evaluación_modulo_3_arianna.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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 estadisticas básicas de las columnas involucradas.\n",
    "\n",
    "- Une los dos conjuntos de datos de la forma más eficiente.\n",
    "\n",
    "### 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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tratamiento de datos\n",
    "# -----------------------------------------------------------------------\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Imputación de nulos usando métodos avanzados estadísticos\n",
    "# -----------------------------------------------------------------------\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "# Librerías de visualización\n",
    "# -----------------------------------------------------------------------\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Configuración\n",
    "# -----------------------------------------------------------------------\n",
    "pd.set_option('display.max_columns', None) # para poder visualizar todas las columnas de los DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Loyalty Number,Year,Month,Flights Booked,Flights with Companions,Total Flights,Distance,Points Accumulated,Points Redeemed,Dollar Cost Points Redeemed\n",
      "0                        100018,2017,1,3,0,3,1521,152,0,0                                                                                                    \n",
      "1                      100102,2017,1,10,4,14,2030,203,0,0                                                                                                    \n",
      "2                        100140,2017,1,6,0,6,1200,120,0,0                                                                                                    \n",
      "3                             100214,2017,1,0,0,0,0,0,0,0                                                                                                    \n",
      "4                             100272,2017,1,0,0,0,0,0,0,0                                                                                                    \n",
      "...                                                   ...                                                                                                    \n",
      "405619                       999902,2018,12,0,0,0,0,0,0,0                                                                                                    \n",
      "405620                       999911,2018,12,0,0,0,0,0,0,0                                                                                                    \n",
      "405621                  999940,2018,12,3,0,3,1233,123,0,0                                                                                                    \n",
      "405622                       999982,2018,12,0,0,0,0,0,0,0                                                                                                    \n",
      "405623                       999986,2018,12,0,0,0,0,0,0,0                                                                                                    \n",
      "\n",
      "[405624 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# Aplico una función para leer los datos del fichero:\n",
    "def leer_datos(ruta, sep=\",\"):\n",
    "\n",
    "    df = pd.read_csv(ruta, sep=sep)\n",
    "    return df\n",
    "\n",
    "# Para hacer la llamada de la función\n",
    "df_customer_flight=leer_datos('customer_flight_activity.csv', sep=\";\")\n",
    "print(df_customer_flight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Abriendo el fichero customer flight activity, me doy cuenta de que tiene todas las variables en una única columna.**\n",
    "\n",
    "**Hay que separarlas, usando la coma cómo separador de columnas.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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>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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100102</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>2030</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100140</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1200</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100214</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100272</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  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   \n",
       "1            14     2030                203               0   \n",
       "2             6     1200                120               0   \n",
       "3             0        0                  0               0   \n",
       "4             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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Voy a dividir la columna en 10 columnas, utilizando las comas para dividir las columnas\n",
    "df_split_flight = df_customer_flight[\n",
    "    'Loyalty Number,Year,Month,Flights Booked,Flights with Companions,Total Flights,Distance,Points Accumulated,Points Redeemed,Dollar Cost Points Redeemed'\n",
    "    ].str.split(',', expand=True) \n",
    "\n",
    "# Voy a asignar los nombres a las columnas:\n",
    "df_split_flight.columns = [\n",
    "    'Loyalty Number', 'Year', 'Month', 'Flights Booked', 'Flights with Companions',\n",
    "    'Total Flights', 'Distance', 'Points Accumulated', 'Points Redeemed',\n",
    "    'Dollar Cost Points Redeemed'\n",
    "]\n",
    "\n",
    "# Con head visualizo las primeras 5 filas de mi nuevo dataframe\n",
    "df_split_flight.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      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",
      "0      480934,Canada,Ontario,Toronto,M2Z 4K1,Female,B...                                                                                                                                            \n",
      "1      549612,Canada,Alberta,Edmonton,T3G 6Y6,Male,Co...                                                                                                                                            \n",
      "2      429460,Canada,British Columbia,Vancouver,V6E 3...                                                                                                                                            \n",
      "3      608370,Canada,Ontario,Toronto,P1W 1K4,Male,Col...                                                                                                                                            \n",
      "4      530508,Canada,Quebec,Hull,J8Y 3Z5,Male,Bachelo...                                                                                                                                            \n",
      "...                                                  ...                                                                                                                                            \n",
      "16732  823768,Canada,British Columbia,Vancouver,V6E 3...                                                                                                                                            \n",
      "16733  680886,Canada,Saskatchewan,Regina,S1J 3C5,Fema...                                                                                                                                            \n",
      "16734  776187,Canada,British Columbia,Vancouver,V5R 1...                                                                                                                                            \n",
      "16735  906428,Canada,Yukon,Whitehorse,Y2K 6R0,Male,Ba...                                                                                                                                            \n",
      "16736  652627,Canada,Manitoba,Winnipeg,R2C 0M5,Female...                                                                                                                                            \n",
      "\n",
      "[16737 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "def leer_datos(ruta, sep=\",\"):\n",
    "\n",
    "    df = pd.read_csv(ruta, sep=sep)\n",
    "    return df\n",
    "\n",
    "# Para hacer la llamada de la función\n",
    "df_customer_loyalty=leer_datos('customer_loyalty_history.csv', sep=\";\")\n",
    "print(df_customer_loyalty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Abriendo el fichero customer loyalty history, me doy cuenta que tenemos el mismo problema del otro fichero, voy a dividir las columnas con el mismo metodo.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </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</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></td>\n",
       "      <td></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></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></td>\n",
       "      <td></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></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</td>\n",
       "      <td>1</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></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></td>\n",
       "      <td></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</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></td>\n",
       "      <td></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        Married         Star  3839.14        Standard   \n",
       "1   College               Divorced         Star  3839.61        Standard   \n",
       "2   College                 Single         Star  3839.75        Standard   \n",
       "3   College                 Single         Star  3839.75        Standard   \n",
       "4  Bachelor  103495        Married         Star  3842.79        Standard   \n",
       "\n",
       "  Enrollment Year Enrollment Month Cancellation Year Cancellation Month  \n",
       "0            2016                2                                       \n",
       "1            2016                3                                       \n",
       "2            2014                7              2018                  1  \n",
       "3            2013                2                                       \n",
       "4            2014               10                                       "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Voy a dividir la columna en 16 columnas, utilizando las comas para dividir las columnas\n",
    "df_split_loyalty = df_customer_loyalty['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'].str.split(',', expand=True) \n",
    "\n",
    "# Voy a asignar los nombres a las columnas:\n",
    "df_split_loyalty.columns = [\n",
    "   '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",
    "\n",
    "df_split_loyalty.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ahora que tengo los ficheros divididos en columnas, voy a analizarlos con una función para comprobar si hay valores nulos, duplicados, atípicos o datos faltantes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe actividad vuelos clientes:\n",
      "Tamaño del dataset:\n",
      "Filas: 405624, Columnas: 10\n",
      "\n",
      "Tipos de datos:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Loyalty Number                 object\n",
       "Year                           object\n",
       "Month                          object\n",
       "Flights Booked                 object\n",
       "Flights with Companions        object\n",
       "Total Flights                  object\n",
       "Distance                       object\n",
       "Points Accumulated             object\n",
       "Points Redeemed                object\n",
       "Dollar Cost Points Redeemed    object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Valores_nulos:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Loyalty Number                 0\n",
       "Year                           0\n",
       "Month                          0\n",
       "Flights Booked                 0\n",
       "Flights with Companions        0\n",
       "Total Flights                  0\n",
       "Distance                       0\n",
       "Points Accumulated             0\n",
       "Points Redeemed                0\n",
       "Dollar Cost Points Redeemed    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filas duplicadas en el dataset:\n",
      "Filas: 1864\n",
      "_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n",
      "\n",
      "Dataframe historial clientes:\n",
      "Tamaño del dataset:\n",
      "Filas: 16737, Columnas: 16\n",
      "\n",
      "Tipos de datos:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Loyalty Number        object\n",
       "Country               object\n",
       "Province              object\n",
       "City                  object\n",
       "Postal Code           object\n",
       "Gender                object\n",
       "Education             object\n",
       "Salary                object\n",
       "Marital Status        object\n",
       "Loyalty Card          object\n",
       "CLV                   object\n",
       "Enrollment Type       object\n",
       "Enrollment Year       object\n",
       "Enrollment Month      object\n",
       "Cancellation Year     object\n",
       "Cancellation Month    object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Valores_nulos:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Loyalty Number        0\n",
       "Country               0\n",
       "Province              0\n",
       "City                  0\n",
       "Postal Code           0\n",
       "Gender                0\n",
       "Education             0\n",
       "Salary                0\n",
       "Marital Status        0\n",
       "Loyalty Card          0\n",
       "CLV                   0\n",
       "Enrollment Type       0\n",
       "Enrollment Year       0\n",
       "Enrollment Month      0\n",
       "Cancellation Year     0\n",
       "Cancellation Month    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filas duplicadas en el dataset:\n",
      "Filas: 0\n"
     ]
    }
   ],
   "source": [
    "def explorar_dataset(df): # Función generica para una visión general del dataset\n",
    "\n",
    "    print(\"Tamaño del dataset:\")# Tamaño del dataset (filas y columnas)\n",
    "    print(f\"Filas: {df.shape[0]}, Columnas: {df.shape[1]}\") # Tamaño del dataset (filas y columnas)\n",
    "    print(\"\\nTipos de datos:\") # Tipos de datos por columna\n",
    "    display(df.dtypes)\n",
    "    print(\"\\nValores_nulos:\") \n",
    "    display(df.isnull().sum())\n",
    "    print(\"Filas duplicadas en el dataset:\")\n",
    "    print(f\"Filas: {df.duplicated().sum()}\") #calcula la cantidad de filas duplicadas en el df\n",
    "\n",
    "# Llamamos a la función para ver los datos de cadauno de los df\n",
    "print(f'Dataframe actividad vuelos clientes:')\n",
    "explorar_dataset(df_split_flight)\n",
    "print('_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _', '\\n')\n",
    "print(f'Dataframe historial clientes:')\n",
    "explorar_dataset(df_split_loyalty)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Loyalty Number  Year Month Flights Booked Flights with Companions  \\\n",
      "42             101902  2017     1              0                       0   \n",
      "227            112142  2017     1              0                       0   \n",
      "478            126100  2017     1              0                       0   \n",
      "567            130331  2017     1              0                       0   \n",
      "660            135421  2017     1              0                       0   \n",
      "...               ...   ...   ...            ...                     ...   \n",
      "404668         949628  2018    12              0                       0   \n",
      "404884         960050  2018    12              0                       0   \n",
      "405111         971370  2018    12              0                       0   \n",
      "405410         988392  2018    12              0                       0   \n",
      "405437         989528  2018    12              0                       0   \n",
      "\n",
      "       Total Flights Distance Points Accumulated Points Redeemed  \\\n",
      "42                 0        0                  0               0   \n",
      "227                0        0                  0               0   \n",
      "478                0        0                  0               0   \n",
      "567                0        0                  0               0   \n",
      "660                0        0                  0               0   \n",
      "...              ...      ...                ...             ...   \n",
      "404668             0        0                  0               0   \n",
      "404884             0        0                  0               0   \n",
      "405111             0        0                  0               0   \n",
      "405410             0        0                  0               0   \n",
      "405437             0        0                  0               0   \n",
      "\n",
      "       Dollar Cost Points Redeemed  \n",
      "42                               0  \n",
      "227                              0  \n",
      "478                              0  \n",
      "567                              0  \n",
      "660                              0  \n",
      "...                            ...  \n",
      "404668                           0  \n",
      "404884                           0  \n",
      "405111                           0  \n",
      "405410                           0  \n",
      "405437                           0  \n",
      "\n",
      "[1864 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "# Mostrar las filas duplicadas del df de actividad de vuelos:\n",
    "df_flight_duplicates = df_split_flight[df_split_flight.duplicated()]\n",
    "print(df_flight_duplicates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observando estas informaciones, puedo ver ya que hay que cambiar algunos tipos de datos en los dos df, porque son todos object, o sea categoricos y algunos necesito que sean numericos para poder calcular porcentajes y estadisticas, como por ejemplo, en el df de la actividad de vuelos: Flights Booked, Flights with Companions, Total Flights, Distance, Points Accumulated, Points Redeemed, Dollar Cost Points Redeemed.**\n",
    "\n",
    "**También veo que hay 1864 filas duplicadas en el dataset de la actividad de vuelos, pero he comprobado y veo que se trata de años, meses, numeros de puntos acumulados y redimidos, el valor en dolares de los puntos, etc. No hace falta modificar esos datos**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Voy a transformar el tipo de datos da categoricos a numerico en las columnas que he identificado antes:\n",
    "\n",
    "def cambiar_tipo_datos(df):\n",
    "    # Diccionario que mapea las columnas a su tipo deseado\n",
    "    tipos_datos = {\n",
    "        'Flights Booked': 'float64', \n",
    "        'Flights with Companions': 'float64', \n",
    "        'Total Flights': 'float64', \n",
    "        'Distance': 'float64', \n",
    "        'Points Accumulated': 'float64', \n",
    "        'Points Redeemed': 'float64', \n",
    "        'Dollar Cost Points Redeemed': 'float64'\n",
    "    }\n",
    "    \n",
    "    # Aplicar la conversión de tipos de datos\n",
    "    df = df.astype(tipos_datos)\n",
    "\n",
    "    return df\n",
    "\n",
    "# Usar la función en tu DataFrame\n",
    "df_split_flight = cambiar_tipo_datos(df_split_flight)\n",
    "\n",
    "# Verificar los tipos de datos después del cambio\n",
    "print(df_split_flight.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primeras 5 filas df Actividad vuelos:\n",
      "  loyalty_number  year month  flights_booked  flights_with_companions  \\\n",
      "0         100018  2017     1             3.0                      0.0   \n",
      "1         100102  2017     1            10.0                      4.0   \n",
      "2         100140  2017     1             6.0                      0.0   \n",
      "3         100214  2017     1             0.0                      0.0   \n",
      "4         100272  2017     1             0.0                      0.0   \n",
      "\n",
      "   total_flights  distance  points_accumulated  points_redeemed  \\\n",
      "0            3.0    1521.0               152.0              0.0   \n",
      "1           14.0    2030.0               203.0              0.0   \n",
      "2            6.0    1200.0               120.0              0.0   \n",
      "3            0.0       0.0                 0.0              0.0   \n",
      "4            0.0       0.0                 0.0              0.0   \n",
      "\n",
      "   dollar_cost_points_redeemed  \n",
      "0                          0.0  \n",
      "1                          0.0  \n",
      "2                          0.0  \n",
      "3                          0.0  \n",
      "4                          0.0  \n",
      "_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n",
      "\n",
      "Primeras 5 filas df Historial clientes:\n",
      "  loyalty_number  year month  flights_booked  flights_with_companions  \\\n",
      "0         100018  2017     1             3.0                      0.0   \n",
      "1         100102  2017     1            10.0                      4.0   \n",
      "2         100140  2017     1             6.0                      0.0   \n",
      "3         100214  2017     1             0.0                      0.0   \n",
      "4         100272  2017     1             0.0                      0.0   \n",
      "\n",
      "   total_flights  distance  points_accumulated  points_redeemed  \\\n",
      "0            3.0    1521.0               152.0              0.0   \n",
      "1           14.0    2030.0               203.0              0.0   \n",
      "2            6.0    1200.0               120.0              0.0   \n",
      "3            0.0       0.0                 0.0              0.0   \n",
      "4            0.0       0.0                 0.0              0.0   \n",
      "\n",
      "   dollar_cost_points_redeemed  \n",
      "0                          0.0  \n",
      "1                          0.0  \n",
      "2                          0.0  \n",
      "3                          0.0  \n",
      "4                          0.0  \n"
     ]
    }
   ],
   "source": [
    "# Ahora quiero estandarizar los nombres de todas las columnas de los dos df:\n",
    "# cambiar todas las etiquetas a minusculas y sustituir los espacios con guion bajo:\n",
    "\n",
    "df_split_flight.columns = [col.lower().replace(\" \", \"_\") for col in df_split_flight.columns] #df actividad vuelos\n",
    "print(f'Primeras 5 filas df Actividad vuelos:')\n",
    "print(df_split_flight.head()) \n",
    "\n",
    "print('_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _', '\\n')\n",
    "\n",
    "df_split_loyalty.columns = [col.lower().replace(\" \", \"_\") for col in df_split_loyalty.columns] #df historial clientes\n",
    "print(f'Primeras 5 filas df Historial clientes:')\n",
    "print(df_split_flight.head()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loyalty_number                  object\n",
      "year                            object\n",
      "month                           object\n",
      "flights_booked                   int64\n",
      "flights_with_companions          int64\n",
      "total_flights                    int64\n",
      "distance                       float64\n",
      "points_accumulated               int64\n",
      "points_redeemed                  int64\n",
      "dollar_cost_points_redeemed    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Voy a transformar otra vez el tipo de datos da float a int en algunas de las columnas que he transformado antes:\n",
    "\n",
    "def cambiar_tipo_datos(df):\n",
    "    # Diccionario que mapea las columnas a su tipo deseado\n",
    "    tipos_datos = {\n",
    "        'flights_booked': 'int64', \n",
    "        'flights_with_companions': 'int64', \n",
    "        'total_flights': 'int64', \n",
    "        'points_accumulated': 'int64', \n",
    "        'points_redeemed': 'int64', \n",
    "    }\n",
    "    \n",
    "    # Aplicar la conversión de tipos de datos\n",
    "    df = df.astype(tipos_datos)\n",
    "\n",
    "    return df\n",
    "\n",
    "# Usar la función en tu DataFrame\n",
    "df_split_flight = cambiar_tipo_datos(df_split_flight)\n",
    "\n",
    "# Verificar los tipos de datos después del cambio\n",
    "print(df_split_flight.dtypes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ahora hay que cambiar los meses, o sea sustituir al numero 1 la palabra 'enero', al 2 la palabra 'febrero' y así sucesivamente.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cambiar meses en columna month:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FASE 2: VISUALIZACIÓN \n",
    "\n",
    "### Usando las herramientas de visualización que has aprendido durante el módulo, contesta a las siguientes preguntas usando la mejor gráfica que consideres:\n",
    "\n",
    "***1. ¿Cómo se distribuye la cantidad de vuelos reservados por mes durante el año?***\n",
    "\n",
    "***2. ¿Existe una relación entre la distancia de los vuelos y los puntos acumulados por los clientes?***\n",
    "\n",
    "***3. ¿Cuál es la distribución de los clientes por provincia o estado?***\n",
    "\n",
    "***4. ¿Cómo se compara el salario promedio entre los diferentes niveles educativos de los clientes?***\n",
    "\n",
    "***5. ¿Cuál es la proporción de clientes con diferentes tipos de tarjetas de fidelidad?***\n",
    "\n",
    "***6. ¿Cómo se distribuyen los clientes según su estado civil y género?***"
   ]
  }
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