{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 6 - String operations! Which month was the snowiest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "line_width has been deprecated, use display.width instead (currently both are\n",
      "identical)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "\n",
    "# Make the graphs a bit prettier, and bigger\n",
    "pd.set_option('display.mpl_style', 'default')\n",
    "figsize(15, 5)\n",
    "\n",
    "# Always display all the columns\n",
    "pd.set_option('display.line_width', 5000) \n",
    "pd.set_option('display.max_columns', 60) \n",
    "\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_2012 = pd.read_csv('./data/weather_2012.csv', parse_dates=True, index_col='Date/Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>89</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.26</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>88</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.27</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>101.23</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)               Weather\n",
       "Date/Time                                                                                                                              \n",
       "2012-01-01 00:00:00      -1.8                -3.9           86                4              8.0           101.24                   Fog\n",
       "2012-01-01 01:00:00      -1.8                -3.7           87                4              8.0           101.24                   Fog\n",
       "2012-01-01 02:00:00      -1.8                -3.4           89                7              4.0           101.26  Freezing Drizzle,Fog\n",
       "2012-01-01 03:00:00      -1.5                -3.2           88                6              4.0           101.27  Freezing Drizzle,Fog\n",
       "2012-01-01 04:00:00      -1.5                -3.3           88                7              4.8           101.23                   Fog"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_2012.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 String operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_description = weather_2012['Weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "is_snowing = weather_description.str.contains('Snow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date/Time\n",
       "2012-01-01 00:00:00    False\n",
       "2012-01-01 01:00:00    False\n",
       "2012-01-01 02:00:00    False\n",
       "2012-01-01 03:00:00    False\n",
       "2012-01-01 04:00:00    False\n",
       "Name: Weather, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_snowing[: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110444bd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dLJaUmyxqVpYKhYLm67FIeWREE4WCbR8bPIO1TZs2aXp6WlLt+jW3UyDX1tZUrVabZZeX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\nAEgggjUAAAAASKD/H+3a72inUazCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106b6d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_snowing.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Use resampling to find the snowiest month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110444b50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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60SSiM11KaRKhy/PxOZTTpcrnUE6XnM+hnC5VPodyuuSOtc+hoaGh5/29jp9ZGx4ejsnJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f94310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_2012[u'Temp (C)'].resample('M', how = np.median).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date/Time\n",
       "2012-01-01 01:00:00    0\n",
       "2012-01-01 02:00:00    0\n",
       "2012-01-01 03:00:00    0\n",
       "2012-01-01 04:00:00    0\n",
       "2012-01-01 05:00:00    0\n",
       "2012-01-01 06:00:00    0\n",
       "2012-01-01 07:00:00    0\n",
       "2012-01-01 08:00:00    0\n",
       "2012-01-01 09:00:00    0\n",
       "Name: Weather, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_snowing.astype(float)[1: 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date/Time\n",
       "2012-01-31    0.240591\n",
       "2012-02-29    0.162356\n",
       "2012-03-31    0.087366\n",
       "2012-04-30    0.015278\n",
       "2012-05-31    0.000000\n",
       "2012-06-30    0.000000\n",
       "2012-07-31    0.000000\n",
       "2012-08-31    0.000000\n",
       "2012-09-30    0.000000\n",
       "2012-10-31    0.000000\n",
       "2012-11-30    0.038889\n",
       "2012-12-31    0.251344\n",
       "Freq: M, Name: Weather, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_snowing.astype(float).resample('M', how = np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1106b6e90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bN3llyRds3749Fi1aFCtWrJjctm3btvjhD38YTz75ZDz99NNx6aWXHnIfAAAAzNyMbjEw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\nAEBB/j++8pFHpUhX4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11120c510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_snowing.astype(float).resample('M', how = np.mean).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Plotting temperature and snowiness stats together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temperature = weather_2012['Temp (C)'].resample('M', how = np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "is_snowing = weather_2012['Weather'].str.contains('Snow')\n",
    "snowiness = is_snowing.astype(float).resample('M', how = np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temperature.name = 'Temperature'\n",
    "snowiness.name = 'Snowiness'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stats = pd.concat([temperature, snowiness], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Snowiness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
       "      <td>-7.05</td>\n",
       "      <td>0.240591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-29</th>\n",
       "      <td>-4.10</td>\n",
       "      <td>0.162356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-31</th>\n",
       "      <td>2.60</td>\n",
       "      <td>0.087366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-30</th>\n",
       "      <td>6.30</td>\n",
       "      <td>0.015278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-31</th>\n",
       "      <td>16.05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Temperature  Snowiness\n",
       "Date/Time                         \n",
       "2012-01-31        -7.05   0.240591\n",
       "2012-02-29        -4.10   0.162356\n",
       "2012-03-31         2.60   0.087366\n",
       "2012-04-30         6.30   0.015278\n",
       "2012-05-31        16.05   0.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110444450>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUkUiEUUiERUXFyuZTKa9SAAAAADIN197Zm3NmjV65ZVXFAqF5HmeQqGQRo0a\npeOOO873sY2NjYpGo5o7d648z1M0GlVDQ4NKSkqysngAAAAA6Ki+dlgbMGCABgwYcFgHi8Vi2rlz\np2666SZJ0uzZsxWPxw/5OWVlZYd17G/C1601X9HFRhc/mtjoYqOLjS5+NLHRxUYXG138cqFJ2rtB\nep6X+nlJSYkSiUTq9mQyyVk1AAAAAGiHkLf/tHUEampq9P7772vHjh069dRTNXbsWEn7Lpv8ajfI\nkSNHHvZZOQAAAADA/2n3sAYAAAAAyB7eFBsAAAAAHMSwBgAAAAAOYlgDAAAAAAcxrAEAAACAg772\nfdY6smQyqYaGBsXjcd5i4P+jiY0uNrrY6OJHExtdbHSx0cWPJja62HKxS14Oa2vXrlV1dbWKi4sV\njUbV1NSkZDKpysrKvH2rAZrY6GKji40ufjSx0cVGFxtd/Ghio4stp7t4eWjy5Mne7t27Pc/zvKqq\nKm/37t3erl27vEmTJgW8suDQxEYXG11sdPGjiY0uNrrY6OJHExtdbLncJS9fs+bt99ZyoVAo9aOX\nx285RxMbXWx0sdHFjyY2utjoYqOLH01sdLHlcpe8fFPs2tpaLVy4UKWlpYpGo9q5c6cSiURunArN\nEprY6GKji40ufjSx0cVGFxtd/Ghio4stl7vk5bAm7ZuwE4mEGhsbFYvFVFJSonA4L080ptDERhcb\nXWx08aOJjS42utjo4kcTG11sudrF/RVmSSgUUjgcTv2YC/+zso0mNrrY6GKjix9NbHSx0cVGFz+a\n2Ohiy9Uu7AaZazvCZAlNbHSx0cVGFz+a2Ohio4uNLn40sdHFltNdsrd3ibtyeUeYbKGJjS42utjo\n4kcTG11sdLHRxY8mNrrYcrlLbpz/yzAvh3eEyRaa2Ohio4uNLn40sdHFRhcbXfxoYqOLLZe75OUG\nI7m8I0y20MRGFxtdbHTxo4mNLja62OjiRxMbXWy53CUvhzUpd3eEySaa2Ohio4uNLn40sdHFRhcb\nXfxoYqOLLVe75O2wBgAAAAAuc3+cBAAAAIA8lNfD2po1aw7563xEExtdbHSx0cWPJja62Ohio4sf\nTWx0seVil7we1hobGw/563xEExtdbHSx0cWPJja62Ohio4sfTWx0seViF16zBgAAAAAOyuszawAA\nAADgqsKgFxCkZDKphoYGxeNxlZSUBL0cJ9DERhcbXWx08aOJjS42utjo4kcTG11sudglL4e1tWvX\nqrq6WsUqEb81AAAWlUlEQVTFxYpGo2pqalIymcyJN8bLFprY6GKji40ufjSx0cVGFxtd/Ghio4st\np7t4eWjy5Mne7t27Pc/zvKqqKm/37t3erl27vEmTJgW8suDQxEYXG11sdPGjiY0uNrrY6OJHExtd\nbLncJS9fs+btt6dKKBRK/ejl8V4rNLHRxUYXG138aGKji40uNrr40cRGF1sud8nL3SBra2u1cOFC\nlZaWKhqNaufOnUokErlxKjRLaGKji40uNrr40cRGFxtdbHTxo4mNLrZc7pKXw5q0b8JOJBJqbGxU\nLBZTSUmJwuG8PNGYQhMbXWx0sdHFjyY2utjoYqOLH01sdLHlahf3V5gloVBI4XA49WMu/M/KNprY\n6GKji40ufjSx0cVGFxtd/Ghio4stV7uwG2Su7QiTJTSx0cVGFxtd/Ghio4uNLja6+NHERhdbTnfJ\n3t4l7srlHWGyhSY2utjoYqOLH01sdLHRxUYXP5rY6GLL5S65cf4vw7wc3hEmW2hio4uNLja6+NHE\nRhcbXWx08aOJjS62XO6SlxuM5PKOMNlCExtdbHSx0cWPJja62Ohio4sfTWx0seVyl7wc1qTc3REm\nm2hio4uNLja6+NHERhcbXWx08aOJjS62XO2St8MaAAAAALjM/XESAAAAAPJQXg9ra9asOeSv8xFN\nbHSx0cVGFz+a2Ohio4uNLn40sdHFlotd8npYa2xsPOSv8xFNbHSx0cVGFz+a2Ohio4uNLn40sdHF\nlotdeM0aAAAAADgor8+sAQAAAICrCoNeQJCSyaQaGhoUj8dVUlIS9HKcQBMbXWx0sdHFjyY2utjo\nYqOLH01sdLHlYpe8HNbWrl2r6upqFRcXKxqNqqmpSclkMifeGC9baGKji40uNrr40cRGFxtdbHTx\no4mNLrac7uLlocmTJ3u7d+/2PM/zqqqqvN27d3u7du3yJk2aFPDKgkMTG11sdLHRxY8mNrrY6GKj\nix9NbHSx5XKXvHzNmrffniqhUCj1o5fHe63QxEYXG11sdPGjiY0uNrrY6OJHExtdbLncJS93g6yt\nrdXChQtVWlqqaDSqnTt3KpFI5Map0CyhiY0uNrrY6OJHExtdbHSx0cWPJja62HK5S14Oa9K+CTuR\nSKixsVGxWEwlJSUKh/PyRGMKTWx0sdHFRhc/mtjoYqOLjS5+NLHRxZarXdxfYZaEQiGFw+HUj7nw\nPyvbaGKji40uNrr40cRGFxtdbHTxo4mNLrZc7cJukLm2I0yW0MRGFxtdbHTxo4mNLja62OjiRxMb\nXWw53SV7e5e4K5d3hMkWmtjoYqOLjS5+NLHRxUYXG138aGKjiy2Xu+TG+b8M83J4R5hsoYmNLja6\n2OjiRxMbXWx0sdHFjyY2uthyuUtebjCSyzvCZAtNbHSx0cVGFz+a2Ohio4uNLn40sdHFlstd8nJY\nk3J3R5hsoomNLja62OjiRxMbXWx0sdHFjyY2uthytUveDmsAAAAA4DL3x0kAAAAAyEMMawAAAADg\nIIY1AAAAAHAQwxoAAAAAOIhhDQAAAAAcVBj0AuCW1tZWNTU16Vvf+lbQS0EOSCaTamhoUDweV0lJ\nSdDLgcN4rgDp4+sIh4vnSsfBsLafWbNmaezYsUEvIxDJZFILFizQ9u3b1dTUpMLCQvXq1UvXX389\ng5shn58rkrR27VpVV1eruLhY0WhUTU1NSiaTOfHmkkHI5+cLz5Ujk8/PlUPJ9y58HR0+nis8V45E\nLjxf8nJYmz9/vu82z/P04YcfBrAaNzz33HO6++67FY1G9dhjj2nChAlavXq1nnrqKU2cODHo5QWG\n54qturpaEydOVCQS0Zw5c3TzzTfL8zxNmzYtr/8y4Pnix3PFxnPFRhcbX0d+PFdsPFdsufx8ycth\nbd26dRo+fLhisdgBt2/cuDGgFQWvublZ8Xhce/fuVXNzsyRp4MCBWrx4ccArCxbPFZvneamfh0Kh\n1I/7356PeL748Vyx8Vyx0cXG15EfzxUbzxVbLj9f8nJYGz16tFpaWtSvX78Dbj/zzDMDWlHwhg4d\nqokTJ6q1tVXDhw9P3V5WVhbgqoLHc8VWWVmpqVOnqrS0VNFoVM8995wSiYQqKyuDXlqgeL748Vyx\n8Vyx0cXG15EfzxUbzxVbLj9fQl6+j9pI2bNnjzzPU+fOnYNeCnKA53lKJBJqbGxULBZTSUmJwmE2\nmIUfzxUgfXwd4XDxXOlYGNYAAAAAwEEFDzzwwANBL+KbtnXrVr344otKJpMqKyvTY489pnfffVcn\nnnii4vF40MsLxNatW7VgwQLV1taqR48eqR0gq6qq9N3vfjfg1QWH54qNLja6+NHERhcbXWx08aOJ\njS62XO6Sl+dEn332WV1wwQXyPE+TJk1SZWWlbrnlFs2ZMyfopQVmxowZOuecc3T++edr2bJleu21\n1yRJW7ZsCXhlweK5YqOLjS5+NLHRxUYXG138aGKjiy2Xu+TlBiNtbW065ZRTdMopp+i9997TCSec\nIElqaWkJeGXBCYVC6t+/vySpb9++WrVqlebNm6e2traAVxYsnis2utjo4kcTG11sdLHRxY8mNrrY\ncrlLXg5rJ598curnU6ZMSf3c9dOg2VRaWnrArwcNGqTu3btr5cqVAa3IDTxXbHSx0cWPJja62Ohi\no4sfTWx0seVyFzYYAQAAAAAH5eVr1gAAAADAdQxrklatWhX0EpxDExtdbHSx0cWPJja62Ohio4sf\nTWx0seVSF4Y1SUuXLg16Cc6hiY0uNrrY6OJHExtdbHSx0cWPJja62HKpC8MaAAAAADgoL98U29K7\nd++gl+AcmtjoYqOLjS5+NLHRxUYXG138aGKjiy1XurAbJAAAAAA4iMsgAWTEZ599FvQSkAP27Nmj\nPXv2BL0M5AieLwdqbW3Vl19+GfQynJJMJrVhwwYlk8mglwJkBZdB7mfWrFkaNGhQ0MsIxNatW7Vg\nwQLV1taqR48e+ta3viVJqqqq0ne/+92AVxecrVu36sUXX1QymVRZWZkee+wxvfvuuzrxxBNz4o0U\ns2XFihXauHFj6r+PP/5Y1dXVam1t1QknnBD08gLzm9/8JnVZRTKZ1MyZM/Uf//EfOuGEE9S1a9dg\nFxeQefPm6YwzzpAkrVy5UjNnztS//uu/qrm5WSeeeGLAqwvODTfcoGQyqR49eqhbt25BL8cZa9eu\n1ZNPPqlPP/1Ura2teu655/TWW2+psLAwZy5ZyoZkMqlZs2ZpxYoVevPNN/X2229rw4YNOvnkk9W5\nc+eglxeItWvX6plnntGnn36qZDKpDz74QEuWLNExxxyj4uLioJfnnHz+HlfK7e9zC4NeQBDmz5/v\nu83zPH344YcBrMYNM2bM0MiRI1VUVKRly5aprKxMV111lbZs2RL00gL17LPPqrKyUh9//LEmTZqk\n22+/Xd26ddPs2bN13333Bb28wLz66qvq06ePBg8erK+upA6Hw+rSpUvAKwvWO++8owsvvFCSNHfu\nXP3gBz9Qp06d9Pzzz+vee+8NeHXB2LhxY+rny5cv19SpU1VYWKhJkybpyiuvDHBlwerTp4++//3v\n6/XXX9ef/vQnXXzxxfre976ncDi/L3j51a9+pYkTJ2r16tVasWKFpkyZIs/z9E//9E8aMmRI0MsL\nzHPPPae7775b0WhUjz32mCZMmKDVq1frqaee0sSJE4NeXiCqq6s1ceJERSIRzZkzRzfffLM8z9O0\nadM0YMCAoJcXGL7HteXy97l5OaytW7dOw4cPVywWO+D2/b+pyDehUEj9+/eXJPXt21erVq3SvHnz\n1NbWFvDKgtXW1qZTTjlFp5xyit57773UWaOWlpaAVxasJ598Um+88YZ+//vf64orrtCZZ56pDz74\nIDWo5Ks9e/Zo27Zt8jxPjY2NqTMBzc3NwS4sQOFwWOvXr9dJJ52koqIihcNhff7554pGo0EvLXC9\nevXSrbfeqsbGRv3Lv/yL7r//fg0aNEgVFRVBLy0wra2t6tKli7p166aCggJJ+/5+CoVCAa8sWM3N\nzYrH49q7d2/qz5OBAwdq8eLFAa8sOPtvufDV8yMUCinft2Lge1xbLn+fm5fD2ujRo9XS0qJ+/fod\ncPuZZ54Z0IqCV1paesCvBw0apO7du2vlypUBrcgNJ598curnU6ZMSf08ny+BlKTCwkJdeeWVuuSS\nS7Rs2TItX7487wdYSSovL9fChQsl7Ttz8pXu3bsHtaTA3XnnnZo3b56qqqq0Z88ePfDAAzr66KN1\n8803B700Z8RiMVVUVOjqq6/O+z9zL7/8ck2YMEHHHnusBg8erEcffTT1j2b5bOjQoZo4caJaW1sP\nGObLysoCXFWwKisrNXXqVJWWlioajeq5555TIpFQZWVl0EsLFN/j2nL5+1x2gwSQtp07d+qjjz7S\nwIEDg14KHNXW1qYvv/xS8Xg8dcYkn+3cuZOzi4dh69atam1t9X2jlY/27Nkjz/Py9jVqFs/zlEgk\n1NjYqFgsppKSkry/lBgdT14Pa8lkUg0NDYrH4yopKQl6OU6giY0uNrrY6OJHExtdbHSx0QXIP3l5\nGeTatWtVXV2t4uJiRaNRNTU1KZlMqrKyMm9flEoTG11sdLHRxY8mNrrY6GKjy+GbNWuWxo4dG/Qy\nnEMXWy50ycthjR2E/Ghio4uNLja6+NHERhcbXWx08WPXQxtdbLncJS+HNXYQ8qOJjS42utjo4kcT\nG11sdLHRxY9dD210seVyl7x8zVptba0WLlyY2kFo586dqR2E8vVfqGhio4uNLja6+NHERhcbXWx0\n8fvoo4/U0tKi008//YDba2pq8vrtL+hiy+UueTmsSewgZKGJjS42utjo4kcTG11sdLHRBchPeTus\nAQAAAIDL+CcZSatWrQp6Cc6hiY0uNrrY6OJHExtdbHSx0cWPJja62HKpC8OapKVLlwa9BOfQxEYX\nG11sdPGjiY0uNrrY6OJHExtdbLnUhWENAAAAABxU8MADDzwQ9CJc0Lt376CX4Bya2Ohio4uNLn40\nsdHFRhcbXfxoYqOLLVe6sMEIAAAAADiIyyABAAAAwEF5Oaxt3bpVs2fP1pw5c1RXV5e6vaqqKsBV\nBYsmNrrY6GKjix9NbHSx0cVGFz+a2Ohiy+UueTmszZgxQ+ecc47OP/98LVu2TK+99pokacuWLQGv\nLDg0sdHFRhcbXfxoYqOLjS42uvjRxEYXWy53KQx6AUEIhULq37+/JKlv375atWqV5s2bp7a2toBX\nFhya2Ohio4uNLn40sdHFRhcbXfxoYqOLLZe75OWwVlpaesCvBw0apO7du2vlypUBrSh4NLHRxUYX\nG138aGKji40uNrr40cRGF1sud2E3SAAAAABwUF6+Zg0AAAAAXMewtp9Zs2YFvQTn0MRGFxtdbHTx\no4mNLja62OjiRxMbXWy50CUvX7M2f/58322e5+nDDz8MYDVuoImNLja62OjiRxMbXWx0sdHFjyY2\nuthyuUteDmvr1q3T8OHDFYvFDrh948aNAa0oeDSx0cVGFxtd/Ghio4uNLja6+NHERhdbLnfJy2Ft\n9OjRamlpUb9+/Q64/cwzzwxoRcGjiY0uNrrY6OJHExtdbHSx0cWPJja62HK5C7tBAgAAAICD8vLM\n2leSyaQaGhoUj8dVUlIS9HKcQBMbXWx0sdHFjyY2utjoYqOLH01sdLHlYpe8HNbWrl2r6upqFRcX\nKxqNqqmpSclkUpWVlRowYEDQywsETWx0sdHFRhc/mtjoYqOLjS5+NLHRxZbTXbw8NHnyZG/37t2e\n53leVVWVt3v3bm/Xrl3epEmTAl5ZcGhio4uNLja6+NHERhcbXWx08aOJjS62XO6Sl++z5u33Mr1Q\nKJT60cvjl+/RxEYXG11sdPGjiY0uNrrY6OJHExtdbLncJS83GKmtrdXChQtVWlqqaDSqnTt3KpFI\n5Map0CyhiY0uNrrY6OJHExtdbHSx0cWPJja62HK5S14Oa9K+CTuRSKixsVGxWEwlJSUKh/PyRGMK\nTWx0sdHFRhc/mtjoYqOLjS5+NLHRxZarXfJ2WAMAAAAAl7k/TmbB1q1bNXv2bM2ZM0d1dXWp26uq\nqgJcVbBoYqOLjS42uvjRxEYXG11sdPGjiY0utlzukpfD2owZM3TOOefo/PPP17Jly/Taa69JkrZs\n2RLwyoJDExtdbHSx0cWPJja62Ohio4sfTWx0seVyl7x8n7VQKKT+/ftLkvr27atVq1Zp3rx5amtr\nC3hlwaGJjS42utjo4kcTG11sdLHRxY8mNrrYcrlLXg5rpaWlB/x60KBB6t69u1auXBnQioJHExtd\nbHSx0cWPJja62Ohio4sfTWx0seVyFzYYAQAAAAAH5eVr1gAAAADAdQxrAAAAAOAghjUAAAAAcBDD\nGgAAAAA4KC93gwQAuGPKlClqampSKBRS7969NXr0aEWj0cP63GXLlumSSy5RJBJp12O/9dZb2r59\nu6655holk0nNmDFDkpRIJBSLxfStb31L3bp107hx41Kf09bWpmeffVa33HKLwmH+zRMAkD3sBgkA\nCNSUKVM0atQo9enTR2+88YZWrVql++6777A+9/bbb9dDDz2kWCzWrse+7777NHHiRN/nz5gxQ4MG\nDdLZZ5/druMCAJAJnFkDAATuq383vOyyy/Tb3/5WmzZtUmlpqaqqqvT5559r27ZtOvvss3XttddK\nkvbs2aMHH3xQO3bs0M9+9jMVFBTorrvu0tFHHy1J2rRpk1544QV5nqdYLKZbbrlF8Xj8gMd8//33\n1bdv3yMa9H7xi1+ovr5emzdv1rx581K3V1dX609/+pM++eQTDR48WNu2bdP69ev18MMPKxwO68sv\nv9Ts2bPV2Ngoz/M0atQoHX/88elmAwB0cFy/AQBwSt++fbVlyxYVFRVp1KhR+slPfqKHH35Y77zz\njr744gtJUiQS0U9/+lN169ZN9957r6ZMmZIa1Pbu3auZM2fqrrvu0gMPPKC//Mu/1Pz5832P89pr\nr+mqq646orXdeuut+ulPf6pQKOS7b+/evbr11lu1dOlSXXfddQqHw0okEpKkX/7yl7r44ot1//33\n64477tDMmTOPNAsAIA9xZg0A4KyCggKtXr1aW7duVSQS0fbt29W9e/fU/daV/PX19dq2bZumT5+e\n+phOnTod8DFbtmxRJBJRaWlpxtbau3dvRaNRffvb31YsFlPXrl21e/duSVJtba127NihV155RdK+\nwa6xsbHdl28CAPIDwxoAwCkbN27UBRdcoC1btuipp57SpZdeqt69eysej5vD2Z8Lh8Pq2bOn7r//\n/oN+zNKlSzVs2LBMLvuQCgoKNGHCBBUVFX1jjwkAyH1cBgkAcMaKFSsUjUZ1/PHHa82aNRo4cKAu\nu+wyRaNRbd261ffxX51tk/7vLFtZWZn27t2rlStXpj5u/yFv+/btSiaTOvXUU9u9ziPdm+uss87S\nr371q3Z/PgAgP3FmDQAQuGeffVZtbW3q06eP7r77bknSeeedp0ceeUTr1q1TWVmZTj311NRg9pVL\nL71UDz/8sHr06KHzzjtPF110kcLhsMaPH6+qqiotWbJEoVBI5557rq644gpJ+wbCr37eXtZr1g5l\n1KhRmjdvnu677z5FIhEVFxfrtttuS2sNAICOj637AQAAAMBBXAYJAAAAAA5iWAMAAAAABzGsAQAA\nAICDGNYAAAAAwEEMawAAAADgIIY1AAAAAHAQwxoAAAAAOOj/ASfGv3UHI6HuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11101d990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x111855c50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1118c6610>], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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irr32Wrndbo0YMULZ2dmt1rJ69WoVFxcrEAjI5XKptLRUDzzwQKttDB48WK+9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\nQC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111855490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.plot(kind='bar', subplots = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}