bda-pt-promoC-modulo-3-evaluacion-final-Sara-D-Riobello.ipynb

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    "**Fase 1: Exploración y Limpieza**\n",
    "\n",
    "1. Exploración Inicial:\n",
    "\n",
    "- Realiza una exploración inicial de los datos para identificar posibles problemas, como valores nulos, atípicos o datos faltantes en las columnas relevantes.\n",
    "\n",
    "- Utiliza funciones de Pandas para obtener información sobre la estructura de los datos, la presencia de valores nulos y estadísticas básicas de las columnas involucradas.\n",
    "  \n",
    "- Une los dos conjuntos de datos de la forma más eficiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 965,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Instalación de bibliotecas: \n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "from IPython.display import display #He añadido esta para mostrar el dataframe en formato de tabla para que sea más legible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 966,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Exploración inicial de los datos de Customer Flight Activity\n",
    "\n",
    "# Primero hago el proceso de exploración y análisis de la primera base de datos\n",
    "\n",
    "def explorar_dataset(filepath, index_col=0, n=5):  #filepath: Ruta del archivo CSV a cargar.\n",
    "     \n",
    "    # Lectura del dataset\n",
    "    \n",
    "    df = pd.read_csv('Customer_Flight_Activity.csv', index_col=index_col)\n",
    "\n",
    "    \n",
    "    df.reset_index(inplace=True)  # Esto convierte el índice actual en una columna y asigna un nuevo índice, para poder manipular la información del índice con la tabla\n",
    "    \n",
    "    # Eliminación de la columna del índice \n",
    "    if isinstance(index_col, int):\n",
    "        print(f\"Usando la columna {index_col} como índice.\")\n",
    "    elif isinstance(index_col, str):\n",
    "        print(f\"Usando la columna '{index_col}' como índice.\")\n",
    "    \n",
    "\n",
    "     # Desactiva la visualización del índice al imprimir\n",
    "    pd.set_option('display.show_dimensions', False)\n",
    "    \n",
    "    # Tamaño y estructura del DataFrame\n",
    "    print(\"\\n--- Información general del DataFrame ---\")\n",
    "    df.info()  \n",
    "    print(\"\\nPrimeras filas del DataFrame:\")\n",
    "    display(df.head(n))  \n",
    "    print(\"\\nFilas aleatorias del DataFrame:\")\n",
    "    display(df.sample(n))  #Aunque head me muestra las primeras filas, he querido añadir esto para poder ver información aleatoria del dataframe y ver diferentes datos \n",
    "    print(f\"\\nTamaño del dataset: {df.size} elementos\")\n",
    "    print(f\"Forma del dataset: {df.shape[0]} filas, {df.shape[1]} columnas\")\n",
    "\n",
    "    # Exploración de columnas\n",
    "    print(\"\\n--- Información sobre las columnas ---\")\n",
    "    pd.set_option('display.max_columns', None)  # Para poder todas las columnas\n",
    "    print(f\"Columnas del dataset: {df.columns.tolist()}\")\n",
    "    print(\"\\nConteo de valores no nulos por columna:\")\n",
    "    print(df.count())  # Conteo de valores no nulos por columna\n",
    "\n",
    "    # Devolución del DataFrame para posibles análisis adicionales\n",
    "    return df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 967,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Usando la columna 0 como índice.\n",
      "\n",
      "--- Información general del DataFrame ---\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 405624 entries, 0 to 405623\n",
      "Data columns (total 10 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   Loyalty Number               405624 non-null  int64  \n",
      " 1   Year                         405624 non-null  int64  \n",
      " 2   Month                        405624 non-null  int64  \n",
      " 3   Flights Booked               405624 non-null  int64  \n",
      " 4   Flights with Companions      405624 non-null  int64  \n",
      " 5   Total Flights                405624 non-null  int64  \n",
      " 6   Distance                     405624 non-null  int64  \n",
      " 7   Points Accumulated           405624 non-null  float64\n",
      " 8   Points Redeemed              405624 non-null  int64  \n",
      " 9   Dollar Cost Points Redeemed  405624 non-null  int64  \n",
      "dtypes: float64(1), int64(9)\n",
      "memory usage: 30.9 MB\n",
      "\n",
      "Primeras filas del DataFrame:\n"
     ]
    },
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       "      <th>4</th>\n",
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       "   Loyalty Number  Year  Month  Flights Booked  Flights with Companions  \\\n",
       "0          100018  2017      1               3                        0   \n",
       "1          100102  2017      1              10                        4   \n",
       "2          100140  2017      1               6                        0   \n",
       "3          100214  2017      1               0                        0   \n",
       "4          100272  2017      1               0                        0   \n",
       "\n",
       "   Total Flights  Distance  Points Accumulated  Points Redeemed  \\\n",
       "0              3      1521               152.0                0   \n",
       "1             14      2030               203.0                0   \n",
       "2              6      1200               120.0                0   \n",
       "3              0         0                 0.0                0   \n",
       "4              0         0                 0.0                0   \n",
       "\n",
       "   Dollar Cost Points Redeemed  \n",
       "0                            0  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                            0  "
      ]
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     "text": [
      "\n",
      "Filas aleatorias del DataFrame:\n"
     ]
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       "      <th></th>\n",
       "      <th>Loyalty Number</th>\n",
       "      <th>Year</th>\n",
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       "      <th>Flights Booked</th>\n",
       "      <th>Flights with Companions</th>\n",
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       "      <th>146358</th>\n",
       "      <td>693138</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>3320</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>93946</th>\n",
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       "      <td>2017</td>\n",
       "      <td>6</td>\n",
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       "        Loyalty Number  Year  Month  Flights Booked  Flights with Companions  \\\n",
       "186862          151183  2017     12               0                        0   \n",
       "35984           216072  2017      3               0                        0   \n",
       "389170          123784  2018     12               0                        0   \n",
       "146358          693138  2017      9              14                        6   \n",
       "93946           603433  2017      6               0                        0   \n",
       "\n",
       "        Total Flights  Distance  Points Accumulated  Points Redeemed  \\\n",
       "186862              0         0                 0.0                0   \n",
       "35984               0         0                 0.0                0   \n",
       "389170              0         0                 0.0                0   \n",
       "146358             20      3320               332.0                0   \n",
       "93946               0         0                 0.0                0   \n",
       "\n",
       "        Dollar Cost Points Redeemed  \n",
       "186862                            0  \n",
       "35984                             0  \n",
       "389170                            0  \n",
       "146358                            0  \n",
       "93946                             0  "
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      "\n",
      "Tamaño del dataset: 4056240 elementos\n",
      "Forma del dataset: 405624 filas, 10 columnas\n",
      "\n",
      "--- Información sobre las columnas ---\n",
      "Columnas del dataset: ['Loyalty Number', 'Year', 'Month', 'Flights Booked', 'Flights with Companions', 'Total Flights', 'Distance', 'Points Accumulated', 'Points Redeemed', 'Dollar Cost Points Redeemed']\n",
      "\n",
      "Conteo de valores no nulos por columna:\n",
      "Loyalty Number                 405624\n",
      "Year                           405624\n",
      "Month                          405624\n",
      "Flights Booked                 405624\n",
      "Flights with Companions        405624\n",
      "Total Flights                  405624\n",
      "Distance                       405624\n",
      "Points Accumulated             405624\n",
      "Points Redeemed                405624\n",
      "Dollar Cost Points Redeemed    405624\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Llamo a la función\n",
    "\n",
    "filepath = 'Customer_Flight_Activity.csv'\n",
    "dataset = explorar_dataset(filepath, index_col=0, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 969,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombres de columnas estandarizados: ['loyalty_number', 'year', 'month', 'flights_booked', 'flights_with_companions', 'total_flights', 'distance', 'points_accumulated', 'points_redeemed', 'dollar_cost_points_redeemed']\n"
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       "      <th>405619</th>\n",
       "      <td>999902</td>\n",
       "      <td>2018</td>\n",
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       "      <td>2018</td>\n",
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       "      <td>3</td>\n",
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       "      <td>999982</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>405623</th>\n",
       "      <td>999986</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loyalty_number  year  month  flights_booked  flights_with_companions  \\\n",
       "0               100018  2017      1               3                        0   \n",
       "1               100102  2017      1              10                        4   \n",
       "2               100140  2017      1               6                        0   \n",
       "3               100214  2017      1               0                        0   \n",
       "4               100272  2017      1               0                        0   \n",
       "...                ...   ...    ...             ...                      ...   \n",
       "405619          999902  2018     12               0                        0   \n",
       "405620          999911  2018     12               0                        0   \n",
       "405621          999940  2018     12               3                        0   \n",
       "405622          999982  2018     12               0                        0   \n",
       "405623          999986  2018     12               0                        0   \n",
       "\n",
       "        total_flights  distance  points_accumulated  points_redeemed  \\\n",
       "0                   3      1521               152.0                0   \n",
       "1                  14      2030               203.0                0   \n",
       "2                   6      1200               120.0                0   \n",
       "3                   0         0                 0.0                0   \n",
       "4                   0         0                 0.0                0   \n",
       "...               ...       ...                 ...              ...   \n",
       "405619              0         0                 0.0                0   \n",
       "405620              0         0                 0.0                0   \n",
       "405621              3      1233               123.0                0   \n",
       "405622              0         0                 0.0                0   \n",
       "405623              0         0                 0.0                0   \n",
       "\n",
       "        dollar_cost_points_redeemed  \n",
       "0                                 0  \n",
       "1                                 0  \n",
       "2                                 0  \n",
       "3                                 0  \n",
       "4                                 0  \n",
       "...                             ...  \n",
       "405619                            0  \n",
       "405620                            0  \n",
       "405621                            0  \n",
       "405622                            0  \n",
       "405623                            0  "
      ]
     },
     "execution_count": 969,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estandarizo los nombres de las columnas del DataFrame con una sola función:\n",
    "\n",
    "def estandarizar_columnas(dataset):\n",
    "    \n",
    "    dataset.columns = dataset.columns.str.strip().str.lower().str.replace(\" \", \"_\").str.replace(r'[^\\w\\s]', '', regex=True)\n",
    "    print(f\"Nombres de columnas estandarizados: {dataset.columns.tolist()}\")\n",
    "    return dataset\n",
    "\n",
    "estandarizar_columnas(dataset) #Llamo a la función"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 970,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Valores únicos por columna ---\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columna</th>\n",
       "      <th>Valores únicos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loyalty_number</td>\n",
       "      <td>16737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>year</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>month</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>flights_booked</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>flights_with_companions</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_flights</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>distance</td>\n",
       "      <td>4746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>points_accumulated</td>\n",
       "      <td>1549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>points_redeemed</td>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dollar_cost_points_redeemed</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Columna  Valores únicos\n",
       "0               loyalty_number           16737\n",
       "1                         year               2\n",
       "2                        month              12\n",
       "3               flights_booked              22\n",
       "4      flights_with_companions              12\n",
       "5                total_flights              33\n",
       "6                     distance            4746\n",
       "7           points_accumulated            1549\n",
       "8              points_redeemed             587\n",
       "9  dollar_cost_points_redeemed              49"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculo y muestro la cantidad de valores únicos por columna.\n",
    "\n",
    "def valores_unicos_por_columna(dataset):\n",
    "    \n",
    "    print(\"\\n--- Valores únicos por columna ---\")\n",
    "    valores_unicos = {col: len(dataset[col].unique()) for col in dataset.columns}\n",
    "    valores_unicos_df = pd.DataFrame(\n",
    "        list(valores_unicos.items()),\n",
    "        columns=['Columna', 'Valores únicos']\n",
    "    )\n",
    "    display(valores_unicos_df)\n",
    "    return valores_unicos_df\n",
    "\n",
    "\n",
    "valores_unicos = valores_unicos_por_columna(dataset) #Llamo a la función"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 971,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Esta función procesa las columnas numéricas (que en este caso son todas) y muestra estadísticas del DataFrame.\n",
    "\n",
    "def procesar_columnas_numericas(dataset, columnas_numericas=None): \n",
    "    \n",
    "    # Si no se especifican columnas, se detectan automáticamente\n",
    "    if columnas_numericas is None:\n",
    "        columnas_numericas = dataset.select_dtypes(include='number').columns.tolist()\n",
    "        print(f\"\\nColumnas numéricas detectadas automáticamente: {columnas_numericas}\")\n",
    "    \n",
    "    # Creo un diccionario para almacenar las estadísticas de cada columna\n",
    "    estadisticas = {}\n",
    "    \n",
    "    # Proceso solo las columnas especificadas\n",
    "    for columna in columnas_numericas:\n",
    "        if columna in dataset.columns:\n",
    "            # Calculo estadísticas para la columna\n",
    "            stats = dataset[columna].describe()\n",
    "            estadisticas[columna] = stats\n",
    "            \n",
    "            # Filtro valores negativos\n",
    "            if (dataset[columna] < 0).any():\n",
    "                print(f\"\\nSe encontraron valores negativos en '{columna}', filtrando...\")\n",
    "                dataset = df[df[columna] >= 0]\n",
    "        else:\n",
    "            print(f\"\\nLa columna '{columna}' no existe en el DataFrame.\")\n",
    "    \n",
    "    # Convierto el diccionario de estadísticas en un DataFrame\n",
    "    df_estadisticas = pd.DataFrame(estadisticas)\n",
    "    print(\"\\nEstadísticas descriptivas:\")\n",
    "    print(df_estadisticas.T)  # Transposición para que las columnas sean filas\n",
    "    \n",
    "    return df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 972,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Columnas numéricas detectadas automáticamente: ['loyalty_number', 'year', 'month', 'flights_booked', 'flights_with_companions', 'total_flights', 'distance', 'points_accumulated', 'points_redeemed', 'dollar_cost_points_redeemed']\n",
      "\n",
      "Estadísticas descriptivas:\n",
      "                                count           mean            std       min  \\\n",
      "loyalty_number               405624.0  550037.873084  258935.286969  100018.0   \n",
      "year                         405624.0    2017.500000       0.500001    2017.0   \n",
      "month                        405624.0       6.500000       3.452057       1.0   \n",
      "flights_booked               405624.0       4.115052       5.225518       0.0   \n",
      "flights_with_companions      405624.0       1.031805       2.076869       0.0   \n",
      "total_flights                405624.0       5.146858       6.521227       0.0   \n",
      "distance                     405624.0    1208.880059    1433.155320       0.0   \n",
      "points_accumulated           405624.0     123.692721     146.599831       0.0   \n",
      "points_redeemed              405624.0      30.696872     125.486049       0.0   \n",
      "dollar_cost_points_redeemed  405624.0       2.484503      10.150038       0.0   \n",
      "\n",
      "                                   25%       50%        75%       max  \n",
      "loyalty_number               326961.00  550834.0  772194.00  999986.0  \n",
      "year                           2017.00    2017.5    2018.00    2018.0  \n",
      "month                             3.75       6.5       9.25      12.0  \n",
      "flights_booked                    0.00       1.0       8.00      21.0  \n",
      "flights_with_companions           0.00       0.0       1.00      11.0  \n",
      "total_flights                     0.00       1.0      10.00      32.0  \n",
      "distance                          0.00     488.0    2336.00    6293.0  \n",
      "points_accumulated                0.00      50.0     239.00     676.5  \n",
      "points_redeemed                   0.00       0.0       0.00     876.0  \n",
      "dollar_cost_points_redeemed       0.00       0.0       0.00      71.0  \n"
     ]
    }
   ],
   "source": [
    "# Llamo a la función\n",
    "df_procesado = procesar_columnas_numericas(dataset, columnas_numericas=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 973,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Después de analizar todas las columnas numéricas, procedo a realizar estadísticas de las que son más relevantes\n",
    "\n",
    "def estadisticas_numericas(dataset, columnas=None):\n",
    " \n",
    "    # Selecciona columnas numéricas especificadas o todas si no se indican\n",
    "    if columnas is not None:\n",
    "        columnas_existentes = [col for col in columnas if col in dataset.columns]\n",
    "        if not columnas_existentes:\n",
    "            raise ValueError(\"Ninguna de las columnas especificadas existe en el DataFrame.\")\n",
    "        dataset = dataset[columnas_existentes]\n",
    "        print(f\"Analizando las siguientes columnas numéricas: {columnas_existentes}\")\n",
    "    else:\n",
    "        print(\"Analizando todas las columnas numéricas del DataFrame.\")\n",
    "\n",
    "    # Calcula estadísticas descriptivas\n",
    "    estadisticas = dataset.select_dtypes(include='number').describe()\n",
    "    estadisticas_transpuestas = estadisticas.T\n",
    "\n",
    "    print(\"\\n--- Estadísticas descriptivas de variables numéricas ---\")\n",
    "    display(estadisticas)\n",
    "\n",
    "    print(\"\\n--- Estadísticas descriptivas transpuestas (numéricas) ---\")\n",
    "    display(estadisticas_transpuestas)\n",
    "\n",
    "    return {\n",
    "        'estadisticas': estadisticas,\n",
    "        'estadisticas_transpuestas': estadisticas_transpuestas\n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 974,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analizando las siguientes columnas numéricas: ['flights_booked', 'flights_with_companions', 'total_flights', 'distance', 'points_accumulated', 'points_redeemed', 'dollar_cost_points_redeemed']\n",
      "\n",
      "--- Estadísticas descriptivas de variables numéricas ---\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>flights_booked</th>\n",
       "      <th>flights_with_companions</th>\n",
       "      <th>total_flights</th>\n",
       "      <th>distance</th>\n",
       "      <th>points_accumulated</th>\n",
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       "      <th>count</th>\n",
       "      <td>405624.000000</td>\n",
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       "      <td>405624.000000</td>\n",
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       "      <td>405624.000000</td>\n",
       "      <td>405624.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.115052</td>\n",
       "      <td>1.031805</td>\n",
       "      <td>5.146858</td>\n",
       "      <td>1208.880059</td>\n",
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       "      <td>30.696872</td>\n",
       "      <td>2.484503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.225518</td>\n",
       "      <td>2.076869</td>\n",
       "      <td>6.521227</td>\n",
       "      <td>1433.155320</td>\n",
       "      <td>146.599831</td>\n",
       "      <td>125.486049</td>\n",
       "      <td>10.150038</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>488.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6293.000000</td>\n",
       "      <td>676.500000</td>\n",
       "      <td>876.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       flights_booked  flights_with_companions  total_flights       distance  \\\n",
       "count   405624.000000            405624.000000  405624.000000  405624.000000   \n",
       "mean         4.115052                 1.031805       5.146858    1208.880059   \n",
       "std          5.225518                 2.076869       6.521227    1433.155320   \n",
       "min          0.000000                 0.000000       0.000000       0.000000   \n",
       "25%          0.000000                 0.000000       0.000000       0.000000   \n",
       "50%          1.000000                 0.000000       1.000000     488.000000   \n",
       "75%          8.000000                 1.000000      10.000000    2336.000000   \n",
       "max         21.000000                11.000000      32.000000    6293.000000   \n",
       "\n",
       "       points_accumulated  points_redeemed  dollar_cost_points_redeemed  \n",
       "count       405624.000000    405624.000000                405624.000000  \n",
       "mean           123.692721        30.696872                     2.484503  \n",
       "std            146.599831       125.486049                    10.150038  \n",
       "min              0.000000         0.000000                     0.000000  \n",
       "25%              0.000000         0.000000                     0.000000  \n",
       "50%             50.000000         0.000000                     0.000000  \n",
       "75%            239.000000         0.000000                     0.000000  \n",
       "max            676.500000       876.000000                    71.000000  "
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Estadísticas descriptivas transpuestas (numéricas) ---\n"
     ]
    },
    {
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       "      <th>flights_booked</th>\n",
       "      <td>405624.0</td>\n",
       "      <td>4.115052</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>405624.0</td>\n",
       "      <td>1208.880059</td>\n",
       "      <td>1433.155320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>6293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>points_accumulated</th>\n",
       "      <td>405624.0</td>\n",
       "      <td>123.692721</td>\n",
       "      <td>146.599831</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>676.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>points_redeemed</th>\n",
       "      <td>405624.0</td>\n",
       "      <td>30.696872</td>\n",
       "      <td>125.486049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar_cost_points_redeemed</th>\n",
       "      <td>405624.0</td>\n",
       "      <td>2.484503</td>\n",
       "      <td>10.150038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                count         mean          std  min  25%  \\\n",
       "flights_booked               405624.0     4.115052     5.225518  0.0  0.0   \n",
       "flights_with_companions      405624.0     1.031805     2.076869  0.0  0.0   \n",
       "total_flights                405624.0     5.146858     6.521227  0.0  0.0   \n",
       "distance                     405624.0  1208.880059  1433.155320  0.0  0.0   \n",
       "points_accumulated           405624.0   123.692721   146.599831  0.0  0.0   \n",
       "points_redeemed              405624.0    30.696872   125.486049  0.0  0.0   \n",
       "dollar_cost_points_redeemed  405624.0     2.484503    10.150038  0.0  0.0   \n",
       "\n",
       "                               50%     75%     max  \n",
       "flights_booked                 1.0     8.0    21.0  \n",
       "flights_with_companions        0.0     1.0    11.0  \n",
       "total_flights                  1.0    10.0    32.0  \n",
       "distance                     488.0  2336.0  6293.0  \n",
       "points_accumulated            50.0   239.0   676.5  \n",
       "points_redeemed                0.0     0.0   876.0  \n",
       "dollar_cost_points_redeemed    0.0     0.0    71.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creo una variable para meter las columnas que quiero procesar\n",
    "columnas_a_procesar = ['flights_booked', \n",
    "    'flights_with_companions', \n",
    "    'total_flights', \n",
    "    'distance', \n",
    "    'points_accumulated', \n",
    "    'points_redeemed', \n",
    "    'dollar_cost_points_redeemed']\n",
    "\n",
    "estadisticas_num = estadisticas_numericas(dataset, columnas=columnas_a_procesar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 975,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Exploración inicial de los datos de Customer Loyalty History usando la misma función que en la anterior base de datos\n",
    "\n",
    "def explorar_dataset(filepath, index_col=0, n=5):\n",
    "    \n",
    "    # Lectura del dataset\n",
    "    df_2 = pd.read_csv('Customer_Loyalty_History.csv', index_col=index_col)\n",
    "\n",
    "    # Restablece el índice para convertirlo en una columna normal\n",
    "    df_2.reset_index(inplace=True)  # Esto convierte el índice actual en una columna y asigna un nuevo índice\n",
    "        \n",
    "    # Elimina la columna del índice (si es necesario)\n",
    "    if isinstance(index_col, int):\n",
    "        print(f\"Usando la columna {index_col} como índice.\")\n",
    "    elif isinstance(index_col, str):\n",
    "        print(f\"Usando la columna '{index_col}' como índice.\")\n",
    "\n",
    "     # Desactiva la visualización del índice al imprimir\n",
    "    pd.set_option('display.show_dimensions', False)\n",
    "    \n",
    "    # Tamaño y estructura del DataFrame\n",
    "    print(\"\\n--- Información general del DataFrame ---\")\n",
    "    df_2.info()  \n",
    "    print(\"\\nPrimeras filas del DataFrame:\")\n",
    "    display(df_2.head(n))  \n",
    "    print(\"\\nFilas aleatorias del DataFrame:\")\n",
    "    display(df_2.sample(n))  \n",
    "    print(f\"\\nTamaño del dataset: {df_2.size} elementos\")\n",
    "    print(f\"Forma del dataset: {df_2.shape[0]} filas, {df_2.shape[1]} columnas\")\n",
    "\n",
    "    # Exploración de columnas\n",
    "    print(\"\\n--- Información sobre las columnas ---\")\n",
    "    pd.set_option('display.max_columns', None)  # Ver todas las columnas\n",
    "    print(f\"Columnas del dataset: {df_2.columns.tolist()}\")\n",
    "    print(\"\\nConteo de valores no nulos por columna:\")\n",
    "    print(df_2.count())  # Conteo de valores no nulos por columna\n",
    "\n",
    "    # Retorna el DataFrame para posibles análisis adicionales\n",
    "    return df_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 976,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usando la columna 0 como índice.\n",
      "\n",
      "--- Información general del DataFrame ---\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16737 entries, 0 to 16736\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Loyalty Number      16737 non-null  int64  \n",
      " 1   Country             16737 non-null  object \n",
      " 2   Province            16737 non-null  object \n",
      " 3   City                16737 non-null  object \n",
      " 4   Postal Code         16737 non-null  object \n",
      " 5   Gender              16737 non-null  object \n",
      " 6   Education           16737 non-null  object \n",
      " 7   Salary              12499 non-null  float64\n",
      " 8   Marital Status      16737 non-null  object \n",
      " 9   Loyalty Card        16737 non-null  object \n",
      " 10  CLV                 16737 non-null  float64\n",
      " 11  Enrollment Type     16737 non-null  object \n",
      " 12  Enrollment Year     16737 non-null  int64  \n",
      " 13  Enrollment Month    16737 non-null  int64  \n",
      " 14  Cancellation Year   2067 non-null   float64\n",
      " 15  Cancellation Month  2067 non-null   float64\n",
      "dtypes: float64(4), int64(3), object(9)\n",
      "memory usage: 2.0+ MB\n",
      "\n",
      "Primeras filas del DataFrame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loyalty Number</th>\n",
       "      <th>Country</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Loyalty Card</th>\n",
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       "      <th>Enrollment Type</th>\n",
       "      <th>Enrollment Year</th>\n",
       "      <th>Enrollment Month</th>\n",
       "      <th>Cancellation Year</th>\n",
       "      <th>Cancellation Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480934</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M2Z 4K1</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>83236.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.14</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>549612</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T3G 6Y6</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.61</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>429460</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>V6E 3D9</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.75</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>608370</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>P1W 1K4</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.75</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>530508</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Hull</td>\n",
       "      <td>J8Y 3Z5</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>103495.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>3842.79</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loyalty Number Country          Province       City Postal Code  Gender  \\\n",
       "0          480934  Canada           Ontario    Toronto     M2Z 4K1  Female   \n",
       "1          549612  Canada           Alberta   Edmonton     T3G 6Y6    Male   \n",
       "2          429460  Canada  British Columbia  Vancouver     V6E 3D9    Male   \n",
       "3          608370  Canada           Ontario    Toronto     P1W 1K4    Male   \n",
       "4          530508  Canada            Quebec       Hull     J8Y 3Z5    Male   \n",
       "\n",
       "  Education    Salary Marital Status Loyalty Card      CLV Enrollment Type  \\\n",
       "0  Bachelor   83236.0        Married         Star  3839.14        Standard   \n",
       "1   College       NaN       Divorced         Star  3839.61        Standard   \n",
       "2   College       NaN         Single         Star  3839.75        Standard   \n",
       "3   College       NaN         Single         Star  3839.75        Standard   \n",
       "4  Bachelor  103495.0        Married         Star  3842.79        Standard   \n",
       "\n",
       "   Enrollment Year  Enrollment Month  Cancellation Year  Cancellation Month  \n",
       "0             2016                 2                NaN                 NaN  \n",
       "1             2016                 3                NaN                 NaN  \n",
       "2             2014                 7             2018.0                 1.0  \n",
       "3             2013                 2                NaN                 NaN  \n",
       "4             2014                10                NaN                 NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Filas aleatorias del DataFrame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loyalty Number</th>\n",
       "      <th>Country</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Loyalty Card</th>\n",
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       "      <th>Enrollment Year</th>\n",
       "      <th>Enrollment Month</th>\n",
       "      <th>Cancellation Year</th>\n",
       "      <th>Cancellation Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11538</th>\n",
       "      <td>839328</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>Halifax</td>\n",
       "      <td>B3J 9S2</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>56384.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>2674.23</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>982542</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Tremblant</td>\n",
       "      <td>H5Y 2S9</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>71908.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>7693.12</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6347</th>\n",
       "      <td>834550</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>V6E 3D9</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married</td>\n",
       "      <td>Nova</td>\n",
       "      <td>5060.30</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14644</th>\n",
       "      <td>379752</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>H2T 9K8</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>83099.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Star</td>\n",
       "      <td>7553.23</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>203550</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Tremblant</td>\n",
       "      <td>H5Y 2S9</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>53610.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>4426.10</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Loyalty Number Country          Province       City Postal Code  \\\n",
       "11538          839328  Canada       Nova Scotia    Halifax     B3J 9S2   \n",
       "2238           982542  Canada            Quebec  Tremblant     H5Y 2S9   \n",
       "6347           834550  Canada  British Columbia  Vancouver     V6E 3D9   \n",
       "14644          379752  Canada            Quebec   Montreal     H2T 9K8   \n",
       "305            203550  Canada            Quebec  Tremblant     H5Y 2S9   \n",
       "\n",
       "       Gender Education   Salary Marital Status Loyalty Card      CLV  \\\n",
       "11538    Male  Bachelor  56384.0        Married         Star  2674.23   \n",
       "2238     Male  Bachelor  71908.0        Married       Aurora  7693.12   \n",
       "6347     Male   College      NaN        Married         Nova  5060.30   \n",
       "14644  Female  Bachelor  83099.0       Divorced         Star  7553.23   \n",
       "305      Male  Bachelor  53610.0        Married         Star  4426.10   \n",
       "\n",
       "      Enrollment Type  Enrollment Year  Enrollment Month  Cancellation Year  \\\n",
       "11538        Standard             2012                 7                NaN   \n",
       "2238         Standard             2013                 6             2017.0   \n",
       "6347         Standard             2016                 6                NaN   \n",
       "14644        Standard             2015                 6                NaN   \n",
       "305          Standard             2013                11                NaN   \n",
       "\n",
       "       Cancellation Month  \n",
       "11538                 NaN  \n",
       "2238                  8.0  \n",
       "6347                  NaN  \n",
       "14644                 NaN  \n",
       "305                   NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tamaño del dataset: 267792 elementos\n",
      "Forma del dataset: 16737 filas, 16 columnas\n",
      "\n",
      "--- Información sobre las columnas ---\n",
      "Columnas del dataset: ['Loyalty Number', 'Country', 'Province', 'City', 'Postal Code', 'Gender', 'Education', 'Salary', 'Marital Status', 'Loyalty Card', 'CLV', 'Enrollment Type', 'Enrollment Year', 'Enrollment Month', 'Cancellation Year', 'Cancellation Month']\n",
      "\n",
      "Conteo de valores no nulos por columna:\n",
      "Loyalty Number        16737\n",
      "Country               16737\n",
      "Province              16737\n",
      "City                  16737\n",
      "Postal Code           16737\n",
      "Gender                16737\n",
      "Education             16737\n",
      "Salary                12499\n",
      "Marital Status        16737\n",
      "Loyalty Card          16737\n",
      "CLV                   16737\n",
      "Enrollment Type       16737\n",
      "Enrollment Year       16737\n",
      "Enrollment Month      16737\n",
      "Cancellation Year      2067\n",
      "Cancellation Month     2067\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Uso de la función\n",
    "filepath_2 = 'Customer_Loyalty_History.csv'\n",
    "dataset_2 = explorar_dataset(filepath_2, index_col=0, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 977,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombres de columnas estandarizados: ['loyalty_number', 'country', 'province', 'city', 'postal_code', 'gender', 'education', 'salary', 'marital_status', 'loyalty_card', 'clv', 'enrollment_type', 'enrollment_year', 'enrollment_month', 'cancellation_year', 'cancellation_month']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loyalty_number</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>loyalty_card</th>\n",
       "      <th>clv</th>\n",
       "      <th>enrollment_type</th>\n",
       "      <th>enrollment_year</th>\n",
       "      <th>enrollment_month</th>\n",
       "      <th>cancellation_year</th>\n",
       "      <th>cancellation_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480934</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M2Z 4K1</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>83236.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.14</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>549612</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T3G 6Y6</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.61</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>429460</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>V6E 3D9</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.75</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>608370</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>P1W 1K4</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>Star</td>\n",
       "      <td>3839.75</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>530508</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Hull</td>\n",
       "      <td>J8Y 3Z5</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>103495.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>3842.79</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16732</th>\n",
       "      <td>823768</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>V6E 3Z3</td>\n",
       "      <td>Female</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>61850.19</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16733</th>\n",
       "      <td>680886</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>Regina</td>\n",
       "      <td>S1J 3C5</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>89210.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>67907.27</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16734</th>\n",
       "      <td>776187</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>V5R 1W3</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>Star</td>\n",
       "      <td>74228.52</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16735</th>\n",
       "      <td>906428</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Yukon</td>\n",
       "      <td>Whitehorse</td>\n",
       "      <td>Y2K 6R0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>-57297.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>10018.66</td>\n",
       "      <td>2018 Promotion</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16736</th>\n",
       "      <td>652627</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Manitoba</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>R2C 0M5</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>75049.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>83325.38</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loyalty_number country          province        city postal_code  \\\n",
       "0              480934  Canada           Ontario     Toronto     M2Z 4K1   \n",
       "1              549612  Canada           Alberta    Edmonton     T3G 6Y6   \n",
       "2              429460  Canada  British Columbia   Vancouver     V6E 3D9   \n",
       "3              608370  Canada           Ontario     Toronto     P1W 1K4   \n",
       "4              530508  Canada            Quebec        Hull     J8Y 3Z5   \n",
       "...               ...     ...               ...         ...         ...   \n",
       "16732          823768  Canada  British Columbia   Vancouver     V6E 3Z3   \n",
       "16733          680886  Canada      Saskatchewan      Regina     S1J 3C5   \n",
       "16734          776187  Canada  British Columbia   Vancouver     V5R 1W3   \n",
       "16735          906428  Canada             Yukon  Whitehorse     Y2K 6R0   \n",
       "16736          652627  Canada          Manitoba    Winnipeg     R2C 0M5   \n",
       "\n",
       "       gender education    salary marital_status loyalty_card       clv  \\\n",
       "0      Female  Bachelor   83236.0        Married         Star   3839.14   \n",
       "1        Male   College       NaN       Divorced         Star   3839.61   \n",
       "2        Male   College       NaN         Single         Star   3839.75   \n",
       "3        Male   College       NaN         Single         Star   3839.75   \n",
       "4        Male  Bachelor  103495.0        Married         Star   3842.79   \n",
       "...       ...       ...       ...            ...          ...       ...   \n",
       "16732  Female   College       NaN        Married         Star  61850.19   \n",
       "16733  Female  Bachelor   89210.0        Married         Star  67907.27   \n",
       "16734    Male   College       NaN         Single         Star  74228.52   \n",
       "16735    Male  Bachelor  -57297.0        Married         Star  10018.66   \n",
       "16736  Female  Bachelor   75049.0        Married         Star  83325.38   \n",
       "\n",
       "      enrollment_type  enrollment_year  enrollment_month  cancellation_year  \\\n",
       "0            Standard             2016                 2                NaN   \n",
       "1            Standard             2016                 3                NaN   \n",
       "2            Standard             2014                 7             2018.0   \n",
       "3            Standard             2013                 2                NaN   \n",
       "4            Standard             2014                10                NaN   \n",
       "...               ...              ...               ...                ...   \n",
       "16732        Standard             2012                12                NaN   \n",
       "16733        Standard             2014                 9                NaN   \n",
       "16734        Standard             2014                 3                NaN   \n",
       "16735  2018 Promotion             2018                 4                NaN   \n",
       "16736        Standard             2015                12             2016.0   \n",
       "\n",
       "       cancellation_month  \n",
       "0                     NaN  \n",
       "1                     NaN  \n",
       "2                     1.0  \n",
       "3                     NaN  \n",
       "4                     NaN  \n",
       "...                   ...  \n",
       "16732                 NaN  \n",
       "16733                 NaN  \n",
       "16734                 NaN  \n",
       "16735                 NaN  \n",
       "16736                 8.0  "
      ]
     },
     "execution_count": 977,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estandarizo los nombres de las columnas del DataFrame con una sola función:\n",
    "\n",
    "def estandarizar_columnas(dataset_2):\n",
    "    \n",
    "    dataset_2.columns = dataset_2.columns.str.strip().str.lower().str.replace(\" \", \"_\").str.replace(r'[^\\w\\s]', '', regex=True)\n",
    "    print(f\"Nombres de columnas estandarizados: {dataset_2.columns.tolist()}\")\n",
    "    return dataset_2\n",
    "\n",
    "\n",
    "estandarizar_columnas(dataset_2) #Llamo a la función"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 978,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Valores únicos por columna ---\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columna</th>\n",
       "      <th>Valores únicos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loyalty_number</td>\n",
       "      <td>16737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>country</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>province</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>postal_code</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gender</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>education</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>salary</td>\n",
       "      <td>5891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>marital_status</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>loyalty_card</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>clv</td>\n",
       "      <td>7984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>enrollment_type</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>enrollment_year</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>enrollment_month</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cancellation_year</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cancellation_month</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Columna  Valores únicos\n",
       "0       loyalty_number           16737\n",
       "1              country               1\n",
       "2             province              11\n",
       "3                 city              29\n",
       "4          postal_code              55\n",
       "5               gender               2\n",
       "6            education               5\n",
       "7               salary            5891\n",
       "8       marital_status               3\n",
       "9         loyalty_card               3\n",
       "10                 clv            7984\n",
       "11     enrollment_type               2\n",
       "12     enrollment_year               7\n",
       "13    enrollment_month              12\n",
       "14   cancellation_year               7\n",
       "15  cancellation_month              13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculo y muestro la cantidad de valores únicos por columna.\n",
    "\n",
    "def valores_unicos_por_columna(dataset_2):\n",
    "    \n",
    "    print(\"\\n--- Valores únicos por columna ---\")\n",
    "    valores_unicos = {col: len(dataset_2[col].unique()) for col in dataset_2.columns}\n",
    "    valores_unicos_df_2 = pd.DataFrame(\n",
    "        list(valores_unicos.items()),\n",
    "        columns=['Columna', 'Valores únicos']\n",
    "    )\n",
    "    display(valores_unicos_df_2)\n",
    "    return valores_unicos_df_2\n",
    "\n",
    "\n",
    "valores_unicos = valores_unicos_por_columna(dataset_2) #Llamo a la función"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 979,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Esta función procesa las columnas numéricas y muestra estadísticas del DataFrame con describe().\n",
    "\n",
    "\n",
    "def procesar_columnas_numericas(dataset_2, columnas_numericas=None):\n",
    "    \n",
    "    # Si no se especifican columnas, las detecta automáticamente\n",
    "    if columnas_numericas is None:\n",
    "        columnas_numericas = dataset_2.select_dtypes(include='number').columns.tolist()\n",
    "        print(f\"\\nColumnas numéricas detectadas automáticamente: {columnas_numericas}\")\n",
    "    \n",
    "    # Esto procesa solo las columnas especificadas\n",
    "    for columna in columnas_numericas:\n",
    "        if columna in dataset_2.columns:\n",
    "            print(f\"\\nEstadísticas de la columna '{columna}':\")\n",
    "            print(dataset_2[columna].describe())\n",
    "            \n",
    "            # Filtro valores negativos\n",
    "            if (dataset_2[columna] < 0).any():\n",
    "                print(f\"\\nSe encontraron valores negativos en '{columna}', filtrando...\")\n",
    "                df = dataset_2[dataset_2[columna] >= 0]\n",
    "        else:\n",
    "            print(f\"\\nLa columna '{columna}' no existe en el DataFrame.\")\n",
    "    \n",
    "    return dataset_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 980,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Columnas numéricas detectadas automáticamente: ['loyalty_number', 'salary', 'clv', 'enrollment_year', 'enrollment_month', 'cancellation_year', 'cancellation_month']\n",
      "\n",
      "Estadísticas de la columna 'loyalty_number':\n",
      "count     16737.000000\n",
      "mean     549735.880445\n",
      "std      258912.132453\n",
      "min      100018.000000\n",
      "25%      326603.000000\n",
      "50%      550434.000000\n",
      "75%      772019.000000\n",
      "max      999986.000000\n",
      "Name: loyalty_number, dtype: float64\n",
      "\n",
      "Estadísticas de la columna 'salary':\n",
      "count     12499.000000\n",
      "mean      79245.609409\n",
      "std       35008.297285\n",
      "min      -58486.000000\n",
      "25%       59246.500000\n",
      "50%       73455.000000\n",
      "75%       88517.500000\n",
      "max      407228.000000\n",
      "Name: salary, dtype: float64\n",
      "\n",
      "Se encontraron valores negativos en 'salary', filtrando...\n",
      "\n",
      "Estadísticas de la columna 'clv':\n",
      "count    16737.000000\n",
      "mean      7988.896536\n",
      "std       6860.982280\n",
      "min       1898.010000\n",
      "25%       3980.840000\n",
      "50%       5780.180000\n",
      "75%       8940.580000\n",
      "max      83325.380000\n",
      "Name: clv, dtype: float64\n",
      "\n",
      "Estadísticas de la columna 'enrollment_year':\n",
      "count    16737.000000\n",
      "mean      2015.253211\n",
      "std          1.979111\n",
      "min       2012.000000\n",
      "25%       2014.000000\n",
      "50%       2015.000000\n",
      "75%       2017.000000\n",
      "max       2018.000000\n",
      "Name: enrollment_year, dtype: float64\n",
      "\n",
      "Estadísticas de la columna 'enrollment_month':\n",
      "count    16737.000000\n",
      "mean         6.669116\n",
      "std          3.398958\n",
      "min          1.000000\n",
      "25%          4.000000\n",
      "50%          7.000000\n",
      "75%         10.000000\n",
      "max         12.000000\n",
      "Name: enrollment_month, dtype: float64\n",
      "\n",
      "Estadísticas de la columna 'cancellation_year':\n",
      "count    2067.000000\n",
      "mean     2016.503145\n",
      "std         1.380743\n",
      "min      2013.000000\n",
      "25%      2016.000000\n",
      "50%      2017.000000\n",
      "75%      2018.000000\n",
      "max      2018.000000\n",
      "Name: cancellation_year, dtype: float64\n",
      "\n",
      "Estadísticas de la columna 'cancellation_month':\n",
      "count    2067.000000\n",
      "mean        6.962748\n",
      "std         3.455297\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%         7.000000\n",
      "75%        10.000000\n",
      "max        12.000000\n",
      "Name: cancellation_month, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Llamo a la función con las columnas deseadas\n",
    "df_procesado2 = procesar_columnas_numericas(dataset_2, columnas_numericas=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 981,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Después de ver todos los datos, uso esta función para realizar estadísticas numéricas de columnas específicas, para ver en este caso salary y clv, que considero más relevantes:\n",
    "\n",
    "def estadisticas_numericas(dataset_2, columnas=None):\n",
    " \n",
    "    # Selección columnas numéricas especificadas o todas si no se indican\n",
    "    if columnas is not None:\n",
    "        columnas_existentes = [col for col in columnas if col in dataset_2.columns]\n",
    "        if not columnas_existentes:\n",
    "            raise ValueError(\"Ninguna de las columnas especificadas existe en el DataFrame.\")\n",
    "        dataset_2 = dataset_2[columnas_existentes]\n",
    "        print(f\"Analizando las siguientes columnas numéricas: {columnas_existentes}\")\n",
    "    else:\n",
    "        print(\"Analizando todas las columnas numéricas del DataFrame.\")\n",
    "\n",
    "    # Calculo estadísticas descriptivas\n",
    "    estadisticas = dataset_2.select_dtypes(include='number').describe()\n",
    "    estadisticas_transpuestas = estadisticas.T\n",
    "\n",
    "    print(\"\\n--- Estadísticas descriptivas de variables numéricas ---\")\n",
    "    display(estadisticas)\n",
    "\n",
    "    print(\"\\n--- Estadísticas descriptivas transpuestas (numéricas) ---\")\n",
    "    display(estadisticas_transpuestas)\n",
    "\n",
    "    return {\n",
    "        'estadisticas': estadisticas,\n",
    "        'estadisticas_transpuestas': estadisticas_transpuestas\n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 982,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analizando las siguientes columnas numéricas: ['salary', 'clv']\n",
      "\n",
      "--- Estadísticas descriptivas de variables numéricas ---\n"
     ]
    },
    {
     "data": {
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       "\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>salary</th>\n",
       "      <th>clv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12499.000000</td>\n",
       "      <td>16737.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79245.609409</td>\n",
       "      <td>7988.896536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>35008.297285</td>\n",
       "      <td>6860.982280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-58486.000000</td>\n",
       "      <td>1898.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>59246.500000</td>\n",
       "      <td>3980.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73455.000000</td>\n",
       "      <td>5780.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>88517.500000</td>\n",
       "      <td>8940.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>407228.000000</td>\n",
       "      <td>83325.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              salary           clv\n",
       "count   12499.000000  16737.000000\n",
       "mean    79245.609409   7988.896536\n",
       "std     35008.297285   6860.982280\n",
       "min    -58486.000000   1898.010000\n",
       "25%     59246.500000   3980.840000\n",
       "50%     73455.000000   5780.180000\n",
       "75%     88517.500000   8940.580000\n",
       "max    407228.000000  83325.380000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Estadísticas descriptivas transpuestas (numéricas) ---\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>12499.0</td>\n",
       "      <td>79245.609409</td>\n",
       "      <td>35008.297285</td>\n",
       "      <td>-58486.00</td>\n",
       "      <td>59246.50</td>\n",
       "      <td>73455.00</td>\n",
       "      <td>88517.50</td>\n",
       "      <td>407228.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clv</th>\n",
       "      <td>16737.0</td>\n",
       "      <td>7988.896536</td>\n",
       "      <td>6860.982280</td>\n",
       "      <td>1898.01</td>\n",
       "      <td>3980.84</td>\n",
       "      <td>5780.18</td>\n",
       "      <td>8940.58</td>\n",
       "      <td>83325.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count          mean           std       min       25%       50%  \\\n",
       "salary  12499.0  79245.609409  35008.297285 -58486.00  59246.50  73455.00   \n",
       "clv     16737.0   7988.896536   6860.982280   1898.01   3980.84   5780.18   \n",
       "\n",
       "             75%        max  \n",
       "salary  88517.50  407228.00  \n",
       "clv      8940.58   83325.38  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Llamada de la función con las columnas que quiero analizar\n",
    "estadisticas_num = estadisticas_numericas(dataset_2, columnas=['salary', 'clv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 983,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Como en la exploraicón del dataset me muestra columnas categóricas, hago un cálculo y muestra de la frecuencia de valores únicos en esas columnas.\n",
    "\n",
    "def frecuencias_categoricas(dataset_2):\n",
    "    \n",
    "    print(\"\\n--- Frecuencia de valores únicos en columnas categóricas ---\")\n",
    "    frecuencias_categoricas = []\n",
    "    for col in dataset_2.select_dtypes(include='object').columns:\n",
    "        frecuencias = dataset_2[col].value_counts().reset_index()\n",
    "        frecuencias.columns = ['Valor', 'Frecuencia']\n",
    "        frecuencias['Columna'] = col\n",
    "        frecuencias_categoricas.append(frecuencias)\n",
    "\n",
    "    if frecuencias_categoricas:\n",
    "        frecuencias_categoricas_df = pd.concat(frecuencias_categoricas, ignore_index=True)\n",
    "        display(frecuencias_categoricas_df)\n",
    "        return frecuencias_categoricas_df\n",
    "    else:\n",
    "        print(\"No se encontraron columnas categóricas.\")\n",
    "        return None\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 984,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Frecuencia de valores únicos en columnas categóricas ---\n"
     ]
    },
    {
     "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>Valor</th>\n",
       "      <th>Frecuencia</th>\n",
       "      <th>Columna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Canada</td>\n",
       "      <td>16737</td>\n",
       "      <td>country</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ontario</td>\n",
       "      <td>5404</td>\n",
       "      <td>province</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>4409</td>\n",
       "      <td>province</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quebec</td>\n",
       "      <td>3300</td>\n",
       "      <td>province</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alberta</td>\n",
       "      <td>969</td>\n",
       "      <td>province</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Star</td>\n",
       "      <td>7637</td>\n",
       "      <td>loyalty_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Nova</td>\n",
       "      <td>5671</td>\n",
       "      <td>loyalty_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Aurora</td>\n",
       "      <td>3429</td>\n",
       "      <td>loyalty_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Standard</td>\n",
       "      <td>15766</td>\n",
       "      <td>enrollment_type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2018 Promotion</td>\n",
       "      <td>971</td>\n",
       "      <td>enrollment_type</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Valor  Frecuencia          Columna\n",
       "0              Canada       16737          country\n",
       "1             Ontario        5404         province\n",
       "2    British Columbia        4409         province\n",
       "3              Quebec        3300         province\n",
       "4             Alberta         969         province\n",
       "..                ...         ...              ...\n",
       "106              Star        7637     loyalty_card\n",
       "107              Nova        5671     loyalty_card\n",
       "108            Aurora        3429     loyalty_card\n",
       "109          Standard       15766  enrollment_type\n",
       "110    2018 Promotion         971  enrollment_type"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frecuencias = frecuencias_categoricas(dataset_2) #Llamada de la función"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 985,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Estadísticas descriptivas de variables categóricas ---\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>loyalty_card</th>\n",
       "      <th>enrollment_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "      <td>16737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>V6E 3D9</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>16737</td>\n",
       "      <td>5404</td>\n",
       "      <td>3351</td>\n",
       "      <td>911</td>\n",
       "      <td>8410</td>\n",
       "      <td>10475</td>\n",
       "      <td>9735</td>\n",
       "      <td>7637</td>\n",
       "      <td>15766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country province     city postal_code  gender education marital_status  \\\n",
       "count    16737    16737    16737       16737   16737     16737          16737   \n",
       "unique       1       11       29          55       2         5              3   \n",
       "top     Canada  Ontario  Toronto     V6E 3D9  Female  Bachelor        Married   \n",
       "freq     16737     5404     3351         911    8410     10475           9735   \n",
       "\n",
       "       loyalty_card enrollment_type  \n",
       "count         16737           16737  \n",
       "unique            3               2  \n",
       "top            Star        Standard  \n",
       "freq           7637           15766  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Esta función realiza un conteo a través de \"describe\" de los valores de cada columna categórica\n",
    "\n",
    "def estadisticas_descriptivas(dataset_2):\n",
    "   \n",
    "    print(\"\\n--- Estadísticas descriptivas de variables categóricas ---\")\n",
    "    estadisticas_categoricas = dataset_2.describe(include=[object])\n",
    "    display(estadisticas_categoricas)\n",
    "\n",
    "    return {'estadisticas_categoricas': estadisticas_categoricas }\n",
    "\n",
    "\n",
    "estadisticas = estadisticas_descriptivas(dataset_2) #Llamada de la función\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 986,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Unión conjunto de datos: \n",
    "\n",
    "# Después de analizar las dos tablas, procedo a su unión con merge\n",
    "# He decidido unir la segunda tabla con la primera, para ver primero la información del cliente y luego su historial.\n",
    "\n",
    "\n",
    "df_unido = pd.merge(dataset_2, dataset, on='loyalty_number', how='inner')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loyalty_number</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>loyalty_card</th>\n",
       "      <th>clv</th>\n",
       "      <th>enrollment_type</th>\n",
       "      <th>enrollment_year</th>\n",
       "      <th>enrollment_month</th>\n",
       "      <th>cancellation_year</th>\n",
       "      <th>cancellation_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>flights_booked</th>\n",
       "      <th>flights_with_companions</th>\n",
       "      <th>total_flights</th>\n",
       "      <th>distance</th>\n",
       "      <th>points_accumulated</th>\n",
       "      <th>points_redeemed</th>\n",
       "      <th>dollar_cost_points_redeemed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315321</th>\n",
       "      <td>438263</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Manitoba</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>R2C 0M5</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>4717.63</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104940</th>\n",
       "      <td>174923</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Hull</td>\n",
       "      <td>J8Y 3Z5</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>89076.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Nova</td>\n",
       "      <td>2799.47</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2095</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16229</th>\n",
       "      <td>201220</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>West Vancouver</td>\n",
       "      <td>V6V 8Z3</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>63517.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>4892.62</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2092</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214972</th>\n",
       "      <td>443865</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>P2T 6G3</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>58083.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Nova</td>\n",
       "      <td>11030.66</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>566033</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M2P 4F6</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>4015.21</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loyalty_number country          province            city postal_code  \\\n",
       "315321          438263  Canada          Manitoba        Winnipeg     R2C 0M5   \n",
       "104940          174923  Canada            Quebec            Hull     J8Y 3Z5   \n",
       "16229           201220  Canada  British Columbia  West Vancouver     V6V 8Z3   \n",
       "214972          443865  Canada           Ontario         Toronto     P2T 6G3   \n",
       "2311            566033  Canada           Ontario         Toronto     M2P 4F6   \n",
       "\n",
       "        gender education   salary marital_status loyalty_card       clv  \\\n",
       "315321    Male   College      NaN        Married         Star   4717.63   \n",
       "104940  Female  Bachelor  89076.0        Married         Nova   2799.47   \n",
       "16229     Male  Bachelor  63517.0        Married         Star   4892.62   \n",
       "214972    Male  Bachelor  58083.0        Married         Nova  11030.66   \n",
       "2311      Male   College      NaN        Married         Star   4015.21   \n",
       "\n",
       "       enrollment_type  enrollment_year  enrollment_month  cancellation_year  \\\n",
       "315321        Standard             2014                 5                NaN   \n",
       "104940        Standard             2014                12                NaN   \n",
       "16229         Standard             2013                 9                NaN   \n",
       "214972        Standard             2012                 9                NaN   \n",
       "2311          Standard             2015                 6             2016.0   \n",
       "\n",
       "        cancellation_month  year  month  flights_booked  \\\n",
       "315321                 NaN  2017     10               0   \n",
       "104940                 NaN  2017     12               5   \n",
       "16229                  NaN  2017      5               3   \n",
       "214972                 NaN  2017      5               0   \n",
       "2311                   7.0  2017      8               0   \n",
       "\n",
       "        flights_with_companions  total_flights  distance  points_accumulated  \\\n",
       "315321                        0              0         0                 0.0   \n",
       "104940                        0              5      2095               209.0   \n",
       "16229                         1              4      2092               209.0   \n",
       "214972                        0              0         0                 0.0   \n",
       "2311                          0              0         0                 0.0   \n",
       "\n",
       "        points_redeemed  dollar_cost_points_redeemed  \n",
       "315321                0                            0  \n",
       "104940                0                            0  \n",
       "16229                 0                            0  \n",
       "214972                0                            0  \n",
       "2311                  0                            0  "
      ]
     },
     "execution_count": 987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unido.sample(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Limpieza de Datos:**\n",
    "   \n",
    "- Elimina o trata los valores nulos, si los hay, en las columnas clave para asegurar que los datos estén completos.\n",
    "  \n",
    "- Verifica la consistencia y corrección de los datos para asegurarte de que los datos se presenten de forma coherente.\n",
    "  \n",
    "- Realiza cualquier ajuste o conversión necesaria en las columnas (por ejemplo, cambiar tipos de datos) para garantizar la adecuación de los datos para el análisis estadístico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Esta función identifica los valores nulos en el DataFrame y devuelve un resumen en forma de DataFrame\n",
    "\n",
    "def identificar_nulos(df_unido): #DataFrame a analizar\n",
    "    \n",
    "    # Calcular valores nulos y porcentaje de nulos\n",
    "    resumen_nulos = df_unido.isnull().sum()\n",
    "    porcentaje_nulos = df_unido.isnull().mean() * 100\n",
    "    \n",
    "    # Crear el DataFrame resumen\n",
    "    resumen_df = pd.DataFrame({\n",
    "        'Valores Nulos': resumen_nulos,\n",
    "        'Porcentaje Nulos (%)': porcentaje_nulos\n",
    "    }).reset_index()\n",
    "    resumen_df.columns = ['Columna', 'Valores Nulos', 'Porcentaje Nulos (%)']\n",
    "    \n",
    "    # Filtrar columnas con valores nulos\n",
    "    resumen_con_nulos = resumen_df[resumen_df['Valores Nulos'] > 0]\n",
    "    \n",
    "    # Mostrar el resumen directamente\n",
    "    print(\"\\n--- Resumen de columnas con valores nulos y porcentajes ---\")\n",
    "    display(resumen_con_nulos)\n",
    "\n",
    "    return resumen_con_nulos \n",
    "    # Devuelve un DataFrame con el resumen y porcentaje de valores nulos por columna.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Resumen de columnas con valores nulos y porcentajes ---\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columna</th>\n",
       "      <th>Valores Nulos</th>\n",
       "      <th>Porcentaje Nulos (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>salary</td>\n",
       "      <td>102672</td>\n",
       "      <td>25.312112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cancellation_year</td>\n",
       "      <td>355560</td>\n",
       "      <td>87.657535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cancellation_month</td>\n",
       "      <td>355560</td>\n",
       "      <td>87.657535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Columna  Valores Nulos  Porcentaje Nulos (%)\n",
       "7               salary         102672             25.312112\n",
       "14   cancellation_year         355560             87.657535\n",
       "15  cancellation_month         355560             87.657535"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resumen_nulos_df = identificar_nulos(df_unido) #Llamada dde la función"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se puede ver, hay una diferencia bastante grande entre salario y año y mes de cancelación, por lo que lo voy tratar de dos maneras diferentes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 990,
   "metadata": {},
   "outputs": [],
   "source": [
    "# En el caso de la columna de salario, he decidido procesarlo con la siguiente función que gestiona los valores nulos en columnas numéricas: \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def gestionar_nulos_numericos(df_unido, columnas_numericas=['salary'], umbral_alto=30):\n",
    "    \n",
    "    #Parámetros:\n",
    "    #columnas_numericas: Lista de columnas numéricas.\n",
    "    #umbral_alto: Porcentaje considerado como \"alto\" para valores nulos.\n",
    "        \n",
    "    columnas_numericas = columnas_numericas or df_unido.select_dtypes(include='number').columns.tolist() #da dos opciones para la búsqueda de nulos\n",
    "\n",
    "    for col in columnas_numericas:\n",
    "        porcentaje_nulos = df_unido[col].isnull().mean() * 100\n",
    "        print(f\"\\nProcesando columna numérica: {col} ({porcentaje_nulos:.2f}% de nulos)\")\n",
    "\n",
    "        if porcentaje_nulos > umbral_alto:\n",
    "            print(\"- Eliminando la columna por alto porcentaje de nulos.\")\n",
    "            df_unido = df_unido.drop(columns=[col])\n",
    "        else:\n",
    "            print(\"- Analizando distribución para imputación.\")  #muestro la gráfica para ver la distribución de valores\n",
    "            df_unido[col].hist(bins=20, edgecolor='black', alpha=0.7)\n",
    "            plt.title(f\"Distribución de {col}\")\n",
    "            plt.xlabel(\"Valores\")\n",
    "            plt.ylabel(\"Frecuencia\")\n",
    "            plt.show()\n",
    "\n",
    "            skewness = df_unido[col].skew()\n",
    "            if abs(skewness) < 0.5:  # Distribución simétrica\n",
    "                print(\"- Distribución simétrica. Imputando con la media.\")\n",
    "                df_unido[col] = df_unido[col].fillna(df_unido[col].mean())\n",
    "            else:  # Distribución asimétrica\n",
    "                print(\"- Distribución asimétrica. Imputando con la mediana.\")\n",
    "                df_unido[col] = df_unido[col].fillna(df_unido[col].median())\n",
    "\n",
    "    return df_unido #Devuelve un DataFrame con los nulos gestionados en columnas numéricas.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 991,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Procesando columna numérica: salary (25.31% de nulos)\n",
      "- Analizando distribución para imputación.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Distribución asimétrica. Imputando con la mediana.\n"
     ]
    }
   ],
   "source": [
    "df_corregido = gestionar_nulos_numericos(df_unido, umbral_alto=30) #Llamada de la función de valores nulos en salario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loyalty_number</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>loyalty_card</th>\n",
       "      <th>clv</th>\n",
       "      <th>enrollment_type</th>\n",
       "      <th>enrollment_year</th>\n",
       "      <th>enrollment_month</th>\n",
       "      <th>cancellation_year</th>\n",
       "      <th>cancellation_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>flights_booked</th>\n",
       "      <th>flights_with_companions</th>\n",
       "      <th>total_flights</th>\n",
       "      <th>distance</th>\n",
       "      <th>points_accumulated</th>\n",
       "      <th>points_redeemed</th>\n",
       "      <th>dollar_cost_points_redeemed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>269739</th>\n",
       "      <td>722089</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>H4G 3T4</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>89932.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>2569.69</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269201</th>\n",
       "      <td>586365</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>H2Y 2W2</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>73479.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Star</td>\n",
       "      <td>2561.58</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2828</td>\n",
       "      <td>282.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288032</th>\n",
       "      <td>980761</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Quebec City</td>\n",
       "      <td>G1B 3L5</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>83710.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>2776.77</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131070</th>\n",
       "      <td>766530</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>M8Y 4K8</td>\n",
       "      <td>Female</td>\n",
       "      <td>College</td>\n",
       "      <td>73479.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Nova</td>\n",
       "      <td>3742.58</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2516</td>\n",
       "      <td>251.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254990</th>\n",
       "      <td>337842</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T9G 1W3</td>\n",
       "      <td>Female</td>\n",
       "      <td>College</td>\n",
       "      <td>73479.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>2441.39</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1464</td>\n",
       "      <td>157.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loyalty_number country province         city postal_code  gender  \\\n",
       "269739          722089  Canada   Quebec     Montreal     H4G 3T4    Male   \n",
       "269201          586365  Canada   Quebec     Montreal     H2Y 2W2    Male   \n",
       "288032          980761  Canada   Quebec  Quebec City     G1B 3L5  Female   \n",
       "131070          766530  Canada  Ontario      Toronto     M8Y 4K8  Female   \n",
       "254990          337842  Canada  Alberta     Edmonton     T9G 1W3  Female   \n",
       "\n",
       "       education   salary marital_status loyalty_card      clv  \\\n",
       "269739  Bachelor  89932.0        Married         Star  2569.69   \n",
       "269201   College  73479.0         Single         Star  2561.58   \n",
       "288032  Bachelor  83710.0        Married         Star  2776.77   \n",
       "131070   College  73479.0         Single         Nova  3742.58   \n",
       "254990   College  73479.0        Married         Star  2441.39   \n",
       "\n",
       "       enrollment_type  enrollment_year  enrollment_month  cancellation_year  \\\n",
       "269739        Standard             2017                 6                NaN   \n",
       "269201        Standard             2018                 1                NaN   \n",
       "288032        Standard             2015                10                NaN   \n",
       "131070        Standard             2016                 3                NaN   \n",
       "254990        Standard             2015                 5                NaN   \n",
       "\n",
       "        cancellation_month  year  month  flights_booked  \\\n",
       "269739                 NaN  2017      4               0   \n",
       "269201                 NaN  2018      6               2   \n",
       "288032                 NaN  2017      8               0   \n",
       "131070                 NaN  2017      7              11   \n",
       "254990                 NaN  2018      2               6   \n",
       "\n",
       "        flights_with_companions  total_flights  distance  points_accumulated  \\\n",
       "269739                        0              0         0                0.00   \n",
       "269201                        0              2      2828              282.00   \n",
       "288032                        0              0         0                0.00   \n",
       "131070                        6             17      2516              251.00   \n",
       "254990                        0              6      1464              157.68   \n",
       "\n",
       "        points_redeemed  dollar_cost_points_redeemed  \n",
       "269739                0                            0  \n",
       "269201                0                            0  \n",
       "288032                0                            0  \n",
       "131070                0                            0  \n",
       "254990                0                            0  "
      ]
     },
     "execution_count": 992,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unido.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se ve en la tabla, month, enrollment month y cancellation month son \"int\" por lo que voy a convertirlos a \"object\". Y como en cancellation month y cancellation year el alto porcentaje de nulos es porque la cuenta sigue en activo, voy a cambiar los nulos por la palabra \"Active\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def procesar_columnas(df_corregido, columnas_active=['cancellation_month', 'cancellation_year']): #Parámetros para las columnas que quiero cambiar los nulos por \"Active\"\n",
    "    \n",
    "    # Diccionario para mapear números a nombres de meses\n",
    "    months = {\n",
    "        1: 'January', 2: 'February', 3: 'March', 4: 'April',\n",
    "        5: 'May', 6: 'June', 7: 'July', 8: 'August',\n",
    "        9: 'September', 10: 'October', 11: 'November', 12: 'December'\n",
    "    }\n",
    "\n",
    "    # Operaciones para asegurarnos de que 'cancellation_year' y 'cancellation_month' solo contienen valores numéricos\n",
    "    df_corregido['cancellation_year'] = pd.to_numeric(df_corregido['cancellation_year'], errors='coerce')\n",
    "    df_corregido['cancellation_month'] = pd.to_numeric(df_corregido['cancellation_month'], errors='coerce')\n",
    "\n",
    "    # Rellenamos los nulos con 0 en 'cancellation_year' y 'cancellation_month' y que son de tipo int\n",
    "    df_corregido['cancellation_year'] = df_corregido['cancellation_year'].fillna(0).astype(int)\n",
    "    df_corregido['cancellation_month'] = df_corregido['cancellation_month'].fillna(0).astype(int)\n",
    "\n",
    "    # Reemplazar 0 en 'cancellation_year' y 'cancellation_month' por NaN (no cancelado)\n",
    "    df_corregido[\"cancellation_year\"] = df_corregido[\"cancellation_year\"].replace(0, np.nan)\n",
    "    df_corregido[\"cancellation_month\"] = df_corregido[\"cancellation_month\"].replace(0, np.nan) ###\n",
    "    \n",
    "    # Convertir 'month', 'enrollment_month' y 'cancellation_month' a nombres de meses\n",
    "    df_corregido['month'] = df_corregido['month'].map(months)\n",
    "    df_corregido['enrollment_month'] = df_corregido['enrollment_month'].map(months)\n",
    "    df_corregido['cancellation_month'] = df_corregido['cancellation_month'].map(months)\n",
    "\n",
    "    # Rellenar los valores nulos con 'Active' en las columnas especificadas\n",
    "    df_corregido[columnas_active] = df_corregido[columnas_active].fillna('Active')\n",
    "\n",
    "    return df_corregido\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Llamamos a la función e indicamos las columnas donde queremos reemplazar nulos con 'Active'\n",
    "df_corregido = procesar_columnas(df_corregido, columnas_active=['cancellation_month', 'cancellation_year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Elimino columna country ya que es el mismo en toda la tabla\n",
    "\n",
    "df_corregido.drop('country', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loyalty_number</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>loyalty_card</th>\n",
       "      <th>clv</th>\n",
       "      <th>enrollment_type</th>\n",
       "      <th>enrollment_year</th>\n",
       "      <th>enrollment_month</th>\n",
       "      <th>cancellation_year</th>\n",
       "      <th>cancellation_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>flights_booked</th>\n",
       "      <th>flights_with_companions</th>\n",
       "      <th>total_flights</th>\n",
       "      <th>distance</th>\n",
       "      <th>points_accumulated</th>\n",
       "      <th>points_redeemed</th>\n",
       "      <th>dollar_cost_points_redeemed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>292864</th>\n",
       "      <td>578114</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>H2T 9K8</td>\n",
       "      <td>Male</td>\n",
       "      <td>College</td>\n",
       "      <td>73479.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Star</td>\n",
       "      <td>2987.95</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2014</td>\n",
       "      <td>September</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2018</td>\n",
       "      <td>May</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370071</th>\n",
       "      <td>739767</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T9G 1W3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>92796.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Star</td>\n",
       "      <td>8733.53</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2018</td>\n",
       "      <td>November</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2018</td>\n",
       "      <td>April</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314347</th>\n",
       "      <td>665548</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>H2Y 4R4</td>\n",
       "      <td>Female</td>\n",
       "      <td>College</td>\n",
       "      <td>73479.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>4654.95</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2013</td>\n",
       "      <td>September</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2018</td>\n",
       "      <td>August</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124128</th>\n",
       "      <td>519984</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>P1J 8T7</td>\n",
       "      <td>Female</td>\n",
       "      <td>College</td>\n",
       "      <td>73479.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Nova</td>\n",
       "      <td>3460.12</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>September</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43737</th>\n",
       "      <td>452684</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>V6E 3Z3</td>\n",
       "      <td>Female</td>\n",
       "      <td>College</td>\n",
       "      <td>73479.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>6707.99</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>April</td>\n",
       "      <td>2017</td>\n",
       "      <td>October</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3421</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loyalty_number          province       city postal_code  gender  \\\n",
       "292864          578114            Quebec   Montreal     H2T 9K8    Male   \n",
       "370071          739767           Alberta   Edmonton     T9G 1W3  Female   \n",
       "314347          665548            Quebec   Montreal     H2Y 4R4  Female   \n",
       "124128          519984           Ontario    Toronto     P1J 8T7  Female   \n",
       "43737           452684  British Columbia  Vancouver     V6E 3Z3  Female   \n",
       "\n",
       "       education   salary marital_status loyalty_card      clv  \\\n",
       "292864   College  73479.0         Single         Star  2987.95   \n",
       "370071  Bachelor  92796.0       Divorced         Star  8733.53   \n",
       "314347   College  73479.0        Married         Star  4654.95   \n",
       "124128   College  73479.0        Married         Nova  3460.12   \n",
       "43737    College  73479.0       Divorced       Aurora  6707.99   \n",
       "\n",
       "       enrollment_type  enrollment_year enrollment_month cancellation_year  \\\n",
       "292864        Standard             2014        September            Active   \n",
       "370071        Standard             2018         November            Active   \n",
       "314347        Standard             2013        September            Active   \n",
       "124128        Standard             2016        September            Active   \n",
       "43737         Standard             2017           August            2018.0   \n",
       "\n",
       "       cancellation_month  year    month  flights_booked  \\\n",
       "292864             Active  2018      May               0   \n",
       "370071             Active  2018    April               0   \n",
       "314347             Active  2018   August               0   \n",
       "124128             Active  2017  January               0   \n",
       "43737               April  2017  October              11   \n",
       "\n",
       "        flights_with_companions  total_flights  distance  points_accumulated  \\\n",
       "292864                        0              0         0                 0.0   \n",
       "370071                        0              0         0                 0.0   \n",
       "314347                        0              0         0                 0.0   \n",
       "124128                        0              0         0                 0.0   \n",
       "43737                         0             11      3421               342.0   \n",
       "\n",
       "        points_redeemed  dollar_cost_points_redeemed  \n",
       "292864                0                            0  \n",
       "370071                0                            0  \n",
       "314347                0                            0  \n",
       "124128                0                            0  \n",
       "43737                 0                            0  "
      ]
     },
     "execution_count": 996,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ejecuto una muestra para ver si se han realizado los cambios\n",
    "\n",
    "df_corregido.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(1864)"
      ]
     },
     "execution_count": 997,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compruebo la cantidad de datos duplicados en la tabla\n",
    "\n",
    "df_corregido.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Esta función Identifica duplicados en filas y columnas de un DataFrame \n",
    "\n",
    "def identificar_duplicados(df_corregido): #Dataframe a analizar\n",
    "    \n",
    "    resultados = {}\n",
    "\n",
    "    # Identificar duplicados en filas\n",
    "    print(\"\\n--- Identificación de duplicados en filas ---\")\n",
    "    filas_duplicadas = df_corregido.duplicated(keep=False)  # Considera todas las filas duplicadas\n",
    "    num_filas_duplicadas = filas_duplicadas.sum()\n",
    "    print(f\"Número de filas duplicadas: {num_filas_duplicadas}\")\n",
    "    \n",
    "    if num_filas_duplicadas > 0:\n",
    "        print(\"Primeras 5 filas duplicadas:\")\n",
    "        print(df_corregido[filas_duplicadas].head())  # Corregido el uso del DataFrame\n",
    "\n",
    "    # Guardar resultados\n",
    "    resultados['num_filas_duplicadas'] = num_filas_duplicadas\n",
    "    resultados['filas_duplicadas'] = df_corregido[filas_duplicadas] if num_filas_duplicadas > 0 else pd.DataFrame()\n",
    "\n",
    "    return resultados #Devuelve un diccionario con información de duplicados en filas \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Identificación de duplicados en filas ---\n",
      "Número de filas duplicadas: 3712\n",
      "Primeras 5 filas duplicadas:\n",
      "     loyalty_number province        city postal_code gender education  \\\n",
      "120          193662    Yukon  Whitehorse     Y2K 6R0   Male  Bachelor   \n",
      "121          193662    Yukon  Whitehorse     Y2K 6R0   Male  Bachelor   \n",
      "159          193662    Yukon  Whitehorse     Y2K 6R0   Male  Bachelor   \n",
      "160          193662    Yukon  Whitehorse     Y2K 6R0   Male  Bachelor   \n",
      "164          193662    Yukon  Whitehorse     Y2K 6R0   Male  Bachelor   \n",
      "\n",
      "      salary marital_status loyalty_card      clv enrollment_type  \\\n",
      "120  51124.0        Married         Star  3844.57        Standard   \n",
      "121  51124.0        Married         Star  3844.57        Standard   \n",
      "159  51124.0        Married         Star  3844.57        Standard   \n",
      "160  51124.0        Married         Star  3844.57        Standard   \n",
      "164  51124.0        Married         Star  3844.57        Standard   \n",
      "\n",
      "     enrollment_year enrollment_month cancellation_year cancellation_month  \\\n",
      "120             2012              May            Active             Active   \n",
      "121             2012              May            Active             Active   \n",
      "159             2012              May            Active             Active   \n",
      "160             2012              May            Active             Active   \n",
      "164             2012              May            Active             Active   \n",
      "\n",
      "     year     month  flights_booked  flights_with_companions  total_flights  \\\n",
      "120  2017   January               0                        0              0   \n",
      "121  2017   January               0                        0              0   \n",
      "159  2018    August               0                        0              0   \n",
      "160  2018    August               0                        0              0   \n",
      "164  2018  November               0                        0              0   \n",
      "\n",
      "     distance  points_accumulated  points_redeemed  \\\n",
      "120         0                 0.0                0   \n",
      "121         0                 0.0                0   \n",
      "159         0                 0.0                0   \n",
      "160         0                 0.0                0   \n",
      "164         0                 0.0                0   \n",
      "\n",
      "     dollar_cost_points_redeemed  \n",
      "120                            0  \n",
      "121                            0  \n",
      "159                            0  \n",
      "160                            0  \n",
      "164                            0  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Llamamos a la función\n",
    "resultados_duplicados = identificar_duplicados(df_corregido)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [],
   "source": [
    "#He creado esta función para gestionar duplicados en filas y columnas del DataFrame.\n",
    "\n",
    "def gestionar_duplicados(df_corregido, conservar_filas=\"first\"): \n",
    "\n",
    "    # Conservar_filas: Estrategia para conservar filas duplicadas:\n",
    "    #- \"first\" (por defecto): Conserva el primer duplicado.\n",
    "    #- \"last\": Conserva el último duplicado.\n",
    "    #- \"none\": Elimina todos los duplicados.\n",
    "                                        \n",
    "    # Gestiono duplicados en filas\n",
    "    print(\"\\n--- Gestión de duplicados en filas ---\")\n",
    "    if df_corregido[['loyalty_number', 'year', 'month']].duplicated().sum() > 0:\n",
    "        if conservar_filas == \"none\":\n",
    "            print(\"Eliminando todas las filas duplicadas...\")\n",
    "            df_corregido = df_corregido.drop_duplicates(subset=['loyalty_number', 'year', 'month'], keep=False)\n",
    "        else:\n",
    "            print(f\"Eliminando duplicados conservando '{conservar_filas}'...\")\n",
    "            df_corregido = df_corregido.drop_duplicates(subset=['loyalty_number', 'year', 'month'], keep=conservar_filas)\n",
    "\n",
    "    return df_corregido #Devuelve un dataframe sin duplicados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Gestión de duplicados en filas ---\n",
      "Eliminando duplicados conservando 'first'...\n"
     ]
    }
   ],
   "source": [
    "# Aplicamos la función para eliminar duplicados\n",
    "df_limpio = gestionar_duplicados(df_corregido, conservar_filas=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loyalty_number</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>gender</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>loyalty_card</th>\n",
       "      <th>clv</th>\n",
       "      <th>enrollment_type</th>\n",
       "      <th>enrollment_year</th>\n",
       "      <th>enrollment_month</th>\n",
       "      <th>cancellation_year</th>\n",
       "      <th>cancellation_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>flights_booked</th>\n",
       "      <th>flights_with_companions</th>\n",
       "      <th>total_flights</th>\n",
       "      <th>distance</th>\n",
       "      <th>points_accumulated</th>\n",
       "      <th>points_redeemed</th>\n",
       "      <th>dollar_cost_points_redeemed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103747</th>\n",
       "      <td>450698</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>V6E 3Z3</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>69221.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>5131.36</td>\n",
       "      <td>2018 Promotion</td>\n",
       "      <td>2018</td>\n",
       "      <td>April</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2018</td>\n",
       "      <td>August</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349562</th>\n",
       "      <td>208668</td>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>Regina</td>\n",
       "      <td>S1J 3C5</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>99306.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Star</td>\n",
       "      <td>7013.44</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2018</td>\n",
       "      <td>July</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2017</td>\n",
       "      <td>March</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246973</th>\n",
       "      <td>711874</td>\n",
       "      <td>Manitoba</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>R6Y 4T5</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>48069.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>2335.88</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2013</td>\n",
       "      <td>September</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2018</td>\n",
       "      <td>March</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4060</td>\n",
       "      <td>438.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377065</th>\n",
       "      <td>619337</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>H2T 2J6</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>93688.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>9802.55</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2018</td>\n",
       "      <td>August</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2017</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256079</th>\n",
       "      <td>933865</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>P5S 6R4</td>\n",
       "      <td>Male</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>72403.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Star</td>\n",
       "      <td>2372.16</td>\n",
       "      <td>2018 Promotion</td>\n",
       "      <td>2018</td>\n",
       "      <td>April</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>2018</td>\n",
       "      <td>December</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loyalty_number          province       city postal_code  gender  \\\n",
       "103747          450698  British Columbia  Vancouver     V6E 3Z3    Male   \n",
       "349562          208668      Saskatchewan     Regina     S1J 3C5    Male   \n",
       "246973          711874          Manitoba   Winnipeg     R6Y 4T5    Male   \n",
       "377065          619337            Quebec   Montreal     H2T 2J6  Female   \n",
       "256079          933865           Ontario    Toronto     P5S 6R4    Male   \n",
       "\n",
       "                   education   salary marital_status loyalty_card      clv  \\\n",
       "103747              Bachelor  69221.0        Married         Star  5131.36   \n",
       "349562              Bachelor  99306.0       Divorced         Star  7013.44   \n",
       "246973              Bachelor  48069.0        Married         Star  2335.88   \n",
       "377065              Bachelor  93688.0        Married         Star  9802.55   \n",
       "256079  High School or Below  72403.0        Married         Star  2372.16   \n",
       "\n",
       "       enrollment_type  enrollment_year enrollment_month cancellation_year  \\\n",
       "103747  2018 Promotion             2018            April            Active   \n",
       "349562        Standard             2018             July            Active   \n",
       "246973        Standard             2013        September            Active   \n",
       "377065        Standard             2018           August            Active   \n",
       "256079  2018 Promotion             2018            April            Active   \n",
       "\n",
       "       cancellation_month  year     month  flights_booked  \\\n",
       "103747             Active  2018    August               0   \n",
       "349562             Active  2017     March               0   \n",
       "246973             Active  2018     March               5   \n",
       "377065             Active  2017  February               0   \n",
       "256079             Active  2018  December               0   \n",
       "\n",
       "        flights_with_companions  total_flights  distance  points_accumulated  \\\n",
       "103747                        0              0         0                0.00   \n",
       "349562                        0              0         0                0.00   \n",
       "246973                        5             10      4060              438.48   \n",
       "377065                        0              0         0                0.00   \n",
       "256079                        0              0         0                0.00   \n",
       "\n",
       "        points_redeemed  dollar_cost_points_redeemed  \n",
       "103747                0                            0  \n",
       "349562                0                            0  \n",
       "246973                0                            0  \n",
       "377065                0                            0  \n",
       "256079                0                            0  "
      ]
     },
     "execution_count": 1002,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_limpio.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fase 2: Visualización**\n",
    "\n",
    "Contesta a las siguientes gráficas usando la mejor gráfica que consideres:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Primero instalo las bibliotecas necesarias \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.max_columns', None) #para poder ver todas las columnas del dataframe en caso de que lo muestre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. ¿Cómo se distribuye la cantidad de vuelos reservados por mes durante el año?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creo un nuevo DataFrame para usar en esta gráfica\n",
    "df_vuelos_mes_matplt = df_limpio.groupby(\"month\")[\"flights_booked\"].sum().reset_index()\n",
    "\n",
    "# Orden de los meses\n",
    "orden_meses = ['January', 'February', 'March', 'April', 'May', 'June', 'July', \n",
    "               'August', 'September', 'October', 'November', 'December']\n",
    "\n",
    "# Me aseguro de que los meses están ordenados como un tipo categórico con esta función\n",
    "df_vuelos_mes_matplt['month'] = pd.Categorical(\n",
    "    df_vuelos_mes_matplt['month'], \n",
    "    categories=orden_meses, \n",
    "    ordered=True\n",
    ")\n",
    "\n",
    "# Vuelvo a ordenar los datos con base en los meses categorizados\n",
    "df_vuelos_mes_matplt = df_vuelos_mes_matplt.sort_values('month')\n",
    "\n",
    "# Creo la figura y un único eje\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "# Creamos la gráfica con Seaborn\n",
    "sns.barplot(\n",
    "    x='month', \n",
    "    y='flights_booked', \n",
    "    hue='month',  # Asignar \"month\" a hue\n",
    "    dodge=False,  # Evitar separación de barras\n",
    "    data=df_vuelos_mes_matplt, \n",
    "    ax=ax, \n",
    "    palette='magma', \n",
    "    legend=False  # Ocultar la leyenda\n",
    ")\n",
    "\n",
    "# Ajustamos los ticks para mejorar la presentación\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "# Título opcional\n",
    "ax.set_title('Number of flights booked per month', fontsize=14)\n",
    "\n",
    "# Etiquetas de los ejes\n",
    "ax.set_xlabel('Month', fontsize=12)\n",
    "ax.set_ylabel('Flights booked', fontsize=12)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Según los resultados, el mayor número de vuelos se concentra en los meses de verano, siendo Julio el mes con más vuelos con casi 20.000, seguido por Junio, Agosto y Diciembre. Como se puede ver, los meses en donde hay periodos vacacionales, hay un incremento considerable de vuelos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. ¿Existe una relación entre la distancia de los vuelos y los puntos acumulados por los cliente?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " #DISTANCE - points_accumulated\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=1, sharex=False, sharey=False, figsize=(10, 5))\n",
    "\n",
    "sns.regplot(\n",
    "    x='distance', \n",
    "    y='points_accumulated', \n",
    "    data=df_limpio, \n",
    "    marker='d',\n",
    "    line_kws={'color': 'black', 'linewidth': 1},\n",
    "    scatter_kws={'color': 'coral', 's': 10},  # Cambié el tamaño de los puntos a 10 para mayor visibilidad\n",
    "    ax=axes  # Aquí también puedes usar `ax=axes` ya que solo hay un subplot.\n",
    ")\n",
    "\n",
    "# Cambiamos los nombres de los ejes y ponemos título\n",
    "axes.set_xlabel('Distance')\n",
    "axes.set_ylabel('Total accumulated points')\n",
    "axes.set_title('Relation between flight distance and accumulated points')\n",
    "\n",
    "# Mostramos la gráfica\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Efectivamente, según la gráfica de dispersión se puede ver que a mayor distancia se acumula un mayor número de puntos. Aún así, vamos a comprobar con valores atípicos: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1005,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))  # Creamos dos subplots\n",
    "\n",
    "# BOXPLOT USANDO SEABORN\n",
    "sns.boxplot(\n",
    "    y=\"distance\", \n",
    "    data=df_limpio, \n",
    "    width=0.5, \n",
    "    color=\"turquoise\", \n",
    "    ax=axes[0]  # Colocamos la gráfica en el subplot 0\n",
    ")\n",
    "\n",
    "axes[0].set_title(\"Distance\")\n",
    "axes[0].set_ylabel(\"Distance\")\n",
    "axes[0].set_xlabel(\"\")  # No necesitamos etiqueta en el eje X\n",
    "\n",
    "# Ajustamos el rango del eje Y (opcional, dependiendo de tus datos)\n",
    "axes[0].set_ylim([df_limpio[\"distance\"].min() - 1, df_limpio[\"distance\"].max() + 1])\n",
    "\n",
    "# BOXPLOT USANDO MATPLOTLIB\n",
    "axes[1].boxplot(\n",
    "    df_limpio[\"points_accumulated\"],  # Usamos directamente la columna como entrada\n",
    "    boxprops={\"color\": \"turquoise\"},  # Cambiamos el color de la línea\n",
    "    capprops={\"color\": \"purple\"}  # Cambiamos el color de los bigotes\n",
    ")\n",
    "\n",
    "axes[1].set_title(\"Points accumulated\")\n",
    "axes[1].set_ylabel(\"Points accumulated\")\n",
    "axes[1].set_xlabel(\"\")  # No necesitamos etiqueta en el eje X\n",
    "\n",
    "# Ajustamos el rango del eje Y (opcional, dependiendo de tus datos)\n",
    "axes[1].set_ylim([df_limpio[\"points_accumulated\"].min() - 1, df_limpio[\"points_accumulated\"].max() + 1])\n",
    "\n",
    "# Mostramos la gráfica\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Según los resultados en los boxplots, los valores atípicos en ambos pueden representar usuarios o vuelos fuera de lo común (distancias muy largas o puntos extremadamente altos)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. ¿Cuál es la distribución de los clientes por provincia o estado?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupamos por 'province' y contamos los clientes únicos por 'loyalty_number' para evitar que cuente al mismo cliente.\n",
    "df_clientes_por_estado = df_limpio.groupby(\"province\")[\"loyalty_number\"].nunique().reset_index(name=\"numero_clientes\")\n",
    "\n",
    "# Crear gráfico de barras horizontales\n",
    "plt.barh(df_clientes_por_estado[\"province\"], df_clientes_por_estado[\"numero_clientes\"], color=\"teal\")\n",
    "\n",
    "# Etiquetas y título\n",
    "plt.xlabel(\"Number of clients\")\n",
    "plt.ylabel(\"Province\")\n",
    "plt.title(\"Client distribution per province\")\n",
    "\n",
    "# Mostrar gráfico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Según el gráfico de barras, la provincia que tiene un mayor número de clientes es Ontario, con más de 120.000. Las provincias con mayor número de clientes son también las más grandes en superficie y población."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. ¿Cómo se compara el salario promedio entre los diferentes niveles educativos de los clientes?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1006,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creo un nuevo Dataframe para agrupar por 'education' y calcular el salario medio\n",
    "df_salario_estudios = df_limpio.groupby(\"education\")[\"salary\"].mean().reset_index(name=\"salario_medio\")\n",
    "\n",
    "# Ordeno por salario medio ascendente\n",
    "df_salario_estudios = df_salario_estudios.sort_values(by=\"salario_medio\", ascending=True)\n",
    "\n",
    "# Creo el gráfico de barras con 'hue' para los colores\n",
    "fig, axes = plt.subplots(1, 1, figsize=(10, 6))\n",
    "\n",
    "sns.barplot(x=\"education\", \n",
    "            y=\"salary\", \n",
    "            data=df_limpio, \n",
    "            ax=axes, \n",
    "            order=df_salario_estudios[\"education\"],\n",
    "            hue=\"education\", \n",
    "            palette=\"mako\", \n",
    "            legend=False)\n",
    "\n",
    "# Añado los valores de salario medio en el gráfico\n",
    "for i, level in enumerate(df_salario_estudios[\"education\"]):\n",
    "    # Colocar el valor de salario medio encima de la barra correspondiente\n",
    "    axes.text(i, df_salario_estudios.loc[df_salario_estudios[\"education\"] == level, \"salario_medio\"].values[0], \n",
    "              f'{df_salario_estudios.loc[df_salario_estudios[\"education\"] == level, \"salario_medio\"].values[0]:.2f}', \n",
    "              horizontalalignment='center', size=12, color='black')\n",
    "\n",
    "plt.title(\"Comparison of average salary by educational level\")\n",
    "plt.ylabel(\"Average Salary\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se puede observar a mayor nivel educativo, el salario promedio es mayor. También se puede ver que apenas hay diferencia entre el salario promedio de clientes con estudios de Bachillerato y Título universitario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. ¿Cuál es la proporción de clientes con diferentes tipos de tarjetas de fidelidad?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cnJzW/fXHH39UJ74dv7sjywlU26rizshnSzW37PtyDJSYLSdaHeOWAoesy7Zkmcgy7Ngk0dm+2t/8oGLoTUKVDUm+0FosZ8YyVqurOnxLW5KFnGl1xnKG1B1pG5KD8Oeff65KjPK90n6ybNkytWP1Vlelo8O1penB1jF1tZ56Qkqer7/+utqYZUeVdSbLWh6X+7IdyrbT34Tan23JFiz7h7SDSULsjBwgu1vPtt7HekuS9SmnnKISqrSzShuWJHlL219vye+V9lTpp9AZSwKWNmEpvUv7p5Tm5CZtspIgJPE4EukXIe3vsgzvuece1V4oJ6Btk4Y8JolJSo5S2yO/X9aztO+2rR3oLakpkX4X8vlz585Vf6VkbTmB6Q8p7cq6lNL0nXfeqU7cpdQrbdXyWzrWrMiJWX96tFsjP/SqU5Js+HK2vHbtWsyYMQP9ZTlrkGRtjRVg0V01oJTy5OxUbnK2Ko3N0nmgJwtMzn7blgCkw4CsVOl01B05q5IGcuk01FFqaqraCOSsqON3SccXC6nmkU4V0mFFyNAlIZ/ZthpZqoFleFNPl2dPq0zbfp90ZmlLHrM8bw2WRCnVQtLBQaqFhOyw0ulKEqrsWNLJpjv9qQ7ujvxWS8mvrc7Wb3/IdiMlITmw9HX/0GMf6+1rJYlJ1aGsa0kKEydOVB1VOpL9TZJ628+Tzi/Csg/K75V9pSe/VWpspDpdbrIfyzFBOgred999qjTc1bqXUpjU1LUtpcp+bHneGuRztm/fruJqmyQ6+x4pvUnTiyw7KdlLadXS+cpCTlTkOCHJoe3yk05q/Vl/kpSlOUE6GD3++ONqsg9pdjrcCbVlu5QCWlfLWiY5kfUrJziWDnyiN9X0spxkGcpJQ9sTjK721f7kB9GrdC5nfXIgk15TUqzvSIKWXp89JQdEWbDSI0t2go4sY/N6S2LsOKuHHJQ6VhHIWZocnOWMuCdeeumldvelt5k43MKWjUumlPvyyy/bVQ/LMpSeplLyslSpW8iJi1TbWEgikTYPy3fJAUMOCNLTuG3JQXruye/srBdbT5dVZ+SMV5bXK6+80m55yZm99ILr6ff1hJy0SBW19CqUZSCzcFkSrWxjcnCQNkyp4umOpQ3H2jO8yEmN9PKWHodtt9W27VjWINuN9HSVdtTOaoZ6sn/Ych/r7rWiq9fLNi29R+WALG2DXZVOpW1LSg8WUvUvPeyl5C2lIkspQ074peTZkXy/7ENC2oTbkqRlKe13dzyQdS/HkhdffLHd47KtSuLpS01XV98j7cLSHmkhscsxR2oUOrYvSxXr7t27VelTthsptbZlSXBtjxXSXijLqr/rT75bmgOuvvpqtY31pHZBjodyQiIlZBkv3ZYlxs5ilv97k2Ms60OOk211POGwRn7odQlVdkxJANLuIXX5ctYgdeBSKpKu6ZZu3T0lG7G048iPljNSaReSA6kU6aVbuiQZ6f7dW3IQkbPIZ555Ri0QOUDL2YnUr8s0YOPHj1cbpbxGzoplOEdPSMlPuszLVHGyIVq6YsvnHc4jjzyizqwkecrZjyQDORuWlSV19x3JMpVOI3KAkLMpmSJQ3ivfbym9SFdzGTYj8cjjltfJsJaeVpnJspJkLfHJmaJsRB1LoJYOEXLAk3UkO7N0lrAMm5HSgbWnVZPk+dFHH2Hs2LGtbXhytig7t5y1WrrVH+63WTokSNVUZweavpATS8uUgTJ8wTJsxlKqsKbHHntM7QvS8UTO/KWzm1SBSUcS2X7lf3vZxzoO/Wp73JAOH3IyJgdRWV7yWkttj2xbsl4kSck6atsRp2N1rQxVkX1Wqhllaj/ZBqWq1kISyldffaVq0+RYJHFKZxYp7ciJmJzQSvKWQoEsO9nW5bggbW2SrCQ5dze8RUqzUnMkw8zks2Tfl+pBOVmWji5dDR/qLemUKMcH+Q2bNm1S+5jEbyl9dmzDlRNaaReVY7Csa9mP25LlIaVT6dQlr5VjmawP2Z46O9HqbD+S3yzrSdaXLAdLopUaBckDn376qVp2PRliItudnITIepDjlezPsp9L3wjpbyKlUqnileUpQ8Bke5X3yMllb9ryZX3K9iTHRUmY0qHpp59+UrUdbUmNQ3/zg9KXrsHSJV0GXku3bBm6Id2fZ82aZX7hhRfUINreTuywZcsW8xlnnKG65ssAZnnfOeecY/7pp58O6dJfUlJy2OneUlNT1VAUGcBuGXQuXcZvv/12NSjcMrGC/G8ZntEdy3fv3r1bdY+X94eFhakJB7qa2KEzMrGDDF+Qbu8ysFomBlizZk23EzvI98jrZQKFsrKyQz5ThsmMGDFCTfsoXeCvvfbaLid26IxMkCAD5uU39WRih48//lgNU5D1JMNWupvYoavl2BMyYYW8Vn5PWzLQWh5vu210NWxGhgrccMMNaoC2dLPvbGKHvk5Htn37drWsejOxQ1+GzVgG+MvrZXC7rOeYmBjzMccco6b9tLCsr08//bTTeG2xj3VHhiRZJjrpbAiNTKghj3c1GUDbiR1kMgH5DbLdd/Z7a2pqzHfffbc5OTlZHZ9kQP/MmTPNTz31VOtQtM8++0x9l2WyjMGDB5uvvvpqc0FBQbe/w/L5MgGJTJ4h60OGJbWd2MEaw2Ys610m9JD4JcaxY8d2O32jZRjfhx9+eMhzEtujjz6qlqMsO9mHZcibrLe2k4h0tQ/I9i3buQxh62wIjWVY36OPPmruja+++kqtG9mOZBjTtGnT1IQbFnLMlX1ejoGyHCTvyDDFjttQV8ccIcdoGcoj2768prOJHfqTH9rqU0J1NV0daLRgOXjJ2D0iV2EZL/juu+92m1Cp+/HRkgzazmplKzJu1q3D2FBXxEk+iUh30qtbqtlkZh7qPWmHlCYoaXPvbo5uLUjBTPpuzJs375AhV66mV22oRETWJO230plG2qBlyjebT2bu4KQnqrT1SfuqdLTqbtpEa5O2aWmvlrZ4aaP+8ssv4eqYUIlINzLbmHQskl6t0sGOekdORmSojHRCkp6sXY011oL0EJfORNLhTMa/nvpnh0lX5ib1vnoHQURE5OjYhkpERGQFTKhERERWwIRKRERkBUyoREREVsCESkREZAVMqERERFbAhEpERGQFTKhERERWwIRKRERkBUyoREREVsCESkREZAVMqERERFbAhEpERGQFTKhERERWwIRKRERkBUyoREREVsCESkREZAVMqERERFbAhEpERGQFTKhERERWwIRKRERkBUyoREREVsCESkREZAVMqERERFbAhEpERGQFTKhERERWwIRKRERkBUyoREREVsCESkREZAVMqERERFbAhEpERGQFTKhERERWwIRKRERkBUyoREREVsCESkREZAVMqERERFbAhEpERGQFTKhERERWwIRKRERkBZ7W+BAi6jmTyYTqqlpUVhhQVVmLysoa9X9lpQE11XVoaTHCaDSp16m/RhMGnDQcNSEe8HBzg7ub+59/3dRfPw9PhHn5ItTbB2Hevgj18kGot696nIhsh3sckZUVF1Uge38hsrMKkZtTjPKy6taEKQm0proWJpO5V585dU4UUhvre/UeX3cPlVgPJlgfhHr5Ikz99UGUjz8SA0IwwNe/l7+OiLrChErUB2azGYX5ZdifVahulgQqt9raBtiDBpMRhQ216taVAA8vJAYEY0hAiEqwQwJDkOAfDF+Wbol6jXsNUQ8U5Jdh2+YMbNuyFxnpucjNLkZDQxMcXa2xGbuqy9StbceKWL/Agwm2zY2lWaLuMaESdSIvt0Ql0K2bM7FtSwaKCivgKkwADtQb1G116YHWx0O8fDAhdAAmhcktWrXXEtFfmFCJAORkF2HrJkmgGdi+dS9Kiiv1DsnuVDU34reSXHUTUoJVyTU0GqNDIuDl7qF3iES6YkIllyS9ZyWB/vrTZqxauUN1HKLe2V9bpW7L8jLg4+6BMSGRquQqSTbeP1jv8IhsjgmVXDKJrvx1m+p5S9bRaDJiU0WRuokoHz9MDB2AmZFxmBgWrYb3EDk7JlRyakyi+ihprMcPRdnqFu7tiyMHxGP+gAQMDmDJlZyXm1n6/xM5EZkQYctG50qiUxedjFTv3o1DtUfDAsMwP3ow5kXFI9DLW+9wiKyKJVRyGjL70LdfrcUXn/2OwoK/hoGQ/cgwVKjbG/t2YFpErEquk8JiWCVMToEJlRze3owDWPrJb/jxuw1obGzWOxzqgWazSQ3JkVuYlw+OGjAYx0QnIIFVwuTAWOVLDknmu/39121Y9slvapiLs3OWKt/DGREUjrPjh2NaeAzcWGolB8MSqg7Wrl2L2bNn44QTTsC3336rdzgOpbKiBl9/vhpfLlvFsaJOKLWmHA/vXqvGuJ4TPxyzI+PURQCIHAFLqDq44oorEBgYiDfffBNpaWkYOHBgnz+rqakJ3t7eNnufXmSu3A8W/4BfftyMpqYWuBpXKaF2FOcXiLMGpagqYU93Xm2S7Bu3UBszGAz4+OOPce211+Lkk0/G4sWLW5+T/0NDQ9u9/osvvmhX9fXggw9iwoQJeOONNzBkyBD4+h6c/i0nJwennXaaStTBwcE455xzUFRUdNj3fffdd6q0LN8bERGBU045BXv32k8VamFBORb96z38/fxH8f3yP1wymboymf7w+YzNuHLj9/g6fy+aTEa9QyLqEhOqjX3yyScYMWIEhg8fjosuughvvfWWunJJb2RmZmLp0qVYtmwZtm7dqoaJSDItLy/Hb7/9hhUrVmDfvn0499xzu32fqK2txS233IKNGzfip59+gru7O04//XT1mXoqK63Gc09+govOegjffbtejScl1x7X+urebbjsj+/wWW466lrY+YzsD9tQbUyqeSWRCmlDraqqUknwyCOP7FV17bvvvouoqCh1XxLojh07kJWVhfj4ePWYPD969Ghs2LABU6dO7fR94swzz2z32ZLg5fndu3djzJgxsDW5wPaH765QnY2c4WouZF2VzY1YvH8nPstLw4KBSThtYDLHs5LdYAnVhqS99I8//sD555+v7nt6eqpSpCTZ3khISGiXFPfs2aMSqSWZilGjRqlqXHmuq/eJjIwMFc/QoUNVVXFiYmJrFbIt1dc34t03v8N5Cx9QCZXJlLpjaGnGkpxUXLXxB3xXkAUTu4KQHWAJ1YYkcba0tLTrhCTVvT4+PnjxxRdVdWvH6t/m5kOrtgICAvr0/Z29b8GCBSrRvv766youqeqVkqmUZm2hqakZXy1bhfcX/4CK8hqbfCc5j+qWJryYuQXfF+7HNcnjMTwoXO+QyIUxodqIJFKpbn366adx3HHHtXtu4cKFWLJkiUpsNTU1ql3TkvwsbZ3dGTlyJHJzc9XNUkqVKtvKykpVUu1KWVmZKjVLMp0zZ456bNWqVbCVDev34NnHP8aBvFKbfSc5J5l96batv2J+dAIuTRyDEG8fvUMiF8SEaiPffPMNKioqcPnllyMkJKTdc9KOKaXX77//Hv7+/rjnnntw4403Yv369e16AXdl/vz5GDt2LC688EI899xzKnlfd911mDdvHqZMmdLl+8LCwlTP3tdeew2xsbGqmveuu+6CLcaSvvjsMqz4boPm30WuQ+p2VhRlY21ZPi5MGIWTYodySkOyKbah2ogkTEl8HZOpJaFKL9u8vDy8//77WL58uUqQUmqV4S6HI8NqvvzyS5Ug586dq75H2kRleE53pIr5o48+wqZNm1Q1780334wnn3wSWlr+9Vr87ZxHmExJ0/ZV6RH8zy0/Y1cVaz/IdjixA9lEXk4xnlr0EbZsStc7FIfkqhM7WMNRA+Jx2ZCxCPM+OPaaSCus8iXN59z98J0VeG/x92jixPWkg1+Kc7G+rEBVA586MIlzBJNmmFBJMzu27cVTjy7B/qxCvUMhF1dnbMHr+7ZjY3khbhk+haVV0gQTKlldY0MT/vvCF+q6pGxRIHuypbIY12/+Cf9MmYSp4bF6h0NOhp2SyKqy9hbg6kufxOefrmQyJbtU1dyIf+1aqzouNXNuYLIillDJar5c+jteem4ZL/JNDkEm299ZVYo7RkxDvH+Q3uGQE2AJlfqtpqYO99/1Bp55/GMmU3IoWbVVaniNTF9I1F8soVK/pO3Jwf13vYnCgjK9QyHqk0aTUU1fKO2rNwybiEBPTrZPfcOESn321eer8MLTn/EapeQUVpceQHpNOW4bPhWjQyL1DoccEKt8qU+9eOWi308v+ojJlJzuuqt3b/8dn+Sk6h0KOSCWUKlXCg6U4t7bX8fezAN6h0KkCRPMeDd7N3Lra3DjsEnwcvfQOyRyECyhUq/aS6+7/GkmU3KZGZbu3bFKDbMh6gkmVOqR9Wt346Zrn0c5r1lKLmR3dRlu3forcuuq9Q6FHAATKh3Wt1+txd23vIL6Op6pk+spbKjFbVt/w5aKIr1DITvHhErdWvz6cjzxyAcwGk16h0Kkm1pjM/61aw1+Kc7ROxSyY+yURF1eJUYmavj2yzV6h0JkF1rMZjyTthFljQ04Kz5F73DIDjGh0iHq6xvx4D1vYd3qXXqHQmRXZHbqxft3oqKpAVcMHctLwVE7TKjUTkV5De68+b+qRy8Rde7L/EyUNzWoS8F5ubPljA5iQqVWBflluOUfLyD/QKneoRDZvd9L81BrbMJ9o2ZwrCopPLUipaS4Ejdf9x8mU6Je2FxRjMf2/AGjmZ32iAmVAJSXVauSqZRQiah31pcX4Om0jTDx+r8ujwnVxVVVGnDrDS8iJ5tj7Ij6amVJHv6TsRlmJlWXxoTqwgyGetx+40vYl5mvdyhEDu/Homy8sneb3mGQjphQXVRdXSPuuOllpKXm6h0KkdP4tmAf3sraoXcYpBMmVBe9/Nrdt76CXTuy9A6FyOksy8vAB9m79Q6DdMCE6mKam1vwf3e8jq2bMvQOhchpLclJxdK8dL3DIBtjQnWx6QQfvPst/LFuj96hEDm9t7N24pv8vXqHQTbEhOpCZG7eVSu36x0Gkct4de821VmJXAMTqotY9slvnOieyMZkEM2LGZuxs4oTprgCJlQXsHlDGl58dqneYRC57FVqHtuzHqWNdXqHQhpjQnVyMpXgA3e/yeuZEumosrkRj+5Zj2aTUe9QSENMqE6srrYB99z6KqqreWZMpLf0mgq8lLlV7zBIQ0yoTkqmQHvkgXeQta9A71CI6E/SQelb9vx1WkyoTuqtV7/F6pWcsYXI3ry+bzt2sZOSU2JCdUI/r9iMd9/6Tu8wiKiLTkqLVCeler1DIStjQnUy6Wm5ePzh9/UOg4gO00lp0Z517KTkZJhQnUitoR733fEGGhqa9A6FiA4jraYC/2UnJafChOpE/vP0Zygs4EXCiRzFD0XZWF6wT+8wyEqYUJ3Eyl+24btv1+sdBhH10mt7tyOrtkrvMMgKmFCdQHlZNZ5atETvMIioD1rMJjyXvglGMydfcXRMqE7giUc+QFWlQe8wiKiP9hoq8VkuL/fm6JhQHdzXX6zG2tW79A6DiKxwDdXs2mq9w6B+YEJ18Hl6X3pumd5hEJFVq37lGjXkiJhQHZTJZMKjD76L+rpGvUMhIivJMFTg8zxW/ToqJlQHteS9H7FjG7vbEzmbD7L3ILeOVb+OiAnVAWWk5+Ht15brHQYRaaBZVf1uholVvw6HCdUBryLz1KNL0NzconcoRKSRtJpyfHEgU+8wqJeYUB3M/75Zh9Td2XqHQUQa+yB7Nw7U1egdBvUCE6qDXTD89Ze/1jsMIrKBRpMRz2ew6teReOodAPWcXJJNZkVyJjn5m1BasQ/1DRVwd/dEcGAMhgyaAX+/sNbXmEwt2Ju7GiVlGTCZjQgPGYzkhHnw9vLv8nPT9v2EorLUdo+FBQ/G2OEL/vxMI9L3/4yyiiz1OfJ5YSHxra/NLdiMxiYDkhPmavK7iXpid3UZVhTtx/ExQ/QOhXqACdVB5OWW4LOPfoWzqarJx8DoMQgKGKDah/fnrcOO9K8wZcwF8PDwUq/Zm7MK5VXZGJl8Ajw9vJGZvRK7M/+HCSPP7Pazw0IGY/iQo1vvu7l5tP5fULILhtoSTBh1Jsorc5C6bwWOmPB3uLm5ob6xGoUluzFx9Dka/nKinvf6nRcVD18PHq7tHat8HcRLzy11yo5IUmKMiRyJAL8IBPpHImXIMapkWFNXop5vaWlEYekeDI2fhbDgQSrxDh9yDKoNherWHXc3D3h7BbTevDx9W5+rq69AROgQ9b0Do8eiuaUezS0N6rnM/b9hSPxMlbyJ9Fbe1ICv2EHJITChOoAN6/Zgze874QqMxoMTVXh5+Ki/kljNZhPCgv+qjpXqYB/vwMMm1MqaA1i75S1s2PEBMvb/2powRYB/BKoMBTCaWlBRlaOqfSXhFpWlwd3dA5FhQzX7jUS9tTQvA9XNnMTF3rEOwc61tBjx4rNL4Qqkyleqd4MDY1XCE83NdXBzc4en58EEayEJsKm5rtvqXkmKvj7BqG+sUlXJO9O/VtXE8nlSKq6tL8PGHR/Cy9MPI5OOR4uxEdkH/sC44QuRlbcOJeUZ8PMJQcqQo1UCJ9JLrbEZH+ek4cqkcXqHQt1gQrVzXyz9Hfuzui+JOYvM7N9QW1+OCSPP6PdnDYgY1vq/JGep2t2w431VapXSrpRChyXMAxL+ek9a1k+IGzAOtXWlKKvMwuTR5yG3cDP25vyOUckn9jsmov74tmAfTo1LQrRvgN6hUBdY5WvHKisNePu1b+EKpKNRWWU2xo1Y2K406OXlr6p8pS21LSmddtfLtyM/3xBVpVvf0PmFnCur81BXX67aUyXphockqE5RUeHJqKw+0I9fRmS9yfPfz96tdxjUDSZUO/b+W9/DUFMPZ6/mlWQqQ2fGjzgNfj7B7Z4P8o9SVbQV1XntOhRJxyUZYtNT8nppQ/XxOvTsXoblSAzDEo5U3yUxSRI/GJ8JZnAcINmHX4tzsc9QqXcY1AUmVDtVWVGjrnXq7CSRSUegEUOPVSXCpuZadZPOQkLaTqW9c1/uKlWKrKktRnrWzwgOiGmXUKXjkSRlYTQ2YV/uatVpqaGxGhXVudiVsVy1h0rbakfZ+RtViTQwIErdDwmKQWnFXhjqSpFftAMhgbE2Wx5E3ZFTu8X7XaODoiNiG6qd+nTJL2hoaIKzKyg5eHDYnvZFu8elI5AkUpE0eDaQ64bdmd+piR1kgoZhie0nXKhvqFSdihQ3d9TWlaGoNE09JkNmZNKGxLjpqu20LXldSXkmJo8+t/WxyDCp5s3HttTP4e8bqpI9kb3YXFGMrRXFmBA2QO9QqAM3s9RvkV2pNdTjnFPvh8Hg3NW91HNTF52MVG9uD3RQcmAonp1wlJqIhOwHq3zt0OefrWQyJaIuZRoq8XspO8vZGyZUO9PY0OSUUwwSkXUtzUvXOwTqgAnVznzz5RpUlPOSTUTUvb2GSuyuKtM7DGqDCdXOZkX66P2f9A6DiBzE1/mc49eeMKHake+X/4Hiogq9wyAiB7GmLB+ljexvYS+YUO2EyWTCkndX6B0GETkQo9mM5QUHx1+T/phQ7cRvP29Fbk6x3mEQkYP5riALTSaj3mEQE6r9WPrxb3qHQEQOqLqlSU1JSPpjQrUDeTnF2LFtr95hEJGD+iafxw97wIRqB5Z/s07vEIjIge2rrcLOqlK9w3B5TKg6MxpN+GH5H3qHQUQO7qsDHEKjNyZUnW1YtwclxbwcExH1z7qyAhQ31OkdhktjQtXZ8q9Z3UtE/SdX7uUQGn0xoeqoqtKANb/v0DsMInISvxTngBcQ0w8Tqo5WfLcBzc0HL6RNRNRfZU0NSK0p1zsMl8WEqqP/sbqXiKxsFS/rphsmVJ2kp+YiM4MbPhFZ1xomVN0woepk+ddr9Q6BiJxQSWM90ljtqwsmVJ0mwv/lx816h0FETmo1S6m6YELVwc7tWaisMOgdBhE5qTWl+XqH4JKYUHWweiWHyhCRdgobapFp4LWVbY0JVQerV27XOwQicnKs9rU9JlQby8ku4nVPiUhzq1nta3NMqDa2bl2q3iEQkQvIrzcgq7ZK7zBciqfeAbiaXwxNCDv7aMR6uKExuxDZm9LR0tSsd1hE5IRWlxzAkIAQvcNwGUyoNlTX2IxtOSVoMZqgKmOCguB/3HQkhfjDp6IaBVsyUFFQpneYROQk1pUX4KLEUXqH4TKYUG1o475ClUzbqmtqwc6S6oN3xg5DwtyJiDIbYcg8gJytGTCbONE1EfVNdm0VDC1NCPT01jsUl8CEakPrMgsO+5rsCgOy5Z/IcAQvmIMhgT5wL6lA3oY0GCpqbBEmETkJOR3fU12OqeExeofiEphQbWh9DxJqW9X1TdhW36RWk8f0MRgSFojQpiZUpmbjwK79msVJRM5jT3UZE6qNMKHaSJmhHrllfS9hGk1mZFreHxeDiJREDPbzgim/FDkbU9FgqLdesETkNHZXsV+GrTCh2sjO3FKrfl5ZbYO6wdsHXnMmIiksAIF19SjZvg/F+zj+jIgOyjBUoMVkgqc7R0lqjQnVRnbkWDehttVsNCG19M/Sa/JgxE4agYFebmjMKULOxjQ0N3JYDpGrajQZsddQieHB4XqH4vSYUG1kZ26Jzb6roLoOqrU2IBB+86chKTQAPlU1B4flHNAusRORfdpVXcaEagNMqDZgNJmw+4A+7Rj1zca/huWMTkbC7PGINJtQty8fOVsyYOowjIeInLNjEjBM7zCcHhOqDWQWVqKh2Qh7kF1Re3BYTngYgk6ehaQgP3iUVuLAxlTUlP2ZeInICRMqaY0J1QZ2WLlDkrXUNDRje4O0r7rDfcpoDAkPRFhTE6rScpC3M0vv8IjISiqbG3Gg3oA4v0C9Q3FqTKg2sMOG7ad9ZTKbsdcyLGdgNCKGDcZgP2+YCkqRs4HDcoicoZTKhKotJlQHHDJjC2W1jeoGr4PDcoaGBSKorg5lO/ejMDNP7/CIqJd2V5dhfnSC3mE4NQ5M0pihoalfEzrYAxmWk1ZajY11LcgaOgghZx2FUefPR/KssfDy8dI7PCLqgbTqcqt8TklJCa699loMHjwYPj4+iImJwfHHH4/Vq1er593c3PDFF1/AFbGEqrEcB0+mnSmsrkeh/BMQAN8/h+X4VtWgcGsmyvPsv3qbyFWvjypNO+5ubv36nDPPPBNNTU145513MHToUBQVFeGnn35CWZl1Oz7Jd3h7O9ak/kyoGsuxDFlxUtJ7eZflN45KQvyscRgA88FhOZvTOSyHyE40m00obqhDjF9Anz+jsrISv//+O3799VfMmzdPPZaQkIBp06ap/xMTE9Xf008/vfW5/fv3Y+/evbjllluwbt061NbWYuTIkVi0aBHmz5/f+tny3ssvvxwZGRmqhHvGGWdg8eLFcCSs8tVYdqlzJ9SOcitqsamiDnvCQuF+8iwMu/A4jDx+GoIieZFjIr3l1vevxiwwMFDdJOE1NjYe8vyGDRvU37fffhsFBQWt9w0GA0466SRVkt2yZQtOOOEELFiwADk5Oe3e/9RTT2H8+PHqNffddx8cjZvZbOYFNzV078e/Y8UONfLTpUk1U2J4IMKbm1GdloNcGZbDTa/Hpi46Gane7GlN/XPF0LFYGNe/CR6WLl2KK6+8EvX19Zg0aZIqqZ533nkYN25caxvq559/joULF3b7OWPGjME111yD66+/vrWEOnHiRPVeR8USqsZcrYTaFWm72VdWg43VDUiPHQDf0+Zi+IXHYvgxk+AX7K93eEQuIa+u/306pA01Pz8fX331lSppSvWvJNbF3VTPSgn1tttuU1W9oaGhqpS7Z8+eQ0qoU6ZMgSNjG6qGpPCfw4TaqYq6RnWDhzc8Z05AUngAguobULZrPwrTc/UOj8gp5dUbrPI5vr6+OPbYY9VNqmavuOIKPPDAA7j00ks7fb0k0xUrVqgq3eTkZPj5+eGss85SHY/aCgjoe/uuPWBC1VBxVZ3dTDloz+TSUmmWq+UkxiF6bDLivD3QnFeM7A2paG5ov9MRUd8U1tdq8rmjRo1qHSrj5eUFo7H9cU+G1EiytXRWkhKrdFZyNkyoGsrm3Lh9UlRTjyL5x88fPkdPVdd69as2oGhrBsocYNYpIntV3lTfr2ujytCYs88+G5dddplqMw0KCsLGjRvxxBNP4LTTTmttC5XOR7NmzVLjVMPCwjBs2DAsW7ZMdUSSNlYp1ZpMzjcCgAlVQ6zu7b/GFiN2W4bljEzCoBnjEO1mRn1WPrJlWE6L8+2URFqRvaW0sb7PQ2ek7XP69Ol49tln1VCY5uZmxMfHq05K99xzj3rN008/rYbIvP7664iLi1Ml0WeeeUYl4ZkzZyIyMhJ33nknqqud7/jIXr4aeumHLXhn5S69w3BaAT5eGBrsB6/yKhzYlIbq4ko4K/by7Vrm+8uQ9toHSDzrZIy+8TL12Nob70f51vb73uBTj8PY267u9rNq9uch9ZX3UL5tN8xGIwITB2Hyw7fDLzpKPb/7xbeR979f4eHrgxFXX4S44+a2vrfglzXI+/5XTH3sYGKxV4+OnYNxoQd/D1kXS6gaKjc06B2CU6ttbMaOErlajhvcJo5AYlggIowtqE7PRe72fRyW4wIq92Qi56sVCEo6dI7a+AXzkXLZea33JQl2p/ZAIdZefy/iTz4GKZedC88AfxiycuH+52w9Ras3IP/HVZj29H2ozSvA9sdeRtS0CfAODUazoRZpr3+I6c8+AHtX3FindwhOiwlVQ5V1TKi2Irkzq9wAddG56CiEnTYICf7eQFE5cjemoq5Km84YpJ+Wunpsffg5jLvjGmS8u/SQ5z18fOAbEdbjz5OEOOCISRh57d9aHwuIi2n935B9AOETRiN0RLK67X7hbdQVFKuEmvrf9zB44fGtJVl7VtTAfUErTKgaqjAcOpMI2XpYjhc8ZoxDUngggusbUb47CwVpHJbjDHY++wYGzJiMyCnjO02o+St+x4EVK+ETHoromVMw7JKzuyylmk0mFK/dhKQLFmL9rQ+hOiML/rHRSLrodMTMma5eE5ycgJyvV6C5xoC6/CKYGpsQMCgG5dv3oCpjH8bcciUcQUkjmw60woSqoYpallDtgdFkRrplWE5CHAaMOTgsx3jg4LCcpnoOy3E0+T+tQnX6Psx67fFOn4+bPxt+MVHwiQhHzd5spL76Hgw5+Zjy7zs6fX1jRRWM9Q3Y+8HnSLnifIy45mKUrN+CTf/3JI54/l+ImDAaUdMmIu7YuVh11Z3w8PbG+HtuUAl659OvYfw91yP7i++xf9n/4B0ShLG3X4OgIYNhj+papJmEtMCEqiEmVPtUXFOPYvnH1x/eR01FclgA/Gtq1dVyynLUgB2yY/VFpdj1n7cw/Zn74eHT+dVIpAOSRXBSAnwiwrD+5gdVO2nbatxWf7a3R8+eiqHnLFD/hwwbgoqdacj58nuVUIW0rcrNIv3tTxA5ZRzcPD2Q+d5SzFn8DIrXbMLWf7+AOW88CXtUb2zROwSnxYSqkcZmI+qauOHau6a2w3JGDEHcEWMQ425GQ1ahGpZjbOY6tDdV6XvRVFGFVVfc3vqY2WhSPXOzP/8fTvzxI7h5eLR7T+iog/PX1h0o6DShSqlS3hOYEN/u8cCEQajYsafTOAzZeTjww2+Y8+ZTyF3+M8LHj4RPaAhij5qJ7Y+9pNp4Pf39YG8aTNymtcKEqhGWTh3TgcpaHJB/QoIRcMIRSA72h1dFNfI3paGqqELv8AhA5ORxmLv42XaPbXvsRQQOjkPSBacfkkxFdebBWXmkpNoZdy8vhIxIRm2uWvutavPyVdVxRzLacMdTr2LU9ZeqpCltsKaWg7MDmS1/7fTShfUtTKhaYULVCBOq46ttbMGOP0uvbhOG/zksx4iaDBmWsxdmE4fl6EESWNDQ9u2THr6+8AoOUo9LtW7+j7+rHrvymLShyvjR8PGjEJx08Hqd4teLbsCIqy5CzNyDnY6Szj8Nmx98Rr0uYuIY1YZavGYjjnj+oUNiyP3mR9W7N3rWVHU/fMwIZLz9CSp2paNk/WY1ftUryD7npa1nCVUzTKgaqZQepuScw3IGRCJkwUAkBvjAvaQCORv2oK6SQxHshbunJ0o3bkfWp9/A2NAI36gIxMw7Asl/O6vd62pz8tFc+9d6k8Q69tar1EQRu55/C4GDB2LSQ7cjfNzIdu9rLK9U7aUzX360XZXy0HMXYMOd/4Z3aAgm3HMD7FVDh3l2yXo4U5JGftmdgzs/XKl3GGQDHu5uGBoWiJDGRpTv2Y+CPe0vSWUNnCmJrMXH3QNLZx2cd5esiyVUDYdqkOus64yyP4flxA9E1KihGOTtCWN+CXI2pKKRtRVkRxpNRnV9Ync3N71DcTpMqBppsdMOCaS9kpoGqGvi+PjB+8gp6mo5AYY6FG/LRMn+Qr3DI0KDsQX+nl56h+F0mFA1whIqWYbl7LEMy0lJxMCpoxDr4YaG/QXI3sRhOaTfWFQmVOtjQtUIS6jUmfyqOuTLP8HB8D/+CCSF+MNHhuVsTkdlYbne4ZELlVDJ+phQNWJ0wovnknXJxB87LcNyxqdg8LxARJllWE4ecrdlclgOaYZDZ7TBhKqRFh4MqRekr312hQHZcicqAiELYpEY6AO3kgrkbUjTOzxyMhw6ow0mVI2whEr9UVXfhG1q0n5PeEwfA3dvmcKOw2bIOtzBHr5acNfkU4ltqGTVDm6leZx5i6zHt5PpGan/mFA1whpfsqa0tHI1IJ/IGnw8WDmpBSZUjfh68eBH1tPcYkK8Z5DeYZCT4MmZNphQNeLvzTFeZF3mSr0jIGfBKl9tMKFqxN+HVSpkXfvTq/QOgZyErzuPT1pgQtWIvw9LqGRdBUW1GODtr3cY5OA83dzg6c5Dvxa4VDXi780zQLK+sGZfvUMgB8cOSdphQtUIS6ikheoCXrmG+seXHZI0w4SqEZZQSQtpqeXwduNuS33nyxKqZrhnaoQlVNJCQ6MRcV4cPkN9xxKqdphQNeLHEippxLOa08ZR37ENVTtMqBrx9fKEtycXL1lf7r4avUMgB8YxqNrhEV9DA4I5xIGsLye3BhFe7O1LfRPs6aN3CE6LCVVD0SEBeodATirSKFefIeq9aF+e6GuFCVVDTKikFUNRs94hkIOK8eVxSStMqBqKCeWZIGkjPbVCzXhD1FtMqNphQtVQDEuopJHaumYOn6E+ifHjcUkrTKgaYpUvacmnlr01qXe83NwR4c32d60woWoomlW+pKH8LIPeIZCDGeDrD3c2FWiGCVVDrPIlLe3LqkIoh0BQL7D9VFtMqBpPPxjk6613GOTEBoC1INRzTKjaYkLVWHwEO46QdhqKW/QOgRxINBOqpphQNTYsJkzvEMjJh89wJ6aeiuGkDprivqixYbFMqKSd6pomxHmzFoR6hlW+2mJC1VgKS6ikMb96Xj2EeoYJVVtMqBpLjgkFe6mTlkqy6/QOgRxApLcf/D15nWYtMaFqLNDXG7GhgXqHQU4sI7MSgR48UFL3RgSH6x2C02NCtQF2TCItmcxmxLqxKo+6NzI4Qu8QnB4Tqg0MiwnVOwRycs1lZr1DIDvHEqr2mFBtgD19SWt70yrApnrqire7O5ICeGKvNSZUG0iJ4ZkhaausogGx3myrp84lB4bB052He61xCdtAXHggIgJ99Q6DnFxQIzsmUedY3WsbTKg2MiFxgN4hkJMry23QOwSyUyOD2CHJFphQbWRCAhMqaSs9owJ+HpzkgQ7FEqptMKHayMTEaL1DICfX0mJCnAfbUenQ2ZHCvNnkZAtMqDaSHB2KYD9eyo20ZSrn8Blqb0QQS6e2woRqI+7ubpg8hKVU0lZWepXeIZCdGcnqXpthQrWhqUmxeodATq6opA7R3rxEF/2FMyTZDhOqDU1LitE7BHIBoU0+eodAdiLUyweJASF6h+EymFBtaHBkMGJDOecqaavyQKPeIZCdOCIiFu683JXNMKHa2BHJrPYlbaWllcPH3UPvMMgOzIiM0zsEl8KEamNHjh6sdwjk5JqaTYjz5PAZVxfg6YXxIVF6h+FSmFBtbOrQGA6fIc25V7Gaz9VNC4/h/L02xqVtY54e7pgzYpDeYZCTy86s1jsE0tmMiIF6h+BymFB1cDSrfUljB/INiPLy0zsM0om0oU8O46gCW2NC1cH05FgE+PDKIKStcCOnm3NVk8Oi4ePBjmm2xoSqA29PD8wezt53pK2agia9QyCdzIhkda8emFB1wmpf0lpaagU83biLuxpZ59PCOTxPD9zbdDIjZSD8vHmpLdJOfUMLBnlx+IyrGR8apYbMkO0xoerE18sTM1NYLUPa8jJwF3c17N2rH+5tOlowKUnvEMjJHdhn0DsEsiFPNzcmVB0xoeroiOSBnNuXNLU/uxphXuzt6yokmYZ48+IIemFC1fkaqadNSdY7DHJyUSaOR3UVJw9krZeemFB1durkJHi4c5o40k59cYveIZANDAkIwZiQSL3DcGlMqDqLDPLHXE5FSBpK21MOD17Cy+mdMnCo3iG4PCZUO3D61GF6h0BOzFDbjDivIL3DIA0FenrhyCiObdcbE6qdTEUYF8bxgqQd31pOQ+fMjo1O5FSDdoAzC9gBNzc3LJySjJdWbIWzKN70I4o3/4zGyhJ13y9qEAbOXojQ5PHq/v7lb6E6axeaDBXw8PZFYNwwDDr6XPh1M2Vas6EKub98hOp9O2FsqEPg4OFIOP5v8A3/axLwnBUfoHT773D38kH80ecgYsys1ufK96xH6fZVSDn3VriawuxagP3fnLZUdHIsq3vtAUuodmLB5GR4eTjP6vAOCsego87B6MsfxujLHkJwwihkfvos6kvy1PP+MYkYsuBKjL36caScdwcAM9KXPAGzydTp55nNZmR89hwaK0qQfPbNGHXFI/AJiUTaB4/B2NSgXlOZvhllu9Zi+Pl3IP6Y85D17ZtorqtRz7U01CHv18+QcMIlcEV791UiyJPX4XVGk8NjEOPH4Xf2wHmO4A4uPNAXJ4wfAmcRmjIJockTVOnRNyIWg446G+7evjAcyFTPD5h0NIIGj4CPTJMWm4i4eWehqboMjVUHS7QdNZYXovZAJhJPvBSBA4fCLyIWCSdeClNLE8p3rVOvqS/LR3DCCAQMHIqI0TPg4ePXWkLO+/kj9Z2ShF2R2QzEggddZ3QKS6d2gwnVjlwyd7RTDqGRUqeUHE3NjapqtyMpYZZuX6mSq3dwRKefYTIeHPrh1maOUjc3d7h5eKEmL03d9x8wGLUFWWipr1V/Tc1N8A2LRk1uGmoL9yN66vFwZY0lRr1DICsb6BeISWHReodBf2Ibqh0ZHBmMY8Yk4Ift++EM6opzsWfxv2BqaVbtpMln3QS/qL8uW1e88Ufk/vyRSrRSik254E64e3S+ScrzkmzzfvkEiSdeBndvHxSt/w7NNeWqbVWEJI1Tbaa7374f7p7eGLrgavW67P8txpAFV6F4008o3vgDPP2DkHjSZapd15VkpFbAd4YHOq9UJ0ckbafSB4Psg5tZGqfIbuwtqsQFL36jqugcnZQqm6rKYGysQ3nqHyjd+htGXHRva1KVds2Wumo0GypRuG45mmoqMPKS+1Qy7IyUOrO+eQP1xTmAmzuCh4w+eDAxAynn397pew6sXKa+P3LcXKQteQJjrnwUlZlbUbxxhWrfdTWTFgxAXhPn93UGfh6eWDztRF5Zxo6wytfOJEWHYo6TTPQgpU3f8GgExA5B/FHnqirZog3ftz7v6euv2lilLTXpzBvRUJaPirRNXX6efM6YK/+Nibe+igk3vaA6H7XUG+ATFtXp6+tL81G2c41qn63J2YOgwcPhFRCM8JHTUFe4H8bGeriagAYefJ3FqQOTmEztDBOqHbps3hg4I7PZBJOxuasnVUnT3NLF821IIpbE2CAdlQqyEJoyuZOPMyP7f29h8PwLVHWztOOajQfbEM2mP/+aXa/ysyTH9U4inFGwpzfOHJSidxjUAROqHRo1KBLTk2LhyHJ/+Rg1Oamql620par72amIGD0TDRXFyF/9lUqGjVWlqMlLx95lL8DNyxshf45TFTteuQMVqRvbjSOtzt6j3i8l2bQPH0dYymSEDB17yPeXbv0Vnv7BqrexCByUgprs3aqXsbS9+kbGwdPX9Xq9ZmRUIMCDpRpHd3b8cPizdGp32CnJTv39yDFYv7cAjqqlthr7vnpVtY/K8BWp7pV2Tkl+0lZqyE1T1b/G+lp4BoSo6tiRl9wPr4CQ1s9oKCtQ7Z8W8lk5Kz5ES20VvAJDETF2NgbOWXjId0snJUnY8nkWgXFJiJ5+ItI/fhpe/sGqk5IrMprMGOgegAxjpd6hUB9F+fhx3l47xU5Jduyq13/A1uxivcMgJzN9Ziyygg72jCbHc+OwSTguJlHvMKgTrPK1YzecMBHsEU/Wti+VpVNHFe8XhGOiE/QOg7rAhGrHxsZH4dixPBMl6yopr0est+u1HzuDixNH8VJ8dowJ1c5df9xE+HjyKhJkXcFNnNfX0QwPCsPMyL8mRiH7w4Rq52JCA3D+rJF6h0FOpjz34AUFyHFckuicw+mcCROqA7h07mhEBPrqHQY5kfT0Cvi6s+bDUUwKG4BxoZ1PYEL2gwnVAfj7eOGa+RP0DoOcSHOLCXGeQXqHQT0gLaYsnToGJlQHsWBSElJiw/QOg5wJO/s6hLlR8UgKDNU7DOoBJlQH4e7uhn+eeOgUe0R9tT+dY1HtXaCnF67oZCYwsk9MqA5kytAYzB/DMWhkHQVFtRjg7a93GNSNvw8ZgzBv9p9wFEyoDua2U6YixN9H7zDISYQ182Btr8aEROK4aI5DdyRMqA4mPNAXt540Re8wyElU5TfqHQJ1wsvNHdcny0xpnMTBkTChOqATJgzBnBEc4E39l5ZWDm83HgbszTnxwzHIn72wHQ33JAd116nTEeTL2W6ofxobjRjkxQO3PUn0D8ZZ8cP1DoP6gAnVQUUF++OmEw9e65OoP9yrWa1oLzzd3HDL8Cnwcueh2RFxrTmwUycnY3qyY1+InPSXt69G7xDoT+fGj8BQjjl1WEyoDu7ehUcgwMdL7zDIgeXk1iDCi7199ZYcGIpzBrOq15ExoTrB5Pk3ncCqX+qfSKOf3iHA1Xv13pwyBR7sIObQuPacwMKpw3DcOI5Xo74zFDXrHYJLuyhxFBICgvUOg/qJCdVJ3HPadCREcoekvklPrVAdYsj2joiIxRlxw/QOg6yACdWJrkjz2Plz4ePFS3JR79XWNXP4jA4G+QXilpQpnMDBSTChOpGk6FDccco0vcMgB+Vt4MmYLfl5eOLeUUfA35OdCp0FE6qTWTA5CadMHKp3GOSA8rMMeofgUm5OmYx4fzbTOBMmVCd0x4JpqrRK1Bv79lch1JMXXrCFswalYGYkpw91NkyoTsjX2xOLzpsDf29PvUMhBzMAvJyb1iaGDsDfEkfrHQZpgAnVSSVGheC+M2aAfR2oNxqKW/QOwalF+/jjjhHT4M4d0ykxoTqxY8Yk4B/HTdQ7DHKw4TPu4MFeCz7uHqoTUpAXL2rhrJhQndzf5ozG6VM5xo16prqmCXHegXqH4ZSuHzaR8/Q6OSZUF3DHgqmYmTJQ7zDIQfjXse3d2hYMTMJRAwbrHQZpjAnVBXi4u+PRc+cgJTZM71DIARTl1OkdglOZFh6DK4aO1TsMsgEmVBeaSemZi4/CgGD24qTuZWZWItCDkw1Yw/jQKNw9cjonvXcRXMsuRJKpJFVe7o26YzKbEesWoHcYDm9kcDjuGzUDXu6cgcpVMKG6GKn2ffS8OfD04KqnrjWXmfUOwaElBYTgwdGz4OvB9mhXwqOqC5oxbCAeOnsWPNw5PII6l5lWwcEzfRTvF4SHxs5GAOfodTlMqC5q/pgEPHDGTA4wp06VVzRgIIfP9FqMbwAeGTsbIV6cwtEVMaG6sBMmDME9C6dzNiXqVGADJyDojQhvX/x77GxE+PjpHQrphAnVxZ06ORl3LpjGpEqHKM2t1zsEhxHq5YN/j52DaF925nJlTKiEM6al4O7TWFKl9tIzytU1O6l7gZ5eeGjMLAzy5wXaXR0TKikLpwzD/y2cwTZVamU0mhHnznbU7vh7eKrevJxSkAQTKrW7OPn9Z8xg719qZarg8Jnu2kwfHz8PI4LD9Q6F7ISb2WzmHkPtrErLw70fr0J9Ey/l5eqio/xhnqR3FPYnwT8Y/xozE5E+nHmM/sISKh1i9vBB+O9l8xEe4Kt3KKSzopI6xHizo01b40Ki8MT4eUymdAgmVCu79NJL4ebmhscee6zd41988YV63FGMGhSJN68+HvER7Gjh6kKaOHzG4qgB8fjXmFmctIE6xYSqAV9fXzz++OOoqKiAI4sLD8KbVx2PsfGReodCOqo80Kh3CHbh7PgU3JIyBV7uPGxS57hlaGD+/PmIiYnBokWLunzN0qVLMXr0aPj4+CAxMRFPP/1063P33HMPpk+ffsh7xo8fj4ceekj9v2HDBhx77LGIjIxESEgI5s2bh82bN1v9t4QG+OKly+Zj3sh4q382OYa0tHL4uPAE7+5ww3XJE3BJ4hiHqmUi22NC1YCHhwceffRRvPDCC8jLyzvk+U2bNuGcc87Beeedhx07duDBBx/Efffdh8WLF6vnL7zwQvzxxx/Yu3dv63t27dqF7du344ILLlD3a2pqcMkll2DVqlVYt24dhg0bhpNOOkk9bm2+Xp54/Py5OHt6itU/m+xfU7MJgzxdc/iMnEjcO+oInBQ7VO9QyAGwl68GbaiVlZWqzXTGjBkYNWoU3nzzTXX/9NNPhyxuSZglJSX44YcfWt93xx134Ntvv1WJU0yYMAFnnnmmSrSWUuvPP/+skmdnTCYTQkND8eGHH+KUU07R7Pd9vDYVz3+3GS1Gk2bfQfZn5tyByPSrhKvNfnT/6BlICeKwGOoZllA1JO2o77zzDvbs2dPucbk/a9asdo/J/YyMDBiNRnVfkq4kRyFJeMmSJeoxi6KiIlx55ZWqZCpVvsHBwTAYDMjJydH0N507YwReufxYXqjcxWRnVsOVDAkIwVPjj2QypV5hQtXQ3Llzcfzxx+Puu+/u9XvPP/98pKWlqXbRNWvWIDc3F+eee27r81Ldu3XrVjz//PPqefk/IiICTU1NVv4Vhxo3OArv/eMkTB0ao/l3kX04kG9AlJdrTPp+SuxQPDPhSMT4cbgQ9Q4n6tSYDJ+R6tvhw4e3PjZy5EisXr263evkfkpKimp/FYMGDVIdjT744APU19erDkgDBgxo9/qXX35ZtZsKSbilpaU2+11hAb544dJj8OpP27B45U6w4cD5hRv9UALnnTA/yNMbNw6bhBmRA/UOhRwUE6rGxo4dq6pq//Of/7Q+duutt2Lq1Kl4+OGHValz7dq1ePHFF1WCbEve98ADD6hS57PPPtvuOanqfe+99zBlyhRUV1fj9ttvh5+fbUsQ7u5uuPbYCRg7OBIPfrYG1fXal45JP9UFjUA0nNKo4AjcPmIqojhZA/UDq3xtQIa6SKchi0mTJuGTTz7BRx99hDFjxuD+++9Xr5EOTW2dddZZKCsrQ11dHRYuXNjuOenoJONc5bMuvvhi3Hjjje1KsLaeWend607CiIFsb3Jm6akV8HJzrkOG/Jpz44dj0bi5TKbUb+zlS1bT1GLEyyu24qM1qTBxs3JK0xfEIqupCs4g3NsXtw2finGhUXqHQk6CCZWsblt2MR5atha5ZdYfE0v6mnXkQGT4OP7wmSlh0bh5+BSEePnoHQo5ESZU0kRDc4sqrX6yNo2lVSeSMDgY9SMd9ypEnm7uuCRxNBbGJXPWI7I6JlTS1Jb9xXh42RrklRv0DoWsZPgp4ahoboCjGR4UhuuSJyKJFwMnjTChkuYamlrw4g9b8On6NA6vcQKzT4pDutFxLvwQ7OmNvyWOxvExiSyVkqaYUMlmNmcV4d9frGPbqoObPCUauRH2vw4ldR4Xk6iqeIPZVko2wIRKNtXcYsSStal4+9edqG1s1jsc6oPAAC8EzfGG0Y4PHVKte13SBAwP5lAush0mVNJFmaEeL/+wFd9s2ctqYAc0dUEMspvsb35fufD3xQmj1NVh3Fm9SzbGhEq62n2gDM98uxHbc0r0DoV6YfbRcUj3sp92VEmdRw8YjL8PGYNQb1+9wyEXxYRKduG7bVl48fstKK6u0zsU6oHkoaGoHmYfU00m+gfj2uQJGB0SqXco5OKYUMmuegO/+/suLFmTyvZVOye1qUknhaK6Rb+kOsgvEOfEj8C8AfHwYPUu2QEmVLI71fWNWLI6FR+vS4WhgYnVXs0+OQ7pLbav9k3wD8a5g0dgdmQc20nJrjChkt0yNDSpeYE/WpvKK9nYoanTYpAdZruOSUkBISqRzogYyPGkZJeYUMnuSfWvTGH44Zo9qKpr1Dsc+lNosA98Z3jgr+soaSMlKAznxY/AtIhYjb+JqH+YUMlh1DU247M/0vHhqj0or3W8qe+c0eQF0chtqtHsGqXnDR6BSWFOehFWcjpMqORwGpuN+GlntkquO3NL9Q7Hpc2ZH4c0D+u2o44LiVKJlJdVI0fDhEoOLb2gHJ+tT8f32/ejvslxr4LiqIanhKFiSKNVrk0q40iPjU5AnH+QVWIjsjUmVHKaDkzfbtmHZX9kIKvEOS6A7Qg83N0w+IRg1Bqb+3QptWnhMTg2JgGTwmI49IUcHhMqOZ1N+wqxbEMGVqbmqeph0tasU+KQ0dzzat/EgBBVEj1yQDwv8E1OhQmVnJZUAf+emocfd2ZjbXo+GluYXLUwfWYssoKqDjvH7pFR8SqRJgeF2Sw2IltiQiWXGXpjSa7rMvLR1KL1YA/XERXuB7eph1bX+rh7YHxoFOZFxWNG5EB4u3voEh+RrTChkku2t67ck4efdmXjj8xCllytYPyCKBQ01SLS2w9Tw2MwLSIG40IGwMeDSZRcBxMquTRpY92yvwjrMguwPrMAe4sq9Q7JoXh6uGPMoEicOHcIxsdFYWhgqN4hEemGCZWojZLqOmzaV4SNWYXYnFWEvHKD3iHZFZk7d8iAEEwZGo1pSbGYPCQa/j5eeodFZBeYUIm6UVRZiy37i7E7vwzp+eVIL6xwqQn7Y0ICMGpQBEYPisCouAiMjItgAiXqAhMqUS/I7nKg3IC0gnKk5ZcjraBC/V9ucPypEKOC/DA0OlQlz9GDIlUijQj00zssIofBhEpkparifcVVyK8woKDCgPzKWvW3oLIWZYZ62MNeJvMmRAX5Y1B4IOIjgjEoIgiDI4LU3/jwIPh6e+odIpFDY0Il0lhTi1ElVkuCldKsDOOpbWiCobFZVSEb/vy/9s//6zpMoyhtl5IQZWYiuXSZ+583uR/k643QAB+E+PsgxO/Pv/7eCPH3bf1fSppxYYFMmkQaYkIlskMmkxkms7k1gRKR/WNCJSIisgJ3a3wIERGRq2NCJSIisgImVCIiIitgQiUiIrICJlQiIiIrYEIlIiKyAiZUIiIiK2BCJSIisgImVCIiIitgQiUiIrICJlQiIiIrYEIlIiKyAiZUIiIiK2BCJSIisgImVCIiIitgQiUiIrICJlQiIiIrYEIlIiKyAiZUIiIiK2BCJSIisgImVCIiIitgQiUiIrICJlQiIiIrYEIlIiKyAiZUIiIiK2BCJSIisgImVCIiIitgQiUiIrICJlQiIiIrYEIlIiKyAiZUIiIiK2BCJSIisgImVCIiIitgQiUiIrICJlQiIiIrYEIlIiKyAiZUIiIiK2BCJSIisgImVCIiIitgQiUiIrICJlQiIiIrYEIlIiKyAiZUIiIiK2BCJSIisgImVCIiIvTf/wM6uCTOxO7vrAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupamos en una variable nueva con 'loyalty_card' y contando los números únicos de 'loyalty_number'\n",
    "\n",
    "df_fidelidad = df_limpio.groupby(\"loyalty_card\")[\"loyalty_number\"].nunique().reset_index(name=\"num_uniq_loyalty\")\n",
    "\n",
    "# Colores para el gráfico\n",
    "colores = sns.color_palette(\"mako\", len(df_fidelidad))\n",
    "\n",
    "# Crear el gráfico de pie\n",
    "plt.pie(df_fidelidad[\"num_uniq_loyalty\"], \n",
    "        labels=df_fidelidad[\"loyalty_card\"], \n",
    "        autopct='%1.1f%%', \n",
    "        colors=colores, \n",
    "        textprops={'fontsize': 10}, \n",
    "        startangle=90)\n",
    "\n",
    "plt.title(\"Clients proportion with different types of loyalty cards\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Casi un 50% de los clientes son de la categoría de Star. Mientras que la categoría Nova va en segundo lugar y Aurora en último. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. ¿Cómo se distribuyen los clientes según su estado civil y género?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupo las columnas 'gender' y 'marital_status' y contando los números únicos de 'loyalty_number' para hacer un recuento real sin clientes duplicados\n",
    "df_distribution = df_limpio.groupby(['gender', 'marital_status'])['loyalty_number'].nunique().reset_index(name=\"num_uniq_loyalty\")\n",
    "df_distribution = df_distribution.sort_values(by=\"marital_status\", ascending=False)\n",
    "\n",
    "# Creamos un gráfico de barras apiladas\n",
    "fig, axes = plt.subplots(1, 1, figsize=(10, 6))\n",
    "\n",
    "# Introducimos los datos en el gráfico de barras apiladas\n",
    "sns.barplot(x=\"marital_status\", \n",
    "            y=\"num_uniq_loyalty\", \n",
    "            hue=\"gender\", \n",
    "            data=df_distribution, \n",
    "            ax=axes, \n",
    "            palette=\"mako\")\n",
    "\n",
    "# Añadimos título y etiquetas\n",
    "plt.title(\"Clients distribution according to marital status and gender\")\n",
    "plt.xlabel(\"Marital status\")\n",
    "plt.ylabel(\"Number of clients\")\n",
    "plt.legend(title=\"Genre\")\n",
    "\n",
    "# Mostramos el gráfico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La distribución de clientes según el estado civil es mayor en casados, con la misma proporción de hombres y mujeres. En cambio, en las categorías los clientes solteros y divorciados hay un mayor número de mujeres que de hombres. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***BONUS***\n",
    "\n",
    " **Fase 3: Evaluación de Diferencias en Reservas de Vuelos por Nivel Educativo**\n",
    "\n",
    " Utilizando un conjunto de datos que hemos compartido, se busca evaluar si existen diferencias significativas en el número de vuelos reservados según el nivel educativo de los clientes. Para ello, los pasos que deberas seguir son:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Preparación de Datos: Filtra el conjunto de datos para incluir únicamente las columnas relevantes: 'Flights Booked' y 'Education'.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cargamos las bibliotecas necesarias para poder visualizar los análisis que queremos hacer:\n",
    "\n",
    "# Evaluación linealidad de las relaciones entre las variables\n",
    "# ------------------------------------------------------------------------------\n",
    "from scipy.stats import shapiro, poisson, chisquare, expon, kstest\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Configuración\n",
    "# -----------------------------------------------------------------------\n",
    "pd.set_option('display.max_columns', None) # para poder visualizar todas las columnas de los DataFrames\n",
    "\n",
    "# Gestión de los warnings\n",
    "# -----------------------------------------------------------------------\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>flights_booked</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27782</th>\n",
       "      <td>3</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147606</th>\n",
       "      <td>18</td>\n",
       "      <td>College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387614</th>\n",
       "      <td>0</td>\n",
       "      <td>Doctor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38054</th>\n",
       "      <td>5</td>\n",
       "      <td>College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278376</th>\n",
       "      <td>11</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        flights_booked education\n",
       "27782                3  Bachelor\n",
       "147606              18   College\n",
       "387614               0    Doctor\n",
       "38054                5   College\n",
       "278376              11  Bachelor"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Según mi interpretación, creo que habría que seleccionar las columnas del Dataframe que queremos analizar, por lo que he hecho esto: \n",
    "\n",
    "df_filtrado = df_limpio[['flights_booked', 'education']] \n",
    "df_filtrado.sample(5) #lo llamo para ver una pequeña muestra\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Análisis Descriptivo: Agrupa los datos por nivel educativo y calcula estadísticas descriptivas básicas (como el promedio, la desviación estandar) del número de vuelos reservados para cada grupo.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>var</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bachelor</th>\n",
       "      <td>4.112363</td>\n",
       "      <td>5.224957</td>\n",
       "      <td>27.300172</td>\n",
       "      <td>251400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>College</th>\n",
       "      <td>4.172349</td>\n",
       "      <td>5.250673</td>\n",
       "      <td>27.569572</td>\n",
       "      <td>101712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doctor</th>\n",
       "      <td>4.177736</td>\n",
       "      <td>5.262027</td>\n",
       "      <td>27.688930</td>\n",
       "      <td>17616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High School or Below</th>\n",
       "      <td>4.181319</td>\n",
       "      <td>5.248480</td>\n",
       "      <td>27.546543</td>\n",
       "      <td>18768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>4.208743</td>\n",
       "      <td>5.224245</td>\n",
       "      <td>27.292736</td>\n",
       "      <td>12192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean       std        var   count\n",
       "education                                                  \n",
       "Bachelor              4.112363  5.224957  27.300172  251400\n",
       "College               4.172349  5.250673  27.569572  101712\n",
       "Doctor                4.177736  5.262027  27.688930   17616\n",
       "High School or Below  4.181319  5.248480  27.546543   18768\n",
       "Master                4.208743  5.224245  27.292736   12192"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Aquí, siguiendo con lo que he interpretado, realizamos estadística descriptiva básica según el nivel educativo\n",
    "\n",
    "estadisticas = df_filtrado.groupby('education')['flights_booked'].agg(['mean', 'std', 'var', 'count'])\n",
    "\n",
    "estadisticas\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Prueba Estadística: Realiza una prueba de hipótesis para determinar si existe una diferencia significativa en el número de vuelos reservados entre los diferentes niveles educativos.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import f_oneway\n",
    "\n",
    "#La función realiza una prueba de hipótesis estadística para determinar si hay diferencias significativas entre las medias de uno o más grupos de datos.\n",
    "def prueba_hipotesis(*args):\n",
    "    \n",
    "    # lo primero  miramos si las varianzas son iguales o no\n",
    "    if len(args) == 2:\n",
    "        p_valor_varianza = stats.levene(*args, center = \"median\")[1] #Se utiliza la prueba de Levene para verificar si las varianzas son iguales\n",
    "    else:\n",
    "        p_valor_varianza = stats.bartlett(*args)[1] #Se utiliza la prueba de Bartlett para verificar la homogeneidad de varianzas. \n",
    "    \n",
    "    if p_valor_varianza > 0.05: # si el p-valor de la prueba de varianzas es mayor, asumimos que las varianzas son iguales\n",
    "        # realizamos la prueba t de Student\n",
    "        t_stat, p_valor = stats.ttest_ind(*args, equal_var=True)\n",
    "    else:\n",
    "        t_stat, p_valor = stats.ttest_ind(*args, equal_var=False)\n",
    "        \n",
    "    # Establecemos un nivel de significancia (alfa)\n",
    "    alfa = 0.05\n",
    "\n",
    "    # comparamos el p-valor con el nivel de significancia\n",
    "    if p_valor < alfa:\n",
    "        print(\"Rechazamos la hipótesis nula.\")\n",
    "        print(\"Hay una diferencia significativa en los vuelos promedio entre los dos niveles educativos.\")\n",
    "    else:\n",
    "        print(\"No podemos rechazar la hipótesis nula.\")\n",
    "        print(\"No hay evidencia suficiente para afirmar una diferencia significativa en los vuelos promedio entre los dos niveles educativos.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Prueba de Hipótesis sobre el número promedio de vuelos según nivel de estudios\n",
    "\n",
    "bachelor = df_filtrado[df_filtrado['education'] == 'Bachelor']['flights_booked']\n",
    "college = df_filtrado[df_filtrado['education'] == 'College']['flights_booked']\n",
    "doctor = df_filtrado[df_filtrado['education'] == 'Doctor']['flights_booked']\n",
    "high_school = df_filtrado[df_filtrado['education'] == 'High School or Below']['flights_booked']\n",
    "master = df_filtrado[df_filtrado['education'] == 'Master']['flights_booked']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rechazamos la hipótesis nula.\n",
      "Hay una diferencia significativa en los vuelos promedio entre los dos niveles educativos.\n"
     ]
    }
   ],
   "source": [
    "# Llamamos a la función que hemos creado para hacer la comparativa según nivel de estudios\n",
    "prueba_hipotesis(bachelor, college)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Estas serían todas las combinaciones posibles según nivel de estudios\n",
    "\n",
    "prueba_hipotesis(bachelor, college)\n",
    "prueba_hipotesis(bachelor, doctor)\n",
    "prueba_hipotesis(bachelor, high_school)\n",
    "prueba_hipotesis(bachelor, master)\n",
    "prueba_hipotesis(college, doctor)\n",
    "prueba_hipotesis(college, high_school)\n",
    "prueba_hipotesis(college, master),\n",
    "prueba_hipotesis(doctor, high_school),\n",
    "prueba_hipotesis(doctor, master),\n",
    "prueba_hipotesis(high_school, master)"
   ]
  }
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