sbecker11/first.ipynb

## first.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "sns.set(style=\"darkgrid\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('fortune500.csv')\n",
    "df.columns = ['year', 'rank', 'company', 'revenue', 'profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>rank</th>\n",
       "      <th>company</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1955</td>\n",
       "      <td>1</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>9823.5</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1955</td>\n",
       "      <td>2</td>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>5661.4</td>\n",
       "      <td>584.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1955</td>\n",
       "      <td>3</td>\n",
       "      <td>U.S. Steel</td>\n",
       "      <td>3250.4</td>\n",
       "      <td>195.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1955</td>\n",
       "      <td>4</td>\n",
       "      <td>General Electric</td>\n",
       "      <td>2959.1</td>\n",
       "      <td>212.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1955</td>\n",
       "      <td>5</td>\n",
       "      <td>Esmark</td>\n",
       "      <td>2510.8</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  rank           company  revenue profit\n",
       "0  1955     1    General Motors   9823.5    806\n",
       "1  1955     2       Exxon Mobil   5661.4  584.8\n",
       "2  1955     3        U.S. Steel   3250.4  195.4\n",
       "3  1955     4  General Electric   2959.1  212.6\n",
       "4  1955     5            Esmark   2510.8   19.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>rank</th>\n",
       "      <th>company</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25495</th>\n",
       "      <td>2005</td>\n",
       "      <td>496</td>\n",
       "      <td>Wm. Wrigley Jr.</td>\n",
       "      <td>3648.6</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25496</th>\n",
       "      <td>2005</td>\n",
       "      <td>497</td>\n",
       "      <td>Peabody Energy</td>\n",
       "      <td>3631.6</td>\n",
       "      <td>175.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25497</th>\n",
       "      <td>2005</td>\n",
       "      <td>498</td>\n",
       "      <td>Wendy's International</td>\n",
       "      <td>3630.4</td>\n",
       "      <td>57.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25498</th>\n",
       "      <td>2005</td>\n",
       "      <td>499</td>\n",
       "      <td>Kindred Healthcare</td>\n",
       "      <td>3616.6</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25499</th>\n",
       "      <td>2005</td>\n",
       "      <td>500</td>\n",
       "      <td>Cincinnati Financial</td>\n",
       "      <td>3614.0</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  rank                company  revenue profit\n",
       "25495  2005   496        Wm. Wrigley Jr.   3648.6    493\n",
       "25496  2005   497         Peabody Energy   3631.6  175.4\n",
       "25497  2005   498  Wendy's International   3630.4   57.8\n",
       "25498  2005   499     Kindred Healthcare   3616.6   70.6\n",
       "25499  2005   500   Cincinnati Financial   3614.0    584"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>rank</th>\n",
       "      <th>company</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1955</td>\n",
       "      <td>229</td>\n",
       "      <td>Norton</td>\n",
       "      <td>135.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1955</td>\n",
       "      <td>291</td>\n",
       "      <td>Schlitz Brewing</td>\n",
       "      <td>100.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>1955</td>\n",
       "      <td>295</td>\n",
       "      <td>Pacific Vegetable Oil</td>\n",
       "      <td>97.9</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1955</td>\n",
       "      <td>297</td>\n",
       "      <td>Liebmann Breweries</td>\n",
       "      <td>96.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1955</td>\n",
       "      <td>353</td>\n",
       "      <td>Minneapolis-Moline</td>\n",
       "      <td>77.4</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  rank                company  revenue profit\n",
       "228  1955   229                 Norton    135.0   N.A.\n",
       "290  1955   291        Schlitz Brewing    100.0   N.A.\n",
       "294  1955   295  Pacific Vegetable Oil     97.9   N.A.\n",
       "296  1955   297     Liebmann Breweries     96.0   N.A.\n",
       "352  1955   353     Minneapolis-Moline     77.4   N.A."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_numberic_profits = df.profit.str.contains('[^0-9.-]')\n",
    "df.loc[non_numberic_profits].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N.A.'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.profit[non_numberic_profits])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.profit[non_numberic_profits])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_sizes, _, _ = plt.hist(df.year[non_numberic_profits], bins=range(1955, 2006))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~non_numberic_profits]\n",
    "df.profit = df.profit.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25131"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_by_year = df.loc[:, ['year', 'revenue', 'profit']].groupby('year')\n",
    "avgs = group_by_year.mean()\n",
    "x = avgs.index\n",
    "y1 = avgs.profit\n",
    "def plot(x, y, ax, title, y_label):\n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.plot(x, y)\n",
    "    ax.margins(x=0, y=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plot(x, y1, ax, 'Increase in mean Fortune 500 company profits from 1955 to 2005', 'Profit (millions)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eGxUdfDIyMujUqdNV75mysjJyc3Np1qwZGzZswMnJiZ49e95wrHW++3afPn1Yt24d+fn5APz73//myy+/vGq9fv368euvvxqT++233/L0008DsHXrVl566SWGDBkCXGqL1ev1JCYmEhQURLt27XjhhRd45plnOHjwIG3atKFevXrGN25aWhpBQUEcOnTIDHt8Se/evdm2bRsnT54EYNOmTTz++OMUFxezdetWnn76aYYNG4aLiwvbt29Hr9ffsW3369ePr776CqUUpaWlxMTE0KdPn2uua21tbXxzNGnSxJij9PR0du/eXeO2rrefl6tfvz6LFi3iwIEDxvWKioro2rUrgwYN4ueff6awsJDS0lJWrFjB4MGDsbGxYcCAAcTExACX3qwnT56kV69eVZ7b0dERT09PYmNjgUuv95gxY2jYsCE2Njb83//9n3Gf1q5dW20uqpOYmEhiYiIA33//PQ8++CANGzas9risyahRo1i/fj2HDx/mscceq3H9mo7n4cOHs2HDBtauXUtISAhw46/LlZydnY3Pu2vXLnQ6HQDdu3fn9OnT7NmzB4CjR4/i7+9Peno6UVFRREdHM3jwYGbNmkX79u05fvx4jft1uZs5Zm+HRqPh+eef5+DBgwDExcVhZ2fH/fffT0FBAW+//Ta5ubkALFu2DH9/f6ytrUlISDCe0efm5vLjjz8aX/fLXf5+6t+/Pz/99JPxLHn58uX07Nnzqi9A58+f5+mnn+bFF19k5syZVb74DBo0iO+//9643pYtWxg4cCDdunUjNzeXP/74A4CffvqJ7t2707BhQ86fP897771HcXExer2eL7/88pqxXq5OnRFdi6+vLydOnGDMmDEAtG/fnrfeesv44VChX79+PP/880yYMAGNRoOjoyMfffQRGo2G6dOn89JLL+Hg4ICjoyM9e/bkzJkzjBo1isDAQEaMGIGDgwP16tVj9uzZ2NnZER0dzbx58/jss88oLy9n2rRpV3U8MKX27dszd+5cXn31VeM3kiVLltCgQQNeeuklFixYwIcffoitrS09evQwNpncCbNnz+btt98mODiYsrIy+vfvz+TJk6+57qOPPsrChQspKytj3LhxvP766/j7++Ph4XFV19Sb3c/LOTs7s2jRIiIiIigrKzM2kdjZ2fHoo49y7NgxRo0aRVlZGYMGDWLYsGEAzJkzh9mzZxMUFIRGo2HBggXXPLP917/+RWRkJMuXL0ej0TBv3jzc3NyIjo7m7bff5t///jd6vZ6XXnqJRx55hF27dt1wPps2bcqiRYtISUnB2dmZBQsWAFR7XNbExcUFLy8v2rVrd0Pf7ms6nrVaLZ07d6a8vNx4tnijr8uVXn/9df75z3/y/fff4+npiaenJ3Dp9Vu8eDELFiygpKQEpRQLFizAw8ODp59+mvDwcIKCgowf6kOHDq1xvy53M8fs7dBoNPzrX//izTffpKysDK1WS3R0NBqNBl9fX8aNG8eYMWMwGAzcf//9vPXWWwBEREQQERHB0KFDKS8vJzQ0lL59+171/I888givv/46b731FrNmzSItLY1Ro0ZhMBho3bq1sePB5aKjoykqKmL58uXGMzU7Ozt++OEHpk6dyj//+U+GDh2KXq8nLCyMVq1aAZd+ejF37lyKiopo3Lgx7733HgBPPfUUZ8+eZfjw4ej1enr16lWlafGaeVE32+4ihDCbXbt28dZbb7F69eo79pzZ2dmMHDmSr7/+Gjc3tzv2vELcqjrfNCeEqBQTE8OQIUN47rnnpAiJWkPOiIQQQliUnBEJIYSwKClEQgghLEoKkRBCCIuSQiSEEMKi6vzviKqTk1OAwWDZfhouLo5kZeVbNIbaQnJRSXJRSXJRyZK5SMsq5H9rE1kw1cckz3/PFiKDQVm8EFXEIS6RXFSSXFSSXFSyVC52Hj5P9oXrj4pxO0zaNJefn09QUBDnzp0DLg3DP3r0aIYOHcqrr75qHEfq6NGjhISE4O/vz6xZs4xDVKSmphIaGkpAQABTpkyhoKAAuDRe3KRJkwgMDCQ0NNQ4DIgQQog7b98xHa3cGprs+U1WiPbv38+YMWNITk4GLhWlqVOnMnfuXH799VcAfvzxRwDCwsKIiIhg7dq1KKWMY3tFRkYyduxY4uPj8fLyIjo6GoBFixbh7e3NmjVrGDVqFPPmzTPVbgghxD1Nl1vEmYx8vO5zrnnlW2SyQhQTE8OcOXOMoyBv27aN7t27G4fmnz17No899hgpKSkUFxcbp0cICQkhPj6esrIy9uzZYxx+vWI5XJqyITg4GICgoCA2b9581UjNQgghbt++45kAeLZpYrJtmOwa0ZVnKadPn8bBwYHp06dz6tQpevToQXh4OEeOHKkyY6BWqyU9PZ2cnBwcHR2Nw4dXLIdLQ5FXPMbGxgZHR0eys7OvGp5fCCHE7fnjmA53bQOaNqpvsm2YrbOCXq9n69atfP/997Ro0YJZs2axdOlS+vTpc9WkZBXzk1w5l8uVty9/jJXVzZ3cubg43vxOmIBWW/MEXvcKyUUlyUUlyUUlc+fiQn4JJ87lMmpwR5N+ZpqtEDVt2pRu3boZZ/oMDAzkq6++IiQkpEpng8zMTFxdXXF2diYvLw+9Xo+1tTU6nc7YzOfq6kpmZibNmzenvLycgoIC48RlNyorK9/ivXG0Wid0ujyLxlBbSC4qSS4qSS4qWSIXW/anYlDQyb0RWVn5JitGZvtBa79+/Th8+DBpaWkA/P7773h6euLu7o69vT0JCQkArFq1Ch8fH2xtbfH29iYuLg6A2NhYfHwu9WH39fU1TkIWFxeHt7f3bc2aKIQQ4mr7jmfi0tCeVs1M24JktjMiNzc35s6dy+TJkykpKeGBBx5gxowZAERFRTF79mzy8/Px9PRk/PjxwKVJycLDw1myZAlubm4sXLgQgGnTphEeHs7QoUNxcnK65mRPQgghbl1xaTmHkrIZ0L1FtZdF7pR7dhoIaZqrXSQXlSQXlSQXlcydi72JGUTHHuKNMQ/SqXUTrKw0d3/TnBBCiLvHH8d1ONa3pUPLRibflhQiIYQQVZTrDew/kUW39i5Y32SP5FshhUgIIUQVf53JpaiknB4dtDWvfAdIIRJCCFHFH8d12Nla4dnGdMP6XE4KkRBCCCODUuw7psOrjQt2ttZm2aYUIiGEEEbJaXnk5pfSo2NTs21TCpEQQgijP47psNJo6NpOCpEQQggL2Hdcx/2tGuNY33yj1UghEkIIAUBaVgFpWYX06Gie3nIVpBAJIYQAIH7XGWysNVKIhBBCmF9qZgFbD6Yx8EEPmjjZm3XbUoiEEEKwYvMp7G2tCerT2uzblkIkhBB1TFFJOWt3n2HbwbQbWv9kygX+OKYjoFcrnBzsTBzd1cw2DYQQQgjTulhYyvq9Z9mQkEJhSTkADerb0r199V2xlVL8uPEkDRvY4dezpblCrUIKkRBC3OUyLxSxdtdZthxIpazcQI/7tTzm3ZJv1h/js1+OEPGMN65NHK752IOnsvnrbC6hj3Wknp1lSoIUIiGEuEsVFpfxzfrj7DqSDkBvz+YEPtIKN5cGALw0vAtzv9zDRysOMWv8Q9hfMWSPQSl+2nQSbeN6+HZvYfb4K8g1IiGEuEut3X2WHYfP82gPD96b3JsJQx8wFiEAbeP6THrckxRdPv+LT+TKeVB3HUnnbEY+w33aYmNtuXIghUgIIe5S+09m0t69EWMGd8C5Yb1rrtOlrQtP9G/DjsPpbPgjxbi8XG9g5eZTtHJ15OEHmpkr5GsyaSHKz88nKCiIc+fOVVn+1VdfMW7cOOPto0ePEhISgr+/P7NmzaK8/NJFttTUVEJDQwkICGDKlCkUFBQAcPHiRSZNmkRgYCChoaHodDpT7oYQQtQ6OXklnEnPp9t1OiJUCOpzH93aufDdb8c5ce4CABv3pZB5oZgRA9phpdGYOtzrMlkh2r9/P2PGjCE5ObnK8hMnTrB06dIqy8LCwoiIiGDt2rUopYiJiQEgMjKSsWPHEh8fj5eXF9HR0QAsWrQIb29v1qxZw6hRo5g3b56pdkMIIWqlAyczAejazqXGda00Gp4P7oxLw3pExx4kPaeQX7Yn06lVY7zMNOfQdeMz1RPHxMQwZ84cXF1djctKS0uJiIjglVdeMS5LSUmhuLiY7t27AxASEkJ8fDxlZWXs2bMHf3//KssBNm7cSHBwMABBQUFs3ryZsrIyU+2KEELUOgdOZuHSsB7uTRvUvDLgUM+Wl0K6UFhcztwv95BXWMaIAe3QWPhsCExYiObNm4e3t3eVZf/6178YMWIELVtW9lXPyMhAq60c10ir1ZKenk5OTg6Ojo7Y2NhUWX7lY2xsbHB0dCQ7O9tUuyKEELVKWbmew8nZdG3vclOFpKWrI08HdqKoRM9DHbW0a9HIhFHeOLN13962bRtpaWn84x//YNeuXcblBoOhSiKVUmg0GuPfy1WXcKUUVlY3V1NdXBxvan1T0WqdLB1CrSG5qCS5qCS5qFSRiz8SMygtM+DTo+VN5+fxAU60dm9MOw/zTvVwPWYrRKtXr+b48eM88cQTFBYWkpmZyd///nfCwsKqdDbIzMzE1dUVZ2dn8vLy0Ov1WFtbo9PpjM18rq6uZGZm0rx5c8rLyykoKKBx48Y3FU9WVj4Gg6p5RRPSap3Q6fIsGkNtIbmoJLmoJLmodHkuNiecxc7GCrdG9reUnxaN61GUX0xRfvENP8bKSmOyL/Bm6749f/581qxZw6pVq3j77bfx8vJi0aJFuLu7Y29vT0JCAgCrVq3Cx8cHW1tbvL29iYuLAyA2NhYfHx8AfH19iY2NBSAuLg5vb29sbWtHZRdCCFNSSrH/ZCad73PG7oofqN6tasXviKKiopg/fz4BAQEUFhYyfvx4AObMmUNMTAxDhgxh7969/P3vfwdg2rRp/PnnnwwdOpRvvvmGiIgIS4YvhBBmk5pVSOaF4hvqLXe30Kgrf2p7j5CmudpFclFJclFJclGpIhdrdp3mh99PEvVin2p/xGoKdaJpTgghxO3bfyKLlq6OZi1CpiaFSAgh7hIFxWWcOHeBbu3rTrMcSCESQoi7xqFT2RiUomu7mof1uZtIIRJCiLvEgZOZONa3pa1bQ0uHckdJIRJCiLuA3qA4eCqbLm1dsLKy/LA8d5IUIiGEuAscO51DflFZnbs+BFKIhBDirrDn6HmsNJpaMVr2nSaFSAgh7gJ7jqTTsWUjHOrVvVFkpBAJIUQtl3WhmOS0i3Wut1wFKURCCFHLHTiVBVAnrw+BFCIhhKj19p/IpLmLA82dHSwdiklIIRJCiFqspFRP4ukcenZuXitmUzUFKURCCFGL7TqaTmm5gb5dW1g6FJORQiSEELWUUooNCefw0Dagcx3stl1BCpEQQtRSJ1IucCYjn0cf8qizzXIghUgIIWqt3xLOUd/eht6dm1s6FJOSQiSEELVQbn4JCX/p6N/VDXu7ujEleHWkEAkhRC20+c9U9AbFwAfdLR2KyZm0EOXn5xMUFMS5c+cA+P777wkKCiI4OJh//OMflJaWAnD06FFCQkLw9/dn1qxZlJeXA5CamkpoaCgBAQFMmTKFgoICAC5evMikSZMIDAwkNDQUnU5nyt0QQgizKtcb+P3PFLzaOtOsjv526HImK0T79+9nzJgxJCcnA5CUlMSyZcv47rvv+PnnnzEYDHzzzTcAhIWFERERwdq1a1FKERMTA0BkZCRjx44lPj4eLy8voqOjAVi0aBHe3t6sWbOGUaNGMW/ePFPthhBCmN2+45lcyC9lUA8PS4diFiYrRDExMcyZMwdXV1cA7OzsmDNnDo6Ojmg0Gjp27EhqaiopKSkUFxfTvXt3AEJCQoiPj6esrIw9e/bg7+9fZTnAxo0bCQ4OBiAoKIjNmzdTVlZmql0RQgiz+i3hHE0b1aNL27o5pM+VbG50xdLSUqysrLCxubGHXHmW4u7ujrv7pbbO7Oxsvv76a+bPn09GRgZarda4nlarJT09nZycHBwdHY3bq1gOVHmMjY0Njo6OZGdn06xZsxvdHVxcHG94XVPSap0sHUKtIbmoJLmodK/lIin1AsfO5vJskCfNmlWdibWu5uK6VSUrK4ulS6lOGhAAACAASURBVJeybt060tLS0Gg0eHh4EBAQwDPPPIOz883/wCo9PZ2JEycyYsQIevXqRUJCQpX+8UopNBqN8e/lqutHr5TCyurmTu6ysvIxGNRNx38nabVO6HR5Fo2htpBcVJJcVLoXc/HTb8ewtbHiwXbOVfbd0rmwstKY7At8tZ/esbGxPP/887i4uPDRRx+xa9cuEhIS+Oijj2jUqBHPPvssK1euvKmNnTx5kqeeeorhw4fz0ksvAdC8efMqnQ0yMzNxdXXF2dmZvLw89Ho9ADqdztjM5+rqSmZmJgDl5eUUFBTQuHHjm9tzIYSoZQqLy9hx+Dy9OjfDsX7dm3eoOtUWogsXLvDjjz8yadIkOnfuTMOGDalfvz4dO3bkueeeY8WKFVy4cOGGN5Sfn89zzz3HtGnTmDBhgnG5u7s79vb2JCQkALBq1Sp8fHywtbXF29ubuLg44FJh9PHxAcDX15fY2FgA4uLi8Pb2xtb23nnRhBB109aD5yktM9wznRQqaJRSN9U+VVZWdlMf+o8++ij/+9//WL9+PVFRUbRr167KfdOmTSMxMZHZs2eTn5+Pp6cn8+fPx87OjpSUFMLDw8nKysLNzY2FCxfSqFEjcnNzCQ8P5+zZszg5OREVFYWHx829cNI0V7tILipJLirdS7kwKMXMpTtp6GDHzHEPXXW/pXNhyqa5GgvR3r172b17NxMnTiQ0NJRjx44xf/58hgwZYpKAzEUKUe0iuagkuah0L+Xi0KksFsbsZ9LjnXnkGkP6WDoXFrlGVOH999+ne/furF+/nsaNG/Prr7/y+eefmyQYIYS4F5WU6vl5WzING9jhfb+rpcMxuxoLkV6vp0+fPmzfvp3Bgwfj4eGBwWAwR2xCCFHnXSgoZcG3f3Ay9QKjB7bDxvreG3mtxj02GAwcOHCAjRs30qdPH44dOyY/HhVCiDvgfHYh8/63lxRdAS+HdKGPl5ulQ7KIGn+dOnnyZF577TVGjhxJy5YtefTRR5k1a5Y5YhNCiDrrxLkLLP7pABoNvDG2B21bNKz5QXVUjYXIz88PPz8/4+1169ZhbV23hyQXQghTSvgrg6W/HMHZyZ7po7vh2qTuD2x6PTUWolOnTvHpp5+Sm5vL5R3sPv74Y5MGJoQQddG6PWf57rfjtHVvyCsjuuLkYGfpkCyuxkIUHh5O165d6dmzZ52eqlYIIUztt4RzfPvbcXp01DIpuDN2ttK6BDdQiIqKipg9e7Y5YhFCiDorv6iMlZtP4dnGmReHeWFlJV/sK9TYa65169ZkZGSYIxYhhKizft6aRFFpOU892l6K0BVqPCMyGAwEBQXh6emJvb29cblcIxJCiBtzPruQ3/el4NutBe7a2jEFTW1SYyF67LHHeOyxx8wRixBC1Ek//H4CGxsrnujf1tKh1Eo1FqLhw4eTkpLC7t27KS8v5+GHH6Z169bmiE0IIe56f53JYd/xTEJ82tKogfSQu5YarxFt2bKFESNGsH79en777TdGjhzJ+vXrzRGbEELc1QxK8d1vJ3BuaI9fz5aWDqfWqvGM6MMPP+Srr76iffv2ABw/fpywsDAGDx5s8uCEEOJutvPweU6n5/F8kHTVvp4az4jKysqMRQigQ4cOxllThRBCXFtJmZ6fNp3ivuZO9PJsZulwarUaC1G9evU4ePCg8fbBgwepX7++SYMSQoi73f/tOUtOXglPDeqAlQwGcF01Ns2FhYUxefJkYweFpKQkPvzwQ5MHJoQQd6sL+SXE7ThNj45aOrZsbOlwar0aC5G3tze//vor+/fvx2Aw0L17d5o0aWKO2IQQ4q60cksS5XoDowa0s3Qod4Vqm+ZWrVoFwBdffMHKlSs5deoUycnJxMbG8sUXX9zQk+fn5xMUFMS5c+cA2L59O8HBwfj5+fHBBx8Y1zt69CghISH4+/sza9YsysvLAUhNTSU0NJSAgACmTJlCQUEBABcvXmTSpEkEBgYSGhqKTqe7tb0XQog77PT5PLYcSGVgD3eaOd/bo2rfqGoL0enTpwE4duzYNf/VZP/+/YwZM4bk5GQAiouLmTlzJtHR0cTFxXHo0CE2bdoEXGr+i4iIYO3atSiliImJASAyMpKxY8cSHx+Pl5cX0dHRACxatAhvb2/WrFnDqFGjmDdv3m0lQQgh7oSycj2frT5CowZ2PNGvjaXDuWtU2zT3yiuvADB//vxbeuKYmBjmzJnDG2+8AcCBAwdo3bo1LVte6ksfHBxMfHw87du3p7i4mO7duwMQEhLC4sWLGTVqFHv27OE///mPcfnf/vY3wsLC2LhxI19//TUAQUFBzJ07l7KyMmxtbW8pViGEuBNWbkkiJbOA6aO70aCefB7dqGoLUXBw8HUf+Msvv1z3/ivPUjIyMtBqtcbbrq6upKenX7Vcq9WSnp5OTk4Ojo6O2NjYVFl+5XPZ2Njg6OhIdnY2zZrdeBdJF5faMd6TVutk6RBqDclFJclFpbslF0eSsli7+wz+j7Tm0V73mWQbd0subla1hejNN9+8oxsyGAxV5jNSSqHRaKpdXvH3ctXNh6SUwsqqxp7oVWRl5WMwqJpXNCGt1gmdLs+iMdQWkotKkotKd0suikvL+ddXCbg0rMfjvVubJGZL58LKSmOyL/DVFqIGDRrc0Q01b968SqcCnU6Hq6vrVcszMzNxdXXF2dmZvLw89Ho91tbWxvXh0tlUZmYmzZs3p7y8nIKCAho3li6SQgjL+GHjSXS5Rbwx9kHq29fYGVlcodqMTZ06tdoHaTQafvvtt5vaULdu3UhKSuL06dN4eHiwevVqRowYgbu7O/b29iQkJPDQQw+xatUqfHx8sLW1xdvbm7i4OIKDg4mNjcXHxwcAX19fYmNjmTx5MnFxcXh7e8v1ISGERRxOyub3P1Lw69mS+1vJT1tuRbWFaMOGDXd0Q/b29rz77rtMnTqVkpISfH19CQgIACAqKorZs2eTn5+Pp6cn48ePB2DOnDmEh4ezZMkS3NzcWLhwIQDTpk0jPDycoUOH4uTkRFRU1B2NVQghbkRhcRmfxx3FzcWBEB+Z4uFWaZRS17xQ8umnn/L888/z9ttvX/OBd/v04XKNqHaRXFSSXFSq7blYtvoIOw6nM3PcQ7Rt0dCk27J0LixyjcjJ6VLvDLn2IoQQV9t3TMe2Q+cJ6nOfyYtQXVdtIXrqqacAePnll80WjBBC3A0Ki8v579q/aOXqyON977N0OHe9Grt3xMXFsXjxYi5cuFBl+Y4dO0wWlBBC1GY/b0sir6CUaSO7YmN9cz8dEVersRC9//77zJ49m1atWpkjHiGEqNVSMgv4LeEc/bu1oI2bNMndCTUWInd3dwYNGmSOWIQQolZTSvHNumPY21oT4iu95O6UGgvRsGHDeO+99/Dx8TEOtwPQs2dPkwYmhBC1TcJfOo6eziH0sY40dLCzdDh1Ro2FaNeuXWzevJmtW7dWWV7TWHNCCFGXlJTp+W7DcTy0jgx4sIWlw6lTaixER44cYfPmzdjb25sjHiGEqJV+3XGa7IslTAr1xPomx7YU11djNps2bWqcqE4IIe5FGblFxO86wyOdm8nU3yZQ4xlRs2bNeOKJJ+jTpw92dpVtonf7yApCCHGjvlt/HGtrDaMGtrd0KHVSjYWoVatW0nVbCHHPOnAyiz9PZDJqQDuaOMklClOothBlZ2fj7Ox83ZEVsrKycHFxMUlgQghhaWXlBr5df4xmzg481rOlpcOps6q9RjRz5ky++OKLq0ZUAMjPz+ezzz4jPDzcpMEJIYQl/b4vhfScIkIHd5ARFEyo2jOi6OhoPv/8c4KCgmjTpg2tW7fGYDBw5swZkpKSGD9+PNHR0eaMVQghzOr4uVyaOTvg1VZafkyp2kJkZWXFxIkT+dvf/sbOnTs5deoUGo2Gxx577KqOC0IIURfpcoto1qS+pcOo82rsrFCvXj0GDBjAgAEDzBCOEELUDkopdLlFdHCX7tqmJo2eQghxDQXF5RSV6NE2rmfpUOo8ixSiVatWMXToUIYOHcp7770HwPbt2wkODsbPz48PPvjAuO7Ro0cJCQnB39+fWbNmGX9cm5qaSmhoKAEBAUyZMoWCggJL7IoQoo7S5RYBoJWmOZMzeyEqKipi3rx5LF++nFWrVrF37142bNjAzJkziY6OJi4ujkOHDrFp0yYAwsLCiIiIYO3atSiliImJASAyMpKxY8cSHx+Pl5eXdJwQQtxRxkLUWAqRqdVYiAoKCoiMjOTpp58mNzeXiIiI2zr70Ov1GAwGioqKKC8vp7y8HEdHR1q3bk3Lli2xsbEhODiY+Ph4UlJSKC4upnv37gCEhIQQHx9PWVkZe/bswd/fv8pyIYS4U4yFqJEUIlOrsRC9/fbbNGzYkKysLOzt7cnPzyciIuKWN+jo6Mi0adMIDAzE19cXd3d3MjIy0Gq1xnVcXV1JT0+/arlWqyU9PZ2cnBwcHR2N01JULBdCiDtFl1tEwwZ22NtZWzqUOq/GXnNHjx5l/vz5bNq0ifr16xMVFUVQUNAtbzAxMZGffvqJ33//HScnJ15//XWSk5PRaDTGdZRSaDQaDAbDNZdX/L3clbdr4uLieMv7cCdptU6WDqHWkFxUklxUslQucgvKaNG0Qa16LWpTLHdSjYXI6orhzvV6/VXLbsbWrVvp3bu3cWigkJAQli1bhrV15bcOnU6Hq6srzZs3R6fTGZdnZmbi6uqKs7MzeXl56PV6rK2tjevfjKysfAwGdcv7cSdotU7odHkWjaG2kFxUklxUsmQuUnX5tPdoVGteC0sfF1ZWGpN9ga+xovTs2ZP333+f4uJitmzZwtSpU+nVq9ctb7BTp05s376dwsJClFJs2LCBbt26kZSUxOnTp9Hr9axevRofHx/c3d2xt7cnISEBuNTbzsfHB1tbW7y9vYmLiwMgNjYWHx+fW45JCCEuV643kHWxWK4PmUmNZ0Svv/46S5cuxcnJiQ8++ID+/fvz4osv3vIG+/Xrx5EjRwgJCcHW1pYuXbowdepU+vbty9SpUykpKcHX15eAgAAAoqKimD17Nvn5+Xh6ejJ+/HgA5syZQ3h4OEuWLMHNzY2FCxfeckxCCHG57IvFKCU95sxFo5SybPuUhUjTXO0iuagkuahkqVwcTsrmX9//SXhoj1ozEZ6ljwtTNs3VeEY0efLkay7/+OOP73gwQghRG2TIb4jMqsZCVPFbHYCysjLWrl2Ll5eXSYMSQghL0uUWYWNtRSNHGdzZHGosRMOHD7/q9rhx40wWkBBCWJoutwht43pY3eTPQsStuel+2EopMjIyTBGLEELUCpcKkTTLmctNXyM6duwYDz/8sMkCEkIIS5LpH8zvpq4RaTQaxowZQ79+/UwalBBCWIpM/2B+N3SNqLS0lKKiIip6eufl5dG4sXxbEELUPTLqtvnVWIi+++473nnnHcrKyoDK8d6OHj1q8uCEEMLcZB4i86uxEH322Wd8++23eHp6miMeIYSwKJn+wfxq7DXXtGlTKUJCiHuGTP9gfjUWon79+vHNN9+Qnp5Obm6u8Z8QQtRFutxi6ahgZjU2zS1dupTS0lLmzp1rXCbXiIQQdZUut4j2Ho0sHcY9pcZCdODAAXPEIYQQFlcx/UPvRs0tHco9pcamOYPBwLJlywgPDyc/P59PPvkEvV5vjtiEEMKsZPoHy6ixEC1YsIC//vqL/fv3o5Riy5YtzJ8/3xyxCSGEWelyiwHkGpGZ1ViIduzYwbvvvou9vT1OTk58/vnnbNu2zRyxCSGEWVV03XZt4mDhSO4tNRYiGxsbrKwqV7Ozs8PGpsZLS0IIcdfJkOkfLKLGitKxY0e+/vpr9Ho9p06d4ssvv6RTp07miE0IIcxKpn+wjBrPiGbNmsXhw4fJyspi7NixFBYWMnPmzNva6IYNGwgJCSEwMJC3334bgO3btxMcHIyfnx8ffPCBcd2jR48SEhKCv78/s2bNory8HIDU1FRCQ0MJCAhgypQpFBQU3FZMQggh0z9YRo2FKCMjg3feeYft27ezc+dOoqKiaNKkyS1v8OzZs8yZM4fo6Gh+/vlnjhw5wqZNm5g5cybR0dHExcVx6NAhNm3aBEBYWBgRERGsXbsWpRQxMTEAREZGMnbsWOLj4/Hy8iI6OvqWYxJCiIrpH2RoH/OrsRA988wzhIaGsmrVKkpLS297g+vWrWPIkCE0b94cW1tbPvjgA+rXr0/r1q1p2bIlNjY2BAcHEx8fT0pKCsXFxXTv3h2AkJAQ4uPjKSsrY8+ePcYpKiqWCyHErZLpHyynxmtEGzduZMuWLaxcuZL3338fPz8/Ro8efcvXiU6fPo2trS2TJ08mLS2NAQMG0KFDB7RarXEdV1dX0tPTycjIqLJcq9WSnp5OTk4Ojo6Oxk4TFcuFEOJWyfQPllNjIbKyssLX1xdfX19OnjzJP/7xD7799ttbHuJHr9ezd+9eli9fjoODA1OmTKFevXpoLrs4WDHVhMFguObyir+Xu/J2TVxcHG8p/jtNq3WydAi1huSikuSikrlykZhyEYCObZvW2vzX1rhuV42FqLy8nA0bNrBixQoOHDjAkCFDeOutt255g02bNqV37944OzsDMHjwYOLj47G2rhzpVqfT4erqSvPmzdHpdMblmZmZuLq64uzsTF5eHnq9Hmtra+P6NyMrKx+DQd3yftwJWq0TOl2eRWOoLSQXlSQXlcyZi5NnsgGwMRhqZf4tfVxYWWlM9gX+hkbfXr58OYGBgWzcuJHZs2dz//333/IGBw4cyNatW7l48SJ6vZ4tW7YQEBBAUlISp0+fRq/Xs3r1anx8fHB3d8fe3p6EhAQAVq1ahY+PD7a2tnh7exMXFwdAbGwsPj4+txyTEELI9A+Wc0MztN533313bIPdunVj4sSJjB07lrKyMvr27cuYMWNo27YtU6dOpaSkBF9fXwICAgCIiopi9uzZ5Ofn4+npyfjx4wGYM2cO4eHhLFmyBDc3NxYuXHjHYhRC3Htk+gfL0Silrts+pdPpmDVrFqdPn+brr79mxowZzJ8//6abwmobaZqrXSQXlSQXlcyZizeWbKe9RyMmBdfOiUAtfVxYtGkuMjKSwYMHY29vT6NGjejUqROzZ882STBCCGEJFdM/yG+ILKPGQpSSksLo0aOxsrLC1taWsLAw0tLSzBGbEEKYhUz/YFk1FqKKbtQV8vPzq9wWQoi7nUz/YFk1dlbw8/Pj9ddfJy8vj++++44ffviBwMBAc8QmhBBmIT9mtawaC9HkyZOJjY3FYDCwfft2nnzySUaPHm2O2IQQwix0/3/6h8ZO9pYO5Z50QxMLDRs2jGHDhhlvb9u2jb59+5osKCGEMKcMmf7Boqq9RnTo0CGeeuopJk+eTHb2pV8cp6am8vLLLzNlyhSzBSiEEKYm0z9YVrWFKDIyEj8/Pzw8PFiyZAnr16/n8ccfp6ioiFWrVpkzRiGEMBmZ/sHyqm2ay8vLY8KECej1evz9/VmzZg2RkZEMHTrUnPEJIYRJyfQPlldtIapf/9K3A2tra0pKSli6dCmdO3c2W2BCCHGn6HKL2H00nbzCMgxKoQxgQKEU5BeVAdJjzpKqLUSXj/zTpEkTKUJCiFpBKUX2xRIaOdphY139TyFLSvXs/SuDbQfTSDyTC0A9O2s0Gg1WGir/Wmlwc3GgTYuG5toFcYVqC5HBYODChQvGgnT5/wEaN25s+uiEEOIyyecv8vW6Y5xMuYj1/y8g7lpH3Js2uPRP24Dc/FK2HkxjT2IGJaV6XBvXZ3j/NvTxcsOlkTS/1UbVFqJjx47xyCOPGItPr169jPdpNJpbnhhPCCFu1sWCUlZsPsmW/Wk4NbBj5IB2FBaXk6LL52TKBXYdqTpDs72dNQ93cqVvFzc6eDS66YkzhXlVW4gSExPNGYcQQlylXG/g//acZdXWJErL9Pg/3IrgvvdR377qR1dRSTmpWQWk6Aqws7HiwQ5amVfoLnJDP2gVQghzO5yUTcwXuzmbno9XG2fGDO6Am0uDa65b396Gdi0a0a5FIzNHKe4EKURCiFrlZMoFVmw+xdHTObi5NOCVEV3p1t5FmtfqMClEQoha4Ux6His2n+LAySwaOtgyZlAHRvndT25OoaVDEyYmhUgIYVGpmQXEbk1ib2IGDvY2jPBty6CHPKhnZ4OtjVznuRdYtBC999575OTk8O6777J9+3bmz59PSUkJgYGBTJ8+HYCjR48ya9YsCgoK8Pb2JjIyEhsbG1JTUwkLCyMrK4s2bdoQFRVFgwbXbj8WQtQ+xaXlxGw4wab9qdjZWhPc5z78H26JQz1bS4cmzKzGifFMZceOHaxcuRKA4uJiZs6cSXR0NHFxcRw6dIhNmzYBEBYWRkREBGvXrkUpRUxMDHBpLLyxY8cSHx+Pl5cX0dHRltoVIcRNOpV6kX9+sYdNf6Yy6CEP3pvcm+E+baUI3aMsUohyc3P54IMPmDx5MgAHDhygdevWtGzZEhsbG4KDg4mPjyclJYXi4mK6d+8OQEhICPHx8ZSVlbFnzx78/f2rLBdC1G4Gg+KX7cm8szyBcr2BN8Y+yNjBHWnoYGfp0IQFWaRpLiIigunTp5OWlgZARkYGWq3WeL+rqyvp6elXLddqtaSnp5OTk4OjoyM2NjZVlt8MFxfHO7Ant0+rdbJ0CLWG5KJSXcxFenYhC79P4EhSNj7d3ZkyshuO9Ws+A6qLubhVdTUXZi9EP/zwA25ubvTu3ZsVK1YAl4YTurxrplIKjUZT7fKKv5e72a6dWVn5GAyq5hVNSKt1QqfLs2gMtYXkolJdzMWOw+f56v/+Qil4Pqgzj3g2oyi/mKL84us+ri7m4lZZOhdWVhqTfYE3eyGKi4tDp9PxxBNPcOHCBQoLC0lJScHaurJ3jE6nw9XVlebNm6PT6YzLMzMzcXV1xdnZmby8PPR6PdbW1sb1hRC1S2pmATG/n+DAySzaezTi+aDOMsq1uIrZC9EXX3xh/P+KFSvYvXu3cRK+06dP4+HhwerVqxkxYgTu7u7Y29uTkJDAQw89xKpVq/Dx8cHW1hZvb2/i4uIIDg4mNjYWHx8fc++KEPcMpRRnM/I5kpyDQz0benTUXrdZ7WJhKau2JrFpXyr2dlaMHtiex3p6YG1lsf5RoharFb8jsre3591332Xq1KmUlJTg6+tLQEAAAFFRUcyePZv8/Hw8PT0ZP348AHPmzCE8PJwlS5bg5ubGwoULLbkLQtQ5+UVlHEnO5uCpLA6dyuZCQanxvuVr/6Lzfc707ORKj45Njb3dysr1rN97jtU7kikpNTDgwRY83q+NdEYQ16VRl8/tcA+Ra0S1i+Si0s3koqxcz+b9aeTml9DHq3m1Y7HdqLSsAv44puPP45mcSruIUtCgng2ebZzp0tYFzzbOXMgvZffRdPYkZpB5oRgbaw1ebVzo4NGI3/elkHmhmK7tXBg9sD0tmt5ePHJcVLJ0LurUNSIhxO0r1xvYciCN1duTyckrQaOBX3ecplOrxvh2d6dHRy22NjU3g1U0uSX8pSPhmI7UzAIA2rg5EdznPrq0c6FN84ZYWVV2BmrsaE/r5k6MHNCOpLQ8Y1H680QmHlpHXnuqO573OZts30XdI4VIiLtIud7A9kPn+WVbMlkXi2nv3oiJQx+ghdaRrQdS2fRnKp/8fBjH+rb07+qGb/cWuDSqR35ROXkFpeQVlpJXVMbFglIyLxSz77gOXW4xGg3c37IxAx/ryIMdmuLcsOYJ5DQaDW1bNKRti4aMfrQ9utwitI3qVylaQtwIKURC3AUMSrHz8Hl+3ppMRm4RbdyceDrgfjzbOBt/ujC0930EPtKaI0nZbPwzlbW7z7Jm1xk0wLUaoa2tNDxwXxOG9r6P7u2b0rDBrV/HsdJoaNbE4ZYfL+5tUoiEqOWKS8v59Jcj7DueSStXx+tOi2Cl0eDV1gWvti7k5JWw8/B5SssNODnY4uRgR0MHWxwd7HBysMWxnq2cvYhaQQqRELVY5oUiFv94kJTMfJ4a1IHB3h5Y3eCPt5s42RP4SGsTRyjE7ZNCJEQtdfxcLh+tOEi5XjF9dDe82rhYOiQhTEIKkRC10NYDafw3PpGmjerxysiut90tW4jaTAqRELWIwaBY9vMhYjed5IHWTZgyzOuGBgYV4m4mhUiIWqK4tJyPVx3mwMksBvXw4MlB7bGxliFxRN0nhUiIWuBiYSkf/rCf5PN5TBnRlZ4dmlo6JCHMRr5uCWFhutwi5i9P4JyugJdDujCkTxtLhySEWckZkRAWdCY9jw9i9lOuN/D6U93p4NHY0iEJYXZSiISwkKOnc/hoxQHq2dkQPuYh3G9zgFAh7lZSiISwgD2JGXz6y2Fcmzjw6uhuNzS2mxB1lRQiIcyotEzP2t1niN2SRDuPRrwyoqt0zxb3PClEQpiB3mBg64E0ft52adoG706uTBz6AHa21pYOTQiLk0IkhAkZlCLhLx0rNp8iPbuQdu4NmRTcmftbNbF0aELUGlKIhDABpRSHk7P5aeMpTqfn4d60AVNHdKF7+6bXHDVbiHuZRQrRRx99xJo1awDw9fXljTfeYPv27cyfP5+SkhICAwOZPn06AEePHmXWrFkUFBTg7e1NZGQkNjY2pKamEhYWRlZWFm3atCEqKooGDaTXkbAspRT7T2Tx685kTqZcxKVhPZ4b+gC9PZvLlAtCVMPsP2jdvn07W7duZeXKlcTGxnL48GFWr17NzJkziY6OJi4ujkOHDrFp0yYAwsLCiIiIYO3atSiliImJASAyMpKxY8cSHx+Pl5cX0dHR5t4VIYz0BgM7D59nzue7WfzTAXLzSgl9rCPvTHqEvl3cpAgJcR1mL0RarZbw8HDs7OywtbWlXbt2JCcn07p1a1q2bImNjQ3BwcHEx8eTkpJCcXEx3bt3ByAkJIT4+HjKysrYs2cP/v7+B6H/ZAAADTNJREFUVZYLYW5l5Xo27kth5tKdLP3lCHqD4rmhDzD/hUcY9JAHtjYyeIkQNTF701yHDh2M/09OTmbNmjX87W9/Q6vVGpe7urqSnp5ORkZGleVarZb09HRycnJwdHTExsamyvKb4eLieJt7cmdotU6WDqHWuJtykXOxmPgdyazZcakXXMdWjXl+WFd63aEmuLspF6YmuahUV3Nhsc4Kx48f54UXXuCNN97A2tqa5ORk431KKTQaDQaDocqF3YrlFX8vd7MXgLOy8jEY1G3tw+3Sap3Q6fIsGkNtcTfkQinFqdSL/JZwjj2JGegNCq+2zkwc+gCdWjdBo9GQlZV/29u5G3JhLpKLSpbOhZWVxmRf4C1SiBISEnjllVeYOXMmQ4cOZffu3eh0OuP9Op0OV1dXmjdvXmV5ZmYmrq6uODs7k5eXh16vx9ra2ri+EKZQVq5n99EMfks4R/L5POrbWzOwhzuDenjQzNnB0uEJcdczeyFKS0vjpZde4v+1d/exUdV7Hsff03mePkGfKIWhcJGL9/ZSylqz6pUSst5W206N9G4A61NqIhIN2UiiPJn+AQRFA9HwjybX8If6B0YFrUCyoJBIdwPbiG5IUWIpbekDnVLaeT7z8N0/CoOsq7IRGJj5vpKT4RxOy/w+mfCZc+bM7+zcuZP7778fgIULF3L27FnOnTvHzJkzaW9vp7m5mRkzZmC32+ns7OSee+5h37591NTUYLVaqa6uZv/+/Xg8Hvbu3UtNTc2tHopKI/FEgnNDfkYuhf7XEuaiL4wITC900fK3P/LAX0px2vWbD0rdKCYRuaXnp7Zs2cLHH3/MrFmzkttWrFjB7Nmzk5dvL1myhPXr12MymTh9+jSbNm3C7/dTUVHBtm3bsNlsnD9/nnXr1jE6Osr06dPZsWMH+fn51/089NTc7SWVWfQO+/jHF130Xbh6Wi0/20bRFAfFU5wU5zv546wp/Pny6bebTV8XV2kWV6U6i5t5au6WF9HtQovo9pKKLGLxBO0dPXzxH+fIdlr5+5K5zJmeS1G+E7stdVPv6OviKs3iqlRnkXafESmVaj1DE7z3RRf9IwHuryhl5UPzdPJRpVJEi0hllGgswWfHznLgP3vJy7ay5u+VVN2lt+VWKpW0iFRGiMUTdH4/wucdPQx4A/x1QSkr/mUe2Q49ClIq1bSIVFoLhKMcPTnA4c5+xnwRpk118m//WknlXD0KUup2oUWk0tLQxSD//l99HPvvQYxogj+VT+XJuvlUzi0kS2e/Vuq2okWk0oIvaNA9MEH3wARn+i9xuvcSFrOJf/7zNP5W7WbWtPScGkWpdKBFpO44gXCU8yMBeod9dA9O0H1+gguXQgCYTOAuzqHpr7NZ+k8zyc+2pfjZKqV+ixaRuu2ICKFIHF/IYNxvMDAaYGAkwHlvgIHRAON+I7nvlBwbc8vyWVJVxh/K8phdmpfS7wAppf7/tIjULReLJxgeCzE0GmBwNMjgaJCQEWd0PIQvaOAPRYnFr/2ysd1qpqzIxV/mFFBWlM2MomxmFudQkOdI0SiUUjeKFpG6aRIiXBgL0XfBT++wjwFvgIHRICNjIRI/mdBjaq6dkgIXhXkOyktzyXVZyXXayHVZyc+2UVrooiDPoRcZKJWmtIjU7yIiBMIxLvkijPkjeMfD9F3w0zfso38kQCQaByDLZGJagZOZRdlUzy+mrDCb0kIXpQUunHZLyqcvUUqljhaR+lUiwkTAYHhscjbqC5cfL06EGfNHuOQ3iMYS1/yMy27BXZLD4srpuKflMKskl7IiF1aLfnajlPo5LaIMJyIEIzFGx8N4k0uI0fFw8jYIV45qYPKqtMI8BwV5Dv5Qls/UHDtTcu1MzbUzNcdOQd7kn2/FLNVKqfSgRZRmEiKEIzGC4RiBcIxgJEYwHGUiGMUXMJgIGkwEDCaCUSYCBuOBCKFI/JrfYbeZKcp3UJzv5E/lBZRMdU4uU5wU5juwmLNSNDqlVDrSIkqRaCyBL2gw5osQjSeIxhLEYpOP0ViceEKSSyIhxBIJ4nHBiCXwh6L4g1H8IQN/KIY/ZOALRgmGY4QiMX7t5hbZDgt52TZyXTZmluRQkV1AUb6DonwHhfkOivKdZDssekSjlLpltIh+ImzE8IeihI044UicsBEjZMQJR2KEjTghY/I/+lAkPvloxAhH4hjROFZrFg6rGbvNgt2alXwUAX8oiu9ycfiCUXyhKBEj/ttP6FfYbWZynVZynFZyXFamFbjIdljJdlhw2S04HRZc9svrDgu5rsmr0PRoRil1u8nYIjrc2c/AaBBf0Li8RDGiv10Ok4VjwWEz47CZyXPZsFnNRGNxjGiCSCxOwBfFiIUwYpO/L8duxeW0MqMkhxyHlWynFZfdwtQpToxIDKvZhMWchcWShTUrC/PldZPJhDnLhNlsIivLhNkEFouZbIclLQslK0uPwq7QLK7SLK5KZRY389/O2Du0KqWUuj2k39tqpZRSdxQtIqWUUimlRaSUUiqltIiUUkqllBaRUkqplNIiUkoplVJaREoppVJKi0gppVRKaREppZRKKS2im8Dv99PY2Eh/fz8An3zyCfX19Xg8HrZs2UIsFgOgu7ubJ598kqamJp599lnGx8cBGBgYoKWlhYcffpjVq1cTCARSNpbf63qy6Orq4tFHH00uixcvprGxEci8LABOnTpFc3MzTU1NrFq1iomJCSAzszh69CgejwePx8PatWuTY06XLHbt2kVDQwMNDQ1s374dgI6ODjweD7W1tezcuTO5b1dXF8uWLaOuro6NGzcmM0qLLETdUCdPnpTGxkapqKiQvr4++fHHH2Xx4sUyPDwsIiJtbW3y3nvvSSKRkNraWjl69KiIiLzxxhuyfft2ERF57rnnpL29XUREdu3aldx+p7neLH4qGAxKQ0ODnDhxQkQyM4uVK1fKkSNHRERk27ZtsmPHDhHJvCzGx8flvvvukzNnzoiIyLvvviubN28WkfTI4tixY7J8+XKJRCJiGIY89dRT8vnnn8uSJUukt7dXotGotLa2Jl8LDQ0N8s0334iIyPr16+WDDz4QkfTIQo+IbrA9e/bQ1tZGSUkJAN9//z1VVVXJ9aVLl3Lo0CFOnTqFy+WipqYGgOeff56Wlhai0SgnTpygrq4OgGXLlnHw4MHUDOZ3ut4sfuqdd97h3nvvpbq6OmOzSCQSyXe1oVAIh8ORkVn09PRQVlbGXXfddc32dMmiuLiYdevWYbPZsFqtzJ07l56eHsrLy3G73VgsFjweDwcPHuT8+fOEw2GqqqqAq2NOlyy0iG6wrVu3Ul1dnVy/++67+fbbbxkcHCQej3Pw4EG8Xi+9vb0UFRWxYcMGHnvsMdra2nC5XIyNjZGTk4PFMjkxenFxMcPDw6kazu9yvVlc4fP52LNnDy+++CJAxmaxbt06Nm3axIMPPkhHRwcrVqzIyCxmz57N0NAQp0+fBuDAgQN4vd60yWLevHnJYunp6eHAgQOYTCaKi4uT+5SUlDA8PMyFCxeu2X5lzOmShRbRTTZnzhzWrl3L6tWraWlpYf78+VitVmKxGMePH2flypV8+umnuN1uXnvtNUTkZzelS5eb1P1SFld89tlnPPTQQxQWFgJkZBbhcJiNGzeye/duvv76ax5//HFeeeWVjMwiLy+P119/nVdffZXm5mZKSkqwWq1pl8WZM2dobW3l5Zdfxu12XzOWK2NNJBL/5/Z0ySJj70d0q0QiESorK9m7dy8w+a7O7XZTXFxMeXk5CxYsAKCxsZE1a9ZQUFCAz+cjHo9jNpsZGRlJnrK40/1SFlccOnSIVatWJdczMYsffvgBu91OZWUlAMuXL+ett97KyCzi8TilpaV89NFHAHz33Xe43e60yqKzs5M1a9awYcMGGhoaOH78OCMjI8m/vzK20tLSa7Z7vV5KSkrSJgs9IrrJgsEgzzzzDH6/H8MweP/996mvr2fRokVcvHgxedrhyy+/pKKiAqvVSnV1Nfv37wdg7969yc+R7nS/lAVMvsM7deoUixYtSu6fiVmUl5czNDREd3c3AIcPH2bBggUZmYXJZKK1tZXh4WFEhN27d1NfX582WQwODvLCCy/w5ptv0tDQAMDChQs5e/Ys586dIx6P097eTk1NDTNmzMBut9PZ2QnAvn37qKmpSZss9Kq5m2Tp0qXS19cnIiJ79uyR+vp6qa2tlbfffju5z8mTJ6W5uVnq6+ultbVVvF6viIj09/fLE088IY888oi0trbKpUuXUjKGG+V6svB6vfLAAw/87GczMYsjR46Ix+ORxsZGefrpp6W3t1dEMjOLr776ShobG6W2tlba2trEMAwRSY8sNm/eLFVVVdLU1JRcPvzwQ+no6BCPxyO1tbWydetWSSQSIiLS1dUlzc3NUldXJy+99JJEIhERSY8s9A6tSimlUkpPzSmllEopLSKllFIppUWklFIqpbSIlFJKpZQWkVJKqZTSIlJKKZVSWkRKKaVSSotIKaVUSv0P6pSL56eV0cgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = avgs.revenue\n",
    "fig, ax = plt.subplots()\n",
    "plot(x, y2, ax, 'Increase in mean Fortune 500 company revenues from 1955 to 2005', 'Revenue (millions)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_with_std(x, y, stds, ax, title, y_label):\n",
    "    ax.fill_between(x, y - stds, y + stds, alpha=0.2)\n",
    "    plot(x, y, ax, title, y_label)\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
    "title = 'Increase in mean and std Fortune 500 company %s from 1955 to 2005'\n",
    "stds1 = group_by_year.std().profit.values\n",
    "stds2 = group_by_year.std().revenue.values\n",
    "plot_with_std(x, y1.values, stds1, ax1, title % 'profits', 'Profit (millions)')\n",
    "plot_with_std(x, y2.values, stds2, ax2, title % 'revenues', 'Revenue (millions)')\n",
    "fig.set_size_inches(14, 4)\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}