jess_question1.ipynb

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    "Observations:\n",
    "\n",
    "1) Country VS Points\n",
    "\n",
    "\t- Even with the US giving the most votes, Germany has the highest ranked wine at an average of 90.0.\n",
    "\t- When averaging out all the wine, the mode doesn't fall far from the top and bottom of the list. Most wines scored around an 88 that were not top or bottom.\n",
    "    - The worst ranked wine only falls at 83.0 points in Brazil, which overall isn't very low. The worst wines in the world aren't even ranked. Only the best, so let's say Brazil is the worst best wine?\n",
    "\n",
    "2) Country VS Price\n",
    "\n",
    "    - Surprisingly, Canada has the highest priced wine at $80.00 and didn't fall that far behind the highest ranked Germany(90.0) in points with 89.5.\n",
    "    - The countries that are most popular for producing wine(US, Italy, Germany) have about the same average pricing \n",
    "    - That also means that the wines might be harder to come by causing an inflated price by the imported country.\n",
    "    - Keep in mind that the dataset includes all variables of wine, not the 8 most popular. \n",
    "    \n",
    "OVERALL, the randomizing of the data doesn't allow a good look at how the origin of a wine can affect the popularity and price. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "% matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>taster_name</th>\n",
       "      <th>taster_twitter_handle</th>\n",
       "      <th>title</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26038</td>\n",
       "      <td>26038</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Raw, gamy aromas of herbal black fruits and we...</td>\n",
       "      <td>Finca Resalso</td>\n",
       "      <td>84</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Ribera del Duero</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michael Schachner</td>\n",
       "      <td>@wineschach</td>\n",
       "      <td>Emilio Moro 2015 Finca Resalso  (Ribera del Du...</td>\n",
       "      <td>Tempranillo</td>\n",
       "      <td>Emilio Moro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115439</td>\n",
       "      <td>115439</td>\n",
       "      <td>US</td>\n",
       "      <td>This wine is mild in acidity and simple in str...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Virginie Boone</td>\n",
       "      <td>@vboone</td>\n",
       "      <td>Rutherford Ranch 2015 Chardonnay (Napa Valley)</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Rutherford Ranch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111361</td>\n",
       "      <td>111361</td>\n",
       "      <td>Austria</td>\n",
       "      <td>This is described as Brut dry, but the perfume...</td>\n",
       "      <td>Sparkling Mosi Brut</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Österreichischer Sekt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Hermann Moser NV Sparkling Mosi Brut Gelber Mu...</td>\n",
       "      <td>Gelber Muskateller</td>\n",
       "      <td>Hermann Moser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>109611</td>\n",
       "      <td>109611</td>\n",
       "      <td>France</td>\n",
       "      <td>This big, ripe wine is opulent and smooth. Wit...</td>\n",
       "      <td>Tradition</td>\n",
       "      <td>87</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Domaine Schoffit 2012 Tradition Pinot Gris (Al...</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Domaine Schoffit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33921</td>\n",
       "      <td>33921</td>\n",
       "      <td>US</td>\n",
       "      <td>The wine has tempting aromas like marzipan and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>12.0</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California Other</td>\n",
       "      <td>Jim Gordon</td>\n",
       "      <td>@gordone_cellars</td>\n",
       "      <td>Leese-Fitch 2014 Chardonnay (California)</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Leese-Fitch</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  country  \\\n",
       "0       26038         26038    Spain   \n",
       "1      115439        115439       US   \n",
       "2      111361        111361  Austria   \n",
       "3      109611        109611   France   \n",
       "4       33921         33921       US   \n",
       "\n",
       "                                         description          designation  \\\n",
       "0  Raw, gamy aromas of herbal black fruits and we...        Finca Resalso   \n",
       "1  This wine is mild in acidity and simple in str...                  NaN   \n",
       "2  This is described as Brut dry, but the perfume...  Sparkling Mosi Brut   \n",
       "3  This big, ripe wine is opulent and smooth. Wit...            Tradition   \n",
       "4  The wine has tempting aromas like marzipan and...                  NaN   \n",
       "\n",
       "   points  price               province          region_1          region_2  \\\n",
       "0      84   15.0         Northern Spain  Ribera del Duero               NaN   \n",
       "1      87   24.0             California       Napa Valley              Napa   \n",
       "2      85    NaN  Österreichischer Sekt               NaN               NaN   \n",
       "3      87   27.0                 Alsace            Alsace               NaN   \n",
       "4      86   12.0             California        California  California Other   \n",
       "\n",
       "         taster_name taster_twitter_handle  \\\n",
       "0  Michael Schachner           @wineschach   \n",
       "1     Virginie Boone               @vboone   \n",
       "2         Roger Voss            @vossroger   \n",
       "3         Roger Voss            @vossroger   \n",
       "4         Jim Gordon      @gordone_cellars   \n",
       "\n",
       "                                               title             variety  \\\n",
       "0  Emilio Moro 2015 Finca Resalso  (Ribera del Du...         Tempranillo   \n",
       "1     Rutherford Ranch 2015 Chardonnay (Napa Valley)          Chardonnay   \n",
       "2  Hermann Moser NV Sparkling Mosi Brut Gelber Mu...  Gelber Muskateller   \n",
       "3  Domaine Schoffit 2012 Tradition Pinot Gris (Al...          Pinot Gris   \n",
       "4           Leese-Fitch 2014 Chardonnay (California)          Chardonnay   \n",
       "\n",
       "             winery  \n",
       "0       Emilio Moro  \n",
       "1  Rutherford Ranch  \n",
       "2     Hermann Moser  \n",
       "3  Domaine Schoffit  \n",
       "4       Leese-Fitch  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "wine_data = pd.read_csv(\"Resources/sample_data.csv\")\n",
    "\n",
    "wine_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>86.521739</td>\n",
       "      <td>23.227273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>87.857143</td>\n",
       "      <td>39.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>89.700000</td>\n",
       "      <td>28.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>83.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>89.500000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>85.631579</td>\n",
       "      <td>18.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>88.824859</td>\n",
       "      <td>38.871622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>90.214286</td>\n",
       "      <td>43.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>86.250000</td>\n",
       "      <td>21.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>88.250000</td>\n",
       "      <td>32.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>88.566667</td>\n",
       "      <td>40.409836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>88.095238</td>\n",
       "      <td>27.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>87.833333</td>\n",
       "      <td>22.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>88.600000</td>\n",
       "      <td>24.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>87.333333</td>\n",
       "      <td>29.270833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>88.747017</td>\n",
       "      <td>36.362110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 points      price\n",
       "country                           \n",
       "Argentina     86.521739  23.227273\n",
       "Australia     87.857143  39.857143\n",
       "Austria       89.700000  28.960000\n",
       "Brazil        83.000000  28.000000\n",
       "Canada        89.500000  80.000000\n",
       "Chile         85.631579  18.263158\n",
       "Croatia       84.000000  13.000000\n",
       "France        88.824859  38.871622\n",
       "Germany       90.214286  43.071429\n",
       "Greece        86.250000  21.750000\n",
       "Hungary       87.000000  13.000000\n",
       "Israel        88.250000  32.500000\n",
       "Italy         88.566667  40.409836\n",
       "New Zealand   88.095238  27.333333\n",
       "Portugal      87.833333  22.066667\n",
       "South Africa  88.600000  24.750000\n",
       "Spain         87.333333  29.270833\n",
       "Turkey        88.000000  15.000000\n",
       "US            88.747017  36.362110"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean Dataframe\n",
    "countries_df = wine_data.loc[:, [\"country\", \"points\", \"price\"]]\n",
    "countries_df = countries_df.groupby([\"country\"]).mean()\n",
    "points= countries_df[\"points\"].astype(float).map(\"{:,.2f}\".format)\n",
    "price = countries_df[\"price\"].astype(float).map(\"${:,.2f}\".format)\n",
    "countries_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "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",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Does the country of wine affect popularity? (2 Graphs)\n",
    "\n",
    "#Country vs Points\n",
    "countries = countries_df.index.values\n",
    "points = countries_df[\"points\"]\n",
    "\n",
    "#Plot Graph\n",
    "x_axis = np.arange(len(countries_df))\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.bar(x_axis, points, color=\"maroon\", align=\"edge\")\n",
    "\n",
    "tick_locations = [value+0.4 for value in x_axis]\n",
    "plt.xticks(tick_locations, countries, rotation=\"vertical\")\n",
    "\n",
    "plt.xlim(-0.25, len(x_axis))\n",
    "plt.ylim((80, 95))\n",
    "\n",
    "plt.title(\"How Does Country Origin Affect Ratings?\")\n",
    "plt.xlabel(\"Country\")\n",
    "plt.ylabel(\"Points/Ratings\")\n",
    "\n",
    "plt.grid(axis=\"y\")\n",
    "plt.show()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Images/CountryVsRatings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "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",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Does the origin of the wine correlate with the price of wine?\n",
    "\n",
    "#Country vs Price\n",
    "countries = countries_df.index.values\n",
    "price = countries_df[\"price\"]\n",
    "\n",
    "x_axis = np.arange(len(countries_df))\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.bar(x_axis, price, color=\"maroon\", align=\"edge\")\n",
    "\n",
    "tick_locations = [value+0.4 for value in x_axis]\n",
    "plt.xticks(tick_locations, countries, rotation=\"vertical\")\n",
    "\n",
    "plt.xlim(-0.25, len(x_axis))\n",
    "\n",
    "plt.title(\"How Does Country Origin Affect Price?\")\n",
    "plt.xlabel(\"Country\")\n",
    "plt.ylabel(\"Price ($)\")\n",
    "\n",
    "plt.grid(axis=\"y\")\n",
    "plt.show()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Images/CountryVsPrice.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}