bliotti/Fortune500.ipynb

## Fortune500.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## activate matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## https://dev.ossys.com/fortune500.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('fortune500.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = ['year', 'rank', 'company', 'revenue', 'profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25500"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "non_numberic_profits = df.profit.str.contains('[^0-9.-]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[non_numberic_profits].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N.A.'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.profit[non_numberic_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.profit[non_numberic_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7yhda2XPAX3Tvc6w9RmjzJcC3F3mfkm0GTgb+tn3O3wV+r+t9rmzbPwf84bTbNcE2zwPP\n0Pki8Z+A98za50znNGjSGYm2oz2upDMabSudvzi2Aqe27YPOjXmeA54CFrre6wbg2fb4nWm3bZSH\nM0UlqQhPuUhSEQa6JBVhoEtSEQa6JBVhoEtSEQa6JBVhoEtSEQa6JBXxf6GgUhz8VjPqAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185cc208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_sizes, _, _ = plt.hist(df.year[non_numberic_profits], bins=range(1955, 2006))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.loc[~non_numberic_profits]\n",
    "df.profit = df.profit.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25131"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "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",
    "\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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q3Dbjnaud+Q+8QHeLhDwPFTL9buB5ty8r8CrJ3e223YpXrf3LeMWYVwPnhxzfS/Cu2w14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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11859add8>"
      ]
     },
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qNh1E1RhjTCt2lFfzwKL1nHzAQI4e2y/S1emxQr0a+o6IHBTWmhhjTJS5e+Fa\nqmrrufm0/SNdlR4t1CO644BLRWQj7tSlAKqqB4etZsYY042t3V7GU0tzueToUYztnxrp6vRooQa6\nU8NaC2OMiTK/enEVKYlxXDs1K9JV6fFCOnWpqpuBdNxIBmcA6T7NGGNME2+tK2TRmkKuOWk/MlIS\nIl2dHi/UZ11eBzwBDPCvx0XkmnBWzBhjuqP6BuWOF1YxPKMXs44ZFenqGEI/dXkFMFlVKwBE5DfA\nYuBP4aqYMcZ0R88s38Lq/DLu+8ZhNr5cFxFqr0sB6gPv632aMcYYr6K6jt+9spZJI9L56kGDI10d\n44Ua6B4BlojIrX7U8XdpZbRwABGZLSIFIvJpIC1DRBaKyDr/t69PFxG5V0RyRORjEZkUmGeWL79O\nRGYF0g8XkU/8PPeKPW7AGBNhD765gcKyan5++gR7AkoXEmpnlD8AlwHFQAlwmar+sY3ZHgWa3mR+\nE/CqqmYBr/r34Hp1ZvnXVcAD4AIjcAswGTgSuKUxOPoyVwXmsxvajTERsya/jL8sWs8Zhwxh0oi+\nbc9gOk2rgU5E0vzfDNzoBY8Dj+Ee6pzR2ryq+iYuMAbNBOb46Tm4YX8a0+eq8y6QLiKDgenAQlUt\nVtUSYCEww+elqepiVVVgbuCzjDGmU9XUNfCjpz+kd1Ict54xIdLVMU201RnlSeB03DMug+PPiX8/\nZh+/b6CqbgNQ1W0iMsCnDwW2BMrl+bTW0vOaSTfGmE533+s5rNi6iwcvPpx+qYmRro5potVAp6qn\n+7+jw1yP5k5mazvSm/9wkatwpzkZMWJEe+pnjDHN+jivlD+/nsPZhw1l+sRBka6OaUargS7YKaQ5\nqvr+Pn7fdhEZ7I/mBgMFPj0PGB4oNwzY6tOnNElf5NOHNVO+pXo+BDwEkJ2dbSOjG2M6RFVtPT96\n+iP6pyZyy5kTI10d04K2Tl3+vpU8BU7ax++bD8wC7vR/nw+kf19E5uE6nuz0wXAB8KtAB5RpwM2q\nWiwiZSJyFLAEuAS7p88Y08l+/8oacgrKmXv5kfTpFR/p6pgWtHXqst2jiIvIU7ijsUwRycP1nrwT\neFpErgBygfN88ReB04AcoBLXwxMf0G4H3vPlblPVxg4u38X17OwFvORfxhjTKZZsKOJvb2/koskj\nOGFc/0hXx7RCXKfFFjJFzm5tZlX9Z4fXKMyys7N12bJlka6GMaYbq6iuY8Y9byIIL113PCmJoT5k\nqvsSkeWqmh3perRHW/+dM1rJU6DbBTpjjPmy7nhxFXklu/n7VUf3iCDX3bV16vKyzqqIMcZ0B2+u\nLeTJJblcefxojhzd6u3Epot/nOHxAAAdj0lEQVRoq9flN1X1cRH5UXP5/okpxhjTI1TX1fOL5z9l\nTGYK108bH+nqmBC1dcyd4v/2DndFjDGmq/vbWxvZVFTJ3MuPJCneRiboLto6dfmg//v/Oqc6xhjT\nNW0t3c19r+UwbcJA62XZzYR0FVVERgPXAKOC86jqmeGpljHGdC2/enEVDar83+n2LMvuJtTuQs/h\nhuX5N9AQvuoYY0zXs3h9Ef/5eBvXTc1ieEZypKtj9lGoga5KVe8Na02MMaYLqqtv4Nb5KxjWtxff\nnTI20tUx7RBqoLtHRG4BXgGqGxPb8axLY4zpVh57dzNrtpfxl28ebh1QuqlQA91BwMW4Z1s2nrps\nz7MujTGm29hRXs0fFq7l+KxMpk8cGOnqmHYKNdB9DRijqjXhrIwxxnQld728mt019dxyxkREmhsd\nzHQHrY4wHvARkB7OihhjTFfyQW4JTy/L44rjRrPfgNRIV8d8CaEe0Q0EVovIe+x9jc5uLzDGRJ2G\nBuXW+SsY0DuRa6ZmRbo65ksKNdDdEtZaGGNMF/JhXikf5e3krnMOJtUe2tzttfWsS1HnjbbKdHzV\njDEmMjbtqAAge1TfNkqa7qCta3Svi8g1IjIimCgiCSJykojMwY0UbowxUSO3uBIRGNq3V6SrYjpA\nW8fkM4DLgaf8Y8BKgSQgFndP3d2q+mF4q2iMMZ0rt7iSwWlJJMbZfXPRoK2HOlcB9wP3i0g8kAns\nVtXSzqicMcZEwpbiSnvUVxQJ9fYCVLVWVbd1RJATkR+KyAoR+VREnhKRJBEZLSJLRGSdiPxdRBJ8\n2UT/Psfnjwp8zs0+fY2ITP+y9TLGGIDNRZWM7GeBLlqEHOg6iogMBa4FslX1QNxp0AuA3+BOhWYB\nJcAVfpYrgBJV3Q+425dDRCb4+SbiTrHeLyJ2nsEY86XsrqmnoKyaEXZEFzU6PdB5cUAvEYkDkoFt\nuMeJPePz5wBn+emZ/j0+f6q4RxTMBOaparWqbgRygCM7qf7GmCiVV1IJYKcuo0jIgU5ERorIyX66\nl4i0a9RxVf0M+B2QiwtwO4HlQKmq1vliecBQPz0U2OLnrfPl+wXTm5mnad2vEpFlIrKssLCwPdU2\nxvQQucUu0NkRXfQIKdCJyJW4o6kHfdIw3Bh1+0xE+uKOxkYDQ4AU4NRmijbem9fcA+a0lfQvJqo+\npKrZqprdv7+NDGyMaZkFuugT6hHd1cCxwC4AVV0HDGjnd54MbFTVQlWtBf4JHAOk+1OZ4ALpVj+d\nBwwH8Pl9gOJgejPzGGNMu+QWV5KSEEtGSkKkq2I6SKiBrjo4coEPOO19GkoucJSIJPtrbVOBlcDr\nwLm+zCzgeT89nz03pZ8LvOafxDIfuMD3yhwNZAFL21knY4wB9txaYKMVRI9QH+L2hoj8FNeB5BTg\ne8C/2/OFqrpERJ4B3gfqgA+Ah4AXgHki8kuf9rCf5WHgMRHJwR3JXeA/Z4WIPI0LknXA1apa3546\nGWNMo9ziSkb1S4l0NUwHCjXQ3YTr5v8J8G3gReBv7f1SVb2FLz4oegPN9Jr0N62f18Ln3AHc0d56\nGGNMkKqSW1zJiePsWn40CSnQqWoD8Ff/MsaYqFRYVk1VbYN1RIkyIQU6EdlIM9fkVHVMh9fIGGMi\npLHHpd1DF11CPXWZHZhOwp1KzOj46hhjTOTYrQXRKaRel6paFHh9pqp/xD3JxBhjooYNzxOdQj11\nOSnwNgZ3hNeuJ6MYY0xXZcPzRKdQT13+PjBdB2wCzu/w2hhjTATZ8DzRKdRel18Jd0WMMSbS7NaC\n6BTqqctE4BxgVHAeVb0tPNUyxpjOVVVbz/ZdNjxPNAr11OXz7BlloDp81THGmMjYYrcWRK1QA90w\nVZ0R1poYY0wE2a0F0SvUhzq/IyIHhbUmxhgTQRbooleoR3THAZf6J6RU48aCU1U9OGw1M8aYTmTD\n80SvUANdcwOjGmNM1LDheaJXqE9G2Ywb5PQkP10Z6rzGGNMd5BZX2mnLKBVSsBKRW4AbgZt9Ujzw\neLgqZYwxnalxeJ6R/SzQRaNQj8q+BpwJVACo6lbsEWDGmChhw/NEt1ADXY2qKn6oHhGx4XeNMVHD\nhueJbqEGuqdF5EEgXUSuBP6LDcJqjIkSdmtBdAu1M8rvgGeAZ4HxwC9U9U/t/VIRSReRZ0RktYis\nEpGjRSRDRBaKyDr/t68vKyJyr4jkiMjHwZEURGSWL79ORGa1tz7GmJ7NhueJbqE+6/KHwD9UdWEH\nfe89wMuqeq6IJADJwE+BV1X1ThG5CbgJ1wHmVCDLvyYDDwCTRSQDuAU3ZJACy0VkvqqWdFAdjTE9\nhA3PE91CPXWZBiwQkbdE5GoRGdjeLxSRNOAE4GEAVa1R1VJgJjDHF5sDnOWnZwJz1XkXd/p0MDAd\nWKiqxT64LQTsMWXGmH1mw/NEt1BPXf4/VZ0IXA0MAd4Qkf+28zvHAIXAIyLygYj8zXduGaiq2/z3\nbQMG+PJDgS2B+fN8WkvpXyAiV4nIMhFZVlhY2M5qG2Oild1DF9329abvAiAfKGJPINpXccAk4AFV\nPQx3y8JNrZRv7jEF2kr6FxNVH1LVbFXN7t/fxpoyxuzRODyP3UMXvUK9Yfy7IrIIeBXIBK78Es+5\nzAPyVHWJf/8MLvBt96ck8X8LAuWHB+YfBmxtJd0YY0KWV2K3FkS7UI/oRgI/UNWJqnqLqq5s7xeq\naj6wRUTG+6SpwEpgPtDYc3IWbgw8fPolvvflUcBOf2pzATBNRPr6HprTfJoxxoRsc5HdWhDtQup1\nqao3ichxInKZqj4iIv2BVFXd2M7vvQZ4wve43ABchgu6T4vIFUAucJ4v+yJwGpCDe8bmZb5OxSJy\nO/CeL3ebqha3sz7GmB7K7qGLfqHeXtDYjX888Ah7nnV5bHu+VFU/9J/X1NRmyiquE0xznzMbmN2e\nOhhjDNjwPD2BPevSGNOj2fA80c+edWmM6dHs1oLo92Wedfm38FXLGGPCr3F4Hgt00S3Uzii/E5FT\ngF3sedZlRz0OzBhjIqKw3A3PY/fQRbeQAh2AD2wLAUQkVkQuUtUnwlYzY4wJsy02PE+P0OqpSxFJ\nE5GbReQ+EZnm72X7Pu6WgPM7p4rGGBMedg9dz9DWEd1jQAmwGPgW8BMgAZjpbxEwxphuy4bn6Rna\nCnRjVPUgABH5G7ADGKGqZWGvmTHGhJkNz9MztBXoahsnVLVeRDZakDPGdCeqSmVNPfWqNDQoDQr1\nDYqqsqGwwq7P9QBtBbpDRGSXnxagl38vuIeWpIW1dsYY005bS3fzrw8+45nleWzcUdFiuQuOGN5i\nnokOrQY6VbXjeWNMl5C/s4qH3tyACIwbmMq4gb3JGtib1MQ9u7Gq2noWrMjnmeV5vJ2zA1U4akwG\n52UPIyE2htgYIUaEmBghRiBWhCnj2zvimOkuQr69wBhjIqG6rp6/vbWRP7+eQ129EhMDVbUNn+cP\n69uL8QN706dXPAtXbaesqo6h6b249qQszpk0jBF2j1yPZ4HOGNMlqSqvrirg9hdWsrmokukTB/Lz\nr05gaHovtpRUsia/jLXby1izvZx128t4P7eEUyYM5NzDh3HU6H7ExNizK41jgc4Y0+XkFJRz239W\n8ubaQvYbkMrjV0zmuKzMz/NH9kthZL8Upk0cFMFamu7CAp0xpssoLKvm/kU5PLZ4M70SYvnF6RO4\n+OiRxMeG+lheY77IAp0xJuJKK2t48M0NPPq/TdTUN3B+9jCunzaezNTESFfNRAELdMaYiCmvrmP2\n2xv565sbKK+p44yDh/CDk7MY0z810lUzUcQCnTGm01XX1TPnnU08sGg9JZW1TJswkB9NG8f+g+zW\nXNPxIhboRCQWWAZ8pqqni8hoYB6QAbwPXKyqNSKSCMwFDgeKgK+r6ib/GTcDVwD1wLWquqDzW2KM\n2Rdrt5dx7VMfsDq/jOOzMvnxtPEcMjw90tUyUSySV3ivA1YF3v8GuFtVs3APkr7Cp18BlKjqfsDd\nvhwiMgG4AJgIzADu98HTGNMFqSqP/m8jp//pbXaUV/PwrGweu2KyBTkTdhEJdCIyDPgqfpRyERHg\nJOAZX2QOcJafnunf4/On+vIzgXmqWq2qG4Ec4MjOaYExZl8UlFVx6SPvceu/V3Ls2H68dN0JTD1g\nYKSrZXqISJ26/CNwA9Dbv+8HlKpqnX+fBwz100OBLQCqWiciO335ocC7gc8MzrMXEbkKuApgxIgR\nHdcKY0ybXl21nRue+Zjy6jpunzmRbx41Evdb1ZjO0emBTkROBwpUdbmITGlMbqaotpHX2jx7J6o+\nBDwEkJ2d3WwZY0zH2llZy28WrObJJblMGJzGvAsOJWtg77ZnNKaDReKI7ljgTBE5DUgC0nBHeOki\nEueP6oYBW335PGA4kCcicUAfoDiQ3ig4jzGmgzU0aEiP1aqpa+Dxdzdz72vr2Lm7liuPH82Pp4+3\nMd9MxHR6oFPVm4GbAfwR3Y9V9SIR+QdwLq7n5SzgeT/LfP9+sc9/TVVVROYDT4rIH4AhQBawtDPb\nYkw0q6ypY/H6It5YW8gbawvJ31nFV8YP4KsHD2bqAQNITth796GqLFy5nV+/tJqNOyo4br9Mfnra\nAUwYYrcMmMjqSvfR3QjME5FfAh8AD/v0h4HHRCQHdyR3AYCqrhCRp4GVQB1wtarWd361jYmcnZW1\npPWK65BrXqrKuoJyFq0p4I21hby3sYSa+gaSE2I5Zmw/jtsvk4Urt/Pyinx6xcdy0gEDOP2gwXxl\n/wHkFJRz+39WsmRjMfsNSOWRS49gyvj+di3OdAmi2rMuWWVnZ+uyZcsiXQ1jvpTlm4v5/StreWd9\nEROHpHHR5JGceeiQvcZmC4Wq8lHeTl7+NJ+XP93GpqJKAPYf1JsTx/XnxHH9OXxU389PO9Y3KO9t\nKuY/H2/lpU/yKaqoITkhlt219fRNTuCHp4zjwiOGE2fPpow6IrJcVbMjXY/2sEBnTDfy0ZZS/rBw\nLW+sLSQzNYGzJw3jzbWFrM4vIyUhlrMOG8o3Jo9g4pA+LX5GfYOybFMxL32azysr8tm6s4q4GOHo\nsf2YceAgTtp/AIP79GqzLnX1DSzZWMxLn24jIzmBb50whrSk+I5srulCLNB1IxboTHe0YutO7l64\nlv+uKqBvcjzfPnEslxw9kuSEOFSV93NLeXJJLv/5eCvVdQ0cMjydKeP6U1ZVR1FFNcUVNRSV11BU\nUU1JRS019Q0kxMVw4rj+zJg4iJMPGEifZAtSpmUW6LoRC3SmOymrquVn//qU+R9tJS0pjqtOGMOl\nx45u8RTlzspann0/jyeX5pJTUE5qYhwZKQn0S02gX0oCGSkJZKQkcuDQNL4yfgAp+3iq0/Rc3TnQ\n2VpuTBeVW1TJt+a+x/rCCq45aT++dfwY+vRq/airT3I8lx83msuOHUVtvZIQZ9fKjLFAZ0wX9O6G\nIr77+HIaFB67/EiO2S+z7ZkCRISEOOvxaAxYoDOmy3lqaS7/99ynjOiXzMOzjmB0Zkqkq2RMt2aB\nzpguoq6+gV++sIpH39nECeP686cLD2vzVKUxpm0W6IzpAnburuX7T77PW+t2cPmxo/npafvbvWjG\ndBALdMZE2NbS3cyavZRNRRXcefZBXHCkjbBhTEeyQGdMBK3bXsYls5dSVlXHnMv2vdOJMaZtFuiM\niZDlm4u5/NFlJMTF8PdvH9Xq00yMMe1ngc6YCFi4cjvff/J9hqT3Yu7lRzI8IznSVTImalmgM6aT\nzVuay0//9QkHDe3D7EuPoF9qYqSrZExUs0BnTCdRVe57LYffL1zLCeP688BFk+wRXMZ0AtvKjOkE\nyzcX85uX17B0YzFnHzaU35x7MPF2+4AxncICnTFhtDp/F79bsIb/riogMzWR2886kIuOHEFMjD2e\ny5jOYoHOmDDILark7v+u5bkPPyM1MY6fTB/PZceOIjnBNjljOpttdcZ0oM9Kd/PgG+t5amkuMSJc\ndcIYvnviWNKTEyJdNWN6rE4PdCIyHJgLDAIagIdU9R4RyQD+DowCNgHnq2qJiAhwD3AaUAlcqqrv\n+8+aBfzcf/QvVXVOZ7bFmEY5BeX85Y31PPfBZwCclz2c66ZmMahPUoRrZoyJxBFdHXC9qr4vIr2B\n5SKyELgUeFVV7xSRm4CbgBuBU4Es/5oMPABM9oHxFiAbUP8581W1pNNbZHqsT/J2cv+iHF5ekU9i\nXAzfPGokV54whqHpvSJdNWOM1+mBTlW3Adv8dJmIrAKGAjOBKb7YHGARLtDNBOaqGwr9XRFJF5HB\nvuxCVS0G8MFyBvBUpzXG9Eiqyjvri/jLG+t5a90OeifFcfWU/bjs2FF2T5wxXVBEr9GJyCjgMGAJ\nMNAHQVR1m4gM8MWGAlsCs+X5tJbSm/ueq4CrAEaMsAfmmvapqK7jnx98xtx3NrGuoJzM1ERunLE/\n3zxqBL2TbDgdY7qqiAU6EUkFngV+oKq73KW45os2k6atpH8xUfUh4CGA7OzsZssY05JNOyqYu3gz\n/1i+hbKqOg4a2offnXcIpx88mKT42EhXzxjThogEOhGJxwW5J1T1nz55u4gM9kdzg4ECn54HDA/M\nPgzY6tOnNElfFM56m+i3c3ctW4oryfWvJRuKWLS2kFgRTj1oMJceM4pJI9Jp5YeZMaaLiUSvSwEe\nBlap6h8CWfOBWcCd/u/zgfTvi8g8XGeUnT4YLgB+JSJ9fblpwM2d0QYTPbYUV/LH/65j7fYycosr\n2bm7dq/8QWlJXHNSFhdNHsHANOtBaUx3FIkjumOBi4FPRORDn/ZTXIB7WkSuAHKB83zei7hbC3Jw\ntxdcBqCqxSJyO/CeL3dbY8cUY9rS0KA8vmQzd760GgEOH5XBIcP7MCIjmREZyQz3rzS79mZMtyeu\nM2PPkZ2drcuWLYt0NUwEbS6q4IZnPmbJxmJOGNefX599kN0OYEwbRGS5qmZHuh7tYU9GMT1GQ4Py\n6DubuGvBauJjYrjrnIM5L3uYXW8zJspZoDM9wvrCcm569mPe21TCV8b351dnH8TgPnYUZ0xPYIHO\nRC1V5b1NJTz89gYWrtxOamIcvz/vEM6eNNSO4ozpQSzQmahTU9fAC59sZfbbm/jks52kJ8fznRPH\ncumxoxjQ23pOGtPTWKAzUUFV2VRUyYufbGPu4k1s31XN2P4p3PG1Azn7sGH0SrAbu43pqSzQmW5p\nZ2UtH+aV8kFuCR9uKeXDLaWUVrp74I7PyuTOcw7mxKz+NsCpMcYCnenaauoa2LijgnUFZazdXs66\n7WWsyS9jw44KAERg3IDeTJ8wiMNGpDN5TD9GZ6ZEuNbGmK7EAp2JmJKKGvJKdlNUUU1xRQ3FFTUU\nVdRQXO7+biqqYNOOCuoa3L2eMQIj+6WQNSCVcw4fxmHD0zloWB97oLIxplUW6ExYNTQon5XuJqeg\nnPWF7uWmKyiuqPlC+bgYISMlgYyUBMZkpjB94kDGDexN1oDejOmfYg9RNsbsMwt0psNU1dazJr+M\nldt2sWrbLlZu3cXq/DLKq+s+L5ORksDY/ilMmzCQ/QakMjwjmczUBDJSEslISSAtKc66/htjOpQF\nOhMSVaWwrJrPSnezfVcV+TuryN9VHZiuYnNRBf4sI6mJcew/qDdnTxrK/oPSyBqYytj+qWSkJES2\nIcaYHscCnflcdV0920qryCvZzaaiCnKLK9lcVMHmIjdkTWVN/V7lE2JjGJCWyMC0JCYMTuOMQ4Yw\nYXBvJgzuw7C+vazHozGmS7BA1wOoKpU19ewor2ZHeQ1F5dUUlleztXQ3eSW7+azE/d1eVkXwGd8J\ncTGMzEhmZL9kjhmbyajMZIb17cXAtCQGpSXRNznBgpkxpsuzQNfNVNXWU1pZS3FFDSWVrqdi6e5a\ndjW+qmrZtbuOnX66uKKGHeXVVNU2fOGzYmOEIelJDEtP5risTIb17cWwvi6YjeyXzMDeSRbIjDHd\nngW6ENXUNVBWVUt5dR1lVe5VXl1HXIyQnBBLckIcyYmxn08nxsWwc7cPSL7bfEllDUXlNZRV1VFd\nV091XYN71TZO11Nbr9Q1KHX1DdTVK3UNDdQ1KLV1DZTurv3C6cOgxLgY0nrFk5YUR59e8b7jRyqZ\nqQn0S02kX0oCmb0TyUxJJLN3Av1TE4mLjenEpWiMMZ2vxwU6VTeqdEFZNYVlVf6vO6VXXl1HeVUt\nFdX1lFXXUeFfZdV11NR98YiovXonxpEYH0NiXCyJcTEkxMWQGB9LYmwMSfExxMXEEBcjxMWKm/Z/\n05Nd8OqbnEBGSjzpya4bfnpyPGlJ8db13hhjmtHjAt2nW3dy/F2v75UWI67be++keFIT40hJjGVo\nei9SE2NJTYojJTGONJ+XmhhH76Q4UpPcdH2Dsrumnoqaeipr6qisqaeiuo7quobPj6qCr/Re8XYU\nZYwxnajHjTAuIoXA5ghXIxPYEeE6dBW2LPawZbGHLYs9usqyGKmq/SNdifbocYGuKxCRZd11SPqO\nZstiD1sWe9iy2MOWxZdn59CMMcZENQt0xhhjopoFush4KNIV6EJsWexhy2IPWxZ72LL4kuwanTHG\nmKhmR3TGGGOimgU6Y4wxUc0CXQcRkdkiUiAinwbSDhGRxSLyiYj8W0TSAnkH+7wVPj/Jpx/u3+eI\nyL3SDQdn25dlISIXiciHgVeDiBzq83rasogXkTk+fZWI3ByYZ4aIrPHL4qZItOXL2MflkCAij/j0\nj0RkSmCeaFgnhovI6/5/vEJErvPpGSKyUETW+b99fbr4tuaIyMciMinwWbN8+XUiMitSberyVNVe\nHfACTgAmAZ8G0t4DTvTTlwO3++k44GPgEP++HxDrp5cCRwMCvAScGum2hXNZNJnvIGBD4H2PWhbA\nN4B5fjoZ2ASMAmKB9cAYIAH4CJgQ6baFcTlcDTzipwcAy4GYKFonBgOT/HRvYC0wAbgLuMmn3wT8\nxk+f5tsqwFHAEp+eAWzwf/v66b6Rbl9XfNkRXQdR1TeB4ibJ44E3/fRC4Bw/PQ34WFU/8vMWqWq9\niAwG0lR1sbo1eS5wVvhr37H2cVkEXQg8BdBDl4UCKSISB/QCaoBdwJFAjqpuUNUaYB4wM9x170j7\nuBwmAK/6+QqAUiA7itaJbar6vp8uA1YBQ3H/0zm+2Bz2tG0mMFedd4F0vyymAwtVtVhVS3DLcEYn\nNqXbsEAXXp8CZ/rp84DhfnocoCKyQETeF5EbfPpQIC8wf55PiwYtLYugr+MDHT1zWTwDVADbgFzg\nd6pajGv3lsD80bIsWloOHwEzRSROREYDh/u8qFsnRGQUcBiwBBioqtvABUPc0Sy0/P+P1vWiw1mg\nC6/LgatFZDnuFEWNT48DjgMu8n+/JiJTcacmmoqW+z9aWhYAiMhkoFJVG6/h9MRlcSRQDwwBRgPX\ni8gYondZtLQcZuN22suAPwLvAHVE2XIQkVTgWeAHqrqrtaLNpGkr6aaJHjd6QWdS1dW405SIyDjg\nqz4rD3hDVXf4vBdx1y8eB4YFPmIYsLXTKhxGrSyLRhew52gO3DLqacviG8DLqloLFIjI/4Bs3K/2\n4BFwVCyLlpaDqtYBP2wsJyLvAOuAEqJknRCReFyQe0JV/+mTt4vIYFXd5k9NFvj0PJr//+cBU5qk\nLwpnvbsrO6ILIxEZ4P/GAD8H/uKzFgAHi0iyvx5zIrDSn64oE5GjfG+yS4DnI1D1DtfKsmhMOw93\n7Qn4/NRNT1sWucBJvpddCq7jwWpcp40sERktIgm4HwXzO7/mHaul5eC3ixQ/fQpQp6pRs334uj8M\nrFLVPwSy5gONPSdnsadt84FL/HpxFLDTL4sFwDQR6et7aE7zaaapSPeGiZYX7mhkG1CL+6V1BXAd\nrkfVWuBO/JNofPlvAitw1ynuCqRn+7T1wH3BebrLqx3LYgrwbjOf06OWBZAK/MOvFyuBnwQ+5zRf\nfj3ws0i3K8zLYRSwBtdJ47+44WGiaZ04DneK8WPgQ/86Ddf7+lXc0eurQIYvL8CffZs/AbIDn3U5\nkONfl0W6bV31ZY8AM8YYE9Xs1KUxxpioZoHOGGNMVLNAZ4wxJqpZoDPGGBPVLNAZY4yJahbojDHG\nRDULdMYYY6La/wcY3EjXHmyAjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117b566a0>"
      ]
     },
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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czF5KKKtYQCjxXmRmDbGUezWwIy6/DVgDbDOzBmAh0JaYXpL8jIiITAMHE3Bn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\n/wGK2DmocWGVQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11859c978>"
      ]
     },
     "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",
    "\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.as_matrix()\n",
    "stds2 = group_by_year.std().revenue.as_matrix()\n",
    "plot_with_std(x, y1.as_matrix(), stds1, ax1, title % 'profits', 'Profit (millions)')\n",
    "plot_with_std(x, y2.as_matrix(), stds2, ax2, title % 'revenues', 'Revenue (millions)')\n",
    "fig.set_size_inches(14, 4)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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