4_analysis_telecommunication_services.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fbff87a2",
   "metadata": {},
   "source": [
    "***Megaline Internet***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0fbe65",
   "metadata": {},
   "source": [
    "# Contenido<a id='contenido'></a>\n",
    "* [Introducción](#int)\n",
    "* [Inicialización de datos](#ini)\n",
    "* [Preparación de datos](#pre)\n",
    "* [Análisis de datos](#ana)\n",
    "* [Prueba de hipótesis](#hip)\n",
    "* [Conclusión general](#con)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06972d3",
   "metadata": {},
   "source": [
    "# Introducción<a id='intr'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e26d14d",
   "metadata": {},
   "source": [
    "El operador de telecomunicaciones Megaline ofrece a sus clientes dos tarifas de prepago, Surf y Ultimate. El departamento comercial quiere saber cuál de los planes genera más ingresos para ajustar el presupuesto de publicidad.\n",
    "Vas a realizar un análisis preliminar de las tarifas basado en una selección de clientes relativamente pequeña. Tendrás los datos de 500 clientes de Megaline: quiénes son los clientes, de dónde son, qué tarifa usan y la cantidad de llamadas que hicieron y los mensajes de texto que enviaron en 2018. Tu trabajo es analizar el comportamiento de los clientes y determinar qué tarifa de prepago genera más ingresos.\n",
    "\n",
    "* Surf\n",
    "    - Pago mensual: 20 USD\n",
    "    - 500 minutos al mes, 50 SMS y 15 GB de datos\n",
    "    - Si se exceden los límites del paquete:\n",
    "    - 1 minuto: 3 centavos\n",
    "    - 1 SMS: 3 centavos\n",
    "    - 1 GB de datos: 10 USD\n",
    "* Ultimate\n",
    "    - Pago mensual: 70 USD\n",
    "    - 3000 minutos al mes, 1000 SMS y 30 GB de datos\n",
    "    - Si se exceden los límites del paquete:\n",
    "    - 1 minuto: 1 centavo\n",
    "    - 1 SMS: 1 centavo\n",
    "    - 1 GB de datos: 7 USD\n",
    "\n",
    "## Objetivos\n",
    "\n",
    "Determinar cuál tarifa de prepago genera más ingresos, por lo que se tiene que probar las siguientes hipótesis:\n",
    "*  El ingreso promedio de los usuarios de las tarifas Ultimate y Surf difiere.\n",
    "*  El ingreso promedio de los usuarios en el área de estados Nueva York - Nueva Jersey es diferente al de los usuarios de otras regiones.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9783f26d",
   "metadata": {},
   "source": [
    "# Inicialización de datos<a id='ini'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feca9b1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-26T20:46:57.198238Z",
     "start_time": "2022-12-26T20:46:57.141969Z"
    }
   },
   "source": [
    "**Librerias y carga de datos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3e9af80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.098477Z",
     "start_time": "2023-01-05T13:45:23.075577Z"
    }
   },
   "outputs": [],
   "source": [
    "# librerias\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats as st\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51ac1f6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.301128Z",
     "start_time": "2023-01-05T13:45:24.098477Z"
    }
   },
   "outputs": [],
   "source": [
    "# carga de datos\n",
    "try:\n",
    "    calls    = pd.read_csv('megaline_calls.csv')\n",
    "    internet = pd.read_csv('megaline_internet.csv')\n",
    "    messages = pd.read_csv('megaline_messages.csv')\n",
    "    plans    = pd.read_csv('megaline_plans.csv')\n",
    "    users    = pd.read_csv('megaline_users.csv')\n",
    "except:\n",
    "    calls    = pd.read_csv('/datasets/megaline_calls.csv')\n",
    "    internet = pd.read_csv('/datasets/megaline_internet.csv')\n",
    "    messages = pd.read_csv('/datasets/megaline_messages.csv')\n",
    "    plans    = pd.read_csv('/datasets/megaline_plans.csv')\n",
    "    users    = pd.read_csv('/datasets/megaline_users.csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1691d1",
   "metadata": {},
   "source": [
    "**Exploración de datos iniciales:**\n",
    "\n",
    "La tabla calls (datos sobre las llamadas):\n",
    "* `id` — identificador único de la llamada\n",
    "* `call_date` — fecha de la llamada\n",
    "* `duration` — duración de la llamada (en minutos)\n",
    "* `user_id` — el identificador del usuario que realiza la llamada\n",
    "\n",
    "La tabla internet (datos sobre las sesiones web):\n",
    "* `id` — identificador único de la sesión\n",
    "* `mb_used` — el volumen de datos gastados durante la sesión (en megabytes)\n",
    "* `session_date` — fecha de la sesión web\n",
    "* `user_id` — identificador del usuario\n",
    "\n",
    "La tabla messages (datos sobre los SMS):\n",
    "* `id` — identificador único del SMS\n",
    "* `message_date` — fecha del SMS\n",
    "* `user_id` — el identificador del usuario que manda el SMS\n",
    "\n",
    "La tabla plans (datos sobre las tarifas):\n",
    "* `plan_name` — nombre de la tarifa\n",
    "* `usd_monthly_fee` — pago mensual en dólares estadounidenses\n",
    "* `minutes_included` — minutos incluidos al mes\n",
    "* `messages_included` — SMS incluidos al mes\n",
    "* `mb_per_month_included` — datos incluidos al mes (en megabytes)\n",
    "* `usd_per_minute` — precio por minuto tras exceder los límites del paquete (por ejemplo, si el paquete incluye 100 minutos el operador cobrará el minuto 101)\n",
    "* `usd_per_message` — precio por SMS tras exceder los límites del paquete\n",
    "* `usd_per_gb` — precio por gigabyte de los datos extra tras exceder los límites del paquete (1 GB = 1024 megabytes)\n",
    "\n",
    "La tabla users (datos sobre los usuarios):\n",
    "* `user_id` — identificador único del usuario\n",
    "* `first_name` — nombre del usuario\n",
    "* `last_name` — apellido del usuario\n",
    "* `age` — edad del usuario (en años)\n",
    "* `reg_date` — fecha de suscripción (dd, mm, aa)\n",
    "* `churn_date` — fecha en que el usuario dejó de usar el servicio (si el valor es ausente, la tarifa se estaba usando cuando se recuperaron estos datos)\n",
    "* `city` — ciudad de residencia del usuario\n",
    "* `plan` — nombre de la tarifa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "359261e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.393538Z",
     "start_time": "2023-01-05T13:45:24.301128Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabla calls\n"
     ]
    },
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
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       "      <td>8.52</td>\n",
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       "      <td>2018-12-28</td>\n",
       "      <td>5.76</td>\n",
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       "      <th>4</th>\n",
       "      <td>1000_380</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id   call_date  duration\n",
       "0   1000_93     1000  2018-12-27      8.52\n",
       "1  1000_145     1000  2018-12-27     13.66\n",
       "2  1000_247     1000  2018-12-27     14.48\n",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 137735 entries, 0 to 137734\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   id         137735 non-null  object \n",
      " 1   user_id    137735 non-null  int64  \n",
      " 2   call_date  137735 non-null  object \n",
      " 3   duration   137735 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 4.2+ MB\n",
      "registros duplicados:  0\n"
     ]
    }
   ],
   "source": [
    "print('tabla calls')\n",
    "display(calls.head())\n",
    "calls.info()\n",
    "print('registros duplicados: ', calls.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "451e7786",
   "metadata": {
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     "end_time": "2023-01-05T13:45:24.479520Z",
     "start_time": "2023-01-05T13:45:24.395065Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabla internet\n"
     ]
    },
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_date</th>\n",
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       "      <th>0</th>\n",
       "      <td>1000_13</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>89.86</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>1000_379</td>\n",
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       "      <td>2018-12-28</td>\n",
       "      <td>660.40</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>270.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_442</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>880.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         id  user_id session_date  mb_used\n",
       "0   1000_13     1000   2018-12-29    89.86\n",
       "1  1000_204     1000   2018-12-31     0.00\n",
       "2  1000_379     1000   2018-12-28   660.40\n",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104825 entries, 0 to 104824\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            104825 non-null  object \n",
      " 1   user_id       104825 non-null  int64  \n",
      " 2   session_date  104825 non-null  object \n",
      " 3   mb_used       104825 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 3.2+ MB\n",
      "registros duplicados:  0\n"
     ]
    }
   ],
   "source": [
    "print('tabla internet')\n",
    "display(internet.head())\n",
    "internet.info()\n",
    "print('registros duplicados: ', internet.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c72c899",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.535339Z",
     "start_time": "2023-01-05T13:45:24.479520Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabla messages\n"
     ]
    },
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>message_date</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_125</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>1000</td>\n",
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       "      <th>4</th>\n",
       "      <td>1000_255</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "         id  user_id message_date\n",
       "0  1000_125     1000   2018-12-27\n",
       "1  1000_160     1000   2018-12-31\n",
       "2  1000_223     1000   2018-12-31\n",
       "3  1000_251     1000   2018-12-27\n",
       "4  1000_255     1000   2018-12-26"
      ]
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 76051 entries, 0 to 76050\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   id            76051 non-null  object\n",
      " 1   user_id       76051 non-null  int64 \n",
      " 2   message_date  76051 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ MB\n",
      "registros duplicados:  0\n"
     ]
    }
   ],
   "source": [
    "print('tabla messages')\n",
    "display(messages.head())\n",
    "messages.info()\n",
    "print('registros duplicados: ', messages.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19026693",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.570412Z",
     "start_time": "2023-01-05T13:45:24.536361Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabla plans\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>usd_monthly_pay</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</th>\n",
       "      <th>plan_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>ultimate</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   messages_included  mb_per_month_included  minutes_included  \\\n",
       "0                 50                  15360               500   \n",
       "1               1000                  30720              3000   \n",
       "\n",
       "   usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute plan_name  \n",
       "0               20          10             0.03            0.03      surf  \n",
       "1               70           7             0.01            0.01  ultimate  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 8 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   messages_included      2 non-null      int64  \n",
      " 1   mb_per_month_included  2 non-null      int64  \n",
      " 2   minutes_included       2 non-null      int64  \n",
      " 3   usd_monthly_pay        2 non-null      int64  \n",
      " 4   usd_per_gb             2 non-null      int64  \n",
      " 5   usd_per_message        2 non-null      float64\n",
      " 6   usd_per_minute         2 non-null      float64\n",
      " 7   plan_name              2 non-null      object \n",
      "dtypes: float64(2), int64(5), object(1)\n",
      "memory usage: 256.0+ bytes\n",
      "registros duplicados:  0\n"
     ]
    }
   ],
   "source": [
    "print('tabla plans')\n",
    "display(plans.head())\n",
    "plans.info()\n",
    "print('registros duplicados: ', plans.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18c9511d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.593955Z",
     "start_time": "2023-01-05T13:45:24.570412Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabla users\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>user_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>plan</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Anamaria</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>28</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Carlee</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Reynaldo</td>\n",
       "      <td>Jenkins</td>\n",
       "      <td>52</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id first_name  last_name  age                                   city  \\\n",
       "0     1000   Anamaria      Bauer   45  Atlanta-Sandy Springs-Roswell, GA MSA   \n",
       "1     1001     Mickey  Wilkerson   28        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "2     1002     Carlee    Hoffman   36   Las Vegas-Henderson-Paradise, NV MSA   \n",
       "3     1003   Reynaldo    Jenkins   52                          Tulsa, OK MSA   \n",
       "4     1004    Leonila   Thompson   40        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "\n",
       "     reg_date      plan churn_date  \n",
       "0  2018-12-24  ultimate        NaN  \n",
       "1  2018-08-13      surf        NaN  \n",
       "2  2018-10-21      surf        NaN  \n",
       "3  2018-01-28      surf        NaN  \n",
       "4  2018-05-23      surf        NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   user_id     500 non-null    int64 \n",
      " 1   first_name  500 non-null    object\n",
      " 2   last_name   500 non-null    object\n",
      " 3   age         500 non-null    int64 \n",
      " 4   city        500 non-null    object\n",
      " 5   reg_date    500 non-null    object\n",
      " 6   plan        500 non-null    object\n",
      " 7   churn_date  34 non-null     object\n",
      "dtypes: int64(2), object(6)\n",
      "memory usage: 31.4+ KB\n",
      "registros duplicados:  0\n"
     ]
    }
   ],
   "source": [
    "print('tabla users')\n",
    "display(users.head())\n",
    "users.info()\n",
    "print('registros duplicados: ', plans.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34bca9be",
   "metadata": {},
   "source": [
    "Se revisaron los datos inicialmente de las 5 tablas, y podemos indicar lo siguiente:\n",
    "\n",
    "\n",
    "Tabla `calls`:\n",
    "  * Hay 137735 registros y 4 columnas.\n",
    "  * En la columna `call_date` el tipo de dato es object, debe cambiarse a tipo datetime.\n",
    "  * No existen valores ausentes ni registros duplicados.\n",
    "\n",
    "Tabla `internet`:\n",
    "  * Hay 104825 registros y 4 columnas.\n",
    "  * En la columna `session_date` el tipo de dato es object, debe cambiarse a tipo datetime.\n",
    "  * No existen valores ausentes ni registros duplicados\n",
    "  \n",
    "Tabla `messages`:\n",
    "  * Hay 76051 registros y 3 columnas.\n",
    "  * En la columna `message_date` el tipo de dato es object, debe cambiarse a tipo datetime.\n",
    "  * No existen valores ausentes ni registros duplicados.\n",
    "\n",
    "Tabla `plans`:\n",
    "  * Hay 2 registros y 8 columnas.\n",
    "  * No existen valores ausentes ni registros duplicados.\n",
    "  \n",
    "Tabla `users`:\n",
    "  * Hay 500 registros y 8 columnas.\n",
    "  * En las columnas `reg_date` y `churn_date` el tipo de dato es object, debe cambiarse a tipo datetime.\n",
    "  * La columna `churn_date` tiene 466 valores ausentes, lo que representa el 93% de los usuarios. El valor ausente representa que la tarifa se estaba usando cuando se generaron estos datos.\n",
    "\n",
    "  * No existen registros duplicados."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de492e9",
   "metadata": {},
   "source": [
    "# Preparación de datos<a id='pre'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb7c6f2",
   "metadata": {},
   "source": [
    "* Convierte los datos en los tipos necesarios\n",
    "* Encuentra y elimina errores en los datos.\n",
    "* Explica qué errores encontraste y cómo los eliminaste. Nota: muchas llamadas tienen duración de 0.0 minutos. Estas pueden ser llamadas perdidas. Tú decides si quieres preprocesar o no estos valores; evalúa cuánto afectaría su ausencia a los resultados de tu análisis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e88a1ed",
   "metadata": {},
   "source": [
    "Vamos a preparar los datos, empezemos realizando el cambio de tipo de datos necesario:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f735b7b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.645672Z",
     "start_time": "2023-01-05T13:45:24.595416Z"
    }
   },
   "outputs": [],
   "source": [
    "# cambiar a tipo fecha todas las fechas de todas las tablas\n",
    "calls['call_date']       = pd.to_datetime(calls['call_date'], format='%Y-%m-%d')\n",
    "internet['session_date'] = pd.to_datetime(internet['session_date'], format='%Y-%m-%d')\n",
    "messages['message_date'] = pd.to_datetime(messages['message_date'], format='%Y-%m-%d')\n",
    "users['reg_date']        = pd.to_datetime(users['reg_date'], format='%Y-%m-%d')\n",
    "users['churn_date']      = pd.to_datetime(users['churn_date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84a959e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.670381Z",
     "start_time": "2023-01-05T13:45:24.645672Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n",
      "datetime64[ns]\n",
      "datetime64[ns]\n",
      "datetime64[ns]\n",
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "# revisar tipos de datos cambiados\n",
    "\n",
    "print(calls.call_date.dtype)\n",
    "print(internet.session_date.dtype)\n",
    "print(messages.message_date.dtype)\n",
    "print(users.reg_date.dtype)\n",
    "print(users.churn_date.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2674270c",
   "metadata": {},
   "source": [
    "Cada llamada individual se redondea, incluso si la llamada duró solo un segundo, se contará como un minuto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df609354",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.685590Z",
     "start_time": "2023-01-05T13:45:24.675387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          9.0\n",
       "1         14.0\n",
       "2         15.0\n",
       "3          6.0\n",
       "4          5.0\n",
       "          ... \n",
       "137730     9.0\n",
       "137731    11.0\n",
       "137732     9.0\n",
       "137733     1.0\n",
       "137734    14.0\n",
       "Name: duration, Length: 137735, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# redondeo de segundos a minutos en duration de calls\n",
    "calls['duration'] = np.ceil(calls['duration'])\n",
    "calls['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93112d3a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.711836Z",
     "start_time": "2023-01-05T13:45:24.686610Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "llamadas con 0 segundos:  26834\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0    0.19\n",
       "6.0    0.06\n",
       "5.0    0.06\n",
       "7.0    0.06\n",
       "8.0    0.06\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# revisar duracion de llamadas en 0 segundos\n",
    "print('llamadas con 0 segundos: ', len(calls.query('duration == 0')))\n",
    "\n",
    "round(calls.duration.value_counts(normalize=True), ndigits=2).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e65494d",
   "metadata": {},
   "source": [
    "26834 de llamadas tienen duración 0, es decir el 19% de los registros. Las llamadas con duración 0 segundos son llamadas perdidas (no efectuadas) por los usuarios.  En este análisis necesitamos saber el tiempo mensual de llamadas, por lo que una llamada con 0 segundos no va a sumar al mensual. Dicho esto, decidimos eliminar todos los registros con duracion 0, ya que no representa información valiosa para nuestro análisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f73d1be8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.728429Z",
     "start_time": "2023-01-05T13:45:24.711836Z"
    }
   },
   "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>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_93</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_145</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_247</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_309</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_380</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137730</th>\n",
       "      <td>1499_199</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137731</th>\n",
       "      <td>1499_200</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137732</th>\n",
       "      <td>1499_201</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137733</th>\n",
       "      <td>1499_202</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137734</th>\n",
       "      <td>1499_203</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137735 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  user_id  call_date  duration\n",
       "0        1000_93     1000 2018-12-27       9.0\n",
       "1       1000_145     1000 2018-12-27      14.0\n",
       "2       1000_247     1000 2018-12-27      15.0\n",
       "3       1000_309     1000 2018-12-28       6.0\n",
       "4       1000_380     1000 2018-12-30       5.0\n",
       "...          ...      ...        ...       ...\n",
       "137730  1499_199     1499 2018-11-21       9.0\n",
       "137731  1499_200     1499 2018-10-20      11.0\n",
       "137732  1499_201     1499 2018-09-21       9.0\n",
       "137733  1499_202     1499 2018-10-10       1.0\n",
       "137734  1499_203     1499 2018-12-29      14.0\n",
       "\n",
       "[137735 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4646b6bb",
   "metadata": {},
   "source": [
    "La tabla `calls` quedó con un total de 110901 registros eliminando las llamadas de 0 segundos, es decir se eliminaron 26834 registros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c774a4f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.744586Z",
     "start_time": "2023-01-05T13:45:24.728429Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         0\n",
       "first_name      0\n",
       "last_name       0\n",
       "age             0\n",
       "city            0\n",
       "reg_date        0\n",
       "plan            0\n",
       "churn_date    466\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# revisamos valores ausentes en users\n",
    "users.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c4e6278",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-27T21:56:34.096883Z",
     "start_time": "2022-12-27T21:56:34.082143Z"
    }
   },
   "source": [
    "Hay 466 valores ausentes en la columna churn_date, esta columna registra la fecha que el usuario dejó de usar el servicio, al estar vacio este valor nos indica que el usuario sigue usando el servicio al término del año 2018. Dicho esto, se decide imputar los datos con la fecha 1 enero del 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd947182",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.777301Z",
     "start_time": "2023-01-05T13:45:24.745265Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>user_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>plan</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Anamaria</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>28</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Carlee</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Reynaldo</td>\n",
       "      <td>Jenkins</td>\n",
       "      <td>52</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>Fidel</td>\n",
       "      <td>Sharpe</td>\n",
       "      <td>67</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>Ariel</td>\n",
       "      <td>Shepherd</td>\n",
       "      <td>49</td>\n",
       "      <td>New Orleans-Metairie, LA MSA</td>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>Donte</td>\n",
       "      <td>Barrera</td>\n",
       "      <td>49</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA MSA</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>Scot</td>\n",
       "      <td>Williamson</td>\n",
       "      <td>51</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>Shena</td>\n",
       "      <td>Dickson</td>\n",
       "      <td>37</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "      <td>2018-05-06</td>\n",
       "      <td>surf</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>466 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id first_name   last_name  age  \\\n",
       "0       1000   Anamaria       Bauer   45   \n",
       "1       1001     Mickey   Wilkerson   28   \n",
       "2       1002     Carlee     Hoffman   36   \n",
       "3       1003   Reynaldo     Jenkins   52   \n",
       "4       1004    Leonila    Thompson   40   \n",
       "..       ...        ...         ...  ...   \n",
       "495     1495      Fidel      Sharpe   67   \n",
       "496     1496      Ariel    Shepherd   49   \n",
       "497     1497      Donte     Barrera   49   \n",
       "498     1498       Scot  Williamson   51   \n",
       "499     1499      Shena     Dickson   37   \n",
       "\n",
       "                                          city   reg_date      plan churn_date  \n",
       "0        Atlanta-Sandy Springs-Roswell, GA MSA 2018-12-24  ultimate        NaT  \n",
       "1              Seattle-Tacoma-Bellevue, WA MSA 2018-08-13      surf        NaT  \n",
       "2         Las Vegas-Henderson-Paradise, NV MSA 2018-10-21      surf        NaT  \n",
       "3                                Tulsa, OK MSA 2018-01-28      surf        NaT  \n",
       "4              Seattle-Tacoma-Bellevue, WA MSA 2018-05-23      surf        NaT  \n",
       "..                                         ...        ...       ...        ...  \n",
       "495  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-09-04      surf        NaT  \n",
       "496               New Orleans-Metairie, LA MSA 2018-02-20      surf        NaT  \n",
       "497     Los Angeles-Long Beach-Anaheim, CA MSA 2018-12-10  ultimate        NaT  \n",
       "498  New York-Newark-Jersey City, NY-NJ-PA MSA 2018-02-04      surf        NaT  \n",
       "499          Orlando-Kissimmee-Sanford, FL MSA 2018-05-06      surf        NaT  \n",
       "\n",
       "[466 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.query('churn_date.isna()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81e6f018",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.795587Z",
     "start_time": "2023-01-05T13:45:24.777301Z"
    }
   },
   "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>user_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>plan</th>\n",
       "      <th>churn_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Anamaria</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>45</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Mickey</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>28</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>surf</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Carlee</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>36</td>\n",
       "      <td>Las Vegas-Henderson-Paradise, NV MSA</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>surf</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Reynaldo</td>\n",
       "      <td>Jenkins</td>\n",
       "      <td>52</td>\n",
       "      <td>Tulsa, OK MSA</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>surf</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>Leonila</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>40</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>surf</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id first_name  last_name  age                                   city  \\\n",
       "0     1000   Anamaria      Bauer   45  Atlanta-Sandy Springs-Roswell, GA MSA   \n",
       "1     1001     Mickey  Wilkerson   28        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "2     1002     Carlee    Hoffman   36   Las Vegas-Henderson-Paradise, NV MSA   \n",
       "3     1003   Reynaldo    Jenkins   52                          Tulsa, OK MSA   \n",
       "4     1004    Leonila   Thompson   40        Seattle-Tacoma-Bellevue, WA MSA   \n",
       "\n",
       "    reg_date      plan churn_date  \n",
       "0 2018-12-24  ultimate 2019-01-01  \n",
       "1 2018-08-13      surf 2019-01-01  \n",
       "2 2018-10-21      surf 2019-01-01  \n",
       "3 2018-01-28      surf 2019-01-01  \n",
       "4 2018-05-23      surf 2019-01-01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# imputamos valores ausentes en churn_date\n",
    "users['churn_date'] = users['churn_date'].fillna('2019-01-01')\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "60d3db4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.812050Z",
     "start_time": "2023-01-05T13:45:24.795587Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "# tipos de datos cambiados\n",
    "\n",
    "users['churn_date'] = pd.to_datetime(users['churn_date'], format='%Y-%m-%d')\n",
    "print(users.churn_date.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88d60b16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.825192Z",
     "start_time": "2023-01-05T13:45:24.812050Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comprobamos que se reemplazaron los valores ausentes\n",
    "users.churn_date.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0f517a",
   "metadata": {},
   "source": [
    "Como observamos fueron reemplazados los valores ausentes de churn_date con fecha 2019-01-01."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fc964c74",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.836271Z",
     "start_time": "2023-01-05T13:45:24.825192Z"
    }
   },
   "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>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_date</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_13</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>89.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_204</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_379</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>660.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>270.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_442</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>880.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  user_id session_date  mb_used\n",
       "0   1000_13     1000   2018-12-29    89.86\n",
       "1  1000_204     1000   2018-12-31     0.00\n",
       "2  1000_379     1000   2018-12-28   660.40\n",
       "3  1000_413     1000   2018-12-26   270.99\n",
       "4  1000_442     1000   2018-12-27   880.22"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# revisamos tabla internet\n",
    "internet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e893dfb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.853051Z",
     "start_time": "2023-01-05T13:45:24.836271Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0mb:  13747\n"
     ]
    }
   ],
   "source": [
    "print('0mb: ', len(internet.query('mb_used == 0.00')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2aa3669",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.886086Z",
     "start_time": "2023-01-05T13:45:24.855135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      0.131142\n",
       "435.77    0.000076\n",
       "414.46    0.000076\n",
       "423.06    0.000067\n",
       "417.46    0.000067\n",
       "Name: mb_used, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet.mb_used.value_counts(normalize=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39d1d90",
   "metadata": {},
   "source": [
    "En la tabla `internet` observamos que 13747 registros (el 13% de los datos) tienen una sesion de datos de 0 mb, estos registros pueden eliminarse ya que considero tampoco contiene información valiosa para este análisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "80b2427f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.905163Z",
     "start_time": "2023-01-05T13:45:24.886086Z"
    }
   },
   "outputs": [],
   "source": [
    "# eliminamos registros de 0 mb\n",
    "indexes_drop_2 = internet.query('mb_used == 0.00').index\n",
    "internet = internet.drop(indexes_drop_2, axis=0).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e34ee7b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.925272Z",
     "start_time": "2023-01-05T13:45:24.905163Z"
    }
   },
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_date</th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1000_13</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>89.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1000_379</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>660.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1000_413</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>270.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1000_442</td>\n",
       "      <td>1000</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>880.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1001_0</td>\n",
       "      <td>1001</td>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>284.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91073</th>\n",
       "      <td>104819</td>\n",
       "      <td>1499_214</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>261.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91074</th>\n",
       "      <td>104820</td>\n",
       "      <td>1499_215</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>218.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91075</th>\n",
       "      <td>104821</td>\n",
       "      <td>1499_216</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>304.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91076</th>\n",
       "      <td>104822</td>\n",
       "      <td>1499_217</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-09-22</td>\n",
       "      <td>292.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91077</th>\n",
       "      <td>104824</td>\n",
       "      <td>1499_219</td>\n",
       "      <td>1499</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>758.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91078 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index        id  user_id session_date  mb_used\n",
       "0           0   1000_13     1000   2018-12-29    89.86\n",
       "1           2  1000_379     1000   2018-12-28   660.40\n",
       "2           3  1000_413     1000   2018-12-26   270.99\n",
       "3           4  1000_442     1000   2018-12-27   880.22\n",
       "4           5    1001_0     1001   2018-08-24   284.68\n",
       "...       ...       ...      ...          ...      ...\n",
       "91073  104819  1499_214     1499   2018-10-18   261.13\n",
       "91074  104820  1499_215     1499   2018-10-20   218.06\n",
       "91075  104821  1499_216     1499   2018-12-30   304.72\n",
       "91076  104822  1499_217     1499   2018-09-22   292.75\n",
       "91077  104824  1499_219     1499   2018-12-24   758.31\n",
       "\n",
       "[91078 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0610081a",
   "metadata": {},
   "source": [
    "La tabla `internet` queda con 91078 registros, después que fueron eliminados los registros de mb_used en 0.0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9994cc5",
   "metadata": {},
   "source": [
    "Vamos a calcular por cada usuario:\n",
    "* El número de llamadas realizadas y minutos utilizados al mes.\n",
    "* La cantidad de los SMS enviados por mes.\n",
    "* El volumen de datos por mes.\n",
    "* Los ingresos mensuales de cada usuario (resta el límite del paquete gratuito del número total de llamadas, SMS y datos; multiplica el resultado por el valor de tarifa de llamadas; agrega el cargo mensual según la tarifa de llamadas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6b79ed9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:24.965109Z",
     "start_time": "2023-01-05T13:45:24.927289Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# agregamos columna mes en 3 tablas\n",
    "calls['month']   = pd.DatetimeIndex(calls['call_date']).month\n",
    "internet['month'] = pd.DatetimeIndex(internet['session_date']).month\n",
    "messages['month']  = pd.DatetimeIndex(messages['message_date']).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae581474",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.025154Z",
     "start_time": "2023-01-05T13:45:24.965109Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>124.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1001</td>\n",
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       "      <td>27</td>\n",
       "      <td>182.0</td>\n",
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       "      <td>49</td>\n",
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       "      <th>3</th>\n",
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       "      <td>426.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>2253</th>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>39</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>1499</td>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "      <td>346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>1499</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>1499</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>1499</td>\n",
       "      <td>12</td>\n",
       "      <td>65</td>\n",
       "      <td>496.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2258 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  calls_used  minutes_used\n",
       "0        1000     12          16         124.0\n",
       "1        1001      8          27         182.0\n",
       "2        1001      9          49         315.0\n",
       "3        1001     10          65         393.0\n",
       "4        1001     11          64         426.0\n",
       "...       ...    ...         ...           ...\n",
       "2253     1498     12          39         339.0\n",
       "2254     1499      9          41         346.0\n",
       "2255     1499     10          53         385.0\n",
       "2256     1499     11          45         308.0\n",
       "2257     1499     12          65         496.0\n",
       "\n",
       "[2258 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# llamadas realizadas y minutos utilizados al mes\n",
    "calls_users = calls.pivot_table(index=['user_id','month'], \n",
    "                                values='duration', \n",
    "                                aggfunc=['count', 'sum']).reset_index()\n",
    "\n",
    "calls_users.columns = ['user_id', 'month', 'calls_used', 'minutes_used']\n",
    "calls_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b6e32c1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.055314Z",
     "start_time": "2023-01-05T13:45:25.025154Z"
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   "outputs": [
    {
     "data": {
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       "      <th>1801</th>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1805</th>\n",
       "      <td>1497</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1806 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  sms_used\n",
       "0        1000     12        11\n",
       "1        1001      8        30\n",
       "2        1001      9        44\n",
       "3        1001     10        53\n",
       "4        1001     11        36\n",
       "...       ...    ...       ...\n",
       "1801     1496      9        21\n",
       "1802     1496     10        18\n",
       "1803     1496     11        13\n",
       "1804     1496     12        11\n",
       "1805     1497     12        50\n",
       "\n",
       "[1806 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SMS enviados por mes\n",
    "sms_users = messages.pivot_table(index=['user_id', 'month'],\n",
    "                                 values='message_date',\n",
    "                                 aggfunc='count').reset_index()\n",
    "\n",
    "sms_users.columns = ['user_id', 'month', 'sms_used']\n",
    "sms_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aba32322",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.084745Z",
     "start_time": "2023-01-05T13:45:25.055314Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>1499</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>1499</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>1499</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>1499</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2277 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  gb_used\n",
       "0        1000     12        2\n",
       "1        1001      8        7\n",
       "2        1001      9       14\n",
       "3        1001     10       22\n",
       "4        1001     11       19\n",
       "...       ...    ...      ...\n",
       "2272     1498     12       23\n",
       "2273     1499      9       13\n",
       "2274     1499     10       20\n",
       "2275     1499     11       17\n",
       "2276     1499     12       22\n",
       "\n",
       "[2277 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GB de datos usados por mes\n",
    "internet_users = internet.pivot_table(index=['user_id', 'month'], \n",
    "                                      values='mb_used', \n",
    "                                      aggfunc='sum').reset_index()\n",
    "\n",
    "# redondea de MB a GB por mes\n",
    "internet_users['mb_used'] = internet_users['mb_used'].apply(\n",
    "    lambda x: math.ceil(x/1024))\n",
    "\n",
    "internet_users.columns = ['user_id', 'month', 'gb_used']\n",
    "internet_users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c850f3",
   "metadata": {},
   "source": [
    "Hemos creado 3 tablas resumen de llamadas, mensajes y datos usados. Vamos a crear una nueva tabla donde nos muestre por usuario todos estos datos juntos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b6544c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.105285Z",
     "start_time": "2023-01-05T13:45:25.086935Z"
    }
   },
   "outputs": [],
   "source": [
    "# tabla resumen de usuarios uniendo calls_users, sms_users y internet_users\n",
    "users_resume = calls_users.merge(     sms_users, on=['user_id', 'month'], how='outer'\n",
    "                         ).merge(internet_users, on=['user_id', 'month'], how='outer'\n",
    "                         ).fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "281e17eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.135375Z",
     "start_time": "2023-01-05T13:45:25.112122Z"
    }
   },
   "outputs": [
    {
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       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>calls_used</th>\n",
       "      <th>minutes_used</th>\n",
       "      <th>sms_used</th>\n",
       "      <th>gb_used</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>9</td>\n",
       "      <td>49.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>19.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1349</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1361</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1482</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1108</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1311</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  calls_used  minutes_used  sms_used  gb_used\n",
       "0        1000     12        16.0         124.0      11.0      2.0\n",
       "1        1001      8        27.0         182.0      30.0      7.0\n",
       "2        1001      9        49.0         315.0      44.0     14.0\n",
       "3        1001     10        65.0         393.0      53.0     22.0\n",
       "4        1001     11        64.0         426.0      36.0     19.0\n",
       "...       ...    ...         ...           ...       ...      ...\n",
       "2288     1349     12         0.0           0.0      61.0     13.0\n",
       "2289     1361      5         0.0           0.0       2.0      2.0\n",
       "2290     1482     10         0.0           0.0       2.0      0.0\n",
       "2291     1108     12         0.0           0.0       0.0      1.0\n",
       "2292     1311      6         0.0           0.0       0.0      2.0\n",
       "\n",
       "[2293 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_resume"
   ]
  },
  {
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     "start_time": "2023-01-05T13:45:25.136382Z"
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    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "summary users\n"
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      "text/plain": [
       "      user_id  month  calls_used  minutes_used  sms_used  gb_used\n",
       "1027     1222      6         2.0          14.0       2.0      0.0\n",
       "1028     1222      7        90.0         699.0      23.0     24.0\n",
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       "1033     1222     12        94.0         637.0      24.0     15.0"
      ]
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    {
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     "text": [
      "internet used per user\n"
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       "      user_id  month  gb_used\n",
       "1054     1222      7       24\n",
       "1055     1222      8       18\n",
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      "messages used per user\n"
     ]
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       "     user_id  month  sms_used\n",
       "830     1222      6         2\n",
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      "calls used per user\n"
     ]
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       "      user_id  month  calls_used  minutes_used\n",
       "1027     1222      6           2          14.0\n",
       "1028     1222      7          90         699.0\n",
       "1029     1222      8          78         526.0\n",
       "1030     1222      9          67         565.0\n",
       "1031     1222     10          83         672.0\n",
       "1032     1222     11          83         618.0\n",
       "1033     1222     12          94         637.0"
      ]
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     "metadata": {},
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   ],
   "source": [
    "# verificamos la tabla resumen de usuarios con las tablas individuales calls_users, sms_users y internet_users, con el fin\n",
    "# de que los merges estén correctamente mezclados sin perder información. El ejemplo a continuación con cualquier usuario\n",
    "\n",
    "user = 1222 # usuario al azar\n",
    "print('summary users')\n",
    "display(users_resume.query('user_id == @user'))\n",
    "\n",
    "print('internet used per user')\n",
    "display(internet_users.query('user_id == @user'))\n",
    "\n",
    "print('messages used per user')\n",
    "display(sms_users.query('user_id == @user'))\n",
    "\n",
    "print('calls used per user')\n",
    "display(calls_users.query('user_id == @user'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec84388",
   "metadata": {},
   "source": [
    "Ok, hasta ahora tenemos nuestra tabla resumen con 2293 registros y 6 columnas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b34043",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-28T23:17:40.410735Z",
     "start_time": "2022-12-28T23:17:40.386252Z"
    }
   },
   "source": [
    "Pero seguiremos sumando nuevas columnas a la tabla. Ahora, calcularemos los ingresos mensuales de cada usuario (resta el límite del paquete gratuito del número total de llamadas, SMS y datos; multiplica el resultado por el valor de tarifa de llamadas; agrega el cargo mensual según la tarifa de llamadas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f2d94068",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.193079Z",
     "start_time": "2023-01-05T13:45:25.176714Z"
    }
   },
   "outputs": [],
   "source": [
    "# agregamos la columna plan en nuestra tabla resumen\n",
    "users_resume = users_resume.merge(users[['user_id', 'plan']], on='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "057af4bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.212046Z",
     "start_time": "2023-01-05T13:45:25.193079Z"
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   },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>calls_used</th>\n",
       "      <th>minutes_used</th>\n",
       "      <th>sms_used</th>\n",
       "      <th>gb_used</th>\n",
       "      <th>plan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ultimate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>9</td>\n",
       "      <td>49.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>surf</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1204</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>ultimate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1349</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1349</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1349</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>surf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1108</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ultimate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  calls_used  minutes_used  sms_used  gb_used      plan\n",
       "0        1000     12        16.0         124.0      11.0      2.0  ultimate\n",
       "1        1001      8        27.0         182.0      30.0      7.0      surf\n",
       "2        1001      9        49.0         315.0      44.0     14.0      surf\n",
       "3        1001     10        65.0         393.0      53.0     22.0      surf\n",
       "4        1001     11        64.0         426.0      36.0     19.0      surf\n",
       "...       ...    ...         ...           ...       ...      ...       ...\n",
       "2288     1204     12         0.0           0.0      78.0     36.0  ultimate\n",
       "2289     1349     10         0.0           0.0      76.0     13.0      surf\n",
       "2290     1349     11         0.0           0.0      72.0     17.0      surf\n",
       "2291     1349     12         0.0           0.0      61.0     13.0      surf\n",
       "2292     1108     12         0.0           0.0       0.0      1.0  ultimate\n",
       "\n",
       "[2293 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_resume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2da341fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.228271Z",
     "start_time": "2023-01-05T13:45:25.212046Z"
    }
   },
   "outputs": [],
   "source": [
    "# renombramos columna plan_name por plan en tabla plans\n",
    "plans.rename({'plan_name': 'plan'}, axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4364f470",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.244543Z",
     "start_time": "2023-01-05T13:45:25.228271Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>usd_monthly_pay</th>\n",
       "      <th>usd_per_gb</th>\n",
       "      <th>usd_per_message</th>\n",
       "      <th>usd_per_minute</th>\n",
       "      <th>plan</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
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      ],
      "text/plain": [
       "   messages_included  mb_per_month_included  minutes_included  \\\n",
       "0                 50                  15360               500   \n",
       "1               1000                  30720              3000   \n",
       "\n",
       "   usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute      plan  \n",
       "0               20          10             0.03            0.03      surf  \n",
       "1               70           7             0.01            0.01  ultimate  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d98de791",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.265053Z",
     "start_time": "2023-01-05T13:45:25.245047Z"
    }
   },
   "outputs": [],
   "source": [
    "# agregamos columnas de la tabla plans y agregamos a nuestra tabla resumen users_resumen. Algunas serán temporales\n",
    "users_resume = users_resume.merge(plans, on='plan')\n",
    "# dejamos ordenada la tabla por user_id\n",
    "users_resume.sort_values(by='user_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3f0991c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.284740Z",
     "start_time": "2023-01-05T13:45:25.265129Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>calls_used</th>\n",
       "      <th>minutes_used</th>\n",
       "      <th>sms_used</th>\n",
       "      <th>gb_used</th>\n",
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       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
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       "      <th>usd_per_gb</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>1000</td>\n",
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       "      <td>0.01</td>\n",
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       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
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       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>1001</td>\n",
       "      <td>12</td>\n",
       "      <td>56.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
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       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
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       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>39.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2262</th>\n",
       "      <td>1499</td>\n",
       "      <td>9</td>\n",
       "      <td>41.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2263</th>\n",
       "      <td>1499</td>\n",
       "      <td>10</td>\n",
       "      <td>53.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2265</th>\n",
       "      <td>1499</td>\n",
       "      <td>12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>1499</td>\n",
       "      <td>11</td>\n",
       "      <td>45.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  calls_used  minutes_used  sms_used  gb_used      plan  \\\n",
       "0        1000     12        16.0         124.0      11.0      2.0  ultimate   \n",
       "723      1001     11        64.0         426.0      36.0     19.0      surf   \n",
       "724      1001     12        56.0         412.0      44.0     19.0      surf   \n",
       "722      1001     10        65.0         393.0      53.0     22.0      surf   \n",
       "720      1001      8        27.0         182.0      30.0      7.0      surf   \n",
       "...       ...    ...         ...           ...       ...      ...       ...   \n",
       "2261     1498     12        39.0         339.0       0.0     23.0      surf   \n",
       "2262     1499      9        41.0         346.0       0.0     13.0      surf   \n",
       "2263     1499     10        53.0         385.0       0.0     20.0      surf   \n",
       "2265     1499     12        65.0         496.0       0.0     22.0      surf   \n",
       "2264     1499     11        45.0         308.0       0.0     17.0      surf   \n",
       "\n",
       "      messages_included  mb_per_month_included  minutes_included  \\\n",
       "0                  1000                  30720              3000   \n",
       "723                  50                  15360               500   \n",
       "724                  50                  15360               500   \n",
       "722                  50                  15360               500   \n",
       "720                  50                  15360               500   \n",
       "...                 ...                    ...               ...   \n",
       "2261                 50                  15360               500   \n",
       "2262                 50                  15360               500   \n",
       "2263                 50                  15360               500   \n",
       "2265                 50                  15360               500   \n",
       "2264                 50                  15360               500   \n",
       "\n",
       "      usd_monthly_pay  usd_per_gb  usd_per_message  usd_per_minute  \n",
       "0                  70           7             0.01            0.01  \n",
       "723                20          10             0.03            0.03  \n",
       "724                20          10             0.03            0.03  \n",
       "722                20          10             0.03            0.03  \n",
       "720                20          10             0.03            0.03  \n",
       "...               ...         ...              ...             ...  \n",
       "2261               20          10             0.03            0.03  \n",
       "2262               20          10             0.03            0.03  \n",
       "2263               20          10             0.03            0.03  \n",
       "2265               20          10             0.03            0.03  \n",
       "2264               20          10             0.03            0.03  \n",
       "\n",
       "[2293 rows x 14 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_resume"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8857eb64",
   "metadata": {},
   "source": [
    "Hemos agregado nuevas columnas de la tabla plans a nuestra tabla resumen. Estas columnas son:\n",
    "- messages_included\t\n",
    "- mb_per_month_included\t\n",
    "- minutes_included\t\n",
    "- usd_monthly_pay\t\n",
    "- usd_per_gb\n",
    "- usd_per_message\t\n",
    "- usd_per_minute\n",
    "\n",
    "varias columnas serán temporales porque se usarán para realizar cálculos.\n",
    "\n",
    "A continuación, renombraremos las nuevas columnas para tener una mejor vista a la tabla resumen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "59166cd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.292958Z",
     "start_time": "2023-01-05T13:45:25.285399Z"
    }
   },
   "outputs": [],
   "source": [
    "users_resume.rename({'messages_included': 'sms', \n",
    "                     'mb_per_month_included':'gb',\n",
    "                     'minutes_included': 'minutes',\n",
    "                     'usd_monthly_pay': 'price',\n",
    "                     'usd_per_gb': 'gb_price',\n",
    "                     'usd_per_message': 'sms_price',\n",
    "                     'usd_per_minute': 'minute_price'},\n",
    "                    axis=1,\n",
    "                   inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fc98d1",
   "metadata": {},
   "source": [
    "Cambiamos Megabytes por Gigabytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "298f9e17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.305481Z",
     "start_time": "2023-01-05T13:45:25.295465Z"
    }
   },
   "outputs": [],
   "source": [
    "# transforma MB a GB \n",
    "users_resume['gb'] = users_resume['gb'].apply(lambda x: math.floor(x/1024))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "61f2618e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.333584Z",
     "start_time": "2023-01-05T13:45:25.305481Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
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       "      <td>426.0</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>339.0</td>\n",
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       "      <td>346.0</td>\n",
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       "      <td>496.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>308.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  calls_used  minutes_used  sms_used  gb_used      plan  \\\n",
       "0        1000     12        16.0         124.0      11.0      2.0  ultimate   \n",
       "723      1001     11        64.0         426.0      36.0     19.0      surf   \n",
       "724      1001     12        56.0         412.0      44.0     19.0      surf   \n",
       "722      1001     10        65.0         393.0      53.0     22.0      surf   \n",
       "720      1001      8        27.0         182.0      30.0      7.0      surf   \n",
       "...       ...    ...         ...           ...       ...      ...       ...   \n",
       "2261     1498     12        39.0         339.0       0.0     23.0      surf   \n",
       "2262     1499      9        41.0         346.0       0.0     13.0      surf   \n",
       "2263     1499     10        53.0         385.0       0.0     20.0      surf   \n",
       "2265     1499     12        65.0         496.0       0.0     22.0      surf   \n",
       "2264     1499     11        45.0         308.0       0.0     17.0      surf   \n",
       "\n",
       "       sms  gb  minutes  price  gb_price  sms_price  minute_price  \n",
       "0     1000  30     3000     70         7       0.01          0.01  \n",
       "723     50  15      500     20        10       0.03          0.03  \n",
       "724     50  15      500     20        10       0.03          0.03  \n",
       "722     50  15      500     20        10       0.03          0.03  \n",
       "720     50  15      500     20        10       0.03          0.03  \n",
       "...    ...  ..      ...    ...       ...        ...           ...  \n",
       "2261    50  15      500     20        10       0.03          0.03  \n",
       "2262    50  15      500     20        10       0.03          0.03  \n",
       "2263    50  15      500     20        10       0.03          0.03  \n",
       "2265    50  15      500     20        10       0.03          0.03  \n",
       "2264    50  15      500     20        10       0.03          0.03  \n",
       "\n",
       "[2293 rows x 14 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_resume"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9172d2",
   "metadata": {},
   "source": [
    "Realizaremos nuevos cálculos sobre recargos adicionales por uso de sms, minutos y datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c2a446cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.345531Z",
     "start_time": "2023-01-05T13:45:25.335092Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# agregamos columna de cargo por mensajes adicionales usados al mes\n",
    "users_resume['sms_charge'] = (users_resume['sms_used'] - users_resume['sms']) * users_resume['sms_price']\n",
    "users_resume['sms_charge'] = users_resume['sms_charge'].apply(lambda x: 0 if(x < 0) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b6643add",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.358531Z",
     "start_time": "2023-01-05T13:45:25.345531Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# agregamos columna de cargo por minutos adicionales usados al mes\n",
    "users_resume['min_charge'] = (users_resume['minutes_used'] - users_resume['minutes']) * users_resume['minute_price']\n",
    "users_resume['min_charge'] = users_resume['min_charge'].apply(lambda x: 0 if(x < 0) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4533c093",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.375211Z",
     "start_time": "2023-01-05T13:45:25.358531Z"
    }
   },
   "outputs": [],
   "source": [
    "# agregamos columna de cargo por datos en GB adicionales usados al mes\n",
    "users_resume['gb_charge'] = (users_resume['gb_used'] - users_resume['gb']) * users_resume['gb_price']\n",
    "users_resume['gb_charge'] = users_resume['gb_charge'].apply(lambda x: 0 if(x < 0) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9f71f5a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.383480Z",
     "start_time": "2023-01-05T13:45:25.375211Z"
    }
   },
   "outputs": [],
   "source": [
    "# agregamos columna de surcharge con el total de recargo adicional \n",
    "users_resume['surcharge'] = users_resume['sms_charge'] + users_resume['min_charge'] + users_resume['gb_charge']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "31ead84e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.395219Z",
     "start_time": "2023-01-05T13:45:25.385503Z"
    }
   },
   "outputs": [],
   "source": [
    "# agregamos columna full_charge con el total a pagar mensual por usuario (valor del plan + recargo)\n",
    "users_resume['full_charge'] = users_resume['surcharge'] + users_resume['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "03b4d497",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.428718Z",
     "start_time": "2023-01-05T13:45:25.395219Z"
    }
   },
   "outputs": [
    {
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       "      <td>393.0</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>70.09</td>\n",
       "      <td>90.09</td>\n",
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       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>39.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2262</th>\n",
       "      <td>1499</td>\n",
       "      <td>9</td>\n",
       "      <td>41.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2263</th>\n",
       "      <td>1499</td>\n",
       "      <td>10</td>\n",
       "      <td>53.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2265</th>\n",
       "      <td>1499</td>\n",
       "      <td>12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>1499</td>\n",
       "      <td>11</td>\n",
       "      <td>45.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  calls_used  minutes_used  sms_used  gb_used      plan  \\\n",
       "0        1000     12        16.0         124.0      11.0      2.0  ultimate   \n",
       "723      1001     11        64.0         426.0      36.0     19.0      surf   \n",
       "724      1001     12        56.0         412.0      44.0     19.0      surf   \n",
       "722      1001     10        65.0         393.0      53.0     22.0      surf   \n",
       "720      1001      8        27.0         182.0      30.0      7.0      surf   \n",
       "...       ...    ...         ...           ...       ...      ...       ...   \n",
       "2261     1498     12        39.0         339.0       0.0     23.0      surf   \n",
       "2262     1499      9        41.0         346.0       0.0     13.0      surf   \n",
       "2263     1499     10        53.0         385.0       0.0     20.0      surf   \n",
       "2265     1499     12        65.0         496.0       0.0     22.0      surf   \n",
       "2264     1499     11        45.0         308.0       0.0     17.0      surf   \n",
       "\n",
       "       sms  gb  minutes  price  gb_price  sms_price  minute_price  sms_charge  \\\n",
       "0     1000  30     3000     70         7       0.01          0.01        0.00   \n",
       "723     50  15      500     20        10       0.03          0.03        0.00   \n",
       "724     50  15      500     20        10       0.03          0.03        0.00   \n",
       "722     50  15      500     20        10       0.03          0.03        0.09   \n",
       "720     50  15      500     20        10       0.03          0.03        0.00   \n",
       "...    ...  ..      ...    ...       ...        ...           ...         ...   \n",
       "2261    50  15      500     20        10       0.03          0.03        0.00   \n",
       "2262    50  15      500     20        10       0.03          0.03        0.00   \n",
       "2263    50  15      500     20        10       0.03          0.03        0.00   \n",
       "2265    50  15      500     20        10       0.03          0.03        0.00   \n",
       "2264    50  15      500     20        10       0.03          0.03        0.00   \n",
       "\n",
       "      min_charge  gb_charge  surcharge  full_charge  \n",
       "0            0.0        0.0       0.00        70.00  \n",
       "723          0.0       40.0      40.00        60.00  \n",
       "724          0.0       40.0      40.00        60.00  \n",
       "722          0.0       70.0      70.09        90.09  \n",
       "720          0.0        0.0       0.00        20.00  \n",
       "...          ...        ...        ...          ...  \n",
       "2261         0.0       80.0      80.00       100.00  \n",
       "2262         0.0        0.0       0.00        20.00  \n",
       "2263         0.0       50.0      50.00        70.00  \n",
       "2265         0.0       70.0      70.00        90.00  \n",
       "2264         0.0       20.0      20.00        40.00  \n",
       "\n",
       "[2293 rows x 19 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_resume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4820d804",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.445396Z",
     "start_time": "2023-01-05T13:45:25.428718Z"
    }
   },
   "outputs": [],
   "source": [
    "# eliminamos columnas innecesaria\n",
    "users_resume.drop(columns=['sms', 'gb', 'minutes', 'price', \n",
    "                           'sms_price', 'gb_price', 'minute_price', 'sms_price',\n",
    "                           'sms_charge', 'gb_charge', 'min_charge', 'surcharge'], \n",
    "                  inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d76031d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.461502Z",
     "start_time": "2023-01-05T13:45:25.450402Z"
    }
   },
   "outputs": [],
   "source": [
    "# agregamos ciudad a la tabla puesto que usaremos esta información en la hipótesis.\n",
    "users_resume = users_resume.merge(users[['user_id','city']], on='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f567c7db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.475180Z",
     "start_time": "2023-01-05T13:45:25.461502Z"
    }
   },
   "outputs": [],
   "source": [
    "# hacemos mas cortas las ciudades\n",
    "#users_resume['city'] = users_resume['city'].str.split(',').str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a954d623",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.493544Z",
     "start_time": "2023-01-05T13:45:25.475180Z"
    }
   },
   "outputs": [],
   "source": [
    "# eliminamos espacios en blanco al principio de la columna city\n",
    "users_resume['city'] = users_resume['city'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "132f9801",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.518772Z",
     "start_time": "2023-01-05T13:45:25.495550Z"
    }
   },
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>calls_used</th>\n",
       "      <th>minutes_used</th>\n",
       "      <th>sms_used</th>\n",
       "      <th>gb_used</th>\n",
       "      <th>plan</th>\n",
       "      <th>full_charge</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ultimate</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Atlanta-Sandy Springs-Roswell, GA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>64.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>12</td>\n",
       "      <td>56.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>90.09</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>20.00</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA MSA</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>1498</td>\n",
       "      <td>12</td>\n",
       "      <td>39.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>100.00</td>\n",
       "      <td>New York-Newark-Jersey City, NY-NJ-PA MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>1499</td>\n",
       "      <td>9</td>\n",
       "      <td>41.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>20.00</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>1499</td>\n",
       "      <td>10</td>\n",
       "      <td>53.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>1499</td>\n",
       "      <td>12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>90.00</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>1499</td>\n",
       "      <td>11</td>\n",
       "      <td>45.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>surf</td>\n",
       "      <td>40.00</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL MSA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  month  calls_used  minutes_used  sms_used  gb_used      plan  \\\n",
       "0        1000     12        16.0         124.0      11.0      2.0  ultimate   \n",
       "1        1001     11        64.0         426.0      36.0     19.0      surf   \n",
       "2        1001     12        56.0         412.0      44.0     19.0      surf   \n",
       "3        1001     10        65.0         393.0      53.0     22.0      surf   \n",
       "4        1001      8        27.0         182.0      30.0      7.0      surf   \n",
       "...       ...    ...         ...           ...       ...      ...       ...   \n",
       "2288     1498     12        39.0         339.0       0.0     23.0      surf   \n",
       "2289     1499      9        41.0         346.0       0.0     13.0      surf   \n",
       "2290     1499     10        53.0         385.0       0.0     20.0      surf   \n",
       "2291     1499     12        65.0         496.0       0.0     22.0      surf   \n",
       "2292     1499     11        45.0         308.0       0.0     17.0      surf   \n",
       "\n",
       "      full_charge                                       city  \n",
       "0           70.00      Atlanta-Sandy Springs-Roswell, GA MSA  \n",
       "1           60.00            Seattle-Tacoma-Bellevue, WA MSA  \n",
       "2           60.00            Seattle-Tacoma-Bellevue, WA MSA  \n",
       "3           90.09            Seattle-Tacoma-Bellevue, WA MSA  \n",
       "4           20.00            Seattle-Tacoma-Bellevue, WA MSA  \n",
       "...           ...                                        ...  \n",
       "2288       100.00  New York-Newark-Jersey City, NY-NJ-PA MSA  \n",
       "2289        20.00          Orlando-Kissimmee-Sanford, FL MSA  \n",
       "2290        70.00          Orlando-Kissimmee-Sanford, FL MSA  \n",
       "2291        90.00          Orlando-Kissimmee-Sanford, FL MSA  \n",
       "2292        40.00          Orlando-Kissimmee-Sanford, FL MSA  \n",
       "\n",
       "[2293 rows x 9 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostramos tabla\n",
    "users_resume"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf372e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-29T19:23:09.843105Z",
     "start_time": "2022-12-29T19:23:09.827405Z"
    }
   },
   "source": [
    "Se realizaron los cálculos de recargos de consumos adicionales de sms, minutos y GB de datos: `sms_charge` , `minutes_charge`, `gb_charge`, y el total de recargo por los tres servicios está en `surcharge` y el valor total a pagar por cada cliente en cada mes en la columna `full_charge` que suma el recargo + valor del plan mensual. También se adicionó la columna city, que usaremos en la hipótesis más adelante. Y se borraron varias columnas temporales.\n",
    "\n",
    "Finalmente, nuestra tabla resumen está depurada, y ha quedado con las columnas necesarias. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e3c3e8",
   "metadata": {},
   "source": [
    "# Análisis de datos<a id='ana'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d911435",
   "metadata": {},
   "source": [
    "Empezamos, describiendo el comportamiento de los clientes a continuación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "41d976da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.535416Z",
     "start_time": "2023-01-05T13:45:25.518772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "surf        0.678\n",
       "ultimate    0.322\n",
       "Name: plan, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.plan.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f989dc1b",
   "metadata": {},
   "source": [
    "En el 2018 el 68% de los usuarios contrataron el plan surf, mientras que el 32% contrataron el plan ultimate. \n",
    "\n",
    "Pero como fueron los consumos de los clientes de cada servicio: llamadas, mensajes y datos. Revisemos a continuación."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9db397",
   "metadata": {},
   "source": [
    "## Análisis de consumo de minutos que requieren los usuarios por mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cd58b5a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.777765Z",
     "start_time": "2023-01-05T13:45:25.535416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostramos uso de minutos por usuarios\n",
    "min_used_surf = users_resume.query('plan==\"surf\"')['minutes_used']\n",
    "min_used_ulti = users_resume.query('plan==\"ultimate\"')['minutes_used']\n",
    "\n",
    "plt.hist(min_used_surf, alpha=0.6, edgecolor ='black', label='surf', density=True)\n",
    "plt.hist(min_used_ulti, alpha=0.6, edgecolor ='black', label='ultimate', density=True)\n",
    "plt.title('Minutes used monthly by users of each plan')\n",
    "plt.legend()\n",
    "plt.xlabel('minutes')\n",
    "plt.ylabel('density user-month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c533ce",
   "metadata": {},
   "source": [
    "Los histogramas de Surf y Ultimate nos muestran que las distribuciones son asimétricas, los datos están sesgados a la derecha en ambos conjuntos de datos; son notables los datos atípicos altos, es decir existen pocos clientes con consumos de más de 1000 minutos al mes.\n",
    "\n",
    "En ambos planes el consumo de minutos que requieren los usuarios mensualmente es algo similar; en el pico más alto notamos que la mayoría de usuarios *Surf* consumen aproximadamente 400 minutos mensuales, y la mayoría de usuarios con plan *Ultimate* consumen 500 minutos mensuales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4f0aa1dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:25.793767Z",
     "start_time": "2023-01-05T13:45:25.777765Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes SURF\n",
      "mean: 429\n",
      "variance: 54933\n",
      "standard deviation: 234\n",
      "\n",
      "Minutes ULTIMATE\n",
      "mean: 430\n",
      "variance: 57764\n",
      "standard deviation: 240\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el promedio, la varianza y la distribución estandar de nuestra distribución \n",
    "mean_min_surf = np.mean(min_used_surf).round(0).astype(int)\n",
    "var_min_surf  = np.var(min_used_surf).round(0).astype(int)\n",
    "std_min_surf  = np.std(min_used_surf).round(0).astype(int)\n",
    "\n",
    "mean_min_ulti = np.mean(min_used_ulti).round(0).astype(int)\n",
    "var_min_ulti  = np.var(min_used_ulti).round(0).astype(int)\n",
    "std_min_ulti  = np.std(min_used_ulti).round(0).astype(int)\n",
    "\n",
    "print('Minutes SURF')\n",
    "print('mean:', mean_min_surf)\n",
    "print('variance:', var_min_surf)\n",
    "print('standard deviation:', std_min_surf)\n",
    "print()\n",
    "print('Minutes ULTIMATE')\n",
    "print('mean:', mean_min_ulti)\n",
    "print('variance:', var_min_ulti)\n",
    "print('standard deviation:', std_min_ulti)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4beb41d2",
   "metadata": {},
   "source": [
    "El consumo promedio de minutos de usuarios *Surf* es de 429 y el de *Ultimate* es de 430, muy similar.\n",
    "\n",
    "De acuerdo a las desviaciones estándar calculadas / varianzas podemos notar que los minutos usados por los usuarios *Ultimate* son un poco más dispersos que en los usuarios *Surf*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381a697b",
   "metadata": {},
   "source": [
    "## Análisis de consumo de mensajes de texto que requieren los usuarios por mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0e92ff4b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.060729Z",
     "start_time": "2023-01-05T13:45:25.796413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostramos uso de sms por usuarios\n",
    "sms_used_surf = users_resume.query('plan == \"surf\"')['sms_used']\n",
    "sms_used_ulti = users_resume.query('plan == \"ultimate\"')['sms_used']\n",
    "\n",
    "plt.hist(sms_used_surf, alpha=0.6,  edgecolor ='black', label='surf', density=True)\n",
    "plt.hist(sms_used_ulti, alpha=0.6,  edgecolor ='black', label='ultimate', density=True)\n",
    "plt.title('SMS used monthly by users of each plan')\n",
    "plt.legend()\n",
    "plt.xlabel('messages')\n",
    "plt.ylabel('density user-month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c909e793",
   "metadata": {},
   "source": [
    "Los histogramas de *Surf* y *Ultimate* nos muestran que las distribuciones son asimétricas, los datos están sesgados totalmente a la derecha en ambos conjuntos de datos; son notables los datos atípicos altos, es decir existen clientes con consumos de más de 100 minutos al mes.\n",
    "\n",
    "El comportamiento de la mayoría de usuarios es similar en ambos planes, usan pocos mensajes de texto; la mayoría de usuarios *Surf*  consumen al menos 10 mensajes mensuales y la mayoría de usuarios *Ultimate* consumen al menos 20 mensajes de texto mensuales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "019794b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.078589Z",
     "start_time": "2023-01-05T13:45:26.061736Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMS SURF\n",
      "mean: 31\n",
      "variance: 1126\n",
      "standard deviation: 34\n",
      "\n",
      "SMS ULTIMATE\n",
      "mean: 38\n",
      "variance: 1207\n",
      "standard deviation: 35\n"
     ]
    }
   ],
   "source": [
    "mean_sms_surf = np.mean(sms_used_surf).round(0).astype(int)\n",
    "var_sms_surf  = np.var(sms_used_surf).round(0).astype(int)\n",
    "std_sms_surf  = np.std(sms_used_surf).round(0).astype(int)\n",
    "\n",
    "mean_sms_ulti = np.mean(sms_used_ulti).round(0).astype(int)\n",
    "var_sms_ulti  = np.var(sms_used_ulti).round(0).astype(int)\n",
    "std_sms_ulti  = np.std(sms_used_ulti).round(0).astype(int)\n",
    "\n",
    "print('SMS SURF')\n",
    "print('mean:', mean_sms_surf)\n",
    "print('variance:', var_sms_surf)\n",
    "print('standard deviation:', std_sms_surf)\n",
    "print()\n",
    "print('SMS ULTIMATE')\n",
    "print('mean:', mean_sms_ulti)\n",
    "print('variance:', var_sms_ulti)\n",
    "print('standard deviation:', std_sms_ulti)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f203a881",
   "metadata": {},
   "source": [
    "El consumo promedio de mensajes de texto de usuarios *Surf* es de 31 y el de *Ultimate* es de 38, no muy diferentes.\n",
    "\n",
    "De acuerdo a las desviaciones estándar calculadas / varianzas podemos notar que los mensajes de texto usados por los usuarios *Ultimate* se dispersan de manera similar a los usuarios *Surf*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49047519",
   "metadata": {},
   "source": [
    "## Análisis de consumo de datos (Gigabytes) que requieren los usuarios por mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0c2baa73",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.291865Z",
     "start_time": "2023-01-05T13:45:26.078589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostramos uso de GB por usuarios\n",
    "gb_used_surf = users_resume.query('plan == \"surf\"')['gb_used']\n",
    "gb_used_ulti = users_resume.query('plan == \"ultimate\"')['gb_used']\n",
    "\n",
    "plt.hist(gb_used_surf, alpha=0.6, edgecolor ='black', label='surf', density=True)\n",
    "plt.hist(gb_used_ulti, alpha=0.6, edgecolor ='black', label='ultimate', density=True)\n",
    "plt.title('GB used monthly by users of each plan')\n",
    "plt.legend()\n",
    "plt.xlabel('GB')\n",
    "plt.ylabel('density user per month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735b53f2",
   "metadata": {},
   "source": [
    "Los histogramas de *Surf* y *Ultimate* nos muestran que las distribuciones casi normales, con un leve sesgo a la derecha en ambos conjuntos de datos; aparecen valores atípicos altos, es decir existen pocos clientes con consumos de más de 40 Gibabytes de datos al mes.\n",
    "\n",
    "El comportamiento de los usuarios es similar para *Surf* y *Ultimate* el pico más alto en ambos casos nos dice que la mayoría de usuarios requieren unos 17 Gigabytes de datos para navegar en internet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ee5177f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.315540Z",
     "start_time": "2023-01-05T13:45:26.293498Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes SURF\n",
      "mean: 17\n",
      "variance: 62\n",
      "standard deviation: 8\n",
      "\n",
      "Minutes ULTIMATE\n",
      "mean: 17\n",
      "variance: 59\n",
      "standard deviation: 8\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el promedio, la varianza y la distribución estandar de nuestra distribución \n",
    "\n",
    "mean_gb_surf = np.mean(gb_used_surf).round(0).astype(int)\n",
    "var_gb_surf  = np.var(gb_used_surf).round(0).astype(int)\n",
    "std_gb_surf  = np.std(gb_used_surf).round(0).astype(int)\n",
    "\n",
    "mean_gb_ulti = np.mean(gb_used_ulti).round(0).astype(int)\n",
    "var_gb_ulti  = np.var(gb_used_ulti).round(0).astype(int)\n",
    "std_gb_ulti  = np.std(gb_used_ulti).round(0).astype(int)\n",
    "\n",
    "print('Minutes SURF')\n",
    "print('mean:', mean_gb_surf)\n",
    "print('variance:', var_gb_surf)\n",
    "print('standard deviation:', std_gb_surf)\n",
    "print()\n",
    "print('Minutes ULTIMATE')\n",
    "print('mean:', mean_gb_ulti)\n",
    "print('variance:', var_gb_ulti)\n",
    "print('standard deviation:', std_gb_ulti)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b2aae2",
   "metadata": {},
   "source": [
    "El consumo promedio de Gigabytes de datos de usuarios *Surf* y *Ultimate* es de 17, tal como nos muestra las distribuciones en los histogramas correspondientes.\n",
    "\n",
    "De acuerdo a las desviaciones estándar calculadas / varianzas podemos notar que los datos consumidos por los usuarios *Surf* y *Ultimate* se comportan igual, sus datos están dispersos de la misma manera en ambos planes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cfd80f6",
   "metadata": {},
   "source": [
    "# Prueba de hipótesis<a id='hip'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2197e5",
   "metadata": {},
   "source": [
    "## **Ingreso promedio de los usuarios de las tarifas Ultimate y Surf**\n",
    "\n",
    "Antes de empezar con la prueba de hipótesis resumiremos los ingresos totales en los usuarios con tarifa *Ultimate* y *Surf*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "05d2f14d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.583917Z",
     "start_time": "2023-01-05T13:45:26.317706Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtramos usuarios surf y ultimate\n",
    "surf = users_resume.query('plan == \"surf\"')['full_charge']\n",
    "ulti = users_resume.query('plan == \"ultimate\"')['full_charge']\n",
    "\n",
    "\n",
    "# histograma de total de ingresos de los usuarios en cada plan\n",
    "plt.hist(surf, alpha=0.5, label ='surf', ec ='black', density=True, range=(20, 300), bins=36)\n",
    "plt.hist(ulti, alpha=0.5, label='ultimate', ec ='black', density=True, range=(20, 300), bins=36)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Total income users Surf and Ultimate')\n",
    "plt.legend()\n",
    "plt.xlabel('income USD')\n",
    "plt.ylabel('users')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06985067",
   "metadata": {},
   "source": [
    "La mayoría de usuarios *Surf* pagan 20 USD y la mayoría de usuarios *Ultimate* pagan 70 USD, es decir la mayoría de los usuarios no pagan más de lo que cuesta el plan de cada tarifa base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d98282b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.743495Z",
     "start_time": "2023-01-05T13:45:26.585425Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TK1aswNy5cxEUFIQlS5Zg06ZNJVomQd+++OILBAYG4ocffsCQIUNgYmKCwMBAvPfee9p9Jk+eDFdXV3z//ff4448/YGFhgY4dOyI0NBSBgYF6q8XDwwMnTpzAl19+iW+++Qa3b9+GjY0N/P390bNnzxK11gQHB2PkyJGYPn06rl69Ck9PT8yfP1+nBcHKygoHDhzA7NmzsWzZMkRHR8PCwgI+Pj7o2rVrmWag/fjjj6FQKLBy5Up8++23aNmyJdavX4++ffvq7Ne5c2f8/fff+O6775CZmYkaNWpg2LBhmDp1qnafTp064fjx4/j6668xceJEJCcnw8nJCUFBQRg4cOAz11hWMpkMmzdvxowZMxAWFoavv/4azs7OGDp0KEJDQ5+pL4q/vz8WLFiAhQsXomPHjlCpVAgLCyvSyfhZtWzZEn5+frCwsHimliOqHmRCCCF1EURUvfn5+aFBgwb4999/pS6FKplz586hcePG+PHHH7UdqIkexZYZIiKqdK5fv46YmBhMmTIFHh4eemvpoaqJQ7OJiKjS+fLLL9GtWzekp6djw4YN2v5IRMXhZSYiIiIyaGyZISIiIoPGMENEREQGjWGGiIiIDFqVH82kVqtx9+5d2NjYVNiU4URERFQ2QgikpaXB09MTRkZPbnup8mHm7t27FT7VPBEREenHrVu3njqDeJUPMzY2NgA0b4atra3E1RAREVFJKBQKeHt7az/Hn6TKh5mCS0u2trYMM0RERAamJF1E2AGYiIiIDBrDDBERERk0hhkiIiIyaAwzREREZNAYZoiIiMigMcwQERGRQWOYISIiIoPGMENEREQGjWGGiIiIDBrDDBERERk0hhkiIiIyaAwzREREZNAYZkh6uVmAEFJXQUREBqrKr5pNlVhOGrBtMnB6DWDpBPi1BfyeA/zaAS51gRKslEpERMQwQ9KIPQZsegtIvqm5n/kAiPqf5gYAls4MN0REVCIMM1SxVLnAvjnAgXmAUAN23kDfRYCJOXDzAHDzoCboMNwQEVEJyYSo2p0VFAoF7OzskJqaCltbW6nLqd4eXAU2jgbuntbcb/Qa0GsuYG6nu1+eErh7Sjfc5GXp7sNwQ0RUpZXm85thhsqfEEDEz8COaZpQYm4P9FkA1H+pZM9nuCEiqnYYZgphmJFY2j3gf+OAa+Ga+wGdgH4/Abaez35MhhsioiqPYaYQhhkJXfwH+Ps9ICtJ0yem60yg5VuAkZ5nBGC4ISKqchhmCmGYkUBOGrD1U+DMGs1994ZA/58B17oVc36GGyIig8cwUwjDTAWLPQpsfAtIiQEgA9pOADpNBUzMpKuJ4YaISL/ycjSDOWKPADFHgGbDgbov6PUUDDOFMMxUEFUusHc2cHB+/pBrH+ClJZqAUNkw3BARlU62Arh1XBNeYo8Ad04CedkPH2/xJvDCPL2ekmGmEIaZCnD/imbIddwZzf3Gg4Dn5xQdcl1ZlTTc1HkeaNAf8GsPGHOKJpKQEAzXVL7S7gGxhzWt7TGHgXsXNH+oFmbpDPi0BnxDNIM73IL0WgLDTCEMM+Xo0SHXFg5A7wVA/X5SV1Y2Tws3ls5AUF9NsPFpAxgZS1crVV15OZoZspNuAInXgaTr+f/eABR3AStnwN5Hc7Pzzv/aF7D31tw3s5T6FZChEELzcxWTH15iD2vuP8rBT/M7z6eNJsA41SrXUM0wUwjDTDlJi88fcr1Tc79mZ6DvT4Cth7R1lYc8paZZNXITcPFvIDPx4WPW7pr5chr0B7xa8K9lKp08JZASWyioFPo39XbRv4RLw8qlUMh55GbnDcit9fc6yLCoVUD8+YeXjGKOABkJj+wkA9wa5Le85AeYskyp8QwYZgphmCkHjw657vYF0GK0/odcV0aqXCB6P3BhI3DpHyA79eFjdt4Pg41HMIMNaajyNB3ii7SwXAdSbgFC9fjnmlkDjgGAU03Nv441NV/b1tAs+ZESm3+7VejrWECZ9vS6LBzzw413fotO4RYeb8O5TExPl5ul6eMSkx9ebh0v+jNibAZ4Ns0PLiGAd0vAwl6ScgswzBTCMKNHUg+5rmzycoDruzXB5vJ/gDL94WOOAUD9/ppg4xrEYFPVqVWFWlhu6F4SSokB1HmPf66pZX5QKQgtNR/+a+1a+p8dIYDslKIhJ/WWppaUWN0Q/jjmdg8vXem08OR/bW4v/c+1EJo/MHIzNB/YysxCX+f/m5tZ6OtH9lPlai6PW7lo3msrV82/1q6abcam0r6+Z5WVrLk8XtDn5c4pQJ2ru4/cVhNYCi4ZeTYFTM2lqfcxGGYKYZjRk0eHXLebCHScIu2Q68okNwu4ukMTbK5s1+1j41L3YbBxDpSuRiobtUpz6UcbVKIffp18s+iHRWEm5o8PLDbuFR8KslMfBp3UgsAT83BbVtLTjyG3LT7k2PtoRjNaOmr2y8vRBIrczEIhI/++MvNhyCj8tc5+jwsm+V8/qWWrrCwcHgacgsBTOPRoQ5ALYCIvvzqeJvVO/uWi/PCSEAXgkY92a7eHwcWnDeBWv9L392OYKYRhpozylMC+2cDB7x4Oue6/VPMfgoqXkw5c2aYJNtfCAZXy4WPuDR8GGwc/yUqkRwih+T7lZmqGoCbf1G1dSbwOJEfrfi8fZSwHHP3zg0rAI4HFw7Auw+akFwo5sY+07sQCGfeffgxjuSbglaXfT2kYmQCmVpqOz6YWmq9NLfLvP+ZrI1NNcEu/r+kzkp6geW0Z95/cmlYcc7v8kOMGWLvkf+1SKPgUul+WFhAhgAdXdDvrpsQW3c+pVqHOum0AB3/pW9JKiWGmEIaZMrh/OX/I9VnN/caD84dc830ssexU4NIWTbC5sUf3F2SNZppgU/8lwK6GdDVWdqo8TUtXwV/jOv8Wt+1Jjz1hW0k+dI1MCwWWmrqtLbZehhVYykKZqWmlKmjR0Qk+t4D0+KLPMTLNDxP5N52v8wNGsY89LowUeo6ZlX4vCanVmks1hQNOekL+/ftA+r2HX2fcf3KrXHHkdo8JPC75YahQq4+RCRB37uElo9gjuoMQAEBmBLg3ym91aa0JMNau+ns/JMIwUwjDzDMQAji+HAifppkUycIB6LNQMxyZnl1mkmY01IWNmmHfhT88fdpogk1QX8DGTboa9U2t1nywJUVrWjZSbmn6Fj0tWORlP9z2pNaQ8mBsprlUUrjDbUFgsfOu9E3zlUJutuYD39jsYTAx1P4nTyNEfvDJDzzp94qGn8IhqLTBR2Zc9FKaiblm9GRBcPFuCcht9PeaKgmGmUIYZkpJEacZcn19l+Z+zS5A3x+r5pBrKaUnAFH/0wSb2MMPt8uMNDMO1+8P1HsRsHKSrsaSUuXmd37NDyw6/94sOgHhM5Pl/7VuUejfx339pP2ecoyq+qFL0ivonP3oZa0iISj/X1WO5nnm9vmXjPInqPMIrhb9FRlmCmGYKYWov4F/3tP8lWFiDnT7Emg52uCusxqc1DtA1GZNsLlz4uF2IxMgoKMm2NR9QdphkjnpjwSVGw+/ftp8KDJjTedQB3/AwVfTcfRZwoaJnD+LVH0IAeQoNH24bGtUn0uYhTDMFMIwUwLZCmDbp8CZ3zT33RsBL/8MuNSRtq7qKPmmZnK+CxuB+HMPtxubAbW6aoJNnef1P+GZEEDGA01IKdK6Ev30Dp8mFpq+JA7++f/6Pbxv78PWDiIqNYaZQhhmniLmCLDprfze8DKg3ftAx8nVogmz0ntwDYjcCFz4C7h/6eF2E3Ogdg9NsKndQ9NyURKqPEBxu5jLQTc1/xaeJ6c4Fo6PBBb//H4l/ppOi2w1ISI9YpgphGHmMfKUwN5ZwKEFmksE9j7AS8s0Q/io8rkX9TDYFF4zxcxa01JTvz9Qq4vme5l8UzewFLS2pMQ+ZbipTNOc7ehfTGjx54ywRFShGGYKYZgpxqNDroOHAD1nc8i1IRBC832L3Ahc2ASkFppfwlj+sMPg4xibaS4BPRpUCvqzSDnxFxEZHLVaIF2ZB1tz/V9KLs3nt4nez14Kfn5+iImJKbJ97Nix+PHHHyGEwMyZM7Fs2TIkJyejVatW+PHHH1G/fn0Jqq0ChACOLwPCP+eQa0MlkwGewZpb15nA7ROaYBO5CUiL0+wjtwMc/YoGFscAwMazWnYkJKLy8d+FOEzddAEfdq+NYW38JKtD0jATEREBlerh+PkLFy6gW7duGDBgAABg7ty5mD9/PlauXInatWvjq6++Qrdu3XD58mXY2FS9MfXlShEH/G+sZi0hgEOuqwKZDPBuobl1/1ozY62lkyaksv8KEZWzXJUa83ZcQWpWLlIySzl/jp5J+ieai4sL3N3dtbd///0XNWvWRIcOHSCEwIIFCzB16lT0798fDRo0wKpVq5CZmYm1a9dKWbbhidwMLG6jCTIm5sDz3wCv/8UgU5UYGWnWfbJ0ZJAhogqx4cRtRD/IgJOVGd5o5y9pLZK2zBSmVCqxZs0afPDBB5DJZLhx4wbi4+PRvXt37T5yuRwdOnTA4cOHMWbMGAmrhWY59dhj0tZQEndPAec3aL72aAz0X84h10REVCZZShUW7roCABjfuRas5NLGiUoTZjZv3oyUlBSMGDECABAfr1nbw81Nd2p3Nze3YvvZFMjJyUFOzsNOkAqFQv/FAsCNfcCumeVzbH2TGQFtJ3LINRER6cWqIzdxT5EDLwcLDGrlI3U5lSfMrFixAs8//zw8PT11tsseaTIXQhTZVtisWbMwc2YFhAyXukCDV8r/PGVlIgeaDtNMg01ERFRGqZm5+GnPNQDAB91qQ24i/XpllSLMxMTEYOfOndi4caN2m7u7OwBNC42Hx8O+HQkJCUVaawqbPHkyPvjgA+19hUIBb29v/Rddt5fmRkREVI0s3X8diuw81HGzQd/gGlKXA0DiDsAFwsLC4OrqihdeeEG7zd/fH+7u7ggPD9duUyqV2LdvH0JCQh57LLlcDltbW50bERERlV2CIhu/HIoGAHzcow6MjSrHgAPJW2bUajXCwsIwfPhwmJg8LEcmk2HixIkIDQ1FYGAgAgMDERoaCktLSwwePFjCiomIiKqn73dfRXauGs18HdClnqvU5WhJHmZ27tyJ2NhYjBo1qshjkyZNQlZWFsaOHaudNG/Hjh2cY4aIiKiC3XyQgXXHbwEAPulZ94n9VysalzMgIiKip3rv99P4++xddKrjgrCRLcv9fKX5/K4UfWaIiIio8oq8m4q/z94FAHzco67E1RTFMENERERP9M32ywCAvsGeCPKsfFc5GGaIiIjosY7eSMTey/dhYiTDB91qS11OsRhmiIiIqFhCCMzddgkAMKilD3ydrCSuqHgMM0RERFSs8Kh7OBWbAgtTY4zvXEvqch6LYYaIiIiKUKmFtq/MqHZ+cLU1l7iix2OYISIioiI2nb6DqwnpsLMwxVvta0pdzhMxzBAREZGOnDwVvgu/AgAY27Em7CxMJa7oyRhmiIiISMdvR2NxJyULbrZyDA/xk7qcp2KYISIiIq30nDws2nMNADCxa22YmxpLXNHTMcwQERGR1s8HbiApQ4kAZysMaOYldTklwjBDREREAIDE9Bws338DAPBh9zowMTaMmGAYVRIREVG5+3HPdWQoVWhYww7PN3CXupwSY5ghIiIi3E7OxJqjMQCAST3rwMhIJnFFJccwQ0RERFiw8yqUKjVCajqhXS1nqcspFYYZIiKiau7KvTRsPHUbADCpZ13IZIbTKgMwzBAREVV7326/DLUAetZ3R7C3vdTllBrDDBERUTV2KjYZO6LuwUgGfNSjttTlPBOGGSIiompKCIE5Wy8BAF5p5oVarjYSV/RsGGaIiIiqqf1XH+BYdBLMTIwwoathtsoADDNERFQNqdUCUXcVUKuF1KVIRq0WmLtN0yozrLUvathbSFzRs2OYISKiaiVPpcY7v51Er+8P4N3fT0FVTQPNlvNxiLyrgLXcBGM71ZK6nDJhmCEiompDrRb45K/z2B55DwDw3/l4zPwnEkJUr0CTq1Jj3o7LAIC32gfA0cpM4orKhmGGiIiqBSEEvtwShb9O3YaxkQwj2/pBJgNWH4nBT3uvS11ehVp/4hZuJmbCycoMb7Tzl7qcMmOYISKiauH7XdcQdugmAODbAY0wvU99fN47CADwzfbLWH/iloTVVZwspQoLd14FAIzvXAtWchOJKyo7hhkiIqrywg5F47udVwAAM1+sj5eaeAEARrb1x9sdagIAJm88j92X7klWY0VZefgmEtJy4OVggUGtfKQuRy8YZoiIqEr76+RtzPwnCgDwQbfaGB7ip/P4Jz3roH/TGlCpBcb+dgqnY5MlqLJipGbmYvHeawA074XcxFjiivSDYYaIiKqsHZHxmPTXOQDAG+38Mb5z0VE7MpkMc15uhA61XZCdq8aolRG4fj+9okutEEv2X4ciOw913GzQN7iG1OXoDcMMERFVSYevPcC7a09DpRZ4pZkXpvaq99gFFE2NjfDTkKZo7GWH5MxcDFtxHPcU2RVccfm6p8hG2KFoAMDHPerA2MiwFpN8EoYZIiIDolILpGXnSl1GpXfmVgreXH0CSpUaPeu7Y3b/hjB6yoe3ldwEv4xoAT8nS9xJycKIsAgoqtB7/f2uq8jOVaOZrwO61HOVuhy9MvwuzEREVVSWUoXL99IQdVeBqLhURN5V4FJcGrJyVRjY3Atf9WsIMxP+TfqoK/fSMCLsODKVKrSr5YyFg4JhYlyy98nJWo7Vo1qh/+LDuBinwFurT2DVqJYG37ck+kEG1kVoRmt90rPuY1uoDBXDDBFRJZCYnoOoOEV+cNH8e/1+Oh43Oe36E7dxMzETS19vBgcDn/BMn24lZWLoimNIycxFEx97LB3arNRBxMfJEitHtsBry47i6I0kfPDHWfwwqMlTW3Yqs/nhV6BSC3Sq44KW/o5Sl6N3DDNERBVIrRa4lZyJqLsKRBYKLvGP6Z/hZGWGIE9bzc3DFvU97XArKRPjfz+N49FJeOmnQ/hlRAsEuFhX8CupfBIU2Rjy8zHcU+SgjpsNwka0eOY5VBrUsMPSoc0wIuw4tpyPg4uNHNP7BBlki8aFO6n45+xdAMDHPepKXE35YJghIionOXkqXL2Xrm1tibybiotxaUjPySt2f39nKwR5FA4utnCxkRf5AK3lao2/3gnBqJURuJmYiZd+OowlrzdDm5pOFfGyKqWUTCWGrjiO2KRM+Dha4tc3WsLesmwtVm1rOWPewGC89/tprDx8E2625ninY009VVxxvtmuWbagb7AngjxtJa6mfDDMEBHpQWpmLiLjUnUuE11LSEdeMdeJzEyMUMfNRhNYamiCS10PW1iXohWhjrsNNo9ri7d+PYHTsSkYuuIYQl9qiIEtvPX5sgxCRk4eRoRF4PK9NLjayPHbm63gamuul2O/2NgT99Ny8OW/UZiz7RJcbOR4pZmXXo5dEY5cT8S+K/dhYiTDB91qS11OuWGYISIqBSEE7qRkFblMdCclq9j97SxMUT+/pSXIU3OZKMDFCqYl7JD6JC42cvw+ujU+2nAW/56Lw6S/zuHGgwxM6lHHoPt3lEZOngpv/XoCZ26lwN7SFGvebAVvR0u9nuONdv5IUGRj6f4b+OSvc3CyNkOnOpV/NJAQAnO3XwIADGrpA18nK4krKj8MM0REj5GrUuNagu5loqi7Ciiyi79M5OVgkR9c7LT9XDztzMu1n4W5qTG+f60JApyt8P3ua1iy7zpuPsjAd68Gw8LMsEfgPE2eSo0Jv5/BoWuJsDIzxsqRLVHbzaZczvVJz7pISMvBptN3MHbNKfz+VmsEe9uXy7n0JTzqHk7HpsDC1LjYyQKrEoYZIqJ8eSo1Npy8jdOxyYiKU+BKfDqUKnWR/UyMZAgsuEyUH1rqedjCzsJUgqoBIyMZPuheB/4uVvjkz/PYFhmPO0uP4OfhzeGmp8stlY1aLTB5o+a1mhkbYfmw5uUaLoyMZJj7SiMkZiix/8p9jFoZgT/fblNpO16r1ELbV2ZUOz+9XXarrGRCiMcM/KsaFAoF7OzskJqaClvbqtnxiYj0Y8bfkVh5+KbONhu5CerpXCayRS1X60o770jEzSS8tfoEkjNz4WFnjp+HN0d9Tzupy9IrIQS+2nIRKw5Gw9hIhp+GNEWP+u4Vcu6MnDwMWn4U526nwsvBAhvHhsDVpvIFhT9P3sZHG87CzsIU+yd1kixol0VpPr8ZZoiIAMSlZqHD3L1QqtQY1dYfLf0dEORhBy8HC4PrfxKTmJG/vlAGLM00l6G6BrlJXZbefL/rKuaHa1bAnjegMV6u4A65D9Jz8PLiw4hJzESQhy3+GNMaNuaVJyzk5KnQ+dt9uJOShcnP18WYDoY3Agso3ec3p44kIgKwZO91KFVqtPRzxLTe9dCzgQd8nCwNLsgAgK+TFTaObYu2tZyQqVRh9K8n8POBG6gKf7uuPBStDTLT+wRVeJABAGdrOVaPaglnazNExSkw5teTyMlTVXgdj/Pb0VjcScmCm628yArhVRXDDBFVe/Gp2fg9f6r3CV0DDXJitEfZWZhi5ciWGNTSB0IAX225iKmbLyC3mD5AhmLjqduY8U8UAGBi10CMbOsvWS2+TlYIG9ESVmbGOHw9ER+uPwv146ZrrkDpOXlYtOcaAGBi19owN62cl0P1jWGGiKq9JfuuQ5mnRgs/B4RUoYnnTI2NEPpSA3z2Qj3IZMDaY7EYtTICqVmGt3hieNQ9fPznOQDAiBA/TOgSKHFFQEMvOywZ2gymxjL8ey4OX26Jkrz16+cDN5CUoUSAsxUGGNB8OGXFMENE1do9RTbWHo8FAEzoUrtKtMoUJpPJ8OZzAVg2tDksTI1x4OoDvLz4MGITM6UurcQOX3+AcWtPQaUWeLmpFz7vXXmWFXgu0AXfDmgMAAg7dBPL9t+QrJbE9Bwszz//h93rlHhxzaqg+rxSIqJiLN6raZVp5uuAtrWqTqvMo7oFuWHD223gbmuOawnp6PfTIZy4mSR1WU919lYKRq86AWWeGt2D3DDn5YaVrh9T3+Aa+OyFegCAWVsvYeOp25LU8eOe68hQqtCwhh2eb1Axo7sqC4YZIqq2EhTZ+D2/VWZiFekr8yQNathh87i2aFDDFkkZSgxefgz/O3NH6rIe6+q9NAwPO44MpQohNZ3w/aAmlba14c3nAjD6OU0fnkl/nsPeywkVev7byZlYczRGc/6e1WcG6AKV86eCiKgCLN53HTl5ajT1sUe7Ws5Sl1Mh3O3MsX5MG3QPcoNSpcaEdWfwXfgVyft6POpWUiZeX3EMKZm5aOxtj2XDmlf6zqyTn6+HvsGeyFMLjP3tFM7eSqmwcy/YeRVKlRohNZ2qzc9yYQwzRFQtJSiysfZYQatM1esr8ySWZiZY8nozjGkfAABYuOsqJqw7g+zcyjG8OEGRjddXHMM9RQ5qu1lj5YgWpVqEUypGRjJ880pjPBfojEylCqNWRiD6QUa5n/fKvTTtpa1JPetWq5/lAgwzRFQtLd1/Azl5ajTxscdzgdXwL1kjGSb3qofZ/RvCxEiGv8/exeDlR/EgPUfSulIzczHsl+OIScyEt6MFfn2jFRyszCStqTTMTIyw+PVmaFDDFokZSgz75RgS0rLL9Zzfbr8MtQB61nev9OtFlReGGSKqdhLSsrX9C6pbq8yjXmvpg9WjWsLW3ASnYlPQ78dDuHovTZJaMpV5GLnyOC7Fp8HFRo41b7QyyLWlrOUmCBvREj6OlriVlIWRYRFIzyl+cdKyOhWbjB1R92AkAz7qUbtczmEIGGaIqNpZtk/TKhPsbY/21bBV5lEhtZyxcWxb+DpZ4nZyFvr/dBj7r9yv0Bpy8lQY8+tJnIpNgZ2FKda80Qq+TlYVWoM+udhoZgl2sjJD5F0F3v71JJR5+p2wUAiBOVsvAQBeaeaFWq7ls2K4IZA8zNy5cwevv/46nJycYGlpieDgYJw8eVL7uBACM2bMgKenJywsLNCxY0dERkZKWDERGbL7aTlYc6ygVabqj2AqqVqu1tg0ti1a+jkiLScPI1dG4Nf81qvylqdSY+K6Mzhw9QEszYwRNrIF6rgb/gezn7MVwka2gKWZMQ5ee4CPNuh3luD9Vx/gWHQSzEyMMKFr9W2VASQOM8nJyWjbti1MTU2xdetWREVFYd68ebC3t9fuM3fuXMyfPx+LFi1CREQE3N3d0a1bN6SlSdMMSkSGbdn+68jOVaOxtz061HaRupxKxdHKDL++2RL9m9aASi0wbfMFzPwnEqpynKZfCIEpm85j64V4mBkbYdnQ5mjq41Bu56tojbzsseT1Ztp+SaH/XdTLcdVqgbnbNK0yw1r7ooa9hV6Oa6gkDTNz5syBt7c3wsLC0LJlS/j5+aFLly6oWVOzwqcQAgsWLMDUqVPRv39/NGjQAKtWrUJmZibWrl0rZelEZIDup+VoWxsmdmGrTHHkJsaYN6AxPuqu+Us/7NBNvLX6RLn0+RBC4OstF7H+xG0YyYDvBwWjXRW87Ne+tgu+GdAIAPDzwWjtLL1lseV8HCLvKmAtN8HYTrXKfDxDJ2mY+fvvv9G8eXMMGDAArq6uaNKkCZYvX659PDo6GvHx8ejevbt2m1wuR4cOHXD48OFij5mTkwOFQqFzIyICgOUHbmhaZbzs0LEOW2UeRyaT4d3OgVg0uAnkJkbYdSkBryw+jLspWXo9z497ruHng9EAgDkvN0LPBh56PX5l8lITL0zpVRcA8PV/F7H59LNPVpirUmPejssAgLfaB8DRgEZ7lRdJw8yNGzewePFiBAYGYvv27Xj77bfx3nvvYfXq1QCA+Ph4AICbm5vO89zc3LSPPWrWrFmws7PT3ry9vcv3RRCRQXiQnoNfj2haZarKytjlrXcjT6x7qzWcreW4FJ+Gvj8e0ttEcKuP3MS3O64AAKb1DsKA5lX/d/Xo5wLwRjvNLMEfbTj7zJ2s15+4hZuJmXCyMtMer7qTNMyo1Wo0bdoUoaGhaNKkCcaMGYPRo0dj8eLFOvs9+ktHCPHYX0STJ09Gamqq9nbr1q1yq5+IDMfy/TeQlatCIy87dKrjKnU5BqOJjwM2jwtBXXcb3E/LwavLjmDr+bgyHXPz6Tv4/H+agRzvdQmsNh/IMpkMU3vVQ5/GmlmC31lzEudvp5bqGFlKFRbuvAoAGN+5FqwMYDLBiiBpmPHw8EBQUJDOtnr16iE2VjMrp7u7ZqGsR1thEhISirTWFJDL5bC1tdW5EVH1lpieg9UFrTLsK1NqXg6W2PB2G3Ss44LsXDXe+e0Uftxz7ZmWQNgZdQ8fbjgLABgR4of3uwbqu9xKzchIhm8HNELbWk7IUKowIuw4bpZiluCVh28iIS0HXg4WGNTKpxwrNSyShpm2bdvi8uXLOtuuXLkCX19fAIC/vz/c3d0RHh6ufVypVGLfvn0ICQmp0FqJyHAtO6BplWlYww6d67JV5lnYmJvi52HNMSLEDwDwzfbL+PjPc6WaO+XI9USMXXsKKrVA/yY18HnvoGoZLOUmxljyejPU99TMEjw87Djupz195uXUzFws3nsNAPBBt9qQm1TutaoqkqRh5v3338fRo0cRGhqKa9euYe3atVi2bBnGjRsHQNMkN3HiRISGhmLTpk24cOECRowYAUtLSwwePFjK0onIQCQW7ivDVpkyMTE2wowX62Pmi/VhJAP+PHkbQ1ccQ3KG8qnPPXc7BW+uioAyT42u9dww55VG1W5l58JszE0RNrIFvB0tEJOYiVErnz5L8JL916HIzkMdNxv0Da5RQZUaBknDTIsWLbBp0yb8/vvvaNCgAb788kssWLAAQ4YM0e4zadIkTJw4EWPHjkXz5s1x584d7NixAzY2hj+hEhGVv+UHopGpVKFBDVt0qcdWGX0YHuKHFfmLPx6LTkL/xYdx4376Y/e/lpCG4b8cR4ZShTYBTlg0uAlMjSWfs1VyrjbmWD2qFRytzHD+TireWfP4WYLvKbIRdkgz8uvjHnVgXI2DYHFkorKt+65nCoUCdnZ2SE1NZf8ZomomKUOJdnN2I1OpwvJhzdEtqPi+dvRsLsenYdTKCNxJyYKdhSmWvN4MbWo66exzKykTA5YcQbwiG4287LB2dGuDWAG7Ip29lYLXlh1FVq4K/YI9MX9gcJFWq6mbzuO3Y7Fo5uuAP99uUy1aGEvz+c1oTERV1s8HbiBTqUJ9T1t0ZauM3tVxt8HmcW0R7G2P1KxcDF1xDOtPPBxBmpCWjaErjiFekY1AV2usHNmSQaYYjb3tsfj1pjAxkmHzmbuYnT+zb4HoBxlYF6F5Xz/pWbdaBJnSYpghoiopOUOJVYdvAmBfmfLkYiPHurda44VGHshTC0z68xxmb72ElEwlhq04jpuJmfBysMCvb7Ti5G5P0LGOK+a8rJkleNn+G/j5wMNZgueHX4FKLdCpjgta+jtKVWKlxohMRFXSzwdvIEOpQpCHLS8vlTNzU2P88FoTBDhb4Yfd17Bk33WsPRYDRXYenK3lWPNGK7jbmUtdZqX3cjMv3E/Pweytl/DVlotwsZGjpos1/jl7FwDwcY+6EldYeTHMEFGVk5yhxMpDNwFwtt+KYmQkw4fd68Df2Qqf/nUeiuw82Jqb4Nc3WsLP2Urq8gzGmPYB+Z19b+KjDWcR4GwNAOgb7IkgT/b7fByGGSKqclYcjEaGUoV6HrbozlaZCtW/qRd8HC3x27FYjGzrh3oe/AAuDZlMhmkvBCEhLQdbzsXh8r00mBjJ8EG32lKXVqkxzBBRlZKSqcRK9pWRVHM/RzT3Y9+OZ2VkJMP8gY2RlK7EkRuJGNzKB75ObN16EoYZIqpSVhyMRnpOHuq627BVhgyW3MQYYSNb4ODVB3iutrPU5VR6DDNEVGWkZBbqK9MlsFrPMEuGz9zUGF0ZyEuEQ7OJqMr45WA00vJbZXrUd5e6HCKqIAwzRFQlpGbmIiy/VeY9tsoQVSsMM0RUJaw4pGmVqeNmg55slSGqVhhmiMjgpWblahfhY6sMUfXDMENEBu+Xg9FIy85DbTdrPN+ArTJE1Q3DDBEZtNSsXPzCVhmiao1hhogMWtghTatMoKs1ejXwkLocIpIAwwwRGSxFdi5+OchWGaLqjmGGiAzWykM3ocjOQy1Xa/RqyFYZouqKYYaIDJIiOxc/H7gBQNMqY8xWGaJqi2GGiAzSqvxWmZouVniBrTJE1RrDDBEZnLTsXPxcqK8MW2WIqjeGGSIyOKsO30RqVi5qulihdyNPqcshIokxzBCRQUnLzsXyA2yVIaKHGGaIyKCsPhKD1KxcBLBVhojyMcwQkcFIz8nD8vwRTOM712KrDBEBYJghIgOy6vBNpGTmIsDZCn3YKkNE+RhmiMggpOfkaeeVebdzLZgY89cXEWnwtwERGYTVR24iOTMX/s5WeLExW2WI6CGGGSKq9DJy8rB8f36rTCe2yhCRLv5GIKJKb/WRGCRn5sLPyRJ9g9kqQ0S6GGaIqFLLKDSC6d3OgWyVIaIi+FuBiCq1NUdjkJShhK+TJfqxVYaIivFMYUYIgQcPHiAxMVHf9RARaWUq87CMfWWI6ClK9ZshPj4ew4YNg4ODA9zc3ODq6goHBweMGjUK9+7dK68aiaiaWnM0BokZSvg4WuKlJjWkLoeIKimTku6oUCgQEhKC9PR0jBw5EnXr1oUQAlFRUfj9999x8OBBnDp1CtbW1uVZLxFVE5nKPCzdx3lliOjpShxmFi5cCGNjY0RGRsLFxUXnsc8++wxt27bF999/jylTpui9SCKqfn47GovEDCW8HS3YKkNET1TiP3W2bNmCKVOmFAkyAODq6orJkyfjn3/+0WtxRFQ9ZSlVWLr/OgBgfKdAmLJVhoieoMS/Ia5cuYKQkJDHPh4SEoLLly/rpSgiqt5+OxaDB+n5rTJN2SpDRE9W4jCjUChgb2//2Mft7e2hUCj0URMRVWNZShWW7Hs4gomtMkT0NCX+LSGEgJHR43eXyWQQQuilKCKqvjStMjnwcrBA/6ZeUpdDRAagxB2AhRCoXbs2ZDLZYx8nIiqL7FwVlubPKzOOrTJEVEIlDjNhYWHlWQcREX47Fov7aTmoYW+Bl9kqQ0QlVOIwM3z48PKsg4iquexcFZbs04xgGtepFsxM2CpDRCVT4jBTnOzsbPzxxx/IyMhAt27dEBgYqK+6iKiaWVuoVeaVZmyVIaKSK3GY+fjjj6FUKrFw4UIAgFKpRJs2bRAZGQlLS0tMmjQJ4eHhaNOmTbkVS0RVU+FWmbGdarJVhohKpcS/MbZu3YouXbpo7//222+IiYnB1atXkZycjAEDBuCrr74qlyKJqGpbdzwWCWk58LQzx4Bm3lKXQ0QGpsRhJjY2FkFBQdr7O3bswCuvvAJfX1/IZDJMmDABp0+fLpciiajqys5VYbG2VYZ9ZYio9Er8W8PIyEhn+PXRo0fRunVr7X17e3skJyfrtzoiqvL+iLiFe4oceNiZY0Bz9pUhotIrcZipW7eudu2lyMhIxMbGolOnTtrHY2Ji4Obmpv8KiajKys5V4ae91wBoWmXkJsYSV0REhqhUHYAHDRqELVu2IDIyEr169YK/v7/28f/++w8tW7YslyKJqGpaf+Jhq8xAtsoQ0TMqccvMyy+/jP/++w+NGjXC+++/jz/++EPncUtLS4wdO1bvBRJR1ZSTp8JPe/L7ynSsyVYZInpmpepp17VrV3z33Xf45JNPYGlpqfPY9OnT0bFjx1KdfMaMGZDJZDo3d3d37eNCCMyYMQOenp6wsLBAx44dERkZWapzEFHltD7iFuIV2XC3NcfAFhzBRETPrsSXmfbv31/sdjs7O9SqVQtWVlbPVED9+vWxc+dO7X1j44d/nc2dOxfz58/HypUrUbt2bXz11Vfo1q0bLl++DBsbm2c6HxFJLydPhZ/2PpxXhq0yRFQWJQ4zT2p1MTY2xjvvvIN58+bB1NS0dAWYmOi0xhQQQmDBggWYOnUq+vfvDwBYtWoV3NzcsHbtWowZM6ZU5yGiymP9iduIS82Gm60cA5uzVYaIyqbEl5mSk5OLvUVHR2Pt2rX4+++/8c0335S6gKtXr8LT0xP+/v547bXXcOOGZsXc6OhoxMfHo3v37tp95XI5OnTogMOHD5f6PERUOeTkqbB4T/4Ipo61YG7KVhkiKpsSt8zY2dk9druvry/MzMwwZcoUTJkypcQnb9WqFVavXo3atWvj3r17+OqrrxASEoLIyEjEx8cDQJHh3m5uboiJiXnsMXNycpCTk6O9r1AoSlwPEZW/DSdu425+q8yr7CtDRHpQpoUmC2vcuPETQ0Zxnn/+ee3XDRs2RJs2bVCzZk2sWrVKOyGfTCbTeY4Qosi2wmbNmoWZM2eWqg4iqhjKPDV+ym+VebtDTbbKEJFe6G3e8Lt378LV1bVMx7CyskLDhg1x9epVbT+aghaaAgkJCU+cnG/y5MlITU3V3m7dulWmmohIfzacvIW7qdlwtZFjUEsfqcshoipCL2EmISEBn332GTp37lym4+Tk5ODixYvw8PCAv78/3N3dER4ern1cqVRi3759CAkJeewx5HI5bG1tdW5EJD1Nq4xmBBNbZYhIn0p8malJkybFXt5JTU3F7du3Ua9ePaxbt65UJ//oo4/Qp08f+Pj4ICEhAV999RUUCgWGDx8OmUyGiRMnIjQ0FIGBgQgMDERoaCgsLS0xePDgUp2HiKRxT5GNozcScSw6CUevJ+JOShZcbOQY3IqtMkSkPyUOM/369St2u62tLerWrYvu3bvrzBFTErdv38agQYPw4MEDuLi4oHXr1jh69Ch8fX0BAJMmTUJWVhbGjh2L5ORktGrVCjt27OAcM0SV1N2ULByLTsSxG0k4Fp2E6AcZOo+bGMkwrXcQW2WISK9kovBS2FWQQqGAnZ0dUlNTecmJSM9uJWXiWHQSjuW3vsQmZeo8LpMB9T1t0crfCa38HdHS3xH2lmYSVUtEhqQ0n996G81ERFWbEAK3krJw9EYijua3vtxJydLZx0gGNKxhh1YBmvDS3M8Rdhalm0iTiKi0GGaIqFhCCNxMzNT0eclveYlLzdbZx9hIhoY17NA6wAmtAhzR3NcBNuYML0RUsRhmiAiAJrxcv5+Oo/n9XY7dSERCWo7OPqbGMjTyskfrAEe08ndCM18HWMn5a4SIpMXfQkTVlFotcDUhvVCH3UQ8SFfq7GNmbIRg7/zwEuCEpj4OsDBj510iqlyeOcwolUpER0ejZs2aMDFhJiKq7NRqgUvxadrwcvxmEpIydMOL3MQITXzsNZeN/J3QxMeeI4+IqNIrdQrJzMzE+PHjsWrVKgDAlStXEBAQgPfeew+enp749NNP9V4kEZWeSi1wMU6hnecl4mYSUjJzdfYxNzVCM18HtPJ3QusAJzT2toPchOGFiAxLqcPM5MmTcfbsWezduxc9e/bUbu/atSumT5/OMEMkkTyVGpF3FTotL2nZeTr7WJoZo5mvA1oHOKF1gCMa1rCHmYneVjUhIpJEqcPM5s2b8ccff6B169Y6MwIHBQXh+vXrei2OiErmv/NxmLzxPFKzdFterOUmaO5X0PLiiAY17GBqzPBCRFVLqcPM/fv3i11QMiMj44mrWRNR+dgRGY/xv5+GSi1gY26Cln6OaJU/2qi+py1MGF6IqIordZhp0aIFtmzZgvHjxwOANsAsX74cbdq00W91RPREey8n4N21miDTL9gT3w5ozPBCRNVOqcPMrFmz0LNnT0RFRSEvLw8LFy5EZGQkjhw5gn379pVHjURUjMPXH2DMryehVKnRq6E7gwwRVVul/s0XEhKCQ4cOITMzEzVr1sSOHTvg5uaGI0eOoFmzZuVRIxE94sTNJLy56gRy8tToUtcVC15twiBDRNUWF5okMjDnbqdgyPJjSMvJw3OBzlg+rDnngiGiKqdcF5pUKBTFbpfJZJDL5TAz44q4ROUl6q4CQ1ccR1pOHlr6O2LZUAYZIqJShxl7e/snjlry8vLCiBEjMH36dBgZsdmbSF+uJaRh6IpjSM3KRRMfe/wyogWXFiAiwjOEmZUrV2Lq1KkYMWIEWrZsCSEEIiIisGrVKnz22We4f/8+vv32W8jlckyZMqU8aiaqdm4+yMDg5ceQmKFEgxq2WDmyJay5wCMREYBnCDOrVq3CvHnzMHDgQO22F198EQ0bNsTSpUuxa9cu+Pj44Ouvv2aYIdKD28mZGPLzMSSk5aCOmw1+HdUKdhamUpdFRFRplPo60JEjR9CkSZMi25s0aYIjR44AANq1a4fY2NiyV0dVniI7F8v338CN++lSl1IpxadmY/DyY7iTkoUAZyusebMVHKzYL42IqLBShxkvLy+sWLGiyPYVK1bA29sbAJCYmAgHB4eyV0dV3uSN5/H1fxfR54eD2Ho+TupyKpX7aTkY/PNRxCZlwtvRAr+NbgUXG7nUZRERVTqlvsz07bffYsCAAdi6dStatGgBmUyGiIgIXLp0CX/++ScAICIiAq+++qrei6Wq5diNRGw5pwkwGUoV3vntFMa0D8DHPepU+zlTkjOUGLriGG7cz4CnnTnWvtkaHnYWUpdFRFQpPdM8MzExMViyZAkuX74MIQTq1q2LMWPGwM/PrxxKLBvOM1M5qdQCfX44iKg4BV5r4Q1bC1Ms238DABBS0wnfD2oCZ+vq2QqRmpWL138+hvN3UuFqI8cfY9rA39lK6rKIiCpUuc0zk5ubi+7du2Pp0qWYNWtWmYqk6m39iVuIilPA1twEH/eoAydrOYK97fHxhrM4fD0RfX44iMWvN0Owt73UpVao9Jw8jAw7jvN3UuFoZYbf3mzFIENE9BSlass3NTXFhQsXuDo2lYkiOxffbr8MAJjQtTac8ltgejX0wP/ebYsAFyvEpWZj4JIjWHssFlV8kmqtLKUKb6yMwKnYFNhZmGLNG60Q6GYjdVlERJVeqTsmDBs2rNgOwEQl9cOuq0jMUKKmixWGtfHVeayWqw3+N64tetR3g1KlxpRN5zHpz3PIzlVJVG3FyM5V4a1fT+BYdBKs5SZYPaolgjx5WZSIqCRK3QFYqVTi559/Rnh4OJo3bw4rK90m8Pnz5+utOKp6rt9PR9ihmwCAab2DYFpMR18bc1Mseb0Zluy7gW+2X8KGk7dxMV6BxUOawdvRsoIrLn+5KjXeXXsKB64+gIWpMcJGtkDjanZ5jYioLEodZi5cuICmTZsCAK5cuaLzGC8/0dN8veUi8tQCneu6omMd18fuJ5PJ8E7HmmjkZYfxv5/GhTsK9Fl0EN+/1gTta7tUYMXlK0+lxsR1Z7DzYgLkJkZYMbw5Wvg5Sl0WEZFB4arZVGH2XE7AyLAImBjJsOP99ghwsS7R8+6kZOGdNSdx7nYqZDLgw261MbZjLRgZGXZ4VqsFPtpwFhtP34GpsQzLhzV/YsAjIqpOSvP5Xb0n86AKk6tS48t/owAAI9v6lTjIAEANewusH9MGg1p6Qwjg2x1X8NavJ6HIzi2vcsudEAJTN5/HxtN3YGwkw6LBTRlkiIie0TOtVBcREYENGzYgNjYWSqVS57GNGzfqpTCqWlYficGN+xlwsjLD+C6BpX6+uakxZvVvhGBve0z7XyR2XryHvosOYcnrzVDH3bBG/AghMPOfKPx+/BaMZMCCV4PRo7671GURERmsUrfMrFu3Dm3btkVUVBQ2bdqE3NxcREVFYffu3bCzsyuPGsnAJabnYMFOTf+qj3vUga35sy+S+GoLH/z5dhvUsLdA9IMM9PvxEP4+e1dfpZY7IQRmb7uElYdvAgDmvtIYfRp7SlsUEZGBK3WYCQ0NxXfffYd///0XZmZmWLhwIS5evIiBAwfCx8enPGokAzcv/ArSsvNQ39MWA5p7l/l4jbzs8c/4dmhXyxlZuSq89/tpfPFPFHJVaj1UW74W7rqKpfs0Mx1/1a8BXmnmJXFFRESGr9Rh5vr163jhhRcAAHK5HBkZGZDJZHj//fexbNkyvRdIhi3qrgLrjmtWUJ/epz6M9dRp19HKDKtGtcTYjjUBAL8cisaQ5ceQkJatl+OXhyX7rmPBzqsANMPSX2/t+5RnEBFRSZQ6zDg6OiItLQ0AUKNGDVy4cAEAkJKSgszMTP1WRwZNCIEv/o2EWgAvNPJAS3/9Djk2NpJhUs+6WPJ6M1jLTXD8ZhJ6f38QJ2OS9HoefQg7FI3ZWy8B0Fxqe6Odv8QVERFVHaUOM8899xzCw8MBAAMHDsSECRMwevRoDBo0CF26dNF7gWS4tl2Ix9EbSZCbGGHy83XL7Tw9G7jjf++2RaCrNRLScvDq0qNYdfhmpVkGYe2xWMz8RzOS673OtTCuUy2JKyIiqlpKPc9MUlISsrOz4enpCbVajW+//RYHDx5ErVq1MG3aNDg4OJRXrc+E88xIIztXha7z9+F2chbe6xKID7rVLvdzZuTkYdJf57DlXBwA4KUmNRD6UkNYmBmX+7kfZ+Op2/hww1kIAbzVPgCTn6/LySWJiEqgNJ/fnDSPysWi3Vfx7Y4r8LAzx64PO8DS7JlmASg1IQRWHIzGrK2XoFIL1HW3wdKhzeDrVPErT285F4fxv5+CWgDD2vhi5ov1GWSIiEqoNJ/fz/QJo1arce3aNSQkJECt1h1B0r59+2c5JFUh8anZ+HHPdQDAp8/XrbAgA2iWQXjzuQDU97TD+N9P4VJ8Gvr8cBALXgtG57puFVZHeNQ9TFh3GmoBvNrcGzP6MMgQEZWXUn/KHD16FIMHD0ZMTEyRPgkymQwqVdVe3Ziebs62S8jKVaG5rwNelGgOlTY1nfDP+HYY+9spnI5NwaiVJzChSyAmdAks92UQ9l25j3G/nUKeWqBvsCdC+zc0+KUXiIgqs1J3AH777bfRvHlzXLhwAUlJSUhOTtbekpIq3ygSqlgnY5Kx6fQdyGSaodhStkZ42Fngj7faYGj+EOiFu65i1KoIpGQqn/LMZ3fkeiLeWn0CSpUazzdwx7wBjfU2HJ2IiIpX6j4zVlZWOHv2LGrVMowRGewzU3HUaoGXfjqEs7dTMbC5F+a+0ljqkrT+OnkbUzadR06eGt6OFljyejPU99TvjNUnY5IxdMUxZCpV6FLXFYtfbwYzEy5/RkT0LMp1oclWrVrh2rVrz1wcVV2bTt/B2dupsJab4KMedaQuR8fLzbywcWwIvB0tcCspC/1/OoyNp27r7fjnb6dixC/HkalUoV0tZ/w4pCmDDBFRBSlRn5lz585pvx4/fjw+/PBDxMfHo2HDhjA11V1np1GjRvqtkAxCek4e5mzTTAr3budacLUxl7iioup72uGfd9thwroz2HflPj5YfxanY1MwrXdQmYLHxTgFhv5yDGk5eWjp54hlw5rB3FS64eBERNVNiS4zGRkZQSaTPXYSsoLHKmMHYF5mqhhzt13CT3uvw9fJEjvebw+5SeX9MFepBRbuuorvd2mWFmjqY4+fhjSDu13pA9i1hHS8uvQIEjOUCPa2x5o3W8FaXnGjt4iIqiq9D82Ojo7WS2FUNcUmZuLnA5qfkc9eCKrUQQbQLIPwQbfaaOxlh4l/nMGp2BT0/uEAFg1uitYBTiU+TkxiBob8fBSJGUrU97TFqlEtGWSIiCTASfOozMb8egLbI+/huUBnrB7V0qDmU7n5IANvrzmJS/FpMDaSYfLzdfFGO/+nvoY7KVkYuOQI7qRkoY6bDX5/qzUcrcwqqGoioqqvXDsAz5o1C7/88kuR7b/88gvmzJlT2sORgTt07QG2R96DsZEM03oHGVSQAQA/ZytsGtsW/YI9oVILfLXlIsb/fhoZOXmPfc49RTYGLz+KOylZCHC2wpo3WzHIEBFJqNRhZunSpahbt+iigfXr18eSJUv0UhQZhjyVGl/kL6A4tLUvarvZSFzRs7EwM8Z3rwZjRp8gmBjJ8O+5OLz00yHcuJ9eZN8H6TkYvPwoYhIz4e1ogd9Gt4KLjVyCqomIqECpw0x8fDw8PDyKbHdxcUFcXJxeiiLD8PvxWFy+lwZ7S1NM7BoodTllIpPJMKKtP35/qzVcbOS4ci8dLy46hO2R8dp9UjKVeP3nY7h+PwMeduZY+2ZreNhZSFg1EREBzxBmvL29cejQoSLbDx06BE9Paaaup4qXkqnEvPArAIAPu9WGvWXVuMzSws8RW8a3Qws/B6Tn5GHMrycxd9slpGQqMXTFcVyKT4OLjRxrR7eGt6Ol1OUSERGeYW2mN998ExMnTkRubi46d+4MANi1axcmTZqEDz/8UO8FUuW0YOdVpGTmoo6bDQa19JG6HL1ytTXH2tGtEfrfRYQduomf9l7H6iMxSM/Jg6OVGda+2Qr+zhW/CjcRERWv1GFm0qRJSEpKwtixY6FUata4MTc3xyeffILJkyfrvUCqfK7eS8OvR2MAAJ/3CYKJcdWb6dbU2AjT+9RHsLc9Pv3rPNJz8mBrboJf32iJQAPtG0REVFWV+lNIJpNhzpw5uH//Po4ePYqzZ88iKSkJn3/+eZkKmTVrFmQyGSZOnKjdJoTAjBkz4OnpCQsLC3Ts2BGRkZFlOg+VjRACX/wbBZVaoHuQG9rWcpa6pHLVN7gGNo0LwbA2vvj9rdZ6X8+JiIjK7pn/pLa2tkaLFi3QoEEDyOVlG80RERGBZcuWFVkKYe7cuZg/fz4WLVqEiIgIuLu7o1u3bkhLSyvT+ejZ7bqYgANXH8DM2AhTX6gndTkVoq67Lb7o24BBhoiokpL8+kB6ejqGDBmC5cuXw8HBQbtdCIEFCxZg6tSp6N+/Pxo0aIBVq1YhMzMTa9eulbDi6isnT4WvtmiGYr/xnD98ndhvhIiIpCd5mBk3bhxeeOEFdO3aVWd7dHQ04uPj0b17d+02uVyODh064PDhw489Xk5ODhQKhc6N9GPloZu4mZgJFxs5xnWqJXU5REREAJ6hA7A+rVu3DqdOnUJERESRx+LjNfN7uLm56Wx3c3NDTEzMY485a9YszJw5U7+FEhLSsvHD7msAgE961uUaREREVGlI1jJz69YtTJgwAWvWrIG5+eNXK350evyC1bkfZ/LkyUhNTdXebt26pbeaq7Nvt19Gek4eGnvZoX+TGlKXQ0REpCXZn9cnT55EQkICmjVrpt2mUqmwf/9+LFq0CJcvXwZQdMbhhISEIq01hcnl8jJ3SCZd526nYMPJ2wCAz/vUh5GRYa2/REREVZtkLTNdunTB+fPncebMGe2tefPmGDJkCM6cOYOAgAC4u7sjPDxc+xylUol9+/YhJCREqrKrHSEEvvgnCkIALzWpgWa+Dk9/EhERUQWSrGXGxsYGDRo00NlmZWUFJycn7faJEyciNDQUgYGBCAwMRGhoKCwtLTF48GApSq6W/jkXhxMxybAwNcYnPYsuMEpERCS1St2Lc9KkScjKysLYsWORnJyMVq1aYceOHbCx4QysFSFLqcKs/y4CAMZ2rAl3u8f3bSIiIpKKTAghpC6iPCkUCtjZ2SE1NRW2trZSl2NQvgu/goW7rqKGvQV2fdgB5qbGUpdERETVRGk+vyWfZ4YqpzspWViy7zoAYOoL9RhkiIio0mKYoWLN+u8icvLUaOXviOcbuEtdDhER0WMxzFARx6OT8O+5OBjJNKtiP2leHyIiIqkxzJAOlVpg5j+alclfa+nDxRWJiKjSY5ghHRtO3ELkXQVszE3wYbfaUpdDRET0VAwzpKXIzsU32zUzL0/sWhtO1pxJmYiIKj+GGdJatPsaEjOUCHCxwrA2vlKXQ0REVCIMMwQAuHE/HWGHogEA03oHwdSYPxpERGQY+IlFAICvt1xErkqgUx0XdKrjKnU5REREJcYwQ9h7OQG7LiXAxEiGz3oHSV0OERFRqTDMVHO5KjW+/DcKADAixA81XawlroiIiKh0GGaquV+PxOD6/Qw4WZlhfJdAqcshIiIqNYaZaiwxPQff7bwCAPioRx3YWZhKXBEREVHpMcxUY/PDryAtOw9BHrYY2Nxb6nKIiIieCcNMNRV1V4Hfj8cCAKb3CYKxEddfIiIiw8QwUw0JIfDFv5FQC+CFhh5oFeAkdUlERETPjGGmGtoeGY+jN5IgNzHCp8/XlbocIiKiMmGYqWayc1X4astFAMCY9gHwdrSUuCIiIqKyYZipZlYcjMbt5Cy425rj7Y41pS6HiIiozBhmqpH41Gz8uOcaAGByr7qwNDORuCIiIqKyY5ipRuZuu4RMpQrNfB3wYmNPqcshIiLSC4aZauJUbDI2nr4DQDMUWybjUGwiIqoaGGaqAbVaYOY/mvWXBjTzQiMve2kLIiIi0iOGmWpg0+k7OHsrBVZmxvi4Zx2pyyEiItIrhpkqLiMnD3O2XQIAjO8SCFcbc4krIiIi0i+GmSrup73XkJCWA18nS4xs6yd1OURERHrHMFOFxSZmYvmBaADA1F71IDcxlrgiIiIi/WOYqcJ+2H0Vyjw12tVyRrcgN6nLISIiKhcMM1VUfGo2Np/RDMX+oHttDsUmIqIqi2GmivrlUDRyVQIt/RzR1MdB6nKIiIjKDcNMFZSalYu1x2IBAG93DJC4GiIiovLFMFMF/XYsBuk5eajtZo2OtV2lLoeIiKhcMcxUMdm5Kvxy8CYAYEz7mjAyYl8ZIiKq2hhmqphNp+/gQXoOPO3M8WIwF5MkIqKqj2GmClGpBZbtvwEAGNXOH6bG/PYSEVHVx0+7KiQ8Kh7RDzJgZ2GKQS19pC6HiIioQjDMVBFCCCzep2mVGdraF1ZyE4krIiIiqhgMM1XEsegknL2VArmJEUZwDSYiIqpGGGaqiCX7rgMABjT3grO1XOJqiIiIKg7DTBVwMU6BvZfvw0gGjH6Ok+QREVH1wjBTBRSMYHq+oQd8nawkroaIiKhiMcwYuNvJmfj77F0AwNvta0pcDRERUcVjmDFwKw5GQ6UWaFvLCQ297KQuh4iIqMIxzBiw5Awl1h2/BQB4uwNbZYiIqHpimDFgq4/EICtXhfqetmhXy1nqcoiIiCTBMGOgspQqrDpyEwAwpkNNyGRcUJKIiKonhhkDteHkLSRlKOHtaIFeDdylLoeIiEgyDDMGKE+lxvIDmuHYo58LgAkXlCQiomqMn4IG6L8L8biVlAVHKzMMaOYtdTlERESSYpgxMEIILNmrWbpgeBs/WJgZS1wRERGRtCQNM4sXL0ajRo1ga2sLW1tbtGnTBlu3btU+LoTAjBkz4OnpCQsLC3Ts2BGRkZESViy9g9ceICpOAQtTYwxr4yt1OURERJKTNMx4eXlh9uzZOHHiBE6cOIHOnTujb9++2sAyd+5czJ8/H4sWLUJERATc3d3RrVs3pKWlSVm2pAoWlHy1hTccrMwkroaIiEh6MiGEkLqIwhwdHfHNN99g1KhR8PT0xMSJE/HJJ58AAHJycuDm5oY5c+ZgzJgxJTqeQqGAnZ0dUlNTYWtrW56ll7vzt1PRZ9FBGBvJsO/jjvBysJS6JCIionJRms/vStNnRqVSYd26dcjIyECbNm0QHR2N+Ph4dO/eXbuPXC5Hhw4dcPjw4cceJycnBwqFQudWVSzZr2mV6dPIg0GGiIgon+Rh5vz587C2toZcLsfbb7+NTZs2ISgoCPHx8QAANzc3nf3d3Ny0jxVn1qxZsLOz0968vavGaJ+YxAxsPR8HQDNJHhEREWlIHmbq1KmDM2fO4OjRo3jnnXcwfPhwREVFaR9/dGZbIcQTZ7udPHkyUlNTtbdbt26VW+0VafmBG1ALoGMdF9TzMOzLZURERPpkInUBZmZmqFWrFgCgefPmiIiIwMKFC7X9ZOLj4+Hh4aHdPyEhoUhrTWFyuRxyubx8i65gD9JzsOHEbQDAmPZslSEiIipM8paZRwkhkJOTA39/f7i7uyM8PFz7mFKpxL59+xASEiJhhRVv1eGbyMlTo7G3PVoHOEpdDhERUaUiacvMlClT8Pzzz8Pb2xtpaWlYt24d9u7di23btkEmk2HixIkIDQ1FYGAgAgMDERoaCktLSwwePFjKsitURk4eVh+JAQC83T6AC0oSERE9QtIwc+/ePQwdOhRxcXGws7NDo0aNsG3bNnTr1g0AMGnSJGRlZWHs2LFITk5Gq1atsGPHDtjY2EhZdoVaF3ELqVm58He2Qvf6XFCSiIjoUZVunhl9M+R5ZnJVanSYuwd3U7Mxq39DDGrpI3VJREREFcIg55mhov4+cxd3U7PhbC3HS01qSF0OERFRpcQwU0kJIbA0f5K8Ue38YG7KBSWJiIiKwzBTSe25nIAr99JhLTfBkFZcUJKIiOhxGGYqqSX7bgAABrfygZ2FqcTVEBERVV4MM5XQqdhkHI9OgqmxDKPa+ktdDhERUaXGMFMJLdmr6SvTL7gG3O3MJa6GiIiocmOYqWSuJaQj/OI9AMCYDgESV0NERFT5McxUMsv334AQQNd6bqjlWn0mByQiInpWDDOVyD1FNjadvgMAeKcjW2WIiIhKgmGmEvnlUDSUKjVa+DmgmS8XlCQiIioJhplKQpGdi7VHYwEAY9rXlLgaIiIiw8EwU0msPRaLtJw8BLpao3NdV6nLISIiMhgMM5VATp4KvxyMBgC81T4ARkYyiSsiIiIyHAwzlcDm03eQkJYDDztz9A3mgpJERESlwTAjMbVaYOl+zdIFb7Tzh5kJvyVERESlwU9Oie2Iuocb9zNga26C11r6SF0OERGRwWGYkZAQAkv2aZYuGNrGF9ZyE4krIiIiMjwMMxI6Hp2EM7dSYGZihBEhXFCSiIjoWbApQEIFfWVeaeYFFxu5xNUQEVUtKpUKubm5UpdBj2FqagpjY2O9HIthRiKX49Ow+1ICZDJg9HNcuoCISF+EEIiPj0dKSorUpdBT2Nvbw93dHTJZ2aYkYZiRyNL9mr4yzzdwh7+zlcTVEBFVHQVBxtXVFZaWlmX+oCT9E0IgMzMTCQkJAAAPD48yHY9hRgJ3UrLw95m7ALh0ARGRPqlUKm2QcXJykrocegILCwsAQEJCAlxdXct0yYkdgCWw4kA08tQCbQKc0NjbXupyiIiqjII+MpaWlhJXQiVR8H0qa98mhpkKlpKpxLoIzYKSb3dkqwwRUXngpSXDoK/vE8NMBfv1SAwylSrU87BF+0BnqcshIqIq7NKlS2jdujXMzc0RHBwsdTnlhn1mKlB2rgorD98EALzdIYB/ORARUbmaPn06rKyscPnyZVhbW0tdTrlhy0wF2nDyNhIzlKhhb4EXGpat5zYREdHjKJVKAMD169fRrl07+Pr6VukO0QwzFSRPpcby/EnyRj/nDxNjvvVERPTQn3/+iYYNG8LCwgJOTk7o2rUrMjIy0LFjR0ycOFFn3379+mHEiBHa+35+fvjqq68wYsQI2NnZYfTo0ZDJZDh58iS++OILyGQyzJgxo0JfT0XiZaYKsi0yHrFJmXCwNMXAFt5Sl0NEVG0IIZCVq6rw81qYGpe4O0FcXBwGDRqEuXPn4qWXXkJaWhoOHDgAIUSJz/fNN99g2rRp+OyzzwAAc+bMQdeuXdGzZ0989NFHVfoyE8NMBSi8oOSwNn6wNOPbTkRUUbJyVQj6fHuFnzfqix4l/n0fFxeHvLw89O/fH76+vgCAhg0blup8nTt3xkcffaSzzcTEBNbW1nB3dy/VsQwNr3VUgMPXE3HhjgLmpkYYHuIndTlERFTJNG7cGF26dEHDhg0xYMAALF++HMnJyaU6RvPmzcupusqPTQQVoKBV5rUWPnC0MpO4GiKi6sXC1BhRX/SQ5LwlZWxsjPDwcBw+fBg7duzADz/8gKlTp+LYsWMwMjIqcrmpuEnmrKyq79I4DDPl7MKdVBy4+gDGRjK80c5f6nKIiKodmUxmEJf3ZTIZ2rZti7Zt2+Lzzz+Hr68vNm3aBBcXF8TFxWn3U6lUuHDhAjp16iRhtZVL5f/uGril+SOYejfygLcjp9cmIqKijh07hl27dqF79+5wdXXFsWPHcP/+fdSrVw9WVlb44IMPsGXLFtSsWRPfffcdVwR/BMNMOYpNzMSWc5oFJd9qHyBxNUREVFnZ2tpi//79WLBgARQKBXx9fTFv3jw8//zzyM3NxdmzZzFs2DCYmJjg/fffZ6vMIxhmytHPB29ALYD2tV1Q39NO6nKIiKiSqlevHrZt21bsY6ampvjpp5/w008/Pfb5N2/eLHb7mTNn9FBd5cfRTOUkMT0H60/cAqBZuoCIiIjKB8NMOVl1JAbZuWo08rJDm4CqO4U0ERGR1BhmykGmMg+rj9wEALzdoSYXlCQiIipHDDPlYN3xW0jJzIWfkyV61K/asy4SERFJjWFGz3JVaqw4GA0AGN0+AMZGbJUhIiIqTwwzevbvubu4k5IFZ2szvNzUS+pyiIiIqjyGGT0SQmDpPs0keSPb+sO8FFNZExER0bNhmNGjvVfu41J8GqzMjPF6K1+pyyEiIqoWGGb0aMlezYKSg1r6wM7SVOJqiIiIqgeGGT05HZuMY9FJMDGS4Y3nuKAkERHpl0wmw+bNm5+4z4gRI9CvX78KqacyYZjRk4K+Mn2Da8DDzkLiaoiIqCq7efMmZDJZkeUKFi5ciJUrV5b7+StbaOLaTHpw4346tkfFA+DSBUREJB07u+q5DiBbZvRg+YEbEALoWs8VgW42UpdDREQGyM/PDwsWLNDZFhwcjBkzZhTZ199f052hSZMmkMlk6NixI4CiLSYdO3bE+PHjMXHiRDg4OMDNzQ3Lli1DRkYGRo4cCRsbG9SsWRNbt27VPkelUuGNN96Av78/LCwsUKdOHSxcuFD7+IwZM7Bq1Sr873//g0wmg0wmw969ewEAd+7cwauvvgoHBwc4OTmhb9++j10EU58YZsooQZGNv07eAQCM6VBT4mqIiKgIIQBlRsXfhCi3l3T8+HEAwM6dOxEXF4eNGzc+dt9Vq1bB2dkZx48fx/jx4/HOO+9gwIABCAkJwalTp9CjRw8MHToUmZmZAAC1Wg0vLy+sX78eUVFR+PzzzzFlyhSsX78eAPDRRx9h4MCB6NmzJ+Li4hAXF4eQkBBkZmaiU6dOsLa2xv79+3Hw4EFYW1ujZ8+eUCqV5fZeALzMVGZhh29CqVKjma8DWvg5Sl0OERE9KjcTCPWs+PNOuQuYWZXLoV1cXAAATk5OcHd/8rI5jRs3xmeffQYAmDx5MmbPng1nZ2eMHj0aAPD5559j8eLFOHfuHFq3bg1TU1PMnDlT+3x/f38cPnwY69evx8CBA2FtbQ0LCwvk5OTonHvNmjUwMjLCzz//rF2TMCwsDPb29ti7dy+6d++u1/egMElbZmbNmoUWLVrAxsYGrq6u6NevHy5fvqyzjxACM2bMgKenJywsLNCxY0dERkZKVLGutOxcrDkaAwAY0559ZYiIqPJp1KiR9mtjY2M4OTmhYcOG2m1ubm4AgISEBO22JUuWoHnz5nBxcYG1tTWWL1+O2NjYJ57n5MmTuHbtGmxsbGBtbQ1ra2s4OjoiOzsb169f1/Or0iVpy8y+ffswbtw4tGjRAnl5eZg6dSq6d++OqKgoWFlp0uzcuXMxf/58rFy5ErVr18ZXX32Fbt264fLly7CxkbZ/yu/HY5GWnYdartboWs9N0lqIiOgxTC01rSRSnLcUjIyMIB65NJWbm1v2Mkx15z2TyWQ62wpaUdRqNQBg/fr1eP/99zFv3jy0adMGNjY2+Oabb3Ds2LEnnketVqNZs2b47bffijxW0JJUXiQNM9u2bdO5HxYWBldXV5w8eRLt27eHEAILFizA1KlT0b9/fwCaa39ubm5Yu3YtxowZI0XZAICcPJV2Qcm32gfAiAtKEhFVTjJZuV3u0ScXFxfExcVp7ysUCkRHRxe7r5mZGQBNZ119O3DgAEJCQjB27FjttkdbVszMzIqcu2nTpvjjjz/g6uoKW1tbvdf1JJWqA3BqaioAwNFR0/ckOjoa8fHxOtfZ5HI5OnTogMOHDxd7jJycHCgUCp1befjf6bu4p8iBm60cfYMluBZLRERVSufOnfHrr7/iwIEDuHDhAoYPHw5j4+LX+HN1dYWFhQW2bduGe/fuaT8/9aFWrVo4ceIEtm/fjitXrmDatGmIiIjQ2cfPzw/nzp3D5cuX8eDBA+Tm5mLIkCFwdnZG3759ceDAAURHR2Pfvn2YMGECbt++rbf6ilNpwowQAh988AHatWuHBg0aAADi4zVztxRczyvg5uamfexRs2bNgp2dnfbm7e1dLvUmZyphbmqEN9r5Q27CBSWJiKhsJk+ejPbt26N3797o1asX+vXrh5o1ix8la2Jigu+//x5Lly6Fp6cn+vbtq7c63n77bfTv3x+vvvoqWrVqhcTERJ1WGgAYPXo06tSpo+1Xc+jQIVhaWmL//v3w8fFB//79Ua9ePYwaNQpZWVnl3lIjE49eoJPIuHHjsGXLFhw8eBBeXl4AgMOHD6Nt27a4e/cuPDw8tPuOHj0at27dKnKZCtC0zOTk5GjvKxQKeHt7IzU1Ve9vZlKGEnITI1jJOSiMiKgyyM7ORnR0NPz9/WFubi51OfQUT/p+KRQK2NnZlejzu1J8Co8fPx5///039u/frw0yALRDvuLj43XCTEJCQpHWmgJyuRxyubx8C87naGVWIechIiKix5P0MpMQAu+++y42btyI3bt3a2c0LODv7w93d3eEh4drtymVSuzbtw8hISEVXS4RERFVQpK2zIwbNw5r167F//73P9jY2Gj7wdjZ2cHCwgIymQwTJ05EaGgoAgMDERgYiNDQUFhaWmLw4MFSlk5ERESVhKRhZvHixQCgXVOiQFhYGEaMGAEAmDRpErKysjB27FgkJyejVatW2LFjh+RzzBAREVHlIGmYKUnfY5lMhhkzZhS70BYRERFRpRmaTUREpC+VZKAuPYW+vk8MM0REVGUUTNNfsAI0VW4F36dHl1worUoxNJuIiEgfjI2NYW9vr1000dLSUrv2EFUeQghkZmYiISEB9vb2j53puKQYZoiIqEopmKOs8CrQVDnZ29trv19lwTBDRERVikwmg4eHB1xdXfWy6jSVD1NT0zK3yBRgmCEioirJ2NhYbx+WVLmxAzAREREZNIYZIiIiMmgMM0RERGTQqnyfmYIJeRQKhcSVEBERUUkVfG6XZGK9Kh9m0tLSAADe3t4SV0JERESllZaWBjs7uyfuIxNVfM5ntVqNu3fvwsbGplpOnKRQKODt7Y1bt27B1tZW6nIMFt9H/eD7qB98H8uO76F+lOf7KIRAWloaPD09YWT05F4xVb5lxsjICF5eXlKXITlbW1v+h9UDvo/6wfdRP/g+lh3fQ/0or/fxaS0yBdgBmIiIiAwawwwREREZNIaZKk4ul2P69OmQy+VSl2LQ+D7qB99H/eD7WHZ8D/WjsryPVb4DMBEREVVtbJkhIiIig8YwQ0RERAaNYYaIiIgMGsMMERERGTSGmSpo1qxZaNGiBWxsbODq6op+/frh8uXLUpdl8GbNmgWZTIaJEydKXYrBuXPnDl5//XU4OTnB0tISwcHBOHnypNRlGZS8vDx89tln8Pf3h4WFBQICAvDFF19ArVZLXVqltn//fvTp0weenp6QyWTYvHmzzuNCCMyYMQOenp6wsLBAx44dERkZKU2xldiT3sfc3Fx88sknaNiwIaysrODp6Ylhw4bh7t27FVYfw0wVtG/fPowbNw5Hjx5FeHg48vLy0L17d2RkZEhdmsGKiIjAsmXL0KhRI6lLMTjJyclo27YtTE1NsXXrVkRFRWHevHmwt7eXujSDMmfOHCxZsgSLFi3CxYsXMXfuXHzzzTf44YcfpC6tUsvIyEDjxo2xaNGiYh+fO3cu5s+fj0WLFiEiIgLu7u7o1q2bdl0/0njS+5iZmYlTp05h2rRpOHXqFDZu3IgrV67gxRdfrLgCBVV5CQkJAoDYt2+f1KUYpLS0NBEYGCjCw8NFhw4dxIQJE6QuyaB88sknol27dlKXYfBeeOEFMWrUKJ1t/fv3F6+//rpEFRkeAGLTpk3a+2q1Wri7u4vZs2drt2VnZws7OzuxZMkSCSo0DI++j8U5fvy4ACBiYmIqpCa2zFQDqampAABHR0eJKzFM48aNwwsvvICuXbtKXYpB+vvvv9G8eXMMGDAArq6uaNKkCZYvXy51WQanXbt22LVrF65cuQIAOHv2LA4ePIhevXpJXJnhio6ORnx8PLp3767dJpfL0aFDBxw+fFjCygxfamoqZDJZhbXAVvmFJqs7IQQ++OADtGvXDg0aNJC6HIOzbt06nDp1ChEREVKXYrBu3LiBxYsX44MPPsCUKVNw/PhxvPfee5DL5Rg2bJjU5RmMTz75BKmpqahbty6MjY2hUqnw9ddfY9CgQVKXZrDi4+MBAG5ubjrb3dzcEBMTI0VJVUJ2djY+/fRTDB48uMIW8WSYqeLeffddnDt3DgcPHpS6FINz69YtTJgwATt27IC5ubnU5RgstVqN5s2bIzQ0FADQpEkTREZGYvHixQwzpfDHH39gzZo1WLt2LerXr48zZ85g4sSJ8PT0xPDhw6Uuz6DJZDKd+0KIItuoZHJzc/Haa69BrVbjp59+qrDzMsxUYePHj8fff/+N/fv3w8vLS+pyDM7JkyeRkJCAZs2aabepVCrs378fixYtQk5ODoyNjSWs0DB4eHggKChIZ1u9evXw119/SVSRYfr444/x6aef4rXXXgMANGzYEDExMZg1axbDzDNyd3cHoGmh8fDw0G5PSEgo0lpDT5ebm4uBAwciOjoau3fvrrBWGYCjmaokIQTeffddbNy4Ebt374a/v7/UJRmkLl264Pz58zhz5oz21rx5cwwZMgRnzpxhkCmhtm3bFpka4MqVK/D19ZWoIsOUmZkJIyPdX9nGxsYcml0G/v7+cHd3R3h4uHabUqnEvn37EBISImFlhqcgyFy9ehU7d+6Ek5NThZ6fLTNV0Lhx47B27Vr873//g42Njfa6sJ2dHSwsLCSuznDY2NgU6WdkZWUFJycn9j8qhffffx8hISEIDQ3FwIEDcfz4cSxbtgzLli2TujSD0qdPH3z99dfw8fFB/fr1cfr0acyfPx+jRo2SurRKLT09HdeuXdPej46OxpkzZ+Do6AgfHx9MnDgRoaGhCAwMRGBgIEJDQ2FpaYnBgwdLWHXl86T30dPTE6+88gpOnTqFf//9FyqVSvu54+joCDMzs/IvsELGTFGFAlDsLSwsTOrSDB6HZj+bf/75RzRo0EDI5XJRt25dsWzZMqlLMjgKhUJMmDBB+Pj4CHNzcxEQECCmTp0qcnJypC6tUtuzZ0+xvw+HDx8uhNAMz54+fbpwd3cXcrlctG/fXpw/f17aoiuhJ72P0dHRj/3c2bNnT4XUJxNCiPKPTERERETlg31miIiIyKAxzBAREZFBY5ghIiIig8YwQ0RERAaNYYaIiIgMGsMMERERGTSGGSIiIjJoDDNERCUgk8mwefPmxz6+d+9eyGQypKSkVFhNRKTBMENEVMiMGTMQHBwsdRlEVAoMM0QkGZVKxYUSiajMGGaISMvPzw8LFizQ2RYcHIwZM2Zo78+YMQM+Pj6Qy+Xw9PTEe++9p31MqVRi0qRJqFGjBqysrNCqVSvs3btX+/jKlSthb2+Pf//9F0FBQZDL5YiJiSlSR8Elm+3bt6NJkyawsLBA586dkZCQgK1bt6JevXqwtbXFoEGDkJmZqX1eTk4O3nvvPbi6usLc3Bzt2rVDREREkePu2rULzZs3h6WlJUJCQrSreq9cuRIzZ87E2bNnIZPJIJPJsHLlSu3zHzx4gJdeegmWlpYIDAzE33//Xez7mJGRAVtbW/z555862//55x9YWVkhLS3tsd8DIio9hhkiKrE///wT3333HZYuXYqrV69i8+bNaNiwofbxkSNH4tChQ1i3bh3OnTuHAQMGoGfPnrh69ap2n8zMTMyaNQs///wzIiMj4erq+tjzzZgxA4sWLcLhw4dx69YtDBw4EAsWLMDatWuxZcsWhIeH44cfftDuP2nSJPz1119YtWoVTp06hVq1aqFHjx5ISkrSOe7UqVMxb948nDhxAiYmJtqVp1999VV8+OGHqF+/PuLi4hAXF4dXX31V+7yZM2di4MCBOHfuHHr16oUhQ4YUOTagWV39tddeQ1hYmM72sLAwvPLKK7CxsSnhO05EJVIhy1kSkUHw9fUV3333nc62xo0bi+nTpwshhJg3b56oXbu2UCqVRZ577do1IZPJxJ07d3S2d+nSRUyePFkIIURYWJgAIM6cOfPEOgpW6N25c6d226xZswQAcf36de22MWPGiB49egghhEhPTxempqbit99+0z6uVCqFp6enmDt37mOPu2XLFgFAZGVlCSGEmD59umjcuHGRmgCIzz77THs/PT1dyGQysXXrVp1jJycnCyGEOHbsmDA2Nta+H/fv3xempqZi7969T3ztRFR6bJkhohIbMGAAsrKyEBAQgNGjR2PTpk3Iy8sDAJw6dQpCCNSuXRvW1tba2759+3D9+nXtMczMzNCoUaMSna/wfm5ubrC0tERAQIDOtoSEBADA9evXkZubi7Zt22ofNzU1RcuWLXHx4sXHHtfDwwMAtMcpaT1WVlawsbF57PNatmyJ+vXrY/Xq1QCAX3/9FT4+Pmjfvv1Tz0NEpWMidQFEVHkYGRlBCKGzLTc3V/u1t7c3Ll++jPDwcOzcuRNjx47FN998g3379kGtVsPY2BgnT56EsbGxzjGsra21X1tYWEAmk5WoHlNTU+3XMplM537BtoIOxAV1P3psIUSRbY8eF0CJOiI/6fzFefPNN7Fo0SJ8+umnCAsLw8iRI0v82omo5NgyQ0RaLi4uiIuL095XKBSIjo7W2cfCwgIvvvgivv/+e+zduxdHjhzB+fPn0aRJE6hUKiQkJKBWrVo6N3d393KvvVatWjAzM8PBgwe123Jzc3HixAnUq1evxMcxMzODSqXSS02vv/46YmNj8f333yMyMhLDhw/Xy3GJSBdbZohIq3Pnzli5ciX69OkDBwcHTJs2TaeVZeXKlVCpVGjVqhUsLS3x66+/wsLCAr6+vnBycsKQIUMwbNgwzJs3D02aNMGDBw+we/duNGzYEL169SrX2q2srPDOO+/g448/hqOjI3x8fDB37lxkZmbijTfeKPFx/Pz8EB0djTNnzsDLyws2NjaQy+XPVJODgwP69++Pjz/+GN27d4eXl9czHYeInowtM0SkNXnyZLRv3x69e/dGr1690K9fP9SsWVP7uL29PZYvX462bduiUaNG2LVrF/755x84OTkB0IzWGTZsGD788EPUqVMHL774Io4dOwZvb+8KqX/27Nl4+eWXMXToUDRt2hTXrl3D9u3b4eDgUOJjvPzyy+jZsyc6deoEFxcX/P7772Wq6Y033oBSqdSOmCIi/ZOJRy+QExGR3vz222+YMGEC7t69CzMzM6nLIaqSeJmJiKgcZGZmIjo6GrNmzcKYMWMYZIjKES8zERGVg7lz5yI4OBhubm6YPHmy1OUQVWm8zEREREQGjS0zREREZNAYZoiIiMigMcwQERGRQWOYISIiIoPGMENEREQGjWGGiIiIDBrDDBERERk0hhkiIiIyaAwzREREZND+DwGJR946JGXcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cobros mensuales promedios de los usuarios en cada plan\n",
    "mean_charge_plan = users_resume.pivot_table(index='month', columns='plan', values='full_charge', aggfunc='mean')\n",
    "\n",
    "plt.plot(mean_charge_plan, label=['surf', 'ultimate'])\n",
    "plt.title('Total income per user monthly')\n",
    "plt.legend()\n",
    "plt.xlabel('user monthly')\n",
    "plt.ylabel('charge USD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca725b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-02T14:06:19.672726Z",
     "start_time": "2023-01-02T14:06:19.657189Z"
    }
   },
   "source": [
    "Como dato adicional, podemos observar que el promedio de ingresos por los usuarios con el plan Ultimate tuvieron un comportamiento regular por los 70 USD en todo el año, notándose una variación desde febrero a junio un poco más alta en consumos, el resto del año el comportamiento es similar. \n",
    "\n",
    "Por otro lado los ingresos obtenidos de los usuarios con plan Surf es ascendente a medida que los meses transcurrieron, empezando desde el consumo base de 20 USD hasta los 70 USD en diciembre. Notablemente terminaron consumiendo más de los servicios que habían contratado."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c41933",
   "metadata": {},
   "source": [
    "### Prueba de hipótesis 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47887a69",
   "metadata": {},
   "source": [
    "A continuación realizaremos la formulación de hipótesis nula H0 e hipótesis alternativa Ha. El nivel de significancia será del 5% asumiendo que el nivel de confianza es del 95%.\n",
    "\n",
    "*  **H0: El ingreso promedio de los usuarios de las tarifas Ultimate y Surf son iguales**.\n",
    "*  Ha: El ingreso promedio de los usuarios de las tarifas Ultimate y Surf son diferentes.\n",
    "*  alpha 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bfa8a510",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.752391Z",
     "start_time": "2023-01-05T13:45:26.745499Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  2.862674510698644e-08\n",
      "Rechazamos la hipótesis nula\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "results = st.ttest_ind(surf, ulti)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    ", \n",
    "if results.pvalue < alpha:\n",
    "    print(\"Rechazamos la hipótesis nula\")\n",
    "else:\n",
    "    print(\"No rechazamos la hipótesis nula\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8ea7ee0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:26.795696Z",
     "start_time": "2023-01-05T13:45:26.755194Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average income users Surf $ 60.71\n",
      "Average income users Ultimate $ 72.31\n"
     ]
    }
   ],
   "source": [
    "# promedios de ingresos de usuarios Surf y usuarios Ultimate\n",
    "print(f'Average income users Surf $ {surf.mean():.2f}')\n",
    "print(f'Average income users Ultimate $ {ulti.mean():.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8f042b",
   "metadata": {},
   "source": [
    "Rechazamos la hipótesis nula, es decir podemos concluir que es falsa la hipótesis de que el ingreso promedio de los usuarios de las tarifas Ultimate y Surf son iguales. El ingreso promedio por usuarios Surf es de 60.71 USD y el ingreso promedio por usuarios Ultimate es de 72.31 USD."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4896ff",
   "metadata": {},
   "source": [
    "## Ingreso promedio de los usuarios en el área de estados Nueva York - Nueva Jersey y  usuarios de otras regiones\n",
    "\n",
    "Antes de empezar con la prueba de hipótesis 2, mostraremos los ingresos en los usuarios de estados New York-Newark Jersey y del resto de estados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b19d0e9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:27.073026Z",
     "start_time": "2023-01-05T13:45:26.795696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtramos usuarios de NY-NJ y usuarios del resto de estados\n",
    "ny_nj  = users_resume.query('city == \"New York-Newark-Jersey City, NY-NJ-PA MSA\"')['full_charge']\n",
    "others = users_resume.query('city != \"New York-Newark-Jersey City, NY-NJ-PA MSA\"')['full_charge']\n",
    "\n",
    "# histograma de total de ingresos de los usuarios dentro y fuera del estado de New York-Newark Jersey.\n",
    "\n",
    "plt.hist(ny_nj, alpha=0.5, label ='New York-Newark-Jersey', ec ='black', density=True, range=(0, 350), bins=35)\n",
    "plt.hist(others, alpha=0.5, label='Others states', ec ='black', density=True, range=(0, 350), bins=35)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Average income in NY-NJ and others states')\n",
    "plt.legend()\n",
    "plt.xlabel('income USD')\n",
    "plt.ylabel('users')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149874fa",
   "metadata": {},
   "source": [
    "Resumiendo nuestro histograma, la mayoría de los usuarios de New York-Newark Jersey pagan entre 20 a 30 USD por consumos en sus planes. Y la mayoría de los usuarios de otras ciudades pagan entre 70 a 80 USD por el consumo de sus servicios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "94fe4904",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:27.253626Z",
     "start_time": "2023-01-05T13:45:27.073026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cobros mensuales promedios de los usuarios dentro y fuera de New York Newak Jersey\n",
    "\n",
    "in_ny = users_resume.query('city == \"New York-Newark-Jersey City, NY-NJ-PA MSA\"')[['full_charge','month']]\n",
    "out_ny = users_resume.query('city != \"New York-Newark-Jersey City, NY-NJ-PA MSA\"')[['full_charge','month']]\n",
    "\n",
    "plt.plot(in_ny.pivot_table(index='month',  values='full_charge', aggfunc='mean'), label='NY-NJ-PA MSA')\n",
    "plt.plot(out_ny.pivot_table(index='month', values='full_charge', aggfunc='mean'), label='other states')\n",
    "\n",
    "plt.title('Average income monthly in and out NY-NJ-PA MSA')\n",
    "plt.legend()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('charge USD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304afbf5",
   "metadata": {},
   "source": [
    "Como dato adicional, podemos observar que en el estado de NY-NJ en el primer semestre los usuarios pagaron por debajo de los 50 usd, y en el último semestre del año sus pagos suben de manera ascendente hasta los 65 USD.\n",
    "\n",
    "Por otro lado los ingresos obtenidos de los usuarios del resto de estados el primer semestre empieza sobre los 50 USD alcanzando hasta final de año un pago de servicios de más de 65 USD."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "427d592a",
   "metadata": {},
   "source": [
    "### Prueba de hipótesis 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df90710",
   "metadata": {},
   "source": [
    "Formulación de hipótesis nula H0 e hipótesis alternativa Ha. El nivel de significancia es del 5% asumiendo que el nivel de confianza es del 95%.\n",
    "\n",
    "*  **H0: El ingreso promedio de los usuarios en el área de estados Nueva York-Nueva Jersey es igual al de los usuarios de otras regiones**.\n",
    "*  Ha: El ingreso promedio de los usuarios en el área de estados Nueva York-Nueva Jersey es diferente al de los usuarios de otras regiones.\n",
    "*  alpha 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "20f7ce1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:27.269030Z",
     "start_time": "2023-01-05T13:45:27.255137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:  0.043557431621342436\n",
      "Rechazamos la hipótesis nula\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "results = st.ttest_ind(ny_nj, others)\n",
    "\n",
    "print('p-value: ', results.pvalue)\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"Rechazamos la hipótesis nula\")\n",
    "else:\n",
    "    print(\"No rechazamos la hipótesis nula\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0a5066cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T13:45:27.303674Z",
     "start_time": "2023-01-05T13:45:27.270238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average income in of NY-NJ-PA MSA is: 59.92\n",
      "Average income out of NY-NJ-PA MSA is: 65.22\n"
     ]
    }
   ],
   "source": [
    "# promedios de ingresos dentro y fuera del estado NY-NJ-PA MSA\n",
    "print(f'Average income in of NY-NJ-PA MSA is: {ny_nj.mean():.2f}')\n",
    "print(f'Average income out of NY-NJ-PA MSA is: {others.mean():.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd68b10",
   "metadata": {},
   "source": [
    "Rechazamos la hipótesis nula, es decir, la afirmación de que el ingreso promedio de los usuarios en el estado \"New York-Newark-Jersey City, NY-NJ-PA MSA\" es igual al de otros estados, se descarta. El ingreso promedio por usuarios del area de estados New York-Newark Jersey es de 59.92 USD y el ingreso promedio de otros estados es de 65.22 USD."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c04598",
   "metadata": {},
   "source": [
    "# Conclusión general<a id='con'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13626fb1",
   "metadata": {},
   "source": [
    "En la fase inicial creamos una tabla resumen con la información de 5 tablas previstas por Megaline, esta tabla es llamada `users_resume` con un total de 2293 registros y 9 columnas, contiene registros de 500 usuarios por mes sobre las llamadas, mensajes y datos usados, cobros por servicios y ciudad/estado al que pertenece. Las columnas en esta tabla son: user_id,month, calls_used, minutes_used, sms_used, gb_used, plan, full_charge, city.\n",
    "\n",
    "Después se analizó el comportamiento de los usuarios respecto a los consumos que necesitaron cada mes en cada tarifa y obtuvimos que:\n",
    "\n",
    "* *Consumo Minutos*: La mayoría de usuarios *Surf* consumen aproximadamente 400 minutos mensuales, y la mayoría de usuarios con plan *Ultimate* consumen 500 minutos mensuales. El consumo promedio de minutos de usuarios *Surf* es de 429 y el de *Ultimate* es de 430, muy similar. Existen clientes atípicos con consumos de más de 1000 minutos al mes en ambos planes.\n",
    "\n",
    "\n",
    "* *Consumo SMS*: En ambos planes los usuarios usan pocos mensajes de texto; la mayoría de usuarios *Surf*  consumen al menos 10 mensajes mensuales y la mayoría de usuarios *Ultimate* consumen al menos 20 mensajes de texto mensuales. Existen usuarios con consumos atípico de más de 100 minutos al mes. El consumo promedio de mensajes de texto de usuarios *Surf* es de 31 y el de *Ultimate* es de 38, no muy diferentes.\n",
    "\n",
    "\n",
    "* *Consumo GB*: El consumo es similar para usuarios *Surf* y *Ultimate* la mayoría de usuarios requieren unos 17 Gigabytes de datos para navegar en internet. Existen usuarios atípicos con consumos de más de 40 Gibabytes de datos al mes. El consumo promedio de Gigabytes de datos de usuarios *Surf* y *Ultimate* es de 17.\n",
    "\n",
    "\n",
    "\n",
    "Finalmente, hicimos 2 pruebas de hipótesis:\n",
    "\n",
    "\n",
    "**Hipotesis 1: El ingreso promedio de los usuarios de las tarifas Ultimate y Surf es igual** \n",
    "\n",
    "Rechazamos la hipótesis nula, es decir es falsa la hipótesis de que el ingreso promedio de los usuarios de las tarifas Ultimate y Surf son iguales. \n",
    "\n",
    "El plan más rentable es el Ultimate. Ya que su ingreso promedio es de 72.31 USD y el de usuarios Surf es de 60.71 USD.\n",
    "\n",
    "**Hipótesis 2: El ingreso promedio de los usuarios en el área de estados Nueva York-Nueva Jersey es igual al de los usuarios de otras regiones.**\n",
    "\n",
    "Rechazamos la hipótesis nula, es decir, el ingreso promedio de los usuarios en el estado \"New York-Newark-Jersey City, NY-NJ-PA MSA\" es diferente al de otros estados. \n",
    "\n",
    "Los usuarios de New York-Newark Jersey no son más rentables que los usuarios del resto de estados, ya que el primero tiene un ingreso promedio de 59.92 USD y el segundo es de 65.22 USD."
   ]
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