode': 'markers',\n   'name': '_line0',\n   'type': 'scatter',\n   'x': [0.0,\n    0.2,\n    0.4,\n    0.6000000000000001,\n    0.8,\n    1.0,\n    1.2000000000000002,\n    1.4000000000000001,\n    1.6,\n    1.8,\n    2.0,\n    2.2,\n    2.4000000000000004,\n    2.6,\n    2.8000000000000003,\n    3.0,\n    3.2,\n    3.4000000000000004,\n    3.6,\n    3.8000000000000003,\n    4.0,\n    4.2,\n    4.4,\n    4.6000000000000005,\n    4.800000000000001],\n   'y': [1.0,\n    0.2530017165184952,\n    -0.5423003089130293,\n    -0.44399794031078654,\n    0.13885028597711233,\n    0.36787944117144233,\n    0.09307413008823949,\n    -0.19950113459002566,\n    -0.16333771416280363,\n    0.051080165611755,\n    0.1353352832366127,\n    0.0342400589643796,\n    -0.07339236590604742,\n    -0.060088587008433,\n    0.01879134278019714,\n    0.049787068367863944,\n    0.012596213757493282,\n    -0.026999542555766767,\n    -0.022105355809443925,\n    0.006912948680839934,\n    0.01831563888873418,\n    0.004633888077982665,\n    -0.009932576627300052,\n    -0.008132105942074103,\n    0.0025431316975542792]}],\n 'layout': {'hovermode': 'closest',\n  'showlegend': False,\n  'xaxis': {'anchor': 'y',\n   'domain': [0.0, 1.0],\n   'mirror': True,\n   'range': [0.0, 5.0],\n   'showgrid': False,\n   'showline': True,\n   'ticks': 'inside',\n   'zeroline': False},\n  'yaxis': {'anchor': 'x',\n   'domain': [0.0, 1.0],\n   'mirror': True,\n   'range': [-0.6000000000000001, 1.2],\n   'showgrid': False,\n   'showline': True,\n   'ticks': 'inside',\n   'zeroline': False}}}"
       }
      ],
-     "prompt_number": 62
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -258,18 +267,18 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3317\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3358\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e5ef510>"
+       "text": "<IPython.core.display.HTML at 0x1061dc210>"
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Another subplotting example, in this case we're using Plotly's defaults. "
+     "source": "Another subplotting example using Plotly's defaults. "
     },
     {
      "cell_type": "code",
@@ -279,13 +288,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3318\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3359\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e5f1550>"
+       "text": "<IPython.core.display.HTML at 0x106578110>"
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -305,13 +314,13 @@
        "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
       },
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3319\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3360\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10f7301d0>"
+       "text": "<IPython.core.display.HTML at 0x106fe8e50>"
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -326,13 +335,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3349\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3361\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110f93b90>"
+       "text": "<IPython.core.display.HTML at 0x1075a9c10>"
       }
      ],
-     "prompt_number": 63
+     "prompt_number": 20
     },
     {
      "cell_type": "heading",
@@ -353,13 +362,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3321\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3362\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1100d3a10>"
+       "text": "<IPython.core.display.HTML at 0x1075a5f50>"
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -374,13 +383,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3322\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3363\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10fe131d0>"
+       "text": "<IPython.core.display.HTML at 0x10e94f110>"
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -395,13 +404,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3323\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3364\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110297550>"
+       "text": "<IPython.core.display.HTML at 0x10ec37b50>"
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -416,13 +425,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3324\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3365\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1102d43d0>"
+       "text": "<IPython.core.display.HTML at 0x10eee2f90>"
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -437,13 +446,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3325\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3366\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x1107c0810>"
+       "text": "<IPython.core.display.HTML at 0x10ef14390>"
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 25
     },
     {
      "cell_type": "heading",
@@ -464,13 +473,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3348\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3367\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e9d6050>"
+       "text": "<IPython.core.display.HTML at 0x10ee04990>"
       }
      ],
-     "prompt_number": 60
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -480,18 +489,18 @@
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig15 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    y = np.random.normal(size=1000).cumsum()\n    x = np.arange(1000)\n\n    # For now, you need to specify both x and y :(\n    # Still figuring out how to specify just one\n    ppl.plot(x, y, label=str(i), linewidth=0.75)\n        \npy.iplot_mpl(fig15)",
+     "input": "fig15 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    y = np.random.normal(size=1000).cumsum()\n    x = np.arange(1000)\n\n    # Specify both x and y\n    ppl.plot(x, y, label=str(i), linewidth=0.75)\n        \npy.iplot_mpl(fig15)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3346\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3368\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110fc72d0>"
+       "text": "<IPython.core.display.HTML at 0x10f94fb10>"
       }
      ],
-     "prompt_number": 56
+     "prompt_number": 28
     },
     {
      "cell_type": "heading",
@@ -511,7 +520,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 44
+     "prompt_number": 29
     },
     {
      "cell_type": "code",
@@ -520,7 +529,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 45
+     "prompt_number": 30
     },
     {
      "cell_type": "code",
@@ -530,13 +539,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3339\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3369\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110fab290>"
+       "text": "<IPython.core.display.HTML at 0x10f21f990>"
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -551,13 +560,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3340\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3370\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x111ab4150>"
+       "text": "<IPython.core.display.HTML at 0x111743490>"
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
@@ -571,7 +580,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 42
+     "prompt_number": 33
     },
     {
      "cell_type": "code",
@@ -581,21 +590,13 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3341\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3371\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x110d1f550>"
+       "text": "<IPython.core.display.HTML at 0x111766710>"
       }
      ],
-     "prompt_number": 48
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": "",
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 34
     }
    ],
    "metadata": {}

diff --git a/Graphing b/Graphing
@@ -127,7 +127,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "One special Plotly feature is that you'll get a URL for your call. The data always lives with the graph. The graph we just made is here:\n\nhttps://plot.ly/~IPython.Demo/3080\n\nAnd I've gone in to make a copy of the graph, and shared the data here:\n\nhttps://plot.ly/~MattSundquist/1190"
+     "source": "One special Plotly feature is that you'll get a URL with your data and graph. You can then go to Plotly's GUI and edit, tweak, and share your graph. Here's the URL:\n\nhttps://plot.ly/~IPython.Demo/3080\n\nAnd I've gone in to make a copy of the graph, and shared the data here:\n\nhttps://plot.ly/~MattSundquist/1190."
     },
     {
      "cell_type": "heading",
@@ -316,23 +316,23 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "[histogram](http://matplotlib.org/examples/statistics/histogram_demo_features.html)"
+     "source": "And a final [histogram](http://matplotlib.org/examples/statistics/histogram_demo_features.html) from the matplotlib gallery. "
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig8 = plt.figure()\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\n\n\n# example data\nmu = 100 # mean of distribution\nsigma = 15 # standard deviation of distribution\nx = mu + sigma * np.random.randn(10000)\n\nnum_bins = 50\n# the histogram of the data\nn, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n# add a 'best fit' line\ny = mlab.normpdf(bins, mu, sigma)\nplt.plot(bins, y, 'r--')\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\nplt.title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\npy.iplot_mpl(fig8, strip_style = True)",
+     "input": "fig8 = plt.figure()\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\n\n\n# example data\nmu = 100 # mean of distribution\nsigma = 15 # standard deviation of distribution\nx = mu + sigma * np.random.randn(10000)\n\nnum_bins = 50\n# the histogram of the data\nn, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n# add a 'best fit' line\ny = mlab.normpdf(bins, mu, sigma)\nplt.plot(bins, y, 'r--')\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\npy.iplot_mpl(fig8, strip_style = True)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3320\" height=\"525\" width=\"100%\"></iframe>",
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3349\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
        "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10fe0f350>"
+       "text": "<IPython.core.display.HTML at 0x110f93b90>"
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 63
     },
     {
      "cell_type": "heading",
@@ -475,7 +475,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "prettyplotlib again improves on matplotlib's defaults, adding an appealing set of defaults. "
+     "source": "And another prettyplotlib example."
     },
     {
      "cell_type": "code",
@@ -502,7 +502,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Another library we really difg is [seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), an awesome project by Michael Waskom. You may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip. The styling isn't yet translated to Plotly, so we'll go to Plotly's default settings. "
+     "source": "Another library we really dig is [seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), by Michael Waskom. You may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip. The styling isn't yet translated to Plotly, so we'll go to Plotly's default settings. "
     },
     {
      "cell_type": "code",

diff --git a/Graphing b/Graphing
@@ -164,86 +164,86 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. "
+     "source": "You can also strip out the matplotlib styling, and use Plotly's default styling."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "tls.mpl_to_plotly(fig3).get_data()",
+     "input": "fig = tls.mpl_to_plotly(fig3)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3315\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 57,
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+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x10e5f1210>"
       }
      ],
-     "prompt_number": 57
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "Or you can get the string to produce the graph using Plotly."
+     "source": "Next up, an example from [pylab](http://matplotlib.org/examples/pylab_examples/arctest.html)."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "tls.mpl_to_plotly(fig3).to_string(pretty = False)",
+     "input": "fig4 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\npy.iplot_mpl(fig4)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
+       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3316\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 58,
-       "text": "\"Figure(\\n    data=Data([\\n        Scatter(\\n            x=[0.0, 0.01, 0.02, 0.029999999999999999, 0.040000000000000001, 0.050000000000000003, 0.059999999999999998, 0.070000000000000007, 0.080000000000000002, 0.089999999999999997, 0.10000000000000001, 0.11, 0.12, 0.13, 0.14000000000000001, 0.14999999999999999, 0.16, 0.17000000000000001, 0.17999999999999999, 0.19, 0.20000000000000001, 0.20999999999999999, 0.22, 0.23000000000000001, 0.23999999999999999, 0.25, 0.26000000000000001, 0.27000000000000002, 0.28000000000000003, 0.28999999999999998, 0.29999999999999999, 0.31, 0.32000000000000001, 0.33000000000000002, 0.34000000000000002, 0.35000000000000003, 0.35999999999999999, 0.37, 0.38, 0.39000000000000001, 0.40000000000000002, 0.41000000000000003, 0.41999999999999998, 0.42999999999999999, 0.44, 0.45000000000000001, 0.46000000000000002, 0.47000000000000003, 0.47999999999999998, 0.48999999999999999, 0.5, 0.51000000000000001, 0.52000000000000002, 0.53000000000000003, 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-0.41361856953180914, -0.39851447575939558, -0.38211290243192081, -0.36450525066426415, -0.34578594359613163, -0.32605198826640425, -0.30540253581112531, -0.28393844178030153, -0.26176182833265149, -0.23897565002509305, -0.21568326486535064, -0.19198801224195713, -0.16799279928639099, -0.14379969715753954, -0.11950954866940107, -0.095221588609376448, -0.071033078016965234, -0.047038953611618299, -0.023331493474257361, -9.0104476020544775e-17, 0.022869498949732101, 0.045194529858818715, 0.066896433587700643, 0.087900604974218713, 0.10813671144866356, 0.1275388902027601, 0.14604592354732276, 0.16360139218795283, 0.18015380624195493, 0.19565671391234482, 0.21006878782598024, 0.22335388913220877, 0.23548110954559864, 0.24642479160106115, 0.2561645274716351, 0.26468513677815952, 0.27197662389572586, 0.27803411533393801, 0.2828577778364218, 0.28645271790948318, 0.28882886355015802, 0.29000082899996177, 0.28998776340228466, 0.28881318428848463, 0.28650479686019009, 0.28309430007307207, 0.2786171805603177, 0.27311249546219446, 0.26662264525142604, 0.25919313766259983, 0.25087234384752405, 0.24171124788738035, 0.23176319079674212, 0.22108361015411482, 0.20972977648871718, 0.19776052754383772, 0.18523600152343281, 0.17221737041078325, 0.15876657442618819, 0.14494605866497509, 0.13081851292775601, 0.1164466157220316, 0.10189278337814718, 0.087218925183439405, 0.072486205396410944, 0.057754812958130106, 0.043083739671049108, 0.028530567566246629, 0.014151266129022342, 8.1976690925703258e-17, -0.013871052285278363, -0.027411868010671404, -0.040574737996370604, -0.053314411924152427, -0.065588230934112562, -0.077356247213697008, -0.088581330357498655, -0.099229260333664276, -0.10926880694967485, -0.11867179576646029, -0.1274131604651251, -0.13547098172474095, -0.14282651272255487, -0.14946419141933984, -0.15537163984234573, -0.16053965062618583, -0.16496216111788936, -0.16863621539611176, -0.17156191459597445, -0.1737423559701157, -0.17518356115312753, -0.17589439413055735, -0.17588646944497877, -0.17517405120020096, -0.17377394345044514, -0.17170537258420662, -0.16898986233252339, -0.16565110204844838, -0.16171480891867487, -0.1572085847794841, -0.15216176821749336, -0.14660528264109668, -0.14057148101105291, -0.13409398791842592, -0.12720753969508478, -0.11994782323628231, -0.11235131420653809, -0.10445511528922737, -0.096296795127030863, -0.087914228584813431, -0.079345438948697292, -0.070628442655187362, -0.061801097122304069, -0.052900952230938332, -0.043965105979150605, -0.035030064805074417, -0.026131609045568792, -0.017304663967931452, -0.0085831767810049826],\\n            name='_line3',\\n            mode='lines+markers',\\n            marker=Marker(\\n                symbol='square',\\n                line=Line(\\n                    color='#000000',\\n                    width=0\\n                ),\\n                size=7,\\n                color='#FF0000',\\n                opacity=1\\n            ),\\n            line=Line(\\n                dash='dashdot',\\n                color='#FF0000',\\n                width=1.75,\\n                opacity=1\\n            )\\n        )\\n    ]),\\n    layout=Layout(\\n        title='Damped oscillation',\\n        xaxis=XAxis(\\n            title='time',\\n            domain=[0.0, 1.0],\\n            range=(0.0, 2.0),\\n            showline=True,\\n            ticks='inside',\\n            showgrid=True,\\n            zeroline=False,\\n            titlefont=Font(\\n                size=11.0,\\n                color='#000000'\\n            ),\\n            anchor='y',\\n            mirror=True\\n        ),\\n        yaxis=YAxis(\\n            title='volts',\\n            domain=[0.0, 1.0],\\n            range=(-1.0, 1.5),\\n            showline=True,\\n            ticks='inside',\\n            showgrid=True,\\n            zeroline=False,\\n            titlefont=Font(\\n                size=11.0,\\n                color='#000000'\\n            ),\\n            anchor='x',\\n            mirror=True\\n        ),\\n        width=480,\\n        height=320,\\n        autosize=False,\\n        margin=Margin(\\n            l=60,\\n            r=47,\\n            b=40,\\n            t=31,\\n            pad=0\\n        ),\\n        hovermode='closest',\\n        titlefont=Font(\\n            size=12.0,\\n            color='#262626'\\n        ),\\n        showlegend=False,\\n        annotations=Annotations([\\n            \\n        ])\\n    )\\n)\""
+       "output_type": "display_data",
+       "text": "<IPython.core.display.HTML at 0x10e6ba410>"
       }
      ],
-     "prompt_number": 58
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "You can also strip out the matplotlib styling, and use Plotly's default styling."
+     "source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. "
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig = tls.mpl_to_plotly(fig3)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
+     "input": "tls.mpl_to_plotly(fig4).get_data()",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3315\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
-       "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e5f1210>"
+       "output_type": "pyout",
+       "prompt_number": 61,
+       "text": "[{'name': '_line0',\n  'x': [0.0,\n   0.20000000000000001,\n   0.40000000000000002,\n   0.60000000000000009,\n   0.80000000000000004,\n   1.0,\n   1.2000000000000002,\n   1.4000000000000001,\n   1.6000000000000001,\n   1.8,\n   2.0,\n   2.2000000000000002,\n   2.4000000000000004,\n   2.6000000000000001,\n   2.8000000000000003,\n   3.0,\n   3.2000000000000002,\n   3.4000000000000004,\n   3.6000000000000001,\n   3.8000000000000003,\n   4.0,\n   4.2000000000000002,\n   4.4000000000000004,\n   4.6000000000000005,\n   4.8000000000000007],\n  'y': [1.0,\n   0.25300171651849518,\n   -0.54230030891302927,\n   -0.44399794031078654,\n   0.13885028597711233,\n   0.36787944117144233,\n   0.09307413008823949,\n   -0.19950113459002566,\n   -0.16333771416280363,\n   0.051080165611754998,\n   0.1353352832366127,\n   0.034240058964379601,\n   -0.073392365906047419,\n   -0.060088587008433003,\n   0.018791342780197139,\n   0.049787068367863944,\n   0.012596213757493282,\n   -0.026999542555766767,\n   -0.022105355809443925,\n   0.0069129486808399343,\n   0.018315638888734179,\n   0.0046338880779826647,\n   -0.0099325766273000524,\n   -0.0081321059420741033,\n   0.0025431316975542792]}]"
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 61
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "First up, an example from [pylab](http://matplotlib.org/examples/pylab_examples/arctest.html)."
+     "source": "Or you can get the string to produce the graph using Plotly."
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "fig4 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\npy.iplot_mpl(fig4)",
+     "input": "tls.mpl_to_plotly(fig4).to_string(pretty = False)",
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3316\" height=\"525\" width=\"100%\"></iframe>",
        "metadata": {},
-       "output_type": "display_data",
-       "text": "<IPython.core.display.HTML at 0x10e6ba410>"
+       "output_type": "pyout",
+       "prompt_number": 62,
+       "text": "\"Figure(\\n    data=Data([\\n        Scatter(\\n            x=[0.0, 0.20000000000000001, 0.40000000000000002, 0.60000000000000009, 0.80000000000000004, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6000000000000001, 1.8, 2.0, 2.2000000000000002, 2.4000000000000004, 2.6000000000000001, 2.8000000000000003, 3.0, 3.2000000000000002, 3.4000000000000004, 3.6000000000000001, 3.8000000000000003, 4.0, 4.2000000000000002, 4.4000000000000004, 4.6000000000000005, 4.8000000000000007],\\n            y=[1.0, 0.25300171651849518, -0.54230030891302927, -0.44399794031078654, 0.13885028597711233, 0.36787944117144233, 0.09307413008823949, -0.19950113459002566, -0.16333771416280363, 0.051080165611754998, 0.1353352832366127, 0.034240058964379601, -0.073392365906047419, -0.060088587008433003, 0.018791342780197139, 0.049787068367863944, 0.012596213757493282, -0.026999542555766767, -0.022105355809443925, 0.0069129486808399343, 0.018315638888734179, 0.0046338880779826647, -0.0099325766273000524, -0.0081321059420741033, 0.0025431316975542792],\\n            name='_line0',\\n            mode='markers',\\n            marker=Marker(\\n                symbol='dot',\\n                line=Line(\\n                    color='#000000',\\n                    width=0\\n                ),\\n                size=30,\\n                color='#0000FF',\\n                opacity=1\\n            )\\n        )\\n    ]),\\n    layout=Layout(\\n        xaxis=XAxis(\\n            domain=[0.0, 1.0],\\n            range=(0.0, 5.0),\\n            showline=True,\\n            ticks='inside',\\n            showgrid=True,\\n            zeroline=False,\\n            anchor='y',\\n            mirror=True\\n        ),\\n        yaxis=YAxis(\\n            domain=[0.0, 1.0],\\n            range=(-0.60000000000000009, 1.2),\\n            showline=True,\\n            ticks='inside',\\n            showgrid=True,\\n            zeroline=False,\\n            anchor='x',\\n            mirror=True\\n        ),\\n        width=480,\\n        height=320,\\n        autosize=False,\\n        margin=Margin(\\n            l=60,\\n            r=47,\\n            b=40,\\n            t=31,\\n            pad=0\\n        ),\\n        hovermode='closest',\\n        showlegend=False\\n    )\\n)\""
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 62
     },
     {
      "cell_type": "markdown",

diff --git a/Graphing b/Graphing
diff --git a/Graphing b/Graphing
diff --git a/Graphing b/Graphing
@@ -11,21 +11,25 @@
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
-     "source": "Sharing with Plotly: matplotlib gallery, prettyplotlib, seaborn, Software Carpentry, and Stack Overflow"
+     "source": "20 Interactive Plots from matplotlib, prettyplotlib, Stack Overflow, and seaborn"
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "%matplotlib inline\nimport matplotlib.pyplot as plt # side-stepping mpl backend\nimport matplotlib.gridspec as gridspec # subplots\nimport numpy as np",
+     "language": "python",
      "metadata": {},
-     "source": "Plotly's matplotlib support lets you make matplotlib plots into interactive, online, and collaborative projects. It's free, online, you own your data, and you control the privacy. It's like a GitHub for data and graphs. You can use the public key below or [sign up](https://plot.ly).  That means you can use your libraries and code, then use Plotly to make your graphs interactive, shareable, and drawn with D3. Let's get started. "
+     "outputs": [],
+     "prompt_number": 16
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": "%matplotlib inline\nimport matplotlib.pyplot as plt # side-stepping mpl backend\nimport matplotlib.gridspec as gridspec # subplots\nimport numpy as np",
+     "input": "from matplotlylib import fig_to_plotly\nusername = \"IPython.Demo\"\napi_key = \"1fw3zw2o13\"",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 35
+     "prompt_number": 17
     },
     {
      "cell_type": "code",
@@ -37,20 +41,11 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 36,
+       "prompt_number": 18,
        "text": "'0.5.9'"
       }
      ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": "from matplotlylib import fig_to_plotly\nusername = \"IPython.Demo\"\napi_key = \"1fw3zw2o13\"",
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 37
+     "prompt_number": 18
     },
     {
      "cell_type": "heading",
@@ -61,7 +56,7 @@
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "These first two are drawn from the excellent work the fine folks at Software Carpenty do. Check out [SWC repo](http://nbviewer.ipython.org/github/swcarpentry/notebooks/blob/master/matplotlib.ipynb) for more."
+     "source": "These first two are drawn from the excellent work at Software Carpenty. Check out [SWC repo](http://nbviewer.ipython.org/github/swcarpentry/notebooks/blob/master/matplotlib.ipynb) for more."
     },
     {
      "cell_type": "code",
@@ -71,14 +66,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3123/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3183/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 48,
-       "text": "<IPython.core.display.HTML at 0x110c2f510>"
+       "prompt_number": 19,
+       "text": "<IPython.core.display.HTML at 0x10bb19b90>"
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 19
     },
     {
      "cell_type": "code",
@@ -87,7 +82,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 46
+     "prompt_number": 20
     },
     {
      "cell_type": "code",
@@ -97,19 +92,19 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3122/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3184/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 47,
-       "text": "<IPython.core.display.HTML at 0x1102d9b90>"
+       "prompt_number": 21,
+       "text": "<IPython.core.display.HTML at 0x10bbdf7d0>"
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
      "metadata": {},
-     "source": "One special Plotly feature is that you'll get a URL for your call. The data always lives with the graph. To for that graph, the graph is here:\n\nhttps://plot.ly/~IPython.Demo/3080\n\nAnd I've gone in to make a copy of the graph, and shared the data here:\n\nhttps://plot.ly/~MattSundquist/1190"
+     "source": "One special Plotly feature is that you'll get a URL for your call. The data always lives with the graph. The graph we just made is here:\n\nhttps://plot.ly/~IPython.Demo/3080\n\nAnd I've gone in to make a copy of the graph, and shared the data here:\n\nhttps://plot.ly/~MattSundquist/1190"
     },
     {
      "cell_type": "heading",
@@ -135,14 +130,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3124/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3185/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 51,
-       "text": "<IPython.core.display.HTML at 0x101ab1210>"
+       "prompt_number": 22,
+       "text": "<IPython.core.display.HTML at 0x10dbac990>"
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -157,14 +152,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3125/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3186/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 52,
-       "text": "<IPython.core.display.HTML at 0x110c70b10>"
+       "prompt_number": 23,
+       "text": "<IPython.core.display.HTML at 0x10db4b090>"
       }
      ],
-     "prompt_number": 52
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -179,14 +174,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3126/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3187/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 53,
-       "text": "<IPython.core.display.HTML at 0x1117ad610>"
+       "prompt_number": 24,
+       "text": "<IPython.core.display.HTML at 0x10e69b810>"
       }
      ],
-     "prompt_number": 53
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -206,14 +201,14 @@
        "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
       },
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3127/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3188/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 54,
-       "text": "<IPython.core.display.HTML at 0x1102cf910>"
+       "prompt_number": 25,
+       "text": "<IPython.core.display.HTML at 0x10e6cff90>"
       }
      ],
-     "prompt_number": 54
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -228,14 +223,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3128/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3189/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 55,
-       "text": "<IPython.core.display.HTML at 0x10f55a490>"
+       "prompt_number": 26,
+       "text": "<IPython.core.display.HTML at 0x10e813ad0>"
       }
      ],
-     "prompt_number": 55
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -250,14 +245,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3129/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3190/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 56,
-       "text": "<IPython.core.display.HTML at 0x112631b50>"
+       "prompt_number": 27,
+       "text": "<IPython.core.display.HTML at 0x10f511390>"
       }
      ],
-     "prompt_number": 56
+     "prompt_number": 27
     },
     {
      "cell_type": "heading",
@@ -278,14 +273,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3130/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3191/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 57,
-       "text": "<IPython.core.display.HTML at 0x112cef250>"
+       "prompt_number": 28,
+       "text": "<IPython.core.display.HTML at 0x10f5c83d0>"
       }
      ],
-     "prompt_number": 57
+     "prompt_number": 28
     },
     {
      "cell_type": "markdown",
@@ -300,14 +295,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3131/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3192/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 58,
-       "text": "<IPython.core.display.HTML at 0x113046190>"
+       "prompt_number": 29,
+       "text": "<IPython.core.display.HTML at 0x10bbe5450>"
       }
      ],
-     "prompt_number": 58
+     "prompt_number": 29
     },
     {
      "cell_type": "markdown",
@@ -322,14 +317,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3132/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3193/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 59,
-       "text": "<IPython.core.display.HTML at 0x112d1aa50>"
+       "prompt_number": 30,
+       "text": "<IPython.core.display.HTML at 0x10bddd4d0>"
       }
      ],
-     "prompt_number": 59
+     "prompt_number": 30
     },
     {
      "cell_type": "markdown",
@@ -344,14 +339,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3133/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3194/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 60,
-       "text": "<IPython.core.display.HTML at 0x112d72250>"
+       "prompt_number": 31,
+       "text": "<IPython.core.display.HTML at 0x10bddc190>"
       }
      ],
-     "prompt_number": 60
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -366,14 +361,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3134/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3195/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 61,
-       "text": "<IPython.core.display.HTML at 0x113085210>"
+       "prompt_number": 32,
+       "text": "<IPython.core.display.HTML at 0x10bdf0b50>"
       }
      ],
-     "prompt_number": 61
+     "prompt_number": 32
     },
     {
      "cell_type": "heading",
@@ -394,14 +389,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3135/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3196/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 62,
-       "text": "<IPython.core.display.HTML at 0x112d21950>"
+       "prompt_number": 33,
+       "text": "<IPython.core.display.HTML at 0x10f5d1050>"
       }
      ],
-     "prompt_number": 62
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
@@ -416,14 +411,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3136/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3197/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 63,
-       "text": "<IPython.core.display.HTML at 0x1136e4850>"
+       "prompt_number": 34,
+       "text": "<IPython.core.display.HTML at 0x10fd206d0>"
       }
      ],
-     "prompt_number": 63
+     "prompt_number": 34
     },
     {
      "cell_type": "heading",
@@ -443,7 +438,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 64
+     "prompt_number": 35
     },
     {
      "cell_type": "code",
@@ -452,7 +447,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 65
+     "prompt_number": 36
     },
     {
      "cell_type": "code",
@@ -462,14 +457,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3137/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3198/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 66,
-       "text": "<IPython.core.display.HTML at 0x112d1b790>"
+       "prompt_number": 37,
+       "text": "<IPython.core.display.HTML at 0x1115dd110>"
       }
      ],
-     "prompt_number": 66
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -484,14 +479,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3138/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3199/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 67,
-       "text": "<IPython.core.display.HTML at 0x1137c7ed0>"
+       "prompt_number": 38,
+       "text": "<IPython.core.display.HTML at 0x110155150>"
       }
      ],
-     "prompt_number": 67
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -505,7 +500,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 68
+     "prompt_number": 39
     },
     {
      "cell_type": "code",
@@ -514,7 +509,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 69
+     "prompt_number": 40
     },
     {
      "cell_type": "code",
@@ -523,7 +518,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 70
+     "prompt_number": 41
     },
     {
      "cell_type": "code",
@@ -533,14 +528,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3139/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3200/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 71,
-       "text": "<IPython.core.display.HTML at 0x1137a8150>"
+       "prompt_number": 42,
+       "text": "<IPython.core.display.HTML at 0x110175790>"
       }
      ],
-     "prompt_number": 71
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -554,7 +549,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 72
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -568,7 +563,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 73
+     "prompt_number": 44
     },
     {
      "cell_type": "code",
@@ -577,7 +572,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 74
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
@@ -589,18 +584,23 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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77JarZ9V6K7l/3R3cv+4O4kaCzon+9Nr2pdR0umGZnJvo49xEH892vEy5y0dr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\n4Oc//7ncZfTY008/jX/6p3/CsmXLcPbsWbnL6bX9+/cjNzdX7jL6ZP3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-       "text": "<matplotlib.figure.Figure at 0x10dd9c510>"
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nq+KetTdxz9qbSJhJuicGMmvbp9PT6aZtcWyin2MT/TzX9TIVnlLa61ppr29h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\nD1okb9U42GWSAAAAAElFTkSuQmCC\n",
+       "text": "<matplotlib.figure.Figure at 0x1102ea390>"
       },
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3140/600/450\" width=\"650\"></iframe>",
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": "/Users/matthewsundquist/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlylib/renderer.py:448: UserWarning: Dang! That path collection is out of this world. I totally don't know what to do with it yet! Plotly can only import path collections linked to 'data' coordinates\n  warnings.warn(\"Dang! That path collection is out of this \"\n"
+      },
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3201/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 75,
-       "text": "<IPython.core.display.HTML at 0x10fe5acd0>"
+       "prompt_number": 46,
+       "text": "<IPython.core.display.HTML at 0x1102aa090>"
       }
      ],
-     "prompt_number": 75
+     "prompt_number": 46
     },
     {
      "cell_type": "markdown",
@@ -615,14 +615,19 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3141/600/450\" width=\"650\"></iframe>",
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": "/Users/matthewsundquist/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlylib/renderer.py:363: UserWarning: Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n  warnings.warn(\"Bummer! Plotly can currently only draw Line2D \"\n"
+      },
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3202/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 76,
-       "text": "<IPython.core.display.HTML at 0x1143242d0>"
+       "prompt_number": 47,
+       "text": "<IPython.core.display.HTML at 0x11038af90>"
       }
      ],
-     "prompt_number": 76
+     "prompt_number": 47
     },
     {
      "cell_type": "markdown",
@@ -637,14 +642,14 @@
      "metadata": {},
      "outputs": [
       {
-       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3142/600/450\" width=\"650\"></iframe>",
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3203/600/450\" width=\"650\"></iframe>",
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 77,
-       "text": "<IPython.core.display.HTML at 0x114324210>"
+       "prompt_number": 48,
+       "text": "<IPython.core.display.HTML at 0x111f07050>"
       }
      ],
-     "prompt_number": 77
+     "prompt_number": 48
     }
    ],
    "metadata": {}

diff --git a/Graphing b/Graphing
diff --git a/Graphing b/Graphing
@@ -0,0 +1,589 @@
+{
+ "metadata": {
+  "name": "Three new matplotlib plots"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": "Sharing with Plotly: matplotlib gallery, prettyplotlib, seaborn, Software Carpentry, and Stack Overflow"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Plotly's matplotlib support lets you make matplotlib plots into interactive, online, and collaborative projects. It's free, online, you own your data, and you control the privacy. It's like a GitHub for data and graphs. You can use the public key below or [sign up](https://plot.ly).  That means you can use your libraries and code, then use Plotly to make your graphs interactive, shareable, and drawn with D3. "
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "%matplotlib inline\nimport matplotlib.pyplot as plt # side-stepping mpl backend\nimport matplotlib.gridspec as gridspec # subplots\nimport numpy as np",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "import plotly\nplotly.__version__",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": "'0.5.9'"
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "from matplotlylib import fig_to_plotly\nusername = \"IPython.Demo\"\napi_key = \"1fw3zw2o13\"",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": "I. Plotly for Teaching: Software Carpentry Notebook"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "You can use Plotly, matplotlib, and IPython to interactive plots. These can be shared, forked, and easily edited as a learning process. These are drawn from the [SWC repo](http://nbviewer.ipython.org/github/swcarpentry/notebooks/blob/master/matplotlib.ipynb). We'll start off with a histogram. "
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig1 = plt.figure()\n\nx = np.array(range(20))\ny = 3 + 0.5 * x + np.random.randn(20)\nz = 2 + 0.9 * x + np.random.randn(20)\n\n#plot the data\nplt.plot(x, y, 'bo')\nplt.hold(True)\nplt.plot(x, z, 'r^')\n\nfig_to_plotly(fig1, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3080/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": "<IPython.core.display.HTML at 0x10df10b90>"
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "We've left plot.show in so you can see both plots. For the rest, we won't show both. One special Plotly feature is that you'll get a URL for your call. Non-coders and coders can work together. You can also edit the data, fork it, and make new graphs. The data always lives with the code, so for that graph, the graph is here:\n\nand the data is here: \n\n. "
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Plotly also reads sup-plots. "
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig2 = plt.figure()\n\nplt.subplot(1, 2, 1)\nplt.plot(x, y, 'rs')\nplt.subplot(1, 2, 2)\nplt.hist(data, 10)\n\nfig_to_plotly(fig2, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3081/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 14,
+       "text": "<IPython.core.display.HTML at 0x10e433950>"
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": "II. matplotlib Gallery graphs"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "For matplotlib experts, you'll recognize these graphs from the [matplotlib gallery](matplotlib.org/gallery.html). Let us know if you find others you like or need translated. \n\nYou can also use Plotly's [APIs](https://plot.ly/api) for Python, MATLAB, R, Perl, Julia, and REST to write to graphs. That means you and I could edit the same graph with any language or the GUI. Then it goes to a profile, like this: https://plot.ly/~IPython.Demo."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig3 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\nfig_to_plotly(fig3, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3082/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 15,
+       "text": "<IPython.core.display.HTML at 0x10e4242d0>"
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "You can also plot with Plotly with pandas, NumPy, datetime, and more of your favorite Python tools. We've already imported numpy and matplotlib; here we've kept them in so you can simply copy and paste these examples into your own NB. "
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig4 = plt.figure()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# make a little extra space between the subplots\nplt.subplots_adjust(wspace=0.5)\n\ndt = 0.01\nt = np.arange(0, 30, dt)\nnse1 = np.random.randn(len(t))                 # white noise 1\nnse2 = np.random.randn(len(t))                 # white noise 2\nr = np.exp(-t/0.05)\n\ncnse1 = np.convolve(nse1, r, mode='same')*dt   # colored noise 1\ncnse2 = np.convolve(nse2, r, mode='same')*dt   # colored noise 2\n\n# two signals with a coherent part and a random part\ns1 = 0.01*np.sin(2*np.pi*10*t) + cnse1\ns2 = 0.01*np.sin(2*np.pi*10*t) + cnse2\n\nplt.subplot(211)\nplt.plot(t, s1, 'b-', t, s2, 'g-')\nplt.xlim(0,5)\nplt.xlabel('time')\nplt.ylabel('s1 and s2')\nplt.grid(True)\n\nplt.subplot(212)\ncxy, f = plt.csd(s1, s2, 256, 1./dt)\nplt.ylabel('CSD (db)')\n\nfig_to_plotly(fig4, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3083/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 16,
+       "text": "<IPython.core.display.HTML at 0x10e5d6590>"
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Another subplotting example. Note if you double-click, Plotly auto-sizes the plot. We initially draw it based on matplotlib defaults, but you can change the zoom and then save and share it how you'd like."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig5 = plt.figure()\n\nfrom pylab import figure, show\nfrom numpy import arange, sin, pi\n\nt = arange(0.0, 1.0, 0.01)\n\nfig = figure(1)\n\nax1 = fig.add_subplot(211)\nax1.plot(t, sin(2*pi*t))\nax1.grid(True)\nax1.set_ylim( (-2,2) )\nax1.set_ylabel('1 Hz')\nax1.set_title('A sine wave or two')\n\nfor label in ax1.get_xticklabels():\n    label.set_color('r')\n\n\nax2 = fig.add_subplot(212)\nax2.plot(t, sin(2*2*pi*t))\nax2.grid(True)\nax2.set_ylim( (-2,2) )\nl = ax2.set_xlabel('Hi mom')\nl.set_color('g')\nl.set_fontsize('large')\n\nfig_to_plotly(fig5, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3084/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 17,
+       "text": "<IPython.core.display.HTML at 0x10dca3c10>"
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "A favorte from the gallery is [Anscombe's quartet](http://matplotlib.org/examples/pylab_examples/anscombe.html). You  might also like Plotly's [blog post](blog.plot.ly/post/68951620673/why-graph-anscombes-quartet) on the subject."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig6 = plt.figure()\n\nfrom __future__ import print_function\n\"\"\"\nEdward Tufte uses this example from Anscombe to show 4 datasets of x\nand y that have the same mean, standard deviation, and regression\nline, but which are qualitatively different.\n\nmatplotlib fun for a rainy day\n\"\"\"\n\nfrom pylab import *\n\nx =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\ny1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\ny2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\ny3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\nx4 = array([8,8,8,8,8,8,8,19,8,8,8])\ny4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])\n\ndef fit(x):\n    return 3+0.5*x\n\n\n\nxfit = array( [amin(x), amax(x) ] )\n\nsubplot(221)\nplot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'I', fontsize=20)\n\nsubplot(222)\nplot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))\ntext(3,12, 'II', fontsize=20)\n\nsubplot(223)\nplot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\ntext(3,12, 'III', fontsize=20)\nsetp(gca(), yticks=(4,8,12), xticks=(0,10,20))\n\nsubplot(224)\n\nxfit = array([amin(x4),amax(x4)])\nplot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'IV', fontsize=20)\n\n#verify the stats\npairs = (x,y1), (x,y2), (x,y3), (x4,y4)\nfor x,y in pairs:\n    print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))\n\nfig_to_plotly(fig6, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
+      },
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3085/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": "<IPython.core.display.HTML at 0x10f3b3810>"
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "[damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig7 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.legend( (l2, l4), ('oscillatory', 'damped'), 'upper right', shadow=True)\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nfig_to_plotly(fig7, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3086/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": "<IPython.core.display.HTML at 0x10f3dc050>"
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "[histogram](http://matplotlib.org/examples/statistics/histogram_demo_features.html)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig8 = plt.figure()\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\n\n\n# example data\nmu = 100 # mean of distribution\nsigma = 15 # standard deviation of distribution\nx = mu + sigma * np.random.randn(10000)\n\nnum_bins = 50\n# the histogram of the data\nn, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n# add a 'best fit' line\ny = mlab.normpdf(bins, mu, sigma)\nplt.plot(bins, y, 'r--')\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\nplt.title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\nfig_to_plotly(fig8, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3087/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": "<IPython.core.display.HTML at 0x10fd3f0d0>"
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": "III. Stack Overflow Answers"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "[histogram](http://stackoverflow.com/questions/5328556/histogram-matplotlib)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig9 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\nhist, bins = np.histogram(x, bins=50)\nwidth = 0.7 * (bins[1] - bins[0])\ncenter = (bins[:-1] + bins[1:]) / 2\nplt.bar(center, hist, align='center', width=width)\n\nfig_to_plotly(fig9, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3088/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": "<IPython.core.display.HTML at 0x10feeb310>"
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "[Density plot](http://stackoverflow.com/questions/4150171/how-to-create-a-density-plot-in-matplotlib/4152016#4152016)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig10 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\ndata = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8\ndensity = gaussian_kde(data)\nxs = np.linspace(0,8,200)\ndensity.covariance_factor = lambda : .25\ndensity._compute_covariance()\nplt.plot(xs,density(xs))\n\nfig_to_plotly(fig10, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3089/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 22,
+       "text": "<IPython.core.display.HTML at 0x10e43f110>"
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "[different lines for different plots](http://stackoverflow.com/questions/4805048/how-to-get-different-lines-for-different-plots-in-a-single-figure/4805456#4805456)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig11 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10)\n\nplt.plot(x, x)\nplt.plot(x, 2 * x)\nplt.plot(x, 3 * x)\nplt.plot(x, 4 * x)\n\nfig_to_plotly(fig11, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3090/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 23,
+       "text": "<IPython.core.display.HTML at 0x10e496d10>"
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig12 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 20\n\n# Have a look at the colormaps here and decide which one you'd like:\n# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\ncolormap = plt.cm.gist_ncar\nplt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])\n\n# Plot several different functions...\nx = np.arange(10)\nlabels = []\nfor i in range(1, num_plots + 1):\n    plt.plot(x, i * x + 5 * i)\n    labels.append(r'$y = %ix + %i$' % (i, 5*i))\n\nfig_to_plotly(fig12, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3091/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 24,
+       "text": "<IPython.core.display.HTML at 0x10ff04710>"
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "[variables as subscripts in math mode](http://stackoverflow.com/questions/23276918/writing-variables-as-subscripts-in-math-mode)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig13 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\n\nmean = [10,12,16,22,25]\nvariance = [3,6,8,10,12]\n\nx = np.linspace(0,40,1000)\n\nfor i in range(4):\n    sigma = np.sqrt(variance[i])\n    y = mlab.normpdf(x,mean[i],sigma)\n    plt.plot(x,y, label=r'$v_{}$'.format(i+1))\n\nplt.xlabel(\"X\")\nplt.ylabel(\"P(X)\")        \n\nplt.legend()\n\nfig_to_plotly(fig13, username, api_key, notebook= True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3092/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 25,
+       "text": "<IPython.core.display.HTML at 0x10ff17e10>"
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": "IV. Prettyplotlib graphs in Plotly"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Some lovely [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) from [prettyplotlib](https://github.com/olgabot/prettyplotlib)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig14 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    x = np.random.normal(loc=i, size=1000)\n    y = np.random.normal(loc=i, size=1000)\n    ax = ppl.scatter(x, y, label=str(i))\n    \nppl.legend(ax)\nax.set_title('prettyplotlib `scatter`')\n\nfig_to_plotly(fig14, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3093/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 26,
+       "text": "<IPython.core.display.HTML at 0x10e49f310>"
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig15 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    y = np.random.normal(size=1000).cumsum()\n    x = np.arange(1000)\n\n    # For now, you need to specify both x and y :(\n    # Still figuring out how to specify just one\n    ppl.plot(x, y, label=str(i), linewidth=0.75)\n    \nppl.legend()\n\nfig_to_plotly(fig15, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3094/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 27,
+       "text": "<IPython.core.display.HTML at 0x110ab2b10>"
+      }
+     ],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": "V. Plotting with seaborn"
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "You may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip."
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "import seaborn as sns\nfrom matplotlylib import fig_to_plotly",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "def sinplot(flip=1):\n    x = np.linspace(0, 14, 100)\n    for i in range(1, 7):\n        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\nfig_to_plotly(fig, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3095/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 30,
+       "text": "<IPython.core.display.HTML at 0x10f5736d0>"
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig16 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n    plt.subplot(211)\n    sinplot()\nplt.subplot(212)\nsinplot(-1)\n\nfig_to_plotly(fig16, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3096/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": "<IPython.core.display.HTML at 0x10f5765d0>"
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "Finally, it\u2019s also possibly to plot each individual observation with a point, rather than joining them. This is more useful for the gestalt it presents than as a quantitative visualization but you may prefer it. [Visualizing the data for each sampling unit](http://stanford.edu/~mwaskom/software/seaborn/tutorial/timeseries_plots.html#specifying-input-data-with-multidimensional-arrays)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "import numpy as np\nnp.random.seed(9221999)\nimport pandas as pd\nfrom scipy import stats, optimize\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set(palette=\"Set2\")",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "def sine_wave(n_x, obs_err_sd=1.5, tp_err_sd=.3):\n    x = np.linspace(0, (n_x - 1) / 2, n_x)\n    y = np.sin(x) + np.random.normal(0, obs_err_sd) + np.random.normal(0, tp_err_sd, n_x)\n    return y",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "sines = np.array([sine_wave(31) for _ in range(20)])",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig17 = plt.figure()\n\nsns.tsplot(sines, err_style=\"unit_points\", color=\"mediumpurple\");\n\nfig_to_plotly(fig17, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3097/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 35,
+       "text": "<IPython.core.display.HTML at 0x110e59bd0>"
+      }
+     ],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": "[plotting regressions](http://stanford.edu/~mwaskom/software/seaborn/tutorial/quantitative_linear_models.html#plotting-simple-regression-with-regplot)"
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig18 = plt.figure()\n\nx, y = np.random.multivariate_normal([1, 5], [(2, -.8), (-.8, 2)], 80).T\nax = sns.regplot(x, y, color=\"seagreen\")\nax.set(xlabel=\"x variable\", ylabel=\"y variable\");\nplt.show()\n\nfig_to_plotly(fig18, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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psJ/JkA/NHiIY0sizOrPid2lb4zU8tu85AtEQVpOFrYuvPu/jqwsqZlS7c1hs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\nzrabre0AfG1N9zeWY39jOQAgIzqlZ812NjJiRgRkfgCDmoi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+       "text": "<matplotlib.figure.Figure at 0x110eada10>"
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": "/Users/matthewsundquist/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlylib/renderer.py:448: UserWarning: Dang! That path collection is out of this world. I totally don't know what to do with it yet! Plotly can only import path collections linked to 'data' coordinates\n  warnings.warn(\"Dang! That path collection is out of this \"\n"
+      },
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3098/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 37,
+       "text": "<IPython.core.display.HTML at 0x110ea6c90>"
+      }
+     ],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "fig19 = plt.figure()\n\nsns.residplot(x, y);\n\nfig_to_plotly(fig19, username, api_key, notebook = True)",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": "/Users/matthewsundquist/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlylib/renderer.py:363: UserWarning: Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n  warnings.warn(\"Bummer! Plotly can currently only draw Line2D \"\n"
+      },
+      {
+       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3099/600/450\" width=\"650\"></iframe>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 38,
+       "text": "<IPython.core.display.HTML at 0x111a90650>"
+      }
+     ],
+     "prompt_number": 38
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}