{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CAOS Workshop Series: Introduction to Python Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chris McCray - 21 February 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic Python syntax (from https://www.stavros.io/tutorials/python/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Python has no mandatory statement termination characters (i.e. semicolons)\n",
    "* Single-line comments start with a #\n",
    "* Variable values are assigned using =\n",
    "* Python automatically sets the data type (i.e. int, float, str)\n",
    "* Basic data structures: lists, tuples, dictionaries (index of the first item is 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python allows for a great deal of freedom, but does have a style guide (PEP 8) to make your code generally cleaner and more user-friendly: https://www.python.org/dev/peps/pep-0008/\n",
    "\n",
    "Many good intro to Python resources can be found online (check them out for more detail than we can go through here) - e.g., Python tutorial: https://docs.python.org/3/tutorial/\n",
    "\n",
    "Python code can be run in scripts, with an IDE (see Spyder), in Jupyter notebooks like this one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter Notebook:\n",
    "* Allows code to be run in blocks/\"cells\"\n",
    "* Allows for easy presentation of figures and code\n",
    "* Can be hosted remotely (i.e. on a server, closer to your data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables, data types, and printing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable types are automatically assigned - no type declarations needed\n",
    "\n",
    "Variable names \n",
    "* Can start with lowercase or uppercase letters (but not numbers or symbols!)\n",
    "* Can contain numbers and certain symbols\n",
    "* Words should be separated by underscores\n",
    "\n",
    "Printing\n",
    "* In Python 3, printing output requires parentheses: print(variable_name)\n",
    "* In Python 2, parentheses are not required (but can be included): print variable_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "#Integers\n",
    "my_integer = 3\n",
    "print(type(my_integer))\n",
    "\n",
    "#Floats\n",
    "my_float = 1.23\n",
    "print(type(my_float))\n",
    "\n",
    "#Strings\n",
    "string_1 = 'This is a string.'\n",
    "string_2 = \"This is also a string!\"\n",
    "print(type(string_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "Test\n",
      "Test 3\n"
     ]
    }
   ],
   "source": [
    "print(my_integer)\n",
    "print('Test')\n",
    "print('Test',my_integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-3085dcc10b55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring_1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmy_integer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int"
     ]
    }
   ],
   "source": [
    "print(string_1+my_integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(string_1+' '+str(my_integer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a string. 3\n"
     ]
    }
   ],
   "source": [
    "print(string_1, my_integer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists and dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "f\n"
     ]
    }
   ],
   "source": [
    "list_one = ['a','b','c','d','e','f']\n",
    "print(list_one[0])\n",
    "print(list_one[-1])"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    }
   ],
   "source": [
    "ages = {'Jim': 23, \n",
    "        'Sarah': 25,\n",
    "        'Tom': 30}\n",
    "\n",
    "\n",
    "print(ages['Jim'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control Flow Tools/Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### While loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "b = 10\n",
    "while b <= 20:\n",
    "    print (b)\n",
    "    b+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### If statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter an integer:11\n",
      "11 is greater than 0.\n"
     ]
    }
   ],
   "source": [
    "x = int(input(\"Please enter an integer:\"))\n",
    "if x < 0:\n",
    "    print(x,'is less than 0.')\n",
    "elif x == 0:\n",
    "    print(x,'is 0.')\n",
    "else:\n",
    "    print(x,'is greater than 0.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat 3\n",
      "horse 5\n",
      "chicken 7\n"
     ]
    }
   ],
   "source": [
    "words = ['cat', 'horse', 'chicken']\n",
    "for w in words:\n",
    "    print(w, len(w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules and functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Python source files (ending in .py) are known as **modules**\n",
    "* Modules contain **functions**, which can be called separated from the module\n",
    "* Below is an example of what's inside the module _welcome.py_ (modified from https://developers.google.com/edu/python/introduction)\n",
    "* Try running welcome.py by typing ```python hello.py``` and your name, in your command line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "#!/usr/bin/env python\n",
    "\n",
    "# import modules used here -- sys is a very standard one\n",
    "import sys\n",
    "\n",
    "# Gather our code in a main() function\n",
    "def main():\n",
    "    print('Hello there', sys.argv[1])\n",
    "    # Command line args are in sys.argv[1], sys.argv[2] ...\n",
    "    # sys.argv[0] is the script name itself and can be ignored\n",
    "\n",
    "def string_information(string):\n",
    "    num_characters = len(string)\n",
    "    num_no_whitespace = (len(string.replace(\" \", \"\")))\n",
    "    print('This string has',num_characters,'characters including \\\\\n",
    "    whitespace and',num_no_whitespace,'without whitespace.')\n",
    "\n",
    "# Standard boilerplate to call the main() function to begin\n",
    "# the program.\n",
    "if __name__ == '__main__':\n",
    "    main()\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions can be imported into other modules:\n",
    "(welcome.py can be downloaded at https://github.com/mccrayc/tutorials/blob/master/1_intro/welcome.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from welcome import string_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "string = 'This Is a Test'\n",
    "string_information(string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.7\n",
      "0.007908163265306122\n",
      "9.920000000000002\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "print( 3.1 + 3.6 )\n",
    "print( 3.1/392   )\n",
    "print( 3.1*3.2   )\n",
    "print( 4**2  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from math import cos\n",
    "print( cos(2*math.pi) )\n",
    "print( math.sqrt(4) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Warning: In Python 2, integer division is \"floor division\"\n",
    "* 5/6 = 0\n",
    "* 6/5 = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumPy\n",
    "From http://www.numpy.org/\n",
    "> NumPy is the fundamental package for scientific computing with Python. It contains among other things\n",
    "* a powerful N-dimensional array object\n",
    "* sophisticated (broadcasting) functions\n",
    "* tools for integrating C/C++ and Fortran code\n",
    "* useful linear algebra, Fourier transform, and random number capabilities\n",
    "\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,3,4],[5,6,7,8]])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2 -1  0  1]\n",
      " [ 2  3  4  5]]\n",
      "[[ 0.54030231 -0.41614684 -0.9899925  -0.65364362]\n",
      " [ 0.28366219  0.96017029  0.75390225 -0.14550003]]\n",
      "[[  2.71828183e+00   7.38905610e+00   2.00855369e+01   5.45981500e+01]\n",
      " [  1.48413159e+02   4.03428793e+02   1.09663316e+03   2.98095799e+03]]\n",
      "[[1 5]\n",
      " [2 6]\n",
      " [3 7]\n",
      " [4 8]]\n"
     ]
    }
   ],
   "source": [
    "#Subtract 3 from each array element\n",
    "print( a-3 )\n",
    "# Get the cosine of each array element\n",
    "print( np.cos(a) )\n",
    "# Calculate e^x for each array element x\n",
    "print (np.exp(a))\n",
    "# Transpose a\n",
    "print( a.T )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy is a very important part of scientific Python, and forms an integral part of nearly all other scientific packages. You should look through https://docs.scipy.org/doc/numpy-dev/user/quickstart.html to get some background in how it works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common plotting library with Python is currently [matplotlib](https://matplotlib.org/), which provides a MATLAB-style interface\n",
    "\n",
    "* There are many ways to create figures and subplots with matplotlib, but here we'll just go over a basic example using the pyplot interface.\n",
    "* Pyplot provides MATLAB-like functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This allows for an interactive figure interface within the jupyter notebook. \n",
    "If you just want to show the figure without interactivity, use %matplotlib inline\n",
    "'''\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0,100,0.01)\n",
    "y1 = np.cos(x)\n",
    "y2 = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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 overriden (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"
    },
    {
     "data": {
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\" width=\"639.2857115639716\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f8bcc028f60>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(x,y1, label='cos(x)')\n",
    "plt.plot(x,y2, label='sin(x)')\n",
    "plt.axhline(y=0, color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Zoom in on the plot\n",
    "plt.xlim([0,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add labels to the axes\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "#Add a title\n",
    "plt.title(\"$sin(x)$ and $cos(x)$\")\n",
    "#Add a grid\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8bc0e6d518>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plot a basic legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Working with real data (CSV format) in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are many ways to read CSV data. Python's standard library includes the \"csv\" package\n",
    "* \"Pandas\" (https://pandas.pydata.org/) is one of the key packages in scientific Python and data science\n",
    "* Pandas makes reading CSVs easy, handles missing data well, and allows for quick calculations and plotting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll read in a CSV file that contains daily weather data for each day in 2017 from Environment and Climate Change Canada for CYUL (Montreal-Trudeau Airport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyul_2017 = pd.read_csv('http://www.cdmccray.com/python_tutorial/eng-daily-01012017-12312017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Data Quality</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Max Temp Flag</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Min Temp Flag</th>\n",
       "      <th>Tmean</th>\n",
       "      <th>...</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Total Snow Flag</th>\n",
       "      <th>Precip</th>\n",
       "      <th>Total Precip Flag</th>\n",
       "      <th>Snow on Grnd (cm)</th>\n",
       "      <th>Snow on Grnd Flag</th>\n",
       "      <th>Dir of Max Gust (10s deg)</th>\n",
       "      <th>Dir of Max Gust Flag</th>\n",
       "      <th>Max_gust</th>\n",
       "      <th>Spd of Max Gust Flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>‡</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>‡</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>‡</td>\n",
       "      <td>-9.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>‡</td>\n",
       "      <td>-10.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-01-10</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-01-12</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-01-14</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-01-15</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-01-16</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-01-17</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>‡</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-01-22</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>‡</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-01-25</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-01-27</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-01-28</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-01-30</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>‡</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2017-12-02</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>‡</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2017-12-03</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2017-12-04</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2017-12-05</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>‡</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2017-12-06</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2017-12-09</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2017-12-10</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>‡</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2017-12-11</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>‡</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2017-12-12</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>‡</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>...</td>\n",
       "      <td>18.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2017-12-13</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>2017-12-14</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>‡</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>‡</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2017-12-16</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>‡</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>‡</td>\n",
       "      <td>-12.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>‡</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>‡</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>2017-12-21</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>‡</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>‡</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>‡</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2017-12-24</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>‡</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2017-12-25</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>‡</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>...</td>\n",
       "      <td>13.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>‡</td>\n",
       "      <td>-18.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>‡</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-26.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>‡</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>‡</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>‡</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Year  Month  Day Data Quality  Tmax Max Temp Flag  Tmin  \\\n",
       "0    2017-01-01  2017      1    1            ‡  -2.4           NaN  -8.7   \n",
       "1    2017-01-02  2017      1    2            ‡   1.8           NaN -10.2   \n",
       "2    2017-01-03  2017      1    3            ‡   0.5           NaN  -5.8   \n",
       "3    2017-01-04  2017      1    4            ‡   2.5           NaN  -5.6   \n",
       "4    2017-01-05  2017      1    5            ‡  -5.3           NaN -10.7   \n",
       "5    2017-01-06  2017      1    6            ‡  -7.7           NaN -16.4   \n",
       "6    2017-01-07  2017      1    7            ‡  -9.6           NaN -18.9   \n",
       "7    2017-01-08  2017      1    8            ‡ -10.9           NaN -20.3   \n",
       "8    2017-01-09  2017      1    9            ‡  -7.0           NaN -21.9   \n",
       "9    2017-01-10  2017      1   10            ‡   1.3           NaN  -8.3   \n",
       "10   2017-01-11  2017      1   11            ‡   5.7           NaN   1.2   \n",
       "11   2017-01-12  2017      1   12            ‡   5.0           NaN  -0.9   \n",
       "12   2017-01-13  2017      1   13            ‡  -0.4           NaN -14.9   \n",
       "13   2017-01-14  2017      1   14            ‡  -7.1           NaN -17.2   \n",
       "14   2017-01-15  2017      1   15            ‡  -7.4           NaN -11.3   \n",
       "15   2017-01-16  2017      1   16            ‡   2.7           NaN  -9.0   \n",
       "16   2017-01-17  2017      1   17            ‡  -0.4           NaN  -5.3   \n",
       "17   2017-01-18  2017      1   18            ‡  -1.3           NaN  -5.3   \n",
       "18   2017-01-19  2017      1   19            ‡   2.0           NaN  -1.3   \n",
       "19   2017-01-20  2017      1   20            ‡   2.2           NaN   0.2   \n",
       "20   2017-01-21  2017      1   21            ‡   2.1           NaN   0.3   \n",
       "21   2017-01-22  2017      1   22            ‡   2.1           NaN  -2.6   \n",
       "22   2017-01-23  2017      1   23            ‡  -0.1           NaN  -3.8   \n",
       "23   2017-01-24  2017      1   24            ‡  -2.3           NaN  -3.8   \n",
       "24   2017-01-25  2017      1   25            ‡   0.3           NaN  -2.4   \n",
       "25   2017-01-26  2017      1   26            ‡   1.4           NaN   0.1   \n",
       "26   2017-01-27  2017      1   27            ‡   0.8           NaN  -3.1   \n",
       "27   2017-01-28  2017      1   28            ‡   0.2           NaN  -3.0   \n",
       "28   2017-01-29  2017      1   29            ‡  -0.3           NaN  -6.5   \n",
       "29   2017-01-30  2017      1   30            ‡  -6.4           NaN -13.7   \n",
       "..          ...   ...    ...  ...          ...   ...           ...   ...   \n",
       "335  2017-12-02  2017     12    2            ‡   3.2           NaN  -0.9   \n",
       "336  2017-12-03  2017     12    3            ‡   2.3           NaN  -1.7   \n",
       "337  2017-12-04  2017     12    4            ‡   5.9           NaN  -3.0   \n",
       "338  2017-12-05  2017     12    5            ‡  10.0           NaN   5.6   \n",
       "339  2017-12-06  2017     12    6            ‡   5.7           NaN  -0.3   \n",
       "340  2017-12-07  2017     12    7            ‡   1.4           NaN  -1.7   \n",
       "341  2017-12-08  2017     12    8            ‡   1.0           NaN  -2.9   \n",
       "342  2017-12-09  2017     12    9            ‡   0.5           NaN  -4.5   \n",
       "343  2017-12-10  2017     12   10            ‡  -1.4           NaN  -5.1   \n",
       "344  2017-12-11  2017     12   11            ‡  -3.8           NaN -12.7   \n",
       "345  2017-12-12  2017     12   12            ‡  -6.8           NaN -10.7   \n",
       "346  2017-12-13  2017     12   13            ‡  -7.2           NaN -16.3   \n",
       "347  2017-12-14  2017     12   14            ‡ -14.5           NaN -19.0   \n",
       "348  2017-12-15  2017     12   15            ‡  -9.4           NaN -20.1   \n",
       "349  2017-12-16  2017     12   16            ‡  -8.0           NaN -15.0   \n",
       "350  2017-12-17  2017     12   17            ‡ -12.4           NaN -21.0   \n",
       "351  2017-12-18  2017     12   18            ‡ -10.1           NaN -14.1   \n",
       "352  2017-12-19  2017     12   19            ‡   3.3           NaN -10.2   \n",
       "353  2017-12-20  2017     12   20            ‡   1.0           NaN -12.2   \n",
       "354  2017-12-21  2017     12   21            ‡ -10.5           NaN -16.2   \n",
       "355  2017-12-22  2017     12   22            ‡ -11.6           NaN -15.2   \n",
       "356  2017-12-23  2017     12   23            ‡  -5.2           NaN -12.0   \n",
       "357  2017-12-24  2017     12   24            ‡  -5.5           NaN  -9.7   \n",
       "358  2017-12-25  2017     12   25            ‡  -6.4           NaN -10.1   \n",
       "359  2017-12-26  2017     12   26            ‡  -7.0           NaN -18.9   \n",
       "360  2017-12-27  2017     12   27            ‡ -18.3           NaN -24.5   \n",
       "361  2017-12-28  2017     12   28            ‡ -20.5           NaN -26.6   \n",
       "362  2017-12-29  2017     12   29            ‡ -18.4           NaN -24.8   \n",
       "363  2017-12-30  2017     12   30            ‡ -17.4           NaN -24.9   \n",
       "364  2017-12-31  2017     12   31            ‡ -18.9           NaN -23.8   \n",
       "\n",
       "    Min Temp Flag  Tmean          ...           Snow  Total Snow Flag Precip  \\\n",
       "0             NaN   -5.6          ...            2.8              NaN    2.4   \n",
       "1             NaN   -4.2          ...            0.0              NaN    0.0   \n",
       "2             NaN   -2.7          ...            3.6              NaN   17.4   \n",
       "3             NaN   -1.6          ...            9.0              NaN    8.0   \n",
       "4             NaN   -8.0          ...            0.0                T    0.0   \n",
       "5             NaN  -12.1          ...            0.0                T    0.0   \n",
       "6             NaN  -14.3          ...            0.0              NaN    0.0   \n",
       "7             NaN  -15.6          ...            0.0              NaN    0.0   \n",
       "8             NaN  -14.5          ...            0.4              NaN    0.4   \n",
       "9             NaN   -3.5          ...            1.0              NaN    2.2   \n",
       "10            NaN    3.5          ...            0.0              NaN    3.0   \n",
       "11            NaN    2.1          ...            0.0              NaN   10.0   \n",
       "12            NaN   -7.7          ...            0.0                T    0.4   \n",
       "13            NaN  -12.2          ...            0.0                T    0.0   \n",
       "14            NaN   -9.4          ...            0.0                T    0.0   \n",
       "15            NaN   -3.2          ...            0.0              NaN    0.0   \n",
       "16            NaN   -2.9          ...            1.4              NaN    1.4   \n",
       "17            NaN   -3.3          ...            6.8              NaN    7.0   \n",
       "18            NaN    0.4          ...            0.0              NaN    0.0   \n",
       "19            NaN    1.2          ...            0.0              NaN    0.0   \n",
       "20            NaN    1.2          ...            0.0              NaN    0.6   \n",
       "21            NaN   -0.3          ...            0.0                T    0.0   \n",
       "22            NaN   -2.0          ...            0.0              NaN    0.0   \n",
       "23            NaN   -3.1          ...            7.0              NaN   12.4   \n",
       "24            NaN   -1.1          ...            3.2              NaN    2.6   \n",
       "25            NaN    0.8          ...            2.2              NaN    1.6   \n",
       "26            NaN   -1.2          ...            3.6              NaN    2.6   \n",
       "27            NaN   -1.4          ...            0.2              NaN    0.2   \n",
       "28            NaN   -3.4          ...            1.0              NaN    1.0   \n",
       "29            NaN  -10.1          ...            0.0                T    0.0   \n",
       "..            ...    ...          ...            ...              ...    ...   \n",
       "335           NaN    1.2          ...            0.0              NaN    0.0   \n",
       "336           NaN    0.3          ...            0.0              NaN    0.0   \n",
       "337           NaN    1.5          ...            0.0              NaN    0.0   \n",
       "338           NaN    7.8          ...            0.0              NaN    9.8   \n",
       "339           NaN    2.7          ...            0.0                T    0.0   \n",
       "340           NaN   -0.2          ...            0.2              NaN    0.2   \n",
       "341           NaN   -1.0          ...            0.0                T    0.0   \n",
       "342           NaN   -2.0          ...            0.0                T    0.0   \n",
       "343           NaN   -3.3          ...            1.8              NaN    1.6   \n",
       "344           NaN   -8.3          ...            0.8              NaN    0.4   \n",
       "345           NaN   -8.8          ...           18.6              NaN   17.2   \n",
       "346           NaN  -11.8          ...            2.4              NaN    1.6   \n",
       "347           NaN  -16.8          ...            0.0              NaN    0.0   \n",
       "348           NaN  -14.8          ...            0.4              NaN    0.4   \n",
       "349           NaN  -11.5          ...            1.8              NaN    1.8   \n",
       "350           NaN  -16.7          ...            0.2              NaN    0.2   \n",
       "351           NaN  -12.1          ...            4.8              NaN    4.4   \n",
       "352           NaN   -3.5          ...            4.4              NaN    7.1   \n",
       "353           NaN   -5.6          ...            0.0              NaN    0.0   \n",
       "354           NaN  -13.4          ...            0.0              NaN    0.0   \n",
       "355           NaN  -13.4          ...            1.4              NaN    1.2   \n",
       "356           NaN   -8.6          ...            9.4              NaN   14.4   \n",
       "357           NaN   -7.6          ...            0.0                T    0.0   \n",
       "358           NaN   -8.3          ...           13.4              NaN   12.6   \n",
       "359           NaN  -13.0          ...            0.0                T    0.0   \n",
       "360           NaN  -21.4          ...            0.0              NaN    0.0   \n",
       "361           NaN  -23.6          ...            0.0              NaN    0.0   \n",
       "362           NaN  -21.6          ...            1.6              NaN    1.6   \n",
       "363           NaN  -21.2          ...            0.2              NaN    0.2   \n",
       "364           NaN  -21.4          ...            0.0                T    0.0   \n",
       "\n",
       "     Total Precip Flag Snow on Grnd (cm)  Snow on Grnd Flag  \\\n",
       "0                  NaN              12.0                NaN   \n",
       "1                  NaN               6.0                NaN   \n",
       "2                  NaN               6.0                NaN   \n",
       "3                  NaN              12.0                NaN   \n",
       "4                    T              13.0                NaN   \n",
       "5                    T              13.0                NaN   \n",
       "6                  NaN              13.0                NaN   \n",
       "7                  NaN              12.0                NaN   \n",
       "8                  NaN              11.0                NaN   \n",
       "9                  NaN              11.0                NaN   \n",
       "10                 NaN              10.0                NaN   \n",
       "11                 NaN               4.0                NaN   \n",
       "12                 NaN               2.0                NaN   \n",
       "13                   T               1.0                NaN   \n",
       "14                   T               1.0                NaN   \n",
       "15                 NaN               1.0                NaN   \n",
       "16                 NaN               1.0                NaN   \n",
       "17                 NaN               3.0                NaN   \n",
       "18                   T               4.0                NaN   \n",
       "19                 NaN               3.0                NaN   \n",
       "20                 NaN               2.0                NaN   \n",
       "21                   T               1.0                NaN   \n",
       "22                   T               0.0                  T   \n",
       "23                 NaN               0.0                  T   \n",
       "24                 NaN               4.0                NaN   \n",
       "25                 NaN               5.0                NaN   \n",
       "26                 NaN               4.0                NaN   \n",
       "27                 NaN               5.0                NaN   \n",
       "28                 NaN               5.0                NaN   \n",
       "29                   T               5.0                NaN   \n",
       "..                 ...               ...                ...   \n",
       "335                NaN               NaN                NaN   \n",
       "336                NaN               NaN                NaN   \n",
       "337                  T               NaN                NaN   \n",
       "338                NaN               NaN                NaN   \n",
       "339                  T               NaN                NaN   \n",
       "340                NaN               0.0                  T   \n",
       "341                  T               0.0                  T   \n",
       "342                  T               NaN                NaN   \n",
       "343                NaN               0.0                  T   \n",
       "344                NaN               2.0                NaN   \n",
       "345                NaN               4.0                NaN   \n",
       "346                NaN               9.0                NaN   \n",
       "347                NaN               4.0                NaN   \n",
       "348                NaN               3.0                NaN   \n",
       "349                NaN               5.0                NaN   \n",
       "350                NaN               5.0                NaN   \n",
       "351                NaN               5.0                NaN   \n",
       "352                NaN               9.0                NaN   \n",
       "353                  T               8.0                NaN   \n",
       "354                NaN               6.0                NaN   \n",
       "355                NaN               5.0                NaN   \n",
       "356                NaN               5.0                NaN   \n",
       "357                  T              12.0                NaN   \n",
       "358                NaN              18.0                NaN   \n",
       "359                  T              19.0                NaN   \n",
       "360                NaN              11.0                NaN   \n",
       "361                NaN              10.0                NaN   \n",
       "362                NaN              10.0                NaN   \n",
       "363                NaN              10.0                NaN   \n",
       "364                  T              10.0                NaN   \n",
       "\n",
       "    Dir of Max Gust (10s deg)  Dir of Max Gust Flag Max_gust  \\\n",
       "0                        28.0                   NaN       54   \n",
       "1                         NaN                   NaN      <31   \n",
       "2                         5.0                   NaN       43   \n",
       "3                        24.0                   NaN       85   \n",
       "4                        24.0                   NaN       76   \n",
       "5                        10.0                   NaN       41   \n",
       "6                         NaN                   NaN      <31   \n",
       "7                        30.0                   NaN       37   \n",
       "8                        15.0                   NaN       41   \n",
       "9                        15.0                   NaN       63   \n",
       "10                       22.0                   NaN       93   \n",
       "11                       26.0                   NaN       57   \n",
       "12                       28.0                   NaN       70   \n",
       "13                       18.0                   NaN       33   \n",
       "14                        NaN                   NaN      <31   \n",
       "15                       23.0                   NaN       59   \n",
       "16                        4.0                   NaN       44   \n",
       "17                        4.0                   NaN       37   \n",
       "18                       26.0                   NaN       33   \n",
       "19                        NaN                   NaN      <31   \n",
       "20                        NaN                   NaN      <31   \n",
       "21                        4.0                   NaN       54   \n",
       "22                        5.0                   NaN       50   \n",
       "23                        3.0                   NaN       65   \n",
       "24                       24.0                   NaN       39   \n",
       "25                       25.0                   NaN       46   \n",
       "26                       25.0                   NaN       63   \n",
       "27                       26.0                   NaN       46   \n",
       "28                       22.0                   NaN       37   \n",
       "29                       23.0                   NaN       35   \n",
       "..                        ...                   ...      ...   \n",
       "335                      10.0                   NaN       17   \n",
       "336                       5.0                   NaN       26   \n",
       "337                      15.0                   NaN       57   \n",
       "338                      26.0                   NaN       59   \n",
       "339                      22.0                   NaN       65   \n",
       "340                      23.0                   NaN       41   \n",
       "341                      20.0                   NaN       54   \n",
       "342                      22.0                   NaN       44   \n",
       "343                      27.0                   NaN       48   \n",
       "344                       2.0                   NaN       33   \n",
       "345                       3.0                   NaN       48   \n",
       "346                      27.0                   NaN       61   \n",
       "347                      24.0                   NaN       50   \n",
       "348                       NaN                   NaN      <31   \n",
       "349                      27.0                   NaN       32   \n",
       "350                       NaN                   NaN      <31   \n",
       "351                       NaN                   NaN      <31   \n",
       "352                      25.0                   NaN       61   \n",
       "353                      29.0                   NaN       63   \n",
       "354                       NaN                   NaN      <31   \n",
       "355                       4.0                   NaN       43   \n",
       "356                      24.0                   NaN       41   \n",
       "357                       4.0                   NaN       43   \n",
       "358                      26.0                   NaN       52   \n",
       "359                      27.0                   NaN       54   \n",
       "360                      27.0                   NaN       37   \n",
       "361                      27.0                   NaN       44   \n",
       "362                      26.0                   NaN       30   \n",
       "363                       NaN                   NaN      <31   \n",
       "364                      27.0                   NaN       37   \n",
       "\n",
       "     Spd of Max Gust Flag  \n",
       "0                     NaN  \n",
       "1                     NaN  \n",
       "2                     NaN  \n",
       "3                     NaN  \n",
       "4                     NaN  \n",
       "5                     NaN  \n",
       "6                     NaN  \n",
       "7                     NaN  \n",
       "8                     NaN  \n",
       "9                     NaN  \n",
       "10                    NaN  \n",
       "11                    NaN  \n",
       "12                    NaN  \n",
       "13                    NaN  \n",
       "14                    NaN  \n",
       "15                    NaN  \n",
       "16                    NaN  \n",
       "17                    NaN  \n",
       "18                    NaN  \n",
       "19                    NaN  \n",
       "20                    NaN  \n",
       "21                    NaN  \n",
       "22                    NaN  \n",
       "23                    NaN  \n",
       "24                    NaN  \n",
       "25                    NaN  \n",
       "26                    NaN  \n",
       "27                    NaN  \n",
       "28                    NaN  \n",
       "29                    NaN  \n",
       "..                    ...  \n",
       "335                   NaN  \n",
       "336                   NaN  \n",
       "337                   NaN  \n",
       "338                   NaN  \n",
       "339                   NaN  \n",
       "340                   NaN  \n",
       "341                   NaN  \n",
       "342                   NaN  \n",
       "343                   NaN  \n",
       "344                   NaN  \n",
       "345                   NaN  \n",
       "346                   NaN  \n",
       "347                   NaN  \n",
       "348                   NaN  \n",
       "349                   NaN  \n",
       "350                   NaN  \n",
       "351                   NaN  \n",
       "352                   NaN  \n",
       "353                   NaN  \n",
       "354                   NaN  \n",
       "355                   NaN  \n",
       "356                   NaN  \n",
       "357                   NaN  \n",
       "358                   NaN  \n",
       "359                   NaN  \n",
       "360                   NaN  \n",
       "361                   NaN  \n",
       "362                   NaN  \n",
       "363                   NaN  \n",
       "364                   NaN  \n",
       "\n",
       "[365 rows x 27 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyul_2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set the index of the Pandas DataFrame to Date\n",
    "cyul_2017.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Data Quality</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Max Temp Flag</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Min Temp Flag</th>\n",
       "      <th>Tmean</th>\n",
       "      <th>Mean Temp Flag</th>\n",
       "      <th>...</th>\n",
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       "      <th>Total Snow Flag</th>\n",
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       "      <th>Total Precip Flag</th>\n",
       "      <th>Snow on Grnd (cm)</th>\n",
       "      <th>Snow on Grnd Flag</th>\n",
       "      <th>Dir of Max Gust (10s deg)</th>\n",
       "      <th>Dir of Max Gust Flag</th>\n",
       "      <th>Max_gust</th>\n",
       "      <th>Spd of Max Gust Flag</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>‡</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>‡</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>‡</td>\n",
       "      <td>-9.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>‡</td>\n",
       "      <td>-10.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-14</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-15</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>‡</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-21</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>‡</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-28</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-29</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>‡</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>‡</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-02</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>‡</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>‡</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-05</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>‡</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>‡</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-07</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-09</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>‡</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>‡</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>‡</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>‡</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>‡</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>‡</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-16</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>‡</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-17</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>‡</td>\n",
       "      <td>-12.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>‡</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>‡</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>‡</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>‡</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>‡</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-23</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>‡</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>‡</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>‡</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>‡</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>‡</td>\n",
       "      <td>-18.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>‡</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-26.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>‡</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>‡</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>‡</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year  Month  Day Data Quality  Tmax Max Temp Flag  Tmin  \\\n",
       "Date                                                                  \n",
       "2017-01-01  2017      1    1            ‡  -2.4           NaN  -8.7   \n",
       "2017-01-02  2017      1    2            ‡   1.8           NaN -10.2   \n",
       "2017-01-03  2017      1    3            ‡   0.5           NaN  -5.8   \n",
       "2017-01-04  2017      1    4            ‡   2.5           NaN  -5.6   \n",
       "2017-01-05  2017      1    5            ‡  -5.3           NaN -10.7   \n",
       "2017-01-06  2017      1    6            ‡  -7.7           NaN -16.4   \n",
       "2017-01-07  2017      1    7            ‡  -9.6           NaN -18.9   \n",
       "2017-01-08  2017      1    8            ‡ -10.9           NaN -20.3   \n",
       "2017-01-09  2017      1    9            ‡  -7.0           NaN -21.9   \n",
       "2017-01-10  2017      1   10            ‡   1.3           NaN  -8.3   \n",
       "2017-01-11  2017      1   11            ‡   5.7           NaN   1.2   \n",
       "2017-01-12  2017      1   12            ‡   5.0           NaN  -0.9   \n",
       "2017-01-13  2017      1   13            ‡  -0.4           NaN -14.9   \n",
       "2017-01-14  2017      1   14            ‡  -7.1           NaN -17.2   \n",
       "2017-01-15  2017      1   15            ‡  -7.4           NaN -11.3   \n",
       "2017-01-16  2017      1   16            ‡   2.7           NaN  -9.0   \n",
       "2017-01-17  2017      1   17            ‡  -0.4           NaN  -5.3   \n",
       "2017-01-18  2017      1   18            ‡  -1.3           NaN  -5.3   \n",
       "2017-01-19  2017      1   19            ‡   2.0           NaN  -1.3   \n",
       "2017-01-20  2017      1   20            ‡   2.2           NaN   0.2   \n",
       "2017-01-21  2017      1   21            ‡   2.1           NaN   0.3   \n",
       "2017-01-22  2017      1   22            ‡   2.1           NaN  -2.6   \n",
       "2017-01-23  2017      1   23            ‡  -0.1           NaN  -3.8   \n",
       "2017-01-24  2017      1   24            ‡  -2.3           NaN  -3.8   \n",
       "2017-01-25  2017      1   25            ‡   0.3           NaN  -2.4   \n",
       "2017-01-26  2017      1   26            ‡   1.4           NaN   0.1   \n",
       "2017-01-27  2017      1   27            ‡   0.8           NaN  -3.1   \n",
       "2017-01-28  2017      1   28            ‡   0.2           NaN  -3.0   \n",
       "2017-01-29  2017      1   29            ‡  -0.3           NaN  -6.5   \n",
       "2017-01-30  2017      1   30            ‡  -6.4           NaN -13.7   \n",
       "...          ...    ...  ...          ...   ...           ...   ...   \n",
       "2017-12-02  2017     12    2            ‡   3.2           NaN  -0.9   \n",
       "2017-12-03  2017     12    3            ‡   2.3           NaN  -1.7   \n",
       "2017-12-04  2017     12    4            ‡   5.9           NaN  -3.0   \n",
       "2017-12-05  2017     12    5            ‡  10.0           NaN   5.6   \n",
       "2017-12-06  2017     12    6            ‡   5.7           NaN  -0.3   \n",
       "2017-12-07  2017     12    7            ‡   1.4           NaN  -1.7   \n",
       "2017-12-08  2017     12    8            ‡   1.0           NaN  -2.9   \n",
       "2017-12-09  2017     12    9            ‡   0.5           NaN  -4.5   \n",
       "2017-12-10  2017     12   10            ‡  -1.4           NaN  -5.1   \n",
       "2017-12-11  2017     12   11            ‡  -3.8           NaN -12.7   \n",
       "2017-12-12  2017     12   12            ‡  -6.8           NaN -10.7   \n",
       "2017-12-13  2017     12   13            ‡  -7.2           NaN -16.3   \n",
       "2017-12-14  2017     12   14            ‡ -14.5           NaN -19.0   \n",
       "2017-12-15  2017     12   15            ‡  -9.4           NaN -20.1   \n",
       "2017-12-16  2017     12   16            ‡  -8.0           NaN -15.0   \n",
       "2017-12-17  2017     12   17            ‡ -12.4           NaN -21.0   \n",
       "2017-12-18  2017     12   18            ‡ -10.1           NaN -14.1   \n",
       "2017-12-19  2017     12   19            ‡   3.3           NaN -10.2   \n",
       "2017-12-20  2017     12   20            ‡   1.0           NaN -12.2   \n",
       "2017-12-21  2017     12   21            ‡ -10.5           NaN -16.2   \n",
       "2017-12-22  2017     12   22            ‡ -11.6           NaN -15.2   \n",
       "2017-12-23  2017     12   23            ‡  -5.2           NaN -12.0   \n",
       "2017-12-24  2017     12   24            ‡  -5.5           NaN  -9.7   \n",
       "2017-12-25  2017     12   25            ‡  -6.4           NaN -10.1   \n",
       "2017-12-26  2017     12   26            ‡  -7.0           NaN -18.9   \n",
       "2017-12-27  2017     12   27            ‡ -18.3           NaN -24.5   \n",
       "2017-12-28  2017     12   28            ‡ -20.5           NaN -26.6   \n",
       "2017-12-29  2017     12   29            ‡ -18.4           NaN -24.8   \n",
       "2017-12-30  2017     12   30            ‡ -17.4           NaN -24.9   \n",
       "2017-12-31  2017     12   31            ‡ -18.9           NaN -23.8   \n",
       "\n",
       "           Min Temp Flag  Tmean Mean Temp Flag         ...           Snow  \\\n",
       "Date                                                   ...                  \n",
       "2017-01-01           NaN   -5.6            NaN         ...            2.8   \n",
       "2017-01-02           NaN   -4.2            NaN         ...            0.0   \n",
       "2017-01-03           NaN   -2.7            NaN         ...            3.6   \n",
       "2017-01-04           NaN   -1.6            NaN         ...            9.0   \n",
       "2017-01-05           NaN   -8.0            NaN         ...            0.0   \n",
       "2017-01-06           NaN  -12.1            NaN         ...            0.0   \n",
       "2017-01-07           NaN  -14.3            NaN         ...            0.0   \n",
       "2017-01-08           NaN  -15.6            NaN         ...            0.0   \n",
       "2017-01-09           NaN  -14.5            NaN         ...            0.4   \n",
       "2017-01-10           NaN   -3.5            NaN         ...            1.0   \n",
       "2017-01-11           NaN    3.5            NaN         ...            0.0   \n",
       "2017-01-12           NaN    2.1            NaN         ...            0.0   \n",
       "2017-01-13           NaN   -7.7            NaN         ...            0.0   \n",
       "2017-01-14           NaN  -12.2            NaN         ...            0.0   \n",
       "2017-01-15           NaN   -9.4            NaN         ...            0.0   \n",
       "2017-01-16           NaN   -3.2            NaN         ...            0.0   \n",
       "2017-01-17           NaN   -2.9            NaN         ...            1.4   \n",
       "2017-01-18           NaN   -3.3            NaN         ...            6.8   \n",
       "2017-01-19           NaN    0.4            NaN         ...            0.0   \n",
       "2017-01-20           NaN    1.2            NaN         ...            0.0   \n",
       "2017-01-21           NaN    1.2            NaN         ...            0.0   \n",
       "2017-01-22           NaN   -0.3            NaN         ...            0.0   \n",
       "2017-01-23           NaN   -2.0            NaN         ...            0.0   \n",
       "2017-01-24           NaN   -3.1            NaN         ...            7.0   \n",
       "2017-01-25           NaN   -1.1            NaN         ...            3.2   \n",
       "2017-01-26           NaN    0.8            NaN         ...            2.2   \n",
       "2017-01-27           NaN   -1.2            NaN         ...            3.6   \n",
       "2017-01-28           NaN   -1.4            NaN         ...            0.2   \n",
       "2017-01-29           NaN   -3.4            NaN         ...            1.0   \n",
       "2017-01-30           NaN  -10.1            NaN         ...            0.0   \n",
       "...                  ...    ...            ...         ...            ...   \n",
       "2017-12-02           NaN    1.2            NaN         ...            0.0   \n",
       "2017-12-03           NaN    0.3            NaN         ...            0.0   \n",
       "2017-12-04           NaN    1.5            NaN         ...            0.0   \n",
       "2017-12-05           NaN    7.8            NaN         ...            0.0   \n",
       "2017-12-06           NaN    2.7            NaN         ...            0.0   \n",
       "2017-12-07           NaN   -0.2            NaN         ...            0.2   \n",
       "2017-12-08           NaN   -1.0            NaN         ...            0.0   \n",
       "2017-12-09           NaN   -2.0            NaN         ...            0.0   \n",
       "2017-12-10           NaN   -3.3            NaN         ...            1.8   \n",
       "2017-12-11           NaN   -8.3            NaN         ...            0.8   \n",
       "2017-12-12           NaN   -8.8            NaN         ...           18.6   \n",
       "2017-12-13           NaN  -11.8            NaN         ...            2.4   \n",
       "2017-12-14           NaN  -16.8            NaN         ...            0.0   \n",
       "2017-12-15           NaN  -14.8            NaN         ...            0.4   \n",
       "2017-12-16           NaN  -11.5            NaN         ...            1.8   \n",
       "2017-12-17           NaN  -16.7            NaN         ...            0.2   \n",
       "2017-12-18           NaN  -12.1            NaN         ...            4.8   \n",
       "2017-12-19           NaN   -3.5            NaN         ...            4.4   \n",
       "2017-12-20           NaN   -5.6            NaN         ...            0.0   \n",
       "2017-12-21           NaN  -13.4            NaN         ...            0.0   \n",
       "2017-12-22           NaN  -13.4            NaN         ...            1.4   \n",
       "2017-12-23           NaN   -8.6            NaN         ...            9.4   \n",
       "2017-12-24           NaN   -7.6            NaN         ...            0.0   \n",
       "2017-12-25           NaN   -8.3            NaN         ...           13.4   \n",
       "2017-12-26           NaN  -13.0            NaN         ...            0.0   \n",
       "2017-12-27           NaN  -21.4            NaN         ...            0.0   \n",
       "2017-12-28           NaN  -23.6            NaN         ...            0.0   \n",
       "2017-12-29           NaN  -21.6            NaN         ...            1.6   \n",
       "2017-12-30           NaN  -21.2            NaN         ...            0.2   \n",
       "2017-12-31           NaN  -21.4            NaN         ...            0.0   \n",
       "\n",
       "           Total Snow Flag  Precip Total Precip Flag  Snow on Grnd (cm)  \\\n",
       "Date                                                                      \n",
       "2017-01-01             NaN     2.4               NaN               12.0   \n",
       "2017-01-02             NaN     0.0               NaN                6.0   \n",
       "2017-01-03             NaN    17.4               NaN                6.0   \n",
       "2017-01-04             NaN     8.0               NaN               12.0   \n",
       "2017-01-05               T     0.0                 T               13.0   \n",
       "2017-01-06               T     0.0                 T               13.0   \n",
       "2017-01-07             NaN     0.0               NaN               13.0   \n",
       "2017-01-08             NaN     0.0               NaN               12.0   \n",
       "2017-01-09             NaN     0.4               NaN               11.0   \n",
       "2017-01-10             NaN     2.2               NaN               11.0   \n",
       "2017-01-11             NaN     3.0               NaN               10.0   \n",
       "2017-01-12             NaN    10.0               NaN                4.0   \n",
       "2017-01-13               T     0.4               NaN                2.0   \n",
       "2017-01-14               T     0.0                 T                1.0   \n",
       "2017-01-15               T     0.0                 T                1.0   \n",
       "2017-01-16             NaN     0.0               NaN                1.0   \n",
       "2017-01-17             NaN     1.4               NaN                1.0   \n",
       "2017-01-18             NaN     7.0               NaN                3.0   \n",
       "2017-01-19             NaN     0.0                 T                4.0   \n",
       "2017-01-20             NaN     0.0               NaN                3.0   \n",
       "2017-01-21             NaN     0.6               NaN                2.0   \n",
       "2017-01-22               T     0.0                 T                1.0   \n",
       "2017-01-23             NaN     0.0                 T                0.0   \n",
       "2017-01-24             NaN    12.4               NaN                0.0   \n",
       "2017-01-25             NaN     2.6               NaN                4.0   \n",
       "2017-01-26             NaN     1.6               NaN                5.0   \n",
       "2017-01-27             NaN     2.6               NaN                4.0   \n",
       "2017-01-28             NaN     0.2               NaN                5.0   \n",
       "2017-01-29             NaN     1.0               NaN                5.0   \n",
       "2017-01-30               T     0.0                 T                5.0   \n",
       "...                    ...     ...               ...                ...   \n",
       "2017-12-02             NaN     0.0               NaN                NaN   \n",
       "2017-12-03             NaN     0.0               NaN                NaN   \n",
       "2017-12-04             NaN     0.0                 T                NaN   \n",
       "2017-12-05             NaN     9.8               NaN                NaN   \n",
       "2017-12-06               T     0.0                 T                NaN   \n",
       "2017-12-07             NaN     0.2               NaN                0.0   \n",
       "2017-12-08               T     0.0                 T                0.0   \n",
       "2017-12-09               T     0.0                 T                NaN   \n",
       "2017-12-10             NaN     1.6               NaN                0.0   \n",
       "2017-12-11             NaN     0.4               NaN                2.0   \n",
       "2017-12-12             NaN    17.2               NaN                4.0   \n",
       "2017-12-13             NaN     1.6               NaN                9.0   \n",
       "2017-12-14             NaN     0.0               NaN                4.0   \n",
       "2017-12-15             NaN     0.4               NaN                3.0   \n",
       "2017-12-16             NaN     1.8               NaN                5.0   \n",
       "2017-12-17             NaN     0.2               NaN                5.0   \n",
       "2017-12-18             NaN     4.4               NaN                5.0   \n",
       "2017-12-19             NaN     7.1               NaN                9.0   \n",
       "2017-12-20             NaN     0.0                 T                8.0   \n",
       "2017-12-21             NaN     0.0               NaN                6.0   \n",
       "2017-12-22             NaN     1.2               NaN                5.0   \n",
       "2017-12-23             NaN    14.4               NaN                5.0   \n",
       "2017-12-24               T     0.0                 T               12.0   \n",
       "2017-12-25             NaN    12.6               NaN               18.0   \n",
       "2017-12-26               T     0.0                 T               19.0   \n",
       "2017-12-27             NaN     0.0               NaN               11.0   \n",
       "2017-12-28             NaN     0.0               NaN               10.0   \n",
       "2017-12-29             NaN     1.6               NaN               10.0   \n",
       "2017-12-30             NaN     0.2               NaN               10.0   \n",
       "2017-12-31               T     0.0                 T               10.0   \n",
       "\n",
       "           Snow on Grnd Flag  Dir of Max Gust (10s deg) Dir of Max Gust Flag  \\\n",
       "Date                                                                           \n",
       "2017-01-01               NaN                       28.0                  NaN   \n",
       "2017-01-02               NaN                        NaN                  NaN   \n",
       "2017-01-03               NaN                        5.0                  NaN   \n",
       "2017-01-04               NaN                       24.0                  NaN   \n",
       "2017-01-05               NaN                       24.0                  NaN   \n",
       "2017-01-06               NaN                       10.0                  NaN   \n",
       "2017-01-07               NaN                        NaN                  NaN   \n",
       "2017-01-08               NaN                       30.0                  NaN   \n",
       "2017-01-09               NaN                       15.0                  NaN   \n",
       "2017-01-10               NaN                       15.0                  NaN   \n",
       "2017-01-11               NaN                       22.0                  NaN   \n",
       "2017-01-12               NaN                       26.0                  NaN   \n",
       "2017-01-13               NaN                       28.0                  NaN   \n",
       "2017-01-14               NaN                       18.0                  NaN   \n",
       "2017-01-15               NaN                        NaN                  NaN   \n",
       "2017-01-16               NaN                       23.0                  NaN   \n",
       "2017-01-17               NaN                        4.0                  NaN   \n",
       "2017-01-18               NaN                        4.0                  NaN   \n",
       "2017-01-19               NaN                       26.0                  NaN   \n",
       "2017-01-20               NaN                        NaN                  NaN   \n",
       "2017-01-21               NaN                        NaN                  NaN   \n",
       "2017-01-22               NaN                        4.0                  NaN   \n",
       "2017-01-23                 T                        5.0                  NaN   \n",
       "2017-01-24                 T                        3.0                  NaN   \n",
       "2017-01-25               NaN                       24.0                  NaN   \n",
       "2017-01-26               NaN                       25.0                  NaN   \n",
       "2017-01-27               NaN                       25.0                  NaN   \n",
       "2017-01-28               NaN                       26.0                  NaN   \n",
       "2017-01-29               NaN                       22.0                  NaN   \n",
       "2017-01-30               NaN                       23.0                  NaN   \n",
       "...                      ...                        ...                  ...   \n",
       "2017-12-02               NaN                       10.0                  NaN   \n",
       "2017-12-03               NaN                        5.0                  NaN   \n",
       "2017-12-04               NaN                       15.0                  NaN   \n",
       "2017-12-05               NaN                       26.0                  NaN   \n",
       "2017-12-06               NaN                       22.0                  NaN   \n",
       "2017-12-07                 T                       23.0                  NaN   \n",
       "2017-12-08                 T                       20.0                  NaN   \n",
       "2017-12-09               NaN                       22.0                  NaN   \n",
       "2017-12-10                 T                       27.0                  NaN   \n",
       "2017-12-11               NaN                        2.0                  NaN   \n",
       "2017-12-12               NaN                        3.0                  NaN   \n",
       "2017-12-13               NaN                       27.0                  NaN   \n",
       "2017-12-14               NaN                       24.0                  NaN   \n",
       "2017-12-15               NaN                        NaN                  NaN   \n",
       "2017-12-16               NaN                       27.0                  NaN   \n",
       "2017-12-17               NaN                        NaN                  NaN   \n",
       "2017-12-18               NaN                        NaN                  NaN   \n",
       "2017-12-19               NaN                       25.0                  NaN   \n",
       "2017-12-20               NaN                       29.0                  NaN   \n",
       "2017-12-21               NaN                        NaN                  NaN   \n",
       "2017-12-22               NaN                        4.0                  NaN   \n",
       "2017-12-23               NaN                       24.0                  NaN   \n",
       "2017-12-24               NaN                        4.0                  NaN   \n",
       "2017-12-25               NaN                       26.0                  NaN   \n",
       "2017-12-26               NaN                       27.0                  NaN   \n",
       "2017-12-27               NaN                       27.0                  NaN   \n",
       "2017-12-28               NaN                       27.0                  NaN   \n",
       "2017-12-29               NaN                       26.0                  NaN   \n",
       "2017-12-30               NaN                        NaN                  NaN   \n",
       "2017-12-31               NaN                       27.0                  NaN   \n",
       "\n",
       "            Max_gust Spd of Max Gust Flag  \n",
       "Date                                       \n",
       "2017-01-01        54                  NaN  \n",
       "2017-01-02       <31                  NaN  \n",
       "2017-01-03        43                  NaN  \n",
       "2017-01-04        85                  NaN  \n",
       "2017-01-05        76                  NaN  \n",
       "2017-01-06        41                  NaN  \n",
       "2017-01-07       <31                  NaN  \n",
       "2017-01-08        37                  NaN  \n",
       "2017-01-09        41                  NaN  \n",
       "2017-01-10        63                  NaN  \n",
       "2017-01-11        93                  NaN  \n",
       "2017-01-12        57                  NaN  \n",
       "2017-01-13        70                  NaN  \n",
       "2017-01-14        33                  NaN  \n",
       "2017-01-15       <31                  NaN  \n",
       "2017-01-16        59                  NaN  \n",
       "2017-01-17        44                  NaN  \n",
       "2017-01-18        37                  NaN  \n",
       "2017-01-19        33                  NaN  \n",
       "2017-01-20       <31                  NaN  \n",
       "2017-01-21       <31                  NaN  \n",
       "2017-01-22        54                  NaN  \n",
       "2017-01-23        50                  NaN  \n",
       "2017-01-24        65                  NaN  \n",
       "2017-01-25        39                  NaN  \n",
       "2017-01-26        46                  NaN  \n",
       "2017-01-27        63                  NaN  \n",
       "2017-01-28        46                  NaN  \n",
       "2017-01-29        37                  NaN  \n",
       "2017-01-30        35                  NaN  \n",
       "...              ...                  ...  \n",
       "2017-12-02        17                  NaN  \n",
       "2017-12-03        26                  NaN  \n",
       "2017-12-04        57                  NaN  \n",
       "2017-12-05        59                  NaN  \n",
       "2017-12-06        65                  NaN  \n",
       "2017-12-07        41                  NaN  \n",
       "2017-12-08        54                  NaN  \n",
       "2017-12-09        44                  NaN  \n",
       "2017-12-10        48                  NaN  \n",
       "2017-12-11        33                  NaN  \n",
       "2017-12-12        48                  NaN  \n",
       "2017-12-13        61                  NaN  \n",
       "2017-12-14        50                  NaN  \n",
       "2017-12-15       <31                  NaN  \n",
       "2017-12-16        32                  NaN  \n",
       "2017-12-17       <31                  NaN  \n",
       "2017-12-18       <31                  NaN  \n",
       "2017-12-19        61                  NaN  \n",
       "2017-12-20        63                  NaN  \n",
       "2017-12-21       <31                  NaN  \n",
       "2017-12-22        43                  NaN  \n",
       "2017-12-23        41                  NaN  \n",
       "2017-12-24        43                  NaN  \n",
       "2017-12-25        52                  NaN  \n",
       "2017-12-26        54                  NaN  \n",
       "2017-12-27        37                  NaN  \n",
       "2017-12-28        44                  NaN  \n",
       "2017-12-29        30                  NaN  \n",
       "2017-12-30       <31                  NaN  \n",
       "2017-12-31        37                  NaN  \n",
       "\n",
       "[365 rows x 26 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyul_2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                         2017\n",
       "Month                           5\n",
       "Day                            29\n",
       "Data Quality                    ‡\n",
       "Tmax                         19.6\n",
       "Max Temp Flag                 NaN\n",
       "Tmin                         12.7\n",
       "Min Temp Flag                 NaN\n",
       "Tmean                        16.2\n",
       "Mean Temp Flag                NaN\n",
       "Heat Deg Days (°C)            1.8\n",
       "Heat Deg Days Flag            NaN\n",
       "Cool Deg Days (°C)              0\n",
       "Cool Deg Days Flag            NaN\n",
       "Rain                            8\n",
       "Total Rain Flag               NaN\n",
       "Snow                            0\n",
       "Total Snow Flag               NaN\n",
       "Precip                          8\n",
       "Total Precip Flag             NaN\n",
       "Snow on Grnd (cm)             NaN\n",
       "Snow on Grnd Flag             NaN\n",
       "Dir of Max Gust (10s deg)      14\n",
       "Dir of Max Gust Flag          NaN\n",
       "Max_gust                       52\n",
       "Spd of Max Gust Flag          NaN\n",
       "Name: 2017-05-29, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyul_2017.loc['2017-05-29']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the warmest and coldest temperatures of 2017 were:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2017-06-18    32.1\n",
       "2017-09-25    31.5\n",
       "2017-06-12    31.4\n",
       "2017-09-27    31.2\n",
       "2017-09-24    30.6\n",
       "Name: Tmax, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyul_2017['Tmax'].nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2017-12-28   -26.6\n",
       "2017-12-30   -24.9\n",
       "2017-12-29   -24.8\n",
       "2017-12-27   -24.5\n",
       "2017-12-31   -23.8\n",
       "Name: Tmin, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyul_2017['Tmin'].nsmallest(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, let's try plotting some of this data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll grab the individual columns we want for our x and y data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_temps = cyul_2017['Tmax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2017-01-01    -2.4\n",
       "2017-01-02     1.8\n",
       "2017-01-03     0.5\n",
       "2017-01-04     2.5\n",
       "2017-01-05    -5.3\n",
       "2017-01-06    -7.7\n",
       "2017-01-07    -9.6\n",
       "2017-01-08   -10.9\n",
       "2017-01-09    -7.0\n",
       "2017-01-10     1.3\n",
       "2017-01-11     5.7\n",
       "2017-01-12     5.0\n",
       "2017-01-13    -0.4\n",
       "2017-01-14    -7.1\n",
       "2017-01-15    -7.4\n",
       "2017-01-16     2.7\n",
       "2017-01-17    -0.4\n",
       "2017-01-18    -1.3\n",
       "2017-01-19     2.0\n",
       "2017-01-20     2.2\n",
       "2017-01-21     2.1\n",
       "2017-01-22     2.1\n",
       "2017-01-23    -0.1\n",
       "2017-01-24    -2.3\n",
       "2017-01-25     0.3\n",
       "2017-01-26     1.4\n",
       "2017-01-27     0.8\n",
       "2017-01-28     0.2\n",
       "2017-01-29    -0.3\n",
       "2017-01-30    -6.4\n",
       "              ... \n",
       "2017-12-02     3.2\n",
       "2017-12-03     2.3\n",
       "2017-12-04     5.9\n",
       "2017-12-05    10.0\n",
       "2017-12-06     5.7\n",
       "2017-12-07     1.4\n",
       "2017-12-08     1.0\n",
       "2017-12-09     0.5\n",
       "2017-12-10    -1.4\n",
       "2017-12-11    -3.8\n",
       "2017-12-12    -6.8\n",
       "2017-12-13    -7.2\n",
       "2017-12-14   -14.5\n",
       "2017-12-15    -9.4\n",
       "2017-12-16    -8.0\n",
       "2017-12-17   -12.4\n",
       "2017-12-18   -10.1\n",
       "2017-12-19     3.3\n",
       "2017-12-20     1.0\n",
       "2017-12-21   -10.5\n",
       "2017-12-22   -11.6\n",
       "2017-12-23    -5.2\n",
       "2017-12-24    -5.5\n",
       "2017-12-25    -6.4\n",
       "2017-12-26    -7.0\n",
       "2017-12-27   -18.3\n",
       "2017-12-28   -20.5\n",
       "2017-12-29   -18.4\n",
       "2017-12-30   -17.4\n",
       "2017-12-31   -18.9\n",
       "Name: Tmax, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_temps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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 overriden (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"
    },
    {
     "data": {
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\" width=\"699.9999970197678\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8bace53630>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=[7,4])\n",
    "plt.plot(max_temps)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has many convenience functions that allow us to quickly plot our data in a much prettier way!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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 overriden (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"
    },
    {
     "data": {
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\" width=\"799.9999965940203\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8baca98eb8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.figure()\n",
    "cyul_2017['Tmax'].plot(color='red', figsize=[8,5])\n",
    "cyul_2017['Tmin'].plot(color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's add a 30-day rolling average to the plot so that we smooth out the variations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyul_2017['Tmean'].rolling(30,min_periods=2,center=True).mean().plot(c='k', label='30-day avg. Tmean')\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, add a title, axis labels, and legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8baba858d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.title('Montreal Daily Temperatures - 2017')\n",
    "plt.ylabel('Temperature (deg C)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With pandas, you can do quick statistics/calculations as well. This is what makes it really powerful for data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmean</th>\n",
       "      <th>Heat Deg Days (°C)</th>\n",
       "      <th>Cool Deg Days (°C)</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Precip</th>\n",
       "      <th>Snow on Grnd (cm)</th>\n",
       "      <th>Dir of Max Gust (10s deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>336.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>15.720548</td>\n",
       "      <td>12.009116</td>\n",
       "      <td>3.234795</td>\n",
       "      <td>7.606354</td>\n",
       "      <td>11.205249</td>\n",
       "      <td>0.811602</td>\n",
       "      <td>2.843611</td>\n",
       "      <td>0.633795</td>\n",
       "      <td>3.459392</td>\n",
       "      <td>6.865546</td>\n",
       "      <td>18.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>8.808321</td>\n",
       "      <td>12.471745</td>\n",
       "      <td>11.354032</td>\n",
       "      <td>11.812289</td>\n",
       "      <td>10.873452</td>\n",
       "      <td>1.749120</td>\n",
       "      <td>6.758139</td>\n",
       "      <td>2.761052</td>\n",
       "      <td>7.166166</td>\n",
       "      <td>7.004749</td>\n",
       "      <td>9.289102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-20.500000</td>\n",
       "      <td>-26.600000</td>\n",
       "      <td>-23.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>-5.300000</td>\n",
       "      <td>-1.900000</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>14.150000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.325000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>17.775000</td>\n",
       "      <td>19.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>26.700000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>42.400000</td>\n",
       "      <td>31.600000</td>\n",
       "      <td>42.400000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year       Month         Day        Tmax        Tmin       Tmean  \\\n",
       "count   365.0  365.000000  365.000000  362.000000  365.000000  362.000000   \n",
       "mean   2017.0    6.526027   15.720548   12.009116    3.234795    7.606354   \n",
       "std       0.0    3.452584    8.808321   12.471745   11.354032   11.812289   \n",
       "min    2017.0    1.000000    1.000000  -20.500000  -26.600000  -23.600000   \n",
       "25%    2017.0    4.000000    8.000000    1.900000   -5.300000   -1.900000   \n",
       "50%    2017.0    7.000000   16.000000   14.150000    5.000000    9.300000   \n",
       "75%    2017.0   10.000000   23.000000   23.325000   13.200000   17.775000   \n",
       "max    2017.0   12.000000   31.000000   32.100000   21.300000   26.700000   \n",
       "\n",
       "       Heat Deg Days (°C)  Cool Deg Days (°C)        Rain        Snow  \\\n",
       "count          362.000000          362.000000  360.000000  361.000000   \n",
       "mean            11.205249            0.811602    2.843611    0.633795   \n",
       "std             10.873452            1.749120    6.758139    2.761052   \n",
       "min              0.000000            0.000000    0.000000    0.000000   \n",
       "25%              0.225000            0.000000    0.000000    0.000000   \n",
       "50%              8.700000            0.000000    0.000000    0.000000   \n",
       "75%             19.900000            0.000000    1.600000    0.000000   \n",
       "max             41.600000            8.700000   42.400000   31.600000   \n",
       "\n",
       "           Precip  Snow on Grnd (cm)  Dir of Max Gust (10s deg)  \n",
       "count  362.000000         119.000000                 336.000000  \n",
       "mean     3.459392           6.865546                  18.928571  \n",
       "std      7.166166           7.004749                   9.289102  \n",
       "min      0.000000           0.000000                   0.000000  \n",
       "25%      0.000000           1.000000                  12.000000  \n",
       "50%      0.000000           5.000000                  22.000000  \n",
       "75%      2.800000          10.000000                  26.000000  \n",
       "max     42.400000          29.000000                  36.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyul_2017.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmean</th>\n",
       "      <th>Heat Deg Days (°C)</th>\n",
       "      <th>Cool Deg Days (°C)</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Precip</th>\n",
       "      <th>Snow on Grnd (cm)</th>\n",
       "      <th>Dir of Max Gust (10s deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>-5.612903</td>\n",
       "      <td>-12.064516</td>\n",
       "      <td>-8.858065</td>\n",
       "      <td>26.858065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409677</td>\n",
       "      <td>1.980645</td>\n",
       "      <td>2.409677</td>\n",
       "      <td>7.083333</td>\n",
       "      <td>20.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.092121</td>\n",
       "      <td>8.416006</td>\n",
       "      <td>8.786526</td>\n",
       "      <td>8.493675</td>\n",
       "      <td>8.493675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.808932</td>\n",
       "      <td>4.290876</td>\n",
       "      <td>4.692430</td>\n",
       "      <td>5.012304</td>\n",
       "      <td>9.142967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-20.500000</td>\n",
       "      <td>-26.600000</td>\n",
       "      <td>-23.600000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>-11.050000</td>\n",
       "      <td>-18.950000</td>\n",
       "      <td>-14.100000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>-6.400000</td>\n",
       "      <td>-12.200000</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>-3.750000</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Month        Day       Tmax       Tmin      Tmean  \\\n",
       "count    31.0   31.0  31.000000  31.000000  31.000000  31.000000   \n",
       "mean   2017.0   12.0  16.000000  -5.612903 -12.064516  -8.858065   \n",
       "std       0.0    0.0   9.092121   8.416006   8.786526   8.493675   \n",
       "min    2017.0   12.0   1.000000 -20.500000 -26.600000 -23.600000   \n",
       "25%    2017.0   12.0   8.500000 -11.050000 -18.950000 -14.100000   \n",
       "50%    2017.0   12.0  16.000000  -6.400000 -12.200000  -8.600000   \n",
       "75%    2017.0   12.0  23.500000   1.200000  -3.750000  -1.500000   \n",
       "max    2017.0   12.0  31.000000  10.000000   5.600000   7.800000   \n",
       "\n",
       "       Heat Deg Days (°C)  Cool Deg Days (°C)       Rain       Snow  \\\n",
       "count           31.000000                31.0  31.000000  31.000000   \n",
       "mean            26.858065                 0.0   0.409677   1.980645   \n",
       "std              8.493675                 0.0   1.808932   4.290876   \n",
       "min             10.200000                 0.0   0.000000   0.000000   \n",
       "25%             19.500000                 0.0   0.000000   0.000000   \n",
       "50%             26.600000                 0.0   0.000000   0.000000   \n",
       "75%             32.100000                 0.0   0.000000   1.700000   \n",
       "max             41.600000                 0.0   9.800000  18.600000   \n",
       "\n",
       "          Precip  Snow on Grnd (cm)  Dir of Max Gust (10s deg)  \n",
       "count  31.000000          24.000000                  26.000000  \n",
       "mean    2.409677           7.083333                  20.076923  \n",
       "std     4.692430           5.012304                   9.142967  \n",
       "min     0.000000           0.000000                   2.000000  \n",
       "25%     0.000000           4.000000                  16.250000  \n",
       "50%     0.200000           5.500000                  24.000000  \n",
       "75%     1.600000          10.000000                  27.000000  \n",
       "max    17.200000          19.000000                  29.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyul_2017[cyul_2017.Month==12].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try making a plot of one of the other fields in cyul_2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()\n",
    "#######"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other useful AOS-related Python packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Xarray - reading, analyzing, editing, creating NetCDF files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* MetPy - https://unidata.github.io/MetPy/latest/\n",
    "    * Meteorological functions and calculations ($\\theta_e$, RH, dew point, etc.)\n",
    "    * Plotting Skew-T diagrams, station plots, radar data, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Cartopy - http://scitools.org.uk/cartopy/\n",
    "    * Plotting data on maps\n",
    "    * Note that you'll come across matplotlib basemap - this is no longer developed and Cartopy is currently the successor to basemap"
   ]
  }
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