Fase3_Bonus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# importamos las librerías que necesitamos\n",
    "\n",
    "# Tratamiento de datos\n",
    "# -----------------------------------------------------------------------\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Visualización\n",
    "# ------------------------------------------------------------------------------\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Evaluar linealidad de las relaciones entre las variables\n",
    "# y la distribución de las variables\n",
    "# ------------------------------------------------------------------------------\n",
    "from scipy.stats import shapiro, levene\n",
    "from scipy.stats import ttest_ind\n",
    "from scipy.stats import mannwhitneyu\n",
    "from scipy.stats import chi2_contingency\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import shapiro, poisson, chisquare, expon, kstest\n",
    "\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\")\n",
    "\n",
    "# Funciones de soporte\n",
    "# -----------------------------------------------------------------------\n",
    "\n",
    "from src import soporte as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loyalty_number</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",
       "      <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>100018</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1521</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T9G 1W3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>92552.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>7919.2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100018</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1320</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T9G 1W3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>92552.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>7919.2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100018</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>2533</td>\n",
       "      <td>253.0</td>\n",
       "      <td>438</td>\n",
       "      <td>36</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T9G 1W3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>92552.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>7919.2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100018</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>924</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T9G 1W3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>92552.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>7919.2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100018</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",
       "      <td>Canada</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>T9G 1W3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>92552.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>7919.2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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          100018  2017      2               2                        2   \n",
       "2          100018  2017      3              14                        3   \n",
       "3          100018  2017      4               4                        0   \n",
       "4          100018  2017      5               0                        0   \n",
       "\n",
       "   total_flights  distance  points_accumulated  points_redeemed  \\\n",
       "0              3      1521               152.0                0   \n",
       "1              4      1320               132.0                0   \n",
       "2             17      2533               253.0              438   \n",
       "3              4       924                92.0                0   \n",
       "4              0         0                 0.0                0   \n",
       "\n",
       "   dollar_cost_points_redeemed country province      city postal_code  gender  \\\n",
       "0                            0  Canada  Alberta  Edmonton     T9G 1W3  Female   \n",
       "1                            0  Canada  Alberta  Edmonton     T9G 1W3  Female   \n",
       "2                           36  Canada  Alberta  Edmonton     T9G 1W3  Female   \n",
       "3                            0  Canada  Alberta  Edmonton     T9G 1W3  Female   \n",
       "4                            0  Canada  Alberta  Edmonton     T9G 1W3  Female   \n",
       "\n",
       "  education   salary marital_status loyalty_card     clv enrollment_type  \\\n",
       "0  Bachelor  92552.0        Married       Aurora  7919.2        Standard   \n",
       "1  Bachelor  92552.0        Married       Aurora  7919.2        Standard   \n",
       "2  Bachelor  92552.0        Married       Aurora  7919.2        Standard   \n",
       "3  Bachelor  92552.0        Married       Aurora  7919.2        Standard   \n",
       "4  Bachelor  92552.0        Married       Aurora  7919.2        Standard   \n",
       "\n",
       "   enrollment_year  enrollment_month  cancellation_year  cancellation_month  \n",
       "0             2016                 8                NaN                 NaN  \n",
       "1             2016                 8                NaN                 NaN  \n",
       "2             2016                 8                NaN                 NaN  \n",
       "3             2016                 8                NaN                 NaN  \n",
       "4             2016                 8                NaN                 NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cargamos el csv obtenido de la fase de exploracion y limpieza\n",
    "df = pd.read_csv(\"archivos/df_union.csv\") \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluación de Diferencias en Reservas de Vuelos por Nivel Educativo ✨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Preparación de Datos: Filtra el conjunto de datos para incluir únicamente las columnas relevantes: 'flights_booked' y 'education' ✨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- flights_booked -> cantidad de vuelos/mes -> int64 -> valor numérico continuo\n",
    "- education -> Nivel educativo alcanzado por el cliente -> var categorica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flights_booked</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   flights_booked education\n",
       "0               3  Bachelor\n",
       "1               2  Bachelor\n",
       "2              14  Bachelor\n",
       "3               4  Bachelor\n",
       "4               0  Bachelor"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtrado = df[['flights_booked', 'education']]\n",
    "df_filtrado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num. filas df_filtrado: 403760\n",
      "Num. columnas df_filtrado: 2\n"
     ]
    }
   ],
   "source": [
    "print(f'Num. filas df_filtrado: {df_filtrado.shape[0]}')\n",
    "print(f'Num. columnas df_filtrado: {df_filtrado.shape[1]}')"
   ]
  },
  {
   "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 ✨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</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",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>252567.0</td>\n",
       "      <td>4.11</td>\n",
       "      <td>5.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>College</th>\n",
       "      <td>102260.0</td>\n",
       "      <td>4.17</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doctor</th>\n",
       "      <td>17731.0</td>\n",
       "      <td>4.18</td>\n",
       "      <td>5.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High School or Below</th>\n",
       "      <td>18915.0</td>\n",
       "      <td>4.18</td>\n",
       "      <td>5.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>12287.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>5.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count  mean   std  min  25%  50%  75%   max\n",
       "education                                                           \n",
       "Bachelor              252567.0  4.11  5.22  0.0  0.0  1.0  8.0  21.0\n",
       "College               102260.0  4.17  5.25  0.0  0.0  1.0  8.0  21.0\n",
       "Doctor                 17731.0  4.18  5.26  0.0  0.0  1.0  8.0  21.0\n",
       "High School or Below   18915.0  4.18  5.24  0.0  0.0  1.0  8.0  21.0\n",
       "Master                 12287.0  4.20  5.21  0.0  0.0  1.0  8.0  21.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupamos por nivel educativo\n",
    "df_agrupado = df_filtrado.groupby('education')['flights_booked']\n",
    "# principales estadisticos de df_vuelos\n",
    "display(df_agrupado.describe().round(2))"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot para visualizar mejor distribuición entre estas dos columnas\n",
    "\n",
    "sns.boxplot(x='flights_booked',\n",
    "            y='education',\n",
    "            data=df_filtrado,\n",
    "            palette=\"Set2\")\n",
    "plt.title('Distribución de Vuelos Reservados por Nivel Educativo')\n",
    "plt.xlabel('Vuelos Reservados')\n",
    "plt.ylabel('Educación');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los valores de la mediana son similares para todos los grupos y todos los grupos presentan valores atipicos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comprobamos si es un problema de contingencia ✨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- La prueba de contingencia se utiliza cuando deseas analizar la relación entre dos variables categóricas, por lo que no aplicaría a este caso al ser flights_booked una variable numérica.\n",
    "\n",
    "- Calculamos igualmente la tabla de contingencia para comprobar que no nos da valores claros que analizar:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>flights_booked</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>123264</td>\n",
       "      <td>7949</td>\n",
       "      <td>8423</td>\n",
       "      <td>11436</td>\n",
       "      <td>8033</td>\n",
       "      <td>9444</td>\n",
       "      <td>8606</td>\n",
       "      <td>9050</td>\n",
       "      <td>8958</td>\n",
       "      <td>8790</td>\n",
       "      <td>8285</td>\n",
       "      <td>9709</td>\n",
       "      <td>6492</td>\n",
       "      <td>6789</td>\n",
       "      <td>4358</td>\n",
       "      <td>4081</td>\n",
       "      <td>2678</td>\n",
       "      <td>2392</td>\n",
       "      <td>1813</td>\n",
       "      <td>962</td>\n",
       "      <td>708</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>College</th>\n",
       "      <td>49426</td>\n",
       "      <td>3115</td>\n",
       "      <td>3508</td>\n",
       "      <td>4612</td>\n",
       "      <td>3209</td>\n",
       "      <td>3881</td>\n",
       "      <td>3505</td>\n",
       "      <td>3687</td>\n",
       "      <td>3613</td>\n",
       "      <td>3614</td>\n",
       "      <td>3407</td>\n",
       "      <td>4081</td>\n",
       "      <td>2677</td>\n",
       "      <td>2698</td>\n",
       "      <td>1814</td>\n",
       "      <td>1696</td>\n",
       "      <td>1145</td>\n",
       "      <td>1023</td>\n",
       "      <td>766</td>\n",
       "      <td>354</td>\n",
       "      <td>308</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doctor</th>\n",
       "      <td>8538</td>\n",
       "      <td>579</td>\n",
       "      <td>589</td>\n",
       "      <td>818</td>\n",
       "      <td>586</td>\n",
       "      <td>605</td>\n",
       "      <td>608</td>\n",
       "      <td>662</td>\n",
       "      <td>665</td>\n",
       "      <td>614</td>\n",
       "      <td>612</td>\n",
       "      <td>690</td>\n",
       "      <td>426</td>\n",
       "      <td>464</td>\n",
       "      <td>326</td>\n",
       "      <td>286</td>\n",
       "      <td>207</td>\n",
       "      <td>158</td>\n",
       "      <td>142</td>\n",
       "      <td>79</td>\n",
       "      <td>52</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High School or Below</th>\n",
       "      <td>9060</td>\n",
       "      <td>634</td>\n",
       "      <td>715</td>\n",
       "      <td>794</td>\n",
       "      <td>580</td>\n",
       "      <td>722</td>\n",
       "      <td>636</td>\n",
       "      <td>701</td>\n",
       "      <td>686</td>\n",
       "      <td>693</td>\n",
       "      <td>628</td>\n",
       "      <td>749</td>\n",
       "      <td>485</td>\n",
       "      <td>499</td>\n",
       "      <td>336</td>\n",
       "      <td>330</td>\n",
       "      <td>186</td>\n",
       "      <td>194</td>\n",
       "      <td>139</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>5840</td>\n",
       "      <td>390</td>\n",
       "      <td>406</td>\n",
       "      <td>568</td>\n",
       "      <td>413</td>\n",
       "      <td>432</td>\n",
       "      <td>492</td>\n",
       "      <td>461</td>\n",
       "      <td>435</td>\n",
       "      <td>453</td>\n",
       "      <td>431</td>\n",
       "      <td>476</td>\n",
       "      <td>302</td>\n",
       "      <td>332</td>\n",
       "      <td>223</td>\n",
       "      <td>189</td>\n",
       "      <td>138</td>\n",
       "      <td>132</td>\n",
       "      <td>98</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "flights_booked            0     1     2      3     4     5     6     7     8   \\\n",
       "education                                                                       \n",
       "Bachelor              123264  7949  8423  11436  8033  9444  8606  9050  8958   \n",
       "College                49426  3115  3508   4612  3209  3881  3505  3687  3613   \n",
       "Doctor                  8538   579   589    818   586   605   608   662   665   \n",
       "High School or Below    9060   634   715    794   580   722   636   701   686   \n",
       "Master                  5840   390   406    568   413   432   492   461   435   \n",
       "\n",
       "flights_booked          9     10    11    12    13    14    15    16    17  \\\n",
       "education                                                                    \n",
       "Bachelor              8790  8285  9709  6492  6789  4358  4081  2678  2392   \n",
       "College               3614  3407  4081  2677  2698  1814  1696  1145  1023   \n",
       "Doctor                 614   612   690   426   464   326   286   207   158   \n",
       "High School or Below   693   628   749   485   499   336   330   186   194   \n",
       "Master                 453   431   476   302   332   223   189   138   132   \n",
       "\n",
       "flights_booked          18   19   20   21  \n",
       "education                                  \n",
       "Bachelor              1813  962  708  347  \n",
       "College                766  354  308  121  \n",
       "Doctor                 142   79   52   25  \n",
       "High School or Below   139   65   65   18  \n",
       "Master                  98   36   23   17  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_filtrado[\"education\"], df_filtrado[\"flights_booked\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Es un problema de Medias ✨\n",
    "Ya que flights_booked es una variable numerica continua "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Los datos siguen una distribucion normal?\n",
    "\n",
    "- Solo las variables numericas continuas pueden tener una dist.normal, esto nos indica que debemos realizar una prueba NO parametrica, ya que education no tiene una dist.normal al ser una var. categorica -> son grupos independientes -> utilizaremos el test de Mann-Whitney\n",
    "\n",
    "- A modo de practica se calculara la dist.normal de flights_booked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTA POST EVALUACION 🚨\n",
    "A/B Testing se hace siempre con 1 var numerica y otra categorica que sirve a modo de etiqueta "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para la columna flights_booked los datos no siguen una distribución normal.\n"
     ]
    }
   ],
   "source": [
    "sp.normalidad(df_filtrado, \"flights_booked\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test de Mann-Whitney\n",
    "- Para realizar este test necesitamos tener dos grupos diferenciados\n",
    "- Dividiremos los estudio en universitarios y no universitarios para el test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos los grupos en funcion de si son estudios universitarios o no\n",
    "university = ['Bachelor', 'Master', 'Doctor']\n",
    "no_university = ['College', 'High School or Below']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Crear una columna nueva a nuestro df para asignar el nombre del grupo segun la division que acabamos de hacer -> estudios universitarios o no\n",
    "df_filtrado['education_level'] = df_filtrado['education'].apply(lambda x: 'University' if x in university else 'No University')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</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",
       "      <th>education_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>269835</th>\n",
       "      <td>9</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>No University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181326</th>\n",
       "      <td>0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226948</th>\n",
       "      <td>16</td>\n",
       "      <td>Master</td>\n",
       "      <td>University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77937</th>\n",
       "      <td>0</td>\n",
       "      <td>College</td>\n",
       "      <td>No University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168737</th>\n",
       "      <td>19</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>University</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        flights_booked             education education_level\n",
       "269835               9  High School or Below   No University\n",
       "181326               0              Bachelor      University\n",
       "226948              16                Master      University\n",
       "77937                0               College   No University\n",
       "168737              19              Bachelor      University"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtrado.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hago 2 grupos en funcion de la columna education_level -> seran el grupo de control y grupo test para aplicar en mannwhitneyu\n",
    "grupo_universitario = df_filtrado[df_filtrado[\"education_level\"] == \"University\"][\"flights_booked\"]\n",
    "grupo_no_universitario = df_filtrado[df_filtrado[\"education_level\"] == \"No University\"][\"flights_booked\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para la métrica flights_booked, las medianas son diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Aplicamos test_man_whitney\n",
    "sp.test_man_whitney(df_filtrado, ['flights_booked'], 'University', 'No University', 'education_level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTA POSTEVALUACION 🚨\n",
    "test_man_whitney evalua como es la distribucion de los datos, pueden tener diferente distribucion pero misma mediana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si p_value < 0.05 -> existe una diferencia significativa\n",
    "\n",
    "Si p_value > 0.05 -> NO existe una diferencia significativa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0029\n"
     ]
    }
   ],
   "source": [
    "_, p_value = mannwhitneyu(grupo_universitario, grupo_no_universitario)\n",
    "\n",
    "print(p_value.round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value < 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analizando el p_value se concluye que SI existe una diferencia significativa entre el nivel de estudios y el número de vuelos realizados, teniendo en cuenta la division de estudios realizada. Tiene sentido ya que los nivel de estudio superiores proporcionan un mayor salario y por tanto una mayor posibilidad de realizar vuelos.\n",
    "\n",
    "- NEXT STEPS: Hacer la prueba con divisiones de estudios diferentes"
   ]
  }
 ],
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