Analisis_Exploratorio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=center style=\"font-family: 'Times New Roman', sans-serif\">Análisis Exploratorio de Datos</h1>\n",
    "<h2 align=center style=\"font-family: 'Times New Roman', sans-serif\"> Accidentes Aereos - Data Analtytics </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h>En este notebook vamos a hacer un análisis de datos de <a href=\"AccidentesAviones.csv\">Accidentes Aviones.csv</a> para intentar encontrar patrones, nulos, faltantes, registros duplicados o variables insignificantes que no brinden información relevante al proyecto. </h>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para iniciar este notebook hay que importar las librerías que utilizaremos y abrir el archivo <em>.csv</em> que analizaremos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import re, datetime\n",
    "from collections import Counter\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abrimos el archivo <em>csv</em> de la siguiente manera."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./AccidentesAviones.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos como inicia el dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Hora Declarada</th>\n",
       "      <th>Ruta</th>\n",
       "      <th>Operador</th>\n",
       "      <th>Número de Vuelo</th>\n",
       "      <th>Ruta de Vuelo</th>\n",
       "      <th>Tipo de Aeronave</th>\n",
       "      <th>Registro</th>\n",
       "      <th>CN LN</th>\n",
       "      <th>Total a Bordo</th>\n",
       "      <th>Pasajeros a Bordo</th>\n",
       "      <th>Tripulación a Bordo</th>\n",
       "      <th>Cantidad de Fallecidos</th>\n",
       "      <th>Fallecidos Pasajeros</th>\n",
       "      <th>Fallecidos Tripulación</th>\n",
       "      <th>En Tierra</th>\n",
       "      <th>Resumen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>September 17, 1908</td>\n",
       "      <td>1718</td>\n",
       "      <td>Fort Myer, Virginia</td>\n",
       "      <td>Military - U.S. Army</td>\n",
       "      <td>?</td>\n",
       "      <td>Demonstration</td>\n",
       "      <td>Wright Flyer III</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>During a demonstration flight, a U.S. Army fly...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>September 07, 1909</td>\n",
       "      <td>?</td>\n",
       "      <td>Juvisy-sur-Orge, France</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>Air show</td>\n",
       "      <td>Wright Byplane</td>\n",
       "      <td>SC1</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Eugene Lefebvre was the first pilot to ever be...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>July 12, 1912</td>\n",
       "      <td>0630</td>\n",
       "      <td>Atlantic City, New Jersey</td>\n",
       "      <td>Military - U.S. Navy</td>\n",
       "      <td>?</td>\n",
       "      <td>Test flight</td>\n",
       "      <td>Dirigible</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>First U.S. dirigible Akron exploded just offsh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>August 06, 1913</td>\n",
       "      <td>?</td>\n",
       "      <td>Victoria, British Columbia, Canada</td>\n",
       "      <td>Private</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>Curtiss seaplane</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>The first fatal airplane accident in Canada oc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>September 09, 1913</td>\n",
       "      <td>1830</td>\n",
       "      <td>Over the North Sea</td>\n",
       "      <td>Military - German Navy</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>Zeppelin L-1 (airship)</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>20</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>14</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>The airship flew into a thunderstorm and encou...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fecha Hora Declarada                                Ruta  \\\n",
       "0  September 17, 1908           1718                 Fort Myer, Virginia   \n",
       "1  September 07, 1909              ?             Juvisy-sur-Orge, France   \n",
       "2       July 12, 1912           0630           Atlantic City, New Jersey   \n",
       "3     August 06, 1913              ?  Victoria, British Columbia, Canada   \n",
       "4  September 09, 1913           1830                  Over the North Sea   \n",
       "\n",
       "                 Operador Número de Vuelo  Ruta de Vuelo  \\\n",
       "0    Military - U.S. Army               ?  Demonstration   \n",
       "1                       ?               ?       Air show   \n",
       "2    Military - U.S. Navy               ?    Test flight   \n",
       "3                 Private               ?              ?   \n",
       "4  Military - German Navy               ?              ?   \n",
       "\n",
       "         Tipo de Aeronave Registro CN LN Total a Bordo Pasajeros a Bordo  \\\n",
       "0        Wright Flyer III        ?     1             2                 1   \n",
       "1          Wright Byplane      SC1     ?             1                 0   \n",
       "2               Dirigible        ?     ?             5                 0   \n",
       "3        Curtiss seaplane        ?     ?             1                 0   \n",
       "4  Zeppelin L-1 (airship)        ?     ?            20                 ?   \n",
       "\n",
       "  Tripulación a Bordo Cantidad de Fallecidos Fallecidos Pasajeros  \\\n",
       "0                   1                      1                    1   \n",
       "1                   1                      1                    0   \n",
       "2                   5                      5                    0   \n",
       "3                   1                      1                    0   \n",
       "4                   ?                     14                    ?   \n",
       "\n",
       "  Fallecidos Tripulación En Tierra  \\\n",
       "0                      0         0   \n",
       "1                      0         0   \n",
       "2                      5         0   \n",
       "3                      1         0   \n",
       "4                      ?         0   \n",
       "\n",
       "                                             Resumen  \n",
       "0  During a demonstration flight, a U.S. Army fly...  \n",
       "1  Eugene Lefebvre was the first pilot to ever be...  \n",
       "2  First U.S. dirigible Akron exploded just offsh...  \n",
       "3  The first fatal airplane accident in Canada oc...  \n",
       "4  The airship flew into a thunderstorm and encou...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h2 id=\"##inicio\" align=center style=\"font-family: 'Times New Roman', sans-serif\">Contenido</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "    <li>\n",
    "        <a href=\"#duplicates\">Duplicados</a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"#nulos\">Valores nulos o faltantes</a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"#outliers\">Valores atípicos</a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"#graph\">Análisis de las variables</a>\n",
    "    </li>\n",
    "</ul>\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"#duplicates\" align=center style=\"font-family: 'Times New Roman', sans-serif; text-decoration: underline\">Duplicados</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder trabajar con este dataframe, debemos eliminar los valores duplicados para que no perjudiquen los análisis. Eliminamos los valores duplicados en la siguiente línea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de registros sin eliminar duplicados: 5008.\n",
      "Cantidad de registros habiendo eliminado duplicados: 5008.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Cantidad de registros sin eliminar duplicados: {len(df)}.\") # Imprimimos por pantalla la cantidad de registros antes de eliminar duplicados\n",
    "df.drop_duplicates() # Eliminamos los valores duplicados\n",
    "print(f\"Cantidad de registros habiendo eliminado duplicados: {len(df)}.\") # Imprimimos la cantidad de registros sin los duplicados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver que en este dataframe no contiene registros duplicados, por lo que podemos proseguir con los análisis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"#nulos\" align=center style=\"font-family: 'Times New Roman', sans-serif; text-decoration: underline\">Valores nulos y faltantes</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observemos que en este dataframe los valores nulos o faltantes no están escritos de la manera tradicional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha                     0\n",
       "Hora Declarada            0\n",
       "Ruta                      0\n",
       "Operador                  0\n",
       "Número de Vuelo           0\n",
       "Ruta de Vuelo             1\n",
       "Tipo de Aeronave          0\n",
       "Registro                  0\n",
       "CN LN                     0\n",
       "Total a Bordo             0\n",
       "Pasajeros a Bordo         0\n",
       "Tripulación a Bordo       0\n",
       "Cantidad de Fallecidos    0\n",
       "Fallecidos Pasajeros      0\n",
       "Fallecidos Tripulación    0\n",
       "En Tierra                 0\n",
       "Resumen                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si buscamos los valores faltantes de esta manera, podríamos caer en la conclusión errónea que el dataframe no tiene valores faltantes, exceptuando al único de la columna <em>Ruta de Vuelo</em>. Para poder hacer la búsqueda concreta de valores faltantes debemos ver alguna parte del dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Hora Declarada</th>\n",
       "      <th>Ruta</th>\n",
       "      <th>Operador</th>\n",
       "      <th>Número de Vuelo</th>\n",
       "      <th>Ruta de Vuelo</th>\n",
       "      <th>Tipo de Aeronave</th>\n",
       "      <th>Registro</th>\n",
       "      <th>CN LN</th>\n",
       "      <th>Total a Bordo</th>\n",
       "      <th>Pasajeros a Bordo</th>\n",
       "      <th>Tripulación a Bordo</th>\n",
       "      <th>Cantidad de Fallecidos</th>\n",
       "      <th>Fallecidos Pasajeros</th>\n",
       "      <th>Fallecidos Tripulación</th>\n",
       "      <th>En Tierra</th>\n",
       "      <th>Resumen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>September 03, 1925</td>\n",
       "      <td>0530</td>\n",
       "      <td>Caldwell, Ohio</td>\n",
       "      <td>Military - U.S. Navy</td>\n",
       "      <td>?</td>\n",
       "      <td>Lakehurst, NJ - S.t Louis, MO</td>\n",
       "      <td>Dirigible ZR-1 Shenandoah (airship)</td>\n",
       "      <td>ZR-1</td>\n",
       "      <td>?</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>The Shenandoah was flying over Southern Ohio w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>September 07, 1925</td>\n",
       "      <td>?</td>\n",
       "      <td>Toul, France</td>\n",
       "      <td>CIDNA</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>Spad 33</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>October 01, 1925</td>\n",
       "      <td>?</td>\n",
       "      <td>Mt. Nittany, Ohio</td>\n",
       "      <td>US Aerial Mail Service</td>\n",
       "      <td>?</td>\n",
       "      <td>New York - San Francisco</td>\n",
       "      <td>De Havilland DH-4</td>\n",
       "      <td>385</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Crashed into a wooded area on Mt. Nittany in f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>November 14, 1925</td>\n",
       "      <td>?</td>\n",
       "      <td>Nouadhibou, Mauritania</td>\n",
       "      <td>Lignes Aeriennes Latecoere</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>Breguet 14</td>\n",
       "      <td>F-AEGZ</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>The pilot suffered sunstroke lost control of t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Fecha Hora Declarada                    Ruta  \\\n",
       "102  September 03, 1925           0530          Caldwell, Ohio   \n",
       "103  September 07, 1925              ?            Toul, France   \n",
       "104    October 01, 1925              ?       Mt. Nittany, Ohio   \n",
       "105   November 14, 1925              ?  Nouadhibou, Mauritania   \n",
       "\n",
       "                       Operador Número de Vuelo  \\\n",
       "102        Military - U.S. Navy               ?   \n",
       "103                       CIDNA               ?   \n",
       "104      US Aerial Mail Service               ?   \n",
       "105  Lignes Aeriennes Latecoere               ?   \n",
       "\n",
       "                     Ruta de Vuelo                     Tipo de Aeronave  \\\n",
       "102  Lakehurst, NJ - S.t Louis, MO  Dirigible ZR-1 Shenandoah (airship)   \n",
       "103                              ?                              Spad 33   \n",
       "104       New York - San Francisco                    De Havilland DH-4   \n",
       "105                              ?                           Breguet 14   \n",
       "\n",
       "    Registro CN LN Total a Bordo Pasajeros a Bordo Tripulación a Bordo  \\\n",
       "102     ZR-1     ?            43                 0                  43   \n",
       "103        ?     ?             ?                 ?                   ?   \n",
       "104      385     ?             1                 0                   1   \n",
       "105   F-AEGZ   168             1                 0                   1   \n",
       "\n",
       "    Cantidad de Fallecidos Fallecidos Pasajeros Fallecidos Tripulación  \\\n",
       "102                     14                    0                     14   \n",
       "103                      ?                    ?                      ?   \n",
       "104                      1                    0                      1   \n",
       "105                      1                    0                      1   \n",
       "\n",
       "    En Tierra                                            Resumen  \n",
       "102         0  The Shenandoah was flying over Southern Ohio w...  \n",
       "103         ?                                                  ?  \n",
       "104         0  Crashed into a wooded area on Mt. Nittany in f...  \n",
       "105         0  The pilot suffered sunstroke lost control of t...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[102:106] # Visualizamos una parte cualquiera del dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como podemos ver, en los registros hay valores <h style='color: #FFFF00'>'?'</h> que representa la ausencia de información en ese registro. Con este nuevo panorama, busquemos los <h style=\"color: #FFAB00\">'?'</h> en el dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha                        0\n",
       "Hora Declarada            1504\n",
       "Ruta                         5\n",
       "Operador                    10\n",
       "Número de Vuelo           3682\n",
       "Ruta de Vuelo              762\n",
       "Tipo de Aeronave            13\n",
       "Registro                   272\n",
       "CN LN                      667\n",
       "Total a Bordo               17\n",
       "Pasajeros a Bordo          221\n",
       "Tripulación a Bordo        219\n",
       "Cantidad de Fallecidos       8\n",
       "Fallecidos Pasajeros       235\n",
       "Fallecidos Tripulación     235\n",
       "En Tierra                   44\n",
       "Resumen                     59\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isin([\"?\"]).sum() # Analizamos la suma de las columnas donde aparece el valor \"?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora podemos observar correctamente la cantidad de valores faltantes que tiene cada columna del dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver en que posiciones están los valores faltantes en el siguiente gráfico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nulls_heatmap_df = df.isin([\"?\"]) # Definimos nuestra matriz que graficaremos.\n",
    "\n",
    "plt.figure(figsize=(12,6))  # Definimos el tamaño de la figura que graficaremos\n",
    "\n",
    "sns.heatmap(nulls_heatmap_df.T, cmap='cividis', cbar=False) # Graficamos el mapa de calor con la matrix que definimos antes.\n",
    "\n",
    "plt.title(\"Mapa de calor de los registros nulos.\") # Colocamos el título\n",
    "plt.ylabel(\"Columnas\") # Colocamos el texto lateral\n",
    "plt.xlabel(\"Índices\") # Colocar el texto inferior\n",
    "plt.xticks([]) # Quitamos todos los índices del dataframe que no prporcionan información relevante\n",
    "\n",
    "plt.show() # Mostramos el gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al observar las líneas amarillas, que representan los valores nulos, podemos observar que existe una relación en los valores nulos de las columnas con la información de los pasajeros. Quiere decir que, normalmente, cuando no contiene información de una de las columnas, las otras tampoco contendrán información. Esto sucede con las columnas <h style=\"color: #23fce1\">'Pasajeros a Bordo', 'Tripulación a Bordo', 'Fallecidos Pasajeros' y 'Fallecidos Tripulación'</h>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para aligerar el tratamiento de los valores faltantes, vamos a transformar los <h style=\"color: #FFAB00\">\"?\"</h> en los valores nulos más tradicionales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(\"?\", None, inplace=True) # Reemplazamos los valores \"?\" con valores None."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"#outliers\" align=center style=\"font-family: 'Times New Roman', sans-serif; text-decoration: underline\">Valores atípicos</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los valores atípicos son datos extremos de la distribución que pueden afectar los análisis por sus valores alejados de los promedios o medianas. Estos valores pueden deberse a varios factores como eventos únicos en su clase, error humano al guardar la información, entre otros. A continuación buscaremos estos valores atípicos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comenzar vamos a analizar las fechas de los accidentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_fechas = [] # Creamos una lista vacía donde irán las fechas procesadas con datetime.strptime\n",
    "for date in df['Fecha']: # Iteramos las fechas\n",
    "    try: # Intentar\n",
    "        fecha = datetime.datetime.strptime(date, \"%B %d, %Y\") # Convertir la cadena en un datetime\n",
    "        list_fechas.append(fecha) # Lo agregan a la lista\n",
    "    except ValueError: # Si ocurre un ValueError\n",
    "        print(f\"'{date}' No es un valor válido para datetime.\") # Imprime la fecha que produce el error\n",
    "\n",
    "df['Fecha Procesada'] = list_fechas # Iniciar la columna 'Fecha Procesada' con la lista de fechas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media = np.mean(df['Fecha Procesada']) # La media de las fechas procesada\n",
    "std = np.std(df['Fecha Procesada']) # La desviación Estándar\n",
    "\n",
    "plt.figure(figsize=(16,6)) # Definimos el tamaño de la figura\n",
    "\n",
    "plt.hist(df['Fecha Procesada'], bins= 35, color='#eafefa', edgecolor='k') # Creamos el histograma\n",
    "\n",
    "plt.xticks([datetime.datetime.strptime(str(year) + '-01-01', \"%Y-%m-%d\") for year in np.arange(1910, 2021, 10)]) # Definimos los valores que se verán en el eje X\n",
    "plt.yticks(np.arange(0, 251, 25)) # Definimos los valores que se verán en el eje Y\n",
    "\n",
    "plt.xlim(datetime.datetime.strptime(\"1905-01-01\", \"%Y-%m-%d\"), datetime.datetime.strptime(\"2025-01-01\", \"%Y-%m-%d\")) # Definimos los límites del gráfico en el eje X\n",
    "\n",
    "# Definimos las líneas que representan los valores de las tres sigmas\n",
    "plt.axvline(media, color='#61AA61', linestyle='--', label= \"Media\")\n",
    "plt.axvline(media + std, color= \"#B9C101\", linestyle= '-.', label= \"Sigma N°1\")\n",
    "plt.axvline(media + std * 2, color= \"#A64421\", linestyle=\":\", label=\"Sigma N°2\")\n",
    "plt.axvline(media + std * 3, color= \"#FF3161\", linestyle=\"-\", label=\"Sigma N°3\")\n",
    "plt.axvline(media - std, color= \"#B9C101\", linestyle= '-.')\n",
    "plt.axvline(media - std * 2, color= \"#A64421\", linestyle=\":\")\n",
    "plt.axvline(media - std * 3, color= \"#FF3161\", linestyle=\"-\")\n",
    "\n",
    "# Colocamos el título y las etiquetas\n",
    "plt.title(\"Histograma de fechas de accidentes.\")\n",
    "plt.xlabel(\"Fechas\")\n",
    "plt.ylabel(\"Cantidad de accidentes de Aviones\")\n",
    "\n",
    "\n",
    "plt.legend() # Mostramos las legendas\n",
    "plt.show() # Mostrar el gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como nos muestra el histograma de fechas, no existen accidentes en fechas atípicas. Quedan todas las fechas incluidas dentro de las tres sigmas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuemos con la columna de <em>Total a Bordo</em>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define los valores enteros no nulos de la columna 'Total a Bordo'\n",
    "totalP = [int(x) for x in df['Total a Bordo'] if x != None]\n",
    "\n",
    "# Declarar la media y la desviación estandar de la columna 'Total a Bordo'\n",
    "media = np.mean(totalP)\n",
    "std = np.std(totalP)\n",
    "\n",
    "plt.figure(figsize=(12,6)) # Definir el tamaño de gráfico\n",
    "\n",
    "sns.histplot(totalP, color='#eafeFa', bins= 30, edgecolor= 'k') # Crear el gráfico \n",
    "\n",
    "# Crear el título y las etiquetas\n",
    "plt.title(\"Histograma de las cantidad de personas a bordo de los accidentes.\")\n",
    "plt.xlabel(\"Cantidad de personas en el accidente\")\n",
    "plt.ylabel(\"Conteo de accidentes\")\n",
    "\n",
    "# Creamos las líneas de las sigmas.\n",
    "plt.axvline(media, color=\"#22ef31\", linestyle=\"--\", label=\"Media\")\n",
    "plt.axvline(media + std, color=\"#42e5a7\", linestyle=\"-.\", label=\"Sigma N°1\")\n",
    "plt.axvline(media + std * 2, color=\"#7285f1\", linestyle=\":\", label=\"Sigma N°2\")\n",
    "plt.axvline(media + std * 3, color=\"#b242fe\", linestyle=\"-\", label=\"Sigma N°3\")\n",
    "\n",
    "# Mostrar las legendas\n",
    "plt.legend()\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El histograma dice que existen algunos accidentes en los que hubo más de 200 personas involucradas en accidentes, lo que constituye valores \"atípicos\". Estos valores atípicos pueden ser irrelevantes debido a que la cantidad de personas que viajan es relativamente aleatorio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"#graph\" align=center style=\"font-family: 'Times New Roman', sans-serif; text-decoration: underline\">Análisis de las variables</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta sección, con ayuda de los gráficos, vamos a indagar en posibles patrones o distribuciones que existan en el dataframe. Intentando conseguir información relevante y util que nos ayude a entender una posible razón de los accidentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comenzar vamos a analizar la columna de resúmenes en búsqueda de la distribución de cuales son las causas más comunes de los accidentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crashed: 3388\n",
      "aircraft: 2656\n",
      "plane: 2258\n",
      "flight: 1222\n",
      "pilot: 1216\n",
      "crew: 1133\n",
      "runway: 1039\n",
      "engine: 1022\n",
      "approach: 982\n",
      "failure: 856\n"
     ]
    }
   ],
   "source": [
    "df['Resumen'].fillna(\"\", inplace=True)\n",
    "\n",
    "# Lista de stop words en inglés\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# Supongamos que ya tienes un DataFrame llamado 'df' con una columna llamada 'Resumen'\n",
    "# Si no tienes el DataFrame, puedes crearlo primero con los datos adecuados\n",
    "\n",
    "# Tokeniza y limpia los resúmenes, eliminando las stop words\n",
    "def tokenize_and_clean(text):\n",
    "    # Divide el texto en palabras\n",
    "    words = re.findall(r'\\w+', text.lower())\n",
    "    # Elimina las stop words\n",
    "    words = [word for word in words if word not in stop_words]\n",
    "    return words\n",
    "\n",
    "# Aplica la función de tokenización y limpieza a la columna 'Resumen'\n",
    "df['Resumen'] = df['Resumen'].apply(tokenize_and_clean)\n",
    "\n",
    "# Crea una lista de todas las palabras en todos los resúmenes\n",
    "all_words = [word for sublist in df['Resumen'].tolist() for word in sublist]\n",
    "\n",
    "# Calcula la frecuencia de cada palabra\n",
    "word_counts = Counter(all_words)\n",
    "\n",
    "# Encuentra las palabras más repetidas (puedes ajustar el número)\n",
    "n_palabras_mas_repetidas = 10  # Por ejemplo, para obtener las 10 palabras más repetidas\n",
    "\n",
    "# Obtiene las n palabras más repetidas\n",
    "palabras_mas_repetidas = word_counts.most_common(n_palabras_mas_repetidas)\n",
    "\n",
    "# Imprime las palabras más repetidas\n",
    "for palabra, frecuencia in palabras_mas_repetidas:\n",
    "    print(f'{palabra}: {frecuencia}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traduciendo la respuesta anterior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accidentado            3388\n",
      "aeronave               2656\n",
      "avión                  2258\n",
      "vuelo                  1222\n",
      "piloto                 1216\n",
      "tripulación            1133\n",
      "pista de aterrizaje    1039\n",
      "motor                  1022\n",
      "aproximación            982\n",
      "falla o fracaso         856\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "   \"accidentado\": 3388,\n",
    "   \"aeronave\": 2656,\n",
    "   \"avión\": 2258,\n",
    "   \"vuelo\": 1222,\n",
    "   \"piloto\": 1216,\n",
    "   \"tripulación\": 1133,\n",
    "   \"pista de aterrizaje\": 1039,\n",
    "   \"motor\": 1022,\n",
    "   \"aproximación\": 982,\n",
    "   \"falla o fracaso\": 856\n",
    "}\n",
    "\n",
    "print(pd.Series(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las palabras más frecuentes en los resúmenes son 'accidentado', 'aeronave' y 'avión', algo que no nos brinda información tan relevante. En todo caso, las palabras más repetidas que nos pueden dar información util son 'pista de aterrizaje', 'motor' y 'falla o fracaso'. Que pueden dar las razones más comunes de porque sucedieron los accidentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora veamos si alguna de las rutas que aparece en la columna de <em>Rutas de Vuelo</em> se repite con frecuencia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de rutas de vuelos, sin contabilizar repetidos: 4245\n",
      "Cantidad de rutas únicas: 3837\n"
     ]
    }
   ],
   "source": [
    "rutas = df['Ruta de Vuelo'].dropna() # elimina los nan y lo almacena en la variable 'rutas'\n",
    "print(f\"Cantidad de rutas de vuelos, sin contabilizar repetidos: {len(rutas)}\") # imprime la cantidad de registros totales\n",
    "print(f\"Cantidad de rutas únicas: {len(rutas.unique())}\") # imprime la cantidad de rutas unicas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Existen alguna ruta que se repita? Veamos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_rutas = {} # define un diccionario vacio\n",
    "for route in rutas: # itera en la Serie de rutas\n",
    "    if route not in dict_rutas: # Si el la ruta no se encuentra como clave del diccionario\n",
    "        dict_rutas[f\"{route}\"] = 1 # Se inicia como clave con valor 1\n",
    "    else: # Si existe la ruta como clave del diccionario\n",
    "        dict_rutas[f\"{route}\"] += 1 # Agrega uno al valor del diccionario\n",
    "\n",
    "dict_rutas = dict(sorted(dict_rutas.items(), key= lambda item: item[1], reverse=True)) # Ordena el diccionario y lo almacena guardado\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training': 96,\n",
       " 'Sightseeing': 31,\n",
       " 'Test flight': 23,\n",
       " 'Sao Paulo - Rio de Janeiro': 7,\n",
       " 'Test': 6,\n",
       " 'Rio de Janeiro - Sao Paulo': 5,\n",
       " 'Demonstration': 4,\n",
       " 'Croydon - Paris': 4,\n",
       " 'Paris - London': 4,\n",
       " 'Barranquilla - Bogota': 4,\n",
       " 'Tallinn - Helsinki': 4,\n",
       " 'Bogota - Barranquilla': 4,\n",
       " 'Sao Paulo - Porto Alegre': 4,\n",
       " 'Villavicencio - Mitu': 4,\n",
       " 'Huambo - Luanda': 4,\n",
       " 'London - Paris': 3,\n",
       " 'Strasbourg - Paris': 3,\n",
       " 'Amsterdam - Paris': 3,\n",
       " 'Paris - Croydon': 3,\n",
       " 'Agadir - Casablanca': 3,\n",
       " 'Santiago de Cuba - Havana': 3,\n",
       " 'Mexico City - Merida': 3,\n",
       " 'Vitoria - Rio de Janeiro': 3,\n",
       " 'Honolulu - San Francisco': 3,\n",
       " 'Patrol': 3,\n",
       " 'Guayaquil - Cuenca': 3,\n",
       " 'Rome - Cairo': 3,\n",
       " 'Villavicencio - Bogota': 3,\n",
       " 'Burbank - Oakland': 3,\n",
       " 'Krasnoyarsk - Irkutsk': 3,\n",
       " 'Istanbul - Ankara': 3,\n",
       " 'Bangkok - Hong Kong': 3,\n",
       " 'Hue - Da Nang': 3,\n",
       " 'Guayaquil - Quito': 3,\n",
       " 'Survey flight': 3,\n",
       " 'Vientiane - Luang Prabang': 3,\n",
       " 'Izmir - Istanbul': 3,\n",
       " 'Quito - Cuenca': 3,\n",
       " 'Kinshasa - Tshikapa': 3,\n",
       " 'Lagos - Abuja': 3,\n",
       " 'Barrow - Wainwright': 3,\n",
       " 'Khartoum - Juba': 3,\n",
       " 'Shuttle': 2,\n",
       " 'Miami - Bimini': 2,\n",
       " 'Charleville - Tambo - Blackall': 2,\n",
       " 'Berlin - Munich': 2,\n",
       " 'Buenos Aires - Montevideo': 2,\n",
       " 'Casablanca - Alicante - Perpignan - Toulouse': 2,\n",
       " 'Barcelona - Madrid': 2,\n",
       " 'Cleveland - Chicago': 2,\n",
       " 'Cleveland - Louisville': 2,\n",
       " 'Frankfurt - Berlin': 2,\n",
       " 'Marseille - Ajaccio - Tunis': 2,\n",
       " 'Boston - Newark': 2,\n",
       " 'Prague - Bratislava': 2,\n",
       " 'Brussels - London': 2,\n",
       " 'Lae - Bulolo': 2,\n",
       " 'Brussels, Belgium - Croydon, England': 2,\n",
       " 'Saigon - Paris': 2,\n",
       " 'Salt Lake City - Cheyenne': 2,\n",
       " 'Buenos Aires - Santiago': 2,\n",
       " 'Cochabamba - La Paz': 2,\n",
       " 'Berlin - Moscow': 2,\n",
       " 'Los Angeles - San Francisco - Oakland': 2,\n",
       " 'Brisbane - Sidney': 2,\n",
       " 'Krakow - Warsaw': 2,\n",
       " 'Moscow - Murmansk': 2,\n",
       " 'Auckland - Palmerston North': 2,\n",
       " 'Cagliari - Rome': 2,\n",
       " 'Frankfurt, Germany - Milan, Italy': 2,\n",
       " 'Chicago - Seattle': 2,\n",
       " 'London - Brussels': 2,\n",
       " 'Berlin - Budapest': 2,\n",
       " 'New York City - Chicago': 2,\n",
       " 'Catania - Rome': 2,\n",
       " 'Toronto - Winnipeg': 2,\n",
       " 'San Francisco - New York City': 2,\n",
       " 'Aberdeen - Stockholm': 2,\n",
       " 'Stockholm - Leuchars': 2,\n",
       " 'Copenhagen - Oslo': 2,\n",
       " 'Rio de Janeiro - Belem': 2,\n",
       " 'New York City - Los Angeles': 2,\n",
       " 'Tapachula - Mexico City': 2,\n",
       " 'Chunking - Shanghai': 2,\n",
       " 'Cairo - Rome': 2,\n",
       " 'Training flight': 2,\n",
       " 'New York City - San Francisco': 2,\n",
       " 'Gander - Shannon': 2,\n",
       " 'Paris - Cairo': 2,\n",
       " 'Chunking - Shanghi': 2,\n",
       " 'New York City - Cleveland': 2,\n",
       " 'Los Angeles - New York City': 2,\n",
       " 'Anchorage - Seattle': 2,\n",
       " 'Chicago - Miami': 2,\n",
       " 'Chicago - New York City': 2,\n",
       " 'Macau - Hong Kong': 2,\n",
       " 'Rome - Athens': 2,\n",
       " 'San Juan - Miami': 2,\n",
       " 'Porto Alegre - Sao Borja': 2,\n",
       " 'London - Glasgow': 2,\n",
       " 'Cuzco - Lima': 2,\n",
       " 'Test Flight': 2,\n",
       " 'Novosibirsk - Moscow': 2,\n",
       " 'Bucaramanga - Cucuta': 2,\n",
       " 'Iloilo - Manila': 2,\n",
       " 'Medellín - Barranquilla': 2,\n",
       " 'Saigon - Karachi - Bahrain - Paris': 2,\n",
       " 'Ribeirao Preto - Sao Paulo': 2,\n",
       " 'Merida - Caracas': 2,\n",
       " 'Hong Kong - Bangkok': 2,\n",
       " 'Tokyo - Osaka': 2,\n",
       " 'Madras - Delhi': 2,\n",
       " 'Sao Paulo - Bauru': 2,\n",
       " 'Tripoli - Kano': 2,\n",
       " 'Minsk - Leningrad': 2,\n",
       " 'Sofia - Varna': 2,\n",
       " 'Calcutta - Delhi': 2,\n",
       " 'Krasnoyarsk - Novosibirsk': 2,\n",
       " 'Vientiane - Saigon': 2,\n",
       " 'Rome - Beirut': 2,\n",
       " 'Ankara - Van': 2,\n",
       " 'Cairo - Luxor': 2,\n",
       " 'Military exercise': 2,\n",
       " 'Saigon - Hanoi': 2,\n",
       " 'Pucallpa - Lima': 2,\n",
       " 'Hong Kong - Jakarta': 2,\n",
       " 'Lima - Cuzco': 2,\n",
       " 'Denver - Portland': 2,\n",
       " 'New York - Miami': 2,\n",
       " 'Frobisher Bay, Baffin Island - Fort Chimo, Quebec': 2,\n",
       " 'New York City - Caracas': 2,\n",
       " 'New York City - Miami': 2,\n",
       " 'Warsaw - Moscow': 2,\n",
       " 'Porto Alegre - Sao Paulo': 2,\n",
       " 'London - Belfast': 2,\n",
       " 'Tipuani - La Paz': 2,\n",
       " 'Rio de Janerio - Fortaleza': 2,\n",
       " 'Athens - Cairo': 2,\n",
       " 'Tokyo - Nagoya': 2,\n",
       " 'San Borja - La Paz': 2,\n",
       " 'Lima - Pucallpa': 2,\n",
       " 'Bogota - Quito': 2,\n",
       " 'La Paz - Tipuani': 2,\n",
       " 'London - Perpignan': 2,\n",
       " 'Training exercise': 2,\n",
       " 'Paris - New York City': 2,\n",
       " 'Sofia - Moscow': 2,\n",
       " 'Bangkok - Bombay': 2,\n",
       " 'Bratislava - Brno': 2,\n",
       " 'Moscow - Irkutsk': 2,\n",
       " 'London - Innsbruck': 2,\n",
       " 'Air Show': 2,\n",
       " 'Manila - Naga': 2,\n",
       " 'Tyumen - Khanty': 2,\n",
       " 'Bogota - Bucaramanga': 2,\n",
       " 'Hong Kong - Da Nang': 2,\n",
       " 'Bogota - Cali': 2,\n",
       " 'San Pedro Sula - Tegucigalpa': 2,\n",
       " 'Santiago - Coihaique': 2,\n",
       " 'Cleveland - Detroit': 2,\n",
       " 'Bucharest - Oradea': 2,\n",
       " 'Manila - Bacolod': 2,\n",
       " 'Silchar - Gauhati': 2,\n",
       " 'Tyumen - Surgut': 2,\n",
       " 'Taipei - Hong Kong': 2,\n",
       " 'Rome - Palermo': 2,\n",
       " 'Bogota - Cartagena': 2,\n",
       " 'Kinshasa - Kisangani': 2,\n",
       " 'Vientiane - Sam Neua': 2,\n",
       " 'Hualien - Taipei': 2,\n",
       " 'Tbilisi - Sukhumi': 2,\n",
       " 'Kariba - Salisbury': 2,\n",
       " 'Military exercises': 2,\n",
       " 'Cucuta - Arauca': 2,\n",
       " 'Positioning': 2,\n",
       " 'Auckland - Christchurch': 2,\n",
       " 'Cincinnati - Detroit': 2,\n",
       " 'Guangzhou - Guilin': 2,\n",
       " 'Tokyo - Honolulu': 2,\n",
       " 'Douala - Yaounde': 2,\n",
       " 'Davao - Manila': 2,\n",
       " 'Medellin - Quibdo': 2,\n",
       " 'Taipei - Los Angeles': 2,\n",
       " 'Madrid - Bilbao': 2,\n",
       " 'Bangkok - Phuket': 2,\n",
       " 'Kaboul-Kunduz': 2,\n",
       " 'Chichen Itza - Cozumel': 2,\n",
       " 'New York, NY - Atlantic City, NJ': 2,\n",
       " 'Local sightseeing': 2,\n",
       " 'Luanda - Cafunfo': 2,\n",
       " 'Nakhichevan - Rostov': 2,\n",
       " 'San Jose - Villavicencio': 2,\n",
       " 'Port Harcourt - Lagos': 2,\n",
       " 'Addis Ababa - Nairobi': 2,\n",
       " 'Luanda - Lucapa': 2,\n",
       " 'Jomsom - Pokhara': 2,\n",
       " 'Kathmandu - New Delhi': 2,\n",
       " 'El Yopal - Mitu': 2,\n",
       " 'Jayapura - Wamena': 2,\n",
       " 'Boston - Los Angeles': 2,\n",
       " 'Skydiving': 2,\n",
       " 'Tehran - Orumiyeh': 2,\n",
       " 'Mogadishu - Djibouti': 2,\n",
       " 'Pointe Noire - Brazzaville': 2,\n",
       " 'Karachi - Islamabad': 2,\n",
       " 'Kathmandu - Lukla': 2,\n",
       " 'Pokhara - Jomsom': 2,\n",
       " 'Air show': 1,\n",
       " 'Venice  Taliedo': 1,\n",
       " 'Paris - Hounslow': 1,\n",
       " 'Washington - Newark': 1,\n",
       " 'Warsaw - Prague - Strasbourg - Paris': 1,\n",
       " 'Geraldton - Derby': 1,\n",
       " 'Croydon - Le Bourget': 1,\n",
       " 'Hamburg - Berlin': 1,\n",
       " 'Venice - London': 1,\n",
       " 'Toulouse - Barcelona - Valencia - Alicante - Malaga - Rabat': 1,\n",
       " 'Paris - Berck-sur-Mer - Croydon': 1,\n",
       " 'Croydon - Manchester': 1,\n",
       " 'Toulon - Algiers': 1,\n",
       " 'Lympne, England - Rotterdam, The Netherlands': 1,\n",
       " 'Tiflis - Suchumi': 1,\n",
       " 'Alicante - Barcelona - Toulouse': 1,\n",
       " 'Oran - Alicante': 1,\n",
       " 'Toulouse - Barcelona': 1,\n",
       " 'Lakehurst, NJ - S.t Louis, MO': 1,\n",
       " 'New York - San Francisco': 1,\n",
       " 'Paris - Cryodon': 1,\n",
       " 'Paris - Prague - Budapest': 1,\n",
       " 'Amsterdam - Rotterdam - Brussels - Paris': 1,\n",
       " 'Lebourget, France - Croydon, England': 1,\n",
       " 'New York - Paris': 1,\n",
       " 'Casablanca -Dakar': 1,\n",
       " 'Delivery flight': 1,\n",
       " 'Casablanca - Oran': 1,\n",
       " 'Kassel - Frankfurt': 1,\n",
       " 'Croydon - Schiphol': 1,\n",
       " 'Old Orchad Beach - Rome': 1,\n",
       " 'Saint Louis - Dakar': 1,\n",
       " 'Dakar - Casablanca - Alicante - Perpignan - Toulouse': 1,\n",
       " 'Minola - Albany - Buffalo': 1,\n",
       " 'Toulouse - Perpignan - Alicante - Casablanca - Dakar': 1,\n",
       " 'Dearborn - Buffalo': 1,\n",
       " 'Essen - Dortmund - Frankfurt': 1,\n",
       " 'Chicago - Dallas': 1,\n",
       " 'Testing': 1,\n",
       " 'Sightseeing over Rotterdam': 1,\n",
       " 'Victoria - Seattle': 1,\n",
       " 'Toul - Clermont-Ferrand': 1,\n",
       " 'Adelaide - Longreach': 1,\n",
       " 'Great Falls - Pocatello - Salt Lake City': 1,\n",
       " 'Casablanca - Alicante - Barcelona - Perpignan - Toulouse': 1,\n",
       " 'Paris - Lyon - Marseille': 1,\n",
       " 'Marseille - Palma de Majorca': 1,\n",
       " 'San Antonio - Amarillo - Tulsa - Denver': 1,\n",
       " 'Colfax - Spokane': 1,\n",
       " 'Cologne -  Berlin': 1,\n",
       " 'Boise, ID - Pasco, WA': 1,\n",
       " 'Brindisi - Athens - Istanbul': 1,\n",
       " 'Los Angeles - Phoenix - El Paso': 1,\n",
       " 'San Diego - Phoenix': 1,\n",
       " 'Tampico - Brownsville, TX': 1,\n",
       " 'Algiers - Marseille': 1,\n",
       " 'Janeiro - Buenos Aires': 1,\n",
       " 'London - Paris - Basil - Zuric': 1,\n",
       " 'St. Paul - Minneapolis': 1,\n",
       " 'Albuquerque - Los Angeles': 1,\n",
       " 'London, England - India': 1,\n",
       " 'Toulouse, France - Casablanca, Morocco': 1,\n",
       " 'Toulouse - Perpignan - Barcelona - Alicante - Casablanca': 1,\n",
       " 'Tijuana - Nogales': 1,\n",
       " 'Naples - Genoa': 1,\n",
       " 'Bremerton - Seattle': 1,\n",
       " 'Matamoros - Mexico City - Juarez': 1,\n",
       " 'Croyden - Amsterdam - Berlin': 1,\n",
       " 'Cagliari, Terranova - Rome': 1,\n",
       " 'Marseille - Berlin': 1,\n",
       " 'Demonistration': 1,\n",
       " 'Antananarivo - Paris': 1,\n",
       " 'Aqua Caliente, Mexico - Los Angeles': 1,\n",
       " 'Casablanca - Alicante': 1,\n",
       " 'Wichita - Kansas City, KS': 1,\n",
       " 'Newark, NJ - Hartford, CT': 1,\n",
       " 'Albany, NY - Jersey City, NJ': 1,\n",
       " 'Stettin - Norway': 1,\n",
       " 'Port Aransas - Kansas City': 1,\n",
       " 'London - Le touquet, France': 1,\n",
       " 'Croydon - Brussels': 1,\n",
       " 'Calgary - Moosejaw': 1,\n",
       " 'Cardington, England - Karachi, Pakistan': 1,\n",
       " 'Berlin - Vienna': 1,\n",
       " 'Casablanca - Bahat - Toulouse': 1,\n",
       " 'Paris, France - London, England': 1,\n",
       " 'Porto Alegre - Laguna - Rio de Janeiro': 1,\n",
       " 'Burbank, CA - Oakland, CA': 1,\n",
       " 'Shanghai - Hankow': 1,\n",
       " 'Portland - Pasco': 1,\n",
       " 'Gisborne - Hastings': 1,\n",
       " 'Winnigeg - Moosejaw': 1,\n",
       " 'Algiers – Marseille': 1,\n",
       " 'Sydney - Melbourne': 1,\n",
       " 'Kansas City - Wichita -  Los Angeles': 1,\n",
       " 'San Diego - Los Angeles': 1,\n",
       " 'Saigon - Karachi': 1,\n",
       " 'Cologne - Saarbrucken': 1,\n",
       " 'Chicago - Detroit': 1,\n",
       " 'Constantinople - Budapest': 1,\n",
       " 'Cincinnati - Atlanta': 1,\n",
       " 'Paris - Bucharest': 1,\n",
       " 'Oakland, CA - Seattle, WA': 1,\n",
       " 'Sydney - New York': 1,\n",
       " 'Newark, NJ  - Camden, NJ - Washington DC': 1,\n",
       " 'Durban - Cape Town': 1,\n",
       " 'Nanking - Peiking': 1,\n",
       " 'Portland,  OR - Pasco, WA': 1,\n",
       " 'Kansas City - Chicago': 1,\n",
       " 'Djakarta - Amsterdam -  Bangkok - Rangoon (Burma)': 1,\n",
       " 'Marseille - Algiers': 1,\n",
       " 'Port Colombus, OH - Louisville, KY': 1,\n",
       " 'Rockford - Chicago, IL - Madison, WI': 1,\n",
       " 'San Francisco - Bakersfield - Los Angeles, CA': 1,\n",
       " 'Chicago - Kalamazoo': 1,\n",
       " 'Phoenix, AZ - Los Angeles, CA': 1,\n",
       " 'Columbus - McKeesport': 1,\n",
       " 'Damascus - Baghdad - Karachi': 1,\n",
       " 'San Diego - Burbank - Oakland': 1,\n",
       " 'Santiago, Chile - Mendosa, Argentina': 1,\n",
       " 'Marseille - Naples - Beyrouth - Saigon': 1,\n",
       " 'Amsterdam - Hanover - Copenagen - Malmö': 1,\n",
       " 'El Paso - Dallas': 1,\n",
       " 'Sydney - Lismore - Brisbane': 1,\n",
       " 'Dakar - Port Etienne - Algiers - Paris': 1,\n",
       " 'London - Cologne - Berlin': 1,\n",
       " 'Nuremberg - Frankfort': 1,\n",
       " 'Little Rock, AR - Memphis, TN': 1,\n",
       " 'Big Springs - San Antonio': 1,\n",
       " 'Mendoza, Argentina - Santiago, Chili': 1,\n",
       " 'Burbank - San Francisco': 1,\n",
       " 'Cologne, Germany - Croydon, England': 1,\n",
       " 'Corfu - Naples': 1,\n",
       " 'Paris - Morroco': 1,\n",
       " 'Athens - Rhodes': 1,\n",
       " 'Amarillo, TX - Albuquerque, NM': 1,\n",
       " 'Ljubjana - Susak, Croatia': 1,\n",
       " 'Newark - Cleveland - Chicago - Oakland': 1,\n",
       " 'Zurich - Basel - Paris': 1,\n",
       " 'Seattle, WA - Dallas, TX': 1,\n",
       " 'Canton - Shanghai': 1,\n",
       " 'Berlin - Hamburg': 1,\n",
       " 'St. Louis, MO - Chicago, IL': 1,\n",
       " 'Quiddo - Cartago': 1,\n",
       " 'Dieppe, France - New Haven, England': 1,\n",
       " 'Croydon, England - Paris, France': 1,\n",
       " 'New York - Chicago': 1,\n",
       " 'Mexico - Burbank': 1,\n",
       " 'San Bernardion - Los Angeles': 1,\n",
       " 'Zurich - Stuttgart - Leipzig - Berlin': 1,\n",
       " 'Kansas City - Saint Joseph - Omaha': 1,\n",
       " 'Launceston, Tasmania - Melbourne, Australia': 1,\n",
       " 'Rabaul - Kaiapit': 1,\n",
       " 'Omaha - Kansas City': 1,\n",
       " 'London - Sydney': 1,\n",
       " 'Berlin - Hanover': 1,\n",
       " 'Kansas City, MO - Chicago, IL': 1,\n",
       " 'Havana - Santiago de Cuba': 1,\n",
       " 'Amsterdam - Batavia, Java': 1,\n",
       " 'Louisville, KY - Nashville, TN': 1,\n",
       " 'Newark, NJ - Columbus, OH': 1,\n",
       " 'Koenigberg - Berlin': 1,\n",
       " 'Jerusalem - Cairo': 1,\n",
       " 'Prague Leipzig Essen Amsterdam Rotterdam': 1,\n",
       " 'Pueblo - El Paso': 1,\n",
       " 'Los Angeles - Albuquerque - Kanasas City -  Washington': 1,\n",
       " 'Billings - Sheridan - Casper - Cheyenne': 1,\n",
       " 'Chicago - St. Louis': 1,\n",
       " 'Medellin - Bogotá': 1,\n",
       " 'Amsterdam - Sweden': 1,\n",
       " 'New Orleans, LA - Mobile, AL': 1,\n",
       " 'Milan - Frankfort - Amsterdam': 1,\n",
       " 'Cardiff - Saint-Helier': 1,\n",
       " 'Palanquero - Tres Esquinas': 1,\n",
       " 'Guidonia - Cairo - Massawa - Asmara': 1,\n",
       " 'Dallas -  Atlanta': 1,\n",
       " 'Helena - Denver': 1,\n",
       " 'Burbank, CA - Salt Lake City, UT': 1,\n",
       " 'Panama City - David': 1,\n",
       " 'Melbourne - Sydney': 1,\n",
       " 'Troy - Teterboro': 1,\n",
       " 'Bahia - Aracaju': 1,\n",
       " 'Dallas - Waco': 1,\n",
       " 'London - Brindisi - Alexandria': 1,\n",
       " 'Le Havre - Paris - Tours - Bordeaux': 1,\n",
       " 'London - Brindisi - Mirabella - Alexandria': 1,\n",
       " 'Newark - Memphis - Little Rock - Dallas - Los Angeles': 1,\n",
       " 'Chicago - Oklahoma City': 1,\n",
       " 'Natal, Brazil - Dakar, Senegal': 1,\n",
       " 'Rio de Janerio - Berlin': 1,\n",
       " 'Mexico - Guatemala': 1,\n",
       " 'Newwark - Buffalo': 1,\n",
       " 'Newark - Los Angeles': 1,\n",
       " 'Miami - Rio de Janeiro': 1,\n",
       " 'Turin - Milan': 1,\n",
       " 'Paris - Brussels - Cologne': 1,\n",
       " 'Malmo - Amsterdam': 1,\n",
       " 'Bergen - Trondheim': 1,\n",
       " 'Belgrade - Zagreb - Ljubljana': 1,\n",
       " 'Saint Pierre - Saint-Hélier': 1,\n",
       " 'Paris - Toulouse - Dakar - Natal - Rio de Janeiro': 1,\n",
       " 'New Orleans - Chicago': 1,\n",
       " 'Hanover - Cologne - London-Gatwick': 1,\n",
       " 'Alexandria - Mirabella - Brindisi - Southampton': 1,\n",
       " 'Addis Ababa, Ethiopia - Rome, Italy': 1,\n",
       " 'El Paso, TX - Pueblo, CO': 1,\n",
       " 'Frankfurt - Erfurt': 1,\n",
       " 'Leipzig - Nuremberg': 1,\n",
       " 'Hanover - London': 1,\n",
       " 'Lisbon - Burgos - Geneva - Munich': 1,\n",
       " 'Marseille - Dakar - Natal - Rio de Janeiro': 1,\n",
       " 'Croydon - Amsterdam': 1,\n",
       " 'Burbank - Las Vegas - Salt Lake City': 1,\n",
       " 'Chicago - Minneapolis - Missoula - Seattle': 1,\n",
       " 'Miami - Charleston - Newark': 1,\n",
       " 'Oakland - San Francisco - Los Angeles': 1,\n",
       " 'Mexico City - Minatitlán - Mérida': 1,\n",
       " 'Salt Lake City - Burbank': 1,\n",
       " 'Elizabethville - Gao - Colomb-Béchar - Oran - Brussels': 1,\n",
       " 'Las Palmas, Canary Islands - Bathhurst, Gambia': 1,\n",
       " 'Southhampton, England - Alexandria, Egypt': 1,\n",
       " 'Newark - Camden - Pittsburg': 1,\n",
       " 'Frankfurt - Lakehurst N.J.': 1,\n",
       " 'Puerto Montt - Punta Arenas': 1,\n",
       " 'San José  San Isidro de El General': 1,\n",
       " 'Lae - Howland Island': 1,\n",
       " 'Rome, Italy - Kartoum': 1,\n",
       " 'Santiago, Chili - Cristobal, Panama': 1,\n",
       " 'Canton - Shanghi': 1,\n",
       " 'Chicago - Daytona': 1,\n",
       " 'Mendosa - Cordoba': 1,\n",
       " 'Alexandria, Egypt  - Athens, Greece': 1,\n",
       " 'Palembang - Singapore': 1,\n",
       " 'Newark - Chicago - Cheyenne - Salt Lake City - Los Angeles': 1,\n",
       " 'Dakar - Casablanca': 1,\n",
       " 'Berlin - Mannheim': 1,\n",
       " 'Cologne - Brussels - London': 1,\n",
       " 'Rome - Munich - Berlin': 1,\n",
       " 'India - London': 1,\n",
       " 'Marseille - Lyon - Paris': 1,\n",
       " 'Bucharest - Vienna - Prague - Paris': 1,\n",
       " 'Milan - Frankfurt': 1,\n",
       " 'Seattle - Spokane - Butte - Billings - Chicago': 1,\n",
       " 'Oakland - Honolulu - Kingman Reef - Pago Pago - Auckland': 1,\n",
       " 'Sioux Lookout - Hudson': 1,\n",
       " 'Marseille, France - Ajaccio, Corsica': 1,\n",
       " 'Cadiz - Pollença -Alghero - Rome': 1,\n",
       " 'Berlin, Germany - Paris, France': 1,\n",
       " 'San Francisco - Winslow, AZ,  Burbank, CA': 1,\n",
       " 'Hanoi, Vietnam - Paris, France': 1,\n",
       " 'Dakar - Casablanca - Oran - Toulouse - Paris': 1,\n",
       " 'Brindisi - Rome': 1,\n",
       " 'Mexico City - Juarez': 1,\n",
       " 'Burbank,- St. Paul': 1,\n",
       " 'Santos - Sao Paulo - Rio de Janeiro - Natal': 1,\n",
       " 'Newark - Cleveland - Chicago': 1,\n",
       " 'Vancouver - Zeballos': 1,\n",
       " 'Seattle - Spokane - Helena - Billings - Fargo': 1,\n",
       " 'Warsaw, Poland - Bucharest, Romania': 1,\n",
       " 'London - Kisumu - Cape Town': 1,\n",
       " 'Alameda - Manila': 1,\n",
       " 'Villahermosa - Veracruz - Mexico City': 1,\n",
       " 'Prague - Strasbourg - Paris': 1,\n",
       " 'Guanabara - Victoria': 1,\n",
       " 'Hong Kong - Chungking': 1,\n",
       " 'Burbank - Winslow - Albuquerque': 1,\n",
       " 'Townsville - Innisfail - Cairns': 1,\n",
       " 'Brussels - Düsseldorf - Berlin': 1,\n",
       " 'Adelaide - Melbourne': 1,\n",
       " 'Jersey - Southhampton': 1,\n",
       " 'Berlin - Amsterdam': 1,\n",
       " 'Winnipeg - Regina - Vancouver': 1,\n",
       " 'Southampton - Sydney': 1,\n",
       " 'Seattle, WA - Medford, OR - Oakland,CA': 1,\n",
       " 'Fukuoka - Taihoku': 1,\n",
       " 'Instructional Flight': 1,\n",
       " 'Zurich - Geneva - Paris': 1,\n",
       " 'Katherine - Adelaide': 1,\n",
       " 'New York City - Bermuda': 1,\n",
       " 'Ketchikan - Juneau': 1,\n",
       " 'Barcelona, Spain - Rome, Italy': 1,\n",
       " 'Chicago - Kansas City - Wichita - Oklahoma City - Dallas': 1,\n",
       " 'London - Cairo- Mombasa - Dar es-Salaam - Lumbo - Maputo': 1,\n",
       " 'Casablanca, Morocco - Dakar, Senegal': 1,\n",
       " 'Haamstede - Vlissingen': 1,\n",
       " 'Heston - Newcastle': 1,\n",
       " 'Vienna - Zurich': 1,\n",
       " 'Cologne - Toulouse - Barcelona - Casablanca': 1,\n",
       " 'Miami - Rio de Janerio': 1,\n",
       " 'London - Hamburg - Copenhagen': 1,\n",
       " 'Calcutta - Dum Dum': 1,\n",
       " 'Berlin - Hanover - Cologne - London': 1,\n",
       " 'Copenhagen - Amsterdam': 1,\n",
       " 'Barranquilla - Girardot': 1,\n",
       " 'Tetouan- Seville - Alicante': 1,\n",
       " 'Egypt - England': 1,\n",
       " 'Rome - Rio de Janerio': 1,\n",
       " 'Batavia - Denpasar - Kupang - Darwin - Sydney': 1,\n",
       " 'Catania - Brindisi - Rome': 1,\n",
       " 'Bogota - Cucuta': 1,\n",
       " 'Calcutta - Jask - Sharjah - Alexandria - London': 1,\n",
       " 'Tripol- Catania - Rome - Milan': 1,\n",
       " 'Stockholm - Perth - Heston': 1,\n",
       " 'Tallinn, Estonia - Helsinki, Finland': 1,\n",
       " 'Melbourne - Canberra': 1,\n",
       " 'Zagreb - Split': 1,\n",
       " 'Baia Mare - Cluj-Napoca - Sibiu - Bucharest': 1,\n",
       " 'Washington D.C. - Detroit': 1,\n",
       " 'Belgrade - Osijek - Zagreb': 1,\n",
       " 'Chungking - Kunming': 1,\n",
       " 'Santa Cruz - Corumba': 1,\n",
       " 'Oakland - Salt Lake City': 1,\n",
       " 'Rome - Seville - Villa Cisneros - Sal - Natal - Rio': 1,\n",
       " 'Rio de Janeiro - Natal - Sal - Seville - Rome': 1,\n",
       " 'Budapest - Maros-Vasarhely': 1,\n",
       " 'Kansas City, MO - New York': 1,\n",
       " 'New York City -  Washington D.C. - Atlanta - Brownsville': 1,\n",
       " 'Luanda - Windhoek - Cape Town': 1,\n",
       " 'Isles of Scilly - Penzance, Cornwall': 1,\n",
       " 'Bucharest - Sofia': 1,\n",
       " 'Rome - Cagliari': 1,\n",
       " 'Curitiba - Sao Paulo': 1,\n",
       " 'Horn Island - Coen - Cairns': 1,\n",
       " 'Marseille - Toulouse': 1,\n",
       " 'Miami - Buenos Aires': 1,\n",
       " 'Athens - Sofia': 1,\n",
       " 'Minneapolis - Fargo': 1,\n",
       " 'New York - Buffalo - Chicago - Detroit': 1,\n",
       " 'Chardzhou - Urgench': 1,\n",
       " 'Almaty - Karaganda - Aktyubinsk - Kuybyshev - Kazan': 1,\n",
       " 'Las Vegas - Los Angeles': 1,\n",
       " 'Darwin - Surabaya, Dutch East Indies': 1,\n",
       " 'Cairo, Egypt - England': 1,\n",
       " 'Brisbane - Darwin': 1,\n",
       " 'Java - Broom, Australia': 1,\n",
       " 'Java - Australia': 1,\n",
       " 'Broome - Kupang': 1,\n",
       " 'Kunming - Chungking': 1,\n",
       " 'Brisbane - Bachelor Field': 1,\n",
       " 'Rome - Tripoli, Lybia': 1,\n",
       " 'Battle Creek - Cincinnati - Florence': 1,\n",
       " 'Horn Island - Amberley': 1,\n",
       " 'Sydenham - Hendon': 1,\n",
       " 'Trinidad - Curacao': 1,\n",
       " 'Oban - Invergordon - Keflavik': 1,\n",
       " 'Lagos - Bathurst - Lisbon - Poole': 1,\n",
       " 'Marseille, France - Dakar, Senegal': 1,\n",
       " 'Juana Díaz-Losey Field - San Juan': 1,\n",
       " 'New York - Botwood - Foynes, Ireland': 1,\n",
       " 'Moscow - Kazan - Sverdlovsk': 1,\n",
       " 'Budapest - Belgrade': 1,\n",
       " 'Palm Springs - New York City': 1,\n",
       " 'Kranoyarsk - Kirensk': 1,\n",
       " 'Chardzhou - Tashkent': 1,\n",
       " 'Salt Lake - Las Vegas - Burbank': 1,\n",
       " 'Copenhagen, Denmark - Budapest, Hungary': 1,\n",
       " 'Prince George - Vancouver': 1,\n",
       " 'Lisbon - London': 1,\n",
       " 'Accra, Ghana - Natal, Brazil': 1,\n",
       " 'S Santiago de Chile - Arequipa - Lima': 1,\n",
       " 'New York - Hamilton - Horta - Lisbon - Marseille': 1,\n",
       " 'Kunming - Assam': 1,\n",
       " 'Kunming - Dinjan': 1,\n",
       " 'Brisbane - Sydney': 1,\n",
       " 'Stockholm, Sweden - RAF Leuchars, Scotland': 1,\n",
       " 'Townsville - Port Moresby': 1,\n",
       " 'Lisbon - Bristol': 1,\n",
       " 'Pope Field - Maxton': 1,\n",
       " 'Noumea - Sydney': 1,\n",
       " 'Mackay - Port Moresby': 1,\n",
       " 'Lagos - Lisbon - Foynes': 1,\n",
       " 'Cleveland - Memphis': 1,\n",
       " 'Bombing run': 1,\n",
       " 'Townsville - Brisbane': 1,\n",
       " 'Maxton - Godman': 1,\n",
       " 'Nashville - Memphis': 1,\n",
       " 'Gander - Mont-Joli': 1,\n",
       " 'Cairo - Djerba - Poole': 1,\n",
       " 'Port Moresby - Townsville,, Queensland, Australia': 1,\n",
       " 'Townsville - Rockhampton - Brisbane': 1,\n",
       " 'Wendover Field - Pocatello AAB': 1,\n",
       " 'Thessaloniki - Belgrade': 1,\n",
       " 'Little Rock - Memphis': 1,\n",
       " 'Quelimane - Lourence Marques': 1,\n",
       " 'Port Moreaby - Nadzab': 1,\n",
       " 'Nome - Fairbanks': 1,\n",
       " 'Vienna - Belgrade - Sofia - Thessaloniki - Athens': 1,\n",
       " 'Montreal, Canada - European air base': 1,\n",
       " 'Calcutta - Dinjan': 1,\n",
       " 'Dijan - Kunming': 1,\n",
       " 'Kunming - Chungking,': 1,\n",
       " 'Barksdale - Portland': 1,\n",
       " 'Cairns or Mareeba, Australia - Los Negros Island': 1,\n",
       " 'England - Long Island, N.Y.': 1,\n",
       " 'Bristol - Preswick': 1,\n",
       " 'Bruning, NB - Pierre, SD': 1,\n",
       " 'San Juan - Antilla - Miami': 1,\n",
       " 'Boston - Gander - Keflavik - Prestwick': 1,\n",
       " 'Athens - Sofia - Belgrade - Vienna': 1,\n",
       " 'Merauke -- Cairns': 1,\n",
       " 'Elmindorf AFB, Anchorage - Ladd Field, Fairbanks': 1,\n",
       " 'Stuttgart - Barcelona': 1,\n",
       " 'Dinjan - Suifu': 1,\n",
       " 'Sydney - Townsville': 1,\n",
       " 'Frankfurt - Berlin - Hamburg - Copenhagen - Oslo': 1,\n",
       " 'Liverpool - New York': 1,\n",
       " 'Chateaudun - Greenham Common': 1,\n",
       " 'Cairo - Juba - Nairobi - Salisbury': 1,\n",
       " 'Near Tegucigalpa - San Pedro Sula': 1,\n",
       " 'Stockholm, Sweden - Berlin Germany': 1,\n",
       " 'San Francisco - Burbank - New York City': 1,\n",
       " 'Chisinau - Kiev - Moscow': 1,\n",
       " 'Twinwood, England - Villacoublay, France': 1,\n",
       " 'Kunming - Tengchung': 1,\n",
       " 'Miami - Leopoldville': 1,\n",
       " 'New York City - Juarez, Mexico - Burbank': 1,\n",
       " 'Chongqing - Kunming': 1,\n",
       " 'Melbourne - Kerang - Mildura - Broken Hill': 1,\n",
       " 'Florence - Rome': 1,\n",
       " 'Thorney Island - Amiens': 1,\n",
       " 'Cooktown - Finschafen': 1,\n",
       " 'Kunming - Sichang': 1,\n",
       " 'Pittsburgh - Morgantown': 1,\n",
       " 'Perth - Adelaide': 1,\n",
       " 'Berlin - Prague - Vienna - Munich': 1,\n",
       " 'Midland, TX - Nashville, TN': 1,\n",
       " 'Ridgewell - Nutts Corner': 1,\n",
       " 'Washington - New York': 1,\n",
       " 'Lorengau - Biak': 1,\n",
       " 'England - Brussels': 1,\n",
       " 'Accra, Ghana - Monrovia, Liberia': 1,\n",
       " 'Perth - Geraldton - Carnarvon - Port Hedland': 1,\n",
       " 'Montreal - Gander - Keflavik - Prestwick - Northolt': 1,\n",
       " 'Kisumu - Durban': 1,\n",
       " 'Boston - NY - Washington DC - Jacksonville - Miami': 1,\n",
       " 'Bedford - New York': 1,\n",
       " 'Mexico City - Nogales': 1,\n",
       " 'Port of Spain - Fort de France': 1,\n",
       " 'Miami - New York City': 1,\n",
       " 'Milne Bay - Dobodura': 1,\n",
       " 'Pitu - Merauke - Bamaga - Townsville': 1,\n",
       " 'Miami - Jacksonville': 1,\n",
       " 'England - Prague': 1,\n",
       " 'London - Cairo': 1,\n",
       " 'London - Brussels - New Delhi': 1,\n",
       " 'Hankou - Peking': 1,\n",
       " 'Shanghai - Canton': 1,\n",
       " 'Ambon, Indonesia - Darwin, Australia': 1,\n",
       " 'London - Valetta - Cairo': 1,\n",
       " 'Zhijiang - Shanghai': 1,\n",
       " 'Bovingdon - Schleissheim': 1,\n",
       " 'Chabua - Karachi': 1,\n",
       " 'Alberquerque -': 1,\n",
       " 'Athens - Naples': 1,\n",
       " 'France - Athens': 1,\n",
       " 'Chabau, India - Karachi, Pakistan': 1,\n",
       " 'Lae - Finschhafen - Jacquinot Bay - Rabaul': 1,\n",
       " 'Merryfield - Bombay': 1,\n",
       " 'India - Cambridge, England': 1,\n",
       " 'Minneapolis - Fargo - Billings': 1,\n",
       " 'Down Ampney - Copenhagen - Oslo': 1,\n",
       " 'Paris - N’Djamena - Bangui - Antananarivo': 1,\n",
       " 'Philadelphia - New York City': 1,\n",
       " 'New York City - Birmingham, AL': 1,\n",
       " 'Kharkov, Ukraine - Moscow': 1,\n",
       " 'New York - Boston': 1,\n",
       " 'Comox, BC - Greenwood,NS': 1,\n",
       " 'Boise - Denver': 1,\n",
       " 'Iloilo - Cebu': 1,\n",
       " 'Dallas - Tucon - San Diego': 1,\n",
       " 'Paris - Prauge': 1,\n",
       " 'Hobart - Melbourne': 1,\n",
       " 'Shanghai - Nanking': 1,\n",
       " 'Stockton, CA - Ogden, UT': 1,\n",
       " 'Karachi - Australia': 1,\n",
       " 'Liverpool - Belfast': 1,\n",
       " 'Shensi - Yenan': 1,\n",
       " 'La Liberiad - Managua': 1,\n",
       " 'Richmond - Atlanta': 1,\n",
       " 'Oslo - Stockholm': 1,\n",
       " 'Kano - Ikeja': 1,\n",
       " 'Topeka - Denver': 1,\n",
       " 'Jalapa - Cordoba': 1,\n",
       " 'Northolt - Oslo': 1,\n",
       " 'Atlantic City - Newark': 1,\n",
       " 'Palestine - London': 1,\n",
       " 'Teterboro - Chicago - San Francisco': 1,\n",
       " 'Stewart - Prince Rupert': 1,\n",
       " 'Copenhagen - Paris': 1,\n",
       " 'Bathrust, - Argentina': 1,\n",
       " 'Brussels - Croydon': 1,\n",
       " 'Brussels - Shannon - Gander - New York City': 1,\n",
       " 'St. Mawan, England - Almaza, Egypt': 1,\n",
       " 'Sichang - Kunming, Yunnan': 1,\n",
       " 'Hong Kong - Singapore': 1,\n",
       " 'Islay - Glascow': 1,\n",
       " 'Belo Horizonte  - Rio de Janerio': 1,\n",
       " 'San Francisco - Cheyenne - Chicago': 1,\n",
       " 'Paris - Casablanca': 1,\n",
       " 'Oakland - Chicago': 1,\n",
       " 'Paris - Toulouse - Casablanca': 1,\n",
       " 'Riga, Latvia - Moscow': 1,\n",
       " 'Chicago - Cleveland': 1,\n",
       " 'Palmdale - Burbank': 1,\n",
       " 'Mexico City - Veracruz': 1,\n",
       " 'London - Amsterdam': 1,\n",
       " 'Guam - Iwo Jima': 1,\n",
       " 'Meshed - Teheran': 1,\n",
       " 'Miramar NAS, California - Sand Point NB, Seattle': 1,\n",
       " 'Grace Park AF - Lucena': 1,\n",
       " 'Kingston, Jamaca - San Jose': 1,\n",
       " 'London - Buenos Aires.': 1,\n",
       " 'Holtville - San Diego': 1,\n",
       " 'Flint, MI - Washville, IL': 1,\n",
       " 'Paris - Shannon': 1,\n",
       " 'Buffalo - Chicago': 1,\n",
       " 'Shanghai - Tsingtao - Beijing': 1,\n",
       " 'Miami - Raleigh - Washington': 1,\n",
       " 'London - Bordeaux  - Lagos': 1,\n",
       " 'Shanghai - Manila': 1,\n",
       " 'Akron, OH - Winston/Salem, NC': 1,\n",
       " 'Puebla - Oaxaca': 1,\n",
       " 'Bogota - Barrancabermeja': 1,\n",
       " 'Croydon - Rome - Salisbury': 1,\n",
       " 'Canton - Chungking - Hong Kong': 1,\n",
       " 'Kobenhavn - Stockholm': 1,\n",
       " 'Shanghai - Hankow - Chungking': 1,\n",
       " 'Paris - Bordeaux - Lisbon': 1,\n",
       " 'New York, NY - Raleigh, NC': 1,\n",
       " 'Havana - Lisbon - Madrid': 1,\n",
       " 'Omaha, NE - Denver, CO': 1,\n",
       " 'Sichang - Chungking': 1,\n",
       " 'Singapore - Siagon': 1,\n",
       " 'Tbilisi - Moscow - Vnoukovo': 1,\n",
       " 'Hassani - Rome - Northolt': 1,\n",
       " 'Lyons - Nice - Paris': 1,\n",
       " 'Medellin, Colombia - San Jose, Costa Rica': 1,\n",
       " 'Negombo - Northolt': 1,\n",
       " 'Cumana - Caracas': 1,\n",
       " 'Lisbon - Dakar': 1,\n",
       " 'Macon - Columbus': 1,\n",
       " 'Lethbridge - Vancouver': 1,\n",
       " 'Nicoya - San Jose': 1,\n",
       " 'Nikolayevsk-on-Amur - Khabarovsk': 1,\n",
       " 'Seoul - Tachikawa': 1,\n",
       " 'Reykjavík - Akureyri': 1,\n",
       " 'Newark - Baltimore - Miami': 1,\n",
       " 'Marseille - Lyon': 1,\n",
       " 'Buenos Aires - Natal - London': 1,\n",
       " 'Pittsburgh - Washington D.C.': 1,\n",
       " 'Karachi - Istanbul': 1,\n",
       " 'Bangu i- Yaoundé - Douala': 1,\n",
       " 'Newark - Miami': 1,\n",
       " 'Paris - Vichy': 1,\n",
       " 'Cairo - Basrah': 1,\n",
       " 'Tihua - Lanzhou': 1,\n",
       " 'Kodiak - Dutch Harbor': 1,\n",
       " 'New York - Buffalo': 1,\n",
       " 'Amsterdam - Malmo': 1,\n",
       " 'Constanta - Bucharest': 1,\n",
       " 'Karachi - Bahrain': 1,\n",
       " 'Tromso - Oslo': 1,\n",
       " 'New York City - Dallas - Los Angeles': 1,\n",
       " 'Foynes, Ireland - Botwood, Newfoundland - New York': 1,\n",
       " 'Marseille - Oran': 1,\n",
       " 'Charlotte, NC - Gainsville, GA': 1,\n",
       " 'Istanbul-Athens-Rome-Geneve-Copenhagen-Stockholm': 1,\n",
       " 'Seattle - Juneau': 1,\n",
       " 'San Francisco - Chicago - New York': 1,\n",
       " 'Bucurest - Prague': 1,\n",
       " 'Fairbanks - Yakutat': 1,\n",
       " 'Zagreb - Titograd': 1,\n",
       " 'Goose Bay - Westover Field, MS': 1,\n",
       " 'El Paso - Memphis': 1,\n",
       " 'Krasnoyarsk - Omsk': 1,\n",
       " 'Karachi - Bombay': 1,\n",
       " 'Dorango - Leon - Mexico City': 1,\n",
       " 'Brussels - Paris': 1,\n",
       " 'Renfrew - Northolt': 1,\n",
       " 'Charlston - Savannah': 1,\n",
       " 'Algiers - Biskra': 1,\n",
       " 'Santa Cruz de Barahona - Santiago de los Caballeros': 1,\n",
       " 'Atlanta - Washington D.C.': 1,\n",
       " 'London - Havana': 1,\n",
       " 'Boston- NY-West Palm Beach- Miami': 1,\n",
       " 'Bandung - Jakarta - Banjoemans - Semarang': 1,\n",
       " 'Giessen - Bad Homberg': 1,\n",
       " 'Florence - Pisa - Rome': 1,\n",
       " 'Pittsburgh, PA - Columbus, OH': 1,\n",
       " 'Rio de Janeiro - Bele - Cayenne': 1,\n",
       " 'Oran - Perpignan - Lyon': 1,\n",
       " 'Vestmannaeyjar - Reykjavik': 1,\n",
       " 'Anchorage - New York City': 1,\n",
       " 'El Secreto - Bogota': 1,\n",
       " 'Delhi - Rome - London': 1,\n",
       " 'London - Berlin': 1,\n",
       " 'Lagos - Kano - Algiers - Paris': 1,\n",
       " 'Belgium - Shannon': 1,\n",
       " 'Kirensk - Badaybo': 1,\n",
       " 'Croydon - Paris - Cannes': 1,\n",
       " 'Leopoldville - Libenga': 1,\n",
       " 'Durban - Johannesburg - London': 1,\n",
       " 'Newark, NJ - Chicago, IL': 1,\n",
       " 'Valence - Bovingdon': 1,\n",
       " 'Petropavlovsk-Kamchatsky - Magadan': 1,\n",
       " 'Albacete - Albacete': 1,\n",
       " 'Birmingham, England - Isle of Man': 1,\n",
       " 'Milano - Brussels': 1,\n",
       " 'Stockholm - Amsterdam - Northolt': 1,\n",
       " 'Saigon - Da Lat': 1,\n",
       " 'Port Meunier - Gaspé': 1,\n",
       " 'Rosario - Bueno Aires': 1,\n",
       " 'Qingdao - Jinan': 1,\n",
       " 'Chaklala – Mauripur': 1,\n",
       " 'Fort-de-France - Port-Etienne - Paris': 1,\n",
       " 'Blenheim - Paraparaumu': 1,\n",
       " 'Maiquetía - Las Piedras - Maracaibo': 1,\n",
       " 'Chicago - Minneapolis': 1,\n",
       " 'Manono - Elizabethville': 1,\n",
       " 'Alma Ata - Balkhash - Karaganda - Moscow': 1,\n",
       " 'Fornebu - Hommelvik': 1,\n",
       " 'Tashkent - Baku - Tbilisi - Sochi': 1,\n",
       " 'Amsterdam - Prestwick - New York City': 1,\n",
       " 'Palmerston - Hamilton': 1,\n",
       " 'Krasnodar - Karachayevsk - Sukhumi - Tbilisi': 1,\n",
       " 'Minneapolis - Edmonton - Anchorage - Tokyo': 1,\n",
       " 'RAF Scrampton - Burtonwood AFB': 1,\n",
       " 'Anchorage - Homer - Yakutat - Annette Island - Seattle': 1,\n",
       " 'Melbourne - Denilquin': 1,\n",
       " 'Isle of Man - Dublin, Ireland': 1,\n",
       " 'Zyryanka - Srednekymsk': 1,\n",
       " 'Karachi - Lohore': 1,\n",
       " 'Chengdu - Shanghai': 1,\n",
       " 'Milan - Brussels': 1,\n",
       " 'Shanghai - Taipei': 1,\n",
       " 'Shanghai - Hong Konk': 1,\n",
       " 'Madrid - Barcelona': 1,\n",
       " 'Athens - Rome - Nice': 1,\n",
       " 'Seattle - New Haven': 1,\n",
       " 'Raleigh, NC - Boston, MA': 1,\n",
       " 'Bermuda - Kingston': 1,\n",
       " 'RAF Scampton - Keflavik - Kansas': 1,\n",
       " 'Homer - Anchorage': 1,\n",
       " 'Trinity - Rimouski': 1,\n",
       " 'Khartoum - Castel Benito': 1,\n",
       " 'Paris - Copenhagen': 1,\n",
       " 'Tingo Maria - Huanuco': 1,\n",
       " 'Manila, Philippines - Hong Kong': 1,\n",
       " 'Coolangatta - Brisbane': 1,\n",
       " 'Auckland - Wellington': 1,\n",
       " 'Yakutsk - Kirensk': 1,\n",
       " 'Barcelona - Torino': 1,\n",
       " 'Daet - Manila': 1,\n",
       " 'Kavallo - Athens': 1,\n",
       " 'Florianpolis - Porto Alegre': 1,\n",
       " 'Batavia - Amsterdam': 1,\n",
       " 'Guildford - Darwin': 1,\n",
       " 'Albuquerque - Burbank': 1,\n",
       " 'Delhi - Bombay': 1,\n",
       " 'Seattle - Chicago': 1,\n",
       " 'New York City - Wilmington': 1,\n",
       " 'Mers Nueva - Ambato': 1,\n",
       " 'Bogota - Ibague': 1,\n",
       " 'Rome - Shannon': 1,\n",
       " 'Belfast - Manchester': 1,\n",
       " 'Churchhill - Winnipeg': 1,\n",
       " 'Leopoldville - Elizabethville': 1,\n",
       " 'Montreal - Comeau Bay': 1,\n",
       " 'Tapacula - Mexico City': 1,\n",
       " 'Las Vegas - Denver': 1,\n",
       " 'Paris - Santa Maria - New York City': 1,\n",
       " 'Boston - Washington D.C. - New Orleans': 1,\n",
       " 'Philadelphia - Cleveland': 1,\n",
       " 'Brussels - Oslo': 1,\n",
       " 'New York City - Mexico City': 1,\n",
       " 'Paris - Lyons - Tunis': 1,\n",
       " 'Sao Palo, Brazil - Jacarezinho, Panama': 1,\n",
       " 'Burbank - Oakland - Sacramento': 1,\n",
       " 'Chengdu - Hoikow': 1,\n",
       " 'New Port News - Washington D.C.': 1,\n",
       " 'Agartala - Calcutta': 1,\n",
       " 'Murcia-San Javier - Albacete': 1,\n",
       " 'Valle grande - Cochabamba': 1,\n",
       " 'Antananarivo - Tamatave': 1,\n",
       " 'Elmendorf - Great Falls': 1,\n",
       " 'Amsterdam - London': 1,\n",
       " 'Ostrava - Prague': 1,\n",
       " 'Rochester Minn. - Minneapolis': 1,\n",
       " 'Hue - Tourane': 1,\n",
       " 'Llandow - Dublin': 1,\n",
       " 'Clark - Kadena - Tachikawa': 1,\n",
       " 'Bogota - Quito - Guayaquil': 1,\n",
       " 'Barksdale - Lajes': 1,\n",
       " 'Popayan - Ipiales': 1,\n",
       " 'Teterboro - Fort Wayne - Detroit': 1,\n",
       " 'Victoria - Salvador': 1,\n",
       " 'San Juan - Wilmington NC': 1,\n",
       " 'Kingston, Jamacia - Maracaibo, Venezuela': 1,\n",
       " 'New York City - Minneapolis - Spokan -Seattle': 1,\n",
       " 'Perth  Adelaide  Melbourne e': 1,\n",
       " 'Paris - Bordeaux - Casablanca - Dakar': 1,\n",
       " 'Delhi - Sringagar': 1,\n",
       " 'Nova Lisboa - Lobito': 1,\n",
       " 'Myrtle Beach - Nashville': 1,\n",
       " 'Tokyo - Kagoshima - Okinawa': 1,\n",
       " 'Rio de Janeiro - Porte Algegre': 1,\n",
       " 'Algiers - Aoulef - Bidon V - Gao': 1,\n",
       " 'Alma-Ata - Karaganda - Moscow': 1,\n",
       " 'Los Angeles - Chicago': 1,\n",
       " 'Changi, Singapore - Kota Baru': 1,\n",
       " 'Ithaca - Syracuse - Utica - Newark': 1,\n",
       " 'Kwajzalein - Tokyo': 1,\n",
       " 'Belgrade - Zagreb': 1,\n",
       " 'Karlstad - Stockholm': 1,\n",
       " 'Ashiya AB - Kimpo AB': 1,\n",
       " 'Casablanca - Algiers - Perpignan': 1,\n",
       " 'Bombay - Cairo - Geneva - London': 1,\n",
       " 'Helena - Butte': 1,\n",
       " 'Rome - Paris': 1,\n",
       " 'Chico, CA - Billings, WY': 1,\n",
       " 'Maldorado - Quincemil': 1,\n",
       " 'Tehran - Qom': 1,\n",
       " 'Paris - Bangui - Dar es-Salaam': 1,\n",
       " 'Casablanca - Perpignan': 1,\n",
       " 'Bombay - Coimbatore': 1,\n",
       " 'Naha AB - Clark AB': 1,\n",
       " 'Vancouver - Penticton': 1,\n",
       " 'Mar del Plata - Buenos': 1,\n",
       " 'Margate - Ladysmith - Johannesburg': 1,\n",
       " 'Butterworth - Kota Bharu': 1,\n",
       " 'Newark - Philadelphia': 1,\n",
       " 'Minneapolis - Seattle': 1,\n",
       " 'Paris - Rome': 1,\n",
       " 'Hong Kong - Iwakuni': 1,\n",
       " 'Vestmannaeyar Island - Reykjavík': 1,\n",
       " 'Douala - Niamey': 1,\n",
       " 'Kansas City, MO - Omaha, NB - Sioux City, Iowa': 1,\n",
       " 'San Marcos - Cartagena': 1,\n",
       " 'Rio de Janeiro - São Paulo - Curitiba - Florianópolis': 1,\n",
       " 'Loring AFB - Lakeheath AFB': 1,\n",
       " 'Irkutsk - Chita - Yakutsk': 1,\n",
       " 'Ushuaia - Río Grande - Buenos Aires': 1,\n",
       " 'Manchester - Belfast': 1,\n",
       " 'Santa Maria - Santa Barbara - Oxnard - Los Angeles': 1,\n",
       " 'Godman AFB - Charleston, WV': 1,\n",
       " 'Miami - Havana': 1,\n",
       " 'Cleveland - Fort Wayne - South Bend - Chicago': 1,\n",
       " 'Carswell - Kirtland': 1,\n",
       " 'Gibraltar - Bordeaux - Bovingdon': 1,\n",
       " 'Santa Cruz de Rio Prado - Presidente Prudent': 1,\n",
       " 'Fort Campbell - Lawton': 1,\n",
       " 'Medellín - Cartagena': 1,\n",
       " 'Johannesburg - Accra - Monrovia - New York': 1,\n",
       " 'Rejeka - Zagreb': 1,\n",
       " 'Salt Lake City - Denver': 1,\n",
       " 'Luqa - Lyneham - Dishforth': 1,\n",
       " 'Calcutta - Tezpur': 1,\n",
       " 'Natal - Recife - Maceió - Aracaju': 1,\n",
       " 'Wau - Bulolo - Lae': 1,\n",
       " 'Pakse - Saigon': 1,\n",
       " 'Vancouver - Tokyo': 1,\n",
       " 'Brussels - Gao - Léopoldville': 1,\n",
       " 'Cochabamba - Rio de Janeiro': 1,\n",
       " 'Hobart Melbourne': 1,\n",
       " 'Boston - Hartford - Cleveland - Chicago - Oakland': 1,\n",
       " 'Perpignan - Oran': 1,\n",
       " 'San Francisco - Oakland - San Francisco': 1,\n",
       " 'Bangalore - Trivandrum': 1,\n",
       " 'Ashiya - Tachikawa AB': 1,\n",
       " 'Mexico City - Minatitlán - Villahermosa': 1,\n",
       " 'Port Elizabeth - Durbin': 1,\n",
       " 'Azores - Westover AFB': 1,\n",
       " 'Kildala - Vancouver': 1,\n",
       " 'Belgrad - Skopje': 1,\n",
       " 'Flores - Guatemala City': 1,\n",
       " 'Oakland - Albuquerque': 1,\n",
       " 'Frankfort - Bordeaux': 1,\n",
       " 'Lodz - Krakow': 1,\n",
       " 'Ankara - Cairo': 1,\n",
       " 'Nagpur - Calcutta': 1,\n",
       " 'Tel Aviv - Rome - Zurich - Amsterdam': 1,\n",
       " 'San Luis Acatlán - Acapulco': 1,\n",
       " 'Mount Hagen - Madang': 1,\n",
       " 'Fort Smith - Newark - Tampa': 1,\n",
       " 'Cairo - Baghdad - Tehran': 1,\n",
       " 'Yakutsk - Vilyuisk': 1,\n",
       " 'Miami - Pittsburgh - Buffalo': 1,\n",
       " 'Point Barrow - Umiat - Fairbanks': 1,\n",
       " 'New York - Williams AFB': 1,\n",
       " 'Northolt - Dublin': 1,\n",
       " 'Tokyo - Elmendorf AFB': 1,\n",
       " 'Buffalo - Newark': 1,\n",
       " 'Costermansville - Leopoldville': 1,\n",
       " ...}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_rutas # imprime por pantalla el diccionario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con esta información, tenemos claro que la frecuencia de accidentes en entrenamientos aéreos es mayor a cualquier otra ruta o situación de vuelo. Le sigue <em>sightseeing</em> y los vuelos de testeo. Hablando de específicamente de rutas, la ruta con más accidentes es la ruta <strong>Rio de Janeiro - Sao Paulo</strong> y viceversa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Habiendo visto en que ruta ocurrieron más accidentes, veamos que tipos de aeronaves tuvieron más accidentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de registros totales de tipos de aeronave: 4995\n",
      "    Cantidad de 'Tipos de aeronaves': 2468\n"
     ]
    }
   ],
   "source": [
    "tipoAeronave = df['Tipo de Aeronave'].dropna() # Almacena en una variable la serie sin valores nan\n",
    "print(f\"Cantidad de registros totales de tipos de aeronave: {len(tipoAeronave)}\") # imprime por pantalla la cantidad de registros totales\n",
    "print(f\"    Cantidad de 'Tipos de aeronaves': {len(tipoAeronave.unique())}\") # imprime por pantalla la cantidad de registros únicos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cuales son los tipos de aeronaves con más accidentes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Douglas DC-3': 333,\n",
       " 'de Havilland Canada DHC-6 Twin Otter 300': 81,\n",
       " 'Douglas C-47A': 70,\n",
       " 'Douglas C-47': 64,\n",
       " 'Douglas DC-4': 41,\n",
       " 'Yakovlev YAK-40': 35,\n",
       " 'Antonov AN-26': 35,\n",
       " 'Junkers JU-52/3m': 30,\n",
       " 'De Havilland DH-4': 27,\n",
       " 'Douglas C-47B': 27,\n",
       " 'Douglas DC-6B': 27,\n",
       " 'Breguet 14': 22,\n",
       " 'Douglas DC-6': 20,\n",
       " 'Curtiss C-46A': 19,\n",
       " 'Curtiss C-46': 19,\n",
       " 'Antonov AN-24': 19,\n",
       " 'Douglas C-47-DL': 18,\n",
       " 'Antonov AN-12': 18,\n",
       " 'McDonnell Douglas DC-9-32': 18,\n",
       " 'Junkers F-13': 17,\n",
       " 'Fokker F-27 Friendship 600': 17,\n",
       " 'Douglas DC-3C': 16,\n",
       " 'Fokker F-27 Friendship 200': 16,\n",
       " 'Tupolev TU-134A': 16,\n",
       " 'Embraer 110P1 Bandeirante': 16,\n",
       " 'Lockheed 18 Lodestar': 15,\n",
       " 'Ilyushin IL-18B': 15,\n",
       " 'de Havilland Canada DHC-6 Twin Otter 100': 15,\n",
       " 'Lockheed C-130H': 15,\n",
       " 'Lockheed C-130H Hercules': 15,\n",
       " 'Boeing 40': 13,\n",
       " 'Ilyushin IL-12': 13,\n",
       " 'Ilyushin IL-14P': 13,\n",
       " 'CASA 212 Aviocar 200': 13,\n",
       " 'Douglas DC-2': 12,\n",
       " 'Douglas DC-3A': 12,\n",
       " 'Curtiss C-46D': 12,\n",
       " 'Ilyushin IL-18': 12,\n",
       " 'Fokker F-27 Friendship 100': 12,\n",
       " 'Antonov AN-24B': 12,\n",
       " 'Convair CV-580': 12,\n",
       " 'de Havilland Canada DHC-6 Twin Otter 200': 12,\n",
       " 'Let 410UVP': 12,\n",
       " 'Curtiss C-46F': 11,\n",
       " 'Douglas DC-6A': 11,\n",
       " 'Fokker F-28 Fellowship 1000': 11,\n",
       " 'Mil Mi-8 (helicopter)': 11,\n",
       " 'Junkers JU-52': 10,\n",
       " 'Douglas C-54A': 10,\n",
       " 'Ilyushin IL-14': 10,\n",
       " 'Tupolev TU-104B': 10,\n",
       " 'Ilyushin IL-18D': 10,\n",
       " 'Fokker F-27 Friendship 400M': 10,\n",
       " 'Douglas C-47A-DL': 9,\n",
       " 'Lisunov Li-2': 9,\n",
       " 'Consolidated PBY-5A Catalina': 9,\n",
       " 'Curtiss C-46F-1-CU': 9,\n",
       " 'Boeing KC-135A': 9,\n",
       " 'Antonov AN-24V': 9,\n",
       " 'Antonov An-26': 9,\n",
       " 'Antonov AN-24RV': 9,\n",
       " 'Embraer 110C Bandeirante': 9,\n",
       " 'McDonnell Douglas MD-82': 9,\n",
       " 'Antonov AN-32': 9,\n",
       " 'Lockheed 10 Electra': 8,\n",
       " 'Lisunov LI-2': 8,\n",
       " 'Douglas DC-4-1009': 8,\n",
       " 'Martin 202': 8,\n",
       " 'Convair CV-240-0': 8,\n",
       " 'de Havilland Canada DHC-3 Otter': 8,\n",
       " 'Tupolev TU-104A': 8,\n",
       " 'Fairchild F-27': 8,\n",
       " 'Antonov AN-2': 8,\n",
       " 'Fokker F-27 Friendship 500': 8,\n",
       " 'Tupolev TU-154B': 8,\n",
       " 'Lockheed Vega': 7,\n",
       " 'Douglas DC-2-112': 7,\n",
       " 'Douglas C-54': 7,\n",
       " 'Martin 404': 7,\n",
       " 'Vickers Viscount 745D': 7,\n",
       " 'Convair CV-440': 7,\n",
       " 'Boeing B-707-321B': 7,\n",
       " 'Tupolev TU-124': 7,\n",
       " 'Antonov AN-12BP': 7,\n",
       " 'Fairchild C-123K': 7,\n",
       " 'Britten-Norman BN-2A Islander': 7,\n",
       " 'McDonnell Douglas DC-9-31': 7,\n",
       " 'Lockheed L-100-30 Hercules': 7,\n",
       " 'Bell 212': 7,\n",
       " 'McDonnell Douglas DC-10-30': 7,\n",
       " 'Antonov 12BP': 7,\n",
       " 'Fokker 100': 7,\n",
       " 'Let 410UVP-E': 7,\n",
       " 'Douglas M-4': 6,\n",
       " 'Boeing 247': 6,\n",
       " 'Boeing 247D': 6,\n",
       " 'Lockheed 14 Super Electra': 6,\n",
       " 'Junkers Ju-52/3m': 6,\n",
       " 'Consolidated LB-30A Liberator': 6,\n",
       " 'Douglas C-47D': 6,\n",
       " 'Lockheed 049 Constellation': 6,\n",
       " 'Lockheed 749A Constellation': 6,\n",
       " 'Avro 685 York C-1': 6,\n",
       " 'Douglas DC-7C': 6,\n",
       " 'Lockheed C-130A Hercules': 6,\n",
       " 'Ilyushin IL-18V': 6,\n",
       " 'McDonnell Douglas DC-9-14': 6,\n",
       " 'de Havilland Canada DHC-6 Twin Otter': 6,\n",
       " 'McDonnell Douglas DC-10-10': 6,\n",
       " 'Ilyushin IL-62M': 6,\n",
       " 'Britten Norman BN-2A Trislander': 6,\n",
       " 'Antonov An-12': 6,\n",
       " 'Lockheed C-130E Hercules': 6,\n",
       " 'Swearingen SA.227AC Metro III': 6,\n",
       " 'de Havilland DHC-6 Twin Otter 300': 6,\n",
       " 'Antonov AN-28': 6,\n",
       " 'Mil Mi-8': 6,\n",
       " 'Fokker F-VII': 5,\n",
       " 'Fairchild FC-2': 5,\n",
       " 'Latecoere 28': 5,\n",
       " 'Junkers W-34': 5,\n",
       " 'Short S-23 (flying boat)': 5,\n",
       " 'Lockheed 14 Electra': 5,\n",
       " 'Lockheed 18 LodeStar': 5,\n",
       " 'Lockheed 18 Loadstar': 5,\n",
       " 'Douglas C-53': 5,\n",
       " 'Douglas C-47 Skytrain (DC-3)': 5,\n",
       " 'Douglas C-47B-DK': 5,\n",
       " 'Douglas C-54D-DC (DC-4)': 5,\n",
       " 'Avro 685 York I': 5,\n",
       " 'Douglas C-54B': 5,\n",
       " 'Li-2': 5,\n",
       " 'Douglas DC-3 (C-47-DL)': 5,\n",
       " 'Bristol 170 Freighter 21': 5,\n",
       " 'Convair CV-240': 5,\n",
       " 'Convair CV-340': 5,\n",
       " 'Lockheed 1049H Super Constellation': 5,\n",
       " 'Nord 2501 Noratlas': 5,\n",
       " 'Lockheed 188A Electra': 5,\n",
       " 'Douglas DC-7B': 5,\n",
       " 'Lockheed C-130B Hercules': 5,\n",
       " 'Antonov An-24B': 5,\n",
       " 'Boeing B-707-321C': 5,\n",
       " 'Britten-Norman BN-2A-6 Islander': 5,\n",
       " 'Bell 206B': 5,\n",
       " 'Boeing B-737-2A8': 5,\n",
       " 'Antonov AN-12V': 5,\n",
       " 'Douglas DC-6BF': 5,\n",
       " 'Lockheed L-188AF Electra': 5,\n",
       " 'Lockheed C-130 Hercules': 5,\n",
       " 'Boeing B-737-200': 5,\n",
       " 'Shorts SC-7 Skyvan 3-100': 5,\n",
       " 'McDonnell Douglas MD-11': 5,\n",
       " 'Tupolev TU-154M': 5,\n",
       " 'Learjet 35A': 5,\n",
       " 'Antonov 12': 5,\n",
       " 'Cessna 208B Grand Caravan': 5,\n",
       " 'Ford 5-AT-B Tri Motor': 4,\n",
       " 'Pitcairn PA-6 Mailwing': 4,\n",
       " 'Ford 5-AT-C Tri Motor': 4,\n",
       " 'de Havilland DH-86': 4,\n",
       " 'Stinson Model A': 4,\n",
       " 'Lockheed 10E Electra': 4,\n",
       " 'Dewoitine D-338': 4,\n",
       " 'Lockheed 14-H2 Super Electra': 4,\n",
       " 'Lockheed Hudson': 4,\n",
       " 'Grumman G-21 Goose': 4,\n",
       " 'Douglas C-47 Dakota': 4,\n",
       " 'Lockheed 049-46-21 Constellation': 4,\n",
       " 'Douglas DC-3D': 4,\n",
       " 'Douglas C-54D': 4,\n",
       " 'de Havilland DH-114 Heron 1B': 4,\n",
       " 'Douglas C-118A': 4,\n",
       " 'Curtiss C-46A-45-CU': 4,\n",
       " 'de Havilland DH-114 Heron 2D': 4,\n",
       " 'Convair CV-240-2': 4,\n",
       " 'Lockheed L-1049H Super Constellation': 4,\n",
       " 'Fairchild C-119G': 4,\n",
       " 'Lockheed L-188C Electra': 4,\n",
       " 'McDonnell Douglas DC-8-43': 4,\n",
       " 'de Havilland Comet 4C': 4,\n",
       " 'Douglas DC-7CF': 4,\n",
       " 'Vickers Viscount 785D': 4,\n",
       " 'Fairchild-Hiller FH-227B': 4,\n",
       " 'McDonnell Douglas DC-8-62': 4,\n",
       " 'Lockheed C-130E': 4,\n",
       " 'Lockheed L-100-20 Hercules': 4,\n",
       " 'Ilyushin IL-62': 4,\n",
       " 'Boeing B-737-222': 4,\n",
       " 'Tupolev TU-154': 4,\n",
       " 'Lockheed L-188A Electra': 4,\n",
       " 'McDonnell Douglas DC-8-63F': 4,\n",
       " 'Boeing B-747': 4,\n",
       " 'Beechcraft 99': 4,\n",
       " 'Tupolev TU-134': 4,\n",
       " 'Britten-Norman BN-2A-21 Islander': 4,\n",
       " 'Antonov An-12BP': 4,\n",
       " 'de Havilland DHC-2': 4,\n",
       " 'Sikorsky S-76': 4,\n",
       " 'Fokker F-28 Fellowship 4000': 4,\n",
       " 'Antonov An-24RV': 4,\n",
       " 'Swearingen SA.226TC Metro II': 4,\n",
       " 'Grumman G-159 Gulfstream I': 4,\n",
       " 'Embraer 120RT Brasilia': 4,\n",
       " 'Mil Mi-17 (helicopter)': 4,\n",
       " 'Antonov An-32': 4,\n",
       " 'Cessna 208B Caravan I': 4,\n",
       " 'Cessna 208B Caravan I Super Cargomaster': 4,\n",
       " 'Beechcraft 1900C-1': 4,\n",
       " 'Mil Mi-8T': 4,\n",
       " 'Dornier 228-201': 4,\n",
       " 'Embraer 110 Bandeirante': 4,\n",
       " 'Fokker 50': 4,\n",
       " 'Mil Mi-17': 4,\n",
       " 'Tupolev Tu-154M': 4,\n",
       " 'Mi-8': 4,\n",
       " 'Farman F-60 Goliath': 3,\n",
       " 'Fokker FG III': 3,\n",
       " 'Dornier Merkur': 3,\n",
       " 'Latecoere 25': 3,\n",
       " 'Latecoere 26': 3,\n",
       " 'Travel Air 4000': 3,\n",
       " 'Dornier Wal': 3,\n",
       " 'Travel Air 6000': 3,\n",
       " 'Lockheed Vega 5': 3,\n",
       " 'Boeing 95': 3,\n",
       " 'Fairchild Pilgrim 100A': 3,\n",
       " 'CAMS 53': 3,\n",
       " 'de Havilland DH-84 Dragon': 3,\n",
       " 'Savoia-Marchetti SM73': 3,\n",
       " 'Martin M-130 (flying boat)': 3,\n",
       " 'Savoia-Marchetti SM-75': 3,\n",
       " 'Consolidated Liberator': 3,\n",
       " 'Consolidated B-24 Liberator': 3,\n",
       " 'Focke-Wulf FW 200': 3,\n",
       " 'Douglas C-47A-10-DK': 3,\n",
       " 'Avro Anson': 3,\n",
       " 'Douglas DC-3 (C-53D-DO)': 3,\n",
       " 'Avro 685 York 1': 3,\n",
       " 'Curtiss C-46E': 3,\n",
       " 'Latecoere 631 (flying boat)': 3,\n",
       " 'Douglas C-47DL': 3,\n",
       " 'Boeing B-29': 3,\n",
       " 'Douglas C-54B-1-DC': 3,\n",
       " 'Douglas C-124A Globemaster': 3,\n",
       " 'SNCASE Languedoc': 3,\n",
       " 'Curtiss C-46A-50-CU': 3,\n",
       " 'Fairchild C-119C': 3,\n",
       " 'de Havilland DH106 Comet 1': 3,\n",
       " 'Convair CV-240-4': 3,\n",
       " 'Bristol 170 Freighter 21E': 3,\n",
       " 'Aeroflot': 3,\n",
       " 'Bristol 170 Freighter 31': 3,\n",
       " 'Canadair C-4 Argonaut': 3,\n",
       " 'de Havilland DH-114 Heron 2B': 3,\n",
       " 'de Havilland DH-114 Heron': 3,\n",
       " 'Noorduyn Norseman': 3,\n",
       " 'Antonov AN-10': 3,\n",
       " 'Fairchild F-27A': 3,\n",
       " 'Avia 14': 3,\n",
       " 'Grumman G-21A Goose': 3,\n",
       " 'Antonov AN-10A': 3,\n",
       " 'Fairchild C-123': 3,\n",
       " 'Fokker F-27 Friendship 400': 3,\n",
       " 'de Havilland Canada C-7A Caribou': 3,\n",
       " 'Lockheed L-100 Hercules': 3,\n",
       " 'Boeing B-727-64': 3,\n",
       " 'McDonnell Douglas DC-8-63CF': 3,\n",
       " 'Antonov AN-12B': 3,\n",
       " 'Boeing B-747-121': 3,\n",
       " 'Boeing Vertol CH-47 (helicopter)': 3,\n",
       " 'Boeing 707-321C': 3,\n",
       " 'Boeing B-727-224': 3,\n",
       " 'McDonnell Douglas DC-9-15': 3,\n",
       " 'Boeing B-707-331B': 3,\n",
       " 'Beechcraft E18S': 3,\n",
       " 'Boeing B-727-231': 3,\n",
       " 'Yakovlev 40': 3,\n",
       " 'Ilyushin IL-14M': 3,\n",
       " 'Embraer 110P Bandeirante': 3,\n",
       " 'McDonnell Douglas DC-8-61': 3,\n",
       " 'de Havilland Canada DHC-4A Caribou': 3,\n",
       " 'CASA 212 Aviocar 100': 3,\n",
       " 'Cessna 404 Titan': 3,\n",
       " 'Tupolev TU-154B-2': 3,\n",
       " 'McDonnell Douglas DC-10-30CF': 3,\n",
       " 'Yakovlev YAK-42': 3,\n",
       " 'de Havilland Canada DHC-6 Twin Otter 310': 3,\n",
       " 'Learjet 25': 3,\n",
       " 'Britten-Norman BN-2A-26 Trislander': 3,\n",
       " 'Cessna 402C': 3,\n",
       " 'Beechcraft 1900C': 3,\n",
       " 'Fairchild FH-227B': 3,\n",
       " 'Ilyushin IL-76MD': 3,\n",
       " 'Ilyushin IL-76TD': 3,\n",
       " 'Antonov 12BK': 3,\n",
       " 'Yakovlev YAK-42D': 3,\n",
       " 'Antonov 32': 3,\n",
       " 'Antonov AN-8': 3,\n",
       " 'Embraer EMB-110 Bandeirante': 3,\n",
       " 'Antonov AN-26B': 3,\n",
       " 'Airbus A320-211': 3,\n",
       " 'Antonov AN-32B': 3,\n",
       " 'Antonov 26B': 3,\n",
       " 'Dornier 228-212': 3,\n",
       " 'Britten-Norman BN-2A-26 Islander': 3,\n",
       " 'Ilyushin 76MD': 3,\n",
       " 'Harbin Yunshuji Y-12-II': 3,\n",
       " 'McDonnell Douglas MD-83': 3,\n",
       " 'Tupolev 154M': 3,\n",
       " 'Bell 206L': 3,\n",
       " 'Mi-8 helicopter': 3,\n",
       " 'Embraer 120ER Brasilia': 3,\n",
       " 'Ilyushin II-76TD': 3,\n",
       " 'Beechcraft 1900D': 3,\n",
       " 'Bell 407': 3,\n",
       " 'Antonov An-12BK': 3,\n",
       " 'Curtiss R-4LM': 2,\n",
       " 'Potez IX': 2,\n",
       " 'Fokker F7': 2,\n",
       " 'Bleriot 155': 2,\n",
       " 'Handley Page W-10': 2,\n",
       " 'Ford 4-AT-B Tri Motor': 2,\n",
       " 'Latecoere 32': 2,\n",
       " 'Ford Tri-motor 4': 2,\n",
       " 'Stearman C-38': 2,\n",
       " 'Ford 5-AT-B Tri-Motor': 2,\n",
       " 'Fairchild 71': 2,\n",
       " 'Junkers G-24': 2,\n",
       " 'de Havilland DH.80 Puss Moth': 2,\n",
       " 'Avro 10': 2,\n",
       " 'Lockheed Orion 9': 2,\n",
       " 'Stinson': 2,\n",
       " 'Stinson SM-6000B': 2,\n",
       " 'Stearman C-3MB': 2,\n",
       " 'Liore-et-Olivier 213': 2,\n",
       " 'De Havilland DH-60': 2,\n",
       " 'Stearman 4': 2,\n",
       " 'Ford 5-AT-C Tri-Motor': 2,\n",
       " 'Lockheed Orion': 2,\n",
       " 'Curtiss AT-32C Condor': 2,\n",
       " 'Sikorsky S-38B': 2,\n",
       " 'Lockheed Vega 5C': 2,\n",
       " 'Tupolev ANT-9': 2,\n",
       " 'Fokker F-XXII': 2,\n",
       " 'Short S-17 Kent': 2,\n",
       " 'Heinkel He-70': 2,\n",
       " 'Sikorsky S-43': 2,\n",
       " 'Short Empire Flying Boat': 2,\n",
       " 'Potez 621': 2,\n",
       " 'Lockheed 14H Super Electra': 2,\n",
       " 'Sikorsky S-42 (flying boat)': 2,\n",
       " 'Douglas DST-A-207A': 2,\n",
       " 'Douglas DC-2-115B': 2,\n",
       " 'Short S-23 Empire Flying Boat': 2,\n",
       " 'Savoia-Marchetti SM.75': 2,\n",
       " 'Junkers JU-53/3m': 2,\n",
       " 'Tupolev ANT-6': 2,\n",
       " 'Short Sunderland': 2,\n",
       " 'Lockheed Hudson VI': 2,\n",
       " 'Consolidated B-24': 2,\n",
       " 'Douglas C-53D-DO': 2,\n",
       " 'Lockheed 18-56 Lodestar': 2,\n",
       " 'Douglas C-47A-60-DL': 2,\n",
       " 'Douglas C-47A-DK': 2,\n",
       " 'Douglas C-47A-90-DL': 2,\n",
       " 'Junkers 52/3m': 2,\n",
       " 'Curtiss-Wright C-46D-CU': 2,\n",
       " 'Avro Lancaster': 2,\n",
       " 'Douglas DC-2-243': 2,\n",
       " 'Douglas C-47B-5-DK': 2,\n",
       " 'Curtiss-Wright C-46F-CU': 2,\n",
       " 'Douglas C-54G': 2,\n",
       " 'Avro 691 Lancastrian 1': 2,\n",
       " 'Lockheed L-049 Constellation': 2,\n",
       " 'Curtiss C-46D-10-CU': 2,\n",
       " 'Douglas DC-3 (C-47A-90-DL)': 2,\n",
       " 'Douglas C-47A-1-DK': 2,\n",
       " 'Douglas C-54B-15-DO': 2,\n",
       " 'Douglas DC-47': 2,\n",
       " 'Avro 685 York': 2,\n",
       " 'Vickers 610 Viking 1B': 2,\n",
       " 'de Havilland Dove 1': 2,\n",
       " 'de Havilland DH-89A Dragon Rapide': 2,\n",
       " 'Curtiss C-46D-20-CU': 2,\n",
       " 'Boeing B-29A Superfortress': 2,\n",
       " 'Douglas C-54 Skymaster': 2,\n",
       " 'Vickers 628 Viking 1B': 2,\n",
       " 'Lockheed 749-79-33 Constellation': 2,\n",
       " 'Curtiss C-46E-1-CS': 2,\n",
       " 'Douglas C-54A-1-DO': 2,\n",
       " 'Curtiss C-46D-CU': 2,\n",
       " 'Curtiss C-46 Commando': 2,\n",
       " 'Douglas R5D-3': 2,\n",
       " 'Curtiss C-46D-15-CU': 2,\n",
       " 'Douglas C-47A-35-DL': 2,\n",
       " 'Avro Shackleton MR-1': 2,\n",
       " 'Bristol 170 Freighter': 2,\n",
       " 'Douglas C-47B-15-DK': 2,\n",
       " 'Curtiss-Wright C-46': 2,\n",
       " 'Convair CV-240-7': 2,\n",
       " 'Convair RB-36H': 2,\n",
       " 'Avro Shackleton MR-2': 2,\n",
       " 'Ilyushin II-14': 2,\n",
       " 'Boeing 377 Stratocruiser 10-26': 2,\n",
       " 'Douglas C54A': 2,\n",
       " 'Convair CV-340-58': 2,\n",
       " 'Douglas C-47-DL (DC-3)': 2,\n",
       " 'Convair CV-440-11': 2,\n",
       " 'Douglas C-54A-10-DC': 2,\n",
       " 'Vickers 615 Viking 1B': 2,\n",
       " 'Douglas C-124C': 2,\n",
       " 'Vickers Viking 1B': 2,\n",
       " 'Convair CV-440-62': 2,\n",
       " 'Vickers 802 Viscount': 2,\n",
       " 'Fairchild C-82A': 2,\n",
       " 'Bristol Britannia 175': 2,\n",
       " 'Lockheed WV-2': 2,\n",
       " 'Vickers Viscount 748D': 2,\n",
       " 'Douglas C-124C Globemaster': 2,\n",
       " 'Lockheed 1049E Super Constellation': 2,\n",
       " 'Lockheed WV-2 Super Constellation': 2,\n",
       " 'Lockheed C-69 Constellation': 2,\n",
       " 'Boeing RC-135E': 2,\n",
       " 'Lockheed 1649A Starliner': 2,\n",
       " 'Boeing 707-123': 2,\n",
       " 'Vickers 785D Viscount': 2,\n",
       " 'Lockheed 188C Electra': 2,\n",
       " 'Curtiss C-46A-40-CU': 2,\n",
       " 'Convair CV-880': 2,\n",
       " 'Vickers 634 Viking 1B': 2,\n",
       " 'Vickers Viscount 837': 2,\n",
       " 'McDonnell Douglas DC-8-21': 2,\n",
       " 'Handley Page Hastings C-2': 2,\n",
       " 'McDonnell Douglas DC-8-53': 2,\n",
       " 'Sud-Aviation Caravelle III': 2,\n",
       " 'Bristol 170 Freighter 31M': 2,\n",
       " 'Lockheed L-749A Constellation': 2,\n",
       " 'de Havilland Comet 4': 2,\n",
       " 'Vickers Viscount 720': 2,\n",
       " 'de Havilland Comet 4B': 2,\n",
       " 'Boeing B-707-123B': 2,\n",
       " 'Boeing B-707-124': 2,\n",
       " 'Boeing B-707-328': 2,\n",
       " 'Antonov An-10A': 2,\n",
       " 'McDonnell Douglas DC-8-33': 2,\n",
       " 'Vickers 757 Viscount': 2,\n",
       " 'Vickers Viscount 812': 2,\n",
       " 'Lockheed P-3A Orion': 2,\n",
       " 'Douglas DC-7': 2,\n",
       " 'Fairchild C-119': 2,\n",
       " 'Tupolev Tu-124': 2,\n",
       " 'Cessna 185': 2,\n",
       " 'Douglas C-47A-25-DK': 2,\n",
       " 'Boeing B-727-22': 2,\n",
       " 'Vickers Vanguard 951': 2,\n",
       " 'Learjet 23': 2,\n",
       " 'Douglas DC-3 / Douglas DC-3': 2,\n",
       " 'Boeing B-707-437': 2,\n",
       " 'Boeing B-727-81': 2,\n",
       " 'Sud Aviation SE-210 Caravelle VIN': 2,\n",
       " 'BAC One-Eleven 203AE': 2,\n",
       " 'Lockheed C-130B': 2,\n",
       " 'Curtiss C-46T': 2,\n",
       " 'Douglas DC-8-54F': 2,\n",
       " 'Bristol 170 Freighter 31E': 2,\n",
       " 'Sud Aviation SE-210 Caravelle III': 2,\n",
       " 'Sikorsky S-61L helicopter': 2,\n",
       " 'Convair CV-990-30A-5': 2,\n",
       " 'Fairchild F-27B': 2,\n",
       " 'Boeing 707-321CF': 2,\n",
       " 'Bell 205A': 2,\n",
       " 'Boeing Vertol CH47A (helicopter)': 2,\n",
       " 'BAC One-Eleven 402AP': 2,\n",
       " 'Antonov AN-22': 2,\n",
       " 'Canadair CL-44J': 2,\n",
       " 'Vickers Viscount 749': 2,\n",
       " 'Piper PA-31': 2,\n",
       " 'BAC One-Eleven 515FB': 2,\n",
       " 'Cessna 402': 2,\n",
       " 'Beech E18S': 2,\n",
       " 'NAMC YS-11A-211': 2,\n",
       " 'Sikorsky CH-53D (helicopter)': 2,\n",
       " 'Boeing 707-327C': 2,\n",
       " 'Lockheed C-141A': 2,\n",
       " 'Sud-Aviation Caravelle VI-N': 2,\n",
       " 'Beechcraft 99A': 2,\n",
       " 'Transall C-160D': 2,\n",
       " 'Antonov An-24': 2,\n",
       " 'Cessna 500 Citation I': 2,\n",
       " 'McDonnell Douglas DC-8-55F': 2,\n",
       " 'Grumman G-21': 2,\n",
       " 'Hawker Siddeley HS-748 2': 2,\n",
       " 'Antonov An-2': 2,\n",
       " 'Lockheed C-141A (L.300)': 2,\n",
       " 'Boeing B-727-2F2': 2,\n",
       " 'Gates Learjet 24B': 2,\n",
       " 'Boeing B-737-2H6': 2,\n",
       " 'Lockheed P-3B Orion': 2,\n",
       " 'Piper PA-31-350 Navajo': 2,\n",
       " 'Boeing B-747-237B': 2,\n",
       " 'Boeing B-727-235': 2,\n",
       " 'Sikorsky S-61N': 2,\n",
       " 'Piper PA-31-350 Chieftain': 2,\n",
       " 'Nord 262A-44': 2,\n",
       " 'Hawker Siddeley Trident 2E': 2,\n",
       " 'de Havilland Canada DHC-5D Buffalo': 2,\n",
       " 'Cessna 402B': 2,\n",
       " 'Antonov An-2TP': 2,\n",
       " 'Boeing B-727': 2,\n",
       " 'Lockheed L-188CF Electra': 2,\n",
       " 'Sikorsky S-62A': 2,\n",
       " 'Beechcraft Super King Air 200': 2,\n",
       " 'Fairchild F-27J': 2,\n",
       " 'Tupolev TU-154B-1': 2,\n",
       " 'Lockheed Hercules C-130': 2,\n",
       " 'de Havilland DHC-5 Buffalo': 2,\n",
       " 'Douglas C-47A-5-DK': 2,\n",
       " 'Fokker F-27 Friendship 600RF': 2,\n",
       " 'McDonnell Douglas MD-81': 2,\n",
       " 'Hawker Siddeley HS-748-209 Srs. 2': 2,\n",
       " 'Arava 201': 2,\n",
       " 'Lockheed L-1011': 2,\n",
       " 'Shorts 330-200': 2,\n",
       " 'Sikorsky CH-53D': 2,\n",
       " 'Bell UH-1H': 2,\n",
       " 'Britten-Norman BN-2A-20 Islander': 2,\n",
       " 'Lockheed C-130A': 2,\n",
       " 'Fokker F-28 Fellowship 3000': 2,\n",
       " 'Boeing 707-336C': 2,\n",
       " 'Cessna 208 Caravan I': 2,\n",
       " 'IAI 1124 Westwind': 2,\n",
       " 'Boeing B-737-2A1': 2,\n",
       " 'Lockheed HC-130P Hercules': 2,\n",
       " 'Embraer 110P2 Bandeirante': 2,\n",
       " 'Antonov An-12B': 2,\n",
       " 'Shorts 360-300': 2,\n",
       " 'Boeing B-727-21': 2,\n",
       " 'Boeing B-747-200': 2,\n",
       " 'de Havilland Canada DHC-7-102': 2,\n",
       " 'Airbus A320-111': 2,\n",
       " 'Boeing B-737-4Y0': 2,\n",
       " 'Boeing B-747-122': 2,\n",
       " 'Bell 412': 2,\n",
       " 'Dassault Falcon 20': 2,\n",
       " 'Military - U.S. Air Force': 2,\n",
       " 'Cessna 208A Caravan I Cargomaster': 2,\n",
       " 'CASA 212-200 Aviocar': 2,\n",
       " 'Antonov AN-26V': 2,\n",
       " 'Beechcraft C99': 2,\n",
       " 'Boeing B-737-3Y0': 2,\n",
       " 'Boeing B-737-291': 2,\n",
       " 'Hindustan Aeronautics 748-2': 2,\n",
       " 'Ilyushin 76TD': 2,\n",
       " 'GAF Nomad N24A': 2,\n",
       " 'Britten-Norman BN-2A-21': 2,\n",
       " 'Airbus A310-304': 2,\n",
       " 'Swearingen SA-227AC Metro III': 2,\n",
       " 'Bell 206B3': 2,\n",
       " 'Saab 340B': 2,\n",
       " 'Britten Norman BN-2A-21 Trislander': 2,\n",
       " 'Airbus A300B4-622R': 2,\n",
       " 'Fokker F-27 Friendship 500F': 2,\n",
       " 'ATR-72-212': 2,\n",
       " 'Airbus A310-324': 2,\n",
       " 'de Havilland Canada DHC-8-102': 2,\n",
       " 'Antonov An-2R': 2,\n",
       " 'Cessna 421C Golden Eagle': 2,\n",
       " 'Boeing B-757-223': 2,\n",
       " 'Britten-Norman BN-2A-27 Islander': 2,\n",
       " 'Let 410': 2,\n",
       " 'Antonov 12B': 2,\n",
       " 'Antonov An-32B': 2,\n",
       " 'Embraer 110P1A Bandeirante': 2,\n",
       " 'Harbin Yunshuji Y-12 II': 2,\n",
       " 'Swearingen SA-226T Metro II': 2,\n",
       " 'Xian Yunshuji Y-7-100C': 2,\n",
       " 'Ilyushin II-76': 2,\n",
       " 'Antonov 32B': 2,\n",
       " 'Hawker Siddeley HS-125': 2,\n",
       " 'Antonov 28': 2,\n",
       " 'Canadair CL-600-2B16 Challenger 604': 2,\n",
       " 'LET 410UVP': 2,\n",
       " 'Learjet 25B': 2,\n",
       " 'Boeing B-737-3Q8': 2,\n",
       " 'Mi-17': 2,\n",
       " 'Lockheed MC-130H Hercules': 2,\n",
       " 'ATR 42-300': 2,\n",
       " 'Yakovlev 42D': 2,\n",
       " 'EMB 721C Sertanejo': 2,\n",
       " 'Canadair CRJ-100ER': 2,\n",
       " 'de Havilland DHC-3 Otter': 2,\n",
       " 'Tupolev 154B-2': 2,\n",
       " 'CASA 212 Aviocar': 2,\n",
       " 'Sikorsky S-76C': 2,\n",
       " 'Antonov 12V': 2,\n",
       " 'Canadair CRJ-200ER': 2,\n",
       " 'Airbus A-320-233': 2,\n",
       " 'de Havilland Canada DHC-2 Beaver': 2,\n",
       " 'Antonov An-28': 2,\n",
       " 'Airbus A320-232': 2,\n",
       " 'Airbus A320-214': 2,\n",
       " 'Boeing 737-8HG': 2,\n",
       " 'Airbus A321-231': 2,\n",
       " 'Britten Norman BN-2A-27 Islander': 2,\n",
       " 'Wright Flyer III': 1,\n",
       " 'Wright Byplane': 1,\n",
       " 'Dirigible': 1,\n",
       " 'Curtiss seaplane': 1,\n",
       " 'Zeppelin L-1 (airship)': 1,\n",
       " 'Zeppelin L-2 (airship)': 1,\n",
       " 'Zeppelin L-8 (airship)': 1,\n",
       " 'Zeppelin L-10 (airship)': 1,\n",
       " 'Schutte-Lanz S-L-10 (airship)': 1,\n",
       " 'Zeppelin L-32 (airship)': 1,\n",
       " 'Zeppelin L-31 (airship)': 1,\n",
       " 'Super Zeppelin (airship)': 1,\n",
       " 'Zeppelin L-34 (airship)': 1,\n",
       " 'Airship': 1,\n",
       " 'Schutte-Lanz S-L-9 (airship)': 1,\n",
       " 'Zeppelin L-22 (airship)': 1,\n",
       " 'Zeppelin L-43 (airship)': 1,\n",
       " 'Zepplin L-48 (air ship)': 1,\n",
       " 'Zeppelin L-23 (airship)': 1,\n",
       " 'Zeppelin L-44 (airship)': 1,\n",
       " 'Zeppelin L-59 (airship)': 1,\n",
       " 'Zeppelin L-70 (airship)': 1,\n",
       " 'Zeppelin L-53 (airship)': 1,\n",
       " 'FD Type Dirigible': 1,\n",
       " 'Caproni Ca.48': 1,\n",
       " 'De Havilland DH.4': 1,\n",
       " 'de Havilland DH-4': 1,\n",
       " 'Curtiss JN-4H': 1,\n",
       " 'Farman F-40': 1,\n",
       " 'Armstrong-Whitworth F-K-8': 1,\n",
       " 'Junkers JL-6': 1,\n",
       " 'Salmson 2-A-2': 1,\n",
       " 'Handley Page HP-16': 1,\n",
       " 'Avro 504': 1,\n",
       " 'De Havilland DH-4\\r\\nDe Havilland DH-4': 1,\n",
       " 'Royal Airship Works ZR-2 (airship)': 1,\n",
       " 'Bristol 28 Tourer': 1,\n",
       " 'Handley Page O/10': 1,\n",
       " 'Dirigible Roma (airship)': 1,\n",
       " 'Aeromarine Model 85 (flying boat)': 1,\n",
       " 'de Havilland DH-18 / Farman F-60 Goliath': 1,\n",
       " 'Bleriot Spad 27': 1,\n",
       " 'LVG C VI': 1,\n",
       " 'Potez 29': 1,\n",
       " 'de Havilland DH-9': 1,\n",
       " 'Lioré-et-Olivier H-13': 1,\n",
       " 'Farman F.60 Goliath': 1,\n",
       " 'Blériot Spad 46': 1,\n",
       " 'de Havilland DH-34': 1,\n",
       " 'Zeppelin Dixmunde (airship)': 1,\n",
       " 'Fokker F.III': 1,\n",
       " 'de Havilland DH-34B': 1,\n",
       " 'Fokker (KLM)  F.III': 1,\n",
       " 'Lioré-et-Olivier LeO H-13': 1,\n",
       " 'Dirigible ZR-1 Shenandoah (airship)': 1,\n",
       " 'Spad 33': 1,\n",
       " 'Curtiss Carrier Pigeon': 1,\n",
       " 'Caudron C-61': 1,\n",
       " 'Sikorsky S-25': 1,\n",
       " 'Ryan M-1': 1,\n",
       " 'de Havilland DH-9C': 1,\n",
       " 'Swallow\\r\\nSwallow': 1,\n",
       " 'SPCA Meteore 63': 1,\n",
       " 'Fokker F-VIII': 1,\n",
       " 'Fokker Universal': 1,\n",
       " 'Farman F-121 Jabiru': 1,\n",
       " 'Latécoère 23 (flying boat)': 1,\n",
       " 'Rohrbach Roland': 1,\n",
       " 'Ford 4': 1,\n",
       " 'AEGK': 1,\n",
       " 'Vickers 74 Vulcan': 1,\n",
       " 'Fokker (KLM) F.III': 1,\n",
       " 'Bleriot Spad 33': 1,\n",
       " 'de Havilland DH.50J': 1,\n",
       " 'Fokker Super Universal': 1,\n",
       " 'Bleriot Spad 56': 1,\n",
       " 'Hamilton H-47': 1,\n",
       " 'Junkers G-31': 1,\n",
       " 'Blériot Spad 66': 1,\n",
       " 'CMASA Wal': 1,\n",
       " 'Fokker F-VIIA': 1,\n",
       " 'Ford 5-AT-B Tri-Motor / B-PW-9D': 1,\n",
       " 'Fokker F-III': 1,\n",
       " 'Cams 53': 1,\n",
       " 'Fokker F-X': 1,\n",
       " 'Domier Delphin III (flying boat)': 1,\n",
       " 'Lockheed 5 Vega': 1,\n",
       " 'Kalinin K-4': 1,\n",
       " 'Fokker FG II': 1,\n",
       " 'de Havilland DH-66 Hercules': 1,\n",
       " 'Latecoere 25-3-R': 1,\n",
       " 'Short Calcutta (flying boat)': 1,\n",
       " 'Loening C-2C': 1,\n",
       " 'Liore et Olivier 190': 1,\n",
       " 'Savoia Marchetti S55': 1,\n",
       " 'Stearman M-2 Speedmail': 1,\n",
       " 'Douglas M-3': 1,\n",
       " 'Arado V1': 1,\n",
       " 'Travel Air B6000': 1,\n",
       " 'Farman 190': 1,\n",
       " 'Travel Air A6000A': 1,\n",
       " 'Farman F-63': 1,\n",
       " 'Ford Tri-Motor 5': 1,\n",
       " 'Junkers F-13ge': 1,\n",
       " 'Sabca F-VII': 1,\n",
       " 'Royal Airship Works R-101': 1,\n",
       " 'Messerschmitt M-20B': 1,\n",
       " 'Handley Page W-8': 1,\n",
       " 'Loening C-W Air Yaht': 1,\n",
       " 'Desoutter II': 1,\n",
       " 'Fokker Universal F-14': 1,\n",
       " 'Fokker F10A Trimotor': 1,\n",
       " 'Messerschmitt M-20': 1,\n",
       " 'Fokker F-VIIB': 1,\n",
       " 'Lockheed Vega 2': 1,\n",
       " 'Lasco Lascowl': 1,\n",
       " 'Fokker F-VIIb-3m': 1,\n",
       " 'Heinkel HE-2': 1,\n",
       " 'De Havilland DH.80': 1,\n",
       " 'Fokker F-VIIb-3M': 1,\n",
       " 'CAMS 56': 1,\n",
       " 'Stinson SM-2A': 1,\n",
       " 'Fairchild': 1,\n",
       " 'Fokker F-10A': 1,\n",
       " 'Northrop Alpha 3': 1,\n",
       " 'Farman F-190': 1,\n",
       " 'Junkers G-23': 1,\n",
       " 'Fokker F-10': 1,\n",
       " 'De Havilland DH-80': 1,\n",
       " 'Ford Tri-motor 5': 1,\n",
       " 'Junkers W-33': 1,\n",
       " 'Ford 5': 1,\n",
       " 'Curtiss Condor 18': 1,\n",
       " 'Ford 5-AT-D': 1,\n",
       " 'Fairchild FC-2W': 1,\n",
       " 'Armstrong Whitworth Argosy II': 1,\n",
       " 'Goodyear-Zeppelin U.S.S. Akron (airship)': 1,\n",
       " 'Cams 53-1': 1,\n",
       " 'Latecoere 28-1': 1,\n",
       " 'Pitcairns PA-6': 1,\n",
       " 'Sikorsky S-38B (flying boat)': 1,\n",
       " 'CMASA Wal (flying boat)': 1,\n",
       " 'Farman F-306': 1,\n",
       " 'Farman F-301': 1,\n",
       " 'Northrop Delta': 1,\n",
       " 'Kalinin K-7': 1,\n",
       " 'Focke-Wulf A-17': 1,\n",
       " 'Dewoitine D-332': 1,\n",
       " 'Breguet 280T': 1,\n",
       " 'Latécoère 28': 1,\n",
       " 'Sikorsky S-38 Flying Boat': 1,\n",
       " 'Sikorsky S-38BB': 1,\n",
       " 'Wibault 282T': 1,\n",
       " 'Curtiss Condor T-32': 1,\n",
       " 'Stinson  SM-6000B': 1,\n",
       " 'de havilland 89 Dragon Rapide': 1,\n",
       " 'de Havilland DH-50A': 1,\n",
       " 'De Havilland DH-61': 1,\n",
       " 'Stinson SM-6000-B': 1,\n",
       " 'Ford 4-AT-E Tri-motor': 1,\n",
       " 'Douglas DC-2-115A': 1,\n",
       " 'Lockheed Orion 9D': 1,\n",
       " 'Wapiti': 1,\n",
       " 'Consolidated Fleetster': 1,\n",
       " 'Goodyear-Zeppelin U.S.S. Macon (airship)': 1,\n",
       " 'Rochrbach Roland': 1,\n",
       " 'de Havilland Dragon 1': 1,\n",
       " 'Farman F-300': 1,\n",
       " 'Fokker F-12': 1,\n",
       " 'Tupolev ANT-20 / I-5': 1,\n",
       " 'Ford model 4-AT-E': 1,\n",
       " 'Boeing 221': 1,\n",
       " 'Stinson SM6000B Tri-motor': 1,\n",
       " 'Ford Tri-Motor / Ford Tri-Motor': 1,\n",
       " 'General 102-E': 1,\n",
       " 'Savoia-Marchetti S-66': 1,\n",
       " 'Lockheed Orion 9E Explorer float plane': 1,\n",
       " 'Waco, model YLC': 1,\n",
       " 'Ford Tri-Motor': 1,\n",
       " 'Boeing B-247-D': 1,\n",
       " 'Latécoère 28-1': 1,\n",
       " 'Caudron C-630 Simoun': 1,\n",
       " 'Short S-8 Calcutta flying boat': 1,\n",
       " 'Douglas DC-2-120': 1,\n",
       " 'Vultee V-1': 1,\n",
       " 'Latecoere 301 (flying boat)': 1,\n",
       " 'Dormier Do-J-Iif Bos Wal flying boat': 1,\n",
       " 'Ford 5-AT-D Tri-Motor': 1,\n",
       " 'Sikorsky S-42A  Flying Boat': 1,\n",
       " 'OFM F-VIIb/3m': 1,\n",
       " 'General Aviation GA-43': 1,\n",
       " 'De Havilland DH-60 Moth': 1,\n",
       " 'Junkers JU-160': 1,\n",
       " 'Spartan Cruiser': 1,\n",
       " 'Saro A-19 Cloud': 1,\n",
       " 'Wibault 280': 1,\n",
       " 'Lockheed 9 Orion': 1,\n",
       " 'Lockheed 10B Electra': 1,\n",
       " 'De Havilland DH-86': 1,\n",
       " 'Stinson SM 6000B': 1,\n",
       " 'CRDA CANT Z-506': 1,\n",
       " 'Boulton and Paul P-71': 1,\n",
       " 'Lockheed  5 Vega': 1,\n",
       " 'Blackburn B-2': 1,\n",
       " 'Fokker F-XII': 1,\n",
       " 'Antonov AN-9': 1,\n",
       " 'Latécoère 300 (float plane.': 1,\n",
       " 'Douglas DC-2-115E': 1,\n",
       " 'Sabca S-73': 1,\n",
       " 'Heinkel He-111V2': 1,\n",
       " 'Short  S-23 (flying boat)': 1,\n",
       " 'Zeppelin LZ-129': 1,\n",
       " 'Sirkorsky S-43 (flying boat)': 1,\n",
       " 'Douglas DC-2-115L': 1,\n",
       " 'Stinson SR-7 Reliant': 1,\n",
       " 'Short Empire flying boat': 1,\n",
       " 'Dewoitine D-333': 1,\n",
       " 'Heinkel He-111': 1,\n",
       " 'Lockhed 10 Electra': 1,\n",
       " 'Wibault 283-T12': 1,\n",
       " 'Junkers JU-38': 1,\n",
       " 'Stinson Reliant SR-9': 1,\n",
       " 'Consolidated PBY-2 / Consolidated PBY-2': 1,\n",
       " 'V6 (airship)': 1,\n",
       " 'Lioré-et-Olivier H-242': 1,\n",
       " 'Cant Z-506': 1,\n",
       " 'Junkers JU.52': 1,\n",
       " 'Lockheed Vega 5B': 1,\n",
       " 'Fairchild 51': 1,\n",
       " 'Heinkel 116': 1,\n",
       " 'De Havilland DH-85': 1,\n",
       " 'Savoia Marchetti S-66': 1,\n",
       " 'Armstrong-Withworth Atlanta': 1,\n",
       " 'Ford Tri-motor F-VIII': 1,\n",
       " 'Douglas DF-151': 1,\n",
       " 'Macchi C-94': 1,\n",
       " 'Unknown / Unknown': 1,\n",
       " 'Ford Tri-motor': 1,\n",
       " 'De Havilland DH-84': 1,\n",
       " 'Dornier DO.18 (float plane)': 1,\n",
       " 'Savbia-Marchetti  S-73P': 1,\n",
       " 'Junkers JU90V2': 1,\n",
       " 'Douglas DC 3-A-SB-3-G-14': 1,\n",
       " 'Nakajima A-T2': 1,\n",
       " 'Lockheed 14-WF62 Super Electra': 1,\n",
       " 'Short S23 ‘C’ Class flying boat': 1,\n",
       " 'Lockheed 14-H Super Electra': 1,\n",
       " 'Fairchild 71(amphibious)': 1,\n",
       " 'Junkers JU-86': 1,\n",
       " 'Boeing 307 Stratoliner': 1,\n",
       " 'Caudron C.635 Simoun': 1,\n",
       " 'Curtiss-Wright C-14 Osprey': 1,\n",
       " 'Koolhoven FK.43': 1,\n",
       " 'de Havilland DH.89 Dragon Rapide': 1,\n",
       " 'Junkers JU86 Z-2': 1,\n",
       " 'Sikorsky S43 (flying boat)': 1,\n",
       " 'De Havilland D-84': 1,\n",
       " 'Airspeed Oxford': 1,\n",
       " 'Savoia-Marchetti SM83': 1,\n",
       " 'Handley Page HP-42': 1,\n",
       " 'Savoia Marchetti SM 73': 1,\n",
       " 'Douglas DC-5': 1,\n",
       " 'Junkers JU-52/3mge': 1,\n",
       " 'Douglas DC-2-115H': 1,\n",
       " 'Lockheed Hudson A16-97': 1,\n",
       " 'Junkers Ju53/2m': 1,\n",
       " 'Junkers JU-90A-1': 1,\n",
       " 'Farman F-224': 1,\n",
       " 'Savoia-Marchetti SM-83': 1,\n",
       " 'Ford 5-AT Tri-Motor': 1,\n",
       " 'Douglas DC-3-3': 1,\n",
       " 'Mitsubishi MC-20': 1,\n",
       " 'Macchi C.100': 1,\n",
       " 'Consolidated 32-2 Liberator I': 1,\n",
       " 'Bloch 220': 1,\n",
       " 'Sikorsky S-42B (flying boat)': 1,\n",
       " 'deHavilland DH-86': 1,\n",
       " 'Douglas DC-2-221': 1,\n",
       " 'Douglas DC-3-A-269': 1,\n",
       " 'Douglas C-49E': 1,\n",
       " 'Liore et Olivier H-246 Air Boat': 1,\n",
       " 'Siebel Si-204': 1,\n",
       " 'Short S-30 (flying boat)': 1,\n",
       " 'Dewoitine D-342': 1,\n",
       " 'Douglas C-39-DO  (DC-2)': 1,\n",
       " 'Sirkorsky 44A (flying boat)': 1,\n",
       " 'GVF PS-84': 1,\n",
       " 'Douglas DC-3 / Lockheed B-34': 1,\n",
       " 'Handley Page HP-57 Halifax': 1,\n",
       " 'Grazhdansky Vozdushnyi Flot PS-84': 1,\n",
       " 'Tupolev A.N.T. 20bis': 1,\n",
       " 'Short S-26 (flying boat)': 1,\n",
       " 'Consolidated C-87': 1,\n",
       " 'Consolidated 32-3 Liberator II': 1,\n",
       " 'Boeing XB-29': 1,\n",
       " 'Boeing B-314 (flying boat)': 1,\n",
       " 'Lockheed C-56B Lodestar': 1,\n",
       " 'Short S-23': 1,\n",
       " 'B-17C Flying Fortress': 1,\n",
       " 'Consolidated Liberator B24 C': 1,\n",
       " 'Short S-25 Sunderland 3 (flying boat)': 1,\n",
       " 'Boeing B-17F / Boeing B-17F': 1,\n",
       " 'Douglas DC3-G102': 1,\n",
       " 'Short Sunderland 3': 1,\n",
       " 'Lockheed L-18-56 Lodestar': 1,\n",
       " 'Consolidated B-24 / Consolidated B-24': 1,\n",
       " 'Pilgrim 100B': 1,\n",
       " 'Lockheed 10C Electra': 1,\n",
       " 'Douglas A-26B Invader': 1,\n",
       " 'R4D-5': 1,\n",
       " 'Douglas C-54A-DO (DC-4)': 1,\n",
       " 'Consolidated Catalina PB2Y-3R': 1,\n",
       " 'Consolidated PB4Y-1': 1,\n",
       " 'Consolidated B-24H': 1,\n",
       " 'Douglas C-54A-DC (DC-4)': 1,\n",
       " 'Lockheed 18-56 Loadstar': 1,\n",
       " 'Junkers JU52/3m': 1,\n",
       " 'C-47 Dakota DT-941': 1,\n",
       " 'Short S.23 Empire Flying Boat': 1,\n",
       " 'Consolidated B24H': 1,\n",
       " 'Avro 683 Lancaster': 1,\n",
       " 'UC-64A Noorduyn Norseman': 1,\n",
       " 'Douglas R4D-6': 1,\n",
       " 'B17G Flying Fortress': 1,\n",
       " 'Douglas C-54E-DO (DC-4)': 1,\n",
       " 'Douglas C-47B-dK': 1,\n",
       " 'de Havilland DH-86A': 1,\n",
       " 'Boeing TB-29A Super Fortress': 1,\n",
       " 'Douglas DC-3-201C /  Army A-26': 1,\n",
       " 'North American B-25D bomber': 1,\n",
       " 'Sikorsky S-43 (flying boat)': 1,\n",
       " 'Consolidated LB-40-A Liberator': 1,\n",
       " 'Short Stirling IV': 1,\n",
       " 'Consolidated LB-30-A Liberator': 1,\n",
       " 'Faucett F-19': 1,\n",
       " 'Curtiss-Wright C-46A-CK': 1,\n",
       " 'Budd RB-1 Conestoga': 1,\n",
       " 'Martin PBM-3S  / Martin PBM-5': 1,\n",
       " 'Boeing B-17G': 1,\n",
       " 'Short Stirling': 1,\n",
       " 'Douglas C-48': 1,\n",
       " 'Douglas C-47 Dakota-DK': 1,\n",
       " 'Five Grumman TBM Avengers': 1,\n",
       " 'Martin PBM Mariner': 1,\n",
       " 'Douglas DC-3-201E': 1,\n",
       " 'Douglas DC-3 Dakota': 1,\n",
       " 'Douglas DC-3-194H': 1,\n",
       " 'Consolidated 32-2 Liberator II': 1,\n",
       " 'Douglas DC-3-227B': 1,\n",
       " 'Douglas DC-3 (C-47DL)': 1,\n",
       " 'de Hvilland 89A Dragon Rapide': 1,\n",
       " 'Vickers Wellington bomber': 1,\n",
       " 'PBY4-2 Privateer / PB4Y-2 Privateer': 1,\n",
       " 'Douglas C-54-DO (DC-4)': 1,\n",
       " 'Boeing B17G': 1,\n",
       " 'Boeing B-17G / Boeing B-17G': 1,\n",
       " 'Supermarine Stranraer (flying boat)': 1,\n",
       " 'Douglas DC-3 (C-47-A5-DL)': 1,\n",
       " 'Douglas C-47A Dakota': 1,\n",
       " 'Curtiss Wright C-46': 1,\n",
       " 'Douglas C-47B-25-DK': 1,\n",
       " 'de Havilland 89A Dragon Rapide': 1,\n",
       " 'Douglas DC-3A-228D': 1,\n",
       " 'Douglas C-54E-5-DO': 1,\n",
       " 'Fairey Firefly MK1': 1,\n",
       " 'AAC-1 Toucan': 1,\n",
       " 'Douglas C-53D-DO (DC-3)': 1,\n",
       " 'Douglas C-47A-90 DL (DC-3)': 1,\n",
       " 'Vickers 620 Viking 1': 1,\n",
       " 'Curtiss-Wright R5C-1': 1,\n",
       " 'Douglas DC-3 (C-47-B-1-DK)': 1,\n",
       " 'Avro  685 York I': 1,\n",
       " 'Douglas DC-3 ( C-53D-DO)': 1,\n",
       " 'Curtiss C-47': 1,\n",
       " 'Lockheed 049 Consellation': 1,\n",
       " 'Douglas DC-3 (C-50A-DO)': 1,\n",
       " 'Sikorsky S-43B (flying boat)': 1,\n",
       " 'Curtis C-46': 1,\n",
       " 'Douglas DC-4 (C-54A-DO)': 1,\n",
       " 'Lockheed C-60 Lodestar': 1,\n",
       " 'Douglas DC-3 ( C-47-DO)': 1,\n",
       " 'Douglas C-47 / Douglas DC-3': 1,\n",
       " 'Douglas C- C47A-30DK': 1,\n",
       " 'Savoia-Marchetti SM-95': 1,\n",
       " 'Doublas C-47': 1,\n",
       " 'Avro York C1': 1,\n",
       " 'Douglas DC-3 (C-47DL) /Vultee BT-13': 1,\n",
       " 'Junkers Ju-52': 1,\n",
       " 'Avro 691 Lancastrian': 1,\n",
       " 'Douglas DC-4 (C-54-DO)': 1,\n",
       " 'Avro 691 Lancastrian 3': 1,\n",
       " 'Douglas DC-3F': 1,\n",
       " 'Short Sandringham 5 (flying boat)': 1,\n",
       " 'Short Sandringham (flying boat)': 1,\n",
       " 'Boeing 314A': 1,\n",
       " ...}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_tiposAeronaves = {} # define un diccionario vacio\n",
    "\n",
    "for aNave in tipoAeronave: # itera entre los elementos de la serie almacenada\n",
    "    if aNave not in dict_tiposAeronaves: # Si no existe una clave con el nombre de la variable:\n",
    "        dict_tiposAeronaves[f\"{aNave}\"] = 1 # Se crea en el diccionario con el valor 1\n",
    "    else: # De existir:\n",
    "        dict_tiposAeronaves[f\"{aNave}\"] += 1 # Se agrega uno al valor\n",
    "\n",
    "dict(sorted(dict_tiposAeronaves.items(), key= lambda item: item[1], reverse= True)) # se imprime el diccionario ordenado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este dataset nos queda claro. Después del análisis podemos decir que la serie de aeronaves Douglas es el tipo de Aeronave con más accidentes registrados, incluyendo distintos modelos de estas aeronaves. Por ejemplo: 'Douglas DC-3' tuvo <strong>333</strong> accidentes registrados, 'Douglas C-47A' y 'Douglas C-47' tuvieron <em>(conjuntamente)</em> tuvieron <strong>134</strong> accidentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los accidentes, en muchas ocasiones y lamentablemente, tuvieron víctimas fatales. Analizaremos la cantidad de víctimas de los accidentes registrados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pasajeros, fallecidos = df['Total a Bordo'], df['Cantidad de Fallecidos'] # se definen dos variables con dos series\n",
    "zipasajeros = zip(pasajeros, fallecidos) # se comprime en una sola las series en una especie de lista de tuplas\n",
    "\n",
    "lista_pasajeros = [] # crea una lista vacia\n",
    "\n",
    "for pas, dead in zipasajeros: # itera en las tuplas, con sus elementos por separado\n",
    "    if pas != None and dead != None: # Si ninguno de los elementos en nulo:\n",
    "        lista_pasajeros.append((int(pas), int(dead))) # agrega a la lista, los valores enteros de los numeros\n",
    "    else: continue # Sino, continua con la siguiente iteracion\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5)) # Define el tamaño\n",
    "\n",
    "plt.scatter([item[0] for item in lista_pasajeros], [item[1] for item in lista_pasajeros], marker= \".\", color='#Fe0435', alpha=0.4) # crea el gráfico\n",
    "\n",
    "# Le agrega título y etiquetas\n",
    "plt.title(\"Diagrama de dispersión de cantidad de pasajeros y víctimas fatales.\")\n",
    "plt.xlabel(\"Cantidad de pasajeros\")\n",
    "plt.ylabel(\"Cantidad de fallecidos\")\n",
    "\n",
    "# Mostra el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este gráfico de dispersión podemos ver que la cantidad de pasajeros y de víctimas es proporcional. Es decir que cuando aumenta la cantidad de pasajeron, también aumenta la cantidad de víctimas.\n",
    "También se puede concluir que cuando en los viajes aumentan la capacidad de pasajeros, los accidentes también disminuyen. Por eso el área de la izquierda está mucho más poblada de accidentes que el área de la derecha."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos como se distribuye la proporción de pasajeros-tripulación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pasajeros, tripulacion = df['Pasajeros a Bordo'], df['Tripulación a Bordo'] # Crea dos variables con las series de las columnas\n",
    "\n",
    "pasaj_trip = zip(pasajeros, tripulacion) # los comprime con el método zip\n",
    "\n",
    "lista_tuplas_pasajeros_tripulacion = [] # crea una lista vacia\n",
    "\n",
    "for pasjr, trpl in pasaj_trip: # itera sobre cada una de las tuplas comprimidas\n",
    "    if pasjr != None and trpl != None: # Si ningun valor es nulo:\n",
    "        lista_tuplas_pasajeros_tripulacion.append((int(pasjr), int(trpl))) # Agrega a la lista una tupla con los valores enteros\n",
    "\n",
    "pasajeros = [Y[0] for Y in lista_tuplas_pasajeros_tripulacion] # Crea una lista con los valores en la posición 0 de las tuplas\n",
    "tripulacion = [Z[1] for Z in lista_tuplas_pasajeros_tripulacion] # Crea una lista con los valores en la posición 1 de las tuplas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12,6)) # Define el tamaño de la figura y la cantidad de subplots que habrá\n",
    "\n",
    "for i in range(0, 2):\n",
    "    if i == 0:    \n",
    "        media_p = np.mean(pasajeros)\n",
    "        media_t = np.mean(tripulacion)\n",
    "        ax[i].pie(x=[media_p, media_t], explode=[0 , 0.1], labels=[\"Pasajeros\", \"Tripulación\"], colors = [\"#Fe0435\", \"#21CaFe\"], autopct=\"%2.f%%\", startangle=25)\n",
    "        ax[i].set_title(\"Proporción de pasajeros y tripulación, en promedio.\")\n",
    "    else:\n",
    "        media_p = np.sum(pasajeros)\n",
    "        media_t = np.sum(tripulacion)\n",
    "        ax[i].pie(x=[media_p, media_t], explode=[0, 0.1], labels=[\"Pasajeros\", \"Tripulación\"], colors = [\"#De0237\", \"#71CaFa\"], autopct=\"%2.f%%\", startangle=25)\n",
    "        ax[i].set_title(\"Proporción de pasajeros y tripulación, sumando.\")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como nos muestran los dos gráficos, en ambas proprociones, hay un tripulante de avión por cada 5 a 6 pasajeros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"##inicio\" align=center style=\"text-decoration: underline\">Volver al inicio</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}