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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "code",
  5.    "execution_count": 1,
  6.    "metadata": {},
  7.    "outputs": [],
  8.    "source": [
  9.     "import pandas as pd\n",
  10.     "import matplotlib.pyplot as plt\n",
  11.     "import seaborn as sns\n",
  12.     "sns.set(style=\"darkgrid\")\n",
  13.     "df = pd.read_csv('fortune500.csv')"
  14.    ]
  15.   },
  16.   {
  17.    "cell_type": "code",
  18.    "execution_count": 2,
  19.    "metadata": {},
  20.    "outputs": [
  21.     {
  22.      "data": {
  23.       "text/html": [
  24.        "<div>\n",
  25.        "<style scoped>\n",
  26.        "    .dataframe tbody tr th:only-of-type {\n",
  27.        "        vertical-align: middle;\n",
  28.        "    }\n",
  29.        "\n",
  30.        "    .dataframe tbody tr th {\n",
  31.        "        vertical-align: top;\n",
  32.        "    }\n",
  33.        "\n",
  34.        "    .dataframe thead th {\n",
  35.        "        text-align: right;\n",
  36.        "    }\n",
  37.        "</style>\n",
  38.        "<table border=\"1\" class=\"dataframe\">\n",
  39.        "  <thead>\n",
  40.        "    <tr style=\"text-align: right;\">\n",
  41.        "      <th></th>\n",
  42.        "      <th>Year</th>\n",
  43.        "      <th>Rank</th>\n",
  44.        "      <th>Company</th>\n",
  45.        "      <th>Revenue (in millions)</th>\n",
  46.        "      <th>Profit (in millions)</th>\n",
  47.        "    </tr>\n",
  48.        "  </thead>\n",
  49.        "  <tbody>\n",
  50.        "    <tr>\n",
  51.        "      <th>0</th>\n",
  52.        "      <td>1955</td>\n",
  53.        "      <td>1</td>\n",
  54.        "      <td>General Motors</td>\n",
  55.        "      <td>9823.5</td>\n",
  56.        "      <td>806</td>\n",
  57.        "    </tr>\n",
  58.        "    <tr>\n",
  59.        "      <th>1</th>\n",
  60.        "      <td>1955</td>\n",
  61.        "      <td>2</td>\n",
  62.        "      <td>Exxon Mobil</td>\n",
  63.        "      <td>5661.4</td>\n",
  64.        "      <td>584.8</td>\n",
  65.        "    </tr>\n",
  66.        "    <tr>\n",
  67.        "      <th>2</th>\n",
  68.        "      <td>1955</td>\n",
  69.        "      <td>3</td>\n",
  70.        "      <td>U.S. Steel</td>\n",
  71.        "      <td>3250.4</td>\n",
  72.        "      <td>195.4</td>\n",
  73.        "    </tr>\n",
  74.        "    <tr>\n",
  75.        "      <th>3</th>\n",
  76.        "      <td>1955</td>\n",
  77.        "      <td>4</td>\n",
  78.        "      <td>General Electric</td>\n",
  79.        "      <td>2959.1</td>\n",
  80.        "      <td>212.6</td>\n",
  81.        "    </tr>\n",
  82.        "    <tr>\n",
  83.        "      <th>4</th>\n",
  84.        "      <td>1955</td>\n",
  85.        "      <td>5</td>\n",
  86.        "      <td>Esmark</td>\n",
  87.        "      <td>2510.8</td>\n",
  88.        "      <td>19.1</td>\n",
  89.        "    </tr>\n",
  90.        "  </tbody>\n",
  91.        "</table>\n",
  92.        "</div>"
  93.       ],
  94.       "text/plain": [
  95.        "   Year  Rank           Company  Revenue (in millions) Profit (in millions)\n",
  96.        "0  1955     1    General Motors                 9823.5                  806\n",
  97.        "1  1955     2       Exxon Mobil                 5661.4                584.8\n",
  98.        "2  1955     3        U.S. Steel                 3250.4                195.4\n",
  99.        "3  1955     4  General Electric                 2959.1                212.6\n",
  100.        "4  1955     5            Esmark                 2510.8                 19.1"
  101.       ]
  102.      },
  103.      "execution_count": 2,
  104.      "metadata": {},
  105.      "output_type": "execute_result"
  106.     }
  107.    ],
  108.    "source": [
  109.     "df.head()"
  110.    ]
  111.   },
  112.   {
  113.    "cell_type": "code",
  114.    "execution_count": 3,
  115.    "metadata": {},
  116.    "outputs": [
  117.     {
  118.      "data": {
  119.       "text/plain": [
  120.        "year         int64\n",
  121.        "rank         int64\n",
  122.        "company     object\n",
  123.        "revenue    float64\n",
  124.        "profit      object\n",
  125.        "dtype: object"
  126.       ]
  127.      },
  128.      "execution_count": 3,
  129.      "metadata": {},
  130.      "output_type": "execute_result"
  131.     }
  132.    ],
  133.    "source": [
  134.     "df.tail()\n",
  135.     "df.columns = ['year','rank','company','revenue','profit']\n",
  136.     "len(df)\n",
  137.     "df.dtypes"
  138.    ]
  139.   },
  140.   {
  141.    "cell_type": "code",
  142.    "execution_count": 4,
  143.    "metadata": {},
  144.    "outputs": [
  145.     {
  146.      "data": {
  147.       "text/html": [
  148.        "<div>\n",
  149.        "<style scoped>\n",
  150.        "    .dataframe tbody tr th:only-of-type {\n",
  151.        "        vertical-align: middle;\n",
  152.        "    }\n",
  153.        "\n",
  154.        "    .dataframe tbody tr th {\n",
  155.        "        vertical-align: top;\n",
  156.        "    }\n",
  157.        "\n",
  158.        "    .dataframe thead th {\n",
  159.        "        text-align: right;\n",
  160.        "    }\n",
  161.        "</style>\n",
  162.        "<table border=\"1\" class=\"dataframe\">\n",
  163.        "  <thead>\n",
  164.        "    <tr style=\"text-align: right;\">\n",
  165.        "      <th></th>\n",
  166.        "      <th>year</th>\n",
  167.        "      <th>rank</th>\n",
  168.        "      <th>company</th>\n",
  169.        "      <th>revenue</th>\n",
  170.        "      <th>profit</th>\n",
  171.        "    </tr>\n",
  172.        "  </thead>\n",
  173.        "  <tbody>\n",
  174.        "    <tr>\n",
  175.        "      <th>228</th>\n",
  176.        "      <td>1955</td>\n",
  177.        "      <td>229</td>\n",
  178.        "      <td>Norton</td>\n",
  179.        "      <td>135.0</td>\n",
  180.        "      <td>N.A.</td>\n",
  181.        "    </tr>\n",
  182.        "    <tr>\n",
  183.        "      <th>290</th>\n",
  184.        "      <td>1955</td>\n",
  185.        "      <td>291</td>\n",
  186.        "      <td>Schlitz Brewing</td>\n",
  187.        "      <td>100.0</td>\n",
  188.        "      <td>N.A.</td>\n",
  189.        "    </tr>\n",
  190.        "    <tr>\n",
  191.        "      <th>294</th>\n",
  192.        "      <td>1955</td>\n",
  193.        "      <td>295</td>\n",
  194.        "      <td>Pacific Vegetable Oil</td>\n",
  195.        "      <td>97.9</td>\n",
  196.        "      <td>N.A.</td>\n",
  197.        "    </tr>\n",
  198.        "    <tr>\n",
  199.        "      <th>296</th>\n",
  200.        "      <td>1955</td>\n",
  201.        "      <td>297</td>\n",
  202.        "      <td>Liebmann Breweries</td>\n",
  203.        "      <td>96.0</td>\n",
  204.        "      <td>N.A.</td>\n",
  205.        "    </tr>\n",
  206.        "    <tr>\n",
  207.        "      <th>352</th>\n",
  208.        "      <td>1955</td>\n",
  209.        "      <td>353</td>\n",
  210.        "      <td>Minneapolis-Moline</td>\n",
  211.        "      <td>77.4</td>\n",
  212.        "      <td>N.A.</td>\n",
  213.        "    </tr>\n",
  214.        "  </tbody>\n",
  215.        "</table>\n",
  216.        "</div>"
  217.       ],
  218.       "text/plain": [
  219.        "     year  rank                company  revenue profit\n",
  220.        "228  1955   229                 Norton    135.0   N.A.\n",
  221.        "290  1955   291        Schlitz Brewing    100.0   N.A.\n",
  222.        "294  1955   295  Pacific Vegetable Oil     97.9   N.A.\n",
  223.        "296  1955   297     Liebmann Breweries     96.0   N.A.\n",
  224.        "352  1955   353     Minneapolis-Moline     77.4   N.A."
  225.       ]
  226.      },
  227.      "execution_count": 4,
  228.      "metadata": {},
  229.      "output_type": "execute_result"
  230.     }
  231.    ],
  232.    "source": [
  233.     "non_numberic_profits = df.profit.str.contains('[^0-9.-]')\n",
  234.     "df.loc[non_numberic_profits].head()"
  235.    ]
  236.   },
  237.   {
  238.    "cell_type": "code",
  239.    "execution_count": 5,
  240.    "metadata": {},
  241.    "outputs": [
  242.     {
  243.      "data": {
  244.       "text/plain": [
  245.        "{'N.A.'}"
  246.       ]
  247.      },
  248.      "execution_count": 5,
  249.      "metadata": {},
  250.      "output_type": "execute_result"
  251.     }
  252.    ],
  253.    "source": [
  254.     "set(df.profit[non_numberic_profits])"
  255.    ]
  256.   },
  257.   {
  258.    "cell_type": "code",
  259.    "execution_count": 6,
  260.    "metadata": {},
  261.    "outputs": [
  262.     {
  263.      "data": {
  264.       "text/plain": [
  265.        "369"
  266.       ]
  267.      },
  268.      "execution_count": 6,
  269.      "metadata": {},
  270.      "output_type": "execute_result"
  271.     }
  272.    ],
  273.    "source": [
  274.     "len(df.profit[non_numberic_profits])"
  275.    ]
  276.   },
  277.   {
  278.    "cell_type": "code",
  279.    "execution_count": 7,
  280.    "metadata": {},
  281.    "outputs": [
  282.     {
  283.      "data": {
  284.       "image/png": "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\n",
  285.       "text/plain": [
  286.        "<Figure size 432x288 with 1 Axes>"
  287.       ]
  288.      },
  289.      "metadata": {},
  290.      "output_type": "display_data"
  291.     }
  292.    ],
  293.    "source": [
  294.     "bin_sizes, _, _ = plt.hist(df.year[non_numberic_profits], bins = range(1955,2006))"
  295.    ]
  296.   },
  297.   {
  298.    "cell_type": "code",
  299.    "execution_count": 8,
  300.    "metadata": {},
  301.    "outputs": [
  302.     {
  303.      "data": {
  304.       "text/plain": [
  305.        "25131"
  306.       ]
  307.      },
  308.      "execution_count": 8,
  309.      "metadata": {},
  310.      "output_type": "execute_result"
  311.     }
  312.    ],
  313.    "source": [
  314.     "df = df.loc[~non_numberic_profits]\n",
  315.     "df.profit = df.profit.apply(pd.to_numeric)\n",
  316.     "len(df)"
  317.    ]
  318.   },
  319.   {
  320.    "cell_type": "code",
  321.    "execution_count": 9,
  322.    "metadata": {},
  323.    "outputs": [
  324.     {
  325.      "data": {
  326.       "text/plain": [
  327.        "year         int64\n",
  328.        "rank         int64\n",
  329.        "company     object\n",
  330.        "revenue    float64\n",
  331.        "profit     float64\n",
  332.        "dtype: object"
  333.       ]
  334.      },
  335.      "execution_count": 9,
  336.      "metadata": {},
  337.      "output_type": "execute_result"
  338.     }
  339.    ],
  340.    "source": [
  341.     "df.dtypes"
  342.    ]
  343.   },
  344.   {
  345.    "cell_type": "code",
  346.    "execution_count": 10,
  347.    "metadata": {},
  348.    "outputs": [],
  349.    "source": [
  350.     "group_by_year = df.loc[:, ['year', 'revenue', 'profit']].groupby('year')\n",
  351.     "avgs = group_by_year.mean()\n",
  352.     "x = avgs.index\n",
  353.     "y1 = avgs.profit\n",
  354.     "def plot(x, y, ax, title, y_label):\n",
  355.     "    ax.set_title(title)\n",
  356.     "    ax.set_ylabel(y_label)\n",
  357.     "    ax.plot(x, y)\n",
  358.     "    ax.margins(x=0, y=0)"
  359.    ]
  360.   },
  361.   {
  362.    "cell_type": "code",
  363.    "execution_count": 11,
  364.    "metadata": {},
  365.    "outputs": [
  366.     {
  367.      "data": {
  368.       "image/png": 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\n",
  369.       "text/plain": [
  370.        "<Figure size 432x288 with 1 Axes>"
  371.       ]
  372.      },
  373.      "metadata": {},
  374.      "output_type": "display_data"
  375.     }
  376.    ],
  377.    "source": [
  378.     "fig, ax = plt.subplots()\n",
  379.     "plot(x, y1, ax, 'Increase in mean Fortune 500 company profits from 1955 to 2005','Profit(millions)')"
  380.    ]
  381.   },
  382.   {
  383.    "cell_type": "code",
  384.    "execution_count": 12,
  385.    "metadata": {},
  386.    "outputs": [
  387.     {
  388.      "data": {
  389.       "image/png": 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\n",
  390.       "text/plain": [
  391.        "<Figure size 432x288 with 1 Axes>"
  392.       ]
  393.      },
  394.      "metadata": {},
  395.      "output_type": "display_data"
  396.     }
  397.    ],
  398.    "source": [
  399.     "y2 = avgs.revenue\n",
  400.     "fig, ax = plt.subplots()\n",
  401.     "plot(x, y2, ax, 'Increase in mean Fortune 500 company revenues from 1955 to 2005', 'Revenue (millions)')"
  402.    ]
  403.   },
  404.   {
  405.    "cell_type": "code",
  406.    "execution_count": 13,
  407.    "metadata": {},
  408.    "outputs": [
  409.     {
  410.      "data": {
  411.       "image/png": 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\n",
  412.       "text/plain": [
  413.        "<Figure size 1008x288 with 2 Axes>"
  414.       ]
  415.      },
  416.      "metadata": {},
  417.      "output_type": "display_data"
  418.     }
  419.    ],
  420.    "source": [
  421.     "def plot_with_std(x, y, stds, ax, title, y_label):\n",
  422.     "    ax.fill_between(x, y - stds, y + stds, alpha=0.2)\n",
  423.     "    plot(x, y, ax, title, y_label)\n",
  424.     "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
  425.     "title = 'Increase in mean and std Fortune 500 company %s from 1955 to 2005'\n",
  426.     "stds1 = group_by_year.std().profit.values\n",
  427.     "stds2 = group_by_year.std().revenue.values\n",
  428.     "plot_with_std(x, y1.values, stds1, ax1, title % 'profits', 'Profit (millions)')\n",
  429.     "plot_with_std(x, y2.values, stds2, ax2, title % 'revenues', 'Revenue (millions)')\n",
  430.     "fig.set_size_inches(14, 4)\n",
  431.     "fig.tight_layout()"
  432.    ]
  433.   }
  434.  ],
  435.  "metadata": {
  436.   "kernelspec": {
  437.    "display_name": "Python 3",
  438.    "language": "python",
  439.    "name": "python3"
  440.   },
  441.   "language_info": {
  442.    "codemirror_mode": {
  443.     "name": "ipython",
  444.     "version": 3
  445.    },
  446.    "file_extension": ".py",
  447.    "mimetype": "text/x-python",
  448.    "name": "python",
  449.    "nbconvert_exporter": "python",
  450.    "pygments_lexer": "ipython3",
  451.    "version": "3.6.5"
  452.   }
  453.  },
  454.  "nbformat": 4,
  455.  "nbformat_minor": 2
  456. }
RAW Paste Data
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