mburke05/gist:8dffcf08d0f375726593439f1d67eb5a

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['datetime', 'title']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from IPython.display import HTML\n",
    "from queries import TOTAL, DEMO, DB_CREDENTIALS\n",
    "import pandas as pd\n",
    "import pyodbc\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "mpl.rc('font',family='Arial Rounded MT Bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "abc = pd.read_csv('joined_table.csv')\n",
    "del abc['Unnamed: 0']\n",
    "abc['date'] = pd.to_datetime(abc['date'])\n",
    "\n",
    "old = abc[abc.date < datetime(year=2015, month=4, day=1)]\n",
    "old = old.groupby('station').mean()\n",
    "old['total'] = old['18+fem'] + old['18+male']\n",
    "old['%female'] = old['18+fem']/old['total']\n",
    "old['%male'] = old['18+male']/old['total']\n",
    "old['\\'15 %female'] = old['%female'].apply(lambda x: round(x*100,2))\n",
    "old['\\'15 %male'] = old['%male'].apply(lambda x: round(x*100,2))\n",
    "\n",
    "\n",
    "new = abc[abc.date > datetime(year=2015, month=12, day=1)]\n",
    "new = new.groupby('station').mean()\n",
    "new['total'] = new['18+fem'] + new['18+male']\n",
    "new['%female'] = new['18+fem']/new['total']\n",
    "new['%male'] = new['18+male']/new['total']\n",
    "new['\\'16 %female'] = new['%female'].apply(lambda x: round(x*100,2))\n",
    "new['\\'16 %male'] = new['%male'].apply(lambda x: round(x*100,2))\n",
    "\n",
    "n = new[new.columns[-2:]].sort_values(by=new.columns[-2], ascending=False)\n",
    "o = old[old.columns[-2:]].sort_values(by=old.columns[-2], ascending=False)\n",
    "t = o.join(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "with pyodbc.connect(DB_CREDENTIALS) as cnxn:\n",
    "    df = pd.read_sql(sql=TOTAL, con=cnxn)\n",
    "    df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#pre tot\n",
    "pre = df.set_index('datetime').sort_index()\n",
    "pre = pre[(pre.index > datetime(year=2014,month=12,day=2)) &                                            (pre.index < datetime(year=2015,month=4,day=1))]\n",
    "tst = pre[datetime(day=8,year=2014,month=12):]\n",
    "tst['delta'] = tst.date - datetime(year=2014, month=12,day=8)\n",
    "tst['delta'] = tst['delta'].apply(lambda x: x.days)\n",
    "tst['week'] = tst.delta // 7\n",
    "tst = tst.groupby('week').mean()\n",
    "del tst['delta']\n",
    "tst['imp'] = tst.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#post tot\n",
    "post = df.set_index('datetime').sort_index()\n",
    "tet = post[datetime(day=7,year=2015,month=12):]\n",
    "tet['delta'] = tet.date - datetime(year=2015, month=12,day=7)\n",
    "tet['delta'] = tet['delta'].apply(lambda x: x.days)\n",
    "tet['week'] = tet.delta // 7\n",
    "tet = tet.groupby('week').mean()\n",
    "del tet['delta']\n",
    "tet['imp'] = tet.imp.apply(lambda x: x/1000000.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
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    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "State of the television (overview): TV totals\n",
    "=================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The following data is TV impressions averaged for fifteen minute periods. Thus the resulting plots shows the average viewership at the given point (here, for instance, we show on 2-01-2015 the, the date of the superbowl in 2015, TV Impressions averaged 74.5M for the entirety of the day (though, as we'll see in future slides, this number understates prime and other highly active dayparts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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RERGZHRRMVcie0VEmp6cD+zUyJSIiIiIyOyiYqpBsQVNY6p+IiIiIiDQeBVMV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SAAAg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\nIiKfun/1G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      "text/plain": [
       "<matplotlib.figure.Figure at 0x32d2b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "tot = df.set_index('datetime').groupby(pd.TimeGrouper('W')).mean()\n",
    "tot['imp'] = tot.imp.apply(lambda x: x/1000000)\n",
    "y_label = {'fontsize':14}\n",
    "title = {'fontsize':30}\n",
    "s_legend = {'fontsize':14, 'handlelength':10}\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(14, 7)\n",
    "ax.plot(tot.index,tot, linewidth=4, color=\"#046865\")\n",
    "ax.set_ylabel('IMPRESSIONS (M)', **y_label)\n",
    "ax.set_title('Total Cable TV Impressions'.upper(), **title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "TV pattern has a cyclicality that follows weather, school, and vacation patterns socially. In 2015 viewership peaks during colder weather and eclipses the 58 Million Viewership mark regularly. Mean impressions are <b>54.8 Million</b> for the duration of the data (15 Months.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (overview): Hot dates\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "tmp = df.groupby('date').mean().sort_values(by='imp', ascending=False)\n",
    "tmp.columns = ['Impressions']\n",
    "tmp.Impressions = tmp.Impressions.apply(lambda x: round(x/1000000.0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Impressions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>74.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-07</th>\n",
       "      <td>71.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>71.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-11</th>\n",
       "      <td>70.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>70.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>69.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-18</th>\n",
       "      <td>69.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>69.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>68.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>68.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Impressions\n",
       "date                   \n",
       "2015-02-01        74.48\n",
       "2016-02-07        71.85\n",
       "2015-01-01        71.63\n",
       "2015-01-11        70.85\n",
       "2016-01-24        70.75\n",
       "2016-01-17        69.49\n",
       "2015-01-18        69.40\n",
       "2015-11-01        69.27\n",
       "2016-01-10        68.70\n",
       "2015-01-04        68.38"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Note that all values fall between 11-01 and 2-07, by calendar date. Values are, again, in millions <b>impressions</b> averaged over the duration of the date. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (overview): Female skewing stations. \n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>'16 %female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALL</th>\n",
       "      <td>76.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMC</th>\n",
       "      <td>76.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC</th>\n",
       "      <td>76.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENT</th>\n",
       "      <td>76.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRVO</th>\n",
       "      <td>75.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIF</th>\n",
       "      <td>74.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLC</th>\n",
       "      <td>74.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LMN</th>\n",
       "      <td>72.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>71.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HGTV</th>\n",
       "      <td>71.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         '16 %female\n",
       "station             \n",
       "HALL           76.97\n",
       "HMC            76.87\n",
       "GMC            76.18\n",
       "ENT            76.12\n",
       "BRVO           75.41\n",
       "LIF            74.55\n",
       "TLC            74.18\n",
       "LMN            72.27\n",
       "OWN            71.93\n",
       "HGTV           71.69"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.sort_values(by=t.columns[2], ascending=False)[:10][[2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Female skewing stations include a variety of <b>family-friendly movie stations</b> (LIF, LMN, HMC), <b>variety/talk shows</b> (OWN), and <b>DIY/hobbyist</b> friendly stations (HGTV, ENT, TLC.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (overview): Male skewing stations. \n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>'16 %male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENN</th>\n",
       "      <td>80.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLBN</th>\n",
       "      <td>77.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VEL</th>\n",
       "      <td>75.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEIN</th>\n",
       "      <td>75.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBAT</th>\n",
       "      <td>74.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOXD</th>\n",
       "      <td>74.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UDN</th>\n",
       "      <td>74.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NBCSN</th>\n",
       "      <td>73.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESPN2</th>\n",
       "      <td>73.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPD</th>\n",
       "      <td>73.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         '16 %male\n",
       "station           \n",
       "ENN          80.35\n",
       "MLBN         77.36\n",
       "VEL          75.72\n",
       "BEIN         75.38\n",
       "NBAT         74.79\n",
       "FOXD         74.50\n",
       "UDN          74.07\n",
       "NBCSN        73.98\n",
       "ESPN2        73.26\n",
       "SPD          73.02"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.sort_values(by=t.columns[3], ascending=False)[:10][[3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Male friendly stations include the usual sports-related subjects (which may be subject to volatility, as we'll see in future looks.) These include <b> ESPN2, NBATV, MLBN, NBCSPORTS, Univision Deportes</b>. ESPN just misses the list with 72.37% of impressions coming from males in 2016. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'15 %female    26.22\n",
       "'15 %male      73.78\n",
       "'16 %female    27.63\n",
       "'16 %male      72.37\n",
       "Name: ESPN  , dtype: float64"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.ix['ESPN  ',:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Impressions by Demographic Shifts\n",
    "=================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The following slides will highlight demographic movements, by age and gender. Doing so allows us to glean the value of specific daypar combinations for specific age-demographic pairings for target markets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4CeHJgP8Soy2F0pjzeO2P+4HfZPhbU6hilGlyyakN2YgC+AmuF8SfVvRVY8wv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\nnkRERERERFzw/wHUwCRvLfEyrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b680c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "y_label = {'fontsize':14}\n",
    "title = {'fontsize':30}\n",
    "s_legend = {'fontsize':14, 'handlelength':10}\n",
    "fig, ax = plt.subplots()\n",
    "sns.set_style(style='whitegrid')\n",
    "fig.set_size_inches(14, 7)\n",
    "ax.plot(tst.index, tst.imp, label='\\'15', linewidth=3, color=\"#21A0A0\")\n",
    "ax.plot(tet.index, tet.imp, label='\\'16', linewidth=5, color=\"#046865\")\n",
    "ax.set_ylabel('IMPRESSIONS (M)', **y_label)\n",
    "ax.set_title('Total Cable TV Impressions, 2015 v. 2016'.upper(), **title)\n",
    "ax.set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax.get_xticks())])\n",
    "ax.legend(**s_legend);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Total impressions were lower in '16 for the majority of weeks, excluding the 3rd and 4th weeks of January (due to seasonal sporting events.) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Total Impression +/- By Week\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kXb58Wc8995yio6P15z//WTt37nTosrirX375RREREWrZsqUuX74s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x34cd3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "y_label = {'fontsize':14}\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(14, 7)\n",
    "tst.columns=['2015']\n",
    "tet.columns=['2016']\n",
    "j = tst.join(tet)\n",
    "j['Difference'] = j['2016'] - j['2015']\n",
    "ax.bar(j.index,j.Difference, width=0.9, color=\"#E53D00\")\n",
    "ax.set_ylabel('+/- Impressions YOY'.upper(), **y_label)\n",
    "ax.set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax.get_xticks())])\n",
    "ax.set_xlim([0,16]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The above shows the difference in impressions for each week comparing year over year. This highlights the most glaring differences in late February and smaller more consistent differences in late Q4. Notable event discrepenacies include, for instance, a difference in timing of the superbowl (Feb 7 in 2016; Feb 1 in 2015) a difference in the timing of the BCS playoffs, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {
    "collapsed": false,
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "with pyodbc.connect(DB_CREDENTIALS) as cnxn:\n",
    "    df = pd.read_sql(sql=DEMO, con=cnxn)\n",
    "    df['date'] = pd.to_datetime(df['date'])\n",
    "    df['imp'] = df['impressions']\n",
    "    del df['impressions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "women = df[df.DemographicGroupDimID < 16].groupby(['date', 'datetime'])['imp', 'date', 'datetime'].sum().reset_index()\n",
    "men = df[df.DemographicGroupDimID > 15].groupby(['date', 'datetime'])['imp', 'date', 'datetime'].sum().reset_index()\n",
    "\n",
    "#########making total male and female tables\n",
    "\n",
    "#pre tot\n",
    "pre = women.set_index('datetime').sort_index()\n",
    "pre = pre[(pre.index > datetime(year=2014,month=12,day=2)) & (pre.index < datetime(year=2015,month=4,day=1))]\n",
    "tst = pre[datetime(day=8,year=2014,month=12):]\n",
    "tst['delta'] = tst.date - datetime(year=2014, month=12,day=8)\n",
    "tst['delta'] = tst['delta'].apply(lambda x: x.days)\n",
    "tst['week'] = tst.delta // 7\n",
    "tst = tst.groupby('week').mean()\n",
    "del tst['delta']\n",
    "tst['imp'] = tst.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#post tot\n",
    "post = women.set_index('datetime').sort_index()\n",
    "tet = post[datetime(day=7,year=2015,month=12):]\n",
    "tet['delta'] = tet.date - datetime(year=2015, month=12,day=7)\n",
    "tet['delta'] = tet['delta'].apply(lambda x: x.days)\n",
    "tet['week'] = tet.delta // 7\n",
    "tet = tet.groupby('week').mean()\n",
    "del tet['delta']\n",
    "tet['imp'] = tet.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#pre tot\n",
    "pre = men.set_index('datetime').sort_index()\n",
    "pre = pre[(pre.index > datetime(year=2014,month=12,day=2)) & (pre.index < datetime(year=2015,month=4,day=1))]\n",
    "tmt = pre[datetime(day=8,year=2014,month=12):]\n",
    "tmt['delta'] = tmt.date - datetime(year=2014, month=12,day=8)\n",
    "tmt['delta'] = tmt['delta'].apply(lambda x: x.days)\n",
    "tmt['week'] = tmt.delta // 7\n",
    "tmt = tmt.groupby('week').mean()\n",
    "del tmt['delta']\n",
    "tmt['imp'] = tmt.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#post tot\n",
    "post = men.set_index('datetime').sort_index()\n",
    "tnt = post[datetime(day=7,year=2015,month=12):]\n",
    "tnt['delta'] = tnt.date - datetime(year=2015, month=12,day=7)\n",
    "tnt['delta'] = tnt['delta'].apply(lambda x: x.days)\n",
    "tnt['week'] = tnt.delta // 7\n",
    "tnt = tnt.groupby('week').mean()\n",
    "del tnt['delta']\n",
    "tnt['imp'] = tnt.imp.apply(lambda x: x/1000000.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Battle of the Sexes.\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zZk2EhITgxIkT5rRjx44JGh7T0r59e4SFhZmf25MnT3Dnzh1UrFgRQPaMaNi3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qG+EvIAGxrJ9EkhRMMTsmCExWLhxRdfpLS0FK/XS0JCQsW5jz/+GLfbTffu3aMS5P+zd99hUV7Z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\nADCA8gQAAAAABlCeAAAAAMCA/wNMab0licreCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bba1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, sharey=True)\n",
    "fig.set_size_inches(14, 7)\n",
    "y_label = {'fontsize':14}\n",
    "title = {'fontsize':30}\n",
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "s_legend = {'fontsize':14, 'handlelength':10}\n",
    "ax[0].plot(tst.index, tst.imp, label='\\'15', linewidth=3, color=\"#FFE900\")\n",
    "ax[0].plot(tet.index, tet.imp, label='\\'16', linewidth=5, color=\"#E53D00\")\n",
    "ax[0].set_ylabel('IMPRESSIONS (M)', **y_label)\n",
    "ax[0].set_title('FEMALE, 2015 v. 2016'.upper(), **title)\n",
    "ax[0].set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax[0].get_xticks())])\n",
    "ax[0].legend(**s_legend);\n",
    "\n",
    "ax[1].plot(tst.index, tmt.imp, label='\\'15', linewidth=3, color=\"#21A0A0\")\n",
    "ax[1].plot(tet.index, tnt.imp, label='\\'16', linewidth=5, color=\"#046865\")\n",
    "ax[1].set_ylabel('IMPRESSIONS (M)', **y_label)\n",
    "ax[1].set_title('MALE, 2015 v. 2016'.upper(), **title)\n",
    "ax[1].set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax[1].get_xticks())])\n",
    "ax[1].legend(**s_legend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "There are no enormous dissimilarities between general male/female demographics. Of note is the fact that the axis in the female graph is ~4M higher (female viewing audiences are higher across all age demographics by ~10-12% per annum.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Stations accumulating <u>female</u> viewers. \n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "t['CHANGE'] = t[t.columns[-1]] - t[t.columns[-3]] \n",
    "t.index.names = ['STATION']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHANGE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RLZC</th>\n",
       "      <td>-12.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REY</th>\n",
       "      <td>-7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKTNS</th>\n",
       "      <td>-6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRNT</th>\n",
       "      <td>-6.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFD</th>\n",
       "      <td>-6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STYL</th>\n",
       "      <td>-5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBN</th>\n",
       "      <td>-5.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIO</th>\n",
       "      <td>-5.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOLF</th>\n",
       "      <td>-4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAM</th>\n",
       "      <td>-3.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CHANGE\n",
       "STATION        \n",
       "RLZC     -12.39\n",
       "REY       -7.75\n",
       "NKTNS     -6.70\n",
       "CRNT      -6.51\n",
       "RFD       -6.25\n",
       "STYL      -5.95\n",
       "FBN       -5.30\n",
       "BIO       -5.13\n",
       "GOLF      -4.15\n",
       "DAM       -3.90"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.sort_values(by='CHANGE')[[-1]][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Note that <b>CRNT</b>, <b>RFD</b>, and <b>FBN</b> all feature heavy or moderate discussions around rural and progressive politics. This is notworthy because, as we'll see, politically and/or news oriented programs gained the most during Q1 '16 compared to it's '15 counterpart. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Stations accumulating <u>male</u> viewers. \n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHANGE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TLC</th>\n",
       "      <td>5.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNTRC</th>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRVO</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM</th>\n",
       "      <td>3.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMC</th>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOGO</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHIL</th>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISJR</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSP</th>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CHANGE\n",
       "STATION        \n",
       "TLC        5.54\n",
       "CNTRC      4.44\n",
       "BRVO       3.97\n",
       "FAM        3.94\n",
       "HMC        3.84\n",
       "LOGO       3.76\n",
       "CHIL       3.68\n",
       "DISJR      3.67\n",
       "OWN        3.65\n",
       "INSP       3.62"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.sort_values(by='CHANGE', ascending=False)[[-1]][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Millenials \n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "mill_men = [21,22,23,24]\n",
    "mill_women = [6,7,8,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "mill_men = df[df.DemographicGroupDimID.isin(mill_men)].groupby(['date', 'datetime'])['imp', 'date', 'datetime'].sum().reset_index()\n",
    "mill_women = df[df.DemographicGroupDimID.isin(mill_women)].groupby(['date', 'datetime'])['imp', 'date', 'datetime'].sum().reset_index()\n",
    "\n",
    "#########making total male and female tables\n",
    "\n",
    "#pre tot\n",
    "pre = mill_women.set_index('datetime').sort_index()\n",
    "pre = pre[(pre.index > datetime(year=2014,month=12,day=2)) & (pre.index < datetime(year=2015,month=4,day=1))]\n",
    "tst = pre[datetime(day=8,year=2014,month=12):]\n",
    "tst['delta'] = tst.date - datetime(year=2014, month=12,day=8)\n",
    "tst['delta'] = tst['delta'].apply(lambda x: x.days)\n",
    "tst['week'] = tst.delta // 7\n",
    "tst = tst.groupby('week').mean()\n",
    "del tst['delta']\n",
    "tst['imp'] = tst.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#post tot\n",
    "post = mill_women.set_index('datetime').sort_index()\n",
    "tet = post[datetime(day=7,year=2015,month=12):]\n",
    "tet['delta'] = tet.date - datetime(year=2015, month=12,day=7)\n",
    "tet['delta'] = tet['delta'].apply(lambda x: x.days)\n",
    "tet['week'] = tet.delta // 7\n",
    "tet = tet.groupby('week').mean()\n",
    "del tet['delta']\n",
    "tet['imp'] = tet.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#pre tot\n",
    "pre = mill_men.set_index('datetime').sort_index()\n",
    "pre = pre[(pre.index > datetime(year=2014,month=12,day=2)) & (pre.index < datetime(year=2015,month=4,day=1))]\n",
    "tmt = pre[datetime(day=8,year=2014,month=12):]\n",
    "tmt['delta'] = tmt.date - datetime(year=2014, month=12,day=8)\n",
    "tmt['delta'] = tmt['delta'].apply(lambda x: x.days)\n",
    "tmt['week'] = tmt.delta // 7\n",
    "tmt = tmt.groupby('week').mean()\n",
    "del tmt['delta']\n",
    "tmt['imp'] = tmt.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#post tot\n",
    "post = mill_men.set_index('datetime').sort_index()\n",
    "tnt = post[datetime(day=7,year=2015,month=12):]\n",
    "tnt['delta'] = tnt.date - datetime(year=2015, month=12,day=7)\n",
    "tnt['delta'] = tnt['delta'].apply(lambda x: x.days)\n",
    "tnt['week'] = tnt.delta // 7\n",
    "tnt = tnt.groupby('week').mean()\n",
    "del tnt['delta']\n",
    "tnt['imp'] = tnt.imp.apply(lambda x: x/1000000.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YunTpEh9++CHt27fPMZd7gwYNAK11SVVVdu/ezYYNG3I9Wc+ePdmzZw/9+vVD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\ntNCFZd5+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20bf6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,1, sharex=True)\n",
    "fig.set_size_inches(14, 7)\n",
    "y_label = {'fontsize':14}\n",
    "title = {'fontsize':30}\n",
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "s_legend = {'fontsize':14, 'handlelength':10}\n",
    "ax[0].plot(tst.index, tst.imp, label='\\'15', linewidth=3, color=\"#FFE900\")\n",
    "ax[0].plot(tet.index, tet.imp, label='\\'16', linewidth=5, color=\"#E53D00\")\n",
    "ax[0].set_ylabel('IMPRESSIONS (M)', **y_label)\n",
    "ax[0].set_title('FEMALE MILLENIALS'.upper(), **title)\n",
    "ax[0].set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax[0].get_xticks())])\n",
    "ax[0].legend(**s_legend);\n",
    "\n",
    "ax[1].plot(tst.index, tmt.imp, label='\\'15', linewidth=3, color=\"#21A0A0\")\n",
    "ax[1].plot(tet.index, tnt.imp, label='\\'16', linewidth=5, color=\"#046865\")\n",
    "ax[1].set_title('MALE MILLENIALS'.upper(), **title)\n",
    "ax[1].set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax[1].get_xticks())])\n",
    "ax[1].legend(**s_legend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Millenials were the most notably impacted group realtive to year over year performance. In spite of the news cycle including a gambit of anti-establishment candidates, young people appear to have watched a smaller share of TV than traditionally had been the case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "heat_map = pd.read_csv('heat_map.csv').rename(columns = {'Unnamed: 0' : 'Station'}).replace([np.inf, -np.inf], np.nan).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "millenials = heat_map[[0,3,4,5,6,-1]].set_index('Station')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "millenials['females'] = millenials[millenials.columns[0]] + millenials[millenials.columns[2]]\n",
    "millenials['males'] = millenials[millenials.columns[1]] + millenials[millenials.columns[3]]\n",
    "millenials['%fem increase'] = millenials.females / millenials.aud\n",
    "millenials['%male increase'] = millenials.males / millenials.aud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Millenial station performances (best and worst), year over year (female.)\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>%fem increase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NKJR</th>\n",
       "      <td>11.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VH1</th>\n",
       "      <td>6.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REY</th>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RLZC</th>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NICK</th>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MUN2</th>\n",
       "      <td>-8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPRT</th>\n",
       "      <td>-9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV1</th>\n",
       "      <td>-9.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TNNK</th>\n",
       "      <td>-10.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DFAM</th>\n",
       "      <td>-10.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         %fem increase\n",
       "Station               \n",
       "NKJR             11.96\n",
       "VH1               6.94\n",
       "REY               4.52\n",
       "RLZC              3.68\n",
       "NICK              3.60\n",
       "MUN2             -8.47\n",
       "SPRT             -9.14\n",
       "TV1              -9.54\n",
       "TNNK            -10.18\n",
       "DFAM            -10.55"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = millenials.sort_values(by=millenials.columns[-2], ascending=False)[[-2]].head()\n",
    "worst = millenials.sort_values(by=millenials.columns[-2], ascending=False)[[-2]].tail()\n",
    "millen = pd.concat([best, worst])\n",
    "millen[millen.columns[-1]] = millen[millen.columns[-1]].apply(lambda x: round(x*100, 2))\n",
    "millen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Millenial station performances (best and worst), year over year (male.)\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>%male increase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NKJR</th>\n",
       "      <td>5.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REY</th>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUND</th>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VH1</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNN</th>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPRT</th>\n",
       "      <td>-7.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TNNK</th>\n",
       "      <td>-7.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOXD</th>\n",
       "      <td>-8.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTV2</th>\n",
       "      <td>-12.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMDY</th>\n",
       "      <td>-12.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         %male increase\n",
       "Station                \n",
       "NKJR               5.43\n",
       "REY                4.28\n",
       "SUND               3.72\n",
       "VH1                3.58\n",
       "CNN                2.76\n",
       "SPRT              -7.09\n",
       "TNNK              -7.53\n",
       "FOXD              -8.19\n",
       "MTV2             -12.02\n",
       "CMDY             -12.69"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = millenials.sort_values(by=millenials.columns[-1], ascending=False)[[-1]].head()\n",
    "worst = millenials.sort_values(by=millenials.columns[-1], ascending=False)[[-1]].tail()\n",
    "millen = pd.concat([best, worst])\n",
    "millen[millen.columns[-1]] = millen[millen.columns[-1]].apply(lambda x: round(x*100, 2))\n",
    "millen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "days = {0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'}\n",
    "mill_men['imp'] = mill_men['imp'].apply(lambda x: round(x/1000000,2))\n",
    "mill_men['HOUR'] = mill_men['datetime'].apply(lambda x: x.hour)\n",
    "mill_men['WEEKDAY'] = mill_men['datetime'].apply(lambda x: x.weekday())\n",
    "mill_men_piv = pd.pivot_table(mill_men, values=\"imp\", index=\"WEEKDAY\", columns=\"HOUR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "mill_women['imp'] = mill_women['imp'].apply(lambda x: round(x/1000000,2))\n",
    "mill_women['HOUR'] = mill_women['datetime'].apply(lambda x: x.hour)\n",
    "mill_women['WEEKDAY'] = mill_women['datetime'].apply(lambda x: x.weekday())\n",
    "mill_women_piv = pd.pivot_table(mill_women, values=\"imp\", index=\"WEEKDAY\", columns=\"HOUR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): When were Millenials watching TV?\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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ZUuB+r9ervLw8SdLp06eVlJRU/GFc3rNQuGeeeUaPP/64nnnmGfXt21dHjhyR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x225cd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(14, 7)\n",
    "y_label = {'fontsize':14}\n",
    "title = {'fontsize':30}\n",
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "sns.heatmap(mill_women_piv, cmap=\"YlGnBu\", ax=ax1)\n",
    "sns.heatmap(mill_men_piv, cmap=\"YlGnBu\", ax=ax2)\n",
    "ax1.set_title('FEMALE MILLENIALS')\n",
    "ax2.set_title('MALE MILLENIALS');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Gen X\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "genx_men = [25,26,27,28]\n",
    "genx_women = [10,11,12,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "genx_men = df[df.DemographicGroupDimID.isin(genx_men)].groupby(['date', 'datetime'])['imp', 'date', 'datetime'].sum().reset_index()\n",
    "genx_women = df[df.DemographicGroupDimID.isin(genx_women)].groupby(['date', 'datetime'])['imp', 'date', 'datetime'].sum().reset_index()\n",
    "\n",
    "#########making total male and female tables\n",
    "\n",
    "#pre tot\n",
    "pre = genx_women.set_index('datetime').sort_index()\n",
    "pre = pre[(pre.index > datetime(year=2014,month=12,day=2)) & (pre.index < datetime(year=2015,month=4,day=1))]\n",
    "tst = pre[datetime(day=8,year=2014,month=12):]\n",
    "tst['delta'] = tst.date - datetime(year=2014, month=12,day=8)\n",
    "tst['delta'] = tst['delta'].apply(lambda x: x.days)\n",
    "tst['week'] = tst.delta // 7\n",
    "tst = tst.groupby('week').mean()\n",
    "del tst['delta']\n",
    "tst['imp'] = tst.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#post tot\n",
    "post = genx_women.set_index('datetime').sort_index()\n",
    "tet = post[datetime(day=7,year=2015,month=12):]\n",
    "tet['delta'] = tet.date - datetime(year=2015, month=12,day=7)\n",
    "tet['delta'] = tet['delta'].apply(lambda x: x.days)\n",
    "tet['week'] = tet.delta // 7\n",
    "tet = tet.groupby('week').mean()\n",
    "del tet['delta']\n",
    "tet['imp'] = tet.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#pre tot\n",
    "pre = genx_men.set_index('datetime').sort_index()\n",
    "pre = pre[(pre.index > datetime(year=2014,month=12,day=2)) & (pre.index < datetime(year=2015,month=4,day=1))]\n",
    "tmt = pre[datetime(day=8,year=2014,month=12):]\n",
    "tmt['delta'] = tmt.date - datetime(year=2014, month=12,day=8)\n",
    "tmt['delta'] = tmt['delta'].apply(lambda x: x.days)\n",
    "tmt['week'] = tmt.delta // 7\n",
    "tmt = tmt.groupby('week').mean()\n",
    "del tmt['delta']\n",
    "tmt['imp'] = tmt.imp.apply(lambda x: x/1000000.0)\n",
    "\n",
    "#post tot\n",
    "post = genx_men.set_index('datetime').sort_index()\n",
    "tnt = post[datetime(day=7,year=2015,month=12):]\n",
    "tnt['delta'] = tnt.date - datetime(year=2015, month=12,day=7)\n",
    "tnt['delta'] = tnt['delta'].apply(lambda x: x.days)\n",
    "tnt['week'] = tnt.delta // 7\n",
    "tnt = tnt.groupby('week').mean()\n",
    "del tnt['delta']\n",
    "tnt['imp'] = tnt.imp.apply(lambda x: x/1000000.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pYnMEUwfRgqn+aMHU04WvX/hdxu4NJE5/ivIXz/DfgkpEvbaUwLg7/d0tIYQQ\nosQoX748gHMH8FWrVjFt2jTMZjOAy3Kis2fPsnTpUlq3bs2bb74JQGhoKIsXL8Zms2Ew5H9dfFHw\n2ouBAwcycOBATp8+zZ49e5xfa9asYc2aNSiKQlhYGD169GD48OHeqhGi8FQ7JPUGxz9aWjFph+4q\n+f/UX7XZSJz9LOSy9g9AtaQTP6oDsW/vwFCtTkF67UoXkxVMtQHbfi0v+Rm0YMoH0wiF31j2fkf8\n6EfAqp2157h0lvj/PUz5D39BX6Gan3snhBB5Y0/4j8Qp/bAc+B4yLf7uTplSCTjj704UFWMggY3v\nJXL0x+ijyud6ad26dQGoVasWM2fOZOHChcTFxdGgQQN27Njhcu3WrVtRVZXWrVtjs9m4ePEiw4YN\nY/DgwSUmiALQXe2CKlWq0LFjR1577TWmT5/OY489RmBgIKqqkpyczNKlS4ujn+JaljoeMr/NTkd8\nCoYbClbVivnYjh90y9eVq+KW5zBf4tKIttjjzxeoLfdGoiHyOzDkOEsieQCkve2b+kWxyzy00yWI\nukxNTiBhYi9Um81PPRNCiPxJnNIPy+71EkSJ/Mm0YNm9nsQp/a56aWxsLEeOHGHQoEG0adOGCRMm\nsGjRIudIVU5//vknAP/++y+tWrWiZcuWtGrVisOHD/v6ERSK15DOZrNx4MABfv75Z/bu3cvBgwed\nm08oikLdunW55ZZb6NSpU7F1VlyDLGsgLccueiGvQGDB1i7Zzv9D8odj3fINtRsRO/cHLg29D+ux\n/S5l9rN/Ef/yg8TM+xFdSFiB2nWhi9KCqcT7wbZHy0sZBDggRLb+L02sxw9y6aX2qBlpHsszf91G\n8qcTiXjytWLumRBC5F/m4Z3+7oIoxfL7+mnUqBGNGjXyWp6cnAzAggULGDBgAEFBQcydO5fBgwez\nadMmwsPDC9VfX/EaSHXq1Injx48DEBwcTFxcHE2bNqVJkyY0atSIsDAfvKkUIje2P7IOs80S0AZC\nC/6mNGnu86jpqa6ZikLk8AXowiOJnr6Wi881x37upMsl1qP7SBjXjeipq1EMAQVu30kXCZGbIPEB\nsGXteJnyPFowNaTw9YsiZ/vnKJeG34+akpjrdSmfTCSwUUsCm7Qqpp4JUQI4EiBtFtEhRlCbFGgt\nqyh+xhubayNSQhSA8cb876Ccm6CgIAB69uzpPGP23LlzfP755/z000906NDBp+0VlNdA6nIQFRYW\nRp06dXAbBavVAAAgAElEQVQ4HOzdu5e9e/c6r1EUhcaNG/Piiy8WfU/FtUVNg6QuoGoLENFdB6Yv\nQdEXqLr0ravI2L7aLT+k40CMDW4DQB9TkZhZG7g46A4c5ksu11l2byBx1rNEvrzQNxus6EwQuRES\n24Jtl5aX8gJaMCW/TyWZ7dzfXBzaGkfihatfrKokTHqMch/+gj4ytug7J4S/qSqYu4N1EzVjAPMB\niPhY+wBJlGiRoz+WNVIi/3KskfKlyzuF16pVy5lXs2ZNQAuoSgqvgdT111/PiRMnSElJYf/+/d4u\n49SpUxJICd9SVUh6NuvsJQBj1qG7BXsj6khLxjz3ebd8XUwlIp6e7JJnuK4u0VO/4eKL90JmhktZ\n+rqP0Jev5rupWpeDKXM7sGYtskwZihZMDfNNG8Kn7JfOcWloaxwXTrmVGa6rS2KNJoRtWeSS77h4\nhsSp/Yie9o3scirKPssysG7KTmd+DQm3QMQKCLjZf/0SV6WPKk/MzHX+7kaZtG/fPpo2bXr1C4VT\nixYtANi1axc9e/YE4NChQ4BrcOVvXgOplStXcuHCBZetCK+kKArlypUrko6Ja1j6O2D5PDsd/iYE\n3Frg6pI/HOfxja/p+bnowkxu+cabmhM1bjEJr3YGh8OlLOXjCejLVSX0IR9tW66LANOGrGBqe1Yj\nw7WdCkNH+qYN4ROOpHguDb8f++k/3Mr0FasTM+c7/v37DNFJZ8g8+JNLuWXnWlKXzSPsUfnQSZRh\njuSsD4OuYD8BCbdD+LsQ3Lf4+yWEKNE8nS/VoEEDWrVqxcaNGxk6dChBQUGsWrWK2rVrc/fdd/ur\nq2687tpns9moUqUKVatW9fpVpUoVjEaj8560NM+LroXIM+tOSMnxZjPoSQjqX/Dqjh0gdfk8t/zA\nZu0IatXN633Bdz2C6cU3PZaZZw8gY8eaAvfJjS4cTOsh4K7svNSXIHW679oQheJIS+bSyHbY/vzN\nrUwXXZGYOd+hL18VdHqixnyOzhTjdl3Suy+ReXRfcXRXCP9IfQ0cWZs86ypwMv5VUC6vp86A5H7a\nbAM1w1sNQohrUMOGDYmNjXUbaZo9ezaPPvooO3fu5LvvvqNt27Z8/PHHpWP783bt2jFy5Eh27dqF\n44pP5XOy2Wzs2LGDkSNH0r59+yLppLhGOP4DczfAqqUNTbTRqAJOh1LtdhJnPeM2qqQEBmMa+tZV\np1mFdnyOsMf+56GfDhLGdyfz/34uUL880oWDaR0E3JOdlzoKUqf4rg1RIKolg/j/PYL1d/fnW4mI\nJmbOJgxVs7fj15evSuSoj9wrsllJGN8DR1pyUXZXCP+wHYL0udnpsFlcSn0EovaAvkF2fsZ7kHAn\n2E8WexeFECVTr1692LZtG1FRUS75ISEhTJgwgV27drFnzx5ef/11YmLcP6j0J68h3ZtvvsmECRPo\n168fYWFhNGjQgIoVKzpPI7548SLnzp3j0KFDpKWl0ahRI95+W87DEQWk2sDcAxyntbQSDaYVoAQX\nuMq0Ve9gPbLXLT+s71gMlWvmqY7wpydjv3Ca9I2funY3I434lx8k9p2dLm+iC0UXBpFrIfEhsP6g\n5aW+AjggdIxv2hD5otqsxI/rRuaBH9zKlOAwYmZuIKDWTW5lQXc8RGjXF9xGQ+2n/8A8ZyBRYz4r\nsj4LUexUFZIHAXYtHXAPBPYG9oOhHkTt1g4gt2StH7Ttg/gmEPEFBLbzV6+FEKLQvAZSDRs2ZNmy\nZezYsYNly5axfft2fv7Z9RNZk8lEy5Yt6datG82b+3bbQ3GNSX0lO3hA0f7A6msUuDr7xTMkvT/a\nLd9Q80bCegzPcz2KohD50vs4Lp3FsneTS5nDfJFLI9sS+/aOq57mnfcGQyFyTVYw9b2Wl/oqWjDl\nfgaWKDqq3U7i5D5YPE3jNAYRPe0bjPW9r92LGDCdzF+2YD1+wCU//dvPCbylDSFt+/i6y0L4R8bn\nYN2SlTBA+FuuMwl0Ydr/6ektIGUYYAU1AcwPQsgYCB1X4B1ZhRDCn646ybBFixa0aNECu93O2bNn\niY+PR1EUYmNjKV++PHq9/OcnCiljBaTNyE6HjofAtoWq0vzGi6geplCZRizI91lQSoCRqEkruPj8\n3diOH3Qps58+QfzLHYiZ9wO64NBC9Tm7wRCI/AYSHwHrd1pe6jhQHVlvOGTnt6KmqirmOc+Rvnmx\ne6HeQPTE5QQ2bplrHYoxkKjxi7nQv4nb+WXm1wdibNAMw3V1fdhrIfzAkQgpI7LTIS+C4Ub36xRF\nO3Q8oKk2hdtxGlAhbSJYd2nHWxRwZ1YhhPAXr2ukrqTX66latSpxcXE0bNiQSpUqSRAlCs92BJKf\nyE4b22ufUBZCxq71ZPywzC0/pEN/AhveUaA6dSHhxExfh75idbcy65E9JIx7FNVmK1DdHikhELka\njPdn56W9lhVQed9JUxSeqqokvT2StG/edy9UFKLGfE5Q8wfzVJehWh1Mw95xbyM9lfjXeqDKWS2i\ntEsdA+p/2ve6KhAyLvfrA5pD9AEIuC87z7pJm+pn3V10/RRCiCKQ50BKCJ9zpIC5M6hZI0e6WhDx\nOSgFf1k6MtIwzxnolq+LLEfEgMLtgqePrUT0zA0oEdFuZZZd6zDPHpDrcQH5pgSD6Wsw5hidS5uY\n9cZFgqmikvLpJFKXzPZYZhr5HsH3dc9XfSEPPE7wA4+75duOHyTpnZcK1EchSgTrfu24isvC5mrT\n+K5GV047Qy/klew8x7+QcBekvS3/vwkhSg0JpIR/qCokPwX237MygrTNJXRRud52NSkfT8B+7qRb\nfsTgOeg8BED5FVC9HtFTV4MxyK0sbe1CUj6eUOg2XChBYFqpjdQ5G5oCqaPlzUYRSFk2j+SFntei\nRQyaTWiHgm3Fbxr6Fvqqtd3yU1fMJ2Pb6gLVKYRfqQ5IHghk7YpqvB8Cu+T9fkUPYZPA9A0okVmZ\nVkgZBEmPg5qa6+1CCFESSCAl/CN9LliWZqfDF0BAo0JVaf3zECkeRhKMTe8juE3vQtWdU2DDO4ga\n+6XHtUrJH40ndc1Cn7UFZAVTX4Exx3SytGmQ+rIEUz6Utu4jkt7wfGBuWL9xhHUfVuC6dSHhRI1b\nDAFGt7KEaU9g/8/9wGghSrSMhWC7PBXPCGEFPKoisANE7wND4+w8yxcQ3wxsx3zSVSGEKCq5BlJp\naWn8/PPPWCwWZ3r69On079+fsWPH8scffxRLJ0UZk7kVUkZmp4Ofg+DC7WCmOhwkznoW7FesUwow\nEjns7aueGZVfwXd3wjRkvscy8+xnydi5zqftoQRqI3bGh7Lz0mZqP0cJpgot/cflJM7wPNoU2u1F\nwp+4yrqPPDDWbULEgBlu+WpSPAkTevl2jZ0QRclxEVJGZadDXgKD+4hrnulrQdR2CHoqO89+GBJu\n0TYjEkKIEsprIHXu3Dnat29P3759ee+99wDo378/H330Edu2bWPp0qV069aNI0eOFFtnRRlgPwtJ\nj+I8b8TQDMJeL3S1aWsXYj20wy0//LHRGKrVKXT9noR2GUxYr5fdC+x2EsZ1I/P3Pb5tUAkE03Iw\nPpKdlz4bUoZLMFUIGbs3kDChl9vBzQAh7Z8kYvAcnwXioV2HENiig1t+5q9bSf50kk/aEKLIpfwP\n1Hjte10NCPVwcHl+KcEQ8QGEfwAEanlqMiR1heQRoFoL34YQQviY10Bq5syZnDt3jrZt29K9e3d+\n+ukn9u/fT2BgINOmTWPUqFFYrVbmz/f8qbwQblQrJHUDxzktrcSCaZkWIBSCPf68x0X7+mp1COs9\nysMdvhP+zBSP0wYvH9hrO33Ctw0qRjAthcBO2Xnpr0PKUECCqfyy/LKVhDGdweb+Ji2oVTdMI9/z\n6WimoihEjvoIXWxlt7KUTydiOfiTz9oSokhYd0HGB9np8PnaLqO+EvwURO0EXY5D09NnQ+J92gdx\nQghRgngNpLZu3UqbNm14/fXXKV++PBs2bACgT58+dOzYkX79+vHggw+yb9++YuusKOVSRoJ1e1ZC\nB6bFoK9W6GqT3hqOmpLolh85/F0UY+GCtKtRdDoiR32Isel9bmWOxAtcGtEWe+IFHzdqhIglrgu7\n0+dRLXIGqJm+basMyzyyl/iXH0S1pLuVBTZrR9SYz1GK4IgHfWSstsZOd8V/vw4HCRN74zBf8nmb\nQviEaoPk57LTxoch8CHv1xdUQGNt3ZQxx+itdSskNIbMLd7vE0KIYuY1kEpPT+f66693pn/66ScU\nRaF169bOvIoVKzrXTwmRq4xFkD4vOx06BYzuwUd+WfZ+R/qmL9zygx/oQ2CTVoWuPy+UACPRk77C\ncMPNbmX2038Q/3IHHOk+3oFKCYCIRRDYzZlVPnwZxDeCzB9921YZZD35f8SPbOvx0GbjzXcTNXE5\nioeNIXwlsNE9hPV51S3fceE0CVOf8O02+kL4Svo7YLt8KHkwhM/L9fJC0UVpxz+ETsb5VsVxHhLv\nhbRZMp1ZCFEieA2kqlatyrZt27Baraxbt474+HhiYmKIi4sDICUlhc2bN1OtWuFHFEQZZzsESTkW\n8gd20hYnF5JqySDRw5lRSkQ0EYNmFbr+/NCFRhAzYx36Cte5lVl//5mE13r4fjMBJQAivoTAHOca\n2X+HxFZgflx70yHc2M78yaWhrT2O/ATUu4Xoad+gC/LhVCUvwvuMwRh3l1u+Zcc3pK54o8jbFyJf\n7Oe0M+wuC30F9DWKtk1FB6GjtTOnlNjLHdFmNyR1BYe5aNsXQoir8BpIde7cmUOHDtG8eXOGDx8O\naNP6FEVhyZIltG3blj/++IMuXfJxboS49jjM2qG7pGlpfR0I/6hg2+ReIfmLqdhPHXfLjxgwA31k\nuULXn1/62MpEz1yPEhbpVmbZsQbz64N8P9KgGCDiCwibi90RmqPBz+FSPUh7B1S7b9ssxewXTmtB\n1CX3tRaGmjcSM3MDutCIYumLYjAQ9eoXHg94TnpnJNZjB4qlH0LkScpIUJO07/V1IGRE8bVtbA3R\nB8DQPDvP8hUk3Aq234qvH0IIcQWvgdSTTz7JU089hV6vJywsjD59+vDUU9rWpPPmzSMhIYHevXvT\nt2/fYuusKGVUFZL6gf1ysBOinYekMxW6auvfR0j5YppbvjHuTkLaP1Ho+gsqoEaDrAN73ddmpX3z\nHimfTvZ9o4oeQl7g8LllEPhodr6aCCkDIaEFWPf7vt1Sxp54gUvD2mA/+5dbmb5yLWJmb0JniinW\nPukrVCPy5Q/dC6yZxI/vjsPD1EMhil3mT9qHM5eFv1noTYLyTV8Von6E4CHZefbj2nlTGZ97vU0I\nIYqS10BKr9czcuRIdu/ezZ49exg9ejT6rIXXa9asYevWrbz66qs+P59HlCFpMyBzVXY6YiEYbix0\ntaqqYp49AKxXbKxgCMA0YgHKlYv4i1ngzXcRNeZzzwf2LnyVtHUfFUm7Vnt5MC0B0wbQZ69vxPaz\n9slt8pBrdiqMI8VM/Ii22P7+3a1MV64KMXO+Qx9byQ89g+C7HiG0y/Nu+fZTxzG/PtgPPRIiB9UK\nyYOy04HdwNjGP31RjNq6rIhFoFwegU+HpMcheSCosmZbCFG8CvSOMzo6muho9+koQjhlbobU0dnp\n4BcgqIdPqk7f8CmZHraJDus5koAaDXzSRmEFt+xKxPNzPZYlznyajN0biq7xwAcg+hCEjAMub5jg\ngPQ3IL4eZCy+phZqOzLSiB/VAesx91E5nSmWmNmbMFSu6eHO4hMxYAaG2o3c8tM3fkraxs/80CMh\nsqTP0w7HBS14CZvj3/6A9rck6mfQ18vOS38HEu4C+9/+65cQ4prjNZDq0aMH9evXz/Xrxhtv5Omn\nny7O/orSwP4vmHsAWQecBtwBYTN9UrXDfImkt93n5usr1yK8zxgPd/hPWNchhPbwsI7AbidhbFcy\njxbh0QFKEISN1wKqgByfHjvOQVJPSLwfbMeKrv0SQs20kDCmM5m/bnMrU0IjiJ61kYAa9f3Qsyv6\nEhhE9LjFKMGhbmXmOc9h+7fsP1eiBLKfgtTx2enQ8doUu5LA0EALpnJOZ7btgfgmYNnov34JIa4p\nXgOpKlWqUKlSJY9foaGhqKqK3W7n2DH5Ay9yUC1g7grqRS2tqwgRy7Qd5nwg6Z2XcJgvuuWbhr2N\nEhjskzZ8KWLAdIJb93TLV9NTiX+pPbYzfxZtBwy1tR2vIpaALsfUNet3EN8QUsaC6n6OUlmg2mwk\nTOyN5Wf3N1VKYDDR09dirNvEDz3zzHBdXUwvvuWWr6ankjC+B2qmTFsSxSxlKKhZRzfob9RmFpQk\nunCIWAxhcwGDlqfGg7kdpL4GqsOv3RNClH1eA6nZs2fz/fffu3xt2rSJXr16Ybdru4DVr1+f999/\nv9g6K0qBlBe1NTkA6CFiKeh9s/bE8stW0ta5L8wPurc7Qbc94JM2fE07sPcjjI3dz7RyJPyXdWCv\ne2Do204oEPQoRB/JeiN0+dc+E9ImagGVpQinGvqB6nBoUyh/WuFeaAggavJKAuPuLP6OXUVw2z4E\nt+ntlm89foCkBaP80CNxzbJ8C5bl2enwt332gZhPKQqEvACRP4Kuclamqo2kmR8EhxxwLYQoOnle\nI3Xw4EE6derErFmzUBSFUaNGsWLFCurUqVOU/ROlSfonkP5udjpsFhjdz8kpCNWaiXnWs275SmgE\npsGv+6SNoqIYA4mevBJDrYZuZfZTx4n/30M4MtKKviO6CAifC1F7wdAsRydOaJ/gmruB/XTR96OI\nqapK0htDSV//sXuhTkfU2EUlN/BWFEzD30Ff5Qa3stRlc8nY/o0feiWuOaoFUnJsdBL0OBjv9l9/\n8sJ4B0Tvh4AcH1plbtCm+ln3+K9fQogy7aqBVFJSEmPHjqVnz54cO3aM+++/n/Xr19OvXz90ft4d\nTZQg1oOQPCA7Hdjdp9NAUhbN9LjjWsSz0/y221p+6MJMxMxYh66c+/oC6+FdJL7W0/cH9noT0Bii\ndkD4u6DkOPPKslzbjCLtdVCLqS9FIHnhWFJXzPdYFvnyhwS3LNln3+lCwokavxgM7p/+J057AvuF\n0h/sihIubWb2sRWKyWdrXIucrgJEfgshOUZvHf9Awp2QvuCa2mRHCFE8co2EVq9eTbt27Vi6dClV\nqlRhwYIFzJ8/nwoVKhRX/0QpoFeSsg7dzcjKaADhH/jk0F0A2+kTJH86yS0/oEEzQh52H6UqqfTl\nqxIza4PHA3sztq/GPO953x/Y642ig+BnIeYoBOU4C05NgZRhkHALWHcWT198KGXRLFI8vFYATC+8\nQUi70nHunbFuUyIGTHfLd5gvkTCxN6q9iA5ZdlzQzhyTtSXXLvtfkJrjvLvQSVqAUlooBgibCqZV\nWhAIQKb2QV9yP1CLYfRfCHHNMHgrGDp0KOvXrwfAZDLRtm1bfvvtN377LfsUcUVRiIuL4667fDN9\nS5RCqoOaMa+CI+uQUyU869DdMN9Ur6qY5wyEzAzXAr2eyBJwZlR+BdS8kegpq7g0/H63c7DSvn4X\nfflqhD8+2svdRUBXHiI+hqAnIfk5sP+flm/7RTvIN6g/hE0DXfEeVFsQqavfI+mdkR7LwvtPIrRL\n6TqTKbTbi1j2bcayc61LfubBn0j5bDLh/cb6piE1HSyrIeMzbSoUdgi4EyI+Bb1/t4UXfpD8As4P\nxQyNIfg5v3anwAIf0aYxJ3UB269aXsanYDsIEcu1jXiEEKKQvAZSv/zyi/N7s9nMBx984PE6k8nE\n7t27fd8zUTqkTcIUvD07Hf4xGOr6rPqM75dg2fOtW35o1xcJuOFmn7VTnAIb3UPUK5+RML67W1ny\n+6+gj61S/CMnxrsh+oA2rS/1NSBrJ7+MD8CyCsJmQFA/n40y+lrad4u0Q5o9COv5EmHFGZz6iKIo\nRP7vIy48cTOOS2ddypI/fg1j41YE3lzAD7FUB1i3am8sLctBTXItt26D+JshbL42YllCn3fhY5bV\nkJljHV7426Do/defwjLcAFE7tcN6Mz7R8my/aiPuEZ9AYEf/9k8IUep5DaQ+//xz9u7diy2XdRuK\nolC/vv/PYBF+krHU9YyRkJcgqLPPqnckJ2J+40W3fH35aoQ/Md79hlIk+N5HsV88TdKbw9zKEmf0\nRxdTiaDb7i/eTilGCH1ZO+wyeQhkrtby1YuQ/CRkfAjh74DhpuLt11VkbP+GxMmPe1z/EPLIAMIH\nTEMppYGAPrIcUa9+waWh97k+PoeDhAm9KP/hQXSmfIwW2o5oI08ZX4DD28GlOsABajIkP6G9sQ5f\nALrYwjwUUdKpadrv/WVBT0PA7f7rj68oIRD+kXaeYfJgIFP74MDcSfubFTpZmw4ohBAF4PV/j8qV\nK/Pwww8XZ19EaZK5GZIeA7Le3AW00v4g+VDSe//DEX/eLd809C10Ib6ZOuhPYY8Oxf7fKVKXznEt\nsNtIeLULMfN/8s85R/rqEPm19ul08vPaYm3IGqVoDCFDIWSsz6ZvFoZl3/fEj+sGHtYMBbfuhWno\nW6U2iLossEkrwvqMIeWTiS75jgunSJz2JFFTVuX+GB0XIGOxFkDZvOxepr9B25kt6DFwXNR+ty9v\nNmD5Cqw7IPxDCGzno0clSpzUKdnBtRKjrTMqKxQFgp/Wpiqau2Y/zrQZYP0ZIhaBvqJ/+yiEKJWu\nusDkl19+Yfz48fTo0YO2bdvStWtXxo4dy4EDB4qjf6Iksh7QPs3DCkC6tQaYlvn0U73Mw7tIW73A\nLT/ork4E3fGQz9rxt4iBMwm6132Kn5qeQvzLD2I785cfepUl8GGI+T8IeZnsz1xs2o5e8Q3AstKv\nu2BlHt5F/OiHwcNBtUF3PEzk6I9L3Ro6b8L7jsXo4dyrjO2rSfvK/RBf1AzIWAaJD8PFypAyxD2I\nUqK19S9ROyH6GISOBX0tCLhNm+aZc22M4xyY20PyIFmsXxbZjmm/15eVknWR+RZwi7ZFujHHBwLW\nHyGhCWRu81u3hBClV67vMmbOnEmPHj1YvHgxBw8e5OTJkxw6dIilS5fSq1cvpk2bVlz9FCWFLevM\nITVZS+uqcPzCmz79o6varCTOetbtTboSHIbpBc/bWpdWik5H1OhPMDa6x63MEX+O+Jfa4TD78UBJ\nJVR7UxV9EAJynCPj+FfbqdH8kLbLVzGznviVSyPboaanupUZm9xL1PglKB62Dy+tFIOByFe/QAmP\nciszvz0c6/GD2rqnzK2Q9AxcrAhJj2atd8k5PTsAAjuDaSXEntXWwATc7r4GSgnVykxrXXdsS39b\nG5WUc3nKDlXNnvIGYLhd23ymrNJFg2kNhE4Asl73jrOQ2DLr6AfZIl0IkXdeA6m1a9eycOFCYmJi\nGDlyJEuXLmXDhg0sXbqUkSNHEhMTwyeffMKqVavy1JDdbmfChAm0aNGCNm3aOHcEzGnVqlXceeed\n3HHHHbz55psFf1SiaDjOg/kB7V/QziCK3IDV7tspEanL5mE78atbfvhTE9GXdz+HqbTTDuxdhaHm\njW5ltn+OcmnUQ6iWdD/0LAfDjRD5I4R/Akq57PzMtXDpRm1akJrp9XZfsv17nEvD70dNSXQrC7jx\ndqKnfI0SGFQsfSlOhgrXETnqQ/cCayYJ41rjOFUTEu+GjPdBNbteE9BCW98Wew5MK7RF9orx6o0G\ntofo38CYY1G+/Zi2o2PqxFJ93pjIYlkO1k1ZCV3WBhNlYyTXK0UHoa+CaYM2jREAu3b0Q9Kj4Ej2\na/eEEKWH1/8tv/jiC6KiolixYgVPPfUUcXFx1KhRg7i4OJ566im++uoroqOjWbRoUZ4aWrVqFWvX\nrmXNmjW89NJLvPzyy5w/n73+JT4+njFjxjBu3DhWrlzJwoUL2bmz9J1jU2Y5kiCxHdhPZGUEgekb\nn288YDv3N8kfjXPLD6jdmNDOpWv76vzQhUcSM2M9unJV3Mqsh3eSMKFX0Z0dlFeKAsF9IOYIBD2L\n89Nc0iH1FW2Xt8wfirQLtvP/cGlYa49r5wzXx2mHHpeB9XPeBN/VkZBOg9zybacuYf7gH9dMXS0I\nGQfRxyFqOwQP0D6Nzy9dOe1Ig/APQbn8s7VB6lhIuAtsf+S/TlEyOJIhZWh2OnigdmD3tSLwfm2q\nn+G27DzLcki4FWyH/dcvIUSp4TWQOn78OA8++KDXw3fLly9P+/btOXbsWJ4aOnToEHXr1iU6Opr7\n7rsPu93Onj3Z00N+/fVXbDYb99xzD+XLl6du3bps3749lxpFsVEt2jQu2+V1cTowLQGj+5qNQjWj\nqpjnDkbNuGINhqJgGrEAxVC2d1bSV6hGzIz1KKERbmUZW1dhnjek+A7szY0uGiLe1dbWGBpl59uP\nQOK9YH48e9TSh+zx57k0tDX28/+4lemr1iZm9rfoPEx9KzNUC2SswNT7bwzV3YvTf4C0rSFakBu5\nDWL+gLDx2hbQhaUoEPwERP+i7X52mW0XJDSC9PdlSlRplDYBHKe173UVIHRi7teXRfrrIGqLFkRe\nZj8K8bdBxpf+65cQolTw+s7UZrMREJD7GgNdPhZy165dm3Xr1nH+/Hl+++037HY7CQkJznKz2YzB\nYMBo1KabhIaGkpSU5K06F/v27ctzP0R+OagZ8wrRIZudOSfjR3Pp3ypA9s/dF89B0K/fE7VjjVt+\n6p2PcjZNB9fI82zsO4PoBc+j2K0u+Wmr3uacVUdq635e7y3e3wUD8C7lw5ZR2fQuel3WeiXL59jS\nV3E6cTAXUzsBhT+HRklLIuatZwk4c9ytzB5ZgfNPvs6pv07BX6cK3VZh+fY5UAk1/kJM6DqiQjZh\n0CWjAFHD4OJLWmyVU/y7cCy0LfZyQcB+H/YjpzlUDP+EyqYFKIod1FRIfobE/z7n7/gx2BwFGPUq\nAvJ3IXdBAX/QoMLrzuVxf10YRPzfJ3K/qQBKz/PwJNEhFakeNRmdzgKkQVJvLpxZzmnzYOwOk787\nWKjCPKkAACAASURBVGCl5zko2+R5KJu8BlLXX389a9as4emnnyY62v0P46VLl1i7di316tXLU0Nd\nu3bl119/pXPnzrRo0YLw8HCX0a7Q0FDsdjs2mw2DwUBKSgqhoaF5qrtp06Z5uk7kk6pqu32lb8rO\nC51EjfKvUCPHZfv27Sv0c+BIS+a/KY/guCJfF1uZ60e/h87DKE2Z1bQp6eVMJLzW060oYu2bXNfo\nNkIeeNytzBfPQ8E0A/swbX2BZQkABl0K1aOnUb38Zgh/FwIKvo27Iy2FS8PbYPUQROmiylP+zS1U\nq1anwPX7ks+eA9sfkPG5tmW540+34oAqYHqmFolvuJbpLGlUWTGJ2Ld3oATkYQ1Ugd0G1qeytkn/\nHYDI4C1EVu0NEQsh0L87a/rvd6GUUFVIHAbWrOnCAfdQs94Yavr4qIDS9zw0BVtHMHdxbv9fLmwl\n5cK3aNvBBz1Z6g4oLn3PQdkkz0PJUBTBrNchpR49enDhwgW6dOnCJ598wt69e/nrr7/4+eef+eij\nj+jUqRMXL17ksccey1ND58+fJzQ0lE2bNtGrVy/sdjvNmzd3ljdt2pSgoCC2bt3K2bNnOXz4sEu5\n8IO0qZCeY9OP4MEQMrpImkr+4FUcF0675ZuGzLu2gqgswff1IGLgLI9lidOeJGPPJo9lfqOvDKbF\nYNqonUl0mW2Ptt4g+XlwmL3f74VqySD+lY5YD+9yK1PCIomZswlDCQmiCs0RD2nvQHwLiK8Naa+5\nB1G6mhDyKkQfJbjrHwS3dg+2rUf3kbTgf0Xf34AmEL0Pgp/PzlMvgPlhbedAR0rR90EUTMbnYN2S\nlTBA+FvuOzdeqwwNIWovBHbJzlMvQfIzkHA7WHf7r29CiBLH64hU165dOXbsGJ9++ilTp2oH8ymK\n4rJG4+mnn+bBBx/MU0PlypUjISGBVq1aYTKZmD17NkFBQXTt2pUZM2ZQq1YtZs6cyaRJk0hJSaF/\n//7cfffdV69YFI30D7QNBC4LfBTC5hbJH9vMo/tJ/eoNt/zA29sTdE8XD3dcG0K7D8P+37+kLp/n\nWpB1YG/sG1sIqN3I883+Eng/GH+DtOmQOhWwAA4tILcsh7A5ENgjT68j1WYlYXx3MvdtditTgkOJ\nmbmegOvjfP8YipNqgcx12siTZQ2Xz2ZzoZi037+gPtr6pKyfnQKYhr9L5v/txn7GNeBKXTqHwKb3\nEtQ8b/8/F5gSDOHzwdgBkvtp20iDtnOg9QeI+EzbXl2UHI5ESBmRnQ55UduVU2TTRUDEMshcBclD\nsw/wte3VgqmgJ7LO2irv334KIfxOUa+yen3//v2sXLmSw4cPk5KSQnh4ODfddBNdunQhLs7/b2Jk\nuLQIWFZnHbibNdEu4F6IXAdKoMfLC/McqHY7Fwc0w3rUdbhVCQym3Kf/h6FSjQLVW1aoDgcJ47uT\n8eNytzJddEVi392FoaK280CJ+12wHYeUwZD5rWt+wH3aFssG7yNJqsNB4uQ+pG/6wr3QGEjM9LUE\nNr3Pxx0uvDw9B6qqbdKQ8RlkLAE13sNFBu3Q0KA+ENgBFO/buWce2cvFgS3A5hqE6UyxlPvoF/Sx\nlQvwSArAcQmSnwPLspy9gJBXtK2mleI716vE/S6UJMnPZ8800FWB6COgK5qdLsvE86CmQeoMSJuG\n9sFQFsWknUUVPNCnh9H7Wpl4DsoAeR5KhqJ4Hq7629+kSROaNCn4+gZRymRuB3N3nEGUobF2eKeX\nIKqwUle+5RZEAYT9P3v3HR5FtT5w/DuzPdlNA0SKF6SoCCICVlBQRFDEgqDitV6xohd772JFxXYV\nuyDXzv3ZETsqCgqiIEWaSEfSs8n2md8fZ9PY3RTI7obwfp5nns2eMzvzpkDy7jnnPeffudsnURDd\nsPfW1ygo3Epw0Xe1+ozCLRReN5zWz8xBz2oeC/xrsXZX+7QE3gHvVdWjFaEvofAAyLgRMm9Woxo1\nmKZJyeTx8ZMoi4W8u95ulklUvSJrosnTdIgkKBluPQSc54DzDFV2vAHs+/Un6+IHKH3mulrtRkk+\nRfeeTavHPkezpGBdh94Kst4C/0iVQJulgAEV90LwUzU6Zd03+XGIxEK/qE2VK7knJy2JajG0DFX9\n0nUulF0DwfdVu1kC3glq9obnKbDHbqouhGj5EiZSq1atYsmSJXWWW9Y0jV69etG1a9ekBCdSLLwE\nSk4E/Oq5pSvkzFTTHJIgsm0jZS/eFtNu3bsX7jOuSco9d0Waw0neA++TP34g4bVLa/WF1y2n8OaT\naPVYM1szVUnTwHk62IdD+Z3gexKVpAfVH9j+/4LnaXAcD6gkqmzKTVS8PyXutXJumYZz4Ekp/RR2\nilEEgbdVAhVKsJ2D3gmcZ6vD2rDiPdvLPP1qAgu+JDCv9kbnwYVf453+AJ7zYv+dJYWmgescsB8F\npedBaLZqD/8MhQeB+1G1n5Wsx0k904Cyy6meaTAUHKPTGtIuxdIFct6DwEyVQEWLURBZDMWDwTEW\n3JPAErsXoBCi5UqYSP3zn/+kpKT+xeHZ2dnMmyeLL3d5kXVQPAzMYvVc20MVDtDj7yPWFEqenIBZ\nEbuDfM51z6FZUzcNaFege3LJmzST/MsOx8jfVKsvuHgORfeeDSffmKboGkDPAs9kcJ4HZZdCOPp/\nhrEGSk5QC7vdj+N9fRreNx6Oe4nsa54lY+hZKQx6B5lBCM6Mrnv6EAjGnqNlgWOMGn2yHQlaw7eS\niEfTdXJueZVtFxyIUbilVl/ZK3diP2gwjt5Nu+9bnSydIOdLqHgsutYyBPjAezkEPwTPS2Bpl7p4\nBPhfrv53h129gSEJbeM5jgf7MVAxGcrvBaL7HgbegOAHqhhMxtWgJbNqphCiuUiYSF133XX88ssv\n9Y5IHXLIIQn7xS7CKFBJVOXGjJoHcj4Fa/JGGv0/fox/9oyY9oyRF2M/4Iik3XdXZm37D1pNmkn+\nFUdiltfeY83/7f/IMm2YvV8B3RI9dLTm9oeSrQ/k/gD+F8F7E5jRveQCMyh/70PKXoqTdABZFxxA\n5uCvoORrVJmF7Q5Nj9+OhipOqkX/aEzQF3O9OvrquOZeub9B/teqylcMixqZc54DjpNipjTuLEvu\nHuTePp2Ca4bW3hzXMCi+5yzavPxraqeAahbIvB7sx0XLpP+u2oMz1dROz/PgHJW6eHZnRj54a7zR\nknFDnWsURT00B2TepEaRvddD4E3VbpZD+U0qaXU/CY5h6Y1TCJF0CROpMWPGMGbMmFTGItLBLIfi\nERBZHm2wQfZ7YDsoabc0fOWUTB4f067n7kHWpQ8m7b4tga1rb/Im/h8F1w+PKS6Q+d1bbB76Vu0X\nWFRSpVUmVxYLmsVaI9myqPUz0bZa5+k12i01z7fWPid6Xq1zKl9b85zK11aep12IFv4CzF8xyqD8\nw/hJlPs0cI9YDIHFyfqyNpk93MD27z1Z+6miEc4zk17ly9FvCO6zb8H72n212iN/r6f4oQvJnfi/\n1CfXtgMh72fw3gq+x1SbWQClp0HwfHA/kbTpwyLKe3N1URO9s1qbKHaepSNkvwHBS6DsCogsUe2R\nFVAyHOyngOcxsOyd3jiFEElTb7GJjRs30qGDmvNrGAZvvPEGK1asoEOHDowaNYrWrVsnPUiRJGYI\nSk6vMd1Dg6zpatpCEpW9ejeRLX/FtGeNfwzdk5vUe7cEjn7HkHPzqxTf+8/6T45EIBKp9bd9nWU6\nm5nM48FzZrqj2AH6XtF1T+eAtUdKb+254C4CC78m9PsPtdr9371HxXvPknnq5SmNB1BVBz2PgmOE\nWjtlbIgG9SoEv1GFKOwpnHq4OwnNVSPAlTxPqgIKounYB0PeQlXIo/yOaKEVVPn0gk/V6FXGDU0+\nCi2ESL+EiVRJSQkXXHABy5YtY8KECVx66aVcddVVfPZZdSnjadOm8eabb9KxY8eUBCuakGlC2Ti1\nh00l9xOqMEAShVYvovztx2La7f2PxbUrrH9pJjKGnkVk2wbKpjTjdVE7yTV0MFkTLo8uHzLjHEZ0\nCluCvu3bzDr6duSaZmzfps3baN9pLNgG7fS6px2lWa3k3vE62/7VB9NbXKuv5D/XYD9gALZuB6Yl\nNuzHQN4i9e594HXVZqyF4qOiVRzvlrUlTcmMRAtMRNlHgmNk+uJpyTQbZExQI8/em8H/SrTDD+V3\nge9VtU7UfrKsTROiBUmYSD3++OMsXbqUPn36MHToUObPn89nn32GxWLhsssuo6SkhOnTp/P444/z\nyCOPpDJm0RTKbwL/tOrnGbdCxpVJvaVpGBQ/cokaJanJ7iDnmmeb33qeZs499nqMbRsonxG7mfGu\nzjnoNHJufhPN2nz3Z4lnc+kC2tvTv1eIdc9O5Nz4EkW3b7ehdTBA0V1n0PqFBeiuzPQEp+dC9n9V\nmfSyy6IFbky1T09wlhoVt+6fnthaGt+zEF4YfeIEzxN1ni6agN4Wsl4G18VQNh7Cv6h2Y63an9E+\nTL1pKVsBCNEiJHzLdNasWRx22GG88cYbdO3alU8+USMXY8aM4YorruDWW29l6NCh/PjjjykLVjSR\nislQUaMymvNCyLw3+bf98AVCS+bGtHvOuRVrx25Jv39Lo2kaWVdMxnPhPVj27IRhd4LdodYm7cIc\nh51A7u3/3eWSqObGNWgUGadcFtMeXvcHpU/8Ow0Rbcd5JuQtVht+VwovhMJ+UPGkKtctdpyxFcpr\nlL3PvFXW6qSS7TDI/Qk8z4FWo8hLcJYqtuK9CQxv+uITQjSJhH+plJaWctBBB1WNEnz99dcAnHDC\nCVXndOnShW+++Sa5EYqm5f8veGvs0WQ/CTxTkj7VIFK4ldLnboppt/5jX9xjb0jqvVsyzWLBc97t\neM67PWbHbtMwwIiukTIiEAmDUflxRH0cbas6J3qeGe2vPj9c+xwjghkOV32srrXdPSqvGwnXPsdI\ncG/A1q0PriFnpmYD2d1A9vhHCS76nvCa2oU6Kj55GXv/Y8k4dmyaIouydIScz9X+Yt6bgADgV/v0\nBD6CrFdkX54d5b1ebRoLYOkOGdenN57dkWZRI1OO06D8dvBNQU0FDkHFQ2pzbvcj4DhDpvsJsYtK\nmEi1bduWhQvVlIAff/yRzZs3k5WVRd++fQEIh8PMnTuXdu1kL5BdRuAzKD2/+rltIGS/CVry3/kv\nffqamPUaANnXTkGzO5J+/92Rpuug62C1Ib+id0+aw0XuXW+Rf1E/zICvVl/JI5dg73EI1g5p3lBd\n0yHjKrAfq8qkh39T7aHPo2XSpyR97WaLE5yt9jGr5H5alewW6aG3As8z4BwH3ishFC0EY2yE0rFg\nmwKep8B6QHrjFEI0WsKpfSNGjGDu3LmMGDGCyy9Xi1XHjBmD1Wpl9uzZnHbaafz666+1RqhEMxb6\nGUpHAWH13NITsj9ISRUh/0+f4fvi9Zh21/DzcBw0OOn3F2J3Zuvcg6wJsevozIoyiu4+EzMUv+x8\nyll7Qe48VXSiMvU3i6D0DCg5B4zYN2JEHGZIrc2p5BgDjuPSF4+oZusLOd+DZ1rtze5Ds6HwICib\nID/nQuxiEiZSl19+OcOHD2f16tX4fD6GDBnCFVdcAcD111/PH3/8weDBg7nsstg5+KKZCa+A4hPU\nnlGgSjPnfKoWfSeZGfBRMjm23LKe3Yqsy6VIiRCpkDHiX7iGxNaRDy2fT+nzt6QhogQ0B7gfhJxv\nQO9U3R6YDoW9Val0UTffE9X7GWmZ4I6tkirSSNPAdQ7krQDXNUDlNOaImuJasA/4XpE1gkLsIhLO\n6XI6nTz++ON4vV4MwyArq3rDxOnTpxMOh9l/f6ms1OxFNkHxcWDmq+daHuTMUmsTUqDstfuJbFwd\n05512SQsObIHmRCpoGka2ddOIbh0HpHNf9bqK3/rURz9huA87Pg0RReH/SjI+02tlfJPVW3Geig+\nBjKuhcyJMlUtnsgGVWq7UuZdKfu/XjSSnqX2VnP9C8r+DaGvVLu5Dcr+Bb7nwPM02PqnN04hRJ3q\n3ejE7XbXSqJWrFjBqlWrCAabyXQQkZhRDCXHg1G5+a0Lcj5O2QahobXL8L7+UEy7/cCjcB1/fkpi\nEEIoujub3DvfjFvVsfj+84jkb05DVHXQsyHrVch6t0bVMxMqHoHCgyFcu4CGGfAT3vIXwSVzsa+a\nT2jlr4Q3r8XwlqjCK7sD7zXVMw8sPcE1Ib3xiPpZe0LOF5D1jpotUik8D4oOgdKLwchPX3xCiDol\nHJGKRCI8/PDDfP7551x99dWMHDmSl156iUceeQTTNAE4+OCDef7553G5ZLfuZsf0Q8nJEF4UbbBA\n9ruqJGsqbm+alDx6KYRDtTusNlVgQioUCZFy9v0PIevi+yl9tnalTKN4G0UTz6bVo581q4qJpmli\n+AdhlLyDseFGItvmYxRDpHgxRlEfImV7YxTbiRRsrlXMphWwreaFdB0tMxvdnYPuyUWLPuruHLTo\nY53tu0JBnMBnEHin+rnnP2qTWNH8aRo4R4PjeCh/AComAUHABP8LEHhXjcK6LlGVAIUQzUbCRGra\ntGlMnTqVzMxMPB4P69atY/LkyZimycEHH0x5eTk///wzzzzzDNdee20qYxb1MSNQehaEvq1u87wM\njtQVBvHNfJXgb9/GtLvH3oCtc2pGxIQQsTLPuJbAgi8J/DSrVnvwl6/wvv4QnnOSv2bKDPiIFG7F\nKNhMpHALRuEWIgWbo49bMAo3q8eirbFvxlQxgNhpw/FPNTDLioiUFcVMbWwQu7PexKvq+XZ9WmaW\nqqCZTGYAvFdUP3ecDfZByb2naHpaJrgngvN88F4FwY9Vu1kE3vEqqXI/DfYBaQ1TCFEtYSI1ffp0\nOnfuzH//+19atWrFU089RTgcZuDAgbz44osYhsHYsWOZOXOmJFLNiWmqik2B/6tuy3wYXOemLIRI\ncT6lz8buWWLp0BXPubemLA4hRCxN18m5dRrbLjgQo3BLrb6yl+/A0Wcw9gOOaPR1TcPAKC3AKNii\nkqMaSVLVxwVbiBRuxvSWNNWnkxpBP0bBZoyCHZj+qGk7NhrmyUXPykOz2eu/R8UjEFkZvV82uCc1\nPk7RfFi7Qc5Hai+1sglgrFHt4V+heCA4z4HMh8Ai288IkW4JE6mtW7cybtw4WrVqBcBXX6mFkGec\ncQYAuq5z2GGH8fLLL6cgTNFgFfeA/7nq565rIOO6lIZQ+uz1GCUFMe3Z1zyL5pBpoEKkmyV3D3Jv\ne42Ca49Tb75UikQoumcsbV7+Fd2jqnqaAZ8aHYo3elQrYdqqNmQWtZkmpreYiLeYyJa1jX655nKj\nZ+WhZ7VCiz7qNR/dBrp2N7obNA/obW9EN1vL3nEtgeNEtb9axSNQfj8Q3QvO/xoE3lPFRFxXyhRO\nIdIoYSKVlZXF5s3q3bd169axbNkybDYbhx9+eNU569atIycnJ/lRiobxTaldscnxT/XOZArXIwV+\nnY1v5qsx7a5jx+I8eGjK4hBC1M3R/1jc/7wJ7/QHarVHtq5j20X90aw2tfaovDRNETaSrgqhWXJt\n+PW9cBgZGN4iTG8xpq883dHtMNPnJeLzEtm6roGvuAW4BS0zKybp0rZPwrbvd+c0qzVyAtCckHkb\nOM8F77VqvRSAWaae+15Um/nah6Q3TiF2UwkTqQEDBvDhhx/idDpZsGABAEOGDMHtdvP333/zzDPP\nMHPmTE499dSUBSvq4J8BZTX2a7IPg6yXQUvy3PwazGCAkkcujWnX3NlkjZe9TIRobjz/upvAwq8J\nLZlbqz2yaU2aIoqlZXjQ8/bEkrcneqt20cc9sbRqp9pzPOjOZ9Dtr0fX4YeANWA9DFzjwXEGZiQT\no7wEs6wIo6xYJVjRR6OsWLV747R7VTuRSJq/Co1nlpcSKS9t3JowTVNTDGMSrTpGxLJapWYd2O7O\n8g/IfgeCX0DZlRBZrtojy6D4WHCMBvej6jwhRMokTKSuvvpqFixYwFtvvQVA27Ztuf56te5l1KhR\n5Ofn065dOyZMkPKqaRf8RhWXIDpFx3pwtGRwA+bWNyHvG5MIr1se0551yYNYWu2Z0liEEPXTrDZy\n73iDbRf2Se26JYsFPbetSoryaiRF0WRJb7VnVZ/uymzABQdA4HQovUjtwwMQngtlc6HsKjTHSVhc\n50H2MNAS/tqLyzRNTJ83mmAVY5SpUS6jMvmq8Txeu+nzNv7rky6mWV2UY2MjXqfr6J68mETLrbkI\n2gPYehyCZm3c110kYD9W7bHmewrK71YjU6BGqgIfQ+ataq81zZneOIXYTST8n619+/Z88skn/PDD\nD5imyeGHH05GRgYAEyZMoLy8nFNPPZXs7OyUBSviCP2mypwT3dfL0l3tFaW7UxpGeP1Kyl6bGNNu\n63kYGSMvTmksQoiGs7brTM4NL1J0x5idvpaWmRU3Mdp+FEnPatX0U8gcJ0Orw6Dsagz/O+ha5Xqt\ngCoLHngH9LZqyrPrPLD2btjnpGloGR7I8GBpu1f9L9iOGQ6pvaziJF6Gt7j2xzXPKSvEKCuCXWEP\nLMPAKMmHknxqjt15gPxPn0NzZ+PoOwTHIcNwHDIM656d0hVpy6DZVbLkOAvKb1RrpgDwQflt4H8F\n3E+AY0RawxRid1DnW0ROp5Njjjkmpn3MGPUL1zAM8vPzad26dXKiE3WL/Aklw8GMrmHQ20HOZ6C3\nSW0cpknx5MshGKjdbrGQc91zMuVDiGbONXg04QvvoeylO2I7LVb0vMSjR5VJkp63J7ozI/XB16S3\nhezXWbTmQvrsuxT8UyG8oLrf2Aq+x9RhPQic54HzrKT+n6lZbVhyWkNO439PmoaBWV6CUVqIUVKA\nURZ9LF6O+fcDGGURDC8Yvp4Y5ZmqamJpodpTq2YRkTQzvSX4v/0f/m//B4D1H/viOHgYjkOOw95n\ncANHHUUMSzvImgbOi1X5+/Bvqj2yGkpOBPsIHNZxQL+0hilES5YwkRo2bBj//ve/GTGi+h2Nxx9/\nnHPOOaeqkt+MGTN44okn+P7775MfqajN+BuKjwMjWr5Yy4KcT8HSOeWhOH+ZRXD+FzHtmadfg61r\nw971FUKkl+e823EeNYrwX8vQMrJUwtQqOnq0i70ZEjFyIONKdYSXqITKPx2MGuXLwwvBuxC814H9\nBJVUOU5M+ZToumi6XlUKnQ5dVaNpqjfQgtGxH+uhkPtDrfWwZiSiRrqiiVXVY41kzCwrrN1XWpCy\nwiLhdX8QXvcH5TOeBJsd+wEDcRwyDOchw7B27S0btjeWfSDkLgDfc2pEyixS7cGP2X/Pz6DiAVXB\nV76uQjS5hInUX3/9xfr166ueb9iwgSlTptCrVy+OPfZYALZt20Z+fn7yoxS1GWVQPAIiq6INDsj+\noMFTVZo2lCKy3ostJGHZsxOe8+9MeTxCiB1n27sntr17pjuMpmXtCe6HIfN+tVDf/6oqHU3lCHoY\ngh+oQ8sD51iVVFn7N88/PAPvQvCz6BMdPM/EFBXSLBa07Fbo2a0adWkzHFLTDCsTq+0SrZqPNft2\nah1YKEjwl68I/vIVZVNuRM9ri+Pg49SI1cFDseTusePX3p1oFsi4HJyng/dWtXkvJroWUm8WRNaC\n+3GiFVmEEE2kwas/I9GqRcauMF+7JTODUHIahOdHG3TIej1tu9iXPnczFm9hTHv2VU/LdA0hRPOh\nWcExXB1GMQTeViNVoR+qzzELwfcfdVh6RKf+nQ2WDumLuyajDLxXVz93XQ62vk12ec1qw5K7R6OT\nFzMYiCZg1clW5O/1/P3lDFxrfsGsKGvwtYzCrfhmvYZvllr3Y9unr1pbdfBx2Hsd0bANindnemvI\neg5cF0HZZdV/K/ieVjNYsl6TQhRCNCEpo7MrMQ0oPR9Cn1e3eZ4B56i0hBNc/AMVHzwX0+48ahTO\nI05MQ0RCCNEAeg64LlZHeCX4p6nDqLFXU2QZlN8E5beAfWh06t8poKVxU/GKe8CIltPT20LmvemL\npQbN7sDSas+Y6qx/dD6CvQ/sTfD3Hwn8PIvAz58R+mNBo9ZvhVb8QmjFL3inP4DmcmM/6Gic0cTK\n0rGbTANMxNYfcr+n8K+R5GVE/2YIvAvFf0P2++rfgBBip7WMRKp4pNo3yT4crN3SHU1ymGZ0M743\nqtsy7wbXJWkJJ7x5LUUPnB/TrmV4yJ7wZMrjEUKIHWLtDu571f+nodlqlCrwLpiVm/gaEJylDi0L\nHKerpMo2ILVT/8JLoOLx6ueZk3aJP4Y1qw1Hn6Nw9DkKLrqPSPE2AvO/IPCTSqyMgs31XyTK9HkJ\n/PAhgR8+BMDSbu+qSoCOvsegZ2Yl69PYNWkO/iy4j7xW+4PvCdUW+haKjoScmWDpmN74hGgB6kyk\n5s6dSzisSsgWFxcDMHPmTFasWAHAzz//3DzeDQp+pA4AS9fqpMp2dMrLgCdNxSTw1fgl6roUMm5P\nSyiBxXMouvVUjOJtMX2ecROxtGkm02CEEKKhNB3sR6vDeBoCM6JT/76uPscsBf+L6rB0Bee56kh2\nkR/TjG64Hi3pbjtKTTncBVly2pBx7Fgyjh2LaZqE1ywm8NNn+H+eRXDRd7HVX+sQ2fwnFe9PoeL9\nKWCxYu95uFpfdcgwbPv22+WKpCSHDu7JoHeA8htUU+R3KDpcFaiytrA1kUKkWMJEKiMjg7lz5zJ3\nbu0d72fOnFnrucuVxmkO8URWg+8ZdWAD28Aao1W9m+fi4fr4XlV7RVRyjAL302n5XCpmvUbxw+Mg\nFIzps+3bj8xTx6c8JiGEaFK6W+015ToPIn+pfXr8U2sU+EH9rim/Ux22QeA8Hxynge5p+ngC/1Uj\nCQBYwPOfXfN32XY0TcPWtTe2rr1xj70Ow19B8NfZVaNV4b+WNfxikTDBRd8RXPQdZS/djp7dCkf/\noVXrqyyt2yfvE2nuNA0yr1fl0ksvAMJgbICigapQlf3IdEcoxC4rYSL17LPPMn/+/DqLS2iaNXYU\n/gAAIABJREFURr9+zWB/AvfTEPxUvXNYNR0DIKTaQl+rue76ntVJlX0o6I2raJQWgY+hbFz1c9sg\nyPpvyivvmIZB2Yu34Z3+QNx+zeUm56aXm36TTSGESCdLJ8i8DTJuhfCP4JsKgTer9+8DNSUwNBvK\nxoPztOjUv6NjquntEKNYVV2r5LoKrL12/rrNkO7MwHnY8TgPOx6A8NZ1BH7+TCVW879Q+2M1kFFS\ngO/LN/F9+SYA1r17VU8D7H0kmmM3LLjgPFutrSsZBaYXzGIoHqoKVqVprbUQu7qEidShhx7KoYce\nmspYdlzGeHWYAQjNUUlVcBaEF9U+z9gS3U9kKqCB9WCVWDmGg/UQVdWpOQn9CCVjoHKveOuBapFo\niivuGL5yiieeg/+7/4vbH8lpy56Pfip7RgkhWi5NA9sR6jAfh8D76ndJ8DOg8g3Hiujo1Wug7wXO\nc6Kl1PfZ8fuW36E2EgY1PStz99lWwtr2H1hPHEfmieMww2FCf8wn8NMs/D/NIrRsHjSiinD4z98J\n//k75W89CnYnjj6DqhIra6cezWOZQirYh0LObCg5IfpzFYDS0WA8pf6OEkI0Sp2Zw5YtW5g9ezbB\nYJAjjjiCrl27VvUVFBTw6quvous6V199dR1XSSHNAfZj1MHDENmkfskFP4Xg56q0bRUTwj+po+Je\n0HLAfmx0tGpY+hdhhpeqvaLwqed6Z8ieCXp2SsOIbF1PwS0nEV75a9x+W49D2HrmPezVvU9K4xJC\niLTRXOA8Ux2RTeD/b3Tq35Lqc4z1UHG/OqyHget8cJzRuAIRoV9UKfZK7snJmTq4C9CsVuw9D8Pe\n8zA8F9yJUVZEYMGXVSNWka3r6r9IpaBfjXL9NAsAvU1HVQnwkGE4+g1Bz8pL0mfRTNj6qk2ci4dD\nZCVggvcKMDZB5sQWMW1UiFRJmEjNmzePSy65BL/fD4DFYuHRRx9l+PDhvPPOO9x///34fD5at27d\nfBKp7Vnaq19ervPBjKj9FIKfQmAWhOdR/S4iaog78K46ACw9VVLlGAa2I1M7ChRZD8XDqncn19pA\nzmdqfnMKBZf+ROEtJ2MUbonb7xpyJjk3vcy635emNC4hhGg2LO3V+pOM6yD8S3TWw+tgFlSfE54L\nZXOhbAI4TlajVPbj6p4FYRrRAhPR31O2oeAYndRPZVeie3JxDR6Na/BoVbRi3R9VyVHw128wA74G\nX8vYtoGKj1+i4uOXQNex7XcwjkOG4Tx4GLYeh6BZm9lslaZg6QK5c6D4RPWGMqik39gEnudBs6U3\nPiF2EQn/d3jooYfw+/0MHTqU9u3b89577/HQQw+xbNkynntO7R3Uv39/7rrrrlTFunM0C9gOVUfm\nnWAUQvDL6mmAlXtzVIosAd8S8D0KuMA+uMZo1T7Je8fGKFTvEhkbonFnQs4nqkRvCvm+fJOiBy6A\noD9uv+fCe3Cfe9vuMx1CCCHqomlg66cO9yMQ/Fitpwp+TFW1PQJqI+DA22rNruOfqqCF9YDY6/lf\njr7hB2AHT3oKDO0KNE3D1mk/bJ32wz1mAmYwQHDx9/ijiVV49aL6L1LJMAgtnUdo6Ty8r94Ddge2\nTvtj7dobW5cD1GPX3ljy2ibvE0oVvQ3kfgUlp0PwE9Xmf1VN+ct6u+VUPRYiiRImUqtXr2bEiBE8\n+uijAPTq1Yvrr7+e5557jrZt23LTTTdx/PHHpyzQJqfngXOMOkxTJU6Vo1Whb4GaVel8EJypDlDT\n7BzRpMp2DOhNtHeFWQElIyFSOcJjg+z/UxvrpYhpmpS9cjfeV++Of4LdSe4tU3Edc3rKYhJCiF2K\nZgfHqeowtoH/DTVSFf6l+hxji3qjzvcoWA9So1TOs9Qft0YBeG+qPjfj+p1bZ7Wb0ewOHP2G4Og3\nBC57mEj+ZgLzP6/eu6okv+EXCwYIrVxIaOVCao5x6TltqpKqqgSr8/5ojmZWybg+WqZae112iUre\nQf2tU3w05HwM+h7pjU+IZi5hIhUIBOjSpUvV8332Uf+J77///rzyyitkZ6d2rU5SaZqqgmTtpaZn\nmOUQnF09WhVZUft8Yy34pqgDq1p8XDlaZe2zY5WazDCUnAmhH6rbsqaqhaEpYgZ8FN1/Pv6v347b\nr7dqR97972PvcXDKYhJCiF2a3gYy/q2O8O/RqX/TVSJVKbwQvAtVdT77CUCkemqg3gkyb0lL6C2F\npXU7MoafS8bwczENg9DKhVVJVXDxHIiE67/IdozibQQXfElwwZfVjbqOtWP36tGrLtHRqz07Ne89\nrTQreF4EvT1UTFRt4flQNACyPwVr17pfL8RurM6Jv5YapaydTrVGaOzYsS0riYpHywTHCeoAiKxR\nI1XBWRD6UpUNrRJWI1ihb6H8FtD2UOuq7MPUHHi9Tf33M031blDww+o292Rwjm3ST6sukfzNFN5y\nMqHlP8ftt+3Tl7wHPpDNdoUQYkdZe4F7EmQ+oAoh+aeq6n9UbkIbhuAHtV/jeRK0jFRH2mJpuo59\n337Y9+2H55xbMCrKCP7ytZoG+PMsIhtX7/jFDYPwuj8Ir/sD/9fvVN/T5cba5YDao1ddDkD3NKLw\nSLJpGrjvBUsHVcYfQ+2bVnSEWl5gawZb3QjRDNWZSC1dupT33nsPgPx8NRT+xx9/VLVpmkbPnj3p\n1q1bksNMM0sXyLhMHWZQlSWvKrG+sPa55t/V5W/RwNq3erTKdlj8BZzlt1UPqQNk3AgZVyX1U6op\ntGIhBTePxNi2MW6/86hR5Nw6Dd2VmbKYhBCixdKs1W/WGUVqzZRvqtqnqib7SHCclJ4YdxN6hgfn\nwJNwDlRf5/DG1VWVAIOLv8coKajnCvUzfV5CS34ktKT299eyx14xa6+se+2DZk1joQfXpWr9XslY\nwK/+pikeBFkz1JvEQohaEiZSVquVzz77jM8++6xW+/Tp02s9z87OZt68eew2NDvYB6mDByCyJVpi\nfZZ6NGvOvTYhvEAdFfeBlhUtsR4dsbJ0goonVaWcSs7z1LuVKeKb/T+K7zsH018Rt999zq14Lryn\neU9LEEKIXZWeC65L1BFeAf5pEPg/tWG8Z0q6o9vtWDt0xdrhMjJPuQzTNDEKthBas4jw6sWE1iwi\ntHoR4b+WQShY/8XqEfl7PZG/1xP48ePqRpsda6ce241e9UZvtWfqijs5ToGcL9SabbNILXcoORE8\nL4Hr3KTe2oxEMCvKMMpLMMtLMMqK0aw2rF17y5u5ollKmEhNnDiRefPmYZpmwhdrmsbBBzd8vczE\niRP58MMPcblcXHzxxZx11lm1+h977DGmTZuGzWbDYrFw3333MWTIkAZfPy0se6r/WFznqnK14V+q\nR6tCP1K1mS6AWQqB/6kDVPW/yMrqfvsI8LyQkspMpmninf4AZS/cGv8Eu4OcG14i47h/Jj0WIYQQ\nqIIS7onqEGmnaRqW1u2wtG4Hh1SPxpjhEOH1KwivWUxodTS5WrO4cXtZJRIKEl71G+FVv9UubpHd\nqmrNVeU0QevePdGdSZr2aR8QLY8+HIx1QBjKzlPl0TNujPt3imkY1UmQtyT6WIzhjSZFNdvLSzC8\nJRje4qqPzfISzIqyuOFoDheOw07ANXg0jsNPRM+QioKieUiYSJ1yyimccsopTXajb7/9lhkzZvDF\nF1+wfPlyxo0bx5AhQ2jbtrqE6JdffsnEiRM58cQTm+y+KaXpqsKerT9k3gZGcbTE+iyVXBnra59f\ns4iF9TDIfjslezeYAT/Fky7C99n0uP167h7k3fce9l6HJz0WIYQQYleiWW3Y9u6Jbe+euIacWdVu\nlBUTWrM4JsEyfd46rtYwRkkBwYVfE1z4dY1ANCwdutUeveraG0u7vRs9i8Q0DEyftyrRqUp6Sq/H\nzH8Aw7sJswKMipsxA69gBLphlpdilBVXJ0kVZWrNdxKYAR/+2TPwz54BdifOQ4fjHDQa54CR6JlN\nVDlZiB1Q5xqpJUuW8MUXX+D3++nXrx/HHnvsDt8oJ0ctqgwGgxiGgdVqxWarThoCgQBr167lueee\n4/777+eII47g3nvvxeXaxUqJ1qTngPM0dZgmRJZXj1YFv6FqgbGlB+R8lJIFxZHCrRTeemrMXO1K\n1i4HkPfgh1j37JT0WIQQQoiWQvfk4DjwSBwHHlnVZhoGkS1/VSdX0WmC4Q0rwDB27oamSWTDSiIb\nVqoEI0pzZWLduxe2rr3J1DMpW/5FrVGfmBGj8hLM8tJGJEErokeaBP34v3sP/3fvgc2O4+BhuI4e\ng/OIkc2rgIfYLWhmgrl7X3zxBRMmTCASUVPTNE3j/PPP58Ybb9yhGwWDQS688EIWLFiAYRicdtpp\n3HfffVX9Pp+Pe++9l3HjxuHxeBg9ejSnn34648ePr/O6CxYs2KF40k3T/Hgcv2C3bKWwYiiGmfxh\nauumVeS+eBXWoi1x+/09j6T47ImYTpmHLIQQQiRN0I9165/YNq/Cumll1aPFW5TuyHZZpsVKYJ9D\n8fc5Fn/PozAzW3iFabFD+vVr2gqUCUeknnrqKTRN45JLLiEvL4+pU6fy2muvcdFFF5GXl9foG73w\nwgts2rSJuXPnkp+fz8knn8yQIUM45phjqs45/fTTq/au6t27N3/88UeDrt3UX5TUGQBAKsZ+/D98\nRNHT4xJOMcgcez3tLn4ArUbJ+4ZasGDBLvw9aDnk+5B+8j1oHuT70DzI96E+A2JaIoVbY6YGhtYu\ngWAgzutbJs3lRnNno2dmo2VmE9mwskGbKGuRMM5lc3AumwMWK47+x+IcNBrXkaegZ7dKQeSJyb+F\n5iEZgy8JE6k///yT4cOHc/XVVwPQqVMnLr30UlavXr1DiZTP56sqXGG1WtE0jdLS0qr+1atXc955\n5/H666/Ttm1bfvvtN84777xG30fUZpom5W89Rumz18cftrfayLnuOTJOuCD1wQkhhBCiiiWvLZa8\ntjj6Vy+lMMNhwhtWViVYlY+RLWvTF2gCmisTLTOaBLmz0d05tZ9v97HmzkF3q4RJd2ejZWTFvKFr\nhsMEf5uN75t38X/7P4yiv+sPJBImMO9TAvM+peTRS3AcdAzOwaNxHnUqlpwG7O8pRAMlTKSCwSCd\nO3euet6pkxo3KS4u3qEbXXjhhaxZs4ajjz4ai8XCqaeeyogRIxg9ejQPP/wwvXr14qKLLmLcuHFE\nIhGOP/54SaR2khkKUvLoZVR88nLcfj27Fbn3/g9Hn6NSHJkQQgghGkKzWrF17oGtcw9cx5xe1W6U\nlxJe83t1WfZogmWWl9ZxtTru48yInwRVJjo1EyD7QnTtKfSMCFoG6JlZaO3eR3MNbqLPukZcViuO\nfkNw9BuCedXTBBd9p5Kq2TMwCuMvVaglEiEw/3MC8z+n5LHLsPcZjGvwaJxHjcKS17b+1wtRhzqL\nTVit1d2W6DsEdZVDr0tubi7PPPNMTPu7775b9fEVV1zBFVdcsUPXF7VFivMpuv00gr99G7ff2nl/\nVVSifZcURyaEEEKInaVnZmE/4AjsBxxR1WaaJpGt66qSqi1LF7JHx05xkyHdnVNjCl1WIzcCPheC\nJ0DJKDC9QCmUDQftdXCOavLPtZJmseA4aDCOgwZj/vsJgr//gP+bd/DNnoGRv6n+CxgGwV++IvjL\nV5RMHo/9wKNwDR6jkqrW7ZIWt2i56kyknnjiCZ544olabRMmTKj6WNd1BgwYwPPPP5+c6MQOCa1d\nSuFNI4lsWhO333HocHLvfBPdLQsxhRBCiJZC0zSse3bCumcnnEecyIoFC+ierLU59qGQMxtKTgBj\nKxCA0tFgPA0ZlyfnnjVoFktVlcSsKx8ntGQuvm/ewffNuxjbNtR/AdMk+Otsgr/OpuSJK7EfMADn\n4DG4jhqFZY+OSY9ftAwJNxoYMGAApmnWOoBazyORCH6/P2XBivr5f5pF/mWHJ0yiMkdPIO+BDyWJ\nEkIIIcTOsfWF3B/A0j3aYIJ3PHhvTdqeUvFouo79gCPIvnIybd/5i9ZT5pJ5xrVYGrqVi2kSXPQ9\npU9OYOvovdh2+QC8b08m3BSbLIsWLeGI1EsvvZTKOMROMk2T8v89TelTV8Xfm8J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GAAAg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\naF2qHRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x35656908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,1, sharex=True)\n",
    "fig.set_size_inches(14, 7)\n",
    "y_label = {'fontsize':14}\n",
    "title = {'fontsize':30}\n",
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "s_legend = {'fontsize':14, 'handlelength':10}\n",
    "ax[0].plot(tst.index, tst.imp, label='\\'15', linewidth=3, color=\"#FFE900\")\n",
    "ax[0].plot(tet.index, tet.imp, label='\\'16', linewidth=5, color=\"#E53D00\")\n",
    "ax[0].set_ylabel('IMPRESSIONS (M)', **y_label)\n",
    "ax[0].set_title('FEMALE GEN-X'.upper(), **title)\n",
    "ax[0].set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax[0].get_xticks())])\n",
    "ax[0].legend(**s_legend);\n",
    "\n",
    "ax[1].plot(tst.index, tmt.imp, label='\\'15', linewidth=3, color=\"#21A0A0\")\n",
    "ax[1].plot(tet.index, tnt.imp, label='\\'16', linewidth=5, color=\"#046865\")\n",
    "ax[1].set_title('MALE GEN-X'.upper(), **title)\n",
    "ax[1].set_xticklabels([(datetime(year=2015,month=12,day=7)+timedelta(days=7*(x))).strftime('%b%#d') for x in list(ax[1].get_xticks())])\n",
    "ax[1].legend(**s_legend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Male and female gen-x'ers alike were down year over year, with a much larger % of lost viewership coming from lost female viewers as males climbed as a portion of total viewership. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "genx = heat_map[[0,7,8,9,10,-1]].set_index('Station')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "genx['females'] = genx[genx.columns[0]] + genx[genx.columns[2]]\n",
    "genx['males'] = genx[genx.columns[1]] + genx[genx.columns[3]]\n",
    "genx['%fem increase'] = genx.females / genx.aud\n",
    "genx['%male increase'] = genx.males / genx.aud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Genx station performances (best and worst), year over year (female.)\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>%fem increase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REY</th>\n",
       "      <td>16.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUND</th>\n",
       "      <td>15.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBN</th>\n",
       "      <td>13.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAC</th>\n",
       "      <td>12.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RLZC</th>\n",
       "      <td>11.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OVTN</th>\n",
       "      <td>-7.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AEN</th>\n",
       "      <td>-9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VH1C</th>\n",
       "      <td>-11.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHIL</th>\n",
       "      <td>-15.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOO</th>\n",
       "      <td>-21.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         %fem increase\n",
       "Station               \n",
       "REY              16.59\n",
       "SUND             15.08\n",
       "FBN              13.87\n",
       "GAC              12.06\n",
       "RLZC             11.42\n",
       "OVTN             -7.21\n",
       "AEN              -9.00\n",
       "VH1C            -11.08\n",
       "CHIL            -15.26\n",
       "CLOO            -21.13"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = genx.sort_values(by=genx.columns[-2], ascending=False)[[-2]].head()\n",
    "worst = genx.sort_values(by=genx.columns[-2], ascending=False)[[-2]].tail()\n",
    "gen = pd.concat([best, worst])\n",
    "gen[gen.columns[-1]] = gen[gen.columns[-1]].apply(lambda x: round(x*100, 2))\n",
    "gen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): Genx station performances (best and worst), year over year (male.)\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>%male increase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FBN</th>\n",
       "      <td>17.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUND</th>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REY</th>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNN</th>\n",
       "      <td>13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UDN</th>\n",
       "      <td>10.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>-13.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEIN</th>\n",
       "      <td>-14.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOO</th>\n",
       "      <td>-14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRNT</th>\n",
       "      <td>-17.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VH1C</th>\n",
       "      <td>-23.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         %male increase\n",
       "Station                \n",
       "FBN               17.42\n",
       "SUND              15.82\n",
       "REY               15.35\n",
       "CNN               13.56\n",
       "UDN               10.22\n",
       "MIL              -13.57\n",
       "BEIN             -14.38\n",
       "CLOO             -14.95\n",
       "CRNT             -17.65\n",
       "VH1C             -23.73"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = genx.sort_values(by=genx.columns[-1], ascending=False)[[-1]].head()\n",
    "worst = genx.sort_values(by=genx.columns[-1], ascending=False)[[-1]].tail()\n",
    "gent = pd.concat([best, worst])\n",
    "gent[gent.columns[-1]] = gent[gent.columns[-1]].apply(lambda x: round(x*100, 2))\n",
    "gent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "days = {0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'}\n",
    "genx_men['imp'] = genx_men['imp'].apply(lambda x: round(x/1000000,2))\n",
    "genx_men['HOUR'] = genx_men['datetime'].apply(lambda x: x.hour)\n",
    "genx_men['WEEKDAY'] = genx_men['datetime'].apply(lambda x: x.weekday())\n",
    "genx_men_piv = pd.pivot_table(genx_men, values=\"imp\", index=\"WEEKDAY\", columns=\"HOUR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "genx_women['imp'] = genx_women['imp'].apply(lambda x: round(x/1000000,2))\n",
    "genx_women['HOUR'] = genx_women['datetime'].apply(lambda x: x.hour)\n",
    "genx_women['WEEKDAY'] = genx_women['datetime'].apply(lambda x: x.weekday())\n",
    "genx_women_piv = pd.pivot_table(genx_women, values=\"imp\", index=\"WEEKDAY\", columns=\"HOUR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "State of the television (2015 v 2016): When were Gen-Xers watching TV?\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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mlE/WA9vnqz7/Rg3QjyTV2HkvbLwVFTb+LpCkUhsZ/2Wp9YzPK7ae1ZKUX2j974KyjcXW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\n0UKxsbGKjY3Vnj1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O3epTUFCg3NxcTZkyxf/YJ598otatW6tHjx7q1q2bWrZsqY8++iig\n3c6dO1VVVaXBgwcrJiZG8fHx2rp168W9AYYjrwHADMHO6mCq85pOAADsio+P14oVK3Tw4EH/Y6Wl\npYqKivLfb9q0qU6ePBnQrrS0VBEREfJ6vZKkqKgonThxIjg7DQAAHFFv0Xn48GFVVVX5/x0eHq4j\nR44EZccAAA3DLQsJRUdHq7y83H//1KlTAUWo9H2RWV1draqqKkVERKisrKzWNqiNvAaAS59b8toJ\ndRadERERGjNmjP/+hAkTAp4DAFwa3LKQQEpKiubNm6eCggKVl5fr6NGjGjBgQMA2ffr0UWRkpLZs\n2aJu3bpp9+7dmjx5coj2+NJAXgOAGdyS106oM43mz5+vwsLCcz7XtWtXx3YIAGCOFi1aKDY2VrGx\nsYqJidHs2bOVlZWlmpoazZw5U127dlVlZaUyMjI0f/58xcXFacGCBcrJyVFZWZmysrI0aNCgUB+G\nq5HXAAC3q7PoHDp0qMLDw8/53Lm+DwwA4E6hXEggOjpamzZt8t8fO3asxo4dG7CN1+sN+GqPtLQ0\npaWlBWsXL3nkNQCYwU0L/zS0Oo9t0KBBKisrkyTNmjXLvxre8ePHNXTo0ODsHQAAqBd5DQBwuzqL\nzu+++06nTp2SJL355pv+7/0qLy/3BxoAwP2c+LJpk687udSQ1wBgBpOzmhUGAMBwHoNXwwMAwBQm\n57XlqcMej4tKZgAAcE7kNQDALeod6XzwwQfVqFEjVVRUaNq0aWrcuLEqKyuDtW8AgAbgpuk1cAZ5\nDQCXPpPzus6i84YbbtCePXskSbGxsQFfMv1v//Zvju8YAAA4P/IaAOB2dRadRUVFioiIUHJysvr3\n76/+/furbdu2wdw3AEADMHkJdpDXAGAKk/O6zqLznnvu0Xvvvaf8/Hy9/vrrkqSf/OQn6t+/v265\n5RYlJycHbScBAPaFGbwwAchrADCFyXldZ9E5evRojR49WtL3Z1E3btyoJUuWaOXKlXr33Xe1bdu2\noO0kAAA4N/IaAOB2dRadZ8+e1V//+ld98MEH+uCDD/Tll1+qadOmGjJkiD/cAADuZ/LCBCCvAcAU\nJud1nUXn2LFj9cUXX6h79+4aMmSIZs6cqcTERDVq1CiY+wcAAOpBXgMA3K7O61Uvv/xy+Xw+ffPN\nNzpw4IAOHDigkpKSYO4bAKABhDl0gzuQ1wBgBpOzus6Rzpdeekn79+/X1q1btWXLFj355JM6ffq0\nrrzySt15550aP358MPcTAGCTydN1QF4DgClMzus6i05J6ty5s2JiYhQXF6drrrlGK1as0FdffaUX\nXniBEAMAwCXIawCAm9VZdP7mN7/Ru+++q6+++krV1dVq0qSJevfurX79+unGG28M5j4CAC6CyUuw\ng7wGAFOYnNd1Fp2fffaZWrduraFDh6pfv37q1asXixIAAOAy5DUAwO3qLDpzc3ODuR8AAIeYfI0I\nyGsAMIXJeV3vNZ0AAAAAAOe5abXZhubaovOb09Z37dsz1n9ULRtXWW4jSd6wCsttqmvKLbcJD/Na\nbiNJYdXW3ws7Z1cqa6y3kSSfjSnrNT7rO1hjc2q8nffCbl922Hv/7PUVbuO9aBpuvbPmzex91B69\nuqX1RtXW989TUW29H0k1ZfY+YxqSySGG0DtQFm5p+5IKe7+RLRufsNwmoibScpvG4ZdZbvO94PxP\n8/nsBW+NgvdZ5JH14PApeCFq5z302Qheu7lbbePvHTuaN7K3g22jrOfh0a5RltucLm9luY0keY5b\n/3u73HoTWOTaohMA0DBMXpgAAABTmJzXnAAHAAAAADiGkU4AMJzJCxMAAGAKk/OaohMAAAAAQoyi\nEwBwyTI5xAAAgPtRdAKA4bh4HwAA9zM5r00+NgAAAABAiDHSCQCGM3kJdgAATGFyXlN0AgAAAECI\nmbwGA0UnABjO5BADAADuR9EJAIbj4n0AANzP5Lw2+dgAAAAAACHGSCcAGI7ptQAAuJ/Jec1IJwAA\nAADAMYx0AoDhPAYvwQ4AgClMzmuKTgAwnMnTdQAAMIXJec30WgAAAACAYxjpBADDcXYRAAD3Mzmv\nTT42AAAAAECIMdIJAIYLM3hhAgAATGFyXlN0AoDhTF6YAAAAU5ic10yvBQAAAAA4hpFOADCcyWdO\nAQAwhcl5zUgnAAAAAMAxjHQCgOHCQ70DAADgvEzOa4pOADCcyavhAQBgCpPzmum1AAAAAADHMNIJ\nAIYzeWECAABMYXJeB2Wk88knnwxGNwAA4CKQ1wAAJzgy0jlz5kwdOHBAPt/385I//fRT7dmzR126\ndNGcOXOc6BIAUAeTz5zi4pDXAOAeJue1I0XnP/7xD3311VcaN26cPB6PduzYoX79+uknP/mJE90B\nAOoRbnCI4eKQ1wDgHibntSPTa3/zm9/o+uuvV0FBgbKystS4cWNlZ2frlltucaI7AABgA3kNAAgG\nR4pOr9er+fPnKz4+XuPHj1d1dbUT3QAALkCYx5kbLn3kNQC4h8lZ7ejqtQ888IA6deqk9957z8lu\nAADARSCvAQBOcvwrU0aPHq3Ro0c73Q0AoA4mf9k0Gg55DQChZXJeB+UrUwAAocP0WgAA3C/YWb13\n714NGjRIycnJSk5OVo8ePfTCCy8EbLNw4UIlJSUpOTlZAwYMUF5enq1jc3ykEwAAAADgLnFxcdq8\nebMkadu2bcrOztaIESMCtsnLy1NOTo5Gjhx5UX0x0gkAhgt36AYAABpOqLK6vLxc06dP19SpUxUX\nF+d/vKKiQvv379fixYs1cOBATZs2TWfOnLF1bBSdAAAAAPAjtWrVKjVq1Ejjxo0LeLympkajRo3S\n888/r/Xr12v79u1asmSJrT6YXgsAhuP6SwAA3C9Ueb18+XKNGDFCHk/tHRgzZox/9LNXr14qKCiw\n1QcjnQAAAAAQYmEeX4Pfzufrr7/Wvn37lJSUVOu5wsJCZWZmavfu3Tp69Kh27NihxMREW8fGSCcA\nGM7kJdgBAIB9NTU1atu2rXr16iVJqqysVEZGhubPn6+EhARNmjRJWVlZqq6u1s0336zMzExb/VB0\nAoDhwpleCwCA64Uirzt37qz8/Hz/fa/Xq9WrV/vvZ2dnKzs7+6L7YXotAAAAAMAxjHQCgOFYSAgA\nAPczOa8pOgEAAAAgxCg6AQCXLJNDDAAAuB/XdAKA4cI8ztzqs3fvXg0aNEjJyclKTk5Wjx499MIL\nLwRss3DhQiUlJSk5OVkDBgxQXl6eg+8CAADuFuysDiZGOgEADS4uLk6bN2+WJG3btk3Z2dkaMWJE\nwDZ5eXnKycnRyJEjQ7GLAAAgSBjpBADDhXt8jtwuRHl5uaZPn66pU6cqLi7O/3hFRYX279+vxYsX\na+DAgZo2bZrOnDnj1FsAAIDrhSqrg4GiEwDgmFWrVqlRo0YaN25cwOM1NTUaNWqUnn/+ea1fv17b\nt2/XkiVLQrSXAADASUyvBQDDhfLs4vLlyzVixAh5PLUvLBkzZox/9LNXr14qKCgI9u4BAOAaJo8G\nmnxsAACFZiEhSfr666+1b98+JSUl1XqusLBQmZmZ2r17t44ePaodO3YoMTHRgaMHAODSYPJCQhSd\nAABH1NTUqG3bturVq5ckqbKyUunp6dq7d68SEhI0adIkZWVlafjw4RoyZIgyMzNDvMcAAMAJTK8F\nAMOF6kxn586dlZ+f77/v9Xq1evVq//3s7GxlZ2eHYtcAAHAdN41MNjRGOgEAAAAAjmGkEwAM56Yl\n0wEAwLmZnNcUnQBgOJOn6wAAYAqT85rptQAAAAAAxzDSCQCGM/nMKQAApjA5r11bdJZVWR+EPVVl\n/SdVY3PqdI2v2nIbn2rsdWZDZHgrG62sv+dna07a6Ef6ttx6X8Wnrbc5cdbeYH6l9R+vztZY//2z\n8zsrSSfOWm9XWmnvvThaEW65zaHT1j9azlbZ+//hC7fxHjaxvn+2r7Kw+yEDXCIOWvz/fviMvc+i\ndk2tf0Y0Dq+03Mbns/dZ5PFYP64ITxPLbc56TltuI0lllWcstzlW4e6MqrCR1ZJ0qsp6BpywsX8l\nNt8/O7n7jzPByWpJ+u476/9Hyg6VW24Tccx6G0nS6bOWm1Qdq7DXFy6Ya4tOAEDDMPnMKQAApjA5\nryk6AcBwdgaDAQBAcJmc1ywkBAAAAABwDCOdAGC4MIO/9wsAAFOYnNeMdAIAAAAAHMNIJwAYjrOL\nAAC4n8l5TdEJAIYzeTU8AABMYXJem1xQAwAAAABCjJFOADCcyUuwAwBgCpPzmpFOAAAAAIBjGOkE\nAMOZvAQ7AACmMDmvKToBwHAmL0wAAIApTM5rptcCAAAAABzDSCcAGM7kM6cAAJjC5LxmpBMAAAAA\n4BhGOgHAcJxdBADA/UzOa4pOADCcx+DpOgAAmMLkvDa5oAYAAAAAhBgjnQBgOINPnAIAYAyT85qR\nTgAAAACAYxjpBADDmXyNCAAApjA5ryk6AQAAACDETJ6CStEJAIYzOcQAAID7UXQCgOE8Hl+odwEA\nAJyHyXnNCXAAAAAAgGMY6QQAwxm8LgEAAMYwOa8pOgEAAAAgxFi9FgBwyTI5xAAAgPs5ck3nrl27\nJEk+n0/r1q3TU089pXfeeceJrgAA5+Fx6IZLH3kNAO5hclY7UnTef//9kqS5c+dqzpw5+vjjjzV9\n+nQ999xzTnQHAABsIK8BAMHgyPTaiooKSdLbb7+t1atXq2vXriosLNT48eP10EMPOdElAKAOYW46\n1QlXIa8BwD1MzmtHRjqrqqr06aefqk2bNmrWrJkkqUmTJvL5zP3uGQAALjXkNQAgGBwpOm+44Qbd\nc8892rNnjxYtWqQ9e/bo9ttv14gRI5zoDgBQD67pRF3IawBwD5Oz2pHptc8884x8Pp8OHDigyMhI\neb1ePf7447rxxhud6A4AUA9Wr0VdyGsAcA+T89qxr0zxeDzq3Lmz//7QoUOd6goAANhEXgMAnMb3\ndAKA4Qw+cQoAgDFMzmtHrukEAAAAAEBipBMAjGfymVMAAExhcl5TdAKA4Uz+3i8AAExhcl4zvRYA\nAAAA4BhGOgHAcAafOAUAwBgm5zUjnQAAAAAAxzDSCQCG83h8od4FAABwHibnNUUnABjO5Ok6AACY\nwuS8ZnotAAAAAMAxjHQCgOE8Jp86BQDAECbnNSOdAAAAAADHMNIJAIbj7CIAAO5ncl5TdAKA4Uye\nrgMAgClMzmuTC2oAAAAAQIgx0gkAhjP4xCkAAMYwOa8Z6QQAAAAAOIaRTgAwnMnXiAAAYAqT85qR\nTgAwnMehGwAAaDihyOqpU6eqd+/eSk5OVmpqqnbt2hXw/Lp165SamqqUlBQtWrTI9rEx0gkAAAAA\nPzI+n0/vv/++lixZot69e9d6vqSkRDNmzNCzzz6rxMREDR06VH369NF1111nuS9GOgHAcGEeZ24A\nAKDhBDurDxw4oNOnT2vOnDlKSUlRTk6OfD6f//mdO3eqqqpKgwcPVkxMjOLj47V161Zbx8ZIJwAA\nAAD8yERGRmrMmDHKzs7WyZMnlZ6eroSEBI0ePVqSVFpaqoiICHm9XklSVFSUTpw4Yasvik4AMByD\nkgAAuF+w89rr9SojI0MxMTGKiYlRly5dVFBQ4H8+KipK1dXVqqqqUkREhMrKyhQVFWWrL6bXAoDh\nPB6fIzcAANBwgp3V27dv18SJE1VcXKzCwkLt27dPiYmJ/uf79OmjyMhIbdmyRYcOHdLu3bttXc8p\nMdIJAAAAAD86N954oz7++GPdeuut8ng8yszM1E9/+lOlp6dr/vz5iouL04IFC5STk6OysjJlZWVp\n0KBBtvqi6AQAwzG9FgAA9wt2XoeFhWnWrFmaNWtWwOOrV6/2/zstLU1paWkX3Zdri8745pWW21zZ\nvNpym8u8jSy3kSRveLTlNo3CrM+Brqo5Y7mNJIV5rP9owzzhNtpYbiJJKrf+o9LRcuuzwb+rsDeD\n/ORZ6wd2pNz6+3e0wnobSfrORl+nTtmbDllaWmO5TUXRScttIr74znIbSfIV7LPcpvjYbsttWl3W\nzXIbSQpPjLHVzgRTp07Ve++9p4iICDVu3Fi/+93v1LNnT//z69at09NPPy2fz6dx48YpOzs7hHsL\nu65tWWFp+26X2QgASZd7m1lu0yS8teU2dvJTknwKzpRzj80/S6usf5SrpMJeX//fCevv4eEz1vP6\nqI0slKSvTlr/2+/ICevvxYkT9n4nysqs/7B8x63/3RxedNRyG0kK/7rUcpvqg0WW23xbusdyG0mK\nubzn+Tf6V31ibfWFC+faohMA0DA8IRjqDOZ3fwEAYIJQ5HWwsJAQAKDBBfO7vwAAMIHHgZtbMNIJ\nAIYLRegE87u/AACAu1F0AoDhQjGlJZjf/QUAgAlMnoJq8rEBAEIkmN/9BQAA3I2RTgAwXCgWJgjm\nd38BAGACkxcSougEADS4YH73FwAAcDeKTgAwnsGnTgEAMIa5eU3RCQCGs/tl8gAAIHhMzmsWEgIA\nAAAAOIaRTgAwnMfD+UUAANzO5Lw298gAAAAAACHHSCcAGM/ca0QAADCHuXlN0QkAhjN5YQIAAExh\ncl4zvRYAAAAA4BhGOgHAeOaeOQUAwBzm5jUjnQAAAAAAxzDSCQCGM3kJdgAATGFyXlN0AoDxzJ2u\nAwCAOczNa3PLaQAAAABAyDHSCQCGM3kJdgAATGFyXjPSCQAAAABwDCOdAGA4k8+cAgBgCpPzmqIT\nAIzHpBYAANzP3Lw298gAAAAAACHHSCcAGM7jMXe6DgAApjA5rxnpBAAAAAA4xpGRzsrKSh07dkyt\nWrXS2rVr9cUXX6h79+5KT083uoIHAHficxfnRl4DgJuY+7nrSNE5ffp0RUVFqbq6Whs2bFC3bt30\n5z//WQUFBZoxY4YTXQIA6mDyani4OOQ1ALiHyXntSNH54YcfauPGjRo2bJjWrl2rjh076siRIxox\nYgQhBgCAS5DXAIBgcOSazubNm2vfvn3q27evCgsLJUkFBQWKjIx0ojsAQL3CHLrhUkdeA4CbmJvV\njox03nfffRo/fryaNWumDRs2KDIyUpWVlZozZ44T3QEAABvIawBAMDhSdN56661KTk7W9u3b9e23\n3yoqKkopKSnq0qWLE90BAOph8jUiuDjkNQC4h8l57dj3dHbo0EEdOnRw6uUBABeIVUhRH/IaANzB\n5Lx2z0RfAAAAAIBxHBvpBAC4hblnTgEAMIe5ec1IJwAAAADAMYx0Avh/7d1vTJX1/8fx10kpFP+N\nFMxCR2QcEVTcMBFEiVjOf4WRC5e2/t6y1kItWI5Z1LrTmuSak7jRP+dmS6ebWjmm5pyOtJg5ZCWY\nOgrU1nMAAA3SSURBVApQUYwiinN9b/x+8MXzvc51XRy9jnD5fNzScz7vc33Odb25Xn7OdXGEx/n4\nfBEAgAHPy3nNohMAPM+7t+sAAOAd3s1r7y6nAQAAAAC3HFc6AcDjvPwV7AAAeIWX85ornQAAAAAA\n13ClEwA8z7ufnAIA4B3ezWsWnQAAAABwi/HttQCAQcvLIQYAAAY+Fp0A4HnevV0HAADv8G5e8/E3\nAAAAAMA1XOkEAI/zefiTUwAAvMLLec2VTgAAAACAa7jSCQAe5+X/bBoAAK/wcl6z6AQAz+OmFgAA\nBj7v5rV33xkAAAAA4JbjSicAeJyXv5gAAACv8HJec6UTAAAAAOAarnQCgOd595NTAAC8w7t5zaIT\nADzOy9+GBwCAV3g5r7m9FgAAAADgGq50AoDn8fkiAAADn3fz2rvvDAAAAABwy3GlEwA8zstfwQ4A\ngFd4Oa99hmEYt3oSAAAAAABv4vZaAAAAAIBrWHQCAAAAAFzDohMAAAAA4BoWnQAAAAAA17DoBAAA\nAAC4hkUnAAAAAMA1LDoBAAAAAK4ZVIvOyspKzZ49W/PmzdP27dv7VdvU1KRHHnlE58+fdzR+48aN\nysnJUXZ2tsrKyuTkvzOtqqpSbm6uMjMz9fHHHzueW1dXl1588UWtX7/ecU1xcbHS09OVkZGh7Oxs\nnTx50rZmx44devTRR5WZmani4mJ1dXXZ1jQ0NCgnJ0cZGRnKyMhQSkqKPvzwQ9u68vJyPfTQQ5o/\nf762bt1qO767u1tvvfWW5syZo/z8fO3du9e2JviYOu0Ps16w64/g5530R3CNk/4wm4eT/giuc9If\nwTVO+qNvTX96I3hbTvqjb42T/jA7Jk56ItSxtOoJsxq7njB7PtxzBjDQRSqvw8lqKXJ5HU5WS/3P\n60hltdT/vPZiVoeai11/RCqrg+uc9kckslqKXF6Hk9Wh6sjrm8AYJOrq6ozk5GTj1KlTRl1dnZGS\nkmI0NDQ4qt2/f7+Rm5trJCcnG7W1tbbjDx8+bKSnpxvnzp0zzp07Z6SlpRn79u2zrKmvrzdmzZpl\nXLp0yThw4ICRnJxsXLhwwdH81q5da8yePdtYuXKlo/GBQMBIT083Tpw44Wi8YRjG+fPnjdTUVKO2\nttZob2831q9fb/z++++O6w3DMI4cOWLMnDnTOHPmjOW4gwcPGjNmzDAuXrxoHD582PD7/bbb+vLL\nL3v33zfffGOkpaVZ1gQfU6f9YdYLdv3R87zf7zdqa2sd9Ufwazrpj1DzsOuP4Don/RFc46Q/rPaT\nVW8E7z8n/dG35scff7TtD7NjsmnTJtueCHUsrd6rWc3evXste8Ks5qOPPgr7nAEMZJHK63Cy2jAi\nl9fhZLVh3Hheu5nVhtG/vPZiVlvNxao/IpXVdvsqVH9EIqsNI3J5HU5Wh6ojr2+OQXOl8/jx4xo7\ndqxSUlLk9/sVGxuro0eP2tbV19ersrJSL7/8suNtTZgwQeXl5UpISNCIESMkSR0dHZY1iYmJ+vzz\nzxUbG6v29nYNGzZMY8aMsd1WRUWFOjo6VFRU5PgT2l9//VV//vmnNmzYoKysLJWXl9vW1tTUaOTI\nkaqqqtKCBQvU3d2tu+++29H2JKmzs1MlJSUqLi7W/fffbzm25313dXUpEAho6NChioqKsqz56aef\nlJycrNjYWOXl5am7u1s1NTWmY82OqZP+MKuz64++z/fsY7v+MHtNu/4INQ+7/jCrs+sPsxq7/rDa\nT1a9Ybb/7PrDrMauP8yOyZgxY2x7wqyuubnZsifMatrb2y17wqxm7NixYZ0zgIEuUnkdTlZLkcvr\ncLJaurG8djurJed57cWstpqLVX9EKqvt9lWo/ohUVkuRy+twsjpUHXl9cwy91RNw6urVq4qJien9\n+/Dhw3Xt2jXbuuTkZG3btk0XLlxwvK3ExEQlJibq4sWLWr16tSZOnKiFCxda1kRFRWnixIl6+OGH\n1dTUpIULF143XzM7d+7UgQMH9MUXX/TrUn10dLSWL1+u1atX69q1ayosLFRqaqoef/zxkDUtLS26\nfPmyCgoKtGbNGhUUFGjGjBl68sknHW1z+/btioqKUlFRke1Yv9+v1NRU5eXlKRAI6IknnlBsbKxl\nzeTJk7Vnzx41Nzfr5MmT6u7uVltbm+lYs2PqpD/M6uz6w+x5u/4wq7HrD7MaJ/1hVmfXH2Y1dv1h\ntZ+sesOszq4/gmt8Pp9tf5gdk7a2NtueMKtbvny5Vq1aFbInzGqWLl2q6OjokD1hVrNkyRJJ6tc5\nAxgMIpXX4WS1FLm8DierpRvLa7ezWnKe117M6lB1dv0Rqay221eh+iNSWR3quLiR1+Fkdag68vrm\nGDRXOmNiYtTZ2dn7946ODlcP+NmzZ1VYWKhAIKBPPvlE0dHRluOvXbumlpYWVVdX6+DBgzp06JB2\n795tWVNdXa2zZ88qJydHlZWV+uGHH/Tuu+/azu3OO+/UihUrFBcXp6SkJCUmJqq+vt6yJiYmRtHR\n0Zo/f74SEhLk9/tVV1dnu60eW7du1aJFi+Tz+WzHVlZWqqmpSUePHtWePXu0a9cuVVdXW9YUFhYq\nNzdXy5Yt09dff62RI0cqPj7e8fxGjBjhqf4wDGPQ9Ed/esP4/9/bcKM/go/JqFGjHPVEf49lqBq7\n1wl+/p9//lFra2u/zhnAYBDJvA7n5zdSeR3OuViK3Pk4nHOxdGN57bWslsLP64H8bzk3s1qKXF6H\nk9VmdeT1zTFoFp1ZWVlqbW1VfX29amtrdfHiRc2ePduVbTU3N+vpp5/W5MmT9dlnnzm6rWXfvn16\n7rnndPXqVf3999/6999/NWTIEMuaiooKnThxQjU1NXrppZc0c+ZMlZaW2m6rpqZGzz77rJqamnTm\nzBk1NjZq+vTpljVZWVnq6urS4cOH1draqp9//llTpkyx3ZYknTt3To2NjZoxY4aj8X/99VfvrRZD\nhw6Vz+dTe3u7ZU1zc7NiYmL07bffasWKFeru7lZmZqaj7Une6w+fzzco+qO/veHz+cLqj5aWFsv+\nMDsmc+bMse2JcI6lWY3d65g9H845AxgMInU+DufnV4pcXodzLpYidz4O51ws3Vheey2rpfDzeiD/\nW86trJYil9fhZHWoOvL65hg0t9c+8MADKisr0wsvvKBAIKD169crKSnJcf2YMWM0fvx4jR8/3nbs\n6dOndeXKFR0/flzZ2dm644479Prrr2vZsmUha5YtW6b6+notWrRIkrRy5UpHt/n0mDRpklpbWx2N\nzc/P17Fjx1RQUCCfz6dnnnlGCxYssKxJTEzUhg0bVFpaqs7OTi1dulSFhYWOthcIBBQfH69p06Y5\nGv/888+roaFBubm5GjJkiAoKCrR48WLLmnHjxqmtrU25ubkaPXq03n//fY0cOdKypu8xjYuLc9wf\nZr1g1x99n6+rq3PUH31rpk6d6qg/Qs3Drj/61qWmpjrqj+D956Q/gufntDf61jntj741o0aNsuwP\ns5/ZdevW2faE1c96qGNhVvPKK69Y9oRZzZo1azRv3rywzxnAQBWpvA4nq6XI5XU4WS2Fn9eRyGqp\n/3ntxay2motVf0Qqq83m56Q/3M5qKXJ5HU5Wh6ojr28On+H022sAAAAAAOinQXN7LQAAAABg8GHR\nCQAAAABwDYtOAAAAAIBrWHQCAAAAAFzDohMAAAAA4BoWnQAAAAAA17DohCd1dXUpJydHe/bs6X3s\njz/+UE5Ojvbv3y9J2rFjh5YsWaK0tDRlZWWppKREly9f7h3/2GOPacuWLde97sKFC/Xpp59KkrZs\n2aIpU6bI7/fL7/dr+vTpWrlypX777bcIvEMAAAY/8hq4PbDohCe1tLSopaVFDQ0NvY9duXJFLS0t\nOn36tHbv3q2SkhJlZmZq586deu+993Ts2DGtWLFCnZ2dkqT6+no1NjZe97oNDQ2qq6vr/bMkHTp0\nSAcPHtSmTZt04sQJbdy4MULvEgCAwY28Bm4PLDpxW/rggw80depUlZaWKikpSXPnzlVZWZnOnj2r\nvXv3On4dwzAUFxen+Ph43XvvvRoyZIgMw3Bx5gAA3D7Ia8Abht7qCQBu2rx5s6qqqq577NKlS2pq\nalJ+fv51j8+aNUuSdOrUKRUUFDjeRnp6ugKBgLq7uzVv3jy99tprNz5xAABuI+Q14G1c6YSnFRUV\nadeuXdq1a1fv73v4fD5JUiAQuG5sz9+DHw/WU9/jq6++0qpVqyT93++QxMfH35S5AwBwuyCvAW/j\nSic8bfTo0UpISJD03/CJjY3VhAkT9P3331839tixY5KkqVOnSpLGjRuntra23ud7/jxq1Kjr6hIT\nE1VcXKza2lq9+eabmjZtWu82AQCAPfIa8DaudMLTgj/l7PHqq6+qrq5O77zzjn755Rd99913evvt\ntzVp0iQtXrxYkpSdna0jR46ourpajY2NqqiokM/nU3Z2tulrrl27Vp2dndq8ebNr7wcAAC8irwFv\n40onPGncuHG65557lJSU1PtYbGys4uLiNGXKFOXl5WnYsGGqqKjQtm3bNHz4cM2dO1fr1q3TXXfd\nJUl64403FBUVpdLSUnV0dOi+++5TeXl5b4glJyfrwQcf7H39tLQ0PfXUU//zDXoAAMAceQ3cHnwG\nX90FAAAAAHAJt9cCAAAAAFzDohMAAAAA4BoWnQAAAAAA17DoBAAAAAC4hkUnAAAAAMA1LDoBAAAA\nAK5h0QkAAAAAcM1/ANPdut5VnyuKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3444d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(14, 7)\n",
    "y_label = {'fontsize':14}\n",
    "title = {'fontsize':30}\n",
    "mpl.rc('font',family='Arial Rounded MT Bold')\n",
    "sns.heatmap(genx_women_piv, cmap=\"YlGnBu\", ax=ax1)\n",
    "sns.heatmap(genx_men_piv, cmap=\"YlGnBu\", ax=ax2)\n",
    "ax1.set_title('FEMALE GENX')\n",
    "ax2.set_title('MALE GENX');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Looking ahead: TV forecasting for Q2 and beyond. \n",
    "=================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "TBD: recommendations and predictions? "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}