Aviation_Exploratory_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aviation Accident Exploratory Notebook\n",
    "\n",
    "## Overview \n",
    "\n",
    "This project analyzes data from decades of aviation accidents to recommend the best aircrafts for a business to invest in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import python packages for analyis, computation and visualizations\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading in the data with pd.read_csv\n",
    "Aviation_data = pd.read_csv('archive/AviationData.csv', low_memory=False, encoding='latin-1')\n",
    "USState_Codes = pd.read_csv('archive/USState_Codes.csv', low_memory=False, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aviation and US State Codes Data\n",
    "\n",
    "The data includes information from the National Transportation Safety Board that includes aviation accident data from 1962 to 2023 about civil aviation accidents and selected incidents in the United States and international waters. This information will be useful for finding the best aircraft models to invest in based on previous accidents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 88889 entries, 0 to 88888\n",
      "Data columns (total 31 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Event.Id                88889 non-null  object \n",
      " 1   Investigation.Type      88889 non-null  object \n",
      " 2   Accident.Number         88889 non-null  object \n",
      " 3   Event.Date              88889 non-null  object \n",
      " 4   Location                88837 non-null  object \n",
      " 5   Country                 88663 non-null  object \n",
      " 6   Latitude                34382 non-null  object \n",
      " 7   Longitude               34373 non-null  object \n",
      " 8   Airport.Code            50249 non-null  object \n",
      " 9   Airport.Name            52790 non-null  object \n",
      " 10  Injury.Severity         87889 non-null  object \n",
      " 11  Aircraft.damage         85695 non-null  object \n",
      " 12  Aircraft.Category       32287 non-null  object \n",
      " 13  Registration.Number     87572 non-null  object \n",
      " 14  Make                    88826 non-null  object \n",
      " 15  Model                   88797 non-null  object \n",
      " 16  Amateur.Built           88787 non-null  object \n",
      " 17  Number.of.Engines       82805 non-null  float64\n",
      " 18  Engine.Type             81812 non-null  object \n",
      " 19  FAR.Description         32023 non-null  object \n",
      " 20  Schedule                12582 non-null  object \n",
      " 21  Purpose.of.flight       82697 non-null  object \n",
      " 22  Air.carrier             16648 non-null  object \n",
      " 23  Total.Fatal.Injuries    77488 non-null  float64\n",
      " 24  Total.Serious.Injuries  76379 non-null  float64\n",
      " 25  Total.Minor.Injuries    76956 non-null  float64\n",
      " 26  Total.Uninjured         82977 non-null  float64\n",
      " 27  Weather.Condition       84397 non-null  object \n",
      " 28  Broad.phase.of.flight   61724 non-null  object \n",
      " 29  Report.Status           82508 non-null  object \n",
      " 30  Publication.Date        75118 non-null  object \n",
      "dtypes: float64(5), object(26)\n",
      "memory usage: 21.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# display information on the aviation dataframe\n",
    "Aviation_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number.of.Engines</th>\n",
       "      <th>Total.Fatal.Injuries</th>\n",
       "      <th>Total.Serious.Injuries</th>\n",
       "      <th>Total.Minor.Injuries</th>\n",
       "      <th>Total.Uninjured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82805.000000</td>\n",
       "      <td>77488.000000</td>\n",
       "      <td>76379.000000</td>\n",
       "      <td>76956.000000</td>\n",
       "      <td>82977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.146585</td>\n",
       "      <td>0.647855</td>\n",
       "      <td>0.279881</td>\n",
       "      <td>0.357061</td>\n",
       "      <td>5.325440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.446510</td>\n",
       "      <td>5.485960</td>\n",
       "      <td>1.544084</td>\n",
       "      <td>2.235625</td>\n",
       "      <td>27.913634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number.of.Engines  Total.Fatal.Injuries  Total.Serious.Injuries  \\\n",
       "count       82805.000000          77488.000000            76379.000000   \n",
       "mean            1.146585              0.647855                0.279881   \n",
       "std             0.446510              5.485960                1.544084   \n",
       "min             0.000000              0.000000                0.000000   \n",
       "25%             1.000000              0.000000                0.000000   \n",
       "50%             1.000000              0.000000                0.000000   \n",
       "75%             1.000000              0.000000                0.000000   \n",
       "max             8.000000            349.000000              161.000000   \n",
       "\n",
       "       Total.Minor.Injuries  Total.Uninjured  \n",
       "count          76956.000000     82977.000000  \n",
       "mean               0.357061         5.325440  \n",
       "std                2.235625        27.913634  \n",
       "min                0.000000         0.000000  \n",
       "25%                0.000000         0.000000  \n",
       "50%                0.000000         1.000000  \n",
       "75%                0.000000         2.000000  \n",
       "max              380.000000       699.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic summary of statistics for numerical columns\n",
    "Aviation_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be outliers that are driving the maximums to shoot way above the average. Although due to the large number of records, it could be a skewed distribution but we shall see in the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62 entries, 0 to 61\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   US_State      62 non-null     object\n",
      " 1   Abbreviation  62 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# display information on the US State Codes dataframe\n",
    "USState_Codes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>US_State</th>\n",
       "      <th>Abbreviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>VI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Washington_DC</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Gulf of mexico</td>\n",
       "      <td>GM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Atlantic ocean</td>\n",
       "      <td>AO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Pacific ocean</td>\n",
       "      <td>PO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          US_State Abbreviation\n",
       "0          Alabama           AL\n",
       "1           Alaska           AK\n",
       "2          Arizona           AZ\n",
       "3         Arkansas           AR\n",
       "4       California           CA\n",
       "..             ...          ...\n",
       "57  Virgin Islands           VI\n",
       "58   Washington_DC           DC\n",
       "59  Gulf of mexico           GM\n",
       "60  Atlantic ocean           AO\n",
       "61   Pacific ocean           PO\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USState_Codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The avaiation dataframe includes 31 columns and close to 90,000 entries. For each incident it covers 31 categories, including but not limited to the investigation type, make and model of the plane, weather and location data, information about the flight, damage to the plane and injuries to passengers, the regulation data and more.\n",
    "There is a lot of missing data which we will sift through in data preparation in order to reach the best conclusions. In the US State Codes dataframe there are two columns, one with the state names and their corresponding code in the neighboring cell. There is no missing data and there are only object data types. This information will be prepared, cleaned, and analyzed to create visualizations which will subsequently aid in the final recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will prepare the data by making decisions on which data to use and which data to remove or replace. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns we're not using\n",
    "Aviation_columns = Aviation_data.drop(['FAR.Description','Schedule', 'Purpose.of.flight', 'Registration.Number', \n",
    "                                         'Publication.Date', 'Airport.Code', 'Airport.Name', 'Amateur.Built', 'Latitude', 'Longitude', 'Accident.Number', 'Engine.Type', 'Broad.phase.of.flight'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Event.Id                      0\n",
       "Investigation.Type            0\n",
       "Event.Date                    0\n",
       "Location                     52\n",
       "Country                     226\n",
       "Injury.Severity            1000\n",
       "Aircraft.damage            3194\n",
       "Aircraft.Category         56602\n",
       "Make                         63\n",
       "Model                        92\n",
       "Number.of.Engines          6084\n",
       "Air.carrier               72241\n",
       "Total.Fatal.Injuries      11401\n",
       "Total.Serious.Injuries    12510\n",
       "Total.Minor.Injuries      11933\n",
       "Total.Uninjured            5912\n",
       "Weather.Condition          4492\n",
       "Report.Status              6381\n",
       "dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display number of nulls in each column\n",
    "Aviation_columns.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANCHORAGE, AK             434\n",
       "MIAMI, FL                 200\n",
       "ALBUQUERQUE, NM           196\n",
       "HOUSTON, TX               193\n",
       "CHICAGO, IL               184\n",
       "                         ... \n",
       "VAN HORNSVILLE, NY          1\n",
       "DELOIT, IA                  1\n",
       "AVA, NY                     1\n",
       "Morona Santiago, Spain      1\n",
       "GEROME, AR                  1\n",
       "Name: Location, Length: 27758, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Location column values\n",
    "Aviation_columns['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     82248\n",
       "Brazil              374\n",
       "Canada              359\n",
       "Mexico              358\n",
       "United Kingdom      344\n",
       "                  ...  \n",
       "Obyan                 1\n",
       "Niger                 1\n",
       "Mauritania            1\n",
       "Benin                 1\n",
       "Chad                  1\n",
       "Name: Country, Length: 219, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Country column values \n",
    "Aviation_columns['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keeping USA country\n",
    "Aviation_columns = Aviation_columns[Aviation_columns['Country'] == 'United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "USState_Codes_dict = USState_Codes.set_index('US_State')['Abbreviation'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alabama': 'AL',\n",
       " 'Alaska': 'AK',\n",
       " 'Arizona': 'AZ',\n",
       " 'Arkansas': 'AR',\n",
       " 'California': 'CA',\n",
       " 'Colorado': 'CO',\n",
       " 'Connecticut': 'CT',\n",
       " 'Delaware': 'DE',\n",
       " 'Florida': 'FL',\n",
       " 'Georgia': 'GA',\n",
       " 'Hawaii': 'HI',\n",
       " 'Idaho': 'ID',\n",
       " 'Illinois': 'IL',\n",
       " 'Indiana': 'IN',\n",
       " 'Iowa': 'IA',\n",
       " 'Kansas': 'KS',\n",
       " 'Kentucky': 'KY',\n",
       " 'Louisiana': 'LA',\n",
       " 'Maine': 'ME',\n",
       " 'Maryland': 'MD',\n",
       " 'Massachusetts': 'MA',\n",
       " 'Michigan': 'MI',\n",
       " 'Minnesota': 'MN',\n",
       " 'Mississippi': 'MS',\n",
       " 'Missouri': 'MO',\n",
       " 'Montana': 'MT',\n",
       " 'Nebraska': 'NE',\n",
       " 'Nevada': 'NV',\n",
       " 'New Hampshire': 'NH',\n",
       " 'New Jersey': 'NJ',\n",
       " 'New Mexico': 'NM',\n",
       " 'New York': 'NY',\n",
       " 'North Carolina': 'NC',\n",
       " 'North Dakota': 'ND',\n",
       " 'Ohio': 'OH',\n",
       " 'Oklahoma': 'OK',\n",
       " 'Oregon': 'OR',\n",
       " 'Pennsylvania': 'PA',\n",
       " 'Rhode Island': 'RI',\n",
       " 'South Carolina': 'SC',\n",
       " 'South Dakota': 'SD',\n",
       " 'Tennessee': 'TN',\n",
       " 'Texas': 'TX',\n",
       " 'Utah': 'UT',\n",
       " 'Vermont': 'VT',\n",
       " 'Virginia': 'VA',\n",
       " 'Washington': 'WA',\n",
       " 'West Virginia': 'WV',\n",
       " 'Wisconsin': 'WI',\n",
       " 'Wyoming': 'WY',\n",
       " 'American Samoa': 'AS',\n",
       " 'Guam': 'GU',\n",
       " 'Marshall Islands': 'MH',\n",
       " 'Micronesia': 'FM',\n",
       " 'Northern Marianas': 'MP',\n",
       " 'Palau': 'PW',\n",
       " 'Puerto Rico': 'PR',\n",
       " 'Virgin Islands': 'VI',\n",
       " 'Washington_DC': 'DC',\n",
       " 'Gulf of mexico': 'GM',\n",
       " 'Atlantic ocean': 'AO',\n",
       " 'Pacific ocean': 'PO'}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USState_Codes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CA'"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Location'].values[1][-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing values with an empty string\n",
    "Aviation_columns['Location'].fillna('', inplace=True)\n",
    "\n",
    "# Only keep state abbreviation in location\n",
    "for index in range(len(Aviation_columns['Location'])):\n",
    "    Aviation_columns['Location'].values[index] = Aviation_columns['Location'].values[index][-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CA', 'TX', 'FL', 'AK', 'AZ', 'CO', 'WA', 'IL', 'MI', 'GA', 'NY', 'OH',\n",
       "       'PA', 'OR', 'NC', 'MO', 'WI', 'AR', 'MN', 'ID', 'NM', 'UT', 'IN', 'VA',\n",
       "       'OK', 'NV', 'LA', 'NJ', 'AL', 'TN', 'KS', 'MT', 'SC', 'MA', 'IA', 'MD',\n",
       "       'MS', 'WY', 'NE', 'KY', 'ND', 'ME', 'CT', 'HI', 'SD', 'WV', 'NH', 'VT',\n",
       "       'RI', 'PR', 'DE', 'GM', 'DC', 'OF', 'AO', 'n,', 'PO', '', 'o,', 'GU',\n",
       "       'VI', 'A,', 'g,', ',', 'UN', 'x,', 'e,', '9,', 'a,', 'd,', 'm,', 'i,',\n",
       "       'H,', 's,', 'C,', '8,', 'O,', 'CB', '7,', 'y,', 'l,'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Location'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    8857\n",
       "TX    5913\n",
       "FL    5825\n",
       "AK    5672\n",
       "AZ    2834\n",
       "      ... \n",
       "O,       1\n",
       "CB       1\n",
       "7,       1\n",
       "y,       1\n",
       "l,       1\n",
       "Name: Location, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States    82248\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airplane             27617\n",
       "Helicopter            3440\n",
       "Glider                 508\n",
       "Balloon                231\n",
       "Gyrocraft              173\n",
       "Weight-Shift           161\n",
       "Powered Parachute       91\n",
       "Ultralight              30\n",
       "Unknown                 14\n",
       "WSFT                     9\n",
       "Powered-Lift             5\n",
       "Blimp                    4\n",
       "UNK                      2\n",
       "Rocket                   1\n",
       "ULTR                     1\n",
       "Name: Aircraft.Category, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aircraft category values\n",
    "Aviation_data['Aircraft.Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Dropping aircrafts that arent airplanes because we only want airplanes\n",
    "Aviation_columns = Aviation_columns[Aviation_columns['Aircraft.Category'] == 'Airplane']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airplane    24229\n",
       "Name: Aircraft.Category, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Aircraft.Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop nulls in Model and Model column\n",
    "Aviation_columns = Aviation_columns.dropna(subset = ['Model', 'Make'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CESSNA              4264\n",
       "Cessna              3460\n",
       "PIPER               2496\n",
       "Piper               1840\n",
       "BEECH                890\n",
       "                    ... \n",
       "Ronald D. Murray       1\n",
       "Eaa Biplane            1\n",
       "Jonsson                1\n",
       "BAREISS DAVID A        1\n",
       "Crisman                1\n",
       "Name: Make, Length: 3702, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Make'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Make'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172           772\n",
       "152           427\n",
       "172N          305\n",
       "182           289\n",
       "172S          273\n",
       "             ... \n",
       "ZENAIR 701      1\n",
       "Vans RV-7       1\n",
       "SA-227-AT       1\n",
       "180 - B         1\n",
       "L16A            1\n",
       "Name: Model, Length: 4788, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Model'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Model'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Non-Fatal     19848\n",
       "Fatal          3028\n",
       "Fatal(1)        357\n",
       "Fatal(2)        242\n",
       "Incident        165\n",
       "Minor           157\n",
       "Serious         114\n",
       "Fatal(3)         80\n",
       "Fatal(4)         63\n",
       "Fatal(5)         23\n",
       "Fatal(6)         15\n",
       "Fatal(7)          9\n",
       "Fatal(8)          7\n",
       "Fatal(10)         4\n",
       "Fatal(12)         3\n",
       "Fatal(17)         1\n",
       "Fatal(27)         1\n",
       "Fatal(11)         1\n",
       "Fatal(14)         1\n",
       "Fatal(228)        1\n",
       "Fatal(13)         1\n",
       "Fatal(16)         1\n",
       "Fatal(9)          1\n",
       "Fatal(65)         1\n",
       "Name: Injury.Severity, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#injury severity column values\n",
    "Aviation_columns['Injury.Severity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Injury.Severity'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill nans with non fatal\n",
    "Aviation_columns['Injury.Severity'] = Aviation_columns['Injury.Severity'].fillna('Non-Fatal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Injury.Severity'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine all fatal values replace nans with fatal \n",
    "Aviation_columns.loc[Aviation_columns['Injury.Severity'].str.contains('Fatal\\('), 'Injury.Severity'] = 'Fatal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Non-Fatal    19929\n",
       "Fatal         3840\n",
       "Incident       165\n",
       "Minor          157\n",
       "Serious        114\n",
       "Name: Injury.Severity, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Injury.Severity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      17420\n",
       "1.0       2077\n",
       "2.0       1118\n",
       "3.0        299\n",
       "4.0        191\n",
       "5.0         60\n",
       "6.0         33\n",
       "7.0         15\n",
       "8.0         10\n",
       "10.0         8\n",
       "9.0          4\n",
       "12.0         3\n",
       "11.0         2\n",
       "14.0         2\n",
       "16.0         1\n",
       "17.0         1\n",
       "49.0         1\n",
       "27.0         1\n",
       "228.0        1\n",
       "13.0         1\n",
       "65.0         1\n",
       "Name: Total.Fatal.Injuries, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Total.Fatal.Injuries'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Non-Fatal', 'Fatal', 'Incident', 'Minor', 'Serious'], dtype=object)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Injury.Severity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Substantial    20508\n",
       "Destroyed       2666\n",
       "Minor            507\n",
       "Unknown           43\n",
       "Name: Aircraft.damage, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Aircraft.damage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "481"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Aircraft.damage'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace aircraft damage nans with unknown\n",
    "Aviation_columns['Aircraft.damage'] = Aviation_columns['Aircraft.damage'].fillna('Unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>Number.of.Engines</th>\n",
       "      <th>Total.Fatal.Injuries</th>\n",
       "      <th>Total.Serious.Injuries</th>\n",
       "      <th>Total.Minor.Injuries</th>\n",
       "      <th>Total.Uninjured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23583.000000</td>\n",
       "      <td>21249.000000</td>\n",
       "      <td>21270.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>23493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.132723</td>\n",
       "      <td>0.342322</td>\n",
       "      <td>0.278608</td>\n",
       "      <td>0.220423</td>\n",
       "      <td>4.306176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.367482</td>\n",
       "      <td>1.892637</td>\n",
       "      <td>1.633074</td>\n",
       "      <td>0.822648</td>\n",
       "      <td>24.239808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>588.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number.of.Engines  Total.Fatal.Injuries  Total.Serious.Injuries  \\\n",
       "count       23583.000000          21249.000000            21270.000000   \n",
       "mean            1.132723              0.342322                0.278608   \n",
       "std             0.367482              1.892637                1.633074   \n",
       "min             0.000000              0.000000                0.000000   \n",
       "25%             1.000000              0.000000                0.000000   \n",
       "50%             1.000000              0.000000                0.000000   \n",
       "75%             1.000000              0.000000                0.000000   \n",
       "max             8.000000            228.000000              137.000000   \n",
       "\n",
       "       Total.Minor.Injuries  Total.Uninjured  \n",
       "count          21613.000000     23493.000000  \n",
       "mean               0.220423         4.306176  \n",
       "std                0.822648        24.239808  \n",
       "min                0.000000         0.000000  \n",
       "25%                0.000000         0.000000  \n",
       "50%                0.000000         1.000000  \n",
       "75%                0.000000         2.000000  \n",
       "max               50.000000       588.000000  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns.describe() # see summary statistics for numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill number of engines with mean\n",
    "Aviation_columns['Number.of.Engines'] = Aviation_columns['Number.of.Engines'].fillna(Aviation_columns['Number.of.Engines'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill injured columns with 0\n",
    "\n",
    "Aviation_columns[['Total.Fatal.Injuries', 'Total.Serious.Injuries', 'Total.Minor.Injuries', 'Total.Uninjured']] = Aviation_columns[['Total.Fatal.Injuries', 'Total.Serious.Injuries', 'Total.Minor.Injuries', 'Total.Uninjured']].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number.of.Engines</th>\n",
       "      <th>Total.Fatal.Injuries</th>\n",
       "      <th>Total.Serious.Injuries</th>\n",
       "      <th>Total.Minor.Injuries</th>\n",
       "      <th>Total.Uninjured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24205.000000</td>\n",
       "      <td>24205.000000</td>\n",
       "      <td>24205.000000</td>\n",
       "      <td>24205.000000</td>\n",
       "      <td>24205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.132723</td>\n",
       "      <td>0.300516</td>\n",
       "      <td>0.244825</td>\n",
       "      <td>0.196819</td>\n",
       "      <td>4.179508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.362729</td>\n",
       "      <td>1.776842</td>\n",
       "      <td>1.533561</td>\n",
       "      <td>0.780335</td>\n",
       "      <td>23.891703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>588.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number.of.Engines  Total.Fatal.Injuries  Total.Serious.Injuries  \\\n",
       "count       24205.000000          24205.000000            24205.000000   \n",
       "mean            1.132723              0.300516                0.244825   \n",
       "std             0.362729              1.776842                1.533561   \n",
       "min             0.000000              0.000000                0.000000   \n",
       "25%             1.000000              0.000000                0.000000   \n",
       "50%             1.000000              0.000000                0.000000   \n",
       "75%             1.000000              0.000000                0.000000   \n",
       "max             8.000000            228.000000              137.000000   \n",
       "\n",
       "       Total.Minor.Injuries  Total.Uninjured  \n",
       "count          24205.000000     24205.000000  \n",
       "mean               0.196819         4.179508  \n",
       "std                0.780335        23.891703  \n",
       "min                0.000000         0.000000  \n",
       "25%                0.000000         0.000000  \n",
       "50%                0.000000         1.000000  \n",
       "75%                0.000000         2.000000  \n",
       "max               50.000000       588.000000  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns.describe() # see summary statistics for numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VMC    22296\n",
       "IMC     1294\n",
       "Unk      111\n",
       "UNK       58\n",
       "Name: Weather.Condition, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weather condition values and counts\n",
    "Aviation_columns['Weather.Condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine unknown columns in weather condition\n",
    "Aviation_columns['Weather.Condition'] = Aviation_columns['Weather.Condition'].replace({'Unk' : 'UNK'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "446"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Weather.Condition'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VMC    22296\n",
       "IMC     1294\n",
       "UNK      169\n",
       "Name: Weather.Condition, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Weather.Condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filled weather condition nans with unknown\n",
    "Aviation_columns['Weather.Condition'] = Aviation_columns['Weather.Condition'].fillna('UNK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Weather.Condition'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2176"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Report.Status'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Probable Cause                                                                                                                                                                                                                                                                                                                                 6364\n",
       "The pilot's failure to maintain directional control during the landing roll.                                                                                                                                                                                                                                                                     56\n",
       "A loss of engine power for undetermined reasons.                                                                                                                                                                                                                                                                                                 49\n",
       "The pilot's failure to maintain directional control during landing.                                                                                                                                                                                                                                                                              43\n",
       "A total loss of engine power for undetermined reasons.                                                                                                                                                                                                                                                                                           38\n",
       "                                                                                                                                                                                                                                                                                                                                               ... \n",
       "A loss of engine power for reasons that could not be determined because postaccident examination of the engine did not reveal any failures or malfunctions that would have precluded normal operation.                                                                                                                                            1\n",
       "The pilot's failure to maintain directional control during takeoff when the airplane encountered standing water on the airstrip.                                                                                                                                                                                                                  1\n",
       "The pilots decision to take off with the airplane over its maximum gross weight due to his failure to perform preflight weight and balance calculations, which affected the airplanes takeoff performance, and his subsequent decision to abort the takeoff with too little runway remaining, which resulted in a runway excursion.\\r\\n\\r       1\n",
       "The pilots exceedance of the airplanes critical angle of attack while maneuvering for landing, which resulted in an aerodynamic stall and impact with terrain. \\r                                                                                                                                                                               1\n",
       "The collapse of a main landing gear for reasons that could not be determined due to no postaccident examination of the airframe.                                                                                                                                                                                                                  1\n",
       "Name: Report.Status, Length: 14437, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Report.Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "Aviation_columns = Aviation_columns.dropna(subset = ['Report.Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "Aviation_columns = Aviation_columns[Aviation_columns['Report.Status'] != 'Foreign']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "Aviation_columns = Aviation_columns[Aviation_columns['Report.Status'] != 'Probable Cause']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The pilot's failure to maintain directional control during the landing roll.                                                                                                                                                                                   56\n",
       "A loss of engine power for undetermined reasons.                                                                                                                                                                                                               49\n",
       "The pilot's failure to maintain directional control during landing.                                                                                                                                                                                            43\n",
       "A total loss of engine power for undetermined reasons.                                                                                                                                                                                                         38\n",
       "The loss of engine power for undetermined reasons.                                                                                                                                                                                                             28\n",
       "                                                                                                                                                                                                                                                               ..\n",
       "The pilots failure to maintain aircraft control during an emergency landing.  Contributing factors were the reported smoke and prevailing gusty wind conditions.                                                                                                1\n",
       "The pilot's selection of unsuitable terrain for landing, resulting in the collapse of the main landing gear, and a nose over.                                                                                                                                   1\n",
       "The pilot did not maintain airspeed following a loss of engine power during takeoff climb, which resulted in an aerodynamic stall. Contributing to the accident was the loss of engine power due to the unlocked engine primer.                                 1\n",
       "The pilot's loss of control of the airplane during landing on water due to an encounter with wind shear, which resulted in the airplane's abnormal contact with the surface.                                                                                    1\n",
       "The pilots improper in-flight fuel management, which resulted in fuel starvation and the right engines complete loss of power. Contributing to the accident was the pilots improper configuration of the airplane following the loss of engine power. \\r     1\n",
       "Name: Report.Status, Length: 14435, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Report.Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-08-31    15\n",
       "2011-03-19    14\n",
       "2011-06-18    14\n",
       "2010-05-29    14\n",
       "2011-07-23    14\n",
       "              ..\n",
       "2021-08-30     1\n",
       "2020-08-04     1\n",
       "2009-01-13     1\n",
       "2014-12-03     1\n",
       "2020-05-24     1\n",
       "Name: Event.Date, Length: 4720, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Event.Date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "Aviation_columns['Event.Date'] = Aviation_columns['Event.Date'].map(lambda x: x[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015',\n",
       "       '2016', '2017', '2018', '2019', '2020', '2021', '2022'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Event.Date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Event.Id                     0\n",
       "Investigation.Type           0\n",
       "Event.Date                   0\n",
       "Location                     0\n",
       "Country                      0\n",
       "Injury.Severity              0\n",
       "Aircraft.damage              0\n",
       "Aircraft.Category            0\n",
       "Make                         0\n",
       "Model                        0\n",
       "Number.of.Engines            0\n",
       "Air.carrier               7131\n",
       "Total.Fatal.Injuries         0\n",
       "Total.Serious.Injuries       0\n",
       "Total.Minor.Injuries         0\n",
       "Total.Uninjured              0\n",
       "Weather.Condition            0\n",
       "Report.Status                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# need to clean up make column\n",
    "Aviation_columns['Make'] = Aviation_columns['Make'].map(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cessna                    4638\n",
       "piper                     2691\n",
       "beech                      935\n",
       "boeing                     291\n",
       "mooney                     238\n",
       "                          ... \n",
       "bailey                       1\n",
       "buhlmann howard              1\n",
       "mckay gene                   1\n",
       "flightstar sportplanes       1\n",
       "wilderoder                   1\n",
       "Name: Make, Length: 2690, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Aviation_columns['Make'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "Aviation_columns['Make'] = Aviation_columns['Make'].map(lambda x: x.title())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#going to filter data by top_ten_makes to make visualizations more comprehensive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filters by top ten makes \n",
    "top_ten_makes = Aviation_columns.Make.value_counts().nlargest(10)\n",
    "topten_df = Aviation_columns[Aviation_columns.Make.isin(top_ten_makes.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot top ten to show - might use this in analysis\n",
    "fig, ax = plt.subplots()\n",
    "top_ten_makes.plot.bar()\n",
    "\n",
    "ax.set_title('Top Ten Airplane Makes')\n",
    "ax.set_xlabel('Top Ten Makes')\n",
    "ax.set_ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "topten_df.to_csv('topten_df.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9607 entries, 63915 to 88661\n",
      "Data columns (total 18 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Event.Id                9607 non-null   object \n",
      " 1   Investigation.Type      9607 non-null   object \n",
      " 2   Event.Date              9607 non-null   object \n",
      " 3   Location                9607 non-null   object \n",
      " 4   Country                 9607 non-null   object \n",
      " 5   Injury.Severity         9607 non-null   object \n",
      " 6   Aircraft.damage         9607 non-null   object \n",
      " 7   Aircraft.Category       9607 non-null   object \n",
      " 8   Make                    9607 non-null   object \n",
      " 9   Model                   9607 non-null   object \n",
      " 10  Number.of.Engines       9607 non-null   float64\n",
      " 11  Air.carrier             5400 non-null   object \n",
      " 12  Total.Fatal.Injuries    9607 non-null   float64\n",
      " 13  Total.Serious.Injuries  9607 non-null   float64\n",
      " 14  Total.Minor.Injuries    9607 non-null   float64\n",
      " 15  Total.Uninjured         9607 non-null   float64\n",
      " 16  Weather.Condition       9607 non-null   object \n",
      " 17  Report.Status           9607 non-null   object \n",
      "dtypes: float64(5), object(13)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# clean dataframe is called topten_df\n",
    "topten_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "topten_df "
   ]
  }
 ],
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