Jupyter notebook

fortune500.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"fortune500.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</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",
       "      <td>25495</td>\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",
       "      <td>25496</td>\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",
       "      <td>25497</td>\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",
       "      <td>25498</td>\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",
       "      <td>25499</td>\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [\"year\", \"rank\", \"company\", \"revenue\", \"profit\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25500"
      ]
     },
     "execution_count": 18,
     "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      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>228</td>\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",
       "      <td>290</td>\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",
       "      <td>294</td>\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",
       "      <td>296</td>\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",
       "      <td>352</td>\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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_numeric_profits = df.profit.str.contains(\"[^0-9.-]\")\n",
    "df.loc[non_numeric_profits].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N.A.'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.profit[non_numeric_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.profit[non_numeric_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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_numeric_profits], bins=range(1955, 2006))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jco/.local/lib/python3.7/site-packages/pandas/core/generic.py:5208: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[~non_numeric_profits]\n",
    "df.profit = df.profit.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25131"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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": 35,
   "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",
    "\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",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}