fortune500.iypnb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a09fec2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "823320ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('fortune500.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b5f322f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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": "ba0aa10c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>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": "c49133a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['year', 'rank', 'company', 'revenue', 'profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ae22ec3",
   "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,
   "id": "be9f8a48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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,
   "id": "659d3387",
   "metadata": {
    "scrolled": true
   },
   "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": "code",
   "execution_count": 9,
   "id": "2a1a1033",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~non_numberic_profits]\n",
    "df.profit = df.profit.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d20d9f1a",
   "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": 11,
   "id": "082e6fac",
   "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": 12,
   "id": "a48afe77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X6x5fnT8xMZG5c+cSGxtraVdKkZKSQoMGDVi+fDmrV68mNTUVjUZDXl5eme6uP8el1+tvenzn22+/LXNsLT09neLiYkJCQizTQkJCynSH1K9f/6braq2AgADL325ubpaN/q9y8GcdO3YEwNnZmddff53IyEji4uIIDQ3F3d29TGHLy8vD3d0djUaDh4cHeXl5ZV4rPz8fDw+P65aRnJxc7jabmppq+YEF4OHhgU6nIyUlhYYNGwJl3xcXF5frHqenp1see3t74+7ubnkcEhJi2S4PHz7MwoULOXPmDMXFxRgMBgYMGFAmnj9/7q5uW3369GHmzJkkJCQQHx+Pp6cn7dq1u259rrr2fU5MTMRoNNK1a1fLNLPZTHBwMFBSoIuKijh8+DB+fn6cPHnSctKJNZ+1P28L1nwOMzMzKSws5P7777dMU0pZuj/Hjx/P+++/z7hx4wAYNWoUTz311F++7lVX1w1K3uNrPxNw/efiZu+xNd8R5XnmmWfIzc1l6NChODs787e//Y0TJ05YXj88PNzy3OHDh7Nx40Z27tzJ2LFjCQgIsOS2UaNGvPLKKzz99NPXHY4pjzXb9p9lZmYybtw4xowZw6BBgyzT3d3dy3zerv7t4eFxw89iXl6e5bN47YmKoaGhPPvssyxfvpynn3663NjtphCWp379+ly5coWcnBy8vb2va//zr7b69eszd+5cIiMjr3vu2rVr+fHHH/n0009p2LAhubm5dOzY0XK8qV27dnz44YcUFxezYsUKJk+ezM6dOwkODmbChAk888wz1bOSFXQ1riFDhlzXtn//fv71r3/x2Wef0bJlS7RabZl1rQo+Pj44OTmRmJhIixYtAEhKSiIoKMjynD+/Pzf6le3m5mb5xQqU+aL/KzfLgTU0Go0lJy1btuTkyZOWL/yTJ09aTopo0aIFn3zyCUopyzqcOnWKMWPGXPeaN9tmAwMDy+ytFhQUkJ2dXSZntyInJ4eCggJLMUxKSrLE/NJLL/Hwww/zr3/9CxcXF956660yP4RuxsXFhXvvvZf169dz7ty5m+4NQtn39WpvzJ49e274o8rBwYEBAwawceNG/P396dmzp+XXf2U+a25ubpZjflByrPMqHx8fXF1d2bRp0w1z7enpydSpU5k6dSqnT5/m0Ucf5a677uLuu++2atnXrn9gYCCJiYll2pOSkujWrdutrtItc3V1ZcaMGcyYMQMo2cNu06ZNuSfvXLv930rbjdzqtn3lyhXGjRtH7969r3u/W7ZsyalTpxg4cCBQ8ln09/fHx8eHFi1akJCQQF5enmW7OXnyZJlCeqvrYTddo+UJDAyke/fuvPnmm1y5coXi4mL27dtX7vMffPBBFi9ebHlDMjMzLdf65Ofn4+zsjI+PD4WFhbzzzjuW+QwGA+vXryc3NxcnJyc8PDwsG8/IkSNZtWoVhw8fRilFQUEBO3bsuO5Xye02evRoPv74Y8sJP7m5uZZusfz8fBwcHPD19cVoNPL+++9XebxXv9AWLVpEXl4ely9f5tNPP71pUfLz8yMlJaXMqdBhYWFs27aNwsJCLly4wOrVq62O4WY5+LMzZ85w4sQJTCYT+fn5vP322wQGBtK8eXOgpCv5008/JSUlhZSUFD799FOGDx8OQHR0NA4ODnz++ecYDAbLHkunTp2uW87NttlBgwbxzTffcOLECQwGA++88w7t2rUr9xezNd577z0MBgP79+9nx44dlr2+/Px86tWrh4uLC0eOHGHjxo239LpDhw7l22+/Zfv27X9ZCK8VGBhIly5dePvtt8nLy8NsNnPx4sUy3bKDBw/m+++/Z8OGDWW+wCrzWWvdurXlPdbr9WVODtJqtYwcOZK5c+daTspJSUlh165dQMkJOhcuXEAphZeXFw4ODrfUNXqtHj16cP78eTZs2IDRaOS7777j7Nmz9OzZs0Kv92cGgwG9Xo9SCqPRiF6vt+zZXt12lVIcOnSIDz74gOeffx4o+dG0a9cuS8/P+vXr2b9/v6VA79mzh8uXL6OUIikpiYULF9KnT59y4/D39y9zvfStbNt5eXmMHz+eDh06WM4EvdbQoUNZvXo1Z8+eJScnhw8//NDyWWzatClhYWEsXboUvV7Ptm3bOHXqlOVSkJ07d1p+TMfFxfHBBx/cdD2gBuwRAsyfP5958+Zx7733UlxcTExMjKWL68+uns03btw4UlNT8fPzY+DAgfTt25dhw4axe/duunXrhk6nY9KkSWVOsV23bh2zZ8/GZDLRtGlTy+nJd911F7Nnz2bWrFlcuHABV1dXOnToUOFr6arKPffcQ35+Pi+++CKXL1/Gy8uLzp07c++991rO1uvfvz/u7u48+uijZbpvqsr06dOZPXs2ffv2xcXFhZEjRzJixIhyn9+pUydatGhB165d0Wg07N27l0cffZQ//viDzp07ExoayuDBg62+xvRmOfiz9PR03njjDVJSUnBzcyMiIoKPPvoIJycnoKSoJiQkWI5XPfDAA4wePRoo6UpdunQp/+///T/+8Y9/0Lx5c5YuXVrupRPlbbOdO3dm0qRJPP/88+Tk5BAREcGiRYusWtcb8ff3x9vbm27duuHm5sYbb7xhKewzZ84kNjaWWbNmER0dzb333ktOTo7Vrx0ZGYlWq6VNmzY37Ga+mfnz57Nw4UIGDhxIfn4+jRo14sknn7S0h4eHW7o1u3fvbplemc9a06ZNefbZZ3nsscdwdXXlxRdf5Msvv7S0v/LKKyxdupS//e1vZGVlERQUxIMPPki3bt24cOECs2fPJjMzE29vbx588MEb/sixho+PD8uWLWPu3Lm88cYbNG7cmGXLlllO7a+s8ePHW35UHDx4kOnTp/P5558TExPDxYsXmTJlChkZGdSvX5+XXnrJ0kVtNBpZvHgx586dw8HBgWbNmrF06VKaNm0KlFwn+8orr5CTk4NOp+Oee+5h8uTJ5cbx3HPPMXXqVIqKipg1axYDBw60etvetm0bf/zxB2fPni1zreKmTZsICQmhe/fuPPHEEzzyyCMUFRXRv3//MpeAvPPOO7z22mt07NiR4OBglixZYsnvnj17eO211ygoKMDPz48hQ4bctFsUQKOqsq9MCHHb7N27l1deeaVar/t65JFHGDx4cLnXnQlRG9h916gQwjaOHDnC8ePHb7h3LURtUiO6RoUQt9eUKVP44YcfeP3118ucxi5EbSRdo0IIIeo06RoVQghRp0khFEIIUadJIRRCCFGnycky5cjKysdstt3hUz8/TzIybHvBvr2QXJSSXJSSXJSydS4Wf30Yf507k0ZH2CyGypBCWA6zWdm0EF6NQZSQXJSSXJSSXJSyVS7Ssws5dCadxweG2WT5VeG2dI3GxsbSu3dvQkNDy4xDptfrmTlzJv369WPw4MFMnz7d0hYfH8+oUaPo378/o0aN4vz585VuE0IIUbUOnim5nVmbpj42jqTibksh7NOnDytWrLjuNk0LFizAxcWFLVu2sGHDBiZNmmRpmzlzJmPGjGHLli2MGTPGchPZyrQJIYSoWgdOp9EgwAP/em62DqXCbkshjIqKuu4+l/n5+axdu5ZJkyZZbm57daiQjIwMjh8/brkZ76BBgzh+/DiZmZkVbhNCCFG1cgsMnL6UTUTLgL9+sh2z2THChIQEdDod77//Pnv37sXDw4NJkyYRFRVlGcrHwcEBKBnlIDAwkKSkJJRSFWqrqhveCiGEKHHobDpKQWQrKYQVYjKZSEhI4M4772TKlCkcPnyYCRMmsG3bNluFVIafn+1vKxUQ4PXXT6ojJBelJBelJBelbJGL4xeyCfBxI7JtcIWHrbIHNiuEwcHBODo6Wroxw8PD8fHxIT4+3jKas8lkwsHBAZPJRGpqKsHBwZbRx2+17VZlZOTZ9Iy0gAAv0tJybbZ8eyK5KCW5KCW5KGWLXOgNJg6cSqVHeAjp6XlotRq72IGoCJtdUO/r60tMTAy//PILUHK2Z0ZGBo0bN8bPz4+wsDDLQKIbN24kLCwMX1/fCrcJIYSoOkfjMyg2momo4d2icJtuuj1nzhy2bt1Keno6Pj4+6HQ6Nm3aREJCAtOmTSM7OxtHR0cmT55Mjx49gJKRhadOnUpOTg7e3t7ExsbSrFmzSrXdCtkjtB+Si1KSi1KSi1K2yMU/NxzjSFwGi1/oioNWW6P3CGX0iXJIIbQfkotSkotSkotStzsXRpOZyUt2E9HSn/GD7gSo0YVQ7jUqhBDilpxKyKZAb6RDLegWBSmEQgghbtHB02k4O2q5s2ntOP9CCqEQQgirmZXi4Jl02jbzw8XJwdbhVAkphEIIIax2ITmXrFw9ES39bR1KlZFCKIQQwmoHTqeh1WgIbyGFUAghRB104HQaoXfo8HRzsnUoVUYKoRBCCKskZeSTlFFQa84WvUoKoRBCCKtcHXuwNh0fBCmEQgghrHTgdBpN6nvh6+1q61CqlBRCIYQQf+l0QjbnEnOIDguydShVTgqhEEKIm1JKsXpHHDpPZ3p1aGDrcKqcFEIhhBA3dehsOmcvX2FI16a15iL6a0khFEKIOkQpxR/nMvj6p7MUGYx/+XyzWfHNznME+brTrd2tj+1aE9hsYF4hhBC3j9ms2H8qle/+e4GLqXkApF8pYsLQNjcdXf7Xo8lcTs9n4rC2OGhr576TFEIhhKjFio1mfj2axPd7L5KaVUh9X3ceH9ia7DwD3/58jmYh3vSPvqOceU2s3X2OpsFeRIbWrmsHryWFUAghaqkdhy6zbnc8V/IMNKnvxbPD2xLRMgCtVoNSiovJuXz9UxxN6nsReofPdfNvP3CZzBw94weG3XSvsaarnfu5QghRx6VmFfD55lME1HPjpdHtmf5oFJGhgWi1JQVNo9Ew7r4wAn3c+HDtUbJy9WXmLygysvHX87Rp6ktYk9ox3FJ5pBAKIUQtdDguA4AnBoXRponvDffo3Fwcefb+u9AXm/lg7R8YTWZL2+bfLpBfZOSBHs1vW8y2ctsKYWxsLL179yY0NJTTp09f1/7+++9f13bo0CGGDBlC//79GTduHBkZGZVuE0KIuuBIXAbBfu4E+rjf9HkN/D0Yd18YcZdzWPXjGQCy8/Rs3ZdAdFggjet73Y5wbeq2FcI+ffqwYsUKGjS4/mLMY8eOcejQoTJtZrOZV155hRkzZrBlyxaioqJYuHBhpdqEEKIuKDIYOXUxi3bN/ax6fsfWgfSPbsT2A5f59WgSG345j8mkGN69WTVHah9uWyGMiooiOPj6a1AMBgOzZs3ijTfeKDP96NGjuLi4EBUVBcDo0aPZvHlzpdqEEKIuOH4+C6NJEd7c+ptjP9CzOa3v0PH55lP8fDiR7uEhBP3F3mRtYfNjhO+++y5DhgyhYcOGZaYnJSUREhJieezr64vZbCY7O7vCbUIIURccPpuOm4sjLRrWs3oeB62Wp4e2xcPNCQcHDUO6NKm+AO2MTS+fOHjwIEePHuXll1+2ZRg35OfnaesQCAio/X3z1pJclJJclJJclLqaC7NZcex8JpGtAwmub30hLHkNWPhCd3LyDbRopKuGKO2TTQvhvn37iIuLo0+fPgAkJyczfvx45s2bR3BwMImJiZbnZmZmotVq0el0FW67FRkZeZjNqnIrWAkBAV6kpeXabPn2RHJRSnJRSnJR6tpcnE/OITNHT2jDehXKjwao5+pwy/NqtRq72IGoCJt2jT711FPs3r2b7du3s337durXr8/y5cvp2rUrbdu2paioiP379wOwatUqBgwYAFDhNiGEqO2OnM1AA9xl5Yky4jbuEc6ZM4etW7eSnp7O448/jk6nY9OmTeU+X6vVMn/+fGbOnIler6dBgwYsWLCgUm1CCFHbHY5Lp1mIN97uzrYOpcbQKKVs1/9nx6Rr1H5ILkpJLkpJLkpdzcWVfAN/f283w7s3Y3DnJrc1BukaFUIIYXNH4tIBCJdu0VsihVAIIWqJI3EZ+Hi50CiwZu6Z2YoUQiGEqAWMJjPH4jNp19yvVo8UUR2kEAohRC1wOiGbIoPplu4mI0pIIRRCiFrg8NkMHB20hDW+flxBcXNSCIUQohY4EpdOWGMfXJwdbB1KjSOFUAgharjLaXmkZBVaPdqEKEsKoRBC1HD7jqcActlERUkhFEKIGm7f8WQa+Hvgr3OzdSg1khRCIYSowQr1Ro6dy6BdC9kbrCgphEIIUYMdi8/EZL61QXhFWVIIhRCiBjt8Nh1PNyeaN/C2dSg1lhRCIYSooQqKitl3KpW77wrGQStf5xUlmRNCiBrqlz+SMRSbGdilqa1DqdGkEAohRA1kVortBy7RvIE3LRrqbB1OjSaFUAghaqDj8ZmkZBXSp0NDW4dS4910hHqj0cj27dvZsWMHJ0+eJDc3Fy8vL1q3bk337t3p27cvjo63bZB7IYQQ/7P9wGW8PZyJah1o61BqvHKr2MqVK/noo49o3rw5HTt2pFevXnh4eJCfn09cXBxff/01b7/9Nk8//TQPPvjg7YxZCCHqtLTsQg6fTee+zk1wdJCOvcoqtxBevHiRr7/+moCAgOva7rnnHiZMmEBqaiqffvqpVQuKjY1ly5YtXL58mQ0bNtCqVSuysrJ49dVXuXjxIs7OzjRu3JhZs2bh6+sLwKFDh5gxYwZ6vZ4GDRqwYMEC/Pz8KtUmhBA13U8HL6PRaOjZPsTWodQK5f6UmDJlyg2L4LUCAwOZMmWKVQvq06cPK1asoEGDBpZpGo2GJ554gi1btrBhwwYaNWrEwoULATCbzbzyyivMmDGDLVu2EBUVVek2IYSo6QzFJnYdTqRDK398vV1tHU6tYNU+9dmzZ0lPTwcgLy+PJUuW8P7771NYWGj1gqKioggODi4zTafTERMTY3ncvn17EhMTATh69CguLi5ERUUBMHr0aDZv3lypNiGEqOn2nkghv8hIbzlJpspYdabLiy++yOLFi/H392f+/PnEx8fj4uLCjBkzWLBgQZUEYjabWblyJb179wYgKSmJkJDS3X5fX1/MZjPZ2dkVbtPpdFbH4+fnWfmVqqSAAC9bh2A3JBelJBel6loulFLsPJzEHfW96BrZCI1GY2mra7moSlYVwsuXL9OsWTOUUmzbto1Nmzbh6upKnz59qiyQ2bNn4+7uzsMPP1xlr1kZGRl5mM3KZssPCPAiLS3XZsu3J5KLUpKLUnUxF2cvX+Hc5SuM7R9KenqeZbo95EKr1djFDkRFWFUIXVxcyMvLIy4ujuDgYHx9fTEajej1+ioJIjY2lgsXLrBs2TK0/7tNUHBwsKWbFCAzMxOtVotOp6twmxBC1GTbD1zCzcWBu9sE2TqUWsWqY4SDBg3i0UcfZcqUKdx///0AHD9+nIYNK99H/c4773D06FGWLl2Ks7OzZXrbtm0pKipi//79AKxatYoBAwZUqk0IIWqqK/kG9p1IpUvbYFyd5frtqqRRSlnV/7d7924cHR3p1KkTAH/88Qd5eXncfffdVi1ozpw5bN26lfT0dHx8fNDpdCxevJhBgwbRpEkTXF1Lzn5q2LAhS5cuBeDAgQPMnDmzzGUQ/v7+lWqzlnSN2g/JRSnJRam6losNv8Tz7a543noyhmA/jzJt9pCLmtw1anUhrGukENoPyUUpyUWpupQLk9nMqx/+lxB/D14a1f66dnvIRU0uhFbtXyckJLB48WJOnDhBQUFBmbYdO3ZUR1xCCCH+Z/vvl8nK1fNwv1a2DqVWsqoQvvzyyzRq1IgpU6bg5uZW3TEJIYSg5HKJdbvjWf/Ledo29ZVR6KuJVYXwzJkzrFy50nJGpxBCiOplNJn5fPMpdv+RRNe7gnlkQChareavZxS3zKrK1rFjR44fP17dsQghhAAK9UbeXX2E3X8kMbRrUx4f2Fpurl2NrNojbNCgAU888QT33HPPdWdfTpo0qVoCE0KIuigrV8/irw9zOS2fx+9tTbdwubF2dbOqEBYWFtKrVy+MRiPJycnVHZMQQtRJl9PyWPT1YfKLjEwe2Y62zWTUnNvBqkI4b9686o5DCCHqtMycIuZ9cQAnJy1Tx3SgcX25d+jtYvXtCc6fP8/GjRtJTU0lMDDQciG8EEKIyluz8xwGo5npj0UR5ONu63DqFKuOvm7fvp3777+f+Ph46tWrR3x8PCNGjODHH3+s7viEEKLWi0/K4b/HkunXsZEUQRuwao9w0aJFfPDBB5bbqwHs3buX2bNnV+kIFEIIUdcopfhy+1m83J247+7Gtg6nTrJqjzA5Odky0O1VkZGRcuKMEEJU0sEz6ZxOyGZY16a4ucjNtG3BqkLYunVrPvnkkzLTPv30U8LCwqolKCGEqAuMJjNf/3SWYD93ureXyyRsxaqfH2+88QbPPPMMn3/+OcHBwSQlJeHm5sayZcuqOz4hhKi1fjp4mZSsQiY90A4HuXOXzVhVCJs3b853333HoUOHLGeNhoeH4+TkVN3xCSFErZRfVMz63fGENfahXXO5XtCWrO6QdnR0vO44oRBCiIrZ+Ot5CoqMjOrdAo1G7iFqS+UWwnvvvZfvv/8egB49epT7RskwTEIIcWtSswr48fdLdGkXzB1BcuG8rZVbCGfPnm35e8GCBbclGCGEqAtW74hDq9UwvFszW4ciuEkhvLYbNDo6ulILiY2NZcuWLVy+fJkNGzbQqlXJ4JLx8fFMnTqV7OxsdDodsbGxlrvVVEebEELY2plL2ew/lcbQrk3x8XKxdTiCmxTCd99916oXsGb0iT59+vDII4/w0EMPlZk+c+ZMxowZw9ChQ1m3bh0zZszg888/r7Y2IYSwJZPZzMofzqDzdGZA9B22Dkf8T7nn6yYnJ1v1zxpRUVEEBweXmZaRkcHx48cZNGgQAIMGDeL48eNkZmZWS5sQQtjad3sucj45l9F9WuLi7GDrcMT/lLtHWN0jTiQlJREUFISDQ8nG4ODgQGBgIElJSSilqrzN19e3WtdHCCFu5mJKLut3xxMdFkh0WJCtwxHXKLcQJiQkWPUCjRo1qrJg7Imfn6etQyAgQM4mu0pyUUpyUaqm5KLYaGLWv/fj7eHMpAcj8fZwrvJl1JRc2KNyC+E999yDRqNBKVXuzBqNhhMnTlRowcHBwaSkpGAymXBwcMBkMpGamkpwcDBKqSpvu1UZGXmYzeWve3ULCPAiLS3XZsu3J5KLUpKLUjUpF6t3xHE+KYdJD7RDX6AnrUBfpa9vD7nQajV2sQNREeUWwpMnT1brgv38/AgLC2Pjxo0MHTqUjRs3EhYWZunCrI42IYS43c5evsL3ey/QrV0w4S38bR2OuAGNutkuXxWZM2cOW7duJT09HR8fH3Q6HZs2bSIuLo6pU6eSk5ODt7c3sbGxNGtWcl1NdbTdCtkjtB+Si1KSi1I1IRf6YhNvfPIbRpOZWeNjqm10CXvIRU3eIyy3EI4fP57ly5cDMGbMmHLvLLNixYrqi86GpBDaD8lFKclFqZqQixXbTvPj75d45cEIwhr7VNty7CEXNbkQlvvzZNiwYZa/R44ceTtiEUKIWuPE+Ux+/P0SfSMbVmsRFJVXbiEcPHiw5e/hw4fflmCEEKI2KCgy8sl3JwjydWdEz+a2Dkf8Bas7rPfv38/x48cpKCgoM33ChAlVHpQQQtRka3bGkZmrZ9rDkbg4yYXz9s6qQjh79my+//57oqKicHEpvTeeDB0ihBBlXUjOZcfBy/SObEjzBvVsHY6wglWFcMOGDWzYsIGgILkbghBClEcpxYofTuPp7sTwbk1tHY6wUrn3Gr1W/fr1cXau+jshCCFEbbLnWApnL11hRI/muLs62TocYSWr9gjfeustpk+fzn333Ye/f9kLQjt27FgtgQkhRE1SqDfy1U9naRrsRdd2t343K2E7VhXCY8eO8fPPP7Nv3z5cXV0t0zUajYxQL4QQwIZfznMl38DzI9qhlfMnahSrCuGiRYtYtmwZnTt3ru54hBCixklMz2fb/gS6tQumWYi3rcMRt8iqY4Rubm5lRqwXQghRQinFyh9O4+zkwIgecs1gTWRVIXzhhReYO3cuaWlpmM3mMv+EEKIuO3A6nWPnsxjerWm1DK8kqp9VXaPTpk0D4Msvv7RMU0pVahgmIYSo6fTFJlb9eIaGAR706tDA1uGICrKqEP7444/VHYcQQtQ43++5QEZOEVPGROCgtaqDTdghqwphgwbyS0cIIa6Vnafnuz0XiQ4LJPQOual2TVbuT5h58+aRlpZ205nT0tKYN29elQclhBD27nxyLkaTmb5RjWwdiqikcvcImzZtysiRI2nevDkdO3akadOmeHh4kJ+fz/nz5/ntt9+Ij4/nmWeeuZ3xCiGEXUjLLgQg0MfNxpGIyiq3EI4ePZoRI0bw448/8vPPP/PDDz+Qm5uLt7c3oaGhjB49ml69euHoWD0jLgshhD1Lyy7ExdkBLze5lVpNd9Mq5uTkxIABAxgwYMDtikcIIWqE9OwiAuq5ySg8tYBdnOb0008/MWzYMIYOHcqQIUPYunUrAPHx8YwaNYr+/fszatQozp8/b5mnom1CCFEVUrMLpVu0lrB5IVRK8eqrrzJ//nzWrVvH/PnzmTJlCmazmZkzZzJmzBi2bNnCmDFjmDFjhmW+irYJIURlKaVIyy4kQOf6108Wds/mhRBAq9WSm5sLQG5uLoGBgWRlZXH8+HEGDRoEwKBBgzh+/DiZmZlkZGRUqE0IIarClXwDxUYzATrZI6wNbH6mi0ajYfHixUycOBF3d3fy8/P5+OOPSUpKIigoCAcHBwAcHBwIDAwkKSkJpVSF2nx9fW22nkKI2uPqGaNSCGsHqwthXFwcmzdvJj09nZkzZxIXF0dxcTGtW7euVABGo5GPPvqIDz74gMjISH7//XcmT57M/PnzK/W6leXn52nT5QMEBHjZOgS7IbkoJbkoZatc/HEhG4DQZv4EBNj+uwJku6gMqwrh999/z6xZs7jnnnvYuHEjM2fOpKCggH/84x989tlnlQrgxIkTpKamEhkZCUBkZCRubm64uLiQkpKCyWTCwcEBk8lEamoqwcHBKKUq1HYrMjLyMJtVpdatMgICvEhLy7XZ8u2J5KKU5KKULXNxLiELDaAxmuzi/bCH7UKr1djFDkRFWHWMcMmSJXz66afMmjXL0uXYunVrTp48WekA6tevT3JyMufOnQNK9jwzMjJo3LgxYWFhbNy4EYCNGzcSFhaGr68vfn5+FWoTQoiqkJZdiI+3C06OdnGahagkq/YIMzMzCQ0NBbBcM6PRaKrk+pmAgADeeOMNJk2aZHm9uXPnotPpeOONN5g6dSoffPAB3t7exMbGWuaraJsQQlRWWnYhAfXk+GBtYVUhbNOmDevWrWPYsGGWaZs2baJdu3ZVEsSQIUMYMmTIddObN2/O119/fcN5KtomhBCVlZZdSNtmfrYOQ1QRqwrh66+/zvjx41m9ejUFBQWMHz+e+Ph4Pvnkk+qOTwgh7Iqh2ER2nkHOGK1FrCqEzZs35/vvv+enn36iZ8+eBAcH07NnTzw8PKo7PiGEsCtpV4oA5GL6WsTqyyfc3NwYOHBgdcYihBB2T64hrH2sKoRjxowp98SYFStWVGlAQghhz6QQ1j5WFcKRI0eWeZyWlsaaNWsYPHhwtQQlhBD2SoZfqn2sKoTDhw+/blr//v157bXXeO6556o8KCGEsFcy/FLtU+GrQYOCgjh16lRVxiKEEHZPRp2ofazaI1y9enWZx0VFRWzdupX27dtXR0xCCGGXrg6/1LaZ3KmqNrGqEK5bt67MY3d3dyIiInjssceqIyYhhLBLV/INGGT4pVrHqkL4n//8p7rjEEIIuydnjNZOVl9HmJubS3x8PPn5+WWm33333VUelBBC2CMphLWTVYXwm2++YdasWbi7u+PqWnqQWKPR8OOPP1ZbcEIIYU/SsovQAH7ecrJMbWJVIVy0aBHvvvsuPXr0qO54hBDCbsnwS7WTVe+myWSia9eu1R2LEELYNRl+qXayqhA++eSTfPjhh5jN5uqORwgh7FbJNYRSCGsbq7pGP/vsM9LT0/nXv/6FTqcr07Zjx45qCEsIIeyLZfglHymEtY1VhXDBggXVHYcQQtg1GX6p9rKqEEZHR1d3HEIIYdfk0onay6pjhAaDgUWLFtGnTx8iIyMB2L17N1988UWVBKHX65k5cyb9+vVj8ODBTJ8+HYD4+HhGjRpF//79GTVqFOfPn7fMU9E2IYSoCCmEtZdVhXDu3LmcPn2ahQsXWu643rJlS1auXFklQSxYsAAXFxe2bNnChg0bmDRpEgAzZ85kzJgxbNmyhTFjxjBjxgzLPBVtE0KIipDhl2ovqwrhDz/8wD/+8Q8iIiLQaktmCQoKIiUlpdIB5Ofns3btWiZNmmQpsv7+/mRkZHD8+HEGDRoEwKBBgzh+/DiZmZkVbhNCiIqS4ZdqL6uOETo5OWEymcpMy8zMvO4M0opISEhAp9Px/vvvs3fvXjw8PJg0aRKurq4EBQXh4OAAgIODA4GBgSQlJaGUqlCbr6/cMV4IUTFp2YUEyhmjtZJVhXDAgAFMmTKF1157DYDU1FTmzp3LfffdV+kATCYTCQkJ3HnnnUyZMoXDhw8zYcIE3n333Uq/dmX4+XnadPkAAQFetg7BbkguSkkuSt2uXCilSLtSRMc29e02//YaV01gVSH8+9//zsKFCxkyZAiFhYX079+fkSNH8uyzz1Y6gODgYBwdHS1dmeHh4fj4+ODq6kpKSgomkwkHBwdMJhOpqakEBwejlKpQ263IyMjDbFaVXr+KCgjwIi0t12bLtyeSi1KSi1K3MxdX8vQYik14ujjYZf7tYbvQajV2sQNREVYdI3R2dmbatGkcPHiQX3/9lQMHDjBt2jScnZ0rHYCvry8xMTH88ssvQMkZnxkZGTRp0oSwsDA2btwIwMaNGwkLC8PX1xc/P78KtQkhREWkyhmjtZpGKfWXuz0TJ05k8ODB9OnTp0qK358lJCQwbdo0srOzcXR0ZPLkyfTo0YO4uDimTp1KTk4O3t7exMbG0qxZM4AKt1lL9gjth+SilOSi1O3Mxa9Hk/jXxhPMfaoT9X3db8syb4U9bBc1eY/QqkL42WefsXHjRuLj4+nbty+DBg2iS5culjNIayMphPZDclFKclHqduZi3e541u+OZ9nLPe1y5Al72C5qciG06h197LHHWL16NWvWrKFRo0bMnTuXbt26MWfOnOqOTwghbE6GX6rdbuldbdKkCc899xyLFi0iNDSUFStWVFdcQghhN2T4pdrNqrNGAS5evMjGjRvZtGkTmZmZDBgwgIkTJ1ZnbEIIYRfSsgtp29TP1mGIamJVIRwxYgTnz5+nd+/evPrqq3Tp0gVHR6trqBBC1FiW4Zdk1Ilay6pqNn78eHr37o2rq2wIQoi6Jf3q8EtyV5lay6pjhAMHDqSwsJC1a9fyz3/+E4CUlBSSk5OrNTghhLA1uYaw9rOqEP72228MGDCADRs28MEHHwBw4cIF3njjjeqMTQghbE6GX6r9rB6GafHixSxfvtxybDA8PJwjR45Ua3BCCGFrMvxS7WdVIbx8+TJ33303gGUIkhuNSCGEELWNDL9U+1l1skzz5s3ZtWsX3bp1s0z79ddfadWqVbUFJoQQt4O+2MSB02lcTMnFbAazUiilMKuSUSfOXr5Cy4b1bB2mqEZWFcKpU6fy9NNP07NnT4qKipgxYwbbt2+3HC8UQgh7kVNgwFGrxd21/K83pRRxl3PY/UcS+06mUKg34eSoxdFBg1ajQaPRoNWU9IA5OWpp38L/Nq6BuN2sKoTt27dn/fr1rF+/nhEjRhAcHMzq1aupX79+dccnhBBWKSgysv6XeH78/RIms8LHy4UGAR409PekQYAHDQI8cHd1Yt+JFHb/kUxKZgHOTlo6hgbS5a5gWt2hQyvdn3WS1VfFBwUF8eSTT1oenzp1irlz57JkyZJqCUwIIaxhVordR5JYszOOvIJiuoWHEOjjxuW0PC6n5fPDhUsYTeYy84Q20nFfp8ZEhgbg5iI3B6nrbroFFBYW8tFHH3Hy5EkaN27M888/T1ZWFm+//Ta//vorw4YNu01hCiHE9c5evsL/bTvN+eRcWjasx5i/taJx/bIjtZvMZlKzCklMzyc7z8BdzXwJ9LG/oZSE7dy0EM6aNYvjx4/TtWtXfv75Z06fPs25c+cYNmwYs2fPlsFuhRA2kZWr5z/bTvPT75fw8XLhqSF3EhMWdMMzOx20WoL9PAj287BBpKImuGkh3LVrF+vWrcPPz4+xY8fSs2dPvvjiC6Kiom5XfEIIYZFbYOD7PRf58cAllIL77m7MfXc3xtVZujdFxd106ykoKMDPr+SO6/Xr18fd3V2KoBDitisoMrLlt4ts3Z+AodjE3W3q8/iQtjiYzX89sxB/4aaF0GQysWfPHq4dxP7Pj69eaC+EEFVNbzDxw+8JbN57kfwiI1GtAxnWtSkh/h4E+HnYfFR2UTvctBD6+fkxbdo0y2OdTlfmsUaj4ccff6yyYN5//33ee+89NmzYQKtWrTh06BAzZsxAr9fToEEDFixYYNlDrWibEKJm2Hs8hZU/nCanoJh2zf0Y3q3ZdSfCCFEVNOra3TsbOnbsGIsWLeLcuXMsW7aMFi1a0L9/f+bNm0dUVBQffPABCQkJzJs3D7PZXKG2W5GRkYfZbLvUBAR4ya/d/5FclKoLuSjUG/li62n+eyyZZiHejO7TkhYNrr+zS13IhbXsIRdarQY/P0+bxlBRVt1rtLoZDAZmzZpVZjSLo0eP4uLiYjkmOXr0aDZv3lypNiGEfTtzKZuZn/zGnuPJDO3alNce7nDDIihEVbKLU63effddhgwZQsOGDS3TkpKSCAkJsTz29fXFbDaTnZ1d4TadTmd1TPbwyyYgQLqBrpJclKqNuTCazKzadoqvfzhNgI8785/rRusmf315Vm3MRUVJLirO5oXw4MGDHD16lJdfftnWoZQhXaP2Q3JRqjbmIiWrgH9uOM65xBy6tK3PmHta4ebi+JfrWRtzUVH2kIua3DVq80K4b98+4uLi6NOnDwDJycmMHz+esWPHkpiYaHleZmYmWq0WnU5HcHBwhdqEEPZDbzCx5beLfLf3Ao5aLROGtiE6LMjWYYk6yOaF8KmnnuKpp56yPO7du7flZJmvvvqK/fv3ExUVxapVqxgwYAAAbdu2paio6JbbhBDVIyffwLH4TDJzi2jX3J+GAR7ljt9nVor/Hk1mzc44svMMRIYG8GCflvh6u97mqIUoYfNCWB6tVsv8+fOZOXNmmcsgKtMmhKgaJrOZuMs5HI3P4I9zmVxILu2WW7PzHMF+7nRsHUh0WBAh/qW3NjtxIYsvt5/hYkoeTYO9mDC0La0a6WywBkKUspvLJ+yNHCO0H5KLUreai9MJ2ew/mUpYEx/aNffDQVvxE8ULiowcjkvn4Ok0jp3PolBvRKvR0KKBN22b+XFXMz90Xi4cOJ3GvhMpnLqYjQIaBHjQsXUgF5JzOXgmHT9vF0b0aE70nUGVGvZItotS9pALOUYohLArcZevsHbXOY6dz0KjgR/+d3Pq7uEhdA8PwcfLxarXySkwcOhMOr+fSuP4+UxMZkU9T2c6tg6gbVM/7mzie90AuL0iGtArogHZeXp+P5XGbydSWLsrHhdnB0b0aMY9UY1wdnKojtUWokKkEApRi5xPzmHtrniOxGXg6ebE33q1oEf7EI6fz2LHocus2x3Phl/OE97Cjx7tG9C2qS9FBhO5BQZyC4rJLTCQU2AgJ9/AiQtZnErIRinwr+fKPVGN6BAaQLMQb6v25HSeLvSJbEifyIZcydPj5KjF3dXpNmRBiFsjhVCIWuBSWh7f/nyOg2fS8XB1ZESPZvSJbGgZlSEyNIDI0ABSswv5+VAiu44kcvBMOhoNlHdwJNjPnfvubkJkqwDuCPIs9+QXa9TztG4PVAhbkEIoRA2363Ain285hbOTlmFdm3JPx0bljroeqHPjgZ7NGdatKQdOp5GQmoenmxNe7k54uzvj5e6Ml3vJYydH6b4UdYMUQiFqKLNZ8dVPZ9m6L4E2TXx4emhbPN2s63p0dNASHRYk1+0JgRRCIWqkgiIjy9Yf5ei5TPpGNmRUnxaVOiNUiLpMCqEQNUxKVgFLVh8hNauQRwaE0rN9A1uHJESNJoVQiBrkyNk05v57PwAvjWpP68Y+No5IiJpPCqEQNcTOQ5f5YutpgnzdeWHEXQT6uNs6JCFqBSmEQtg5pRTrfznPut3xdGgdyPh7W5d7VqgQ4tbJp0kIO2Y2K77YeoodhxLp0rY+Lz/SkazMfFuHJUStIoVQCDtVbDTx0frjHDidxsBOjRnRoxmODnJmqBBVTQqhEHaooKiYJauPcPrSFR7s05J7OjaydUhC1FpSCIWwM1m5et756hDJGQU8PaQNMXfKRe9CVCcphELYkRMXsvhk03Hyi4z8/W/h3NnE19YhCVHrSSEUwg6cT85hzY44jp3PwtfbhSljOtC4vpetwxKiTpBCKIQNJWXk8+2uePafTMXTzYlRvVvQu0MDueG1ELeRFEIhbCAzp4j1v8Sz+0gyTo5ahnRpQv/oO+T6QCFswOafuqysLF599VUuXryIs7MzjRs3ZtasWfj6+nLo0CFmzJiBXq+nQYMGLFiwAD8/P4AKtwlhS6lZBXy/9yK//JGEUtC7QwMGdW6Ct4ezrUMTos6y+UVJGo2GJ554gi1btrBhwwYaNWrEwoULMZvNvPLKK8yYMYMtW7YQFRXFwoULASrcJoStJKTm8dH6Y7z28R5++SOJLncFM++pToy5p5UUQSFszOaFUKfTERMTY3ncvn17EhMTOXr0KC4uLkRFRQEwevRoNm/eDFDhNiFut7OXrvDu14eZ+clvHDqbTv+OdxA7oTOPDmiNv87N1uEJIbCDrtFrmc1mVq5cSe/evUlKSiIkJMTS5uvri9lsJjs7u8JtOp3O6lj8/DyrZJ0qIyBAzhq8qiblwmgy898/kti4+xzH4zPxcnfmoQGtua9LU7zcK7/3V5NyUd0kF6UkFxVnV4Vw9uzZuLu78/DDD7Nt2zabxpKRkYfZrGy2/IAAL9LScm22fHtSU3KRk29g56HL7DiUSFaungCdK6P7tKRHeAguzg4U5espytdXahk1JRe3g+SilD3kQqvV2MUOREXYTSGMjY3lwoULLFu2DK1WS3BwMImJiZb2zMxMtFotOp2uwm1CVDWlFOeTc/lh/yX2nUzBaFK0aerL2P6htGvmh1arsXWIQoi/YBeF8J133uHo0aN8/PHHODuXdB21bduWoqIi9u/fT1RUFKtWrWLAgAGVahOiolKzC0lMzyctu5C07ELSs4tK/r5SiKHYjIuzA93DQ+gT2ZBgPw9bhyuEuAUapZTt+v+AM2fOMGjQIJo0aYKrqysADRs2ZOnSpRw4cICZM2eWuQzC398foMJt1pKuUfthy1wUGYx8vSOOnw5ctkxzcXIgQOdKgM4N/3puNAjwoGPrwNtyDaBsF6UkF6XsIRc1uWvU5oXQXkkhtB+2ysXx85l89v1JMq4U0SeqITFhQQTo3PByd0KjsU2Xp2wXpSQXpewhFzW5ENpF16gQ9qRQb+Trn86y41AiQT5uTH24Ay0b6mwdlhCimkghFOIaR+Mz+Oz7k2Tl6hkQfQfDujXF2Unu+ylEbSaFUAjgYkoum3+7yJ5jKQT7uTPt4UiaN6hn67CEELeBFEJRZ5mV4sjZDLbtT+DEhSycnbQM7NSYoV2byOgPQtQhUghFnaM3mNj9RxI/7E8gJasQHy8XRvZsTrfwEDzdnGwdnhDiNpNCKGo9o8nMxZQ84hKvEJ+Yw5G4DAr0RpoGe/P0kGZEhgbg6GDz2+4KIWxECqGoVYwmM8mZBVxKyyM+MZdziVe4kJKL0VRyKYyPlwvtmvvRu0NDmjfwttllEEII+yGFUNQ4xUYzuQUGcguKSckqIDE9n8vp+SSm55OaVYjpf9d/OjtqaVLfi76RjWgW4k2zEG98vV1tHL0Qwt5IIRR2RSlFdp6BpIx8kjIKSM4oIKeomIzsQkvxKzKYysyj0UCgzo0Qfw86tAqggb8HIf/7J12eQoi/IoVQ2ExugYGLqXkkpORxKS3PUvyuLXSuzg4E+3vg7uxAoK4enu5OeLk74+XuhJebMwE6V4L93OUsTyFEhUkhFNXKUGwiO09PVm7Jv8vp+SSk5nExJZfsPIPlefU8nQnx86Bz2/oE+3kQ7OdOsJ8HOk9nAgO9bX77KCFE7SWFUFRKkcFIalbJiAyp2YWkZhWSkVNE9v8KX36RsczzHbQagv3cCWvsS6NATxoFedIo0BPvKhiwVgghKkIKobipYqOZzJwi0nOKyLhSRPqVQtKvFJF+pYjUrEJy8g1lnu/p5oR/vZKRGVo20uHj6YKPlws6Txd0Xi4E6txwcpTjdkII+yGFsA5RSmEwmikoMlKgN1JQVExBkZG8wmJy8g3kFBjIyS8mp8BAbr6BK/kGcvINXDsGh1ajwdfbBf96roQ39yPQx41AH3cCdW4E6Nxwd5VNSghRs8i3lh0ymxX5hcVcyTdQbDRRbDSX/DOZMRrNGE0Kk1lhMpsxmRRmpTCZFEaTmfz/Fba8wpIzLEv+Lia/sJgCvdFyPd2NODtq8fZwxsvdGR8vFxrX98LP2xW/eq741yv538fLBQet7NEJIWoPKYRWMprM5BUWU6g3UmQwUaQ3UmgwUWQoeXx1eoHeWNKmL5lWaDDioNXg4uRQ8s+59H8nB62lcOUWGP73fzH5RcVUZpRIB62m5OxKNyc83Zxo4O+Bp5sT7q5OuLs64u7iWPK/qyPuLk54ujni7eGMi5ODXGAuhKhzpBCW47s9F0hMz7d0FxYUFf/lPA5aDS7Ojrg4aXFzdkTn7UKQkztmpSguNqMvNpFXWExmrh5DsYlikxk3Fyc8XR3xr+fKHUFe/ytYjvj7eGDQF+PoqMHRQYuTgwOOjlocteDgoMVBq0Gr1eCg1Zb8rylZvpurEy5O2lpX0LTa2rU+lSG5KCW5KGXrXNh6+ZUhI9QLIYSo0+RgjxBCiDpNCqEQQog6TQqhEEKIOk0KoRBCiDpNCqEQQog6TQqhEEKIOk0KoRBCiDpNCqEQQog6TQqhEEKIOk0K4W0QGxtL7969CQ0N5fTp05bpO3bsYPjw4QwePJiHH36YhIQES5ter2fmzJn069ePwYMHM336dEtbfHw8o0aNon///owaNYrz58/fztWplFvNxaVLlxg6dKjlX+/evYmOjrbMV5dyAfDTTz8xbNgwhg4dypAhQ9i6daulra7l4mZtNTkXWVlZPPnkk/Tv35/Bgwfz3HPPkZmZCcChQ4cYMmQI/fv3Z9y4cWRkZFjmq2ibAJSodvv27VOJiYmqV69e6tSpU0oppbKzs1V0dLQ6d+6cUkqptWvXqnHjxlnmmT17tnrrrbeU2WxWSimVlpZmaRs7dqxau3atZb6xY8ferlWptIrk4lpz5sxRb775puVxXcqF2WxWUVFRlueeOHFCtW/fXplMJqVU3crFX20zNTkXWVlZas+ePZbHb7/9tnrttdeUyWRSffv2Vfv27VNKKbV06VI1depUpZSqcJsoIYXwNrr2Q3748GE1cOBAS1tWVpZq1aqVysjIUHl5eSoyMlLl5eVd9xrp6ekqMjJSGY1GpZRSRqNRRUZGqoyMjNuzElXE2lxcS6/Xq5iYGHX06FGlVN3LhdlsVtHR0Wr//v1KKaV+++031a9fP6VU3cvFzdpqSy6u2rx5s3r00UfV4cOH1X333WeZnpGRodq3b6+UUhVuEyWka9RGmjZtSnp6OkeOHAFgw4YNACQlJZGQkIBOp+P999/n/vvvZ+zYsezfv9/SHhQUhIODAwAODg4EBgaSlJRkmxWpAjfLxbW2b99OUFAQbdq0sbTXpVxoNBoWL17MxIkT6dWrF88++yyxsbGW9rqUi5u11aZcmM1mVq5cSe/evUlKSiIkJMTS5uvri9lsJjs7u8JtooQMw2QjXl5eLFq0iHnz5qHX6+nevTve3t44ODhgMplISEjgzjvvZMqUKRw+fJgJEyawbds2W4ddLW6Wi2utWbOGESNG2CjK2+NmuTAajXz00Ud88MEHREZG8vvvvzN58mQ2bdpk67Crxc1y8Vd5qi1mz56Nu7s7Dz/8cK39/NsDKYQ21LlzZzp37gxAeno6y5cv54477qCoqAhHR0cGDRoEQHh4OD4+PsTHxxMSEkJKSgomk8lSNFNTUwkODrblqlRaebm4KiUlhX379jF//nzLtODg4DqVixMnTpCamkpkZCQAkZGRuLm5ERcXR4MGDepULm7WVlhYWCtyERsby4ULF1i2bBlarZbg4GASExMt7ZmZmWi1WnQ6XYXbRAnpGrWhtLQ0oKT745133mH06NG4u7vj6+tLTEwMv/zyC1ByBlxGRgaNGzfGz8+PsLAwNm7cCMDGjRsJCwvD19fXZutRFcrLxVXffvstPXr0wMfHxzKtruWifv36JCcnc+7cOQDi4uLIyMjgjjvuqHO5uFlbbcjFO++8w9GjR1m6dCnOzs4AtG3blqKiIsthklWrVjFgwIBKtYkSMjDvbTBnzhy2bt1Keno6Pj4+6HQ6Nm3axOuvv86BAwcoLi6mS5cuTJs2DRcXFwASEhKYNm0a2dnZODo6MnnyZHr06AGUfAFOnTqVnJwcvL29iY2NpVmzZrZcRatVJBcA/fv35/XXX6d79+5lXq+u5WL9+vX885//RKMpGQ38hRdeoG/fvkDdy8XN2mpyLs6cOcOgQYNo0qQJrq6uADRs2JClS5dy4MABZs6ciV6vp0GDBixYsAB/f3+ACrcJKYRCCCHqOOkaFUIIUadJIRRCCFGnSSEUQghRp0khFEIIUadJIRRCCFGnSSEUQghRp0khFEIIUadJIRRCCFGn/X9zxAU9fLRf9AAAAABJRU5ErkJggg==\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": 13,
   "id": "c8fe40fe",
   "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()"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}