dukarc/Learning.ipynb

## Learning.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\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,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('fortune500.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['year', 'rank', 'company', 'revenue', 'profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25500"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "bento_obj_id": "139835980860368"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=25500, step=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "bento_obj_id": "139834812537600"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {
      "bento_obj_id": "139834812538552"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "bento_obj_id": "139835980675616"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "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</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1955</td>\n",
       "      <td>229</td>\n",
       "      <td>Norton</td>\n",
       "      <td>135.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1955</td>\n",
       "      <td>291</td>\n",
       "      <td>Schlitz Brewing</td>\n",
       "      <td>100.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>1955</td>\n",
       "      <td>295</td>\n",
       "      <td>Pacific Vegetable Oil</td>\n",
       "      <td>97.9</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1955</td>\n",
       "      <td>297</td>\n",
       "      <td>Liebmann Breweries</td>\n",
       "      <td>96.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1955</td>\n",
       "      <td>353</td>\n",
       "      <td>Minneapolis-Moline</td>\n",
       "      <td>77.4</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  rank                company  revenue profit\n",
       "228  1955   229                 Norton    135.0   N.A.\n",
       "290  1955   291        Schlitz Brewing    100.0   N.A.\n",
       "294  1955   295  Pacific Vegetable Oil     97.9   N.A.\n",
       "296  1955   297     Liebmann Breweries     96.0   N.A.\n",
       "352  1955   353     Minneapolis-Moline     77.4   N.A."
      ]
     },
     "execution_count": 8,
     "metadata": {
      "bento_obj_id": "139834812625808"
     },
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N.A.'}"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "bento_obj_id": "139834812187368"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.profit[non_numeric_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "bento_obj_id": "139834812203760"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.profit[non_numeric_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wLzlFdel70KBBeuyxxzRjxgx98sknmjNnjt555x116xb8cD5fZnvqjLlPaupCbs/O7qE+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2dd44fa4a8>"
      ]
     },
     "metadata": {
      "bento_obj_id": "139834812245160"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_sizes, _, _ = plt.hist(df.year[non_numeric_profits], bins=range(1955, 2006))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~non_numeric_profits]\n",
    "df.profit = df.profit.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25131"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "bento_obj_id": "139834812203568"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "bento_obj_id": "139834812028968"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
    "\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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y8Y6IiKjmjpxJh9Vmx+CeJYvf3MwtPXnHcD1XoSMiIqq5S1fzAACdWwdWuJ07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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2dd442add8>"
      ]
     },
     "metadata": {
      "bento_obj_id": "139834811395544"
     },
     "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W42XiJyIiegQXb/55T79jq+o/LU8ul+EfgzqggaMtth+9hlvJmWXeP3jmNvIL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\ngX/tPM2Gj85wS34KgVB72Ld6Aleccr+cNcHAtNt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2dd4276390>"
      ]
     },
     "metadata": {
      "bento_obj_id": "139834809607056"
     },
     "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": null,
   "metadata": {},
   "outputs": [],
   "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 Frotune 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()"
   ]
  }
 ],
 "metadata": {
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3rc1+"
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 },
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}