1  {
   2   "cells": [
   3    {
   4     "cell_type": "markdown",
   5     "metadata": {},
   6     "source": [
   7      "# Assignment 4\n",
   8      "\n",
   9      "Before working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to **Preview the Grading** for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment.\n",
  10      "\n",
  11      "This assignment requires that you to find **at least** two datasets on the web which are related, and that you visualize these datasets to answer a question with the broad topic of **economic activity or measures** (see below) for the region of **Ann Arbor, Michigan, United States**, or **United States** more broadly.\n",
  12      "\n",
  13      "You can merge these datasets with data from different regions if you like! For instance, you might want to compare **Ann Arbor, Michigan, United States** to Ann Arbor, USA. In that case at least one source file must be about **Ann Arbor, Michigan, United States**.\n",
  14      "\n",
  15      "You are welcome to choose datasets at your discretion, but keep in mind **they will be shared with your peers**, so choose appropriate datasets. Sensitive, confidential, illicit, and proprietary materials are not good choices for datasets for this assignment. You are welcome to upload datasets of your own as well, and link to them using a third party repository such as github, bitbucket, pastebin, etc. Please be aware of the Coursera terms of service with respect to intellectual property.\n",
  16      "\n",
  17      "Also, you are welcome to preserve data in its original language, but for the purposes of grading you should provide english translations. You are welcome to provide multiple visuals in different languages if you would like!\n",
  18      "\n",
  19      "As this assignment is for the whole course, you must incorporate principles discussed in the first week, such as having as high data-ink ratio (Tufte) and aligning with Cairo’s principles of truth, beauty, function, and insight.\n",
  20      "\n",
  21      "Here are the assignment instructions:\n",
  22      "\n",
  23      " * State the region and the domain category that your data sets are about (e.g., **Ann Arbor, Michigan, United States** and **economic activity or measures**).\n",
  24      " * You must state a question about the domain category and region that you identified as being interesting.\n",
  25      " * You must provide at least two links to available datasets. These could be links to files such as CSV or Excel files, or links to websites which might have data in tabular form, such as Wikipedia pages.\n",
  26      " * You must upload an image which addresses the research question you stated. In addition to addressing the question, this visual should follow Cairo's principles of truthfulness, functionality, beauty, and insightfulness.\n",
  27      " * You must contribute a short (1-2 paragraph) written justification of how your visualization addresses your stated research question.\n",
  28      "\n",
  29      "What do we mean by **economic activity or measures**?  For this category you might look at the inputs or outputs to the given economy, or major changes in the economy compared to other regions.\n",
  30      "\n",
  31      "## Tips\n",
  32      "* Wikipedia is an excellent source of data, and I strongly encourage you to explore it for new data sources.\n",
  33      "* Many governments run open data initiatives at the city, region, and country levels, and these are wonderful resources for localized data sources.\n",
  34      "* Several international agencies, such as the [United Nations](http://data.un.org/), the [World Bank](http://data.worldbank.org/), the [Global Open Data Index](http://index.okfn.org/place/) are other great places to look for data.\n",
  35      "* This assignment requires you to convert and clean datafiles. Check out the discussion forums for tips on how to do this from various sources, and share your successes with your fellow students!\n",
  36      "\n",
  37      "## Example\n",
  38      "Looking for an example? Here's what our course assistant put together for the **Ann Arbor, MI, USA** area using **sports and athletics** as the topic. [Example Solution File](./readonly/Assignment4_example.pdf)"
  39     ]
  40    },
  41    {
  42     "cell_type": "code",
  43     "execution_count": 1,
  44     "metadata": {
  45      "collapsed": false
  46     },
  47     "outputs": [],
  48     "source": [
  49      "import pandas as pd\n",
  50      "import numpy as np\n",
  51      "import matplotlib.pyplot as plt\n",
  52      "import seaborn as sns\n",
  53      "%matplotlib notebook\n",
  54      "\n",
  55      "# Removes default white gridlines from the plot:\n",
  56      "plt.rcParams[\"axes.grid\"] = False"
  57     ]
  58    },
  59    {
  60     "cell_type": "code",
  61     "execution_count": 2,
  62     "metadata": {
  63      "collapsed": false
  64     },
  65     "outputs": [
  66      {
  67       "data": {
  68        "application/javascript": [
  69         "/* Put everything inside the global mpl namespace */\n",
  70         "window.mpl = {};\n",
  71         "\n",
  72         "\n",
  73         "mpl.get_websocket_type = function() {\n",
  74         "    if (typeof(WebSocket) !== 'undefined') {\n",
  75         "        return WebSocket;\n",
  76         "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
  77         "        return MozWebSocket;\n",
  78         "    } else {\n",
  79         "        alert('Your browser does not have WebSocket support.' +\n",
  80         "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
  81         "              'Firefox 4 and 5 are also supported but you ' +\n",
  82         "              'have to enable WebSockets in about:config.');\n",
  83         "    };\n",
  84         "}\n",
  85         "\n",
  86         "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
  87         "    this.id = figure_id;\n",
  88         "\n",
  89         "    this.ws = websocket;\n",
  90         "\n",
  91         "    this.supports_binary = (this.ws.binaryType != undefined);\n",
  92         "\n",
  93         "    if (!this.supports_binary) {\n",
  94         "        var warnings = document.getElementById(\"mpl-warnings\");\n",
  95         "        if (warnings) {\n",
  96         "            warnings.style.display = 'block';\n",
  97         "            warnings.textContent = (\n",
  98         "                \"This browser does not support binary websocket messages. \" +\n",
  99         "                    \"Performance may be slow.\");\n",
 100         "        }\n",
 101         "    }\n",
 102         "\n",
 103         "    this.imageObj = new Image();\n",
 104         "\n",
 105         "    this.context = undefined;\n",
 106         "    this.message = undefined;\n",
 107         "    this.canvas = undefined;\n",
 108         "    this.rubberband_canvas = undefined;\n",
 109         "    this.rubberband_context = undefined;\n",
 110         "    this.format_dropdown = undefined;\n",
 111         "\n",
 112         "    this.image_mode = 'full';\n",
 113         "\n",
 114         "    this.root = $('<div/>');\n",
 115         "    this._root_extra_style(this.root)\n",
 116         "    this.root.attr('style', 'display: inline-block');\n",
 117         "\n",
 118         "    $(parent_element).append(this.root);\n",
 119         "\n",
 120         "    this._init_header(this);\n",
 121         "    this._init_canvas(this);\n",
 122         "    this._init_toolbar(this);\n",
 123         "\n",
 124         "    var fig = this;\n",
 125         "\n",
 126         "    this.waiting = false;\n",
 127         "\n",
 128         "    this.ws.onopen =  function () {\n",
 129         "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
 130         "            fig.send_message(\"send_image_mode\", {});\n",
 131         "            if (mpl.ratio != 1) {\n",
 132         "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
 133         "            }\n",
 134         "            fig.send_message(\"refresh\", {});\n",
 135         "        }\n",
 136         "\n",
 137         "    this.imageObj.onload = function() {\n",
 138         "            if (fig.image_mode == 'full') {\n",
 139         "                // Full images could contain transparency (where diff images\n",
 140         "                // almost always do), so we need to clear the canvas so that\n",
 141         "                // there is no ghosting.\n",
 142         "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
 143         "            }\n",
 144         "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
 145         "        };\n",
 146         "\n",
 147         "    this.imageObj.onunload = function() {\n",
 148         "        this.ws.close();\n",
 149         "    }\n",
 150         "\n",
 151         "    this.ws.onmessage = this._make_on_message_function(this);\n",
 152         "\n",
 153         "    this.ondownload = ondownload;\n",
 154         "}\n",
 155         "\n",
 156         "mpl.figure.prototype._init_header = function() {\n",
 157         "    var titlebar = $(\n",
 158         "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
 159         "        'ui-helper-clearfix\"/>');\n",
 160         "    var titletext = $(\n",
 161         "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
 162         "        'text-align: center; padding: 3px;\"/>');\n",
 163         "    titlebar.append(titletext)\n",
 164         "    this.root.append(titlebar);\n",
 165         "    this.header = titletext[0];\n",
 166         "}\n",
 167         "\n",
 168         "\n",
 169         "\n",
 170         "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
 171         "\n",
 172         "}\n",
 173         "\n",
 174         "\n",
 175         "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
 176         "\n",
 177         "}\n",
 178         "\n",
 179         "mpl.figure.prototype._init_canvas = function() {\n",
 180         "    var fig = this;\n",
 181         "\n",
 182         "    var canvas_div = $('<div/>');\n",
 183         "\n",
 184         "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
 185         "\n",
 186         "    function canvas_keyboard_event(event) {\n",
 187         "        return fig.key_event(event, event['data']);\n",
 188         "    }\n",
 189         "\n",
 190         "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
 191         "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
 192         "    this.canvas_div = canvas_div\n",
 193         "    this._canvas_extra_style(canvas_div)\n",
 194         "    this.root.append(canvas_div);\n",
 195         "\n",
 196         "    var canvas = $('<canvas/>');\n",
 197         "    canvas.addClass('mpl-canvas');\n",
 198         "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
 199         "\n",
 200         "    this.canvas = canvas[0];\n",
 201         "    this.context = canvas[0].getContext(\"2d\");\n",
 202         "\n",
 203         "    var backingStore = this.context.backingStorePixelRatio ||\n",
 204         "\tthis.context.webkitBackingStorePixelRatio ||\n",
 205         "\tthis.context.mozBackingStorePixelRatio ||\n",
 206         "\tthis.context.msBackingStorePixelRatio ||\n",
 207         "\tthis.context.oBackingStorePixelRatio ||\n",
 208         "\tthis.context.backingStorePixelRatio || 1;\n",
 209         "\n",
 210         "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
 211         "\n",
 212         "    var rubberband = $('<canvas/>');\n",
 213         "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
 214         "\n",
 215         "    var pass_mouse_events = true;\n",
 216         "\n",
 217         "    canvas_div.resizable({\n",
 218         "        start: function(event, ui) {\n",
 219         "            pass_mouse_events = false;\n",
 220         "        },\n",
 221         "        resize: function(event, ui) {\n",
 222         "            fig.request_resize(ui.size.width, ui.size.height);\n",
 223         "        },\n",
 224         "        stop: function(event, ui) {\n",
 225         "            pass_mouse_events = true;\n",
 226         "            fig.request_resize(ui.size.width, ui.size.height);\n",
 227         "        },\n",
 228         "    });\n",
 229         "\n",
 230         "    function mouse_event_fn(event) {\n",
 231         "        if (pass_mouse_events)\n",
 232         "            return fig.mouse_event(event, event['data']);\n",
 233         "    }\n",
 234         "\n",
 235         "    rubberband.mousedown('button_press', mouse_event_fn);\n",
 236         "    rubberband.mouseup('button_release', mouse_event_fn);\n",
 237         "    // Throttle sequential mouse events to 1 every 20ms.\n",
 238         "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
 239         "\n",
 240         "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
 241         "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
 242         "\n",
 243         "    canvas_div.on(\"wheel\", function (event) {\n",
 244         "        event = event.originalEvent;\n",
 245         "        event['data'] = 'scroll'\n",
 246         "        if (event.deltaY < 0) {\n",
 247         "            event.step = 1;\n",
 248         "        } else {\n",
 249         "            event.step = -1;\n",
 250         "        }\n",
 251         "        mouse_event_fn(event);\n",
 252         "    });\n",
 253         "\n",
 254         "    canvas_div.append(canvas);\n",
 255         "    canvas_div.append(rubberband);\n",
 256         "\n",
 257         "    this.rubberband = rubberband;\n",
 258         "    this.rubberband_canvas = rubberband[0];\n",
 259         "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
 260         "    this.rubberband_context.strokeStyle = \"#000000\";\n",
 261         "\n",
 262         "    this._resize_canvas = function(width, height) {\n",
 263         "        // Keep the size of the canvas, canvas container, and rubber band\n",
 264         "        // canvas in synch.\n",
 265         "        canvas_div.css('width', width)\n",
 266         "        canvas_div.css('height', height)\n",
 267         "\n",
 268         "        canvas.attr('width', width * mpl.ratio);\n",
 269         "        canvas.attr('height', height * mpl.ratio);\n",
 270         "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
 271         "\n",
 272         "        rubberband.attr('width', width);\n",
 273         "        rubberband.attr('height', height);\n",
 274         "    }\n",
 275         "\n",
 276         "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
 277         "    // upon first draw.\n",
 278         "    this._resize_canvas(600, 600);\n",
 279         "\n",
 280         "    // Disable right mouse context menu.\n",
 281         "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
 282         "        return false;\n",
 283         "    });\n",
 284         "\n",
 285         "    function set_focus () {\n",
 286         "        canvas.focus();\n",
 287         "        canvas_div.focus();\n",
 288         "    }\n",
 289         "\n",
 290         "    window.setTimeout(set_focus, 100);\n",
 291         "}\n",
 292         "\n",
 293         "mpl.figure.prototype._init_toolbar = function() {\n",
 294         "    var fig = this;\n",
 295         "\n",
 296         "    var nav_element = $('<div/>')\n",
 297         "    nav_element.attr('style', 'width: 100%');\n",
 298         "    this.root.append(nav_element);\n",
 299         "\n",
 300         "    // Define a callback function for later on.\n",
 301         "    function toolbar_event(event) {\n",
 302         "        return fig.toolbar_button_onclick(event['data']);\n",
 303         "    }\n",
 304         "    function toolbar_mouse_event(event) {\n",
 305         "        return fig.toolbar_button_onmouseover(event['data']);\n",
 306         "    }\n",
 307         "\n",
 308         "    for(var toolbar_ind in mpl.toolbar_items) {\n",
 309         "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
 310         "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
 311         "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
 312         "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
 313         "\n",
 314         "        if (!name) {\n",
 315         "            // put a spacer in here.\n",
 316         "            continue;\n",
 317         "        }\n",
 318         "        var button = $('<button/>');\n",
 319         "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
 320         "                        'ui-button-icon-only');\n",
 321         "        button.attr('role', 'button');\n",
 322         "        button.attr('aria-disabled', 'false');\n",
 323         "        button.click(method_name, toolbar_event);\n",
 324         "        button.mouseover(tooltip, toolbar_mouse_event);\n",
 325         "\n",
 326         "        var icon_img = $('<span/>');\n",
 327         "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
 328         "        icon_img.addClass(image);\n",
 329         "        icon_img.addClass('ui-corner-all');\n",
 330         "\n",
 331         "        var tooltip_span = $('<span/>');\n",
 332         "        tooltip_span.addClass('ui-button-text');\n",
 333         "        tooltip_span.html(tooltip);\n",
 334         "\n",
 335         "        button.append(icon_img);\n",
 336         "        button.append(tooltip_span);\n",
 337         "\n",
 338         "        nav_element.append(button);\n",
 339         "    }\n",
 340         "\n",
 341         "    var fmt_picker_span = $('<span/>');\n",
 342         "\n",
 343         "    var fmt_picker = $('<select/>');\n",
 344         "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
 345         "    fmt_picker_span.append(fmt_picker);\n",
 346         "    nav_element.append(fmt_picker_span);\n",
 347         "    this.format_dropdown = fmt_picker[0];\n",
 348         "\n",
 349         "    for (var ind in mpl.extensions) {\n",
 350         "        var fmt = mpl.extensions[ind];\n",
 351         "        var option = $(\n",
 352         "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
 353         "        fmt_picker.append(option)\n",
 354         "    }\n",
 355         "\n",
 356         "    // Add hover states to the ui-buttons\n",
 357         "    $( \".ui-button\" ).hover(\n",
 358         "        function() { $(this).addClass(\"ui-state-hover\");},\n",
 359         "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
 360         "    );\n",
 361         "\n",
 362         "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
 363         "    nav_element.append(status_bar);\n",
 364         "    this.message = status_bar[0];\n",
 365         "}\n",
 366         "\n",
 367         "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
 368         "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
 369         "    // which will in turn request a refresh of the image.\n",
 370         "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
 371         "}\n",
 372         "\n",
 373         "mpl.figure.prototype.send_message = function(type, properties) {\n",
 374         "    properties['type'] = type;\n",
 375         "    properties['figure_id'] = this.id;\n",
 376         "    this.ws.send(JSON.stringify(properties));\n",
 377         "}\n",
 378         "\n",
 379         "mpl.figure.prototype.send_draw_message = function() {\n",
 380         "    if (!this.waiting) {\n",
 381         "        this.waiting = true;\n",
 382         "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
 383         "    }\n",
 384         "}\n",
 385         "\n",
 386         "\n",
 387         "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
 388         "    var format_dropdown = fig.format_dropdown;\n",
 389         "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
 390         "    fig.ondownload(fig, format);\n",
 391         "}\n",
 392         "\n",
 393         "\n",
 394         "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
 395         "    var size = msg['size'];\n",
 396         "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
 397         "        fig._resize_canvas(size[0], size[1]);\n",
 398         "        fig.send_message(\"refresh\", {});\n",
 399         "    };\n",
 400         "}\n",
 401         "\n",
 402         "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
 403         "    var x0 = msg['x0'] / mpl.ratio;\n",
 404         "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
 405         "    var x1 = msg['x1'] / mpl.ratio;\n",
 406         "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
 407         "    x0 = Math.floor(x0) + 0.5;\n",
 408         "    y0 = Math.floor(y0) + 0.5;\n",
 409         "    x1 = Math.floor(x1) + 0.5;\n",
 410         "    y1 = Math.floor(y1) + 0.5;\n",
 411         "    var min_x = Math.min(x0, x1);\n",
 412         "    var min_y = Math.min(y0, y1);\n",
 413         "    var width = Math.abs(x1 - x0);\n",
 414         "    var height = Math.abs(y1 - y0);\n",
 415         "\n",
 416         "    fig.rubberband_context.clearRect(\n",
 417         "        0, 0, fig.canvas.width, fig.canvas.height);\n",
 418         "\n",
 419         "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
 420         "}\n",
 421         "\n",
 422         "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
 423         "    // Updates the figure title.\n",
 424         "    fig.header.textContent = msg['label'];\n",
 425         "}\n",
 426         "\n",
 427         "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
 428         "    var cursor = msg['cursor'];\n",
 429         "    switch(cursor)\n",
 430         "    {\n",
 431         "    case 0:\n",
 432         "        cursor = 'pointer';\n",
 433         "        break;\n",
 434         "    case 1:\n",
 435         "        cursor = 'default';\n",
 436         "        break;\n",
 437         "    case 2:\n",
 438         "        cursor = 'crosshair';\n",
 439         "        break;\n",
 440         "    case 3:\n",
 441         "        cursor = 'move';\n",
 442         "        break;\n",
 443         "    }\n",
 444         "    fig.rubberband_canvas.style.cursor = cursor;\n",
 445         "}\n",
 446         "\n",
 447         "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
 448         "    fig.message.textContent = msg['message'];\n",
 449         "}\n",
 450         "\n",
 451         "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
 452         "    // Request the server to send over a new figure.\n",
 453         "    fig.send_draw_message();\n",
 454         "}\n",
 455         "\n",
 456         "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
 457         "    fig.image_mode = msg['mode'];\n",
 458         "}\n",
 459         "\n",
 460         "mpl.figure.prototype.updated_canvas_event = function() {\n",
 461         "    // Called whenever the canvas gets updated.\n",
 462         "    this.send_message(\"ack\", {});\n",
 463         "}\n",
 464         "\n",
 465         "// A function to construct a web socket function for onmessage handling.\n",
 466         "// Called in the figure constructor.\n",
 467         "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
 468         "    return function socket_on_message(evt) {\n",
 469         "        if (evt.data instanceof Blob) {\n",
 470         "            /* FIXME: We get \"Resource interpreted as Image but\n",
 471         "             * transferred with MIME type text/plain:\" errors on\n",
 472         "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
 473         "             * to be part of the websocket stream */\n",
 474         "            evt.data.type = \"image/png\";\n",
 475         "\n",
 476         "            /* Free the memory for the previous frames */\n",
 477         "            if (fig.imageObj.src) {\n",
 478         "                (window.URL || window.webkitURL).revokeObjectURL(\n",
 479         "                    fig.imageObj.src);\n",
 480         "            }\n",
 481         "\n",
 482         "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
 483         "                evt.data);\n",
 484         "            fig.updated_canvas_event();\n",
 485         "            fig.waiting = false;\n",
 486         "            return;\n",
 487         "        }\n",
 488         "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
 489         "            fig.imageObj.src = evt.data;\n",
 490         "            fig.updated_canvas_event();\n",
 491         "            fig.waiting = false;\n",
 492         "            return;\n",
 493         "        }\n",
 494         "\n",
 495         "        var msg = JSON.parse(evt.data);\n",
 496         "        var msg_type = msg['type'];\n",
 497         "\n",
 498         "        // Call the  \"handle_{type}\" callback, which takes\n",
 499         "        // the figure and JSON message as its only arguments.\n",
 500         "        try {\n",
 501         "            var callback = fig[\"handle_\" + msg_type];\n",
 502         "        } catch (e) {\n",
 503         "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
 504         "            return;\n",
 505         "        }\n",
 506         "\n",
 507         "        if (callback) {\n",
 508         "            try {\n",
 509         "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
 510         "                callback(fig, msg);\n",
 511         "            } catch (e) {\n",
 512         "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
 513         "            }\n",
 514         "        }\n",
 515         "    };\n",
 516         "}\n",
 517         "\n",
 518         "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
 519         "mpl.findpos = function(e) {\n",
 520         "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
 521         "    var targ;\n",
 522         "    if (!e)\n",
 523         "        e = window.event;\n",
 524         "    if (e.target)\n",
 525         "        targ = e.target;\n",
 526         "    else if (e.srcElement)\n",
 527         "        targ = e.srcElement;\n",
 528         "    if (targ.nodeType == 3) // defeat Safari bug\n",
 529         "        targ = targ.parentNode;\n",
 530         "\n",
 531         "    // jQuery normalizes the pageX and pageY\n",
 532         "    // pageX,Y are the mouse positions relative to the document\n",
 533         "    // offset() returns the position of the element relative to the document\n",
 534         "    var x = e.pageX - $(targ).offset().left;\n",
 535         "    var y = e.pageY - $(targ).offset().top;\n",
 536         "\n",
 537         "    return {\"x\": x, \"y\": y};\n",
 538         "};\n",
 539         "\n",
 540         "/*\n",
 541         " * return a copy of an object with only non-object keys\n",
 542         " * we need this to avoid circular references\n",
 543         " * http://stackoverflow.com/a/24161582/3208463\n",
 544         " */\n",
 545         "function simpleKeys (original) {\n",
 546         "  return Object.keys(original).reduce(function (obj, key) {\n",
 547         "    if (typeof original[key] !== 'object')\n",
 548         "        obj[key] = original[key]\n",
 549         "    return obj;\n",
 550         "  }, {});\n",
 551         "}\n",
 552         "\n",
 553         "mpl.figure.prototype.mouse_event = function(event, name) {\n",
 554         "    var canvas_pos = mpl.findpos(event)\n",
 555         "\n",
 556         "    if (name === 'button_press')\n",
 557         "    {\n",
 558         "        this.canvas.focus();\n",
 559         "        this.canvas_div.focus();\n",
 560         "    }\n",
 561         "\n",
 562         "    var x = canvas_pos.x * mpl.ratio;\n",
 563         "    var y = canvas_pos.y * mpl.ratio;\n",
 564         "\n",
 565         "    this.send_message(name, {x: x, y: y, button: event.button,\n",
 566         "                             step: event.step,\n",
 567         "                             guiEvent: simpleKeys(event)});\n",
 568         "\n",
 569         "    /* This prevents the web browser from automatically changing to\n",
 570         "     * the text insertion cursor when the button is pressed.  We want\n",
 571         "     * to control all of the cursor setting manually through the\n",
 572         "     * 'cursor' event from matplotlib */\n",
 573         "    event.preventDefault();\n",
 574         "    return false;\n",
 575         "}\n",
 576         "\n",
 577         "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
 578         "    // Handle any extra behaviour associated with a key event\n",
 579         "}\n",
 580         "\n",
 581         "mpl.figure.prototype.key_event = function(event, name) {\n",
 582         "\n",
 583         "    // Prevent repeat events\n",
 584         "    if (name == 'key_press')\n",
 585         "    {\n",
 586         "        if (event.which === this._key)\n",
 587         "            return;\n",
 588         "        else\n",
 589         "            this._key = event.which;\n",
 590         "    }\n",
 591         "    if (name == 'key_release')\n",
 592         "        this._key = null;\n",
 593         "\n",
 594         "    var value = '';\n",
 595         "    if (event.ctrlKey && event.which != 17)\n",
 596         "        value += \"ctrl+\";\n",
 597         "    if (event.altKey && event.which != 18)\n",
 598         "        value += \"alt+\";\n",
 599         "    if (event.shiftKey && event.which != 16)\n",
 600         "        value += \"shift+\";\n",
 601         "\n",
 602         "    value += 'k';\n",
 603         "    value += event.which.toString();\n",
 604         "\n",
 605         "    this._key_event_extra(event, name);\n",
 606         "\n",
 607         "    this.send_message(name, {key: value,\n",
 608         "                             guiEvent: simpleKeys(event)});\n",
 609         "    return false;\n",
 610         "}\n",
 611         "\n",
 612         "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
 613         "    if (name == 'download') {\n",
 614         "        this.handle_save(this, null);\n",
 615         "    } else {\n",
 616         "        this.send_message(\"toolbar_button\", {name: name});\n",
 617         "    }\n",
 618         "};\n",
 619         "\n",
 620         "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
 621         "    this.message.textContent = tooltip;\n",
 622         "};\n",
 623         "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
 624         "\n",
 625         "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
 626         "\n",
 627         "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
 628         "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
 629         "    // object with the appropriate methods. Currently this is a non binary\n",
 630         "    // socket, so there is still some room for performance tuning.\n",
 631         "    var ws = {};\n",
 632         "\n",
 633         "    ws.close = function() {\n",
 634         "        comm.close()\n",
 635         "    };\n",
 636         "    ws.send = function(m) {\n",
 637         "        //console.log('sending', m);\n",
 638         "        comm.send(m);\n",
 639         "    };\n",
 640         "    // Register the callback with on_msg.\n",
 641         "    comm.on_msg(function(msg) {\n",
 642         "        //console.log('receiving', msg['content']['data'], msg);\n",
 643         "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
 644         "        ws.onmessage(msg['content']['data'])\n",
 645         "    });\n",
 646         "    return ws;\n",
 647         "}\n",
 648         "\n",
 649         "mpl.mpl_figure_comm = function(comm, msg) {\n",
 650         "    // This is the function which gets called when the mpl process\n",
 651         "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
 652         "\n",
 653         "    var id = msg.content.data.id;\n",
 654         "    // Get hold of the div created by the display call when the Comm\n",
 655         "    // socket was opened in Python.\n",
 656         "    var element = $(\"#\" + id);\n",
 657         "    var ws_proxy = comm_websocket_adapter(comm)\n",
 658         "\n",
 659         "    function ondownload(figure, format) {\n",
 660         "        window.open(figure.imageObj.src);\n",
 661         "    }\n",
 662         "\n",
 663         "    var fig = new mpl.figure(id, ws_proxy,\n",
 664         "                           ondownload,\n",
 665         "                           element.get(0));\n",
 666         "\n",
 667         "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
 668         "    // web socket which is closed, not our websocket->open comm proxy.\n",
 669         "    ws_proxy.onopen();\n",
 670         "\n",
 671         "    fig.parent_element = element.get(0);\n",
 672         "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
 673         "    if (!fig.cell_info) {\n",
 674         "        console.error(\"Failed to find cell for figure\", id, fig);\n",
 675         "        return;\n",
 676         "    }\n",
 677         "\n",
 678         "    var output_index = fig.cell_info[2]\n",
 679         "    var cell = fig.cell_info[0];\n",
 680         "\n",
 681         "};\n",
 682         "\n",
 683         "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
 684         "    var width = fig.canvas.width/mpl.ratio\n",
 685         "    fig.root.unbind('remove')\n",
 686         "\n",
 687         "    // Update the output cell to use the data from the current canvas.\n",
 688         "    fig.push_to_output();\n",
 689         "    var dataURL = fig.canvas.toDataURL();\n",
 690         "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
 691         "    // the notebook keyboard shortcuts fail.\n",
 692         "    IPython.keyboard_manager.enable()\n",
 693         "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
 694         "    fig.close_ws(fig, msg);\n",
 695         "}\n",
 696         "\n",
 697         "mpl.figure.prototype.close_ws = function(fig, msg){\n",
 698         "    fig.send_message('closing', msg);\n",
 699         "    // fig.ws.close()\n",
 700         "}\n",
 701         "\n",
 702         "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
 703         "    // Turn the data on the canvas into data in the output cell.\n",
 704         "    var width = this.canvas.width/mpl.ratio\n",
 705         "    var dataURL = this.canvas.toDataURL();\n",
 706         "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
 707         "}\n",
 708         "\n",
 709         "mpl.figure.prototype.updated_canvas_event = function() {\n",
 710         "    // Tell IPython that the notebook contents must change.\n",
 711         "    IPython.notebook.set_dirty(true);\n",
 712         "    this.send_message(\"ack\", {});\n",
 713         "    var fig = this;\n",
 714         "    // Wait a second, then push the new image to the DOM so\n",
 715         "    // that it is saved nicely (might be nice to debounce this).\n",
 716         "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
 717         "}\n",
 718         "\n",
 719         "mpl.figure.prototype._init_toolbar = function() {\n",
 720         "    var fig = this;\n",
 721         "\n",
 722         "    var nav_element = $('<div/>')\n",
 723         "    nav_element.attr('style', 'width: 100%');\n",
 724         "    this.root.append(nav_element);\n",
 725         "\n",
 726         "    // Define a callback function for later on.\n",
 727         "    function toolbar_event(event) {\n",
 728         "        return fig.toolbar_button_onclick(event['data']);\n",
 729         "    }\n",
 730         "    function toolbar_mouse_event(event) {\n",
 731         "        return fig.toolbar_button_onmouseover(event['data']);\n",
 732         "    }\n",
 733         "\n",
 734         "    for(var toolbar_ind in mpl.toolbar_items){\n",
 735         "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
 736         "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
 737         "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
 738         "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
 739         "\n",
 740         "        if (!name) { continue; };\n",
 741         "\n",
 742         "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
 743         "        button.click(method_name, toolbar_event);\n",
 744         "        button.mouseover(tooltip, toolbar_mouse_event);\n",
 745         "        nav_element.append(button);\n",
 746         "    }\n",
 747         "\n",
 748         "    // Add the status bar.\n",
 749         "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
 750         "    nav_element.append(status_bar);\n",
 751         "    this.message = status_bar[0];\n",
 752         "\n",
 753         "    // Add the close button to the window.\n",
 754         "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
 755         "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
 756         "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
 757         "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
 758         "    buttongrp.append(button);\n",
 759         "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
 760         "    titlebar.prepend(buttongrp);\n",
 761         "}\n",
 762         "\n",
 763         "mpl.figure.prototype._root_extra_style = function(el){\n",
 764         "    var fig = this\n",
 765         "    el.on(\"remove\", function(){\n",
 766         "\tfig.close_ws(fig, {});\n",
 767         "    });\n",
 768         "}\n",
 769         "\n",
 770         "mpl.figure.prototype._canvas_extra_style = function(el){\n",
 771         "    // this is important to make the div 'focusable\n",
 772         "    el.attr('tabindex', 0)\n",
 773         "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
 774         "    // off when our div gets focus\n",
 775         "\n",
 776         "    // location in version 3\n",
 777         "    if (IPython.notebook.keyboard_manager) {\n",
 778         "        IPython.notebook.keyboard_manager.register_events(el);\n",
 779         "    }\n",
 780         "    else {\n",
 781         "        // location in version 2\n",
 782         "        IPython.keyboard_manager.register_events(el);\n",
 783         "    }\n",
 784         "\n",
 785         "}\n",
 786         "\n",
 787         "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
 788         "    var manager = IPython.notebook.keyboard_manager;\n",
 789         "    if (!manager)\n",
 790         "        manager = IPython.keyboard_manager;\n",
 791         "\n",
 792         "    // Check for shift+enter\n",
 793         "    if (event.shiftKey && event.which == 13) {\n",
 794         "        this.canvas_div.blur();\n",
 795         "        // select the cell after this one\n",
 796         "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
 797         "        IPython.notebook.select(index + 1);\n",
 798         "    }\n",
 799         "}\n",
 800         "\n",
 801         "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
 802         "    fig.ondownload(fig, null);\n",
 803         "}\n",
 804         "\n",
 805         "\n",
 806         "mpl.find_output_cell = function(html_output) {\n",
 807         "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
 808         "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
 809         "    // IPython event is triggered only after the cells have been serialised, which for\n",
 810         "    // our purposes (turning an active figure into a static one), is too late.\n",
 811         "    var cells = IPython.notebook.get_cells();\n",
 812         "    var ncells = cells.length;\n",
 813         "    for (var i=0; i<ncells; i++) {\n",
 814         "        var cell = cells[i];\n",
 815         "        if (cell.cell_type === 'code'){\n",
 816         "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
 817         "                var data = cell.output_area.outputs[j];\n",
 818         "                if (data.data) {\n",
 819         "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
 820         "                    data = data.data;\n",
 821         "                }\n",
 822         "                if (data['text/html'] == html_output) {\n",
 823         "                    return [cell, data, j];\n",
 824         "                }\n",
 825         "            }\n",
 826         "        }\n",
 827         "    }\n",
 828         "}\n",
 829         "\n",
 830         "// Register the function which deals with the matplotlib target/channel.\n",
 831         "// The kernel may be null if the page has been refreshed.\n",
 832         "if (IPython.notebook.kernel != null) {\n",
 833         "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
 834         "}\n"
 835        ],
 836        "text/plain": [
 837         "<IPython.core.display.Javascript object>"
 838        ]
 839       },
 840       "metadata": {},
 841       "output_type": "display_data"
 842      },
 843      {
 844       "data": {
 845        "text/html": [
 846         "<img 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\" width=\"640\">"
 847        ],
 848        "text/plain": [
 849         "<IPython.core.display.HTML object>"
 850        ]
 851       },
 852       "metadata": {},
 853       "output_type": "display_data"
 854      }
 855     ],
 856     "source": [
 857      "# Read and clean data for manufacturing employees:\n",
 858      "manuf = pd.read_csv('manuf.csv')\n",
 859      "manuf['DATE'] = pd.to_datetime(manuf['DATE'])\n",
 860      "manuf = manuf.rename(columns={'ANNA426MFG':'Manufacturing'})\n",
 861      "\n",
 862      "# Read and clean data for employees in professional and business services:\n",
 863      "prof_business = pd.read_csv('prof_business.csv')\n",
 864      "prof_business['DATE'] = pd.to_datetime(manuf['DATE'])\n",
 865      "prof_business = prof_business.rename(columns={'ANNA426PBSVN':'Professional services'})\n",
 866      "prof_business = prof_business.dropna() # NaN values causing problems in plotting.\n",
 867      "\n",
 868      "# Read and clean data for average weekly wages for employees in private organisations:\n",
 869      "avg_weekly = pd.read_csv('avg_weekly.csv')\n",
 870      "avg_weekly['DATE'] = pd.to_datetime(avg_weekly['DATE'])\n",
 871      "avg_weekly = avg_weekly.rename(columns={'ENUC114640510':'Weekly wages'})\n",
 872      "\n",
 873      "# Monthly date range:\n",
 874      "#dates = pd.date_range(start='1/1/1990', end='1/2/2019', freq='MS')\n",
 875      "\n",
 876      "fig, ax1 = plt.subplots()\n",
 877      "ax1.plot(manuf['DATE'],manuf['Manufacturing'])\n",
 878      "ax1.plot(prof_business['DATE'], prof_business['Professional services'])\n",
 879      "ax1.set_xlabel('Year')\n",
 880      "ax1.set_ylabel('Thousands of persons')\n",
 881      "ax1.tick_params('y')\n",
 882      "\n",
 883      "# frameon turns the frame around the legend on. framealpha adjusts the transparency of the box.\n",
 884      "ax1.legend(loc='upper left', fontsize=9, frameon=True, framealpha=1)\n",
 885      "\n",
 886      "ax2 = ax1.twinx()\n",
 887      "ax2.plot(avg_weekly['DATE'],avg_weekly['Weekly wages'], 'r', label='Private sector weekly wages')\n",
 888      "ax2.set_ylabel('Dollars per week')\n",
 889      "ax2.tick_params('y')\n",
 890      "ax2.legend(loc='upper right', fontsize=9, frameon=True, framealpha=1)\n",
 891      "\n",
 892      "plt.title('Employment trends in Ann Arbor, Michigan')\n",
 893      "fig.tight_layout()\n",
 894      "plt.show()"
 895     ]
 896    },
 897    {
 898     "cell_type": "code",
 899     "execution_count": null,
 900     "metadata": {
 901      "collapsed": false
 902     },
 903     "outputs": [],
 904     "source": []
 905    }
 906   ],
 907   "metadata": {
 908    "kernelspec": {
 909     "display_name": "Python 3",
 910     "language": "python",
 911     "name": "python3"
 912    },
 913    "language_info": {
 914     "codemirror_mode": {
 915      "name": "ipython",
 916      "version": 3
 917     },
 918     "file_extension": ".py",
 919     "mimetype": "text/x-python",
 920     "name": "python",
 921     "nbconvert_exporter": "python",
 922     "pygments_lexer": "ipython3",
 923     "version": "3.6.2"
 924    }
 925   },
 926   "nbformat": 4,
 927   "nbformat_minor": 1
 928  }
 929