zgpeace/HelloWorld.ipynb

## HelloWorld.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "print('Hello World!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "time.sleep(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, John!'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def say_hello(recipient):\n",
    "    return 'Hello, {}!'.format(recipient)\n",
    "\n",
    "say_hello('John')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is a level 1 heading\n",
    "\n",
    "## This is a level 2 heading\n",
    "\n",
    "This is some plain text that forms a paragragp. Add emphasis via **bold** and __bold__, or *italic* and _italic_.\n",
    "\n",
    "Paragraphs must be separated by an empty line.\n",
    "\n",
    "* Sometime we want to include lists.\n",
    "* Which can be bulleted using asterisks.\n",
    "\n",
    "1. Lists can also be numbered.\n",
    "2. If we want an ordered list.\n",
    "\n",
    "[It is possible to include hyperlinks](https://www.example.com)\n",
    "\n",
    "Inline code uses single backticks: `foo()`, and code blocks use triple backticks:\n",
    "```\n",
    "bar()\n",
    "```\n",
    "\n",
    "Or can be indented by 4 spaces:\n",
    "    \n",
    "    foo()\n",
    "    \n",
    "And finally, adding images is easy: ![Alt text](https://www.example.com/image.jpg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def square(x):\n",
    "    return x * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 squared is 9\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randint(1, 10)\n",
    "y = square(x)\n",
    "print('%d squared is %d' % (x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is 3 squared 9?\n"
     ]
    }
   ],
   "source": [
    "print('Is %d squared %d?' % (x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is 3 squared is 10?\n"
     ]
    }
   ],
   "source": [
    "y = 10\n",
    "print('Is %d squared is %d?' % (x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download file from \n",
    "# https://github.com/zgpeace/fortune500\n",
    "fileName = 'fortune500.csv'\n",
    "df = pd.read_csv(fileName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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 (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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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 (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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['year', 'rank', 'company', 'revenue', 'profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25500"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "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      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N.A.'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.profit[non_numberic_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.profit[non_numberic_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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_numberic_profits], bins=range(1955, 2006))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 24,
   "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": 25,
   "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": 26,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}