0. Lecture Drills.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Plotting of Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 새로운 윈도우가 아닌 현재 창에 뜨도록 함\n",
    "%matplotlib inline    \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JmhLvLJakxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDUuVTXtGnZKkm3AbdOu4zFYA9w97SImzDGvfK2NF5bvmA+qqrWjGpZdECxXSWarambadUySY175WhsvrMwxOzUkSY0zCCSpcQbB5Jw77QKmwDGvfK2NF1bgmL1GIEmN84xAkhpnEEhS4wyCMUuyW5Lrk1w8om19kiu69huTHDuNGsdtkTEflOSybrxXJlk3jRrHKcnmJN9I8vUksyPak+SjSW7pxv3CadQ5TksY83OTXJPkZ0lOn0aN47aEMb+x+/3emOTqJC+YRp3jsPu0C1iB3gl8C9hnRNtZwAVV9fEkhwB/AWyYYG192dGY/wD4TFX9cZKXA2cDJ0+yuJ4cVVUL3VR0DPCc7udw4OPd43K3ozHfA7wDOGGC9UzCjsb8PeDIqro3yTEMLiIvy9+zZwRj1L3bfRXwiQW6FP/0YvkUYMsk6urTEsZ8CHBZt3wFcPwk6pqy4xmEX1XVtcDqJM+YdlF9qqqtVXUd8PNp1zIpVXV1Vd3brV4LLNuzXYNgvD4CnAE8vED77wEnJbmdwdnA2ydUV58WG/MNwOu65dcAq5LsN4nCelTApUk2Jdk4ov0A4AdD67d325azxca8Eu3MmE8FvjKBmnphEIxJkuOArVW1aQfd3gB8uqrWAccCn02ybH8HSxzz6cCRSa4HjgTuALZPor4eHVFVL2QwBfS2JC+d154Rz1nun9NebMwr0ZLGnOQoBkHwu5MsbpyW7YvQLugI4NVJNgN/Crw8yefm9TkVuACgqq4B9mDwBVbL1aJjrqotVfXaqjoM+M/dtvsnXukYVdWW7nErcBHw4nldbgcOHFpfxzKfBlzCmFecpYw5yaEMpkWPr6ofTbbC8TEIxqSqzqyqdVW1ATgRuLyqTprX7fvAbwAkeR6DINg20ULHaCljTrJm6KznTOBTEy5zrJLslWTV3DJwNHDTvG5fAt7UfXroJcD9VXXnhEsdmyWOeUVZypiTrAcuBE6uqu9Mvsrx8VNDPUvyPmC2qr4EvBv4n0l+h8FUwSm1Am/tnjfmlwFnJyngKuBt06xtDPYHLkoCg7+f86vqkiSnAVTVOQyu/xwL3AI8ALx5SrWOy6JjTvJ0YJbBhyEeTvIu4JCq+vG0in6clvJ7fi+wH/Cxrt/25fqtpH7FhCQ1zqkhSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIj1OSF3XfSb9Hd0fqN5P86rTrkpbKG8qkMUjyfgZfGbIncHtVnT3lkqQlMwikMUjyJOA64B+Bf1VVD025JGnJnBqSxuOpwN7AKgZnBtKy4RmBNAZJvsTgq7ifBTyjqn57yiVJS+a3j0qPU5I3MfjmyfOT7AZcneTlVXX5tGuTlsIzAklqnNcIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklq3P8HrsYpxZbjkZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(5, 5, 'o')\n",
    "\n",
    "plt.ylabel('y')\n",
    "plt.xlabel('x')\n",
    "plt.title('Plottling example')\n",
    "\n",
    "plt.show()    # .show이후에 작성되는 코드는 plotting에 반영되지 않음\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.show 이후에 작성해도 active figure를 찾아서 반영하도록 함\n",
    "%matplotlib notebook    \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plotting example')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(5, 5, '*')\n",
    "plt.show()\n",
    "\n",
    "plt.ylabel('y')\n",
    "plt.xlabel('x')\n",
    "plt.title('Plotting example')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>India</th>\n",
       "      <th>China</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981.0</th>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983.0</th>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984.0</th>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        India  China\n",
       "                    \n",
       "1980.0   8880   5123\n",
       "1981.0   8670   6682\n",
       "1982.0   8147   3308\n",
       "1983.0   7338   1863\n",
       "1984.0   5704   1527"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. names에 3항목인 경우 -> 3열 생성\n",
    "df = pd.read_csv('immigration.txt', names = ['', 'India', 'China'])\n",
    "df.drop(index=0, inplace=True)       # 첫번째 행 삭제\n",
    "df.set_index('')      # 첫번째 열을 index로 옮김"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>India</th>\n",
       "      <th>China</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      India  China\n",
       "1980   8880   5123\n",
       "1981   8670   6682\n",
       "1982   8147   3308\n",
       "1983   7338   1863\n",
       "1984   5704   1527"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. names에 3항목인 경우 -> 2열 생성\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('immigration.txt', names = ['India', 'China'])\n",
    "df.drop(index=np.nan, inplace=True)     # 첫번째 행 삭제\n",
    "df.index = df.index.astype('int64')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-c954f31bcb74>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m     \u001b[1;31m# numeric data가 없다고 뜸 --> data type 확인\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    845\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    259\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 261\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    262\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    408\u001b[0m         \u001b[1;31m# no non-numeric frames or series allowed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    409\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"no numeric data to plot\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    412\u001b[0m         \u001b[1;31m# GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "df.plot()     # numeric data가 없다고 뜸 --> data type 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India    object\n",
       "China    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes     # 이민자 수가 object로 인식됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India    int64\n",
       "China    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type을 int로 변경\n",
    "for x in df.columns :\n",
    "    df[x] = df[x].astype('int64')    \n",
    "df.dtypes  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# of Immigrants')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='line')\n",
    "\n",
    "plt.title('Immigrant comparison')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('# of Immigrants')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: unrecognized arguments: # 기존 figure를 변경하는 오류가 발생하여 inline backend 불러옴\n"
     ]
    }
   ],
   "source": [
    "# Histogram\n",
    "%matplotlib inline     # 기존 figure를 변경하는 오류가 발생하여 inline backend 불러옴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2762d997ec8>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.India.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2762da27608>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['China'].plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x224cd7c8048>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['India'] = df['India'].astype(int)\n",
    "df['China'] = df['China'].astype(int)\n",
    "df.dtypes\n",
    "\n",
    "df.plot()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}