Task_3_Blog_Source_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Airline Safety Visualization Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mary Donovan Martello"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The goal of this project was to effectively tell a story with data to different types of audiences. The topic was to examine airline safety data, determine how safe current airline travel is, and tell the story to an internal data science audience, internal executive audience, and to an external audience via a blog, an infographic, and a presentation.  This notebook includes the code for the blog to an external audiance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2:  Blog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import squarify    \n",
    "from collections import defaultdict\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.patches import Ellipse\n",
    "from matplotlib.text import OffsetFrom\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "\n",
    "# Plotting pretty figures and avoid blurry images\n",
    "#%config InlineBackend.figure_format = 'retina'\n",
    "# Larger scale for plots in notebooks\n",
    "#sns.set_context('talk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "bill = pd.read_excel('640Calcs.xlsx', sheet_name=5)\n",
    "depart = pd.read_excel('640Calcs.xlsx', sheet_name=7)\n",
    "airline = pd.read_excel('air_incidents_byairline.xlsx')\n",
    "airlineFatY = pd.read_excel('airline-safety_byairline.xlsx')\n",
    "usWorld = pd.read_excel('640Calcs.xlsx', sheet_name=4)\n",
    "smSourc = pd.read_excel('640Calcs.xlsx', sheet_name=8)\n",
    "smFreq = pd.read_excel('640Calcs.xlsx', sheet_name=9)\n",
    "fatal = pd.read_excel('AirlineFatalsbyYear.xlsx')\n",
    "rev = pd.read_excel('640Calcs.xlsx', sheet_name=10)\n",
    "auto2018 = pd.read_excel('640Calcs.xlsx', sheet_name=11)\n",
    "departRatio = pd.read_excel('640Calcs.xlsx', sheet_name=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set reParams\n",
    "plt.rcParams.update({\n",
    "    #\"figure.facecolor\":  (1.0, 0.0, 0.0, 0.0),  # red   with alpha = 100%\n",
    "    #\"axes.facecolor\":    (0.0, 1.0, 0.0, 0.0),  # green with alpha = 100%\n",
    "    #\"savefig.facecolor\": (0.0, 0.0, 1.0, 0.0),  # blue  with alpha = 100%\n",
    "    'axes.spines.right': False,\n",
    "    'axes.spines.top': False,\n",
    "    'axes.labelcolor': 'grey',\n",
    "    #'axes.titlecolor': 'grey',\n",
    "    'text.color': 'grey',\n",
    "    'xtick.color': 'grey',\n",
    "    'ytick.color': 'grey',\n",
    "    'axes.edgecolor': 'grey',\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Vehicle Miles</th>\n",
       "      <th>Air Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>152.693848</td>\n",
       "      <td>12.847213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>150.915594</td>\n",
       "      <td>81.479168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>150.577731</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>148.387543</td>\n",
       "      <td>3.346909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>144.472175</td>\n",
       "      <td>1.771134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Vehicle Miles  Air Miles\n",
       "0  2000     152.693848  12.847213\n",
       "1  2001     150.915594  81.479168\n",
       "2  2002     150.577731   0.000000\n",
       "3  2003     148.387543   3.346909\n",
       "4  2004     144.472175   1.771134"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bill.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auto Fatality Rate Much Higher than Air Fatality Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fatalities per miles traveled\n",
    "fig, ax = plt.subplots()\n",
    "ax.text(0.2, 0.2, 'Air Fatalities', transform=ax.transAxes, color='blue', fontsize=14, fontweight='bold')\n",
    "ax.text(0.5, 0.85, 'Auto Fatalities', transform=ax.transAxes, color='grey', fontsize=14, fontweight='bold')\n",
    "ax.text(0.2, 0.5, 'Air fatalities are always much lower than auto fatalties on the same scale', transform=ax.transAxes, \n",
    "        color='navy', fontsize=12, fontweight='bold')\n",
    "ax.text(0.5, 0.4, 'Even with the 9/11 terrorist fatalities!', transform=ax.transAxes, color='navy', fontsize=12, fontweight='bold')\n",
    "ax.plot(bill.Year, bill['Air Miles'], marker='', color='blue', linewidth=2)\n",
    "#ax.plot(bill.Year, bill['Vehicle Miles'], marker='o', markerfacecolor='grey', markersize=12, color='grey', linewidth=4)\n",
    "markers_on = [0, 18]\n",
    "ax.plot(bill.Year, bill['Vehicle Miles'], marker='o', markerfacecolor='grey', markersize=12, markevery=markers_on, \n",
    "        color='grey', linewidth=4)\n",
    "# set details of the graph\n",
    "ax.set(xlabel='Year', ylabel='Fatalities/10 Billion Miles')\n",
    "#from matplotlib.ticker import StrMethodFormatter\n",
    "ax.get_xaxis().set_major_formatter(\n",
    "    StrMethodFormatter('{x:.0f}'))\n",
    "ax.yaxis.set_label_coords(-0.1, 0.7)\n",
    "ax.xaxis.set_label_coords(0.05, -0.12)\n",
    "ax.set_title('Fatalities per vehicle miles vs air miles', loc='left', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Passenger Departures in Millions</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3143000</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>3327000</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>3561000</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>3810000</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>4093000</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Passenger Departures in Millions  Fatalities\n",
       "0  2013                           3143000         265\n",
       "1  2014                           3327000         990\n",
       "2  2015                           3561000         560\n",
       "3  2016                           3810000         325\n",
       "4  2017                           4093000          59"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depart.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Sixties</th>\n",
       "      <th>Seventies</th>\n",
       "      <th>Eighties</th>\n",
       "      <th>Nineties</th>\n",
       "      <th>Tens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350000</td>\n",
       "      <td>750000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>2700000</td>\n",
       "      <td>7900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sixties  Seventies  Eighties  Nineties     Tens\n",
       "0   350000     750000   1300000   2700000  7900000\n",
       "1        0          0         0         0        0\n",
       "2        0          0         0         0        0\n",
       "3        0          0         0         0        0\n",
       "4        0          0         0         0        0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departRatio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Departures without a fatality is drastically increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# departures without a fatality\n",
    "fig, ax = plt.subplots()\n",
    "# set x axis points\n",
    "pos = np.arange(0, 5)\n",
    "# create separate bars\n",
    "bar1 = plt.bar(pos, departRatio[\"Sixties\"], color='lightslategray')\n",
    "bar2 = plt.bar(pos+0.25, departRatio[\"Seventies\"], color='darkgray')\n",
    "bar3 = plt.bar(pos+0.5, departRatio[\"Eighties\"], color='gray')\n",
    "bar4 = plt.bar(pos+0.75, departRatio[\"Nineties\"], color='dimgray')\n",
    "bar5 = plt.bar(pos+1.0, departRatio[\"Tens\"], color='blue')\n",
    "\n",
    "\n",
    "# set details of the graph\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    FuncFormatter(lambda y, p: format(int(y), ',')))\n",
    "#plt.xticks(pos+0.25, depart['Year'], rotation='horizontal')\n",
    "plt.ylabel('Departures')\n",
    "ax.text(0.375, 0.5, 'Ratio of number of departures to 1 fatality', transform=ax.transAxes, \n",
    "        color='navy', fontsize=12, fontweight='bold')\n",
    "ax.text(0.4, 0.41, 'has IMMENSELY increased', transform=ax.transAxes, \n",
    "        color='navy', fontsize=12, fontweight='bold')\n",
    "#plt.title('Departures without a fatality')\n",
    "ax.legend((bar1, bar2, bar3, bar4, bar5), ('68-77', '78-87', '88-97', '98-07', '08-17'))\n",
    "#ax.set_xticklabels([])\n",
    "ax.yaxis.set_label_coords(-0.2, 0.9)\n",
    "ax.set_title('Departures without a fatality', loc='left', fontsize=15)\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Auto US</th>\n",
       "      <th>Air World</th>\n",
       "      <th>Air US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>36560</td>\n",
       "      <td>562</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Auto US  Air World  Air US\n",
       "0  2018    36560        562       1\n",
       "1     0        0          0       0\n",
       "2     0        0          0       0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto2018 = pd.read_excel('640Calcs.xlsx', sheet_name=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Airplane crashes attract attention; auto crashes not so much"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2018 fatalities auto vs air\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# set x axis points\n",
    "pos = np.arange(0, 3)\n",
    "# create separate bars\n",
    "bar1 = plt.bar(pos, auto2018[\"Air US\"], color='grey')\n",
    "bar2 = plt.bar(pos+0.25, auto2018[\"Air World\"], color='white')\n",
    "bar3 = plt.bar(pos+0.5, auto2018[\"Auto US\"], color='blue')\n",
    "plt.yscale('log')\n",
    "# set details of the graph\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    FuncFormatter(lambda y, p: format(int(y), ',')))\n",
    "#plt.xticks(pos+0.25, depart['Year'], rotation='horizontal')\n",
    "plt.ylabel('Fatalities')\n",
    "\n",
    "ax.text(0.45, 0.5, 'Too many auto deaths every year to be news', transform=ax.transAxes, \n",
    "        color='navy', fontsize=12, fontweight='bold')\n",
    "ax.text(0.0525, 0.1, '1', transform=ax.transAxes, \n",
    "        color='black', fontsize=12, fontweight='bold')\n",
    "ax.text(0.42, 0.9, '36,560', transform=ax.transAxes, \n",
    "        color='black', fontsize=12, fontweight='bold')\n",
    "\n",
    "#plt.title('Departures without a fatality')\n",
    "ax.legend((bar1, bar2, bar3), ('US Air Deaths', '', 'US Auto Deaths'))\n",
    "#ax.set_xticklabels([])\n",
    "ax.yaxis.set_label_coords(-0.2, 0.9)\n",
    "ax.set_title('Fatalities by Air vs Auto in 2018', loc='left', fontsize=15)\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Safety increasing as number of departures have been increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# departures\n",
    "# set up plot\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.plot(depart['Year'], depart['Passenger Departures in Millions'])\n",
    "ax.text(0.05, 0.7, 'Number of air passengers', transform=ax.transAxes, \n",
    "        color='navy', fontsize=12, fontweight='bold')\n",
    "ax.text(0.07, 0.63, 'has been steadily increasing', transform=ax.transAxes, \n",
    "        color='navy', fontsize=12, fontweight='bold')\n",
    "# set details of the graph\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    FuncFormatter(lambda y, p: format(int(y), ',')))\n",
    "ax.yaxis.set_label_coords(-0.158, 0.9)\n",
    "ax.xaxis.set_label_coords(0.05, -0.12)\n",
    "ax.set_title('Air Passenger Departures Annually', loc='left', fontsize=15)\n",
    "ax.set(xlabel='Year', ylabel='Departures')\n",
    "#ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>Accidents</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>47</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>38</td>\n",
       "      <td>1429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>56</td>\n",
       "      <td>2432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>50</td>\n",
       "      <td>2226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>46</td>\n",
       "      <td>2068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Accidents  Fatalities\n",
       "0  1970         47        1400\n",
       "1  1971         38        1429\n",
       "2  1972         56        2432\n",
       "3  1973         50        2226\n",
       "4  1974         46        2068"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fatal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 3)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fatal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>Accidents</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2019</td>\n",
       "      <td>14</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Accidents  Fatalities\n",
       "46  2016         11         288\n",
       "47  2017          5          31\n",
       "48  2018         11         542\n",
       "49  2019         14         267\n",
       "50  2020          1         176"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fatal.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent = usWorld.iloc[10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>US Fatalities</th>\n",
       "      <th>World Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  US Fatalities  World Fatalities\n",
       "10  2010              0               943\n",
       "11  2011              0               525\n",
       "12  2012              0               478\n",
       "13  2013              0               265\n",
       "14  2014              0               990\n",
       "15  2015              0               560\n",
       "16  2016              0               325\n",
       "17  2017              0                59\n",
       "18  2018              1               562\n",
       "19  2019              1               287"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US Airline Fatalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lEdztQ0RERFLPTrt6rbXn+c9XWGuvAL4GfLXH+2RyGbC4x/v/A24xxtQA64Ev++VfBtYbY6YAt/jrpY2PGgY3ojekOjSyd93mqMckIiIiyWGga/wK/ecRfTwGl1nEkLW2AjgR+I3/PgAcBfzJX+Ue4FT/9Sn+e/zlR/vrp4XahmbycrKoLBncfHw95/ITERGR9LTTrl5jzB3+y6eNMa/0XGatPSRmUQ3ercC3cQkpQCmwwRgTGnm8Ehjvvx4PrAAwxnRaazf666/tuUFr7VxgLkBRUVFMg4+muoYWJo0uJDtrcLlsaC4/TekiIiKSviId1fuzCMvizlp7EtBgjPl3j+K+sh4vgmXbGGPmGWNmGWNmBYOpczeL2oYWasoHN6IXID83m/KRw9TiJyIiksZ22uJnrf0UcDAwptc1fSOB7FgGNgiHACdba08A8nGx3QqMstbm+K1+FcBqf/2VQCWw0lqbAxQBTfEPO/pa2ztZuX4LZ86q3KXPV5UEWabET0REJG0N1OKXh7uWL4ftr+/bBHw+tqFFxhhzjTGmwhgzAXc3kWeNMV8AniMc4wXAI/7rR/33+MufNcbs0OKXipY0uoEZgx3YEVJVUsgKJX4iIiJpa6Br/F4AXrDW3m2MWRanmKLlKuB+a+11wJvAnX75ncDvrLV1uJa+tLn1XG2Dm2VnsFO5hFSVBPnLm1vZ2tFFfm6yNOiKiIhItAzU1XurMeabwM+ttX1dB3dyzCLbBcaY54Hn/ddLgAP7WGcrcEZcA4uTuoYWsrMCVJcWDrxyH6pLg3gerFzfOug7f4iIiEjyG2gC59/5zzfFOhAZutr6FiaUBsnLiXTMzvZCU8Asb1LiJyIiko4G6ur9t//8QnzCkaGoa2zZ5W5ecC1+gO7ZKyIikqYG6up9hz6mOgkxxsyIekSyS9o7u1m2rpUT9hq3y9soLcyjMC9bU7qIiIikqYG6ek+KSxQyZEvXbaar29vlEb0AgUCAypKgJnEWERFJUwN19abaSN6MVVu/a/fo7a26NLhtWhgRERFJLwO1+AFgrT0Id6eOabi5/bKBzcaYkTGMTQahrqGFQAAmjxla4ldVEuT5/zTS3e2RNcjbvomIiEhyi3T458+Bc4BaoAD4CklyyzZxahuaqSguoCBvaPPvVZUW0tbZTUNzW5QiExERkWQR8bwfxpg6INsY02WMuQs4MnZhyWDVNbQwZYitfeBa/AAN8BAREUlDEXX1Aq3W2jxgkbX2BmANsGuzBEvUdXV7LFm7mcN2HzPkbVWXhKZ02cyBE0uGvD0RERFJHpG2+J3vr3spsBmoBE6LVVAyOCuaWmnv7B7ywA6A8cUFZAXQPXtFRETSUKQtfqcaY24DtgIWwFp7GXBbrAKTyNU2RGdEL0Budha7jSpgmRI/ERGRtBNpi98FfZRdGMU4ZAhqG5qB6CR+4KZ00d07RERE0s9Ad+44BzgXmGitfbTHohHAulgGJpGra2ihfOQwRubnRmV7VSVBnnqvPirbEhERkeQxUFfvq7iBHKOBn/QobwbejlVQMjh1DS3UlI2I2vaqSgpZt7mdlrZOhg+L9GoAERERSXaR3LljGfCp+IQjg+V5HnUNLZw5qzJq29w2pcu6Vqbvpjm6RURE0oXu3JHiVm/cSmt7V9Su7wN3jR/A8qbNSvxERETSiO7ckeLqojiiN6SqVJM4i4iIpCPduSPF1da7Eb01UUz8RubnMiqYq5G9IiIiaUZ37khxHzW2UBzMpXT4sKhut7okqBY/ERGRNDOUO3ecHqugJHK19dEd0RtSqcRPREQk7Qw0j1+VMWa5P7oXety5QxLP8zxqG1o4cca4qG+7ujTIP979hM6ubnKyI74iQERERJLYQH/RHw69sNb+OcaxyCCtbWln45YOpoyJ3vV9IVUlQTq7PdZs3Br1bYuIiEhiDJT4BXq8nhTLQGTwQiN6a8pjkfi5Szg1wENERCR9DJT4ef28liRQF+V79PZUrSldRERE0s5Ao3pnWms34Vr+CvzX+O89TeCcWHUNLQwflsPYkflR33b5yHzysrNY1rQ56tsWERGRxBjolm3Z8QpEBq+2oYXJZcMJBAIDrzxI2VkBKkoKWK6uXhERkbSh4ZoprLahJaoTN/dWpSldRERE0ooSvxS1sbWDxua2mFzfF1JdEmT5ulY8T5d3ioiIpINI79yRtKy1+cCLwDDc8fzJGGOstROB+4ES4A3gfGNMu7V2GHAvsD+wDjjLGLM0IcEPQV1j9G/V1ltlSZDmtk42tHZQXJgXs/2IiIhIfKRDi18bcJQxZiawD3CctfYg4P+AW4wxNcB64Mv++l8G1htjpgC3+OulnNBULjFt8Sv1p3RRd6+IiEhaSPnEzxjjGWNa/Le5/sMDjgL+5JffA5zqvz7Ff4+//GhrbfRHR8RYbX0Lw3KyqCgOxmwfoSldlq3TyF4REZF0kPJdvQDW2mzg38AU4BfAR8AGY0ynv8pKYLz/ejywAsAY02mt3QiUAmt7bXMuMBegqKgo1ocwaHWNLUwaM5zsrNjlrJV+UrlCLX4iIiJpIS0SP2NMF7CPtXYU8FdgWh+rhUYo9JUp7TB6wRgzD5gHMG/evKQb3VBb38L+1cUx3UdBXjZlI4bp7h0iIiJpIuW7ensyxmwAngcOAkZZa0OJbQWw2n+9EqgE8JcXAU3xjXRoWts7WbVhS0wHdoRoShcREZH0kfKJn7V2jN/Sh7W2AJgDLAaeAz7vr3YB8Ij/+lH/Pf7yZ40xSdeitzMfNbhr7mI5sCOkqlSJn4iISLpI+cQPGAc8Z619G1gAzDfG/B24CrjCWluHu4bvTn/9O4FSv/wK4OoExDwk26ZyKY9Pi98nm7aytaMr5vsSERGR2Er5a/yMMW8D+/ZRvgQ4sI/yrcAZcQgtZmrrW8jJCmybbiWWqkuDeB6sXL8lLi2MIiIiEjvp0OKXceoaWpgwupDc7Nh/fVUlLrlc3qQpXURERFKdEr8UVNfQwpQx8Wl9qypxU7os18heERGRlKfEL8W0dXaxdN3muFzfBzB6eB7BvGzdvUNERCQNKPFLMUvXttLtxWdEL0AgEKCqJKhJnEVERNKAEr8UU9vgRvTGc6BFVUlQkziLiIikASV+KaauoYVAACbH6Ro/CE/i7HkpNd2hiIiI9KLEL8XUNrRQWRwkPzc7bvusLg3S1tlNQ3Nb3PYpIiIi0afEL8V81NAS9/n0qvz5AtXdKyIiktqU+KWQzq5uljRujss9envaNqWLBniIiIikNCV+KWTF+i20d3XHvcVv/KgCsgKwfJ0mcRYREUllSvxSSG19/Ef0AuTlZDGuqEAtfiIiIilOiV8KqWtsAeKf+IEb4KFJnEVERFKbEr8UUlffwtiR+YzIz437vqtKgrptm4iISIpT4pdC6hpb4nartt6qSoOs29xOS1tnQvYvIiIiQ6fEL0V0d3vUNbTEdeLmnqpL3JQuavUTERFJXUr8UsSaTVtpbe9KXIufpnQRERFJeUr8UsS2Eb0JavGrKg0lfprSRUREJFUp8UsRdQ1uRG9N+YiE7L+oIJeigly1+ImIiKQwJX4poq6hhZLCPEoK8xIWQ3VpULdtExERSWFK/FJEbQLu0dtbZUlQLX4iIiIpTIlfCvA8N6I33vfo7a26JMiq9Vvo7OpOaBwiIiKya5T4pYDGljY2bulIeItfdWmQzm6PNRu3JjQOERER2TVK/FLAtoEdZYkZ2BFSqSldREREUpoSvxQQSvwS3+LnJnHWAA8REZHUpMQvBdQ1tDBiWA7lI4clNI6xI/PJzQ6oxU9ERCRFKfFLAbX1LUwuG04gEEhoHNlZASqLg5rEWUREJEUp8UsBdY2JH9EbUlmiufxERERSlRK/JLextYPG5raEX98XUl0aZPm6VjzPS3QoIiIiMkg5iQ5gqKy1lcC9wFigG5hnjLnNWlsCPABMAJYCZxpj1ltrA8BtwAlAK3ChMeaNRMQeibpGd4/emvLkSPyqSoI0t3WyobWD4gTeRUREREQGLx1a/DqBK40x04CDgEustdOBq4FnjDE1wDP+e4DjgRr/MRf4VfxDjlxtvT+id0xip3IJqdKULiIiIikr5RM/Y8yaUIudMaYZWAyMB04B7vFXuwc41X99CnCvMcYzxrwGjLLWjotz2BGrbWghPzeL8cUFiQ4F6DGlixI/ERGRlJPyXb09WWsnAPsCrwPlxpg14JJDa22Zv9p4YEWPj630y9b02tZcXIsgRUVFMY17Z+oaWpg0ejjZWYkd0RtSWeIS0OXrNLJXREQk1aRN4metHQ78GfimMWaTtba/VfvKoHYYqWCMmQfMA5g3b17CRjLUNbQwa0Jxona/g2BeDmNGDFNXr4iISApK+a5eAGttLi7p+70x5i9+cX2oC9d/bvDLVwKVPT5eAayOV6yDsbmtk1UbtiTNVC4hVZrSRUREJCWlfIufP0r3TmCxMebmHoseBS4ArvefH+lRfqm19n5gNrAx1CWcbD5qTI5btfVWXRLktSXrEh2GiIiIDFLKJ37AIcD5wDvW2kV+2XdwCd+D1tovA8uBM/xlj+OmcqnDTedyUXzDjVz4Hr3JMaI3pKo0yF8XraKts4thOdmJDkdEREQilPKJnzHmZfq+bg/g6D7W94BLYhpUlNQ2tJCTFaC6NJjoULZTVRLE82Dl+i1MHpNcrZEiIiLSv7S4xi9d1TW0MHF0IbnZyfU1hRLR5brOT0REJKUkV0Yh26lraEm66/vA3a8XYJmmdBEREUkpSvySVFtnF8vWbU66Eb0AY4YPoyA3m+VNWxIdioiIiAyCEr8k9fHazXR7MDkJE79AIEBVSZDlTWrx2xVLl0Ig4B533z3w+tdeG14/5Igj3PsjjohJiJJGnn8+fP48/3z/6114oVtnwoT4xBUvd98dPv6lS11Zuh6rSCSU+CWp0IjemiQb0RtSVRpMqUmcQ4lS71/0oT8AgV7Dg/70Jzj4YCgpgYICqKiAo4+Ge+8deizDhsHs2e4xZsyubWP6dPf56dOHHk8mmzDBffcXXpjoSBJv8mR3Tu27b6IjSX2D/edOJJ5SflRvuqqtbyEQgEljChMdSp+qS4K8VNuI53kEemdNKe6RR+AMf/KfsWNdcrB6NTz7LFRWwhe/OLTtjxsHr70W2brt7X2X//KXQ4shntrbIS8v0VHERmcnZGfv+I9DIgy1nr//ffcQkfSmFr8kVdfYQlVJkPzc5Jwnr6o0yNaObhqb2xIdStT98Y/u+fDDXcL3xhvwySdQVzdwy9A//gGHHgplZe6P8MiRcNhhrjykr9aAnt1RDz0Es2a5zz/+eN/76aurN/T5q66CSy+F0lIXx2WXuQQlpL0d/vd/YY89XOtjaSmcey6sXBlep74ezj8fdtvNrVNW5o7rvvv6P/aeXYq//jUceSTk54eT1A8/hLPPDtdNTQ3ceCN0d4e34Xlw++2w//4QDMLw4e71q6+G13nqKTjqKFe3BQWulepvf+u7fn/yEzjvPBgxAsaPh+uu236dZcvc+3vu6bvlt6ee3YN33w0TJ7rj2LjRLb//fjjoICgsdI+jjoJXXtl+G/X18PWvQ1WV+2xZGXz2s+HlW7bAd78LU6a45SUlbvkbb4TXGehcuf12t/1g0H121ar+j6m/4wv5xz/gkEOguNjV9cSJ8LnPwccf73xbA31HV1zh9jV6tKsTgB/8wJWNHAlLlriy5mb49rddfQwb5upjzhxoanLLPQ9+8QuYOdPtp6gITj4Z3n8/smPu6aabYJ993D5yc913c9pp7rwN6Vn3jzzifrYLCmDqVPj738PrTJwY/sxFF+myDEkynufpMcDjjjvu8OLtMze/4H3prn/Ffb+Reu6Deq/6qr97//p4XaJDicjhh3seeF519fblF1zgyiFcdsYZ7v3YsZ53992eV1vred3dke3nxhs9LzfX8yZN8rx99/W84cPdtnJyPG/RIrfOxx+H93nXXa7srrvCZXl5nrfbbp43ZYrnPfyw5xmzY4yh4zn88HBZaJ3cXM8rKfG88ePDZfPmhdc76SRXlpXleXvv7XnFxe59VZXnNTW5dU47zZUVFnrefvu5esvK8rwvf7n/Y3/uue2PYfRoz5s2zX04AYIAACAASURBVPNuu83V4ahRbtmoUZ43Y4bbHnjepZeGt3HppeFtFBd73p57et6wYeF6eughzwsE3PKKCldH4MoeemjH+s3N9bxx41wsobKnnvK81as9b/ZsFye45bNnu0d/QudKbq6LfffdPa+83PPWr/e8m24Kb3/yZM+rrAyv++qr7vNr17p6DK03ZYrnTZiw/fc6Z054+dSpnjdihHtdUOB5b7458Lny2GPhZSUl7jwsLAyXPffcwMcX+hlpbAzXT2Wl582c6bYJnvfSS/1vJ5LvaOtWdw6A551+uvvZyM117+++263T1ubOvVDslZWuzrOy3Hfc+3yZNs19H+B5RUWe99FHO9ZX6HO9j9XzPO/EE11dTZvmeXvt5XnZ2eH9btmy47Zycz2vpsZ9N+C+q3XrPO/vf/e8ffYJrzdpkjuvLr64/zoTiYF+c5qEJ1Wp8Ih34tfR2eVN+c5j3o8efz+u+x2Mjxqaveqr/u49tHBFokOJyGASv/nzw7/0Q4+yMs+76CLPW7Zs5/v5+GOXCIQ0NYX/eH/ve+F1dpb4nXuu53V1ufLOzsEnfhMnet6GDe6P1W67ubKzznLrvPBCeL0nn3Rl69eHE6PrrnNle+3l3t97b3j7jY3h5LUvPRO/I44I/7Hs7HR1B+4P96ZNrvy++7xtCejy5a5eQgnDySeHP9/U5HlLlrjXEyeG6yiUjH/lK962RKp3/R50kEsgGhvDicVVV4VjDiViF1zQ/3GF9DxXbr/dlXV3e15LSzi5uuYaV97V5Xmf+YwrmzPHlVkb/vzvfx/e7sKF7vnZZ8PLb7zRla1ZE06YTzvNle3sXDnssPB5vmGDKz/vvPD6g0n8Fi70tiU0mzeH13vrLc9raOh/O5F8R57nee++63n5+a48lLCdeWZ4+T33hOP+0Y/C5R9+6M6hnudL6Ff01q3unwVw++xdXztL/N591/Pa28Pv588Pf+7pp3fc1hVXuLJHHgmXPfGEK+vrZ1wkzvrNadTVm4SWN7XS0eUl7cAOgIriIIEAKTPAYzDXYM2ZAwsWwJe+5LoHARoa4K67XHdn604Oub3ddZmVlblrv0pKXHcVuG7jSHzjG5Dl/2Rm70JP/8knuy6v/Pxwl1OoO+3118PrHXusq5fiYli71pWFrj0MdT9eeKG76P+EE+COO1zXbyS+9jW3/9AxhPb74YeuKy8QcF2w4Lp6//UvV+ee58quuCL8+eJidxyNjeEuxj/8wdVRIAC/+Y0rq6uDdb1uIX3WWa4bdPRo9530rItdVVAAX/2qex0IuG7Fzf4A9x//2JVlZ7vuTgjXaagOJkxwXesh++/vnhcsCJeFlo8d67rMARYu3DGW3ufKO++418ce684BgDPP3KXDZM89YdIkd/6WlblBH+ed54539Oi+PzOY72jPPeH6693r+nr3s3bHHeFtheorNxe+9a1weU2N67rveb587WtuP/n58N57rizS62hDli93dT1ypIv7mGPCy/r62T3/fPfcc4DVUM8tkXjQ4I4kVLvtHr3JN5VLSF5OFrsVFbA8RSZxLvTHyISuDQoJ/REa3quq990X7rzTvV66FP7nf+B3v3N/HN5801331JeTToLaWsjJgb33dn+I3nzTJYRdXZHFOnZsZOv1Z9So8Osc/yc89Acy9Axw4IE7JsRVVe75hz90x/jkk/Duu/Dyy/DEE/Dgg/DWWwPH0PsYQvstLXXXa/VWUBBOnvrTM/aJE8OJXE8dHdu/31ld7KqysnCy1Xt7U6eGE66QXRn4EelnIjlXdvV48/Ph3/925/3rr7uE7w9/gN//Htasgcsv3/m+IvmOQtOrAGzY4P4B6fmd7UzPfc2cGf5HISTSf1LAXVN46qnu53TECJeMd3bCIv/u73397IbizOnxV3So55ZIPKjFLwnVpUDiB/hz+aVGi98++7jn5mbX+tDZ6f6oPfecK585M7zuz38Of/1r+A/UhAmupS9k5Mi+97FunUv6wF2ovmiRu+A/GUZ8hhx4YPj1FVe4VpHXXoN//hNuuMG1nIAblHD44fDTn7rRzL/4hSt/++0dW9UGs9/CQneRf2i/Tz0FF1/sWhQPOCBcV7feCm3+uKGNG12CUFYWHniw117w0kvh7Tz4IFxzzeCT5qB/G+yBks6d2Wuv8HaOOsoNRAnFdffdYK1bNnu2e1661MUbEkqkDzggXPb737vnTz4Jn6OzZkUWC7h63bTJvf7znwd7RM6mTfDBB26g0H33uQEmodbHZ5/t+zOD+Y7mz4fbbnNJ9IwZ7js477zwQKRQfXV0wM03h/fx0UfQ0rL9+XLOOeH9vPaaO1//+78jP9bQP2fg/tlZsMANktpVwR63Vx/KuSUSC0r8klBdQwvjivIZPiy5G2SrU2guv69/3XW7guumy8tzf0g3b3Z/PK65Jrzuyy+70XyFhW7k6557hhOiffaBadP63kdJiZvvD8AY1+K333671l0bK0ccAccf716ffTbsvruLs6jIJXqh0aNXXx1undt///DxV1SE63EwvvMdt4/ly6G62rWoTprk9hEaKT1hAlxyiXv98MOuxWbGDDf9TWji4VDX4N/+5sr33detN2EC3HLL4OOaOtU9/+Uv7jgvumjw2wgG3fcNbgTz+PEurrIyd6784Q9u2SWXuGMH1wVdU+O60UPz5h15pLvMAFzX5rRp7vzbsMG1ZkUy1cq3v+2ely519Tt5MjzwwOCPCdzlDZ/6lPuOZsxwdRVK+GbM6P9zkXxH69a5793z3Kjzxx5zLWivv+5GnIM7P/fbz72+6ipXd1OnujpZu9a1KH7962751Ve75TNnuvNz1qxwV3sk9twz/HN63HHuZ+Ib34j8872NGePqLRTb7Nnws5/t+vZEokmJXxJK1nv09lZZEmRtSzstbZ0Dr5xgFRWuJeass6C83LUyFBW5FponnoATTwyv+9Wvhq9tq6+H//zHtVKcf777Y5bTTz4eCLjWlQMOcH9Eurpcy82uTtIcK3/9q2uFmjrVTWeycqVLEq68MjzlxFlnuVa65mZ33diIEXDKKa6udqUFc/fd3R/1s89223rvPdeid8QRrnUv5Kc/dcnTvvu6aymXLHEJUE1NOK4nnnDfW3s7LF7skqIzzhhcC0/Idde5KVjy8lzSG7pGbrC+/W33XR90kGsp+/BDl8hccAF85StundJS1xr1ta+5+SCXLnXrnnBCeDuPPuqS5EmTXMtWVpa7fOCVV8Kt1jtz0kmuxXr8ePdPzeTJ8Ktf7doxlZa6RHjcOBfrsmVue1ddFU50+xLJdzR3rrtubvfd3WUFFRXhxOiHP3Qt0Hl5LuH/1rdcfaxZ45LRww8Pt7r//OfunJk50y37+GMX78UXw+mnR36sU6fCb3/rksn2dncNY2hap10RmtJoyhQ3Rc+//hWeOkgk0QKeLkoY0Lx587y5c+fGZV/d3R57mic5+8BKzGf3jMs+d9Xf317NpX94kycuO5Rp4/rp/xQREZF46/dfdLX4JZnVG7ewpaMrqUf0hlSVuAtZlq1Lje5eERGRTKfEL8mkwojekOoSN1R2RYpc5yciIpLplPglmY/8xK8mBRK/omAuI/NzWNakYWsiIiKpQIlfkqmtb6G0MI/iwtS4q311aSHLm7YkOgwRERGJgBK/JFPXmBojekOqSoMpM4mziIhIplPil0Q8z6O2vpma8hRK/EqCrFy/hc6u7kSHIiIiIgNQ4pdEGlva2LS1kyljUifxqy4J0tntsWbj1kSHIiIiIgNQ4pdE6ur9gR3lyT+VS0hoSpdUuYOHiIhIJlPil0RSaSqXkKpSJX4iIiKpQolfEqlraGFEfg5lI4YlOpSIjSsqIDc7oEmcRUREUoASvyRS29DMlLLhBHblZqgJkp0VoKI4qEmcRUREUoASvyRS17A5JSZu7q2qJKhJnEVERFKAEr8ksaG1nbUtbSl1fV9IVUmQZeta8Twv0aGIiIjITijxSxJ1227VljojekOqS4M0b+1k45aORIciIiIiO5GT6ACGylr7W+AkoMEYs5dfVgI8AEwAlgJnGmPWW2sDwG3ACUArcKEx5o1ExN1bKo7oDan0p3RZtq6VUcHUuNWciIhIJkqHFr+7geN6lV0NPGOMqQGe8d8DHA/U+I+5wK/iFOOA6hpayM/NYvyogkSHMmjVmtJFREQkJaR84meMeRFo6lV8CnCP//oe4NQe5fcaYzxjzGvAKGvtuPhEunO1DS1MHjOcrKzUGdEbUlmsxE9ERCQVpHxXbz/KjTFrAIwxa6y1ZX75eGBFj/VW+mVrem/AWjsX1ypIUVFRbKMFPmpo4YAJxTHfTywUDsth9PBhLFunkb0iIiLJLF0Tv/701ZzW51BUY8w8YB7AvHnzYjpcdXNbJ6s2bOHc8qpY7iamqkuDavETERFJcinf1duP+lAXrv/c4JevBCp7rFcBrI5zbDv4qNEN7Jg8JvUGdoRUlQRZrrt3iIiIJLV0bfF7FLgAuN5/fqRH+aXW2vuB2cDGUJdwItXW+1O5lKd24vfwolW0dXYxLCc70eGIiIhIH1I+8bPW/hE4AhhtrV0JGFzC96C19svAcuAMf/XHcVO51OGmc7ko7gH3Ye+KIr517B5U+dOipKKqkiCeByvXb0nplksREZF0FtDdFgY2b948b+7cuYkOI6ktXNrE52//J3dddABH7lE28AdEREQkVvqdIiRdr/GTOAu1Vuo6PxERkeSlxE+iYsyIYRTkZrNMiZ+IiEjSUuInUREIBNzIXk3pIiIikrSU+EnUVJYEWd6kSZxFRESSlRI/iZrQJM4aMCQiIpKclPhJ1FSVBNna0U1jc1uiQxEREZE+KPGTqKkq9Uf26jo/ERGRpKTET6ImNKWLRvaKiIgkJyV+EjUVxQUEArBMLX4iIiJJSYmfRM2wnGx2KypghRI/ERGRpKTET6KqsqSAZes0pYuIiEgyUuInUVVdUsjypi2JDkMG4b3VG/nNS0tYvUHf267Y3NbJr19cwkeNLYkORURkQDmJDkDSS1VpkLUtbWxu66RwmE6vZLVpawePLlrNAwtW8M6qjQDc9kwt1526F6fsMz7B0aWOfy9bz+UPLGJ5Uys/mf8fvnPCNM4/qJpAoN/7o4uIJJRa/CSqQiN7NaVL8vE8jwVLm7jywbc48IdP872H36Wjq5trPzudv136aWrKhnPZ/Yv4xh/fZGNrR6LDTWodXd385Kn/cMbtr9LV7XH7efsze2Ip//PIe1xw1wLqN21NdIgiIn1Sk4xEVc/Eb9q4kQmORgDWtrTxlzdWcv+CFSxp3MzwYTl8bt8Kzj6gkhkVRdtapx782qe4/YWPuPXpWhZ83MRNZ8zk0zWjExx98qlraOHyBxbxzqqNnL5fBdeePJ0R+bkcu2c59722jB8+vphjb32RH39ub47fe1yiwxUR2Y4SP4mq6tAkzprLL6G6uj1eqm3kgQUrmP9+PZ3dHrOqi7n485M5ccY4gnk7/ujnZGdx6VE1HLb7GL75wCLOu/N1LjpkAlcdN5X83OwEHEVy8TyPe/+5jB89vphgXja/+sJ+2yV2gUCA8z81gYOnjObyBxZx8e/f4LT9xnPtyXsyMj83gZGLiIQp8ZOoGhXMY2R+jrp6E2Tl+lYeWriShxauYPXGrZQU5nHRIRM464BKppSNiGgbMypG8dg3DuX6JxZz1ytLebl2LbectQ97jS+KcfTJq37TVr71p7d58cNGjthjDDecPoOykfl9rjt5zHD+fPHB/OzZOn7xXB2vL2ni5jNnMntSaZyjFhHZkRI/ibqq0qAmcY6j9s5unl5cz/0LVvBSbSMAh9aM4XsnTWfOtHLycgZ/KW9BXjb2lL04alo533roLT73y1e4/Jjd+dphk8nOyqyBC4+/s4bv/PUdtnZ08b+n7sV5s6sGHLyRm53FFcfszhF7jOGKBxZx9q9fY+6hk7jiM7szLEetpyKSOEr8JOqqSwp5f82mRIeR9uoamnlgwQr+/MYqmja3s1tRPv91VA1nzKqgojgYlX0cvvsYnvzmYXzv4Xe54R//4dnFDdxy1j5UlkRn+8ls09YOrn3kPf7y5ipmVhRx81n7MHnM8EFtY7+qYh77r0O57rHF3PHiEl6sXcutZ+3DHmMja30VEYk2JX4SdZUlQZ56/xO6ur2Max2Ktdb2Th57ew0PLFjBwmXryckKcMz0cs46oJJDa8bEpL6LC/P4+bn7cvSbZZhH3uO4W1/EnLwnZ+xfkbbTlry2ZB1XPvgWn2zayn8dXcM3jppCbvauTYJQOCyHH5+2N3OmlXHVn9/msz97mW8ftwdfOmQiWfr5EJE4U+InUVddGqSjy2P1hi0Z0TIUa57n8c6qjdy/YAWPLlpNS1snk8YU8p0TpnLafhWMHj4s5jEEAgFO26+CAyeWcOWDb/HtP73NM4vr+dHn9qY0DvuPl7bOLm5+6kPmvbSE6pIgD339U+xXVRyVbR89rZx/fPMwrv7zO1z32GKeWdzAT86cyW6jCqKyfRGRSCjxk6gLTemyoqk1Lomf53msb+2gqCA3bVoYPc+jsaWNJ975hPsXrGDxmk3k52Zx4t67cfaBlcyqLk5Ia1tFcZA/fPUg7nx5CTc9+SHH3voSN35+BkdOLYt7LNH2wSeb+Ob9i/jgk2bOnV3Fd0+YFvVJyEcPH8avv7g/Dy1cif3bexx764tcd+penDxzt7RtPRWR5KLET6IulPgta2rl4Bjto7Orm4XL1vP0+/XMX1zPsnWt5GQFGDcqn4pRQSqKC6gods+VJe65fGR+0iSGnuexobWDleu3sGJ9KyvXt7Jy/Rb/4V63tncBMKOiiB9+bi8+O3O3pJgWJDsrwNzDJnNozRguf2ARF929gC/MruK7J07rc5qYZNfd7XHnyx9z45P/YWRBDr+9cBZHTS2P2f4CgQBnHlDJQZNKufzBRVx2/yLmv1/PdafuxahgXsz2KyICSvwkBsYV5ZOTFYj6lC4tbZ289GEj89+v59n/NLChtYO87CwOnlLKuQdWsWlrx7bk6cXaRuo3tW33+ZysALuNKvCTwnBiGHqOZmLYM7ELJ3XbJ3eb/cQuZGR+DhXFQSaUFvLpKWMYX1zAQZNK2HO35JxGZdq4kTx8ySHcPP9Dfv3SEl79aB23nLUP+1SOSnRoEVu1YQtXPriI15Y0ccz0cq4/LX5d11WlwW2TZt8y/0MWLl2vSbNFJOaU+EnU5WRnUVFcEJVJnOs3bWX++/U8vbieV+vW0d7VzahgLkftUcYx08s5dPcxDO+nO25rRxdrNm5l5fpWVjRtn4A9/59GGpq3Twxzs3skhqFWw5JwYlg2IpwYep7Hxi29E7vtX7e0dW63/RHDcqgoCVJVGuTgKaXhFsniIOOLCygqSHxr3mDl52bznROmceQeZVz54CJO/9WrfOOoKVxy5K4PhogHz/N4ZNFqvv/Iu3R3e9xw+gzOmBX/wSrZWQEuOXIKh2vSbBGJk4DneYmOIenNmzfPmzt3bqLDSClf/O2/WL+5nb9949OD+pzneXzwSfO2Lty3V24E3ICRY6aVM2d6ObOqi8mJQlKxtaOL1Ru2bEvUVvRqmWvsJzHMz8lm1YYdE7vhw3J6tSSGu5krioMpmdgNxsYtHdhH/elPKkdxy5kzmTTI6U/iYUNrO999+F0ee3sNs6qLufnMfagqTfwgpK0dXVz/xAfc/epSasqGZ/yk2SIyJP3+F6vELwJK/Abvew+/w6OLVvP2tccOuG5HVzcLPm7iKb9lb+X6LQDsWzWKOdPK+cz0cqaUDY97a8zWji5WbdixJW9Le9cO3cWVxUFGFuToAn3gsbfdhMftnd1898RpfCGCCY/j5aXaRv77obdY19LO5cfsztcPT74JqUMxNm1u55tzkjNGEUl6/f7SUFevxER1SSGbtnayobW9zwvWm7d28IJ/vd5zHzSwaWsnw3Ky+PSU0Vx65BSOmlZG2Yi+b4kVL/m52UweM3zQk/ZmuhNnjGP/6mK+9ae3+N7D7/LM4nr+7/MzEvp99mxNm1I2nDsvOCBpW9MOrXGTZn/34Xe58cn/8NwHDUnTKikiqS8jEz9r7XHAbUA28BtjzPUJDinthKZxWd7Uui3xW71hC08vrmf++/W8tmQdHV0eJYV5HLvnWOZML+fQmtEpOSpUdjS2KJ97LjqQe/+5lB8/8QHH3foSPz5tb47dc2zcY3l31UYuu/9NPmrczIUHT+Dq45P/+rlRwTx+fs6+HDOtnO8/8i7H3/Yi5rN7JuQ6RBFJLxnX1WutzQY+BI4BVgILgHOMMe/39xl19Q7e4jWbOP62l7h8zu54eMx/v573VrvbuE0aU8gx08o5Zno5+1YVqxsrzdU1NPPNBxbx7qpNnLF/BebkPfsdkBNNXd3ethGzpcPzuOmMmRxaMybm+422VRu28N8PvsU/l6zjM9PL+XEcRx6LJBvP8+jq9ujs9uj2/Ofu7Z+7unut0xVeN7ysm+5u3LPnEc9UaOq4kYyP/cTt6urt4UCgzhizBMBaez9wCtBv4ieDF5rL75anPyQQgP2rirnm+KnMmV6urtMMM6VsBH+5+BB++kwtv3y+jr+9vTouLW6dXR4tbZ2cNGNcSs+RN35UAb//ymx++8rH3PCP//Cp658lmJfcLZYi0dDd7dHt0StJS3RUQ/d/p+/NWQdUJWz/mZj4jQdW9Hi/EpjdeyVr7VxgLkBRUXJeC5TMCoflcP1pe5OVFeCoqWVxua2YJK+8nCz++9g9OHJqGX97azXx6mmYPamU4/cam/Ldo1lZAb5y6CQ+XTOahxaupLOrO9EhicRcIBAgO6vHw3+fkxUgy3/O7v0IrZMdICsQICcri+wsyO753Gud7KwA8ex4ikNr305lYuLX19e7w18hY8w8YB64rt5YB5WOzj4wcf/RSHLav7qY/aujc+/bTDR17Ei+f9L0RIchIikseWdYjZ2VQGWP9xXA6gTFIiIiIhI3mdjitwCosdZOBFYBZwPnJjYkERERkdjLuBY/Y0wncCnwJLAYeNAY815ioxIRERGJvUxs8cMY8zjweKLjEBEREYmnjGvxExEREclUSvxEREREMoQSPxEREZEMocRPREREJENk3L16d4W1thFYFuPdjAbWxngfqUJ1Eaa6CFNdOKqHMNVFmOoiTHUBa40xx/W5xPM8PZLgce211y5MdAzJ8lBdqC5UF6oH1YXqQnURm4e6ekVEREQyhBI/ERERkQyhxC95zEt0AElEdRGmughTXTiqhzDVRZjqIkx1sRMa3CEiIiKSIdTiJyIiIpIhlPiJiIiIZIicRAeQzqy1lcC9wFigG5hnjLnNWlsCPABMAJYCZxpj1ltrA8BtwAlAK3ChMeYNf1sXAN/zN32dMeaeeB7LUESrHqy1+wC/AkYCXcAPjTEPxPt4hiLK50QX8I6/6eXGmJPjeSxDFeW6uAE4EffP7HzgMmNMSlzHsgv1MBW4C9gP+K4x5qYe2zoOV0fZwG+MMdfH81iGKlp10d924nw4QxLl82Ip0Iz7vdlpjJkVx0MZsijXxeXAVwAP9/vzImPM1jgeTsKpxS+2OoErjTHTgIOAS6y104GrgWeMMTXAM/57gOOBGv8xF5fk4J/cBpgNHAgYa21xPA9kiKJSD7g/9l80xuwJHAfcaq0dFb/DiIpo1QXAFmPMPv4jpZI+X7R+Pg4GDgFmAHsBBwCHx/E4hmqw9dAE/BdwU8+NWGuzgV/g6mk6cI6/nVQSlbrYyXZSSbTqIuRI/3dFSiV9vmj9jIz3y2cZY/bC/YN0dnwOIXko8YshY8yaUIuEMaYZWAyMB04BQi129wCn+q9PAe41xnjGmNeAUdbaccCxwHxjTJMxZj2uRaPvGbmTULTqwRjzoTGm1t/OaqABGBPHQxmyKJ4TKS+KdeEB+UAeMAzIBerjdiBDNNh6MMY0GGMWAB29NnUgUGeMWWKMaQfu97eRMqJVFzvZTsqI4nmR8qJcFzlAgbU2BwgCq2McftJR4hcn1toJwL7A60C5MWYNuBMaKPNXGw+s6PGxlX5Zf+UpZ4j10HM7B+L+0H8U24hjJwp1kW+tXWitfc1aeyopbCh1YYz5J/AcsMZ/PGmMWRyfyKMrwnroT9r8noAh10V/20lJUagLD3jKWvtva+3cWMUZD0OpC2PMKlwr4HLc74qNxpinYhlvMlLiFwfW2uHAn4FvGmM27WTVQB9l3k7KU0oU6iG0nXHA73DXZnRHN8r4iFJdVPndNufiur0nRznMuBhqXVhrpwDTgApconOUtfaw6EcaW4Ooh/6kxe8JiEpdRHU7iRSlYzjEGLMf7jKAS1Lx5wOGXhf+JVKnABOB3YBCa+150Y0y+SnxizFrbS7uRP29MeYvfnF9qLvOf27wy1cClT0+XoFrhu6vPGVEqR6w1o4EHgO+53f3pZxo1YXf3Y0xZgnwPO6/4JQSpbr4HPCaMabFGNMCPIG7DihlDLIe+pPyvycganXR33ZSSrTqosfvigbgr7jLAlJKlOpiDvCxMabRGNMB/AU4OFYxJyuN6o0hfxTincBiY8zNPRY9ClwAXO8/P9Kj/FJr7f24gRwbjTFrrLVPAj/qMaDjM8A18TiGaIhiPeThfmnda4x5KG4HEEVRrItioNUY02atHY0b3HBDvI4jGqJYF8uBr1prf4xr9TocuDVOhzFku1AP/VkA1FhrJwKrcBetnxv9iGMnWnWxk+2kjCjWRSGQZYxp9l9/BvhBbKKOjSj+jCwHDrLWBoEtwNHAwuhHnNx0544YstZ+GngJN2Q81CX5Hdy1CQ8CVbgT8QxjTJN/cv8cN3CjFdeVudDf1pf8z4KbxuSuuB3IEEWrHvwm+buA93ps/kJjzKL4HMnQRbEuDgbu8LeRBdxqjLkzrgczRFGsi2zgl8BhuK7NfxhjrojrwQzBLtTDWNwfq5H+z2xinAAAAjhJREFU+i3AdGPMJmvtCbikNxv4rTHmh3E9mCGKVl3gRnjvsB1jzONxOpQhi2JdjMb9wwyusecPmXpe+D8jFjgLN1L4TeArxpi2eB5PoinxExEREckQusZPREREJEMo8RMRERHJEEr8RERERDKEEj8RERGRDKHET0RERCRDKPETERERyRBK/EREREQyhBI/ERERkQyhxE9EREQkQyjxExEREckQOYkOIFP49xl9CXef3Sf8sjOBLxljjktocCIiIpIRdK/eOLLW7gU8BOyLu4n6IuA4Y8xHQ9hmjjGmM0ohioiISBpT4hdn1tobgM1AIdBsjPlfa+0FwCVAHvAqcKkxpttaOw/YDygAHjDG/MDfxkrgDuA44FZjzEMJOBQRERFJMbrGL/4scC5wPHCD3wr4OeBgY8w+uO73s/11rzbGzAJmAsdYa6f32M5mY8whSvpEREQkUkr84swYsxl4APidMaYNmAMcACy01i4CDgcm+6ufY619A3gDmAb0TPweiF/UIiIikg40uCMxuv0HQAD4rTHm+z1XsNbWAJcBBxpjNlhr7wPye6yyOS6RioiISNpQi1/iPQ2caa0dDWCtLbXWVgEjgWZgk7V2HHBsAmMUERGRNKDEL8GMMe/grvt72lr7NvAUUI7r3n0feBf4NfBKwoIUERGRtKBRvSIiIiIZQi1+IiIiIhlCiZ+IiIhIhlDiJyIiIpIhlPiJiIiIZAglfiIiIiIZQomfiIiISIZQ4iciIiKSIf4/n4/QkJiLh+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# US air fatalities\n",
    "# set up plot\n",
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "ax.plot(usWorld.Year, usWorld['US Fatalities'])\n",
    "ax.text(0.2, 0.3, 'US airlines recent record is excellant', transform=ax.transAxes, color='blue', fontsize=14, fontweight='bold')\n",
    "\n",
    "# set details of the graph\n",
    "ax.yaxis.set_label_coords(-0.1, 0.7)\n",
    "ax.xaxis.set_label_coords(0.05, -0.12)\n",
    "ax.set_title('US Air Fatalities', loc='left', fontsize=15)\n",
    "ax.get_xaxis().set_major_formatter(\n",
    "    StrMethodFormatter('{x:.0f}'))\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    FuncFormatter(lambda y, p: format(int(y), ',')))\n",
    "ax.set(xlabel='Year', ylabel='Fatalities')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>airline</th>\n",
       "      <th>avail_seat_km_per_week</th>\n",
       "      <th>incidents_85_99</th>\n",
       "      <th>fatal_accidents_85_99</th>\n",
       "      <th>fatalities_85_99</th>\n",
       "      <th>incidents_00_14</th>\n",
       "      <th>fatal_accidents_00_14</th>\n",
       "      <th>fatalities_00_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aer lingus</td>\n",
       "      <td>320906734</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aeroflot*</td>\n",
       "      <td>1197672318</td>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>128</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aerolineas argentinas</td>\n",
       "      <td>385803648</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aeromexico*</td>\n",
       "      <td>596871813</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>air canada</td>\n",
       "      <td>1865253802</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 airline  avail_seat_km_per_week  incidents_85_99  \\\n",
       "0             aer lingus               320906734                2   \n",
       "1              aeroflot*              1197672318               76   \n",
       "2  aerolineas argentinas               385803648                6   \n",
       "3            aeromexico*               596871813                3   \n",
       "4             air canada              1865253802                2   \n",
       "\n",
       "   fatal_accidents_85_99  fatalities_85_99  incidents_00_14  \\\n",
       "0                      0                 0                0   \n",
       "1                     14               128                6   \n",
       "2                      0                 0                1   \n",
       "3                      1                64                5   \n",
       "4                      0                 0                2   \n",
       "\n",
       "   fatal_accidents_00_14  fatalities_00_14  \n",
       "0                      0                 0  \n",
       "1                      1                88  \n",
       "2                      0                 0  \n",
       "3                      0                 0  \n",
       "4                      0                 0  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 8)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlineFatY.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US Airlines Safer than Foreign Airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# by airline\n",
    "fig, ax = plt.subplots()\n",
    "# set x axis points\n",
    "pos = np.arange(len(airlineFatY['airline']))\n",
    "yLab = airlineFatY['airline'].tolist()\n",
    "y = airlineFatY['airline']\n",
    "# horizontal bars\n",
    "plt.barh(pos, airlineFatY['fatalities_00_14'], width, align='center', color=['blue', 'blue', 'blue', 'blue', 'blue', 'blue',\n",
    "                                                                      'grey', 'blue', 'grey', 'blue', 'blue', 'blue',\n",
    "                                                                      'grey', 'blue', 'grey', 'blue', 'blue', 'blue', \n",
    "                                                                      'blue', 'blue', 'blue', 'blue', 'orange', 'blue'\n",
    "                                                                     ])\n",
    "ax.text(0.10, 0.2, 'Almost all FOREIGN airlines', transform=ax.transAxes, color='blue', fontsize=14, fontweight='bold')\n",
    "\n",
    "# set details of the graph\n",
    "ax.yaxis.set_label_coords(-0.03, 0.7)\n",
    "ax.xaxis.set_label_coords(0.05, -0.12)\n",
    "ax.set_title('Fatalities by Airline 2000-2014', loc='left', fontsize=15)\n",
    "width = 0.75 # width of the bars\n",
    "widthPad = 1\n",
    "#plt.yticks(pos, airlineFatY['airline'], rotation = (0), fontsize = 11, va='bottom', ha='right')\n",
    "\n",
    "plt.ylabel(\"Airlines\")\n",
    "plt.xlabel('Fatalities')\n",
    "_, xmax = plt.xlim()\n",
    "#plt.xlim(0, xmax+300)\n",
    "for i, v in enumerate(y):\n",
    "    ax.text(550, i, v, color='black', fontweight='bold',fontsize=10, ha='left', va='center')\n",
    "ax.set_yticks([])\n",
    "# plt.tick_params(\n",
    "#     axis='y',          # changes apply to the x-axis\n",
    "#     which='both',      # both major and minor ticks are affected\n",
    "#     bottom=False,      # ticks along the bottom edge are off\n",
    "#     top=False,         # ticks along the top edge are off\n",
    "#     labelbottom=False) # labels along the bottom edge are off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>US Fatalities</th>\n",
       "      <th>World Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>89</td>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>531</td>\n",
       "      <td>1144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>22</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>13</td>\n",
       "      <td>552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  US Fatalities  World Fatalities\n",
       "0  2000             89              1148\n",
       "1  2001            531              1144\n",
       "2  2002              0              1112\n",
       "3  2003             22               705\n",
       "4  2004             13               552"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usWorld.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '2000'),\n",
       " Text(0, 0, '2000'),\n",
       " Text(0, 0, '2002'),\n",
       " Text(0, 0, '2005'),\n",
       " Text(0, 0, '2007'),\n",
       " Text(0, 0, '2010'),\n",
       " Text(0, 0, '2012'),\n",
       " Text(0, 0, '2015'),\n",
       " Text(0, 0, '2017')]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# foregin vs US fatalities\n",
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "ax.text(0.05, 0.58, '9/11', transform=ax.transAxes, color='black', fontsize=14)\n",
    "ax.text(0.05, 0.5, 'Attacks', transform=ax.transAxes, color='black', fontsize=14)\n",
    "ax.text(0.65, 0.15, 'US Airlines', transform=ax.transAxes, color='blue', fontsize=14, fontweight='bold')\n",
    "ax.text(0.75, 0.8, 'Foreign Airlines', transform=ax.transAxes, color='grey', fontsize=14, fontweight='bold')\n",
    "ax.text(0.15, 0.3, 'Foreign airlines much higher', transform=ax.transAxes, color='blue', fontsize=14, fontweight='bold')\n",
    "ax.text(0.2, 0.24, 'even with 9/11', transform=ax.transAxes, color='blue', fontsize=14, fontweight='bold')\n",
    "ax.plot(usWorld.Year, usWorld['US Fatalities'], marker='', color='blue', linewidth=2)\n",
    "markers_on = [0, 5, 10, 14, 18, 19]\n",
    "ax.plot(usWorld.Year, usWorld['World Fatalities'], marker='o', markerfacecolor='grey', markersize=12, markevery=markers_on,\n",
    "        color='grey', linewidth=4)\n",
    "# set details of the graph\n",
    "ax.yaxis.set_label_coords(-0.07, 0.7)\n",
    "ax.xaxis.set_label_coords(0.05, -0.12)\n",
    "ax.set_title('Domestic Airlines vs Foreign Airlines: Fatalities', loc='left', fontsize=15)\n",
    "ax.set_ylabel(ylabel='Fatalities', fontsize=13)\n",
    "ax.set_xlabel(xlabel='Year', fontsize=12)\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    FuncFormatter(lambda y, p: format(int(y), ',')))\n",
    "tick_labels = ('2000', '2000', '2002', '2005', '2007', '2010', '2012', '2015', '2017')\n",
    "ax.set_xticklabels(tick_labels)\n",
    "#ax.set(xlabel='Year', ylabel='Fatalities')"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}