example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "814c65d3",
   "metadata": {
    "scrolled": true
   },
   "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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06110395",
   "metadata": {},
   "source": [
    "Our goal is to find out how the **profits of the largest companies in the US changed historically**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34633839",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('fortune500.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e60311c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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 (in millions)</th>\n",
       "      <th>Profit (in millions)</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 (in millions) Profit (in millions)\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,
   "id": "f071b401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Company</th>\n",
       "      <th>Revenue (in millions)</th>\n",
       "      <th>Profit (in millions)</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 (in millions)  \\\n",
       "25495  2005   496        Wm. Wrigley Jr.                 3648.6   \n",
       "25496  2005   497         Peabody Energy                 3631.6   \n",
       "25497  2005   498  Wendy's International                 3630.4   \n",
       "25498  2005   499     Kindred Healthcare                 3616.6   \n",
       "25499  2005   500   Cincinnati Financial                 3614.0   \n",
       "\n",
       "      Profit (in millions)  \n",
       "25495                  493  \n",
       "25496                175.4  \n",
       "25497                 57.8  \n",
       "25498                 70.6  \n",
       "25499                  584  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d754556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['year', 'rank', 'company', 'revenue', 'profit']\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ae1b798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25500"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96cf3f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344ecf4f",
   "metadata": {},
   "source": [
    "Hmm, not float64?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6564197b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_numberic_profits = df.profit.str.contains('[^0-9.-]')\n",
    "df.loc[non_numberic_profits].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c8bbbb0",
   "metadata": {},
   "source": [
    "Just as we suspected! Some of the values are strings, which have been used to indicate missing data. Are there any other values that have crept in?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9fabe0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N.A.'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.profit[non_numberic_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04d95cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.profit[non_numberic_profits])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5deb0151",
   "metadata": {},
   "source": [
    "It’s a small fraction of our data set, though not completely inconsequential as it is still around 1.5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c6dc0e4",
   "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b826782a",
   "metadata": {},
   "source": [
    "We remove the N.A. rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b9f2b5fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~non_numberic_profits]\n",
    "df.profit = df.profit.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6acd331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25131"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c2e500",
   "metadata": {},
   "source": [
    "Let's plot average profit by year. We might as well plot the revenue as well, so first we can define some variables and a method to reduce our code!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5691fb2e",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "184827ba",
   "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)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a721812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z82Q2m1FK8c033+Ds7AyUnm4qe/zhw4cTHR1NRkYGjz32GHXq1MFkMtGyZUu+++47y+OlpKTg6enJ//73vxvu69fbB0wmU6WvKUdHRzZt2sRvv/3Gr7/+yt/+9jciIyMtp/oq8+fX/59zfPX+d/VrrrLn6WaZzWaWL19ued1OmDCB/v37c+LECU6cOGE5hQeludDpdGzZsgU3NzfLqa6yfbeq9fyZUqrCa7FMcXExERERnDlzhm+++QYfHx+gdP8/cuSIZbmUlBTc3d1xcXGxvDau/l/Dhg3Jyclh1apVPP/88xXei6uK967uTXczevXqxaZNmyw9Of75z3/y1VdfXbNc79692bBhg+VN6euvv2bcuHFAaS+cKVOmEBwcjEaj4ciRI5hMJk6cOEFoaCgtW7bk+eef55lnnuHkyZO0aNECR0dHywsnOTmZ0NBQjh49ap2NBh5++GF27drF2bNnAdi2bRuPP/44RUVF7Ny5k3HjxjFkyBC8vLzYvXs3JpPpjq27T58+rFy5EqUUxcXFrF692nI++s/s7e0tL4569epZcpSZmcn+/furXNeNtvNqzs7OLF26lLi4OMtyBQUFdOzYkf79+7NlyxYyMjJQSvHtt9/y6KOPAqVHF99++y0Aly9fZseOHfTr1++aOHr27MmaNWuA0mtm48aN4/z583Tq1ImVK1da2qOjoyvNRWUyMzPZvn07UHp9TKfT0bp169t+HgMDAzl+/Dg//fTTDT+plnF1daVz5858+eWXQOkHulGjRvHzzz8D8Nhjj/HHH3+wevVqnnzySaD0qOP8+fPs27cPgOPHjxMYGFih6F+Pp6enZR+4cOGC5UjqRq+pVatWMWPGDPr06cP06dPp06cPp0+frnK7rta3b1+++eYbjEYjZrOZlStX0rt371t6jJs1e/ZsS6/MgwcPcvr0aXr16oWdnR0LFizg4sWLAKxatYo2bdrQsGFDLl++zKJFiygsLMRkMvHVV18RHBx8zWNrtVpMJhNKKTp16kR8fLxlnz99+jT79u2je/fu19yvrJfc1YUISl/LR44cISEhAYBvvvnGcoQ2YMAA1qxZQ0lJCTk5OWzYsIFHH32UOnXqsHLlSn766ScAjh07RlxcHH379r1hXmrVkdH1BAQEcObMGUaNGgXAAw88wFtvvWVJVJm+ffvy3HPPMX78eDQaDa6urnz44YdoNBpeeuklpkyZgru7O87OznTr1o0LFy4wfPhwgoKCCA8Px8XFBScnJyIjI3FwcGDZsmUsWLCAzz77jJKSEqZNm0bXrl2ttt2tWrVi3rx5vPzyy5ZPJcuXL8fFxYUpU6awePFili1bhr29PV26dOHChQt3bN2RkZHMnz+fsLAwjEYjffv2ZdKkSdddtl+/fixatAij0cjYsWN59dVXCQwMxMfH57ovmlvZzqt5enqydOlSZs+ejdFoxNXVlY8++ggHBwfatm3LlClTGDduHEajkU6dOvHcc88BMHXqVN58801CQkIwmUxMnz6dZs2aXRPH7NmzefPNNwkLC0MpxfPPP4+vry9RUVHMmzePtWvXUlxcTFhYGMOGDSMxMfGm81n2JhwVFYWTkxMfffQR9vb2t/08Ojg4EBgYSHp6uqXTRlWioqJ46623CAsLo7i4mNDQUB5//HHL4wUHB7N79246duwIlOb7gw8+YPHixRQVFaGUYvHixTRp0uSG65k8eTIRERFs27aN+++/33Kq8EavqXbt2rF3716Cg4NxdnamcePGt9y9efLkySxatIghQ4ZQUlJCx44dmTVr1i09xs2aN28ekZGRfPTRR7i4uFh+t27dmsjISCZPnozJZKJhw4YsWbIEgJEjR3Lx4kWGDh2KyWSiR48eFU4jltHr9bRv356goCC+/vpr3n//fd566y0KCwvRaDQsXLiQFi1aVLjPgQMH+OWXX7jvvvss75MAr776Kn379mXhwoW88MILGI1GmjVrxqJFi4DSzgwXLlxg8ODBGI1GRowYYXnNLlu2jPnz5/PPf/4Te3t73nvvvSr3NY261XMwQgiruXTpEmFhYRw6dOiOPWZ+fj5PPfUUc+bMqXCNSQhbqvWn6YQQ5Xbs2MEjjzxCjx49pBCJGkWOjIQQQticHBkJIYSwOSlGQgghbE6KkRBCCJuTYiSEEMLmav33jCqTlZWH2WzbvhteXq5kZFQxrPo9QnJRTnJRTnJRzpa5yC8s4aN1v/POP278xdW/4p4tRmazsnkxKotDlJJclJNclJNclLNVLg6cTCUtu6Ba11Htp+kMBgOhoaFcunQJKB3G/8knnyQkJISXX36Z4uJioHS4kPDwcAIDA5k5c6ZliJikpCTLEPKTJ08mLy8PKB2SZOLEiQQFBTFmzJgKYyQJIYS4cw6eSsPd1bHqBf+Cai1GR44cYdSoUZZxjQwGA1OnTmXevHls2LABKJ1hE2D69OnMmjWLH3/8EaUUq1evBmDu3LmMHj2a2NhYfH19WbZsGQBLly7Fz8+PjRs3Mnz4cBYsWFCdmyKEEPekIqOJP+Iz6XBfvWpdT7UWo9WrVzNnzhzLCMq7du2ic+fOllGPIyMjeeyxx0hMTKSwsNAyvcKwYcOIjY3FaDSyb98+AgMDK7RD+VS4AKGhoWzfvv2aUZ6FEEL8NX/EZ1JcYqZDi5sbx/B2Ves1oz8frZw/f94yUOeFCxfw8/MjIiKCY8eOWYboh9LB/lJSUsjKysLV1dUy9HhZO5QO5V92H61Wi6urK5mZmdedcVEIIcTtOXgqjTpOWu5vXLda12PVDgwmk4mdO3fy7bff0rhxY2bOnMknn3xy3aHaK5tHp7K5V+D6k55VxsvL9aaXrU56/Y3nOLqXSC7KSS7KSS7KWTsXJpOZuLMZdO/QEG99LSpG9evXp1OnTpZZBoOCgvjPf/7DsGHDSE9PtyyXlpaGt7c3np6eGAwGTCYT9vb2lnYonQkzPT2dhg0bUlJSgsFgwMPD46Zjycgw2LyXjl7vRlpark1jqCkkF+UkF+UkF+VskYvjCZkYCoy0b+ZBRoahWj/EW/VLr3369OGPP/4gOTkZgF9++YUOHTrQpEkTHB0dOXDgAADR0dH4+/uj0+nw8/MjJiamQjuUzlMUHR0NQExMDH5+fn95BkYhhBDlDp5OR6e1w7eFV7Wvy6pHRo0aNWLevHlMmjSJoqIi2rVrx+uvvw6UTt4VGRlJXl4e7du3t0yONWfOHCIiIli+fDmNGjWyTDY1bdo0IiIiCAkJwc3NjaioKGtuihBC1GpKKQ6dTqPDfZ44OthX+/ru2Skk5DRdzSK5KCe5KCe5KGftXCRczmHeV/sZH9yOPh0bYWenqT2n6YQQQtwdDp5KQ6OBTg9U/yk6kGIkhBDiOg6dSqdNUw/cXByssj4pRkIIISpIycwnMT2Ph1rpq174DpFiJIQQooKDp0vH+nyodX2rrVOKkRBCiAoOnkqjWQNX6rs7W22dUoyEEEJYZBuKOJeYQ5fW1jtFB1KMhBBCXOXw6XQU0MWK14tAipEQQoirHDydhreHM030day6XilGQgghgNLpxY8nZPFQ6/o3HJS6OkgxEkIIAcDPBy5iMit6tLf+VDxSjIQQQpCbX8zG3y7wUKv63NeweqeLuB4pRkIIIdiw5zxFRhPhAS1tsn4pRkIIUcsYS0xsO5zIj3svXHeS0j9Lv1LAloOX6P1gIxrXt27HhTJWnUJCCCFE9SkoKmHr4UR+2nuRK3nFADho7ejXxeeG9/t+RzygYUifFlaI8vqkGAkhxF0uJ7+YzfsvseXAJfKLSmh/Xz0mhLVn076LrNp8mqYN3Higift173spzcDuo5cJ7N4Mz7pOVo68nBQjIYS4S5WYzKz+5QzbDydhLDHTpbWe4J7NadGotAPCfQ3dmPfVPpat+505f+uOe51rR+Beu+0cTo5agns2t3b4Fcg1IyGEuEvtOXqZzfsv4dfWm/nP9WDKsActhQigjpOOKUMfJK+whI+/P4rJbK5w/1MXszl8Jp3gh5vh6qyzdvgVSDESQoi71JGzGdRzc+TZkHY08rp+x4NmDdwYN6gNJy5ks2brOUu7Uor/bjuLu6sDj/o1tVbIlarWYmQwGAgNDeXSpUsV2leuXMnYsWMtt5OSkhgzZgyDBg1i8uTJ5OXlAZCTk8PEiRMJCgpizJgxpKWVDmteXFzM9OnTCQoKYujQoZw9e7Y6N0MIIWocY4mZPxIy6fRA1aMl9PJtRP8uTYjde4F9J1IBOHImgzOXrvB47xY46uytEfINVVsxOnLkCKNGjSIhIaFC+5kzZ/j4448rtM2dO5fRo0cTGxuLr68vy5YtA2Dp0qX4+fmxceNGhg8fzoIFCwBYsWIFzs7ObNy4kTfeeIOIiIjq2gwhhKiRTl3MpqjYRMeWNzct+MgBrWjZpC5fbDjOpTQDa7adpUE9Z/p2bFTNkd6caitGq1evZs6cOXh7e1vaiouLmT17NtOmTbO0GY1G9u3bR2BgIADDhg0jNjYWgK1btxIWFgZAaGgo27dvx2g0snXrVh5//HEAunXrRlZWFklJSdW1KUIIUeMcOZuOTmtHu+b1bmp5rb0dfx/yII46Oxb+5wCJ6XkM9b8frX3NuFpTbVEsWLAAPz+/Cm3/3//3/xEeHo6PT3mf96ysLFxdXdFqSzv26fV6UlJSAEhNTUWvLx3GXKvV4urqSmZmZoX2svtcvny5ujZFCCFqFKUUcWcyaNe83i2dYqvn5sjkIb4UFZtp3tANv7beVd/JSqzWtXvXrl0kJyczY8YMfvvtN0v79b4dfKPzn3Z216+flbVXxsvL9ZaWry56vZutQ6gxJBflJBflJBflynJxKTWX1OwCwge0uuX86PVu1K/vit7DGS8rzuRaFasVo/Xr13P69GkGDx5Mfn4+6enpvPjii7z77rsYDAZMJhP29vakpaVZTu15e3uTnp5Ow4YNKSkpwWAw4OHhgbe3N2lpaTRvXtov/ur73KyMDANmc9XDZFQnvd6NtLRcm8ZQU0guykkuykkuyl2di1/2XgDgfm/X28qPl4sOc3HJLd3Xzk5TrR/irXaycOHChWzcuJHvv/+e+fPn4+vry9KlS9HpdPj5+RETEwNAdHQ0/v7+AAQEBBAdHQ1ATEwMfn5+6HQ6AgIC+P777wHYv38/jo6ONG7c2FqbIoQQNhV3Nh0ffR283G03YsKdViOuXM2ZM4fVq1cTHBzM/v37efHFFwGYNm0ahw8fJiQkhFWrVjF79mwAxo4dS3FxMSEhISxYsIDFixfbMHohhLCe/MISTl+6QseW9W0dyh2lUTczpGstJKfpahbJRTnJRTnJRbmyXOw7kcry6KPMeKoLrXw8rLb+WnOaTgghxF935Ew6dZy0tGx8/YFP71ZSjIQQ4i5hNit+P5fBgy29sLO78agLdxspRkIIcZeIT84hN99406Mu3E2kGAkhxF3iyNkM7DQafFtIMRJCCGEjcWfSeaBJXZtP91AdpBgJIcRdIONKARdSDXR6oHZ16S4jxUgIIe4C+46VjtlZG68XgRQjIYS4K+w/nkJ9dyca17/+JHp3OylGQghRwxlLTBw+nUbHll5VTqR3t5JiJIQQNdyJC6UT6dXW60UgxUgIIWq8I2fScXSwp20zD1uHUm2kGAkhRA2mlOLImQw6t9Kj0978RHp3GylGQghRgx07n0VGTiG9OtbuaXKkGAkhRA225cAlXJ119OkkxUgIIYQNpF8p4PCZdAI6N8ZBV3tP0YEUIyGEqLG2HkoC4JHOTWwcSfWTYiSEEDWQscTE9iNJPNRKX6umF6+MFCMhhKiB9h5PxVBgpH+X2n9UBFYoRgaDgdDQUC5dugTAt99+S2hoKGFhYcyYMYPi4mIAjh8/Tnh4OIGBgcycOZOSkhIAkpKSGDNmDIMGDWLy5Mnk5eUBkJOTw8SJEwkKCmLMmDGkpaVV96YIIYTVbDl4iUZeLrRrXs/WoVhFtRajI0eOMGrUKBISEgCIj4/n888/55tvvuGHH37AbDazatUqAKZPn86sWbP48ccfUUqxevVqAObOncvo0aOJjY3F19eXZcuWAbB06VL8/PzYuHEjw4cPZ8GCBdW5KUIIYTXnknKIT86lfxefWjv8z59VazFavXo1c+bMwdvbGwAHBwfefPNNXF1d0Wg0tG7dmqSkJBITEyksLKRz584ADBs2jNjYWIxGI/v27SMwMLBCO8DWrVsJCwsDIDQ0lO3bt2M0Gqtzc4QQwiq2HLyEo4M9vXwb2joUq9He7ILFxcXY2dmh1d70Xa45WmnSpAlNmpSe/8zMzGTlypUsXLiQ1NRU9Hq9ZTm9Xk9KSgpZWVm4urpa1lnWDlS4j1arxdXVlczMTBo0aHBTsXl5ud70dlQnvd7N1iHUGJKLcpKLcvdaLq4Yith7PJXAh5vTzKfiKbranIsbVpaMjAw++eQTNm3aRFJSEnZ2dvj4+DBo0CCeeeYZPD09b2ulKSkpTJgwgfDwcHr06MHBgwevWUaj0aCUum57Zezsbv5ALyPDgNl87eNbk17vRlpark1jqCkkF+UkF+XuxVxs2JNAiclMz3beFbbd1rmws9NU64f4St+9o6Ojee655/Dy8uKTTz4hLi6OAwcO8OGHH+Lu7s7f/vY31q1bd8srPHv2LKNGjWLo0KFMmTIFgAYNGpCenm5ZJi0tDW9vbzw9PTEYDJhMpgrtAN7e3pb7lJSUYDAY8PDwuOV4hBCipjCZzWw9lEi75vVq7bxFlam0GOXk5PDf//6XiRMn8sADD+Dg4ICzszOtW7fm2WefZe3atVy5cuWWVmYwGHj22WeZNm0a48ePt7Q3adIER0dHDhw4AJQWQn9/f3Q6HX5+fsTExFRoBwgICCA6OhqAmJgY/Pz80Olq37zwQoh7x5EzGWTkFNG/i4+tQ7E6jbreubAbMBqNt/ym379/f/7973+zefNmoqKiaNmyZYX/TZs2jRMnThAZGUleXh7t27dn4cKFODg4kJiYSEREBBkZGTRq1IglS5bg7u5OdnY2ERERXLx4ETc3N6KiovDxufknUE7T1SySi3KSi3L3Wi7e/foQKVn5LJrUE/s/XXawdS6q+zRdlcVo//797N27lwkTJjBixAjOnTvHwoULCQ4OrragrEGKUc0iuSgnuSh3L+UiKT2PyM9+IzzgfkJ63nfN/22dC5tdMyrz7rvv0rlzZzZv3kz9+vXZsGEDX3zxRbUFJIQQ9xqT2cy6HefQ2mvoW8tH565MlcXIZDLRq1cvdu/ezaOPPoqPjw9ms9kasQkhRK1XWFzCP9f8zoGTaTzeuwV1XRxsHZJNVFmMzGYzcXFxbN26ld69e3Pq1Cn5cqkQQtwBVwxFLFp1iN/PZfB0YBtCe91n65BspspvsE6aNIlXXnmFJ554Ah8fH/r378/MmTOtEZsQQtRayRl5vLf6CDn5xbwQ3pFOD9S3dUg2VWUxGjhwIAMHDrTc3rRpE/b2tXuSJyGEqE6nLmbzzzVx2NtpeH10F1o0qmvrkGyuymJ05swZPv/8c7KzsyuMiPCvf/2rWgMTQojaaN+JVD793zG83J146clOeHs42zqkGqHKYvTaa6/RpUsXunXrds+MHiuEENXh8Ol0/hV9lJY+7rwQ3hFXZ/mifpkqi5HRaCQyMtIasQghRK1VYjLzzc+naVy/Dq+O6IyDTi53XK3K3nTNmzcnNTXVGrEIIUStteXAJVKzCxjR/wEpRNdR5ZGR2WwmNDSUDh064OjoaGmXa0ZCCHFzDAVGftiVgG8LT3zv97J1ODVSlcXoscce47HHHrNGLEIIUSv9sCueguISnuz/gK1DqbGqLEZDhw4lMTGRvXv3UlJSQvfu3WnevLk1YhNCiLteSmY+vxxMpG/Hxvjoa8aknjVRldeMduzYQXh4OJs3b+bnn3/miSeeYPPmzdaITQgh7nrfbT2LVmvH0L4tbB1KjVblkdH777/Pf/7zHx54oPTw8vTp00yfPp1HH3202oMTQoi72ckLWRw8lcbQvi1wd3Ws+g73sCqPjIxGo6UQAbRq1coy86oQQojrMyvFt1vOUM/NkYHdm9k6nBqvymLk5OTE77//brn9+++/4+ws3xgWQogb+e1YCgmXcwkPuB9H6cpdpSpP002fPp1JkybRvHlzlFIkJCTw/vvvWyM2IYS4KxUbTazZdpbmDd14uENDW4dzV6iyGPn5+bFhwwaOHDmCUopOnTpRr149a8QmhBB3pZ/2XSQzp4jnQttjJ8Oo3ZRKT9N9//33AHz55ZesW7eOc+fOER8fT3R0NF9++eVNr8BgMBAaGsqlS5cA2L17N2FhYQwcOJD33nvPstzx48cJDw8nMDCQmTNnUlJSAkBSUhJjxoxh0KBBTJ48mby8PABycnKYOHEiQUFBjBkzhrS0tFvfeiGEuMMycwrZ8Ot5HmpVnzbN5IP7zaq0GJ0/fx6AU6dOXffnZhw5coRRo0aRkJAAQGFhIW+88QbLli0jJiaGo0ePsm3bNqD0dOCsWbP48ccfUUqxevVqAObOncvo0aOJjY3F19eXZcuWAbB06VL8/PzYuHEjw4cPZ8GCBbedBCGEuBOUUnwRcxwUjBjQytbh3FUqPU33wgsvALBw4cLbfvDVq1czZ84cXnvtNQDi4uJo3rw5TZs2BSAsLIzY2FgeeOABCgsL6dy5MwDDhg3jgw8+YPjw4ezbt4+PPvrI0v7UU08xffp0tm7dysqVKwEIDQ1l3rx5GI1GdDoZBVcIYRu/HErkWEIWTwe2kakhblGlxSgsLOyGd/zf//5X5YP/+WglNTUVvV5vue3t7U1KSso17Xq9npSUFLKysnB1dUWr1VZo//NjabVaXF1dyczMpEGDBlXGBeDlVTO+Ca3Xu9k6hBpDclFOclHubslFUrqB77ae5aHWep54rE21TLlzt+TidlRajGbNmnXHV3b15HxlNBrNLbdXxs6uyp7qFhkZBszmax/fmvR6N9LScm0aQ00huSgnuSh3t+TCbFa8u+ogdhoNYx5tRXq64Y6vw9a5sLPTVOuH+EqLUZ06de74yho0aEB6errldmpqKt7e3te0p6Wl4e3tjaenJwaDAZPJhL29vaUdSo+q0tPTadiwISUlJRgMBjw8PO54zEIIUZUf913gzKUrPBfaHs+6TrYO565UaTGaOnVqpXfSaDT8/PPPt7yyTp06ER8fz/nz5/Hx8WH9+vWEh4fTpEkTHB0dOXDgAF27diU6Ohp/f390Oh1+fn7ExMQQFhZmaQcICAggOjqaSZMmERMTg5+fn1wvEkJYXWKagXXbz9GltZ6HO9zcZQJxrUqL0ZYtW+74yhwdHXnnnXeYOnUqRUVFBAQEMGjQIACioqKIjIwkLy+P9u3b8/TTTwMwZ84cIiIiWL58OY0aNWLJkiUATJs2jYiICEJCQnBzcyMqKuqOxyuEEDdSYjLz2frjODtqeTqweq4T3Ss06noXZoBPP/2U5557jvnz51/3jnf7VORyzahmkVyUk1yUq+m5iN5xjh92JTBlqC9d23hX67psnQubXTNycyvttSHXYYQQ4lrxyTms332enh0aVHshuhdUWoxGjhwJwD/+8Q+rBSOEEHcDk9nMlzHHcXd1YPRjrW0dTq1Q5dh0MTExfPDBB1y5cqVC+549e6otKCGEqMm2HEzkUloeU4b6UsdJOk7dCVUWo3fffZfIyEiaNZP5OIQQIievmOgd8XS4rx5dWuurvoO4KVUWoyZNmjBgwABrxCKEEDXemm1nKTaaGP1Ya+k9dwdVWYyGDBnCokWL8Pf3twzLA9CtW7dqDUwIIWqac0k57IhLZlD3ZjTyuvMDA9zLqixGu3btYvfu3ezcubNC+82MTSeEELWFWSlWbjqJex0HwnrfZ+twap0qi1FcXBzbt2/H0dHRGvEIIUSNtDMumfjkXJ4LbY+zY5VvneIWVTmyqF6vt0x0J4QQ96L8QiNrtp3lAR93GfKnmlRZ3ps1a8bgwYPp1asXDg4Olva7fQQGIYS4WdE74jEUGHlFOi1Um5sqRtKtWwhxr7qUamDLwUQe6dyEZg1q73xCtlZpMcrMzMTT0/OGIzBkZGTg5eVVLYEJIYStKaVYuekULk5ahvrfb+twarVKrxm98cYbfPnll9eMvABgMBj47LPPiIiIqNbghBDClg6dTufkxWyG+d+Pq7OMtFCdKj0yWrZsGV988QWhoaG0aNGC5s2bYzKZuHjxIvHx8Tz99NMsW7bMmrEKIYRVnb6UjU5rh3+nxrYOpdartBjZ2dkxYcIEnnrqKX799VfOnTuHnZ0dAwcOvKYzgxBC1EZp2YXoPZyxs5NOC9Wtyg4MTk5OPPLIIzzyyCNWCEcIIWqOtOwC9O4yjbg1VPk9IyGEuBcppUqLkYezrUO5J9ikGH3//feEhIQQEhLCokWLADh+/Djh4eEEBgYyc+ZMyxdtk5KSGDNmDIMGDWLy5Mnk5eUBkJOTw8SJEwkKCmLMmDGkpaXZYlOEELVUboGRwmIT+npSjKzB6sWooKCABQsWsGLFCr7//nv279/P7t27mT59OrNmzeLHH39EKcXq1asBmDt3LqNHjyY2NhZfX19Lp4mlS5fi5+fHxo0bGT58OAsWLLD2pggharG07AIAOTKykiqLUV5eHnPnzmXcuHFkZ2cze/Zsy9HJ7TCZTJjNZgoKCigpKaGkpAStVkthYSGdO3cGYNiwYcTGxmI0Gtm3bx+BgYEV2gG2bt1KWFgYAKGhoWzfvh2j0XjbcQkhxNWkGFlXlcVo/vz51K1bl4yMDBwdHTEYDMyePfu2V+jq6sq0adMICgrC39+fJk2aoNPp0OvLJ6nS6/WkpKSQlZWFq6urZeqKsnaA1NRUy320Wi2urq5kZmbedlxCCHG1tOxCAOpLBwarqLI33fHjx1m4cCHbtm3D2dmZqKgoQkNDb3uFJ06cYM2aNfzyyy+4ubnx6quvsmvXrmuW02g0KKWu214ZO7ubP+vo5eV608tWJ71ehhcpI7koJ7koZ6tcGApL8KzriE9jD5us/3pq835RZTH68xu8yWS6pTf9P9u5cyc9e/a0DCM0bNgwPv/8c9LT0y3LpKWl4e3tjaenJwaDAZPJhL29vaUdwNvbm/T0dBo2bEhJSQkGgwEPD4+bjiMjw4DZfG2xsya93o20tFybxlBTSC7KSS7K2TIXFy/n4FnXqcY8F7beL+zsNNX6Ib7KqtKtWzfeffddCgsL2bFjB1OnTqVHjx63vcK2bduye/du8vPzUUqxZcsWunfvjqOjIwcOHAAgOjoaf39/dDodfn5+xMTEVGgHCAgIIDo6GoCYmBj8/PzQ6WS4DiHEnVH6HSO5XmQtGnW9c2FXMRqNfPLJJ2zduhWTyUTfvn35+9///pcm2/vkk09Yu3YtOp2OBx98kDlz5hAfH09kZCR5eXm0b9+ehQsX4uDgQGJiIhEREWRkZNCoUSOWLFmCu7s72dnZREREcPHiRdzc3IiKisLHx+emY5Ajo5pFclFOclHOVrkoMZl5/t2thPW+jyF9a8YAqbbeL6r7yKjKYlRbSTGqWSQX5SQX5WyVi5TMfGZ88isTQtvRy7eR1dd/PbbeL6q7GFV5zWjSpEnXbf/Xv/51x4MRQoiaIFW6dVtdlcWo7Ds+UHrKbsuWLbRp06ZagxJCCFuS7xhZX5XFaOjQoRVuDxs2jKeeeqraAhJCCFtLyy5Ap7XDvY7MTmAtt9xH22w2k5qaWh2xCCFEjVA2dcSNvtco7qxbvmZ06tQpunfvXm0BCSGErcnUEdZ3S9eMNBoNo0aNok+fPtUalBBC2ErZ1BFtmnrYOpR7yk1dMyouLqagoMAyPE9ubu4tjXYghBB3C0PZ1BHSecGqqixG//73v4mKirKMiK2UQqPRcPz48WoPTgghrK1sgFSZx8i6bqoYff3113To0MEa8QghhE2lZucD0q3b2qrsTafX66UQCSHuGTJ1hG1UWYx69+7NqlWrSElJITs72/IjhBC1UVp2Ae6uDjjq7G0dyj2lytN0n3zyCcXFxcybN8/SJteMhBC1VXp2gZyis4Eqi1FcXJw14hBCiBohLbuA1k3r2TqMe06Vp+nMZjOff/45ERERGAwGPv74Y0wmkzViE0IIqyoxmcnMKULvIdeLrK3KYrR48WJOnjzJkSNHUEqxY8cOFi5caI3YhBDCqjKuFKKQnnS2UGUx2rNnD++88w6Ojo64ubnxxRdfsGvXLmvEJoQQVlU2Wre3fMfI6qosRlqtFju78sUcHBzQaqu81CSEEHcdmcfIdqqsKq1bt2blypWYTCbOnTvHV199Rdu2ba0RmxBCWJVMHWE7VR4ZzZw5kz/++IOMjAxGjx5Nfn4+b7zxxl9a6ZYtWxg2bBiDBg1i/vz5AOzevZuwsDAGDhzIe++9Z1n2+PHjhIeHExgYyMyZMykpKQEgKSmJMWPGMGjQICZPnkxeXt5fikkIIWTqCNupshilpKTw9ttvs3v3bn799VeioqKoV+/2uz1evHiROXPmsGzZMv73v/9x7Ngxtm3bxhtvvMGyZcuIiYnh6NGjbNu2DYDp06cza9YsfvzxR5RSrF69GoC5c+cyevRoYmNj8fX1ZdmyZbcdkxBCgEwdYUtVFqPx48czZswYoqOjKS4u/ssr3LRpE8HBwTRs2BCdTsd7772Hs7MzzZs3p2nTpmi1WsLCwoiNjSUxMZHCwkI6d+4MlM4yGxsbi9FoZN++fZbpLcrahRDidpVNHSHXi2yjymtGv/zyCzt27GDdunVERUUxcOBAnnzyydu+bnT+/Hl0Oh3PPvssaWlp9OvXj1atWqHX6y3LeHt7k5KSQmpqaoV2vV5PSkoKWVlZuLq6WjpSlLULIcTtkqkjbKvKYmRnZ0dAQAABAQGcPXuWGTNm8PXXX9/2cEAmk4n9+/ezYsUKXFxc+Pvf/46z87VPvkajscyfdLPtt8LLy/WWlq8uer2brUOoMSQX5SQX5ayVi6wLWQA80Nyzxua/psZ1J1RZjEpKStiyZQtr164lLi6O4OBg3nrrrdteYf369enZsyeenp4ADBgwgNjYWOztywclTE1NxdvbmwYNGpCenm5pT0tLw9vbG09PTwwGAyaTCXt7e0v7rcjIMGA2X1vUrEmvdyMtLdemMdQUkotykoty1szFqfgMABzsqJH5t/V+YWenqdYP8VVeM+rTpw8rVqwgODiYrVu3EhkZSZs2bW57hf369WPnzp3k5ORgMpnYsWMHgwYNIj4+nvPnz2MymVi/fj3+/v40adIER0dHDhw4AEB0dDT+/v7odDr8/PyIiYmp0C6EELer7DtGMnWEbVR5ZPTNN99w33333bEVdurUiQkTJjB69GiMRiO9e/dm1KhR3H///UydOpWioiICAgIYNGgQAFFRUURGRpKXl0f79u15+umnAZgzZw4REREsX76cRo0asWTJkjsWoxDi3iNTR9iWRl3vAsxV0tLSmDlzJufPn2flypW8/vrrLFy48JZPi9U0cpquZpFclJNclLNmLhavOkiJWfHGU12tsr5bZev9wuan6ebOncujjz6Ko6Mj7u7utG3blsjIyGoLSAghbKH0O0bSk85WqixGiYmJPPnkk9jZ2aHT6Zg+fTrJycnWiE0IIaxCpo6wvSqLkUajwWw2W24bDIYKt4UQ4m4nU0fYXpUdGAYOHMirr75Kbm4u33zzDd999x1BQUHWiE0IIawiTUbrtrkqi9GkSZOIjo7GbDaze/duRowYwZNPPmmN2IQQwipkHiPbu6mJiYYMGcKQIUMst3ft2kXv3r2rKyYhhLCqVJk6wuYqvWZ09OhRRo4cyaRJk8jMzARKp22YMmUKkydPtlqAQghR3WTqCNurtBjNnTuXgQMH4uPjw/Lly4mJiSEkJITCwkK+//57a8YohBDVSqaOsL1KT9Pl5uYyfvx4TCYTgYGBbNy4kfnz5xMSEmLN+IQQolqVTR3RpqmHrUO5p1VajMpG0ra3t6eoqIhPP/2Udu3aWS0wIYS4U64Yith7PJWMnELMSqEUlt8lJrNMHVEDVFqMrh4lyNPTUwqREKLGyMwpxM1Fh05b+ThyJSYzR86ks+v3y8SdzcCsFI4O9thpNNhpSr9DaacBjZ0GvYcTbZp5WG8DxDUqLUZms5krV66glEIpZfm7jIeHhzXiE0IIi5TMfL7++TRxZzOw02jwrudME30dmtSvg4/elSb6OhQbzez6PZlfj6VgKDDi7upAYI+m9HmwEY286th6E0QlKi1Gp06d4uGHH7YUoB49elj+p9FobntyPSGEuFUFRSWs35PAT3svotPaMbhPC8xmRWJ6HpdSDRw8mcbVwx5r7e3o0ro+vR9sRPv76mFvV+VgM8LGKi1GJ06csGYcQghxDbNZsftoMt9tPcsVQzF9HmxEeMD9uLs6Vliu2GgiOSOfxHQDJpOiSxs9dZx0Nopa3I6b+tKrEEJYW3xyDou/PsSJ81m0aFSXqcM6cn/jutdd1kFnT/OGbjRvWHun5a7tpBgJIWqUxDQD0TvjOXAyDQ83R8YHt6PXgw2xky+k1mpSjIQQNUJqVj7f74zn1z9ScHSw5/He9zEmuD15uYW2Dk1YgRQjIYRNZeYU8sOuBHbGJaO11zCoRzOCHm6Oq7MOFyedFKN7hE2L0aJFi8jKyuKdd97h+PHjREZGYjAY8PPzY+7cuWi1WpKSkpg+fToZGRm0aNGCqKgo6tSpQ05ODq+++ioXL17E09OTpUuXotfrbbk5QohbUGIy88OuBGJ/Ow9Avy5NCOnZHI8/dU4Q9wab9Xfcs2cP69ats9yePn06s2bN4scff0QpxerVq4HSMfJGjx5NbGwsvr6+LFu2DIClS5fi5+fHxo0bGT58OAsWLLDJdgghbt3lzHzeXnGA9bsT6NbWm7cnPsyYx1pLIbqH2aQYZWdn89577zFp0iSgdGrzwsJCOnfuDMCwYcOIjY3FaDSyb98+AgMDK7QDbN26lbCwMABCQ0PZvn07RqPR+hsjhLhpSim2HU7kzS/3kpZdwN+H+PJcWAfqu8tQPPc6m5ymmz17Ni+99BLJyckApKamVjjFptfrSUlJISsrC1dXV7RabYX2P99Hq9Xi6upKZmYmDRo0uKkYvLxc7+Qm3Ta9XrqilpFclKuNubhiKOLD7w7z69HLdGpVn5dGdcHrJopQbczF7arNubB6Mfruu+9o1KgRPXv2ZO3atUDFcfDKaDSaStsrY3cL37LOyDBgNl/7+Nak17uRlpZr0xhqCslFudqYi6PxGXy+4Th5BUae7PcAA7s3xVxcUuV21sZc3C5b58LOTlOtH+KtXoxiYmJIS0tj8ODBXLlyhfz8fDQaDenp6ZZl0tLS8Pb2xtPTE4PBgMlkwt7e3tIO4O3tTXp6Og0bNqSkpASDwSDj5QlRw2TmFLJm2zn2/HGZRl4uvDS8E80a1N5P9+L2Wb0Yffnll5a/165dy969e1m4cCGhoaEcOHCArl27Eh0djb+/PzqdDj8/P2JiYggLC7O0AwQEBBAdHc2kSZOIiYnBz88PnU6G/xCiOiilSMkq4Oi5DDQaDV1a66nnVnlng8LiEmJ+vcCPey+gFAQ/3JzHe9+Hg67yUbbFva3GfM8oKiqKyMhI8vLyaN++PU8//TQAc+bMISIiguXLl9OoUSOWLFkCwLRp04iIiCAkJAQ3NzeioqJsGb4QtU5hcQnHz2dx9Fwmv5/LIP1K+fd9Vm06RaumHnRv503XNt6413EASseS2/l7Muu2n+NKXjE92jcg3P9+6stcQaIKGnW9CzP3ALlmVLNILsrdSi7MZsWvxy5zMdVA93YNaNHo+mO33ayMK4UcPJXG4TPpnLqYjclcOgdQ++b18L3fC98WnpSYzOw7nsreE6kkpeeh0UDbZvXwvd+TPUcvcyktj5ZN6jKyfytaNnH/S/HIflHO1rmo7mtGUoxsyNY7V00iuSh3M7kwK8W+46l8vzOey5n5aDSgFDRv6MYjnRvTo30DnBxu7sRHSmY++0+mcvBUGvHJpettoq9Dx/u98L3fi1Y+7mjtr985KDHNwN7/V5hSMvOp7+7E8H4P4NdGf8PORjdL9otyts6FFKNqIsWoZpFclLtRLsxKcfBkGt/vjCcxPY8m+joM6dOCds3rseePFLYeTiQxLQ8nB3t6dmhIQOfG+OhdMRQayc03YsgvJiffSG5+MZk5RRw5m05iWh4ALRrVpWsbPV1b62ng6XJLMSulSL9SiIerIzrtnfv6ouwX5Wydi1rXm04IcXsOn0knevs5LqQaaOjpwvOPd6BbO2/LaNYDuvrQv0sTzibmsPVwIjvikvnlUKLlqOnPNBpo5ePBqAGt6NpGj2ddp9uOTaPRoJfrQuIvkGIkRA1XYjLz9ebT/HIoEW8PZyaEtqNH+wbXnb1Uo9HwgI87D/i4M3JAK347lkJOXjFuLjrq1nHAzVmHm4sDbnUccHXWygyoosaQYiREDWYoMLJs3e+cuJBNUI9mDPW/v9LrN3/m6qxjQFefao5QiDtDipEQNVRSeh4f/DeOzNxCJoS2o5dvI1uHJES1kWIkRA0UdzaDj384is7ejtdGd+GBv9hFWoiaToqREDWIUorobWf54n9Haap3ZWp4R7zcb79jgRB3CylGQtQQJSYz//npJNuPJNO1tZ4Joe1xdJDhc8S9QYqREDVAUbGJ5d8fJe5sBk8+2pqBXZtYumwLcS+Qfp1C2FhufjHvfnOI389lMDawDWOD2kkhEvccOTISwobSrxSw5NsjpF8p5O9DHqRrG33VdxKiFpJiJISNXEo1sGT1YYqMZl4Z0Yk2zerZOiQhbEaKkRA2cPJCFh+s+R1HnR0zxnTBx7v6xvwS4m4gxUgIKzKZzew4ksyqzafRezjx0pOdqO8uY7oJIcVICCtQSnHgZBprt5/jcmY+bZp6MGXYg7g6y+zEQoAUIyGq3R8JmazZepaEy7k0rl+Hfwx7kIda1b8j8/0IUVtIMRKimpxLymHNtrMcP5+FV11Hng1pR88ODbGzkyIkxJ/ZpBh9+OGHbNy4EYCAgABee+01du/ezcKFCykqKiIoKIiXXnoJgOPHjxMZGYnBYMDPz4+5c+ei1WpJSkpi+vTpZGRk0KJFC6KioqhTp44tNkeICk5dzGb9ngSOnsvEzUXHqAGteOShJnd00jkhahurvzp2797Nzp07WbduHdHR0fzxxx+sX7+eN954g2XLlhETE8PRo0fZtm0bANOnT2fWrFn8+OOPKKVYvXo1AHPnzmX06NHExsbi6+vLsmXLrL0pQlgopThyJp23/3OAd1YeJCE5l6H+9/PO8z15rFtTKURCVMHqrxC9Xk9ERAQODg7odDpatmxJQkICzZs3p2nTpmi1WsLCwoiNjSUxMZHCwkI6d+4MwLBhw4iNjcVoNLJv3z4CAwMrtAthbWaz4rdjKbz55T7e/28cWTmFjH60Fe/+vRdhve7D2VHOhAtxM6z+SmnVqpXl74SEBGJiYhg7dix6ffk3z729vUlJSSE1NbVCu16vJyUlhaysLFxdXdFqtRXab0V1zuV+K/R6N1uHUGPcTbkwFBjZvPcCG3ad43JGPj7errw48iECuvjc9OR3N3I35aK6SS7K1eZc2Oxj2+nTp3n++ed5/fXX0Wq1xMfHV/i/RqNBKXXN/W7UfisyMgyYzdc+jjXp9W6kpeXaNIaa4m7JRWKagZ8PJrL7aDLFRjMP+LgzZagvD7XWY6fRkJWZ95fXcbfkwhokF+VsnQs7O021foi3STE6cOAAL7zwAm+88QYhISHs3buX9PR0y/9TU1Px9vamQYMGFdrT0tLw9vbG09MTg8GAyWTC3t7e0i5EdTCZzRw5k8HPBy5x/HwWWns7Hm7fgAFdfWjesPZ+UhXCmqxejJKTk5kyZQrvvfcePXv2BKBTp07Ex8dz/vx5fHx8WL9+PeHh4TRp0gRHR0cOHDhA165diY6Oxt/fH51Oh5+fHzExMYSFhVnahbhdZqW4lGogNauAtOyrfwrJyCnEZFZ41nUkPOB+/Ds1xs3FwdYhC1GraNT1znlVo/nz57NmzRqaNWtmaRs5ciT33XefpWt3QEAAM2bMQKPRcOLECSIjI8nLy6N9+/YsXLgQBwcHEhMTiYiIICMjg0aNGrFkyRLc3W9+amY5TVez2DIXqdkFfBVznBMXsi1trs469B5O6D2c0Xs406JRXTo94IW9XfX3+ZH9opzkopytc1Hdp+msXoxqCilGNYstcmFWil8OJvLd1jPYaTQM9b+fNk090Hs427QXnOwX5SQX5Wydi1p5zUgIW0vJyufLmBOcupiN7/2ePDOoLZ51nWwdlhD3LClG4p5iVoqf919izbaz2Nvb8bfgtvR5sJGMEyeEjUkxEvcEs1IcPZfB/3YncDYxh44tvRg3qC313BxtHZoQAilGopYrKjax+2gym/Zf4nJmPh6uDjwb0o5evg3laEiIGkSKkaiVsnKL+PnAJbYdTiSvsIT7GroxMaw9fm2978gICUKIO0uKkagV8gtLiL+cw7nEK5xNyuGP+EzMStGllZ7HujWllY+7HAkJUYNJMRJ3nYKiEpIz8rmQmsu5pBzOJeWQnJ5HWUf9Rl4uDOjqw4CuPug9ZEpvIe4GUoxEjaOUoshoIjffyJW8YpIz8khKzyMpPZ+kdAMZOUWWZV2dddzfuC7d23nTsrE7LRq54eIkU3kLcbeRYiSszmQ2k36lkOT0fJIz80jOyCevsISMKwUYCozk5hsxlpgr3Edrb0djLxda+XgQUL8OTerXoYm+DnoPZzn9JkQtIMVIVBulFBlXCrmYauBCqoHENAPJGfmkZOVTYiof/aKui44GXnXwcHWkqd4VNxcH3Fx0uLroqOviQENPF/QezjJdtxC1mBQj8ZcopSgoMpFlKCI7t4iMnEIu/b/iczHVQEFRCQAaQF/PmcZedXiwpReNvFxo5FWHRl4u1HHS2XyoEyGEbUkxEjeklMJQYCQ1u6B0ROusAlKzC8jMKSTLUEx2bhFFRlOF+zjq7PHxrkOP9g1o5u1KU29XfPSuODrY22grhBA1nRQjQUFRCRlXCknPKSz9faWA9CuFpGcXkpqdT0FRxWJTz80Rr7pONPV2peP9XtRzc8TDzYF6ro7Uq+tEfXcn7OQ6jhDiFkgxqmWUUhQWm8gvLCGv0EhBUQl5hSXk5heTk28kJ6+49O+80ttXDEXkFZZUeAyd1o767k54uTvxQJNG6Os54+3hjHc9Z/QeTui0coQjhLizpBjZSInJjCG/mGxDEcYSc/mPqfS3yWzGZFKYzQqTWVFiNmM2K4pLzOQVGDEUGDHkG8kt+7vASF6BkfyiEm40KYizo5a6Ljrq1nGgkacLbZp6WApPfXdnvNydqOuikx5qQgirkmJ0lWKjCUNB6dFEYbGJguISCotMV/1dQkGxicKiEvLLlikqoajYhE5rh6ODPY46+/LfOnvsNBpyC4rLC8f/+112Yf92OejscHPWUcdZh5uzDr2HM3WctLg46XBx1OLipMXFUWtpc3Mp/ZGjGiFETXTPFqNfDiWSlJ5Hbn4xuflGcvOLKSw2VXk/rdYOJwctTjo7HB201Hd3wkFnj8lkpqik9MuaVwzFFJWYKTaaMCtFHScddZy0NPZyoY6PAy5OWuo4a/H0cKG4sASdVoPW3s7yY2+vQWuvwU5jh72dBns7DXb2Guw0pd+3cXHS4aCtfeOrSdftcpKLcpKLcrbMRXWv+56d6VUIIUTNUfs+XgshhLjrSDESQghhc1KMhBBC2JwUIyGEEDYnxUgIIYTNSTESQghhc1KMhBBC2JwUIyGEEDYnxUgIIYTNSTGqBgaDgdDQUC5dugTA2rVrCQ4OJiwsjPnz51NSUjouXWpqKhMnTmTIkCGMHDnSsnxOTg4TJ04kKCiIMWPGkJaWZrNt+atuJhcZGRkMHjzY8tO/f38eeugh4N7LBcClS5cYM2YMgwcPZuzYsSQmJgJQXFzM9OnTCQoKYujQoZw9e9Zm2/JX3Wwu4uLiCA8PJywsjOeff97y/NeWXHz44YeEhIQQEhLC4sWLAdi9ezdhYWEMHDiQ9957z7Ls8ePHCQ8PJzAwkJkzZ1pylJSUxJgxYxg0aBCTJ08mLy/PJtvylylxRx0+fFiFhoaqDh06qIsXL6qzZ8+qvn37qpSUFKWUUnPmzFFffPGFUkqpcePGqVWrVimllFq1apWaNm2aUkqpuXPnqo8//lgppdS6dess7XebW8lFGZPJpJ566in1ww8/KKXuzVy8+uqrauXKlUoppf7973+rV155RSml1GeffaZmzZqllFJq79696oknnrDBlvx1N5sLs9msAgIC1J49e5RSSm3YsEE9//zzSqnakYtdu3apESNGqKKiIlVcXKyefvpp9b///U8FBASoCxcuKKPRqMaPH6+2bt2qlFIqJCREHTp0SCml1IwZMyz7yMSJE9X69euVUkp9+OGHavHixTbZnr9KjozusNWrVzNnzhy8vb0BOHnyJJ07d7bc7tevH5s3byYzM5MTJ04wcuRIAMLDw3nxxRcB2Lp1K2FhYQCEhoayfft2jEaj9TfmL7rZXFxtzZo1ODs7W7b/XsyF2WzGYDAAUFBQgJOTE1Cai8cffxyAbt26kZWVRVJSkrU35S+72VxkZWVRWFjIww8/bGnfuXMnxcXFtSIXer2eiIgIHBwc0Ol0tGzZkoSEBJo3b07Tpk3RarWEhYURGxtLYmIihYWFdO7cGYBhw4YRGxuL0Whk3759BAYGVmi/G0kxusMWLFiAn5+f5Xbbtm05cuQIycnJmEwmYmNjSU9P5+LFizRu3Ji3336bxx9/nBdeeAGdTgeUnr7T6/UAaLVaXF1dyczMtMn2/BU3m4syJpOJ5cuX88orr1ja7sVcTJs2ja+++oq+ffvyxRdf8NxzzwEVcwGlb2aXL1+27obcATebi3r16uHi4sLOnTsB2LBhA0ajkaysrFqRi1atWlmKS0JCAjExMWg0mgrb5e3tTUpKynW3NyUlhaysLFxdXdFqtRXa70ZSjKpZixYteOWVV5g8eTJjxoyhTZs26HQ6SkpKOHbsGL169eKHH35gwIABREREVPo4dnZ3/1NVWS7K7NixgxYtWtCmTZsbPk5tz8Xrr7/OvHnz2LFjB3PnzuUf//gHqpLB9WtzLjQaDR988AEff/wxQ4YMITc3Fw8Pjwr7zNXu1lycPn2a8ePH8/rrr9OsWbNr/q/RaK77/N+o/W50dz57d5GioiI6duxIdHQ033zzDY0bN6Zp06bo9Xrq1KlDv379gNJTUHFxcUDpp6GyT8klJSUYDAY8PDxstQl3TGW5KLN582aCg4Mr3Odey0VmZibnzp3j0UcfBSAwMJC0tDSysrLw9vau0IEjLS3Ncmrrbnaj/UKr1bJixQqio6MZOnQoZrMZDw+PWpOLAwcO8Mwzz/DKK68wdOhQGjRoUOFsQWpqKt7e3te0l22vp6cnBoMBk8lUof1uJMWomuXn5zNu3DgMBgPFxcWsWLGC4OBgmjVrRoMGDdi2bRsAv/zyCx06dAAgICCA6OhoAGJiYvDz86v00+DdpLJclDl8+HCF0zdw7+WiXr16ODo6sn//fqD0zapOnTp4enoSEBDA999/D8D+/ftxdHSkcePGttyMO+JG+8Ubb7xh+ZD2xRdfMGjQIOzs7GpFLpKTk5kyZQpRUVGEhIQA0KlTJ+Lj4zl//jwmk4n169fj7+9PkyZNcHR05MCBAwBER0fj7++PTqfDz8+PmJiYCu13JRt3oKi1+vXrpy5evKiUUmr16tUqODhYDRw4UH3wwQeWZc6ePaueeuopFRISokaMGKHi4+OVUkplZWWp559/XgUHB6sRI0ZYHududTO5UEqpjh07qsLCwgpt92Iujhw5op544gkVGhqqRowYof744w+llFKFhYXqtddeU8HBwWrIkCHq6NGjNtmGO+VmczFkyBAVGBioXnjhBZWbm6uUqh25eOutt1Tnzp3V448/bvlZtWqV2r17twoLC1MDBw5UCxYsUGazWSml1PHjx1V4eLgaNGiQevnll1VRUZFSSqlLly6pp556SgUFBanx48er7OxsW27WbZOZXoUQQticnKYTQghhc1KMhBBC2JwUIyGEEDYnxUgIIYTNSTESQghhc1KMhBBC2JwUIyGEEDYnxUgIIYTN/f+FYqp336rHUwAAAABJRU5ErkJggg==\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)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2145cd9d",
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44698469",
   "metadata": {},
   "source": [
    "The standard deviations are huge! Some Fortune 500 companies make billions while others lose billions, and the risk has increased along with rising profits over the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9e9eb58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17c234ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}